Tumor mutational burden reference materials for assay standardization.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e14746-e14746
Author(s):  
Matt Butler ◽  
Yves Konigshofer ◽  
Omoshile Clement ◽  
Li Liu ◽  
Chen Zhao ◽  
...  
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Reference Materials ◽  
Large Scale ◽  
Ground Truth ◽  
Consensus Method ◽  
Mutational Burden ◽  
Whole Exome ◽  
Tumor Mutational Burden ◽  
Genomic Aberrations

e14746 Background: Next Generation Sequencing based assays are designed to detect genomic aberrations in a limited number of target regions. However, there is a need for accurate measurement of tumor mutational burden (TMB) as low as 4 to as high as 50. As TMB assessment is added to various targeted panels, consistent results between panels are required to advance the broad use of this biomarker. Properly designed reference materials aid measurement standardization and are required to demonstrate assay concordance. We developed reference materials that vary in TMB score, tumor content 5-50% and are prepared in FFPE format. Methods: Seven lung and two breast tumor cell lines as well as matched “normal” lymphoblastoid cell lines were expanded in cell culture. Genomic DNA (gDNA) from each cell line was extracted. Tumor/normal mixes were made by mixing DNA and by embedding cells in FFPE blocks. Whole exome sequencing (WES) results were obtained using Agilent SureSelectXT for library construction and an Illumina Novaseq for sequencing. The Friends of Cancer Research TMB consensus method for analyzing WES data was used to filter variants and calculate TMB scores. Results: The cell lines were grown at large scale to produce extractable gDNA. 100% gDNA tumor, 30% gDNA tumor mixes and 30% FFPE cell line mixes were prepared. Preliminary results show that a clinically-relevant range of TMB values ranging from 4 to 35 mutations per million bases. The several thousand mutations that were observed across the lines were found in a variety of genes, which may explain why TMB in targeted panels is influenced by the specific target regions. Also, the initial results show that 30% cell line mixes showed similar TMB results to 100% gDNA. Conclusions: Our approach with wide ranging TMB values as tumor normal mixes is flexible and can be used to test different tumors and assays. For this study we established WES as the ground truth measurement for comparison to other assay formats and obtained comparison data from other panels. This approach also allows laboratories to test additional variables including formalin fixation, sample extraction, gene panel size, target regions, sequencing depth, filtering and limits of detection.

scholarly journals 61 Tumor mutational burden assessments by two commercial targeted sequencing assays

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A66-A66
Author(s):  
Ruchi Chaudhary ◽  
Gitanjali Vaidya ◽  
Meeta Sunil ◽  
SM Sakthivel Murugan ◽  
VL Ramprasad ◽  
...  
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Large Intestine ◽  
Positive Control ◽  
Negative Control ◽  
Targeted Sequencing ◽  
Failure Rates ◽  
Mutational Burden ◽  
Ffpe Samples ◽  
Tumor Mutational Burden

BackgroundTumor mutational burden (TMB) is a key biomarker for immune checkpoint inhibitor across several cancer types. While TMB as calculated from whole exome sequencing of the tumor tissue is still the gold standard, enabling TMB in clinical labs requires targeted sequencing panels for faster turnaround time and low input requirements. Herein, we assess two commercially available, targeted sequencing (research-use-only) TMB assays for the possibility of offering in the Medgenome labs.MethodsTwo assays, Oncomine Tumor Mutation Load Assay (or Oncomine) by Thermo Fisher Scientific and QIAseq Tumor Mutational Burden Panel (or QIAseq) by Qiagen, were studied. One negative control (NA12878), five positive control (A549, lung; T47D, breast; SKMEL2, skin; HCT-15, large intestine; HCT116, large intestine) cell lines, and 18 FFPE (13 colon, 1 lung, 1 testicular, and 1 oral cancer; 2 healthy) samples were ran on both assays. Sample QC was performed through measuring DNA fragmentation on TapeStation and concentration on Qubit. Failure rates on FFPE samples were investigated. TMB values by both assays were compared on all samples, as well as with expected TMB on cell line samples. Expected TMB on the negative control was considered zero; expected TMB for positive cell lines was calculated by restricting somatic mutations (from cBioPortal.com) to each panel, normalizing by panel size, and averaging. TMB values of 3 samples with known MSI were evaluated and signature patterns of relatively high TMB samples were studied.ResultsOn cell line samples, high correlation (r2 = 0.9994) was observed between TMB values by both assays. TMB values were consistently zero on negative control by both assays. Both assays estimated lower than expected TMB on positive control samples. 6/18 FFPE samples failed on both assays, with Oncomine’s error mode was high deamination (i.e., number of C:G>T:A mutations at low allelic frequency) and QIAseq’s was low confidence (i.e., < 0.9 Mb sequenced panel). All 6 failed samples showed either low DNA integrity (DIN<2) or low concentration (<6 ng/µl). A combined analysis of all QC pass samples showed high correlation (r2 = 0.97) between two assays. TMB values on two MSI cell lines was > 50 by both assays, but 14 by QIAseq and 33 by Oncomine on one MSI FFPE sample. Four out of five FFPE samples with > 25 TMB by both assays displayed MSI signature patterns from COSMIC or incorporated a pathogenic mutation in MLH1 gene.ConclusionsPreliminary analyses showed comparative accuracy and failure rates on FFPE samples. Future analyses will aim at comparison with WES based TMB on reference cell line and FFPE material.

scholarly journals Analysis of tumor mutational burden: correlation of five large gene panels with whole exome sequencing

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Carina Heydt ◽  
Jan Rehker ◽  
Roberto Pappesch ◽  
Theresa Buhl ◽  
Markus Ball ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Whole Exome Sequencing ◽  
Large Gene ◽  
Mutational Burden ◽  
Whole Exome ◽  
Tumor Mutational Burden ◽  
Gene Panels

scholarly journals Clinical advantage of targeted sequencing for unbiased tumor mutational burden estimation in samples with low tumor purity

2020 ◽  
Vol 8 (2) ◽  
pp. e001199
Author(s):  
Tae Hee Hong ◽  
Hongui Cha ◽  
Joon Ho Shim ◽  
Boram Lee ◽  
Jongsuk Chung ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Whole Exome Sequencing ◽  
Predictive Ability ◽  
Progression Free Survival ◽  
Superior Performance ◽  
Enhanced Sensitivity ◽  
Mutational Burden ◽  
Tumor Purity ◽  
Whole Exome ◽  
Tumor Mutational Burden

BackgroundTumor mutational burden (TMB) measurement is limited by low tumor purity of samples, which can influence prediction of the immunotherapy response, particularly when using whole-exome sequencing-based TMB (wTMB). This issue could be overcome by targeted panel sequencing-based TMB (pTMB) with higher depth of coverage, which remains unexplored.MethodsWe comprehensively reanalyzed four public datasets of immune checkpoint inhibitor (ICI)-treated cohorts (adopting pTMB or wTMB) to test each biomarker’s predictive ability for low purity samples (cut-off: 30%). For validation, paired genomic profiling with the same tumor specimens was performed to directly compare wTMB and pTMB in patients with breast cancer (paired-BRCA, n=165) and ICI-treated patients with advanced non-small-cell lung cancer (paired-NSCLC, n=156).ResultsLow tumor purity was common (range 30%–45%) in real-world samples from ICI-treated patients. In the survival analyzes of public cohorts, wTMB could not predict the clinical benefit of immunotherapy when tumor purity was low (log-rank p=0.874), whereas pTMB could effectively stratify the survival outcome (log-rank p=0.020). In the paired-BRCA and paired-NSCLC cohorts, pTMB was less affected by tumor purity, with significantly more somatic variants identified at low allele frequency (p<0.001). We found that wTMB was significantly underestimated in low purity samples with a large proportion of clonal variants undetected by whole-exome sequencing. Interestingly, pTMB more accurately predicted progression-free survival (PFS) after immunotherapy than wTMB owing to its superior performance in the low tumor purity subgroup (p=0.054 vs p=0.358). Multivariate analysis revealed pTMB (p=0.016), but not wTMB (p=0.32), as an independent predictor of PFS even in low-purity samples. The net reclassification index using pTMB was 21.7% in the low-purity subgroup (p=0.016).ConclusionsOur data suggest that TMB characterization with targeted deep sequencing might have potential strength in predicting ICI responsiveness due to its enhanced sensitivity for hard-to-detect variants at low-allele fraction. Therefore, pTMB could act as an invaluable biomarker in the setting of both clinical trials and practice outside of trials based on its reliable performance in mitigating the purity-related bias.

scholarly journals Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration

2018 ◽  
Author(s):  
James M McFarland ◽  
Zandra V Ho ◽  
Guillaume Kugener ◽  
Joshua M Dempster ◽  
Phillip G Montgomery ◽  
...  
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Large Scale ◽  
Cancer Cell Line ◽  
Analytical Framework ◽  
Quality Parameters ◽  
Diverse Range ◽  
Hierarchical Bayesian Inference ◽  
Genome Scale

The availability of multiple datasets together comprising hundreds of genome-scale RNAi viability screens across a diverse range of cancer cell lines presents new opportunities for understanding cancer vulnerabilities. Integrated analyses of these data to assess differential dependency across genes and cell lines are challenging due to confounding factors such as batch effects and variable screen quality, as well as difficulty assessing gene dependency on an absolute scale. To address these issues, we incorporated estimation of cell line screen quality parameters and hierarchical Bayesian inference into an analytical framework for analyzing RNAi screens (DEMETER2; https://depmap.org/R2-D2). We applied this model to individual large-scale datasets and show that it substantially improves estimates of gene dependency across a range of performance measures, including identification of gold-standard essential genes as well as agreement with CRISPR-Cas9-based viability screens. This model also allows us to effectively integrate information across three large RNAi screening datasets, providing a unified resource representing the most extensive compilation of cancer cell line genetic dependencies to date.

scholarly journals Hi-C detects novel structural variants in HL-60 and HL-60/S4 cell lines

2018 ◽  
Author(s):  
Elsie C. Jacobson ◽  
Ralph S. Grand ◽  
Jo K. Perry ◽  
Mark H. Vickers ◽  
Ada L. Olins ◽  
...  
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Myeloid Leukemia ◽  
Large Scale ◽  
Rna Seq ◽  
Structural Variants ◽  
The Stability ◽  
Different Sources ◽  
Identification And Characterization

AbstractCancer cell lines often have large structural variants (SVs) that evolve over time. There are many reported differences in large scale SVs between HL-60 and HL-60/S4, two cell lines derived from the same acute myeloid leukemia sample. However, the stability and variability of inter- and intra-chromosomal structural variants between different sources of the same cell line is unknown. Here, we used Hi-C and RNA-seq to identify and compare large SVs in HL-60 and HL-60/S4 cell lines. Comparisons with previously published karyotypes identified novel SVs in both cell lines. Hi-C was used to characterize the known expansion centered on the MYC locus. The MYC expansion was integrated into known locations in HL-60/S4, and a novel location (chr4) in HL-60. The HL-60 cell line has more within-line structural variation than the HL-60/S4 derivative cell line. Collectively we demonstrate the usefulness of Hi-C and with RNA-seq data for the identification and characterization of SVs.

scholarly journals Landscape of somatic alterations in large-scale solid tumors from an Asian population

2021 ◽  
Author(s):  
Kai Wang ◽  
Qun Wu ◽  
Herui Yao
Keyword(s):  
Large Scale ◽  
Asian Population ◽  
Chinese Patients ◽  
Mutational Burden ◽  
American Patient ◽  
Tumor Tissues ◽  
Tumor Mutational Burden ◽  
Somatic Alterations ◽  
Comprehensive Comparison ◽  
Blood Specimens

Abstract Extending the benefits of tumor molecular profiling for all cancer patients will require comprehensive analysis of tumor genomes across distinct patient populations world-wide. In this study, we performed deep next-generation DNA sequencing (NGS) from tumor tissues and matched blood specimens from over 10,000 patients in China by using a 450-gene comprehensive assay, developed and implemented under international clinical regulations. We performed a comprehensive comparison of somatically altered genes, the distribution of tumor mutational burden (TMB), gene fusion patterns and the spectrum of various somatic alterations between Chinese and American patient populations. In total, 64% of cancers from Chinese patients in this study were found to have clinically actionable genomic alterations, which may affect clinical decisions related to targeted therapy or immunotherapy. These findings describe the similarities and differences between tumors from Chinese and American patients, providing valuable information for personalized medicine.

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