Analytical and clinical validation of the BostonGene tumor portrait assay.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e15085-e15085
Author(s):  
Katerina Nuzhdina ◽  
Yaroslav Lozinsky ◽  
Svetlana Podsvirova ◽  
Arthur Baisangurov ◽  
Kelley Morgan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Copy Number ◽  
False Negative ◽  
Molecular Profile ◽  
Precision Oncology ◽  
Clinical Validation ◽  
Gene Fusions ◽  
Call Rate ◽  
Rna Seq

e15085 Background: Analysis of the genetic and transcriptomic profile of solid tumors via next-generation sequencing (NGS) assays is fundamental to propel precision medicine into clinical practice. NGS technology applied to tumor analysis allows for the characterization of somatic alterations, clonality, altered gene expression, and other parameters using a small amount of tissue. Therefore, to uncover cancer-promoting and suppressing activity of the tumor and the tumor microenvironment (TME), we developed the BostonGene TUMOR PORTRAIT assay, integrating whole-exome sequencing (WES) and mRNA sequencing (RNA-seq). Here, we demonstrate the analytical and clinical validity of the assay. Methods: The accuracy, reproducibility, and robustness of the BostonGene assay were evaluated using reference genomic DNA, reference RNA, well-characterized cell lines, and fresh frozen (FF) tumor and normal tissue containing known single nucleotide variants (SNVs), indels, copy number alterations (CNAs), gene fusions and a reference RNA Spike-In mix containing known levels of specific transcripts. The analysis was performed using the BostonGene automated pipeline. Additionally, we demonstrated high concordance of gene expression measured using two orthogonal techniques, RNA-seq and RT-PCR. Results: The BostonGene TUMOR PORTRAIT assay demonstrated high specificity (>98.1%, >99.8%, >96.9%) and sensitivity (>98.3%, >99.2%, >97.1%) for the detection of SNVs, indels, and CNAs, respectively, with low false-positive and false-negative rates. The assay demonstrated a 100% concordance in the mutation (SNV/indel) call rate across all replicates, and a 100% concordance in the mutation (SNV/indel) and copy number variation (CNV) call rate across all runs. The measurement of gene expression from RNA-seq was achieved with high accuracy (>0.96%) and low variation across genes (<6.0%), demonstrating the ability of the assay to provide transcriptomic information. Furthermore, gene fusions were detected in RNA-seq data with high sensitivity (>95.8%) and specificity (>99%) in a reproducible manner. Using the integrated pipeline that utilizes both WES and RNA-seq, we were able to accurately compute all disease-relevant molecular parameters including tumor genomics, tumor transcriptome phenotype, expression of clinically actionable therapeutic targets, tumor microenvironment composition, and tumor clonality within the single BostonGene TUMOR PORTRAIT report. Conclusions: Analytical and clinical validation results show that the BostonGene TUMOR PORTRAIT assay, based on the sequencing of DNA and RNA, provides a comprehensive and accurate view of the tumor molecular profile, identifying all clinically actionable genetic, transcriptomic, and TME targets. Validation of the BostonGene TUMOR PORTRAIT assay provides a solid foundation for the future development of precision oncology.

scholarly journals Systematic discovery of gene fusions in pediatric cancer by integrating RNA-seq and WGS

2021 ◽  
Author(s):  
Ianthe A.E.M. van Belzen ◽  
Casey Cai ◽  
Marc van Tuil ◽  
Shashi Badloe ◽  
Eric Strengman ◽  
...  
Keyword(s):  
Decision Making ◽  
Pediatric Cancer ◽  
Copy Number ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Specific Gene ◽  
Precision Oncology ◽  
Gene Fusions ◽  
Rna Seq ◽  
High Confidence

Background Gene fusions are important cancer drivers in pediatric cancer and their accurate detection is essential for diagnosis and treatment. Clinical decision-making requires high confidence and precision of detection. Recent developments show RNA sequencing (RNA-seq) is promising for genome-wide detection of fusion products, but hindered by many false positives that require extensive manual curation and impede discovery of pathogenic fusions. Results We developed Fusion-sq to detect tumor-specific gene fusions by integrating and 'fusing' evidence from RNA-seq and whole genome sequencing (WGS) using intron-exon gene structure. In a pediatric pan-cancer cohort of 130 patients, we identified 165 high confidence tumor-specific gene fusions and their underlying structural variants (SVs). This includes all clinically relevant fusions known to be present in this cohort (30 patients). Fusion-sq distinguishes healthy-occurring from tumor-specific fusions, and resolves fusions in amplified regions and copy number unstable genomes. A high gene fusion burden is associated with copy number instability. We identified 27 potentially pathogenic fusions involving oncogenes or tumor-suppressor genes characterised by underlying SVs or expression changes indicative of activating or disruptive effects. Conclusions Our results indicate how clinically relevant and potentially pathogenic gene fusions can be identified and their functional effects investigated by combining WGS and RNA-seq. Integrating RNA fusion predictions with underlying SVs advances fusion detection beyond extensive manual filtering. Taken together, we developed a method for identifying candidate fusions that is suitable for precision oncology applications. Our method provides multi-omics evidence for assessing the pathogenicity of tumor-specific fusions for future clinical decision making.

A Comparison of Clinical FISH and Sequencing Based FISH Estimates in Multiple Myeloma: An Mmrf Commpass Analysis

Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 374-374 ◽  
Author(s):  
Chase Miller ◽  
Jennifer Yesil ◽  
Mary Derome ◽  
Andrea Donnelly ◽  
Jean Marrian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Rna Sequencing ◽  
Plasma Cell ◽  
Copy Number ◽  
Target Gene ◽  
False Negative ◽  
Single Individual ◽  
Sequencing Data ◽  
Target Gene Expression

Abstract Fluorescent in situ hybridization (FISH) is commonly used in the multiple myeloma field to subtype and risk-stratify patients. There are many benefits to FISH based assays, which are widely used around the world and represent true single cell assays. However, there are significant discrepancies in the specific assays, utilization of reflex testing strategies, and enumeration requirements between clinical centers. By comparison next-generation sequencing tests can be designed to simultaneously detect the copy number abnormalities and translocations detected by clinical FISH along with gene mutations that cannot be detected by FISH. As part of the MMRF CoMMpass Study we have compared the results attained using clinical FISH assays compared to sequencing based FISH (Seq-FISH) results. Clinical FISH reports from a random subset of 339 CoMMpass patients were extraction by a single individual based on the ISCN result lines of each report. To validate the accuracy of the central data extraction, two independent cross validations of 10% of the cohort were performed, after which our data entry error rate is expected to be less than 0.348%. The Seq-FISH results were extracted from the whole genome sequencing data available from each patient using a rapid and fully automated informatics process and the results were cross-validated using the matching exome sequencing data for copy number abnormalities and by RNA sequencing data for dysregulated immunoglobulin translocation target genes. There were 230 patients with clinical FISH and Seq-FISH results. In this cohort, 151 translocations were identified by Seq-FISH. This includes translocations to MYC, CCND2, MAFA, and those involving IgK and IgL, which are not tested by clinical FISH. After filtering non-tested translocations there are 118 translocations identified by Seq-FISH. Only 97 of these translocations had a clinical FISH assay performed with 89 (91.75%) of these being detected by clinical FISH, yet spiked target gene expression was observed in all 89 cases by RNA sequencing. Conversely, 93 translocations were called by clinical FISH, of these 89 were called by Seq-FISH(95.7%). Of the 4 translocations only called by clinical FISH, 3 were t(4;14) and 1 was a t(11;14). In two of these t(4;14) cases we did observe spiked target gene expression by RNA sequencing, suggesting these are false negatives by Seq-FISH. However, the remaining two events appear to be false positive clinical FISH results. The t(4;14) event was only observed in 1/200 cells and a co-occuring t(11;14) was also called, which was confirmed by Seq-FISH and spiked gene expression. Similarly, the one t(11;14) was observed in 3/56 cells but a del13q14 was seen in 47/50 cells, unfortunately RNA sequencing data is not available to cross-validate in this case. Plasma cell enrichment or identification is commonly used to prepare myeloma samples for FISH because even in myeloma, the total plasma cell percentage can be low (median 8.3% in the MMRF CoMMpass Baseline Cohort). Therefore, performing FISH on a sample without performing purification or plasma cell identification will indiscriminately assay non-plasma cells and limit the efficacy of the assay. We looked at the two most common translocations in myeloma, t(4;14) and t(11;14), to test the effect of enrichment on sensitivity. Sensitivity was higher for both sets of translocations in the enriched cohort. There was 1 false negative in the enriched population, yielding sensitivities of 100% (32/32) and 95%(19/20) for CCND1 and WHSC1 respectively. For those reports that did not indicate enrichment was performed the observed sensitivities were 86.36% (19/22) and 92.86% (13/14). Seq-FISH identified almost all of the translocations called by clinical FISH and simultaneously; it identified 30 translocations missed by clinical FISH. The translocations that were not reported by clinical FISH can be attributed to a mixture of the correct assay not being performed and the translocation being missed even though the assay was performed. We believe that Seq-FISH is a viable alternative to clinical FISH, with similar specificity and greater sensitivity. It is important to note that a single Seq-FISH assay is sufficient to investigate all translocations, while each translocation must be investigated separately with clinical FISH. As such, Seq-FISH obviates the concern that a translocation would be missed because the correct assay was not performed. Disclosures McBride: Instat: Employment.

scholarly journals Effect of method of deduplication on estimation of differential gene expression using RNA-seq

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3091 ◽  
Author(s):  
Anna V. Klepikova ◽  
Artem S. Kasianov ◽  
Mikhail S. Chesnokov ◽  
Natalia L. Lazarevich ◽  
Aleksey A. Penin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Cancer Genomics ◽  
False Negative ◽  
Read Length ◽  
Rna Seq ◽  
Negative Results ◽  
Paired Samples ◽  
Highly Expressed Genes ◽  
Differential Gene

BackgroundRNA-seq is a useful tool for analysis of gene expression. However, its robustness is greatly affected by a number of artifacts. One of them is the presence of duplicated reads.ResultsTo infer the influence of different methods of removal of duplicated reads on estimation of gene expression in cancer genomics, we analyzed paired samples of hepatocellular carcinoma (HCC) and non-tumor liver tissue. Four protocols of data analysis were applied to each sample: processing without deduplication, deduplication using a method implemented in samtools, and deduplication based on one or two molecular indices (MI). We also analyzed the influence of sequencing layout (single read or paired end) and read length. We found that deduplication without MI greatly affects estimated expression values; this effect is the most pronounced for highly expressed genes.ConclusionThe use of unique molecular identifiers greatly improves accuracy of RNA-seq analysis, especially for highly expressed genes. We developed a set of scripts that enable handling of MI and their incorporation into RNA-seq analysis pipelines. Deduplication without MI affects results of differential gene expression analysis, producing a high proportion of false negative results. The absence of duplicate read removal is biased towards false positives. In those cases where using MI is not possible, we recommend using paired-end sequencing layout.

scholarly journals MLW-gcForest: a multi-weighted gcForest model towards the staging of lung adenocarcinoma based on multi-modal genetic data

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Yunyun Dong ◽  
Wenkai Yang ◽  
Jiawen Wang ◽  
Juanjuan Zhao ◽  
Yan Qiang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Adenocarcinoma ◽  
Copy Number ◽  
Genetic Data ◽  
Small Samples ◽  
High Dimensional ◽  
Methylation Data ◽  
Rna Seq ◽  
Number Variation ◽  
Staging Model

Abstract Background Lung cancer is one of the most common types of cancer, among which lung adenocarcinoma accounts for the largest proportion. Currently, accurate staging is a prerequisite for effective diagnosis and treatment of lung adenocarcinoma. Previous research has used mainly single-modal data, such as gene expression data, for classification and prediction. Integrating multi-modal genetic data (gene expression RNA-seq, methylation data and copy number variation) from the same patient provides the possibility of using multi-modal genetic data for cancer prediction. A new machine learning method called gcForest has recently been proposed. This method has been proven to be suitable for classification in some fields. However, the model may face challenges when applied to small samples and high-dimensional genetic data. Results In this paper, we propose a multi-weighted gcForest algorithm (MLW-gcForest) to construct a lung adenocarcinoma staging model using multi-modal genetic data. The new algorithm is based on the standard gcForest algorithm. First, different weights are assigned to different random forests according to the classification performance of these forests in the standard gcForest model. Second, because the feature vectors generated under different scanning granularities have a diverse influence on the final classification result, the feature vectors are given weights according to the proposed sorting optimization algorithm. Then, we train three MLW-gcForest models based on three single-modal datasets (gene expression RNA-seq, methylation data, and copy number variation) and then perform decision fusion to stage lung adenocarcinoma. Experimental results suggest that the MLW-gcForest model is superior to the standard gcForest model in constructing a staging model of lung adenocarcinoma and is better than the traditional classification methods. The accuracy, precision, recall, and AUC reached 0.908, 0.896, 0.882, and 0.96, respectively. Conclusions The MLW-gcForest model has great potential in lung adenocarcinoma staging, which is helpful for the diagnosis and personalized treatment of lung adenocarcinoma. The results suggest that the MLW-gcForest algorithm is effective on multi-modal genetic data, which consist of small samples and are high dimensional.

scholarly journals OTHR-14. An immunogenomic analysis of melanoma brain metastases (MBM) compared to extracranial metastases (ECM)

2021 ◽  
Vol 3 (Supplement_3) ◽  
pp. iii17-iii17
Author(s):  
Lucy Boyce Kennedy ◽  
Amanda E D Van Swearingen ◽  
Jeff Sheng ◽  
Dadong Zhang ◽  
Xiaodi Qin ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Pathway Analysis ◽  
Immune Cell ◽  
Single Gene ◽  
Enrichment Analysis ◽  
Cell Types ◽  
Gene Set Enrichment Analysis ◽  
Molecular Profile ◽  
Cancer Center ◽  
Rna Seq

Abstract Background MBM have a unique molecular profile compared to ECM. Methods We analyzed a previously published dataset from MD Anderson Cancer Center, including RNA-seq on surgically resected, FFPE MBM and ECM from the same patients. STAR pipeline was used to estimate mRNA abundance. DESeq2 package was used to perform differential gene expression (DGE) analyses. Pathway analysis was performed using Gene Set Enrichment Analysis (GSEA). Paired DGE and GSEA compared MBM vs. lymph node (LN) metastases (n = 16) and MBM vs. skin mets (n = 10). CIBERSORTx estimated relative abundance of immune cell types in MBM and ECM. GATK Mutect2 pipeline was used to call somatic mutations using paired normal tumor samples. Mutations were annotated using the Ensembl Variant Effect Predictor and visualized using the Maftools package in R. RNA-seq was available on 54 human primary cutaneous melanomas (CM). Gene Ontology or KEGG Pathway analysis was performed using goana function of limma package in R. Results Paired GSEA found that autophagy pathways may be up-regulated in MBM vs. LN and MBM vs. skin mets. On a single-gene level, the most strongly up-regulated genes in autophagy pathways were GFAP and HBB. Fold changes in other autophagy-related genes were low and did not reach significance. Comparison between CM which recurred in brain vs. CM which did not recur identified up-regulation of autophagy pathways. CIBERSORTx identified an increased proportion of immune suppressive M2 macrophages compared to tumor suppressive M1 macrophages in MBMs and ECMs. Conclusion Up-regulation of autophagy pathways was observed in patient-matched MBM vs. LN and skin mets. This finding was driven by up-regulation of GFAP and HBB, which could reflect changes in the tumor microenvironment. Higher M2:M1 ratio may contribute to an immune suppressive tumor microenvironment and may be targetable. Validation of our findings in an independent Duke dataset is ongoing.

scholarly journals Analytical and Clinical Validation of an Amplicon-based Next Generation Sequencing Assay for Ultrasensitive Detection of Circulating Tumor DNA

2021 ◽  
Author(s):  
Jonathan Poh ◽  
Kao Chin Ngeow ◽  
Michelle Pek ◽  
Kian-Hin Tan ◽  
Jing Shan Lim ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Copy Number ◽  
Point Mutations ◽  
Circulating Tumor Dna ◽  
Clinical Validation ◽  
Gene Fusions ◽  
Copy Number Alterations ◽  
Next Generation ◽  
Tumor Dna ◽  
Generation Sequencing

Next-generation sequencing of circulating tumor DNA presents a promising approach to cancer diagnostics, complementing conventional tissue-based diagnostic testing by enabling minimally invasive serial testing and broad genomic coverage through a simple blood draw to maximize therapeutic benefit to patients. LiquidHALLMARK® is an amplicon-based next-generation sequencing assay developed for the genomic profiling of plasma-derived cell-free DNA. The comprehensive 80-gene panel profiles point mutations, insertions/deletions, copy number alterations, and gene fusions, and further detects oncogenic viruses (EBV and HBV) and microsatellite instability. Here, the analytical and clinical validation of the assay is reported. Analytical validation using reference genetic materials demonstrated a sensitivity of 99.38% for point mutations and 95.83% for insertions/deletions at 0.1% variant allele frequency (VAF), and a sensitivity of 91.67% for gene fusions at 0.5% VAF, with high specificity even at 0.1% VAF (99.11% per-base). The limit of detection for copy number alterations, EBV, HBV, and microsatellite instability were also empirically determined. Orthogonal comparison of EGFR variant calls made by LiquidHALLMARK and a reference allele-specific PCR method for 355 lung cancer specimens revealed an overall concordance of 93.80%, while external validation with cobas® EGFR Mutation Test v2 for 50 lung cancer specimens demonstrated an overall concordance of 84.00%, with a 100% concordance rate for EGFR variants above 0.4% VAF. Clinical application of LiquidHALLMARK in 1,592 consecutive patients demonstrated a high detection rate (74.8% alteration-positive in cancer samples) and broad actionability (50.0% of cancer samples harboring alterations with biological evidence for actionability). Among ctDNA-positive lung cancers, 72.5% harbored at least one biomarker with a guideline-approved drug indication. These results establish the high sensitivity, specificity, accuracy, and precision of the LiquidHALLMARK assay and supports its clinical application for blood-based genomic testing.

scholarly journals Integration of Genetic, Transcriptomic, and Immune Profiles Reveals Genomically-Distinct Populations in Low-Grade Lymphomas

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 2764-2764
Author(s):  
Keenan T. Hartert ◽  
Kerstin Wenzl ◽  
Jordan Krull ◽  
Michelle Manske ◽  
Vivekananda Sarangi ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Mayo Clinic ◽  
Copy Number ◽  
Research Funding ◽  
Marginal Zone ◽  
Low Grade ◽  
Rna Seq ◽  
Copy Number Alterations ◽  
Genetics Research

Introduction: Current classifications of low-grade B-cell lymphomas (LGL), including splenic marginal zone lymphomas (SMZL), nodal marginal zone lymphomas (NMZL), and Lymphoplasmacytic lymphomas (LPL) are based on a mixture of clinical features and morphologic, immunophenotypic, and genetic findings from the tumor biopsy specimen. While this approach to classification makes pathologic diagnosis more precise, the corresponding clinical impact for the timing and choice of treatment is limited, and differentiating between cases can be challenging. Although LGL are considered indolent and the 10-year overall survival (OS) is about 80%, 70% of cases will eventually require treatment and approximately 30% of patients display a more aggressive phenotype and have a poor prognosis. Consequently, further investigations into the driving genetic, biological, and immune mechanisms of LGL are essential for early identification of high-risk patients and design of personalized treatments. Materials and Methods: RNA-seq was performed on 63 newly diagnosed LGL patient samples from the Mayo Clinic/University of Iowa Lymphoma SPORE: SMZL (N=48), NMZL (N=6), LPL (N=5), MALT (N=2), and BCL (N=2) as well as 5 normal memory B cell controls (CD19+CD27+). For identification of biologic clusters, filtered RNA-seq data was analyzed using the Non-negative Matrix Factorization (NMF) clustering tool from the Broad Institute against the normal samples. Each cluster was analyzed for differential gene expression. This analysis generated cluster-specific T values for each gene. Genes that were significantly associated with a cluster (FDR<0.05) were analyzed for genetic ontology. Cibersort was used to deconvolute the tumor microenvironment (TME). We performed whole exome sequencing (WES) analyses on 60/63 matched LGL to characterize mutations and copy number alterations. Results: WES revealed the presence of previously reported mutations in genes such as MYD88, SPEN, NOTCH, and KLF2 as well as copy number alterations 7q31.2, BCL6, TNFAIP3, and BCL2. NMF clustering of RNA-seq data resulted in a best fit of 5 clusters, named LGL1 through LGL4 and "Normals". Pathologic subtypes were not exclusive to a specific cluster. Differential RNA expression analysis resulted in gene sets that were differentially expressed in each cluster. A cluster signature of the top 1% of associated genes (N=94) was created for each. LGL1 genes were associated with higher BCR signaling. LGL2 was characterized by regulatory dysfunction, particularly concerning mitochondrial integrity. LGL3 genes were associated with high TME and NOTCH and STAT signaling components. Specifically, LGL3 was associated with high CD8+ T-cell and M2 macrophage TME presence. LGL4 was distinguished by the presence of aggressive genetic programs related to B-cell activation such as NF-kB and IRF4. Consequently, LGL4 patients had significantly poorer rates of event-free survival (EFS) (P=0.029) and OS (P=0.006) when compared against the other groups. The top 1% gene signature (N=94) was next validated against an independent cohort of 84 LGL samples with gene expression data (SMZL [N=34], NZML [N=24], and LPL [N=24]). A similar cluster pattern emerged, with pathological subtypes distributing across LGL classifications based on respective transcriptomic signatures. Association of DNA variants within each cluster was also analyzed, but due to the low frequency of genomic variants detected in LGL, nothing was significantly associated. However, we did see an enrichment of BCL2, BCL6, and TNFAIP3 alterations in LGL4 and an overall lower driver gene mutation frequency in LGL2. Conclusions: Gaining a greater understanding of LGL based on their genetic, biologic, and immune profiles will establish a valuable platform for clinicians to identify high-risk cases and make better therapeutic decisions. Using a large cohort of well-annotated cases we identify novel clusters of LGL that present unique genomic and clinical profiles. Our study lays the groundwork for a precision therapy approach in LGL in which DNA or RNA profiles can be used to identify patients early in treatment who may not benefit from the current standard of care and who would benefit from closer monitoring and targeted agents. Disclosures Anagnostou: American Society of Hematology, Mayo Clinic/Iowa Lymphoma SPORE, Mayo Clinic Immune Monitoring Core, Mayo Clinic Hematology Small Grant: Research Funding. Ansell:Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment. Cerhan:Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; NanoString: Research Funding. Novak:Celgene Coorperation: Research Funding.

An immunogenomic analysis of melanoma brain metastases (MBM) compared to extracranial metastases (ECM).

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 9521-9521
Author(s):  
Lucy Kennedy ◽  
Amanda E.D. Van Swearingen ◽  
Jeff Sheng ◽  
Dadong Zhang ◽  
Xiaodi Qin ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Pathway Analysis ◽  
Immune Cell ◽  
Single Gene ◽  
Fold Change ◽  
Gene Set Enrichment Analysis ◽  
Molecular Profile ◽  
Cancer Center ◽  
Rna Seq ◽  
Primary Cutaneous Melanoma

9521 Background: Previous work has shown that MBM have a unique molecular profile compared to ECM. Description of the biology of MBM will facilitate the design of rational therapies for patients (pts) with MBM. Methods: We analyzed a previously published dataset from MD Anderson Cancer Center, which includes RNA-seq on surgically resected FFPE MBM (88 tumors from 74 pts) and surgically resected ECM from the same pts (50 from 34 pts). WES on 18 matched pairs of MBM and ECM was available. The STAR pipeline was used to estimate mRNA abundance. The DESeq2 package was used to perform differential gene expression (DGE) analyses. Pathway analysis was performed using Gene Set Enrichment Analysis (GSEA). Paired DGE and GSEA analyses comparing MBM vs. lymph node metastases (LN mets, n = 16) and MBM vs. skin mets (n = 10) were performed. CIBERSORT estimated relative abundance of immune cell types in MBM and ECM. The GATK Mutect2 pipeline was used to call somatic mutations using paired normal tumor samples. Mutations were annotated using the Ensembl Variant Effect Predictor and visualized using the Maftools package in R. RNA-seq was available on 54 primary cutaneous melanoma (CM) pt samples, including 19 CM which did not recur, 19 CM which recurred as MBM, and 16 CM which recurred as ECM. Gene Ontology or KEGG Pathway analysis was performed using goana function of limma package in R. Results: Comparing MBM vs. LN and MBM vs. skin mets, paired DGE identified 136 and 89 up-regulated genes with a fold change > 2 and false-discovery rate (FDR) q-value < 0.05. Moreover, 308 and 659 down-regulated genes with a fold change < 0.5 were identified in MBM vs. LN and MBM vs. skin mets, respectively (q < 0.05). Paired GSEA found that autophagy signaling pathways may be up-regulated in MBM vs. LN and MBM vs. skin mets. On a single-gene level, comparing both MBM vs. LN and skin mets, the most strongly up-regulated genes in autophagy pathways were GFAP and HBB, whereas fold changes in the majority of other autophagy-related genes were low and did not reach significance. Comparison between CM which recurred in brain vs. CM which did not recur identified up-regulation of autophagy pathways. No difference in autophagy pathway expression was observed comparing between CM with any recurrence vs. without recurrence. CIBERSORT identified an increased proportion of immune suppressive M2 macrophages compared to tumor suppressive M1 macrophages in both MBMs and ECMs. Conclusions: Up-regulation of autophagy pathways was observed in pt-matched MBM vs. LN and skin mets. This finding seemed to be driven by up-regulation of GFAP and HBB, which could reflect changes in the tumor microenvironment (TME). Future studies using single-cell RNA-seq or spatial transcriptomic technology will dissect the TME. A higher M2:M1 ratio may contribute to an immune suppressive tumor microenvironment in MBM and ECM and is targetable. Validation of our findings in an independent Duke dataset is ongoing.

Large-Scale High Resolution Integration of Copy Number and Gene Expression in DLBCL Reveals Focal and Frequent Deletions in Chromatin Modifying Genes with Outcome Correlation

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 295-295
Author(s):  
Fong Chun Chan ◽  
Susana Ben-Neriah ◽  
Raymond Lim ◽  
Sandy Hu ◽  
Sanja Rogic ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Resolution ◽  
Clinical Outcome ◽  
Copy Number ◽  
Large Scale ◽  
Progression Free Survival ◽  
Rna Seq ◽  
Free Survival ◽  
Modifying Genes ◽  
Genomic Aberrations

Abstract Abstract 295 Introduction: Diffuse large B-cell lymphoma (DLBCL) is the most common type of aggressive non-Hodgkin lymphoma (NHL), accounting for approximately 30–40% of all new lymphoma cases. While standard therapy using rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) has significantly increased the survival of DLBCL patients, approximately one third of DLBCL patients still remain unresponsive to or relapse after standard treatment. Further investigation into the genomic architecture of DLBCL will contribute to elucidating the causes of the poor outcomes in this subgroup of patients. While the copy number and the gene expression profiles of DLBCL specimens have been well described as separate analyses, a large-scale high resolution integration of both orthologous measurements has yet to be reported. The integration of these two data types in a clinically well-annotated cohort of DLBCL is crucial as it can potentially distinguish driver from passenger genomic aberrations and reveal associations with clinical outcome. Methods and Patients: Affymetrix SNP 6.0 microarrays were used to ascertain the copy number profiles in 151 pretreatment biopsies of DLBCL that were representative of the population of DLBCL patients treated at the British Columbia Cancer Agency. Clinical outcome data were available for all 151 patients with 142 patients receiving R-CHOP or R-CHOP-like treatment. Matching RNA-seq libraries were used to quantitate the gene expression levels in 91 samples. The SNP 6.0 pre-processing method cRMAv2 was used to generate raw probe intensities that were then normalized to 1258 HapMap3 SNP 6.0 arrays. Copy number state calls were predicted using HMM-Dosage. RNA-seq data were aligned using the split-read aware aligner GSNAP and gene expression values were generated using the metric reads per kilobase of transcript per million mapped reads. DriverNet analyses were utilized to predict functionally relevant driver genes and outcome correlations in R-CHOP treated patients were performed using Cox regression and the log-rank test. Results: The copy number landscape derived from the SNP 6.0 microarrays revealed previously reported large scale chromosomal deletions in chromosome 6p and amplifications in chromosomes 3, 7 and 18. By integrating the gene expression with copy number data, we found that gene copy number was correlated with its own gene expression (classified as being cis-correlated) in 23.5% of genes. In addition, we investigated copy number aberrations which were highly correlated with gene expression across the genome (classified as trans-correlated). This analysis revealed aberration hotspots in genomic locations 3q26-q28 (TBL1XR1, BCL6, TP63), 17p12 (NCOR1, MAP2K4), 18q11.1-q11.2 (RBBP8) and 22q11.21 (BID, IL17RA) suggesting that these hotspots regulate important pathways that may contribute to the pathogenesis of DLBCL. We identified previously reported focal amplifications (e.g. REL) and deletions (e.g. B2M, CDKN2A). Moreover, we identified novel focal deletions, including homozygous deletions, in chromatin modifying genes: LCOR (7.9%), RCOR1 (9.9%), and NCOR1 (17.9%), all of which were cis-correlated and were validated using fluorescence in situ hybridization. DriverNet analyses identified RCOR1 deletions as one of the main driver alterations. RCOR1 deletions were also found to be associated with progression-free survival (5-year progression-free survival: deleted 40% vs. non-deleted 75%, p=0.0188). Discussion: Our systematic integration of SNP 6.0 and RNA-seq data confirmed findings of previous studies and also revealed novel genomic aberration hotspots and highly focal and frequent deletions in chromatin modifying genes. Results derived from our large-scale high resolution data set indicate the feasibility and efficacy of integrative genomic analyses in revealing novel and pathogenetically relevant genomic aberrations in lymphoid cancers. The discovery of the association of RCOR1 deletions with progression-free survival suggests that RCOR1 deletions could be used as a prognostic marker and might indicate a molecular phenotype that can be targeted by novel therapeutic agents in DLBCL. Disclosures: No relevant conflicts of interest to declare.

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