Ultra-Deep Sequencing Reveals the Mutational Landscape of Classical Hodgkin Lymphoma.

The malignant Hodgkin and Reed Sternberg (HRS) cells of classical Hodgkin lymphoma (cHL) are scarce in affected lymph nodes, creating a challenge to detect driver somatic mutations. As an alternative to cell purification techniques, we hypothesized that ultra-deep exome sequencing would allow genomic study of HRS cells, thereby streamlining analysis and avoiding technical pitfalls. To test this, 31 cHL tumor/normal pairs were exome sequenced to approximately 1,000× median depth of coverage. An orthogonal error-corrected sequencing approach verified >95% of the discovered mutations. We identified mutations in genes novel to cHL including: CDH5 and PCDH7, novel stop gain mutations in IL4R, and a novel pattern of recurrent mutations in pathways regulating Hippo signaling. As a further application of our exome sequencing, we attempted to identify expressed somatic single-nucleotide variants (SNV) in single-nuclei RNA sequencing (snRNA-seq) data generated from a patient in our cohort. Our snRNA analysis identified a clear cluster of cells containing a somatic SNV identified in our deep exome data. This cluster has differentially expressed genes that are consistent with genes known to be dysregulated in HRS cells (e.g., PIM1 and PIM3). The cluster also contains cells with an expanded B-cell clonotype further supporting a malignant phenotype. This study provides proof-of-principle that ultra-deep exome sequencing can be utilized to identify recurrent mutations in HRS cells and demonstrates the feasibility of snRNA-seq in the context of cHL. These studies provide the foundation for the further analysis of genomic variants in large cohorts of patients with cHL. SIGNIFICANCE: Our data demonstrate the utility of ultra-deep exome sequencing in uncovering somatic variants in Hodgkin lymphoma, creating new opportunities to define the genes that are recurrently mutated in this disease. We also show for the first time the successful application of snRNA-seq in Hodgkin lymphoma and describe the expression profile of a putative cluster of HRS cells in a single patient.

Endothelial cells are a key target of IFN-g during response to combined PD-1/CTLA-4 ICB treatment in a mouse model of bladder cancer.

To explore mechanisms of response to combined PD-1/CTLA-4 immune checkpoint blockade (ICB) treatment in individual cell types, we generated scRNA-seq using a mouse model of invasive urothelial carcinoma with three conditions: untreated tumor, treated tumor, and tumor treated after CD4+ T cell depletion. After classifying tumor cells based on detection of somatic variants and assigning non-tumor cell types using SingleR, we performed differential expression analysis, overrepresentation analysis, and gene set enrichment analysis (GSEA) within each cell type. GSEA revealed that endothelial cells were enriched for upregulated IFN-g response genes when comparing treated cells to both untreated cells and cells treated after CD4+ T cell depletion. Functional analysis showed that knocking out IFNgR1 in endothelial cells inhibited treatment response. Together, these results indicated that IFN-g signaling in endothelial cells is a key mediator of ICB induced anti-tumor activity.

Mutations associated with progression in follicular lymphoma predict inferior outcomes at diagnosis: Alliance A151303.

Follicular lymphoma (FL) is clinically heterogeneous, with select patients tolerating extended watch-and-wait, whereas others require prompt treatment, suffer progression of disease within 24 months of treatment (POD24), and/or experience aggressive histologic transformation (t-FL). Because our understanding of the relationship between genetic alterations in FL and patient outcomes remains limited, we conducted a clinicogenomic analysis of 370 patients with FL or t-FL (from Cancer and Leukemia Group B/Alliance trials 50402/50701/50803, or real-world cohorts from Washington University School of Medicine, Cleveland Clinic, or University of Miami). FL subsets by grade, stage, watch-and-wait, or POD24 status did not differ by mutation burden, whereas mutation burden was significantly higher in relapsed/refractory (rel/ref) FL and t-FL than in newly diagnosed (dx) FL. Nonetheless, mutation burden in dx FL was not associated with frontline progression-free survival (PFS). CREBBP was the only gene more commonly mutated in FL than in t-FL yet mutated CREBBP was associated with shorter frontline PFS in FL. Mutations in 20 genes were more common in rel/ref FL or t-FL than in dx FL, including 6 significantly mutated genes (SMGs): STAT6, TP53, IGLL5, B2M, SOCS1, and MYD88. We defined a mutations associated with progression (MAP) signature as ≥2 mutations in these 7 genes (6 rel/ref FL or t-FL SMGs plus CREBBP). Patients with dx FL possessing a MAP signature had shorter frontline PFS, revealing a 7-gene set offering insight into FL progression risk potentially more generalizable than the m7-Follicular Lymphoma International Prognostic Index (m7-FLIPI), which had modest prognostic value in our cohort. Future studies are warranted to validate the poor prognosis associated with a MAP signature in dx FL, potentially facilitating novel trials specifically in this high-risk subset of patients.

Endothelial cells are a key target of IFN-g during response to combined PD-1/CTLA-4 ICB treatment in a mouse model of bladder cancer.

To explore mechanisms of response to combined PD-1/CTLA-4 immune checkpoint blockade (ICB) treatment in individual cell types, we generated scRNA-seq using a mouse model of invasive urothelial carcinoma with three conditions: untreated tumor, treated tumor, and tumor treated after CD4+ T cell depletion. After classifying tumor cells based on detection of somatic variants and assigning non-tumor cell types using SingleR, we performed differential expression analysis, overrepresentation analysis, and gene set enrichment analysis (GSEA) within each cell type. GSEA revealed that endothelial cells were enriched for upregulated IFN-g response genes when comparing treated cells to both untreated cells and cells treated after CD4+ T cell depletion. Functional analysis showed that knocking out IFNgR1 in endothelial cells inhibited treatment response. Together, these results indicated that IFN-g signaling in endothelial cells is a key mediator of ICB induced anti-tumor activity.

Hematopoietic cell transplantation donor-derived memory-like NK cells functionally persist after transfer into patients with leukemia.

Natural killer (NK) cells are innate lymphoid cells that eliminate cancer cells, produce cytokines, and are being investigated as a nascent cellular immunotherapy. Impaired NK cell function, expansion, and persistence remain key challenges for optimal clinical translation. One promising strategy to overcome these challenges is cytokine-induced memory-like (ML) differentiation, whereby NK cells acquire enhanced antitumor function after stimulation with interleukin-12 (IL-12), IL-15, and IL-18. Here, reduced-intensity conditioning (RIC) for HLA-haploidentical hematopoietic cell transplantation (HCT) was augmented with same-donor ML NK cells on day +7 and 3 weeks of N-803 (IL-15 superagonist) to treat patients with relapsed/refractory acute myeloid leukemia (AML) in a clinical trial (NCT02782546). In 15 patients, donor ML NK cells were well tolerated, and 87% of patients achieved a composite complete response at day +28, which corresponded with clearing high-risk mutations, including TP53 variants. NK cells were the major blood lymphocytes for 2 months after HCT with 1104-fold expansion (over 1 to 2 weeks). Phenotypic and transcriptional analyses identified donor ML NK cells as distinct from conventional NK cells and showed that ML NK cells persisted for over 2 months. ML NK cells expressed CD16, CD57, and high granzyme B and perforin, along with a unique transcription factor profile. ML NK cells differentiated in patients had enhanced ex vivo function compared to conventional NK cells from both patients and healthy donors. Overall, same-donor ML NK cell therapy with 3 weeks of N-803 support safely augmented RIC haplo-HCT for AML.

Phase 1/dose expansion trial of brentuximab vedotin and lenalidomide in relapsed or refractory diffuse large B-cell lymphoma.

New therapies are needed for patients with relapsed/refractory (rel/ref) diffuse large B-cell lymphoma (DLBCL) who do not benefit from or are ineligible for stem cell transplant and chimeric antigen receptor therapy. The CD30-targeted, antibody-drug conjugate brentuximab vedotin (BV) and the immunomodulator lenalidomide (Len) have demonstrated promising activity as single agents in this population. We report the results of a phase 1/dose expansion trial evaluating the combination of BV/Len in rel/ref DLBCL. Thirty-seven patients received BV every 21 days, with Len administered continuously for a maximum of 16 cycles. The maximum tolerated dose of the combination was 1.2 mg/kg BV with 20 mg/d Len. BV/Len was well tolerated with a toxicity profile consistent with their use as single agents. Most patients required granulocyte colony-stimulating factor support because of neutropenia. The overall response rate was 57% (95% CI, 39.6-72.5), complete response rate, 35% (95% CI, 20.7-52.6); median duration of response, 13.1 months; median progression-free survival, 10.2 months (95% CI, 5.5-13.7); and median overall survival, 14.3 months (95% CI, 10.2-35.6). Response rates were highest in patients with CD30+ DLBCL (73%), but they did not differ according to cell of origin (P = .96). NK cell expansion and phenotypic changes in CD8+ T-cell subsets in nonresponders were identified by mass cytometry. BV/Len represents a potential treatment option for patients with rel/ref DLBCL. This combination is being further explored in a phase 3 study (registered on https://clinicaltrials.org as NCT04404283). This trial was registered on https://clinicaltrials.gov as NCT02086604.

Bam-readcount - rapid generation of basepair-resolution sequence metrics.

Bam-readcount is a utility for generating low-level information about sequencing data at specific nucleotide positions. Originally designed to help filter genomic mutation calls, the metrics it outputs are useful as input for variant detection tools and for resolving ambiguity between variant callers1,2. In addition, it has found broad applicability in diverse fields including tumor evolution, single-cell genomics, climate change ecology, and tracking community spread of SARS-CoV-2.3-6.

Phase I Trial of N-803, an IL15 Receptor Agonist, with Rituximab in Patients with Indolent Non-Hodgkin Lymphoma.

PURPOSE: N-803 is an IL15 receptor superagonist complex, designed to optimize in vivo persistence and trans-presentation, thereby activating and expanding natural killer (NK) cells and CD8+ T cells. Monoclonal antibodies (mAbs) direct Fc receptor-bearing immune cells, including NK cells, to recognize and eliminate cancer targets. The ability of IL15R agonists to enhance tumor-targeting mAbs in patients has not been reported previously. PATIENTS AND METHODS: Relapsed/refractory patients with indolent non-Hodgkin lymphoma were treated with rituximab and intravenous or subcutaneous N-803 on an open-label, dose-escalation phase I study using a 3+3 design (NCT02384954). Primary endpoint was maximum tolerated dose. Immune correlates were performed using multidimensional analysis via mass cytometry and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) which simultaneously measures protein and single-cell RNA expression. RESULTS: This immunotherapy combination was safe and well tolerated and resulted in durable clinical responses including in rituximab-refractory patients. Subcutaneous N-803 plus rituximab induced sustained proliferation, expansion, and activation of peripheral blood NK cells and CD8 T cells, with increased NK cell and T cells present 8 weeks following last N-803 treatment. CITE-seq revealed a therapy-altered NK cell molecular program, including enhancement of AP-1 transcription factor. Furthermore, the monocyte transcriptional program was remodeled with enhanced MHC expression and antigen-presentation genes. CONCLUSIONS: N-803 combines with mAbs to enhance tumor targeting in patients, and warrants further investigation in combination with immunotherapies.

Massive metagenomic data analysis using abundance-based machine learning.

BACKGROUND: Metagenomics is the application of modern genomic techniques to investigate the members of a microbial community directly in their natural environments and is widely used in many studies to survey the communities of microbial organisms that live in diverse ecosystems. In order to understand the metagenomic profile of one of the densest interaction spaces for millions of people, the public transit system, the MetaSUB international Consortium has collected and sequenced metagenomes from subways of different cities across the world. In collaboration with CAMDA, MetaSUB has made the metagenomic samples from these cities available for an open challenge of data analysis including, but not limited in scope to, the identification of unknown samples. RESULTS: To distinguish the metagenomic profiling among different cities and also predict unknown samples precisely based on the profiling, two different approaches are proposed using machine learning techniques; one is a read-based taxonomy profiling of each sample and prediction method, and the other is a reduced representation assembly-based method. Among various machine learning techniques tested, the random forest technique showed promising results as a suitable classifier for both approaches. Random forest models developed from read-based taxonomic profiling could achieve an accuracy of 91% with 95% confidence interval between 80 and 93%. The assembly-based random forest model prediction also reached 90% accuracy. However, both models achieved roughly the same accuracy on the testing test, whereby they both failed to predict the most abundant label. CONCLUSION: Our results suggest that both read-based and assembly-based approaches are powerful tools for the analysis of metagenomics data. Moreover, our results suggest that reduced representation assembly-based methods are able to simultaneous provide high-accuracy prediction on available data. Overall, we show that metagenomic samples can be traced back to their location with careful generation of features from the composition of microbes and utilizing existing machine learning algorithms. Proposed approaches show high accuracy of prediction, but require careful inspection before making any decisions due to sample noise or complexity. REVIEWERS: This article was reviewed by Eugene V. Koonin, Jing Zhou and Serghei Mangul.