Unraveling the Functional Complexity of Cancer Genomes through Chromosome Engineering

Introduction

Cancers arise through genetic and epigenetic alterations that drive the transformation of single cells into malignant tumors. Among genetic changes, copy number alterations (CNAs) are recurrent chromosomal events that increase or decrease the dosage of specific regions of DNA. CNAs can affect up to 30% of a cancer cell genome and are associated with poor clinical outcomes.

Current Challenges

Despite their pervasiveness, the functional effects of specific CNAs on cancer phenotypes remain largely unknown. Current approaches cannot faithfully recapitulate the unique properties of these chromosomal alterations. Indeed, CNAs can uniquely affect the expression of hundreds of linked genes and change DNA topology, which in turn can promote intra-tumor heterogeneity. This is illustrated by the random segregation of oncogenes in extra chromosomal DNA (ecDNA).

Research Objectives

In order to study the functional role of CNAs in cancer, this proposal employs MACHETE, a novel genome engineering toolkit that enables the generation of megabase-sized deletions, gains, and oncogene amplification in ecDNA. Using pancreatic ductal adenocarcinoma (PDAC) as a disease model, we will:

1. Engineer the major CNAs in this lethal tumor to dissect their role in:

   • Immune evasion
   • Metastasis
   • Response to therapy

2. Study whether the order of CNA acquisition leads to divergent or convergent phenotypes, a highly relevant yet unexplored aspect of cancer biology.

Methodology

Overall, by combining the MACHETE genome engineering platform with in vivo cancer models and molecular approaches, this proposal will begin to systematically dissect the function of recurrent CNAs in PDAC, with direct implications for therapy.

Broader Implications

Importantly, the methods and conceptual framework of this proposal are broadly applicable to other cancers and diseases characterized by similar chromosomal alterations. Understanding their underlying biology may lead to a new class of CNA-based clinical targets.