Host Factors

HLA and germline modifiers of resistance

HLA typing has been identified as a predictive biomarker in novel immunotherapies to guide treatment. HLA-DR expression strongly correlates with response to anti-PD-1. MHC-II positivity on tumor cells is associated with therapeutic response, progression-free and overall survival, as well as CD4+ and CD8+ tumor infiltrates [76]. Host germline genetics also affects response to ICIs in NSCLC and other tumor types. Maximal heterozygosity at HLA-I loci (A, B, and C) improves overall survival after immunotherapy compared to patients who are homozygous for at least one HLA locus. In two independent cancer cohorts, patients with the HLA-B44 supertype had extended survival, whereas the HLA-B62 supertype (including HLA-B*15:01) or somatic LOH at HLA-I, was associated with poor outcome. Molecular dynamics simulations of HLA-B*15:01 have revealed unique elements that may impair CD8+ T cell recognition of neoantigens [77].

Antitumor T cells recognize not only tumor-specific mutant peptides like neoantigens, but also cancer germline antigens (CGAs), germline proteins whose expression is typically restricted to germline cells but is upregulated in tumor cells. Tumor types harboring higher numbers of nonsynonymous somatic variants have higher response rates to immunotherapy, due to greater numbers of neoantigens [28]. In some cases of MMR deficiency, whole-exome sequencing confirmed a 20-fold-higher level of nonsynonymous mutation-associated neoantigen load compared to MMR-proficient

512 Biomarkers for the Immune Checkpoint Inhibitors

patients; this is consistent with other reports demonstrating an association between higher mutational load and response to anti-PD-1 in NSCLC [27]. In one example, a patient with metastatic lung adenocarcinoma showed an exceptional response to atezolizumab (anti-PD-Ll). Whole-exome sequencing of the patient's tumor and blood revealed gain-of-function somatic alterations in Janus kinase 3 (JAK3) as well as germline mutations in the same allele [78]. These studies demonstrate the impact of germline mutations that can influence neoantigen formation or immune signaling and may serve as future predictors of sensitivity to immunotherapy.

Environment: Enteric Microbiome

Several studies point to the gut microbiome as a promising pantumor biomarker predicting clinical response to chemotherapeutic agents and immunotherapies (anti-PD-1 or CTLA-4) [62, 79]. The increased abundance of specific bacteria in the gut microbiome correlates with a higher CD8+ T-cell density in responders to PD-1 inhibitors. Sequencing of the oral and gut microbiome of cancer patients also shows a correlation between higher gut microbiome diversity and response to anti-PD-1 monotherapy [62]. Significant decreases in TILs and lack of response to CTLA-4 inhibitors are observed in tumors of mice housed in germ-free conditions [79]. In some cases, primary resistance of immunotherapy can be attributed to abnormal gut microbiome composition. Antibiotics inhibit the clinical benefit of anti-PD-1 monotherapy in patients with advanced cancer including NSCLC and fecal microbiota transplantation (FMT) from cancer patients who responded to immunotherapy into germ-free or antibiotic-treated mice improved the antitumor effects of PD-1 blockade, whereas FMT from nonresponding patients failed to do so. In addition, metagenomic analysis of patient stool samples at diagnosis revealed correlations between clinical responses to immunotherapy and the relative abundance of Akkermansia muciniphila. Oral supplementation with A. muciniphila after FMT with non-responder feces restored the efficacy of PD-1 blockade in an interleukin-12-dependent manner by increasing the recruitment of CCR9+CXCR3+CD4+ T lymphocytes into mouse tumor beds [80]. Currently, microbiome analysis has been integrated as a correlative study in many clinical trials.

Evaluation for Immune Related Adverse Events to ICIs 513

Evaluation for Tumor Response to Immune Checkpoint Inhibitors

The Response Evaluation Criteria in Solid Tumors (RECIST) was first developed as a standard to evaluate tumor responses to chemotherapeutic agents in 2000, and was subsequently updated to RECIST version 1.1 in 2009. The criteria are based on dimensional evaluation provided by computed tomography (CT) and rely on the assumption that the therapeutic effect produces CT-detectable tumor shrinkage from baseline [81]. A growing body of evidence suggests that RECIST tends to underestimate the benefit of immunotherapy. While most patients responsive to ICIs have similar time to tumor response comparable to that of chemotherapy, delayed tumor responses with even initial tumor progression have been observed in patients receiving immunotherapy. For instance, one study showed RECIST underestimated the benefit of pembrolizumab in approximately 15% of treated 327 patients [82]. As RECIST criteria are not suitable to catch some atypical responses, a so-called immune-related response criterion (irRC) has been proposed to provide more rigorous characterization of all patterns of response observed in immunotherapy treatment [82]. Further validation in prospective clinical trials is ongoing to validate irRC in evaluating clinical response to ICIs.

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