Abstract 5489: Utilization of cancer cell line screening and bioinformatic analyses to identify optimal developmental pathways for the novel anticancer agent BOLD-100
Bibliographic record
Abstract
Abstract Cell line screening of unique compounds can provide mechanistic insights and identify optimal drug combination partners. Bioinformatics analysis of cell screen data and correlation to publicly available datasets can support identification of appropriate patient populations for subsequent preclinical and clinical development. BOLD-100 is a first-in-class ruthenium-based anticancer agent currently being tested in a Phase 1b/2a clinical trial in combination with FOLFOX in the treatment of advanced gastrointestinal cancers. BOLD-100 works by altering the unfolded protein response through selective GRP78 inhibition; and inducing reactive oxygen species which causes DNA damage and cell cycle arrest. Collectively, these result in cell death in a range of different cancer types, and in combination with many different classes of anticancer agents. To determine optimal indications for BOLD-100 development, BOLD-100 was tested against 316 cancer cell lines in 72-hour Cell Titer Glo assays with downstream bioinformatic analysis and validation experiments. Multiple cancer types showed preferential response to BOLD-100, including bladder, esophageal, pancreatic, multiple myeloma and ovarian cancers. Subtype analysis identified potential populations of increased responsiveness, including in bladder cancer where BOLD-100 had increased response in luminal and mixed subtypes, as compared to basal subtypes. Utilizing bladder cancer as a case study, subsequent combination testing of BOLD-100 in combination with fluorouracil or cisplatin demonstrated that BOLD-100 enhanced cell death across different bladder cancer cell lines through synergistic interactions with these standard-of-care agents. The pan-cancer response profile of BOLD-100 was compared against 449 other anticancer drug responses that are part of the GDSC database. BOLD-100 displayed limited correlation with existing drugs, suggesting a unique mechanism of action and clinical utility where standard-of-care agents have limited efficacy. Pharmacogenomic analysis of the cell screen data indicated potential pathways and genes of relevance to BOLD-100 response, including increased response in KRAS-mutant cancers. Collectively, BOLD-100 showed a unique sensitivity profile across a panel of over 300 cancer cell lines, identifying multiple potential indications for future development. Subsequent investigations into several cancer types of interest and drug combinations are ongoing. Citation Format: Paromita Raha, Brian Park, Adam Carie, Jim Pankovich, Mark Bazett. Utilization of cancer cell line screening and bioinformatic analyses to identify optimal developmental pathways for the novel anticancer agent BOLD-100 [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 5489.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".