[논문]A deep learning and similarity-based hierarchical clustering approach for pathological stage prediction of papillary renal cell carcinoma

Lee, Sugi; Jung, Jaeeun; Park, Ilkyu; Park, Kunhyang; Kim, Dae-Soo

doi:10.1016/j.csbj.2020.09.029

[해외논문] A deep learning and similarity-based hierarchical clustering approach for pathological stage prediction of papillary renal cell carcinoma 원문보기

Computational and structural biotechnology journal, v.18, 2020년, pp.2639 - 2646

Lee, Sugi (Department of Bioinformatics, KRIBB School of Bioscience, Korea University of Science and Technology (UST), 217 Gajeong-ro, Yuseong-gu, Daejeon, Republic of Korea) , Jung, Jaeeun (Department of Environmental Disease Research Centers, Korea Research Institute of Bioscience & Biotechnology (KRIBB), 125 Gwahak-ro, Yuseong-gu, Daejeon, Republic of Korea) , Park, Ilkyu (Department of Bioinformatics, KRIBB School of Bioscience, Korea University of Science and Technology (UST), 217 Gajeong-ro, Yuseong-gu, Daejeon, Republic of Korea) , Park, Kunhyang (Department of Core Facility Management Center, Korea Research Institute of Bioscience & Biotechnology (KRIBB), 125 Gwahak-ro, Yuseong-gu, Daejeon, Republic of Korea) , Kim, Dae-Soo (Department of Bioinformatics, KRIBB School of Bioscience, Korea University of Science and Technology (UST), 217 Gajeong-ro, Yuseong-gu, Daejeon, Republic of Korea)

Abstract ▼ AI-Helper

Papillary renal cell carcinoma (pRCC), which accounts for 10–15% of renal cell carcinomas, is the second most frequent renal cell carcinoma. pRCC patient classification is difficult because of disease heterogeneity, histologic subtypes, and variations in both disease progression and patient outcomes. Nevertheless, symptom-based patient classification is indispensable in deciding treatment options. Here we introduce a prediction method for distinguishing pRCC pathological tumour stages using deep learning and similarity-based hierarchical clustering approaches. Differentially expressed genes (DEGs) were identified from gene expression data of pRCC patients retrieved from TCGA. Thirty-three of these genes were distinguished based on expression in early or late stage pRCC using the Wilcoxon rank sum test, confidence interval, and LASSO regression. Then, a deep learning model was constructed to predict tumour progression with an accuracy of 0.942 and area under curve of 0.933. Furthermore, pathological sub-stage information with an accuracy of 0.857 was obtained via similarity-based hierarchical clustering using 18 DEGs between stages I and II, and 11 DEGs between stages III and IV, identified through Wilcoxon rank sum test and quantile approach. Additionally, we offer this classification process as an R function. This is the first report of a model distinguishing the pathological tumour stages of pRCC using deep learning and similarity-based hierarchical clustering methods. Our findings are potentially applicable for improving early detection and treatment of pRCC and establishing a clearer classification of the pathological stages in other tumours.

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참고문헌 (39)

1 Rahimi A, Gonen M. Discriminating early- and late-stage cancers using multiple kernel learning on gene sets. Bioinformatics 2018;34:i412？21. doi: 10.1093/bioinformatics/bty239.
2 Fedele C. Tothill R.W. McArthur G.A. Navigating the challenge of tumor heterogeneity in cancer therapy Cancer Discovery 4 2 2014 146 148 24501303

상세보기
3 Muglia VF, Prando A. Renal cell carcinoma: histological classification and correlation with imaging findings. Radiol Bras 2015;48:166？74. doi: 10.1590/0100-3984.2013.1927.
4 Arik D, Acikalin MF, Can C. Papillary renal cell carcinoma and collecting duct carcinoma combination. A case report and review of synchronous renal cell carcinoma subtypes in the same kidney. Arch Med Sci 2015;11:686？90. doi: 10.5114/aoms.2015.52378.
5 Idikio H.A. Human cancer classification: a systems biology- based model integrating morphology, cancer stem cells, proteomics, and genomics J Cancer 2 2011 107 115 10.7150/jca.2.107 21479129

상세보기
6 Ravaud A. Oudard S. De Fromont M. Chevreau C. Gravis G. Zanetta S. Theodore C. Jimenez M. Sevin E. Laguerre B. Rolland F. Ouali M. Culine S. Escudier B. First-line treatment with sunitinib for type 1 and type 2 locally advanced or metastatic papillary renal cell carcinoma: a phase II study (SUPAP) by the French Genitourinary Group (GETUG) Ann Oncol 26 6 2015 1123 1128 10.1093/annonc/mdv149 25802238

상세보기
7 Delahunt B. Eble J.N. Papillary renal cell carcinoma: a clinicopathologic and immunohistochemical study of 105 tumors Mod Pathol 10 1997 537 544 9195569

상세보기
8 Warren A.Y. Harrison D. WHO/ISUP classification, grading and pathological staging of renal cell carcinoma: standards and controversies World J Urol 36 12 2018 1913 1926 10.1007/s00345-018-2447-8 30123932

상세보기
9 Ricketts CJ, De Cubas AA, Spellman PT, Kimryn Rathmell W, Linehan WM. The cancer genome atlas comprehensive molecular characterization of renal cell carcinoma. Cell Rep 2018;23:313？26. doi: 10.1016/j.celrep.2018.03.075.
10 Vogelzang N.J. Stadler W.M. Kidney cancer The Lancet 352 9141 1998 1691 1696 10.1016/S0140-6736(98)01041-1

상세보기
11 Linehan W.M. Spellman P.T. Ricketts C.J. Creighton C.J. Fei S.S. Davis C. Comprehensive molecular characterization of papillary renal-cell carcinoma N Engl J Med 374 2016 135 145 10.1056/NEJMoa1505917 26536169

상세보기
12 Waldert M. Haitel A. Marberger M. Katzenbeisser D. Ozsoy M. Stadler E. Comparison of type I and II papillary renal cell carcinoma (RCC) and clear cell RCC BJU Int 102 2008 1381 1384 10.1111/j.1464-410X.2008.07999.x 18782311

상세보기
13 Lee J. Chae H.K. Lee W. Nam W. Lim B. Choi S.Y. Kyung Y.S. You D. Jeong I.G. Song C. Hong B. Hong J.H. Ahn H. Kim C.-S. Comparison of prognosis in types 1 and 2 papillary renal cell carcinoma and clear cell renal cell carcinoma in T1 stage Korean J Urol Oncol 16 3 2018 119 125

상세보기
14 Liu K. Ren Y. Pang L. Qi Y. Jia W. Tao L. Papillary renal cell carcinoma: A clinicopathological and whole-genome exon sequencing study Int J Clin Exp Pathol 8 2015 8311 8335 26339402

상세보기
15 Jagga Z. Gupta D. Classification models for clear cell renal carcinoma stage progression, based on tumor RNAseq expression trained supervised machine learning algorithms BMC Proc 8 S6 2014 10.1186/1753-6561-8-S6-S2

상세보기
16 Kaur H, Bhalla S, Raghava GPS. Classification of early and late stage liver hepatocellular carcinoma patients from their genomics and epigenomics profiles. PLoS One 2019;14. doi: 10.1371/journal.pone.0221476.
17 Zar JH. Biostatistical Analysis ？ ERRATA. Prentice Hall New Jersey USA 2010:663. doi: 10.1037/0012764.
18 Radovic M. Ghalwash M. Filipovic N. Obradovic Z. Minimum redundancy maximum relevance feature selection approach for temporal gene expression data BMC Bioinf 18 1 2017 10.1186/s12859-016-1423-9

상세보기
19 Melkumova L.E. Shatskikh S.Y. Comparing Ridge and LASSO estimators for data analysis Procedia Eng 201 2017 746 755 10.1016/j.proeng.2017.09.615

상세보기
20 Candel A. Parmar V. Lelell E. Arora A. Lanford J. Deep learning with H 2 O 5th ed. 2016 Mountain View CA, USA
21 Alemu H, Wu W, Zhao J. Feedforward Neural Networks with a Hidden Layer Regularization Method. Symmetry (Basel) 2018;10:525. doi: 10.3390/sym10100525.
22 Wang H. He X. Detecting differential expressions in GeneChip microarray studies: a quantile approach J Am Stat Assoc 102 477 2007 104 112

상세보기
23 Smid M, Rodriguez-Gonzalez FG, Sieuwerts AM, Salgado R, Prager-Van Der Smissen WJC, Vlugt-Daane M Van Der, et al. Breast cancer genome and transcriptome integration implicates specific mutational signatures with immune cell infiltration. Nat Commun 2016;7:1？9. doi: 10.1038/ncomms12910.
24 Liao W, Ying Yang M, Zhan N, Rosenhahn B. Triplet-Based Deep Similarity Learning for Person Re-Identification 2017:385？93.
25 Sorlie T. Perou C.M. Tibshirani R. Aas T. Geisler S. Johnsen H. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications Proc Natl Acad Sci 98 19 2001 10869 10874 11553815
26 Perou C.M. Sørlie T. Eisen M.B. van de Rijn M. Jeffrey S.S. Rees C.A. Pollack J.R. Ross D.T. Johnsen H. Akslen L.A. Fluge Ø. Pergamenschikov A. Williams C. Zhu S.X. Lønning P.E. Børresen-Dale A.-L. Brown P.O. Botstein D. Molecular portraits of human breast tumours Nature 406 6797 2000 747 752 10.1038/35021093 10963602

상세보기
27 Vogelstein B. Papadopoulos N. Velculescu V.E. Zhou S. Diaz L.A. Kinzler K.W. Cancer Genome Landscapes Science 339 6127 2013 1546 1558 23539594

상세보기
28 He Z. Sun M. Ke Y. Lin R. Xiao Y. Zhou S. Zhao H. Wang Y. Zhou F. Zhou Y. Identifying biomarkers of papillary renal cell carcinoma associated with pathological stage by weighted gene co-expression network analysis Oncotarget 8 17 2017 27904 27914 28427189

상세보기
29 Singh N.P. Bapi R.S. Vinod P.K. Machine learning models to predict the progression from early to late stages of papillary renal cell carcinoma Comput Biol Med 100 2018 92 99 10.1016/j.compbiomed.2018.06.030 29990647

상세보기
30 Gao F. Wang W. Tan M. Zhu L. Zhang Y. Fessler E. Vermeulen L. Wang X. DeepCC: a novel deep learning-based framework for cancer molecular subtype classification Oncogenesis 8 9 2019 10.1038/s41389-019-0157-8

상세보기
31 Karabulut E.M. Ibrikci T. Discriminative deep belief networks for microarray based cancer classification Biomed Res 28 2017 1016 1024
32 Fakoor R, Ladhak F, Nazi A, Huber M. Using deep learning to enhance cancer diagnosis and classification 2013.
33 Liu J, Lichtenberg T, Hoadley KA, Poisson LM, Lazar AJ, Cherniack AD, et al. An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics. Cell 2018;173:400-416.e11. doi: 10.1016/j.cell.2018.02.052.
34 Huo D. Hu H. Rhie S.K. Gamazon E.R. Cherniack A.D. Liu J. Yoshimatsu T.F. Pitt J.J. Hoadley K.A. Troester M. Ru Y. Lichtenberg T. Sturtz L.A. Shelley C.S. Benz C.C. Mills G.B. Laird P.W. Shriver C.D. Perou C.M. Olopade O.I. Comparison of breast cancer molecular features and survival by African and European ancestry in the cancer genome atlas JAMA Oncol 3 12 2017 1654 10.1001/jamaoncol.2017.0595 28472234
35 Verhaak RGW, Hoadley KA, Purdom E, Wang V, Qi Y, Wilkerson MD, et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 2010;17:98？110. doi: 0.1016/j.ccr.2009.12.020.
36 Shirkhorshidi AS, Aghabozorgi S, Wah TY. A comparison study on similarity and dissimilarity measures in clustering continuous data. PLoS One 2015;10:e0144059. doi: 10.1371/journal.pone.0144059.
37 Chen L, Yuan L, Qian K, Qian G, Zhu Y, Wu CL, et al. Identification of biomarkers associated with pathological stage and prognosis of clear cell renal cell carcinoma by co-expression network analysis. Front Physiol 2018;9. doi: 10.3389/fphys.2018.00399.
38 Ljungberg B. Bensalah K. Canfield S. Dabestani S. Hofmann F. Hora M. Kuczyk M.A. Lam T. Marconi L. Merseburger A.S. Mulders P. Powles T. Staehler M. Volpe A. Bex A. EAU guidelines on renal cell carcinoma: 2014 update Eur Urol 67 5 2015 913 924 10.1016/j.eururo.2015.01.005 25616710

상세보기
39 Coppin C, Kollmannsberger C, Le L, Porzsolt F, Wilt TJ. Targeted therapy for advanced renal cell cancer (RCC): A Cochrane systematic review of published randomised trials. BJU Int 2011;108:1556？63. doi: 10.1111/j.1464-410X.2011.10629.x.

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