Abstract 7101: Remodeling of cancer cells by chemotherapy
Bibliographic record
Abstract
Abstract Cisplatin is a commonly administrated chemotherapy drug for cancer treatment. Although the direct mechanism of cisplatin is largely well-defined, it is challenging to comprehensively assess its systemic effects. Cellular responses to perturbations are intricate processes involving modifications to protein complexes across diverse cellular compartments. Therefore, substantial research endeavors have been directed towards unraveling protein organization in cells. We recently built a multiscale integrated cell map, MuSIC, that combines fluorescence-stained images and interactions derived from affinity purification mass spectrometry to allow comprehensive characterization of cellular structure and functional networks of 5,254 proteins of the human bone osteosarcoma epithelial line (U2-OS). To exploit the biology of U2OS, we integrated dynamics into this map to determine the proteome reorganization upon cisplatin exposure. Thus, we developed a data-driven approach integrating high throughput size exclusion chromatography mass spectrometry (SEC-MS) data with large-scale cell maps built from static-state information to systematically map at all scales the remodeling of cell architecture in drug-treated cells. Here, we acquired drug-perturbed protein-protein interactions (PPIs) from the SEC-MS data by treating U2-OS with cisplatin. Integrating the differential interactions identified from SEC-MS PPIs with the scaffold large-scale hierarchy (MuSIC) allows retention of the upper cellular organization to systematically map drug impact on protein complexes in the cell. This pipeline serves as an explorative method to identify the drug-responsive organelles, alterations of hierarchical structures, and translocation of proteins. Apart from the well known alterations of DNA damage repair complexes, we also recapitulate remodeling in metabolism pathways, splicing, cAMP-dependent kinase activity, and histone modification. We identify 26 novel protein assemblies containing members that are previously associated with cisplatin biology. This pipeline exploits the potential of a well-established network by integrating the high throughput data modality, allowing for the screening of a range of chemical agents and stimuli to generate differential networks. Citation Format: Gege Qian, Leah Schaffer, Kyung-Mee Moon, Leonard Foster, Trey Ideker, Mengzhou Hu, Sahar Alkhairy, Emma Lundberg. Remodeling of cancer cells by chemotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7101.
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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 | 0.001 |
| 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".