Cellular barcodes could provide crucial insights into ovarian cancer
May 6, 2022
Van Andel Institute Postdoctoral Fellow Dr. Ben Johnson has earned a Mentored Investigator Grant from Ovarian Cancer Research Alliance to support a groundbreaking approach to ovarian cancer research — adding barcodes to ovarian cancer cells to study how these cancers start, recur and resist treatment. Dr. Johnson, who works in the lab of VAI Associate Professor Dr. Hui Shen, also uses other computational techniques to better understand how ovarian cancers work and how we might be able to better treat them.
We caught up with Dr. Johnson to chat about ovarian cancer research, his project and how Big Data is changing research.
What are some of the challenges in studying and treating ovarian cancer?
Dr. Johnson: Ovarian cancer traditionally has been treated as one disease but, in recent years, it has become clear that multiple subtypes exist with distinct features that present unique challenges in the clinic to treat each type.
The most common subtype is called high-grade serous ovarian cancer and comprises about 75% of total cases — making it one of the most intensely studied types. We have a pretty good idea which tissue it starts in — which is important for understanding how to treat it — but the process of normal tissue transforming into a tumor remains poorly understood. One idea is that a rare population of cells called ovarian cancer stem-like cells might provide a way for high-grade serous ovarian cancer tumors to start, restart and resist treatment.
Understanding this process matters because it contributes to two key challenges of treating ovarian cancer — that is, ovarian cancer usually is detected in the late stages of the disease and comes back in about 80% of cases within five years. By understanding how ovarian cancer stem-like cells turn into all cells that make up a tumor, we can design tests to detect cancer sooner and treatments to specifically target these cells and processes.
Another challenge of studying ovarian cancer is that these tumors are composed of many different cell types, with each individual cell interacting with other cells, all of which function slightly differently. Ideally, we would be able to break the tumor apart into the individual cells and study it in its constituent pieces — single cells. At VAI, we have recently made significant strides to studying these single cells at higher resolution than what has been shown before. These insights help us understand which genetic switches are flipped “on” and which are flipped “off” in the cells — an important indicator of the processes that drive each malignant cell. This new technology (STORM-seq) is helping us understand the roles each single cell is playing in a tumor and begin to answer questions that were not previously able to be addressed.
Tell us about your project. What do you hope to find?
Dr. Johnson: One of the ways ovarian cancer tumors might start is through ovarian cancer stem-like cells. This has significant implications both from a basic understanding of tumor development and impacting treatment decisions in the doctor’s office. If this population of cells is the one that can lead to starting and restarting tumors, we need to develop treatments that specifically target these cells. However, it still isn’t clear how ovarian cancer stem-like cells give rise to the cells that make up an ovarian cancer tumor.
In this project, we are using a system in the lab to give each ovarian cancer stem-like cell a unique barcode that we can then trace as each cell divides into two cells, keeping track of which original cell it came from. This type of system allows us to ask questions about the specific genes and environmental cues, such as those seen within a patient, influence the cell types produced from ovarian cancer stem-like cells.
We hope to find patterns of gene expression that define how an ovarian cancer stem-like cell can “decide” to either make more of itself or produce the cell types that make up an ovarian cancer tumor. These patterns will hopefully reveal targets that we can ultimately develop treatments against, leading to better patient outcomes. I am deeply grateful to Ovarian Cancer Research Alliance for their support of this important project.
What is Big Data and how can it help us move the needle on ovarian cancer?
Dr. Johnson: Within the context of biomedical research, Big Data can be defined as vast data sets collected across hundreds to hundreds of thousands of patients with the goal of identifying patterns or associations that could lead to better patient outcomes. These improvements include earlier detection of disease, stratifying people based on risk or guiding treatment plans.
Notably, during the last decade, The Cancer Genome Atlas (TCGA) consortium made landmark discoveries in many different types of cancer, including ovarian cancer, using large data sets built by profiling many different aspects of tumors. The impact of this work continues to have real-world effects by couching new discoveries in a broader context that would otherwise not be possible.
However, as more and more data are collected by researchers, we need to come up with ways to effectively manage and process all this information to ensure it is used effectively. I believe that large-scale analyses will continue to make significant strides that would otherwise not be possible, but at the same time, there exists a need for high-resolution techniques within individual patients to uncover key processes that might be missed otherwise.
I think this is where the technology we are developing, STORM-seq, fits as a complement to other approaches by providing a precise tool, like a scalpel, to dissect out how these processes are occurring by measuring more features within and across single cells. Together, I think Big Data approaches and our more zoomed-in view of single cells can have a significant impact on ovarian cancer patient outcomes.
Research reported in this publication is supported by Ovarian Cancer Research Alliance [Mentored Investigator Grant, no. 891749}. The content is solely the responsibility of the authors and does not necessarily represent the official views of Ovarian Cancer Research Alliance.