NDLI: Predicting breast cancer drug response using a multiple-layer cell line drug response network model

Content Provider	Springer Nature : BioMed Central
Author	Huang, Shujun Hu, Pingzhao Lakowski, Ted M.
Abstract	Background Predicting patient drug response based on a patient’s molecular profile is one of the key goals of precision medicine in breast cancer (BC). Multiple drug response prediction models have been developed to address this problem. However, most of them were developed to make sensitivity predictions for multiple single drugs within cell lines from various cancer types instead of a single cancer type, do not take into account drug properties, and have not been validated in cancer patient-derived data. Among the multi-omics data, gene expression profiles have been shown to be the most informative data for drug response prediction. However, these models were often developed with individual genes. Therefore, this study aimed to develop a drug response prediction model for BC using multiple data types from both cell lines and drugs. Methods We first collected the baseline gene expression profiles of 49 BC cell lines along with IC50 values for 220 drugs tested in these cell lines from Genomics of Drug Sensitivity in Cancer (GDSC). Using these data, we developed a multiple-layer cell line-drug response network (ML-CDN2) by integrating a one-layer cell line similarity network based on the pathway activity profiles and a three-layer drug similarity network based on the drug structures, targets, and pan-cancer IC50 profiles. We further used ML-CDN2 to predict the drug response for new BC cell lines or patient-derived samples. Results ML-CDN2 demonstrated a good predictive performance, with the Pearson correlation coefficient between the observed and predicted IC50 values for all GDSC cell line-drug pairs of 0.873. Also, ML-CDN2 showed a good performance when used to predict drug response in new BC cell lines from the Cancer Cell Line Encyclopedia (CCLE), with a Pearson correlation coefficient of 0.718. Moreover, we found that the cell line-derived ML-CDN2 model could be applied to predict drug response in the BC patient-derived samples from The Cancer Genome Atlas (TCGA). Conclusions The ML-CDN2 model was built to predict BC drug response using comprehensive information from both cell lines and drugs. Compared with existing methods, it has the potential to predict the drug response for BC patient-derived samples.
Related Links	https://bmccancer.biomedcentral.com/counter/pdf/10.1186/s12885-021-08359-6.pdf
Ending Page	15
Page Count	15
Starting Page	1
File Format	HTM / HTML
ISSN	14712407
DOI	10.1186/s12885-021-08359-6
Journal	BMC Cancer
Issue Number	1
Volume Number	21
Language	English
Publisher	BioMed Central
Publisher Date	2021-05-31
Access Restriction	Open
Subject Keyword	Cancer Research Oncology Surgical Oncology Health Promotion and Disease Prevention Biomedicine Medicine Public Health Breast cancer Drug response Network model Data integration Medicine/Public Health
Content Type	Text
Resource Type	Article
Subject	Cancer Research Oncology Genetics
Journal Impact Factor	3.4/2023
5-Year Journal Impact Factor	3.8/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Predicting response to multidrug regimens in cancer patients using cell line experiments and regularised regression models

Predicting multi-level drug response with gene expression profile in multiple myeloma using hierarchical ordinal regression

Dysregulated miR-183 inhibits migration in breast cancer cells

Low-risk susceptibility alleles in 40 human breast cancer cell lines

Predicting the response to neoadjuvant chemotherapy for breast cancer: wavelet transforming radiomics in MRI

Tumor-suppressor activity of RRIG1 in breast cancer

Effects of EpCAM overexpression on human breast cancer cell lines

Development and validation of a radiopathomic model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer patients

Integration of breast cancer care in a middle-income country: learning from Suandok Breast Cancer Network (SBCN)

Predicting breast cancer drug response using a multiple-layer cell line drug response network model

Similar Documents

Predicting response to multidrug regimens in cancer patients using cell line experiments and regularised regression models

Predicting multi-level drug response with gene expression profile in multiple myeloma using hierarchical ordinal regression

Dysregulated miR-183 inhibits migration in breast cancer cells

Low-risk susceptibility alleles in 40 human breast cancer cell lines

Predicting the response to neoadjuvant chemotherapy for breast cancer: wavelet transforming radiomics in MRI

Tumor-suppressor activity of RRIG1 in breast cancer

Effects of EpCAM overexpression on human breast cancer cell lines

Development and validation of a radiopathomic model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer patients

Integration of breast cancer care in a middle-income country: learning from Suandok Breast Cancer Network (SBCN)

Predicting breast cancer drug response using a multiple-layer cell line drug response network model