Canadian (CAN) real-world outcomes for relapsed/recurrent (R/R) metastatic triple-negative breast cancer (mTNBC) in the first-line or later (1L+) setting by early or late recurrence status.
Bibliographic record
Abstract
e18798 Background: Little epidemiological data characterizing the mTNBC population in Canada exist, limiting the understanding of treatment patterns, attrition rates, and unmet need for CAN patients (pts) with R/R mTNBC. This study examined real-world outcomes and palliative treatment patterns for CAN pts with 1L+ R/R mTNBC, stratified by early vs late recurrence. Methods: This retrospective study used real-world, population-based data from the Oncology Outcomes database in Alberta to identify pts diagnosed with early stage (I to III) TNBC between 2010-2019 who progressed to mTNBC. Algorithms reliably identified recurrent pts as those who received 2+ cycles of systemic therapy > 240 days from the initiation of adjuvant treatment or surgery. Clinical outcomes from 1L+ treatment of mTNBC, including time to next treatment (TTNT) or death and overall survival (OS), were ascertained. Diagnosis, demographics, treatment patterns, and attrition rates were stratified by treatment-free interval (TFI), early (8-12 mo TFI) or late ( > 12 mo TFI). Results: Data from 181 CAN pts with R/R mTNBC (recurred early, n = 59; recurred late, n = 122) were analyzed. Baseline characteristics were generally comparable between groups. Pts who recurred late were on average older at recurrence (59 y vs 55 y, P= 0.03) and less likely to be stage III (21% vs 51%, P< 0.001) at initial diagnosis. The median time to recurrence was 17 mo. The most common 1L therapies were capecitabine monotherapy (35%), other chemotherapy combinations (16%), and taxane monotherapy (12%), with a median duration of therapy (DOT) of 4.9 mo, 3.5 mo, and 4.5 mo, respectively. Rates of attrition between lines of therapy (LOT) were relatively high, with 96 pts (53%) initiating 2L therapy (among the 85 pts who did not start 2L treatment, 63 had died). Median DOT was 3.6 mo for 1L, 3.0 mo for 2L, and 3.0 mo for 3L. Stratifying by early or late recurrence did not show differences in TTNT (4 mo vs 4 mo; HR, 0.94; 95% CI, 0.64-1.38; P= 0.75) or OS (10 mo vs 11 mo; HR, 0.96; 95% CI, 0.64-1.44; P= 0.84). Exploratory analyses showed improved OS (23 mo vs 10 mo; HR, 0.60; 95% CI, 0.40-0.92; P= 0.02) in pts with TFI ≥24 mo (n = 61) but not in pts with TFI 12-24 mo (11 vs 10 mo; HR: 0.96; 95% CI: 0.64-1.44; P= 0.84) vs pts with TFI of 8-12 mo. Conclusions: This retrospective study showed higher than expected rates of early recurrence and high attrition between LOT in a CAN breast cancer population that progressed to mTNBC. The short DOT across lines was similar and consistent with CAN practice. OS for 1L+ R/R mTNBC were similar for individuals who recurred between 8-12 vs 12-24 mo. These findings confirmed the poor prognosis of pts with R/R mTNBC and the need for improved therapies especially in 1L, where the most common treatments, including capecitabine, may not deliver the most meaningful impact.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".