Chimeric antigen receptor (CAR) T cells have revolutionized the treatment of hematological malignancies. Unfortunately, this improvement has yet to be translated into the solid tumor field. Current immunodeficient models used in pre-clinical testing often overestimate the efficacy of CAR T cell therapy as they fail to recapitulate the immunosuppressive tumor microenvironment characteristic of solid tumors. As CAR T cell monotherapy is unlikely to be curative for many solid tumors, combination therapies must be investigated, for example, stromal remodeling agents and immunomodulators. The evaluation of these combination therapies requires a fully immunocompetent mouse model in order to recapitulate the interaction between the host’s immune system and the CAR T cells. This review will discuss the need for improved immunocompetent murine models for the pre-clinical evaluation of CAR T cells, the current use of such models and future directions.

Cardiac amyloidosis (CA) is a progressive condition resulting from the deposition of amyloid fibrils in the heart, which leads to severe diastolic dysfunction and restrictive cardiomyopathy. The disease has two main subtypes: light-chain and transthyretin (TTR) CA, with the latter subdivided into wild-type and hereditary forms. Despite advances in diagnostic imaging, early detection remains a challenge due to non-specific symptoms that mimic other cardiac conditions. Treatment has evolved significantly with targeted therapies like TTR stabilizers, gene silencers, and RNA interference, showing promise in altering disease progression. However, barriers such as high costs, limited availability of genetic testing, and inadequate multidisciplinary care continue to impede comprehensive management. Future strategies should focus on integrating novel gene-editing therapies, expanding access to diagnostics, and enhancing multidisciplinary care models to improve outcomes. Overall, early diagnosis, equitable access to therapies, and personalized management plans are crucial to advancing care for CA patients.

Background: The first-generation Bruton’s tyrosine kinase inhibitor (BTKi), ibrutinib, is associated with significant cardiotoxicity. Second-generation agents were developed to mitigate this risk and offer an improved safety profile. This systematic review and meta-analysis of six direct comparator studies compares the cardiac safety profiles of first- and second-generation BTKi. Methods: We systematically searched Medline, Embase, and Cochrane for studies directly comparing first- and second-generation BTKi. Data from 6 studies, encompassing 14 455 patients (12 816 in the first-generation arm and 1639 in the second-generation arm), were included. The primary outcomes were the incidence of atrial fibrillation (AF) and cardiac events, defined by the Medical Dictionary for Regulatory Activities system organ class. All pooled analyses were conducted using a random-effects model. Publication bias was evaluated visually using a funnel plot, quantitatively with Egger’s regression test, and corrected using the Duval and Tweedie trim-and-fill method. Results: Compared to second-generation agents, ibrutinib was associated with a significantly higher risk of AF (OR 2.50, 95% CI, 1.97-3.17), total cardiac events (OR 1.53, 95% CI, 1.18-1.99), and heart failure (OR 2.08, 95% CI, 1.13-3.83). This translated to a 3-fold higher rate of treatment discontinuation for a cardiac cause (OR 3.32, 95% CI, 1.46-7.55). No significant difference in all-cause mortality was found. Conclusion: Second-generation BTKi may provide a more favorable cardiovascular safety profile than ibrutinib, resulting in fewer key cardiac events and less treatment-limiting toxicity. These findings should inform clinical decision-making, especially for patients with increased risk for cardiovascular disease.

Immune checkpoint inhibitors (ICIs) have translated the scope of cancer therapy, but their immune-restorative mechanism can lead to immune-related adverse events (irAEs), which can affect several organs with varying severity. Early identification of patients at risk of irAEs is prudent to inform clinical decisions in precision oncology and remains a challenge as current clinical and biomarker methods still lack predictive accuracy. Artificial Intelligence (AI) presents a promising strategy for improving early detection, risk stratification as well as monitoring of irAEs. This narrative review summarizes the current AI modalities for detecting and predicting irAEs risk occurring with ICI therapy, including clinical Machine Learning models, radiomics-based approaches, natural language processing (NLP) systems and the integration of modalities with multimodal AI frameworks. Clinical Machine Learning models demonstrate moderate predictive performance whereas radiomics-derived modeling appears promising for pneumonitis. NLP and language models have achieved higher accuracy for retrospective irAEs detection. Multimodal AI applications offer theoretical potential through diverse data integration that captures the complex biology of irAEs; however, current evidence is limited. All these modalities face limitations of inadequate sample sizes, retrospective design, heterogeneous outcome definitions, class imbalance, and insufficient external validation. AI-based models have significant potential for personalized immunotherapy monitoring but require prospective multicenter validation, standardized datasets and clinically interpretable frameworks prior to implementation. Future advances in multimodal modeling can also enable precise prediction and early detection of irAEs, ultimately improving the safety and effectiveness of cancer immunotherapy.