BACKGROUND. PD-L1 expression and tumor mutational burden (TMB) have emerged as important biomarkers of response to immune checkpoint inhibitor (ICI) therapy. These biomarkers have each succeeded and failed in predicting responders for different cancer types. We sought to describe the PD-L1 expression landscape across the spectrum of ICI-responsive human cancers, and to determine the relationship between PD-L1 expression, TMB, and response rates to ICIs. METHODS. We assessed 9887 clinical samples for PD-L1 expression and TMB. RESULTS. PD-L1 expression and TMB are not significantly correlated within most cancer subtypes, and they show only a marginal association at the tumor sample level (Pearson's correlation 0.084). Across distinct tumor types, PD-L1 expression and TMB have nonoverlapping effects on the response rate to PD-1/PD-L1 inhibitors and can broadly be used to categorize the immunologic subtypes of cancer. CONCLUSION. Our results indicate that PD-L1 expression and TMB may each inform the use of ICIs, point to different mechanisms by which PD-L1 expression regulates ICI responsiveness, and identify new opportunities for therapeutic development.

Endogenous retroviruses (ERVs) have contributed to more than 8% of the human genome. The majority of these elements lack function due to accumulated mutations or internal recombination resulting in a solitary (solo) LTR, although members of one group of human ERVs (HERVs), HERV-K, were recently active with members that remain nearly intact, a subset of which is present as insertionally polymorphic loci that include approximately full-length (2-LTR) and solo-LTR alleles in addition to the unoccupied site. Several 2-LTR insertions have intact reading frames in some or all genes that are expressed as functional proteins. These properties reflect the activity of HERV-K and suggest the existence of additional unique loci within humans. We sought to determine the extent to which other polymorphic insertions are present in humans, using sequenced genomes from the 1000 Genomes Project and a subset of the Human Genome Diversity Project panel. We report analysis of a total of 36 nonreference polymorphic HERV-K proviruses, including 19 newly reported loci, with insertion frequencies ranging from <0.0005 to >0.75 that varied by population. Targeted screening of individual loci identified three new unfixed 2-LTR proviruses within our set, including an intact provirus present at Xq21.33 in some individuals, with the potential for retained infectivity.

A key constraint in genomic testing in oncology is that matched normal specimens are not commonly obtained in clinical practice. Thus, while well-characterized genomic alterations do not require normal tissue for interpretation, a significant number of alterations will be unknown in whether they are germline or somatic, in the absence of a matched normal control. We introduce SGZ (somatic-germline-zygosity), a computational method for predicting somatic vs. germline origin and homozygous vs. heterozygous or sub-clonal state of variants identified from deep massively parallel sequencing (MPS) of cancer specimens. The method does not require a patient matched normal control, enabling broad application in clinical research. SGZ predicts the somatic vs. germline status of each alteration identified by modeling the alteration’s allele frequency (AF), taking into account the tumor content, tumor ploidy, and the local copy number. Accuracy of the prediction depends on the depth of sequencing and copy number model fit, which are achieved in our clinical assay by sequencing to high depth (>500x) using MPS, covering 394 cancer-related genes and over 3,500 genome-wide single nucleotide polymorphisms (SNPs). Calls are made using a statistic based on read depth and local variability of SNP AF. To validate the method, we first evaluated performance on samples from 30 lung and colon cancer patients, where we sequenced tumors and matched normal tissue. We examined predictions for 17 somatic hotspot mutations and 20 common germline SNPs in 20,182 clinical cancer specimens. To assess the impact of stromal admixture, we examined three cell lines, which were titrated with their matched normal to six levels (10–75%). Overall, predictions were made in 85% of cases, with 95–99% of variants predicted correctly, a significantly superior performance compared to a basic approach based on AF alone. We then applied the SGZ method to the COSMIC database of known somatic variants in cancer and found >50 that are in fact more likely to be germline.

Purpose: Merkel cell carcinoma (MCC) is a rare, aggressive cutaneous malignancy, which has demonstrated sensitivity to immune checkpoint inhibitor therapy. Here, we perform the largest genomics study in MCC to date to characterize the molecular landscape and evaluate for clinical and molecular correlates to immune checkpoint inhibitor response. Experimental Design: Comprehensive molecular profiling was performed on 317 tumors from patients with MCC, including the evaluation of oncogenic mutations, tumor mutational burden (TMB), mutational signatures, and the Merkel cell polyomavirus (MCPyV). For a subset of 57 patients, a retrospective analysis was conducted to evaluate for clinical and molecular correlates to immune checkpoint inhibitor response and disease survival. Results: Genomic analyses revealed a bimodal distribution in TMB, with 2 molecularly distinct subgroups. Ninety-four percent (n ¼ 110) of TMB-high specimens exhibited an ultraviolet light (UV) mutational signature. MCPyV genomic DNA sequences were not identified in any TMB-high cases (0/117), but were in 63% (110/175) of TMB-low cases. For 36 evaluable patients treated with checkpoint inhibitors, the overall response rate was 44% and response correlated with survival at time of review (100% vs. 20%, P < 0.001). Response rate was 50% in TMB-high/UV-driven and 41% in TMB-low/MCPyV-positive tumors (P ¼ 0.63). Response rate was significantly correlated with line of therapy: 75% in firstline, 39% in second-line, and 18% in third-line or beyond (P ¼ 0.0066). PD-1, but not PD-L1, expression was associated with immunotherapy response (77% vs. 21%, P ¼ 0.00598, for PD-1 positive and negative, respectively). Conclusions: We provide a comprehensive genomic landscape of MCC and demonstrate clinicogenomic associates of immunotherapy response.

Neoantigen presentation arises as a result of tumor-specifi c mutations and is a critical component of immune surveillance that can be abrogated by somatic LOH of the human leukocyte antigen class I (HLA-I) locus. To understand the role of HLA-I LOH in oncogenesis and treatment, we utilized a pan-cancer genomic dataset of 83,644 patient samples, a small subset of which had treatment outcomes with immune checkpoint inhibitors (ICI). HLA-I LOH was common (17%) and unexpectedly had a nonlinear relationship with tumor mutational burden (TMB). HLA-I LOH was frequent at intermediate TMB, yet prevalence decreased above 30 mutations/megabase, suggesting highly mutated tumors require alternate immune evasion mechanisms. In ICI-treated patients with nonsquamous non-small cell lung cancer, HLA-I LOH was a signifi cant negative predictor of overall survival. Survival prediction improved when combined with TMB, suggesting TMB with HLA-I LOH may better identify patients likely to benefi t from ICIs. SIGnIFICAnCE:This work shows the pan-cancer landscape of HLA-I LOH, revealing an unexpected "Goldilocks" relationship between HLA-I LOH and TMB, and demonstrates HLA-I LOH as a signifi cant negative predictor of outcomes after ICI treatment. These data informed a combined predictor of outcomes after ICI and have implications for tumor vaccine development.