Poster Presentation 37th Lorne Cancer Conference 2025

MIOFlo reveals temporal transcriptional relationships across the cancer cell-state landscape (#148)

Alexander Tong* 1 2 3 , Manik Kuchroo* 4 5 , Xingzhi Sun* 1 , Shabarni Gupta 6 , Aarthi Venkat 7 , Dhananjay Bhaskar 1 8 9 , Beatriz P. Perez San Juan 6 , Laura Rangel 6 , Brandon Zhu 10 , John G. Lock 11 , Christine Chaffer 6 12 , Smita Krishnaswamy 1 8
  1. Department of Computer Science, Yale University, New Haven, CT, USA
  2. Department of Computer Science and Operations Research, University of Montreal, Montreal, QC, Canada
  3. Mila – Quebec AI Institute, Montreal, QC, Canada
  4. Department of Neuroscience, Yale University, New Haven, CT, USA
  5. Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
  6. The Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
  7. Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA, New Haven, CT, USA
  8. Department of Genetics, Yale University, New Haven, CT, USA
  9. Kavli Institute for Neuroscience, Yale University, New Haven, CT, USA
  10. Yale College, New Haven, CT, USA
  11. Systems Microscopy, University of New South Wales, Sydney, NSW, Australia
  12. St Vincent’s Clinical School, University of New South Wales, Sydney, NSW, Australia

Cell-state plasticity is the underlying mechanism through which cancer cells undergo dynamic, non-genetic changes contributing to their proliferation and metastasis. Cancer cells can be thought to exist within a spectrum of phenotypic states connected by trajectories defining dynamic transitions within a cancer cell-state landscape. At one end of this spectrum, are the metastasis-seeding cancer stem cells (CSCs) with invasive mesenchymal properties critical to tumour initiation and therapy resistance. Non-CSCs generated through differentiation of CSCs are progeny cells with epithelial properties sitting on the other end of this spectrum. The dynamic cell-state transitions between these extremes are regulated by intrinsic programs such as the epithelial-to-mesenchymal transition (EMT) and the reverse mesenchymal-to-epithelial transition (MET). This study aims to define the dynamic transitions that drive CSCs into a chemotherapy-sensitive non-CSC-like state through the MET program in the cancer cell-state landscape.

We developed a neural ODE network called MIOFlow that learns continuous dynamics from static transcriptomic data and applied it to time-lapsed single-cell expression measurements from an in-vitro triple-negative breast cancer MET differentiation system. This led to the identification of core transcriptional factors associated with the emergence of the epithelial trajectory (non-CSC state). We constructed a gene regulatory network to sketch the transcriptional circuitry underlying the MET. The regulatory effect of one gene, the estrogen-related receptor alpha (ESRRA) was validated using orthogonal approaches including RNAi, western blotting, and immunofluorescence. Indeed, the knockdown of ESRRA increased epithelial cell state marker CDH1 (E-cadherin) expression. The downregulation of ESRRA within tumorsphere was validated temporally, together with the concomitant downregulation of mesenchymal cell-state marker ZEB1 and upregulation of CDH1. Furthermore, inhibition of ESRRA in triple-negative breast cancer cells prevented metastatic seeding in lungs via in vivo tail vein injections.

MIOFlow, thus presents an innovative approach to characterizing the dynamic molecular programs derived from time-point-based transcriptomic datasets. Using this approach, we have defined the key transcription factors and validated ESRRA as a key driver of MET in the cancer which have broad implications for translational research aimed at driving CSCs out of their aggressive state to inhibit metastasis and chemotherapy-resistant disease.

  1. Tong, Alexander, Manik Kuchroo, Shabarni Gupta, Aarthi Venkat, Beatriz P. San Juan, Laura Rangel, Brandon Zhu, John G. Lock, Christine L. Chaffer, and Smita Krishnaswamy. "Learning transcriptional and regulatory dynamics driving cancer cell plasticity using neural ODE-based optimal transport." bioRxiv (2023): 2023-03.
  2. Tong, Alexander, Jessie Huang, Guy Wolf, David Van Dijk, and Smita Krishnaswamy. "Trajectorynet: A dynamic optimal transport network for modeling cellular dynamics." In International conference on machine learning, pp. 9526-9536. PMLR, 2020.
  3. Burkhardt, Daniel B., Beatriz P. San Juan, John G. Lock, Smita Krishnaswamy, and Christine L. Chaffer. "Mapping phenotypic plasticity upon the cancer cell state landscape using manifold learning." Cancer discovery 12, no. 8 (2022): 1847-1859.