Casey S. Greene

An Analytic Pipeline to Obtain Reliable Genetic Ancestry Estimates from Tumor-Derived RNA Sequencing Data

Abstract Background: Germline genetics may influence tumor molecular characteristics and ultimately cancer survival. Studies of tumor characteristics, including our epithelial ovarian cancer (EOC) studies of Black women in the United States, may have RNA sequencing (RNA-seq) data from archival tumor tissue but lack germline DNA for at least some individuals. Incomplete germline DNA measurements impede analyses of important measures such as global genetic ancestry, often used in downstream analyses, by reducing sample sizes. Methods: The study population consists of 184 women who participated in two population-based studies of EOC with both germline and formalin-fixed, paraffin-embedded (FFPE) tumor samples and an additional 58 women diagnosed with EOC from the same two studies with only FFPE tumor tissue. We used tumor RNA-seq data to calculate proportions of African, European, and Asian genetic ancestry using a pipeline built on the packages SeqKit, HISAT2, SAMtools, BCFtools, PLINK, and ADMIXTURE. Women from the 1000 Genomes Project were used as the reference populations, and germline genetic ancestry estimates from blood or saliva were used as the baseline comparison. We evaluated multiple quality control strategies to improve genetic ancestry estimation. Results: Correlations between tumor RNA-seq–derived estimates of genetic ancestry from our pipeline and germline-derived African and European genetic ancestry ranged between 0.76 and 0.94. Conclusions: RNA-seq data from archival FFPE tumor tissue can be confidently and efficiently used to approximate global genetic ancestry in an admixed population when germline DNA is unavailable. Impact: This approach supports analyses of genetic ancestry and cancer when germline samples are not available.

Molecular Subtypes of High-Grade Serous Ovarian Cancer across Racial Groups and Gene Expression Platforms

Abstract Background: High-grade serous carcinoma (HGSC) gene expression subtypes are associated with differential survival. We characterized HGSC gene expression in Black individuals and considered whether gene expression differences by self-identified race may contribute to poorer HGSC survival among Black versus White individuals. Methods: We included newly generated RNA sequencing data from Black and White individuals and array-based genotyping data from four existing studies of White and Japanese individuals. We used K-means clustering, a method with no predefined number of clusters or dataset-specific features, to assign subtypes. Cluster- and dataset-specific gene expression patterns were summarized by moderated t-scores. We compared cluster-specific gene expression patterns across datasets by calculating the correlation between the summarized vectors of moderated t-scores. After mapping to The Cancer Genome Atlas–derived HGSC subtypes, we used Cox proportional hazards models to estimate subtype-specific survival by dataset. Results: Cluster-specific gene expression was similar across gene expression platforms and racial groups. Comparing the Black population with the White and Japanese populations, the immunoreactive subtype was more common (39% vs. 23%–28%) and the differentiated subtype was less common (7% vs. 22%–31%). Patterns of subtype-specific survival were similar between the Black and White populations with RNA sequencing data; compared with mesenchymal cases, the risk of death was similar for proliferative and differentiated cases and suggestively lower for immunoreactive cases [Black population HR = 0.79 (0.55, 1.13); White population HR = 0.86 (0.62, 1.19)]. Conclusions: Although the prevalence of HGSC subtypes varied by race, subtype-specific survival was similar. Impact: HGSC subtypes can be consistently assigned across platforms and self-identified racial groups.

Performance of computational algorithms to deconvolve heterogeneous bulk ovarian tumor tissue depends on experimental factors

Abstract Background Single-cell gene expression profiling provides unique opportunities to understand tumor heterogeneity and the tumor microenvironment. Because of cost and feasibility, profiling bulk tumors remains the primary population-scale analytical strategy. Many algorithms can deconvolve these tumors using single-cell profiles to infer their composition. While experimental choices do not change the true underlying composition of the tumor, they can affect the measurements produced by the assay. Results We generated a dataset of high-grade serous ovarian tumors with paired expression profiles from using multiple strategies to examine the extent to which experimental factors impact the results of downstream tumor deconvolution methods. We find that pooling samples for single-cell sequencing and subsequent demultiplexing has a minimal effect. We identify dissociation-induced differences that affect cell composition, leading to changes that may compromise the assumptions underlying some deconvolution algorithms. We also observe differences across mRNA enrichment methods that introduce additional discrepancies between the two data types. We also find that experimental factors change cell composition estimates and that the impact differs by method. Conclusions Previous benchmarks of deconvolution methods have largely ignored experimental factors. We find that methods vary in their robustness to experimental factors. We provide recommendations for methods developers seeking to produce the next generation of deconvolution approaches and for scientists designing experiments using deconvolution to study tumor heterogeneity.

Development and Validation of the Gene Expression Predictor of High-grade Serous Ovarian Carcinoma Molecular SubTYPE (PrOTYPE)

Abstract Purpose: Gene expression–based molecular subtypes of high-grade serous tubo-ovarian cancer (HGSOC), demonstrated across multiple studies, may provide improved stratification for molecularly targeted trials. However, evaluation of clinical utility has been hindered by nonstandardized methods, which are not applicable in a clinical setting. We sought to generate a clinical grade minimal gene set assay for classification of individual tumor specimens into HGSOC subtypes and confirm previously reported subtype-associated features. Experimental Design: Adopting two independent approaches, we derived and internally validated algorithms for subtype prediction using published gene expression data from 1,650 tumors. We applied resulting models to NanoString data on 3,829 HGSOCs from the Ovarian Tumor Tissue Analysis consortium. We further developed, confirmed, and validated a reduced, minimal gene set predictor, with methods suitable for a single-patient setting. Results: Gene expression data were used to derive the predictor of high-grade serous ovarian carcinoma molecular subtype (PrOTYPE) assay. We established a de facto standard as a consensus of two parallel approaches. PrOTYPE subtypes are significantly associated with age, stage, residual disease, tumor-infiltrating lymphocytes, and outcome. The locked-down clinical grade PrOTYPE test includes a model with 55 genes that predicted gene expression subtype with >95% accuracy that was maintained in all analytic and biological validations. Conclusions: We validated the PrOTYPE assay following the Institute of Medicine guidelines for the development of omics-based tests. This fully defined and locked-down clinical grade assay will enable trial design with molecular subtype stratification and allow for objective assessment of the predictive value of HGSOC molecular subtypes in precision medicine applications. See related commentary by McMullen et al., p. 5271

Homologous Recombination Deficiency and Survival in Ovarian High-Grade Serous Carcinoma by Self-Reported Race

Abstract Background: Half of ovarian high-grade serous carcinomas (HGSC) have homologous recombination deficiency (HRD). However, HRD is not well characterized in Black individuals who experience worse survival after a diagnosis of HGSC. The objective of this study was to characterize ovarian HGSC HRD and examine its association with survival by self-reported race. Methods: HRD features were identified using matched tumor–normal whole-exome and RNA sequencing in an HGSC cohort. We calculated age- and stage-adjusted HR and 95% confidence intervals (CI) for survival, comparing individuals with a feature to those without, separately by self-reported race. Results: Any HRD was associated with a 32% reduced risk of death in Black individuals compared with a 62% reduction in White individuals (Black HR = 0.68; 95% CI, 0.43–1.09; White HR = 0.38; 95% CI, 0.14–1.04). More of the germline and somatic variants detected among Black individuals were unannotated or variants of uncertain significance (VUS; germline 65% vs. 45%; somatic 62% vs. 50%). Black individuals with germline unannotated/VUS were more likely to have tumors with HRD scarring and a first-degree family history of breast or ovarian cancer compared with those without (HRD scar 71.4% vs. 49.6%; family history 68.4% vs. 34.6%). Conclusions: HRD testing informs precision-based medicine approaches that improve outcomes, but a higher proportion of VUS among Black individuals may complicate referral for such care leading to worse outcomes for Black individuals. Impact: Our findings emphasize the importance of recruiting diverse individuals in genomics research and better characterizing VUS.

Genomic Characterization of High-Grade Serous Ovarian Carcinoma Reveals Distinct Somatic Features in Black Individuals

Abstract Black individuals experience worse survival after a diagnosis of high-grade serous ovarian carcinoma (HGSC) than White individuals and are underrepresented in ovarian cancer research. To date, the understanding of the molecular and genomic heterogeneity of HGSC is based primarily on the evaluation of tumors from White individuals. In the present study, we performed whole-exome sequencing on HGSC samples from 211 Black patients to identify significantly mutated genes and characterize mutational signatures, assessing their distributions by gene expression subtypes. The occurrence and frequency of somatic mutations and signatures by self-reported race were compared with historic data from The Cancer Genome Atlas (TCGA). Despite technical differences (e.g., formalin-fixed vs. fresh-frozen tissue), the distribution of mutations and their variant classifications for major HGSC genes were nearly identical across study populations. However, de novo significantly mutated gene analysis identified genes not previously reported in TCGA analysis, including the oncogene KRAS and the potential tumor suppressor OBSCN. The prevalence of the homologous recombination deficiency signature was higher among Black individuals with the immunoreactive gene expression subtype compared with the mesenchymal and proliferative subtypes. These findings were confirmed by comparing the data from Black patients with those from 123 White patients with identical tissue collection and processing. Overall, this study suggests that, although most features of HGSC tumor phenotypes are similar in Black and White populations, there may be clinically relevant differences. If validated, these phenotypes may be important for clinical decision-making and would have been missed by characterizing tumors from White individuals only. Significance: Elucidation of the somatic mutational landscape of high-grade serous ovarian carcinoma in Black individuals, who experience poor survival and are underrepresented in research, could inform patient prognosis and enable precision medicine opportunities.