Afatinib as a novel potential treatment option for <i>NRG1</i> fusion-positive tumors.
Bibliographic record
Abstract
110 Background: Neuregulin 1 (NRG1) is a growth factor that binds HER3/4 and activates ErbB signalling pathways. NRG1 gene fusions function as oncogenic drivers and represent a potential therapeutic target across tumour types. Afatinib, an ErbB-family blocker, is a potential treatment option for some patients with NRG1 fusion-positive ( NRG1+) tumours, as supported by preclinical evidence and clinical case reports. Of 13 patients with NRG1+ lung adenocarcinoma (ADC; n = 9), pancreatic ADC (n = 2), cholangiocarcinoma (n = 1), and ovarian cancer (n = 1) treated with afatinib, eight patients achieved a best response of partial response (PR; median duration 7.3 months, range 3–12), three patients had stable disease (SD) and two patients had progressive disease. Methods: Here, we report the clinico-pathological and molecular characteristics and current status of four additional cases of afatinib-treated NRG1+ tumours. Results: Patient 1, 66 year-old female, never-smoker with metastatic non-mucinous lung ADC. CD74-NRG1 fusion was identified, and 5th-line afatinib initiated. PR is ongoing after 16 months. Patient 2, 43 year-old female, non-smoker with advanced invasive mucinous lung ADC. CD74-NRG1 fusion was identified and 3rd-line afatinib initiated (PR, 18 months); treatment is ongoing following local progression. Patient 3, 69 year-old male, with KRAS-mutated metastatic colorectal cancer. Following a right hemicolectomy and liver/lung metastasectomies, a novel POMK-NRG1 fusion was detected and afatinib initiated (SD, 4 months). Eight months after initiation, afatinib treatment is ongoing, in combination with radiotherapy, with SD. Patient 4, 54 year-old male, with KRAS-wild-type metastatic pancreatic cancer. Following progression on chemotherapy, APP-NRG1 fusion was detected as part of the Personalized Oncogenomics study (NCT02155621), and afatinib initiated; PR is ongoing after 7 months. Conclusions: These findings add to a growing body of evidence that afatinib is a potential treatment option for patients with NRG1+ tumours. Prospective study is ongoing/planned in the Drug Rediscovery Protocol trial (DRUP; NCT02925234) and Targeted Agent and Profiling Utilization Registry study (TAPUR; NCT02693535).
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".