Abstract B018: Identifying a novel aggressive subtype of cervical cancer predominantly affecting minority populations through long-read whole genome sequencing
Abstract Purpose of the study: YAP1 is the most frequently amplified oncogene in cervical cancer. The Cancer Genome Atlas Research Network study showed that the YAP1 (11q22) oncogene is amplified in 11% of cervical cancer cases. Our lab recently identified that YAP1 oncogene amplification is associated with a 12-year earlier age of diagnosis, poorer survival, and defies a novel aggressive cervical cancer subtype that is more frequent in minority populations. The present study aims to characterize 19 YAP1 amplified cervical cancers from Guatemala and Venezuela via Oxford Nanopore technology (ONT) whole genome long-read sequencing. Methods: In this study, 380 cervical cancer patients were assessed for YAP1 amplification using a TaqMan assay, with 45 samples identified as positive for YAP1 amplification. These 45 samples were then subjected to whole genome sequencing with 5x coverage using Oxford Nanopore Technologies (ONT) to confirm the amplification status. A subset of 19 YAP1-amplified samples underwent deeper sequencing via ONT-LSK114 library preparation. The HPV integration status in these samples was determined by uploading the sequencing data (fastq_pass files) to the Cancer Genomics Cloud, where breakpoints were manually examined using the Integrated Genome Viewer (IGV). HPV and human DNA segments were further analyzed and identified using bioinformatics tools such as BLAT and BLAST. Results: In our study, we confirmed YAP1 amplification in 24 out of 45 tumor samples using barcode sequencing, and due to limited DNA availability (1 µg), only 19 of these samples underwent deep sequencing with 25-30x coverage. Notably, we observed a higher frequency of HPV integration (11/19) in YAP1-amplified tumors compared to those with episomal HPV status, suggesting a potential link between HPV integration and YAP1 amplification. We noticed that YAP1 amplification is driven by Breakage-Fusion-Bridge (BFB) events, which also lead to the co-amplification of the nearby BIRC2/3 genes, contributing to genomic instability and immune escape. This mechanism was further supported by a similar pattern observed in the TCGA whole genome sequence data, where 27 out of 31 YAP1-amplified cervical tumors also exhibited BFB events. Finally, treatment with the PIK3CA inhibitor Alpelisib resulted in a decrease in YAP1 protein levels in CaSki cells, a cervical cancer cell line with mutated PIK3CA and amplified YAP1 expression. Conclusions: These findings highlight a possible interplay between HPV integration and BFB-driven amplification of YAP1 and its neighboring genes, offering insights into potential therapeutic targets for this novel aggressive subtype of cervical cancer. Citation Format: Sonam Tulsyan, Hong Lou, Yi Xie, Emma Robinson, Danielle Kayembe, Eduardo Gharzouzi, Roberto Orozco, Enrique Alvirez, Michael Dean. Identifying a novel aggressive subtype of cervical cancer predominantly affecting minority populations through long-read whole genome sequencing [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr B018.
Authors
- Roberto Orozco (ORCID: https://orcid.org/0000-0002-3785-2827)
- Eduardo Gharzouzi (ORCID: https://orcid.org/0000-0002-4223-867X)
- Enrique Alvirez
- Sonam Tulsyan (ORCID: https://orcid.org/0000-0001-7201-571X)
- Yi Xie (ORCID: https://orcid.org/0000-0003-2150-8973)
- Emma Robinson
- Michael Dean
- Hong Lou
- D.L. Kayembe
Institutions
- Hospital General San Juan de Dios (GT)
- Liga Nacional Contra el Cáncer (GT)
- Frederick National Laboratory for Cancer Research (US)
- National Cancer Institute (US)
Publication Details
- Journal
- Cancer Research
- Published
- 2025-03-11
- DOI
- https://doi.org/10.1158/1538-7445.genfunc25-b018
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00