Optimization and validation of the international metabolic prognostic index for CD19 CAR-T in large B-cell lymphoma.

While CD19-directed CAR T-cell therapy represents a transformative immunotherapy for relapsed/refractory large B-cell lymphoma (r/r LBCL), more than 50% of patients ultimately progress or relapse. Recently, the International Metabolic Prognostic Index (IMPI) - incorporating age, stage, and metabolic tumor volume (MTV) - was shown to improve prognostication for LBCL frontline treatment. Here, we examine its utility to predict toxicity and survival in CAR-T recipients.

CAR T-cell therapy response varies by extranodal disease site in large B-cell lymphoma.

The role of extranodal (EN) sites as potential sanctuary regions resistant to CD19-directed chimeric antigen receptor T-cell (CAR-T) therapy in large B-cell lymphoma (LBCL) remains unclear. To investigate this, we retrospectively analyzed 283 adults treated with commercial CD19 CAR-T therapy, assessing 958 PET-CT scans across four time points: pre-apheresis, pre-lymphodepletion, best response, and relapse. EN involvement prior to CAR-T therapy was common (76%).

An inflammatory biomarker signature of response to CAR-T cell therapy in non-Hodgkin lymphoma.

Disease progression is a substantial challenge in patients with non-Hodgkin lymphoma (NHL) undergoing chimeric antigen receptor T cell (CAR-T) therapy. Here we present InflaMix (INFLAmmation MIXture Model), an unsupervised quantitative model integrating 14 pre-CAR-T infusion laboratory and cytokine measures capturing inflammation and end-organ function. Developed using a cohort of 149 patients with NHL, InflaMix revealed an inflammatory signature associated with a high risk of CAR-T treatment failure, including increased hazard of death or relapse (hazard ratio, 2.

Correlating High-dimensional longitudinal microbial features with time-varying outcomes with FLORAL.

Correlating time-dependent patient characteristics and matched microbiome samples can be helpful to identify biomarkers in longitudinal microbiome studies. Existing approaches typically repeat a pre-specified modeling approach for all taxonomic features, followed by a multiple testing adjustment step for false discovery rate (FDR) control. In this work, we develop an alternative strategy of using log-ratio penalized generalized estimating equations, which directly models the longitudinal patient characteristic of interest as the outcome variable and treats microbial features as high-dimensional compositional covariates.

Scalable log-ratio lasso regression for enhanced microbial feature selection with FLORAL.

Identifying predictive biomarkers of patient outcomes from high-throughput microbiome data is of high interest, while existing computational methods do not satisfactorily account for complex survival endpoints, longitudinal samples, and taxa-specific sequencing biases. We present FLORAL, an open-source tool to perform scalable log-ratio lasso regression and microbial feature selection for continuous, binary, time-to-event, and competing risk outcomes, with compatibility for longitudinal microbiome data as time-dependent covariates. The proposed method adapts the augmented Lagrangian algorithm for a zero-sum constraint optimization problem while enabling a two-stage screening process for enhanced false-positive control.

Enhanced Feature Selection for Microbiome Data using FLORAL: Scalable Log-ratio Lasso Regression.

Identifying predictive biomarkers of patient outcomes from high-throughput microbiome data is of high interest, while existing computational methods do not satisfactorily account for complex survival endpoints, longitudinal samples, and taxa-specific sequencing biases. We present FLORAL (https://vdblab.github.