Abstract B21: Investigating targeted driver mutations and PD-L1 expression for improved therapy of non-small cell lung cancer
Bibliographic record
Abstract
Abstract Most lung cancer patients are diagnosed at an advanced stage, limiting their treatment options to chemotherapies that have very low response rate. New therapies that target driver gene mutations (e.g., EGFR, ALK, ROS1, BRAF) are being used to treat patients who have tumors with these mutations. In addition, a type of immunotherapy called immune checkpoint inhibitor is being used to treat lung cancer patients. For instance, patients with tumors that express PD-L1 are responsive to anti-PD-1/PD-L1 therapy. Thus, being able to identify the presence of driver mutations and PD-L1 will help patients to benefit from different therapies. A total of 844 cases of non-small cell lung cancer samples have been profiled for the presence of EGFR, KRAS, BRAF, PIK3CA, and HER2 mutations by SNaPshot/sizing genotyping. Immunohistochemistry (IHC) was used to identify the protein expression of ALK and PD-L1. Histologic examination was performed to determine the pathologic type, grade, and lymphatic/vascular invasion. Statistical analysis revealed a number of correlations between the presence of the mutations, PD-L1 expression and the patient pathologic data. Specifically, it was determined that women had lung tumors with a significantly greater number of EGFR mutations than men (p value = 0.001). Examining the presence of ALK, EGFR, KRAS, BRAF, PIK3CA, and HER2 mutations against the presence of pleural invasions yielded a p-value of 0.071, which implies evidence that different mutations are not associated with pleural invasion. However, EGFR mutations were associated with the absence of vascular and lymphatic invasions in lung cancer patients (p value = 0.001, 0.002 respectively). In addition, while the expression of PD-L1 does not associate with the patients who express KRAS mutation, it is associated with lung cancer patients who express EGFR mutation (p value = 0.002). Knowing the mutational and PD-L1 status in lung cancer patients will help patients benefit from targeted therapies and/or checkpoint inhibitors. Citation Format: Akram Alwithenani, Marika Forsythe, Mathieu Castonguay, Wenda Greer, Gorden Flowerdew, Drew Bethune, Harry Henteleff, Madelaine Plourde, Aneil Mujoomdar, Daniel French, Micheal Johnston, Paola Marcato, Zhaolin Xu. Investigating targeted driver mutations and PD-L1 expression for improved therapy of non-small cell lung cancer [abstract]. In: Proceedings of the Fifth AACR-IASLC International Joint Conference: Lung Cancer Translational Science from the Bench to the Clinic; Jan 8-11, 2018; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(17_Suppl):Abstract nr B21.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".