Abstract 4819: Gene-expression profiles predict sorafenib efficacy in wild-type EGFR non-small cell lung cancer (NSCLC)
Bibliographic record
Abstract
Abstract Background: Results from our Biomarkers-Integrated Approaches of Targeted Therapy for Lung Cancer Elimination (BATTLE) program suggest that patients with chemorefractory wild-type (wt) EGFR NSCLC including those with mutant KRAS may benefit from sorafenib. Using 3 different approaches, we tested the hypothesis that gene expression profiles from wild-type (wt) EGFR tumors may predict sorafenib efficacy by capturing effects on multiple targets. Material and Methods: Baseline tumor biopsies from 37 BATTLE patients (pts) with EGFR wt tumors and treated with sorafenib were profiled (Affymetrix Human Gene 1.ST), as well as 68 EGFR wt NSCLC cell lines with available IC50 to sorafenib (Illumina HumanWG-6 v3.0 expression beadchip). (i) We first developed an In vitro Sorafenib Signature (ISS). Correlation of IC50 with each individual probe expression level was computed. Most significant probes were summarized by the first principal component (PC), and correlated with IC50 of sorafenib. To validate the signature, the first PC was computed in BATTLE samples, and progression-free survival (PFS) of pts with high- vs. low-sensitivity signature was compared based on the median of the first PC. (ii) Alternatively, we developed a Clinical Sorafenib Signature (CSS) using BATTLE samples. We compared 23 (62%) pts who achieved 8-week disease control with 14 (38%) who did not (t-test). Most significant probesets were summarized by the first PC and PFS of pts with a high- vs. low-sensitivity signature were compared. To validate the signature, the first PC was computed in cell lines and correlated with IC50 of sorafenib. (iii) Finally, we tested a previously reported KRAS mutation gene expression signature derived by comparing genes differentially expressed in mutant vs. wt KRAS early stage resected lung adenocarcinomas, in 124 BATTLE samples including 24 mutant KRAS. Results: (i) The ISS included 50 probes. The first PC was correlated with the IC50 of sorafenib (rho = –0.71, P < 0.0001). The ISS was then tested in BATTLE and PFS was significantly different in pts with the high- (median PFS 3.61 months) vs. the low-sensitivity signature (median PFS 1.84 months, log-rank P = 0.0263). (ii) The CSS developed in BATTLE included 80 probesets summarized using the first PC. PFS was significantly different in pts with the high- vs. the low-sensitivity signature (log-rank P < 0.0001). The CSS was then tested in cell lines and the first PC was signicantly correlated with IC50 of sorafenib (rho = 0.24, P = 0.0483). (iii) Finally, the KRAS signature was significantly associated with KRAS mutation, but no association was observed with outcome in pts treated with sorafenib in BATTLE. Conclusion: We report 2 gene expression signatures, ISS and CSS, that predicted benefit from sorafenib in patients with chemorefractory NSCLC and in vitro sensitivity to sorafenib respectively. Further validation is planned in our ongoing BATTLE-2 program. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4819. doi:1538-7445.AM2012-4819
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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) | 0.002 | 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".