Triple-negative breast cancer (TNBC) accounts for 15% of the ~34 million breast cancer cases diagnosed annually and is characterised by the absence of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression. TNBC is more aggressive and has significantly higher early relapse rates compared to other breast cancer subtypes. A key factor driving aggressive disease is the high intra-/intertumoural and interpatient heterogeneity. TNBC have diverse molecular subtypes, the nature of which remain controversial and are rapidly evolving. Higher extracellular matrix (ECM) stiffness within the TNBC tumour microenvironment has also been associated with poorer prognosis, drug resistance and metastasis. A significant challenge is stratifying patients to ensure treatment regimens are effective: resistance leads to exacerbation of disease during the therapy period and evolution of the tumour microenvironment. There is an urgent need for innovative means to study dynamic changes in TNBC biology.
This project builds on our recent work using innovative multimodal approaches to profile spatial molecular changes in TNBC tissues before, during and after treatment. The overarching goal is to combine advanced imaging of patient-derived organoids with novel bioinformatics and AI-based approaches to define molecular signatures that predict drug resistance and identify new targets for therapeutic intervention.
The key aims are:
1. Explore multimodal spatial omics datasets using bioinformatics and AI tools, to identify pathways that correlate with tumour biomechanics and clinical imaging data.
2. Perform advanced live microscopy using TNBC patient-derived organoids within biomechanically-tuned 3D scaffolds to experimentally validate identified pathways and spatiotemporal changes within specific cell subpopulations.
3. Target pathways using CRISPR and/or commercial or clinically validated drugs to determine contributions from biomechanical-related pathways to tumour cell proliferation, survival and invasion.
Data arising from these studies will define novel TNBC patient-specific molecules contributing to disease resistance and their potential therapeutic relevance
Biophysical contributions to triple negative breast cancer progression
Representative Publications
• Lawson C, Peel S, Jayo A, Corrigan A, Iyer P, Baxter Dalrymple M, Marsh RJ, Cox S, van Audenhove I, Gettemans J, Parsons M. Nuclear fascin regulates cancer cell survival. 2022. eLife. doi: 10.7554/eLife.79283 • Pfisterer K, Levitt J, Lawson CD., Marsh RJ, Heddleston JM, Wait E, Ameer-Beg S, Cox S, Parsons M. FMNL2 regulates dynamics of fascin in filopodia. J Cell Biol. 2020. doi: 10.1083/jcb.201906111 • Pike R, Ortiz-Zapater E, Lumicisi B, Santis G, Parsons M. KIF22 co-ordinates CAR and EGFR dynamics to promote cancer cell proliferation. Science Signaling. 2018. DOI: 10.1126/scisignal.aaq1060
– Verghese G, Li, M, Liu, F, Lohan A, Cherian Kurian, N, Meena S, Gazinska P, Shah A, Oozeer A, Chan, T, Opdam, M, Linn S, Gillett C, Alberts E, Hardiman T, Jones S, Thavaraj S, Jones L, Salgado R, Pinder, SE, Rane S, Sethi A, Grigoriadis, A. Multiscale deep learning framework captures systemic immune features in lymph nodes predictive of triple negative breast cancer outcome in large-scale studies. J Path 2023 DOI:10.1002/path.6088 – Wall I, Boulat V, Shah A, Blenman KRM, Wu Y, Alberts E, Calado DP, Salgado R, Grigoriadis A. Leveraging the Dynamic Immune Environment Triad in Patients with Breast Cancer: Tumour, Lymph Node, and Peripheral Blood. Cancers (Basel). 2022 Sep 17;14(18):4505. doi: 10.3390/cancers14184505. PMID: 36139665 – Quist J, Taylor L, Staaf J, Grigoriadis A. Random Forest Modelling of High-Dimensional Mixed-Type Data for Breast Cancer Classification Cancers (Basel). 2021 Feb 27;13(5):991. doi: 10.3390/cancers13050991. PMID: 33673506