Lynch syndrome cancer vaccines: A roadmap for the development of precision immunoprevention strategies

Hereditary cancer syndromes (HCS) account for 5~10% of all cancer diagnosis. Lynch syndrome (LS) is one of the most common HCS, caused by germline mutations in the DNA mismatch repair (MMR) genes. Even with prospective cancer surveillance, LS is associated with up to 50% lifetime risk of colorectal, endometrial, and other cancers. While significant progress has been made in the timely identification of germline pathogenic variant carriers and monitoring and early detection of precancerous lesions, cancer-risk reduction strategies are still centered around endoscopic or surgical removal of neoplastic lesions and susceptible organs. Safe and effective cancer prevention strategies are critically needed to improve the life quality and longevity of LS and other HCS carriers. The era of precision oncology driven by recent technological advances in tumor molecular profiling and a better understanding of genetic risk factors has transformed cancer prevention approaches for at-risk individuals, including LS carriers. MMR deficiency leads to the accumulation of insertion and deletion mutations in microsatellites (MS), which are particularly prone to DNA polymerase slippage during DNA replication. Mutations in coding MS give rise to frameshift peptides (FSP) that are recognized by the immune system as neoantigens. Due to clonal evolution, LS tumors share a set of recurrent and predictable FSP neoantigens in the same and in different LS patients. Cancer vaccines composed of commonly recurring FSP neoantigens selected through prediction algorithms have been clinically evaluated in LS carriers and proven safe and immunogenic. Preclinically analogous FSP vaccines have been shown to elicit FSP-directed immune responses and exert tumor-preventive efficacy in murine models of LS. While the immunopreventive efficacy of “off-the-shelf” vaccines consisting of commonly recurring FSP antigens is currently investigated in LS clinical trials, the feasibility and utility of personalized FSP vaccines with individual HLA-restricted epitopes are being explored for more precise targeting. Here, we discuss recent advances in precision cancer immunoprevention approaches, emerging enabling technologies, research gaps, and implementation barriers toward clinical translation of risk-tailored prevention strategies for LS carriers. We will also discuss the feasibility and practicality of next-generation cancer vaccines that are based on personalized immunogenic epitopes for precision cancer immunoprevention.

Introduction

Cancer prevention strategies are generally centered around the reduction of cancer risks. Modifiable cancer risk factors include tobacco use, alcohol consumption, obesity, diabetes, and infection with oncogenic viruses, such as human papillomaviruses (HPV) and hepatitis B virus (HBV). Lifestyle changes and receiving prophylactic vaccines against HPV and HBV can significantly reduce these risks (1). In contrast, genetic predisposition to cancer is not modifiable. Individuals with hereditary cancer syndromes (HCS) account for 5 to 10% of all cancer cases (2). They are clinically identifiable by genetic testing (3–5), are well characterized with predictable ages of disease onset, organ involvement, and molecular pathophysiology, and can be closely monitored for early cancer detection and diagnosis based on HCS guidelines (6). Cancer risk mitigation strategies for HCS carriers, therefore, include primary prevention of cancer as well as detection and elimination of cancer precursors and early-stage (in situ) cancers before they progress to invasive cancers, the approach referred to as cancer interception (7). While much progress has been made in the development of new or improved methods of detecting cancer early (8), with the exception of aspirin for Lynch syndrome (LS) (9) there are currently no effective cancer preventive or interceptive approaches available to them other than surgical (endoscopic or surgical) removals. Most of the conventional anticancer therapeutic agents are too toxic for cancer interception.

Vaccine-preventable infectious diseases (10) are a good example of illnesses that can be safely prevented if vaccines are used as recommended. The unprecedented speed of successful development and deployment of COVID-19 mRNA vaccines in 2020 ~ 2021 was a tremendous scientific achievement that had culminated from years of research in relevant scientific disciplines, including coronavirus virology, vaccinology, and innovative mRNA vaccine technology, and a strong public-private partnership (11, 12). Prerequisites for successful vaccine development generally include identification and characterization of causative agents, understanding of disease pathogenesis and pathophysiology, and availability of suitable preclinical tools and animal models, which recapitulate human disease conditions and host immune responses and therefore can provide a proof of principle for in vivo vaccine efficacy. Compared to prophylactic vaccines against infectious pathogens that have generally fulfilled these prerequisites throughout the history of vaccine development (13), the development of cancer vaccines in general has been met with more challenges (14).

The effect of cancer vaccine was first evaluated in cancer patients in 1959 (15) shortly after Burnet postulated the concept of cancer immunosurveillance (16). While important advancements were made in cancer immunology and vaccinology over the last several decades, the majority of cancer vaccine research has focused on eliciting effective antitumor immunity to treat advanced cancer (17). Various therapeutic cancer vaccines were extensively evaluated in patients with advanced cancer with little success, likely due to the immunosuppressive factors locally in the tumor microenvironment (TME) and systemically (18). The concept of cancer vaccines for immunoprevention started gaining more traction in the last 20 years owing to the pioneering work of Finn, Disis, and others against non-viral cancers (14, 19–25). Target antigens selected for cancer preventive vaccines have predominantly been tumor-associated or tumor-specific antigens that are overexpressed or specifically expressed in cancer precursors and cancer cells and proven immunogenic across different HLA types (26, 27). Cancer vaccines with such commonly expressed (shared) tumor antigens can be more easily studied for efficacy in a well-defined high-risk cohort and thus can be streamlined for further development.

More recently, successful immunotherapy outcomes with immune checkpoint inhibitors (ICI) for various cancers have clearly shown that the immune system can mount strong antitumor immune responses leading to complete remission in some cases if systemic and local immunosuppression in the TME is effectively blocked (28, 29). Interestingly, antitumor immune responses unleashed by immune checkpoint blockade have been shown to target a large repertoire of tumor antigens that are unique to individual patients (i.e., personalized antigens) (30–32). It is conceivable that more robust and durable antitumor immunity can be elicited by cancer vaccines in the prevention or interception setting, wherein local immunity in the TME is less compromised and there is still low clonal heterogeneity of tumor antigens (22, 33, 34). Naturally, questions arise as to whether immunopreventive cancer vaccines can be developed based on personalized tumor antigens and whether such personalized vaccines are more efficacious and desirable than cancer vaccines that target shared/common tumor antigens. This review will discuss the development of LS vaccines as a model strategy for preventing cancer in HCS cohorts, emerging enabling technologies, research gaps and implementation barriers for cancer immunoprevention, and research trajectory towards next-generation precision cancer vaccines for immunoprevention.

Precision cancer prevention

Apart from the modifiable risk reduction strategies discussed earlier, cancer prevention for high-risk cohorts can be improved by determination of risks based on oncogenic mechanisms inherent to a specific cohort, closer monitoring of affected individuals for early cancer detection, and timely and effective interventions developed specifically for each high-risk group. These risk-tailored cancer prevention strategies are interchangeably referred to as personalized or precision cancer prevention. For the purpose of this review, which is focused on cancer prevention strategies for HCS cohorts, in particular LS, we define precision cancer prevention as risk-tailored cancer prevention strategies informed by underlying oncogenic mechanisms responsible for the development and progression of cancer and molecular alterations targetable for cancer prevention and interception in high-risk populations (35). We will use the term “personalized” when we refer to tumor antigens unique to each individual as opposed to shared or commonly expressed antigens (36).

The concept of precision oncology was originally introduced as cancer genomics-informed “personalized or precision” cancer medicine to facilitate the decision on treatment choices for individual cancer patients (37). The common denominators of precision cancer prevention and precision cancer medicine strategies are the involvement of molecular and immune mechanisms of oncogenesis in the decision-making process for interventions rationally and uniquely developed for individuals. Neoantigens are newly acquired and expressed “non-self” antigens arising from gene mutations, exogenous genes (e.g., viral proteins), or alternative antigen processing. The host immune system recognizes these neo-peptides presented with MHC molecules on the cell surface as non-self, mount immune responses against them, and eliminate the neoantigen-expressing aberrant cells from the body (38). During tumor development and progression, tumors accumulate numerous gene mutations, which, if translated, give rise to neoantigens (39, 40). These neoantigens expressed in cancers can be targeted by the host immune system for surveillance and elimination. Although it’s been long postulated that tumor neoantigens would serve as promising cancer vaccine antigens, the discovery and neoepitope selection was a major hurdle until recently. The advent of next-generation sequencing technologies and rapid development of powerful computational analytical tools, which enable comprehensive “mutanome” analysis of individual tumors and the identification of personalized immunogenic neoepitopes, has led to seminal neoantigen cancer vaccine studies in melanoma patients either as peptide-based (41) or mRNA-based vaccines (42). Both studies demonstrated immunized patients mounted robust T cell responses to unique neoantigens, associated with prolonged and objective responses in some cases. The long-term outcome study of patients who received personal neoantigen vaccines demonstrated the clinical benefit at a median follow-up of 4 years post-vaccination and long-term persistence of memory T cells specific to personal neoantigens as well as the evidence of epitope spreading (43).

While there is mounting evidence to suggest that immune responses directed against personal neoantigens can block or control cancer growth and clinically benefit vaccinated patients (30–32, 41–43), the approach cannot be generally translated into the prevention setting unless tumor neoantigens could be “predicted” in individuals who have yet to develop cancers. The first breakthrough observation was made by Kloor, von Knebel Doeberitz, and colleagues, who demonstrated that insertion/deletion frameshift (FS) mutations could be predicted based on the known genetic sequences of coding microsatellites (cMS) and that the specific mutation frequencies could be evaluated in LS/mismatch repair deficiency (MMRd)-associated tumors (44, 45). They discovered that colonic adenomas from LS carriers harbored MMRd-driven FS mutations in the cMS regions at high frequencies for certain genes at levels similar to those found in colorectal carcinomas (46). They further demonstrated frameshift peptides (FSP)-specific T cell responses could be observed not only in LS patients with colorectal cancer (CRC), but also in cancer-free asymptomatic LS carriers (47). These findings clearly demonstrated that LS carriers harbored FSP neoantigens before the onset of overt CRC tumorigenesis and the host immune system was capable of mounting anti-FSP immunity, which may play a role in keeping tumor growth in check. Using commonly recurring (broadly shared) FSP (rFSP) neoantigens as vaccine antigens, they subsequently proposed an rFSP neoantigen-based cancer vaccine for MMRd cancers and successfully demonstrated the safety and immunogenicity in Phase I/IIa clinical trial (48), providing the proof of principle of rFSP neoantigen-based cancer vaccine strategies for cancer prevention and interception in LS carriers.

Tumor-specific neoantigens can be vastly heterogenous and immune responses are restricted by MHC molecules (38). Their expression levels also vary among different neoantigens. Because of the intra- and inter-individual heterogeneity of tumor-specific neoantigens and the diversity of immune responses that are determined by HLA alleles, it is extremely challenging, if not impossible, to develop broadly applicable cancer vaccines targeting shared neoantigens for different HCS carriers, with LS being an exception as discussed earlier. In this regard, the development of personalized cancer preventive vaccines may be more straightforward. Similar to the approach used for the development of precision cancer therapeutic vaccines (41, 42), personalized immunogenic epitopes for preventive vaccines can be identified from molecular and immuno-neoepitope analysis of precancerous lesions. The question, however, is whether there is an advantage to personalize FSP-based cancer preventive vaccines for individual LS carriers when shared FSP antigens can be readily identified. Considering the amount of time and resources required to generate such personalized FSP vaccines for nearly one million LS carriers in the US alone, the concept of personalized immunopreventive cancer vaccines is prohibitively impractical for the LS cohort at this time. At the same time, the debate on the use of personalized neoantigen based vaccines for cancer prevention should also involve the lifetime disease severity and progression trajectory. For example, consider children with constitutional mismatch repair deficiency (CMMRD) syndrome, one of the most aggressive forms of childhood cancer predisposition syndromes, resulting from biallelic deleterious germline mutations in the MMR genes (49). As in LS, DNA MMR deficiency can trigger FS mutations in the cMS, giving rise to FSP neoantigens in these children (50). Children with CMMRD develop brain tumors, hematological malignancies (in particular, non-Hodgkin lymphomas of T-cell lineage, T cell ALL and AML), gastrointestinal and other LS-associated cancers, sarcomas (e.g., osteosarcoma and rhabdomyosarcoma), and other childhood cancers (e.g., neuroblastoma and Wilms tumor) (49, 51). Cancers arising in children with CMMRD have the highest mutational and MS insertion-deletion (MS-indel) burden, are resistant to chemo-radiation interventions, and considered lethal (52). Children with CMMRD therefore may clinically benefit from receiving personalized cancer preventive vaccines. If we aim to develop and deploy risk-tailored and risk-weighted precision cancer prevention strategies, the critical first step is to identify the genetic predisposition carriers and investigate the pathophysiology of oncogenesis in each HCS population.

Advances in genetic predisposition screening technologies

Of the ~140,000 new diagnoses of CRC each year in the United States, ~25% to 30% of patients diagnosed have a first or second degree relative (parents, siblings, children, uncles, aunts and first cousins) with CRC (53). The most common inherited CRC syndromes is LS, which is diagnosed by germline autosomal dominant mutations of DNA MMR genes, MLH1, MSH2, MSH6 and PMS2, or structural variations in EPCAM that drive MSH2 epigenetic inactivation (54, 55). LS is estimated to occur in approximately 1:280 individuals (56). A related syndrome is CMMRD discussed above, a much less frequent pediatric autosomal recessive disease where children inherit bi-allelic MLH1, MSH2, MSH6 or PMS2 mutations that drive aggressive cancer predisposition and are affected with cancers as often as every 2-3 years in early life and commonly perish from brain, GI, and hematopoietic malignancies (50). Additionally, autosomal recessive mutations of MMR genes, MSH3 or MLH3, cause colorectal polyposis and CRC, which is a separate syndrome characterized by distinct patient phenotype and familial inheritance pattern (57–60). This review section focuses on LS.

Historically, LS was referred to as hereditary non-polyposis colorectal cancer (HNPCC). However, LS is now preferred to highlight that that these patients and their families have higher rates of multiple other cancers, most notably endometrial and gastric cancers, but also including ovarian, pancreatic-biliary, urinary tract (kidney, renal pelvis, ureter, bladder, and prostate), small intestinal, brain cancers and sebaceous neoplasms of the skin, among others (61).

Overall, there are two primary strategies for diagnosis of LS. Historically, family history followed by germline DNA mutation testing performed on patients with personal or family history of cancer suspicious for LS was the primary approach. This approach uses different clinical criteria, most notably the Amsterdam or Bethesda criteria (53), both of which focus on age on onset for CRC, and family cancer history of first-, second- and third-degree relatives for LS associated cancers. However, family history taking, while virtually costless and in principle universally implemented, was found to significantly underdiagnose LS (62–65). In part, at least in the United States, this is driven by growing provider economic and corporate medical pressures on primary care physicians to rapidly provide comprehensive medical care for ever growing panels of patients (which can average 1 patient every 15 minutes in some clinics anecdotally) and primarily focus on symptomatic crises rather than less urgent preventative medical care that shortchange history and disease interception strategies. Additionally, there are non-trivial rates of non-paternity, which range from ~0.4% to as high as 30% in different populations but roughly averaging to ~1% across different societies (66, 67), further confounding family history taking. Most importantly, population screening studies of CRC and endometrial cancer (68, 69), primarily by immunohistochemistry (IHC) testing, provided direct data revealing that many LS patients had limited family history and/or later onset cancers. In addition to the practical health services implementation issues with family history taking discussed above, this is driven by the fact that family history does not capture patients who have de novo LS mutations (aka, a new mutation not inherited from either biological parent) and because many LS patients carry MSH6 or PMS2 mutations, which confer lower overall lifetime cancer risk than MLH1 or MSH2 mutation carriers (54, 56).

Underdiagnosis of LS is not unique to the United States. It also remains a challenging issue in Europe (70, 71). A Swedish study has demonstrated that one third of LS patients referred for genetic testing already had cancer, indicating that these individuals’ genetic risk was unknown until they developed LS-associated cancers (72). This proportion of individuals diagnosed with LS due to cancer diagnosis has not changed over the decades (72). A survey across 14 Western European countries showed that the quality of family history taking was thought to be generally poor and there were virtually no specific campaigns or strategies in place to increase the public awareness of hereditary cancers except in one country (Germany) (73). Another study has also reported that family history has been poorly documented even in the electronic health record (74) contributing to the deficiency in family history taking approach. Thus, while personal and family history taking approach has been the cornerstone of LS diagnosis, it significantly undercounts its prevalence.

The second primary approach to LS diagnosis starts not with clinical criteria per se, but molecular screening of CRC, endometrial, pancreatic and other tumor specimens for evidence of MMRd from patients who are diagnosed with carcinomas. This can be performed as polymerase chain reaction (PCR)-based microsatellite instability (MSI) testing of newly or previously archived tumors after diagnosis, immunohistochemistry (IHC) for MMR genes, or direct tumor gene panel sequencing and mutation burden analysis to identify patients who then have germline testing for MMR gene mutations (which is sometimes but not always done simultaneously with tumor sequencing) (54). Importantly, these molecularly initiated approaches also identify patients who have sporadic (aka non-LS) MMRd tumors, which arise via somatic (non-CMMRD congenital) bi-allelic mutation and/or epigenetic inactivation of MMR genes, primarily MLH1 (54).

Cascade testing involves targeted mutation analysis testing of blood relatives from patients who are affected by a genetic disease (4, 75–77). Recent clinical trial and cost effectiveness research studies have provided evidence that primary molecular screening augmented by follow-up targeted cascade testing of family members of affected LS (and other genetic disease) probands is a cost effective public health strategy to diagnose a high percentage of LS mutation carriers that is predicted to identify almost all affected individuals after approximately a decade of implementation (4, 75–77).

Analysis of circulating cell-free tumor DNA (cfDNA) using liquid biopsy has enabled non-invasive detection of tumor mutations. Currently, liquid biopsy is an established technology to replace and/or augment tumor biopsy sequencing, including the detection of minimal residual disease (MRD) after surgery or chemotherapy. Liquid biopsy can be used for detection of LS germline mutations (78) and for detecting MMRd tumors, which carry very elevated tumor mutation burdens. Currently, the highest sensitivity for liquid biopsy detection of MMRd tumor DNA is shown by low pass whole genome sequencing of cfDNA and mutation signature analysis (79), which draws on sequence data from low pass coverage sampling of the ~23,000,000 microsatellites encoded in the human genome (50). However further studies, and perhaps additional augmentation by orthogonal technologies including protein and microbial analyte data streams, are needed before this population surveillance strategy becomes useful for LS diagnosis, screening, and surveillance.

Because of the high tumor mutation burden, it has been long been appreciated that MMRd tumors have elevated numbers of tumor infiltrating lymphocytes (TILs) and other enriched histopathology features of the tumor microenvironment (54). Recently, there has been evidence that machine learning of histopathology images of tumors can detect “MMRdness (80)”. However, although innovative and expected to improve in the future with advances in computational analysis, the sensitivity and specificity of ML histopathology detection of MMRd status may currently be problematic and not just yet ready for clinical application (81).

In summary, identification of LS is important because it is a clinically actionable diagnosis driving increased tumor surveillance, chemoprevention [primarily aspirin, discussed at length elsewhere (9)] and post-tumor therapy choice [immune checkpoint inhibition, discussed at length elsewhere (82)] in a high-risk cancer population. Screening and diagnosis of LS mutation carriers is an evolving paradigm that begins with personal and family history as its cornerstone, but requires additional molecular, computational and health services analyses to reach its potential for improving survival for affected patients. This point is particularly true regarding underrepresented and underserved minority populations, which often have lower rates of information collection or access from detailed family history taking, in addition to less access to preventative medical care and cost-intensive medical technologies (62–65).

Lynch syndrome vaccines for immunoprevention

LS is an ideal model disease for testing the potential of cancer immunopreventive approaches due to the pathogenesis of LS-associated tumors. The penetrance of the disease varies widely depending on the MMR gene affected in the germline, with MLH1 and MSH2 genes being associated with relatively high cancer risk (about 50%: Prospective Lynch Syndrome Database or PLSD) (83) and found in about 90% of LS-associated tumors. In addition to the affected MMR gene, further factors are suspected to influence individual cancer risk in LS carriers, as also within-gene and even within-family differences in cancer risk have been observed. These factors include other genetic constellations (polygenic risk score) and environmental influences. More recently, the possible influence of the immunological factors has also been gaining substantial attention, which is related to the growing knowledge about a close interplay between arising MMR-deficient tumor cell clones and the immune microenvironment.

The driving force of carcinogenesis in LS is MMRd, leading to the inability of affected cells to repair base mismatches occurring during DNA replication. When unrepaired, such errors cause indel mutations, typically indels of one or two nucleotides at short repetitive DNA stretches (microsatellites). The molecular phenotype of MMR-deficient cells is characterized by the accumulation of insertion/deletion mutations at microsatellites and called MSI as discussed earlier. Microsatellite regions are distributed over the entire human genome. Because approximately 99% of the genome has no protein-coding function, most microsatellite mutations do not have an immediate effect on a cell’s functional phenotype. However, mutations at microsatellites in protein-coding genomic regions (coding microsatellites, cMS), which are mostly mononucleotide repeats, can have drastic consequences on the function of the encoded protein and its immunological properties. Due to an insertion or a deletion of one or two nucleotides, the entire subsequent reading frame is shifted (frameshift mutations). This can lead to premature stop codons and translation of truncated, non-functional proteins. cMS mutations affecting tumor suppressor genes can drive tumorigenesis in the mutated cell. Simultaneously, the newly translated FSP sequence often contains numerous epitopes, which are foreign to the host’s immune system, rendering the affected cells highly immunogenic.

Thus, the carcinogenic process of LS cancers is tightly linked to the generation of highly immunogenic antigens. This provides a basis for immune-modulatory therapies, e.g., using immune checkpoint inhibitors, which can reactivate a pre-existing, but exhausted immune response. However, for a largely applicable preventive approach, the predictability of the antigens plays a crucial role. The Darwinian selection principles behind the evolution of MSI cancers allow the prediction of antigens before a cancer develops (Figure 1A). As microsatellite mutations that confer proliferation and growth advantage (by disabling tumor suppressor genes while evading immune recognition and elimination) will be selected for, the respective antigens are over-represented in cancer precursors and manifest cancers and thus serve as promising vaccine targets.

FIGURE 1

Figure 1 Schematic illustration of MMRd tumor clone selection process and workflow for selecting a pool of shared immunogenic neoantigens. (A) Schematic illustration of Darwinian selection process underlying MMRd cancer evolution. The random accumulation of indel mutations in cMS, caused by impairment of the MMR system, is followed by a non-random persistence of mutations. Cell clones carrying mutations that promote tumor outgrowth or provide other survival advantages, such as immune evasion, are positively selected. This evolutionary selection of mutations leads to recurrent cMS mutation patterns in MMRd cancers and thus a predictable pool of FSPs. (B) Strategy for the selection of shared, immunogenic FSPs for immunoprevention. Screening of a genome-wide cMS database forms the basis for the identification of recurrent cMS mutations shared by MMRd cancers. Accounting for mutation frequency in MMRd cancers, immunogenicity prediction in silico and immunogenicity testing in vitro, a pool of candidate neoantigens can be selected for FSP vaccines.

Using a comprehensive bioinformatics approach, we and others previously characterized cMS across the human genome, establishing two major findings: (1) the mutation frequency of a microsatellite largely depends on its length, following a sigmoid curve; (2) cMS mutations with a frequency higher than what was predicted based on microsatellite length are the likely drivers of tumorigenesis, reflecting selection during MSI cancer evolution. Using the information about length-adjusted mutation frequency, it is possible to predict the relevance of specific mutations in the carcinogenic process and their frequencies.

Using this approach, we were able to trace the evolution of MSI cancers down to a few recurrent mutations shared across tumors and patients, opening a new avenue in the field of cancer prevention. By identifying key driver mutations in the MSI carcinogenic process, we predicted the resulting peptide structures resulting from these mutations and demonstrated their ability to induce T cell responses in vitro (47, 84) (Figure 1B). In the next step, these candidates were combined in a trivalent vaccine containing three recurrent and immunogenic antigens derived from AIM2 (-1 deletion), TAF1B (-1 deletion) and HT001 (-1 deletion) frameshift cMS mutations that were shared by more than 85% of MSI tumors. This vaccine was evaluated in a first-in-human Phase I/IIa clinical trial analyzing the safety and immunogenicity of a cancer vaccine in a total of 22 patients (48). The study demonstrated a favorable safety profile with no treatment-related severe systemic adverse effects observed in any of the vaccinated patients. However, grade 2 local injection site reactions have been observed in 3/22 vaccinated study participants, indicating that vaccination-related side effects need to be accounted for in future vaccine formulations and strategies, particularly in LS carriers with pre-existing FSP-specific immune responses. Importantly, all patients vaccinated per protocol demonstrated FSP-specific cellular (predominantly CD4 T cells) and humoral immune response against at least one vaccine antigen.

Although the results of the first clinical trial with the trivalent FSP peptide vaccine described above were highly encouraging, antitumor efficacy was not the primary objective, which would have required a long-term follow-up if tumor recurrence was used as the primary endpoint. To ask whether the FSP-based cancer vaccine could prevent or intercept CRC tumorigenesis, pre-clinical studies in mouse models have been performed. Based on the VCMsh2 LS mouse model developed by Kucherlapati and Edelman et al. (85), which recapitulates human LS-associated intestinal tumorigenesis by biallelic Villin-dependent conditional knockout of Msh2 in the entire intestinal epithelium, the preventive effect of rFSP vaccination was evaluated. Bioinformatics analysis of 488,235 cMS in the murine genome combined with the gene expression and mutation frequency data identified thirteen candidates possibly relevant for the MSI tumorigenesis in Lynch mice (86). The immunological assessment including epitope prediction and immunogenicity analysis revealed four promising candidates for vaccination. Vaccination with these candidates, Nacad (-1 deletion), Maz (-1 deletion), Senp6 (-1 deletion), and Xirp1 (-1 deletion) alone or in combination with non-steroidal anti-inflammatory drugs (NSAIDs) (aspirin or naproxen), which have been examined for chemo preventive effects in LS patients (9, 87, 88), elicited robust T cell immune responses as measured by IFNγ ELISpot and a significant tumor-preventive effect in VCMsh2 mice. Interestingly, the tumor-preventive effect was strongest in the rFSP vaccine plus naproxen combination arm, supporting the hypothesis that NSAIDs may enhance vaccine-induced antitumor efficacy by reshaping the immune microenvironment in the intestinal mucosa and enhancing immune surveillance (89, 90). Clinically, this suggests that the reduction of tumor incidence by NSAIDs, reported for Lynch carriers in retrospective (91) and controlled prospective studies (9, 87, 88), could further be enhanced by FSP vaccines.

In addition to the peptide based FSP vaccination approach, further studies pursuing a different vaccination approach are currently underway, including viral vector-based FSP antigen delivery with a substantially higher number of antigens (over 200), which have been derived from human MSI tumors as analyzed by in vitro, in vivo and ex vivo tools. The study is currently recruiting LS patients for evaluation in clinical trials (92).

HLA genotype and tumor antigen evolution

Both clinical and preclinical data on FSP vaccines’ effectiveness hinted at the importance of epitope selection for eliciting CD4 or CD8 T cell responses. Although the dominant role of cytotoxic CD8 T cell response has been suggested in the efficacy of tumor cell elimination, immune responses engaging CD4 T cell response have been gaining more relevance with growing knowledge in tumor immunology (93, 94). Specifically, the human trivalent FSP vaccine trial (48) demonstrated that FSP-specific immune responses were predominantly mediated by CD4-positive T cells, whereas less than 50% of vaccinated patients developed significant CD8-positive T cell responses. Although this may in part be related to the use of long peptides and Montanide ISA51 used as an adjuvant, the response pattern may also reflect the availability of epitopes in the FSP sequences compatible with the HLA genotype of vaccinated individuals.

The induction of cellular immune responses requires the presentation of epitopes through HLA (or MHC) molecules. Whereas CD4-positive T cell receptors interact with HLA class II molecules, CD8-positive T cells interact with HLA class I molecules. As the structure of the HLA molecules determines the binding affinity and presentation of certain epitopes (95, 96), the HLA type of an individual influences the epitope repertoire presentable by tumor cells and antigen-presenting cells. As the repertoire of HLA molecules is large and the HLA class I and HLA class II antigen-encoding gene loci are highly diverse, the clinical efficacy of cancer vaccines will vary depending on an individual’s HLA genotype. Observations from the recent COVID-19 pandemic regarding the disease course and responses to vaccination with SARS-COV-2 antigens support this association (97–99). Thus, although the shared nature of the driver cMS mutations among different patients and tumors allows the use of a limited set of recurrent candidate neoantigens, response to vaccination and tumor-preventive effectiveness might be substantially improved by the adaptation of the epitopes derived from these neoantigens to the specific HLA type of an individual. Accounting for HLA type specificity is therefore an important task for future immune prevention and interception approaches, potentially enabling higher vaccine effectiveness by individualization of the vaccine formulation to a person’s HLA genotype (100).

It is plausible to assume, that HLA genotype influences not only immune responses induced by vaccination, but also natural immune responses in LS carriers prior to and after tumor manifestation (47, 101, 102). If HLA type in fact influences immune surveillance, the HLA genotype may influence the tumor incidence or tumor risk in LS carriers. Studies analyzing the possible effect of the individual HLA type on cancer risk in LS have been initiated (100, 103). The findings expected from these studies will guide the future development of next-generation HLA-adapted individualized neoantigen vaccines.

Significant recent advances in vaccine technology and immunology indicate that individualized vaccine strategies hold potential for future cancer immune prevention and interception. At the same time, this strategy poses a challenge to the production process of vaccines. Even if certain HLA genotypes with similar peptide-binding characteristics can be united under an HLA supertype, vaccine formulations need to be adapted and produced on a relatively short-term. Peptide-based vaccination, though offering a robust and well-studied and evaluated technology, may lack sufficient flexibility for adaptations, particularly in the scenario of therapeutic application in patients with manifest cancer.

The application scenario (preventive, interceptive or therapeutic) also directly affects the maximal possible level of individualization. Preventive applications can maximally target likely antigens derived from predictable mutation events. The selection of candidate epitopes is restricted to the candidate antigens proven highly relevant and immunogenic. Personalization according to the current knowledge would concern the HLA genotype-adjusted epitope selection, although not the candidate spectrum itself. Such “warehouse” vaccines (104, 105) transferred to the high-risk scenario of LS would focus on the most frequently shared FSP neoantigens adjusted to the predominant HLA alleles (Figure 2). In this setting, peptide-based vaccine may possibly offer a time- and cost-saving solution. In a therapeutic or intercepting approach, the presence of a manifest tumor would enable the analysis of tumor mutanomes and peptidomes (36), binding affinity to the specific HLA molecules of the patient, and immunogenicity, thus enabling the construction of a vaccine based on the given specifics of the tumor and patient. However, this process is time-consuming and may not be ideal for timely initiation of interventions required for cancer interception and treatment.

FIGURE 2

Figure 2 Conceptual illustration of next-generation FSP preventive cancer vaccines. After the selection of immunogenic FSP frequently shared among MMRd cells, FSP epitopes can be HLA supertype-adjusted, facilitating higher vaccine effectiveness. Vaccine delivery can be pursued, among others, with different approaches including peptide, viral vector-based, or mRNA technology. Immunologically, the first step will be vaccine-induced priming, during which the delivered neoantigens encounter antigen-presenting cells, most importantly dendritic cells at the injection site. Neoantigen-loaded dendritic cells traffic to lymph nodes which are primary sites of T cell priming. There, vaccine-derived antigens on HLA class I and II molecules are presented to CD8⁺ and CD4⁺ T cells. Activated T cells proliferate and mature into effector T cells which leave the lymph nodes entering the periphery. Ideally, neoantigen-specific CD8⁺ T cells can induce apoptosis in MMRd cells, which present the respective antigen on HLA class I molecules, and thereby prevent cancer.

Throughout the continuum of tumor development, the immune system constantly interacts with emerging tumor cell clones, the process known as tumor immunoediting. When developing immunopreventive or immunotherapeutic approaches, tumor immune evasion should be taken into consideration, as the host immune system will not recognize “escape” tumor clones effectively. The interplay between arising tumor cell clones and host’s immune cells and characteristics of tumor evolution have been illustrated by several studies (101, 106–110). Such an interplay is particularly pronounced in the scenario of the highly immunogenic MSI cancers. Among those, the ones with LS background could be exposed to a longer process of immunoediting, as the LS carriers have been reported to present with microscopic lesions with normal histomorphology but lacking MMR protein expression (111). Such lesions were proposed to induce the systemic and local immune responses measurable in the blood and colon of tumor-free LS carriers, respectively (47, 102), suggesting the process initiated long before a clinically detectable tumor. We have previously shown that higher frameshift mutation frequency is correlated with lower immunogenicity of resulting neoantigens, whereas a lower frequency of mutations correlated with highly immunogenic antigens (101). This inverse correlation suggests counter-selection of cell clones expressing highly immunogenic neoantigens according to the tumor immunoediting concept. On the other hand, such counterselection may be negated in cases where antigen presentation machinery is dysregulated. In fact, general alterations of the antigen presentation machinery are common in Lynch-associated cancers and observed at higher frequency in advanced lesions. Such evasion phenomena, including complete breakdown of HLA class I-mediated antigen presentation following mutations of the Beta-2-microglobulin (B2M) gene, can interfere with the effectiveness of vaccines. Immune evasion is considered one of the most important reasons for the limited success of previous cancer vaccine trials with a therapeutic design. Therefore, transferring vaccine approaches towards earlier stages (interception) or entirely to cancer prevention in high-risk individuals marks a paradigm change with high potential for re-shaping the field of anti-cancer vaccines and cancer prevention in general. The complexity of this biological process and limited possibilities for experimental investigation calls for mathematical modeling approaches that could account for possible variables and predict the repertoires of relevant antigens or even model the possible outcomes of immune interception approaches.

Advances in next-generation sequencing together with the development of in silico epitope prediction algorithms and ex vivo assessments of immune responses revolutionized the identification of tumor neoepitopes suitable for cancer vaccines (30, 112). Tumor neoantigens originating from genomic alterations beyond those resulting from MMRd-triggered frameshift mutations can also be targeted by the immune system as non-self, leading to tumor-specific T cell responses (32, 113, 114). In fact, vaccination strategies targeting specific tumor neoantigens have demonstrated effective T cell responses against tumor specific antigens and potential clinical benefit (41, 42, 115–117). Neoantigen-based cancer therapies are highly personalized, requiring the development of a vaccine for each individual patient, which limits scalability and availability. To ensure broader applicability, cancer vaccines targeting shared immunogenic neoantigens originating from functionally relevant driver mutations on genes such as KRAS (114, 118), TP53 (119, 120), BRAF (121), PIK3CA (122), and EGFR (123, 124) or from recurrent gene fusions typically occurring in sarcomas (125, 126) have been pursued. However, compared to FSP in LS cancers, the pool of such antigens is limited and their effectiveness in an HLA-diverse population needs to be investigated for other HCS setting. Therefore, the development of personalized cancer vaccines may be warranted for cancer prevention in other HCS carriers, who unlike in LS do not share recurrent neoantigens for “off-the-shelf” vaccine formulations with defined and validated neoantigens. Neoantigen based approaches are promising and likely to improve further with advances in neoantigen prediction pipelines.

Computational tools predictive of peptide binding affinity to specific HLA molecules are available (96, 127–129). These are typically neural network-based algorithms that were trained using existing allele-specific peptides such as those stored in the Immune Epitope Database (130) and Dana-Farber Repository for Machine Learning in Immunology (131). Recently, HLA immune-peptidomics-based approaches to discover HLA-restricted peptides generated large-scale datasets of endogenous HLA-bound peptides that resulted in the development of more accurate epitope prediction algorithms. These algorithms not only predict epitope binding to a specific HLA allele but also consider epitope expression and proteasomal processing to predict epitope presentation more accurately (132). The final frontier in the prediction of neoantigens is the development of an algorithm that can accurately predict antigenicity. Neoantigens can be presented on surface HLA, however not all presented epitopes generate a T cell response, and even presented viral epitopes may not be recognized by T cells (41, 42, 115–117, 133) in some cases. The development of an accurate machine learning algorithm requires thousands of validated T cell epitopes per HLA allele, which are currently unavailable for many alleles. There are ways to overcome this obstacle. Researchers observed more stable HLA ligands yield more immunogenic epitopes (134, 135) and epitopes mimicking pathogen-derived known antigens are more immunogenic (136, 137). Incorporation of these motifs to the neoantigen prediction pipelines may improve vaccine outcomes. Utilization of all these informatics and genomics pipelines may identify validated immunogenic shared neoantigens for LS patients in an HLA-type specific manner that could be the basis of a cancer preventative vaccine. It is plausible that such vaccines may prevent not only LS-associated CRC, but also endometrial and other extracolonic tumors (61, 138) as certain neoantigens are likely shared by the spectrum of cancers arising in LS (Figure 3).

FIGURE 3

Figure 3 Immunopreventive potential of neoantigens shared by tumors in different organs. In addition to the characteristic LS-associated tumors in the colorectum and endometrium, the tumor spectrum in LS also encompasses other organs such as brain, skin, and kidney tumors. It is possible that certain neoantigens are shared across different tumor types (red, purple, green, and aqua blue antigens in the upper panel). Immunopreventive vaccines with these shared neoantigens that are further adjusted by an individual’s HLA genotype may allow the development of a personalized, preventive vaccine without organ restriction. Such an approach would be particularly valuable for cancers without screening options. This is illustrated by the example in the lower panel of the figure, where one of the shared neoantigens (green) is not included in the vaccine formulation because this green antigen does not contain neoepitopes that bind to the individual’s HLA molecules.

Emerging technologies, research gaps, and translational barriers

Emerging new mRNA-based vaccine technologies (139, 140) have transformed the medical field by offering a technological platform with high adaptive capacity, allowing rapid translation of newly gained genomic knowledge into clinical applications for the prevention and treatment of human diseases (42, 141–143). In addition to its high immunogenicity, flexibility and versatility, relatively straight forward regulatory requirements successfully established during the COVID-19 pandemic make mRNA-based vaccination approaches attractive for personalized medical interventions such as precision cancer preventive vaccines. Other innovations in mRNA vaccine platforms include the use of self-amplifying RNA (saRNA) (144–148), circular RNA (circRNA) (149, 150) and modified LNP formulations for mRNA delivery (151–155). While these non-linear mRNA molecule-based vaccines are expected to offer “amplified” expression of encoded proteins in vivo requiring lower RNA doses (saRNA) and improved scalability with potentially lower toxicity concerns (circRNA), novel formulations of LNP can be developed to steer the host immune system towards mounting specific immune responses desired for the intended applications (e.g., Th1 vs. Th2 immunity) (156). Innovative engineering of RNA molecules and their delivery systems is expected to further help advance the optimization of next-gen RNA vaccine design strategies for precision cancer preventive vaccines for LS and other HCS.

The gold standard of screening for immunogenic recurrent neoantigens, for example for “off the shelf” vaccines, has been to test an individuals’ autologous T cells for immune responses to specific neoantigen peptides in vitro as measured by interferon gamma production either by ELISPOT or fluorescent activated cell sorting assays. For personalized neoantigen-based therapeutic cancer vaccines, however, because of the large number of potential candidate neoantigens, individualized immunogenicity screening is challenging and time-consuming, which is not ideal due to the urgency for starting vaccinations in patients who have established cancers. Thus, immunizing peptides were selected based on the basis of HLA binding predictions for personalized cancer therapeutic vaccines (41, 42). For off the shelf antigens, one new approach is screening in transgenic mice with human HLA. Currently, these are available for selected alleles including HLA-A2 (157), HLA-A1 (158), and HLA-B7 (159). Importantly, these models have ablated endogenous murine MHC (157). More recently, a series of HLA class I knock-in (KI) mouse strains have been generated (160). In these novel HLA class I transgenic mice, a chimeric HLA class I molecule (α1/α2 domain of HLA-A and α3 domain of H-2D^b) was covalently linked with 15 aa to human Beta-2-Microglobulin (B2M) and introduced into the endogenous mouse B2m locus, resulting in the loss of endogenous mouse MHC class molecules in homozygous KI mice. HLA-restricted, epitope-specific cytotoxic T cells (CTLs) were induced in HLA KI mice upon vaccination (160). These HLA mouse models can be used as a filter towards selecting human common neoantigens as potential vaccine cargo. However, given the diversity of human HLA alleles (161), and the lack of HLA-C mouse models, these models can only be used for frequent HLA alleles.

As with any other agents under development, the demonstration of efficacy is of paramount importance to the successful development of cancer preventive vaccines. If a cancer-free period is used as a primary endpoint for efficacy in cancer prevention studies, however, it will require a larger number of study subjects and long-term follow-up in order to obtain conclusive evidence (162, 163) even in the LS and other cohorts with an increased cancer risk. Therefore, potential surrogate biomarkers, if carefully selected and included in cancer prevention clinical trials, will help delineate clinical correlates of cancer-preventive efficacies. Long-term fortification of the host immune defense against cancer is the ultimate goal of cancer preventive vaccines. Such vaccines must be able to drive and maintain antitumor immune surveillance that can effectively intercept and eliminate emerging tumor precursor cells while avoiding the immune evasion. Because immune biomarkers of cancer preventive vaccines’ efficacies have yet to be fully elucidated, multi-pronged research strategies are needed to establish immune correlates of protection, including emerging knowledge from immune biomarker studies conducted in cancer patients (164, 165), which may inform the direction of research. For example, clinically beneficial adaptive antitumor immune responses have been characterized locally in the TME and systemically. Tumor infiltrates in the TME with higher densities of antigen-specific Th1 cells, CTLs, and memory T cells, and lower densities of immunosuppressive T regulatory cells and myeloid derived suppressive cells (MDSC) are generally predictive of better outcomes in cancer patients (164, 166–172). Systemically, immune signature of more favorable responses to ICI immunotherapy has been observed in patients, who at baseline had a diverse TCR repertoire (173), a higher number of CD8⁺ effector T cells in the periphery and at the tumor margin (174, 175), and a lower level of MDSC (176–178), and had higher levels of TCR repertoire (173, 179), increased levels of CD127^low PD-1^low CD4 T cells (180), and peripheral expansion of CD8⁺ T cells (181–183) at post-treatment. Evaluation of some of these immune biomarkers that are linked to favorable clinical outcomes should be included in preclinical and clinical studies of candidate cancer vaccines, so the immune response profiles can be correlated with in vivo antitumor efficacies observed in vaccinated animals and with surrogate biomarkers of efficacies in human study subjects, respectively.

More recently, the roles of tissue-resident memory CD8⁺ T (T_RM) cells have been extensively studied in cancer immunosurveillance. T_RM cells are known to function as “pathogen alert” system against invading pathogens for the local organ systems (184–187). Mounting evidence suggests that T_RM cells are a critical component of the host immune surveillance and defense mechanisms against developing cancer (184–186, 188–190). A higher number of intratumoral T_RM cells is predictive of better overall survival (191–194). Mechanistically, these cells in the TME express immune checkpoint receptors (ICR) and exert antitumor effector functions when ICR are blocked by ICI (188, 189, 195), thus linking the presence of T_RM in the TME to more favorable responses to ICI in cancer patients. Furthermore, T_RM cells have been shown to recognize neoantigens (192, 193, 196) and can amplify ICI-mediated antitumor immunity not only by exerting effector functions but also promoting epitope spreading through dendritic cells (193, 196). Preclinical studies have shown T_RM cells can be induced by vaccination against tumor neoantigens and that vaccine-induced T_RM cells potentiated the host antitumor immunity, rejecting tumor challenge (192, 197, 198). In contrast to clinically beneficial cell-mediated antitumor immune responses widely reported to date, the prognostic value of B cell-mediated humoral immune responses in cancer patients has yet to be fully elucidated (164). Recently, a higher number of B cells in the TME and the presence of intratumoral tertiary lymphoid structures, which include B cell follicles as well as T cells, macrophages, and dendritic cells, have been observed in patients who had better clinical outcomes (164, 199). The role of B-cell mediated immunity for cancer control warrants further investigation.

As discussed earlier, in the first clinical study with trivalent FSP vaccine in patients with LS-associated or non-LS associated MSI-H CRC, all evaluable vaccinated patients showed FSP-specific humoral and predominantly CD4⁺ T cell responses (48). In the VcMsh2 LS mouse study discussed earlier, murine rFSP vaccination elicited robust FSP antigen-specific T cell responses (CD4⁺ and/or CD8⁺) and humoral immune responses systemically and upregulated intratumoral Th1 signaling pathway more so than Th2. Moreover, intestinal tumors from vaccinated VcMsh2 mice had significantly elevated levels of CD4⁺ and CD8⁺ T cell infiltrates as compared to control tumors (86). These immune response findings are consistent with what has been reported clinically and suggest that rFSP vaccine-induced immune responses were responsible for the observed cancer preventive efficacy in vivo. Cancer preventive vaccines, regardless of whether based on commonly shared tumor antigens or personalized neoantigen repertoire (predicted or omics-informed), should be able to at a minimum elicit clinically beneficial antitumor immunity discussed above ideally with long-term memory. To learn and establish immune correlates of protection against cancer, these immune parameters should be included in clinical trials for cancer preventive vaccines as part of investigative biomarker analysis.

In the premalignant and pre-invasive stage setting, there is accumulating evidence to suggest pro-tumorigenic MDSC and immune escape mechanisms mediated through immune checkpoints and immune-suppressive interleukins are already present and contributing to the malignant progression (200–202), which may suppress or hinder adaptive antitumor immune responses to cancer vaccines. Immune profiling of LS polyps has previously demonstrated a significantly increased level of pro-inflammatory and immune checkpoint molecules (203). Cancer vaccine-induced antitumor immunity, therefore, may not be sufficient to effectively prevent or arrest tumorigenic process especially in the setting of intercepting premalignant lesions. According to current knowledge, about two-thirds of LS CRC develop from MMRd crypts, suggesting loss of function of the MMR system as the initiating somatic event (204–206). In such a scenario, the generation of MMRd-triggered FSP likely precedes local immunosuppression, potentially opening a window for vaccine-mediated interception. However, the differential effectiveness of rFSP vaccines for the prevention of LS CRC triggered by MMRd vs. those resulting from MMR-proficient adenomas and subsequent MMR inactivation needs to be evaluated in future studies.

The addition of immunomodulatory agents to cancer vaccines may be warranted to induce more robust adaptive immune responses for LS and other high-risk cohorts. For example, chemo preventive effects of NSAID (aspirin and naproxen) have been extensively studied in LS patients (9, 87, 88). In addition to directly reducing the level of pro-tumorigenic prostaglandin E2 (88), naproxen has been shown to potentiate antitumor immune responses by rFSP vaccination in the VcMsh2 mouse study (86) and boosted immune surveillance in LS patients (88). In preclinical models of CRC tumorigenesis, naproxen administration has been shown to decrease the expression of PD-L1 in colon tumors and increase the density of CD8⁺ TILs (207). Since the efficacy of ICI has already been demonstrated in LS and MSI-high cancer patients, ICI-based treatment is being considered for immunointerception in the premalignant setting in LS cohort (208). There are other classes of immunomodulatory agents that can be potentially used to boost the host immune responses to cancer-preventive and interceptive vaccines (209–212). Feasibility, efficacy, and safety of combination of cancer preventive vaccines and these newer immunomodulatory agents should be explored especially for LS and high-risk cohorts through preclinical and clinical research.

Concluding remarks

The first clinical study with FSP neoantigen-based cancer vaccine (NCT01461148) was launched more than a decade ago in MMR-deficient colon cancer patients (48). This seminal study, which was built on the culmination of many years of extensive research on LS tumor molecular biology and endogenous immunity led by the same group, Kloor, von Knebel Doeberitz, and their colleagues, has dramatically changed the landscape of neoantigen-based cancer vaccine research. Over the last decade, there has been an explosion of research on tumorigenesis and genetic triggers, tumor immune surveillance, immune checkpoint mechanisms that can unleash antitumor immunity, contexture of tumor-immune microenvironment, and dynamic interplay between evolving tumors and immune defense, all of which are generating the consensus that better cancer control and favorable outcomes are achievable if tumorigenesis is intercepted earlier than later. Together with technological advances in tumor genomic landscape profiling, cancer vaccinology, and innovative immunomodulatory agents, precision cancer prevention and interception for LS carriers is within the reach. There are, however, remaining questions that must be addressed. For example, even if technical challenges of personalized FSP vaccine production can be overcome, can personalized neoantigen-based precision cancer vaccines lead to more efficacious and long-term immune protection than shared FSP neoantigen-based vaccines in LS carriers? To remain cancer free, how long do LS carriers need to maintain antitumor immune memory? Does the combination of immunomodulatory agents help sustain the durability of immune protection in LS carriers? There are translational barriers that also need to be overcome before the true benefit of precision cancer preventive vaccines are realized for LS carriers. The preclinical research field will greatly benefit from better preclinical models that can more closely mimic human LS tumorigenesis and human immune system. Newer generation of humanized preclinical models may help bridge the inter-species knowledge gap that has been a major obstacle in translational research for LS and other cancer vaccines. Lastly, as next-generation novel surrogate markers emerge from preclinical and clinical studies in the next decade, regulatory approval pathways will have to be reviewed and improved for scientific harmonization without delay. The success of FSP neoantigen-based cancer vaccines for LS cancer prevention will hopefully demonstrate the potential marketability of cancer preventive vaccines in the next decade, which will bring an increasing interest from the private sector and can lead to the partnership opportunities between academia, government, and industry for the betterment of quality of life for LS and other high-risk populations.

Author contributions

SS, AA, DK, SL, and MK wrote this review. LB, JG, and MKD critically reviewed and revised the manuscript. All authors contributed to the article and approved the submitted version.

Funding

This work was supported by NIH grants U54 CA 272688 (DK, JG, MKD, SL, MK), U01 CA233056 (JG, MKD, SL, MK), and R01 HL157174 (DK), grants from German Cancer Aid (Deutsche Krebshilfe, 70113455, 70113890), German Research Foundation (Deutsche Forschungsgemeinschaft, GE 592-9/1), and Donations against Cancer, NCT Heidelberg, and contracts from NCI PREVENT program (HHSN2612015000391).

Acknowledgments

The authors thank Drs. Olivera (Olja) J. Finn and Mary L. (Nora) Disis for helpful discussions, Drs. Asad Umar and Robert Shoemaker for supporting the PREVENT program, and all patients and patient organizations (SemiColon, Germany) involved in the research on vaccines against Lynch syndrome tumors.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Author disclaimer

The opinions expressed by the authors are their own and this material should not be interpreted as representing the official viewpoint of the US Department of Health and Human Services, the National Institutes of Health, or the National Cancer Institute.

Abbreviations

HCS, Hereditary cancer syndromes; LS, Lynch syndrome; MMR, DNA mismatch repair; FSP, Frameshift peptides; TME, Tumor microenvironment; ICI, Immune checkpoint inhibitors; cMS, Coding microsatellites; MMRd, DNA mismatch repair deficiency; CRC, Colorectal cancer; rFSP, Recurring FSP; CMMRD, Constitutional mismatch repair deficiency; Indel, Insertion-deletion; HNPCC, Hereditary non-polyposis colorectal cancer; IHC, Immunohistochemistry; MSI, Microsatellite instability; cfDNA, Cell-free tumor DNA; TILs, Tumor infiltrating lymphocytes; NSAIDs, Non-steroidal anti-inflammatory drugs; saRNA, self-amplifying RNA; circRNA, circular RNA; B2M, Beta-2-Microglobulin; MDSC, Myeloid derived suppressive cells; T_RM, Tissue-resident memory CD8+ T cells; ICR, Immune checkpoint receptors.

References

1. Williams PA, Zaidi SK, Sengupta R. AACR cancer progress report 2022: Decoding cancer complexity, integrating science, and transforming patient outcomes. Clin Cancer Res (2022) 28(19):4178–9. doi: 10.1158/1078-0432.CCR-22-2588

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Tomasetti C, Vogelstein B. Cancer etiology. variation in cancer risk among tissues can be explained by the number of stem cell divisions. Science (2015) 347(6217):78–81. doi: 10.1126/science.1260825

PubMed Abstract | CrossRef Full Text | Google Scholar

3. Tsaousis GN, Papadopoulou E, Apessos A, Agiannitopoulos K, Pepe G, Kampouri S, et al. Analysis of hereditary cancer syndromes by using a panel of genes: novel and multiple pathogenic mutations. BMC Cancer (2019) 19(1):535. doi: 10.1186/s12885-019-5756-4

PubMed Abstract | CrossRef Full Text | Google Scholar

4. Offit K, Tkachuk KA, Stadler ZK, Walsh MF, Diaz-Zabala H, Levin JD, et al. Cascading after peridiagnostic cancer genetic testing: An alternative to population-based screening. J Clin Oncol (2020) 38(13):1398–408. doi: 10.1200/JCO.19.02010

PubMed Abstract | CrossRef Full Text | Google Scholar

5. Yurgelun MB, Uno H, Furniss CS, Ukaegbu C, Horiguchi M, Yussuf A, et al. Development and validation of the PREMMplus model for multigene hereditary cancer risk assessment. J Clin Oncol (2022), 40(35):4083–94. doi: 10.1200/JCO.22.00120

PubMed Abstract | CrossRef Full Text | Google Scholar

6. Walsh MF, Cadoo K, Salo-Mullen EE, Dubard-Gault M, Stadler ZK, Offit K. Genetic factors: Hereditary cancer predisposition syndromes. In: Niederhuber JE, Armitage JO, Doroshow JH, Kastan MB, Tepper JE, editors. Abeloff’s clinical oncology, 6th ed. Philadelphia, PA: Elsevier (2020). p. 180–208.e11.

Google Scholar

7. Blackburn EH. Cancer interception. Cancer Prev Res (Phila) (2011) 4(6):787–92. doi: 10.1158/1940-6207.CAPR-11-0195

PubMed Abstract | CrossRef Full Text | Google Scholar

8. Crosby D, Bhatia S, Brindle KM, Coussens LM, Dive C, Emberton M, et al. Early detection of cancer. Science (2022) 375(6586):eaay9040. doi: 10.1126/science.aay9040

PubMed Abstract | CrossRef Full Text | Google Scholar

9. Burn J, Sheth H, Elliott F, Reed L, Macrae F, Mecklin JP, et al. Cancer prevention with aspirin in hereditary colorectal cancer (Lynch syndrome), 10-year follow-up and registry-based 20-year data in the CAPP2 study: a double-blind, randomised, placebo-controlled trial. Lancet (2020) 395(10240):1855–63. doi: 10.1016/S0140-6736(20)30366-4

PubMed Abstract | CrossRef Full Text | Google Scholar

10. Muhoza P, Danovaro-Holliday MC, Diallo MS, Murphy P, Sodha SV, Requejo JH, et al. Routine vaccination coverage - worldwide, 2020. MMWR Morb Mortal Wkly Rep (2021) 70(43):1495–500. doi: 10.15585/mmwr.mm7043a1

PubMed Abstract | CrossRef Full Text | Google Scholar

11. Tregoning JS, Flight KE, Higham SL, Wang Z, Pierce BF. Progress of the COVID-19 vaccine effort: viruses, vaccines and variants versus efficacy, effectiveness and escape. Nat Rev Immunol (2021) 21(10):626–36. doi: 10.1038/s41577-021-00592-1

PubMed Abstract | CrossRef Full Text | Google Scholar

12. Corey L, Mascola JR, Fauci AS, Collins FS. A strategic approach to COVID-19 vaccine R&D. Science (2020) 368(6494):948–50. doi: 10.1126/science.abc5312

PubMed Abstract | CrossRef Full Text | Google Scholar

13. Pollard AJ, Bijker EM. A guide to vaccinology: from basic principles to new developments. Nat Rev Immunol (2021) 21(2):83–100. doi: 10.1038/s41577-020-00479-7

PubMed Abstract | CrossRef Full Text | Google Scholar

14. Finn OJ. Cancer vaccines: between the idea and the reality. Nat Rev Immunol (2003) 3(8):630–41. doi: 10.1038/nri1150

PubMed Abstract | CrossRef Full Text | Google Scholar

15. Graham JB, Graham RM. The effect of vaccine on cancer patients. Surg Gynecol Obstet (1959) 109(2):131–8. doi: 10.1097/00006534-195911000-00016

PubMed Abstract | CrossRef Full Text | Google Scholar

16. Burnet M. Cancer: a biological approach. III. viruses associated with neoplastic conditions. IV. practical applications. Br Med J (1957) 1(5023):841–7. doi: 10.1136/bmj.1.5023.841

PubMed Abstract | CrossRef Full Text | Google Scholar

17. Dobosz P, Dzieciatkowski T. The intriguing history of cancer immunotherapy. Front Immunol (2019) 10:2965. doi: 10.3389/fimmu.2019.02965

PubMed Abstract | CrossRef Full Text | Google Scholar

18. Schlom J, Hodge JW, Palena C, Tsang KY, Jochems C, Greiner JW, et al. Therapeutic cancer vaccines. Adv Cancer Res (2014) 121:67–124. doi: 10.1016/B978-0-12-800249-0.00002-0

PubMed Abstract | CrossRef Full Text | Google Scholar

19. Barnd DL, Lan MS, Metzgar RS, Finn OJ. Specific, major histocompatibility complex-unrestricted recognition of tumor-associated mucins by human cytotoxic T cells. Proc Natl Acad Sci U.S.A. (1989) 86(18):7159–63. doi: 10.1073/pnas.86.18.7159

PubMed Abstract | CrossRef Full Text | Google Scholar

20. Finn OJ. Premalignant lesions as targets for cancer vaccines. J Exp Med (2003) 198(11):1623–6. doi: 10.1084/jem.20031787

PubMed Abstract | CrossRef Full Text | Google Scholar

21. Finn OJ. Vaccines for cancer prevention: a practical and feasible approach to the cancer epidemic. Cancer Immunol Res (2014) 2(8):708–13. doi: 10.1158/2326-6066.CIR-14-0110

PubMed Abstract | CrossRef Full Text | Google Scholar

22. Finn OJ. A believer's overview of cancer immunosurveillance and immunotherapy. J Immunol (2018) 200(2):385–91. doi: 10.4049/jimmunol.1701302

PubMed Abstract | CrossRef Full Text | Google Scholar

23. Disis ML, Gooley TA, Rinn K, Davis D, Piepkorn M, Cheever MA, et al. Generation of T-cell immunity to the HER-2/neu protein after active immunization with HER-2/neu peptide-based vaccines. J Clin Oncol (2002) 20(11):2624–32. doi: 10.1200/JCO.2002.06.171

PubMed Abstract | CrossRef Full Text | Google Scholar

24. Knutson KL, Schiffman K, Cheever MA, Disis ML. Immunization of cancer patients with a HER-2/neu, HLA-A2 peptide, p369-377, results in short-lived peptide-specific immunity. Clin Cancer Res (2002) 8(5):1014–8.

PubMed Abstract | Google Scholar

25. Lollini PL, Cavallo F, Nanni P, Forni G. Vaccines for tumour prevention. Nat Rev Cancer (2006) 6(3):204–16. doi: 10.1038/nrc1815

PubMed Abstract | CrossRef Full Text | Google Scholar

26. Salazar LG, Fikes J, Southwood S, Ishioka G, Knutson KL, Gooley TA, et al. Immunization of cancer patients with HER-2/neu-derived peptides demonstrating high-affinity binding to multiple class II alleles. Clin Cancer Res (2003) 9(15):5559–65.

PubMed Abstract | Google Scholar

27. Park KH, Gad E, Goodell V, Dang Y, Wild T, Higgins D, et al. Insulin-like growth factor-binding protein-2 is a target for the immunomodulation of breast cancer. Cancer Res (2008) 68(20):8400–9. doi: 10.1158/0008-5472.CAN-07-5891

PubMed Abstract | CrossRef Full Text | Google Scholar

28. Zou W, Wolchok JD, Chen L. PD-L1 (B7-H1) and PD-1 pathway blockade for cancer therapy: Mechanisms, response biomarkers, and combinations. Sci Transl Med (2016) 8(328):328rv4. doi: 10.1126/scitranslmed.aad7118

PubMed Abstract | CrossRef Full Text | Google Scholar

29. Wei SC, Duffy CR, Allison JP. Fundamental mechanisms of immune checkpoint blockade therapy. Cancer Discovery (2018) 8(9):1069–86. doi: 10.1158/2159-8290.CD-18-0367

PubMed Abstract | CrossRef Full Text | Google Scholar

30. Schumacher TN, Schreiber RD. Neoantigens in cancer immunotherapy. Science (2015) 348(6230):69–74. doi: 10.1126/science.aaa4971

PubMed Abstract | CrossRef Full Text | Google Scholar

31. Rizvi NA, Hellmann MD, Snyder A, Kvistborg P, Makarov V, Havel JJ, et al. Cancer immunology. mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science (2015) 348(6230):124–8. doi: 10.1126/science.aaa1348

PubMed Abstract | CrossRef Full Text | Google Scholar

32. Linnemann C, van Buuren MM, Bies L, Verdegaal EM, Schotte R, Calis JJ, et al. High-throughput epitope discovery reveals frequent recognition of neo-antigens by CD4+ T cells in human melanoma. Nat Med (2015) 21(1):81–5. doi: 10.1038/nm.3773

PubMed Abstract | CrossRef Full Text | Google Scholar

33. Spira A, Disis ML, Schiller JT, Vilar E, Rebbeck TR, Bejar R, et al. Leveraging premalignant biology for immune-based cancer prevention. Proc Natl Acad Sci U S A (2016) 113(39):10750–8. doi: 10.1073/pnas.1608077113

PubMed Abstract | CrossRef Full Text | Google Scholar

34. Chen B, Scurrah CR, McKinley ET, Simmons AJ, Ramirez-Solano MA, Zhu X, et al. Differential pre-malignant programs and microenvironment chart distinct paths to malignancy in human colorectal polyps. Cell (2021) 184(26):6262–80.e26. doi: 10.1016/j.cell.2021.11.031

PubMed Abstract | CrossRef Full Text | Google Scholar

35. Rebbeck TR, Burns-White K, Chan AT, Emmons K, Freedman M, Hunter DJ, et al. Precision prevention and early detection of cancer: Fundamental principles. Cancer Discovery (2018) 8(7):803–11. doi: 10.1158/2159-8290.CD-17-1415

PubMed Abstract | CrossRef Full Text | Google Scholar

36. Lin MJ, Svensson-Arvelund J, Lubitz GS, Marabelle A, Melero I, Brown BD, et al. Cancer vaccines: the next immunotherapy frontier. Nat Cancer (2022) 3(8):911–26. doi: 10.1038/s43018-022-00418-6

PubMed Abstract | CrossRef Full Text | Google Scholar

37. Garraway LA, Verweij J, Ballman KV. Precision oncology: an overview. J Clin Oncol (2013) 31(15):1803–5. doi: 10.1200/JCO.2013.49.4799

PubMed Abstract | CrossRef Full Text | Google Scholar

38. Schumacher TN, Scheper W, Kvistborg P. Cancer neoantigens. Annu Rev Immunol (2019) 37:173–200. doi: 10.1146/annurev-immunol-042617-053402

PubMed Abstract | CrossRef Full Text | Google Scholar

39. Greenman C, Stephens P, Smith R, Dalgliesh GL, Hunter C, Bignell G, et al. Patterns of somatic mutation in human cancer genomes. Nature (2007) 446(7132):153–8. doi: 10.1038/nature05610

PubMed Abstract | CrossRef Full Text | Google Scholar

40. Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA Jr., Kinzler KW. Cancer genome landscapes. Science (2013) 339(6127):1546–58. doi: 10.1126/science.1235122

PubMed Abstract | CrossRef Full Text | Google Scholar

41. Ott PA, Hu Z, Keskin DB, Shukla SA, Sun J, Bozym DJ, et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature (2017) 547(7662):217–21. doi: 10.1038/nature22991

PubMed Abstract | CrossRef Full Text | Google Scholar

42. Sahin U, Derhovanessian E, Miller M, Kloke BP, Simon P, Lower M, et al. Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer. Nature (2017) 547(7662):222–6. doi: 10.1038/nature23003

PubMed Abstract | CrossRef Full Text | Google Scholar

43. Hu Z, Leet DE, Allesoe RL, Oliveira G, Li S, Luoma AM, et al. Personal neoantigen vaccines induce persistent memory T cell responses and epitope spreading in patients with melanoma. Nat Med (2021) 27(3):515–25. doi: 10.1038/s41591-020-01206-4

PubMed Abstract | CrossRef Full Text | Google Scholar

44. von Knebel Doeberitz M, Kloor M. Towards a vaccine to prevent cancer in lynch syndrome patients. Fam Cancer (2013) 12(2):307–12. doi: 10.1007/s10689-013-9662-7

PubMed Abstract | CrossRef Full Text | Google Scholar

45. Kloor M, von Knebel Doeberitz M. The immune biology of microsatellite-unstable cancer. Trends Cancer (2016) 2(3):121–33. doi: 10.1016/j.trecan.2016.02.004

PubMed Abstract | CrossRef Full Text | Google Scholar

46. Woerner SM, Kloor M, Mueller A, Rueschoff J, Friedrichs N, Buettner R, et al. Microsatellite instability of selective target genes in HNPCC-associated colon adenomas. Oncogene (2005) 24(15):2525–35. doi: 10.1038/sj.onc.1208456

PubMed Abstract | CrossRef Full Text | Google Scholar

47. Schwitalle Y, Kloor M, Eiermann S, Linnebacher M, Kienle P, Knaebel HP, et al. Immune response against frameshift-induced neopeptides in HNPCC patients and healthy HNPCC mutation carriers. Gastroenterology (2008) 134(4):988–97. doi: 10.1053/j.gastro.2008.01.015

PubMed Abstract | CrossRef Full Text | Google Scholar

48. Kloor M, Reuschenbach M, Pauligk C, Karbach J, Rafiyan MR, Al-Batran SE, et al. A frameshift peptide neoantigen-based vaccine for mismatch repair-deficient cancers: A phase I/IIa clinical trial. Clin Cancer Res (2020) 26(17):4503–10. doi: 10.1158/1078-0432.CCR-19-3517

PubMed Abstract | CrossRef Full Text | Google Scholar

49. Tabori U, Hansford JR, Achatz MI, Kratz CP, Plon SE, Frebourg T, et al. Clinical management and tumor surveillance recommendations of inherited mismatch repair deficiency in childhood. Clin Cancer Res (2017) 23(11):e32–e7. doi: 10.1158/1078-0432.CCR-17-0574

PubMed Abstract | CrossRef Full Text | Google Scholar

50. Chung J, Negm L, Bianchi V, Stengs L, Das A, Liu ZA, et al. Genomic microsatellite signatures identify germline mismatch repair deficiency and risk of cancer onset. J Clin Oncol (2022) 41(4):766–77. doi: 10.1136/jmedgenet-2014-102284

PubMed Abstract | CrossRef Full Text | Google Scholar

51. Wimmer K, Kratz CP, Vasen HF, Caron O, Colas C, Entz-Werle N, et al. Diagnostic criteria for constitutional mismatch repair deficiency syndrome: suggestions of the European consortium 'care for CMMRD' (C4CMMRD). J Med Genet (2014) 51(6):355–65. doi: 10.1136/jmedgenet-2014-102284

PubMed Abstract | CrossRef Full Text | Google Scholar

52. Das A, Sudhaman S, Morgenstern D, Coblentz A, Chung J, Stone SC, et al. Genomic predictors of response to PD-1 inhibition in children with germline DNA replication repair deficiency. Nat Med (2022) 28(1):125–35. doi: 10.1038/s41591-021-01581-6

PubMed Abstract | CrossRef Full Text | Google Scholar

53. Carethers JM, Stoffel EM. Lynch syndrome and lynch syndrome mimics: The growing complex landscape of hereditary colon cancer. World J Gastroenterol (2015) 21(31):9253–61. doi: 10.3748/wjg.v21.i31.9253

PubMed Abstract | CrossRef Full Text | Google Scholar

54. Stadler ZK. Diagnosis and management of DNA mismatch repair-deficient colorectal cancer. Hematol Oncol Clin North Am (2015) 29(1):29–41. doi: 10.1016/j.hoc.2014.09.008

PubMed Abstract | CrossRef Full Text | Google Scholar

55. Rumilla K, Schowalter KV, Lindor NM, Thomas BC, Mensink KA, Gallinger S, et al. Frequency of deletions of EPCAM (TACSTD1) in MSH2-associated lynch syndrome cases. J Mol Diagn (2011) 13(1):93–9. doi: 10.1016/j.jmoldx.2010.11.011

PubMed Abstract | CrossRef Full Text | Google Scholar

56. Haraldsdottir S, Rafnar T, Frankel WL, Einarsdottir S, Sigurdsson A, Hampel H, et al. Comprehensive population-wide analysis of lynch syndrome in Iceland reveals founder mutations in MSH6 and PMS2. Nat Commun (2017) 8:14755. doi: 10.1038/ncomms14755

PubMed Abstract | CrossRef Full Text | Google Scholar

57. Olkinuora A, Nieminen TT, Martensson E, Rohlin A, Ristimaki A, Koskenvuo L, et al. Biallelic germline nonsense variant of MLH3 underlies polyposis predisposition. Genet Med (2019) 21(8):1868–73. doi: 10.1038/s41436-018-0405-x

PubMed Abstract | CrossRef Full Text | Google Scholar

58. Lipkin SM, Wang V, Jacoby R, Banerjee-Basu S, Baxevanis AD, Lynch HT, et al. MLH3: a DNA mismatch repair gene associated with mammalian microsatellite instability. Nat Genet (2000) 24(1):27–35. doi: 10.1038/71643

PubMed Abstract | CrossRef Full Text | Google Scholar

59. Lipkin SM, Wang V, Stoler DL, Anderson GR, Kirsch I, Hadley D, et al. Germline and somatic mutation analyses in the DNA mismatch repair gene MLH3: Evidence for somatic mutation in colorectal cancers. Hum Mutat (2001) 17(5):389–96. doi: 10.1002/humu.1114

PubMed Abstract | CrossRef Full Text | Google Scholar

60. Adam R, Spier I, Zhao B, Kloth M, Marquez J, Hinrichsen I, et al. Exome sequencing identifies biallelic MSH3 germline mutations as a recessive subtype of colorectal adenomatous polyposis. Am J Hum Genet (2016) 99(2):337–51. doi: 10.1016/j.ajhg.2016.06.015

PubMed Abstract | CrossRef Full Text | Google Scholar

61. Yurgelun MB, Hampel H. Recent advances in lynch syndrome: Diagnosis, treatment, and cancer prevention. Am Soc Clin Oncol Educ Book (2018) 38:101–9. doi: 10.1200/EDBK_208341

PubMed Abstract | CrossRef Full Text | Google Scholar

62. Muller C, Lee SM, Barge W, Siddique SM, Berera S, Wideroff G, et al. Low referral rate for genetic testing in racially and ethnically diverse patients despite universal colorectal cancer screening. Clin Gastroenterol Hepatol (2018) 16(12):1911–8.e2. doi: 10.1016/j.cgh.2018.08.038

PubMed Abstract | CrossRef Full Text | Google Scholar

63. Muller C, Matthews L, Kupfer SS, Weiss JM. Effective identification of lynch syndrome in gastroenterology practice. Curr Treat Options Gastroenterol (2019) 17(4):666–80. doi: 10.1007/s11938-019-00261-2

PubMed Abstract | CrossRef Full Text | Google Scholar

64. Faust N, Muller C, Prenner J, Lee SM, Kupfer SS. Low rates of genetic counseling and testing in individuals at risk for lynch syndrome reported in the national health interview survey. Gastroenterology (2020) 158(4):1159–61. doi: 10.1053/j.gastro.2019.11.297

PubMed Abstract | CrossRef Full Text | Google Scholar

65. Muller C, Nielsen SM, Hatchell KE, Yang S, Michalski ST, Hamlington B, et al. Underdiagnosis of hereditary colorectal cancers among Medicare patients: Genetic testing criteria for lynch syndrome miss the mark. JCO Precis Oncol (2021) 5:1103–11. doi: 10.1200/PO.21.00132

CrossRef Full Text | Google Scholar

66. Bellis MA, Hughes K, Hughes S, Ashton JR. Measuring paternal discrepancy and its public health consequences. J Epidemiol Community Health (2005) 59(9):749–54. doi: 10.1136/jech.2005.036517

PubMed Abstract | CrossRef Full Text | Google Scholar

67. Greeff JM, Erasmus JC. Three hundred years of low non-paternity in a human population. Heredity (Edinb) (2015) 115(5):396–404. doi: 10.1038/hdy.2015.36

PubMed Abstract | CrossRef Full Text | Google Scholar

68. Hampel H, Frankel WL, Martin E, Arnold M, Khanduja K, Kuebler P, et al. Screening for the lynch syndrome (hereditary nonpolyposis colorectal cancer). N Engl J Med (2005) 352(18):1851–60. doi: 10.1056/NEJMoa043146

PubMed Abstract | CrossRef Full Text | Google Scholar

69. Mills AM, Longacre TA. Lynch syndrome screening in the gynecologic tract: Current state of the art. Am J Surg Pathol (2016) 40(4):e35–44. doi: 10.1097/PAS.0000000000000608

PubMed Abstract | CrossRef Full Text | Google Scholar

70. Seppala TT, Latchford A, Negoi I, Sampaio Soares A, Jimenez-Rodriguez R, Sanchez-Guillen L, et al. European Guidelines from the EHTG and ESCP for lynch syndrome: an updated third edition of the mallorca guidelines based on gene and gender. Br J Surg (2021) 108(5):484–98. doi: 10.1002/bjs.11902

PubMed Abstract | CrossRef Full Text | Google Scholar

71. Snowsill T, Huxley N, Hoyle M, Jones-Hughes T, Coelho H, Cooper C, et al. A model-based assessment of the cost-utility of strategies to identify lynch syndrome in early-onset colorectal cancer patients. BMC Cancer (2015) 15:313. doi: 10.1186/s12885-015-1254-5

PubMed Abstract | CrossRef Full Text | Google Scholar

72. Bernstedt SW, Bjork J, Fritzell K, Spigelman AD, Bjorck E, Backman AS. Room for improvement: One third of lynch syndrome patients presenting for genetic testing in a highly specialised centre in Stockholm already have cancer. Hered Cancer Clin Pract (2021) 19(1):18. doi: 10.1186/s13053-021-00171-4

PubMed Abstract | CrossRef Full Text | Google Scholar

73. Vasen HF, Moslein G, Alonso A, Aretz S, Bernstein I, Bertario L, et al. Recommendations to improve identification of hereditary and familial colorectal cancer in Europe. Fam Cancer (2010) 9(2):109–15. doi: 10.1007/s10689-009-9291-3

PubMed Abstract | CrossRef Full Text | Google Scholar

74. Singh H, Schiesser R, Anand G, Richardson PA, El-Serag HB. Underdiagnosis of lynch syndrome involves more than family history criteria. Clin Gastroenterol Hepatol (2010) 8(6):523–9. doi: 10.1016/j.cgh.2010.03.010

PubMed Abstract | CrossRef Full Text | Google Scholar

75. Kahn RM, Ahsan MD, Chapman-Davis E, Holcomb K, Nitecki R, Rauh-Hain JA, et al. Barriers to completion of cascade genetic testing: how can we improve the uptake of testing for hereditary breast and ovarian cancer syndrome? Fam Cancer (2022). doi: 10.1007/s10689-022-00316-x

PubMed Abstract | CrossRef Full Text | Google Scholar

76. Frey MK, Ahsan MD, Bergeron H, Lin J, Li X, Fowlkes RK, et al. Cascade testing for hereditary cancer syndromes: Should we move toward direct relative contact? a systematic review and meta-analysis. J Clin Oncol (2022) 40(35):4129–43. doi: 10.1200/JCO.22.00303

PubMed Abstract | CrossRef Full Text | Google Scholar

77. Frey MK, Kahn RM, Chapman-Davis E, Tubito F, Pires M, Christos P, et al. Prospective feasibility trial of a novel strategy of facilitated cascade genetic testing using telephone counseling. J Clin Oncol (2020) 38(13):1389–97. doi: 10.1200/JCO.19.02005

PubMed Abstract | CrossRef Full Text | Google Scholar

78. Slavin TP, Banks KC, Chudova D, Oxnard GR, Odegaard JI, Nagy RJ, et al. Identification of incidental germline mutations in patients with advanced solid tumors who underwent cell-free circulating tumor DNA sequencing. J Clin Oncol (2018) 36(35):3459–65. doi: 10.1200/JCO.18.00328

CrossRef Full Text | Google Scholar

79. Zviran A, Schulman RC, Shah M, Hill STK, Deochand S, Khamnei CC, et al. Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring. Nat Med (2020) 26(7):1114–24. doi: 10.1038/s41591-020-0915-3

PubMed Abstract | CrossRef Full Text | Google Scholar

80. Kather JN, Pearson AT, Halama N, Jager D, Krause J, Loosen SH, et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med (2019) 25(7):1054–6. doi: 10.1038/s41591-019-0462-y

PubMed Abstract | CrossRef Full Text | Google Scholar

81. Hildebrand LA, Pierce CJ, Dennis M, Paracha M, Maoz A. Artificial intelligence for histology-based detection of microsatellite instability and prediction of response to immunotherapy in colorectal cancer. Cancers (Basel) (2021) 13(3):391. doi: 10.3390/cancers13030391

PubMed Abstract | CrossRef Full Text | Google Scholar

82. Rousseau B, Foote MB, Maron SB, Diplas BH, Lu S, Argiles G, et al. The spectrum of benefit from checkpoint blockade in hypermutated tumors. N Engl J Med (2021) 384(12):1168–70. doi: 10.1056/NEJMc2031965

PubMed Abstract | CrossRef Full Text | Google Scholar

83. Dominguez-Valentin M, Sampson JR, Seppala TT, Ten Broeke SW, Plazzer JP, Nakken S, et al. Cancer risks by gene, age, and gender in 6350 carriers of pathogenic mismatch repair variants: findings from the prospective lynch syndrome database. Genet Med (2020) 22(1):15–25. doi: 10.1038/s41436-019-0596-9

PubMed Abstract | CrossRef Full Text | Google Scholar

84. Woerner SM, Yuan YP, Benner A, Korff S, von Knebel Doeberitz M, Bork P. SelTarbase, a database of human mononucleotide-microsatellite mutations and their potential impact to tumorigenesis and immunology. Nucleic Acids Res (2010) 38(Database issue):D682–9. doi: 10.1093/nar/gkp839

PubMed Abstract | CrossRef Full Text | Google Scholar

85. Kucherlapati MH, Lee K, Nguyen AA, Clark AB, Hou H Jr., Rosulek A, et al. An Msh2 conditional knockout mouse for studying intestinal cancer and testing anticancer agents. Gastroenterology (2010) 138(3):993–1002 e1. doi: 10.1053/j.gastro.2009.11.009

PubMed Abstract | CrossRef Full Text | Google Scholar

86. Gebert J, Gelincik O, Oezcan-Wahlbrink M, Marshall JD, Hernandez-Sanchez A, Urban K, et al. Recurrent frameshift neoantigen vaccine elicits protective immunity with reduced tumor burden and improved overall survival in a lynch syndrome mouse model. Gastroenterology (2021) 161(4):1288–302.e13. doi: 10.1053/j.gastro.2021.06.073

PubMed Abstract | CrossRef Full Text | Google Scholar

87. Burn J, Gerdes AM, Macrae F, Mecklin JP, Moeslein G, Olschwang S, et al. Long-term effect of aspirin on cancer risk in carriers of hereditary colorectal cancer: an analysis from the CAPP2 randomised controlled trial. Lancet (2011) 378(9809):2081–7. doi: 10.1016/S0140-6736(11)61049-0

PubMed Abstract | CrossRef Full Text | Google Scholar

88. Reyes-Uribe L, Wu W, Gelincik O, Bommi PV, Francisco-Cruz A, Solis LM, et al. Naproxen chemoprevention promotes immune activation in lynch syndrome colorectal mucosa. Gut (2021) 70(3):555–66. doi: 10.1136/gutjnl-2020-320946

PubMed Abstract | CrossRef Full Text | Google Scholar

89. Wang D, Dubois RN. Eicosanoids and cancer. Nat Rev Cancer (2010) 10(3):181–93. doi: 10.1038/nrc2809

PubMed Abstract | CrossRef Full Text | Google Scholar

90. Wang D, Dubois RN. The role of COX-2 in intestinal inflammation and colorectal cancer. Oncogene (2010) 29(6):781–8. doi: 10.1038/onc.2009.421

PubMed Abstract | CrossRef Full Text | Google Scholar

91. Ait Ouakrim D, Dashti SG, Chau R, Buchanan DD, Clendenning M, Rosty C, et al. Aspirin, ibuprofen, and the risk of colorectal cancer in lynch syndrome. J Natl Cancer Inst (2015) 107(9):djv170. doi: 10.1093/jnci/djv170

PubMed Abstract | CrossRef Full Text | Google Scholar

92. Leoni G, D'Alise AM, Cotugno G, Langone F, Garzia I, De Lucia M, et al. A genetic vaccine encoding shared cancer neoantigens to treat tumors with microsatellite instability. Cancer Res (2020) 80(18):3972–82. doi: 10.1158/0008-5472.CAN-20-1072

PubMed Abstract | CrossRef Full Text | Google Scholar

93. Toes RE, Ossendorp F, Offringa R, Melief CJ. CD4 T cells and their role in antitumor immune responses. J Exp Med (1999) 189(5):753–6. doi: 10.1084/jem.189.5.753

PubMed Abstract | CrossRef Full Text | Google Scholar

94. Axelrod ML, Cook RS, Johnson DB, Balko JM. Biological consequences of MHC-II expression by tumor cells in cancer. Clin Cancer Res (2019) 25(8):2392–402. doi: 10.1158/1078-0432.CCR-18-3200

PubMed Abstract | CrossRef Full Text | Google Scholar

95. Falk K, Rotzschke O, Stevanovic S, Jung G, Rammensee HG. Allele-specific motifs revealed by sequencing of self-peptides eluted from MHC molecules. Nature (1991) 351(6324):290–6. doi: 10.1038/351290a0

PubMed Abstract | CrossRef Full Text | Google Scholar

96. Jurtz V, Paul S, Andreatta M, Marcatili P, Peters B, Nielsen M. NetMHCpan-4.0: Improved peptide-MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data. J Immunol (2017) 199(9):3360–8. doi: 10.4049/jimmunol.1700893

PubMed Abstract | CrossRef Full Text | Google Scholar

97. Langton DJ, Bourke SC, Lie BA, Reiff G, Natu S, Darlay R, et al. The influence of HLA genotype on the severity of COVID-19 infection. HLA (2021) 98(1):14–22. doi: 10.1111/tan.14284

PubMed Abstract | CrossRef Full Text | Google Scholar

98. Crocchiolo R, Gallina AM, Pani A, Campisi D, Cento V, Sacchi N, et al. Polymorphism of the HLA system and weak antibody response to BNT162b2 mRNA vaccine. HLA (2022) 99(3):183–91. doi: 10.1111/tan.14546

PubMed Abstract | CrossRef Full Text | Google Scholar

99. Francis JM, Leistritz-Edwards D, Dunn A, Tarr C, Lehman J, Dempsey C, et al. Allelic variation in class I HLA determines CD8(+) T cell repertoire shape and cross-reactive memory responses to SARS-CoV-2. Sci Immunol (2022) 7(67):eabk3070. doi: 10.1126/sciimmunol.abk3070

PubMed Abstract | CrossRef Full Text | Google Scholar

100. Ahadova A, Witt J, Haupt S, Gallon R, Huneburg R, Nattermann J, et al. Is HLA type a possible cancer risk modifier in lynch syndrome? Int J Cancer (2023) 152(10):2024–31. doi: 10.1002/ijc.34312

PubMed Abstract | CrossRef Full Text | Google Scholar

101. Ballhausen A, Przybilla MJ, Jendrusch M, Haupt S, Pfaffendorf E, Seidler F, et al. The shared frameshift mutation landscape of microsatellite-unstable cancers suggests immunoediting during tumor evolution. Nat Commun (2020) 11(1):4740. doi: 10.1038/s41467-020-18514-5

PubMed Abstract | CrossRef Full Text | Google Scholar

102. Bohaumilitzky L, Kluck K, Huneburg R, Gallon R, Nattermann J, Kirchner M, et al. The different immune profiles of normal colonic mucosa in cancer-free lynch syndrome carriers and lynch syndrome colorectal cancer patients. Gastroenterology (2022) 162(3):907–19.e10. doi: 10.1053/j.gastro.2021.11.029

PubMed Abstract | CrossRef Full Text | Google Scholar

103. Witt J, Haupt S, Ahadova A, Bohaumilitzky L, Fuchs V, Ballhausen A, et al. A simple approach for detecting HLA-A*02 alleles in archival formalin-fixed paraffin-embedded tissue samples and an application example for studying cancer immunoediting. HLA (2023) 101(1):24–33. doi: 10.1111/tan.14846

PubMed Abstract | CrossRef Full Text | Google Scholar

104. Nelde A, Maringer Y, Bilich T, Salih HR, Roerden M, Heitmann JS, et al. Immunopeptidomics-guided warehouse design for peptide-based immunotherapy in chronic lymphocytic leukemia. Front Immunol (2021) 12:705974. doi: 10.3389/fimmu.2021.705974

PubMed Abstract | CrossRef Full Text | Google Scholar

105. Nelde A, Rammensee HG, Walz JS. The peptide vaccine of the future. Mol Cell Proteomics (2021) 20:100022. doi: 10.1074/mcp.R120.002309

PubMed Abstract | CrossRef Full Text | Google Scholar

106. McGranahan N, Rosenthal R, Hiley CT, Rowan AJ, Watkins TBK, Wilson GA, et al. Allele-specific HLA loss and immune escape in lung cancer evolution. Cell (2017) 171(6):1259–71.e11. doi: 10.1016/j.cell.2017.10.001

PubMed Abstract | CrossRef Full Text | Google Scholar

107. McGranahan N, Swanton C. Cancer evolution constrained by the immune microenvironment. Cell (2017) 170(5):825–7. doi: 10.1016/j.cell.2017.08.012

PubMed Abstract | CrossRef Full Text | Google Scholar

108. McGranahan N, Swanton C. Clonal heterogeneity and tumor evolution: Past, present, and the future. Cell (2017) 168(4):613–28. doi: 10.1016/j.cell.2017.01.018

PubMed Abstract | CrossRef Full Text | Google Scholar

109. Laughney AM, Hu J, Campbell NR, Bakhoum SF, Setty M, Lavallee VP, et al. Regenerative lineages and immune-mediated pruning in lung cancer metastasis. Nat Med (2020) 26(2):259–69. doi: 10.1038/s41591-019-0750-6

PubMed Abstract | CrossRef Full Text | Google Scholar

110. Anagnostou V, Smith KN, Forde PM, Niknafs N, Bhattacharya R, White J, et al. Evolution of neoantigen landscape during immune checkpoint blockade in non-small cell lung cancer. Cancer Discovery (2017) 7(3):264–76. doi: 10.1158/2159-8290.CD-16-0828

PubMed Abstract | CrossRef Full Text | Google Scholar

111. Kloor M, Huth C, Voigt AY, Benner A, Schirmacher P, von Knebel Doeberitz M, et al. Prevalence of mismatch repair-deficient crypt foci in lynch syndrome: a pathological study. Lancet Oncol (2012) 13(6):598–606. doi: 10.1016/S1470-2045(12)70109-2

PubMed Abstract | CrossRef Full Text | Google Scholar

112. Garraway LA, Lander ES. Lessons from the cancer genome. Cell (2013) 153(1):17–37. doi: 10.1016/j.cell.2013.03.002

PubMed Abstract | CrossRef Full Text | Google Scholar

113. McGranahan N, Furness AJ, Rosenthal R, Ramskov S, Lyngaa R, Saini SK, et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science (2016) 351(6280):1463–9. doi: 10.1126/science.aaf1490

PubMed Abstract | CrossRef Full Text | Google Scholar

114. Tran E, Ahmadzadeh M, Lu YC, Gros A, Turcotte S, Robbins PF, et al. Immunogenicity of somatic mutations in human gastrointestinal cancers. Science (2015) 350(6266):1387–90. doi: 10.1126/science.aad1253

PubMed Abstract | CrossRef Full Text | Google Scholar

115. Hilf N, Kuttruff-Coqui S, Frenzel K, Bukur V, Stevanovic S, Gouttefangeas C, et al. Actively personalized vaccination trial for newly diagnosed glioblastoma. Nature (2019) 565(7738):240–5. doi: 10.1038/s41586-018-0810-y

PubMed Abstract | CrossRef Full Text | Google Scholar

116. Keskin DB, Anandappa AJ, Sun J, Tirosh I, Mathewson ND, Li S, et al. Neoantigen vaccine generates intratumoral T cell responses in phase ib glioblastoma trial. Nature (2019) 565(7738):234–9. doi: 10.1038/s41586-018-0792-9

PubMed Abstract | CrossRef Full Text | Google Scholar

117. Ott PA, Hu-Lieskovan S, Chmielowski B, Govindan R, Naing A, Bhardwaj N, et al. A phase ib trial of personalized neoantigen therapy plus anti-PD-1 in patients with advanced melanoma, non-small cell lung cancer, or bladder cancer. Cell (2020) 183(2):347–62.e24. doi: 10.1016/j.cell.2020.08.053

PubMed Abstract | CrossRef Full Text | Google Scholar

118. Tran E, Robbins PF, Lu YC, Prickett TD, Gartner JJ, Jia L, et al. T-Cell transfer therapy targeting mutant KRAS in cancer. N Engl J Med (2016) 375(23):2255–62. doi: 10.1056/NEJMoa1609279

PubMed Abstract | CrossRef Full Text | Google Scholar

119. Malekzadeh P, Pasetto A, Robbins PF, Parkhurst MR, Paria BC, Jia L, et al. Neoantigen screening identifies broad TP53 mutant immunogenicity in patients with epithelial cancers. J Clin Invest (2019) 129(3):1109–14. doi: 10.1172/JCI123791

PubMed Abstract | CrossRef Full Text | Google Scholar

120. Hsiue EH, Wright KM, Douglass J, Hwang MS, Mog BJ, Pearlman AH, et al. Targeting a neoantigen derived from a common TP53 mutation. Science (2021) 371(6533):eabc8697. doi: 10.1126/science.abc8697

PubMed Abstract | CrossRef Full Text | Google Scholar

121. Veatch JR, Lee SM, Fitzgibbon M, Chow IT, Jesernig B, Schmitt T, et al. Tumor-infiltrating BRAFV600E-specific CD4+ T cells correlated with complete clinical response in melanoma. J Clin Invest (2018) 128(4):1563–8. doi: 10.1172/JCI98689

PubMed Abstract | CrossRef Full Text | Google Scholar

122. Castle JC, Uduman M, Pabla S, Stein RB, Buell JS. Mutation-derived neoantigens for cancer immunotherapy. Front Immunol (2019) 10:1856. doi: 10.3389/fimmu.2019.01856

PubMed Abstract | CrossRef Full Text | Google Scholar

123. Li F, Deng L, Jackson KR, Talukder AH, Katailiha AS, Bradley SD, et al. Neoantigen vaccination induces clinical and immunologic responses in non-small cell lung cancer patients harboring EGFR mutations. J Immunother Cancer (2021) 9(7):e002531. doi: 10.1136/jitc-2021-002531corr1

PubMed Abstract | CrossRef Full Text | Google Scholar

124. Sampson JH, Heimberger AB, Archer GE, Aldape KD, Friedman AH, Friedman HS, et al. Immunologic escape after prolonged progression-free survival with epidermal growth factor receptor variant III peptide vaccination in patients with newly diagnosed glioblastoma. J Clin Oncol (2010) 28(31):4722–9. doi: 10.1200/JCO.2010.28.6963

PubMed Abstract | CrossRef Full Text | Google Scholar

125. Dupain C, Gracia C, Harttrampf AC, Riviere J, Geoerger B, Massaad-Massade L. Newly identified LMO3-BORCS5 fusion oncogene in Ewing sarcoma at relapse is a driver of tumor progression. Oncogene (2019) 38(47):7200–15. doi: 10.1038/s41388-019-0914-3

PubMed Abstract | CrossRef Full Text | Google Scholar

126. Yang W, Lee KW, Srivastava RM, Kuo F, Krishna C, Chowell D, et al. Immunogenic neoantigens derived from gene fusions stimulate T cell responses. Nat Med (2019) 25(5):767–75. doi: 10.1038/s41591-019-0434-2

PubMed Abstract | CrossRef Full Text | Google Scholar

127. Lin HH, Ray S, Tongchusak S, Reinherz EL, Brusic V. Evaluation of MHC class I peptide binding prediction servers: applications for vaccine research. BMC Immunol (2008) 9:8. doi: 10.1186/1471-2172-9-8

PubMed Abstract | CrossRef Full Text | Google Scholar

128. Mei S, Li F, Leier A, Marquez-Lago TT, Giam K, Croft NP, et al. A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction. Brief Bioinform (2020) 21(4):1119–35. doi: 10.1093/bib/bbz051

PubMed Abstract | CrossRef Full Text | Google Scholar

129. Sarkizova S, Klaeger S, Le PM, Li LW, Oliveira G, Keshishian H, et al. A large peptidome dataset improves HLA class I epitope prediction across most of the human population. Nat Biotechnol (2020) 38(2):199–209. doi: 10.1038/s41587-019-0322-9

PubMed Abstract | CrossRef Full Text | Google Scholar

130. Vita R, Mahajan S, Overton JA, Dhanda SK, Martini S, Cantrell JR, et al. The immune epitope database (IEDB): 2018 update. Nucleic Acids Res (2019) 47(D1):D339–D43. doi: 10.1093/nar/gky1006

PubMed Abstract | CrossRef Full Text | Google Scholar

131. Zhang GL, Lin HH, Keskin DB, Reinherz EL, Brusic V. Dana-Farber Repository for machine learning in immunology. J Immunol Methods (2011) 374(1-2):18–25. doi: 10.1016/j.jim.2011.07.007

PubMed Abstract | CrossRef Full Text | Google Scholar

132. Abelin JG, Keskin DB, Sarkizova S, Hartigan CR, Zhang W, Sidney J, et al. Mass spectrometry profiling of HLA-associated peptidomes in mono-allelic cells enables more accurate epitope prediction. Immunity (2017) 46(2):315–26. doi: 10.1016/j.immuni.2017.02.007

PubMed Abstract | CrossRef Full Text | Google Scholar

133. Keskin DB, Reinhold BB, Zhang GL, Ivanov AR, Karger BL, Reinherz EL. Physical detection of influenza a epitopes identifies a stealth subset on human lung epithelium evading natural CD8 immunity. Proc Natl Acad Sci U.S.A. (2015) 112(7):2151–6. doi: 10.1073/pnas.1423482112

PubMed Abstract | CrossRef Full Text | Google Scholar

134. Rasmussen M, Fenoy E, Harndahl M, Kristensen AB, Nielsen IK, Nielsen M, et al. Pan-specific prediction of peptide-MHC class I complex stability, a correlate of T cell immunogenicity. J Immunol (2016) 197(4):1517–24. doi: 10.4049/jimmunol.1600582

PubMed Abstract | CrossRef Full Text | Google Scholar

135. Prachar M, Justesen S, Steen-Jensen DB, Thorgrimsen S, Jurgons E, Winther O, et al. Identification and validation of 174 COVID-19 vaccine candidate epitopes reveals low performance of common epitope prediction tools. Sci Rep (2020) 10(1):20465. doi: 10.1038/s41598-020-77466-4

PubMed Abstract | CrossRef Full Text | Google Scholar

136. Luksza M, Riaz N, Makarov V, Balachandran VP, Hellmann MD, Solovyov A, et al. A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature (2017) 551(7681):517–20. doi: 10.1038/nature24473

PubMed Abstract | CrossRef Full Text | Google Scholar

137. Luksza M, Sethna ZM, Rojas LA, Lihm J, Bravi B, Elhanati Y, et al. Neoantigen quality predicts immunoediting in survivors of pancreatic cancer. Nature (2022) 606(7913):389–95. doi: 10.1038/s41586-022-04735-9

PubMed Abstract | CrossRef Full Text | Google Scholar

138. Pande M, Wei C, Chen J, Amos CI, Lynch PM, Lu KH, et al. Cancer spectrum in DNA mismatch repair gene mutation carriers: results from a hospital based lynch syndrome registry. Fam Cancer (2012) 11(3):441–7. doi: 10.1007/s10689-012-9534-6

PubMed Abstract | CrossRef Full Text | Google Scholar

139. Sahin U, Kariko K, Tureci O. mRNA-based therapeutics–developing a new class of drugs. Nat Rev Drug Discovery (2014) 13(10):759–80. doi: 10.1038/nrd4278

CrossRef Full Text | Google Scholar

140. Weissman D, Kariko K. mRNA: Fulfilling the promise of gene therapy. Mol Ther (2015) 23(9):1416–7. doi: 10.1038/mt.2015.138

PubMed Abstract | CrossRef Full Text | Google Scholar

141. Polack FP, Thomas SJ, Kitchin N, Absalon J, Gurtman A, Lockhart S, et al. Safety and efficacy of the BNT162b2 mRNA covid-19 vaccine. N Engl J Med (2020) 383(27):2603–15. doi: 10.1056/NEJMoa2034577

PubMed Abstract | CrossRef Full Text | Google Scholar

142. Baden LR, El Sahly HM, Essink B, Kotloff K, Frey S, Novak R, et al. Efficacy and safety of the mRNA-1273 SARS-CoV-2 vaccine. N Engl J Med (2021) 384(5):403–16. doi: 10.1056/NEJMoa2035389

PubMed Abstract | CrossRef Full Text | Google Scholar

143. Liu MA, Zhou T, Sheets RL, Meyer H, Knezevic I. WHO informal consultation on regulatory considerations for evaluation of the quality, safety and efficacy of RNA-based prophylactic vaccines for infectious diseases, 20-22 April 2021. Emerg Microbes Infect (2022) 11(1):384–91. doi: 10.1080/22221751.2022.2026742

PubMed Abstract | CrossRef Full Text | Google Scholar

144. Geall AJ, Verma A, Otten GR, Shaw CA, Hekele A, Banerjee K, et al. Nonviral delivery of self-amplifying RNA vaccines. Proc Natl Acad Sci U.S.A. (2012) 109(36):14604–9. doi: 10.1073/pnas.1209367109

PubMed Abstract | CrossRef Full Text | Google Scholar

145. Vogel AB, Lambert L, Kinnear E, Busse D, Erbar S, Reuter KC, et al. Self-amplifying RNA vaccines give equivalent protection against influenza to mRNA vaccines but at much lower doses. Mol Ther (2018) 26(2):446–55. doi: 10.1016/j.ymthe.2017.11.017

PubMed Abstract | CrossRef Full Text | Google Scholar

146. Erasmus JH, Archer J, Fuerte-Stone J, Khandhar AP, Voigt E, Granger B, et al. Intramuscular delivery of replicon RNA encoding ZIKV-117 human monoclonal antibody protects against zika virus infection. Mol Ther Methods Clin Dev (2020) 18:402–14. doi: 10.1016/j.omtm.2020.06.011

PubMed Abstract | CrossRef Full Text | Google Scholar

147. Lundstrom K. Self-replicating RNA viruses for vaccine development against infectious diseases and cancer. Vaccines (Basel) (2021) 9(10):1187. doi: 10.3390/vaccines9101187

PubMed Abstract | CrossRef Full Text | Google Scholar

148. Palmer CD, Rappaport AR, Davis MJ, Hart MG, Scallan CD, Hong SJ, et al. Individualized, heterologous chimpanzee adenovirus and self-amplifying mRNA neoantigen vaccine for advanced metastatic solid tumors: phase 1 trial interim results. Nat Med (2022) 28(8):1619–29. doi: 10.1038/s41591-022-01937-6

PubMed Abstract | CrossRef Full Text | Google Scholar

149. Qu L, Yi Z, Shen Y, Lin L, Chen F, Xu Y, et al. Circular RNA vaccines against SARS-CoV-2 and emerging variants. Cell (2022) 185(10):1728–44.e16. doi: 10.1016/j.cell.2022.03.044

PubMed Abstract | CrossRef Full Text | Google Scholar

150. Seephetdee C, Bhukhai K, Buasri N, Leelukkanaveera P, Lerdwattanasombat P, Manopwisedjaroen S, et al. A circular mRNA vaccine prototype producing VFLIP-X spike confers a broad neutralization of SARS-CoV-2 variants by mouse sera. Antiviral Res (2022) 204:105370. doi: 10.1016/j.antiviral.2022.105370

PubMed Abstract | CrossRef Full Text | Google Scholar

151. Blakney AK, McKay PF, Yus BI, Aldon Y, Shattock RJ. Inside out: optimization of lipid nanoparticle formulations for exterior complexation and in vivo delivery of saRNA. Gene Ther (2019) 26(9):363–72. doi: 10.1038/s41434-019-0095-2

PubMed Abstract | CrossRef Full Text | Google Scholar

152. Alameh MG, Tombacz I, Bettini E, Lederer K, Sittplangkoon C, Wilmore JR, et al. Lipid nanoparticles enhance the efficacy of mRNA and protein subunit vaccines by inducing robust T follicular helper cell and humoral responses. Immunity (2021) 54(12):2877–92.e7. doi: 10.1016/j.immuni.2021.11.001

PubMed Abstract | CrossRef Full Text | Google Scholar

153. Zhang H, You X, Wang X, Cui L, Wang Z, Xu F, et al. Delivery of mRNA vaccine with a lipid-like material potentiates antitumor efficacy through toll-like receptor 4 signaling. Proc Natl Acad Sci U.S.A. (2021) 118(6):e2005191118. doi: 10.1073/pnas.2005191118

PubMed Abstract | CrossRef Full Text | Google Scholar

154. Bevers S, Kooijmans SAA, Van de Velde E, Evers MJW, Seghers S, Gitz-Francois J, et al. mRNA-LNP vaccines tuned for systemic immunization induce strong antitumor immunity by engaging splenic immune cells. Mol Ther (2022) 30(9):3078–94. doi: 10.1016/j.ymthe.2022.07.007

PubMed Abstract | CrossRef Full Text | Google Scholar

155. Chen J, Ye Z, Huang C, Qiu M, Song D, Li Y, et al. Lipid nanoparticle-mediated lymph node-targeting delivery of mRNA cancer vaccine elicits robust CD8(+) T cell response. Proc Natl Acad Sci U S A (2022) 119(34):e2207841119. doi: 10.1073/pnas.2207841119

PubMed Abstract | CrossRef Full Text | Google Scholar

156. Huang X, Kong N, Zhang X, Cao Y, Langer R, Tao W. The landscape of mRNA nanomedicine. Nat Med (2022) 28(11):2273–87. doi: 10.1038/s41591-022-02061-1

PubMed Abstract | CrossRef Full Text | Google Scholar

157. Pajot A, Michel ML, Fazilleau N, Pancre V, Auriault C, Ojcius DM, et al. A mouse model of human adaptive immune functions: HLA-A2.1-/HLA-DR1-transgenic h-2 class I-/class II-knockout mice. Eur J Immunol (2004) 34(11):3060–9. doi: 10.1002/eji.200425463

PubMed Abstract | CrossRef Full Text | Google Scholar

158. Depla E, van der Aa A, Livingston BD, Crimi C, Allosery K, De Brabandere V, et al. Rational design of a multiepitope vaccine encoding T-lymphocyte epitopes for treatment of chronic hepatitis b virus infections. J Virol (2008) 82(1):435–50. doi: 10.1128/JVI.01505-07

PubMed Abstract | CrossRef Full Text | Google Scholar

159. Alexander J, Oseroff C, Sidney J, Sette A. Derivation of HLA-B*0702 transgenic mice: functional CTL repertoire and recognition of human B*0702-restricted CTL epitopes. Hum Immunol (2003) 64(2):211–23. doi: 10.1016/S0198-8859(02)00786-3

PubMed Abstract | CrossRef Full Text | Google Scholar

160. Harada N, Fukaya S, Wada H, Goto R, Osada T, Gomori A, et al. Generation of a novel HLA class I transgenic mouse model carrying a knock-in mutation at the beta2-microglobulin locus. J Immunol (2017) 198(1):516–27. doi: 10.4049/jimmunol.1502367

PubMed Abstract | CrossRef Full Text | Google Scholar

161. Lank SM, Golbach BA, Creager HM, Wiseman RW, Keskin DB, Reinherz EL, et al. Ultra-high resolution HLA genotyping and allele discovery by highly multiplexed cDNA amplicon pyrosequencing. BMC Genomics (2012) 13:378. doi: 10.1186/1471-2164-13-378

PubMed Abstract | CrossRef Full Text | Google Scholar

162. Meyskens FL Jr., Curt GA, Brenner DE, Gordon G, Herberman RB, Finn O, et al. Regulatory approval of cancer risk-reducing (chemopreventive) drugs: moving what we have learned into the clinic. Cancer Prev Res (Phila) (2011) 4(3):311–23. doi: 10.1158/1940-6207.CAPR-09-0014

PubMed Abstract | CrossRef Full Text | Google Scholar

163. Cuzick J. Preventive therapy for cancer. Lancet Oncol (2017) 18(8):e472–e82. doi: 10.1016/S1470-2045(17)30536-3

PubMed Abstract | CrossRef Full Text | Google Scholar

164. Bruni D, Angell HK, Galon J. The immune contexture and immunoscore in cancer prognosis and therapeutic efficacy. Nat Rev Cancer (2020) 20(11):662–80. doi: 10.1038/s41568-020-0285-7

PubMed Abstract | CrossRef Full Text | Google Scholar

165. Hiam-Galvez KJ, Allen BM, Spitzer MH. Systemic immunity in cancer. Nat Rev Cancer (2021) 21(6):345–59. doi: 10.1038/s41568-021-00347-z

PubMed Abstract | CrossRef Full Text | Google Scholar

166. Galon J, Costes A, Sanchez-Cabo F, Kirilovsky A, Mlecnik B, Lagorce-Pages C, et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science (2006) 313(5795):1960–4. doi: 10.1126/science.1129139

PubMed Abstract | CrossRef Full Text | Google Scholar

167. Bindea G, Mlecnik B, Fridman WH, Pages F, Galon J. Natural immunity to cancer in humans. Curr Opin Immunol (2010) 22(2):215–22. doi: 10.1016/j.coi.2010.02.006

PubMed Abstract | CrossRef Full Text | Google Scholar

168. Galon J, Angell HK, Bedognetti D, Marincola FM. The continuum of cancer immunosurveillance: prognostic, predictive, and mechanistic signatures. Immunity (2013) 39(1):11–26. doi: 10.1016/j.immuni.2013.07.008

PubMed Abstract | CrossRef Full Text | Google Scholar

169. Chao JL, Savage PA. Unlocking the complexities of tumor-associated regulatory T cells. J Immunol (2018) 200(2):415–21. doi: 10.4049/jimmunol.1701188

PubMed Abstract | CrossRef Full Text | Google Scholar

170. Ostrand-Rosenberg S, Fenselau C. Myeloid-derived suppressor cells: Immune-suppressive cells that impair antitumor immunity and are sculpted by their environment. J Immunol (2018) 200(2):422–31. doi: 10.4049/jimmunol.1701019

PubMed Abstract | CrossRef Full Text | Google Scholar

171. Veglia F, Sanseviero E, Gabrilovich DI. Myeloid-derived suppressor cells in the era of increasing myeloid cell diversity. Nat Rev Immunol (2021) 21(8):485–98. doi: 10.1038/s41577-020-00490-y

PubMed Abstract | CrossRef Full Text | Google Scholar

172. Barth SD, Schulze JJ, Kuhn T, Raschke E, Husing A, Johnson T, et al. Treg-mediated immune tolerance and the risk of solid cancers: Findings from EPIC-Heidelberg. J Natl Cancer Inst (2015) 107(11):djv224. doi: 10.1093/jnci/djv224

PubMed Abstract | CrossRef Full Text | Google Scholar

173. Cader FZ, Hu X, Goh WL, Wienand K, Ouyang J, Mandato E, et al. A peripheral immune signature of responsiveness to PD-1 blockade in patients with classical Hodgkin lymphoma. Nat Med (2020) 26(9):1468–79. doi: 10.1038/s41591-020-1006-1

PubMed Abstract | CrossRef Full Text | Google Scholar

174. Tumeh PC, Harview CL, Yearley JH, Shintaku IP, Taylor EJ, Robert L, et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature (2014) 515(7528):568–71. doi: 10.1038/nature13954

PubMed Abstract | CrossRef Full Text | Google Scholar

175. Watson RA, Tong O, Cooper R, Taylor CA, Sharma PK, de Los Aires AV, et al. Immune checkpoint blockade sensitivity and progression-free survival associates with baseline CD8(+) T cell clone size and cytotoxicity. Sci Immunol (2021) 6(64):eabj8825. doi: 10.1126/sciimmunol.abj8825

PubMed Abstract | CrossRef Full Text | Google Scholar

176. Meyer C, Cagnon L, Costa-Nunes CM, Baumgaertner P, Montandon N, Leyvraz L, et al. Frequencies of circulating MDSC correlate with clinical outcome of melanoma patients treated with ipilimumab. Cancer Immunol Immunother (2014) 63(3):247–57. doi: 10.1007/s00262-013-1508-5

PubMed Abstract | CrossRef Full Text | Google Scholar

177. Gebhardt C, Sevko A, Jiang H, Lichtenberger R, Reith M, Tarnanidis K, et al. Myeloid cells and related chronic inflammatory factors as novel predictive markers in melanoma treatment with ipilimumab. Clin Cancer Res (2015) 21(24):5453–9. doi: 10.1158/1078-0432.CCR-15-0676

PubMed Abstract | CrossRef Full Text | Google Scholar

178. Martens A, Wistuba-Hamprecht K, Geukes Foppen M, Yuan J, Postow MA, Wong P, et al. Baseline peripheral blood biomarkers associated with clinical outcome of advanced melanoma patients treated with ipilimumab. Clin Cancer Res (2016) 22(12):2908–18. doi: 10.1158/1078-0432.CCR-15-2412

PubMed Abstract | CrossRef Full Text | Google Scholar

179. Hogan SA, Courtier A, Cheng PF, Jaberg-Bentele NF, Goldinger SM, Manuel M, et al. Peripheral blood TCR repertoire profiling may facilitate patient stratification for immunotherapy against melanoma. Cancer Immunol Res (2019) 7(1):77–85. doi: 10.1158/2326-6066.CIR-18-0136

PubMed Abstract | CrossRef Full Text | Google Scholar

180. Spitzer MH, Carmi Y, Reticker-Flynn NE, Kwek SS, Madhireddy D, Martins MM, et al. Systemic immunity is required for effective cancer immunotherapy. Cell (2017) 168(3):487–502.e15. doi: 10.1016/j.cell.2016.12.022

PubMed Abstract | CrossRef Full Text | Google Scholar

181. Huang AC, Postow MA, Orlowski RJ, Mick R, Bengsch B, Manne S, et al. T-Cell invigoration to tumour burden ratio associated with anti-PD-1 response. Nature (2017) 545(7652):60–5. doi: 10.1038/nature22079

PubMed Abstract | CrossRef Full Text | Google Scholar

182. Fairfax BP, Taylor CA, Watson RA, Nassiri I, Danielli S, Fang H, et al. Peripheral CD8(+) T cell characteristics associated with durable responses to immune checkpoint blockade in patients with metastatic melanoma. Nat Med (2020) 26(2):193–9. doi: 10.1038/s41591-019-0734-6

PubMed Abstract | CrossRef Full Text | Google Scholar

183. Valpione S, Galvani E, Tweedy J, Mundra PA, Banyard A, Middlehurst P, et al. Immune-awakening revealed by peripheral T cell dynamics after one cycle of immunotherapy. Nat Cancer (2020) 1(2):210–21. doi: 10.1038/s43018-019-0022-x

PubMed Abstract | CrossRef Full Text | Google Scholar

184. Gebhardt T, Wakim LM, Eidsmo L, Reading PC, Heath WR, Carbone FR. Memory T cells in nonlymphoid tissue that provide enhanced local immunity during infection with herpes simplex virus. Nat Immunol (2009) 10(5):524–30. doi: 10.1038/ni.1718

PubMed Abstract | CrossRef Full Text | Google Scholar

185. Jiang X, Clark RA, Liu L, Wagers AJ, Fuhlbrigge RC, Kupper TS. Skin infection generates non-migratory memory CD8+ T(RM) cells providing global skin immunity. Nature (2012) 483(7388):227–31. doi: 10.1038/nature10851

PubMed Abstract | CrossRef Full Text | Google Scholar

186. Ariotti S, Hogenbirk MA, Dijkgraaf FE, Visser LL, Hoekstra ME, Song JY, et al. T Cell memory. skin-resident memory CD8(+) T cells trigger a state of tissue-wide pathogen alert. Science (2014) 346(6205):101–5. doi: 10.1126/science.1254803

PubMed Abstract | CrossRef Full Text | Google Scholar

187. Yenyuwadee S, Sanchez-Trincado Lopez JL, Shah R, Rosato PC, Boussiotis VA. The evolving role of tissue-resident memory T cells in infections and cancer. Sci Adv (2022) 8(33):eabo5871. doi: 10.1126/sciadv.abo5871

PubMed Abstract | CrossRef Full Text | Google Scholar

188. Mami-Chouaib F, Blanc C, Corgnac S, Hans S, Malenica I, Granier C, et al. Resident memory T cells, critical components in tumor immunology. J Immunother Cancer (2018) 6(1):87. doi: 10.1186/s40425-018-0399-6

PubMed Abstract | CrossRef Full Text | Google Scholar

189. Park SL, Gebhardt T, Mackay LK. Tissue-resident memory T cells in cancer immunosurveillance. Trends Immunol (2019) 40(8):735–47. doi: 10.1016/j.it.2019.06.002

PubMed Abstract | CrossRef Full Text | Google Scholar

190. Kok L, Masopust D, Schumacher TN. The precursors of CD8(+) tissue resident memory T cells: from lymphoid organs to infected tissues. Nat Rev Immunol (2022) 22(5):283–93. doi: 10.1038/s41577-021-00590-3

PubMed Abstract | CrossRef Full Text | Google Scholar

191. Djenidi F, Adam J, Goubar A, Durgeau A, Meurice G, de Montpreville V, et al. CD8+CD103+ tumor-infiltrating lymphocytes are tumor-specific tissue-resident memory T cells and a prognostic factor for survival in lung cancer patients. J Immunol (2015) 194(7):3475–86. doi: 10.4049/jimmunol.1402711

PubMed Abstract | CrossRef Full Text | Google Scholar

192. Nizard M, Roussel H, Diniz MO, Karaki S, Tran T, Voron T, et al. Induction of resident memory T cells enhances the efficacy of cancer vaccine. Nat Commun (2017) 8:15221. doi: 10.1038/ncomms15221

PubMed Abstract | CrossRef Full Text | Google Scholar

193. Menares E, Galvez-Cancino F, Caceres-Morgado P, Ghorani E, Lopez E, Diaz X, et al. Tissue-resident memory CD8(+) T cells amplify anti-tumor immunity by triggering antigen spreading through dendritic cells. Nat Commun (2019) 10(1):4401. doi: 10.1038/s41467-019-12319-x

PubMed Abstract | CrossRef Full Text | Google Scholar

194. Toh JWT, Ferguson AL, Spring KJ, Mahajan H, Palendira U. Cytotoxic CD8+ T cells and tissue resident memory cells in colorectal cancer based on microsatellite instability and BRAF status. World J Clin Oncol (2021) 12(4):238–48. doi: 10.5306/wjco.v12.i4.238

PubMed Abstract | CrossRef Full Text | Google Scholar

195. Boddupalli CS, Bar N, Kadaveru K, Krauthammer M, Pornputtapong N, Mai Z, et al. Interlesional diversity of T cell receptors in melanoma with immune checkpoints enriched in tissue-resident memory T cells. JCI Insight (2016) 1(21):e88955. doi: 10.1172/jci.insight.88955

PubMed Abstract | CrossRef Full Text | Google Scholar

196. Caushi JX, Zhang J, Ji Z, Vaghasia A, Zhang B, Hsiue EH, et al. Transcriptional programs of neoantigen-specific TIL in anti-PD-1-treated lung cancers. Nature (2021) 596(7870):126–32. doi: 10.1038/s41586-021-03752-4

PubMed Abstract | CrossRef Full Text | Google Scholar

197. Sun YY, Peng S, Han L, Qiu J, Song L, Tsai Y, et al. Local HPV recombinant vaccinia boost following priming with an HPV DNA vaccine enhances local HPV-specific CD8+ T-cell-Mediated tumor control in the genital tract. Clin Cancer Res (2016) 22(3):657–69. doi: 10.1158/1078-0432.CCR-15-0234

PubMed Abstract | CrossRef Full Text | Google Scholar

198. Galvez-Cancino F, Lopez E, Menares E, Diaz X, Flores C, Caceres P, et al. Vaccination-induced skin-resident memory CD8(+) T cells mediate strong protection against cutaneous melanoma. Oncoimmunology (2018) 7(7):e1442163. doi: 10.1080/2162402X.2018.1442163

PubMed Abstract | CrossRef Full Text | Google Scholar

199. Kinker GS, Vitiello GAF, Ferreira WAS, Chaves AS, Cordeiro de Lima VC, Medina TDS. B cell orchestration of anti-tumor immune responses: A matter of cell localization and communication. Front Cell Dev Biol (2021) 9:678127. doi: 10.3389/fcell.2021.678127

PubMed Abstract | CrossRef Full Text | Google Scholar

200. Ma P, Beatty PL, McKolanis J, Brand R, Schoen RE, Finn OJ. Circulating myeloid derived suppressor cells (MDSC) that accumulate in premalignancy share phenotypic and functional characteristics with MDSC in cancer. Front Immunol (2019) 10:1401. doi: 10.3389/fimmu.2019.01401

PubMed Abstract | CrossRef Full Text | Google Scholar

201. Mascaux C, Angelova M, Vasaturo A, Beane J, Hijazi K, Anthoine G, et al. Immune evasion before tumour invasion in early lung squamous carcinogenesis. Nature (2019) 571(7766):570–5. doi: 10.1038/s41586-019-1330-0

PubMed Abstract | CrossRef Full Text | Google Scholar

202. Pang X, Fan HY, Tang YL, Wang SS, Cao MX, Wang HF, et al. Myeloid derived suppressor cells contribute to the malignant progression of oral squamous cell carcinoma. PLoS One (2020) 15(2):e0229089. doi: 10.1371/journal.pone.0229089

PubMed Abstract | CrossRef Full Text | Google Scholar

203. Chang K, Taggart MW, Reyes-Uribe L, Borras E, Riquelme E, Barnett RM, et al. Immune profiling of premalignant lesions in patients with lynch syndrome. JAMA Oncol (2018) 4(8):1085–92. doi: 10.1001/jamaoncol.2018.1482

PubMed Abstract | CrossRef Full Text | Google Scholar

204. Staffa L, Echterdiek F, Nelius N, Benner A, Werft W, Lahrmann B, et al. Mismatch repair-deficient crypt foci in lynch syndrome–molecular alterations and association with clinical parameters. PLoS One (2015) 10(3):e0121980. doi: 10.1371/journal.pone.0121980

PubMed Abstract | CrossRef Full Text | Google Scholar

205. Ahadova A, Gallon R, Gebert J, Ballhausen A, Endris V, Kirchner M, et al. Three molecular pathways model colorectal carcinogenesis in lynch syndrome. Int J Cancer (2018) 143(1):139–50. doi: 10.1002/ijc.31300

PubMed Abstract | CrossRef Full Text | Google Scholar

206. Ahadova A, Seppala TT, Engel C, Gallon R, Burn J, Holinski-Feder E, et al. The "unnatural" history of colorectal cancer in lynch syndrome: Lessons from colonoscopy surveillance. Int J Cancer (2021) 148(4):800–11. doi: 10.1002/ijc.33224

PubMed Abstract | CrossRef Full Text | Google Scholar

207. Cecil DL, Gad EA, Corulli LR, Drovetto N, Lubet RA, Disis ML. COX-2 inhibitors decrease expression of PD-L1 in colon tumors and increase the influx of type I tumor-infiltrating lymphocytes. Cancer Prev Res (Phila) (2022) 15(4):225–31. doi: 10.1158/1940-6207.CAPR-21-0227

PubMed Abstract | CrossRef Full Text | Google Scholar

208. Roudko V, Cimen Bozkus C, Greenbaum B, Lucas A, Samstein R, Bhardwaj N. Lynch syndrome and MSI-h cancers: From mechanisms to "Off-The-Shelf" cancer vaccines. Front Immunol (2021) 12:757804. doi: 10.3389/fimmu.2021.757804

PubMed Abstract | CrossRef Full Text | Google Scholar

209. Guerriero JL, Sotayo A, Ponichtera HE, Castrillon JA, Pourzia AL, Schad S, et al. Class IIa HDAC inhibition reduces breast tumours and metastases through anti-tumour macrophages. Nature (2017) 543(7645):428–32. doi: 10.1038/nature21409

PubMed Abstract | CrossRef Full Text | Google Scholar

210. Chen S, Song Z, Zhang A. Small-molecule immuno-oncology therapy: Advances, challenges and new directions. Curr Top Med Chem (2019) 19(3):180–5. doi: 10.2174/1568026619666190308131805

PubMed Abstract | CrossRef Full Text | Google Scholar

211. Verma V, Jafarzadeh N, Boi S, Kundu S, Jiang Z, Fan Y, et al. MEK inhibition reprograms CD8(+) T lymphocytes into memory stem cells with potent antitumor effects. Nat Immunol (2021) 22(1):53–66. doi: 10.1038/s41590-020-00818-9

PubMed Abstract | CrossRef Full Text | Google Scholar

212. Sasikumar PG, Ramachandra M. Small molecule agents targeting PD-1 checkpoint pathway for cancer immunotherapy: Mechanisms of action and other considerations for their advanced development. Front Immunol (2022) 13:752065. doi: 10.3389/fimmu.2022.752065

PubMed Abstract | CrossRef Full Text | Google Scholar