A multi-classifier system integrated by clinico-histology-genomic analysis for predicting recurrence of papillary renal cell carcinoma

Kang Bo Huang; Cheng Peng Gui; Yun Ze Xu; Xue Song Li; Hong Wei Zhao; Jia Zheng Cao; Yu Hang Chen; Yi Hui Pan; Bing Liao; Yun Cao; Xin Ke Zhang; Hui Han; Fang Jian Zhou; Ran Yi Liu; Wen Fang Chen; Ze Ying Jiang; Zi Hao Feng; Fu Neng Jiang; Yan Fei Yu; Sheng Wei Xiong; Guan Peng Han; Qi Tang; Kui Ouyang; Gui Mei Qu; Ji Tao Wu; Ming Cao; Bai Jun Dong; Yi Ran Huang; Jin Zhang; Cai Xia Li; Pei Xing Li; Wei Chen; Wei De Zhong; Jian Ping Guo; Zhi Ping Liu; Jer Tsong Hsieh; Dan Xie; Mu Yan Cai; Wei Xue; Jin Huan Wei; Jun Hang Luo

doi:10.1038/s41467-024-50369-y

A multi-classifier system integrated by clinico-histology-genomic analysis for predicting recurrence of papillary renal cell carcinoma

Kang Bo Huang, Cheng Peng Gui, Yun Ze Xu, Xue Song Li, Hong Wei Zhao, Jia Zheng Cao, Yu Hang Chen, Yi Hui Pan, Bing Liao, Yun Cao, Xin Ke Zhang, Hui Han, Fang Jian Zhou, Ran Yi Liu, Wen Fang Chen, Ze Ying Jiang, Zi Hao Feng, Fu Neng Jiang, Yan Fei Yu, Sheng Wei XiongGuan Peng Han, Qi Tang, Kui Ouyang, Gui Mei Qu, Ji Tao Wu, Ming Cao, Bai Jun Dong, Yi Ran Huang, Jin Zhang, Cai Xia Li, Pei Xing Li, Wei Chen, Wei De Zhong, Jian Ping Guo, Zhi Ping Liu, Jer Tsong Hsieh, Dan Xie, Mu Yan Cai, Wei Xue, Jin Huan Wei, Jun Hang Luo

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

Integrating genomics and histology for cancer prognosis demonstrates promise. Here, we develop a multi-classifier system integrating a lncRNA-based classifier, a deep learning whole-slide-image-based classifier, and a clinicopathological classifier to accurately predict post-surgery localized (stage I–III) papillary renal cell carcinoma (pRCC) recurrence. The multi-classifier system demonstrates significantly higher predictive accuracy for recurrence-free survival (RFS) compared to the three single classifiers alone in the training set and in both validation sets (C-index 0.831-0.858 vs. 0.642-0.777, p < 0.05). The RFS in our multi-classifier-defined high-risk stage I/II and grade 1/2 groups is significantly worse than in the low-risk stage III and grade 3/4 groups (p < 0.05). Our multi-classifier system is a practical and reliable predictor for recurrence of localized pRCC after surgery that can be used with the current staging system to more accurately predict disease course and inform strategies for individualized adjuvant therapy.

Original language	English (US)
Article number	6215
Journal	Nature communications
Volume	15
Issue number	1
DOIs	https://doi.org/10.1038/s41467-024-50369-y
State	Published - Dec 2024
Externally published	Yes

ASJC Scopus subject areas

General Chemistry
General Biochemistry, Genetics and Molecular Biology
General Physics and Astronomy

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10.1038/s41467-024-50369-y

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Huang, K. B., Gui, C. P., Xu, Y. Z., Li, X. S., Zhao, H. W., Cao, J. Z., Chen, Y. H., Pan, Y. H., Liao, B., Cao, Y., Zhang, X. K., Han, H., Zhou, F. J., Liu, R. Y., Chen, W. F., Jiang, Z. Y., Feng, Z. H., Jiang, F. N., Yu, Y. F., ... Luo, J. H. (2024). A multi-classifier system integrated by clinico-histology-genomic analysis for predicting recurrence of papillary renal cell carcinoma. Nature communications, 15(1), Article 6215. https://doi.org/10.1038/s41467-024-50369-y

Huang, KB, Gui, CP, Xu, YZ, Li, XS, Zhao, HW, Cao, JZ, Chen, YH, Pan, YH, Liao, B, Cao, Y, Zhang, XK, Han, H, Zhou, FJ, Liu, RY, Chen, WF, Jiang, ZY, Feng, ZH, Jiang, FN, Yu, YF, Xiong, SW, Han, GP, Tang, Q, Ouyang, K, Qu, GM, Wu, JT, Cao, M, Dong, BJ, Huang, YR, Zhang, J, Li, CX, Li, PX, Chen, W, Zhong, WD, Guo, JP, Liu, ZP , Hsieh, JT, Xie, D, Cai, MY, Xue, W, Wei, JH & Luo, JH 2024, 'A multi-classifier system integrated by clinico-histology-genomic analysis for predicting recurrence of papillary renal cell carcinoma', Nature communications, vol. 15, no. 1, 6215. https://doi.org/10.1038/s41467-024-50369-y

@article{e530543540aa4299abecbbcc1bb54b31,

title = "A multi-classifier system integrated by clinico-histology-genomic analysis for predicting recurrence of papillary renal cell carcinoma",

abstract = "Integrating genomics and histology for cancer prognosis demonstrates promise. Here, we develop a multi-classifier system integrating a lncRNA-based classifier, a deep learning whole-slide-image-based classifier, and a clinicopathological classifier to accurately predict post-surgery localized (stage I–III) papillary renal cell carcinoma (pRCC) recurrence. The multi-classifier system demonstrates significantly higher predictive accuracy for recurrence-free survival (RFS) compared to the three single classifiers alone in the training set and in both validation sets (C-index 0.831-0.858 vs. 0.642-0.777, p < 0.05). The RFS in our multi-classifier-defined high-risk stage I/II and grade 1/2 groups is significantly worse than in the low-risk stage III and grade 3/4 groups (p < 0.05). Our multi-classifier system is a practical and reliable predictor for recurrence of localized pRCC after surgery that can be used with the current staging system to more accurately predict disease course and inform strategies for individualized adjuvant therapy.",

author = "Huang, {Kang Bo} and Gui, {Cheng Peng} and Xu, {Yun Ze} and Li, {Xue Song} and Zhao, {Hong Wei} and Cao, {Jia Zheng} and Chen, {Yu Hang} and Pan, {Yi Hui} and Bing Liao and Yun Cao and Zhang, {Xin Ke} and Hui Han and Zhou, {Fang Jian} and Liu, {Ran Yi} and Chen, {Wen Fang} and Jiang, {Ze Ying} and Feng, {Zi Hao} and Jiang, {Fu Neng} and Yu, {Yan Fei} and Xiong, {Sheng Wei} and Han, {Guan Peng} and Qi Tang and Kui Ouyang and Qu, {Gui Mei} and Wu, {Ji Tao} and Ming Cao and Dong, {Bai Jun} and Huang, {Yi Ran} and Jin Zhang and Li, {Cai Xia} and Li, {Pei Xing} and Wei Chen and Zhong, {Wei De} and Guo, {Jian Ping} and Liu, {Zhi Ping} and Hsieh, {Jer Tsong} and Dan Xie and Cai, {Mu Yan} and Wei Xue and Wei, {Jin Huan} and Luo, {Jun Hang}",

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AU - Huang, Kang Bo

AU - Gui, Cheng Peng

AU - Xu, Yun Ze

AU - Li, Xue Song

AU - Zhao, Hong Wei

AU - Cao, Jia Zheng

AU - Chen, Yu Hang

AU - Pan, Yi Hui

AU - Liao, Bing

AU - Cao, Yun

AU - Zhang, Xin Ke

AU - Han, Hui

AU - Zhou, Fang Jian

AU - Liu, Ran Yi

AU - Chen, Wen Fang

AU - Jiang, Ze Ying

AU - Feng, Zi Hao

AU - Jiang, Fu Neng

AU - Yu, Yan Fei

AU - Xiong, Sheng Wei

AU - Han, Guan Peng

AU - Tang, Qi

AU - Ouyang, Kui

AU - Qu, Gui Mei

AU - Wu, Ji Tao

AU - Cao, Ming

AU - Dong, Bai Jun

AU - Huang, Yi Ran

AU - Zhang, Jin

AU - Li, Cai Xia

AU - Li, Pei Xing

AU - Chen, Wei

AU - Zhong, Wei De

AU - Guo, Jian Ping

AU - Liu, Zhi Ping

AU - Hsieh, Jer Tsong

AU - Xie, Dan

AU - Cai, Mu Yan

AU - Xue, Wei

AU - Wei, Jin Huan

AU - Luo, Jun Hang

PY - 2024/12

Y1 - 2024/12

N2 - Integrating genomics and histology for cancer prognosis demonstrates promise. Here, we develop a multi-classifier system integrating a lncRNA-based classifier, a deep learning whole-slide-image-based classifier, and a clinicopathological classifier to accurately predict post-surgery localized (stage I–III) papillary renal cell carcinoma (pRCC) recurrence. The multi-classifier system demonstrates significantly higher predictive accuracy for recurrence-free survival (RFS) compared to the three single classifiers alone in the training set and in both validation sets (C-index 0.831-0.858 vs. 0.642-0.777, p < 0.05). The RFS in our multi-classifier-defined high-risk stage I/II and grade 1/2 groups is significantly worse than in the low-risk stage III and grade 3/4 groups (p < 0.05). Our multi-classifier system is a practical and reliable predictor for recurrence of localized pRCC after surgery that can be used with the current staging system to more accurately predict disease course and inform strategies for individualized adjuvant therapy.

AB - Integrating genomics and histology for cancer prognosis demonstrates promise. Here, we develop a multi-classifier system integrating a lncRNA-based classifier, a deep learning whole-slide-image-based classifier, and a clinicopathological classifier to accurately predict post-surgery localized (stage I–III) papillary renal cell carcinoma (pRCC) recurrence. The multi-classifier system demonstrates significantly higher predictive accuracy for recurrence-free survival (RFS) compared to the three single classifiers alone in the training set and in both validation sets (C-index 0.831-0.858 vs. 0.642-0.777, p < 0.05). The RFS in our multi-classifier-defined high-risk stage I/II and grade 1/2 groups is significantly worse than in the low-risk stage III and grade 3/4 groups (p < 0.05). Our multi-classifier system is a practical and reliable predictor for recurrence of localized pRCC after surgery that can be used with the current staging system to more accurately predict disease course and inform strategies for individualized adjuvant therapy.

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A multi-classifier system integrated by clinico-histology-genomic analysis for predicting recurrence of papillary renal cell carcinoma

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