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cancerAlign: Stratifying tumors by unsupervised alignment across cancer types

Gao, Bowen and Luo, Yunan and Ma, Jianzhu and Wang, Sheng (2020) cancerAlign: Stratifying tumors by unsupervised alignment across cancer types. . (Unpublished)

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Tumor stratification, which aims at clustering tumors into biologically meaningful subtypes, is the key step towards personalized treatment. Large-scale profiled cancer genomics data enables us to develop computational methods for tumor stratification. However, most of the existing approaches only considered tumors from an individual cancer type during clustering, leading to the overlook of common patterns across cancer types and the vulnerability to the noise within that cancer type. To address these challenges, we proposed cancerAlign to map tumors of the target cancer type into latent spaces of other source cancer types. These tumors were then clustered in each latent space rather than the original space in order to exploit shared patterns across cancer types. Due to the lack of aligned tumor samples across cancer types, cancerAlign used adversarial learning to learn the mapping at the population level. It then used consensus clustering to integrate cluster labels from different source cancer types. We evaluated cancerAlign on 7,134 tumors spanning 24 cancer types from TCGA and observed substantial improvement on tumor stratification and cancer gene prioritization. We further revealed the transferability across cancer types, which reflected the similarity among them based on the somatic mutation profile. cancerAlign is an unsupervised approach that provides deeper insights into the heterogeneous and rapidly accumulating somatic mutation profile and can be also applied to other genome-scale molecular information.

Item Type:Report or Paper (Discussion Paper)
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URLURL TypeDescription Paper ItemCode
Luo, Yunan0000-0001-7728-6412
Additional Information:The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. This version posted November 20, 2020. The authors have declared no competing interest.
Subject Keywords:Tumor stratification, somatic mutation, adversarial learning
Record Number:CaltechAUTHORS:20201123-135803046
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Official Citation:cancerAlign: Stratifying tumors by unsupervised alignment across cancer types. Bowen Gao, Yunan Luo, Jianzhu Ma, Sheng Wang. bioRxiv 2020.11.17.387860; doi:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:106791
Deposited By: Tony Diaz
Deposited On:23 Nov 2020 22:08
Last Modified:16 Nov 2021 18:56

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