Abstract 5541: Dissecting chemotherapy resistance development in triple negative breast cancer through single cell multiome and lineage tracing
Bibliographic record
Abstract
Abstract Triple negative breast cancer (TNBC) is a breast cancer subtype that manifests fast disease progression and very poor overall survival. Despite initial response to standard of care, development of therapy resistance remains the major challenge in TNBC clinical management. Delineating the mechanisms underlying resistance development has been impeded by the still unknown cell population of origin and lack of longitudinal molecular profiling. To investigate this question, we applied a CRISPR based lineage tracing system into TNBC cell lines. Longitudinal samples from an Antibody Drug Conjugate (ADC), Sacituzumab Govitican sensitive to resistance were collected in vitro via stepwise drug dose escalation and in vivo via xnenograft serial transplantation. We leveraged cellular barcoding with single cell multiome from 10X genomics to simultaneously capture clonal identity, accessible chromatin and gene expression and hence, directly linked lineage with functional cell states. Preliminary results suggest that TNBC cells adopted distinct cell states shortly after drug exposure with characteristics including upregulation of epithelial-to-mesenchymal transition (EMT) genes, stemness markers and downregulation of immune related pathways. Clonal level analysis revealed only a subset of clones survived initial treatment and are enriched in specific “primed” cell states. Next, we will construct single cell lineage trees and quantify clonal dynamics and cell state transition rates. We will identify and validate regulators of cellular plasticity and explore if potential drug combinations will prevent emergence of resistance cell state. Overall, this study will build a comprehensive map of the phenotypic trajectory towards chemotherapy resistance and pinpoints biomarkers of therapy response in triple negative breast cancer. Citation Format: Chufan Zhang, Chu Pan, Dave Cescon, Mathieu Lupien. Dissecting chemotherapy resistance development in triple negative breast cancer through single cell multiome and lineage tracing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5541.
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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 | 0.000 | 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".