ctDNA-based mutational landscape following anti-EGFR antibodies in metastatic colorectal cancer (mCRC) to uncover novel resistance mechanisms in the CCTG CO.26 trial.
Bibliographic record
Abstract
117 Background: Previous studies have identified MAPK and PIK3CA/AKT/mTOR pathways as common mechanisms of acquired resistance to anti-EGFR antibodies (EGFRab) in mCRC. However, such alterations do not account for all patients that become treatment resistant. Using paired whole-exome (WES; tissue) and circulating tumor DNA (ctDNA; plasma) sequencing, we performed characterization of the EGFRab resistance landscape in patients with mCRC. Methods: Post-treatment (ctDNA; plasma) sequencing was performed for 169 patients with mCRC, with 112 (66%) patients also receiving pre-treatment (WES; primary tumor) sequencing as part of the CO.26 trial. 66 (39%) patients received EGFRab previously at a median of 132.0 days prior to plasma sampling. Using bespoke bioinformatics pipelines (WES) coupled with the GuardantOMNI assay (plasma), we compared somatic mutation (SNV/indel, CNV and fusion) frequencies between pre- and post-EGFRab timepoints, and contrasted results between the two groups. Results: Significant increases in de novo acquisition of EGFR (p = 5.6e-4), KRAS (p = 0.011), ZNF217 (p = 0.0022), MAP2K1 (p = 0.0078) and LRP1B (p = 0.017) SNV/indels were unique to the EGFRab group and often occurred as multiple, low allele frequency events in the same patient. De novo copy number amplification of known resistance genes EGFR/ BRAF/ MET were observed in EGFRab-treated patients (p < 0.05), along with SMO (p = 6.8e-7), PTEN inhibitory gene PREX2 (p = 5.6e-4), FLT3 (p = 2.0e-5), NOTCH4 (p = 6.3e-5), ERBB2 (7.4e-4), KMT2A (p = 3.7e-4) and ARID1B (p = 0.0014). Genes impacted by fusion events in EGFRab-treated patients included BRAF-KIAA1549 (1 patient) and MET-CAV1 (1 patient), and these events were not detected in matched pre-treatment samples. EGFRab-treated patients were found to acquire a combination of multiple (≥5) mutation events (SNV/indel, CNV or fusion) at much higher frequency compared to non-EGFRab-treated patients (67% versus 25% of patients, p = 8.7e-8). Tumor mutation burden (TMB) was not significantly different (p = 0.71) between treatment groups prior to therapy initiation, while post-treatment TMB was significantly higher (p = 1.8e-7) in EGFRab-treated patients (median 25.4 versus 13.1 mut/mb). Conclusions: In addition to previously established resistance pathways, we identified acquired alterations in additional genes such as SMO, PREX2 and epigenetic modifiers KMT2A/ARID1B in EGFRab-treated patients . Moreover, we highlight the phenomenon by which EGFRab-treated tumors acquire multiple concurrent resistance mutations and heightened TMB. Our analysis provides novel insight into the landscape of resistance mechanisms to EGFRab in mCRC while highlighting the potential role for immunotherapy post-EGFRab. Clinical trial information: NCT02870920.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".