Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma

Abstract

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.

Burden of tumor mutations, neoepitopes, and other variants are dubious predictors of cancer immunotherapy response and overall survival

“…This suggests that the prospective clinical utilization of TMB is likely subject to many of these same issues, and may result in unintended harms, whether due to omission of therapy for individuals with "low" TMB who might nonetheless benefit, or due to increased risk of toxicity in a "high" TMB population subject to overuse of immunotherapy. Indeed, a recent study of metastatic melanoma patients (62) found significantly different burdens of nonsynonymous mutations between disease subgroups, but not between progressors/responders, highlighting the instability of this metric.…”

“…Tumors with higher TMB have been hypothesized to have more neoantigens that can be recognized by the immune system in response to checkpoint inhibition, yet the data presented here and data previously published (2) support the use of substantially different "absolute" TMB thresholds for immunotherapy response prediction across different diseases. Further, evidence suggests that other genomic factors, such as tumor purity and clonal heterogeneity, may further modulate the relationship between TMB and immunotherapy response (62,72) . This suggests an added layer of as-of-yet undefined complexity not captured in the current bulk metrics, and likely related to disease-specific biology.…”

CoMut: Visualizing integrated molecular information with comutation plots

“…To illustrate the features of CoMut, we created a comutation plot visualizing a cohort from a study of selective response to immunotherapy in melanoma (Liu et al, 2019) (Figure 1). We obtained mutation and clinical data from the supplement and used allele-specific copy number profiles from ABSOLUTE (Carter et al, 2012) to classify allele-specific copy number alterations.…”

“…A comutation plot generated with CoMut using data provided inLiu et al, 2019. For visualization purposes, only 52 samples are shown.…”

Whole exome precision oncology targeting synthetic lethal vulnerabilities across the tumor transcriptome

“…To evaluate the accuracy of SR based predictions, we collected a set of 11 immune checkpoint therapy datasets that included both pre-treatment transcriptomics data and therapy response information (either by RECIST or PFS). Our collection includes five melanoma datasets [35][36][37][38][39] and glioma 40 , and renal cell carcinoma 41 cohorts treated with anti-PD1 or anti-CTLA4. Figure 3A shows that higher SR-scores are indeed associated with better response to immune checkpoint blockade, with AUCs greater than 0.7 in 7 out of 10 datasets, where RECIST information is available (Figure 3B), and Figure S2 shows the corresponding precision-recall curves.…”