Computational Screening of Anti-Cancer Drugs Identifies a New BRCA Independent Gene Expression Signature to Predict Breast Cancer Sensitivity to Cisplatin
The development of therapies that target specific disease subtypes has dramatically improved outcomes for patients with breast cancer. However, survival gains have not been uniform across patients, even within a given molecular subtype. Large collections of publicly available drug screening data mat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/14/10/2404 |
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| author | Jean Berthelet Momeneh Foroutan Dharmesh D. Bhuva Holly J. Whitfield Farrah El-Saafin Joseph Cursons Antonin Serrano Michal Merdas Elgene Lim Emmanuelle Charafe-Jauffret Christophe Ginestier Matthias Ernst Frédéric Hollande Robin L. Anderson Bhupinder Pal Belinda Yeo Melissa J. Davis Delphine Merino |
| author_facet | Jean Berthelet Momeneh Foroutan Dharmesh D. Bhuva Holly J. Whitfield Farrah El-Saafin Joseph Cursons Antonin Serrano Michal Merdas Elgene Lim Emmanuelle Charafe-Jauffret Christophe Ginestier Matthias Ernst Frédéric Hollande Robin L. Anderson Bhupinder Pal Belinda Yeo Melissa J. Davis Delphine Merino |
| author_sort | Jean Berthelet |
| collection | DOAJ |
| description | The development of therapies that target specific disease subtypes has dramatically improved outcomes for patients with breast cancer. However, survival gains have not been uniform across patients, even within a given molecular subtype. Large collections of publicly available drug screening data matched with transcriptomic measurements have facilitated the development of computational models that predict response to therapy. Here, we generated a series of predictive gene signatures to estimate the sensitivity of breast cancer samples to 90 drugs, comprising FDA-approved drugs or compounds in early development. To achieve this, we used a cell line-based drug screen with matched transcriptomic data to derive in silico models that we validated in large independent datasets obtained from cell lines and patient-derived xenograft (PDX) models. Robust computational signatures were obtained for 28 drugs and used to predict drug efficacy in a set of PDX models. We found that our signature for cisplatin can be used to identify tumors that are likely to respond to this drug, even in absence of the BRCA-1 mutation routinely used to select patients for platinum-based therapies. This clinically relevant observation was confirmed in multiple PDXs. Our study foreshadows an effective delivery approach for precision medicine. |
| first_indexed | 2024-03-10T03:12:11Z |
| format | Article |
| id | doaj.art-03fba90710854702b0036094e42aa149 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-10T03:12:11Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-03fba90710854702b0036094e42aa1492023-11-23T10:22:41ZengMDPI AGCancers2072-66942022-05-011410240410.3390/cancers14102404Computational Screening of Anti-Cancer Drugs Identifies a New BRCA Independent Gene Expression Signature to Predict Breast Cancer Sensitivity to CisplatinJean Berthelet0Momeneh Foroutan1Dharmesh D. Bhuva2Holly J. Whitfield3Farrah El-Saafin4Joseph Cursons5Antonin Serrano6Michal Merdas7Elgene Lim8Emmanuelle Charafe-Jauffret9Christophe Ginestier10Matthias Ernst11Frédéric Hollande12Robin L. Anderson13Bhupinder Pal14Belinda Yeo15Melissa J. Davis16Delphine Merino17Olivia Newton-John Cancer Research Institute, Melbourne, VIC 3084, AustraliaBioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, AustraliaBioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, AustraliaBioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, AustraliaOlivia Newton-John Cancer Research Institute, Melbourne, VIC 3084, AustraliaBioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, AustraliaOlivia Newton-John Cancer Research Institute, Melbourne, VIC 3084, AustraliaOlivia Newton-John Cancer Research Institute, Melbourne, VIC 3084, AustraliaGarvan Institute of Medical Research, Darlinghurst, NSW 2010, AustraliaCRCM, Inserm, CNRS, Institut Paoli-Calmettes, Aix-Marseille, Epithelial Stem Laboratory, Equipe Labellisée LIGUE Contre le Cancer, 13009 Marseille, FranceCRCM, Inserm, CNRS, Institut Paoli-Calmettes, Aix-Marseille, Epithelial Stem Laboratory, Equipe Labellisée LIGUE Contre le Cancer, 13009 Marseille, FranceOlivia Newton-John Cancer Research Institute, Melbourne, VIC 3084, AustraliaVictorian Comprehensive Cancer Centre, The University of Melbourne Centre for Cancer Research, Melbourne, VIC 3000, AustraliaOlivia Newton-John Cancer Research Institute, Melbourne, VIC 3084, AustraliaOlivia Newton-John Cancer Research Institute, Melbourne, VIC 3084, AustraliaOlivia Newton-John Cancer Research Institute, Melbourne, VIC 3084, AustraliaBioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, AustraliaOlivia Newton-John Cancer Research Institute, Melbourne, VIC 3084, AustraliaThe development of therapies that target specific disease subtypes has dramatically improved outcomes for patients with breast cancer. However, survival gains have not been uniform across patients, even within a given molecular subtype. Large collections of publicly available drug screening data matched with transcriptomic measurements have facilitated the development of computational models that predict response to therapy. Here, we generated a series of predictive gene signatures to estimate the sensitivity of breast cancer samples to 90 drugs, comprising FDA-approved drugs or compounds in early development. To achieve this, we used a cell line-based drug screen with matched transcriptomic data to derive in silico models that we validated in large independent datasets obtained from cell lines and patient-derived xenograft (PDX) models. Robust computational signatures were obtained for 28 drugs and used to predict drug efficacy in a set of PDX models. We found that our signature for cisplatin can be used to identify tumors that are likely to respond to this drug, even in absence of the BRCA-1 mutation routinely used to select patients for platinum-based therapies. This clinically relevant observation was confirmed in multiple PDXs. Our study foreshadows an effective delivery approach for precision medicine.https://www.mdpi.com/2072-6694/14/10/2404breast cancerpharmacogenomicspredictive modelingdrug sensitivityprecision medicinecisplatin |
| spellingShingle | Jean Berthelet Momeneh Foroutan Dharmesh D. Bhuva Holly J. Whitfield Farrah El-Saafin Joseph Cursons Antonin Serrano Michal Merdas Elgene Lim Emmanuelle Charafe-Jauffret Christophe Ginestier Matthias Ernst Frédéric Hollande Robin L. Anderson Bhupinder Pal Belinda Yeo Melissa J. Davis Delphine Merino Computational Screening of Anti-Cancer Drugs Identifies a New BRCA Independent Gene Expression Signature to Predict Breast Cancer Sensitivity to Cisplatin Cancers breast cancer pharmacogenomics predictive modeling drug sensitivity precision medicine cisplatin |
| title | Computational Screening of Anti-Cancer Drugs Identifies a New BRCA Independent Gene Expression Signature to Predict Breast Cancer Sensitivity to Cisplatin |
| title_full | Computational Screening of Anti-Cancer Drugs Identifies a New BRCA Independent Gene Expression Signature to Predict Breast Cancer Sensitivity to Cisplatin |
| title_fullStr | Computational Screening of Anti-Cancer Drugs Identifies a New BRCA Independent Gene Expression Signature to Predict Breast Cancer Sensitivity to Cisplatin |
| title_full_unstemmed | Computational Screening of Anti-Cancer Drugs Identifies a New BRCA Independent Gene Expression Signature to Predict Breast Cancer Sensitivity to Cisplatin |
| title_short | Computational Screening of Anti-Cancer Drugs Identifies a New BRCA Independent Gene Expression Signature to Predict Breast Cancer Sensitivity to Cisplatin |
| title_sort | computational screening of anti cancer drugs identifies a new brca independent gene expression signature to predict breast cancer sensitivity to cisplatin |
| topic | breast cancer pharmacogenomics predictive modeling drug sensitivity precision medicine cisplatin |
| url | https://www.mdpi.com/2072-6694/14/10/2404 |
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