Citations

Abstract Background : Next- generation sequencing provides comprehensive information about individuals’ genetic makeup and is commonplace in oncology clinical practice. However, the utility of genetic information in clinical decision-making process has not been examined extensively from a real-world, data-driven perspective. Through mining real-world data (RWD) from clinical notes, we could extract patients’ genetic information and further associate treatment decisions with genetic information. Methods : We proposed a real-world evidence (RWE) study framework that incorporates context-based natural language processing (NLP) methods and data quality examination before final association analysis. The framework was demonstrated in a Foundation-tested women cancer cohort (N=196). Upon retrieval of patients’ genetic information using NLP system, we assessed the completeness of genetic data captured in unstructured clinical notes according to a genetic data-model. We examined the distribution of different topics regarding BRCA1/2 throughout patients’ treatment process, and then analyzed the association between BRCA1/2 mutation status and the discussion/prescription of targeted therapy . Results: We identified seven topics in clinical context of genetic mentions including: Information, Evaluation, Insurance, Order, Negative, Positive, and Variants of unknown significance (VUS) . O ur rule-based system achieved a precision of 0.87, recall of 0.93 and F- measure of 0.91. Our machine learning system achieved a precision of 0.901, recall of 0.899 and F- measure of 0.9 for four-topic classification and a precision of 0.833, recall of 0.823 and F- measure of 0.82 for seven-topic classification. We found in result-containing sentences, capture of BRCA1/2 mutation information was 75%, but detailed variant information (e.g. variant types) is largely missing. Using cleaned RWD, significant associations were found between BRCA1/2 positive mutation and targeted therapies. Conclusions : In conclusion, we demonstrated a framework to generate RWE using RWD from different clinical sources. Rule-based NLP system achieved the best performance for resolving contextual variability when extracting RWD from unstructured clinical notes . Data quality issues such as incompleteness and discrepancies exist thus manual data cleaning is needed before further analysis can be performed. Finally, we were able to use cleaned RWD to evaluate real-world utility of genetic information to initiate a prescription of targeted therapy.

Abstract Introduction: Acquired resistance to platinum remains a major clinical problem in ovarian cancer since systemic therapies have limited efficacy in this setting. There is growing evidence that the tumor microenvironment (TME) contributes to such resistance, as well as the pathogenesis of the disease. The study of the TME is limited by technical challenges of separating the epithelial from the stromal component. To overcome this limitation, we performed RNA sequencing (RNA-Seq) for three pairs of sensitive and induced resistant patient-derived xenograft models since the epithelial transcripts originate from human tissue and the stroma component is murine. Methods: To determine which TME genes might be contributing to chemotherapy resistance, three platinum-sensitive ovarian cancer PDX lines were treated repeatedly with carboplatin/paclitaxel until resistance occurred. Primary platinum-sensitive and induced platinum resistant tumors were characterized histologically by H&E and Masson Trichrome staining. The three pairs of sensitive and resistant PDX models were then subjected to RNA-Seq. Raw sequencing data were processed through Xenome to classify the reads into epithelial carcinoma (human) or TME (mouse) through Mayo Clinic’s internal RNA-Seq pipeline for transcriptome quantification. Differential expression analysis between induced resistant and sensitive models was carried out for both carcinoma and TME components using edgeR paired analysis. Gene set enrichment analysis was used to identify overrepresented biologic functions for differentially expressed genes between the pairs. Top significantly varied, differentially expressed genes between the pairs were validated using species-specific qPCR. Results: Histologic analysis of the matched sensitive/resistant pairs revealed a marked increase of the stromal component in the resistant tumors evident by H&E and Masson trichrome staining. A total number of 95 and 312 genes were differentially expressed in the epithelial and TME component, respectively (FDR<0.05; absolute log2 fold-change >1.5). The stemness marker SOX2 was significantly upregulated in the human component of the resistant tumors as confirmed by qPCR. Genes involved in stroma remodeling were upregulated in the “murine” component of the resistant tumors, and upregulation was confirmed by qPCR (normalized to a murine housekeeper gene). Gene set enrichment analysis showed that the upregulated genes in TME of resistant models were enriched for cytoskeleton related functions, while the downregulated genes were enriched for extracellular matrix-related functions. Conclusion: A bioinformatics strategy able to map human and murine reads separately may identify resistance mechanisms attributed to the two tumor subcompartments and could lead to strategies to potentially target each separately or concurrently. Citation Format: Valentina Zanfagnin, Yuanhang Liu, Pritha Chanana, Xiaonan Pritha, Beverly Long, Joe Pathoulas, Brad Evans, Jaime Davila, Chen Wang, Saravut John Weroha. Transcriptional profiling of tumor stroma using ovarian cancer PDX models with induced platinum-resistance [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr B18.

Abstract Background: Gene expression-based molecular subtypes of high-grade serous tubo-ovarian cancer (HGSOC) are distinguished by differential immune and stromal infiltration and may provide opportunities for targeted therapies. Integration of molecular subtypes into clinical trials has been hindered by inconsistent subtyping methodology. Methods: Adopting two independent approaches, we derived and internally validated algorithms for molecular subtype prediction from gene-expression array data in 1,650 tumors. We applied resulting models to assign labels to 3829 HGSOCs from the Ovarian Tumor Tissue Analysis (OTTA) consortium evaluated on NanoString. Using the labeled NanoString data, we developed, confirmed, and validated a minimal gene set, clinical-grade test and prediction tool. We also used the OTTA dataset to evaluate associations between molecular subtype, biologic, and clinical features. Findings: The locked-down test included a model with 55 genes that predicted HGSOC molecular subtype with >95% accuracy. Subtype varied between primary and metastatic site taken at the time of primary surgery, and was significantly associated with age, stage, CD8+ lymphocyte infiltration, residual disease, and outcome. In multivariable models, molecular subtypes lose their prognostic significance in the presence of risk factors such as residual disease and BRCA1/2 germline mutations. Interpretation: We validated the Predictor of high-grade-serous Ovarian carcinoma molecular subTYPE, or PrOTYPE, following the Institute of Medicine guidelines for the development of omics-based tests. This simple-to-use, cost-effective, fully defined, and locked-down clinical-grade assay will facilitate molecular subtype stratification into clinical trial design. PrOTYPE will allow for objective assessment of HGSOC molecular subtype predictive value in precision medicine applications. Citation Format: Aline Talhouk, Joshy George, Chen Wang, Ellen Goode, Susan Ramus, Jennifer Doherty, David Bowtell, Michael Anglesio, OTTA Consortium. PrOTYPE (Predictor of high-grade-serous Ovarian carcinoma molecular subTYPE): The development and validation of a clinical-grade consensus classifier for the molecular subtypes of high-grade serous tubo-ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr A03.

Abstract Background: Next-generation sequencing provides comprehensive information about individuals’ genetic makeup and is commonplace in oncology clinical practice. However, the utility of genetic information in clinical decision-making process has not been examined extensively from a real-world, data-driven perspective. Through mining real-world data (RWD) from clinical notes, we could extract patients’ genetic information and further associate treatment decisions with genetic information.Methods: We proposed a real-world evidence (RWE) study framework that incorporates context-based natural language processing (NLP) methods and data quality examination before final association analysis. The framework was demonstrated on a Foundation-tested women cancer cohort (N=196). Upon retrieval of patients’ genetic information using NLP system, we assessed completeness of genetic data captured in unstructured clinical notes according a genetic data-model. We examined the distribution of different topics regarding BRCA1/2 throughout patients’ treatment process, and then analyzed the association between BRCA1/2 mutation status and the discussion/prescription of targeted therapy. Results: We identified seven topics in clinical context of genetic mentions including: Information, Evaluation, Insurance, Order, Negative, Positive, and Variants of unknown significance (VUS). Our rule-based system achieved a precision of 0.87, recall of 0.93 and F-measure of 0.91. Our machine learning system achieved a precision of 0.901, recall of 0.899 and F-measure of 0.9 for four-topic classification and a precision of 0.833, recall of 0.823 and F-measure of 0.82 for seven-topic classification. We found in result-containing sentences, capture of BRCA1/2 mutation information was 75%, but detailed variant information (e.g. variant types) is largely missing. Using cleaned RWD, significant associations were found between BRCA1/2 positive mutation and targeted therapies.Conclusions: In conclusion, we demonstrated a framework to generate RWE using RWD from different clinical sources. Rule-based NLP system achieved the best performance for resolving contextual variability when extracting RWD from unstructured clinical notes. Data quality issue such as incompleteness and discrepancies exist thus manual data cleaning is needed before further analysis can be performed. Finally, we were able to use cleaned RWD to evaluate real-world utility of genetic information to initiate prescription of targeted therapy.

Abstract The Hippo pathway effector Yes-associated protein (YAP) is localized to the nucleus and transcriptionally active in a number of tumor types, including a majority of human cholangiocarcinomas. YAP activity has been linked to chemotherapy resistance and has been shown to rescue KRAS and BRAF inhibition in RAS/RAF-driven cancers; however, the underlying mechanisms of YAP-mediated chemoresistance have yet to be elucidated. Herein, we report that the tyrosine phosphatase SHP2 directly regulates the activity of YAP by dephosphorylating pYAPY357 even in the setting of RAS/RAF mutations, and that diminished SHP2 phosphatase activity is associated with chemoresistance in cholangiocarcinomas. A screen for YAP-interacting tyrosine phosphatases identified SHP2, and characterization of cholangiocarcinomas cell lines demonstrated an inverse relationship between SHP2 levels and pYAPY357. Human sequencing data demonstrated lower SHP2 levels in cholangiocarcinomas tumors as compared with normal liver. Cell lines with low SHP2 expression and higher levels of pYAPY357 were resistant to gemcitabine and cisplatin. In cholangiocarcinomas cells with high levels of SHP2, pharmacologic inhibition or genetic deletion of SHP2 increased YAPY357 phosphorylation and expression of YAP target genes, including the antiapoptotic regulator MCL1, imparting resistance to gemcitabine and cisplatin. In vivo evaluation of chemotherapy sensitivity demonstrated significant resistance in xenografts with genetic deletion of SHP2, which could be overcome by utilizing an MCL1 inhibitor. Implications: These findings demonstrate a role for SHP2 in regulating YAP activity and chemosensitivity, and suggest that decreased phosphatase activity may be a mechanism of chemoresistance in cholangiocarcinoma via a MCL1-mediated mechanism.

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

5510 Background: Transcriptional profiling of ovarian cancers has proven to be complex. As such it has been difficult to validate existing signatures across studies. Cancer Genome Atlas (TCGA) researchers have identified four molecular subtypes of high-grade serous ovarian cancer (HGSOC). However, survival duration did not differ significantly for the TCGA subtypes. Potential limitations of the TCGA data include short clinical follow-up (45% were alive at the time of last follow-up) and the need to unify gene expression measures from multiple platforms. Methods: Clinically annotated stage-II–IV HGSOC samples (n = 175) with >70% tumor cell content were profiled using the Agilent Whole Human Genome 4 x 44K chip. To identify subtypes non-negative matrix factorization (NMF) of mRNA expression was performed using ~2000 genes with the highest variability across patients. In parallel differentially expressed genes were identified using the Rosetta Similarity Search Tool as well as analysis of variance based on genes known to be involved in epithelial to mesenchymal transition. Results: Median follow-up time was 35 months (range, 0–202 months, 12% were alive at the time of last follow-up). NMF clustering confirmed four HGSOC subtypes (immunoreactive, differentiated, proliferative, and mesenchymal) on the basis of gene content in the clusters. Pathway signatures with therapeutic potential were identified for individual or multiple subtypes. Survival differed significantly between the four molecular subgroups in univariate (Hazard Ratio [HR] 2.4, 95% CI 1.5-4.1, p = 0.007) and multivariate (HR 2.3, 95% CI 1.3-4.0, p = 0.003) analyses when accounting for age, stage, grade, and postoperative residual tumor. Using the supervised clustering approach two distinct molecular subtypes of HGSOC based on epithelial and mesenchymal gene expression signatures were identified with significantly different survival outcomes. Conclusions: Here we independently validate and expand upon the molecular TCGA classification of HGSOC. The potential of these two prognostic classifiers may lie in their ability to recognize categories of patients that are more likely to respond to particular therapies.

PURPOSE The Fast Healthcare Interoperability Resources (FHIR) is emerging as a next-generation standards framework developed by HL7 for exchanging electronic health care data. The modeling capability of FHIR in standardizing cancer data has been gaining increasing attention by the cancer research informatics community. However, few studies have been conducted to examine the capability of FHIR in electronic data capture (EDC) applications for effective cancer clinical trials. The objective of this study was to design, develop, and evaluate an FHIR-based method that enables the automation of the case report forms (CRFs) population for cancer clinical trials using real-world electronic health records (EHRs). MATERIALS AND METHODS We developed an FHIR-based computational pipeline of EDC with a case study for modeling colorectal cancer trials. We first leveraged an existing FHIR-based cancer profile to represent EHR data of patients with colorectal cancer, and then we used the FHIR Questionnaire and QuestionnaireResponse resources to represent the CRFs and their data population. To test the accuracy of and overall quality of the computational pipeline, we used synoptic reports of 287 Mayo Clinic patients with colorectal cancer from 2013 to 2019 with standard measures of precision, recall, and F1 score. RESULTS Using the computational pipeline, a total of 1,037 synoptic reports were successfully converted as the instances of the FHIR-based cancer profile. The average accuracy for converting all data elements (excluding tumor perforation) of the cancer profile was 0.99, using 200 randomly selected records. The average F1 score for populating nine questions of the CRFs in a real-world colorectal cancer trial was 0.95, using 100 randomly selected records. CONCLUSION We demonstrated that it is feasible to populate CRFs with EHR data in an automated manner with satisfactory performance. The outcome of the study provides helpful insight into future directions in implementing FHIR-based EDC applications for modern cancer clinical trials.

Abstract Background: Traditionally, mutational burden and mutational signatures have been assessed by tumor-normal pair DNA sequencing. The requirement of having both normal and tumor samples is not always feasible from a clinical perspective, and led us to investigate the efficacy of using RNA sequencing of only the tumor sample to determine the mutational burden and signatures, and subsequently molecular cause of the cancer. The potential advantages include reducing the cost of testing, and simultaneously providing information on the gene expression profile and gene fusions present in the tumor.Results: In this study, we devised supervised and unsupervised learning methods to determine mutational signatures from tumor RNA-seq data. As applications, we applied the methods to a training set of 587 TCGA uterine cancer RNAseq samples, and examined in an independent testing set of xxx TCGA colorectal cancer RNAseq samples. Both diseases are known associated with microsatellite instable high (MSI-H) and driver defects in DNA polymerase ɛ (POLɛ). From RNAseq called variants, we found majority (>95%) are likely germline variants, leading to C>T enriched germline variants (dbSNP) widely applicable in tumor and normal RNAseq samples. We found significant associations between RNA-derived mutational burdens and MSI/POLɛ status, and insignificant relationship between RNAseq total coverage and derived mutational burdens. Additionally we found that over 80% of variants could be explained by using the COSMIC mutational signature-5, -6 and -10, which are implicated in natural aging, MSI-H, and POLɛ, respectively. For classifying tumor type, within UCEC we achieved a recall of 0.56 and 0.78, and specificity of 0.66 and 0.99 for MSI and POLɛ respectively. By applying learnt RNA signatures from UCEC to COAD, we were able to improve our classification of both MSI and POLɛ. Conclusions: Taken together, our work provides a novel method to detect RNAseq derived mutational signatures with effective procedures to remove likely germline variants. It can leads to accurate classification of underlying driving mechanisms of DNA damage deficiency.