Individualizing the Oncological Treatment of Patients With Metastatic Non–Clear Cell Renal Cell Carcinoma by Using Gene Sequencing and Patient-Reported Outcomes: Protocol for the INDIGO Study
Bibliographic record
Abstract
BACKGROUND: No phase 3 studies have yet been conducted for patients with non-clear cell (CC) renal cell carcinoma (RCC) exclusively due to the rare occurrence of the disease and the heterogenicity in tumor morphology. Consequently, there is no evidence of the optimal treatment, and new approaches are needed. One approach is individualizing treatment based on the gene sequencing of tumor tissue. Additionally, recent studies involving the patient-reported outcomes (PROs) of patients treated for metastatic cancer have shown significant benefits for quality of life, median overall survival, and overall survival. The use of gene sequencing and PROs can be of great importance to patients with rare cancer types, including patients with non-CC RCC, and should be investigated in clinical trials, especially for cases where evidence based on phase 3 studies is difficult to obtain. OBJECTIVE: We describe the INDIGO study, in which patients, based on gene analyses, will be allocated into 4 treatment arms containing 14 treatments and use electronic PROs. We aim to improve the treatment of patients with non-CC RCC. The end points in the study will be the overall response rate (complete and partial) in the total patient population, which will be based on the RECIST (Response Evaluation Criteria in Solid Tumors) version 1.1 criteria, and the time to treatment failure. METHODS: INDIGO is a prospective phase 2 trial, and 30 patients will be enrolled. The patients will receive systemic treatment based on genetic analyses of their tumor tissue. All patients will receive electronic questionnaires in a dedicated app-a questionnaire regarding symptoms and side effects and another regarding health-related quality of life. Depending on the treatment regimen, the patients will be seen by a medical doctor every third, fourth, or sixth week, and the effect of the systemic treatment will be evaluated every 6 weeks via a computed tomography scan. The study has been approved by the Danish Medicines Agency and the National Committee on Health Research Ethics (approval number: H-19041833), complies with good clinical practice guidelines, follows the General Data Protection Regulation, and is registered at the Capital Region of Denmark. RESULTS: Recruitment started in March 2020, and at the time of submitting this paper (June 2022), a total of 9 patients have been enrolled. CONCLUSIONS: We aim to explore methods for improving the treatment outcomes of patients with non-CC RCC, and the INDIGO study will contribute further data on personalized medicine for rare types of RCC and provide new knowledge on the active use of electronic PROs. TRIAL REGISTRATION: ClinicalTrials.gov NCT04644432, https://clinicaltrials.gov/ct2/show/NCT04644432 ; European Union Drug Regulating Authorities Clinical Trials Database 2019-001316-38, https://tinyurl.com/2p8mb4aw. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/36632.
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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 | 0.032 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".