Impact of genetic variants involved in the lipid metabolism pathway on progression free survival in patients receiving bevacizumab-based chemotherapy in metastatic colorectal cancer: a retrospective analysis of FIRE-3 and MAVERICC trials
Bibliographic record
Abstract
Background: Antiangiogenic drug (AAD)-triggered oxygen and nutrient depletion through suppression of angiogenesis switches glucose-dependent to lipid-dependent metabolism. Blocking fatty acid oxidation can enhance AAD-mediated anti-tumor effects in colorectal cancer (CRC). Therefore, we hypothesised that genetic variants in the lipid metabolism pathway may predict clinical outcomes [overall response rate (ORR), overall survival (OS) and progression-free survival (PFS)] in metastatic CRC (mCRC) patients receiving bevacizumab-based first-line treatment. Methods: Genomic DNA from blood samples of patients enrolled in FIRE-3 (a global, randomised, open-label, phase 3 trial, between 2007-6-23 and 2012-9-19, discovery cohort: FOLFIRI/bevacizumab arm, n = 107; control cohort: FOLFIRI/cetuximab arm, n = 129) and MAVERICC (a global, randomised, open-label, phase II study, between 2011-8 and 2015-7, in United States, Canada, Estonia, Ireland, Switzerland, Norway, and Portugal. Validation cohort: FOLFIRI/bevacizumab arm, n = 163) trials, was genotyped using the OncoArray-500 K beadchip panel. The impact on OS and PFS of 17 selected SNPs in 7 genes involved in the lipid metabolism pathway (CD36, FABP4, LPCAT1/2, CPT1A, FASN, ACACA) was analysed using Kaplan-Meier curves, the log-rank test for univariate analyses and likelihood ratio tests of Cox proportional hazards regression parameters for multivariable analyses. ORR and SNP associations were evaluated using Chi-square or Fisher's exact tests. Findings: 0.017) on PFS Interpretation: polymorphisms may predict outcome of bevacizumab-based treatment in patients with mCRC. These findings support a possible role of the lipid metabolism pathway in contributing to resistance to anti-VEGF treatment. Funding: This work was supported by the National Cancer Institute [P30CA 014089 to H.-J.L.], Gloria Borges WunderGlo Foundation, Dhont Family Foundation, Victoria and Philip Wilson Research Fund, San Pedro Peninsula Cancer Guild, Ming Hsieh Research Fund, Eddie Mahoney Memorial Research Fund, Shanghai Sailing Program (22YF1407000), China National Postdoctoral Program for Innovative Talents (BX20220084), China Postdoctoral Science Foundation (2022M710768), National Natural Science Foundation of China (82202892).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".