Abstract 2625: Personalized chemotherapy treatments for microsatellite instable tumors
Bibliographic record
Abstract
Abstract Introduction: Esophagogastric adenocarcinomas with microsatellite instability (MSI) high status often display chemoresistance, as the current therapies are ineffective. It is hypothesized that the poor response of MSI-high esophagogastric adenocarcinomas to therapies is due to the extensive genomic mutational heterogeneity, which can be overcome by identifying, stratifying, and targeting multiple genetic alterations concurrently. The goals of this project are to: 1) Identify a matched retrospective cohort of 20 MSI-high to 20 microsatellite stable organoids, derived from biobanked tumor tissue. 2) Analyze mismatch repair protein function/loss, to confirm that the primary tumor microsatellite status is recapitulated in 3D organoid models. 3) Test standard of care chemotherapies, and explore alternative approaches to prevent chemoresistance by evaluating targeted agents based on genomic sequencing. Methods: Patient-derived organoids grow for ~14 days, after which they are enzymatically disrupted and plated in Matrigel. I will confirm that the organoid models accurately represent clinal response, by treating them with the same chemotherapy received by the patient. 10 different drug concentrations are added in triplicate using a drug dispenser, then after 72 hours, organoid viability is determined with a plate reader. Results: Genomic characterization of 13/226 MSI-high cases has been conducted with a 234 gene gastric cancer specific panel to identify and target genomic alterations. Preliminary robotic drug screens guided by these mutation profiles have been conducted, yielding promising alternative treatments. Conclusions: By targeting the particular mutations found in MSI-high patients, it could help explain the chemoresistance mechanism, so promising personalized treatment approaches for esophagogastric adenocarcinomas can be clinically implemented. Sources of Funding: This project is funded by the Inez and Willena Beaton Award in Oncology, valued at $7,500 and provided by the RI-MUHC. The operating grants that support the research are $120,000 over 2 years from the Cancer Research Society, and $1,200,000 USD over 3 years given by the Department of Defense. Citation Format: Adam Hoffman, Sanjima Pal, Veena Sangwan, Lorenzo Ferri. Personalized chemotherapy treatments for microsatellite instable tumors [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 2625.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".