Factors Determining Long Term Anti-Tumor Responses to Immune Checkpoint Blockade Therapy

Background: Drug–drug interactions (DDIs) pose new challenges beyond traditional pharmacodynamics in the context of optimizing the treatment options with immune checkpoint inhibitors (ICIs). To alleviate cancer-related pain, analgesics are of absolute vital importance as chronic medications used by cancer patients. However, the possible outcome of ICI treatment concomitant with analgesics remains unclear.

Methods: Original articles describing the possible influence of analgesics use on ICI treatment published before December 1, 2021 were retrieved from PubMed, Embase, and the Cochrane Library. Odds ratio (OR) with 95% confidence interval (CI) for objective response rate (ORR), hazard ratio (HR) with 95% CI for progression-free survival (PFS), and overall survival (OS) were calculated using the random-effects or fixed-effects model, and heterogeneity was assessed using the χ²-based Q-test. Publication bias was examined by funnel plot analysis.

Results: A total of 11 studies involving 4,404 patients were included. The pooled OR showed that opioid use decreased the response of opioid users to ICIs compared to non-opioid users (OR = 0.49, 95% CI = 0.37–0.65, p < 0.001). Compared to patients who did not receive opioids, opioid users had an increased risk of progression and mortality (HR = 1.61, 95% CI = 1.37–1.89, p < 0.001; HR = 1.67, 95% CI =1.30–2.14, p < 0.001, respectively). Furthermore, the concomitant use of non-steroidal anti-inflammatory drugs (NSAIDs) was not significantly associated with differences in ORR, PFS, and OS in patients treated with ICIs (OR = 1.40, 95% CI = 0.84–2.32, p = 0.190; HR = 0.90, 95% CI = 0.77–1.06, p = 0.186; HR = 0.90, 95% CI = 0.71–1.14, p = 0.384, respectively).

Conclusion: The concomitant use of opioids during ICI treatment has an adverse effect on patient prognosis, while the use of NSAIDs is not significantly associated with the prognosis in patients treated with ICIs.

Remarkable progress has been made in the field of anti-tumor immunity, nevertheless many questions are still open. Thus, even though memory T cells have been implicated in long-term anti-tumor protection, particularly in prevention of cancer recurrence, the bases of their variable effectiveness in tumor patients are poorly understood. Two types of memory T cells have been described according to their traffic pathways: recirculating and tissue-resident memory T cells. Recirculating tumor-specific memory T cells are found in the cell infiltrate of solid tumors, in the lymph and in the peripheral blood, and they constantly migrate in and out of lymph nodes, spleen, and bone marrow. Tissue-resident tumor-specific memory T cells (TRM) permanently reside in the tumor, providing local protection.

Anti-PD-1/PD-L1, a type of immune checkpoint blockade (ICB) therapy, can considerably re-invigorate T cell response and lead to successful tumor control, even in patients at advanced stages. Indeed, ICB has led to unprecedented successes against many types of cancers, starting a ground-breaking revolution in tumor therapy. Unfortunately, not all patients are responsive to such treatment, thus further improvements are urgently needed. The mechanisms underlying resistance to ICB are still largely unknown. A better knowledge of the dynamics of the immune response driven by the two types of memory T cells before and after anti-PD-1/PD-L1 would provide important insights on the variability of the outcomes. This would be instrumental to design new treatments to overcome resistance.

Here we provide an overview of T cell contribution to immunity against solid tumors, focusing on memory T cells. We summarize recent evidence on the involvement of recirculating memory T cells and TRM in anti-PD-1/PD-L1-elicited antitumor immunity, outline the open questions in the field, and propose that a synergic action of the two types of memory T cells is required to achieve a full response. We argue that a T-centric vision focused on the specific roles and the possible interplay between TRM and recirculating memory T cells will lead to a better understanding of anti-PD-1/PD-L1 mechanism of action, and provide new tools for improving ICB therapeutic strategy.

Background: Only a proportion of patients with bladder cancer may benefit from durable response to immune checkpoint inhibitor (ICI) therapy. More precise indicators of response to immunotherapy are warranted. Our study aimed to construct a more precise classifier for predicting the benefit of immune checkpoint inhibitor therapy.

Methods: This multi-cohort study examined the top 20 frequently mutated genes in five cohorts of patients with bladder cancer and developed the TP53/PIK3CA/ATM mutation classifier based on the MSKCC ICI cohort. The classifier was then validated in a validation set consisting of IMvigor210 cohort and Broad/Dana-Farber cohort. The molecular profile and immune infiltration characteristics in each subgroup as defined by this classifier were explored.

Results: Among all 881 patients with bladder cancer, the mutation frequency of TP53, PIK3CA, and ATM ranked in the top 20 mutated genes. The TP53/PIK3CA/ATM mutation classifier was constructed based on the Memorial Sloan Kettering Cancer Center (MSKCC) ICI cohort and only showed predictive value for patients with bladder cancer who received ICI therapy (median overall survival: low-risk group, not reached; moderate-risk group, 13.0 months; high-risk group, 8.0 months; P<0.0001). Similar results were found in subgroups of MSKCC ICI cohort defined by tumor mutation burden. Multivariate Cox analysis revealed that the risk group defined by the classifier served as an independent prognostic factor for overall survival in patients with bladder cancer. Efficacy of the classifier was verified in a validation set consisting of IMvigor210 cohort and Broad/Dana-Farber cohort. Lower expression of PD-1/PD-L1 and less tumor immune infiltration were observed in the high-risk group than the other two groups of the TCGA cohort and the IMvigor210 cohort.

Conclusion: Our study constructed a TP53/PIK3CA/ATM mutation classifier to predict the benefit of immune checkpoint inhibitor therapy for patients with bladder cancer. This classifier can potentially complement the tumor mutation burden and guide clinical ICI treatment decisions according to distinct risk levels.