Integrating mutation and gene expression cross-sectional data to infer cancer progression

Fleck, Julia L.; Pavel, Ana B.; Cassandras, Christos G.

doi:10.1186/s12918-016-0255-6

Cited by 27 publications

(31 citation statements)

References 28 publications

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“…Recent work using mathematical optimization models shows promise as a way to integrate molecular data from cell lines with drug-sensitivity information to infer resistance mechanisms (Fleck et al, 2016;Fleck et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Predicting drug sensitivity of cancer cells based on DNA methylation levels

Fleck

Miranda

Baião

et al. 2020

Preprint

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Cancer cell lines, which are cell cultures developed from tumor samples, represent one of the least expensive and most studied preclinical models for drug development. Accurately predicting drug response for a given cell line based on molecular features may help to optimize drug-development pipelines and explain mechanisms behind treatment responses. In this study, we focus on DNA methylation profiles as one type of molecular feature that is known to drive tumorigenesis and modulate treatment responses. Using genome-wide, DNA methylation profiles from 987 cell lines from the Genomics of Drug Sensitivity in Cancer database, we applied machine-learning algorithms to evaluate the potential to predict cytotoxic responses for eight anti-cancer drugs. We compared the performance of five classification algorithms and four regression algorithms that use diverse methodologies, including tree-, probability-, kernel-, ensemble-, and distance-based approaches. For both types of algorithm, we artificially subsampled the data to varying degrees, aiming to understand whether training models based on relatively extreme outcomes would yield improved performance. We also performed an information-gain analysis to examine which genes were most predictive of drug responses. Finally, we used tumor data from The Cancer Genome Atlas to evaluate the feasibility of predicting clinical responses in humans based on models derived from cell lines. When using classification or regression algorithms to predict discrete or continuous responses, respectively, we consistently observed excellent predictive performance when the training and test sets both consisted of cell-line data. However, classification models derived from cell-line data failed to generalize effectively for tumors.

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Section: Discussionmentioning

confidence: 99%

Predicting drug sensitivity of cancer cells based on DNA methylation levels

Fleck

Miranda

Baião

et al. 2020

Preprint

Self Cite

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“…More recent studies focus on integrating gene expression with DNA methylation data, as of Cappelli et al and Li et al, for knowledge extraction beneficial to prognosis [21,22]. Moving towards prognosis approaches, Fleck et al propose a method based on the integration of mutations and gene expression to detect how mutations can lead to changes in gene expression, and, consequently, cancer progression [23]. In the same direction, Yu et al and Zafeiris et al propose the use of artificial neural networks for disease classification and biomarker discovery respectively [24,25].…”

Section: -1-literature Reviewmentioning

confidence: 99%

Developing an Integrated Genomic Profile for Cancer Patients with the Use of NGS Data

2019

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Next Generation Sequencing (NGS) technologies has revolutionized genomics data research by facilitating high-throughput sequencing of genetic material that comes from different sources, such as Whole Exome Sequencing (WES) and RNA Sequencing (RNAseq). The exploitation and integration of this wealth of heterogeneous sequencing data remains a major challenge. There is a clear need for approaches that attempt to process and combine the aforementioned sources in order to create an integrated profile of a patient that will allow us to build the complete picture of a disease. This work introduces such an integrated profile using Chronic Lymphocytic Leukemia (CLL) as the exemplary cancer type. The approach described in this paper links the various NGS sources with the patients’ clinical data. The resulting profile efficiently summarizes the large-scale datasets, links the results with the clinical profile of the patient and correlates indicators arising from different data types. With the use of state-of-the-art machine learning techniques and the association of the clinical information with these indicators, which served as the feature pool for the classification, it has been possible to build efficient predictive models. To ensure reproducibility of the results, open data were exclusively used in the classification assessment. The final goal is to design a complete genomic profile of a cancer patient. The profile includes summarization and visualization of the results of WES and RNAseq analysis (specific variants and significantly expressed genes, respectively) and the clinical profile, integration/comparison of these results and a prediction regarding the disease trajectory. Concluding, this work has managed to produce a comprehensive clinico-genetic profile of a patient by successfully integrating heterogeneous data sources. The proposed profile can contribute to the medical research providing new possibilities in personalized medicine and prognostic views.

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“…69,70 Integrating mutation and gene expression data set to identify patterns of molecular events relevant to initiation and progression of tumor may be of importance for OC diagnosis, prognosis, and therapy. 71 The tumor suppressor gene p53 is commonly mutated in OC. 72,73 Alterations in the p53 gene make it functionally inactive in oral tumors.…”

Section: Resultsmentioning

confidence: 99%

Signature of genetic associations in oral cancer

Sharma

Nandan²,

Sharma

et al. 2017

Tumour Biol.

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Oral cancer etiology is complex and controlled by multi-factorial events including genetic events. Candidate gene studies, genome-wide association studies, and next-generation sequencing identified various chromosomal loci to be associated with oral cancer. There is no available review that could give us the comprehensive picture of genetic loci identified to be associated with oral cancer by candidate gene studies-based, genome-wide association studies-based, and nextgeneration sequencing-based approaches. A systematic literature search was performed in the PubMed database to identify the loci associated with oral cancer by exclusive candidate gene studies-based, genome-wide association studies-based, and next-generation sequencing-based study approaches. The information of loci associated with oral cancer is made online through the resource "ORNATE." Next, screening of the loci validated by candidate gene studies and next-generation sequencing approach or by two independent studies within candidate gene studies or nextgeneration sequencing approaches were performed. A total of 264 loci were identified to be associated with oral cancer by candidate gene studies, genome-wide association studies, and next-generation sequencing approaches. In total, 28 loci, that is, 14q32.33, and 5q14.2 (XRCC4), were validated to be associated with oral cancer. "ORNATE" gives a snapshot of genetic loci associated with oral cancer. All 28 loci were validated to be linked to oral cancer for which further fine-mapping followed by geneby-gene and gene-environment interaction studies is needed to confirm their involvement in modifying oral cancer.

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Integrating mutation and gene expression cross-sectional data to infer cancer progression

Cited by 27 publications

References 28 publications

Predicting drug sensitivity of cancer cells based on DNA methylation levels

Predicting drug sensitivity of cancer cells based on DNA methylation levels

Developing an Integrated Genomic Profile for Cancer Patients with the Use of NGS Data

Signature of genetic associations in oral cancer

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