Third-line treatment patterns in HER2-positive metastatic breast cancer: a retrospective analysis of real-world data in Canada
Bibliographic record
Abstract
There is an increasing demand for real-world data pertaining to the usage of cancer treatments, especially in settings where no standard treatment is specifically recommended. This study presents the first real-world analysis of third-line treatment patterns in HER2-positive metastatic breast cancer (mBC) patients in Canada. The purpose was to assess evolution of clinical practice and identify unmet needs in post-second-line therapy. Retrospective data from medical records of 66 patients who received third-line treatment before 31st October 2018, and data from 56 patients who received third-line treatment after this date, extracted from the Personalize My Treatment (PMT) cancer patient registry, were analyzed. In the first cohort, the study revealed heterogeneity in the third-line setting, with trastuzumab, lapatinib, and T-DM1 being the main treatment options. Even though data were collected before the wide availability of tucatinib, neratinib and trastuzumab deruxtecan in Canada, the PMT cohort revealed the emergence of new therapeutic combinations and a shift from lapatinib usage to T-DM1 choice was observed. These findings underscore the evolving nature of third-line treatment strategies in Canada, a facet that is intrinsically tied to the availability of new drugs. The absence of a consensus on post-second-line treatment highlights the pressing need for more efficient therapeutic alternatives beyond the currently available options. This study not only offers valuable insights into the present landscape of third-line treatment in Canada but validates the significance and effectiveness of the PMT registry as a tool for generating pan-Canadian real-world evidence in oncology and its capacity to provide information on evolution of therapeutic practices.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".