Abstract 5084: Functional genomic classification of breast cancer using pooled lentivirus shRNA screens
Bibliographic record
Abstract
Abstract Targeted therapies for most breast cancers are lacking because our knowledge of essential genes driving tumor proliferation is still primitive for each subtype. Although ongoing intensive efforts to genetically characterize large numbers of breast tumors are providing a plethora of new data on genomic abnormalities (e.g., amplifications, deletions, somatic mutations), it can be difficult to access which of these actively drive pathogenesis. Even if an oncogene or tumor suppressor is identified, these often are not amenable to targeted therapy (eg. KRAS, c-MYC, p53, etc). 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 objective of this project was to identify subtype-specific targets for human breast cancer using a genome-wide shRNA screen, as well as to compare “functional genomic” and genomic classification schemes. We screened a panel of 53 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. Most of these were enriched in housekeeping functions, although several genes involved in cellular signal transduction also were identified, including mTORC1, RAPTOR, TGFBR2, DDR1 and DDR2. In addition, we observed several “subtype-specific” genes, whose essentiality is restricted to a defined subtype. Furthermore, unsupervised and supervised clustering of our functional screening results identified potential “drivers” unique to each subtype. These include amplified/overexpressed genes such as ERBB2, ERBB3, FOXA1, SPDEF, TFAP2C, and CCDN1 known to be specific to the luminal and HER2 subtypes, respectively. Finally, integration of gene expression, copy number variation, and functional screening results identified potential synthetic lethal interactions with common genetic changes. Our study represents an extensive functional genetic survey of three 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. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5084. doi:1538-7445.AM2012-5084
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".