Response to ERBB3-Directed Targeted Therapy in <i>NRG1</i> -Rearranged Cancers
Bibliographic record
Abstract
Abstract NRG1 rearrangements are oncogenic drivers that are enriched in invasive mucinous adenocarcinomas (IMA) of the lung. The oncoprotein binds ERBB3–ERBB2 heterodimers and activates downstream signaling, supporting a therapeutic paradigm of ERBB3/ERBB2 inhibition. As proof of concept, a durable response was achieved with anti-ERBB3 mAb therapy (GSK2849330) in an exceptional responder with an NRG1-rearranged IMA on a phase I trial (NCT01966445). In contrast, response was not achieved with anti-ERBB2 therapy (afatinib) in four patients with NRG1-rearranged IMA (including the index patient post-GSK2849330). Although in vitro data supported the use of either ERBB3 or ERBB2 inhibition, these clinical results were consistent with more profound antitumor activity and downstream signaling inhibition with anti-ERBB3 versus anti-ERBB2 therapy in an NRG1-rearranged patient-derived xenograft model. Analysis of 8,984 and 17,485 tumors in The Cancer Genome Atlas and MSK-IMPACT datasets, respectively, identified NRG1 rearrangements with novel fusion partners in multiple histologies, including breast, head and neck, renal, lung, ovarian, pancreatic, prostate, and uterine cancers. Significance: This series highlights the utility of ERBB3 inhibition as a novel treatment paradigm for NRG1-rearranged cancers. In addition, it provides preliminary evidence that ERBB3 inhibition may be more optimal than ERBB2 inhibition. The identification of NRG1 rearrangements across various solid tumors supports a basket trial approach to drug development. Cancer Discov; 8(6); 686–95. ©2018 AACR. See related commentary by Wilson and Politi, p. 676. This article is highlighted in the In This Issue feature, p. 663
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".