Evelyn Calderon-Espinosa1,2,3
Kirsten De Ridder1,2
Thomas Benoot1,2Yanina Jansen4Domien Vanhonacker5
Robbe Heestermans6
Ann De Becker6
Ivan Van Riet6
Lore Decoster7
Cleo Goyvaerts1,2*- 1Laboratory for Molecular and Cellular Therapy (LMCT), Translational Oncology Research Center (TORC), Department of Biomedical Sciences, Vrije Universiteit Brussel, Brussels, Belgium
- 2Laboratory for Molecular Imaging and Therapy (MITH), Vrije Universiteit Brussel, Brussels, Belgium
- 3Department of Chemistry, University of Warwick, Warwick, United Kingdom
- 4Department of Thoracic Surgery, University Hospitals Leuven, Leuven, Belgium
- 5Department of Anesthesiology, Perioperative and Pain Medicine, Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- 6Department of Hematology, Team Hematology and Immunology (HEIM), Translational Oncology Research Center (TORC), Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- 7Department of Medical Oncology, Team Laboratory for Medical and Molecular Oncology (LMMO), Translational Oncology Research Center (TORC), Vrije Universiteit Brussel (VUB), Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
Modest response rates to immunotherapy observed in advanced lung cancer patients underscore the need to identify reliable biomarkers and targets, enhancing both treatment decision-making and efficacy. Factors such as PD-L1 expression, tumor mutation burden, and a ‘hot’ tumor microenvironment with heightened effector T cell infiltration have consistently been associated with positive responses. In contrast, the predictive role of the abundantly present tumor-infiltrating myeloid cell (TIMs) fraction remains somewhat uncertain, partly explained by their towering variety in terms of ontogeny, phenotype, location, and function. Nevertheless, numerous preclinical and clinical studies established a clear link between lung cancer progression and alterations in intra- and extramedullary hematopoiesis, leading to emergency myelopoiesis at the expense of megakaryocyte/erythroid and lymphoid differentiation. These observations affirm that a continuous crosstalk between solid cancers such as lung cancer and the bone marrow niche (BMN) must take place. However, the BMN, encompassing hematopoietic stem and progenitor cells, differentiated immune and stromal cells, remains inadequately explored in solid cancer patients. Subsequently, no clear consensus has been reached on the exact breadth of tumor installed hematopoiesis perturbing cues nor their predictive power for immunotherapy. As the current era of single-cell omics is reshaping our understanding of the hematopoietic process and the subcluster landscape of lung TIMs, we aim to present an updated overview of the hierarchical differentiation process of TIMs within the BMN of solid cancer bearing subjects. Our comprehensive overview underscores that lung cancer should be regarded as a systemic disease in which the cues governing the lung tumor-BMN crosstalk might bolster the definition of new biomarkers and druggable targets, potentially mitigating the high attrition rate of leading immunotherapies for NSCLC.
1 Introduction
Lung cancer remains the leading cause of cancer related death, reflected by a staggering 1.79 million deaths in 2020 alone (1). Non-small cell lung cancer (NSCLC) accounts for ~85% of all cases and is further subdivided into adenocarcinoma (LUAD), squamous cell carcinoma, and large cell carcinoma. The other 10-15% of lung cancers are represented by fast spreading small cell lung cancer, more slowly growing carcinoid tumors and rare types such as adenoid cystic carcinomas and sarcomas.
Though current lung cancer incidence mortality rates are highest in economically developed countries, they are gradually decreasing, mirroring declines in tobacco smoking next to therapeutic improvements for advanced-stage patients, including targeted and immunotherapies (1). At present, the main biological targets of immunotherapy are immune checkpoints, which aid cancer cells to deceive the patients’ immune system. Via delivery of immune checkpoint inhibitors, mostly monoclonal antibodies targeting the Programmed Death - (Ligand) 1 (PD-(L)1) or Cytotoxic T-Lymphocyte Associated protein-4 (CTLA-4) pathways, antitumor immunity can be unleashed (2). Alas, immune checkpoint blockade (ICB) can coincide with serious immune-related adverse events, objective response rates remain <25% in unselected NSCLC patients and most responders eventually develop progressive disease. These sobering facts reflect the multitude of unaddressed resistance mechanisms to immunotherapy that remain today (3).
Histological expression of PD-L1 and tumor mutational burden (TMB) emerged as predictive biomarkers for response to ICB in NSCLC (4). Unfortunately, their predictive power is often unsatisfactory and doesn’t explain the nature of the resistance hubs that could aid oncologists during (combination) treatment decision making.
In search of additional biomarkers and targetable mechanisms of resistance to ICB, ample studies assigned a leading role to the tumor micro-environment (TME) (5, 6). Cytometry-based compositional analyses studies of the lung TME collectively demonstrated a predominance of T lymphocytes followed by B lymphocytes, neutrophils (Neu), and mononuclear phagocytes (7–11). Most bulk RNA sequencing studies of baseline NSCLC biopsies boil down to the observation that a ‘hot’ TME, characterized by activated effector T cells, interferon γ (IFNγ) signature and/or presence of an antigen presentation machinery, has strong predictive power for response to ICB. Noteworthy, most of these predictive gene signatures depart from a predefined lymphocyte-related gene set (12–15). As a result, most of these studies omit the relevance of the abundantly present Neu and mononuclear phagocytes for response and/or resistance to ICB.
By zooming in on the few studies that used a less or unbiased approach to establish an ICB predictive gene set (16–18), decisive roles for myeloid cell-related signatures have been demonstrated. When Hwang et al. tested the predictive value using a 395-gene panel, significant predictive power could be assigned to a T cell next to a M1 macrophage (Mac) characterizing signature (19). By using virtual microdissection of the bulk transcriptome of the TCGA LUAD cohort at single-cell resolution, Wu et al. defined two myeloid cell infiltration clusters, MSC1 and MSC2, significantly associated with a longer and shorter overall survival (OS) and response to ICB resp. In brief, the MSC2 signature was characterized by LAMP3+ dendritic cells (DCs), TIMP1+ Macs and S100A8+ Neu while the MSC1 profile showed higher levels of CD1c+ or CLEC9A+ DCs; IFITM2+, SELENOP+ or PPARg+ Macs and IFITM2+ Neu (20, 21). When Salcher et al. focused on the ICB predictive power of Neu subclusters within the NSCLC TME specifically, they defined a 38-gene encompassing tissue-resident Neu signature that was associated with anti-PD-L1 therapy failure in the randomized POPLAR and OAK clinical trials (22). Similarly, Peng et al. recently showed that NSCLC patients with a high TMB but low ‘Neu differentiation expression gene score’ were most likely to benefit from ICB (23).
Hence predictive signatures for ICB efficacy are likely to span more genes than the ones linked to activated lymphocytes and suggest that the lung tumor infiltrating myeloid cells (TIMs) deserve more attention for their biomarker potential to predict both response and resistance to ICB (9, 10, 21, 22, 24–28). Unlike lymphocytes, the proliferative and longevity potential of most myeloid cell subsets is low. This implies that TIM’s ampleness within the lung TME is the result of their continuous recruitment from their main cradle: the bone marrow niche (BMN). Nevertheless, the crosstalk between progressive solid cancers such as NSCLC and the distant BMN remains vastly understudied. This is attributable to the fact that in depth analysis of the BMN is hampered by the marrow’s inaccessible location, low fractions of hematopoietic stem and progenitor cells (HSPCs) (<1%) next to the heterogeneous and continuously differentiating character of hematopoiesis leading to its difficult nature to culture ex vivo. Thus, untapped biomarker and target potential might lie in the cues that govern tumor instituted alterations in hematopoiesis and TIM-propagating myelopoiesis in particular.
2 Recent insights in hematopoiesis
In adult life, the primary source of myeloid cells is the BMN, where the vital biological process known as hematopoiesis occurs. Hematopoiesis exhibits a continuous hierarchical differentiation process that is tightly regulated by lineage-determining transcription factors (TFs) of which many are highly conserved in mouse and human (29). During embryonal development, intra-aortic cells within the aorta-gonad-mesonephros generate the first multilineage hematopoietic stem cells (HSCs). The latter represent a scarce population of self-renewing cells that translocate to the BMN to constitute most hematopoietic cells during adult life. In addition, HSC-independent hematopoiesis is also initiated during embryogenesis which partly persists in the adult hematopoietic system. HSC-independent lineages are either short-lived, such as primitive erythrocytes, or long-lived, such as tissue-resident Macs (30, 31). Myeloid cells represent a heterogeneous crowd of subsets with embryonic and/or hematopoietic origin (32–34). Fate mapping, transposon, Cre-loxP-mediated barcoding and single cell (sc) RNA-seq studies are continuing to refine our understanding of the compelling hematopoietic trajectories, of which we provide an updated overview below (35, 36).
2.1 LSK compartment: HSC and MPPs
The murine Lin−Sca-1+c-Kit+ (LSK) or human CD34+ compartment represents the HSCs, including the multipotent progenitors (MPPs). They lodge at the top of the hierarchical ladder and withhold high self-renewing but low proliferative capacity (37). For years, HSCs were subdivided in long-term (LT-HSCs) and short-term HSCs (ST-HSCs) according to their reconstituting potential (38). Yet recent scRNA-seq-based characterization of the LSK compartment with functional reconstitution assays, redistributed this compartment into HSCs and six different types of MPPs (Figure 1, Supplementary Table S1). Within the latter, erythroid-primed MPP2, myeloid-primed MPP3, and lympho-erythromyeloid-primed MPP4 have been defined (39, 40). Sommerkamp et al. further report on a close relationship between MPP1 and MPP5 placing them downstream of HSCs but upstream of the less potent MPP2/3/4, while MPP6 resides in a cloud with MPP5, be it with higher multilineage potential than the latter. Further, MPP5 seems to act as a reservoir during emergency myelopoiesis (37). These findings suggest that a functional hierarchy, consisting of progenitors at varying degrees of lineage priming is already installed within the multipotent HSC/MPP fraction. Multi- and oligopotency characterizing TFs are Hlf and ESAM which are downregulated upon specification and commitment resulting in progressive loss of alternative fates along all lineages (35, 39). Additionally, the stem/progenitor state is stabilized by the regulation of the Gata2, Tal1/Scl and Fli1 triad (41).
Figure 1 Overview of solid tumor installed cellular and molecular hematopoietic perturbations in human and mouse models. Hierarchical model of continuous hematopoiesis in mouse is shown. Interactions between remote tumor, Bone marrow, stromal compartment, spleen and premetastatic niche formation are indicated. Red arrows indicate increase, blue arrows indicate decrease, green and yellow arrows indicate lack of consensus on changes in the abundance of the respective populations within the bone marrow niche during solid cancer progression. Dashed lines indicate mobilization of cells. Orange lines indicate the alternative trajectory of neutrophils due to the bone marrow-tumor crosstalk. LK, Lin-Sca-1-c-Kit+; HSC, Hematopoietic Stem Cell; MPP 1/2/3/4/5/6, MultiPotent Progenitor 1/2/3/4/5/6; LMPP, Lymphoid-primed MultiPotent Progenitor; EMkMPP, Erythroid-Megakaryocyte-primed MultiPotent Progenitor; EoMP, Eosinophil-basophil-Mast cell Progenitor; MegE, pre-Megakaryocyte-Erythroid progenitor; PreE, Pre-colony-forming-Erythroid progenitor; MkP, Megakaryocyte Progenitor; CMP, Common Myeloid Progenitor; MEP, Megakaryocyte/Erythrocyte Progenitor; LPP, Lymphoid-Primed Progenitor; MDP, Monocyte/Dendritic cell Progenitor; GMP, Granulocyte/Monocyte Progenitor; GP, Granulocyte Progenitor; CLP, Common Lymphoid Progenitor; NK cell, Natural Killer cell; pDCP, plasmacytoid Dendritic Cell Progenitor; McDP, Monocyte/conventional Dendritic cell Progenitor; cMoP, common Monocyte Progenitor; Mo, monocytes; CDP, Common Dendritic cell Progenitor; (c)DC1/2A/2B, (conventional) Dendritic Cell 1/2A/2B; pDC, plasmacytoid Dendritic Cell; G0, granulocyte progenitor; G1/G2/G3, pre/pro/immature neutrophils; G4,G5a,b,c, mature neutrophils; (pre)Neu, (pre)neutrophils; Mac, macrophage; (ST)/(LT)-HSC, Short-term/long-term- HSC; TA(M)/(N), Tumor associated Mac/Neu; TIM, Tumor infiltrated myeloid cells; sRAGE, soluble form of the receptor for advanced glycation end; PTHrP, parathyroid hormone-related protein; ATP, adenosine triphosphate; GM-CSF, granulocyte-macrophage colony-stimulating factor; G-CSF, granulocyte colony-stimulating factor; M-CSF, macrophage colony-stimulating factor; SCF, stem cell factor; IL1/4/6/8: Interleukin 1/4/6/8; VEGF, vascular endothelial growth factor; PIGF, placental growth factor; ATF3, activating transcription factor 3; CXCR2, C-X-C Motif Chemokine Receptor 2; IGF1, insulin-like growth factor 1; TGFβ, transforming growth factor beta; HTRA1, HtrA serine peptidase 1; LOX, hypoxia-installed lysyl oxidase. Created with BioRender.com.
2.2 Committed-lineage progenitors
Downstream of the multipotent HSCs and MPPs, the original lineage bifurcation model strictly separated oligopotent Common Lymphoid Progenitors (CLP) from Common Myeloid Progenitors (CMP) which could further diverge into Megakaryocyte/Erythrocyte Progenitors (MEPs), Monocyte (Mo)/Dendritic cell (DC) Progenitors (MDPs) and Granulocyte/Mo Progenitors (GMPs). However, this model has been the subject of ongoing revision in the scRNA-seq era. On the one hand, a Lymphoid-primed MultiPotent Progenitor (LMPP) with combined lymphoid and myeloid potential has been shown to gradually lose myeloid potential by upregulating the interleukin-7 receptor α-chain (IL7Rα) and differentiate into Dach1- Lymphoid-Primed Progenitors (LPPs) prior to CLPs (29, 33, 35, 42–45). On the other hand, erythroid-versus-myeloid fate decisions seem to be made prior to CMP commitment (36, 40, 46), calling for in-depth revision of all CMP progeny (MEPs, MDPs and GMPs) as well (46). While the Flt3L+CD115lo CMPs give rise to oligopotent MDPs, the Flt3-CD115- CMPs give rise to GMPs. The former are defined by TF co-expression of CEBPA, IRF8 and RUNX1 (44). MDPs can give rise to IRF8/SATB1 plasmacytoid DC (pDC) Progenitors (pDCPs) and HLA-DR/IRF8/CEBPA Mo/conventional DC (cDC) Progenitors (McDPs). The latter bifurcate in CD11c/CD172ab/CD14 (human) or Ly6C+ Mo (mouse) or IRF8/HLA-DR/CD84/CD172ab Common DC Progenitors (CDPs) (44). In contrast, GMPs (Kit, CD34, Sox4) are very heterogeneous at the transcriptomic and proteomic level and can adjust their functional output towards common Mo Progenitors (cMoP) and Granulocyte Progenitors (GPs) (32).
2.3 DC trajectory
In general, DC progenitors rely on Flt3 and Flt3L for their differentiation and proliferation into pDCs, cDC1 and cDC2. In addition, cDC1 and cDC2 differentiation depends on the specific TF expression of IRF8, BATF3 and ZFP366 versus IRF4 and KLF4 resp. While XCR1/Clec9A/CADM1 cDC1s seem to originate solely from CDPs, recent paradigm shifts are sharpening the ontogeny of cDC2s and pDCs. In case of cDC2s, murine T-bet+ DC2A and RORγt+ DC2B have been described which align with human CD5+ DC2s and CD14+ DC3s (47). Recently, Liu et al. further demonstrated these DC subsets have a different ontogeny. Whereas ESAM+ DC2s are derived from CDPs, the CD172a+ CD16/32+ DC3 fraction seems to develop directly from Ly6Chi McDPs (48). Further, CD123+ pDCs can be derived from MDPs, yet they predominantly originate from IL7Rα+ LPPs requiring high expression of IRF8 and TCF4 for pDC development and identity resp. Although MDP and LPP derived pDC subsets can secrete IFN type 1, only the former share with cDCs the ability to process and present antigens (49).
2.4 Monocyte differentiation
Both in mice and human, Mos are subdivided in IRF8-dependent Ly6Chi or CD14+CD16- classical and IRF8/Nur77-dependent Ly6C- or CD14loCD16+ non-classical Ly6C- Mo resp. Also, they can emerge from at least two different lineage tracks defined by the complementary oligopotent GMPs and MDPs (32, 34). Interestingly, GMPs produce “Neu-like” Ly6Chi Mo while MDP-derived Mo show similarities in gene expression with Macs and DCs. Both GMP- and MDP-derived Mo yield Macs but only the MDP pathway gives rise to MoDCs (32, 34). In general, Mos development largely depends on the expression of Csf1r (CD115) (48, 50) to result in the expression of Mo characterizing markers such as Fcer1g, F13a1, Irf8, Klf4, Zeb2 and Irf5 (35, 51). Relative to other myeloid cells, Mos have an exceedingly short transit time through the BMN and are rapidly released into the circulation after their last division, governed by CCR2 and CX3CR1, where the latter receptor prompts survival of Ly6Clo Mo. In contrast, CXCR4-signaling represents an anchoring force that retains Ly6Chi Mos in the BMN, identifying them as transitional pre-Mos that replenish the mature Mo pool for peripheral responses (35, 51). Notably, Mos also contribute to the replenishment of yolk sac-derived tissue-residing Macs in tissue and gender dependent fashion (32).
2.5 Granulopoiesis
The neutrophil differentiation trajectory is defined by at least 5 stages (G0-G5) where the earliest GPs (G0), embedded in the GMPs, initiate granulopoiesis through Runx1, Cebpa, Gstm1, Per3 and Ets1 expression (35, 52–55). Subsequent differentiation into proNeu (G1), results in full granulocyte commitment and maturation via TF ThPOK (35, 52–55). This is followed by a transcriptional shift to Gfi1 and Cebpe in the preNeu (G2) which represses Mos on MDP fate (Irf8, Klf4 and Zeb2). The latter are still proliferative in the BMN and spleen but hardly found in blood due to their poor motility. Further silencing of Gata1 and Gata2 results in non-proliferating immature Neu (G3). Finally, PU.1, Cebpd, Runx1, and Klf6 upregulation drives the generation of mature Neu (G4 mainly in BMN and G5a, b and c mainly in blood) which substitute their proliferative capacity for trafficking and effector functions. In line, they are characterized by Neu lineage characteristic expression of Elastase (Elane) and ficollin (Fcnb) (53). Notably, the temporal expression of the Cebp-family members mirrors the pattern of granule enzyme expression; primary (e.g. Mpo), secondary (e.g. Ltf) and tertiary granules (e.g. Mmp8) are expressed at the G0-G1, G2-G3 and G4-G5 stage, corresponding to Cebpa, Cebpe, and Cebpd expression resp. After maturation, Neus are retained in the BMN through CXCR4 while CXCR2 drives their release into the circulation (56). During inflammation, granulocyte colony-stimulating factor (G-CSF) further potentiates their mobilization.
Recent scRNA-seq studies on murine and human myeloid progenitors suggest that eosinophils, basophils (characterized by high expression of TFs Gfi1, Il5ra, Prg2, Epx or Lmo4, Runx1 resp.) and mast-cells, bifurcate directly from MPPs instead of GMPs. Specifically, Gata1 expression has been shown to establish an early branch point to separate lineage-commitment. Whereas Gata1- progenitors commit to the LMPPs and GMPs to hatch lymphocytes and Neu/Mo-restricted lineages resp., Gata1+ progenitors give rise to co-segregated commitment of Eosinophil/basophil/Mast cell Progenitors (EoMP) and a Megakaryocyte/Erythroid lineage (MegE) (57–59). The inflection point at erythroid and megakaryocyte lineage bifurcation is characterized by downregulation of RUNX1, GATA2 and PBX1 (44).
2.6 Stromal involvement in hematopoietic regulation
In the highly specialized BMN, HSCs team up with stromal cells of mesenchymal, endothelial and neuronal origin (60–64). To safeguard the balance between HSC maintenance and output, extrinsic and intrinsic factors are continuously engaged. Extrinsic factors include hypoxia, growth factors (GFs), direct cell-cell contacts, and signaling pathway activating morphogens, next to intrinsic factors encompassing TFs, cell cycle regulators, epigenetic proteins, and miRNAs, as extensively reviewed elsewhere (65). The curated orchestration of hematopoiesis is further reflected in the establishment of bidirectional interactions between HSCs and a variety of endothelial cells and mesenchymal stromal cells (MSCs) in spatially restricted microanatomical niches (64, 66).
The type of blood vessels has been used to subdivide the BMN into a central sinusoidal niche, an arteriolar niche and a peripheral endosteal niche with transition zone vessels. While the former comprises highly permeable vessels to promote HSC proliferation, activation and leukocyte trafficking, the latter two support HSC regeneration and maintenance by keeping them in a quiescent state through reduced reactive oxygen species (ROS) availability (67–69). In addition, the endosteum harbors bone-modulating osteoblasts and osteoclasts, shown to support HSC maintenance and retention amongst others mediated through production of G-CSF, stromal-cell-derived factor 1 (CXCL12 or SDF-1), stem cell factor (SCF) and Notch ligand jagged 1 (JAG1) (70–73).
The vast majority of HSCs reside in the central sinusoidal niche in direct contact with sinusoidal endothelial cells and perivascular mesenchymal progenitors including adipogenic leptin receptor+ CXCL12-abundant reticular (CAR) cells (69, 74–80). Via their paracrine secretion of CXCL12 and SCF, the CXCR4 and c-Kit receptor on HSCs are respectively triggered, cardinal for hematopoietic multilineage differentiation (76, 81, 82). Baccin et al. additionally allocated an osterix+ osteogenic CAR population to the vicinity of arterioles and non-vascular regions and concluded that the adipogenic- and osteogenic CAR cell populations represent the main producers of key cytokines and GFs (such as CXCL12), suggesting their localization within the sinusoidal and arteriolar niches resp. establishes unique hubs of HSC differentiation and maintenance (61). In addition, Li et al. recently defined 3 stromal niche populations with distinct HSC regulatory impact (64). In addition, they predicted that bidirectional communication enables HSCs and derivates to regulate stromal cells as well. Hence, homeostasis reinstating feedback mechanisms can be activated such as adipogenic CAR cell activation through IL1 secretion of activated platelets (83).
Overall, technological advances are currently sharpening our understanding of the intricate hematopoietic process. Nevertheless, we cannot speak of a complete fathoming of all hematopoietic tributaries yet, calling for continued research of the BMN in healthy and diseased subjects.
3 Recent advances in singe cell omics reveal a complex lung TIM crowd
scRNA-seq analysis represents a very powerful tool that recently barged into the onco-immunology field. Through this approach, unbiased and in-depth insights have emerged that foster an unprecedented understanding of biological phenomena on transcriptional single cell level. Especially compared to flow cytometry- and microscopy-based approaches which rely on antibodies to pre-defined markers. In recent years, scRNA-seq of NSCLC biopsies markedly deepened our understanding of the TME in which especially the TIM compartment appeared to hold an underestimated myriad of subsets with distinct progenitors and functions (21–23, 25–27, 84–89). Moreover, TIMs have garnered increasing attention for their potential as biomarkers to predict both response and resistance to ICB. Yet each scRNA-seq study used different tumor samples and sequencing techniques. As a result, a plethora of TIM subsets have been described, yet to our knowledge, no side-by-side overview of the most recent scRNA-seq studies has been generated to define the subsets, perpetuated by most of these studies.
So far, consensus has been reached on at least 5 main ontogenetically discernable human lung TIM populations: DCs, Mos, Macs, Neus and mast cells (Table 1). For lung TME-associated DCs, 5 subclusters have consistently been defined: cDC1 and cDC2, mature regulatory DCs (mregDCs, derived from cDC1 and cDC2), pDCs and Mo-derived DCs (Mo-DCs). Lung tumor-associated Mos are repeatedly reported to be of classical or patrolling non-classical origin. In the case of Macs and Neus, much less consensus has been reached across different scRNA-seq studies. We found that for at least 11 Mac and 6 Neu subclusters, two or more independent scRNA-seq studies reported on their presence. The Mac subclusters comprise 3 tissue resident and 8 Mo-derived fractions, while Neus are represented by 2 normal adjacent tissue-associated Neu (NAN) and 4 tumor-associated Neu (TAN) fractions.
Table 1 Overview of major lung TIM clusters with respective subclusters defined by scRNA-seq and their respective reported association with response to ICB.
In terms of TIM subcluster specific prognostic and predictive biomarker potential, unanimity also remains scarce. If we only consider the correlations that were validated by more than one scRNA-seq-based study, consensus has been reached for the positive and negative association of cDC2 and mregDCs resp. with response to ICB. In addition, mast cells and TAN-3 have been associated with a better and worse OS, irrespective of the treatment type. Singular studies further ascribe a positive ICB predictive or prognostic role to MoDCs and CX3CR1hi Mo. In contrast, a negative correlation with response to ICB has been reported for pDCs, dividing TUBB+ Macs, CXCL5+ Macs and CHI3LA+ Macs while the presence of NAN 1&2, OLR1+ Mo, CXCL5+ Macs, dividing TUBB+ Macs, TIMP1+ Macs and CXCL9+ Macs has been associated with worse OS. However, subcluster specific discrepancies have been reported as well. For example, Leader et al. defined the Lung Cancer Activation Module (LCAM) to predict response to anti-PD-(L)1 therapy using scRNA-seq data from lung tumor biopsies at baseline (12). While NSCLC patients with high TMB, mutated TP53, and LCAMhi profile with SPP1+ Macs (next to PD1+ CXCL13+ T cells, IgG+ plasma cells) showed superior responses, the opposite held true for patients with low TMB, TP53 wild type and LCAMlo profile characterized by mregDCs and PPARg+ Macs (next to naïve and central memory T cells and reduced plasma/B cell ratios). Akin, Wu et al. found that LAMP3+ DCs were the only subtype enriched in non-responders, which seems partly in line with the resting mregDC-rich LCAMlo profile. In contrast, both Wu et al. and Hu et al. associated the enrichment of PPARg+ Macs (next to NAN-3) with a major pathological response to ICB, while Hu et al. further defined a negative correlation between SPP1+ Macs (next to TAN-3) and ICB response. Importantly, Hu et al. analyzed samples after neoadjuvant anti-PD-1 ICB with chemotherapy instead of baseline biopsies (21, 90).
The high variability in subcluster definitions and denoted biomarker potential of lung TIMs can be partly explained by the disparity of the scRNA-seq studies. First, the use of different scRNA-seq platforms has been shown to hamper proper data alignment (22). Second, scRNA-seq studies encompass tumor biopsies from patients with diverse profiles (early versus late stage, histological and genetic subtype) and sampling procedures (intra- versus peritumoral, derived from primary versus metastatic lesion, pre- or posttreatment) (22, 23, 85, 89). Considering that TIM subcluster phenotypes and functions are eminently impressionable by local traits in time and space, evaluation, and comparison of TIM subclusters between different tumor biopsies might be a clinical challenge. To illustrate, both tumor-associated cDC1 and cDC2 have been reported to execute antitumor immune promoting and suppressing activity (88, 94). Within the diversified family of tumor-associated Macs and Neus, this ambiguity seems even more pronounced with assigned functions ranging from pro-angiogenic, pro-inflammatory and immunosuppressive to antigen presenting, CD8+ T cell stimulating and tumor eradicating (95, 96).
In conclusion, scRNA-seq delivered an overwhelming amount of new info regarding lung TIM subclusters. However, as the exact TIM clustering method is subject to tumor sample and experimental heterogeneity, their denoted identity and biomarker potential should always be interpreted with caution. Instead of using a snapshot of the TIM composition to predict response to therapy, the question arises whether it is not more interesting to systemically monitor which tumor-derived myelopoiesis perturbing cues are responsible for the influx of TIMs so these can serve as a means of biomarkers and potential targets simultaneously. To illustrate, macrophage-colony stimulating factor (M-CSF or CSF-1) is known to be secreted by tumors to alter remote myelopoiesis. While levels of plasma CSF-1 have been associated with resistance to ICB in NSCLC patients (97), blockade of its receptor CSF1R is currently being investigated to treat advanced malignancies (98).
4 Crosstalk between solid cancer and the BMN
The lifespan of the majority of myeloid cell subsets is relatively short. Consequently, the abundance of TIMs within the TME reflects their ongoing recruitment from their primary reservoir: the BMN. Although the communication between solid cancers and the distant BMN remains largely unexplored, the wealth of preclinical and clinical investigations underscore that the progression of solid cancers is a systemic phenomenon, utilizing both intra- and extra-medullary hematopoiesis. Ample preclinical and clinical studies convey that solid cancer progression is a systemic process by exploiting intra- and extra-medullary hematopoiesis. After listing all studies that evaluated solid cancer-related hematopoietic alterations (Table 2), it becomes apparent that the latter are most often characterized by emergency myelopoiesis, commonly at the expense of megakaryocyte/erythroid and lymphoid differentiation (111–115, 121, 122, 124–126, 132, 134–137, 139, 140, 144).
Table 2 Overview of preclinical and clinical studies which investigated the type and location of hematopoiesis and myelopoiesis perturbing cues in tumor bearing subjects.
4.1 Emergency myelopoiesis
Paracrine signals from the tumor most often instigate emergency myelopoiesis, entailing activation, expansion and/or mobilization of HSPCs and immunosuppressive myeloid progenitors. In brief, most studies report on a drastic elevation of cells in the LSK compartment (HSCs and MPPs, mainly MPP3), GMPs and early stage committed Neu progenitors in the BMN (Figure 1), blood and even in the TME (53, 99, 109, 113, 119, 121, 122, 124–126, 129, 131–134, 141, 142, 145). Although immunosuppressive Ly6C+ Mo and Ly6G/Gr1+ granulocytic myeloid cells, a.k.a. mo- and g-myeloid-derived suppressor cells resp., have been shown to dominate peripheral blood and tumors (126, 146),, trends for BMN-residing CMPs are less clearly defined than for GMPs. For example, Wu et al. describes a marked decrease of CMPs in blood of 133 solid cancer patients (in contrast to significant increase in HSCs, MPPs and GMPs) (122). Akin, higher frequencies of GMPs but not CMPs have been reported in murine breast and lung cancer models (124, 125, 137, 142). In contrast, Al Sayed et al. showed in fibrosarcoma and different lung models that next to HSCs, MPPs and GMPs also CMPs increased due to their higher proliferative capacity within the BMN (132). In addition, Meyer et al. observed that the increase in immature granulocytes was at the expense of CDPs, pre-DCs and especially cDC1s (130).
4.2 Secreted myelopoiesis perturbing cues
Cancer-forced emergency myelopoiesis is orchestrated by tumor secreted myeloid-lineage-specific GFs and cytokines such as granulocyte-macrophage colony-stimulating factor (GM-CSF), G-CSF, M-CSF, SCF, IL6, IL8, vascular endothelial growth factor (VEGF), Placental growth factor (PlGF) and IL1 (100, 101, 108, 109, 115, 116, 121, 122, 147). GM-CSF is by far the most documented tumor-derived hematopoietic perturbator with reported role in amongst others, the expansion of GMPs and immunosuppressive Ly6C+ Mo and Ly6G+/Gr1+ granulocytes in BM, blood and spleen. Moreover, it has been linked to the de-differentiation of erythroid progenitors to multipotent immunosuppressive myeloid progenitors leading to anemia. Notably, anemia is shown to be strongly associated with poor prognosis in most cancer types as well as a compelling risk factor that can obstruct ICB therapy efficacy (122, 124, 135–137). Less consensus has been reached on the role of G-CSF with reports ranging from ‘essential for suppressive Neu expansion and polarization’ at the expense of cDC1 generation (123, 124, 130) over ‘minor impact on HSPC and GMP proliferation and/or immunosuppressive myeloid cell skewing’ (109, 116, 122, 126). Notably several studies reported on the lack of hematopoietic perturbation similarity between different tumor models (121, 130, 142), calling for careful interpretation of studies using different mouse models in terms of tumor type, stage, site and mode of engraftment.
4.3 Stromal involvement in hematopoietic regulation during cancer
Interestingly, recent studies on how tumor secreted factors are molding hematopoiesis exactly, are revealing a key role for BMN-residing stromal and differentiated immune cells. Akin, cancer-related bone loss as a result of deteriorated osteoclast and osteoblast numbers and activity has been reported (127, 128, 137, 138). It seems that this mainly involves the direct crosstalk between tumor cells and MSCs, which are skewed towards osteoprogenitors at the expense of adipogenic differentiation (141, 142). Mechanistically, spontaneous orthotopic lung cancer models have been shown to secrete a soluble form of the receptor for advanced glycation end products (sRAGE) which can provoke osteocalcin+ osteoblast activity with enhanced CXCR2 expression and subsequent mobilization of tumor-infiltrating SiglecFhi Neu (127, 128). Zhang et al. further describe the involvement of tumor-derived parathyroid hormone-related protein (PTHrP) which bolsters osteoblast maturation by modulating the expression of RANKL and its decoy receptor osteoprotegerin (OPG) (138). Osteoclast activity can subsequently release niche and hematopoiesis perturbing factors such as insulin-like growth factor 1 (IGF1) and TGFβ (137, 138). In addition, breast cancer-derived ATP has been shown to activate the NLRP3/inflammasome and release IL1β by BMN-residing Nestin1+ CXCL12 secreting MSCs which results in decreased CXCL12 and SCF expression to increase HSC mobilization, ATF3 promoting CMP/GMP cluster formation and expansion of CD14+ Mo that differentiate to tumor-associated macrophages (TAMs) (139, 140). Finally, HtrA serine peptidase 1 (HTRA1) on tumor-derived vesicles has been reported to upregulate MMP13 in osteoprogenitors which subsequently induce CD41- GMP clustering and systemic accumulation of protumoral Neu (142).
Aside from stromal cells, also BMN-residing basophils were recently shown to fuel emergency myelopoiesis via their production of IL4 during orthotopic lung tumor progression, resulting in GMP expansion and subsequent increase in the number of lung TIMs such as TREM2+ Macs (143). From these relatively disparate findings, it appears that further research is needed to ascertain the exact tumor-specific direct and indirect crosstalk with the different members of the BMN to install tumor promoting emergency hematopoiesis.
4.4 Extra-medullary alterations in hematopoiesis
Next to intra- also extra-medullary alterations in hematopoiesis have been reported (108, 119, 120, 124, 132, 134–136). Bayne et al. used the pancreatic adenocarcinoma model KPC to show that Lin-/c-Kit+ HSCs within the spleen were highly proliferative precursors of Neu and that this phenomenon was much less pronounced in the BMN (116). In line, Sio et al. report on increased splenic but not BMN cellularity with increase in ST-HSCs, LT-HSCs, LKs (Lin-Sca-1-c-Kit+), MPPs, CMPs, GMPs, MEPs and erythroblasts. In contrast, the BMN only revealed an increase in ST-HSCs, MPPs and GMPs whereas the number of LT-HSCs, LKs, MEPs and CMPs decreased (121). In contrast, Casbon et al. found in the PyMT transgenic breast carcinoma (BC) model that the BMN was the main site of Ly6G+ cell generation, in accordance with increased frequencies of marrow but not splenic HSCs, MPPs and GMPs (124). Unlike BM, the majority (80–90%) of spleen-residing myeloid progenitors were MEPs, which is in line with later studies on the generation of erythroid differentiated myeloid cells in tumor bearing subjects (135, 136). As such the splenic red pulp acts as an extra reservoir of immunosuppressive TAM and TAN precursors (recruitment to tumor via CCR2) that promote solid tumor progression and metastasis (117). Mechanistically, overproduction of the peptide hormone angiotensin II in tumor bearing mice triggers S1P1 signaling in HSCs and as such amplifies splenic but not BM-derived HSCs which acts upstream of a potent Mac amplification program (118). These findings indicate HSPC mobilization from the BMN to the spleen as well as extramedullary HSPC proliferation (107, 116). For emergency granulopoiesis specifically, preNeus have not been found in blood, ruling out their mobilization from the BMN to spleen making their splenic expansion likely attributable to the production of GM-CSF and IL3 in the spleen microenvironment (53, 148).
4.5 Premetastatic niche formation
In addition, systemic tumor-HSPC crosstalk represents a cornerstone for tissue specific premetastatic niche formation. On the one hand systemic release of tumor-derived factors can downregulate CXCR4 resulting in the proliferation and mobilization of HSPCs like VEGFR1+ HSPCs, myeloid cell skewed LSKs and α-SMA+ cancer-associated fibroblasts activating Sca-1+c-Kit- cells to the premetastatic niche (104, 111–113). Primary tumor-derived factors reported to be involved in these processes are GM-CSF, G-CSF, Flt3L, IGF1, hypoxia-installed lysyl oxidase (LOX), MMP2, tumor necrosis factor alpha (TNFα), VEGF, PlGF, TGFβ and osteopontin (5, 100–103, 106, 110–113, 121, 126, 132, 137, 138) as well as exosome-based delivery of MET oncoproteins and TLR3 triggering small nuclear RNAs (114, 149). These factors support recruitment of HSPCs next to Ly6G+ Neu and Ly6C Mo myeloid cell skewing at the premetastatic niche which further involves the expression of chemo-attractants like S100A8 and S100A9, CXCL-1, -2, -5 and -12 next to VEGF, TNFα, MIP-2, MMP9, fibronectin, largely dictated by the cellular composition of the TME (25, 106, 149, 150). On the other hand, the BMN is also actively involved in the provision of angiogenic cells such as BM-derived VEGFR2+ endothelial progenitor cells and vasculogenic c-Kit+Tie2+ progenitor cells that contribute to neovascularization of premetastatic sites (101, 151). As such, solid cancer - BMN crosstalk not only impacts hematopoiesis but also tumor vascularization and even angiogenesis as CCL2 gradients recruit CCR2+ inflammatory Mos, which produces VEGF to increase vascular permeability (107, 128). This is further strengthened by the observation that anti-c-Kit neutralizing antibody treatment suppressed tumor angiogenesis in a murine colon and human prostate model (105).
Overall, the distant solid tumor – BMN crosstalk can result in a plethora of hematopoiesis perturbing cues which ultimately result in the lamentable characteristics of tumor progression ranging from metastasis over bone loss and anemia. Although this crosstalk may thus be the ideal target to nip the progression of solid tumors in the bud, the exact signals, and cellular actors of this crosstalk are just begun to be uncovered. Our overview further shows that so far both similar and different observations have been made, based on the experimental setup and tumor type, ratifying a great deal of research remains to be done.
5 Clinical targeting of myelopoiesis perturbing cues
From the extensive list of identified solid cancer installed myelopoiesis perturbing cues (Table 2), only a handful have so far been clinically targeted in NSCLC patients. These include IL6 with Tocilizumab (152–154); CCL2 with Carlumab (155), IL1β with Canakinumab (156–162), VEGF-A with Bevacizumab (163) or Ramucirumab (anti-VEGFR2 (164)), CSF1R with Cabiralizumab (165) or Pexidartinib/PLX3397 (166), IGF1R with Figitumumab (167) and IL4 with Dupilumab (168). While Bevacizumab and Ramucirumab are FDA approved to treat advanced NSCLC in combination with chemo or targeted therapy based on their significant improvement in progression free survival and OS [161, 162], all other agents are still under clinical evaluation. This as monotherapy (Carlumab and Canakinumab) or in combination with chemotherapy (Canakinumab and Figitumumab) or ICB (Tocilizumab, Canakinumab, Cabiralizumab, Pexidaetinib and Dupilumab). As these are mainly represented by phase I/II clinical trials probing drug safety and tolerability, efficacy data are scarce (152–154, 156, 157, 159, 162, 165, 167). However, in case of anti-IL1β therapy with Canakinumab, at least three trials demonstrated a lack of efficacy when delivered as mono- or in combination with chemotherapy (158, 160, 161). Similarly, the phase I/IIa clinical trial in which the safety and ORR were evaluated in solid cancer patients upon combined CSF1R inhibitor Pexidaetinib with anti-PD-1 Pembrolizumab, was terminated prematurely due to lack insufficient evidence of clinical efficacy (166). Concerning Carlumab (anti-CCL2), although preliminary antitumor activity was demonstrated in different solid cancers, including NSCLC (155), it failed to prove clinical benefit in a phase II study in metastatic prostate cancer (169). Interestingly, IL-4Rα blocking antibody dupilumab given in conjunction with PD-(L)1 ICB in NSCLC patients who had progressed on ICB alone reduced circulating Mo, expanded tumor-infiltrating CD8+ T cells, and in one out of six patients, drove a near-complete clinical response two months after treatment, advocating for its further clinical evaluation (143).
6 Conclusion and future perspectives
TIMs within the TME stand as prominent culprits in fostering resistance against ICB, while the BMN serves as the principal nurturing ground for myeloid cells throughout adulthood. Based on recent and, whenever possible, scRNA-seq data, we provide a comprehensive overview of solid cancer-mediated hematopoietic alterations with particular focus on myelopoiesis in the BMN of NSCLC patients. Overall numerous hematopoiesis perturbing cues have been identified that boil down to emergency myelopoiesis following rise in TIMs, at the expense of megakaryocyte/erythroid and lymphoid differentiation. Despite the wealth of novel information provided by cutting edge technologies such as scRNA-seq, this has led to an ongoing evolution in our understanding of hematopoiesis and the identification of lung TIM subcluster, often hampering consensus in terms of their predictive value for OS to ICB. Recent studies also reveal a previously undescribed role for the MSCs in the BMN, advocating for more research into the latter instead of blood- and tumor-derived HSPCs and immune cells. Overall, these findings emphasize that NSCLC should be considered as a systemic disease, underscoring the need for further investment in biomarker monitoring by integration of innovative technologies and models that extend beyond snapshots of the primary disease site. Additionally, we believe hematopoietic perturbing cues hold untapped target potential to improve the current sobering response rates to immunotherapy for advanced NSCLC patients.
Author contributions
EC-E: Investigation, Writing – original draft, Writing – review & editing, Visualization. KR: Writing – original draft, Investigation. TB: Writing – original draft, Investigation. YJ: Writing – review & editing. DV: Writing – review & editing. RH: Writing – review & editing. AB: Writing – review & editing. IR: Writing – review & editing. LD: Writing – review & editing. CG: Conceptualization, Funding acquisition, Investigation, Supervision, Writing – original draft, Writing – review & editing.
Funding
The author(s) declare financial support was received for the research, authorship, and/or publication of this article. EC-E was supported by Eutopia PhD co-tutelle fellowship and Wetenschappelijk Fonds Willy Gepts of the UZ Brussel, KR was supported by Fonds Wetenschappelijk Onderzoek (FWO, G041721N) and Kom op tegen Kanker Emmanuel van der Schueren, TB was supported by an ORC Cancer Research fellowship and Kom op tegen Kanker Emmanuel van der Schueren, RH was supported by the Fonds Wetenschappelijk Onderzoek (FWO, 1SE9324N) and CG was supported by FWO, Eutopia and Wetenschappelijk Fonds Willy Gepts of the UZ Brussel.
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.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2024.1397469/full#supplementary-material
Supplementary Table 1 | Surface markers of murine hematopoietic cell subsets.
Glossary
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Keywords: lung cancer, hematopoiesis, immunotherapy, emergency myelopoiesis, bone marrow, biomarkers
Citation: Calderon-Espinosa E, De Ridder K, Benoot T, Jansen Y, Vanhonacker D, Heestermans R, De Becker A, Van Riet I, Decoster L and Goyvaerts C (2024) The crosstalk between lung cancer and the bone marrow niche fuels emergency myelopoiesis. Front. Immunol. 15:1397469. doi: 10.3389/fimmu.2024.1397469
Received: 07 March 2024; Accepted: 15 July 2024;
Published: 01 August 2024.
Edited by:
Zhihui Zhang, Chinese Academy of Medical Sciences and Peking Union Medical College, ChinaReviewed by:
Isabel Ben-Batalla, German Cancer Research Center (DKFZ), GermanyXiaoxin Hao, Chinese Academy of Medical Sciences, China
Copyright © 2024 Calderon-Espinosa, De Ridder, Benoot, Jansen, Vanhonacker, Heestermans, De Becker, Van Riet, Decoster and Goyvaerts. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Cleo Goyvaerts, cleo.goyvaerts@vub.be