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.
Gene expression profiling is widely used in oncology research and in clinical settings for decision making. Despite the cross-platform correlation of gene expression values, ideally, each measurement should be evaluated against a cohort of samples sequenced using the same methodology. Clinical samples, preserved as FFPEs, often undergo exome capture-based RNA-seq; research samples, stored as fresh/frozen (FF), undergo poly-A RNA-seq, producing high quality expression data. Thus, development of sequencing protocols and data processing algorithms are necessary to provide the same quality gene expression measurements from FFPE samples. Further, while several batch effect correction algorithms exist to neutralize the batch effect between samples across large cohorts, the majority cannot be applied to an individual sample, raising the need to develop an algorithm for single sample projection to improve gene expression-based personalized clinical decision-making. To improve the quality of RNA reads from FFPE tissues, exome capture enrichment of RNA transcripts was optimized and the concordance with poly-A RNA-seq was increased by adding non coding 3’ and 5’ UTR region to the probes. After testing the performance of multiple different extraction methods, a 0.88 correlation was achieved between exome-capture-based and poly-A RNA-seq protocols. To further align the sequencing methodologies, we designed a batch-correction ML-based algorithm by performing a series of paired RNA-seq experiments from the same sample using exome-capture-based and poly-A RNA-seq; we applied linear modeling on the training subset (N = 64) and verified the performance on the validation subset (N = 24). For each gene, 5-20 correlated genes belonging to the TCGA combined pan-cancer datasets were selected and trained using the Lasso model. Over 82% of genes (total N = 20,062) correlated across the two RNA-seq methodologies for each sample after correction (ccc value > 0.5), and approximately 94% of cancer-specific and microenvironment-related genes correlated (ccc value > 0.5). The algorithm significantly outperformed other batch correction methods, with ccc values > 0.8 for 51.37% of the 20,062 genes compared with ~3% for PCA, 26% for MNN, and 28% for ComBat. Our algorithm showed improved performance by correction of 77% of the 1,890 clinically-relevant genes (ccc values > 0.8) compared with 15% for PCA, 39% for MNN, and 40% for ComBat. Here, we developed combinatory technology with a batch correction algorithm trained and developed on FFPE or FF tumor samples, using exome capture-based sequencing or poly-A RNA-seq, that enables the projection of a single sample onto a larger cohort. Future application of this correction tool will enable direct analysis of gene expression of single tumor samples to support potential gene expression-based treatment decisions. Citation Format: Nikita Kotlov, Kirill Shaposhnikov, Cagdas Tazearslan, Ilya Cheremushkin, Madison Chasse, Artur Baisangurov, Svetlana Podsvirova, Svetlana Korkova, Yaroslav Lozinsky, Katerina Nuzhdina, Elena Vasileva, Dmitry Kravchenko, Krystle Nomie, John Curran, Nathan Fowler, Alexander Bagaev. Combinatory technologies for single sample gene expression projection onto a cohort sequenced with a different technology for personalized clinical decision-making [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1216.
Minimally invasive next-generation sequencing-based (NGS) liquid biopsy (LB) tests using cell-free DNA (cfDNA) are increasingly becoming an important tool for the clinical management of cancer patients. The ability to accurately detect molecular alterations from a single plasma sample is valuable for biomarker discovery, diagnostics, and disease monitoring. However, challenges for this assay include low concentrations of circulating tumor DNA (ctDNA) within the cfDNA plasma fraction and PCR and sequencing errors, which can lead to decreased sensitivity in variant calling. Further, somatic mosaicism due to clonal hematopoiesis of indeterminate potential (CHIP) in plasma makes accurate interpretation of LB results a challenge. Incorrect classification of false positives can lead to inappropriate therapeutic management. Here, we present an LB assay composed of a proprietary error reduction algorithm designed to eliminate false positives due to PCR and NGS errors and a white blood cell sequencing (WBC-seq) component to detect and bioinformatically filter out CHIP-related mutations. Consisting of a custom panel of 216 FDA/NCCN pan-cancer genes, our cfDNA LB assay was analytically validated with commercially available reference standards and human cell lines. At a clinically relevant sequencing depth of 4000x, we observed a limit of detection (LOD) of 0.2% variant allele frequency for single nucleotide variants (SNVs) and insertions and deletions (indels) with overall accuracies of 98% and 99% and sensitivities of 91% and 83%, respectively. To assess the LB assay performance on clinical samples, orthogonal whole-exome sequencing (WES) with subsequent standard somatic variant calling was performed on paired peripheral blood lymphocytes (PBLs) and plasma samples from 12 patients. The WES assay results were concordant with those obtained using our LB assay for SNVs (CCC = 0.75) and indels (CCC = 0.90). Further, our assay detected an additional 53% and 50% SNVs and indels, respectively, compared to WES. Finally, 8 out of 11 clinically actionable mutations were detected by both WES and the LB assay, while 3 additional SNVs were detected in the LB assay. Taken together, the use of WBC-seq to filter CHIP-derived mutations combined with our error reduction algorithm resulted in a substantial reduction in false positive variant calls without a compromise in the sensitivity or classification accuracy of SNVs and indels. Concordance was observed between traditional WES analysis and the LB assay developed here. These findings highlight the potential for our LB cfDNA assay to be used to improve personalized therapeutic options and guide clinical decision making. Citation Format: Anastasiya Yudina, Alexey Efremov, Danielle Sookiasian, Ekaterina Nuzhdina, Svetlana Podsvirova, Madison Chasse, Tori Conroy, Noel English, Sergey Starikov, Olesia Klimchuk, Dmitry Tabakov, Anna Love, Kushal Suryamohan, Artur Baisangurov, Cagdas Tazearslan, Nathan Fowler, Alexander Bagaev. Analytical validation of a liquid biopsy test using cell-free circulating tumor DNA for mutational profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1042.
Gastric Cancer (GC) is the second leading cause of cancer-related mortality (9.7% of the total) and most patients with advanced disease will die within one year of diagnosis. GC is histologically classified into intestinal, diffuse and the mixed types, and into four molecular subtypes based on genetic profiling (i.e. microsatellite instable (MSI), EBV positive, chromosomal instable, and genomically stable). Although the molecular annotation is meaningful, the tumor immune microenvironment (TIME) has largely not been evaluated. Using an integrated collection of 11 public cohorts (n=2,270), we identified 5 unique GC TIME phenotypes by unsupervised clustering of gene expression signatures of RNA that describe the composition and biology of TIME and properties of malignant cells: two stromal-enriched subtypes - 1) Mesenchymal-wnt activated, high stroma activation and WNT signaling and 2) Fibrotic, only high stroma activation; two immune enriched subtypes 3) Inflamed non-fibrotic, high immune infiltration and low stromal compartment and 4) B-cell inflamed, high B cell activation; and 5) Immune Depleted, lowest immune and the highest malignant cell properties. The clusters were robust, being identified across all 11 datasets and across all stages of disease. Intestinal, diffuse, and mixed histologies were identified in each cluster. These clusters will be orthogonally validated using immunohistochemistry. The current molecular subtypes were represented in each of our TIME clusters, with some enrichment. Specifically, we found enrichment of MSI molecular subtype, characterized by hypermutation and microsatellite instability, in “Inflamed, non-fibrotic” cluster whereas the MSS/EMT subtype, associated with poor overall survival, was enriched in “Mesenchymal, wnt activated” cluster. We also saw specific enrichment of EBV positive tumors, known to have good prognosis, in both immune-enriched clusters. These results show a high concordance with the current TCGA/ACRG molecular subtypes of GC. These TIME clusters are prognostic in GC. The “Inflamed, non-fibrotic” cluster demonstrated a better overall and relapse free survival whereas stromal enriched clusters exhibited the worst (p<0.001, HR=2.27). Interestingly, the most aggressive “Mesenchymal, wnt activated” subtype was also enriched of metastatic samples. These results were confirmed using an independent validation cohort (n=231) from four other datasets. Additionally, comparison of matching on-treatment vs baseline biopsies from 7 patients treated with cabazitaxel further suggested that TIME changed upon treatment and, in some cases, was indicative of poor response to taxanes. We have defined and characterized the TIME for GC. The GC microenvironment is both prognostic for patient outcome and predictive of response to cytotoxic therapy. Citation Format: Prashant V. Thakkar, Olga Kudryashova, Daria Melikhova, Naira Samarina, Sandrine Degryse, Alexander Bagaev, Felix Frenkel, Svetlana Podsvirova, Dmitry Tychinin, Sandipto Sarkar, Rhonda K. Yantiss, Nathan Fowler, Manish A. Shah. Tumor immune microenvironment based molecular functional clustering reveals a prognostic signature that predicts overall survival in patients with gastric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3182.
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