Introduction: Endometrial cancer accounts for ~76,000 deaths amongst women worldwide. In the United States, it is estimated 65,620 new cases and 12,590 deaths occurring in 2020, being the fourth most common gynecologic malignancy in the country. It has been reported that specific molecular characteristics in tissues are associated with poorer prognosis. Publicly available data generated from bulk molecular profiling of tumors has provided a molecular taxonomy of endometrial cancers with common alterations occurring in a number of known oncogenic pathways. However, little is known about the degree of heterogeneity in endometrial cancer. Thus, we have set out to utilize single cell technologies to assess tumor heterogeneity in endometrial cancers. Methods: Our preliminary study includes serous and mixed lineage endometrial tumors from five (3 African Americans, 1 Hispanic/Latino, 1 Caucasian) cases. In addition to bulk whole exome sequencing, we utilized single cell whole genome sequencing to assess global copy number heterogeneity. OCT-embedded frozen tumor sections were dissociated to collect nuclei, which were subjected to single nuclei sequencing using the 10x ChromiumTM single cell copy number variation (scCNV) assay targeting 500 cells per sample. Single cell libraries were quality assessed and sequenced using the Illumina NovaSeq6000 system. Data were processed using the 10X Genomics CellRanger pipeline for assessment of copy number heterogeneity. Results: We generated scCNV data on a total of 1,546 cells across five tumors. Hierarchical clustering and visual inspection of scCNV data reveals obvious somatic copy number tumor cell heterogeneity in all samples, including single and mixed histology tumors. Heterogeneity was mostly distinguished by whole chromosome or chromosome arm level gains or losses. Clonal focal amplifications were detected at 5p, 8p, 8q, and 17q encompassing known oncogenes. As we are able to identify clustered diploid populations of normal cells, we are using these data to perform segregation analysis for CGH analysis and the identification of somatic mutations in clonal populations of cells. These data are being compared to bulk exome sequencing data to determine the power of scCNV for detecting clonal populations of tumor cells. Conclusions: This project used high resolution single cell sequencing in five endometrial cancers with varying histologies. scCNV analysis provided clear evidence of heterogeneity in all tumors that were assessed in our study. This single cell whole genome data is being further analyzed for the presence of focal events, breakpoints and mutations that further stratify clonal tumor cell components in these cases. We plan to further expand this cohort to establish additional evidence for heterogeneity in endometrial cancer. Citation Format: Enrique I. Velazquez Villarreal, Lee D. Gibbs, Dana Mahinbakht, Diane M. Da Silva, Lynda D. Roman, David W. Craig, John D. Carpten. Identifying genomic heterogeneity at single-cell resolution in endometrial cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2199.

Single-cell sequencing is an important and powerful tool for improving our understanding of cancer heterogeneity. While previous reports have largely focused on single-cell RNA-seq, we examined the utility of novel approaches for single-cell copy-number analysis and clonotype detection using 10x Chromium™ Technology capable of processing thousands of single cells. In contrast to bulk sequencing, this technology is able to call cell clonotypes down to 1% of 1,000 cell inputs, while providing detection of CNVs down to 100 Kb events on clones consisting of 10 or more cells. As a pilot study, we processed 300 single cells from COLO829 through the system, generating a barcoded short-read library, which was sequenced to a raw depth of approximately 600 million reads. Previous efforts had characterized COLO829 by bulk whole-genome sequencing of tumor/normal cell-line lineages grown and sequenced across four laboratories to help establish tumor/normal reference standards. We used barcode-aware bioinformatic analysis to recover reads derived from single cells, analyzing them in 20 Kb cassettes of reads providing both regional copy number and genome-wide ploidy estimates. This high resolution of CNV detection is enabled by aggregation of reads across cells within the same clone. Somewhat surprisingly, we find the presence of multiple clones within a single cell line growth evident by large-scale copy number changes, in some cases spanning entire chromosome arms. We show an ability to better resolve absolute copy number, whereas previous analysis approaches on bulk sequencing have lagged due to a reliance on indirect inferences. Consideration of prior publications on this cell line are consistent with the undescribed existence of clonal heterogeneity in these, and when considered with these data provide greater insight into copy number-driven events. Overall, our results suggest that single-cell copy number provides unique and important insight into larger-scale events, providing unique and distinct insight into cancer heterogeneity. Citation Format: Enrique I. Velazquez Villarreal, Vijay Kumar, Yifeng Yin, John D. Carpten, David W. Craig. Leveraging new methods in single-cell copy number analysis and clonotype detection to uncover and characterize hidden subclones within standard cell lines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 437.

Renal cell carcinoma (RCC) is one of the most lethal urological malignancies and is responsible for around 80 percent of all primary renal neoplasms. In the US, every year are reported approximately 74,000 new cases and almost 15,000 deaths. It has been reported significant racial disparities in survival for renal cell carcinoma (RCC) between African Americans patients (AA), Hispanics and Caucasians Americans (CA) but more efforts are needed in order to have a high resolution genomic profile of the tumor. Currently, RCC has been characterized by extensive cancer heterogeneity using bulk sequencing. In order to get new insight in the study of RCC cancer heterogeneity, we applied DNA single cell sequencing since it has the potential to improve our understanding of this genetic feature by providing sub-clonal and variant information in high resolution. To this end, we studied cancer heterogeneity applying single cell copy number analysis and clonotype detection in four RCC tumors. 10x Chromium™ Technology was used for processing single cells. This technology provides 100 Kb CNV events, calling clonotypes down to 10 of 1000 cell inputs. The most representative sample resulted with more than 50% of tumor content. Analysis of the tumor cells showed variable median ploidies. In addition, regional copy number was estimated by processing our data in 20 Kb cases of reads. Multiple sub-clones were identified in sample number one where four clusters of sub-clones characterized cancer heterogeneity. By using Fast maximum-likelihood and Bayesian Information Criterion, the clustering process were implemented on the most representative sample to select the optimal clustering solution. This allowed us to provide a better insight of sub-clonal evolution. We detected copy number changes on entire chromosome arms and mutations at variant detection level. For this last, we identified previously reported VHL gene mutations that have been reported in RCC samples as signatures of clinical prognosis. The use of single cell copy number analysis has the potential to uncover and characterize the evolution of hidden sub-clones, highlighting their important uses in cancer health disparities research to identify genomic racial differences in RCC risk and progression of AA, Hispanics and CA patients. Citation Format: Enrique I. Velazquez Villarreal, David W. Craig, John D. Carpten. Introducing single-cell sequencing genomic DNA copy number analysis to study cancer heterogeneity in renal cell carcinoma and its potential benefits in cancer health disparities research [abstract]. In: Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2019 Sep 20-23; San Francisco, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr A004.

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Endometrial cancer is the fourth most common gynecologic malignancy in the US. Tumor- associated genetic signatures are known to be associated with poorer prognosis of patients with endometrial cancer. In general, frequent gene alterations are enrichments of p53 and KRAS, PTEN, PIK3CA and PI3K/AKT mutations with variable CNV. Considerable evidence for intratumor heterogeneity indicate that bulk genomic sequencing approaches may be insufficient since endometrial cancer is characterized by extensive tumor heterogeneity. Here, to get new insight about endometrial cancer heterogeneity, we applied single cell sequencing since it has the potential to improve our understanding of this genetic feature by providing clonotype and variant information in high resolution. We examined cancer heterogeneity using single cell DNA sequencing Copy Number Variation (sc-DNAseq-CNV) analysis and clonotype detection in three tumors from patients with endometrial cancer. The technology used for processing single cell was 10x ChromiumTM Technology that provides detection of 100 Kb CNV events, calling clonotypes down to 10 of 1000 cell inputs. By processing these samples resulted in the sequencing of around 800 cells. Genome-wide ploidy analysis of the tumor cells showed variable median ploidies per sample. The regional copy number was estimated by processing our data in 20 Kb cases of reads. Multiple sub-clones were identified per sample number one where four clusters of sub-clones characterized cancer heterogeneity. Copy number changes on entire chromosome arms and mutations at variant detection level were detected since the resolution provided by single cell technology applied. Among the variant detection we identified previously reported mutations in POLE, POLE2, TP53, FGFR2, ARID1A, CTNNBI, PIK3CA, PIK3RI, KRAS and SWI/SNF in specific sub clones. Our study addresses the predominant challenge to determine tumor heterogeneity by performing better approaches to detect genomic differences and identifying molecular signatures among women with endometrial cancer. Better understanding of tumor heterogeneity may benefit patients diagnosed with endometrial cancer, or patients with specific molecular characteristics linked to endometrial cancer. Citation Format: Enrique I. Velazquez Villarreal, John D. Carpten, David W. Craig. Understanding endometrial cancer heterogeneity at single-cell resolution [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2507.