A phase Ib study of an anti-HER2 inhibitor, lapatinib, in combination with a c-MET and VEGFR inhibitor, foretinib, in HER2-positive metastatic breast cancer (MBC): Results from NCIC CTG IND.198.
Bibliographic record
Abstract
518 Background: The mechanisms of resistance to targeted anti-HER 2 therapy are unclear. Proposed pathways include MET, VEGF and AXL. Multi-targeted pathway inhibition may delay or prevent acquired resistance to HER2 inhibition. Foretinib, an oral multi-kinase inhibitor of MET and VEGFR2, as well as PDGFRB, AXL, FLT3, TIE-2, RET and RON kinases, has pre-clinical anti-tumor activity in breast tumor models. This phase 1b study sought to establish the associated toxicities, pharmacokinetics (PK) and recommended phase II doses (RP2D) of this combination of oral tyrosine kinases inhibitors in a cohort of HER-2 positive MBC patients. Methods: Women with HER2 positive (determined locally) MBC, PS 0-2, and no limit on number of prior chemotherapies or lines of anti-HER2 therapies were enrolled. A 3+3 dose escalation design was utilized. 4 dose levels were planned with starting doses of foretinib 30 mg and lapatinib 750 mg PO OD (dose level 1) on a q 4 weekly cycle. Correlative studies from primary archival tissue are planned. Results: 19 patients were enrolled, all of whom were evaluable for toxicity assessment and 16 were evaluable for response. Median age was 60 years (34-86), 95% were PS 0-1, 53% were ER- and 95% had at least one prior anti-HER2 based regimen. A median of 2 cycles (range: 1-20) was delivered across 4 dose levels. At the 4th dose level (foretinib 45 mg/lapatinib 1250 mg) dose limiting toxicities were documented in 4/7 patients. These included grade 3 fatigue (2 cases); grade 3 ALT elevation; grade 3 diarrhea and grade 2 joint effusion. There was only one grade 4 non-hematological toxicity (grade 4 vomiting: dose level 1) across all dose levels. One patient discontinued treatment due to toxicity with grade 3 limb edema and grade 3 proteinuria. PK of both drugs from DL1-3 did not appear to demonstrate a significant interaction. The RP2D was declared to be foretinib 45 mg and lapatinib 1000 mg PO OD. The full efficacy data and correlative studies will be presented at the meeting. Conclusions: The combination of foretinib and lapatinib can safely be delivered together, though at lower doses than either agent alone.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".