How Cancer Cells Collectively Decide to Grow or Stay Dormant
Cancer cells communicate to make a collective decision to initiate a growing tumor, stay dormant or reach a threshold beyond which the tumor does not grow. The Huang Lab is working with the University of Texas Austin to construct mathematical models of how cell-cell communication influences tumor growth dynamics.
Waddington’s epigenetic landscape. By depicting the underside of the surface, Waddington illustrated the idea that genes can change the landscape during evolution. Image courtesy: tandfonline.com.
Despite many decades of progress in understanding the biology of cancer, scientists still know very little about how and why cancer recurs after primary treatment, nor why some precancerous lesions progress to cancer while many don’t. Even with clear cancer-associated mutations, such as mutations in the BRCA1 and 2 genes that are strongly linked to an increased risk of breast cancer, the risk of cancer is never 100%. ISB’s Sui Huang, MD, PhD, is pursuing a project using mathematical theories and modeling combined with experimental data that could explain how cells progress to cancer or don’t. Huang and his colleagues are studying cancer cells’ “tipping point,” how cells become destabilized, which allows them to shift from one state to another — i.e., from dormant to fast-growing tumors.
- Funded by National Cancer Institute
- Led by Sui Huang, MD, PhD
- Key collaborators:
- Amy Brock, PhD, University of Texas at Austin
- Thea Tlsty, PhD, University of California, San Francisco
- Lorenzo Ferri, MD, PhD, McGill University
- Hong Qian, PhD, University of Washington
What is Cancer’s ‘Tipping Point’?
Researchers have long been searching for molecular biomarkers that would predict whether or when pre-cancer will progress to cancer, or if dormant cancer after treatment will progress to metastatic recurrence, with little success. Huang believes that these transitions are spurred not by individual mutations or changes in expression of specific genes, but by a larger pattern of instability that precedes a cell-state shift. All our cells possess the ability to change from one state to another, a phenomenon known as phenotypic plasticity, but in an adult body, cells are typically stably locked into a given type. A liver cell will never change into a brain cell. Cancer is an unusual exception: Tumor growth relies on cells shifting without mutations from a more mature, less malignant form into a less mature, fast-growing form.
To understand the biology behind this shift, Huang and his colleagues are studying how gene expression changes in cancer cells that stay dormant versus those that are primed to shift to rapid, cancer-like growth. In one type of experiment, the researchers use massively-parallel “microcultures” of leukemia cells to catch instability; only some of these small collections of cells will exhibit cancer-like growth while the rest remain stable at low numbers. The team analyzes how genes are switched on in individual cells in each of these microcultures in an attempt to capture the earliest stages of the transition. They found that the transition is marked not by changes in specific genes but by a pattern of collective changes in gene expression. This phase appears to be a stage of general destabilization, characterized by gene expression changes in many different directions, although the average state remains unchanged — without analyzing at the level of single cells, this instability would not be detectable.
The researchers have also replicated these findings using samples taken directly from patients, either biopsies from ovarian cancer patients or from people with Barrett’s esophagus, a precancerous state that can occasionally transition to esophageal cancer. They’re also exploring a phenomenon known as treatment-induced progression, in which the few cancer cells that survive traditional treatments such as chemotherapy actually seem primed to spur new tumor growth.
The team is now continuing their work using leukemia cells in the lab with the goal of defining “early warning signals” that predict a dormant cancer cell’s likelihood of switching to rapid growth. They’re also planning to analyze other kinds of cancer tissues from patients. Finally, they’re working to establish more realistic animal models of cancer, those that replicate the variability between patients that makes it so hard to find biomarkers. Current mouse models of cancer are often designed with an aggressive tumor burden so that 100% of animals develop cancer, but there are no risk factors that result in cancer in 100% of humans. The team is developing mouse models using smaller numbers of cancer cells to more accurately represent the disease.
Citations
- Strasser MK, Gibbs DL, et al. Concerted epithelial and stromal changes during progression of Barrett’s Esophagus to invasive adenocarcinoma exposed by multi-scale, multi-omics analysis. bioRxiv. 2023. doi: 10.1101/2023.06.08.544265.
- Angelini E, Wang Y, Zhou JX, Qian H, Huang S. A model for the intrinsic limit of cancer therapy: Duality of treatment-induced cell death and treatment-induced stemness. PLoS Comput Biol. 2022. doi: 10.1371/journal.pcbi.1010319.
- Huang S. Reconciling Non-Genetic Plasticity with Somatic Evolution in Cancer. Trends Cancer. 2021. doi: 10.1016/j.trecan.2020.12.007.
- S. Huang. The logic of cancer treatment: Why it is so hard to cure cancer
- Treatment-induced progression, hyper-progression and the Nietzsche effect. In: “Rethinking Cancer: A New Paradigm for the Post-genomics Era,” edited by B. Strauss, M. Bertolaso, I. Ernberg and M. J. Bissell. MIT Press. 2021.
- Mojtahedi M, Skupin A, Zhou J, Castaño IG, Leong-Quong RY, Chang H, Trachana K, Giuliani A, Huang S. Cell Fate Decision as High-Dimensional Critical State Transition. PLoS Biol. 2016. doi: 10.1371/journal.pbio.2000640.
Contact Dr. Sui Huang
Professor
ISB