Abstract IA41: Novel Approaches for Accessing Molecular Heterogeneity
Authors: Gibbs Lee D. , Roman Lynda , Webb Michelle , Bassiouni Rania , Williams Stephen R , Weisenfeld Neil I. , Delaney Nigel F. , Yin Yifeng , Rotimi Solomon , Chew Jennifer , Frey Meghan , Qian Jing , Miller Heather , Murderspach Laila , Da Silva Diane , McEachron Troy , Craig David W. , Carpten John D. 
Published: December 2020

Abstract
Ovarian cancer is the fifth leading cause of cancer-related deaths in women and is the deadliest gynecological malignancy in the United States. The standard treatment of ovarian cancer is based on debulking surgery followed by platinum- and taxane-based chemotherapy, and has remained the same over the past three decades. Over those years, molecular targeted and combination therapies have been developed and clinically approved, however the overall survival rate has not improved significantly due to chemo-resistance. Further, the majority of patients experience recurrence of treatment-resistant tumors. The genomic diversity within a tumor and the varying cell types within its microenvironment has placed significant importance on heterogeneity and its clinical implications. Intra-tumor heterogeneity has often been blamed for treatment failure in ovarian carcinoma. Consequently, intra-tumor heterogeneity is a key factor driving drug resistance, therapeutic failure, and poor outcomes and poses a significant challenge to personalized cancer medicine. Intra-tumor heterogeneity is a hallmark of cancer where the molecular and cellular interactions within the tumor microenvironment can dictate a cancer’s fate. Molecular profiling of bulk tissue specimens using methods such as whole-transcriptome sequencing are limited in their ability to resolve fine grain molecular signatures and hinder our utility to dissect the underlying biology of individual tumors. Although informatics approaches are available that attempt to disentangle tissue heterogeneity from bulk tumor data, emerging spatial whole transcriptome sequencing technologies allow a more precise delineation of cellular and molecular substructure in a comprehensive unbiased approach. Spatial Transcriptomics (ST) enables high-throughput whole transcriptomic sequencing within a single intact tissue by using a glass slide arrayed with barcoded cDNA primers at a resolution of 100um (3-30 cells). This workflow requires no tissue dissociation keeping fragile cell types intact. Resulting data from this workflow is overlaid on the tissue, displayed as a “cluster reference map”, providing comprehensive unbiased transcriptional substructure and unique possibilities for subsequent in situ analysis. The overall goal of this study is to utilize ST to unveil the unexplored landscape of intra-tumor heterogeneity in ovarian cancer and determine its translational relevance. Here, we applied ST to profile gene expression in fresh frozen OCT embedded sections from nine high grade ovarian patients. Three serial 5-micron frozen sections were placed on proprietary 10x Genomics Spatial Transcriptomics (ST) slides and processed using manufacturer specifications. Libraries were sequenced on the Illumina NovaSeq 6000 system and data was processed using 10X Genomics analytical tools. Next, we aggregated the transcriptional profiles of serial sections from each case increasing our power to cluster similar regions and identify differentially expressed genes within these tissues. Using serial sections of solid tumors from each subject we were not only able to profile each section at 100um resolution but also spatially resolve gene expression signatures and cluster regions of tissue based on these signatures. This ST workflow reliably quantitated an average spatial distribution of 19,285 genes per section in our ovarian cancer cohort. Interestingly, within our cohort we have extreme outliers that include primary tumors that did not have any response to standard adjuvant chemotherapy, paclitaxel and carboplatin, and patients that sustained a durable response to standard adjuvant chemotherapy and diagnosed as disease free ≥3 years and then presented with recurrent disease. We identified tumor heterogeneity as unique spatially-resolved gene expression clusters across each tissue section defined by individual gene sets associated with tumorigenic molecular processes, immune cell quantity and localization. These approaches highlight the power of spatial whole-transcriptomic sequencing in solid tumor studies to help unravel the complexity of heterogeneous cancers and provide a comprehensive characterization of transcriptional substructure within a single tissue section.
Citation Format: Lee D. Gibbs, Lynda Roman, Michelle Webb, Rania Bassiouni, Stephen R Williams, Neil I. Weisenfeld, Nigel F. Delaney, Yifeng Yin, Solomon Rotimi, Jennifer Chew, Meghan Frey, Jing Qian, Heather Miller, Laila Murderspach, Diane Da Silva, Troy McEachron, David W. Craig, John D. Carpten. Novel Approaches for Accessing Molecular Heterogeneity [abstract]. In: Proceedings of the AACR Virtual Conference: Thirteenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2020 Oct 2-4. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(12 Suppl):Abstract nr IA41.