Researchers in Singapore unveil a breakthrough RNA strategy that simultaneously silences KRAS mutations and activates immune defenses in hard-to-treat tumors.
As precision oncology moves deeper into RNA-based and immune-modulating therapies, clinical laboratories are finding themselves at the center of a rapidly evolving frontier. New research from Singapore signals just how quickly that future is arriving. In two complementary studies, scientists at the Yong Loo Lin School of Medicine, National University of Singapore (NUS Medicine), unveiled a dual-action RNA strategy that targets KRAS—one of cancer’s most stubborn and historically “undruggable” genes—while simultaneously jump-starting the immune system to recognize and attack tumors.
For lab leaders, the findings hint at a coming era in which molecular diagnostics, immune-response markers, and vesicle-based delivery technologies converge in routine care.
Researchers from NUS Medicine, together with collaborators from Nanyang Technological University (NTU), A*STAR, and international partners, focused on KRAS because of its prevalence and difficulty to treat. KRAS mutations lock the gene’s molecular switch in a permanent “on” state, driving constant cell growth and helping tumors hide from immune detection. These mutations appear in more than 90% of pancreatic cancers and are also common in lung and colorectal malignancies. Traditional drug approaches have faltered because the KRAS protein binds its signaling molecules too tightly and lacks accessible pockets for small-molecule inhibitors.
A Dual RNA Strategy to Break KRAS Resistance
To get around these challenges, the team paired two RNA tools: antisense oligonucleotides (ASOs) to silence mutant KRAS and an immunomodulatory RNA (immRNA) to activate RIG-I, an innate immune pathway usually triggered by viral infections. Turning on RIG-I sends an antiviral-like alarm through the cell, prompting immune activation that can help unmask tumor cells. Both RNA agents were delivered using red blood cell–derived extracellular vesicles (RBCEVs), natural carriers that can transport nucleic acid drugs safely and efficiently into tumor tissue.
The first study, published in Theranostics, demonstrated that this ASO–immRNA combination effectively killed KRAS-driven cancer cells in lung, colorectal, and pancreatic models. The therapy blocked oncogenic KRAS activity while converting “cold” tumors—those typically invisible to immune attack—into “hot” tumors that attract immune cells. In laboratory models, the approach reduced tumor burden, improved survival, and spared healthy cells.
Preclinical Progress in Pancreatic Cancer
The second study, appearing in the Journal of Controlled Release, advanced the platform for pancreatic ductal adenocarcinoma (PDAC). PDAC is one of the deadliest human cancers, with a five-year survival rate around 10%. It often spreads throughout the peritoneal cavity, leaving patients with few effective treatment options.
In preclinical models of PDAC with peritoneal metastasis, the dual-RNA therapy markedly suppressed tumor growth, restricted abdominal spread, and extended survival. Importantly, safety testing showed no observable toxicity. Investigators say this strengthens the case for eventual clinical trials and highlights the broader versatility of extracellular vesicles as delivery vehicles across multiple RNA-based modalities.
Associate professor Minh Le, Department of Pharmacology, and Institute for Digital Medicine (WisDM), NUS Medicine noted, “Our EV platform precisely targets mutants, sparing healthy tissue, and synergizes KRAS knockdown with RIG-I activation to unleash interferons, immunogenic cell death, and T-cell memory—halting tumor growth and extending survival without toxicity.” (Photo credit: NUS)
For clinical laboratories, these advances signal more than a scientific milestone—they point to a near future in which labs may need to measure KRAS knockdown, track immune-activation signatures, quantify extracellular vesicle uptake, and support increasingly complex molecular workflows. While the therapy remains in the preclinical phase, the implications are clear: RNA-based therapeutics and EV-mediated delivery are moving quickly toward clinical reality, and laboratories will play a central role in bringing those innovations to patients.
This article was created with the assistance of Generative AI and has undergone editorial review before publishing.
Congress included lab relief in its latest funding bill, signaling growing awareness of PAMA’s impact. Here’s what lab leaders should do next.
During a Nov. 12 webinar hosted by Dark Daily, the discussion centered on why a small laboratory provision ended up in the massive continuing resolution (CR) to reopen the federal government, and what it signals about congressional awareness of the urgent issues surrounding PAMA.
Panelists Susan Van Meter, president, American Clinical Laboratory Association (ACLA) and Jay Weiss, PhD, president and co-owner, Allermetrix explained that the inclusion reflects years of coordinated, persistent advocacy by ACLA, NILA, laboratorians, and industry partners, who have made the case that impending cuts on January 1 would be devastating. Although Congress has repeatedly delayed PAMA cuts and reporting requirements—five and six times respectively—stakeholders emphasized that this should never be taken for granted.
Both speakers highlighted that many lawmakers were initially unaware that laboratories were facing up to 15% reductions on roughly 800 codes beginning in 2026, with ripple effects extending to private insurers because their rates are indexed to Medicare. As Congress searched for a bipartisan path to reopen the government, advocates successfully argued that a short-term delay of the PAMA “cliff” needed to be included in the CR. That delay now runs only until January 30.
“We have been working very deliberately around the clock since the beginning of this year to encourage Congress to move forward legislation that would reform PAMA and address the reductions. It’s been a complicated legislative year,” said Susan Van Meter, president, ACLA. (Photo credit: ACLA)
RESULTS Act
The panel then outlined the RESULTS Act, a bipartisan proposal intended to fix structural flaws in PAMA. Its main provisions include:
Replacing lab-reported commercial rates with data from a nonprofit claims database for widely available tests
Requiring labs to report only for low-volume codes (100 or fewer labs)
Reducing reporting burden significantly
Eliminating three years of up to 15% cuts
Capping future reductions at 5% annually
Ensuring Medicare rates are based on actual, adjudicated claims instead of outdated 2016 data.
Speakers noted that commercial plans already submit claims data to independent entities such as FAIR Health, so the framework is neither novel nor untested. Claims would be reported only after full adjudication, typically six months post-submission.
On timing, the panel acknowledged that passing the RESULTS Act before Jan. 30 is ambitious, especially after weeks of congressional inaction during the shutdown fight. Still, they characterized the inclusion of lab relief in the CR as a strong signal that lawmakers view PAMA reform as legitimate and urgent. A Congressional Budget Office score has not yet been issued for the RESULTS Act, though preliminary scoring requests have been made.
What Lab Leaders Can Do
Both speakers urged labs to prepare dual budgets—one assuming RESULTS Act passage, another reflecting full PAMA implementation. Contingency planning may include staff reductions, automation investments, operational cuts, and pursuing non-payer revenue streams. They also emphasized that PAMA cuts would affect far more than Medicare because commercial payers peg rates to the CLFS.
Finally, both panelists stressed that advocacy in the next few weeks is critical. They encouraged labs to contact lawmakers through email, phone, or in-person visits—and especially to invite members of Congress to tour their laboratories. The StopLabCuts.org campaign has already generated 150,000 messages to Capitol Hill; speakers urged the audience to double that volume. With strong bipartisan sponsorship in both chambers, they said the RESULTS Act has momentum—but is not guaranteed without significant grassroots pressure.
If you missed the live webinar, view it on demand, here.
This article was created with the assistance of Generative AI and has undergone editorial review before publishing.
A new analysis shows why models fall short in practice, how liability and equity issues slow adoption, and what lab leaders should consider as AI becomes a growing part of diagnostic workflows.
Artificial intelligence (AI) has made notable advances in medical imaging, but radiologists are not being displaced. For laboratory and diagnostic leaders, a recent analysis in Works in Progress highlights why AI has not replaced human expertise in radiology—and what this means for managing technology adoption in labs and hospitals.
In 2016, AI pioneer Geoffrey Hinton declared that “people should stop training radiologists now.” Since then, more than 700 FDA-cleared radiology AI models have entered the market, covering everything from stroke detection to lung cancer screening.
Companies such as Annalise.ai, Lunit, Aidoc, and Qure.ai offer tools that can identify dozens of diseases across modalities, reorder worklists, or generate structured draft reports. “On paper, radiology looks like the perfect target for automation,” the article noted, citing its reliance on digital images, pattern recognition, and quantitative benchmarks. Yet demand for radiologists has never been higher. In 2025, US residency programs offered a record 1,208 positions, and vacancy rates remain high as well.
Why Hasn’t AI Taken Over?
For leaders overseeing diagnostic services, three key elements are why AI has not replaced radiologists.
First, models struggle in real-world deployment. “Performance can drop by as much as 20 percentage points” when systems trained on narrow datasets are applied across different scanners, imaging protocols, or patient populations, the article explained. What works in a benchmark test may falter in a hospital with diverse workflows.
Second, liability and regulatory hurdles remain high. Assistive models that require physician review face fewer barriers, but autonomous systems must self-abort on poor image quality, identify unfamiliar equipment, and withstand rigorous scrutiny. Insurers have also drawn hard lines: one malpractice policy states that “coverage applies solely to interpretations reviewed and authenticated by a licensed physician; no indemnity is afforded for diagnoses generated autonomously by software.” Another bluntly imposes an “Absolute AI Exclusion.” For labs, this underscores the importance of risk management before deploying AI tools.
Photo credit: “Artificial Intelligence – Resembling Human Brain” by deepakiqlect is licensed under CC BY 2.0. To view a copy of this license, visit https://creativecommons.org/licenses/by/2.0/?ref=openverse.
Photo credit: “Cancer” by davis.steve32 is licensed under CC BY 2.0. To view a copy of this license, visit https://creativecommons.org/licenses/by/2.0/?ref=openverse.
Third, radiologists do much more than read scans. “Human radiologists spend a minority of their time on diagnostics and the majority on other activities, like talking to patients and fellow clinicians,” the commentary pointed out. Oversight of imaging protocols, interdisciplinary consultations, and patient communication all fall outside the reach of algorithms. Even as AI improves, demand for imaging may increase rather than decrease—a version of the Jevons paradox where greater efficiency leads to higher use. “The better the machines, the busier radiologists have become,” the article observed.
For laboratory leaders, the takeaway is not to fear replacement but to prepare for integration. AI tools are proving valuable in triaging urgent cases, flagging abnormalities, and drafting reports, but they remain narrow in scope—stroke, lung cancer, and breast lesions account for about 60% of models, yet represent only a fraction of total imaging work. As the article concluded, “Models can lift productivity, but their implementation depends on behavior, institutions and incentives.”
The challenge for labs is to create environments where AI augments human expertise rather than attempts to replace it. That means aligning technology adoption with clinical needs, providing training for staff, and working with insurers and regulators to ensure coverage and compliance.
For now, radiologists and the labs that support them are not going away. They are adapting, and AI will be a partner in that evolution.
New program draws bipartisan criticism and concern from patients and doctors.
Shrewd labs will keep an eye on the latest Centers for Medicare & Medicaid Services (CMS) prior authorization pilot that leans on artificial intelligence (AI) to determine treatment options for Medicare patients. While the Wasteful and Inappropriate Service Reduction Model pilot (WISeR) doesn’t directly mention lab tests, staying on the pulse of this growing trend will keep labs thinking ahead on how to minimize impact on bottom line, paperwork, and workflows when these pilots infiltrate lab testing.
An article from POLITICO reported that CMS will start a pilot version of the program as early as January 2026 in six states including Ohio, Texas, Oklahoma, Ariz., N.J., and Wash. Private AI companies will assist and focus on “services that have been vulnerable to fraud, waste and abuse in the past,” the article noted. The voluntary model is slated to span six years through December 31, 2031, according to the Centers for Disease Control and Prevention (CDC).
Among the types of procedures encumbered by the pilot program are knee arthroscopy for osteoarthritis, skin and tissue substitutions, and electrical nerve stimulator implants, CMS noted. All outpatient and emergency services would currently be excluded, they added, as well as “services that would pose a substantial risk to patients if substantially delayed.”
“All recommendations for non-payment will be determined by appropriately licensed clinicians who will apply standardized, transparent, and evidence-based procedures to their review,” CMS added.
The premise of the pilot is to eliminate wasteful spending, with CMS citing 25% of US healthcare spending falling in this category. “According to the Medicare Payment Advisory Commission Medicare spent up to $5.8 billion in 2022 on unnecessary or inappropriate services with little to no clinical benefit,” their website noted.
A Sour Reception
The pilot program is receiving a less-than-warm welcome from both parties—doctors, and patients alike, Politico noted. “It’s been referred to as the AI death panel. You get more money if you’re that AI tech company if you deny more claims. That is going to lead to people getting hurt,” Greg Landsman (D-Ohio) said during the committee hearing.
Landsman noted in the article from POLITICO that a bipartisan desire to put a halt to the program exists among growing concerns about patient harm coming from the program. Landsman “called for the program to be shut down until an independent review board could be erected to review the liability questions and ensure the AI prior authorization pilot doesn’t harm patients.”
“I’m concerned that this AI model will result in denials of lifesaving care and incentivize companies to restrict care,” Frank Pallone (D-N.J.) and House Energy and Commerce Committee ranking member said at the subcommittee meeting on the use of AI in health care held on Sept. 3.
“We have pretty good evidence that prior authorization as a process itself is fraught, adding that AI’s ability to improve the process for patients remains unproven,” Michelle Mello, Stanford University health law professor and witness at the hearing, said.
Looking Ahead
The involvement of AI in healthcare will only continue, and learning what aspects positively impact healthcare versus cause damage will continue to evolve.
Worth noting, there are already two unrelated lawsuits, against UnitedHealthcare and Cigna, that challenge the safety of AI use to deny patient care, POLITICO noted in the article.
Laboratory leaders should keep their eyes open and their ears to the ground on not only the pilot but all AI healthcare trends.
AI tool finds possible bird flu infections overlooked in emergency departments.
Researchers at the University of Maryland School of Medicine (UMSOM) have demonstrated a powerful new use of artificial intelligence (AI) to detect potential bird flu infections that may otherwise go unnoticed in hospital emergency departments. Their findings, published in Clinical Infectious Diseases, show that generative AI can rapidly scan electronic medical records for high-risk exposures to H5N1 avian influenza.
A press release explained that the team applied a large language model (LLM) to 13,494 emergency visits across the University of Maryland Medical System in 2024. All patients had acute respiratory symptoms—such as cough, fever, or congestion—or conjunctivitis, which can mirror early signs of bird flu. The model flagged 76 notes that mentioned possible high-risk exposures, such as working as a butcher or on a farm with livestock.
Often these exposures were “incidentally” documented—for example, as part of a patient’s occupation—rather than because clinicians suspected avian influenza. After review, 14 patients were confirmed to have had recent, relevant exposure to poultry, wild birds, or livestock. None were specifically tested for H5N1, but the model was able to surface “needle in a haystack” cases buried among thousands of routine respiratory illnesses.
“This study shows how generative AI can fill a critical gap in our public health infrastructure by detecting high-risk patients that would otherwise go unnoticed,” said corresponding author Katherine E. Goodman, PhD, JD, assistant professor of Epidemiology & Public Health at UMSOM.
“With H5N1 continuing to circulate in U.S. animals, our biggest danger nationwide is that we don’t know what we don’t know,” noted Katherine E. Goodman, PhD, JD, assistant professor of Epidemiology & Public Health at UMSOM. (Photo credit: UMSOM)
Goodman emphasized that without consistent tracking of potential exposures, infections may be slipping through the cracks.
“Because we are not tracking how many symptomatic patients have potential bird flu exposures, and how many of those patients are being tested, infections could be going undetected,” she said. “It’s vital for healthcare systems to monitor for potential human exposure and to act quickly on that information.”
Bird Flu’s Spread in U.S. Livestock and Birds
The urgency stems from the current spread of H5N1 among U.S. livestock and birds. Since early 2024, the virus has been detected in more than 1,075 dairy herds across 17 states, with over 175 million poultry and wild birds testing positive. Human cases remain rare—70 confirmed with one fatality as of mid-2025, according to the CDC—but experts caution that many more may have gone undetected due to limited testing. The emergence of strains capable of human-to-human spread remains a concern.
Efficiency and scalability were key strengths of the AI approach. “The AI review required only 26 minutes of human time and cost just 3 cents per patient note, demonstrating high scalability and efficiency,” said co-author Anthony Harris, MD, MPH, acting chair of Epidemiology & Public Health at UMSOM. “This method has the potential to create a national network of clinical sentinel sites for emerging infectious disease surveillance.”
The LLM used, GPT-4 Turbo, proved highly accurate when tested on 10,000 historical emergency visits from 2022–2023, showing a 90% positive predictive value and a 98% negative predictive value. Still, researchers noted the model occasionally flagged lower-risk exposures, such as to dogs, underscoring the need for human review.
Next Steps for Real-Time Monitoring
Looking ahead, the team hopes to integrate the tool directly into electronic health records for real-time alerts. This would allow clinicians to ask about exposures, order targeted tests, and isolate patients if needed. Currently, CDC surveillance depends on mandated lab reports, with no system to track whether clinicians are documenting animal exposure in symptomatic patients.
UMSOM dean Mark T. Gladwin, MD, called the work a glimpse of what’s possible with big data and AI. “We are at the forefront of a disruptive but incredibly promising revolution around big data and artificial intelligence,” he said. “As this study demonstrates, [we] can use AI and big data to identify early signals of emerging infectious diseases like bird flu to enable us to take action sooner to test for these diseases and keep them from spreading.”
The project relied on the University of Maryland Institute for Health Computing (UM-IHC), a collaboration established in 2022 to combine computational, clinical, and data resources across the state’s medical and academic institutions.
