Meagan Montesion

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. FUNDING: Funding was provided by Foundation Medicine Inc., the Johns Hopkins Bloomberg-Kimmel Institute for Cancer Immunotherapy, the Viragh Foundation, the National Cancer Institute Specialized Program of Research Excellence (SPORE) in Gastrointestinal Cancers (P50 CA062924), the NIH Center Core Grant (P30 CA006973), the Norman & Ruth Rales Foundation, and the Conquer Cancer Foundation.

Background Non-small cell lung cancer (NSCLC) patients bearing targetable oncogene alterations typically derive limited benefit from immune checkpoint blockade (ICB), which has been attributed to low tumor mutation burden (TMB) and/or PD-L1 levels. We investigated oncogene-specific differences in these markers and clinical outcome. Methods Three cohorts of NSCLC patients with oncogene alterations (n=4189 total) were analyzed. Two clinical cohorts of advanced NSCLC patients treated with ICB monotherapy [MD Anderson (MDACC; n=172) and Flatiron Health-Foundation Medicine Clinico-Genomic Database (CGDB; n=894 patients)] were analyzed for clinical outcome. The FMI biomarker cohort (n=4017) was used to assess the association of oncogene alterations with TMB and PD-L1 expression. Results High PD-L1 expression (PD-L1 ≥50%) rate was 19%–20% in classic EGFR , EGFR exon 20 and HER2 -mutant tumors, and 34%–55% in tumors with ALK , BRAF V600E, ROS1 , RET , or MET alterations. Compared with KRAS- mutant tumors, BRAF non-V600E group had higher TMB (9.6 vs KRAS 7.8 mutations/Mb, p=0.003), while all other oncogene groups had lower TMB (p<0.001). In the two clinical cohorts treated with ICB, molecular groups with EGFR , HER2 , ALK , ROS1 , RET , or MET alterations had short progression-free survival (PFS; 1.8–3.7 months), while BRAF V600E group was associated with greater clinical benefit from ICB (CGDB cohort: PFS 9.8 months vs KRAS 3.7 months, HR 0.66, p=0.099; MDACC cohort: response rate 62% vs KRAS 24%; PFS 7.4 vs KRAS 2.8 months, HR 0.36, p=0.026). KRAS G12C and non-G12C subgroups had similar clinical benefit from ICB in both cohorts. In a multivariable analysis, BRAF V600E mutation (HR 0.58, p=0.041), PD-L1 expression (HR 0.57, p=0.022), and high TMB (HR 0.66, p<0.001) were associated with longer PFS. Conclusions High TMB and PD-L1 expression are predictive for benefit from ICB treatment in oncogene-driven NSCLCs. NSCLC harboring BRAF mutations demonstrated superior benefit from ICB that may be attributed to higher TMB and higher PD-L1 expression in these tumors. Meanwhile EGFR and HER2 mutations and ALK , ROS1 , RET , and MET fusions define NSCLC subsets with minimal benefit from ICB despite high PD-L1 expression in NSCLC harboring oncogene fusions. These findings indicate a TMB/PD-L1-independent impact on sensitivity to ICB for certain oncogene alterations.

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.

Abstract Neoantigen presentation arises as a result of tumor-specific 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 significant negative predictor of overall survival. Survival prediction improved when combined with TMB, suggesting TMB with HLA-I LOH may better identify patients likely to benefit 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 significant negative predictor of outcomes after ICI treatment. These data informed a combined predictor of outcomes after ICI and have implications for tumor vaccine development. This article is highlighted in the In This Issue feature, p. 211