Patient survival with immune checkpoint inhibitors and targeted agents in phase 1 trials: A propensity score weighted analysis.
Bibliographic record
Abstract
2580 Background: There have been important changes in early drug development units with an unprecedented increase of immune-oncology (IO) trials. Currently at the Vall d’Hebron Institute Oncology (VHIO) close to 50% of our Phase 1 trials (Ph1t) portfolio includes IO drugs, while from 2011 to 2015 more than 80% of our trials assessed targeted agents (TA). We wanted to investigate whether this swift had a positive impact on patient (pts) outcome. Methods: We performed a retrospective analysis of the pts treated with IO and TA at VHIO Ph1t Unit from Jun’11 to May’18. Only patients treated with IO in ≥ 2nd line were included (and without an approved IO therapy as per standard-of-care) and those with TA classified as tiers II-III-IV by the ESMO scale for clinical actionability of molecular targets ESCAT (which also represents unapproved indications). The aim of this study was to compare overall survival (OS) for the two cohorts. Given the non-randomized nature of the study a propensity score weighting (PSW) was used to control for selection bias in treatment effect estimation. Results: Out of 545 eligible pts, 281 (51.5%) received TA and 264 (48.5%) IO, with unadjusted median OS (mOS) of 7.7 months (m) and 9.2m, respectively. In univariate analysis, OS was associated with tumor type, number of previous treatment lines, regimen (monotherapy vs combination), and clinical-laboratory prognostic factors (Vioscore: albumin < 3.5 g/dl; LDH > upper limit of normal; neutrophil/[leukocytes minus neutrophils] ratio (dNLR) > 3; more than 2 sites of metastasis; and presence of liver metastasis) (p < 0.05). After adjusting for these factors in a PSW model, the IO group showed statistically significant longer OS with HR = 0.75 (CI95% 0.65 – 0.86, p < 0.0001). The In a stratified analysis by tumor type we found no significant heterogeneity in the relative benefit of IO over TA. Conclusions: In real world data from our Ph1t population, treatment with IO was associated with longer OS than treatment with TA, even after adjusting for known prognostic factors and treatment selection biases. These results suggest that the likelihood of patient benefit with IO therapies in Ph1t is increasing.
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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 | 0.027 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".