scholarly journals Systematic Discovery of the Functional Impact of Somatic Genome Alterations in Individual Tumors through Tumor-specific Causal Inference

2018 ◽  
Author(s):  
Chunhui Cai ◽  
Gregory F. Cooper ◽  
Kevin N. Lu ◽  
Xiaojun Ma ◽  
Shuping Xu ◽  
...  
Keyword(s):  
Causal Inference ◽  
Cancer Biology ◽  
The Cancer Genome Atlas ◽  
Precision Oncology ◽  
Causal Relationships ◽  
Functional Impact ◽  
Disease Mechanisms ◽  
Cancer Genome Atlas ◽  
Somatic Genome ◽  
Molecular Phenotypes

AbstractWe report a tumor-specific causal inference (TCI) framework, which discovers causative somatic genome alterations (SGAs) through inferring causal relationships between SGAs and molecular phenotypes (e.g., transcriptomic, proteomic, or metabolomic changes) within an individual tumor. We applied the TCI algorithm to tumors from The Cancer Genome Atlas (TCGA) and identified those SGAs that causally regulate the differentially expressed genes (DEGs) within each tumor. Overall, TCI identified 634 SGAs that cause cancer-related DEGs in a significant number of tumors, including most of the previously known drivers and many novel candidate cancer drivers. The inferred causal relationships are statistically robust and biologically sensible, and multiple lines of experimental evidence support the predicted functional impact of both well-known and novel candidate drivers. By identifying major candidate drivers and revealing their functional impact in a tumor, TCI shed light on disease mechanisms of each tumor, providing useful information for advancing cancer biology and precision oncology.Significance statementsCancer is mainly caused by SGAs. Precision oncology involves identifying and targeting tumor-specific aberrations resulting from causative SGAs. TCI is a novel computational framework for discovering the causative SGAs and their impact on oncogenic processes, thus revealing tumor-specific disease mechanisms. This information can be used to guide precision oncology.

scholarly journals Tumour-specific Causal Inference Discovers Distinct Disease Mechanisms Underlying Cancer Subtypes

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Yifan Xue ◽  
Gregory Cooper ◽  
Chunhui Cai ◽  
Songjian Lu ◽  
Baoli Hu ◽  
...  
Keyword(s):  
Causal Inference ◽  
The Cancer Genome Atlas ◽  
Common Disease ◽  
Precision Oncology ◽  
Breast Cancers ◽  
Cancer Subtypes ◽  
Disease Mechanisms ◽  
Distinct Disease ◽  
Somatic Genome ◽  
Survival Patterns

Abstract Cancer is a disease mainly caused by somatic genome alterations (SGAs) that perturb cellular signalling systems. Furthermore, the combination of pathway aberrations in a tumour defines its disease mechanism, and distinct disease mechanisms underlie the inter-tumour heterogeneity in terms of disease progression and responses to therapies. Discovering common disease mechanisms shared by tumours would provide guidance for precision oncology but remains a challenge. Here, we present a novel computational framework for revealing distinct combinations of aberrant signalling pathways in tumours. Specifically, we applied the tumour-specific causal inference algorithm (TCI) to identify causal relationships between SGAs and differentially expressed genes (DEGs) within tumours from the Cancer Genome Atlas (TCGA) study. Based on these causal inferences, we adopted a network-based method to identify modules of DEGs, such that the member DEGs within a module tend to be co-regulated by a common pathway. Using the expression status of genes in a module as a surrogate measure of the activation status of the corresponding pathways, we divided breast cancers (BRCAs) into five subgroups and glioblastoma multiformes (GBMs) into six subgroups with distinct combinations of pathway aberrations. The patient groups exhibited significantly different survival patterns, indicating that our approach can identify clinically relevant disease subtypes.

scholarly journals Network-aware mutation clustering of cancer

2018 ◽  
Author(s):  
Swetansu Pattnaik ◽  
Catherine Vacher ◽  
Hong Ching Lee ◽  
Warren Kaplan ◽  
David M. Thomas ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Cancer Biology ◽  
Difficult Problem ◽  
The Cancer Genome Atlas ◽  
Precision Oncology ◽  
Protein Protein Interactions ◽  
Dna Mutation ◽  
Mutation Profile ◽  
Cancer Genome Atlas ◽  
Protein Modules

AbstractThe grouping of cancers across tissue boundaries is central to precision oncology, but remains a difficult problem. Here we present EPICC (Experimental Protein Interaction Clustering of Cancer), a novel technique to cluster cancer patients based on DNA mutation profile, that leverages knowledge of protein-protein interactions to reduce noise and amplify biological signal. We applied EPICC to data from The Cancer Genome Atlas (TCGA), and both recapitulated known cancer clusterings, and identified new cross-tissue cancer groups that may indicate novel cancer molecular subtypes. Investigation of EPICC clusters revealed new protein modules which were recurrently mutated across cancers, and indicate new avenues for research into cancer biology. EPICC leveraged the Vodafone DreamLab citizen science platform, and we provide our results as a resource for researchers to investigate the role of protein modules in cancer.

scholarly journals Integrated Dissection of lncRNA-Perturbated Triplets Reveals Novel Prognostic Signatures Across Cancer Types

2020 ◽  
Vol 21 (17) ◽  
pp. 6087
Author(s):  
Yunzhen Wei ◽  
Limeng Zhou ◽  
Yingzhang Huang ◽  
Dianjing Guo
Keyword(s):  
Cancer Biology ◽  
Noncoding Rna ◽  
The Cancer Genome Atlas ◽  
Dynamic Changes ◽  
Computational Framework ◽  
Potential Biomarker ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Tcga Dataset ◽  
Pan Cancer

Long noncoding RNA (lncRNA)/microRNA(miRNA)/mRNA triplets contribute to cancer biology. However, identifying significative triplets remains a major challenge for cancer research. The dynamic changes among factors of the triplets have been less understood. Here, by integrating target information and expression datasets, we proposed a novel computational framework to identify the triplets termed as “lncRNA-perturbated triplets”. We applied the framework to five cancer datasets in The Cancer Genome Atlas (TCGA) project and identified 109 triplets. We showed that the paired miRNAs and mRNAs were widely perturbated by lncRNAs in different cancer types. LncRNA perturbators and lncRNA-perturbated mRNAs showed significantly higher evolutionary conservation than other lncRNAs and mRNAs. Importantly, the lncRNA-perturbated triplets exhibited high cancer specificity. The pan-cancer perturbator OIP5-AS1 had higher expression level than that of the cancer-specific perturbators. These lncRNA perturbators were significantly enriched in known cancer-related pathways. Furthermore, among the 25 lncRNA in the 109 triplets, lncRNA SNHG7 was identified as a stable potential biomarker in lung adenocarcinoma (LUAD) by combining the TCGA dataset and two independent GEO datasets. Results from cell transfection also indicated that overexpression of lncRNA SNHG7 and TUG1 enhanced the expression of the corresponding mRNA PNMA2 and CDC7 in LUAD. Our study provides a systematic dissection of lncRNA-perturbated triplets and facilitates our understanding of the molecular roles of lncRNAs in cancers.

scholarly journals Identification of novel alternative splicing isoform biomarkers and their association with overall survival in colorectal cancer

2019 ◽  
Author(s):  
Hongtao Jia ◽  
Aili Wang ◽  
Haifeng Lian ◽  
Yuanyuan Shen ◽  
Qian Wang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
Alternative Splicing ◽  
Cancer Biology ◽  
The Cancer Genome Atlas ◽  
Normal Tissues ◽  
Eukaryotic Gene ◽  
Cancer Genome Atlas ◽  
Alternative Splicing Events ◽  
Therapeutic Tools

Abstract Alternative splicing is an important mechanism of regulating eukaryotic gene expression. Understanding the most common alternative splicing events in colorectal cancer (CRC) will help developing diagnostic, prognostic or therapeutic tools in CRC. Publicly available RNA-seq data of 31 pairs of CRC and normal tissues and 18 pairs of metastatic and normal tissues were used to identify alternative splicing events using PSI and DEXSeq methods. The highly significant splicing events were used to search a database of The Cancer Genome Atlas (TCGA). We identified alternative splicing events in 10 genes marking the signature of CRC (more inclusion of CLK1-E4, COL6A3-E6, CD44v8-10, alternative first exon regulation of ARHGEF9, CHEK1, HKDC1 and HNF4A) or metastasis (decrease of SERPINA1-E1a, CALD-E5b, E6 and FBLN2-E9). Except for CHEK1, all other 9 splicing events were confirmed by TCGA data with 382 CRC tumors and 52 normal controls. Two splicing events (COL6A3 and HKDC1) were found to be significantly associated with patient overall survival. The alternative splicing signatures of the 10 genes are highly consistent with previous reports and/or relevant to cancer biology. The significant association of higher expression of the COL6A3 E5-E6 junction and HKDC1 E1-E2 with better overall survival was firstly reported. This study might be of significant value in the future biomarker, prognosis marker and therapeutics development of CRC.

scholarly journals Systematic discovery of the functional impact of somatic genome alterations in individual tumors through tumor-specific causal inference

2019 ◽  
Vol 15 (7) ◽  
pp. e1007088 ◽  
Author(s):  
Chunhui Cai ◽  
Gregory F. Cooper ◽  
Kevin N. Lu ◽  
Xiaojun Ma ◽  
Shuping Xu ◽  
...  
Keyword(s):  
Causal Inference ◽  
Functional Impact ◽  
Somatic Genome

Genomic profiling of circulating tumor DNA (ctDNA) from patients (pts) with pancreatic ductal adenocarcinoma (PDA).

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 4128-4128
Author(s):  
Nathan Bahary ◽  
Jie He ◽  
Mark Bailey ◽  
Shan Zhong ◽  
Gerald Li ◽  
...  
Keyword(s):  
Cancer Biology ◽  
Clinical Care ◽  
Circulating Tumor Dna ◽  
The Cancer Genome Atlas ◽  
Ductal Adenocarcinoma ◽  
Genomic Profiling ◽  
Tissue Samples ◽  
Comprehensive Genomic Profiling ◽  
Cancer Genome Atlas ◽  
Genomic Characterization

4128 Background: PDA is a lethal and increasingly common malignancy and tissue samples for genomic characterization may be limited. As PDA has a high and consistent frequency of KRAS, p53 and CDKN2A mutations it serves as a robust indication to test the utility of ctDNA in accurately characterizing genomic alterations (GA). A prior study suggested significant differences between ctDNA and tissue base profiling but assays were not conducted on the same platform (PMID27833075). We undertook this study to see whether ctDNA could recapitulate the known genomic hallmarks of tumor based profiling. Methods: Hybrid-capture based genomic profiling of 62 genes (FoundationACT) was performed on ctDNA from 78 pts with advanced PDA with samples received in the course of clinical care. The fraction of ctDNA in the blood was estimated using the maximum somatic allele frequency (MSAF) for each sample. Frequencies of alterations in these common drivers were then compared to those seen in tumors of pts who underwent comprehensive genomic profiling (CGP) tissue testing performed on the same core platform, FoundationOne, and The Cancer Genome Atlas (TCGA). Results: Pt characteristics: Median age 65 (range, 47-88); Female (33) /Male (45). FoundationACT results show that 53/78 (68%) cases had MSAF >0 (56%-78%%, 95% CI). ≥1 GA was reported in 81% of the cases with evidence of ctDNA in the blood. The most common GA detected by FoundationACT (based on cases with evidence of ctDNA in blood) vs FoundationOne were in KRAS (59% vs 89%, p< 0.0001), TP53 (69% vs.74%, p=0.19), and CDKN2A (14% vs.45%). Other detected clinically relevant GA detected by FoundationACT included: BRCA1, ERBB2, NF1, PIK3CA. Conclusions: This study demonstrates significant differences between the established driver oncogenic alterations for PDA, as assessed by ct DNA and tissue based genomic profiling which are unlikely to be explained by differences in assay, but rather novel cancer biology. At present use of ctDNA genomic profiling in PDA should not routinely replace tissue based genomic characterization. [Table: see text]

scholarly journals Identification of novel alternative splicing isoform biomarkers and their association with overall survival in colorectal cancer

2019 ◽  
Author(s):  
Haifeng Lian ◽  
Aili Wang ◽  
Yuanyuan Shen ◽  
Qian Wang ◽  
Zhenru Zhou ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
Alternative Splicing ◽  
Cancer Biology ◽  
The Cancer Genome Atlas ◽  
Normal Tissues ◽  
Eukaryotic Gene ◽  
Cancer Genome Atlas ◽  
Alternative Splicing Events ◽  
Therapeutic Tools

Abstract Alternative splicing is an important mechanism of regulating eukaryotic gene expression. Understanding the most common alternative splicing events in colorectal cancer (CRC) will help developing diagnostic, prognostic or therapeutic tools in CRC. Publicly available RNA-seq data of 31 pairs of CRC and normal tissues and 18 pairs of metastatic and normal tissues were used to identify alternative splicing events using PSI and DEXSeq methods. The highly significant splicing events were used to search a database of The Cancer Genome Atlas (TCGA). We identified alternative splicing events in 10 genes marking the signature of CRC (more inclusion of CLK1-E4, COL6A3-E6, CD44v8-10, alternative first exon regulation of ARHGEF9, CHEK1, HKDC1 and HNF4A) or metastasis (decrease of SERPINA1-E1a, CALD-E5b, E6 and FBLN2-E9). Except for CHEK1, all other 9 splicing events were confirmed by TCGA data with 382 CRC tumors and 52 normal controls. Two splicing events (COL6A3 and HKDC1) were found to be significantly associated with patient overall survival. The alternative splicing signatures of the 10 genes are highly consistent with previous reports and/or relevant to cancer biology. The significant association of higher expression of the COL6A3 E5-E6 junction and HKDC1 E1-E2 with better overall survival was firstly reported. This study might be of significant value in the future biomarker, prognosis marker and therapeutics development of CRC.

scholarly journals Using The Cancer Genome Atlas (TCGA) as an inquiry tool in the undergraduate classroom

2020 ◽  
Author(s):  
William Hankey ◽  
Nicholas Zanghi ◽  
Mackenzie Crow ◽  
Whitney Dow ◽  
Austin Kratz ◽  
...  
Keyword(s):  
Undergraduate Students ◽  
Cancer Biology ◽  
Statistical Tests ◽  
The Cancer Genome Atlas ◽  
Biomedical Science ◽  
Biomedical Sciences ◽  
Future Health ◽  
Scientific Databases ◽  
Molecular Features ◽  
Cancer Genome Atlas

Undergraduate students in the biomedical sciences are often interested in future health-focused careers. This presents opportunities for instructors in genetics, molecular biology and cancer biology to capture their attention using lab experiences built around clinically relevant data. As biomedical science in general becomes increasingly dependent on high-throughput data, well-established scientific databases such as TCGA have become publicly available tools for medically relevant inquiry. The best feature of this database is that it bridges the molecular features of cancer to human clinical outcomes, allowing students to see a direct connection between the molecular sciences and their future professions. We have developed and tested a learning module that leverages the power of TCGA datasets to engage students to use the data to generate and test hypotheses and to apply statistical tests to evaluate significance. (Peer reviewed/published version: https://www.frontiersin.org/articles/10.3389/fgene.2020.573992/full)

scholarly journals Using The Cancer Genome Atlas as an Inquiry Tool in the Undergraduate Classroom

2020 ◽  
Vol 11 ◽  
Author(s):  
William Hankey ◽  
Nicholas Zanghi ◽  
Mackenzie M. Crow ◽  
Whitney H. Dow ◽  
Austin Kratz ◽  
...  
Keyword(s):  
Cancer Biology ◽  
Statistical Tests ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Biomedical Science ◽  
Biomedical Sciences ◽  
Future Health ◽  
Molecular Features ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Undergraduate students in the biomedical sciences are often interested in future health-focused careers. This presents opportunities for instructors in genetics, molecular biology, and cancer biology to capture their attention using lab experiences built around clinically relevant data. As biomedical science in general becomes increasingly dependent on high-throughput data, well-established scientific databases such as The Cancer Genome Atlas (TCGA) have become publicly available tools for medically relevant inquiry. The best feature of this database is that it bridges the molecular features of cancer to human clinical outcomes—allowing students to see a direct connection between the molecular sciences and their future professions. We have developed and tested a learning module that leverages the power of TCGA datasets to engage students to use the data to generate and test hypotheses and to apply statistical tests to evaluate significance.

scholarly journals Identification of novel alternative splicing isoform biomarkers and their association with overall survival in colorectal cancer

2020 ◽  
Author(s):  
Haifeng Lian ◽  
Aili Wang ◽  
Yuanyuan Shen ◽  
Qian Wang ◽  
Zhenru Zhou ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
Alternative Splicing ◽  
Cancer Biology ◽  
The Cancer Genome Atlas ◽  
Sample Type ◽  
Normal Tissues ◽  
Eukaryotic Gene ◽  
Cancer Genome Atlas ◽  
Therapeutic Tools

Abstract Background: Alternative splicing (AS) is an important mechanism of regulating eukaryotic gene expression. Understanding the most common AS events in colorectal cancer (CRC) will help developing diagnostic, prognostic or therapeutic tools in CRC.Methods: Publicly available RNA-seq data of 28 pairs of CRC and normal tissues and 18 pairs of metastatic and normal tissues were used to identify AS events using PSI and DEXSeq methods. Result: The highly significant splicing events were used to search a database of The Cancer Genome Atlas (TCGA). We identified AS events in 9 genes in CRC (more inclusion of CLK1-E4, COL6A3-E6, CD44v8-10, alternative first exon regulation of ARHGEF9, CHEK1, HKDC1 and HNF4A) or metastasis (decrease of SERPINA1-E1a, CALD-E5b, E6). Except for CHEK1, all other 8 splicing events were confirmed by TCGA data with 382 CRC tumors and 51 normal controls. The combination of three splicing events was used to build a logistic regression model that can predict sample type (CRC or normal) with near perfect performance (AUC=1). Two splicing events (COL6A3 and HKDC1) were found to be significantly associated with patient overall survival. The AS features of the 9 genes are highly consistent with previous reports and/or relevant to cancer biology. Conclusions: The significant association of higher expression of the COL6A3 E5-E6 junction and HKDC1 E1-E2 with better overall survival was firstly reported. This study might be of significant value in the future biomarker, prognosis marker and therapeutics development of CRC.

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