King, Rebecca

Abstract Introduction: Pirtobrutinib, a highly selective non-covalent BTK inhibitor, demonstrated encouraging efficacy in Richter transformation (RT) of chronic lymphocytic leukemia (CLL) in the BRUIN trial (Wierda et al, Lancet Haematology, 2024). However, a control arm was not included in this phase 1/2 trial, and how pirtobrutinib compares to usual care is unknown. To address this gap of knowledge, we sought to compare efficacy outcomes in BRUIN to that in a control cohort consisting of RT patients treated with usual care at Mayo Clinic. Methods: The study was approved by the Mayo Clinic IRB. Patient-level data from BRUIN (n=79; investigator-assessed) were obtained from the study sponsor. Patients with histologically confirmed RT (large B-cell lymphoma) who received at least one line of therapy (n=230) were identified from the Mayo Clinic CLL Database. Multivariable logistic regression was employed to calculate propensity scores for baseline covariates (ie, age at RT treatment line, number of prior lines of CLL treatment [truncated at 3], number of prior lines of RT treatment [truncated at 3], and LDH). For each Mayo patient, propensity scores for each line of RT therapy were calculated, and the line with the highest propensity score was chosen as the index RT treatment line. Using the inverse probability treatment weighting (IPTW) methodology, individual weights were computed, and standardized mean differences (SMDs) were examined for each of the unadjusted and weighted baseline covariates to ensure that balance was obtained between BRUIN cases and Mayo controls. Overall survival (OS; the primary endpoint) and progression-free survival (PFS) were analyzed using unweighted and weighted Cox proportional hazards models. Unweighted and weighted overall response rates (ORRs) and complete response (CR) rates were analyzed using logistic regression. Results: The final analysis dataset contained 79 BRUIN cases (median age 67 [range 26-95], 66% male) and 204 Mayo controls (median age 69 [range 30-89], 74% male). The BRUIN cohort had more patients with ECOG PS ≥1 (61% vs 40%, p=0.001) and elevated LDH (81% vs 63%, p=0.003) than the Mayo cohort. The proportion of patients with TP53 aberration was not statistically different in both cohorts (57% vs. 44%, p=0.15). The index line of RT treatment in the Mayo cohort included chemoimmunotherapy (n=167, 80%) and novel agent-based therapy (eg, cBTKi, venetoclax, PI3Ki, immune checkpoint inhibitor, etc; n=42, 20%). Median lines of prior CLL therapy were 2 (IQR 1-4) vs 1 (IQR 0-3) in the BRUIN and Mayo cohorts, respectively; median lines of prior RT therapy were 2 (IQR 1-2) vs 0 (IQR 0-2). The SMDs of the weighted baseline factors met pre-determined criteria (-0.25≤SMD≤0.25), indicating that balance between the two cohorts was achieved. The median follow-up was 18.3 months (range 0.7-32.9) in the BRUIN cohort and 122.8 months (range 0-280.2) in the Mayo cohort. The unweighted median OS was 12.5 months (95% CI 7.0-20.6) in the BRUIN cohort and 5.7 months (95% CI 3.9-10.7) in the Mayo cohort. The unweighted HR was 0.76 (95% CI 0.54-1.07, p=0.12). After IPTW, the weighted HR was 0.63 (95% CI 0.50-0.78, p<0.001), significantly favoring the BRUIN cohort. The unweighted median PFS was 3.6 months (95% CI 2.8-5.1) in the BRUIN cohort and 3.1 months (95% CI 2.5-4.3) in the Mayo cohort. The unweighted HR was 0.98 (95%CI 0.72-1.34, p=0.92). After IPTW, the weighted HR was 0.78 (95% CI 0.64-0.95, p=0.01), again favoring the BRUIN cohort. Response was evaluable/available in 69 patients in BRUIN and 182 patients in Mayo cohort. The unweighted ORR was 58% vs 53% in BRUIN and Mayo cohorts, respectively (OR=0.83, 95% CI 0.47-1.45, p=0.51), and the CR rate was 16% vs 36% (OR=2.94, 95% CI 1.43-5.88, p=0.003). After IPTW, the weighted OR was 0.65 (95% CI 0.45-0.93, p=0.02) for ORR, favoring the BRUIN cohort, and 1.89 (95%CI 1.23-2.86, p=0.02) for CR rate, favoring the Mayo cohort. Conclusions: This individual patient-level IPTW-based analysis demonstrated that single agent pirtobrutinib is associated with improved OS, PFS, and ORR compared to usual care in patients with RT. These results support the choice of pirtobrutinib over traditional therapies for RT. Although these results are encouraging, the durability of response to pirtobrutinib remains suboptimal. Ongoing trials and other future research will evaluate the efficacy of pirtobrutinib in combination with other treatments in this setting.

Abstract Despite a characteristic indolent course, a substantial subset of follicular lymphoma (FL) patients has an early relapse with a poor outcome. Thus far, efforts to identify factors that predict survival have been unsuccessful. However, we and others have demonstrated the prognostic relevance of CD4+ T cells in the tumor microenvironment (TME) of FL and developed a bio-clinical risk model (called BioFLIPI) that offers improved risk stratification (Mondello et al, BCJ 2021). However, the mechanisms defining different BioFLIPI scores remain to be fully understood. To identify the programs associated with the BioFLIPI scores, we first interrogated the RNA-seq of purified B cells from 67 newly diagnosed FL1-3A (BioFLIPI 1 n=21; BioFLIPI 2-3 n=36; BioFLIPI 4 n=10). We found 1,156 upregulated genes and downregulation of 930 genes in BioFLIPI 4 vs 1, including well-known immune molecules such as CD70, IL10RA, CD69, and CCR6 (FDR<0.05). Gene set enrichment analysis (GSEA) using the MSigDB confirmed a negative enrichment in multiple immune response and interferon gamma signatures and revealed striking positive enrichment for cell proliferation and antigen presentation pathways in BioFLIPI 4 compared to BioFLIPI 1 tumors. The same pathways were dysregulated in BioFLIPI 2-3 but a lower magnitude. To investigate the somatic mutations associated with different BioFLIPI scores, we performed WES of the same 67 FLs and further expanded this cohort for a total of 90 patients (BioFLIPI 1 n=26; BioFLIPI 2-3 n=51; BioFLIPI 4 n=13). We found enrichment for mutations in several regulators of the germinal center in BioFLIPI 4 and 2-3, such as MEF2B (p=0.029), FOXO1 (p=0.038) and IRF8 (p=0.035), suggesting the cooperation of multiple oncogenic events promoting lymphomagenesis. In contrast, BioFLIPI 1 tumors harbored a higher rate of mutations in TNFRSF14 gene, known to enhance anti-lymphoma immune response. To investigate whether BioFLIPI modulates immune signatures of the microenvironment, we performed TME deconvolution by applying CIBERSORTx to bulk RNA-seq of these 67 FLs. We found that FLs with BioFLIPI 4 and 2-3 were enriched for CD4+ Tcm exh (p=0.02), Tem exh (p=0.04), Tfhexh (p=0.07) and NK cells (p=0.01) compared to BioFLIPI 1. In line with this data, CyTOF performed on a subset of these patients (BioFLIPI 1 n=12; BioFLIPI 2-3 n=25; BioFLIPI 4 n=6) confirmed progressive increase of CD4+ Tcm exh (p=0.006), Tem exh (p=0.009) and Tfh exh (p=0.025) cells from BioFLIPI 1 to BioFLIPI 4. Single cell RNA/BCR-seq of 21 FLs (BioFLIPI 1 n=6; BioFLIPI 2-3 n=12; BioFLIPI 4 n=3) corroborated a higher abundance of Tem exh, cytotoxic (CTL) Tem exh, Tfhexh and NK cells in BioFLIPI 4 and 2-3. Conversely, CD8+ naïve T cells were prevalent in BioFLIPI 1 tumors. Differential gene expression in T cell clusters from BioFLIPI 4 vs BioFLIPI 1 FL revealed a marked upregulation of multiple immune genes involved in antigen presentation (e.g., HLA-DRB, HLA-DQA, HLA-DPA/B) and immune response (e.g., ETS1, TNFSF8, IL7R). GSEA showed a positive enrichment for antigen presentation signatures and negative enrichment for TNFα signaling and Oxphos metabolism. Similar findings were observed in BioFLIPI 2-3 FLs. Using CellChat analysis, we found a stronger interaction between malignant B cells and Tem exh and CTL Tem exh cells in BioFLIPI 4 and 2-3 vs BioFLIPI 1 tumors. This was linked to an increased strenght of MHCI-CD8 interaction and concordant decrease of MHCII-CD4. Notably, the suppressive BTLA-TNFRSF14 signaling axis was present in BioFLIPI 4 and 2-3 but not in BioFLIPI 1 FLs. This was accompanied by a decreased signaling through TNF/LTA-TNFRSF1B, CD80/CD86-CD28/CTLA4. Linking the BioFLIPI-specific immunogenicity to lymphomagenesis, we found that FL patients with BioFLIPI 4 had two-three folds higher risk of transformation to an aggressive lymphoma (12% vs 7%) or death (21% vs 3%) than those with BioFLIPI 1, suggesting that this immune dysfunction favors the expansion of a dominant tumor clone and promotes a more aggressive form of FL. Collectively, our findings demonstrate that BioFLIPI scores in FL reflect distinct patterns of tumor immunogenicity that shape the TME. Notably, high BioFLIPI scores are paradoxically associated with enhanced antigen presentation alongside a suppressed immune response. This suggests a sustained antigen-specific T cell response that may drive immune exhaustion, promote tumor progression, and ultimately lead to histologic transformation

Abstract Background: Recent advances in computer vision have given rise to image foundation models (FMs), large-scale neural networks pre-trained on millions of unlabeled histopathology patches, which can learn generalizable representations of tissue morphology. In many cancers, these FMs have predicted genetic mutations and prognoses directly from histology, suggesting that morphology encodes “hidden” molecular information. In lymphoma, this field is nascent but growing.General-purpose histopathology FMs have limited representation of lymphoid morphology. We developed and evaluated LymphoVision, a lymphoma-specialized image foundation model pretrained with self-supervised learning, to extract clinically meaningful features from routine hematoxylin and eosin (H&E) histology. Methods: LymphoVision was pre-trained using knowledge distillation and consistency (DINOv2) on over 37 million multi-resolution patches (5x, 10x, 20x, 40x) sampled using a density-aware strategy from 31,211 H&E whole slide images (WSIs) from 9,155 archival lymphoma and reactive lymphoproliferative cases. To ensure diverse, representative samples, our patch sampling method uses an efficient graph-based clustering algorithm and prioritizes morphologically informative regions by clustering patch embeddings and performing density sampling within each cluster. The pretrained encoder was evaluated using a clustering-constrained attention multiple instance learning (MIL) framework on three diagnostic tasks: (1) cell-of-origin (COO) classification in diffuse large B-cell lymphoma (DLBCL), distinguishing germinal center B-cell (GCB) from non-GCB subtypes based on the Hans immunohistochemistry algorithm; (2) follicular lymphoma (FL) grading, differentiating low-grade (grades 1–2) from high-grade (grade 3); and (3) multiclass lymphoma subtype classification across six categories: DLBCL, FL, marginal zone lymphoma (MZL), mantle cell lymphoma (MCL), classical Hodgkin lymphoma (CHL), and benign/reactive lymphoid conditions. WSI-level labels were derived from pathology reports, and none of the evaluation cases were included in pretraining. Models used a ViT-Giant LymphoVision backbone to generate 20x patch embeddings, followed by MIL using 50% training data and 2-fold cross-validation (25% validation, 25% test). Performance was assessed using area under the receiver operating characteristic curve (AUC), accuracy, recall (sensitivity) and F1 score. Results: For DLBCL COO classification (total n=358 cases), LymphoVision achieved a mean test AUC of 0.93 (range 0.92–0.94), accuracy of 84.5% and F1 of 0.85 across two independent test folds (n=91 per fold, 50% GCB). These results outperformed general-purpose FMs on the same task and cases, including Virchow-v2 (AUC 0.85) and UNI-v2 (AUC 0.88), despite those models being pretrained on 10–100 times more images. For FL grading (total n=660 cases), the test set in each fold included 166 cases (18% grade 3), yielding a mean test AUC of 0.88 (range 0.87–0.90), accuracy of 89.2% and F1 of 0.80. In multiclass lymphoma subtype classification (total n=1,176 cases), the test set included 296 cases per fold. LymphoVision achieved a mean test AUC of 0.98 (range 0.97-0.99), mean test accuracy of 87.1% and mean F1 of 0.84. Class-wise recall was 92.0% for DLBCL (n=112), 88.2% for FL (n=72), 77.2% for MZL (n=29), 70.8% for MCL (n=35), 87.5% for CHL (n=24), and 95.8% for benign/reactive lymphoid conditions (n=24). Conclusion: LymphoVision is a lymphoma-specialized FM trained using self-supervision that achieves high diagnostic accuracy across multiple clinically meaningful tasks using weak supervision and standard H&E images, suggesting the encoder has learned fine-grained lymphoma morphology. Based on historical benchmarks from the 1997 International Lymphoma Study Group classification project, expert hematopathologists achieved diagnostic agreement rates of 55% to 84% using H&E morphology alone for the non-Hodgkin lymphoma (NHL) entities included in our multiclass task. LymphoVision approached or exceeded these benchmarks. These findings underscore the value of our approach using disease-specialized pretraining and intelligent patch sampling for data-efficient histopathology learning. LymphoVision has the potential to augment diagnostics, enable scalable AI-powered classification in real-world workflows, and serve as a foundation for prognostic and theragnostic applications. External validation and multimodal integration are ongoing.