Dataset Information

Differentiating between cancer and normal tissue samples using multi-hit combinations of genetic mutations.

ABSTRACT: Cancer is known to result from a combination of a small number of genetic defects. However, the specific combinations of mutations responsible for the vast majority of cancers have not been identified. Current computational approaches focus on identifying driver genes and mutations. Although individually these mutations can increase the risk of cancer they do not result in cancer without additional mutations. We present a fundamentally different approach for identifying the cause of individual instances of cancer: we search for combinations of genes with carcinogenic mutations (multi-hit combinations) instead of individual driver genes or mutations. We developed an algorithm that identified a set of multi-hit combinations that differentiate between tumor and normal tissue samples with 91% sensitivity (95% Confidence Interval (CI) = 89-92%) and 93% specificity (95% CI = 91-94%) on average for seventeen cancer types. We then present an approach based on mutational profile that can be used to distinguish between driver and passenger mutations within these genes. These combinations, with experimental validation, can aid in better diagnosis, provide insights into the etiology of cancer, and provide a rational basis for designing targeted combination therapies.

SUBMITTER: Dash S

PROVIDER: S-EPMC6353925 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Differentiating between cancer and normal tissue samples using multi-hit combinations of genetic mutations.

Dash Sajal S Kinney Nicholas A NA Varghese Robin T RT Garner Harold R HR Feng Wu-Chun WC Anandakrishnan Ramu R

Scientific reports 20190130 1

Cancer is known to result from a combination of a small number of genetic defects. However, the specific combinations of mutations responsible for the vast majority of cancers have not been identified. Current computational approaches focus on identifying driver genes and mutations. Although individually these mutations can increase the risk of cancer they do not result in cancer without additional mutations. We present a fundamentally different approach for identifying the cause of individual i ...[more]

PMID: 30700767

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Project description:Background & aimsLiver disease carries significant healthcare burden and frequently requires a combination of blood tests, imaging, and invasive liver biopsy to diagnose. Distinguishing between inflammatory liver diseases, which may have similar clinical presentations, is particularly challenging. In this study, we implemented a machine learning pipeline for the identification of diagnostic gene expression biomarkers across several alcohol-associated and non-alcohol-associated liver diseases, using either liver tissue or blood-based samples.MethodsWe collected peripheral blood mononuclear cells (PBMCs) and liver tissue samples from participants with alcohol-associated hepatitis (AH), alcohol-associated cirrhosis (AC), non-alcohol-associated fatty liver disease, chronic HCV infection, and healthy controls. We performed RNA sequencing (RNA-seq) on 137 PBMC samples and 67 liver tissue samples. Using gene expression data, we implemented a machine learning feature selection and classification pipeline to identify diagnostic biomarkers which distinguish between the liver disease groups. The liver tissue results were validated using a public independent RNA-seq dataset. The biomarkers were computationally validated for biological relevance using pathway analysis tools.ResultsUtilizing liver tissue RNA-seq data, we distinguished between AH, AC, and healthy conditions with overall accuracies of 90% in our dataset, and 82% in the independent dataset, with 33 genes. Distinguishing 4 liver conditions and healthy controls yielded 91% overall accuracy in our liver tissue dataset with 39 genes, and 75% overall accuracy in our PBMC dataset with 75 genes.ConclusionsOur machine learning pipeline was effective at identifying a small set of diagnostic gene biomarkers and classifying several liver diseases using RNA-seq data from liver tissue and PBMCs. The methodologies implemented and genes identified in this study may facilitate future efforts toward a liquid biopsy diagnostic for liver diseases.Lay summaryDistinguishing between inflammatory liver diseases without multiple tests can be challenging due to their clinically similar characteristics. To lay the groundwork for the development of a non-invasive blood-based diagnostic across a range of liver diseases, we compared samples from participants with alcohol-associated hepatitis, alcohol-associated cirrhosis, chronic hepatitis C infection, and non-alcohol-associated fatty liver disease. We used a machine learning computational approach to demonstrate that gene expression data generated from either liver tissue or blood samples can be used to discover a small set of gene biomarkers for effective diagnosis of these liver diseases.

Project description:For organoid preparation, we first treated the following 4 groups of 8-12 week old mice with tamoxifen: 1) CDX2P-CreERT2, Apc flox/+, Kras LSL-G12D/+, Trp53 flox/flox mice (n=2); 2) CDX2P-CreERT2, Apc flox/+, Kras LSL-G12D/+,Trp53 R270H/flox mice (n=3); 3) CDX2P-CreERT2, Apc flox/flox mice (n=3); 4) Wild-type control mice (n=4). The CDX2P-CreERT2 transgene expresses a tamoxifen (TAM)-regulated Cre protein (CreERT2) under control of human CDX2 regulatory sequences, allowing for TAM-inducible targeting of flox alleles in adult mouse terminal ileum, cecum, and colon epithelium. Treating the mice having CDX2P-CreERT2 transgene with tamoxifen permits the Cre recombinase to enter the cell nucleus and recombine the floxed alleles for Apc, Kras, and Trp53, resulting in deletion mutations in Apc and Trp53, and an activating, oncogenic mutation in Kras (G12D mutation). The Trp53 R270H allele carries a constitutive R270H mutation, which is the mouse equivalent of human TP53 R273H mutation. Colon tumors were induced by TAM treatment in all the mice from the first three groups and organoids were derived from the tumors of each mouse. We also derived organoids from the normal colon epithelium in the 4th group of mice as controls. All organoids were generated and propagated using a slightly modified TMDU protocol as described in PMID:20872391. Organoids were cultured for 4 days and then harvested. RNA was purified from the organoids, and targets for Affymetrix arrays were synthesized from the mRNAs. We used Affymetrix Mouse Gene 2.1 ST plate arrays, which hold 41345 probe-sets, but we largely analyzed just those 24562 probe-sets that were mapped to Entrez gene IDs. Raw data was processed with the Robust Multi-array Average algorithm (RMA). Data is log2-transformed transcript abundance estimates. We fit a one-way ANOVA model to the 4 groups of samples. We supply a supplementary excel workbook that holds the same data as the data matrix file for those 24562 probe-sets, but also holds the probe-set annotation at the time we analyzed the data, and some simple statistical calculations, which selects subsets of the probe-sets as differentially expressed between pairs of groups. It also shows data and analysis from a separate experiment of RNA purified directly from 3 groups of mice with genotypes like those of the organoid data except that no group of mice with CDX2P-CreERT2 Apc flox/flox genotype were used. It also joins a statistical summary of differences between 9 human tumors with TP53 missense mutations at codon 273 and 36 tumors with TP53 null mutations assayed with RNA-seq by the TCGA project. A separate supplementary file of the TCGA data is also provided. Consumers should consider obtaining more up-to-date probe-set annotation for the array platform.

Dataset Information

Differentiating between cancer and normal tissue samples using multi-hit combinations of genetic mutations.

Publications

Differentiating between cancer and normal tissue samples using multi-hit combinations of genetic mutations.

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