Alain Kaldany, Zev R Leopold, Juliana E Kim, Hiren V Patel, Arnav Srivastava, Alexandra L Tabakin, Eric A Singer
Lymph node involvement in renal cell carcinoma (RCC) portends a poor prognosis. However, the role of lymph node dissection (LND) at the time of tumor resection is not fully understood. Conflicting data have been published regarding the survival implications of LND during RCC surgery, and the optimal patient population for which LND might be beneficial has yet to be identified. Based on recent data characterizing the outcomes of node-positive RCC, some have advocated for revising the current staging guidelines to better reflect these findings. Given the paucity of high-quality evidence supporting or refuting the routine use of LND in RCC, further research is needed to shed light on this important topic. There are a number of ongoing clinical trials evaluating the role of perioperative (neoadjuvant and adjuvant) systemic therapy, which include patients with node-positive RCC, and will serve to guide changes in treatment practices for this patient population moving forward.
{"title":"Dissecting the role of lymphadenectomy in the management of renal cell carcinoma: past, present, and future.","authors":"Alain Kaldany, Zev R Leopold, Juliana E Kim, Hiren V Patel, Arnav Srivastava, Alexandra L Tabakin, Eric A Singer","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Lymph node involvement in renal cell carcinoma (RCC) portends a poor prognosis. However, the role of lymph node dissection (LND) at the time of tumor resection is not fully understood. Conflicting data have been published regarding the survival implications of LND during RCC surgery, and the optimal patient population for which LND might be beneficial has yet to be identified. Based on recent data characterizing the outcomes of node-positive RCC, some have advocated for revising the current staging guidelines to better reflect these findings. Given the paucity of high-quality evidence supporting or refuting the routine use of LND in RCC, further research is needed to shed light on this important topic. There are a number of ongoing clinical trials evaluating the role of perioperative (neoadjuvant and adjuvant) systemic therapy, which include patients with node-positive RCC, and will serve to guide changes in treatment practices for this patient population moving forward.</p>","PeriodicalId":74040,"journal":{"name":"Kidney cancer journal : official journal of the Kidney Cancer Association","volume":"18 4","pages":"103-108"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8772661/pdf/nihms-1723267.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39852433","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}
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.
{"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
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.
{"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
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.
{"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}
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