52. A ClinGen Somatic curation effort focused on EGFR variants
Bibliographic record
Abstract
As next generation sequencing becomes a routine part of clinical diagnostic and follow up workup for tumor assessment, consensus on cancer variant interpretation and expanded knowledgebase curation is needed. EGFR (Epidermal Growth Factor Receptor) is a well recognized oncogene and EGFR SNVs, CNVs, indels, and fusions have important predictive, diagnostic, and prognostic roles in a variety of cancer types. A definitive collection of tumor-specific EGFR somatic variants and their responses to FDA-approved EGFR inhibitors has not yet been assembled and EGFR -specific guidelines for defining variant oncogenicity have not been proposed. Due to their growing clinical relevance, the ClinGen Somatic Clinical Domain Working Group Solid Tumor Taskforce (STTf) performed a pilot curation effort on 15 EGFR fusions, and a number of curation challenges were noted. For instance, EGFR fusions can be primary events in cancer or part of complex molecular alterations (e.g., involving amplification). The group will compile a list of EGFR fusions and collect data on characteristics such as genomic breakpoints, tumor-type associations, functional evidence, and sensitivity to inhibitors. We are forming an EGFR Somatic Cancer Variant Curation Expert Panel ( EGFR SC-VCEP) to develop oncogenicity classification recommendations specific to EGFR fusions with future expansion to other EGFR sequence changes. The Step 1 ClinGen SC-VCEP application is in-progress for this effort. The results of this expert-led curation and the resulting guidelines will be publicly available through multiple avenues including the CIViC knowledgebase and ClinVar.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.044 | 0.047 |
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".