CfDNA NGS detection of fusions acquired as mechanisms of resistance in the post-EGFR setting across multiple solid tumors.
Bibliographic record
Abstract
3053 Background: Oncogenic fusions are rare but have targetable, FDA-approved therapies across all solid tumor types. These fusions have been associated with resistance to EGFR-TKI (EGFRi) in advanced non-small cell lung cancer (NSCLC) and anti-EGFR antibodies in colorectal cancer (CRC), but the prevalence of acquired fusions and their associated clinical outcomes have not been well-studied. We examined fusions in >5,000 patients with advanced NSCLC or CRC who had a history of EGFR-directed therapy in the GuardantINFORM clinical-genomic database. Methods: Fusions in ALK, FGFR1-3, NTRK1-3, ROS1 and RET were examined in a cohort of 2,379 patients with NSCLC and 1,072 with CRC tested with Guardant360 or Guardant360 CDx (Guardant Health, Redwood City, CA) before approved EGFR-targeting therapies, and in 3,531 patients with NSCLC and 2,132 patients with CRC tested after EGFR therapy. Fusion clonality was calculated as the fusion variant allele fraction (VAF):max somatic VAF for each patient, with subclonal fusions defined as having clonality < 0.5. Clinical outcomes were compared between patients with and without fusions in the post-EGFR population, adjusting for age, gender, regimen/line of therapy, and max VAF. Comparisons of fusion prevalence pre- and post-EGFR therapy were performed using Fisher’s exact test. Results: Fusions were detected more frequently in patients with NSCLC and CRC after EGFR therapy relative to pretreatment (NSCLC: 3.2% vs. pre-EGFR 0.5%, p<0.05; CRC: 6.4% vs. pre-EGFR 1.1%, p<0.05). Fusions in RET were most enriched in the post-EGFR NSCLC population (1.0% vs. pre-EGFR 0.1%, OR=11.6, p<0.05), while fusions in the FGFR family were most enriched in the post-EGFR CRC population (3.0% vs. pre-EGFR 0.3%, OR=10.8, p<0.05). The majority of fusions post-EGFR were subclonal in patients with NSCLC (86%) and CRC (96%), consistent with these fusions emerging as mechanisms of resistance. In patients with NSCLC, clinical outcomes (as measured by time-to-next-treatment (TTNT)) were significantly shorter in patients who acquired fusions compared to those without fusions (median 5.9 vs. 9.5 months, log-rank p=0.01, adjusted HR=1.7, adjusted p=0.02). Patients with CRC showed no significant difference in TTNT between those with and without acquired fusions. In patients with NSCLC who acquired fusions, 45% received an EGFRi in the next line and 23% received a TKI targeted to their fusion; TTNT on these therapies was not significantly different (median 5.3 months vs. 6.4 months, respectively). Conclusions: Fusions in patients with NSCLC and CRC were enriched post-EGFR therapy and were associated with inferior clinical outcomes in NSCLC. Recognition of the subclonal prevalence of acquired fusions provides important context as oncologists consider the next line of therapy. This work highlights the potential utility of genomic profiling in patients over the course of treatment.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".