Whole-genome and transcriptome analysis enhances precision cancer treatment options
Bibliographic record
Abstract
BACKGROUND: Recent advances are enabling delivery of precision genomic medicine to cancer clinics. While the majority of approaches profile panels of selected genes or hotspot regions, comprehensive data provided by whole-genome and transcriptome sequencing and analysis (WGTA) present an opportunity to align a much larger proportion of patients to therapies. PATIENTS AND METHODS: Samples from 570 patients with advanced or metastatic cancer of diverse types enrolled in the Personalized OncoGenomics (POG) program underwent WGTA. DNA-based data, including mutations, copy number and mutation signatures, were combined with RNA-based data, including gene expression and fusions, to generate comprehensive WGTA profiles. A multidisciplinary molecular tumour board used WGTA profiles to identify and prioritize clinically actionable alterations and inform therapy. Patient responses to WGTA-informed therapies were collected. RESULTS: Clinically actionable targets were identified for 83% of patients, of which 37% of patients received WGTA-informed treatments. RNA expression data were particularly informative, contributing to 67% of WGTA-informed treatments; 25% of treatments were informed by RNA expression alone. Of a total 248 WGTA-informed treatments, 46% resulted in clinical benefit. RNA expression data were comparable to DNA-based mutation and copy number data in aligning to clinically beneficial treatments. Genome signatures also guided therapeutics including platinum, poly-ADP ribose polymerase inhibitors and immunotherapies. Patients accessed WGTA-informed treatments through clinical trials (19%), off-label use (35%) and as standard therapies (46%) including those which would not otherwise have been the next choice of therapy, demonstrating the utility of genomic information to direct use of chemotherapies as well as targeted therapies. CONCLUSIONS: Integrating RNA expression and genome data illuminated treatment options that resulted in 46% of treated patients experiencing positive clinical benefit, supporting the use of comprehensive WGTA profiling in clinical cancer care.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".