Immune checkpoint blockers in solid organ transplant recipients and cancer: the INNOVATED cohort
Bibliographic record
Abstract
BACKGROUND: Patients with solid organ transplant (SOT) and solid tumors are usually excluded from clinical trials testing immune checkpoint blockers (ICB). As transplant rates are increasing, we aimed to evaluate ICB outcomes in this population, with a special focus on lung cancer. METHODS: We conducted a multicenter retrospective cohort study collecting real data of ICB use in patients with SOT and solid tumors. Clinical data and treatment outcomes were assessed by using retrospective medical chart reviews in every participating center. Study endpoints were: overall response rate (ORR), 6-month progression-free survival (PFS), and grade ≥3 immune-related adverse events. RESULTS: From August 2016 to October 2022, 31 patients with SOT (98% kidney) and solid tumors were identified (36.0% lung cancer, 19.4% melanoma, 13.0% genitourinary cancer, 6.5% gastrointestinal cancer). Programmed death-ligand 1 expression was positive in 29% of tumors. Median age was 61 years, 69% were males, and 71% received ICB as first-line treatment. In the whole cohort the ORR was 45.2%, with a 6-month PFS of 56.8%. In the lung cancer cohort, the ORR was 45.5%, with a 6-month PFS of 32.7%, and median overall survival of 4.6 months. The grade 3 immune-related adverse events rate leading to ICB discontinuation was 12.9%. Allograft rejection rate was 25.8%, and risk of rejection was similar regardless of the type of ICB strategy (monotherapy or combination, 28% versus 33%, P = 1.0) or response to ICB treatment. CONCLUSIONS: ICB could be considered a feasible option for SOT recipients with some advanced solid malignancies and no alternative therapeutic options. Due to the risk of allograft rejection, multidisciplinary teams should be involved before ICB therapy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".