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. 2024 Feb 2;29(2):e266-e274.
doi: 10.1093/oncolo/oyad239.

Immune-Related Adverse Event Likelihood Score Identifies "Pure" IRAEs Strongly Associated With Outcome in a Phase I-II Trial Population

Luca Mazzarella  1   2 , Federica Giugliano  1 , Eleonora Nicolo  1 , Angela Esposito  1 , Edoardo Crimini  1 , Giulia Tini  2 , Jacopo Uliano  1 , Chiara Corti  1 , Paolo D'Amico  1 , Pamela Trillo Aliaga  1 , Carmine Valenza  1 , Matteo Repetto  1 , Gabriele Antonarelli  2 , Liliana Ascione  1 , Grazia Vivanet  1 , Pierpaolo Berton Giachetti  1 , Ida Minchella  1 , Carmen Belli  1 , Marzia Locatelli  1 , Carmen Criscitiello  1 , Giuseppe Curigliano  1   3
Affiliations

Immune-Related Adverse Event Likelihood Score Identifies "Pure" IRAEs Strongly Associated With Outcome in a Phase I-II Trial Population

Luca Mazzarella et al. Oncologist. .

Abstract

Background: Immune-related adverse events (IRAE) pose a significant diagnostic and therapeutic challenge in patients treated with immune-oncology (IO) drugs. IRAEs have been suggested to correlate with better outcome, but studies are conflicting. Estimating the true incidence of IRAEs is particularly difficult in the early phase I/II trial setting. A key issue is the lack of IRAE diagnostic criteria, necessary to discriminate "pure" IRAEs from other treatment-related adverse events not sustained by an autoimmune process.

Methods: In patients treated with immune-oncology (IO) drugs in phases I-II trials at our institute, we identified high confidence (HC) or low confidence (LC) IRAEs by clinical consensus. We empirically developed an IRAE likelihood score (ILS) based on commonly available clinical data. Correlation with outcome was explored by multivariate Cox analysis. To mitigate immortal time-bias, analyses were conducted (1) at 2-month landmark and (2) modeling IRAEs as time-dependent covariate.

Results: Among 202 IO-treated patients, 29.2% developed >1 treatment-related adverse events (TRAE). Based on ILS >5, we classified patients in no IRAE (n = 143), HC IRAE (n = 24), or LC IRAE (n = 35). hazard ratios (HR) for HC were significantly lower than LC patients (HR for PFS ranging 0.24-0.44, for OS 0.18-0.23, all P < .01).

Conclusion: ILS provides a simple system to identify bona fide IRAEs, pruning for other treatment-related events likely due to different pathophysiology. Applying stringent criteria leads to lower and more reliable estimates of IRAE incidence and identifies events with significant impact on survival.

Keywords: biomarker; clinical trial; immune-related adverse events; immunotherapy; predictive factors.

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Conflict of interest statement

Luca Mazzarella reports having a consulting or advisory role for Tethis Inc. Carmen Criscitiello reports personal fees for consulting, advisory role, and speakers’ bureau from Lilly, Roche, Novartis, MSD, Seagen, Daiichi Sankyo, AstraZeneca, Gilead, and Pfizer. Giuseppe Curigliano reports having a consulting or advisory role for AstraZeneca, Boehringer Ingelheim, Bristol Myers Squibb, Daiichi-Sankyo, Eli Lilly, Foundation Medicine, GlaxoSmithKline, Novartis, Pfizer, Roche/Genentech, Samsung, Exact Sciences, Merck and Seagent; having served on speakers bureau for Daiichi-Sankyo, Eli Lilly, Foundation Medicine, Novartis, Pfizer, Roche/Genentech and Samsung; having received travel, accommodations and expenses support from Roche/Genentech, Daichii-Sankyo and Pfizer; having received honoraria from Ellipses Pharma; and having received research funding from Merck. The other authors indicated no financial relationships.

Figures

Figure 1.
Figure 1.
Tumor type and treatment administered. (A) Tumor-type prevalence by anatomical district. (B) Treatment administered. Classification as in ref.
Figure 2.
Figure 2.
Treatment-related event summary. (A) Time to onset. Dashed line indicates time thresholds for ILS scoring. (B) Events by grade.
Figure 3.
Figure 3.
ROC analysis of ILS. (A) ILS distribution between HC and LC IRAEs. Vertical line indicates optimal cutoff at ILS ≥ 5. (B) ROC curve. Dot indicates optimal cutoff at ILS ≥ 5.
Figure 4.
Figure 4.
IRAEs as predictor of progression-free survival. Patients were assigned to the HC or LC group by ILS and PFS calculated in the unsorted population (“any IRAE”) or in the LC vs HC groups (A,B): Kaplan-Meier curve showing survival in the “uncorrected” analysis (no landmark, IRAE not modelled as time-dependent variable); (C,D) Kaplan-Meier curve showing survival in the landmark analysis (landmark at 8 weeks). (E,F) Simon-Makuch plot showing survival in the analysis with IRAE as time-dependent covariate. (G) Summary of multivariate Cox analysis (full table in Supplementary Tables S5-S6).
Figure 5.
Figure 5.
IRAEs as predictors of overall survival. Patients were assigned to the HC or LC group by ILS and OS calculated in the unsorted population (“any IRAE”) or in the LC vs HC groups (A,B) Kaplan-Meier curve showing survival in the “uncorrected” analysis (no landmark, IRAE not modelled as time-dependent variable); (C,D) Kaplan-Meier curve showing survival in the landmark analysis (landmark at 8 weeks). (E,F) Simon-Makuch plot showing survival in the analysis with IRAE as time-dependent covariate. (G) Summary of any IRAEariate Cox analysis (full table in Supplementary Tables S7 and S8).
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