Multicellular immune ecotypes within solid tumors predict real-world therapeutic benefits with immune checkpoint inhibitors.

Background: Cancer initiation, progression, and immune evasion depend on the tumor microenvironment (TME). Thus, understanding the TME immune architecture is essential for understanding tumor metastasis and therapy response. This study aimed to create an immune cell states (CSs) atlas using bulk RNA-seq data enriched by eco-type analyses to resolve the complex immune architectures in the TME. Methods: We employed EcoTyper, a machine-learning (ML) framework, to study the real-world prognostic significance of immune CSs and multicellular ecosystems, utilizing molecular data from 1,610 patients with multiple malignancies who underwent immune checkpoint inhibitor (ICI) therapy within the ORIEN Avatar cohort, a well-annotated real-world dataset. Results: Our analysis revealed consistent ICI-specific prognostic TME carcinoma ecotypes (CEs) (including CE1, CE9, CE10) across our pan-cancer dataset, where CE1 being more lymphocyte-deficient and CE10 being more proinflammatory. Also, the analysis of specific immune CSs across different cancers showed consistent CD8+ and CD4+ T cell CS distribution patterns. Furthermore, survival analysis of the ORIEN ICI cohort demonstrated that ecotype CE9 is associated with the most favorable survival outcomes, while CE2 is linked to the least favorable outcomes. Notably, the melanoma-specific prognostic EcoTyper model confirmed that lower predicted risk scores are associated with improved survival and better response to immunotherapy. Finally, de novo discovery of ecotypes in the ORIEN ICI dataset identified Ecotype E3 as significantly associated with poorer survival outcomes. Conclusion: Our findings offer important insights into refining the patient selection process for immunotherapy in real-world practice and guiding the creation of novel therapeutic strategies to target specific ecotypes within the TME.

Role of ERα and Aromatase in Juvenile Gigantomastia.

CONTEXT: Approximately 150 patients with juvenile gigantomastia have been reported in the literature but the underlying biologic mechanisms remain unknown. OBJECTIVE: To conduct extensive clinical, biochemical, immunochemical, and genetic studies in 3 patients with juvenile gigantomastia to determine causative biologic factors. METHODS: We examined clinical effects of estrogen by blockading estrogen synthesis or its action. Breast tissue aromatase expression and activity were quantitated in 1 patient and 5 controls. Other biochemical markers, including estrogen receptor α (ERα), cyclin D1 and E, p-RB, p-MAPK, p-AKT, BCL-2, EGF-R, IGF-IR ß, and p-EGFR were assayed by Western blot. Immunohistochemical analyses for aromatase, ERα and ß, PgR, Ki67, sulfotransferase, estrone sulfatase, and 17ßHD were performed in all 3 patients. The entire genomes of the mother, father, and patient in the 3 families were sequenced. RESULTS: Blockade of estrogen synthesis or action in patients resulted in demonstrable clinical effects. Biochemical studies on fresh frozen tissue revealed no differences between patients and controls, presumably due to tissue dilution from the large proportion of stroma. However, immunohistochemical analysis of ductal breast cells in the 3 patients revealed a high percent of ERα (64.1% ± 7.8% vs reference women 9.6%, range 2.3-15%); aromatase score of 4 (76%-100% of cells positive vs 30.4% ± 5.6%); PgR (69.5% ± 15.2% vs 6.0%, range 2.7%-11.9%) and Ki67 (23.7% ± 0.54% vs 4.2%). Genetic studies were inconclusive although some intriguing variants were identified. CONCLUSION: The data implicate an important biologic role for ERα to increase tissue sensitivity to estrogen and aromatase to enhance local tissue production as biologic factors involved in juvenile gigantomastia.

The T Cell Immunoscore as a Reference for Biomarker Development Utilizing Real-World Data from Patients with Advanced Malignancies Treated with Immune Checkpoint Inhibitors.

BACKGROUND: We aimed to determine the prognostic value of an immunoscore reflecting CD3+ and CD8+ T cell density estimated from real-world transcriptomic data of a patient cohort with advanced malignancies treated with immune checkpoint inhibitors (ICIs) in an effort to validate a reference for future machine learning-based biomarker development. METHODS: Transcriptomic data was collected under the Total Cancer Care Protocol (NCT03977402) Avatar® project. The real-world immunoscore for each patient was calculated based on the estimated densities of tumor CD3+ and CD8+ T cells utilizing CIBERSORTx and the LM22 gene signature matrix. Then, the immunoscore association with overall survival (OS) was estimated using Cox regression and analyzed using Kaplan-Meier curves. The OS predictions were assessed using Harrell's concordance index (C-index). The Youden index was used to identify the optimal cut-off point. Statistical significance was assessed using the log-rank test. RESULTS: Our study encompassed 522 patients with four cancer types. The median duration to death was 10.5 months for the 275 participants who encountered an event. For the entire cohort, the results demonstrated that transcriptomics-based immunoscore could significantly predict patients at risk of death (p-value < 0.001). Notably, patients with an intermediate-high immunoscore achieved better OS than those with a low immunoscore. In subgroup analysis, the prediction of OS was significant for melanoma and head and neck cancer patients but did not reach significance in the non-small cell lung cancer or renal cell carcinoma cohorts. CONCLUSIONS: Calculating CD3+ and CD8+ T cell immunoscore using real-world transcriptomic data represents a promising signature for estimating OS with ICIs and can be used as a reference for future machine learning-based biomarker development.

Induction of PARP7 Creates a Vulnerability for Growth Inhibition by RBN2397 in Prostate Cancer Cells.

The ADP-ribosyltransferase PARP7 modulates protein function by conjugating ADP-ribose to the side chains of acceptor amino acids. PARP7 has been shown to affect gene expression in prostate cancer cells and certain other cell types by mechanisms that include transcription factor ADP-ribosylation. Here, we use a recently developed catalytic inhibitor to PARP7, RBN2397, to study the effects of PARP7 inhibition in androgen receptor (AR)-positive and AR-negative prostate cancer cells. We find that RBN2397 has nanomolar potency for inhibiting androgen-induced ADP-ribosylation of the AR. RBN2397 inhibits the growth of prostate cancer cells in culture when cells are treated with ligands that activate the AR, or the aryl hydrocarbon receptor, and induce PARP7 expression. We show that the growth-inhibitory effects of RBN2397 are distinct from its enhancement of IFN signaling recently shown to promote tumor immunogenicity. RBN2397 treatment also induces trapping of PARP7 in a detergent-resistant fraction within the nucleus, which is reminiscent of how inhibitors such as talazoparib affect PARP1 compartmentalization. Because PARP7 is expressed in AR-negative metastatic tumors and RBN2397 can affect cancer cells through multiple mechanisms, PARP7 may be an actionable target in advanced prostate cancer. Significance: RBN2397 is a potent and selective inhibitor of PARP7 that reduces the growth of prostate cancer cells, including a model for treatment-emergent neuroendocrine prostate cancer. RBN2397 induces PARP7 trapping on chromatin, suggesting its mechanism of action might be similar to clinically used PARP1 inhibitors.

Whole genome sequencing identifies structural variants contributing to hematologic traits in the NHLBI TOPMed program.

Genome-wide association studies have identified thousands of single nucleotide variants and small indels that contribute to variation in hematologic traits. While structural variants are known to cause rare blood or hematopoietic disorders, the genome-wide contribution of structural variants to quantitative blood cell trait variation is unknown. Here we utilized whole genome sequencing data in ancestrally diverse participants of the NHLBI Trans Omics for Precision Medicine program (N = 50,675) to detect structural variants associated with hematologic traits. Using single variant tests, we assessed the association of common and rare structural variants with red cell-, white cell-, and platelet-related quantitative traits and observed 21 independent signals (12 common and 9 rare) reaching genome-wide significance. The majority of these associations (N = 18) replicated in independent datasets. In genome-editing experiments, we provide evidence that a deletion associated with lower monocyte counts leads to disruption of an S1PR3 monocyte enhancer and decreased S1PR3 expression.

CCL22 mutations drive natural killer cell lymphoproliferative disease by deregulating microenvironmental crosstalk.

Chronic lymphoproliferative disorder of natural killer cells (CLPD-NK) is characterized by clonal expansion of natural killer (NK) cells where the underlying genetic mechanisms are incompletely understood. In the present study, we report somatic mutations in the chemokine gene CCL22 as the hallmark of a distinct subset of CLPD-NK. CCL22 mutations were enriched at highly conserved residues, mutually exclusive of STAT3 mutations and associated with gene expression programs that resembled normal CD16dim/CD56bright NK cells. Mechanistically, the mutations resulted in ligand-biased chemokine receptor signaling, with decreased internalization of the G-protein-coupled receptor (GPCR) for CCL22, CCR4, via impaired ß-arrestin recruitment. This resulted in increased cell chemotaxis in vitro, bidirectional crosstalk with the hematopoietic microenvironment and enhanced NK cell proliferation in vivo in transgenic human IL-15 mice. Somatic CCL22 mutations illustrate a unique mechanism of tumor formation in which gain-of-function chemokine mutations promote tumorigenesis by biased GPCR signaling and dysregulation of microenvironmental crosstalk.

Integrated bioinformatic pipeline using whole-exome and RNAseq data to identify germline variants correlated with cancer.

Germline Variants (GVs) are effective in predicting cancer risk and may be relevant in predicting patient outcomes. Here we provide a bioinformatic pipeline to identify GVs from the TCGA lower grade glioma cohort in Genomics Data Commons. We integrate paired whole exome sequences from normal and tumor samples and RNA sequences from tumor samples to determine a patient's GV status. We then identify the subset of GVs that are predictive of patient outcomes by Cox regression. For complete details on the use and execution of this protocol, please refer to Chatrath et al. (2019) and Chatrath et al. (2020).

Detecting molecular subtypes from multi-omics datasets using SUMO.

We present a data integration framework that uses non-negative matrix factorization of patient-similarity networks to integrate continuous multi-omics datasets for molecular subtyping. It is demonstrated to have the capability to handle missing data without using imputation and to be consistently among the best in detecting subtypes with differential prognosis and enrichment of clinical associations in a large number of cancers. When applying the approach to data from individuals with lower-grade gliomas, we identify a subtype with a significantly worse prognosis. Tumors assigned to this subtype are hypomethylated genome wide with a gain of AP-1 occupancy in demethylated distal enhancers. The tumors are also enriched for somatic chromosome 7 (chr7) gain, chr10 loss, and other molecular events that have been suggested as diagnostic markers for "IDH wild type, with molecular features of glioblastoma" by the cIMPACT-NOW consortium but have yet to be included in the World Health Organization (WHO) guidelines.

Protocol for integrative subtyping of lower-grade gliomas using the SUMO pipeline.

Grouping patients into subtypes with homogeneous molecular features can guide diagnosis and therapeutic interventions. SUMO is a computational pipeline that uses nonnegative matrix factorization of patient-similarity networks to integrate continuous multi-omic datasets for molecular subtyping of a disease. Here, we present a detailed protocol to demonstrate its use in determining subtypes of lower-grade gliomas by integrating gene expression, DNA methylation, and miRNA expression data from the TCGA-LGG cohort. For complete details on the use and execution of this profile, please refer to Sienkiewicz et al. (2022).

Genomic landscape of TCRαß and TCRγδ T-large granular lymphocyte leukemia.

Large granular lymphocyte (LGL) leukemia comprises a group of rare lymphoproliferative disorders whose molecular landscape is incompletely defined. We leveraged paired whole-exome and transcriptome sequencing in the largest LGL leukemia cohort to date, which included 105 patients (93 T-cell receptor αß [TCRαß] T-LGL and 12 TCRγδ T-LGL). Seventy-six mutations were observed in 3 or more patients in the cohort, and out of those, STAT3, KMT2D, PIK3R1, TTN, EYS, and SULF1 mutations were shared between both subtypes. We identified ARHGAP25, ABCC9, PCDHA11, SULF1, SLC6A15, DDX59, DNMT3A, FAS, KDM6A, KMT2D, PIK3R1, STAT3, STAT5B, TET2, and TNFAIP3 as recurrently mutated putative drivers using an unbiased driver analysis approach leveraging our whole-exome cohort. Hotspot mutations in STAT3, PIK3R1, and FAS were detected, whereas truncating mutations in epigenetic modifying enzymes such as KMT2D and TET2 were observed. Moreover, STAT3 mutations co-occurred with mutations in chromatin and epigenetic modifying genes, especially KMT2D and SETD1B (P < .01 and P < .05, respectively). STAT3 was mutated in 50.5% of the patients. Most common Y640F STAT3 mutation was associated with lower absolute neutrophil count values, and N647I mutation was associated with lower hemoglobin values. Somatic activating mutations (Q160P, D170Y, L287F) in the STAT3 coiled-coil domain were characterized. STAT3-mutant patients exhibited increased mutational burden and enrichment of a mutational signature associated with increased spontaneous deamination of 5-methylcytosine. Finally, gene expression analysis revealed enrichment of interferon-γ signaling and decreased phosphatidylinositol 3-kinase-Akt signaling for STAT3-mutant patients. These findings highlight the clinical and molecular heterogeneity of this rare disorder.

Genomic analyses of the metastasis-derived prostate cancer cell lines LNCaP, VCaP, and PC3-AR.

BACKGROUND: The lymph node metastasis-derived LNCaP, the bone metastasis-derived PC3 (skull), and VCaP (vertebral) cell lines are widely used as preclinical models of human prostate cancer (CaP) and have been described in more than 19,000 publications. Here, we report on short-read whole-genome sequencing and genomic analyses of LNCaP, VCaP, and PC3 cells stably transduced with WT AR (PC3-AR). METHODS: LNCaP, VCaP, and PC3-AR cell lines were sequenced to an average depth of more than 30-fold using Illumina short-read sequencing. Using various computational methods, we identified and compared the single-nucleotide variants, copy-number profiles, and the structural variants observed in the three cell lines. RESULTS: LNCaP cells are composed of multiple subpopulations, which results in nonintegral copy number states and a high mutational load when the data is analyzed in bulk. All three cell lines contain pathogenic mutations and homozygous deletions in genes involved in DNA mismatch repair, along with deleterious mutations in cell-cycle, Wnt signaling, and other critical cellular processes. PC3-AR cells have a truncating mutation in TP53 and do not express the p53 protein. The VCaP cells contain a homozygous gain-of-function mutation in TP53 (p.R248W) that promotes cancer invasion, metastasis, and progression and has also been observed in prostate adenocarcinomas. In addition, we detect the signatures of chromothripsis of the q arms of chromosome 5 in both PC3-AR and VCaP cells, strengthening the association of TP53 inactivation with chromothripsis reported in other systems. CONCLUSIONS: Our work provides a resource for genetic, genomic, and biological studies employing these commonly-used prostate cancer cell lines.

Frequent somatic TET2 mutations in chronic NK-LGL leukemia with distinct patterns of cytopenias.

Chronic natural killer large granular lymphocyte (NK-LGL) leukemia, also referred to as chronic lymphoproliferative disorder of NK cells, is a rare disorder defined by prolonged expansion of clonal NK cells. Similar prevalence of STAT3 mutations in chronic T-LGL and NK-LGL leukemia is suggestive of common pathogenesis. We undertook whole-genome sequencing to identify mutations unique to NK-LGL leukemia. The results were analyzed to develop a resequencing panel that was applied to 58 patients. Phosphatidylinositol 3-kinase pathway gene mutations (PIK3CD/PIK3AP1) and TNFAIP3 mutations were seen in 5% and 10% of patients, respectively. TET2 was exceptional in that mutations were present in 16 (28%) of 58 patient samples, with evidence that TET2 mutations can be dominant and exclusive to the NK compartment. Reduced-representation bisulfite sequencing revealed that methylation patterns were significantly altered in TET2 mutant samples. The promoter of TET2 and that of PTPRD, a negative regulator of STAT3, were found to be methylated in additional cohort samples, largely confined to the TET2 mutant group. Mutations in STAT3 were observed in 19 (33%) of 58 patient samples, 7 of which had concurrent TET2 mutations. Thrombocytopenia and resistance to immunosuppressive agents were uniquely observed in those patients with only TET2 mutation (Games-Howell post hoc test, P = .0074; Fisher's exact test, P = .00466). Patients with STAT3 mutation, inclusive of those with TET2 comutation, had lower hematocrit, hemoglobin, and absolute neutrophil count compared with STAT3 wild-type patients (Welch's t test, P ≤ .015). We present the discovery of TET2 mutations in chronic NK-LGL leukemia and evidence that it identifies a unique molecular subtype.

Germline variants predictive of tumor mutational burden and immune checkpoint inhibitor efficacy.

High tumor mutational burden (TMB) is associated with response to checkpoint blockade in several cancers. We identify pathogenic germline variants associated with increased TMB (GVITMB). GVITMB were found in 7 genes using a pan-cancer approach (APC, FANCL, SLC25A13, ERCC3, MSH6, PMS2, and TP53) and 38 gene sets (e.g., those involved in DNA repair and programmed cell death). GVITMB were also associated with mutational signatures related to the dysfunction of the gene carrying the variant, somatic mutations that further affect the gene or pathway with the variant, or transcriptomic changes concordant with the expected effect of the variant. In a validation cohort of 140 patients with cutaneous melanoma, we found that patients with GVITMB had prolonged progression-free survival (p = 0.0349, hazard ratio = 0.688) and responded favorably (p = 0.0341, odds = 1.842) when treated with immune checkpoint inhibitors. Our results suggest that germline variants can influence the molecular phenotypes of tumors and predict the response to immune checkpoint inhibitors.

Germline Variants That Affect Tumor Progression.

Germline variants have a rich history of being studied in the context of cancer risk. Emerging studies now suggest that germline variants contribute not only to cancer risk but to tumor progression as well. In this opinion article, we discuss the initial discoveries associating germline variants with patient outcome and the mechanisms by which germline variants affect molecular pathways. Germline variants affect molecular pathways through amino acid changes, alteration of splicing patterns or expression of genes, influencing the selection for somatic mutations, and causing genome-wide mutational enrichment. These molecular alterations can lead to tumor phenotypes that become clinically apparent such as metastasis, alterations to the immune microenvironment, and modulation of therapeutic response. Overall, the growing body of evidence suggests that germline variants play a larger role in tumor progression than has been previously appreciated and that germline variation holds substantial potential for improving personalized medicine and patient outcomes.

Upregulation of human endogenous retrovirus-K (HML-2) mRNAs in hepatoblastoma: Identification of potential new immunotherapeutic targets and biomarkers.

PURPOSE: Hepatoblastoma is the most common liver malignancy in children. In order to advance therapy against hepatoblastoma, novel immunologic targets and biomarkers are needed. Our purpose in this investigation is to examine hepatoblastoma transcriptomes for the expression of a class of genomic elements known as Human Endogenous Retrovirus (HERVs). HERVs are abundant in the human genome and are biologically active elements that have been associated with multiple malignancies and proposed as immunologic targets in a subset of tumors. A sub-family of HERVs, HERV-K(HML-2) (HERV-K), have been shown to be tightly regulated in fetal development, making investigation of these elements in pediatric tumors paramount. METHODS: We first created a HERVK-FASTA file utilizing 91 previously described HML-2 proviruses. We then concatenated the file onto the GRCh38.95 cDNA library from Ensembl. We used this reference database to evaluate existing RNA-seq data from 10 hepatoblastoma tumors and 3 normal liver controls (GEO accession ID: GSE8977575). Quantification and differential proviral expression analysis between hepatoblastoma and normal liver controls was performed using the pseudo-alignment program Salmon and DESeq2, respectively. RESULTS: HERV-K mRNA was expressed in hepatoblastoma from multiple proviral loci. All expressed HERV-K proviral loci were upregulated in hepatoblastoma compared to normal liver controls. Five HERV-K proviruses (1q21.3, 3q27.2, 7q22.2, 12q24.33 and 17p13.1) were significantly differentially expressed (p-adjusted value <0.05, |log2 fold change| > 1.5) across conditions. The provirus at 17p13.1 had an approximately 300-fold increased expression in hepatoblastoma as compared to normal liver. This was in part due to the near absence of HERV-K mRNA at the 17p13.1 locus in fully differentiated liver samples. CONCLUSIONS: Our investigation demonstrates that HERV-K is expressed from multiple loci in hepatoblastoma and that the expression is increased for several proviruses compared to normal liver controls. Our results suggest that HERV-K mRNA expression may be useful as a biomarker in hepatoblastoma, given the large differential expression profiles in hepatoblastoma, with very low mRNA levels in liver control samples.

Cancer-specific CTCF binding facilitates oncogenic transcriptional dysregulation.

BACKGROUND: The three-dimensional genome organization is critical for gene regulation and can malfunction in diseases like cancer. As a key regulator of genome organization, CCCTC-binding factor (CTCF) has been characterized as a DNA-binding protein with important functions in maintaining the topological structure of chromatin and inducing DNA looping. Among the prolific binding sites in the genome, several events with altered CTCF occupancy have been reported as associated with effects in physiology or disease. However, hitherto there is no comprehensive survey of genome-wide CTCF binding patterns across different human cancers. RESULTS: To dissect functions of CTCF binding, we systematically analyze over 700 CTCF ChIP-seq profiles across human tissues and cancers and identify cancer-specific CTCF binding patterns in six cancer types. We show that cancer-specific lost and gained CTCF binding events are associated with altered chromatin interactions, partially with DNA methylation changes, and rarely with sequence mutations. While lost bindings primarily occur near gene promoters, most gained CTCF binding events exhibit enhancer activities and are induced by oncogenic transcription factors. We validate these findings in T cell acute lymphoblastic leukemia cell lines and patient samples and show that oncogenic NOTCH1 induces specific CTCF binding and they cooperatively activate expression of target genes, indicating transcriptional condensation phenomena. CONCLUSIONS: Specific CTCF binding events occur in human cancers. Cancer-specific CTCF binding can be induced by other transcription factors to regulate oncogenic gene expression. Our results substantiate CTCF binding alteration as a functional epigenomic signature of cancer.

Human endogenous retrovirus-K mRNA expression and genomic alignment data in hepatoblastoma.

Human Endogenous Retroviruses are a class of genomic elements that are the result of ancient retroviral infection of the human germline. Many are biologically active elements that have been implicated in multiple diseases including cancer. The most recent class to invade the human genome is the HERV-K(HML-2) (HERV-K) family. Approximately 90 HERV-K proviruses and many smaller elements have been identified to date in the human genome. Additional proviruses are continually being discovered with the rapid advancement of deep-sequencing and long-read sequencing technologies. HERV-K proviruses are poorly annotated in human transcriptome databases making their analysis in RNA-seq data difficult. To enable analysis, we compiled the sequences of 91 HERV-K proviruses identified in NCBI GenBank (ID JN675007-JN675097) and created a proviral alignment tool for visualizing RNA-seq reads aligned across individual proviruses. This allowed us to analyse publicly available RNA-seq data from 10 hepatoblastoma samples and 3 normal liver controls (GEO Accession ID: GSE89775). This data report includes the raw FASTA sequence files of the HERV-K proviruses from NCBI, a differential gene expression list between hepatoblastoma samples, and genomic alignment figures from 5 HERV-K proviruses identified as differentially expressed in the companion research article "Upregulation of Human Endogenous Retrovirus-K (HML-2) mRNAs in hepatoblastoma: Identification of potential new immunotherapeutic targets and biomarkers [1]. The data provided here are available for other research groups interested in evaluating individual HERV-K proviral expression using RNA-seq data. Furthermore, the data analysis is highly flexible and will accommodate the addition of other HERV-K proviruses.

The pan-cancer landscape of prognostic germline variants in 10,582 patients.

BACKGROUND: While clinical factors such as age, grade, stage, and histological subtype provide physicians with information about patient prognosis, genomic data can further improve these predictions. Previous studies have shown that germline variants in known cancer driver genes are predictive of patient outcome, but no study has systematically analyzed multiple cancers in an unbiased way to identify genetic loci that can improve patient outcome predictions made using clinical factors. METHODS: We analyzed sequencing data from the over 10,000 cancer patients available through The Cancer Genome Atlas to identify germline variants associated with patient outcome using multivariate Cox regression models. RESULTS: We identified 79 prognostic germline variants in individual cancers and 112 prognostic germline variants in groups of cancers. The germline variants identified in individual cancers provide additional predictive power about patient outcomes beyond clinical information currently in use and may therefore augment clinical decisions based on expected tumor aggressiveness. Molecularly, at least 12 of the germline variants are likely associated with patient outcome through perturbation of protein structure and at least five through association with gene expression differences. Almost half of these germline variants are in previously reported tumor suppressors, oncogenes or cancer driver genes with the other half pointing to genomic loci that should be further investigated for their roles in cancers. CONCLUSIONS: Germline variants are predictive of outcome in cancer patients and specific germline variants can improve patient outcome predictions beyond predictions made using clinical factors alone. The germline variants also implicate new means by which known oncogenes, tumor suppressor genes, and driver genes are perturbed in cancer and suggest roles in cancer for other genes that have not been extensively studied in oncology. Further studies in other cancer cohorts are necessary to confirm that germline variation is associated with outcome in cancer patients as this is a proof-of-principle study.

Whole Genome Sequencing of Spontaneously Occurring Rat Natural Killer Large Granular Lymphocyte Leukemia Identifies JAK1 Somatic Activating Mutation.

Large granular lymphocyte (LGL) leukemia arises spontaneously in elderly Fischer (F344) rats. This rodent model has been shown to emulate many aspects of the natural killer (NK) variant of human LGL leukemia. Previous transplantation of leukemic material into young F344 rats resulted in several strains of rat NK (RNK) primary leukemic cells. One strain, RNK-16, was adapted into the RNK-16 cell line and established as an aggressive NK-LGL leukemia model. Whole genome sequencing of the RNK-16 cell line identified 255,838 locations where the RNK16 had an alternate allele that was different from F334, including a mutation in Jak1. Functional studies showed Jak1 Y1034C to be a somatic activating mutation that mediated increased STAT signaling, as assessed by phosphoprotein levels. Sanger sequencing of Jak1 in RNK-1, -3, -7, and -16 found only RNK-16 to harbor the Y1034C Jak1 mutation. In vivo studies revealed that rats engrafted with RNK-16 primary material developed leukemia more rapidly than those engrafted with RNK-1, -3, and -7. Additionally, ex vivo RNK-16 spleen cells from leukemic rats exhibited increased STAT1, STAT3, and STAT5 phosphorylation compared to other RNK strains. Therefore, we report and characterize a novel gain-of-function Jak1 mutation in a spontaneous LGL leukemia model that results in increased downstream STAT signaling.

Genomics of LGL leukemia and select other rare leukemia/lymphomas.

Genomic analysis of cancer offers the hope of identifying new treatments or aiding in the selection of existing treatments. Rare leukemias pose additional challenges in this regard as samples may be hard to acquire and when found the underlying pathway may not be attractive to drug development since so few individuals are affected. In this case, it can be useful to identify common mutational overlap among subsets of rare leukemias to increase the number of individuals that may benefit from a targeted therapy. This chapter examines the current mutational landscape of large granular lymphocyte (LGL) leukemia with a focus on STAT3 mutations, the most common mutation in LGL leukemia to date. We examined the linkage between these mutations and autoimmune symptoms and disorders, in cases of obvious and suspected LGL leukemia. We then summarized and compared mutations in a set of other rare leukemias that also have JAK/STAT signaling pathway activation brought about by genomic changes. These include T-cell acute lymphoblastic leukemia (T-ALL), T-cell prolymphocytic leukemia (T-PLL), cutaneous T-cell lymphoma (CTCL), select peripheral T-cell lymphoma (PTCL), and adult T-cell leukemia/lymphoma (ATLL). Though STAT3 activation is common in these leukemias, the way in which it is achieved, such as the activating cytokine pathway and/or the co-mutational background, is quite diverse.