Large-scale 2D, 3D maps could enable ‘much more precise’ cancer diagnosis, treatment
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Traditional histology typically guides treatment decisions for patients with colorectal cancer and various other malignancies, but it is unable to reveal a tumor’s molecular or structural features.
To address these limitations, researchers at Harvard Medical School have begun to use histology combined with single-cell imaging technologies to develop large-scale 2D and 3D spatial maps of colorectal cancer.
The maps, characterized in a paper published in Cell, show molecular information on top of histologic features to provide a more extensive view of the structure of the cancer and its interaction with the immune environment.
The maps are part of a larger effort to create atlases for various cancer types. The information will be available to the scientific community through NCI’s Human Tumor Atlas Network.
“Traditional histology provides knowledge of tumor gross morphology, size, whether the tumor is podunculated — hanging by a little thread — or whether it’s flat,” lead author Peter K. Sorger, PhD, the Otto Krayer professor of systems pharmacology at Harvard Medical School's Blavatnik Institute, told Healio. “However, it doesn’t tell you what the grade of the tumor is, or its molecular structure. Our belief is that this map will help us provide a much more precise diagnosis and, eventually, a much more precise treatment.”
Sorger spoke with Healio about how the project came about, the information it provides and the future applicability of this technology.
Healio: What inspired you to develop this map of colorectal cancer?
Sorger: Spatial profiling, or high-plex tissue imaging, has emerged in the research setting over the last 5 years. Our program, which gave rise to this paper, is part of the Human Tumor Atlas Network program. The NCI put together this program, which is meant to be the spatial or visual analog to the Cancer Genome Atlas program. We created this as part of that program.
Histology gives us 150 years of knowledge about cancer, and now we can combine that with detailed information about cellular composition, oncogenic pathways and more.
Healio: How well has this map worked in guiding treatment approaches for patients with cancer?
Sorger: In a sense, these are the first data sets of this type. This will be impactful, in part, due to this connection we have made with conventional histology. Most cancers are diagnosed through histology, or through cytology for liquid cancers. This map provides a lot more information on those cancers using a related method. We hope it will be impactful to patients, maybe even in the fairly short term. We’re probably looking at 2 to 5 years to work out what the impact would be in a real-world diagnostic setting, which in this case would be a pathology or histology lab.
Healio: Will the information gleaned from these maps enable more targeted treatment for these cancers?
Sorger: Yes. We would try to use molecular methods to develop a targeted treatment. The cancer for which this has advanced the most is lung cancer. Lung cancers are separated into nearly a dozen genetic subtypes and are treated differently as a consequence. So, the purpose here would be to inform precision medicine but using image-based features from biopsies.
Interestingly, we could use this information not only to indicate when we would need to use these treatments, but also to determine when we might withhold treatment. We tend to think about that last, but immune-oncology treatment can be very toxic. As this treatment becomes more widespread in early cancers, there is a lot of concern about not overprescribing. So, precision medicine can not only help us identify the right drug, but also to determine when a watchful waiting strategy would be appropriate. Also, these methods that we have are much more amenable to computational analysis.
Healio: How else might this technology be used to improve cancer diagnosis?
Sorger: The other main area we have been working on is melanoma. We are explicitly trying to distinguish, in very early disease, the dangerous from the nondangerous cases. We’re trying to find cases where surgical resection could be curative. With melanoma, you go in with a lesion and a piece is taken out. The question then is, do you need cytotoxic immunotherapy or are you going to be fine if left on your own? We want to know when you can wait and when you can’t, because patients often want things taken out right away.
Healio: What is next for this project?
Sorger: We have one complete reconstruction of colorectal cancer, and then a lot of 2D information, as well. There’s a need to get to a bigger data set. The second thing we’d like to do is make this much more accessible to people. These technologies have been expensive recently, but they don’t need to be. The method we use is freely available. You have to buy the antibodies and the microscope, but there are no licenses. So, I think we want to democratize this as much as we can.
We also plan to study this approach in other cancers, such as breast cancer and brain cancer. A more aggressive type of breast cancer, for example, is going to be treated very differently than a more indolent one. Patients often worry about whether they have a grade 1 or grade 2 tumor, and they worry about the staging. That staging is already being done with histology, but we believe this will make that staging more precise.
Healio: Is there anything else you’d like to mention?
Sorger: The NCI program is trying to make all the data and methods of acquiring the data publicly accessible. So, in principle, people can reanalyze this data. In genomics, we do that all the time, but we haven’t done that much with imaging data. So, democratizing this is not only letting people collect their own data, but also letting other people collect their data. That’s a big part of the program, and it’s partially realized. We’re still working on that.
For more information:
Peter K. Sorger, PhD, can be reached at Harvard Medical School, Systems Biology, Warren Alpert Blvd. 438, 200 Longwood Ave., Boston, MA 02115; email: peter_sorger@hms.harvard.edu.
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