Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature

doi:10.1007/s11684-023-0982-1

Front. Med.

2023, Vol. 17

Issue (4) : 768-780 https://doi.org/10.1007/s11684-023-0982-1

RESEARCH ARTICLE

Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature

Jiaqi Dai^1,³, Tao Wang², Ke Xu^1,³, Yang Sun^1,³, Zongzhe Li^1,³, Peng Chen^1,³, Hong Wang³, Dongyang Wu³, Yanghui Chen³, Lei Xiao³, Hao Liu³, Haoran Wei³, Rui Li^1,³, Liyuan Peng¹, Ting Yu¹, Yan Wang^1,³, Zhongsheng Sun²(

), Dao Wen Wang^1,³(

)

¹. Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
². Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing 100101, China
³. Hubei Key Laboratory of Genetics and Molecular Mechanism of Cardiologic Disorders, Huazhong University of Science and Technology, Wuhan 430030, China

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Abstract

Previous studies have revealed that patients with hypertrophic cardiomyopathy (HCM) exhibit differences in symptom severity and prognosis, indicating potential HCM subtypes among these patients. Here, 793 patients with HCM were recruited at an average follow-up of 32.78 ± 27.58 months to identify potential HCM subtypes by performing consensus clustering on the basis of their echocardiography features. Furthermore, we proposed a systematic method for illustrating the relationship between the phenotype and genotype of each HCM subtype by using machine learning modeling and interactome network detection techniques based on whole-exome sequencing data. Another independent cohort that consisted of 414 patients with HCM was recruited to replicate the findings. Consequently, two subtypes characterized by different clinical outcomes were identified in HCM. Patients with subtype 2 presented asymmetric septal hypertrophy associated with a stable course, while those with subtype 1 displayed left ventricular systolic dysfunction and aggressive progression. Machine learning modeling based on personal whole-exome data identified 46 genes with mutation burden that could accurately predict subtype propensities. Furthermore, the patients in another cohort predicted as subtype 1 by the 46-gene model presented increased left ventricular end-diastolic diameter and reduced left ventricular ejection fraction. By employing echocardiography and genetic screening for the 46 genes, HCM can be classified into two subtypes with distinct clinical outcomes.

Keywords machine learning methods hypertrophic cardiomyopathy genetic risk

Corresponding Author(s): Zhongsheng Sun,Dao Wen Wang

Just Accepted Date: 31 March 2023 Online First Date: 08 May 2023 Issue Date: 12 October 2023

Cite this article:

Jiaqi Dai,Tao Wang,Ke Xu, et al. Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature[J]. Front. Med., 2023, 17(4): 768-780.

URL:

https://academic.hep.com.cn/fmd/EN/10.1007/s11684-023-0982-1
https://academic.hep.com.cn/fmd/EN/Y2023/V17/I4/768

Tab.1 Characteristics of subtypes in the study population

Fig.1 Consensus clustering identified two subtypes that correlated with various clinical features. (A) Hierarchical subtype heat map shows the clinical characteristics and echocardiography features of the two subtypes. (B) Characteristic plots of the two subtypes, including their most representative echo variables. A positive value indicates overrepresentation of this variable in the applicable subtype. A negative value indicates underrepresentation of the corresponding variable. (C) Variable importance for clustering measured by random forests. (D) Supervised decision tree modeling provided availability in clinical practice.

Fig.2 Event-free survival stratified by subtypes as determined by the consensus clustering. Kaplan–Meier curves for (A) cardiovascular death, (B) the combined outcome of cardiovascular death and heart transplant, (C) all-cause death and (D) lifelong likelihood of progression to NYHA class III/IV, respectively. Age was used as the time scale, and events occurring before and during the follow-up were included. The probability values were calculated with the log-rank test.

Fig.3 Machine learning model construction. (A) Machine-identified genes with increased mutation burden in subtype 1. (B) ROC curves for the models based on the 46 feature genes with different classifiers. AUC was determined via stratified fivefold cross-validation. (C) Individual networks for HCM patients with reduced LVEF and enrichment analysis of the 46-gene set and endophenotype for each patient network. The rows correspond to the gene ontology (GO) classifications for genes, and the columns denote samples.

Tab.2 Characteristics of second population subtyping by the genetic model

1	BJ Maron, R Mathenge, SA Casey, LC Poliac, TF Longe. Clinical profile of hypertrophic cardiomyopathy identified de novo in rural communities. J Am Coll Cardiol 1999; 33(6): 1590–1595 https://doi.org/10.1016/S0735-1097(99)00039-X pmid: 10334429
2	Y Zou, L Song, Z Wang, A Ma, T Liu, H Gu, S Lu, P Wu, Y Zhang dagger, L Shen dagger, Y Cai, Y Zhen double dagger, Y Liu, R Hui. Prevalence of idiopathic hypertrophic cardiomyopathy in China: a population-based echocardiographic analysis of 8080 adults. Am J Med 2004; 116(1): 14–18 https://doi.org/10.1016/j.amjmed.2003.05.009 pmid: 14706660
3	MJ Eriksson, B Sonnenberg, A Woo, P Rakowski, TG Parker, ED Wigle, H Rakowski. Long-term outcome in patients with apical hypertrophic cardiomyopathy. J Am Coll Cardiol 2002; 39(4): 638–645 https://doi.org/10.1016/S0735-1097(01)01778-8 pmid: 11849863
4	BJ Maron, EJ Rowin, SA Casey, MS Link, JR Lesser, RH Chan, RF Garberich, JE Udelson, MS Maron. Hypertrophic cardiomyopathy in adulthood associated with low cardiovascular mortality with contemporary management strategies. J Am Coll Cardiol 2015; 65(18): 1915–1928 https://doi.org/10.1016/j.jacc.2015.02.061 pmid: 25953744
5	I Olivotto, F Cecchi, C Poggesi, MH Yacoub. Patterns of disease progression in hypertrophic cardiomyopathy: an individualized approach to clinical staging. Circ Heart Fail 2012; 5(4): 535–546 https://doi.org/10.1161/CIRCHEARTFAILURE.112.967026 pmid: 22811549
6	KM Harris, P Spirito, MS Maron, AG Zenovich, F Formisano, JR Lesser, S Mackey-Bojack, WJ Manning, JE Udelson, BJ Maron. Prevalence, clinical profile, and significance of left ventricular remodeling in the end-stage phase of hypertrophic cardiomyopathy. Circulation 2006; 114(3): 216–225 https://doi.org/10.1161/CIRCULATIONAHA.105.583500 pmid: 16831987
7	P Melacini, C Basso, A Angelini, C Calore, F Bobbo, B Tokajuk, N Bellini, G Smaniotto, M Zucchetto, S Iliceto, G Thiene, BJ Maron. Clinicopathological profiles of progressive heart failure in hypertrophic cardiomyopathy. Eur Heart J 2010; 31(17): 2111–2123 https://doi.org/10.1093/eurheartj/ehq136 pmid: 20513729
8	H Watkins, WJ McKenna, L Thierfelder, HJ Suk, R Anan, A O’Donoghue, P Spirito, A Matsumori, CS Moravec, JG Seidman, CE Seidman. Mutations in the genes for cardiac troponin T and α-tropomyosin in hypertrophic cardiomyopathy. N Engl J Med 1995; 332(16): 1058–1065 https://doi.org/10.1056/NEJM199504203321603 pmid: 7898523
9	R Coppini, CY Ho, E Ashley, S Day, C Ferrantini, F Girolami, B Tomberli, S Bardi, F Torricelli, F Cecchi, A Mugelli, C Poggesi, J Tardiff, I Olivotto. Clinical phenotype and outcome of hypertrophic cardiomyopathy associated with thin-filament gene mutations. J Am Coll Cardiol 2014; 64(24): 2589–2600 https://doi.org/10.1016/j.jacc.2014.09.059 pmid: 25524337
10	AR Harper, A Goel, C Grace, KL Thomson, SE Petersen, X Xu, A Waring, E Ormondroyd, CM Kramer, CY Ho, S; HCMR Investigators; Tadros R Neubauer, JS Ware, CR Bezzina, M Farrall, H Watkins. Common genetic variants and modifiable risk factors underpin hypertrophic cardiomyopathy susceptibility and expressivity. Nat Genet 2021; 53(2): 135–142 https://doi.org/10.1038/s41588-020-00764-0 pmid: 33495597
11	SR Ommen, S Mital, MA Burke, SM Day, A Deswal, P Elliott, LL Evanovich, J Hung, JA Joglar, P Kantor, C Kimmelstiel, M Kittleson, MS Link, MS Maron, MW Martinez, CY Miyake, HV Schaff, C Semsarian, P Sorajja. 2020 AHA/ACC guideline for the diagnosis and treatment of patients with hypertrophic cardiomyopathy: a report of the American College of Cardiology/American Heart Association Joint Committee on clinical practice guidelines. Circulation 2020; 142(25): e558–e631 pmid: 33215931
12	J Dai, Z Li, W Huang, P Chen, Y Sun, H Wang, D Wu, Y Chen, C Li, L Xiao, H Liu, H Wei, R Li, Q Duan, L Peng, X Song, T Yu, Y Wang, DW Wang. Rbm20 is a candidate gene for hypertrophic cardiomyopathy. Can J Cardiol 2021; 37(11): 1751–1759 https://doi.org/10.1016/j.cjca.2021.07.014 pmid: 34333030
13	MD Wilkerson, DN Hayes. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 2010; 26(12): 1572–1573 https://doi.org/10.1093/bioinformatics/btq170 pmid: 20427518
14	J Ingles, J Goldstein, C Thaxton, C Caleshu, EW Corty, SB Crowley, K Dougherty, SM Harrison, J McGlaughon, LV Milko, A Morales, BA Seifert, N Strande, K Thomson, J Peter van Tintelen, K Wallace, R Walsh, Q Wells, N Whiffin, L Witkowski, C Semsarian, JS Ware, RE Hershberger, B Funke. Evaluating the clinical validity of hypertrophic cardiomyopathy genes. Circ Genom Precis Med 2019; 12(2): e002460 https://doi.org/10.1161/CIRCGEN.119.002460 pmid: 30681346
15	S Richards, N Aziz, S Bale, D Bick, S Das, J Gastier-Foster, WW Grody, M Hegde, E Lyon, E Spector, K Voelkerding, HL; ACMG Laboratory Quality Assurance Committee Rehm. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 2015; 17(5): 405–424 https://doi.org/10.1038/gim.2015.30 pmid: 25741868
16	J Li, T Zhao, Y Zhang, K Zhang, L Shi, Y Chen, X Wang, Z Sun. Performance evaluation of pathogenicity-computation methods for missense variants. Nucleic Acids Res 2018; 46(15): 7793–7804 https://doi.org/10.1093/nar/gky678 pmid: 30060008
17	G Zhou, O Soufan, J Ewald, REW Hancock, N Basu, J Xia. NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res 2019; 47(W1): W234–W241 https://doi.org/10.1093/nar/gkz240 pmid: 30931480
18	MV Kuleshov, MR Jones, AD Rouillard, NF Fernandez, Q Duan, Z Wang, S Koplev, SL Jenkins, KM Jagodnik, A Lachmann, MG McDermott, CD Monteiro, GW Gundersen, A Ma’ayan. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res 2016; 44(W1): W90–W97 https://doi.org/10.1093/nar/gkw377 pmid: 27141961
19	BA Maron, RS Wang, S Shevtsov, SG Drakos, E Arons, O Wever-Pinzon, GS Huggins, AO Samokhin, WM Oldham, Y Aguib, MH Yacoub, EJ Rowin, BJ Maron, MS Maron, J Loscalzo. Individualized interactomes for network-based precision medicine in hypertrophic cardiomyopathy with implications for other clinical pathophenotypes. Nat Commun 2021; 12(1): 873 https://doi.org/10.1038/s41467-021-21146-y pmid: 33558530
20	JB Geske, KC Ong, KC Siontis, VB Hebl, MJ Ackerman, DO Hodge, VM Miller, RA Nishimura, JK Oh, HV Schaff, BJ Gersh, SR Ommen. Women with hypertrophic cardiomyopathy have worse survival. Eur Heart J 2017; 38(46): 3434–3440 https://doi.org/10.1093/eurheartj/ehx527 pmid: 29020402
21	EP Rhee, Q Yang, B Yu, X Liu, S Cheng, A Deik, KA Pierce, K Bullock, JE Ho, D Levy, JC Florez, S Kathiresan, MG Larson, RS Vasan, CB Clish, TJ Wang, E Boerwinkle, CJ O’Donnell, RE Gerszten. An exome array study of the plasma metabolome. Nat Commun 2016; 7(1): 12360 https://doi.org/10.1038/ncomms12360 pmid: 27453504
22	J Wessel, AY Chu, SM Willems, S Wang, H Yaghootkar, JA Brody, M Dauriz, MF Hivert, S Raghavan, L Lipovich, B Hidalgo, K Fox, JE Huffman, P An, Y Lu, LJ Rasmussen-Torvik, N Grarup, MG Ehm, L Li, AS Baldridge, A Stančáková, R Abrol, C Besse, A Boland, J Bork-Jensen, M Fornage, DF Freitag, ME Garcia, X Guo, K Hara, A Isaacs, J Jakobsdottir, LA Lange, JC Layton, M Li, Zhao J Hua, K Meidtner, AC Morrison, MA Nalls, MJ Peters, M Sabater-Lleal, C Schurmann, A Silveira, AV Smith, L Southam, MH Stoiber, RJ Strawbridge, KD Taylor, TV Varga, KH Allin, N Amin, JL Aponte, T Aung, C Barbieri, NA Bihlmeyer, M Boehnke, C Bombieri, DW Bowden, SM Burns, Y Chen, YD Chen, CY Cheng, A Correa, J Czajkowski, A Dehghan, GB Ehret, G Eiriksdottir, SA Escher, AE Farmaki, M Frånberg, G Gambaro, F Giulianini, WA 3rd Goddard, A Goel, O Gottesman, ML Grove, S Gustafsson, Y Hai, G Hallmans, J Heo, P Hoffmann, MK Ikram, RA Jensen, ME Jørgensen, T Jørgensen, M Karaleftheri, CC Khor, A Kirkpatrick, AT Kraja, J Kuusisto, EM Lange, IT Lee, WJ Lee, A Leong, J Liao, C Liu, Y Liu, CM Lindgren, A Linneberg, G Malerba, V Mamakou, E Marouli, NM Maruthur, A Matchan, R McKean-Cowdin, O McLeod, GA Metcalf, KL Mohlke, DM Muzny, I Ntalla, ND Palmer, D Pasko, A Peter, NW Rayner, F Renström, K Rice, CF Sala, B Sennblad, I Serafetinidis, JA Smith, N Soranzo, EK Speliotes, EA Stahl, K Stirrups, N Tentolouris, A Thanopoulou, M Torres, M Traglia, E Tsafantakis, S Javad, LR Yanek, E Zengini, DM Becker, JC Bis, JB Brown, LA Cupples, T Hansen, E Ingelsson, AJ Karter, C Lorenzo, RA Mathias, JM Norris, GM Peloso, WH Sheu, D Toniolo, D Vaidya, R Varma, LE Wagenknecht, H Boeing, EP Bottinger, G Dedoussis, P Deloukas, E Ferrannini, OH Franco, PW Franks, RA Gibbs, V Gudnason, A Hamsten, TB Harris, AT Hattersley, C Hayward, A Hofman, JH Jansson, C Langenberg, LJ Launer, D Levy, BA Oostra, CJ O'Donnell, S O'Rahilly, S Padmanabhan, JS Pankow, O Polasek, MA Province, SS Rich, PM Ridker, I Rudan, MB Schulze, BH Smith, AG Uitterlinden, M Walker, H Watkins, TY Wong, E; EPIC-InterAct Consortium; Laakso M Zeggini, IB Borecki, DI Chasman, O Pedersen, BM Psaty, ES Tai, Duijn CM van, NJ Wareham, DM Waterworth, E Boerwinkle, WH Kao, JC Florez, RJ Loos, JG Wilson, TM Frayling, DS Siscovick, J Dupuis, JI Rotter, JB Meigs, RA Scott, MO Goodarzi. Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility. Nat Commun 2015; 6(1): 5897 https://doi.org/10.1038/ncomms6897 pmid: 25631608
23	O Zuk, SF Schaffner, K Samocha, R Do, E Hechter, S Kathiresan, MJ Daly, BM Neale, SR Sunyaev, ES Lander. Searching for missing heritability: designing rare variant association studies. Proc Natl Acad Sci USA 2014; 111(4): E455–E464 https://doi.org/10.1073/pnas.1322563111 pmid: 24443550
24	BJ Maron, P Spirito. Implications of left ventricular remodeling in hypertrophic cardiomyopathy. Am J Cardiol 1998; 81(11): 1339–1344 https://doi.org/10.1016/S0002-9149(98)00164-7 pmid: 9631972
25	HG Klues, A Schiffers, BJ Maron. Phenotypic spectrum and patterns of left ventricular hypertrophy in hypertrophic cardiomyopathy: morphologic observations and significance as assessed by two-dimensional echocardiography in 600 patients. J Am Coll Cardiol 1995; 26(7): 1699–1708 https://doi.org/10.1016/0735-1097(95)00390-8 pmid: 7594106
26	MC Wu, S Lee, T Cai, Y Li, M Boehnke, X Lin. Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet 2011; 89(1): 82–93 https://doi.org/10.1016/j.ajhg.2011.05.029 pmid: 21737059
27	AJ Marian, E Braunwald. Hypertrophic cardiomyopathy: genetics, pathogenesis, clinical manifestations, diagnosis, and therapy. Circ Res 2017; 121(7): 749–770 https://doi.org/10.1161/CIRCRESAHA.117.311059 pmid: 28912181
28	BJ Maron, MS Maron, BA Maron, J Loscalzo. Moving beyond the sarcomere to explain heterogeneity in hypertrophic cardiomyopathy: Jacc review topic of the week. J Am Coll Cardiol 2019; 73(15): 1978–1986 https://doi.org/10.1016/j.jacc.2019.01.061 pmid: 31000001
29	MW Friederich, S Timal, CA Powell, C Dallabona, A Kurolap, S Palacios-Zambrano, D Bratkovic, TGJ Derks, D Bick, K Bouman, KC Chatfield, N Damouny-Naoum, MK Dishop, TC Falik-Zaccai, F Fares, A Fedida, I Ferrero, RC Gallagher, R Garesse, M Gilberti, C González, K Gowan, C Habib, RK Halligan, L Kalfon, K Knight, D Lefeber, L Mamblona, H Mandel, A Mory, J Ottoson, T Paperna, GJM Pruijn, PF Rebelo-Guiomar, A Saada, B Jr Sainz, H Salvemini, MH Schoots, JA Smeitink, MJ Szukszto, Horst HJ Ter, den Brandt F van, Spronsen FJ van, JA Veltman, E Wartchow, LT Wintjes, Y Zohar, MA Fernández-Moreno, HN Baris, C Donnini, M Minczuk, RJ Rodenburg, Hove JLK Van. Pathogenic variants in glutamyl-tRNAGln amidotransferase subunits cause a lethal mitochondrial cardiomyopathy disorder. Nat Commun 2018; 9(1): 4065 https://doi.org/10.1038/s41467-018-06250-w pmid: 30283131
30	TM Marin, K Keith, B Davies, DA Conner, P Guha, D Kalaitzidis, X Wu, J Lauriol, B Wang, M Bauer, R Bronson, KG Franchini, BG Neel, MI Kontaridis. Rapamycin reverses hypertrophic cardiomyopathy in a mouse model of LEOPARD syndrome-associated PTPN11 mutation. J Clin Invest 2011; 121(3): 1026–1043 https://doi.org/10.1172/JCI44972 pmid: 21339643
31	LY Lee, J Loscalzo. Network medicine in pathobiology. Am J Pathol 2019; 189(7): 1311–1326 https://doi.org/10.1016/j.ajpath.2019.03.009 pmid: 31014954

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