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Cancer Innovation 2022, 1(2): 146-167 https://doi.org/10.1002/cai2.25
Original Article | Open Access | Issue | Published: 30 August 2022

Definition and verification of novel metastasis and recurrence related signatures of ccRCC: A multicohort study

Show Author's Information Aimin Jiang1,  Qingyang Pang1,  Xinxin Gan1,  Anbang Wang2,  Zhenjie Wu1,  Bing Liu3,  Peng Luo4( ),  Le Qu5( ),  Linhui Wang1 ( )
Department of Urology, Changhai Hospital, Naval Medical University (Second Military Medical University), Shanghai, China
Department of Urology, Changzheng Hospital, Naval Medical University (Second Military Medical University), Shanghai, China
Department of Urology, The Third Affiliated Hospital, Naval Medical University (Second Military Medical University), Shanghai, China
Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
Department of Urology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China
Keywords:
machine learning, metastasis, recurrence, clear cell renal cell carcinoma, multiple omics, single‐cell sequencing
Cite this article:
Jiang A, Pang Q, Gan X, et al. Definition and verification of novel metastasis and recurrence related signatures of ccRCC: A multicohort study. Cancer Innovation, 2022, 1(2): 146-167. https://doi.org/10.1002/cai2.25
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Abstract Full text About this article
Background

Cancer metastasis and recurrence remain major challenges in renal carcinoma patient management. There are limited biomarkers to predict the metastatic probability of renal cancer, especially in the early‐stage subgroup. Here, our study applied robust machine‐learning algorithms to identify metastatic and recurrence‐related signatures across multiple renal cancer cohorts, which reached high accuracy in both training and testing cohorts.

Methods

Clear cell renal cell carcinoma (ccRCC) patients with primary or metastatic site sequencing information from eight cohorts, including one out‐house cohort, were enrolled in this study. Three robust machine‐learning algorithms were applied to identify metastatic signatures. Then, two distinct metastatic‐related subtypes were identified and verified; matrix remodeling associated 5 (MXRA5), as a promising diagnostic and therapeutic target, was investigated in vivo and in vitro.

Results

We identified five stable metastasis‐related signatures (renin, integrin subunit beta‐like 1, MXRA5, mesenchyme homeobox 2, and anoctamin 3) from multicenter cohorts. Additionally, we verified the specificity and sensibility of these signatures in external and out‐house cohorts, which displayed a satisfactory consistency. According to these metastatic signatures, patients were grouped into two distinct and heterogeneous ccRCC subtypes named metastatic cancer subtype 1 (MTCS1) and type 2 (MTCS2). MTCS2 exhibited poorer clinical outcomes and metastatic tendencies than MTCS1. In addition, MTCS2 showed higher immune cell infiltration and immune signature expression but a lower response rate to immune blockade therapy than MTCS1. The MTCS2 subgroup was more sensitive to saracatinib, sunitinib, and several molecular targeted drugs. In addition, MTCS2 displayed a higher genome mutation burden and instability. Furthermore, we constructed a prognosis model based on subtype biomarkers, which performed well in training and validation cohorts. Finally, MXRA5, as a promising biomarker, significantly suppressed malignant ability, including the cell migration and proliferation of ccRCC cell lines in vitro and in vivo.

Conclusions

This study identified five robust metastatic signatures and proposed two metastatic probability clusters with stratified prognoses, multiomics landscapes, and treatment options. The current work not only provided new insight into the heterogeneity of renal cancer but also shed light on optimizing decision‐making in immunotherapy and chemotherapy.


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Abstract
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About this article

Definition and verification of novel metastasis and recurrence related signatures of ccRCC: A multicohort study

Show Author's information Aimin Jiang1, Qingyang Pang1, Xinxin Gan1, Anbang Wang2, Zhenjie Wu1, Bing Liu3, Peng Luo4( ), Le Qu5( ), Linhui Wang1 ( )
Department of Urology, Changhai Hospital, Naval Medical University (Second Military Medical University), Shanghai, China
Department of Urology, Changzheng Hospital, Naval Medical University (Second Military Medical University), Shanghai, China
Department of Urology, The Third Affiliated Hospital, Naval Medical University (Second Military Medical University), Shanghai, China
Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
Department of Urology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China

Abstract

Background

Cancer metastasis and recurrence remain major challenges in renal carcinoma patient management. There are limited biomarkers to predict the metastatic probability of renal cancer, especially in the early‐stage subgroup. Here, our study applied robust machine‐learning algorithms to identify metastatic and recurrence‐related signatures across multiple renal cancer cohorts, which reached high accuracy in both training and testing cohorts.

Methods

Clear cell renal cell carcinoma (ccRCC) patients with primary or metastatic site sequencing information from eight cohorts, including one out‐house cohort, were enrolled in this study. Three robust machine‐learning algorithms were applied to identify metastatic signatures. Then, two distinct metastatic‐related subtypes were identified and verified; matrix remodeling associated 5 (MXRA5), as a promising diagnostic and therapeutic target, was investigated in vivo and in vitro.

Results

We identified five stable metastasis‐related signatures (renin, integrin subunit beta‐like 1, MXRA5, mesenchyme homeobox 2, and anoctamin 3) from multicenter cohorts. Additionally, we verified the specificity and sensibility of these signatures in external and out‐house cohorts, which displayed a satisfactory consistency. According to these metastatic signatures, patients were grouped into two distinct and heterogeneous ccRCC subtypes named metastatic cancer subtype 1 (MTCS1) and type 2 (MTCS2). MTCS2 exhibited poorer clinical outcomes and metastatic tendencies than MTCS1. In addition, MTCS2 showed higher immune cell infiltration and immune signature expression but a lower response rate to immune blockade therapy than MTCS1. The MTCS2 subgroup was more sensitive to saracatinib, sunitinib, and several molecular targeted drugs. In addition, MTCS2 displayed a higher genome mutation burden and instability. Furthermore, we constructed a prognosis model based on subtype biomarkers, which performed well in training and validation cohorts. Finally, MXRA5, as a promising biomarker, significantly suppressed malignant ability, including the cell migration and proliferation of ccRCC cell lines in vitro and in vivo.

Conclusions

This study identified five robust metastatic signatures and proposed two metastatic probability clusters with stratified prognoses, multiomics landscapes, and treatment options. The current work not only provided new insight into the heterogeneity of renal cancer but also shed light on optimizing decision‐making in immunotherapy and chemotherapy.

Keywords: machine learning, metastasis, recurrence, clear cell renal cell carcinoma, multiple omics, single‐cell sequencing

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Publication history

Received: 30 May 2022
Revised: 27 June 2022
Accepted: 14 July 2022
Published: 30 August 2022
Issue date: August 2022

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© 2022 The Authors.

Acknowledgements

The authors would like to thank Dr. Jianming Zeng (University of Macau), and all the members of his bioinformatics team, Biotrainee, for generously sharing their experience and codes. The use of the Biorstudio high‐performance computing cluster (https://biorstudio.cloud) at Biotrainee and the Shanghai HS Biotech Co., Ltd., for conducting the research reported in this paper.

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