Citations

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.

Background: Multiple myeloma (MM) remains an incurable plasma cell (PC) malignancy. Despite improvements in clinical outcomes, variability in treatment response exists. Some patients experience innate resistance, while others develop resistance over the course of treatment. Identifying signatures of treatment response and resistance is essential to predicting effective treatment strategies. Given the phenotypic clonal heterogeneity observed in MM, we hypothesize that resistant and sensitive subpopulations have unique phenotypic signatures. We used mass cytometry (CyTOF) to characterize primary MM samples and correlated their phenotypic signatures with treatment response. Methods: We characterized 102 primary MM samples by CyTOF using 37 antibodies (see figure). Frozen whole BM specimens were thawed, stained with antibodies, barcoded, and analyzed on a Helios mass cytometer. Data was processed using Cytobank or Pathsetter and analyzed using t-distributed stochastic neighbor embedding (t-SNE) algorithm. T-SNE plots for CD45+ cells were created using lineage defining markers. T-SNE plots of neoplastic PCs (CD138+,CD3-,CD16-,CD19-,cytoplasmic kappa or lambda+) were created using all remaining markers. A differential analysis of each sample's clusters was performed against all other events. Z-score values, representing marker changes from the differential analysis, for each cluster were used to group similar clusters across all samples, allowing us to compare sub populations of PCs between patients (see figure). From these groups of clusters with similar changes in markers, we calculated the proportion of cells with increases or decreases in markers for each sample. Results were compared to IMWG 90-day response criteria using a random forest model. Results: We performed mass cytometry on 102 samples from 68 unique MM patients. Median age was 66 years (range 34-95) with a 1.5:1 male:female ratio. 37 (36%) had NDMM, 55 (54%) had RRMM, 2 (2%) had SMM and 8 cases (8%) had unknown status. FISH studies revealed 47 (46%) had hyperdiploidy without a recurrent primary IgH rearrangement, 31 (30%) had t(11;14), 13 (13%) had t(4;14), 5 (5%) had 14;16 or 14;20, 3 (3%) had hyperhaploidy and 3 (3%) had an undefined primary. 54 (53%) had 1q gain/amp, 22 (22%) had TP53 deletion, 22 (22%) had a MYC rearrangement and 71% had high mSMART risk. Nearly all patients received an immunomodulatory compound, a proteasome inhibitor and dexamethasone. 2 (2%) had complete response (CR), 10 (9.8%) very good partial response (VGPR), 36 (35%) had partial response (PR), 14 (14%) had minimal response (MR), 13 (13%) had stable disease (SD) and 17 (17%) had progressive disease (PD). Characterization of CD45+ cells identified a relatively conserved and reproducible map from each patient. Notable differences included an ~2-fold reduction in the percentage of total B cells (CD45+,CD3-,CD19+)/total CD45+ cells in cases with high risk mSMART, RRMM and TP53 deletion compared to cases with standard risk, NDMM or normal TP53 status. Similar B-cell reductions were found in RRMM vs. NDMM samples from matched-patients. In contrast to the conserved t-SNE structure of the CD45+ population, the neoplastic PC population displayed dramatically reduced t-SNE structural consistency. Heterogeneity between patient samples and between the same patient samples over time was observed. We focused our analysis on 22 patients with serial samples. The proportion of each t-SNE group present in each patient sample was calculated and then correlated to the 90-day response metric. We identified a phenotypic group whose proportion increased in relation to disease progression (R2=0.85). This group had elevated BCL-2, pSTAT3, CD56 levels and reduced CD45 and CD81 levels. Evaluating individual markers, we found an increase in the proportion of cells with elevated BCL-2, pS6, Ki67, and IKZF1 levels in relation to disease progression. We also observed an increase in the proportion of cells with reduced pCREB levels and a reduction in the proportion of cells with reduced CD147 levels in relation to disease progression. Conclusions: We have utilized CyTOF to characterize phenotypic and functional markers in MM cells on a single cell level and can identify unique subgroups that change in relation to disease progression. Future analysis will correlate phenotypic signatures with ISS stage, mSMART risk, cytogenetic abnormality and type of treatment. Figure 1 Disclosures Kumar: Kite Pharma: Consultancy, Research Funding; Oncopeptides: Consultancy, Other: Independent Review Committee; IRC member; Celgene/BMS: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Takeda: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Sanofi: Research Funding; MedImmune: Research Funding; Karyopharm: Consultancy; Merck: Consultancy, Research Funding; Carsgen: Other, Research Funding; Cellectar: Other; Novartis: Research Funding; Adaptive Biotechnologies: Consultancy; AbbVie: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Genecentrix: Consultancy; Genentech/Roche: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Janssen Oncology: Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments; Amgen: Consultancy, Other: Research funding for clinical trials to the institution, Consulting/Advisory Board participation with no personal payments, Research Funding; Tenebio: Other, Research Funding; BMS: Consultancy, Research Funding; Dr. Reddy's Laboratories: Honoraria.

Introduction: Novel targeted therapies have revolutionized treatments for both upfront and relapsed CLL. However, effective therapies have not been established for progressive CLL after developing resistance to both BTK inhibitor and BCL-2 inhibitors. Similarly, Richter's transformation (RS) remains an area of unmet clinical need. Hypothesizing that progressive CLL and RS possess significant but differential molecular abnormalities, we aimed to dissect the initial genomic profiles in a clinical cohort of patients seen in our routine clinical practice using targeted DNA sequencing and transcriptome analysis. Methods: Twelve CLL patients who had lymph node biopsies for evaluation of clinical progression at Mayo Clinic were included in this study. RNA sequencing and targeted DNA sequencing on a focused oncology panel of 1,711 genes were performed on Illumina HiSeq through Tempus Labs, Inc. (Chicago, IL). Differentially expressed (DE) genes were identified using edgeR at the cutoff of FDR <=0.05 and fold-change >=2. Somatic mutations and copy number alternations (CNAs) were identified through Mutect2 and PatternCNV, respectively. CN loss was defined to have log2(ratio) <=-0.93 and CN gain to have log2(ratio) >= 0.585. Pathway analysis was done through the Enrichr package. Results: Among the 12 patients, 7 had confirmed RS (large cell lymphoma histology) and 5 had CLL based on pathological findings. The clinical and therapeutic history for each patient is illustrated in Fig 1A. FISH results from 11 of the patients showed chromosomal abnormalities including del(13q), del(11q), del(17p) and tri(12) (Fig 1A). Hierarchical clustering of expression profiles from the top 10,000 most variable genes in expression revealed two major clusters (Fig 1A). Cluster C1 contains five RS cases, of which case S207 did not receive ibrutinib, S256 was on high-dose steroid and anti-CD20 post-ibrutinib therapy for >1 year before biopsy, and case S025 was treated for < 6 months with ibrutinib. These three form a subgroup separate from the other two RS in C1 who had 8 and 42 months of continuous ibrutinib therapy pre-biopsy, respectively. C2 contains two RS and five CLL cases. Within C2, CLL and RS cases showed similar transcriptomic profiles. Over 90% of the DE genes showed an overall increase of expression in C1, particularly within the 3-sample subgroup (Fig 1B). These genes are enriched in pathways known to play key roles in CLL, including PI3K-AKT signaling, NOTCH signaling and RAS/MAPK signaling, while genes up-regulated in C2 are enriched in WNT signaling. The results suggest marked differences in gene expression profiles and indicate molecular heterogeneity among RS and CLL patients. In addition, targeted DNA sequencing data were analyzed to identify somatic mutations and CNAs. The analysis confirmed the FISH results of del(13q), del(11q), del(17p) or tri(12) in 8 of the cases (Fig 1A). The mutation profiles are similar between C1 and C2 for the recurrently mutated genes published in CLL. However, we identified recurrent CNAs that occurred preferentially in C1 (covering 9 genes) or C2 (48 genes) (Fig 1C). Manual inspections of the reads coverage revealed that 5 of the 9 genes are from chr1q21.1 to chr1q22 that showed clear CN gains largely in C1. Of the 48 genes, 6 are from chrXp11 that showed CN losses, and 27 are from chr17q12 to chr17q25 that showed CN gains. Therefore, the majority of the 57 genes are from large-scale CNAs. Notably, for the CNAs mostly occurring in C2, the affected genes are enriched in WNT signaling, Cell cycle/protein kinase activity and chromatin modifying enzymes. Collectively, the results support the variability of transcriptomic signatures and genetic lesions between RS and CLL. Conclusion: This study revealed a noticeable heterogeneity in gene expression and genetic variation between RS and CLL, albeit in a small cohort. The vast majority of the DE genes showed elevated expression in C1 containing only RS cases, and these genes are enriched in PI3K-AKT signaling, NOTCH signaling, and RAS/MAPK signaling. The pattern is more pronounced for a subgroup in C1 who were overall less exposed to ibrutinib. For somatic variation, there is a strong tendency of occurrence of recurrent CNAs towards C2 cohort, mainly involving gains of chr17q and deletions of chrXp11. Given these advances we are encouraged to further pursue the genetic abnormalities with defined roles in aggressive and transformed CLL. Disclosures Liu: Eisal: Research Funding; Genentech: Research Funding; GRAIL: Research Funding; Menarini Silicon Biosystems: Research Funding; Merck: Research Funding; Seattle Genetics: Research Funding; Tesaro: Research Funding. Parikh:Genentech: Honoraria; Ascentage Pharma: Research Funding; AbbVie: Honoraria, Research Funding; Verastem Oncology: Honoraria; Pharmacyclics: Honoraria, Research Funding; GlaxoSmithKline: Honoraria; Merck: Research Funding; MorphoSys: Research Funding; TG Therapeutics: Research Funding; AstraZeneca: Honoraria, Research Funding; Janssen: Honoraria, Research Funding. Wang:Incyte: Research Funding; Innocare: Research Funding; Novartis: Research Funding. Kenderian:Gilead: Research Funding; Humanigen: Consultancy, Patents & Royalties, Research Funding; MorphoSys: Research Funding; Lentigen: Research Funding; Novartis: Patents & Royalties, Research Funding; Torque: Consultancy; Mettaforge: Patents & Royalties; BMS: Research Funding; Tolero: Research Funding; Sunesis: Research Funding; Juno: Research Funding; Kite: Research Funding. Kay:Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Juno Theraputics: Membership on an entity's Board of Directors or advisory committees; Cytomx: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Agios Pharma: Membership on an entity's Board of Directors or advisory committees; Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; MEI Pharma: Research Funding; Acerta Pharma: Research Funding; Rigel: Membership on an entity's Board of Directors or advisory committees; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Abbvie: Research Funding; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding. Braggio:DASA: Consultancy; Bayer: Other: Stock Owner; Acerta Pharma: Research Funding. Ding:Astra Zeneca: Research Funding; DTRM: Research Funding; Beigene: Membership on an entity's Board of Directors or advisory committees; alexion: Membership on an entity's Board of Directors or advisory committees; MEI Pharma: Membership on an entity's Board of Directors or advisory committees; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; Octapharma: Membership on an entity's Board of Directors or advisory committees; Abbvie: Research Funding.

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.