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Unlocking Insights Into Pancreatic Cancer And Cachexia

In an interview with Targeted Oncology, Sanjay Goel, MD, MS, discussed the mystery of cachexia in pancreatic cancer and why it remains a challenging symptom to target.

As 1 of the most lethal cancers, pancreatic cancer remains a disease warranting research and clinical studies. While the lack of targetable mutations and tumor microenvironment make treating pancreatic cancer difficult, 1 of the most challenging symptoms is unrelated to the tumor: cachexia, or more colloquially, wasting syndrome.

Cachexia leads to significant muscle mass loss and contributes to poor outcomes in pancreatic cancer treatment.

"It's characterized by significant losses in muscle mass, and it's not purely a nutritional deficiency. There are people who believe that overall cachexia is responsible for the deaths of maybe even up to half of all the patients [with pancreatic cancer]," Sanjay Goel, MD, MS told Targeted OncologyTM, in an interview.

In the interview, Goel, medical oncologist, director of phase 1/investigational therapeutics, and professor of medicine at Rutgers Robert Wood Johnson Medical School, discussed a study that aimed to identify the mechanism of cachexia in pancreatic cancer and what future research in this area holds.

Targeted Oncology: What is the current treatment landscape in pancreatic cancer?

Goel: Pancreas cancer is probably 1 of the most lethal cancers. Even those who are diagnosed at an early stage unfortunately end up with a very high risk of relapse. Ultimately, the majority of patients end up with an advanced cancer, and they succumb to the disease, typically in less than a year.

In terms of the current treatment landscape, for patients with advanced pancreatic cancer, it is a combination of chemotherapy cocktails, and there are 2 commonly used ones. One is called FOLFIRINOX, which is a combination of 5-fluouracil, oxaliplatin, and irinotecan. This is generally reserved for those with a better performance status. The other common combination is called gemcitabine and nab-paclitaxel. This is used in patients who may not be able to tolerate FOLFIRINOX. Typically, patients would get 1 of these in the first-line and then go on to the other in the second-line.

There is another option with liposomal irinotecan [Onivyde], but generally that's not considered very active. For the rare patient, patients who have a mutation in the BRCA gene, there is a drug called olaparib [Lynparza] that can be used. There is always the option of clinical trials if the patient is still in a good physical condition to get onto a trial after having gone through these therapies.

What makes pancreatic cancer so difficult to treat?

We have not discovered or found any mutations in this cancer that have so far been considered to be druggable, unlike non–small cell lung cancer or even gastric cancer, another [gastrointestinal]-related cancer, closer to the pancreas, except for BRCA, which, as I said, a small fraction [of patients] do have it and we can use targeted therapies like olaparib. I think the other major issue with pancreas cancer is [that] most of the time by the time it's discovered, or by the time a patient has symptoms and presents to the healthcare system, [the cancer] is already advanced. That makes treatment extremely challenging.

Another important issue is this extensive fibrosis around the pancreas cancer itself, and the tumor microenvironment is not very friendly for the infiltration of immune cells. Therefore, despite years of attempts, immune therapy, which has otherwise played quite a significant role in improving the outcomes for several other cancer types, has been found to be a major challenge in the treatment of pancreas cancer.

A vast majority of [patients with] pancreas cancer actually end up with a clinical condition called cachexia, which is an active, catabolic process, essentially. To put it simply, the body starts eating itself. It is characterized by significant losses in muscle mass, and it is not purely a nutritional deficiency. There are people who believe that overall cachexia is responsible for the deaths of maybe even up to half of all the patients [with pancreatic cancer].

What was the goal of this study?

Caris [Life Sciences] is a bio diagnostics company, and they have an excellent platform of running next-[generation] sequencing. Most of it until now has been done on tumor tissues. Several years ago, they launched what's called the precision oncology alliance [POA] to collaborate with academic physicians to really try and use the extensive tissue samples that they have. We can try to leverage or exploit the vast database to answer some critical clinical questions, and it's divided into several different groups, and I am lucky to be part of the gastrointestinal group.

The Caris POA had several presentations at [the 2024 ASCO Gastrointestinal Cancers Symposium (ASCO GI). One of the ideas of this particular abstract was to study cachexia in greater detail in patients with pancreas cancer and use some of the knowledge that was gained from looking at loss of muscle mass in other conditions. For example, the myostatin/activin pathway has been actually extensively studied in neurological disorders, including patients with motor neuron disease, etc. The investigators led this project out of the University of Southern California, and they decided to see whether they could perform an in-depth study looking at the myostatin pathway in patients with pancreas cancer. That was the main idea.

Can you summarize your findings?

I think the novelty in this approach was that there is a large database. Because the analysis had already been done on 1000s of patient samples, essentially what was required was to go into data mining, overall, there were about 9600 samples that were available at Caris with pancreatic ductal adenocarcinoma, and they all had undergone whole transcriptome sequencing and next-gen DNA sequencing that is fairly standard for all the specimens. Then, based on prior research, there were certain genes that are known to be activators of the myostatin/activin pathway and certain that constrain repressors. Focusing on these specific genes, they derived something called a cachexia gene score. They looked at activators on 1 side and repressors on the other side and then divided the patient into 4 quartiles. What Caris has done is also contracted with insurance companies and other sources of data to derive overall outcomes. This is from basically insurance claims, so that one can gauge an understanding of the overall survival of these patients.

Then [we] looked at a few immune markers, including what is called PD-L1, which is a biomarker for certain immunotherapy drugs, mutational burden, [and] microsatellite status. There were some differences found in the highest and lowest quartile. But in terms of the practical application of that, it is hard to say because immunotherapy is not really approved in pancreas cancer yet, expect in the few patients who are [microsatellite instability-high]. On the cachexia score, there was a relationship to the amount of inflammation that was found in a tumor setting. This knowledge could actually be used to maybe target future therapy. Similarly, they also looked at something called immune-related gene expression. Several of them were found to be higher in the highest quartile of the cachexia score. So again, something that could prove useful in the future.

Coming to the survival, what was interesting was, if we look at 2 specific proteins called SMAD3 and SMAD7, they are both part of the SMAD family. SMAD3 is considered an activator of the cachexia pathway. As expected, what was found is that those patients who had a highest SMAD3 score actually had a lower survival. On the other hand, SMAD7 is considered a repressor of cachexia, and patients who had a highest SMAD7 score had a better survival. I think these would be the main findings. Apart from that, they also looked at correlation between the cachexia score and lipid metabolizing genes.

In summary, I can say that what was finally found from these trials is expression of SMAD3 and SMAD7, which are linked with cachexia, have an influence on survival. The other thing is that there is a link between cachexia genes and the lipid metabolizing genes and inflammatory markers. In terms of clinical translation though, at least on 1 front, I would say that the results have been a little disappointing. There has been a trial looking at drug from [Eli Lilly and Company and Loxo Oncology, Inc.] that could inhibit myostatin. Interestingly, even though in a clinical trial in patients with neurological disorders, it seemed to have a positive outcome; when it was looked at patients with pancreas cancer, there was no difference in survival. That approach, at least in that particular drug, was not successful. But I think where these data can take us further and maybe consider inhibitors of SMAD3 or activators of SMAD7 to try a different approach to counteract cachexia.

The other way maybe to further dig into the data to look at the relationship between cachexia and inflammation, or cachexia and the lipid pathway, and then go further from there and see whether any of those could be used. Interestingly, more than 20 years ago, it was proposed that if we can figure out a way to counteract or block cachexia, that may be way better than developing the so-called chemotherapy cocktails.

Are there any takeaways for the community oncologist?

One thing that community oncologists could take away is that they will know that there are people working on counteracting cachexia as a way improve outcomes. I think the other good thing is their awareness that such a program exists within the Caris POA too and that we can use the vast knowledge base that we have to come up with novel and innovative ideas to gain further knowledge. But in terms of immediately managing a patient with pancreas cancer, I'm not sure there's a direct application yet. But I think it's always good to know that it acts as an important entity. Anyways, advancing knowledge is good, and maybe the interaction with patients could be different.

A New AI-based Risk Prediction System Could Help Catch Deadly Pancreatic Cancer Cases Earlier

A new AI system could help detect the most common form of pancreatic cancer, new research has found.

Pancreatic cancer is a difficult disease to detect. The pancreas itself is hidden by other organs in the abdomen, making it tough to spot tumors during tests. Patients also rarely experience symptoms in the early stages, meaning that the majority of cases are diagnosed at an advanced stage—once it's already spread to other parts of the body. This makes it much harder to cure.

As a result, it's essential to try to catch pancreatic cancer at the earliest stage possible. A team of researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) worked with Limor Appelbaum, a staff scientist in the department of radiation oncology at the Beth Israel Deaconess Medical Center in Boston, to develop an AI system that predicts a patient's likelihood of developing pancreatic ductal adenocarcinoma (PDAC), the most common form of the cancer. 

The system outperformed current diagnostic standards and could someday be used in a clinical setting to identify patients who could benefit from early screening or testing, helping catch the disease earlier and save lives. The research was published in the journal eBioMedicine last month.

The researchers' goal was a model capable of predicting a patient's risk of being diagnosed with PDAC in the next six to 18 months, making early-stage detection and cure more likely. To develop it, they examined existing electronic health records.

The resulting system, known as PRISM, consists of two AI models. The first uses artificial neural networks to spot patterns in the data, which include patients' ages, medical history, and lab results. It then calculates a risk score for an individual patient. The second AI model was fed the same data to generate a score, but used a simpler algorithm.

The researchers fed the two models anonymized data from 6 million electronic health records, 35,387 of which were PDAC cases, from 55 health-care organizations in the US.

The team used the models to evaluate patients' PDAC risk every 90 days until there was no more sufficient data or or the patient was diagnosed with pancreatic cancer. They followed up on all enrolled patients from six months after their first risk evaluation until 18 months after their last risk evaluation to see if they were diagnosed with PDAC in that time. 

Among people who developed pancreatic cancer, the neural network identified 35% of them as high risk six to 18 months before their diagnosis, which the authors say is a significant improvement over current screening systems. For most of the general population, there is no recommended screening routine for pancreatic cancer the way there is for breast or colon cancer, and the current standard screening criteria catch around 10% of cases.

Given how important it is to detect the disease at the earliest stage possible, the system looks promising, says Michael Goggins, a professor of pathology and pancreatic cancer specialist at Johns Hopkins University School of Medicine, who was not involved in the project.

"It would be anticipated that such a model would improve the current landscape," he says. "But it really needs to be very early to make the biggest impact."

It's possible that some people could have developed advanced pancreatic cancer within the six-to-18-month window, meaning it could be too late to treat them effectively by the time they've received a risk assessment, he says.

While this particular study is retrospective, looking at existing data and tasking the models with making hypothetical predictions, the team has started work on a study that will gather gather data on existing patients, compute their risk factors, and wait to see how accurate the predictions are, says Martin Rinard, a professor of electrical engineering and computer science at MIT, who worked on the project.

In the past, other AI models built with data from a particular hospital sometimes didn't work as well when provided with data from another hospital, he points out. That could be down to all kinds of reasons, such as different populations, procedures, and practices.

"Because we have what is coming close to data from essentially a very significant fraction of the entire population of the United States, we have hopes that the model will work better across organizations and not be tied to a specific organization," he says. "And because we're working with so many organizations, it also gives us a bigger training set."

In the future, PRISM could be deployed in two ways, says Appelbaum.

First, it could help single out patients for pancreatic cancer testing. Second, it could offer a broader type of screening, prompting people without symptoms to take a blood or saliva test that may indicate whether they need further testing.

"There are tens of thousands of these models for different cancers out there, but most of them are stuck in the literature," she adds. "I think we have the pathway to take them to clinical practice, which is why I started all of this—so that we can actually get it to people and detect cancer early. It has the potential to save many, many lives."

University Of Oklahoma Spots Pancreatic Cancer With Multispectral Optoacoustics

01 Feb 2024

A $3 million grant will fund a study combining imaging technique with new contrast agent.

A research study at the University of Oklahoma (OU) will investigate how optoacoustic techniques can play a part in detection of pancreatic cancer.

Backed by $3 million from the National Cancer Institute, the study is said to be the first of its specific kind in the world.

"Pancreatic cancer is one of the hardest cancers to cure because it is difficult to detect cancer cells at the microscopic level," said Ajay Jain from OU College of Medicine.

"Because there are usually no early symptoms it is typically not diagnosed until after it has spread, and outcomes are very poor, about a 9-percent overall chance of survival. Surgery and chemotherapy offer the patients the best chance, but for surgery to work, we have to remove all the cancer, and that is difficult to do."

The study will combine multispectral optoacoustic tomography (MSOT) and a newly formulated contrast agent, with the goal of assessing if the combination can detect pancreatic cancer cells down to 200 microns in size.

Optoacoustic, or photoacoustic, imaging has been an attractive technique in cancer diagnosis ever since the method began its clinical translation after the pioneering work of Lihong Wang, now at Caltech.

Early clinical trials showed that it was a potentially versatile technique in cancer detection, able to distinguish malignant from healthy tissues in breast cancer potentially even without a contrast agent, and detecting cervical cancer with high accuracy, thanks to its ability to map hemoglobin in blood vessels.

Developments since then have seen the same principles applied to a range of bioimaging scenarios, with photoacoustic microscopy (PAM) breaking speed and resolution barriers in brain imaging.

Real-time identification of cancer cells

The new study builds on research at Oklahoma's Stephenson Cancer Center into clinical translation of its multispectral optoacoustic approach, in which multiwavelength illumination of tissue allows for the mapping of multiple chromophores.

In 2020 OU reviewed the progress of MSOT translation towards clinical use for the imaging of breast, prostate, bowel and skin cancers and concluded that, alongside developments in the infrared sources and ultrasound detectors employed, the nature of the optoacoustic dyes added to the tissues as exogenous contrast agents for imaging was a key challenge.

OU's Lacey McNally has now devised a contrast agent intended to be unique to pancreatic cancer cells. When this agent is delivered intravenously it can differentiate pancreatic cancer cells from other cells because the environment of pancreatic cancer is acidic. When the contrast agent encounters that acidity, its dye essentially "turns on" and becomes optoacoustically active.

"This is a hybrid approach that accomplishes what a CT scan cannot," McNally said. "Pancreatic cancer often creates tentacles that spread out beyond the primary tumor. Currently, there is no way for the surgeon to know where they are. But if the surgery team can use this MSOT approach in the operating room, it can tell them in real time where the cancer has metastasized so they can remove it."

Clinical trials underway at OU are applying MSOT to the study of breast cancer, and the $3 million grant will allow McNally and team to pursue several other clinical studies as well.

"This type of research collaboration between a translational scientist and a surgeon is extremely unusual," commented McNally. "The scientific and medical communities have made great strides in treating some types of cancer, but pancreatic cancer patients have the poorest survival. The outcome of this research could fundamentally change people's lives."

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