Machine learning with autophagy-related proteins for discriminating renal cell carcinoma subtypes.

He, Zhaoyue; Liu, He; Moch, Holger; Simon, Hans-Uwe (2020). Machine learning with autophagy-related proteins for discriminating renal cell carcinoma subtypes. Scientific reports, 10(1), p. 720. Springer Nature 10.1038/s41598-020-57670-y

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Machine learning techniques have been previously applied for classification of tumors based largely on morphological features of tumor cells recognized in H&E images. Here, we tested the possibility of using numeric data acquired from software-based quantification of certain marker proteins, i.e. key autophagy proteins (ATGs), obtained from immunohistochemical (IHC) images of renal cell carcinomas (RCC). Using IHC staining and automated image quantification with a tissue microarray (TMA) of RCC, we found ATG1, ATG5 and microtubule-associated proteins 1A/1B light chain 3B (LC3B) were significantly reduced, suggesting a reduction in the basal level of autophagy with RCC. Notably, the levels of the ATG proteins expressed did not correspond to the mRNA levels expressed in these tissues. Applying a supervised machine learning algorithm, the K-Nearest Neighbor (KNN), to our quantified numeric data revealed that LC3B provided a strong measure for discriminating clear cell RCC (ccRCC). ATG5 and sequestosome-1 (SQSTM1/p62) could be used for classification of chromophobe RCC (crRCC). The quantitation of particular combinations of ATG1, ATG16L1, ATG5, LC3B and p62, all of which measure the basal level of autophagy, were able to discriminate among normal tissue, crRCC and ccRCC, suggesting that the basal level of autophagy would be a potentially useful parameter for RCC discrimination. In addition to our observation that the basal level of autophagy is reduced in RCC, our workflow from quantitative IHC analysis to machine learning could be considered as a potential complementary tool for the classification of RCC subtypes and also for other types of tumors for which precision medicine requires a characterization.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Pharmacology

UniBE Contributor:

He, Zhaoyue; Liu, He and Simon, Hans-Uwe

Subjects:

600 Technology > 610 Medicine & health

ISSN:

2045-2322

Publisher:

Springer Nature

Language:

English

Submitter:

Sabrina Cookman

Date Deposited:

17 Feb 2020 09:21

Last Modified:

23 Feb 2020 02:49

Publisher DOI:

10.1038/s41598-020-57670-y

PubMed ID:

31959887

BORIS DOI:

10.7892/boris.140000

URI:

https://boris.unibe.ch/id/eprint/140000

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