Inactivation of <i>NF1</i> Promotes Resistance to EGFR Inhibition in <i>KRAS/NRAS/BRAFV600</i> -Wild-Type Colorectal Cancer
Bibliographic record
Abstract
Abstract Through the use of an unbiased, genome-scale CRISPR modifier screen, we identified NF1 suppression as a mechanism of resistance to EGFR inhibition in NRAS/KRAS/BRAFV600-wild-type colorectal cancer cells. Reduced NF1 expression permitted sustained signaling through the MAPK pathway to promote cell proliferation in the presence of EGFR inhibition. Targeting of MEK in combination with EGFR inhibition leads to synergistic antiproliferative activity. Human KRAS/NRAS/BRAFV600-wild-type colorectal cancer cell lines with NF1 mutations displayed reduced NF1 mRNA or protein expression and were resistant to EGFR blockade by gefitinib or cetuximab. Cooccurring loss-of-function mutations in PTEN were associated with resistance to dual EGFR/MEK inhibition but cotreatment with a PI3K inhibitor further suppressed proliferation. Loss of NF1 may be a useful biomarker to identify patients that are less likely to benefit from single-agent anti-EGFR therapy in colorectal cancer and may direct potential combination strategies. Implications: This study suggests that further clinical validation of NF1 status as predictor of response to anti-EGFR targeting antibodies in patients with colorectal cancer with KRAS/NRAS/BRAFV600-wild-type tumors is warranted.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".