Technological innovation and scientific advances in understanding cancer at the molecular level have accelerated the discovery and development of both diagnostics and therapeutics. Circulating tumor cells (CTCs) and plasma circulating tumor DNA (ctDNA) are non-invasive prognostic markers that have been associated with metastatic and aggressive disease. Both CTCs and ctDNA allow molecular characterization of a tumor that is inaccessible or too risky to biopsy. The analysis of genomic aberrations in both sample types provides insights into drug resistance and can help determine appropriate, targeted cancer treatments. Mutations found in the primary or metastatic tumor can be identified in both CTCs and ctDNA as well as novel mutations that may reflect intratumoral and intermetastatic heterogeneity. When collected and evaluated over an extended period of time, changes in the CTC and/or ctDNA mutational profile can offer guidance into the effectiveness of a treatment, indicate the progression of disease, and detect recurrence of disease earlier. We have performed whole exome sequencing of CTCs and ctDNA from a metastatic triple negative breast cancer (TNBC) patient to better understand the evolution of tumor heterogeneity during therapy. The patient was enrolled in the Intensive Trial of OMics in Cancer clinical Trial (ITOMIC-001) and initially received weekly cisplatin infusions followed by additional targeted therapy. Longitudinal peripheral blood samples were collected over a period of 272 days following enrollment in the clinical trial. CTCs were identified using the AccuCyte-CyteFinder system (RareCyte, Seattle WA). We used next generation sequencing, and computational biology tools to analyze genomic DNA from multiple CTCs, white blood cells (WBCs) and ctDNA from various time points. We observed similar genomic aberrations in both CTCs and ctDNA that could be classified into three groups: a) a static group that remains unchanged during the course of therapy, b) a sample-specific group that is unique to each time point and c) an intermediate group that has variants that are short-lived but are present across multiple time points. Variants identified in the liquid biopsy samples were compared with variants observed in primary breast tumor, metastatic bone marrow tumor and publically available pan-cancer datasets. We then performed meta-analysis on somatic variants to identify changes in affected networks in response to therapy over time. Several key nodes were identified that could rationally have been targeted for therapy using compounds currently in clinical trials. We then compared and combined the perturbed networks obtained from the CTCs and ctDNA to better understand the etiology of TNBC. These studies represent the first step of a synergistic partnership between the genetic information obtained from the analysis of CTCs and ctDNA with innovative health care for patients with metastatic breast cancer. Citation Format: Kellie Howard, Sharon Austin, Fang Yin Lo, Arturo Ramirez, Debbie Boles, John Pruitt, Elisabeth Mahen, Heather Collins, Amanda Leonti, Lindsey Maassel, Christopher Subia, Tuuli Saloranta, Nicole Christopherson, Kerry Deutsch, Jackie Stilwell, Eric Kaldjian, Michael Dorschner, Sibel Blau, Anthony Blau, Marcia Eisenberg, Steven Anderson, Anup Madan. Meta-analysis of genomic aberrations identified in CTCs andctDNA in triple negative breast cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 498.
The emergence of Next Generation Sequencing (NGS) along with computational biology has broadened the scope in which diverse cellular processes can be interrogated. While there has been considerable progress in understanding the impact of genetic and epigenetic mechanisms in tumorigenesis using whole genomic, epigenomic and transcriptional analysis by NGS, there has been little consideration of the importance of interplay between these processes. We performed a comparative analysis of array and NGS technologies to identify differentially methylated CpG sites in colorectal cancer cell lines. NGS had more specificity in addition to profiling more CpG sites relative to Illumina 450K arrays. Base-level resolution of sequencing data can identify any strand specific methylation bias. Our analysis shows that methylation frequency between the sense and antisense strand are highly correlated (average R2 ∼ 0.81), and coefficient of variance (CV) between the strands is generally low (about half of observed sites have <10% CV). However, a small percentage of bases had strand specific biases. Using a minimum of 100% CV and difference in methylation frequency greater than 50% as filtering criteria, we found 1210, 569, 638, and 1484 CpG sites have strand specific biases with 7 overlapping bases among the samples tested. Further investigation will identify whether these bases are random or reside within a particular region, where biases occur, and what genes are potentially affected. We also used NGS and publically available gene expression datasets in colorectal cancer cell lines-HCT116 and HCT116 DKO (cell line with genetic knockouts of both DNA methyltransferases DNMT1 and DMNT3b) to identify roles of differential methylation in regulating gene expression. A majority of genes were down-regulated between HCT116 and HCT116 DKO cell lines including those involved in chromatin, nucleic acid, and nucleotide binding and cell cycle regulation. Interestingly, many differentially expressed genes are also involved in immune response. We then used bisulfite treated genomic data to evaluate genetic regulation of gene expression. For this, we converted bisulfite treated data into genomic space using custom in-house developed bio-informatics tools that were first tested using DNA isolated from NA12878 cell line. Our analysis showed that 65% of the known variants detected in NA12878 cell line by the genome in a bottle consortium can be identified by bisulfite sequencing of promoter associated CpG islands. One limitation of this analysis is the inability to identify C>T genomic variants. This data is being analyzed to evaluate effects of genetic mutations in promoter binding sites on gene expression in colorectal cancers. Comparative analysis of genetic and epigenetic regulation of gene expression will allow better understanding of gene regulatory networks in colorectal cancer. Citation Format: Claire Olson, Fang Yin Lo, Kerry Deutsch, Sharon Austin, Kellie Howard, Amanda Leonti, Lindsey Maassel, Christopher Subia, Tuuli Saloranta, Nicole Christopherson, Kathryn Shiji, Shradha Patil, Steven Anderson, Anup Madan. Synergistic effects of promoter associated DNA methylation and genetic alterations to better understand oncogenic gene expression profiles. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4436.
The success of immunotherapy for the treatment of metastatic cancers relies on the prediction and identification of potential neo-antigens. In recent years expression levels of these neo-antigens along with other immune system related genes have been evaluated in an effort to better understand response rates for immunotherapy in various cancers. Gene expression levels can be assessed by numerous techniques including hybridization-based or direct sequencing technologies. Two platforms-HTG Molecular and NanoString nCounter have been utilized to profile changes in gene expression and offer unique advantages for analyzing challenging specimens such as formalin-fixed paraffin embedded (FFPE) tissues. The NanoString nCounter platform utilizes hybridized fluorescent probes targeted against genes of interest for a non-amplified measurement of gene expression. Several studies have been shown that the NanoString platform has good sensitivity, specificity, and reproducibility for the assessment of gene expression levels from FFPE samples. The HTG platform is relatively new and also uses a hybridization based method to enrich genes of interest without first isolating RNA. To determine the robustness of the HTG platform, we profiled a set of 30 metastatic prostate cancer samples using the HTG Molecular EdgeSeq Immuno-Oncology Assay. In these experiments, we found that expression data obtained by using both extracted RNA and lysate from FFPE slides was highly reproducible (Spearman coefficient > 0.85). In addition, the expression profile of targeted genes obtained by using different slides from the same blocks was also highly correlated (Spearman coefficient > 0.90). Our experiments also showed a high correlation between gene expressions profiles obtained by HTG, the NanoString PanCancer Immune Profiling panel and RNA-Seq from the same set of 30 metastatic prostate cancer samples. Further analysis to evaluate and compare the sensitivity of different platforms is being performed and results of these will be presented. Citation Format: Kimberly Kruse, Fang Yin Lo, Ryan Fleming, Douglas Chung, Vickie Satele, Lindsey Maassel, Tuuli Saloranta, Inah Golez, Saman Tahir, Sally Dow, Evan Anderson, Spencer Chee, Raghavee Venkatramanan, Steve Anderson, Peter S. Nelson, Colm Morrissey, Anup Madan, Sharon Austin, Kellie Howard. A cross comparison of technologies for the detection of immune system related gene expression signatures in clinical FFPE samples of metastatic prostate cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3983. doi:10.1158/1538-7445.AM2017-3983
Colorectal cancer (CRC) is the third most common type of cancer in the United States. Although chemotherapy, radiation and targeted therapies can improve survival rates, recent studies have shown the potential benefit of immunotherapies to improve outcomes for patients with advanced CRC. Targeted therapies that use monoclonal antibodies (mAbs) to EGFR have been shown to benefit some CRC patients. Until recently, KRAS has been the only predictive biomarker for anti-EGFR therapy for metastatic CRC. However, 40% to 60% of patients with wild-type KRAS do not respond to anti-EGFR therapy. Therefore, to accurately predict patients’ response to treatments and improve clinical outcomes, additional prediction and treatment methods are imperative. One of the many efforts to improve prediction for CRC patient's response to the anti-EGFR therapy is the development of gene expression based RAS signature scores for identification of RAS activated tumors independent of mutations in the KRAS gene. Recently there have been major advances in immunotherapeutic approaches in a wide variety of cancers. In solid tumors such as melanoma and colon cancers, immune checkpoints have been shown to improve clinical outcomes. There is considerable effort being placed on combinations of targeted therapy and immunotherapies to improve responses for these cancers. Similarly, since no single treatment can apply to all CRC patients, we aim to stratify patients using a combination of three methods: 1. RAS signature score based on the expression profile of 18 genes. This RAS signature score enables measurements of mitogen-activated protein/extracellular signal-regulated kinase (MEK) pathway functional output independent of tumor genotype. 2. Expression profile of immune checkpoint inhibitor target genes, such as PD1 and PDL1, and 3. In-silico prediction of neo-antigens and peptide binding affinity between tumor antigens derived from mutations and human HLA alleles. 55 FFPE samples were selected from a cohort of 468 samples with matching FF samples. These 55 samples have about 1:1:1 ratio of high, medium and low RAS scores. Here we showed our ability to obtain RAS signature scores with concordant results using different platforms including RNA-seq, targeted RNA-seq, Nanostring and Affymetrix microarray. Samples that have RAS activating mutations such as KRAS and BRAF have significant higher RAS scores (p<0.001). Interestingly, expression of PD-L1 was significantly lower in tumor samples harboring mutations of genes such as MET, PTEN, NRAS, FBXW7, and GNAS. Kruskal-Wallis test showed that the expression of PD-L1 was significantly lower in samples with higher RAS signature scores (p<0.05). Combined with further prediction of tumor antigen derived from genes with missense mutations, we provide a combinatorial method for stratifying metastatic CRC patients. Citation Format: FangYin Lo, Sharon Austin, Kellie Howard, Mollie McWhorter, Heather Collins, Amanda Leonti, Lindsey Maassel, Christopher Subia, Tuuli Saloranta, Nicole Christopherson, Kathryn Shiji, Shradha Patil, Saman Tahir, Sally Dow, Evan Anderson, Jon Oblad, Kerry Deutsch, Timothy Yeatman, Steven Anderson, Anup Madan. Stratification of metastatic colorectal cancer patients using DNA and RNA sequencing and in-silico prediction of tumor antigens for consideration in immunotherapy. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3946.
Myeloma is a genetically heterogeneous disease and is sub-classified based on the presence of structural variants and genetic mutations. Structural variants/copy number changes are historically identified by traditional methods such as karyotyping and fluorescence in situ hybridization (FISH). Although microarray based genome wide analyses greatly improve the resolution of structural variation, they may be limited by probe density. Consequently, identification of structural variation may be insensitive to specific disrupted gene(s), neglecting the sequence complexity that might underlie these rearrangements. Determination of the specific breakpoints of structural variants at the nucleotide level is required for a better understanding of the genetic causes and to enhance the development of therapeutics for patients. The emergence of Next-Generation Sequencing (NGS) technology has led to the identification of structural variants in the genome at a higher resolution relative to currently used cytogenetic methods. We analyzed DNA extracted from a set of patients with multiple myeloma, who had Affymetrix SNP array (∼2.7 million probes) data, by whole exome sequencing (WES) at 100X coverage on the Illumina HiSeq platform to identify the full spectrum of associated genomic aberrations. Sequence data was mapped to the hg19 reference sequence and analyzed by various in-house developed and open source data analytic tools. Additionally, a custom sequence analysis pipeline was written to interrogate chromosomal deletions and translocations in these samples. Our analysis showed that ∼43% (6/14) of patients have deletions in chr17p and/or chr13q. We further confirmed structural variants using the Integrative Genomics Viewer (IGV). These data indicate the efficacy of WES for the precise determination of translocation and inversion breakpoints. In addition, we were able to identify single nucleotide variants (SNVs) and insertions/deletions (indels) in these samples. We then used the Ingenuity Variant Analysis (IGV) program to identify clinically actionable variants. These datasets are being further analyzed by various pathway analysis tools to define possible pathogenic mechanisms in multiple myeloma. Citation Format: Kerry Deutsch, Fang Yin Lo, Claire Olson, Sharon Austin, Kellie Howard, Amanda Leonti, Lindsey Maassel, Christopher Subia, Tuuli Saloranta, Nicole Christopherson, Kathryn Shiji, Shradha Patil, Stuart Schwartz, Peter Papenhausen, Steven Anderson, Anup Madan. Investigation of genetic architecture of multiple myeloma by next generation sequencing. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 104.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.