Vemulapalli, Abhijith C

e13683 Background: Immune checkpoint inhibitors (ICIs) have transformed cancer treatment but are frequently complicated by immune-related adverse events (irAEs), which may result in treatment interruption, hospitalization and mortality. Reliable pre-treatment tools to identify patients at higher risk for irAEs, particularly checkpoint inhibitor pneumonitis (CIP) remain limited. Artificial intelligence (AI)-based prediction models have emerged but their clinical performance and readiness for implementation are unclear. Methods: We systematically searched PubMed, Embase, Scopus and Web of Science (inception-January 2025) for studies developing AI-based models to predict irAEs or CIP in patients receiving ICIs. Eligible studies included radiomics, deep learning, natural language processing and clinical machine-learning approaches. Two reviewers independently extracted data on cancer type, toxicity endpoint, modality, algorithm, validation strategy and model performance. Study design and reporting characteristics were assessed. Due to substantial methodological heterogeneity, results were synthesized descriptively. Results: Fifteen studies met inclusion criteria, including mixed solid tumor cohorts and lung-predominant populations. Predictive targets included any-grade irAEs and organ-specific toxicities, most commonly pneumonitis. Model approaches ranged from clinical and electronic health record–based models to CT- and PET/CT-based radiomics and multimodal imaging-clinical frameworks. Reported discriminatory performance varied widely, with AUC values ranging from 0.64 to > 0.90 across model types. The most consistent performance was observed in deep learning and multimodal radiomics-clinical models for pneumonitis, with AUCs frequently between 0.75 and 0.92, whereas several clinical-only models demonstrated more modest performance. Most studies were retrospective and single-center, used heterogeneous irAE definitions and lacked standardized imaging protocols. External validation and calibration assessment were uncommon. Conclusions: AI-based models for predicting irAEs in ICI-treated patients demonstrate promising performance, particularly for pneumonitis using radiomics and multimodal approaches. However, current evidence is limited by heterogeneity, scarce external validation and uncertain generalizability. Prospective, multi-institutional validation and standardized outcome definitions are needed before AI-driven irAE risk prediction can be integrated into routine oncology practice to guide surveillance and patient counseling.

Abstract Background: Hematologic toxicity, including early and prolonged neutropenia, anemia, and thrombocytopenia, is among the most common and clinically significant complications after CD19, CD20, CD22 and BCMA-directed CAR-T therapy. Several clinical scoresmost notably CAR-HEMATOTOX (CAR-HT) and the refined ALL-HThave been developed to predict post-infusion cytopenias, but their comparative performance across diseases has not been systematically evaluated. No prior review has summarized existing models or assessed the need for AI-based predictive approaches. Methods: We conducted a systematic review of studies that developed or validated predictive models or risk scores for hematologic toxicity after CAR-T therapy in hematologic malignancies. Eligible studies included clinical, cytokine-based, or composite predictors and reported discrimination metrics such as area under the curve (AUC). Extracted variables included CAR-T product, target antigen, disease type, predictors used, model type, validation strategy, and performance statistics. Results were synthesized qualitatively due to heterogeneity in model structure and outcome definitions. Results: Studies on the CAR-HT score in B-cell lymphomas and multiple myelomas reported AUC values of 0.89 and 0.82, respectively, for predicting severe or long-term neutropenia after CD19 and BCMA-directed CAR-T therapy. Studies on the ALL-HT score (a modified/refined version of CAR-HT replacing ferritin with bone-marrow burden to improve discrimination in B-ALL) reported AUC 0.84-0.90 for predicting prolonged neutropenia and worse overall survivability (OS) after CD19/CD22 CAR-T. A multicenter study including aggressive/indolent NHL, MM/PCL, and ALL patients treated with CD19, CD20, or BCMA CAR-T reported AUC 0.87 (eIPM-Pre) and 0.88 (eIPM-Post) for predicting ICA-HT. A CD19 CAR-T study in B-ALL and LBCL reported AUC 0.85 for an early hematotoxicity nomogram using TNF- and CRP. Conclusion: Existing prediction tools for hematologic toxicity after CAR-T therapy demonstrate strong but disease-specific performance. Yet inconsistent endpoints and heterogeneous predictors limit clinical generalizability. No current model incorporates contemporary AI or machine-learning methods, underscoring a significant unmet need. Advanced predictive frameworks may allow earlier identification of patients at risk for severe or prolonged cytopenias and guide more individualized supportive care, with the potential to reduce downstream complications. Citation Format: Mansha Gupta, Abhijith Vemulapalli, Swathi Cherukuri, Rithish Nimmagadda, Akhil Jain. Predictive models for hematologic toxicity after CAR-T therapy: A systematic review of current risk scores and unmet AI needs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5202.

175 Background: BRAF V600E mutations occur in ~8–12% of metastatic colorectal cancer (mCRC) and are linked to poor prognosis, therapy resistance, and limited response to anti-EGFR monotherapy. While the BEACON trial established encorafenib plus cetuximab as standard, the added value of triplet regimens remains unclear. Clarifying the efficacy and toxicity of doublet vs triplet therapies is essential to optimize care in this high-risk group. Methods: Studies published across PubMed, Embase, and Google Scholar from inception till May 2025 with reported outcomes of the desired targeted therapies for BRAF V600E-mutated mCRC were screened and finally 12 studies included in this review. Data on efficacy parameters such as overall survival (OS), progression-free survival (PFS), objective response rate (ORR), disease control rate (DCR) as well as safety indicators such as adverse events (AEs), and discontinuation rates were extracted and analyzed. Results: In the ≥2nd-line setting, doublet and triplet regimens (doublet plus binimetinib) demonstrated improved outcomes compared with cetuximab plus irinotecan/FOLFIRI, with higher OS (7.2–9.2 vs 5.6 months), PFS (4.2–4.6 vs 1.5 months), DCR (65–75% vs 31%), and a comparable incidence of severe AEs (3–5% vs 4%). Panitumumab plus vemurafenib, also in the ≥2nd-line setting, reported an OS of 7.6 months, PFS of 3.2 months, and a DCR of 27%. In the 1st-line setting, the doublet regimen plus mFOLFOX6 with longer treatment duration (~50 weeks) showed improved outcomes compared with the doublet regimen plus higher-dose cetuximab, with higher OS (30.3 vs 19.5 months), PFS (12.8 vs 6.8 months), ORR (65.7% vs 45.6%), but also a higher incidence of severe AEs (46% vs 30%). Similarly, the cetuximab + vemurafenib + irinotecan regimen outperformed conventional chemotherapy (mFOLFOX6, FOLFOXIRI, or CAPOX) ± bevacizumab, with improved OS (25.3 vs 10–15 months), PFS (11.9 vs 7.5 months), ORR (63.2% vs 37–40%), DCR (94.7% vs 75%), and a lower rate of grade ≥3 AEs (34.2% vs 65%). Conclusions: The survival benefit observed with the doublet regimen was comparable to that of the triplet, but with lower rates of adverse events and treatment discontinuation, supporting current evidence that the doublet remains the preferred option for patients with BRAF V600E–mutated mCRC who have received one or more prior lines of therapy. Although adding conventional agents such as mFOLFOX6 to the doublet in the first-line setting improved OS, PFS, ORR, and DCR, this approach was associated with higher toxicity and discontinuation rates, raising questions about its overall benefit. Emerging data on vemurafenib in combination with cetuximab and conventional agents show promise, but further studies directly comparing encorafenib- versus vemurafenib-based regimens are warranted to optimize outcomes in this patient population.