
Can a Blood Test Tell if Pancreatic Cancer Therapy Works?
A new blood test that uses artificial intelligence (AI) may offer a faster, more accurate way to determine whether pancreatic cancer treatment is working. Known as ARTEMIS-DELFI, the test detects DNA fragments shed by pancreatic tumors that circulate in blood.
In two large clinical trials of immunotherapy for metastatic pancreatic cancer, researchers successfully applied the test to identify patients with therapeutic responses. Two months after starting treatment, ARTEMIS-DELFI was better at predicting outcomes than imaging or other existing clinical and molecular markers.
'Early determination of response to therapy is crucial for patients with pancreatic or other cancers, especially when treated with immunotherapy where we do not have good biomarkers of response,' senior author Victor Velculescu, MD, PhD, codirector of the Cancer Genetics and Epigenetics Program at Johns Hopkins Kimmel Cancer Center in Baltimore told Medscape Medical News.
'We want to know as quickly as we can if the therapy is helping the patient or not. If it is not working, we want to be able to switch to another therapy,' Velculescu added in a news release.
The study was published online in Science Advances .
Real-Time Monitoring of Therapeutic Response
Pancreatic cancer, one of the deadliest cancers, often goes diagnosed at an advanced stage, with limited treatment options and a dismal prognosis.
Traditional methods for assessing treatment response — such as CT imaging and the CA19-9 tumor marker — are often unreliable, especially during immunotherapy, where pseudo-progression can obscure outcomes.
To address this challenge, Velculescu and colleagues developed two advanced liquid biopsy techniques that leverage cell-free DNA (cfDNA) for real-time, noninvasive therapeutic monitoring — ARTEMIS-DELFI (AI-driven, tumor-independent genome-wide cfDNA fragmentation profiles and repeat landscapes) and WGMAF (tumor-informed whole-genome mutant allele fraction).
Both techniques were tested in participants in the phase 2 CheckPAC trial, which evaluated nivolumab with or without ipilimumab in combination with radiation in patients with refractory metastatic pancreatic cancer.
Researchers established strong correlations between cfDNA signals and patient outcomes. With WGMAF, molecular responders to treatment had significantly longer median progression-free survival (PFS: 157 days vs 51 days; hazard ratio [HR], 0.23; P = .0053) and overall survival (OS: 319 days vs 126 days; HR, 0.29; P = .011) than nonresponders.
With ARTEMIS-DELFI, responders also had significantly longer PFS (105 days vs 53 days; HR, 0.28; P =.0024) and OS (233 days vs 172 days; HR, 0.12; P < .0001) when than nonresponders.
The researchers validated ARTEMIS-DELFI in the PACTO trial, which assessed gemcitabine/nab-paclitaxel with or without tocilizumab for advanced pancreatic cancer. Once again, ARTEMIS-DELFI scores were significant predictors of survival ( P = .048).
Both WGMAF and ARTEMIS-DELFI outperformed standard imaging and molecular markers in predicting treatment outcomes within 2 months. However, ARTEMIS-DELFI was more effective and practical, as it relies solely on DNA fragments circulating in blood.
'The 'fast-fail' ARTEMIS-DELFI approach may be particularly useful in pancreatic cancer where changing therapies quickly could be helpful in patients who do not respond to the initial therapy,' lead study author Carolyn Hruban, PhD, postdoctoral researcher at the Dana-Farber Cancer Institute in Boston, said in a news release. 'It's simpler, likely less expensive, and more broadly applicable than using tumor samples.'
Limitations and Future Directions
The researchers noted their analysis drew from a relatively small sample size – 40 patients each in CheckPAC and PACTO — with fewer evaluable by WGMAF due to tumor biopsy constraints. Some liquid biopsy timepoints did not align exactly with imaging assessments. As with most pancreatic cancer studies, there were few responders, limiting the pool of patients to analyze. Additionally, cut points for determining molecular response were derived from quantiles of observed responses rather than predefined thresholds.
The researchers plan future prospective studies to determine whether ARTEMIS-DELFI can help clinicians more efficiently identify effective therapies and improve outcomes across various cancers.
Earlier this year, the researchers reported that a variation of the cell-free fragmentation monitoring approach called DELFI-tumor fraction (DELFI-TF) was helpful in assessing colon cancer therapy response.
Velculescu and colleagues have founded a company, DELFI Diagnostics, which has developed a fragmentomics monitoring test for research purposes and eventual use within clinical care.
Nice Validation, More Work Ahead
Commenting on this research for Medscape Medical News , Karyn A. Goodman, MD, MS, professor and vice chair of clinical research, Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York City, noted that 'the approach of using circulating tumor DNA to evaluate response to therapy is being evaluated for many tumor types, including pancreas cancer.'
'This study of the ARTEMIS-DELFI test is a nice validation of this approach using a different method of detecting tumor-related cfDNA that does not require a biopsy to establish the types of mutations in a particular tumor,' said Goodman, associate director of clinical research at The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York City.
Goodman noted that the test does not use AI to make treatment decisions but rather to help identify the features in the cfDNA that correlate with tumor burden and treatment response.
'If the ARTEMIS-DELFI test detected a lower level of tumor-related cfDNA, this correlated with a good response to therapy and appeared to be more accurate than typical imaging tests,' observed Goodman, who wasn't involved in the study.
Although emphasizing that further prospective studies are needed, Goodman called the results 'very exciting,' as they point to a future with more noninvasive options for monitoring treatment response in pancreatic cancer.
Hashtags

Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles


Bloomberg
22 minutes ago
- Bloomberg
Israel-Backed Gaza Aid Group Suspends Operations for Second Day
An Israel- and US-backed mechanism to distribute food in Gaza suspended operations for a second day following a series of deadly incidents near its sites that drew international criticism. The Gaza Humanitarian Foundation, a Swiss-based nonprofit, launched in Gaza last week following a months-long Israeli blockade of the territory, and says it has handed out enough food staples for millions of meals. But the roll-out has been dogged by overcrowding and at least one incident in which Israeli forces, citing a security threat, fired toward Palestinians headed to a GHF aid center.


CNET
25 minutes ago
- CNET
Get Your Hands on These Anker Soundcore Open-Ear Earbuds While They're Down to Just $53
There are a lot of options for good running earbuds out there, so finding the one that fits you can be tricky. One really good option is to go with an open-ear style though, as it can help you feel a bit cooler and more comfortable. Well, right now you get your hands on the Soundcore C30i by Anker for $53 as long as you use the on-page coupon to save yourself 25%. This is a great chance to grab these buds for less, and they've got loads of features that make them really good for all kinds of workouts. These open-ear earbuds are available in two colors, black and white. Their firm-shell design is lightweight and comfortable. It also has a secure clip-on design so they won't fall out. You can take them anywhere you go, as they're water-resistant. We haven't tested these specific buds, but they promise clear and high-quality audio, giving you the best listening experience. Hey, did you know? CNET Deals texts are free, easy and save you money. If you're looking for more listening options, check out our list of the best deals on earbuds and headphones going on right now. Why this deal matters Anker's Soundcore open earbuds are designed to fit comfortably in your ears with a tight grip so it doesn't fall out while exercising. The $53 earbuds are now significantly cheaper than Bose's Ultra Open Earbuds which run around $299.


Fast Company
25 minutes ago
- Fast Company
The real data revolution hasn't happened yet
The Gartner Hype Cycle is a valuable framework for understanding where an emerging technology stands on its journey into the mainstream. It helps chart public perception, from the 'Peak of Inflated Expectations' through the 'Trough of Disillusionment,' and eventually up the 'Slope of Enlightenment' toward the 'Plateau of Productivity.' In 2015, Gartner removed big data from the Hype Cycle. Analyst Betsy Burton explained that it was no longer considered an 'emerging technology' and 'has become prevalent in our lives.' She's right. In hindsight, it's remarkable how quickly enterprises recognized the value of their data and learned to use it for their business advantage. Big data moved from novelty to necessity at an impressive pace. Yet in some ways, I disagree with Gartner. Adoption has been widespread, but effectiveness is another matter. Do most enterprises truly have the tools and infrastructure to make the most of the data they hold? I don't believe they do. Which is why I also don't believe the true big data revolution has happened yet. But it's coming. Dissecting the Stack A key reason big data is seen as mature, even mundane, is that people often confuse software progress with overall readiness. The reality is more nuanced. Yes, the software is strong. We have robust platforms for managing, querying, and analyzing massive datasets. Many enterprises have assembled entire software stacks that work well. But that software still needs hardware to run on. And here lies the bottleneck. Most data-intensive workloads still rely on traditional central processing units (CPUs)—the same processors used for general IT tasks. This creates challenges. CPUs are expensive, energy hungry, and not particularly well suited to parallel processing. When a query needs to run across terabytes or even petabytes of data, engineers often divide the work into smaller tasks and process them sequentially. This method is inefficient and time-consuming. It also ends up requiring more total computation than a single large job would. Even though CPUs can run at high clock speeds, they simply don't have enough cores to efficiently handle complex queries at scale. As a result, hardware has limited the potential of big data. But now, that's starting to change with the rise of accelerated computing. Breaking the Bottleneck Accelerated computing refers to running workloads on specialized hardware designed to outperform CPUs. This could mean field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs) built for a specific task. More relevant to big data, though, are graphics processing units (GPUs). GPUs contain thousands of cores and are ideal for tasks that benefit from parallel processing. They can dramatically speed up large-scale data operations. Interestingly, GPU computing and big data emerged around the same time. Nvidia launched CUDA (compute unified device architecture) in 2006, enabling general-purpose computing on graphics hardware. Just two years earlier, Google's MapReduce paper laid the foundation for modern big data processing. Despite this parallel emergence, GPUs haven't become a standard part of enterprise data infrastructure. That's due to a mix of factors. For one, cloud-based access to GPUs was limited until relatively recently. When I started building GPU-accelerated software, SoftLayer—now absorbed into IBM Cloud—was the only real option. There was also a perception problem. Many believed GPU development was too complex and costly to justify, especially for general business needs. And for a long time, few ready-made tools existed to make it easier. Those barriers have largely fallen. Today, a rich ecosystem of software exists to support GPU-accelerated computing. CUDA tools have matured, benefiting from nearly two decades of continuous development. And renting a top-tier GPU, like Nvidia's A100, now costs as little as $1 per hour. With affordable access and a better software stack, we're finally seeing the pieces fall into place. The Real Big Data Revolution What's coming next will be transformative. Until now, most enterprises have been constrained by hardware limits. With GPU acceleration more accessible and a mature ecosystem of supporting tools, those constraints are finally lifting. The impact will vary by organization. But broadly, companies will gain the ability to run complex data operations across massive datasets, without needing to worry about processing time or cost. With faster, cheaper insights, businesses can make better decisions and act more quickly. The value of data will shift from how much is collected to how quickly it can be used. Accelerated computing will also enable experimentation. Freed from concerns about query latency or resource drain, enterprises can explore how their data might power generative AI, smarter applications, or entirely new user experiences. Gartner took big data off the Hype Cycle because it no longer seemed revolutionary. Accelerated computing is about to make it revolutionary again.