Abstract P1-03-11: In vivo rapid discovery of tumor suppressors in breast cancer
Bibliographic record
Abstract
Abstract In breast cancer, genome sequencing of patient have identified hundreds of genetic mutations associated with cancer, but which of these mutations drive tumor formation and which are simply bystanders is largely unknown. This is especially true for mutations which individually occur in a small percentage of patients, but when considered together these less common mutations make a significant proportion of the mutational landscape of breast cancer. To functionally test these genes, we performed a CRISPR screen in vivo to identify which of these genes function as tumor suppressors in a mouse model of breast cancer. To do this, we created a library of sgRNAs targeting 215 putative tumor suppressor genes that are found to be mutated in human patients but had not been previously validated as tumor suppressors. We delivered this library via lentivirus to the adult mammary glands of tumor susceptible mice so each lentiviral infected cell has a knockout of one of the genes in the library as well as an oncogenic PIK3CA mutation. Importantly, each infected cell is surrounded by unmodified normal cells, mimicking the initiation of human breast tumors. As expected, mice injected with the putative tumor suppressor library formed tumor significantly faster and reached endpoint sooner than control library injected mice. Tumors from tumor suppressor library injected mice were deep sequenced which revealed known tumor suppressors such as APC and NF1, as well as an enrichment in multiple epigenetic regulators previously unidentified as tumor suppressors in breast cancer. Further validation using independent individual sgRNAs targeting each of the 4 most commonly targeted epigenetic regulators confirmed their role as tumor suppressors. To test the role of these genes in a human context, we used CRISPR to knockout these epigenetic regulators in normal human mammary epithelial MCF10A cells carrying the same PIK3CA mutation. MCF10A lines with epigenetic regulator knockout formed abnormal and protrusive spheres when seeded in matrigel and were capable of forming tumors when engrafted into immunocompromised mice unlike the parent MCF10A line, indicative of transformation into cancerous cells. To identify the mechanism of action of these epigenetic regulators as tumor suppressors, RNA-seq and ATAC-seq were performed on tumors with knockout of epigenetic genes. When compared to control tumors without epigenetic knockout, pathway analysis revealed a role for these epigenetic regulators in EMT, differentiation and cell metabolism. Analysis of ATAC-seq data is currently underway to identify enhancers associated with these transcriptional changes. Additionally, primary cell lines were isolated from knockout tumors and used to perform a drug screen to find drug sensitivities specific to these regulators. This screen identified several compound classes, including CBP/p300 inhibitors which are more effective at killing epigenetic knockout tumor cell lines than control tumor cell lines or normal mouse mammary epithelial (NMUMG) cells. Overall, these data are the first to identify several epigenetic regulators as tumor suppressors in breast cancer and to test them in a parallel and syngeneic background to establish their mechanism and drug susceptibility. By studying these genes, we seek to not only find treatments that target tumors in which these genes are mutated but also to understand the greater role of epigenetic regulators in tumor suppression. Citation Format: Ellen R Langille, Sampath Loganathan, Daniel Schramek. In vivo rapid discovery of tumor suppressors in breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-03-11.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".