Despite impressive durable responses, immune checkpoint inhibitors do not provide a long-term benefit to the majority of patients with cancer. Understanding genomic correlates of response and ...resistance to checkpoint blockade may enhance benefits for patients with cancer by elucidating biomarkers for patient stratification and resistance mechanisms for therapeutic targeting. Here we review emerging genomic markers of checkpoint blockade response, including those related to neoantigens, antigen presentation, DNA repair, and oncogenic pathways. Compelling evidence also points to a role for T cell functionality, checkpoint regulators, chromatin modifiers, and copy-number alterations in mediating selective response to immune checkpoint blockade. Ultimately, efforts to contextualize genomic correlates of response into the larger understanding of tumor immune biology will build a foundation for the development of novel biomarkers and therapies to overcome resistance to checkpoint blockade.
The management of advanced-stage renal cell carcinoma (RCC) has been transformed by the development of immune-checkpoint inhibitors (ICIs). Nonetheless, most patients do not derive durable clinical ...benefit from these agents. Importantly, unlike other immunotherapy-responsive solid tumours, most RCCs have only a moderate mutational burden, and paradoxically, high levels of tumour CD8
T cell infiltration are associated with a worse prognosis in patients with this disease. Building on the successes of antibodies targeting the PD-1 and CTLA4 immune checkpoints, multiple innovative immunotherapies are now in clinical development for the treatment of patients with RCC, including ICIs with novel targets, co-stimulatory pathway agonists, modified cytokines, metabolic pathway modulators, cell therapies and therapeutic vaccines. However, the successful development of such novel immune-based treatments and of immunotherapy-based combinations will require a disease-specific framework that incorporates a deep understanding of RCC immunobiology. In this Review, using the structure provided by the well-described cancer-immunity cycle, we outline the key steps required for a successful antitumour immune response in the context of RCC, and describe the development of promising new immunotherapies within the context of this framework. With this approach, we summarize and analyse the most encouraging targets of novel immune-based therapies within the RCC microenvironment, and review the landscape of emerging antigen-directed therapies for this disease.
PD-1 blockade has transformed the management of advanced clear cell renal cell carcinoma (ccRCC), but the drivers and resistors of the PD-1 response remain incompletely elucidated. Here, we analyzed ...592 tumors from patients with advanced ccRCC enrolled in prospective clinical trials of treatment with PD-1 blockade by whole-exome and RNA sequencing, integrated with immunofluorescence analysis, to uncover the immunogenomic determinants of the therapeutic response. Although conventional genomic markers (such as tumor mutation burden and neoantigen load) and the degree of CD8
T cell infiltration were not associated with clinical response, we discovered numerous chromosomal alterations associated with response or resistance to PD-1 blockade. These advanced ccRCC tumors were highly CD8
T cell infiltrated, with only 27% having a non-infiltrated phenotype. Our analysis revealed that infiltrated tumors are depleted of favorable PBRM1 mutations and enriched for unfavorable chromosomal losses of 9p21.3, as compared with non-infiltrated tumors, demonstrating how the potential interplay of immunophenotypes with somatic alterations impacts therapeutic efficacy.
The determination of molecular features that mediate clinically aggressive phenotypes in prostate cancer remains a major biological and clinical challenge
. Recent advances in interpretability of ...machine learning models as applied to biomedical problems may enable discovery and prediction in clinical cancer genomics
. Here we developed P-NET-a biologically informed deep learning model-to stratify patients with prostate cancer by treatment-resistance state and evaluate molecular drivers of treatment resistance for therapeutic targeting through complete model interpretability. We demonstrate that P-NET can predict cancer state using molecular data with a performance that is superior to other modelling approaches. Moreover, the biological interpretability within P-NET revealed established and novel molecularly altered candidates, such as MDM4 and FGFR1, which were implicated in predicting advanced disease and validated in vitro. Broadly, biologically informed fully interpretable neural networks enable preclinical discovery and clinical prediction in prostate cancer and may have general applicability across cancer types.
Immune-checkpoint blockade (ICB) has demonstrated efficacy in many tumor types, but predictors of responsiveness to anti-PD1 ICB are incompletely characterized. In this study, we analyzed a ...clinically annotated cohort of patients with melanoma (n = 144) treated with anti-PD1 ICB, with whole-exome and whole-transcriptome sequencing of pre-treatment tumors. We found that tumor mutational burden as a predictor of response was confounded by melanoma subtype, whereas multiple novel genomic and transcriptomic features predicted selective response, including features associated with MHC-I and MHC-II antigen presentation. Furthermore, previous anti-CTLA4 ICB exposure was associated with different predictors of response compared to tumors that were naive to ICB, suggesting selective immune effects of previous exposure to anti-CTLA4 ICB. Finally, we developed parsimonious models integrating clinical, genomic and transcriptomic features to predict intrinsic resistance to anti-PD1 ICB in individual tumors, with validation in smaller independent cohorts limited by the availability of comprehensive data. Broadly, we present a framework to discover predictive features and build models of ICB therapeutic response.
Everolimus, an inhibitor of the mammalian target of rapamycin (mTOR), is effective in treating tumors harboring alterations in the mTOR pathway. Mechanisms of resistance to everolimus remain ...undefined. Resistance developed in a patient with metastatic anaplastic thyroid carcinoma after an extraordinary 18-month response. Whole-exome sequencing of pretreatment and drug-resistant tumors revealed a nonsense mutation in TSC2, a negative regulator of mTOR, suggesting a mechanism for exquisite sensitivity to everolimus. The resistant tumor also harbored a mutation in MTOR that confers resistance to allosteric mTOR inhibition. The mutation remains sensitive to mTOR kinase inhibitors.
We present an in silico approach to identifying neoepitopes derived from intron retention events in tumor transcriptomes. Using mass spectrometry immunopeptidome analysis, we show that retained ...intron neoepitopes are processed and presented on MHC I on the surface of cancer cell lines. RNA-derived neoepitopes should be considered for prospective personalized cancer vaccine development.
Cancer genomes contain large numbers of somatic mutations but few of these mutations drive tumor development. Current approaches either identify driver genes on the basis of mutational recurrence or ...approximate the functional consequences of nonsynonymous mutations by using bioinformatic scores. Passenger mutations are enriched in characteristic nucleotide contexts, whereas driver mutations occur in functional positions, which are not necessarily surrounded by a particular nucleotide context. We observed that mutations in contexts that deviate from the characteristic contexts around passenger mutations provide a signal in favor of driver genes. We therefore developed a method that combines this feature with the signals traditionally used for driver-gene identification. We applied our method to whole-exome sequencing data from 11,873 tumor-normal pairs and identified 460 driver genes that clustered into 21 cancer-related pathways. Our study provides a resource of driver genes across 28 tumor types with additional driver genes identified according to mutations in unusual nucleotide contexts.
In this multicenter phase II trial, we evaluated atezolizumab combined with bevacizumab in patients with advanced renal cell carcinoma (RCC) with variant histology or any RCC histology with ≥ 20% ...sarcomatoid differentiation.
Eligible patients may have received previous systemic therapy, excluding prior bevacizumab or checkpoint inhibitors. Patients underwent a baseline biopsy and received atezolizumab 1,200 mg and bevacizumab 15 mg/kg intravenously every 3 weeks. The primary end point was overall response rate (ORR) by RECIST version 1.1. Additional end points were progression-free survival (PFS), toxicity, biomarkers of response as determined by programmed death-ligand 1 (PD-L1) status, and on-therapy quality-of-life (QOL) metrics using the Functional Assessment of Cancer Therapy Kidney Symptom Index-19 and the Brief Fatigue Inventory.
Sixty patients received at least 1 dose of either study agent; the majority (65%) were treatment naïve. The ORR for the overall population was 33% and 50% in patients with clear cell RCC with sarcomatoid differentiation and 26% in patients with variant histology RCC. Median PFS was 8.3 months (95% CI, 5.7 to 10.9 months). PD-L1 status was available for 36 patients; 15 (42%) had ≥ 1% expression on tumor cells. ORR in PD-L1-positive patients was 60% (n = 9)
19% (n = 4) in PD-L1-negative patients. Eight patients (13%) developed treatment-related grade 3 toxicities. There were no treatment-related grade 4-5 toxicities. QOL was maintained throughout therapy.
In this study, atezolizumab and bevacizumab demonstrated safety and resulted in objective responses in patients with variant histology RCC or RCC with ≥ 20% sarcomatoid differentiation. This regimen warrants additional exploration in patients with rare RCC, particularly those with PD-L1-positive tumors.
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