Abstract 47: Identifying genetic vulnerabilities of chromosome 4p large copy number variants in triple negative breast cancer
Bibliographic record
Abstract
Abstract Triple Negative Breast Cancer (TNBC) is characterized by the absence of common oncogenic drivers, limiting its treatment options; however, it exhibits recurrent large chromosomal deletions. We previously showed that chromosome 4p (chr4p) loss is a frequently observed large copy number variant in TNBC and is associated with poor prognosis. We also showed that chr4p deletion is an early event in tumor evolution and confers on cells a proliferative advantage. Here, we set out to uncover the genetic vulnerabilities associated with chr4p deletion in TNBC to identify novel therapeutic avenues for TNBC and enhance our understanding of the genetic mechanisms that maintain chr4p deletion in the genome. Whole genome sequence analysis of our TNBC Primary Tumor(PT)/Patient-Derived Xenograft (PDX) panel identified samples with copy neutral and deletion status of chr4p. These deletion regions span a large fraction of the chr4p arm. RNAseq analysis revealed that chr4p deletion is functionally significant since gene expression of ~80% of genes was reduced upon chr4p deletion. Chr4p deletion was associated with global transcriptomic changes and differentially expressed genes were enriched for proliferation, DNA replication, cell migration, activation of the innate immune response and protein translation. PDX-derived cell models from these samples showed lentiviral infectivity based on a control lentivirus expressing GFP. Additionally, these PDX cell models were shown to be editable using CRISPR-Cas9 through the targeting of core essential genes. We will use a pooled CRISPR-Cas9 approach to systematically screen for genetic vulnerabilities in TNBC PDX-derived cell models harbouring chr4p copy neutral or deletion state. To further investigate the genetic mechanisms buffering chr4p loss, we have leveraged publicly-available CRISPR-Cas9 genome-wide genetic screen data from the Cancer DepMap to identify putative synthetic lethal (pSL) partners with chr4p deletion in TNBC. This was accomplished by developing regression models and integrating them with results from established methods such as drugZ and MAGeCK. We identified pSL partners for chr4p at gene, segmental and arm levels. pSLs were enriched for mitochondrial, protein translation and proliferation pathways. We will validate the top candidates from these analyses in our cohort of chr4p deletion and chr4p copy neutral TNBC PDX-derived cell models and integrate them with the pooled CRISPR screens. Together, this work aims to reveal potential TNBC-specific therapeutic avenues for precision oncology. Citation Format: Michael Schwartz, Rohan Dandage, Lynn Karam, Alain Pacis, Hellen Kuasne, Anne-Marie Fortier, Sidong Huang, Guillaume Bourque, Traver Hart, Elena Kuzmin, Morag Park. Identifying genetic vulnerabilities of chromosome 4p large copy number variants in triple negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 47.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".