Abstract B004: PISCA-box: A user-friendly interface for Phylogenetic Inference using Somatic Chromosomal Alterations (PISCA)
Authors: Grant Heather E. , Alcraft Rachel , Bousquets-Muñoz Pablo , Gabbutt Calum , Berner Alison , Yalchin Mehmet , Maley Carlo C. , Graham Trevor A. , Mallo Diego 
Published: February 2024

PISCA, originally introduced by Martinez et al. in 2018, represents a pivotal Bayesian phylogenetics tool for the modeling of tumor evolution using multi-region somatic chromosomal alteration (SCA) data. PISCA takes allele-specific copy number data, typically obtained from deep genome sequencing or SNP arrays, or absolute copy number data from low-pass genome sequencing methodologies. It extends the classic BEAST1 framework and inherits a rich repertoire of evolutionary models. Importantly, PISCA leverages longitudinal sampling to estimate SCA mutational clock rates, either employing strict clock models, where mutation rates remain constant throughout the evolutionary tree, or relaxed clocks, which allow each branch or subtree to possess its distinct mutation rate. This nuanced approach empowers PISCA to account for the heterogeneous rates of mutations, a pivotal consideration in understanding tumor evolution dynamics. However, PISCA has historically posed a formidable entry barrier for wider community adoption due to platform and Java dependencies for installation, as well as the need for manual or custom-scripted XML file generation. Here we showcase PISCA-box, a user-friendly interface designed to streamline the generation and testing of XML files. Our PISCA-box Docker image works on any desktop machine to create a locally hosted webpage, where users can input SCA data, sampling dates, select clock and demographic models, and set priors for key parameters - similar to the BEAST XML generator, BEAUTi. The Docker or Singularity installation can then be used for longer analyses using high-performance computing resources. To demonstrate its practical utility, we present novel colorectal cancer data. First, we examine data from a patient with a long-standing history of inflammatory bowel disorder (IBD), a known high-risk factor for colorectal cancer. This patient had undergone surveillance colonoscopies for many years, which provided 38 samples, and subsequently an additional 118 samples were collected from a total colectomy. All were analyzed with low-coverage whole genome sequencing. We find some samples from surveillance and colectomy form lineages with overlapping copy number events, that we used to construct a phylogeny, finding a cancer-adjacent clade that appears to evolve rapidly. A second dataset of multi-region samples from a cohort of exceptional survivors of oligometastatic colorectal cancer who lived &gt;60 months from metastatic diagnosis with biopsies/resections across 3-10 time points is examined and temporal models are used to estimate the ages of metastatic clades. PISCA therefore harnesses the power of longitudinal SCA data to enable a comprehensive study of SCA dynamics. We are currently expanding this framework to include fluctuating methylation clocks which PISCA-box will soon include. By providing this accessible tool, we enable researchers to readily apply Bayesian phylogenetics to real-world clinical datasets to better understand tumor evolution.

Citation Format: Heather E. Grant, Rachel Alcraft, Pablo Bousquets-Muñoz, Calum Gabbutt, Alison Berner, Mehmet Yalchin, Carlo C. Maley, Trevor A. Graham, Diego Mallo. PISCA-box: A user-friendly interface for Phylogenetic Inference using Somatic Chromosomal Alterations (PISCA) [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr B004.