Extended Data Fig. 1: Workflow visual abstract.

(a) Our workflow involved 1) using 14 preinfusion laboratory measures in a model derivation cohort from MSK to build a Gaussian mixture model we named InflaMix. InflaMix clustered patients into two groups, one with an inflammatory blood profile and another with a noninflammatory profile. 2) We then evaluated the association between inflammatory cluster assignment by InflaMix and clinical outcomes after CAR-T therapy. 3). Finally, we then used InflaMix to predict patient cluster assignment in three independent validation cohorts (Cohort 2–MSK patients with LBCL, Cohort 3–SMC and HMH patients with LBCL, and Cohort 4–patients from all 3 centers with either FL or MCL) and evaluate their associations with clinical outcomes. (b) In developing InflaMix from the derivation cohort (Cohort I), we 1) normalized every laboratory value by their upper limit of normal to standardize measurements across different assays. 2) We then systemically log transformed any laboratory measures with distribution skew > 1 that improved > 90% by log transformation. 3) Laboratory values for patients across all cohorts were then scaled by the mean and standard deviation of laboratory values in the derivation cohort. These normalized, log-transformed, and scaled values are then used for cluster assignment by mixture modeling. These cluster assignments were then used as predictors in fitted regression models of disease response and survival to evaluate their associations with clinical outcomes. FL, follicular lymphoma; HMH, Hackensack Meridian Health; MCL, mantle cell lymphoma; MSK, Memorial Sloan Kettering Cancer Center; SMC, Sheba Medical Center.