Stefan Karl Alig

CD19-directed chimeric antigen receptor T cell therapy (CAR19) improves survival in relapsed/refractory large B cell lymphoma compared with immunochemotherapy with intent for autologous hematopoietic cell transplantation (autoHCT). However, major toxicities include prolonged cytopenias, infection and secondary hematologic malignancies. Here, to investigate these complications, we studied patients with lymphoma receiving CAR19 and compared clinical outcomes to propensity-matched autoHCT-treated controls. CAR19-treated patients exhibited impaired immune reconstitution and increased infection. Bone marrow analysis showed that prolonged post-CAR cytopenias are associated with clonal cytopenias of undetermined significance and interferon-mediated inflammation. Clonal hematopoiesis (CH) commonly expanded following CAR19 infusion and was associated with impaired immune reconstitution and treatment-related myeloid malignancy (tMN). Post-CAR tMN was molecularly and clinically comparable to post-autoHCT tMN. Single-cell DNA analysis revealed mostly independent CH clones, while CAR integration into CH-mutant T cells occurred frequently and may drive expansion and persistence. Together, these findings implicate CH expansion and marrow inflammation in key CAR19-associated toxicities.

Body composition analysis (BCA) provides an objective assessment of metabolic states, but its prognostic value in diffuse large B-cell lymphoma (DLBCL) remains unclear. We applied machine learning–supported BCA to computed tomography imaging from patients with newly diagnosed DLBCL enrolled in the prospective phase 3 PETAL trial to quantify radiologic sarcopenia. We assessed BCA results in relation to survival after first-line immunochemotherapy, treatment-related hematologic toxicities, and molecular disease features. Patients in the lowest tertile of normalized skeletal muscle mass exhibited inferior survival after adjustment for established risk factors. Cause-specific time-to-event analyses revealed that sarcopenia was not associated with lymphoma-specific death but strongly predicted nonrelapse mortality, indicating a potential role as a biomarker of host vulnerability. Consistent with these findings, sarcopenic patients had a higher probability of experiencing hematologic toxicity during immunochemotherapy, and sarcopenia was the only independent risk factor for higher-grade hematotoxicity in multivariable analyses. Longitudinal BCA revealed inferior survival in patients with early muscle loss during therapy. Baseline sarcopenia and treatment-emergent muscle loss were not correlated, suggesting that these represent distinct biological phenomena, and only a small fraction of the interindividual variability in muscle mass could be attributed to age and lymphoma burden. Both phenotypes were independent of DLBCL molecular clusters, and no recurrently mutated gene was associated with lower skeletal muscle mass. Taken together, our results establish baseline sarcopenia and treatment-emergent muscle loss as orthogonal risk factors for adverse outcomes in DLBCL, supporting the evaluation of BCA for risk stratification in personalized lymphoma therapy.

TableS1. Patient characteristics of the CHL study training cohorts. Table S2. Gene list for the targeted sequencing panel and HLGen. Table S3 Patient Demographic and molecular alterations. Table S4. HLGen correlation with Age and EBV in Training and Validation cohort. Table S5. Antibodies used for immunohistochemistry staining. Table S6. Differential gene expression analysis followed by gene ontology analysis in L-428 model. Table S7. Differential gene expression analysis followed by gene ontology analysis in U-HO1 model + IL-3. Table S8 Differential gene expression analysis followed by gene ontology analysis in U-HO1 model + IL-5. Table S9 Gene expression changes across all three experimental models. Table S10. Classification methods for HLGen. Table S11 HLGen Prediction in Training cohort. Table S12 HLGen Prediction in Stanford cohort (Alig and colleagues, Nature 2024). Table S13. Antibodies used for immunohistochemistry staining. Table S14. IMC Clustering Strategy. Table S15. Antibodies used for flow cytometry staining. Table S16. Antibodies used for Western blotting.

Figure S1. Co-occurrence of mutation in CHL. Figure S2. STAT6 copy number gain. Figure S3. Frequent mutations according to key clinical features. Figure S4. Copy number alterations according to EB Virus (EBV) status on HRS cells. Figure S5. Multivariable analyses on thymic mutation. Figure S6. Coding mutation count according to key clinical features in CHL. Figure S7. Impact of molecular profile on survival in CHL. Figure S8. STAT6 mutations in CHL. Figure S9. Impact of molecular profile on survival in elderly CHL. Figure S10. MHC status on HRS cells and their molecular correlates. Figure S11. Molecular Subgroups of CHL. Figure S12. Correlation between molecular subtypes and key clinical features. Figure S13. Molecular subtype and progression free survival (PFS) in Hodgkin lymphoma. Figure S14. Overview of the imaging mass cytometry (IMC) analysis and panel. Figure S15. Spatial tumor-microenvironment correlates with molecular subtype in CHL. Figure S16. Membrane map depicting major immune cell populations in each molecular subgroup. Figure S17. Correlation between molecular subtypes and key features. Figure S18. Evidence of CSF2RB mutant allele expression at the protein level in L-1236 KO model. Figure S19. CSF2RB mutations confer cytokine-dependent gain-of-function signaling in the L-428 model. Figure S20. CSF2RB mutations promote activation of the STAT5 signaling pathway. Figure S21. Co-expression of CSF2RB-WT and E788* mutant enhances downstream cytokine responses. Figure S22. Characterization of extrinsic phenotypes of CSF2RB mutations. Figure S23. Tumor-microenvironment characterization with molecular subtype in CHL. Figure S24. Cohort and study design overview for the HLGen development. Figure S25. Oncoplot of the CHL samples from Stanford ctDNA cohort (n = 293), with clinical annotation according to molecular subtype. Figure S26. Impact of feature list reduction on HLGen classifier performance. Figure S27. Graphical summary of molecular subtypes and their clinical and tumor-microenvironment ecosystem correlates in classic Hodgkin lymphoma. Figure S28. GeoMx analyses on CD30+ HRS cells. Figure S29. Segmentation on Imaging Mass Cytometry. Figure S30. Gating Strategy of regulatory T cell population.

TableS1. Patient characteristics of the CHL study training cohorts. Table S2. Gene list for the targeted sequencing panel and HLGen. Table S3 Patient Demographic and molecular alterations. Table S4. HLGen correlation with Age and EBV in Training and Validation cohort. Table S5. Antibodies used for immunohistochemistry staining. Table S6. Differential gene expression analysis followed by gene ontology analysis in L-428 model. Table S7. Differential gene expression analysis followed by gene ontology analysis in U-HO1 model + IL-3. Table S8 Differential gene expression analysis followed by gene ontology analysis in U-HO1 model + IL-5. Table S9 Gene expression changes across all three experimental models. Table S10. Classification methods for HLGen. Table S11 HLGen Prediction in Training cohort. Table S12 HLGen Prediction in Stanford cohort (Alig and colleagues, Nature 2024). Table S13. Antibodies used for immunohistochemistry staining. Table S14. IMC Clustering Strategy. Table S15. Antibodies used for flow cytometry staining. Table S16. Antibodies used for Western blotting.

Figure S1. Co-occurrence of mutation in CHL. Figure S2. STAT6 copy number gain. Figure S3. Frequent mutations according to key clinical features. Figure S4. Copy number alterations according to EB Virus (EBV) status on HRS cells. Figure S5. Multivariable analyses on thymic mutation. Figure S6. Coding mutation count according to key clinical features in CHL. Figure S7. Impact of molecular profile on survival in CHL. Figure S8. STAT6 mutations in CHL. Figure S9. Impact of molecular profile on survival in elderly CHL. Figure S10. MHC status on HRS cells and their molecular correlates. Figure S11. Molecular Subgroups of CHL. Figure S12. Correlation between molecular subtypes and key clinical features. Figure S13. Molecular subtype and progression free survival (PFS) in Hodgkin lymphoma. Figure S14. Overview of the imaging mass cytometry (IMC) analysis and panel. Figure S15. Spatial tumor-microenvironment correlates with molecular subtype in CHL. Figure S16. Membrane map depicting major immune cell populations in each molecular subgroup. Figure S17. Correlation between molecular subtypes and key features. Figure S18. Evidence of CSF2RB mutant allele expression at the protein level in L-1236 KO model. Figure S19. CSF2RB mutations confer cytokine-dependent gain-of-function signaling in the L-428 model. Figure S20. CSF2RB mutations promote activation of the STAT5 signaling pathway. Figure S21. Co-expression of CSF2RB-WT and E788* mutant enhances downstream cytokine responses. Figure S22. Characterization of extrinsic phenotypes of CSF2RB mutations. Figure S23. Tumor-microenvironment characterization with molecular subtype in CHL. Figure S24. Cohort and study design overview for the HLGen development. Figure S25. Oncoplot of the CHL samples from Stanford ctDNA cohort (n = 293), with clinical annotation according to molecular subtype. Figure S26. Impact of feature list reduction on HLGen classifier performance. Figure S27. Graphical summary of molecular subtypes and their clinical and tumor-microenvironment ecosystem correlates in classic Hodgkin lymphoma. Figure S28. GeoMx analyses on CD30+ HRS cells. Figure S29. Segmentation on Imaging Mass Cytometry. Figure S30. Gating Strategy of regulatory T cell population.

The activating BRAF mutation V600E occurs in 8%–12% of metastatic colorectal cancers (mCRC) and is associated with peritoneal carcinomatosis (PC) and poor prognosis. Targeted inhibition with the oral BRAF inhibitor encorafenib, combined with intravenous cetuximab targeting epithelial growth factor receptor (EGFR) and standard chemotherapy FOLFOX, has improved outcomes and received FDA approval in 2025 based on results from the phase III BREAKWATER trial. We report a 26-year-old male with rapidly progressive, BRAF V600E-mutant, microsatellite-stable (MSS) rectal adenocarcinoma, liver metastases, and PC presenting with bowel obstruction and inability for oral intake. To enable targeted therapy, encorafenib was administered rectally and via nasogastric tube, together with intravenous cetuximab and FOLFOX (FOLFOX+EC). Most adverse events (AEs) were present before treatment and improved during FOLFOX+EC, indicating tumor association. Severe AEs included emesis, fatigue, pain, and elevated cholestatic liver enzymes. Treatment-related AEs were manageable with anaemia (°3), neutropenia (°4), and thrombocytopenia (°1). No encorafenib-specific toxicities (rash, pyrexia, QTc prolongation, or ocular effects) were observed. Pharmacokinetic analysis showed trough plasma exposure comparable to oral dosing in patients without PC, and CT imaging at weeks 4 and 8 revealed early tumor shrinkage. This case demonstrates that rectal and nasogastric administration of encorafenib is feasible, achieves therapeutic plasma concentrations, and induces objective and clinical remission in the context of FOLFOX+EC. Short-term safety appeared manageable, though increased infection risk cannot be excluded.

The tissue architecture of classic Hodgkin lymphoma (cHL) is unique among cancers and is characterized by rare malignant Hodgkin and Reed–Sternberg cells that coevolve with a complex ecosystem of immune cells in the tumor microenvironment (TME). The lack of a comprehensive systems-level interrogation has hindered the description of disease heterogeneity and clinically relevant molecular subtypes. In this study, we employed an integrative, multimodal approach to characterize cHL tumors using malignant cell sequencing, spatial transcriptomics, and imaging mass cytometry. We identified four molecular subtypes (CST, CN913, STB, and CN2P), each characterized by distinct clinical features, mutational patterns, malignant cell gene expression profiles, and spatial architecture involving immune cell populations. Functional modeling of CSF2RB mutations, a characteristic feature of the CST subtype, revealed dysregulated oncogenic signaling and unique TME cross-talk. These findings highlight the significance of multidimensional profiling in elucidating patterns of molecular alterations that drive immune ecosystems and underlie therapeutically exploitable vulnerabilities. Significance: Our systematic and comprehensive genomic and spatially resolved profiling of Hodgkin lymphoma revealed a paradigmatic link between a genetic disease subtype and TME composition and function. Insights into mutation-driven mechanisms of deregulated cytokine signaling will pave the way for therapeutic interventions that interfere with oncogenic signaling and cellular ecosystems in cancer.

PURPOSE: Large B-cell lymphomas (LBCLs) are curable, but patients with residual disease after therapy invariably experience progression. Ultrasensitive methods to detect circulating tumor DNA (ctDNA) as minimal residual disease (MRD) may improve the determination of remission. METHODS: We integrated data from five prospective studies of frontline anthracycline-based chemotherapy in patients with LBCL. Tumor-specific phased variants were identified from pretreatment samples and monitored at landmark time points. Serial plasma specimens were blindly analyzed for detectable ctDNA as MRD. MRD status was compared with conventional response criteria for prognosis of progression-free survival (PFS). RESULTS: We studied ctDNA-MRD in 137 patients by monitoring 409 plasma specimens over time. Detectable ctDNA rates decreased during therapy with 55% and 78% of patients achieving undetectable ctDNA after two cycles and at the end of therapy, respectively. After a median follow-up of 37 months, the 2-year PFS for patients with detectable versus undetectable ctDNA after two cycles was 67% versus 96% ( P = .0025; hazard ratio [HR], 6.9) and after therapy was 29% versus 97% ( P < .0001; HR, 28.7), respectively. Ninety-two (94%) patients with undetectable ctDNA at the end of therapy remained alive without progression, while 19 (68%) patients with detectable ctDNA progressed or died. MRD status at the end of therapy had greater prognostic utility than conventional lymphoma response criteria using positron emission tomography (PET) scans (HR, 3.6 for positive PET and 28.3 for detectable ctDNA). CONCLUSION: Ultrasensitive ctDNA detection after frontline LBCL therapy is more prognostic than conventional radiographic response criteria. A refined definition of remission with ctDNA-MRD may improve clinical and psychological outcomes for patients with LBCL.