Abstract PR01: Functional characterization of breast cancer using pooled lentivirus shRNA screens
Bibliographic record
Abstract
Abstract Breast cancer is the most common invasive malignancy and the second leading cause of cancer death in U.S. women. Early detection and improved therapy have led to >85% 5-year survival, but still, half of currently affected women will succumb from metastatic disease. This poor outcome reflects our incomplete knowledge of essential genes driving tumor proliferation for each subtype. From genomic data alone, it can be difficult to access which genetic alterations drive pathogenesis because most of these are functionally irrelevant “passenger” mutations. Even if an oncogene or tumor suppressor is identified, these often are not amenable to targeted therapy. However, unanticipated gene/pathway dependencies can arise as a consequence of these genetic abnormalities in cancer cells (“synthetic lethality”). The recent development of lentiviral-based shRNA libraries enables genome-wide screening of cultured cancer cells in a pooled format, facilitating the identification of genes necessary for cancer cell proliferation and survival in cell culture as well as potential synthetic lethal interactions. The overall objectives of this project were to identify subtype-specific targets and synthetic lethal interactions for common mutations in human breast cancer using genome-wide shRNA screens, as well as to compare “functional genomic” and genomic classification schemes. We screened a panel of > 75 breast cancer lines using an 80,000 lentiviral shRNA library targeting 16,000 genes in a pooled format. We identified several classes of gene “dropouts,” including general essential genes, which are required for survival or growth in more than 70% of all cell lines, irrespective of subtype. Using a newly developed scoring algorithm that allows for precise measurement of statistical significance between two groups, we also identified several “subtype-specific” genes, whose essentiality is restricted to a defined subtype. These include well-known HER2 subtype-specific genes, ERBB2, ERBB3, and TFAP2C and luminal subtype-specific gene FOXA1, SPDEF, GATA3, ESR1, and CCDN1 as well as newly identified BRD4 and CHD4. These two genes were further validated as luminal-specific and results to explain their luminal subtype-specificity will be presented. In addition, the unprecedented number of lines allows the identification of synthetic lethal interaction with common breast cancer somatic mutation such as PIK3CA and PTEN. Finally, integration of gene expression, copy number variation, and functional screening results identified potential biomarkers with common genetic changes and functional drivers. Overall, our study represents an extensive functional genetic survey of four major breast cancer subtypes, reveals complexities between genomic and functional genomic results, and uncovers several unexpected gene dependencies and potential novel therapeutic target for each subtype. This abstract is also presented as Poster A22. Citation Format: Richard Marcotte, Azin Sayad, Maliha Haider, Kevin Brown, Troy Ketela, Jason Moffat, Benjamin G. Neel. Functional characterization of breast cancer using pooled lentivirus shRNA screens. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Synthetic Lethal Approaches to Cancer Vulnerabilities; May 17-20, 2013; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(5 Suppl):Abstract nr PR01.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".