CliPP-on-Web: Scalable Subclonal Reconstruction for Cancer Research

Advancing Tumor Evolution Studies with Efficient Analysis

CliPP (Clonal structure identification through Pairwise Penalization) is an advanced computational framework for identifying clonal and subclonal mutations using a regularized likelihood model. Designed for researchers investigating tumor heterogeneity, CliPP-on-Web provides rapid and accurate subclonal reconstruction without requiring programming expertise.

Addressing Computational Challenges in Subclonal Analysis

Intra-tumor heterogeneity plays a critical role in tumor evolution and therapeutic resistance. However, large-scale subclonal reconstruction has traditionally been constrained by computational limitations. CliPP-on-Web overcomes these challenges by enabling high-throughput, efficient analysis of sequencing data at scale.

Efficient, Large-Scale Analysis

Leveraging an optimized likelihood-based clustering model, CliPP has successfully processed whole-genome and whole-exome sequencing data from over 10,000 tumor samples across TCGA and PCAWG. These datasets are accessible for further exploration in the Data Resources tab.

Key Features

Analyze Your Own Data: Upload tumor sequencing data and obtain rapid subclonal reconstruction results.
Access Curated Datasets: Explore pre-processed subclonal analyses from PCAWG and TCGA.
Driver Mutation Subclonality: Investigate the clonal architecture of key driver mutations in TCGA.

For detailed methodology, refer to our BioRxiv preprint: https://www.biorxiv.org/content/10.1101/2024.07.03.601939v1 .

For issues with the app, please contact:

Aaron Wu - aw80@rice.edu

Quang Tran - qmtran@mdanderson.org

Video Tutorial

Watch the video below to learn how to use the CliPP-on-Web application:

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TCGA
PCAWG

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Reference:

Martínez-Jiménez, Francisco, et al. 'A compendium of mutational cancer driver genes.' Nature Reviews Cancer 20.10 (2020): 555-572.

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Manuscript under preparation:

Yujie Jiang, Matthew D Montierth, Kaixian Yu, Shuangxi Ji, Quang Tran, Xiaoqian Liu, Jessica C Lal, Shuai Guo, Aaron Wu, Seung Jun Shin, Shaolong Cao, Ruonan Li, Yuxin Tang, Tom Lesluyes, Scott Kopetz, Pavlos Msaouel, Anil K. Sood, Christopher Jones, Jaffer Ajani, Sumit K Subudhi, Ana Aparicio, Padmanee Sharma, John Paul Shen, Marek Kimmel, Jennifer R. Wang, Maxime Tarabichi, Rebecca Fitzgerald, Peter Van Loo, Hongtu Zhu, Wenyi Wang. Subclonal mutational load predicts survival and response to immunotherapy in cancers with low to moderate TMB. https://www.biorxiv.org/content/10.1101/2024.07.03.601939v1 .