Jessen, Erik

Abstract BackgroundOverexpression of c-Myc is required for the progression of pre-malignant plasma cells in monoclonal gammopathy of undetermined significance (MGUS) to malignant plasma cells in multiple myeloma (MM). c-Myc also increases glutamine anaplerosis into the tricarboxylic acid (TCA) cycle within cancer cells. Whether increased glutamine anaplerosis is associated with the progression of pre-malignant to malignant plasma cells is unknown.MethodsHuman volunteers (N = 7) and patients with MGUS (N = 11) and MM (N = 12) were prospectively recruited to undergo an intravenous infusion of 13C-labelled glutamine followed by a bone marrow aspiration to obtain bone marrow cells and plasma.ResultsDespite notable heterogeneity, stable isotope resolved metabolomics (SIRM) revealed that the mean 13C-labelled glutamine anaplerosis into the TCA cycle was higher in malignant compared to pre-malignant bone marrow plasma cells relative to the remainder of their paired bone marrow mononuclear cells. RNA sequencing demonstrated a higher relative mRNA expression of c-Myc and glutamine transporters such as ASCT2 and SN2 in malignant compared to pre-malignant bone marrow plasma cells. Finally, higher quantitative levels of TCA cycle intermediates in the bone marrow plasma differentiated MM from MGUS patients.ConclusionMeasurement of the in vivo activity of glutamine anaplerosis into the TCA cycle provides novel insight into the metabolic changes associated with the transformation of pre-malignant plasma cells in MGUS to malignant plasma cells in MM.

Abstract Background: Traditionally, mutational burden and mutational signatures have been assessed by tumor-normal pair DNA sequencing. The requirement of having both normal and tumor samples is not always feasible from a clinical perspective, and led us to investigate the efficacy of using RNA sequencing of only the tumor sample to determine the mutational burden and signatures, and subsequently molecular cause of the cancer. The potential advantages include reducing the cost of testing, and simultaneously providing information on the gene expression profile and gene fusions present in the tumor. Results: In this study, we devised supervised and unsupervised learning methods to determine mutational signatures from tumor RNA-seq data. As applications, we applied the methods to a training set of 587 TCGA uterine cancer RNA-seq samples, and examined in an independent testing set of 521 TCGA colorectal cancer RNA-seq samples. Both diseases are known associated with microsatellite instable high (MSI-H) and driver defects in DNA polymerase ɛ (POLɛ).From RNA-seq called variants, we found majority (>95%) are likely germline variants, leading to C>T enriched germline variants (dbSNP) widely applicable in tumor and normal RNA-seq samples. We found significant associations between RNA-derived mutational burdens and MSI/POLɛ status, and insignificant relationship between RNA-seq total coverage and derived mutational burdens. Additionally we found that over 80% of variants could be explained by using the COSMIC mutational signature-5, -6 and -10, which are implicated in natural aging, MSI-H, and POLɛ, respectively. For classifying tumor type, within UCEC we achieved a recall of 0.56 and 0.78, and specificity of 0.66 and 0.99 for MSI and POLɛ respectively. By applying learnt RNA signatures from UCEC to COAD, we were able to improve our classification of both MSI and POLɛ. Conclusions: Taken together, our work provides a novel method to detect RNA-seq derived mutational signatures with effective procedures to remove likely germline variants. It can leads to accurate classification of underlying driving mechanisms of DNA damage deficiency.

Abstract Objectives Sclerosing pneumocytomas are rare pulmonary neoplasms that are typically benign. However, rare patients experience progressive disease, and therapy targeting specific genetic underpinnings could be an attractive therapeutic option. Recent studies have found recurrent AKT 1 mutations in sclerosing pneumocytoma, but little is known about whether oncogenic fusion genes may also be present. Methods To better understand the genetic background, 10 sclerosing pneumocytomas were subjected to next-generation sequencing cancer mutation panel testing (n = 9) and/or RNA sequencing (n = 3). The patients were all women (average age, 47 years; range, 17-74 years). Results Eight patients had solitary sclerosing pneumocytomas, while one had two tumors, and one had many bilateral tumors. Recurrent mutations were noted in genes involved in the mTOR pathway, including AKT1, PIK3R1, and PTEN. AKT1 alterations were particularly common, present in 78%. No recurrent genetic fusions were identified. The patient in our study with multiple bilateral lesions was treated with the mammalian target of rapamycin (mTOR) inhibitor everolimus, with no objective radiographic evidence of treatment response after 4 months. Conclusions Our data further support that abnormal activation of the mTOR pathway is a consistent genetic event in sclerosing pneumocytoma. This warrants further exploration to determine if mTOR pathway inhibitors may be effective in patients with metastatic or recurrent disease.

AbstractObjectivesSteatohepatitic hepatocellular carcinoma is a distinct variant of hepatocellular carcinoma strongly associated with underlying nonalcoholic steatohepatitis. The molecular biology of steatohepatitic hepatocellular carcinoma is not fully elucidated, and thus we aimed to investigate the molecular underpinnings of this entity.MethodsTranscriptomic analysis using RNAseq was performed on eight tumor-nonneoplastic pairs of steatohepatitic hepatocellular carcinoma with comparison to conventional hepatocellular carcinoma transcriptomes curated in The Cancer Genome Atlas. Immunohistochemistry was used to validate key RNA-level findings.ResultsSteatohepatitic hepatocellular carcinoma demonstrated a distinctive differential gene expression profile compared with The Cancer Genome Atlas curated conventional hepatocellular carcinomas (n = 360 cases), indicating the distinctive steatohepatitic hepatocellular carcinoma morphology is associated with a unique gene expression profile. Pathway analysis comparing tumor-nonneoplastic pairs revealed significant upregulation of the hedgehog pathway based on GLI1 overexpression and significant downregulation of carnitine palmitoyltransferase 2 transcript. Glutamine synthetase transcript was significantly upregulated, and fatty acid binding protein 1 transcript was significantly downregulated and immunohistochemically confirmed, indicating steatohepatitic hepatocellular carcinoma tumor cells display a zone 3 phenotype.ConclusionsSteatohepatitic hepatocellular carcinoma demonstrates a distinctive morphology and gene expression profile, phenotype of zone 3 hepatocytes, and activation of the hedgehog pathway and repression of carnitine palmitoyltransferase 2, which may be important in tumorigenesis.

SummaryWe sought to dissect the tumour microenvironment in a small cohort (N = 10) of patients with POEMS at diagnosis and after therapy using mass cytometry. We included 10 MGUS patients as controls. We identified 29 immune cell subsets in the CD45+ and CD3+ compartments. Double positive T cells and PD‐1 positive CD4 T cells were expanded and naïve CD4 T cells were decreased in the bone marrow of patients with newly diagnosed/progressing POEMS. These findings provide evidence for possible antigenic‐driven selection as a driver of disease pathogenesis in POEMS.

Abstract Background: Traditionally, mutational burden and mutational signatures have been assessed by tumor-normal pair DNA sequencing. The requirement of having both normal and tumor samples is not always feasible from a clinical perspective, and led us to investigate the efficacy of using RNA sequencing of only the tumor sample to determine the mutational burden and signatures, and subsequently molecular cause of the cancer. The potential advantages include reducing the cost of testing, and simultaneously providing information on the gene expression profile and gene fusions present in the tumor.Results: In this study, we devised supervised and unsupervised learning methods to determine mutational signatures from tumor RNA-seq data. As applications, we applied the methods to a training set of 587 TCGA uterine cancer RNAseq samples, and examined in an independent testing set of xxx TCGA colorectal cancer RNAseq samples. Both diseases are known associated with microsatellite instable high (MSI-H) and driver defects in DNA polymerase ɛ (POLɛ). From RNAseq called variants, we found majority (>95%) are likely germline variants, leading to C>T enriched germline variants (dbSNP) widely applicable in tumor and normal RNAseq samples. We found significant associations between RNA-derived mutational burdens and MSI/POLɛ status, and insignificant relationship between RNAseq total coverage and derived mutational burdens. Additionally we found that over 80% of variants could be explained by using the COSMIC mutational signature-5, -6 and -10, which are implicated in natural aging, MSI-H, and POLɛ, respectively. For classifying tumor type, within UCEC we achieved a recall of 0.56 and 0.78, and specificity of 0.66 and 0.99 for MSI and POLɛ respectively. By applying learnt RNA signatures from UCEC to COAD, we were able to improve our classification of both MSI and POLɛ. Conclusions: Taken together, our work provides a novel method to detect RNAseq derived mutational signatures with effective procedures to remove likely germline variants. It can leads to accurate classification of underlying driving mechanisms of DNA damage deficiency.