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Multiple myeloma (MM) is characterized by the uncontrolled proliferation of plasma cells. Despite recent treatment advances, it is still incurable as disease progression is not fully understood. To investigate MM and its immune environment, we apply single cell RNA and linked-read whole genome sequencing to profile 29 longitudinal samples at different disease stages from 14 patients. Here, we collect 17,267 plasma cells and 57,719 immune cells, discovering patient-specific plasma cell profiles and immune cell expression changes. Patients with the same genetic alterations tend to have both plasma cells and immune cells clustered together. By integrating bulk genomics and single cell mapping, we track plasma cell subpopulations across disease stages and find three patterns: stability (from precancer to diagnosis), and gain or loss (from diagnosis to relapse). In multiple patients, we detect “B cell-featured” plasma cell subpopulations that cluster closely with B cells, implicating their cell of origin. We validate AP-1 complex differential expression (JUN and FOS) in plasma cell subpopulations using CyTOF-based protein assays, and integrated analysis of single-cell RNA and CyTOF data reveals AP-1 downstream targets (IL6 and IL1B) potentially leading to inflammation regulation. Our work represents a longitudinal investigation for tumor and microenvironment during MM progression and paves the way for expanding treatment options. Clonal evolution in multiple myeloma (MM) needs to be understood in both the tumor and its microenvironment. Here the authors perform single-cell multi-omics profiling of samples from MM patients at different stages, finding transitions in the immune cell composition throughout progression.

Kaposi's sarcoma (KS) is a highly prevalent cancer in AIDS patients, especially in sub-Saharan Africa. Kaposi's sarcoma-associated herpesvirus (KSHV) is the etiological agent of KS and other cancers like Primary Effusion Lymphoma (PEL). In KS and PEL, all tumors harbor latent KSHV episomes and express latency-associated viral proteins and microRNAs (miRNAs). The exact molecular mechanisms by which latent KSHV drives tumorigenesis are not completely understood. Recent developments have highlighted the importance of aberrant long non-coding RNA (lncRNA) expression in cancer. Deregulation of lncRNAs by miRNAs is a newly described phenomenon. We hypothesized that KSHV-encoded miRNAs deregulate human lncRNAs to drive tumorigenesis. We performed lncRNA expression profiling of endothelial cells infected with wt and miRNA-deleted KSHV and identified 126 lncRNAs as putative viral miRNA targets. Here we show that KSHV deregulates host lncRNAs in both a miRNA-dependent fashion by direct interaction and in a miRNA-independent fashion through latency-associated proteins. Several lncRNAs that were previously implicated in cancer, including MEG3, ANRIL and UCA1, are deregulated by KSHV. Our results also demonstrate that KSHV-mediated UCA1 deregulation contributes to increased proliferation and migration of endothelial cells.

Therapy targeting the BRAF-MEK cascade created a treatment revolution for patients with BRAF mutant advanced melanoma. Unfortunately, 80% patients treated will progress by 5 years follow-up. Thus, it is imperative we study mechanisms of melanoma progression and therapeutic resistance. We created a scRNA (single cell RNA) atlas of 128,230 cells from 18 tumors across the treatment spectrum, discovering melanoma cells clustered strongly by transcriptome profiles of patients of origins. Our cell-level investigation revealed gains of 1q and 7q as likely early clonal events in metastatic melanomas. By comparing patient tumors and their derivative cell lines, we observed that PD1 responsive tumor fraction disappears when cells are propagated in vitro . We further established three anti-BRAF-MEK treatment resistant cell lines using three BRAF mutant tumors. ALDOA and PGK1 were found to be highly expressed in treatment resistant cell populations and metformin was effective in targeting the resistant cells. Our study suggests that the investigation of patient tumors and their derivative lines is essential for understanding disease progression, treatment response and resistance.

Gammaherpesviruses, including the human pathogens Epstein-Barr virus (EBV) and Kaposi's sarcoma-associated herpesvirus (KSHV), establish lifelong latent infection in B cells and are associated with a variety of tumors. In addition to protein coding genes, these viruses encode numerous microRNAs (miRNAs) within their genomes. While putative host targets of EBV and KSHV miRNAs have been previously identified, the specific functions of these miRNAs during in vivo infection are largely unknown. Murine gammaherpesvirus 68 (MHV68) is a natural pathogen of rodents that is genetically related to both EBV and KSHV, and thus serves as an excellent model for the study of EBV and KSHV genetic elements such as miRNAs in the context of infection and disease. However, the specific targets of MHV68 miRNAs remain completely unknown. Using a technique known as qCLASH (quick crosslinking, ligation, and sequencing of hybrids), we have now identified thousands of Ago-associated, direct miRNA-mRNA interactions during lytic infection, latent infection and reactivation from latency. Validating this approach, detailed molecular analyses of specific interactions demonstrated repression of numerous host mRNA targets of MHV68 miRNAs, including Arid1a, Ctsl, Ifitm3 and Phc3. Notably, of the 1,505 MHV68 miRNA-host mRNA targets identified in B cells, 86% were shared with either EBV or KSHV, and 64% were shared among all three viruses, demonstrating significant conservation of gammaherpesvirus miRNA targeting. Pathway analysis of MHV68 miRNA targets further revealed enrichment of cellular pathways involved in protein synthesis and protein modification, including eIF2 Signaling, mTOR signaling and protein ubiquitination, pathways also enriched for targets of EBV and KSHV miRNAs. These findings provide substantial new information about specific targets of MHV68 miRNAs and shed important light on likely conserved functions of gammaherpesvirus miRNAs.

Traditional gene expression deconvolution methods assess a limited number of cell types, therefore do not capture the full complexity of the tumor microenvironment (TME). Here, we integrate nine deconvolution tools to assess 79 TME cell types in 10,592 tumors across 33 different cancer types, creating the most comprehensive analysis of the TME. In total, we found 41 patterns of immune infiltration and stroma profiles, identifying heterogeneous yet unique TME portraits for each cancer and several new findings. Our findings indicate that leukocytes play a major role in distinguishing various tumor types, and that a shared immune-rich TME cluster predicts better survival in bladder cancer for luminal and basal squamous subtypes, as well as in melanoma for RAS-hotspot subtypes. Our detailed deconvolution and mutational correlation analyses uncover 35 therapeutic target and candidate response biomarkers hypotheses (including CASP8 and RAS pathway genes).

Kaposi's sarcoma-associated herpesvirus (KSHV), also named HHV-8, is the etiological agent of Kaposi sarcoma (KS), Primary effusion lymphoma (PEL), and Multicentric Castleman's disease. After de novo infection, KSHV genomes rapidly circularize and acquire a chromatin state that favors latency. During latency, the KSHV episome is decorated with distinct epigenetic marks that segregate the viral genome into transcriptionally active and repressed domains, enabling persistent silencing of lytic genes while retaining the capacity for reactivation. Transcription activity of chromatin is regulated at multiple levels, including the incorporation of histone variants such as H3.3, by a specific set of histone chaperones such as HIRA and DAXX. The interaction between LANA and these interphase active chaperones suggests that H3.3 deposition is a critical driver of early chromatinization and the long-term stability of KSHV latency. We detected rapid H3.3 deposition on KSHV episomes and on episomes within long-term infected cells. Moreover, we demonstrated that genetically disrupting the host H3.3 chaperone HIRA pathway by CRISPR/Cas9-mediated knockout impacted the regulation of LANA and maintenance of viral latency that was not altered in DAXX knockout cells. Collectively, these results support a role for HIRA-mediated H3.3 deposition in the regulation of KSHV latency.

Therapeutic drug treatments of solid tumors are often undermined by various resistance mechanisms. Identification of drug-resistance phenotypes at the single cell level is challenging because conventional molecular methods are cell-destructive, labor-intensive, and cost-prohibitive. To overcome these challenges, we developed an orthogonal approach to drug-resistance phenotyping, through the use of deep-learning-driven morphology analysis of single, high resolution cell images. Specifically, we trained deep learning-based drug resistance classifiers using cell images from 5 different cell lines that were rendered resistant to 5 different therapeutic agents, using a foundation model framework. With high accuracy, the classifier correctly predicted naive or resistance phenotypes across all cancer types and across all the therapeutic agent types (chemotherapeutic, targeted) tested. These results showed that morphology can capture complex phenotype information in the context of drug treatment. To demonstrate the potential clinical utility of the drug resistance classifier, it was applied to a dissociated tumor biopsy and the resulting phenotype predictions were in close concordance with scRNASeq analysis of the biopsy. Our study highlights the potential of deep-learning-driven morphology analysis to provide complex phenotype information, and ultimately shape oncology drug treatment strategies at the patient-level in a clinical context.Competing Interest StatementEL, MGM, and AD have no conflicts of interest. AJ, KS, ZL, CC, TP, RC, SCB, MR are current or former employees at Deepcell, Inc. SS, VP, MB, CR are current or former employees of Abbvie. MPL has unrelated research funding from Roche, Novartis, Molecular Partners, Oncobit, and Scailyte.

Therapeutic drug treatments of solid tumors are often undermined by various resistance mechanisms. Identification of drug-resistance phenotypes at the single cell level is challenging because conventional molecular methods are cell-destructive, labor-intensive, and cost-prohibitive. To overcome these challenges, we developed an orthogonal approach to drug-resistance phenotyping, through the use of deep-learning-driven morphology analysis of single, high resolution cell images. Specifically, we trained deep learning-based drug resistance classifiers using cell images from 5 different cell lines that were rendered resistant to 5 different therapeutic agents, using a foundation model framework. With high accuracy, the classifier correctly predicted naive or resistance phenotypes across all cancer types and across all the therapeutic agent types (chemotherapeutic, targeted) tested. These results showed that morphology can capture complex phenotype information in the context of drug treatment. To demonstrate the potential clinical utility of the drug resistance classifier, it was applied to a dissociated tumor biopsy and the resulting phenotype predictions were in close concordance with scRNASeq analysis of the biopsy. Our study highlights the potential of deep-learning-driven morphology analysis to provide complex phenotype information, and ultimately shape oncology drug treatment strategies at the patient-level in a clinical context.