WINTHER: An international study to select rational therapeutics based on the analysis of matched tumor and normal biopsies in subjects with advanced malignancies.
Bibliographic record
Abstract
TPS11625 Background: Today, personalized cancer medicine implies matching the patient’s tumor genomic characteristics with molecularly and immune targeted agents. Although there are an increasing number of DNA aberrations that can now be matched to a cognate therapy, some patients do not display such druggable oncogene drivers. Methods: WINTHER is an open non-randomized study involving 6 cancer centers in France, Spain, Israel, Canada and USA applying genomic and also transcriptomic assays to guide treatment decisions. The novelty of the WINTHER approach lies in the use of tumor and matched normal tissue biopsies together and an algorithm for predicting efficacy of therapies. The aim is to provide a rational therapeutic choice for all of the patients enrolled in the study whether or not they harbor actionable DNA alterations. The study endpoint is the comparison of the progression-free-survival (PFS) under the WINTHER selected therapy to the PFS of the last therapeutic line. Patients included have refractory metastatic cancer of any histological type, with at least one prior therapeutic regimen and performance status of 0 to 1. Patients who have received a matched treatment based on a molecular anomaly as their immediate prior therapy were excluded. After consent, patients undergo a tumor and histologically-matched normal tissue biopsy. Extracted DNA and RNA of both tumor and normal from frozen tissues at the local center under common standard operating procedures are sent to centralized laboratories for omics investigations. DNA is investigated at Foundation Medicine Inc. and RNA at Gustave Roussy using Agilent technology. For RNA, the WINTHER algorithm is applied on the differential RNA expression data between tumor and normal tissues and establishes the list of drugs with the presumed higher score of efficacy for each patient. Patients with actionable genomic events enter in ARM A, and patients without any druggable anomaly of the DNA enter in ARM B and are treated using the WINTHER algorithm RNA-based treatment decision tool. To date, the trial has recruited 303 patients. Clinical trial information: NCT01856296.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".