Whole genome and transcriptome sequencing of lung cancer: Options for personalized cancer treatment.
Bibliographic record
Abstract
e20567 Background: Targeted therapy against driver mutations has revolutionized lung cancer management. The Personalized OncoGenomics (POG) program uses whole genome and transcriptome derived information to build pathways and identify potential therapeutic targets. We examined the lung adenocarcinoma (LUAD) patients enrolled in POG in order to identify novel cancer drivers and correlate the findings with clinical characteristics. Methods: Patients with advanced LUAD and survival > 6 months were eligible. Blood, archival and fresh tumour specimens were subjected to comprehensive DNA and RNA sequencing. SNV data were compared to the TCGA-LUAD cohort using the cBioPortal platform. Whole tumor transcriptome data were compared to matched normal blood specimens. Clinical characteristics were collected by chart review. Results: 30 POG LUAD cases were analyzed. Baseline characteristics; 47% female, median age 60, 57% never/light smokers, biopsy site - 50% lung, 50% metastatic lesion. High mutations rates in TP53, KRAS, NF1 were comparable to the TCGA-LUAD cohort. Four genes ( GOLGA6L2, FAM186A, ARMCX4 and RBMXL3), were mutated 17-27% of the time in POG patients, while the rate in TCGA-LUAD was < 1%. Driver mutations ( KRAS and EGFR) and known fusions (ROS1 and RET) were present in 63%.Other potential drivers included ERBB3, ERBB2, SDC:NRG1 fusion were identified. Copy number alterations and expression data revealed variations in cell cycle, mTOR, androgen receptor,HSP90, MET and Wee1 proteins, all potential targets for therapy. PD-L1 over-expression and a strong smoking signature were not mutually exclusive to EGFR copy gain and FGFR3 overexpression. Conclusions: The molecular signature of NSCLC is complex and involves multiple key oncogenic drivers. Whole genome sequencing and transcriptome data should be used together to map out the pathways of carcinogenesis and reliably identify targets for therapy.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".