scholarly journals Rapid advancement in cancer genomic big data in the pursuit of precision oncology

2021 ◽  
Author(s):  
Tiara Bunga Mayang Permata ◽  
Sri Mutya Sekarutami ◽  
Endang Nuryadi ◽  
Angela Giselvania ◽  
Soehartati Gondhowiardjo
Keyword(s):  
Big Data ◽  
Open Access ◽  
Cancer Cell ◽  
Cancer Cell Line ◽  
Genomic Data ◽  
The Cancer Genome Atlas ◽  
Clinical Samples ◽  
Precision Oncology ◽  
Cancer Data ◽  
User Friendly

In the current big data era, massive genomic cancer data are available for open access from anywhere in the world. They are obtained from popular platforms, such as The Cancer Genome Atlas, which provides genetic information from clinical samples, and Cancer Cell Line Encyclopedia, which offers genomic data of cancer cell lines. For convenient analysis, user-friendly tools, such as the Tumor Immune Estimation Resource (TIMER), which can be used to analyze tumor-infiltrating immune cells comprehensively, are also emerging. In clinical practice, clinical sequencing has been recommended for patients with cancer in many countries. Despite its many challenges, it enables the application of precision medicine, especially in medical oncology. In this review, several efforts devoted to accomplishing precision oncology and applying big data for use in Indonesia are discussed. Utilizing open access genomic data in writing research articles is also described.

scholarly journals De novo Prediction of Cell-Drug Sensitivities Using Deep Learning-based Graph Regularized Matrix Factorization

2021 ◽  
Author(s):  
Shuangxia Ren ◽  
Yifeng Tao ◽  
Ke Yu ◽  
Yifan Xue ◽  
Russell Schwartz ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cancer Cells ◽  
Anticancer Drugs ◽  
Cancer Cell ◽  
Matrix Factorization ◽  
De Novo ◽  
Cancer Cell Line ◽  
The Cancer Genome Atlas ◽  
Precision Oncology ◽  
Chemical Structures

Application of artificial intelligence (AI) in precision oncology typically involves predicting whether the cancer cells of a patient (previously unseen by AI models) will respond to any of a set of existing anticancer drugs, based on responses of previous training cell samples to those drugs. To expand the repertoire of anticancer drugs, AI has also been used to repurpose drugs that have not been tested in an anticancer setting, i.e., predicting the anticancer effects of a new drug on previously unseen cancer cells de novo. Here, we report a computational model that addresses both of the above tasks in a unified AI framework. Our model, referred to as deep learning-based graph regularized matrix factorization (DeepGRMF), integrates neural networks, graph models, and matrix-factorization techniques to utilize diverse information from drug chemical structures, their impact on cellular signaling systems, and cancer cell cellular states to predict cell response to drugs. DeepGRMF learns embeddings of drugs so that drugs sharing similar structures and mechanisms of action (MOAs) are closely related in the embedding space. Similarly, DeepGRMF also learns representation embeddings of cells such that cells sharing similar cellular states and drug responses are closely related. Evaluation of DeepGRMF and competing models on Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) datasets show its superiority in prediction performance. Finally, we show that the model is capable of predicting effectiveness of a chemotherapy regimen on patient outcomes for the lung cancer patients in The Cancer Genome Atlas (TCGA) dataset.

MEpurity: estimating tumor purity using DNA methylation data

2019 ◽  
Author(s):  
Bowen Liu ◽  
Xiaofei Yang ◽  
Tingjie Wang ◽  
Jiadong Lin ◽  
Yongyong Kang ◽  
...  
Keyword(s):  
Fundamental Property ◽  
Cancer Cell Line ◽  
The Cancer Genome Atlas ◽  
Supplementary Information ◽  
Estimation Methods ◽  
Normal Sample ◽  
Cancer Data ◽  
Tumor Purity ◽  
Cancer Genome Atlas ◽  
Beta Mixture Model

Abstract Motivation Tumor purity is a fundamental property of each cancer sample and affects downstream investigations. Current tumor purity estimation methods either require matched normal sample or report moderately high tumor purity even on normal samples. It is critical to develop a novel computational approach to estimate tumor purity with sufficient precision based on tumor-only sample. Results In this study, we developed MEpurity, a beta mixture model-based algorithm, to estimate the tumor purity based on tumor-only Illumina Infinium 450k methylation microarray data. We applied MEpurity to both The Cancer Genome Atlas (TCGA) cancer data and cancer cell line data, demonstrating that MEpurity reports low tumor purity on normal samples and comparable results on tumor samples with other state-of-art methods. Availability and implementation MEpurity is a C++ program which is available at https://github.com/xjtu-omics/MEpurity. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Quantification of cancer cell migration with an integrated experimental-computational pipeline

2017 ◽  
Author(s):  
Edwin F. Juarez ◽  
Carolina Garri ◽  
Ahmadreza Ghaffarizadeh ◽  
Paul Macklin ◽  
Kian Kani
Keyword(s):  
Breast Cancer ◽  
Cell Migration ◽  
Cancer Cell ◽  
Cancer Cell Line ◽  
Cancer Cell Migration ◽  
Computational Pipeline ◽  
Migration Assay ◽  
User Friendly ◽  
Migration Of Cells

AbstractWe describe an integrated experimental-computational pipeline for quantifying cell migration in vitro. This pipeline is robust to image noise, open source, and user friendly. The experimental component uses the Oris cell migration assay (Platypus Technologies) to create migration regions. The computational component of the pipeline creates masks in Matlab (MathWorks) to cell-covered regions, uses a genetic algorithm to automatically select the migration region, and outputs a metric to quantify the migration of cells. In this work we demonstrate the utility of our pipeline by quantifying the effects of a drug (Taxol) and of the secreted Anterior Gradient 2 (sAGR2) protein in the migration of MDA-MB-231 cells (a breast cancer cell line). In particular, we show that blocking sAGR2 reduces migration of MDA-MB-231 cells.

scholarly journals Comparing cancer cell lines and tumor samples by genomic profiles

2015 ◽  
Author(s):  
Rileen Sinha ◽  
Nikolaus Schultz ◽  
Chris Sander
Keyword(s):  
Cell Lines ◽  
Cancer Cell ◽  
Cancer Cell Line ◽  
Cancer Cell Lines ◽  
Response To Therapy ◽  
Computational Method ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Tumor Material

Cancer cell lines are often used in laboratory experiments as models of tumors, although they can have substantially different genetic and epigenetic profiles compared to tumors. We have developed a general computational method, TumorComparer, to systematically quantify similarities and differences between tumor material when detailed genetic and molecular profiles are available. The comparisons can be flexibly tailored to a particular biological question by placing a higher weight on functional alterations of interest (weighted similarity). In a first pan-cancer application, we have compared 260 cell lines from the Cancer Cell Line Encyclopaedia (CCLE) and 1914 tumors of six different cancer types from The Cancer Genome Atlas (TCGA), using weights to emphasize genomic alterations that frequently recur in tumors. We report the potential suitability of particular cell lines as tumor models and identify apparently unsuitable outlier cell lines, some of which are in wide use, for each of the six cancer types. In future, this weighted similarity method may be generalized for use in a clinical setting to compare patient profiles consisting of genomic patterns combined with clinical attributes, such as diagnosis, treatment and response to therapy.

scholarly journals The utility of homologous recombination deficiency biomarkers across cancer types

2021 ◽  
Author(s):  
Shiro Takamatsu ◽  
J.B. Brown ◽  
Ken Yamaguchi ◽  
Junzo Hamanishi ◽  
Koji Yamanoi ◽  
...  
Keyword(s):  
Homologous Recombination ◽  
Cancer Cell Line ◽  
Parp Inhibitors ◽  
The Cancer Genome Atlas ◽  
Clinical Samples ◽  
Repair Deficiency ◽  
Dna Repair Deficiency ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Recombination Pathway

AbstractBackgroundGenomic alterations in BRCA1/2 and genomic scar signatures are associated with homologous recombination DNA repair deficiency (HRD) and serve as therapeutic biomarkers for platinum and PARP inhibitors in breast and ovarian cancers. However, the clinical significance of these biomarkers in other homologous recombination repair-related genes or other cancer types is not fully understood.ResultsWe analyzed the datasets of all solid cancers from The Cancer Genome Atlas and Cancer Cell Line Encyclopedia, and found that the association between biallelic alterations in the homologous recombination pathway genes and genomic scar signatures differed greatly depending on gender and the presence of somatic TP53 mutation. Additionally, HRD cases identified by a combination of these indicators showed higher sensitivity to DNA-damaging drugs than non-HRD cases both in clinical samples and cell lines.ConclusionOur work provides novel proof of the utility of HRD analysis for all cancer types and will improve the precision and efficacy of chemotherapy selection in clinical oncology.

scholarly journals ACE: A Workbench using Evolutionary Genetic Algorithms for analysing association in TCGA Data

2018 ◽  
Author(s):  
Alan Gilmore ◽  
Kienan I Savage ◽  
Paul O’Reilly ◽  
Aideen C Roddy ◽  
Philip D Dunne ◽  
...  
Keyword(s):  
Data Streams ◽  
Genomic Data ◽  
Molecular Data ◽  
The Cancer Genome Atlas ◽  
Modern Methods ◽  
Evolutionary Genetic ◽  
Cancer Genome Atlas ◽  
User Friendly ◽  
Short Time ◽  
Genome Atlas

AbstractModern methods in generating molecular data have dramatically scaled in recent years, allowing researchers to efficiently acquire large volumes of information. However, this has increased the challenge of recognising interesting patterns within the data. Atlas Correlation Explorer (ACE) is a user-friendly workbench for seeking associations between attributes in the cancer genome atlas (TCGA) database. It allows any combination of clinical and genomic data streams to be selected for searching, and highlights significant correlations within the chosen data. It is based on an evolutionary algorithm which is capable of producing results for very large searches in a short time.

scholarly journals Large-scale Characterization of Drug Responses of Clinically Relevant Proteins in Cancer Cell Lines

2020 ◽  
Author(s):  
Wei Zhao ◽  
Jun Li ◽  
Mei-Ju Chen ◽  
Zhenlin Ju ◽  
Nicole K. Nesser ◽  
...  
Keyword(s):  
Cancer Cell ◽  
Large Scale ◽  
Predictive Power ◽  
Drug Sensitivity ◽  
Cancer Cell Line ◽  
Protein Drug ◽  
Systematic Map ◽  
Data Portal ◽  
Protein Response ◽  
User Friendly

SummaryPerturbation biology is a powerful approach to developing quantitative models of cellular behaviors and gaining mechanistic insights into disease development. In recent years, large-scale resources for phenotypic and mRNA responses of cancer cell lines to perturbations have been generated. However, similar large-scale protein response resources are not available, resulting in a critical knowledge gap for elucidating oncogenic mechanisms and developing effective cancer therapies. Here we generated and compiled perturbed expression profiles of ~210 clinically relevant proteins in >12,000 cancer cell-line samples in response to >150 drug compounds using reverse-phase protein arrays. We show that integrating protein response signals substantially increases the predictive power for drug sensitivity and aids in gaining insights into mechanisms of drug resistance. We build a systematic map of protein-drug connectivity and develop an open-access, user-friendly data portal for community use. Our study provides a valuable information resource for a broad range of quantitative modeling and biomedical applications.HighlightsA large collection of cancer cell line protein responses to drug perturbationsPerturbed protein responses greatly increase predictive power for drug sensitivityBuild a systematic map of protein-drug connectivity based on response profilesDevelop a user-friendly, interactive data portal for community use

scholarly journals Performance Comparisons of AlexNet and GoogLeNet in Cell Growth Inhibition IC50 Prediction

2021 ◽  
Vol 22 (14) ◽  
pp. 7721
Author(s):  
Yeeun Lee ◽  
Seungyoon Nam
Keyword(s):  
Cell Line ◽  
Cancer Cell ◽  
Model Performance ◽  
Gastric Cancer Cell Line ◽  
Cancer Cell Line ◽  
Roc Curves ◽  
Precision Oncology ◽  
Breast Cancer Patients ◽  
Molecular Fingerprints ◽  
Drug Responsiveness

Drug responses in cancer are diverse due to heterogenous genomic profiles. Drug responsiveness prediction is important in clinical response to specific cancer treatments. Recently, multi-class drug responsiveness models based on deep learning (DL) models using molecular fingerprints and mutation statuses have emerged. However, for multi-class models for drug responsiveness prediction, comparisons between convolution neural network (CNN) models (e.g., AlexNet and GoogLeNet) have not been performed. Therefore, in this study, we compared the two CNN models, GoogLeNet and AlexNet, along with the least absolute shrinkage and selection operator (LASSO) model as a baseline model. We constructed the models by taking drug molecular fingerprints of drugs and cell line mutation statuses, as input, to predict high-, intermediate-, and low-class for half-maximal inhibitory concentration (IC50) values of the drugs in the cancer cell lines. Additionally, we compared the models in breast cancer patients as well as in an independent gastric cancer cell line drug responsiveness data. We measured the model performance based on the area under receiver operating characteristic (ROC) curves (AUROC) value. In this study, we compared CNN models for multi-class drug responsiveness prediction. The AlexNet and GoogLeNet showed better performances in comparison to LASSO. Thus, DL models will be useful tools for precision oncology in terms of drug responsiveness prediction.

scholarly journals TP53 isoform junction reads based analysis in malignant and normal contexts

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Suleyman Vural ◽  
Lun-Ching Chang ◽  
Laura M. Yee ◽  
Dmitriy Sonkin
Keyword(s):  
Cancer Cell ◽  
Cancer Cell Line ◽  
Tissue Expression ◽  
The Cancer Genome Atlas ◽  
Rna Seq ◽  
Wild Type ◽  
Suppressor Activity ◽  
Tumor Suppressor Activity ◽  
Cancer Genome Atlas ◽  
Alternative Isoforms

AbstractTP53 is one of the most frequently altered genes in cancer; it can be inactivated by a number of different mechanisms. NM_000546.6 (ENST00000269305.9) is by far the predominant TP53 isoform, however a few other alternative isoforms have been described to be expressed at much lower levels. To better understand patterns of TP53 alternative isoforms expression in cancer and normal samples we performed exon-exon junction reads based analysis of TP53 isoforms using RNA-seq data from The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), and Genotype-Tissue Expression (GTEx) project. TP53 C-terminal alternative isoforms have abolished or severely decreased tumor suppressor activity, and therefore, an increase in fraction of TP53 C-terminal alternative isoforms may be expected in tumors with wild type TP53. Despite our expectation that there would be increase of fraction of TP53 C-terminal alternative isoforms, we observed no substantial increase in fraction of TP53 C-terminal alternative isoforms in TCGA tumors and CCLE cancer cell lines with wild type TP53, likely indicating that TP53 C-terminal alternative isoforms expression cannot be reliably selected for during tumor progression.

Sign in / Sign up

Export Citation Format

Share Document