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Checkpoint inhibitor-based immunotherapies that target cytotoxic T lymphocyte antigen 4 (CTLA4) or the programmed cell death 1 (PD1) pathway have achieved impressive success in the treatment of different cancer types. Yet, only a subset of patients derive clinical benefit. It is thus critical to understand the determinants driving response, resistance and adverse effects. In this Review, we discuss recent work demonstrating that immune checkpoint inhibitor efficacy is affected by a combination of factors involving tumour genomics, host germline genetics, PD1 ligand 1 (PDL1) levels and other features of the tumour microenvironment, as well as the gut microbiome. We focus on recently identified molecular and cellular determinants of response. A better understanding of how these variables cooperate to affect tumour–host interactions is needed to optimize the implementation of precision immunotherapy.This Review discusses recent work demonstrating that immune checkpoint inhibitor efficacy is affected by a combination of factors involving tumour genomics, host germline genetics, programmed cell death 1 ligand 1 (PDL1) levels and other features of the tumour microenvironment, as well as the gut microbiome.

Immunotherapy works by activating the patient's own immune system to fight cancer. For effective tumor killing, CD8 + T cells recognize tumor peptides presented by human leukocyte antigen class I (HLA-I) molecules. In humans, there are three major HLA-I genes ( HLA-A, HLA-B , and HLA-C ). Chowell et al. asked whether germline HLA-I genotype influences how T cells recognize tumor peptides and respond to checkpoint inhibitor immunotherapies (see the Perspective by Kvistborg and Yewdell). They examined more than 1500 patients and found that heterozygosity at HLA-I loci was associated with better survival than homozygosity for one or more HLA-I genes. Thus, specific HLA-I mutations could have implications for immune recognition and for the design of epitopes for cancer vaccines and immunotherapies. Science , this issue p. 582 ; see also p. 516 Human leukocyte antigen superfamilies predict immunotherapy response. CD8 + T cell–dependent killing of cancer cells requires efficient presentation of tumor antigens by human leukocyte antigen class I (HLA-I) molecules. However, the extent to which patient-specific HLA-I genotype influences response to anti–programmed cell death protein 1 or anti–cytotoxic T lymphocyte–associated protein 4 is currently unknown. We determined the HLA-I genotype of 1535 advanced cancer patients treated with immune checkpoint blockade (ICB). Maximal heterozygosity at HLA-I loci (“A,” “B,” and “C”) improved overall survival after ICB compared with patients who were homozygous for at least one HLA locus. In two independent melanoma cohorts, patients with the HLA-B44 supertype had extended survival, whereas the HLA-B62 supertype (including HLA-B*15:01) or somatic loss of heterozygosity at HLA-I was associated with poor outcome. Molecular dynamics simulations of HLA-B*15:01 revealed different elements that may impair CD8 + T cell recognition of neoantigens. Our results have important implications for predicting response to ICB and for the design of neoantigen-based therapeutic vaccines.

Functional diversity of the highly polymorphic human leukocyte antigen class I (HLA-I) genes underlies successful immunologic control of both infectious disease and cancer. The divergent allele advantage hypothesis dictates that an HLA-I genotype with two alleles with sequences that are more divergent enables presentation of more diverse immunopeptidomes1–3. However, the effect of sequence divergence between HLA-I alleles—a quantifiable measure of HLA-I evolution—on the efficacy of immune checkpoint inhibitor (ICI) treatment for cancer remains unknown. In the present study the germline HLA-I evolutionary divergence (HED) of patients with cancer treated with ICIs was determined by quantifying the physiochemical sequence divergence between HLA-I alleles of each patient’s genotype. HED was a strong determinant of survival after treatment with ICIs. Even among patients fully heterozygous at HLA-I, patients with an HED in the upper quartile respond better to ICIs than patients with a low HED. Furthermore, HED strongly impacts the diversity of tumor, viral and self-immunopeptidomes and intratumoral T cell receptor clonality. Similar to tumor mutation burden, HED is a fundamental metric of diversity at the major histocompatibility complex–peptide complex, which dictates ICI efficacy. The data link divergent HLA allele advantage to immunotherapy efficacy and unveil how ICI response relies on the evolved efficiency of HLA-mediated immunity.

Despite the availability of major histocompatibility complex (MHC)-binding peptide prediction algorithms, the development of T-cell vaccines against pathogen and tumor antigens remains challenged by inefficient identification of immunogenic epitopes. CD8⁺ T cells must distinguish immunogenic epitopes from nonimmunogenic self peptides to respond effectively against an antigen without endangering the viability of the host. Because this discrimination is fundamental to our understanding of immune recognition and critical for rational vaccine design, we interrogated the biochemical properties of 9,888 MHC class I peptides. We identified a strong bias toward hydrophobic amino acids at T-cell receptor contact residues within immunogenic epitopes of MHC allomorphs, which permitted us to develop and train a hydrophobicity-based artificial neural network (ANN-Hydro) to predict immunogenic epitopes. The immunogenicity model was validated in a blinded in vivo overlapping epitope discovery study of 364 peptides from three HIV-1 Gag protein variants. Applying the ANN-Hydro model on existing peptide-MHC algorithms consistently reduced the number of candidate peptides across multiple antigens and may provide a correlate with immunodominance. Hydrophobicity of TCR contact residues is a hallmark of immunogenic epitopes and marks a step toward eliminating the need for empirical epitope testing for vaccine development.

T cell discrimination of self and non-self is the foundation of the adaptive immune response, and is orchestrated by the interaction between T cell receptors (TCRs) and their cognate ligands presented by major histocompatibility (MHC) molecules. However, the impact of host immunogenetic variation on the diversity of the TCR repertoire remains unclear. Here, we analyzed a cohort of 666 individuals with TCR repertoire sequencing. We show that TCR repertoire diversity is positively associated with polymorphism at the human leukocyte antigen class I (HLA-I) loci, and diminishes with age and cytomegalovirus (CMV) infection. Moreover, our analysis revealed that HLA-I polymorphism and age independently shape the repertoire in healthy individuals. Our data elucidate key determinants of human TCR repertoire diversity, and suggest a mechanism underlying the evolutionary fitness advantage of HLA-I heterozygosity.

The effects of cytokine and protein stabilizing carriers, such as serum albumin, on tumor response to immune checkpoint blockade (ICB) is not well understood. By examining 1714 patients across 16 cancer types, we found that high pretreatment serum albumin level predicts favorable tumor radiographic response following ICB treatment in a dose-dependent fashion. Serum albumin is a candidate biomarker that can be combined with tumor mutational burden (TMB) for additional predictive capacity, and the tumor response rate to ICB was ~49% in the albumin-high/TMB-high group.

BACKGROUNDRecurrent and/or metastatic (R/M) head and neck squamous cell carcinoma (HNSCC) is generally an incurable disease, with patients experiencing median survival of under 10 months and significant morbidity. While immune checkpoint blockade (ICB) drugs are effective in approximately 20% of patients, the remaining experience limited clinical benefit and are exposed to potential adverse effects and financial costs. Clinically approved biomarkers, such as tumor mutational burden (TMB), have a modest predictive value in HNSCC.METHODSWe analyzed clinical and genomic features, generated using whole-exome sequencing, in 133 ICB-treated patients with R/M HNSCC, of whom 69 had virus-associated and 64 had non-virus-associated tumors.RESULTSHierarchical clustering of genomic data revealed 6 molecular subtypes characterized by a wide range of objective response rates and survival after ICB therapy. The prognostic importance of these 6 subtypes was validated in an external cohort. A random forest-based predictive model, using several clinical and genomic features, predicted progression-free survival (PFS), overall survival (OS), and response with greater accuracy than did a model based on TMB alone. Recursive partitioning analysis identified 3 features (systemic inflammatory response index, TMB, and smoking signature) that classified patients into risk groups with accurate discrimination of PFS and OS.CONCLUSIONThese findings shed light on the immunogenomic characteristics of HNSCC tumors that drive differential responses to ICB and identify a clinical-genomic classifier that outperformed the current clinically approved biomarker of TMB. This validated predictive tool may help with clinical risk stratification in patients with R/M HNSCC for whom ICB is being considered.FUNDINGFundación Alfonso Martín Escudero, NIH R01 DE027738, US Department of Defense CA210784, The Geoffrey Beene Cancer Research Center, The MSKCC Population Science Research Program, the Jayme Flowers Fund, the Sebastian Nativo Fund, and the NIH/NCI Cancer Center Support Grant P30 CA008748.

BackgroundImmune checkpoint blockade (ICB) has revolutionized our approach to cancer treatment. However, the response rate of immune checkpoint blockade (ICB) is still low. With the accumulation of large-scale ICB data, efforts to use these data to build machine learning predictors of ICB response are rising. However, there are several shared concerns about these models, including their black box nature that limits interpretability and the potential risk of overfitting during model training, which have so far impeded their clinical translation.MethodsHere we analyzed ~ 3000 samples across 18 solid tumor types from multiple cohorts with more than 20 clinical, pathologic, and genomic features measured. We developed, trained, and evaluated 20 machine-learning models to identify the most predictive model for ICB response by comparing their performance on test sets and importantly, performance difference between training vs test sets, using a repeated cross-validation procedure. The machine learning models include decision trees, Gaussian processes, support vector machine, XGBoost, and deep neural networks, among others. Finally, we developed the LOgistic Regression-based Immunotherapy-response Score (LORIS) using a transparent, compact 6-feature logistic LASSO regression model. This approach was validated for developing both pan-cancer and NSCLC-specific models across multiple independent datasets (figure 1).ResultsThe linear LASSO regression model outperforms all other models and biomarkers by having the highest performance on cross-validation sets and notably, the smallest performance difference between training and cross-validation sets (figure 2). LORIS outperforms previous signatures in ICB response prediction and can identify patients more likely to respond to ICB treatment, importantly, even those with low TMB or tumor PD-L1 expression levels (figure 3). LORIS consistently predicts both the short-term and the long-term survival across almost all cancer types (figure 3). Most importantly, ICB response probability increases near-monotonically (from 0% to 100%) with the LORIS, which can be used in both patient inclusion and exclusion. In contrast, ICB response probability is ~20% in low TMB patients and not always higher with higher TMB (figure 4). Finally, this approach is also effective in developing cancer-type-specific models for predicting ICB response (figure 5).ConclusionsOur study identifies important clinical features linked to ICB response and survival, allowing for more accurate and interpretable predictions using just a few clinically readily measurable features. We expect that this method will help improve clinical decision-making practices in precision medicine to maximize patient benefit.Abstract 526 Figure 1Overview of the studyAbstract 526 Figure 2Robust prediction of pan-cancer objective response to immunotheraphy by a 6-variable logistic LASSO regression model.Abstract 526 Figure 3LORIS predicts patient outcomes following immunotherapy for both pan-cancer and individual cancer types.Abstract 526 Figure 4Monotonic relationship between LORIS and patient objective response probability & survival following immunotherapy.Abstract 526 Figure 5Robust prediction of response to immunotherapy in NSCLC with logistic LASSO regression.

Only a fraction of patients with cancer respond to immune checkpoint blockade (ICB) treatment, but current decision-making procedures have limited accuracy. In this study, we developed a machine learning model to predict ICB response by integrating genomic, molecular, demographic and clinical data from a comprehensively curated cohort (MSK-IMPACT) with 1,479 patients treated with ICB across 16 different cancer types. In a retrospective analysis, the model achieved high sensitivity and specificity in predicting clinical response to immunotherapy and predicted both overall survival and progression-free survival in the test data across different cancer types. Our model significantly outperformed predictions based on tumor mutational burden, which was recently approved by the U.S. Food and Drug Administration for this purpose 1 . Additionally, the model provides quantitative assessments of the model features that are most salient for the predictions. We anticipate that this approach will substantially improve clinical decision-making in immunotherapy and inform future interventions. A combination of genomic and clinical features improves predictions of response to immune checkpoint blockade.

In cancer, evolutionary forces select for clones that evade the immune system. Here we analyzed >10,000 primary tumors and 356 immune-checkpoint-treated metastases using immune dN/dS, the ratio of nonsynonymous to synonymous mutations in the immunopeptidome, to measure immune selection in cohorts and individuals. We classified tumors as immune edited when antigenic mutations were removed by negative selection and immune escaped when antigenicity was covered up by aberrant immune modulation. Only in immune-edited tumors was immune predation linked to CD8 T cell infiltration. Immune-escaped metastases experienced the best response to immunotherapy, whereas immune-edited patients did not benefit, suggesting a preexisting resistance mechanism. Similarly, in a longitudinal cohort, nivolumab treatment removes neoantigens exclusively in the immunopeptidome of nonimmune-edited patients, the group with the best overall survival response. Our work uses dN/dS to differentiate between immune-edited and immune-escaped tumors, measuring potential antigenicity and ultimately helping predict response to treatment. Immune dN/dS is the ratio of nonsynonymous to synonymous mutations in the MHC-bound immunopeptidome. Application of immune dN/dS to cancer datasets shows that it distinguishes immune evasion and escape and predicts response to immunotherapies.