Real-world data of TAS-102 therapy in refractory metastatic colorectal cancer (mCRC): The experience of the University Hospital of Montreal (CHUM).
Bibliographic record
Abstract
137 Background: Colorectal cancer poses a substantial healthcare burden due to its elevated rates of diagnosis and mortality. Managing refractory metastatic cases remains a challenge in our practice due to the paucity of therapeutic options. Treatment with Trifluridine-Tipiracil (TAS-102) prolonged overall survival among patients (pts) with mCRC. This study investigates the efficacy of TAS-102 therapy in this patient population and explores various prognostic factors influencing clinical outcomes. Methods: We retrospectively reviewed the data of 53 patients with refractory mCRC treated with at least 1 cycle of TAS-102 at CHUM between March 2018 and January 2023. Multivariate and univariate analyses were employed to evaluate the impact of age, number of prior treatments, presence of KRAS mutation, gender, tumor origin, and Eastern Cooperative Oncology Group (ECOG) performance status on progression-free survival (PFS) and overall survival (OS). Survival analysis was conducted using the Kaplan-Meier method, and Cox regression analysis assessed prognostic factors. Results: The median age was 61 years (23-82), and 27 pts (50.9%) were females. Twenty-two pts (41.5%) had their primary tumor located in the left colon, 17 pts (32.1%) had right-sided tumors, and 14 pts (26.4%) with rectal tumors. 52% of pts received TAS-102 after 2 lines of therapy and 43% beyond third line. The performance status was: ECOG 0/1 in 46 pts (86.8%), and ECOG 2 in 7 pts (13.2%). In our refractory mCRC pts, median PFS was 3 months and median OS 7 months. Univariate analysis demonstrated no significant differences in PFS or OS based on patient sex, KRAS mutation status, tumor sidedness, or the number of prior treatments. In univariate and multivariate analysis ECOG 0/1 pts compared to ECOG 2 pts had a significant improvement in both PFS (HR = 0.33; p = 0.0511) and OS (HR = 0.21; p = 0.0086). Conclusions: In our real-world experience, clinical outcomes were comparable to the findings from the RECOURSE trial, suggesting improved PFS and OS with TAS-102 in heavily pre-treated mCRC patients. ECOG performance status remains a significant prognostic factor and underscores its importance in treatment decisions for this patient population.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".