A phase II, multicenter study of encorafenib/binimetinib followed by a rational triple-combination after progression in patients with advanced BRAF V600-mutated melanoma (LOGIC2).
Bibliographic record
Abstract
10022 Background: LOGIC2 evaluates the benefit of a 3rd agent added to encorafenib (enco)/binimetinib (bini), selected at progression based on the genetic tumor evolution. Methods: In part I/run-In, pts were treated with enco/bini until disease progression (as defined per RECIST v1.1). Foundation One NGS was applied on a baseline sample and on a PD sample. Based on the genetic evolution between the biopsy at inclusion (bxI) and at progression (bxPD) and clinical considerations, pts entered part II and received one of four 3rd agent additions to enco/bini combinations: A. LEE011 (CDK4/6 inhibitor), B. BKM120 (PI3K inhibitor), C. INC280 (c-Met inhibitor), or D. BGJ398 (FGFR inhibitor). An adaptive Bayesian logistic regression model (BLRM) guided by the escalation with overdose control (EWOC) principle was used to make dose escalation decisions. Assessments include objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), and safety. Data cutoff for this analysis was May 12, 2019. Data is as is. Part 1 of study is ongoing. Part 2 of study is closed to enrollment. Results: 58 pts enrolled into part II (group A=38; B=6; C=13; D=1). 29 pts were assigned to treatment based on bxPD results (Table). In groups A, B, and C, the confirmed ORR was 5.3%, 0%, and 0%, and the DCR was 26.3%, 16.7%, and 15.4%, with median PFS of 2.1, 1.6, and 2.2 months, respectively. Safety was consistent with known profiles of the individual agents. Conclusions: Triple therapy is feasible when a 3rd agent is added to enco/bini at progression based on genetic alterations, although activity observed was low. Further exploration to identify patterns of resistance susceptible to the addition of a 3rd agent is needed. Gene alterations for enrollment into part 2. Clinical trial information: NCT02159066. [Table: see text]
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.006 | 0.002 |
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".