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Mapping pancreatic cancer to improve immunotherapy: A Q&A with Gregory Beatty, MD

Gregory L. Beatty, MD, PhD
Gregory L. Beatty, MD, PhD

Training the body’s own immune system to attack cancer cells is known as immunotherapy and it’s a treatment option that’s been highly effective for many cancer types, including lung cancer and melanoma. However, despite decades of research, immunotherapy has yet to break through for some of the most challenging types of cancer, including pancreatic cancer, which has a five-year survival rate of about 10 percent. 

If you spend much time listening to researchers who are trying to change this, you’re bound to hear the phrase “tumor microenvironment.” This refers to the many different types of cells that exist side by side within a tumor, and it’s a big reason why the current immunotherapy approaches have had limited success in certain cancer types

Gregory L. Beatty, MD, PhD, an associate professor of Hematology-Oncology and member of Penn Medicine’s Abramson Cancer Center, and his team are focused on improving immunotherapy for pancreatic cancer. They’re using an innovative approach to repurpose an old technology to map the tumor microenvironment and find better ways for immunotherapy drugs to navigate the landscape.  

We sat down with Beatty to learn more about his work and how it’s making an impact. 

What is the tumor microenvironment and why does it matter in pancreatic cancer treatment and research?

For solid tumors like pancreatic cancer or glioblastoma, the tumor isn’t just a solid mass of cancer cells. Inside the tumor, we find cancer cells, along with microbes, blood vessels, fibroblasts, and many different types of immune cells, including T cells, B cells, myeloid cells, macrophages, and more. The way that these cells are distributed throughout the tumor can vary from person to person, even if they have the same type of cancer. It’s like each tumor is a unique city with different neighborhoods of cells. 

In some of the cancers where immunotherapy has been most successful, like melanoma, the tumor microenvironment is less heterogeneous, or diverse, and it’s easier for the trained immune cells to reach the cancer cells. In a tumor like pancreatic cancer, we know that the tumor microenvironment is a barrier to cancer immunotherapy. There are so many different types of cells interacting with each other. These cell neighborhoods protect the cancer cells and in doing so, block the ability of the immune system to eliminate the cancer. 

Our goal is to find how and why the cells are grouped in different ways and to figure out their effects on the immune response to cancer so that we can develop strategies to make immunotherapy work better for patients, particularly those who have very few effective treatment options.

Two images displaying the same pancreatic ductal adenocarcinoma tissue. On the left is a standard H&E stain, showing the cell in pink. On the right is the advanced IHC stain, showing cells in purple, yellow, brown, and teal.
The image on the left shows the standard "H&E" (Hematoxylin and Eosin) stain that’s frequently used by pathologists for cancer diagnosis. The image on the right shows the advanced IHC stain developed by Beatty’s team; this view shows regulatory T cells in purple, CD8 T cells in yellow, CD68 macrophages in brown and CK19 tumor cells in teal. Both images are from the same pancreatic ductal adenocarcinoma tissue.

Tell us about the technology your team adapted to study the tumor microenvironment. 

In my laboratory, we’ve worked to combine advances in machine learning with a very old-school technology that leverages standard techniques that pathologists have used for decades called immunohistochemistry (IHC). Pathologists routinely stain biopsy tissue samples with one or two different colors and look at them under the microscope to determine the cancer type and where the cancerous tissue stops and ends. 

We’ve started to layer more colors on the same tissue to identify other types of cells in the tumor tissue. It sounds simple but isn’t always so easy to do. We’re using a specialized machine that we’ve programmed to automatically paint certain cells different colors. Then, we use AI and machine learning to analyze the samples and determine patterns that the naked eye wouldn’t be able to see. 

As interest in the tumor microenvironment has grown, other technologies have also been developed that can provide a much more high-dimensional view than what we routinely do. But our approach has been designed to create a cost- and time-effective way to reliably analyze a high volume of samples. We can run 60 samples a day, with room to grow, whereas some of the other approaches can take weeks or longer before results are available. 

What have you learned with this technique? 

Our team recently published two studies using this IHC technique: In Cell Reports Medicine, we looked at the different neighborhoods of cells within the pancreatic cancer tumor microenvironment to better understand how the different cells influence each other. With our IHC technique, we found that T cells drive the presence of microbes in tumors, which then recruit other immune cells, like B cells and myeloid cells, to establish a microenvironment that is more vulnerable to attack by the immune system. This finding confirmed the importance of T cells as master orchestrators of tumor destruction and emphasized the importance of finding ways to improve the penetration of T cells into tumors.

In another recent study in Gastroenterology, led by Max Miller Wattenberg, MD, an instructor of Hematology-Oncology, we used the same technique to look at biopsies taken from patients with pancreatic cancer enrolled in a clinical trial and who had received chemotherapy before surgery. We found that we could predict which patients’ cancers would respond favorably to chemotherapy based on the immune cell neighborhoods and their proximity to the cancer cell neighborhoods within the tumor microenvironment. This finding confirmed the importance of the tumor microenvironment in directing outcomes for patients and opens new areas of study to understand why only some tumors have the right neighborhoods and others do not.

How could this research help improve immunotherapy response for cancer patients? 

By finding patterns from these maps of cell locations within tumors, we hope to provide a roadmap to overcome the barriers that prevent immunotherapy from penetrating throughout the tumor microenvironment and attacking cancer cells. 

We are also looking forward to deploying this technique in other clinical trials, including an upcoming study led by Kim Reiss, MD, an associate professor of Hematology-Oncology, that will test an exciting immunotherapy approach for patients with pancreatic cancer. For this study, we will use our IHC technology to predict which patients’ cancers will respond to the drugs in the study. Ultimately, the goal is to figure out which patients are most likely to see their cancers respond to therapy. With this knowledge, we can then better match patients to the drugs that are most likely to work for the specific makeup of their tumor.

Conversely, we will also look for new patterns in the tumor microenvironment where the drugs do not work to figure out the next barriers that we will need to overcome to make immunotherapy more successful. Our findings so far provide hope that we are on the right track to decoding the intricacies that make these tumors so hard to treat.

Overall, this work represents one piece of the larger puzzle to improving immunotherapy for cancer patients. Importantly, our success is reliant on collaborations and forging new partnerships with clinical and translational research colleagues. Together, we hope to improve outcomes for patients who have yet to truly benefit from advances in using the immune system to treat cancer.


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