Why Leukemia Cells Escape Immune Attack—and How to Stop Them

Immunotherapy strategies that harness the body’s innate immune system have long focused on a central concept: cancer cells evade destruction by displaying “don’t eat me” signals that inhibit macrophages. Blocking these signals, most notably CD47, has been a major therapeutic goal. Yet in acute myeloid leukemia (AML), clinical responses to CD47-targeting therapies have been inconsistent, raising questions about whether additional immune evasion mechanisms are at play.

A new study published in Science by researchers at Mass General Brigham, Dana-Farber Cancer Institute, and the Broad Institute suggests that the field may have been overlooking a more dominant signal. The team identifies CD43, a heavily glycosylated surface protein, as a key regulator of macrophage evasion in AML.

Revisiting macrophage immune evasion

Macrophages are critical components of the innate immune system, capable of recognizing and engulfing tumor cells through phagocytosis. This process is regulated by a balance between pro-phagocytic “eat me” signals and inhibitory “don’t eat me” signals expressed on the surface of cancer cells.

Therapeutic efforts have largely focused on CD47, a well-characterized inhibitory signal that binds to SIRPα on macrophages to suppress phagocytosis. However, the limited success of CD47 inhibitors in AML has suggested that this pathway may not fully account for immune evasion in these cancers.

To systematically explore alternative mechanisms, the researchers performed a genome-scale loss-of-function screen in AML cell lines, turning off genes one by one and assessing their impact on macrophage recognition.

CD43 emerges as a dominant signal

The results were unexpected. While CD47 had only a modest effect, CD43 stood out as a major determinant of whether leukemia cells were engulfed by macrophages.

The study reveals that CD43 functions not simply as a surface marker, but as part of a broader protective structure. Specifically, its sialylated form creates a dense, glycosylation-based barrier that interferes with immune recognition.

As described by the authors, “Sialylated CD43 forms a glyco-immune barrier that restrains anti-leukemic immunity.”

This finding introduces a new conceptual framework for immune evasion in AML—one that emphasizes the role of glycosylation and surface architecture, rather than individual receptor–ligand interactions alone.

Explaining limits of current therapies

The identification of CD43 helps clarify why targeting CD47 alone has not produced the expected therapeutic outcomes in AML. If CD43-mediated shielding plays a dominant role, then blocking CD47 may be insufficient to restore effective macrophage activity.

The study suggests that immune evasion in AML is more complex than previously appreciated, involving multiple overlapping mechanisms that together suppress phagocytosis.

By uncovering this additional layer, the work highlights the need for combination strategies or alternative targets in macrophage-based immunotherapy.

A new therapeutic opportunity

From a translational perspective, CD43 represents a promising new target. Interventions that disrupt its glycosylation or block its function could weaken the protective barrier surrounding leukemia cells, making them more susceptible to immune clearance.

Because CD43 operates through a distinct mechanism, targeting it could complement existing therapies rather than replace them. Combining CD43 inhibition with CD47 blockade or other immunotherapies may enhance overall efficacy.

The findings also point to the broader relevance of glyco-immune interactions in cancer. Similar glycosylation-dependent barriers may exist in other tumor types, suggesting that the implications of this work could extend beyond AML.

A shift toward glyco-immunology

The study reflects a growing recognition of the role of glycobiology in cancer and immunology. While protein-based signaling pathways have dominated the field, complex carbohydrate structures on the cell surface are increasingly understood to play critical roles in immune recognition.

By identifying CD43 as a key mediator of immune evasion, the research highlights how these glycosylated molecules can shape interactions between cancer cells and the immune system.

Looking ahead

Although the findings are based on preclinical models, the researchers believe that they provide a strong rationale for further investigation in patient samples and clinical settings. Future studies will be needed to determine how CD43 expression varies across AML subtypes and whether it correlates with treatment response.

If validated, targeting CD43 could represent a new direction for immunotherapy in AML—one that addresses a fundamental mechanism of immune escape.

More broadly, the work highlights the importance of revisiting established paradigms in cancer biology. By moving beyond well-studied targets like CD47 and systematically exploring the full landscape of immune interactions, researchers are uncovering new vulnerabilities that could be exploited therapeutically.

The post Why Leukemia Cells Escape Immune Attack—and How to Stop Them appeared first on Inside Precision Medicine.

Combined Small-Cell Lung Cancer Evolution Insights May Improve Diagnosis, Treatment

A spatial multi-omics study of the rare lung cancer subtype known as combined small-cell lung cancer (cSCLC) has shown that these mixed tumors, which contain features of both small-cell and non-small-cell lung cancer (NSCLC), arise from a single ancestral cell that evolves and transitions between the two cancer types over time.

The results “provide a foundation for understanding cSCLC evolution and advancing innovative diagnostics and therapeutics,” write the authors in Cell Reports Medicine.

cSCLC represents approximately 2–5% of all SCLC cases, with diagnosis primarily based on pathologic evaluation of surgically resected tumor specimens. This can lead to underdiagnosis because small biopsies may not capture the full histological diversity of the tumor. Although cSCLC is more diverse and has a worse prognosis than typical SCLC, it is usually treated the same way because its underlying biology and tumor environment are not well understood.

Traditionally, the mixed histologies within cSCLC were thought to arise from independent tumor populations, but recent studies have revealed that the different histological components share common genetic mutations.

To investigate further, Zhuo Wang, from Fudan University in Shanghai, China, and colleagues applied spatially resolved genomic and transcriptomic sequencing, alongside single-cell RNA sequencing, to 19 treatment-naive cSCLC tumors.

“We found that these tumors are not simply mixtures of different cancer types,” said Wei Wei, PhD, associate professor at the Institute for Systems Biology in Seattle and co-corresponding author of the study. “They are dynamic systems, with cancer cells actively changing their identity. That flexibility may help explain why they are so difficult to treat.”

The team reports that the different tumor components originate from a single clone but later diverge as they acquire different mutations and copy-number changes.

The study also revealed that tumor cells can exist in intermediate or hybrid states, carrying features of multiple cancer types at once. About one-third of the SCLC-like tumor cells analyzed showed these mixed identities, suggesting that cancer progression is not a simple on-off switch, but a continuum.

In addition, the researchers found that different regions within the same tumor create distinct microenvironments. Some areas were rich in immune cells, while others were largely immune-excluded. Dense bands of fibroblasts often separated these regions. Those fibroblast-rich boundaries may help wall off parts of the tumor from immune attack.

“By combining spatial genomics, single-cell analysis, and multi-region sequencing, we were able to trace how these tumors evolve across both space and time,” said Fudan University’s Qihui Shi, PhD, co-corresponding author of the study. “This approach allowed us to capture transitional cell states that are not visible using conventional methods.”

Finally, the researchers developed “cSCLC Detector,” a four-gene diagnostic tool that may help identify these mixed tumors more accurately. The tool was built on a key insight from the study: although the small-cell and non-small-cell parts of cSCLC can look very different under the microscope, they come from the same ancestral tumor and share early trunk mutations.

In independent biopsy and blood samples, the assay, which detects mutations in the NSCLC-specific driver genes EGFR, KRAS, BRAF, and PIK3CA, identified cSCLC-like cases in 14% of samples, compared with a prevalence of 2%–5% estimated from surgically resected specimens.

The findings highlight the importance of understanding not just the genetic mutations in cancer, but also how cancer cells change state and interact with their environment.

“Cancer is not static,” Wei said. “To treat it effectively, we need to understand how it evolves—not just what it is at a single point in time.”

The post Combined Small-Cell Lung Cancer Evolution Insights May Improve Diagnosis, Treatment appeared first on Inside Precision Medicine.

STAT+: Access granted: CMS greenlights more than 150 participants for chronic care experiment

More than 150 companies and providers have been provisionally approved to participate in an experimental Medicare program meant to expand access to technology-supported chronic care. They include popular mental health apps, wearable device makers, a life sciences company tied to Google, and startups that help large health systems manage heart failure patients.

Announced late last year by the Center for Medicare and Medicaid Innovation, the ACCESS model will pay participants set rates to treat chronic conditions like diabetes, hypertension, high cholesterol, musculoskeletal pain, anxiety, and depression. The payments are tied to measurable health outcomes; the model is meant as an alternative to paying for individual technology services. The initial deadline to participate in the first ACCESS cohort was April 1, but CMMI Monday announced it will extend the deadline to allow more to join.

CMS officials say the large number of applications to participate in ACCESS exceeded their expectations and that the enthusiasm suggests modest payment rates and restrictions did not discourage digital health companies from applying. According to officials, most of the participants had not previously served Medicare patients. 

Continue to STAT+ to read the full story…

<![CDATA[New youth-focused substance use guidelines spotlight prevention, brain development, and coordinated mental health support to improve outcomes early.]]>
<![CDATA[“Each person the right to tell their own story in their own way”: An Italian-Canadian psychiatrist traces family journeys across 3 countries, revealing migration’s lessons on identity, belonging, and resilience.]]>

STAT+: Trump goes soft on insurance, and a medical underwriting chart

This is the online version of STAT’s weekly email newsletter Health Care Inc. Sign up here.

We watched the Artemis II astronauts splash down safely last week. A reminder that legitimately amazing things can still happen. Parachute your thoughts here: bob.herman@statnews.com.

Tough talk, soft stance

A few months ago, President Trump confidently said he would be meeting with the country’s largest health insurance companies to pressure them to lower their premiums. The message was just that — a message to give the appearance that Trump officials were willing to crack down on health insurers, which have been at the center of Americans’ disdain of the health care system for decades.

Continue to STAT+ to read the full story…

Why opinion on AI is so divided

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

In an industry that doesn’t stand still, Stanford’s AI Index, an annual roundup of key results and trends, is a chance to take a breath. (It’s a marathon, not a sprint, after all.)

This year’s report, which dropped today, is full of striking stats. A lot of the value comes from having numbers to back up gut feelings you might already have, such as the sense that the US is gunning harder for AI than everyone else: It hosts 5,427 data centers (and counting). That’s more than 10 times as many as any other country.  

There’s also a reminder that the hardware supply chain the AI industry relies on has some major choke points. Here’s perhaps the most remarkable fact: “A single company, TSMC, fabricates almost every leading AI chip, making the global AI hardware supply chain dependent on one foundry in Taiwan.” One foundry! That’s just wild.

But the main takeaway I have from the 2026 AI Index is that the state of AI right now is shot through with inconsistencies. As my colleague Michelle Kim put it today in her piece about the report: “If you’re following AI news, you’re probably getting whiplash. AI is a gold rush. AI is a bubble. AI is taking your job. AI can’t even read a clock.” (The Stanford report notes that Google DeepMind’s top reasoning model, Gemini Deep Think, scored a gold medal in the International Math Olympiad but is unable to read analog clocks half the time.)

Michelle does a great job covering the report’s highlights. But I wanted to dwell on a question that I can’t shake. Why is it so hard to know exactly what’s going on in AI right now?  

The widest gap seems to be between experts and non-experts. “AI experts and the general public view the technology’s trajectory very differently,” the authors of the AI Index write. “Assessing AI’s impact on jobs, 73% of U.S. experts are positive, compared with only 23% of the public, a 50 percentage point gap. Similar divides emerge with respect to the economy and medical care.”

That’s a huge gap. What’s going on? What do experts know that the public doesn’t? (“Experts” here means US-based researchers who took part in AI conferences in 2023 and 2024.)

I suspect part of what’s going on is that experts and non-experts base their views on very different experiences. “The degree to which you are awed by AI is perfectly correlated with how much you use AI to code,” a software developer posted on X the other day. Maybe that’s tongue-in-cheek, but there’s definitely something to it.

The latest models from the top labs are now better than ever at producing code. Because technical tasks like coding have right or wrong results, it is easier to train models to do them, compared with tasks that are more open-ended. What’s more, models that can code are proving to be profitable, so model makers are throwing resources at improving them.

This means that people who use those tools for coding or other technical work are experiencing this technology at its best. Outside of those use cases, you get more of a mixed bag. LLMs still make dumb mistakes. This phenomenon has become known as the “jagged frontier”: Models are very good at doing some things and less good at others.

The influential AI researcher Andrej Karpathy also had some thoughts. “Judging by my [timeline] there is a growing gap in understanding of AI capability,” he wrote in reply to that X post. He noted that power users (read: people who use LLMs for coding, math, or research) not only keep up to date with the latest models but will often pay $200 a month for the best versions. “The recent improvements in these domains as of this year have been nothing short of staggering,” he continued.

Because LLMs are still improving fast, someone who pays to use Claude Code will in effect be using a different technology from someone who tried using the free version of Claude to plan a wedding six months ago. Those two groups are speaking past each other.

Where does that leave us? I think there are two realities. Yes, AI is far better than a lot of people realize. And yes, it is still pretty bad at a lot of stuff that a lot of people care about (and it may stay that way). Anyone making bets about the future on either side should bear that in mind.

Neural Mechanism Underlying Sensory Behavior Revealed in C. elegans

Animal behavior reflects a complex interplay between an animal’s brain and its sensory surroundings. In a new study published in Nature Neuroscience titled, “Neural sequences underlying directed turning in Caenorhabditis elegans,” researchers from Massachusetts Institute of Technology (MIT) have shown how neuron circuits within C. elegans nematode worms respond to odors and generate movement as they pursue favorable versus unfavorable smells. The results inform understanding of the basic principles of the sensory nervous system for therapeutic applications. 

“Across the animal kingdom, there are just so many remarkable behaviors,” said Steven Flavell, PhD, associate professor at the Picower Institute at MIT, Howard Hughes Medical Institute (HHMI) investigator, and corresponding author of the study. “With modern neuroscience tools, we are finally gaining the ability to map their mechanistic underpinnings.” 

Whether moving toward a food source or away from a predator, animals must integrate sensory stimuli to navigate to favorable locations. The neural circuits for navigation are tasked with generating directed movement while simultaneously integrating sensory input to update behavior. Understanding how neural circuits select, execute and adapt sensory-guided navigation behaviors uncovers basic principles of how nervous systems are organized to integrate sensory information and control behavior. 

In C. elegans, the authors identified error-correcting turns during navigation and used whole-brain calcium imaging and cell-specific perturbations to determine their neural underpinnings. Defined neurons activated in a stereotyped order during each turn. Distinct neurons in this sequence respond to the spatial distribution of attractive and aversive olfactory cues, anticipate upcoming turn directions and drive movement, linking key features of this sensorimotor behavior across time. 

“One thing that really excited us about this study is that we were able to see what a sensorimotor arc looks like at the scale of a whole nervous system: all the bits and pieces, from responses to the sensory cue until the behavioral response is implemented,” Flavell said.  

The electrical activity of more than 100 neurons was tracked during sensory movement. Notably, C. elegans only have 302 neurons total. Instead of random movements, the worms executed turns with advantageous timing and at well-chosen angles.  

The activity of SAA neurons was crucial for integrating odor detection with planned movement and predicted the direction of upcoming turns. Several neurons showed different activity patterns depending on the location of odors were and whether the worm was moving forward or in reverse. 

Additionally, the neuromodulator, tyramine, was essential for turning and shifting gears. When the worms moved in reverse, tyramine from the neuron RIM enabled other neurons in the sequence to change their activity appropriately to execute the turns. In several experiments, the scientists knocked out RIM tyramine, which disrupted the navigation behaviors and the sequence of neural activity. 

The post Neural Mechanism Underlying Sensory Behavior Revealed in <i>C. elegans</i> appeared first on GEN – Genetic Engineering and Biotechnology News.

<![CDATA[Exercise boosts recovery in depression and psychosis, and improves cognition and quality of life; clinicians can now use the 5A model to make movement a routine psychiatric tool.]]>
<![CDATA[Artemis II astronauts spark a psychiatrist’s look at “joy trains,” music, and meaning—practical ways teams rekindle joy and prevent burnout.]]>