Eric Devor

scRNA-seq Can Identify Different Cell Populations in Ovarian Cancer Bulk RNA-seq Experiments

High-grade serous ovarian cancer (HGSC) is a heterogeneous disease. RNA sequencing (RNAseq) of bulk solid tissue is of limited use in these populations due to heterogeneity. Single-cell RNA-seq (scRNA-seq) allows for the identification of diverse genetic compositions of heterogeneous cell populations. New computational methodologies are now available that use scRNAseq results to estimate cell type proportions in bulk RNAseq data. We performed bulk RNA-seq gene expression analysis on 112 HGSC specimens and 12 benign fallopian tube (FT) controls. We identified several publicly available scRNAseq datasets for use as annotation and reference datasets. Deconvolution was performed with MUlti-Subject SIngle Cell Deconvolution (MuSiC) to estimate cell type proportions in the bulk RNA-seq data. Datasets from the Cancer Genome Atlas (TCGA). HGSC repositories were also evaluated. Clinical variables and percentages of cell types were compared for differences in clinical outcomes and treatment results. Pathway enrichment analysis was also performed. Different annotations for referenced scRNA-seq datasets used for deconvolution of bulk RNA-seq data revealed different cellular proportions that were significantly associated with clinical outcomes; for example, higher proportions of macrophages were associated with a better response to primary chemotherapy. Our deconvolution study of bulk RNAseq HGSC samples identified cell populations within the tumor that may be associated with some of the observed clinical outcomes.

Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence

Endometrial cancer (EC) incidence and mortality continues to rise. Molecular profiling of EC promises improvement of risk assessment and treatment selection. However, we still lack robust and accurate models to predict those at risk of failing treatment. The objective of this pilot study is to create models with clinical and genomic data that will discriminate patients with EC at risk of disease recurrence. We performed a pilot, retrospective, case–control study evaluating patients with EC, endometrioid type: 7 with recurrence of disease (cases), and 55 without (controls). RNA was extracted from frozen specimens and sequenced (RNAseq). Genomic features from RNAseq included transcriptome expression, genomic, and structural variation. Feature selection for variable reduction was performed with univariate ANOVA with cross-validation. Selected variables, informative for EC recurrence, were introduced in multivariate lasso regression models. Validation of models was performed in machine-learning platforms (ML) and independent datasets (TCGA). The best performing prediction models (out of >170) contained the same lncRNA features (AUC of 0.9, and 95% CI: 0.75, 1.0). Models were validated with excellent performance in ML platforms and good performance in an independent dataset. Prediction models of EC recurrence containing lncRNA features have better performance than models with clinical data alone.

TP53 Sequencing and p53 Immunohistochemistry Predict Outcomes When Bevacizumab Is Added to Frontline Chemotherapy in Endometrial Cancer: An NRG Oncology/Gynecologic Oncology Group Study

PURPOSE The status of p53 in a tumor can be inferred by next-generation sequencing (NGS) or by immunohistochemistry (IHC). We examined the association between p53 IHC and sequence and whether p53 IHC alone, or integrated with TP53 NGS, predicts the outcome. METHODS From GOG-86P, a randomized phase II study of chemotherapy combined with either bevacizumab or temsirolimus in advanced endometrial cancer, 213 cases had p53 protein expression data measured by IHC and TP53 NGS data. An analysis was designed to integrate p53 expression by IHC with the presence or absence of a TP53 mutation. These variables were further correlated with progression-free survival (PFS) and overall survival (OS) in the chemotherapy plus bevacizumab arms versus the chemotherapy plus temsirolimus arm. RESULTS In the analysis of p53 IHC, the most striking treatment effect favoring bevacizumab was in cases where p53 was overexpressed (PFS hazard ratio [HR]: 0.46, 95% CI, 0.26 to 0.88; OS HR: 0.31, 95% CI, 0.16 to 0.62). On integrated analysis, patients with TP53 missense mutations and p53 protein overexpression had a similar treatment effect on PFS (HR: 0.41, 95% CI, 0.22 to 0.83) and OS (HR: 0.28, 95% CI, 0.14 to 0.59) favoring bevacizumab plus chemotherapy relative to temsirolimus plus chemotherapy. Concordance between TP53 NGS and p53 IHC was 88%. Concordance was 92% when cases with TP53 mutations and POLE mutations or mismatch repair deficiency were removed. CONCLUSION IHC for p53 alone or when integrated with sequencing for TP53 identifies a specific, high-risk tumor genotype/phenotype for which bevacizumab is particularly beneficial in improving outcomes when combined with chemotherapy.

Using Genomic Variation to Distinguish Ovarian High-Grade Serous Carcinoma from Benign Fallopian Tubes

The preoperative diagnosis of pelvic masses has been elusive to date. Methods for characterization such as CA-125 have had limited specificity. We hypothesize that genomic variation can be used to create prediction models which accurately distinguish high grade serous ovarian cancer (HGSC) from benign tissue. Methods: In this retrospective, pilot study, we extracted DNA and RNA from HGSC specimens and from benign fallopian tubes. Then, we performed whole exome sequencing and RNA sequencing, and identified single nucleotide variants (SNV), copy number variants (CNV) and structural variants (SV). We used these variants to create prediction models to distinguish cancer from benign tissue. The models were then validated in independent datasets and with a machine learning platform. Results: The prediction model with SNV had an AUC of 1.00 (95% CI 1.00–1.00). The models with CNV and SV had AUC of 0.87 and 0.73, respectively. Validated models also had excellent performances. Conclusions: Genomic variation of HGSC can be used to create prediction models which accurately discriminate cancer from benign tissue. Further refining of these models (early-stage samples, other tumor types) has the potential to lead to detection of ovarian cancer in blood with cell free DNA, even in early stage.

Identification of Novel Fusion Transcripts in High Grade Serous Ovarian Cancer

Fusion genes are structural chromosomal rearrangements resulting in the exchange of DNA sequences between genes. This results in the formation of a new combined gene. They have been implicated in carcinogenesis in a number of different cancers, though they have been understudied in high grade serous ovarian cancer. This study used high throughput tools to compare the transcriptome of high grade serous ovarian cancer and normal fallopian tubes in the interest of identifying unique fusion transcripts within each group. Indeed, we found that there were significantly more fusion transcripts in the cancer samples relative to the normal fallopian tubes. Following this, the role of fusion transcripts in chemo-response and overall survival was investigated. This led to the identification of fusion transcripts significantly associated with overall survival. Validation was performed with different analytical platforms and different algorithms to find fusion transcripts.

Identification of Novel lncRNAs in Ovarian Cancer and Their Impact on Overall Survival

Long non-coding RNA’s (lncRNA) are RNA sequences that do not encode proteins and are greater than 200 nucleotides in length. They regulate complex cellular mechanisms and have been associated with prognosis in various types of cancer. We aimed to identify lncRNA sequences that are associated with high grade serous ovarian cancer (HGSC) and assess their impact on overall survival. RNA was extracted from 112 HGSC patients and 12 normal fallopian tube samples from our Biobank tissue repository. RNA was sequenced and the Ultrafast and Comprehensive lncRNA detection and quantification pipeline (UClncR) was used for the identification of lncRNA sequences. Univariate logistic and multivariate lasso regression analyses identified lncRNA that was associated with HGSC. Univariate and multivariate Cox proportional hazard ratios were used to evaluate independent predictors of survival. 1943 of 16,325 investigated lncRNA’s were differentially expressed in HGSC as compared to controls (p < 0.001). Nine of these demonstrated association with cancer after multivariate lasso regression. Our multivariate analysis of survival identified four lncRNA’s associated with survival in HGSC. Three out of these four were found to be independently significant after accounting for all clinical covariates. Lastly, seven lncRNAs were independently associated with initial response to chemotherapy; four portended a worse response, while three were associated with improved response. More research is needed, but there is potential for these lncRNAs to be used as biomarkers of HGSC or predictors of treatment outcome in the future.