Predicting tumor mutation burden and VHL mutation from renal cancer pathology slides with self‐supervised deep learning
Abstract Background Tumor mutation burden (TMB) and VHL mutation play a crucial role in the management of patients with clear cell renal cell carcinoma (ccRCC), such as guiding adjuvant chemotherapy and improving clinical outcomes. However, the time‐consuming and expensive high‐throughput sequencing...
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| Format: | Article |
| Language: | English |
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Wiley
2024-08-01
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| Series: | Cancer Medicine |
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| Online Access: | https://doi.org/10.1002/cam4.70112 |
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| author | Qingyuan Zheng Xinyu Wang Rui Yang Junjie Fan Jingping Yuan Xiuheng Liu Lei Wang Zhuoni Xiao Zhiyuan Chen |
| author_facet | Qingyuan Zheng Xinyu Wang Rui Yang Junjie Fan Jingping Yuan Xiuheng Liu Lei Wang Zhuoni Xiao Zhiyuan Chen |
| author_sort | Qingyuan Zheng |
| collection | DOAJ |
| description | Abstract Background Tumor mutation burden (TMB) and VHL mutation play a crucial role in the management of patients with clear cell renal cell carcinoma (ccRCC), such as guiding adjuvant chemotherapy and improving clinical outcomes. However, the time‐consuming and expensive high‐throughput sequencing methods severely limit their clinical applicability. Predicting intratumoral heterogeneity poses significant challenges in biology and clinical settings. Our aimed to develop a self‐supervised attention‐based multiple instance learning (SSL‐ABMIL) model to predict TMB and VHL mutation status from hematoxylin and eosin‐stained histopathological images. Methods We obtained whole slide images (WSIs) and somatic mutation data of 350 ccRCC patients from The Cancer Genome Atlas for developing SSL‐ABMIL model. In parallel, 163 ccRCC patients from Clinical Proteomic Tumor Analysis Consortium cohort was used as independent external validation set. We systematically compared three different models (Wang‐ABMIL, Ciga‐ABMIL, and ImageNet‐MIL) for their ability to predict TMB and VHL alterations. Results We first identified two groups of populations with high‐ and low‐TMB (cut‐off point = 0.9). In two independent cohorts, the Wang‐ABMIL model achieved the highest performance with decent generalization performance (AUROC = 0.83 ± 0.02 and 0.8 ± 0.04 in predicting TMB and VHL, respectively). Attention heatmaps revealed that the Wang‐ABMIL model paid the highest attention to tumor regions in high‐TMB patients, while in VHL mutation prediction, non‐tumor regions were also assigned high attention, particularly the stromal regions infiltrated by lymphocytes. Conclusions Our results indicated that SSL‐ABMIL can effectively extract histological features for predicting TMB and VHL mutation, demonstrating promising results in linking tumor morphology and molecular biology. |
| format | Article |
| id | doaj-art-958a9f80ac0d44b48c3f379119d3a381 |
| institution | Kabale University |
| issn | 2045-7634 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cancer Medicine |
| spelling | doaj-art-958a9f80ac0d44b48c3f379119d3a3812024-11-26T10:12:30ZengWileyCancer Medicine2045-76342024-08-011316n/an/a10.1002/cam4.70112Predicting tumor mutation burden and VHL mutation from renal cancer pathology slides with self‐supervised deep learningQingyuan Zheng0Xinyu Wang1Rui Yang2Junjie Fan3Jingping Yuan4Xiuheng Liu5Lei Wang6Zhuoni Xiao7Zhiyuan Chen8Department of Urology Renmin Hospital of Wuhan University Wuhan Hubei ChinaCentre for Reproductive Science Renmin Hospital of Wuhan University Wuhan Hubei ChinaDepartment of Urology Renmin Hospital of Wuhan University Wuhan Hubei ChinaUniversity of Chinese Academy of Sciences Beijing ChinaDepartment of Pathology Renmin Hospital of Wuhan University Wuhan Hubei ChinaDepartment of Urology Renmin Hospital of Wuhan University Wuhan Hubei ChinaDepartment of Urology Renmin Hospital of Wuhan University Wuhan Hubei ChinaCentre for Reproductive Science Renmin Hospital of Wuhan University Wuhan Hubei ChinaDepartment of Urology Renmin Hospital of Wuhan University Wuhan Hubei ChinaAbstract Background Tumor mutation burden (TMB) and VHL mutation play a crucial role in the management of patients with clear cell renal cell carcinoma (ccRCC), such as guiding adjuvant chemotherapy and improving clinical outcomes. However, the time‐consuming and expensive high‐throughput sequencing methods severely limit their clinical applicability. Predicting intratumoral heterogeneity poses significant challenges in biology and clinical settings. Our aimed to develop a self‐supervised attention‐based multiple instance learning (SSL‐ABMIL) model to predict TMB and VHL mutation status from hematoxylin and eosin‐stained histopathological images. Methods We obtained whole slide images (WSIs) and somatic mutation data of 350 ccRCC patients from The Cancer Genome Atlas for developing SSL‐ABMIL model. In parallel, 163 ccRCC patients from Clinical Proteomic Tumor Analysis Consortium cohort was used as independent external validation set. We systematically compared three different models (Wang‐ABMIL, Ciga‐ABMIL, and ImageNet‐MIL) for their ability to predict TMB and VHL alterations. Results We first identified two groups of populations with high‐ and low‐TMB (cut‐off point = 0.9). In two independent cohorts, the Wang‐ABMIL model achieved the highest performance with decent generalization performance (AUROC = 0.83 ± 0.02 and 0.8 ± 0.04 in predicting TMB and VHL, respectively). Attention heatmaps revealed that the Wang‐ABMIL model paid the highest attention to tumor regions in high‐TMB patients, while in VHL mutation prediction, non‐tumor regions were also assigned high attention, particularly the stromal regions infiltrated by lymphocytes. Conclusions Our results indicated that SSL‐ABMIL can effectively extract histological features for predicting TMB and VHL mutation, demonstrating promising results in linking tumor morphology and molecular biology.https://doi.org/10.1002/cam4.70112artificial intelligenceattention‐based multiple instance learningclear cell renal cell carcinomaself‐supervised learningtumor mutation burdenVHL mutation |
| spellingShingle | Qingyuan Zheng Xinyu Wang Rui Yang Junjie Fan Jingping Yuan Xiuheng Liu Lei Wang Zhuoni Xiao Zhiyuan Chen Predicting tumor mutation burden and VHL mutation from renal cancer pathology slides with self‐supervised deep learning Cancer Medicine artificial intelligence attention‐based multiple instance learning clear cell renal cell carcinoma self‐supervised learning tumor mutation burden VHL mutation |
| title | Predicting tumor mutation burden and VHL mutation from renal cancer pathology slides with self‐supervised deep learning |
| title_full | Predicting tumor mutation burden and VHL mutation from renal cancer pathology slides with self‐supervised deep learning |
| title_fullStr | Predicting tumor mutation burden and VHL mutation from renal cancer pathology slides with self‐supervised deep learning |
| title_full_unstemmed | Predicting tumor mutation burden and VHL mutation from renal cancer pathology slides with self‐supervised deep learning |
| title_short | Predicting tumor mutation burden and VHL mutation from renal cancer pathology slides with self‐supervised deep learning |
| title_sort | predicting tumor mutation burden and vhl mutation from renal cancer pathology slides with self supervised deep learning |
| topic | artificial intelligence attention‐based multiple instance learning clear cell renal cell carcinoma self‐supervised learning tumor mutation burden VHL mutation |
| url | https://doi.org/10.1002/cam4.70112 |
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