Poster Presentation 36th Lorne Cancer Conference 2024

The temporal dynamics of cancer resistance (#124)

Christina Bui 1 2 , Vanina Rodriguez 1 , Estefania Matino Echarri 1 , Christine Chaffer 1 2
  1. Garvan Institute, Darlinghurst, NSW, Australia
  2. St Vincent's Clinical School, UNSW Medicine, UNSW Sydney, Darlinghurst, NSW, Australia

Breast cancer is the most frequently diagnosed malignancy worldwide with around 2.3 million people diagnosed in 2020 and a projected 3 million diagnoses by 2040.  It is the leading cause of cancer-related mortality among women worldwide1.

A subset of breast cancers, known as triple-negative breast cancers (TBNCs), are characterised by a lack of receptors for oestrogen, progesterone, and human epidermal growth factor 2, and accounts for approximately 20% of breast tumours worldwide. TBNCs are often more aggressive, more likely to become drug resistant and prone to metastasis and recurrence, making them difficult to treat. Unfortunately, there are currently no curative therapies for patients reaching the resistant phase of disease.

Recent studies have found that chemotherapy can induce cancer cell plasticity and have shown that plasticity has a major role in tumour progression, metastasis, and resistance to therapy. Previously, we have established that TNBC is particularly adept at dynamically evolving its properties and characteristics in response to treatment and other environmental challenges2-5. This ability is known as cancer cell plasticity and can include changes in their shape, size, growth rate and gene.

In this project, we have applied single-cell multi-omics and spatial transcriptomic approaches to visualise changes in gene expression and chromatin accessibility across the genome in patient derived xenograft models of TBNC. This was performed in a temporal manner to capture the dynamic changes associated with those cell state changes. Critically, integration of these large multi-modal single-cell datasets requires development of new computational tools and machine learning algorithms. We have combined our expertise in cell state plasticity with single-cell technologies and the development of novel machine learning tools to build a more comprehensive understanding of the temporal dynamics of cancer resistance.

We aim to describe the first comprehensive analysis of the dynamic and temporal regulation of cell state changes and illustrate how, when and where they can be manipulated. This project will present an unprecedented opportunity to advance strategies for personalised medicine as defining mechanisms to enhance inherent plasticity affords a novel and innovative approach to create new stem cells, exploitable for therapeutic purposes.

  1. Arnold M, Morgan E, Rumgay H, et al: Current and future burden of breast cancer: Global statistics for 2020 and 2040. The Breast 66:15-23, 2022
  2. Christine L Chaffer, Nemanja D Marjanovic, Tony Lee, et al: Poised Chromatin at the ZEB1 Promoter Enables Breast Cancer Cell Plasticity and Enhances Tumorigenicity. Cell 154:61-74, 2013
  3. Chaffer CL, Brueckmann I, Scheel C, et al: Normal and neoplastic nonstem cells can spontaneously convert to a stem-like state. Proceedings of the National Academy of Sciences 108:7950-7955, 2011
  4. Castaño Z, San Juan BP, Spiegel A, et al: IL-1β inflammatory response driven by primary breast cancer prevents metastasis-initiating cell colonization. Nature Cell Biology 20:1084-1097, 2018
  5. Burkhardt DB, San Juan BP, Lock JG, et al: Mapping Phenotypic Plasticity upon the Cancer Cell State Landscape Using Manifold Learning. Cancer Discovery 12:1847-1859, 2022