Erratum: DTX3L E3 ligase targets p53 for degradation at poly ADP-ribose polymerase-associated DNA damage sites.

[This corrects the article DOI: 10.1016/j.isci.2023.106444.].

A Hitchhiker's guide to high-dimensional tissue imaging with multiplexed ion beam imaging.

Advancements in multiplexed tissue imaging technologies are vital in shaping our understanding of tissue microenvironmental influences in disease contexts. These technologies now allow us to relate the phenotype of individual cells to their higher-order roles in tissue organization and function. Multiplexed Ion Beam Imaging (MIBI) is one of such technologies, which uses metal isotope-labeled antibodies and secondary ion mass spectrometry (SIMS) to image more than 40 protein markers simultaneously within a single tissue section. Here, we describe an optimized MIBI workflow for high-plex analysis of Formalin-Fixed Paraffin-Embedded (FFPE) tissues following antigen retrieval, metal isotope-conjugated antibody staining, imaging using the MIBI instrument, and subsequent data processing and analysis. While this workflow is focused on imaging human FFPE samples using the MIBI, this workflow can be easily extended to model systems, biological questions, and multiplexed imaging modalities.

Epstein-Barr Virus Orchestrates Spatial Reorganization and Immunomodulation within the Classic Hodgkin Lymphoma Tumor Microenvironment.

Classic Hodgkin Lymphoma (cHL) is a tumor composed of rare malignant Hodgkin and Reed-Sternberg (HRS) cells nested within a T-cell rich inflammatory immune infiltrate. cHL is associated with Epstein-Barr Virus (EBV) in 25% of cases. The specific contributions of EBV to the pathogenesis of cHL remain largely unknown, in part due to technical barriers in dissecting the tumor microenvironment (TME) in high detail. Herein, we applied multiplexed ion beam imaging (MIBI) spatial pro-teomics on 6 EBV-positive and 14 EBV-negative cHL samples. We identify key TME features that distinguish between EBV-positive and EBV-negative cHL, including the relative predominance of memory CD8 T cells and increased T-cell dysfunction as a function of spatial proximity to HRS cells. Building upon a larger multi-institutional cohort of 22 EBV-positive and 24 EBV-negative cHL samples, we orthogonally validated our findings through a spatial multi-omics approach, coupling whole transcriptome capture with antibody-defined cell types for tu-mor and T-cell populations within the cHL TME. We delineate contrasting transcriptomic immunological signatures between EBV-positive and EBV-negative cases that differently impact HRS cell proliferation, tumor-immune interactions, and mecha-nisms of T-cell dysregulation and dysfunction. Our multi-modal framework enabled a comprehensive dissection of EBV-linked reorganization and immune evasion within the cHL TME, and highlighted the need to elucidate the cellular and molecular fac-tors of virus-associated tumors, with potential for targeted therapeutic strategies.

MAPS: pathologist-level cell type annotation from tissue images through machine learning.

Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resource intensive and necessitate iterative expert input, thereby constraining their scalability and practicality for extensive datasets. We introduce MAPS (Machine learning for Analysis of Proteomics in Spatial biology), a machine learning approach facilitating rapid and precise cell type identification with human-level accuracy from spatial proteomics data. Validated on multiple in-house and publicly available MIBI and CODEX datasets, MAPS outperforms current annotation techniques in terms of speed and accuracy, achieving pathologist-level precision even for typically challenging cell types, including tumor cells of immune origin. By democratizing rapidly deployable and scalable machine learning annotation, MAPS holds significant potential to expedite advances in tissue biology and disease comprehension.

Nivolumab for relapsed/refractory classical Hodgkin lymphoma: 5-year survival from the pivotal phase 2 CheckMate 205 study.

Patients with relapsed/refractory (R/R) classical Hodgkin lymphoma (cHL) for whom autologous hematopoietic cell transplantation (auto-HCT) had failed experienced frequent and durable responses to nivolumab in the phase 2 CheckMate 205 trial. We present updated results (median follow-up, ∼5 years). Patients with R/R cHL who were brentuximab vedotin (BV)-naive (cohort A), received BV after auto-HCT (cohort B), or received BV before and/or after auto-HCT (cohort C) were administered with nivolumab 3 mg/kg IV every 2 weeks until progression or unacceptable toxicity. Patients in cohort C with complete remission (CR) for 1 year could discontinue nivolumab and resume upon relapse. Among 243 patients (cohort A, n = 63; B, n = 80; and C, n = 100), the objective response rate (ORR) was 71.2% (95% confidence interval [CI], 65.1-76.8); the CR rate was 21.4% (95% CI, 16.4-27.1). Median duration of response, CR, and partial remission were 18.2 (95% CI, 14.7-26.1), 30.3, and 13.5 months, respectively. Median progression-free survival was 15.1 months (95% CI, 11.3-18.5). Median overall survival (OS) was not reached; OS at 5 years was 71.4% (95% CI, 64.8-77.1). In cohort C, all 3 patients who discontinued in CR and were subsequently re-treated achieved objective response. No new or unexpected safety signals were identified. This 5-year follow-up of CheckMate 205 demonstrated favorable OS and confirmed efficacy and safety of nivolumab in R/R cHL after auto-HCT failure. Results suggest patients may discontinue treatment after persistent CR and reinitiate upon progression. This trial was registered at www.clinicaltrials.gov as #NCT02181713.

MYD88L265P augments proximal B-cell receptor signaling in large B-cell lymphomas via an interaction with DOCK8.

Diffuse large B-cell lymphoma (DLBCL) is a clinically and genetically heterogeneous disease with at least 5 recognized molecular subtypes. Cluster 5 (C5)/MCD tumors frequently exhibit concurrent alterations in the toll-like receptor (TLR) and B-cell receptor (BCR) pathway members, MYD88L265P and CD79B, and have a less favorable prognosis. In healthy B cells, the synergy between TLR and BCR signaling pathways integrates innate and adaptive immune responses and augments downstream NF-κB activation. In addition, physiologic TLR9 pathway engagement via MYD88, protein tyrosine kinase 2 (PYK2), and dedicator of cytokinesis 8 (DOCK8) increases proximal BCR signaling in healthy murine B cells. Although C5/MCD DLBCLs are selectively sensitive to Bruton tyrosine kinase (BTK) inhibition in in vitro studies and certain clinical trials, the role of mutated MYD88 in proximal BCR signaling remains undefined. Using engineered DLBCL cell line models, we found that concurrent MYD88L265P and CD79B alterations significantly increased the magnitude and duration of proximal BCR signaling, at the level of spleen tyrosine kinase and BTK, and augmented PYK2-dependent DOCK8 phosphorylation. MYD88L265P DLBCLs have significantly increased colocalization of DOCK8 with both MYD88 and the proximal BCR-associated Src kinase, LYN, in comparison with MYD88WT DLBCLs, implicating DOCK8 in MYD88L265P/proximal BCR cross talk. Additionally, DOCK8 depletion selectively decreased proximal BCR signaling, cellular proliferation, and viability of DLBCLs with endogenous MYD88L265P/CD79BY196F alterations and increased the efficacy of BTK blockade in these lymphomas. Therefore, MYD88L265P/DOCK8-enhanced proximal BCR signaling is a likely mechanism for the increased sensitivity of C5/MCD DLBCLs to BTK blockade.

MAPS: Pathologist-level cell type annotation from tissue images through machine learning.

Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resource intensive and necessitate iterative expert input, thereby constraining their scalability and practicality for extensive datasets. We introduce MAPS (Machine learning for Analysis of Proteomics in Spatial biology), a machine learning approach facilitating rapid and precise cell type identification with human-level accuracy from spatial proteomics data. Validated on multiple in-house and publicly available MIBI and CODEX datasets, MAPS outperforms current annotation techniques in terms of speed and accuracy, achieving pathologist-level precision even for challenging cell types, including tumor cells of immune origin. By democratizing rapidly deployable and scalable machine learning annotation, MAPS holds significant potential to expedite advances in tissue biology and disease comprehension.

Five-year follow-up of KEYNOTE-087: pembrolizumab monotherapy for relapsed/refractory classical Hodgkin lymphoma.

Previous analyses of the phase 2 KEYNOTE-087 (NCT02453594) trial of pembrolizumab monotherapy demonstrated effective antitumor activity with acceptable safety in patients with relapsed or refractory (R/R) classical Hodgkin lymphoma (cHL). However, long-term response durability and outcome of patients who receive a second course after treatment discontinuation after complete response (CR) remain of clinical interest. We present KEYNOTE-087 data after >5 years of median follow-up. Patients with R/R cHL and progressive disease (PD) after autologous stem cell transplantation (ASCT) and brentuximab vedotin (BV; cohort 1), salvage chemotherapy and BV without ASCT (cohort 2), or ASCT without subsequent BV (cohort 3), received pembrolizumab for ≤2 years. Patients in CR who discontinued treatment and subsequently experienced PD were eligible for second-course pembrolizumab. Primary end points were the objective response rate (ORR) using blinded central review and safety. The median follow-up was 63.7 months. ORR was 71.4% (95% confidence interval [CI], 64.8-77.4; CR, 27.6%; partial response, 43.8%). Median duration of response (DOR) was 16.6 months; median progression-free survival was 13.7 months. A quarter of responders, including half of complete responders, maintained a response for ≥4 years. Median overall survival was not achieved. Among 20 patients receiving second-course pembrolizumab, ORR for 19 evaluable patients was 73.7% (95% CI, 48.8-90.8); median DOR was 15.2 months. Any-grade treatment-related adverse events occurred in 72.9% of patients and grade 3 or 4 adverse events occurred in 12.9% of patients; no treatment-related deaths occurred. Single-agent pembrolizumab can induce durable responses, particularly in patients achieving CR. Second-course pembrolizumab frequently reinduced sustained responses after relapse from initial CR.

Pembrolizumab in relapsed or refractory primary mediastinal large B-cell lymphoma: final analysis of KEYNOTE-170.

Previous analyses of the phase 2 KEYNOTE-170 (NCT02576990) study demonstrated effective antitumor activity and acceptable safety of pembrolizumab 200 mg given every 3 weeks for up to 35 cycles (∼2 years) in patients with relapsed/refractory (R/R) primary mediastinal B-cell lymphoma (PMBCL) whose disease progressed after or who were ineligible for autologous stem cell transplantation. The end points included objective response rate (ORR), progression-free survival (PFS), and duration of response (DOR) according to the investigator per 2007 Response Criteria; overall survival (OS); and safety. In this final analysis, median duration of follow-up was 48.7 months (range, 41.2-56.2). The ORR was 41.5% (complete response, 20.8%; partial response, 20.8%). The median DOR was not reached; no patients who achieved a complete response progressed at the data cutoff. The median PFS was 4.3 months; the 4-year PFS rate was 33.0%. The median OS was 22.3 months; the 4-year OS rate was 45.3%. At the data cutoff, 30 patients (56.6%) had any-grade treatment-related adverse events (AEs); the most common were neutropenia, asthenia, and hypothyroidism. Grade 3/4 treatment-related AEs occurred in 22.6% of the patients; no grade 5 AEs occurred. After 4 years of follow-up, pembrolizumab continued to provide durable responses, with promising trends for long-term survival and acceptable safety in R/R PMBCL.

Diffuse large B-cell lymphomas have spatially defined, tumor immune microenvironments revealed by high-parameter imaging.

Diffuse large B-cell lymphoma (DLBCL) not otherwise specified is the most common aggressive non-Hodgkin lymphoma and a biologically heterogeneous disease. Despite the development of effective immunotherapies, the organization of the DLBCL tumor-immune microenvironment (TIME) remains poorly understood.We interrogated the intact TIME of 51 de novo DLBCLs with triplicate sampling to characterize 337 995 tumor and immune cells using a 27-plex antibody panel that captured cell lineage, architectural, and functional markers. We spatially assigned individual cells, identified local cell neighborhoods, and established their topographical organization in situ. We found that the organization of local tumor and immune cells can be modeled by 6 composite cell neighborhood types (CNTs). Differential CNT representation divided cases into 3 aggregate TIME categories: immune-deficient, dendritic cell-enriched (DC-enriched), and macrophage-enriched (Mac-enriched). Cases with immune-deficient TIMEs have tumor cell-rich CNTs, in which the few infiltrating immune cells are enriched near CD31+ vessels, in keeping with limited immune activity. Cases with DC-enriched TIMEs selectively include tumor cell-poor/immune cell-rich CNTs with high numbers of CD11c+ DCs and antigen-experienced T cells also enriched near CD31+ vessels, in keeping with increased immune activity. Cases with Mac-enriched TIMEs selectively include tumor cell-poor/immune cell-rich CNTs with high numbers of CD163+ macrophages and CD8 T cells throughout the microenvironment, accompanied by increased IDO-1 and LAG-3 and decreased HLA-DR expression and genetic signatures in keeping with immune evasion. Our findings reveal that the heterogenous cellular components of DLBCL are not randomly distributed but organized into CNTs that define aggregate TIMEs with distinct cellular, spatial, and functional features.