Cancer Genomics Consortium 2023 Webinar Series - CGC 2022 Annual Meeting Poster Winners

Explore the latest findings in cancer genomics presented by winners of the 2022 Cancer Genomics Consortium Annual Meeting poster competition. Dive into two compelling presentations: one examining copy number alterations in childhood brain tumors and their potential for predicting survival, and another investigating single-cell RNA sequencing and co-occurring cellular state analysis in high-grade serous ovarian cancer. Learn about the implementation of new technologies in clinical genetic/genomic testing, standards for interpreting sequence and copy number variants, germline predisposition to cancer, and novel approaches for detecting structural variants in both constitutional and cancer samples. Gain insights into ovarian cancer survival, surgical biopsies, analysis strategies, and the application of single-cell RNA sequencing to both discovery and bulk data recovery. Examine the impact of copy number alterations, including examples of quiet genomes, detected copy events, and adjusted cutoffs for calling copy events. Discover the use of GISTIC (Genomic Identification of Significant Targets in Cancer) in identifying recurrently altered regions and their association with survival changes. Explore clustering patterns among recurrent regions containing known cancer genes, providing a comprehensive overview of cutting-edge research in cancer genomics.

Intro
Ovarian Cancer Survival
Surgical Biopsies
High-Grade Serous Ovarian Cancer
Analysis Strategy
Ground Truths
singleR Annotation
Manual Annotation
100% Reproducible / Colab Code
Final scRNA Annotations
Applied to scRNA (Discovery)
Applied to Bulk Data (Recovery)
Semenkovich Home Research Team
Example: Quiet genome without correction
Example: Detected copy event without correction
Example: Cutoff for calling copy even adjusted
Example: Recentering with adjusted cutoff
Copy number alteration is common, some cases extensive
Correlation may increase when split by diagnosis
Correlation changed with combined cohort
GISTIC-Genomic Identification of Significant Targets in Cancer
GISTIC identifies 24 recurrently altered regions in Wash U
Some recurrently altered regions associated with changes in survival contain known cancer genes
Clustering shows possible patterns among recurrent regions containing known cancer genes