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Subpopulations of tumor cells characterized by mutation profiles may confer differential fitness and consequently influence the prognosis of cancers. Understanding subclonal architecture has the potential to provide biological insight in tumor evolution and advance cancer precision treatment. Recent subclonal reconstruction methods require heavy computational resources, prior knowledge of the number of subclones, and extensive postprocessing. These drawbacks can be addressed by using a regularized likelihood modeling approach, which is novel to the field. Therefore, we propose a model-based method, Clonal structure identification through pair-wise Penalization, or CliP, to address these drawbacks. To evaluate the performance of CliP against other methods, we generated a benchmark dataset of 4,050 simulated samples with varied tumor purity, read depth, copy number alteration rate, and true numbers of clusters. Our results suggest that CliP outperforms popular methods such as PhyloWGS in accuracy and shows similar robustness in most scenarios. We further compared CliP performance against 10 other subclonal reconstruction methods to the consensus subclonal reconstruction results on whole-genome sequencing (WGS) data from the Pan-Cancer Analysis of Whole Genomes (PCAWG, n = 1,993). Our result shows that of all 11 methods, CliP achieves the highest correlation with the consensus calls. In terms of speed, CliP can finish running a sample with 5,000 SNVs within one minute, which is ~1,000 times faster than MCMC-based algorithms. Next, we profiled the subclonal structures of 7,711 patient samples with well-annotated clinical outcomes from The Cancer Genome Atlas (TCGA) across 32 cancer types applying CliP to the whole-exome sequencing (WES) data. This is the largest and most complete pan-cancer characterization of intratumor heterogeneity (ITH) through the lens of subclonal reconstruction. We further used CliP outputs to address a commonly asked question on which sequencing platform to use for cancer evolution studies: the cost-effect WES data at higher read depth versus the more comprehensive WGS data at lower read depth. There were a total of 588 tumor samples from 21 cancer types, for which both PCAWG and TCGA have profiled using the WGS and WES platform, respectively. Using both datasets to compare results from these samples as benchmark, we observed that for most cancer types, subclonal reconstruction from WES is as informative as that from the matched WGS data. In summary, our study represents a significant methodological advancement in subclonal reconstruction and highlights the importance of measuring tumor subclone structure. Citation Format: Yujie Jiang, Kaixian Yu, Matthew D. Montierth, Shuangxi Ji, Seung Jun Shin, Shuai Guo, Shaolong Cao, Yuxin Tang, Scott Kopetz, Pavlos Msaouel, Jennifer R. Wang, Marek Kimmel, Peter Van Loo, Hongtu Zhu, Wenyi Wang. Pan-cancer analysis of intra-tumor heterogeneity in 9,116 cancers using a novel regularized likelihood model. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4272. |
