Abstract A2-07: Integrative functional genomics of breast cancer
Bibliographic record
Abstract
Abstract Breast cancer is the most common invasive malignancy and second leading cause of cancer death in women. Earlier detection and better therapy have led to >85% 5-year survival. Still, half of affected women will die from breast cancer, reflecting incomplete knowledge of how to target this disease. Large-scale genomic technologies enable the identification of genetic/epigenetic abnormalities, but defining which defects are functionally important remains challenging. In addition, the products of many oncogenes and tumor suppressor genes are not “druggable”. However, such abnormalities can cause unanticipated gene/pathway dependencies (“synthetic lethality”), providing alternate avenues for drug development. Lentiviral-based shRNA libraries enable genome-wide screening of cultured cancer cells in a pooled format, facilitating the identification of genes necessary for cancer cell proliferation and survival in cultured cells. If enough lines are tested and companion genomic data are available, “functional” and “genomic” signatures can be compared. Such information can suggest new drug targets, partnered to specific biomarkers. We screened a panel of > 75 breast cancer cell lines using an 80,000 lentiviral shRNA library targeting 16,000 genes, and integrated the screen results with gene expression, somatic copy-number alteration (SCNA), miRNA expression, somatic mutation and reverse-phase protein array (RPPA) data derived from the same lines. We also developed a new mixed effect regression model (siMEM) that provides increased power for interrogating screen results. The resultant analyses reveal several general features associated with pooled shRNA screens: 1) Increased gene expression, when associated with increased essentiality, specifically enriches for known and novel driver genes. Conversely, high gene expression decreases essentiality for numerous housekeeping genes, likely because of shRNA titration; 2) Increased essentiality with heterozygous copy loss expands the number of reported CYCLOPS and GO genes; 3) Integration of essentiality with recurrent SCNAs identifies novel cis and trans dependencies, suggesting new driver genes and synthetic lethal interactions, respectively. In addition, we identified several classes of gene “dropouts” that are required for survival or growth of most cell lines, irrespective of subtype and several “subtype-specific” genes, whose essentiality is restricted to a defined subtype. These include well-known subtype-specific genes, as well new ones, such as BRD4. We confirmed that BRD4 associates with the estrogen receptor (ER) and acts as an ER co-activator, a function dependent on its BET domain. However, we also found that BRD4 has BET domain-independent functions. Remarkably, the BET domain-dependent functions of BRD4 can be abrogated by PIK3CA mutations, most likely via activation of an estrogen-independent/ER-dependent survival pathway. Consequently, breast cancer lines harboring PIK3CA mutations are resistant to BET domain inhibitors (BET-I), but are sensitive to a combination of BET-I and PI3K pathway inhibitors in vitro and in vivo. Overall, our study represents an extensive functional genetic survey of breast cancer, reveals complexities between genomic and functional genomic results, uncovers unexpected gene dependencies and suggests potential novel therapeutic targets and drug combinations for genetically defined breast cancer subtypes. Citation Format: Richard Marcotte, Azin Sayad, Cathy Iorio, Jason Moffat, Benjamin G. Neel. Integrative functional genomics of breast cancer. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-07.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".