Abstract B24: De novo and acquired resistance to first-line standard therapy in colorectal cancer: from cell lines to metastatic tumors
Bibliographic record
Abstract
Abstract Introduction: Personalized medicine (PM) is a concept that has raised high expectations amongst scientists, clinicians, and patients. An emerging approach is to examine tumor biopsy material for genomic changes that are known targets of currently available therapeutic agents, with the assumption that a clinical benefit will be observed if the target is inhibited. While striking anecdotal reports are predictable from this approach, the clinical impact of these agents is limited by the inevitable development of therapeutic resistance. Our focus is on the design of parallel research programs using both in vitro and in vivo strategies, in an effort to delay or inhibit resistance. We present here preliminary data for a signature of resistance to standard first-line treatment - fluorouracil, folinic acid, oxaliplatin and bevacizumab (FOLFOX/B) using cell line models of resistance to this regimen. In parallel, we are conducting a prospective study to identify biomarkers of clinical resistance to first-line therapy in patients with metastatic colorectal cancer (CRC) (NCT00984048). Methods: Ten established CRC cell lines were treated with FOLFOX/B and categorized as resistant or sensitive based on IC50 values. In parallel, patients who consented to an initial biopsy and one at disease progression following an initial response were identified as intrinsically resistant or as having acquired resistance during treatment. CRC cell lines that were initially sensitive were rendered resistant to mimic the acquired resistance in patients, by serial passages with gradual increases in concentration of the combination regimen. We compared microarray data from three sensitive and three resistant cell lines. Results: We found a different expression pattern from microarray data comparing sensitive and resistant cell lines, thereby indicating a potential signature of resistance to FOLFOX/B. Interestingly, we found that the Src family kinase Lyn was overexpressed in resistant cells lines. Treating cells with non-cytotoxic concentration of dasatinib, a dual Src family kinase and Abl inhibitor, sensitized both the parental sensitive cells and the cells with acquired resistance to FOLFOX/B, thereby suggesting that combination treatment with dasatinib may be effective in delaying or inhibiting resistance. We have thus far collected needle core biopsies from liver metastases from forty patients who agreed to partake in this multi-center trial. Eligible patients have confirmed metastatic CRC, measurable disease, and consent to three needle-core biopsies (NCBs) of a non-resectable liver metastasis before treatment and at resistance, as well as serial blood collection throughout the study. Using standard operating procedures developed for this trial, we were able to both preserve morphology and obtain high-quality genomic material from biopsy tissue. We will determine if the resistance signature and overexpression of Lyn observed in the resistant CRC cell lines are similarly demonstrated in patients that were intrinsically resistant to FOLFOX/B. Conclusions: We have designed parallel in vitro and in vivo experiments to study resistance to standard first-line treatment for mCRC. These studies provide insight on metastatic signatures of resistance and suggest combination therapies to delay or inhibit therapeutic resistance in patients.
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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) | 0.001 | 0.000 |
| 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 | 0.001 |
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".