Giant-leaps during tumorigenesis: Dissecting saltatory evolution in cancer ‘in the making’

Introduction

Tumorigenesis is an evolutionary process driven by gradual mutation acquisition to confer selective advantages to somatic cells. DNA rearrangements are the most common cancer driver mutation, outnumbering base substitutions. Rearrangements amplify, disrupt and fuse genes or alter their gene regulation, ultimately driving adaptive changes to initiate cancer, metastasis and therapy resistance.

Background

Cancer genomics has revealed that most of these alterations arise as complex genomic rearrangements (CGRs), where a multitude of changes arise in a short time. While in most cases deleterious to the cell, in rare cases CGRs confer an adaptive malignant phenotype in one giant leap through saltatory evolution events (SEEs) – reminiscent of the “hopeful monsters” theorized by Goldschmidt.

Importance of SEEs

Given their potential to overcome strong selective barriers, it has been proposed that SEEs draw the line between benign lesions and lethal cancer. Understanding their mechanisms is thus fundamental to tumor biology. However, the identity of cells undergoing SEEs has remained elusive, with cancer genomics studies focusing typically on long-established cancers rather than early or even initiating cancer cells.

Role of Nuclear Atypias

Intriguingly, CGRs are largely explained by cascades of atypical cell nuclei (i.e., nuclear atypias) fueling rearrangement formation, providing an opportunity to study SEEs.

Project Objectives

Building a novel automated AI-driven framework that couples imaging and single-cell multi-omics, we will leverage nuclear atypias as a phenotypic indicator to dissect principles and mechanisms of SEEs ‘in the making’, and advance fundamental cancer biology.

Methodology

Using highly controllable cell lines and organoid models of colorectal cancer (CrC), a tumor thought to be driven by SEEs, we will:

1. Unravel pathways,
2. Explore genetic contexts, and
3. Investigate chromosome-level constraints determining SEEs.

Future Directions

Finally, via validation in patient samples, we will pave the way to determining SEEs in future clinical studies to advance precision oncology.