Patient-Specific Network for Personalized Breast Cancer Therapy with Multi-Omics Data
The development of new computational approaches that are able to design the correct personalized drugs is the crucial therapeutic issue in cancer research. However, tumor heterogeneity is the main obstacle to developing patient-specific single drugs or combinations of drugs that already exist in cli...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-02-01
|
| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/23/2/225 |
| _version_ | 1827589495174725632 |
|---|---|
| author | Claudia Cava Soudabeh Sabetian Isabella Castiglioni |
| author_facet | Claudia Cava Soudabeh Sabetian Isabella Castiglioni |
| author_sort | Claudia Cava |
| collection | DOAJ |
| description | The development of new computational approaches that are able to design the correct personalized drugs is the crucial therapeutic issue in cancer research. However, tumor heterogeneity is the main obstacle to developing patient-specific single drugs or combinations of drugs that already exist in clinics. In this study, we developed a computational approach that integrates copy number alteration, gene expression, and a protein interaction network of 73 basal breast cancer samples. 2509 prognostic genes harboring a copy number alteration were identified using survival analysis, and a protein–protein interaction network considering the direct interactions was created. Each patient was described by a specific combination of seven altered hub proteins that fully characterize the 73 basal breast cancer patients. We suggested the optimal combination therapy for each patient considering drug–protein interactions. Our approach is able to confirm well-known cancer related genes and suggest novel potential drug target genes. In conclusion, we presented a new computational approach in breast cancer to deal with the intra-tumor heterogeneity towards personalized cancer therapy. |
| first_indexed | 2024-03-09T00:57:33Z |
| format | Article |
| id | doaj.art-4fe5870f322c4b71a6d45a05911bcb80 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T00:57:33Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-4fe5870f322c4b71a6d45a05911bcb802023-12-11T16:47:15ZengMDPI AGEntropy1099-43002021-02-0123222510.3390/e23020225Patient-Specific Network for Personalized Breast Cancer Therapy with Multi-Omics DataClaudia Cava0Soudabeh Sabetian1Isabella Castiglioni2Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Via F.Cervi 93, Segrate, 20090 Milan, ItalyInfertility Research Center, Shiraz University of Medical Sciences, Shiraz, IranDepartment of Physics “Giuseppe Occhialini”, University of Milan-Bicocca Piazza dell’Ateneo Nuovo, 20126 Milan, ItalyThe development of new computational approaches that are able to design the correct personalized drugs is the crucial therapeutic issue in cancer research. However, tumor heterogeneity is the main obstacle to developing patient-specific single drugs or combinations of drugs that already exist in clinics. In this study, we developed a computational approach that integrates copy number alteration, gene expression, and a protein interaction network of 73 basal breast cancer samples. 2509 prognostic genes harboring a copy number alteration were identified using survival analysis, and a protein–protein interaction network considering the direct interactions was created. Each patient was described by a specific combination of seven altered hub proteins that fully characterize the 73 basal breast cancer patients. We suggested the optimal combination therapy for each patient considering drug–protein interactions. Our approach is able to confirm well-known cancer related genes and suggest novel potential drug target genes. In conclusion, we presented a new computational approach in breast cancer to deal with the intra-tumor heterogeneity towards personalized cancer therapy.https://www.mdpi.com/1099-4300/23/2/225protein networkbioinformaticsbreast cancercopy number alteration |
| spellingShingle | Claudia Cava Soudabeh Sabetian Isabella Castiglioni Patient-Specific Network for Personalized Breast Cancer Therapy with Multi-Omics Data Entropy protein network bioinformatics breast cancer copy number alteration |
| title | Patient-Specific Network for Personalized Breast Cancer Therapy with Multi-Omics Data |
| title_full | Patient-Specific Network for Personalized Breast Cancer Therapy with Multi-Omics Data |
| title_fullStr | Patient-Specific Network for Personalized Breast Cancer Therapy with Multi-Omics Data |
| title_full_unstemmed | Patient-Specific Network for Personalized Breast Cancer Therapy with Multi-Omics Data |
| title_short | Patient-Specific Network for Personalized Breast Cancer Therapy with Multi-Omics Data |
| title_sort | patient specific network for personalized breast cancer therapy with multi omics data |
| topic | protein network bioinformatics breast cancer copy number alteration |
| url | https://www.mdpi.com/1099-4300/23/2/225 |
| work_keys_str_mv | AT claudiacava patientspecificnetworkforpersonalizedbreastcancertherapywithmultiomicsdata AT soudabehsabetian patientspecificnetworkforpersonalizedbreastcancertherapywithmultiomicsdata AT isabellacastiglioni patientspecificnetworkforpersonalizedbreastcancertherapywithmultiomicsdata |