{"title":"Harnessing Big Data with Machine Learning in Precision Oncology.","authors":"Nirmish Singla, Shyamli Singla","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>While multi-level molecular \"omic\" analyses have undoubtedly increased the sophistication and depth with which we can understand cancer biology, the challenge is to make this overwhelming wealth of data relevant to the clinician and the individual patient. Bridging this gap serves as the cornerstone of precision medicine, yet the expense and difficulty of executing and interpreting these molecular studies make it impractical to routinely implement them in the clinical setting. Herein, we propose that machine learning may hold the key to guiding the future of precision oncology accurately and efficiently. Training deep learning models to interpret the histopathologic or radiographic appearance of tumors and their microenvironment-a phenotypic microcosm of their inherent molecular biology-has the potential to output relevant diagnostic, prognostic, and therapeutic patient-level data. This type of artificial intelligence framework may effectively shape the future of precision oncology by fostering multidisciplinary collaboration.</p>","PeriodicalId":74040,"journal":{"name":"Kidney cancer journal : official journal of the Kidney Cancer Association","volume":"18 3","pages":"83-84"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7644120/pdf/nihms-1640922.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38578734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Payal Kapur, A. Christie, Satwik Rajaram, J. Brugarolas
{"title":"What morphology can teach us about renal cell carcinoma clonal evolution.","authors":"Payal Kapur, A. Christie, Satwik Rajaram, J. Brugarolas","doi":"10.52733/kcj18n3-a1","DOIUrl":"https://doi.org/10.52733/kcj18n3-a1","url":null,"abstract":"While cancer is a clonal process, cumulative evidence suggest that tumors are rather heterogenous and are composed of multiple genetically-distinct subclones that arise at different times and either persist and co-exist, expand and evolve, or are eliminated. A paradigm of tumor heterogeneity is renal cell carcinoma (RCC). By exploiting morphological traits and building upon a framework around three axes (architecture, cytology and the microenvironment), we review recent advances in our understanding of RCC evolution leading to an integrated molecular genetic and morphologic evolutionary model with both prognostic and therapeutic implications. The ability to predict cancer evolution may have profound implications for clinical care and is central to oncology.","PeriodicalId":74040,"journal":{"name":"Kidney cancer journal : official journal of the Kidney Cancer Association","volume":"18 3 1","pages":"68-76"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42420363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Payal Kapur, Alana Christie, Satwik Rajaram, James Brugarolas
{"title":"What morphology can teach us about renal cell carcinoma clonal evolution.","authors":"Payal Kapur, Alana Christie, Satwik Rajaram, James Brugarolas","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>While cancer is a clonal process, cumulative evidence suggest that tumors are rather heterogenous and are composed of multiple genetically-distinct subclones that arise at different times and either persist and co-exist, expand and evolve, or are eliminated. A paradigm of tumor heterogeneity is renal cell carcinoma (RCC). By exploiting morphological traits and building upon a framework around three axes (architecture, cytology and the microenvironment), we review recent advances in our understanding of RCC evolution leading to an integrated molecular genetic and morphologic evolutionary model with both prognostic and therapeutic implications. The ability to predict cancer evolution may have profound implications for clinical care and is central to oncology.</p>","PeriodicalId":74040,"journal":{"name":"Kidney cancer journal : official journal of the Kidney Cancer Association","volume":"18 3","pages":"68-76"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8232548/pdf/nihms-1634438.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39110987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Uveal Melanoma and Kidney Cancer: More Similar than Meets the Eye.","authors":"Nirmish Singla","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":74040,"journal":{"name":"Kidney cancer journal : official journal of the Kidney Cancer Association","volume":"18 2","pages":"61"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7405959/pdf/nihms-1613131.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38241521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Roy Elias, Akanksha Sharma, Nirmish Singla, James Brugarolas
{"title":"Next Generation Sequencing in Renal Cell Carcinoma: Towards Precision Medicine.","authors":"Roy Elias, Akanksha Sharma, Nirmish Singla, James Brugarolas","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":74040,"journal":{"name":"Kidney cancer journal : official journal of the Kidney Cancer Association","volume":"17 4","pages":"94-104"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7089604/pdf/nihms-1066623.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37765649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}