Evaluating the role of immune-checkpoint inhibitor (ICI) combinations in patients (pts) with unselected “cold” tumors enrolled in early clinical trials (CT).
Bibliographic record
Abstract
2597 Background: In order to improve the expected response rate (ORR) of less than 10% in cold tumors, several ICI combinations are being evaluated in clinical trials. However, most of these trials don’t require any biomarker and pts are included based solely in histology. We aimed to assess the benefit of ICI combinations in pts with unselected cold tumors included in early CT. Methods: ICI naïve pts with cold tumors treated from 2015 to 2021 with ICI combinations in early CT at VHIO were reviewed. Clinico-pathological data and anti-tumor activity were extracted from a prospective database. ORR was defined as per RECIST v1.1 and clinical benefit rate (CBR) as complete/partial response (CR/PR) + stable disease (SD) for ≥ 4 months (m). Kaplan Meier estimates of progression-free survival (PFS) and overall survival (OS) were calculated and a Cox model according to LIPI (Lung Immune Prognostic Index = baseline LDH and derived neutrophil to lymphocyte ratio) was constructed. Immune-related adverse events (irAE) were classified as per CTCAE v.4.03. Hyperprogressive disease (HPD) was evaluated using RECIST v1.1 (Matos et al, 2020). Results: Out of 97 pts, median age was 62y, 61% had ECOG 0 and 29.8% had LIPI 0 (good prognostic score). Most pts had microsatellite stable (MSS) colorectal cancer (60.8%) or ovarian cancer (14.4%). Regimens included anti-PD1/L1 + another ICI in 69% (most commonly anti-LAG3 [26,8%] and CD40 agonist [20.9%]), anti-PD1/L1 + other molecule in 21.7% (most commonly SHP2 inhibitor [33.3%] and anti p53-HDM2 [28.5%]) and bispecific antibodies in 9.3% (anti-PD1/L1 + anti-LAG3 or CD137 agonist). No patient achieved a response. CBR was 15.3% (11 pts with MSS colorectal cancer, 2 ovarian cancer, 1 olfactory neuroblastoma, 1 paraganglioma). 33 pts (34%) presented irAE, 15 pts (15.5%) had irAE ≥ G2, 4 pts (4.1%) had G3 irAE (dry mouth, hypertransaminasemia, myocarditis and neutrophils count decreased) and 1 patient (1%) had G4 hyperglicemia. 58 pts (59.7%) had progressive disease (PD) as best response, 19 of these pts (32.7%) presented irAE. Overall, 20 pts (20.6%) met definition of HPD, representing 34.4% of pts with PD as best response. Median PFS for overall and CBR population were 1.9 m (CI95% 1.7-2.0) and 5.9 m (5.4-NR), respectively. Median OS for overall population was 7.6 m (5.9-9.5), with a trend for improved OS if LIPI good score vs. others (12.6 m vs. 6.2 m, hazard ratio 1.9, (CI 95% 1.1-3.3), p = 0.02). Among hyperprogressors, median OS was 5.33 m (3.39 - NR) and significantly worse LIPI scores (intermediate [1] or poor [2]) were observed as compared to pts with CBR (75% vs 53.3% p = 0.001). Conclusions: ICI combinations demonstrated very limited activity in pts with unselected cold tumors. However, the risk for irAE and HPD remain substantial. Further drug-biomarker co-development strategies are urgently needed to increase the risk benefit ratio for these pts.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".