Resource For Biomarker Discovery Research Articles

Abstract 950: Functional characterization of atypical somatic variants in the Gundersen Precision Oncology Cohort

Abstract The generation and widespread sharing of large somatic mutation datasets has revolutionized our understanding of genetic mechanisms driving cancer initiation and progression. For well-understood mutations, this has created the opportunity for individualized therapeutic intervention in a wide range of malignancies. Beyond these well-characterized mutations, there exists a wide range of alterations that remain poorly understood and which require some kind of functional genomics analysis to determine whether such patients are eligible for the targeted therapy used for more typical mutations in that gene, whether these alterations are passenger mutations that don’t affect throughput on the associated signaling pathway, or reflect another more nuanced phenomenon. Whether using ectopic overexpression in cell lines or generation of genetically modified mice, addressing this problem at scale using functional genomics approaches is challenging. A key limitation is the absence of the original tissue context in which that variant co-evolved with other genetic alterations as well as data on the clinical response of the patient to successive lines of therapy. This can be addressed, in part, by the banking of tissue from patient cohorts who have had comprehensive genomic profiling as part of their routine clinical care. The Gundersen Precision Oncology Cohort is a growing dataset of FFPE specimens and longitudinal clinical data from more than 750 patients who have had comprehensive cancer genome profiling using assays from a wide range of diagnostic companies. We use this resource for biomarker discovery and functional classification of many atypical cancer variants. Current subgroups include atypical point mutations in BRAF, ERBB2 and ERBB3, as well as fusions affecting BRAF, PIK3CA and ROS1. We present examples illustrating the value of this approach for classification of variants detected in a range of tumor types. Citation Format: Peter Feiszt, Paraic A. Kenny. Functional characterization of atypical somatic variants in the Gundersen Precision Oncology Cohort [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 950.

Integrated meta-analysis of colorectal cancer public proteomic datasets for biomarker discovery and validation.

The cancer biomarker field has been an object of thorough investigation in the last decades. Despite this, colorectal cancer (CRC) heterogeneity makes it challenging to identify and validate effective prognostic biomarkers for patient classification according to outcome and treatment response. Although a massive amount of proteomics data has been deposited in public data repositories, this rich source of information is vastly underused. Here, we attempted to reuse public proteomics datasets with two main objectives: i) to generate hypotheses (detection of biomarkers) for their posterior/downstream validation, and (ii) to validate, using an orthogonal approach, a previously described biomarker panel. Twelve CRC public proteomics datasets (mostly from the PRIDE database) were re-analysed and integrated to create a landscape of protein expression. Samples from both solid and liquid biopsies were included in the reanalysis. Integrating this data with survival annotation data, we have validated in silico a six-gene signature for CRC classification at the protein level, and identified five new blood-detectable biomarkers (CD14, PPIA, MRC2, PRDX1, and TXNDC5) associated with CRC prognosis. The prognostic value of these blood-derived proteins was confirmed using additional public datasets, supporting their potential clinical value. As a conclusion, this proof-of-the-concept study demonstrates the value of re-using public proteomics datasets as the basis to create a useful resource for biomarker discovery and validation. The protein expression data has been made available in the public resource Expression Atlas.

Upgrading the Repertoire of miRNAs in Gastric Adenocarcinoma to Provide a New Resource for Biomarker Discovery.

Recent studies have uncovered microRNAs (miRNAs) that have been overlooked in early genomic explorations, which show remarkable tissue- and context-specific expression. Here, we aim to identify and characterize previously unannotated miRNAs expressed in gastric adenocarcinoma (GA). Raw small RNA-sequencing data were analyzed using the miRMaster platform to predict and quantify previously unannotated miRNAs. A discovery cohort of 475 gastric samples (434 GA and 41 adjacent nonmalignant samples), collected by The Cancer Genome Atlas (TCGA), were evaluated. Candidate miRNAs were similarly assessed in an independent cohort of 25 gastric samples. We discovered 170 previously unannotated miRNA candidates expressed in gastric tissues. The expression of these novel miRNAs was highly specific to the gastric samples, 143 of which were significantly deregulated between tumor and nonmalignant contexts (p-adjusted < 0.05; fold change > 1.5). Multivariate survival analyses showed that the combined expression of one previously annotated miRNA and two novel miRNA candidates was significantly predictive of patient outcome. Further, the expression of these three miRNAs was able to stratify patients into three distinct prognostic groups (p = 0.00003). These novel miRNAs were also present in the independent cohort (43 sequences detected in both cohorts). Our findings uncover novel miRNA transcripts in gastric tissues that may have implications in the biology and management of gastric adenocarcinoma.

Proteomic Analysis of Urinary Microvesicles Reveal Biomarkers for Neurologic Disease

Background: Extracellular vesicles (EVs) harbor thousands of proteins that hold promise for biomarker development. Usually difficult to purify, EVs in urine are relatively easily obtained and have demonstrated efficacy for kidney disease prediction. Herein, we further characterize the proteome of urinary EVs to explore the potential for biomarkers unrelated to kidney dysfunction, focusing on Parkinson's disease (PD). Methods: Using a quantitative mass spectrometry approach, we measured urinary EV proteins from a discovery cohort of 50 subjects. EVs in urine were classified into subgroups and EV proteins were ranked by abundance and variability over time. Enriched pathways and ontologies in stable EV proteins were identified and proteins that predict PD were further measured in a cohort of 108 subjects. Findings: Hundreds of commonly expressed urinary EV proteins with stable expression over time were distinguished from proteins expressed in few individual with high variability over time. Bioinformatic analyses reveal a striking enrichment of endolysosomal proteins linked to Parkinson's, Alzheimer's, Huntington's disease, and other neurologic disorders. Tissue and biofluid enrichment analyses show broad representation of EVs from across the body without bias towards kidney or urine proteins. Among the proteins linked to neurological diseases, SNAP23 and calbindin were the most elevated in PD cases with 86% prediction success for disease diagnosis in the discovery cohort and 76% prediction success in the replication cohort. Interpretation: Urinary EVs are an underutilized but highly accessible resource for biomarker discovery with particular promise for neurological diseases like PD. Funding Statement: Research support was provided by NIH/NINDS U01 NS097028 and R01 NS064934. The authors acknowledge core resources provided by the UAB Comprehensive Cancer Center, UAB Institutional Core Funding Mechanism, and core resources made possible through NCI P30 CA013148. Declaration of Interests: The authors have no competing conflicts of interests to declare. Ethics Approval Statement: All protocols were approved by an institutional review board.

Abstract 4528: RNAseq RNAaccess is the preferred method for expression profiling of low quality FFPE gastric cancer samples

Abstract Clinical trial tissue samples are a valuable resource for biomarker discovery and are frequently collected as Formalin-Fixed, Paraffin-Embedded (FFPE) blocks. FFPE tissues pose challenges for next generation sequencing (NGS)-based expression analysis due to poor yield from highly fragmented RNA. The objective of the current study was to evaluate and compare RNAseq RNAaccess (RR) and Nanostring (NS) platforms for molecular profiling of highly fragmented gastric cancer FFPE tissue. RR (TruSeq RNAaccess, Illumina) and NS (PanCancer immune) were performed according to manufacturer protocol using RNA extracted from sourced FFPE gastric cancer tissues (N=6) with a range of DV200 scores (6-34%) and case-matched fresh-frozen (FF) tissue (N=6). For NS, 200ng and 400ng RNA input levels were tested. Data quality from the 400ng samples proved to be superior, resulting in two more samples passing QC. A final sample size of 4 matched pairs was utilized for cross-platform comparisons. For RR, 100ng RNA was used. Pearson correlation coefficients were examined for the following comparisons: 1) FFPE vs. FF samples from RR, 2) FFPE from NS vs. FF from RR, and 3) FFPE NS vs. FFPE RR. Analyses were performed on log2 transformed expression data, for all available genes (N= 770 common genes from RR and NS) and an 18-gene IFNγ signature (IFNγ). All correlations were plotted against a range of DV200 scores from the FFPE samples in order to evaluate a potential tissue quality cutoff for RR, specifically at DV200 levels below Illumina guideline of 30. All RR data samples yielded &amp;gt;80% exonic rate in uniquely mapped reads. Conversely, samples with DV200&amp;lt;14 analyzed by NS failed to pass QC due to low fraction of genes detected above background signal. Comparing FFPE to FF tissue from RR, samples with DV200&amp;gt;10 were highly correlated (r≥0.92), globally or for IFNγ. Comparisons of FF RR to FFPE NS were also well correlated (global median r=0.8; IFNγ median r=0.86). Similarly, expression of FFPE RR was well correlated with FFPE NS (global median r=0.81; IFNγ median r=0.89). Altogether, NS and RR data were highly comparable and minimally impacted by DV200 scores below 30. RR generates gene expression profiles from FFPE tissue that are highly concordant with case-matched FF tissues. This study supports using RR for transcriptional profiling of poor quality gastric cancer FFPE samples. Samples below the Illumina suggested DV200 cutoff of 30 generated good quality RR and NS expression data. IFNγ scores derived from NS and RR from the same tissue were highly concordant. However, NS required higher RNA input level to compensate for low DV200 score and no acceptable expression data was generated on samples below DV200 14. This study will be expanded with data on FFPE samples with DV200&amp;lt;10 to identify a lower limit of RNA quality for RR. Citation Format: Emon Elboudwarej, Marianna Zavodovskaya, Xi Zhao, Luting Zhuo, Carrie Baker Brachmann, Jinfeng Liu. RNAseq RNAaccess is the preferred method for expression profiling of low quality FFPE gastric cancer samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4528.

244 - Genomics of Drug Sensitivity in Cancer (GDSC): A resource for biomarker discovery in cancer cells

244 - Genomics of Drug Sensitivity in Cancer (GDSC): A resource for biomarker discovery in cancer cells

The beagle dog MicroRNA tissue atlas: identifying translatable biomarkers of organ toxicity.

BackgroundMicroRNAs (miRNA) are varied in length, under 25 nucleotides, single-stranded noncoding RNA that regulate post-transcriptional gene expression via translational repression or mRNA degradation. Elevated levels of miRNAs can be detected in systemic circulation after tissue injury, suggesting that miRNAs are released following cellular damage. Because of their remarkable stability, ease of detection in biofluids, and tissue specific expression patterns, miRNAs have the potential to be specific biomarkers of organ injury. The identification of miRNA biomarkers requires a systematic approach: 1) determine the miRNA tissue expression profiles within a mammalian species via next generation sequencing; 2) identify enriched and/or specific miRNA expression within organs of toxicologic interest, and 3) in vivo validation with tissue-specific toxicants. While miRNA tissue expression has been reported in rodents and humans, little data exists on miRNA tissue expression in the dog, a relevant toxicology species. The generation and evaluation of the first dog miRNA tissue atlas is described here.ResultsAnalysis of 16 tissues from five male beagle dogs identified 106 tissue enriched miRNAs, 60 of which were highly enriched in a single organ, and thus may serve as biomarkers of organ injury. A proof of concept study in dogs dosed with hepatotoxicants evaluated a qPCR panel of 15 tissue enriched miRNAs specific to liver, heart, skeletal muscle, pancreas, testes, and brain. Dogs with elevated serum levels of miR-122 and miR-885 had a correlative increase of alanine aminotransferase, and microscopic analysis confirmed liver damage. Other non-liver enriched miRNAs included in the screening panel were unaffected. Eli Lilly authors created a complimentary Sprague Dawely rat miRNA tissue atlas and demonstrated increased pancreas enriched miRNA levels in circulation, following caerulein administration in rat and dog.ConclusionThe dog miRNA tissue atlas provides a resource for biomarker discovery and can be further mined with refinement of dog genome annotation. The 60 highly enriched tissue miRNAs identified within the dog miRNA tissue atlas could serve as diagnostic biomarkers and will require further validation by in vivo correlation to histopathology. Once validated, these tissue enriched miRNAs could be combined into a powerful qPCR screening panel to identify organ toxicity during early drug development.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2958-x) contains supplementary material, which is available to authorized users.

Abstract LB-226: Integrative pharmacogenomic analysis of patient-derived xenografts of Chinese hepatocellular carcinoma

Abstract Hepatocellular carcinoma (HCC) is the sixth most common cancer worldwide and the third leading cause of cancer-related deaths. Patient derived xenografts (PDX), generated by direct engraftment of tumor samples from the patients into immunocompromised mice can be used as an in vivo platform that provides the opportunity to test proposed personalized medicine strategies. In this study, comprehensive molecular characterizations of 28 Chinese HCC PDX models were done by both whole genome sequencing (WGS) and RNAseq technology. After removing contaminated murine DNA/RNA reads by a proprietary algorithm (unpublished) to eliminate homology bias, more than 35 million reads per sample were analyzed in the RNAseq experiment, and at least average 40X coverage at each base of DNA in the WGS experiment. In order to find meaningful targets with high confidence, we integrated multiple lines of evidences from both DNA and RNA levels. Out of 346 gene fusion events were identified in 28 HCC PDXs, 51 occurred in more than one sample and 13 were supported by structural variation evidence from WGS dataset. The same passages (between p3-p5) of 24 PDX tissues were also treated with Sorafenib in the efficacy studies and showed differential responses. The expression of 482 genes (2%) is statistically significantly correlated with drug sensitivity (p-value &amp;lt;0.05), 198 of them (41%) are positively correlated. In the single nucleotide variation (SNV) and indel analysis of WGS dataset, 162 genes were identified as highly mutated genes (p-value &amp;lt;0.05), including classical cancer driver gene TP53. Total 27401 novel SNVs in these 162 genes were identified, 991 (3.6%) of them are nonsynonymous. 557 (2%) SNVs are statistically significantly correlated with drug sensitivity status (p-value&amp;lt;0.05), 28 are nonsynonymous. Pathway analysis shows that these drug responses related genes are enriched in focal adhension formation, induction of apoptosis, P53 signaling pathway, DNA damage response and cell cycle checkpoint. Comparing to other similar studies, our approach of integrative analysis of WGS (vs. Whole exome sequencing) and RNAseq (vs. microarray) provided the most comprehensive genetic insights/landscapes of HCC patients and genetic alterations’ relationship to the drug responses. It also demonstrated that PDX models provide rich information for preclinical studies of experimental therapeutics, and an excellent resource for biomarker discovery and translational research. Citation Format: Hui Zhou, Haisong Lu, Yujian Shi, Gang Hu, He Zhou, Xueyan Yang, Limei Ouyang, Xiaolong Zhu, Zhuolin Gong, Weikang Tao. Integrative pharmacogenomic analysis of patient-derived xenografts of Chinese hepatocellular carcinoma. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr LB-226. doi:10.1158/1538-7445.AM2014-LB-226

Abstract 4127: Expression profiling of paired formalin-fixed, paraffin-embedded (FFPE) and fresh-frozen tissue samples on Ion Torrent PGMTM.

Abstract Archived formalin-fixed paraffin-embedded (FFPE) specimens represent excellent resources for biomarker discovery, but it has been a major challenge to study gene expression in these samples due to mRNA degradation and modification during fixation and processing. We have developed methods for small RNA and whole transcriptome analysis of FFPE samples on the Ion Torrent PGM™ System using Ion total RNA-Seq Kit v2 and RiboMinus Eukaryote System v2. Paired FFPE and fresh-frozen RNA samples from lung adenocarcinoma tissue were used in this study. Small RNA libraries were prepared using the Ion Total RNA-Seq kit v2 with modified protocol, followed by sequencing on PGM™ system. Results show that small RNA extracted from FFPE sample was successfully converted to small RNA library. Although percentage of reads mapped to miRBase was slightly lower when compared to fresh-frozen sample, the overall miRBase coverage and detection sensitivity were comparable. For whole transcriptome analysis, RiboMinus Eukaryote System v2 kit was used to deplete rRNA from FFPE samples, followed by library construction using the Ion Total RNA-Seq kit v2 with a modified protocol. Significant reduction in rRNA reads was observed after sequencing. High RefSeq correlations were observed between fresh-frozen and FFPE samples. In conclusion, a full spectrum of gene expression data, from both small RNA and whole transcriptome, were generated from FFPE samples using Ion Total RNA-Seq kit v2 with modified protocols. High correlations were observed between paired fresh-frozen and FFPE samples, demonstrating the reliability of the modified workflow for gene expression profiling on FFPE samples. This study provides feasibility for systematic gene expression analysis on FFPE samples from cancer and other diseases. Citation Format: Jian Gu. Expression profiling of paired formalin-fixed, paraffin-embedded (FFPE) and fresh-frozen tissue samples on Ion Torrent PGMTM. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4127. doi:10.1158/1538-7445.AM2013-4127

UniPep - a database for human N-linked glycosites: a resource for biomarker discovery

UniPep, a database of human N-linked glycosites is presented as a resource for biomarker discovery