AACR 2026: MRI-ctDNA Combo Informs HPV-Related Throat Cancer Treatment

At the 2026 annual meeting of the American Association for Cancer Research (AACR), researchers from Memorial Sloan Kettering Cancer Center (MSKCC) presented new evidence that a blood-based biomarker, combined with advanced imaging, could enable real-time adjustment of cancer treatment in patients with HPV-related throat cancer. Findings from this clinical study (NCT03323463) highlight a potentially important shift in care, particularly for HPV-associated oropharyngeal cancer, a disease with generally high cure rates but ongoing efforts to reduce treatment-related toxicity. Rather than waiting until therapy is complete, clinicians may be able to tailor treatment intensity based on early indicators of response.

Circulating tumor DNA (ctDNA) has already shown promise for detecting minimal residual disease (MRD), but its role in guiding treatment decisions during therapy remains largely unexplored. To address this gap, a research team led by Bill H. Diplas, MD, PhD, a radiation oncology fellow at MSKCC, investigated whether serial ctDNA measurements, paired with weekly MRI scans, could provide a more precise and dynamic view of tumor response. In collaboration with Labcorp and Biocartis, the MSKCC researchers developed a personalized ctDNA assay that combined two strategies: detection of patient-specific tumor mutations and quantification of DNA from high-risk HPV strains, particularly HPV-16 and HPV-18, using anchored multiplex PCR and high-throughput sequencing.

The study enrolled 158 patients with HPV-associated oropharyngeal cancer who had undergone primary tumor resection followed by risk-adapted chemoradiotherapy guided by hypoxia assessment. MRIs were performed pretreatment and weekly following treatment to determine tumor volume, and blood samples were collected before treatment and weekly during therapy, yielding nearly 1,000 samples from 119 patients (mean 8.2 samples/patient) up to 126 weeks.

At baseline, ctDNA was identified in 93.9% of patients—outperforming either mutation-based (89.4%) or HPV-based (80.3%) methods alone. ctDNA levels also correlated with tumor size and biological features such as cell death and viral load. Notably, ctDNA emerged as a faster and more sensitive indicator of treatment response than imaging. Changes in ctDNA levels appeared earlier and across a broader dynamic range than tumor size reductions observed on MRI. By the second week of therapy, ctDNA measurements could already distinguish patients likely to require more intensive treatment.

The identification of patients with high-risk disease was significantly improved by combining on-treatment ctDNA assessment with imaging in a multimodal model, outperforming any modality alone. These results underscore the complementary nature of molecular signals in blood and structural changes seen on imaging.

Similar multimodal strategies that integrate ctDNA with imaging have been explored in other cancers, including breast and lung, primarily in research settings. Studies suggest that combining these approaches can improve prediction of treatment response and enable earlier detection of resistance. Broader analyses across colorectal, lung, and breast cancers further support the value of integrating molecular and imaging data to refine models of response and survival. However, most of these approaches remain investigational, and the use of ctDNA to guide real-time treatment decisions is only beginning to be tested in prospective trials.

Although further validation is needed, this study establishes a framework for real-time, personalized treatment in oropharyngeal cancer. If translated into clinical practice, such an approach could accelerate the shift toward adaptive therapy—where decisions are guided not only by how tumors appear on imaging, but by how they respond at the molecular level throughout treatment.

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Autoencoder-Enhanced Convolutional Neural Networks for Plantar Pressure–Based Gait Pattern Recognition: Model Development and Cross-Validated Evaluation Study

<strong>Background:</strong> Plantar pressure imaging is a stable modality that reflects gait-related biomechanical characteristics and has been used increasingly for gait assessment and recognition. However, plantar pressure images are high dimensional and nonlinear, making manual feature engineering and conventional machine learning insufficient to capture discriminative patterns. <strong>Objective:</strong> This study aimed to develop a gait pattern recognition model based on plantar pressure using an autoencoder (AE)-enhanced convolutional neural network (CNN) and to evaluate its performance against baseline deep learning and classical machine learning approaches. <strong>Methods:</strong> A total of 13 healthy volunteers (aged 18-24 years) were recruited. Plantar pressure data were collected during treadmill walking using an in-shoe pressure measurement system and converted into frame-wise plantar pressure images. We compared a lightweight CNN (Light CNN), an AE-CNN cascade model, and an encoder-augmented CNN with an additional bottleneck layer. Model development used participant-wise data partitioning, and performance was evaluated using accuracy, precision, recall, and <i>F</i><sub>1</sub>-score. <strong>Results:</strong> The proposed encoder-augmented CNN achieved the best overall performance (<i>F</i><sub>1</sub>-score=96.20%), outperforming the Light CNN (<i>F</i><sub>1</sub>-score=94.44%) and AE-CNN cascade (<i>F</i><sub>1</sub>-score=92.45%). Confusion matrices and learning curves further indicated stable training behavior and consistent classification performance across gait patterns. <strong>Conclusions:</strong> Integrating representation learning (AE-based compression) with CNN-based classification improved the recognition of gait patterns from plantar pressure images. This pilot study included only healthy participants. Future work should validate generalizability in larger and clinically diverse cohorts and further investigate participant-level evaluation and model interpretability, as well as deployment feasibility.

Early life may have breathed oxygen earlier than believed

Around 2.3 billion years ago, a pivotal period known as the Great Oxidation Event set the evolutionary course for oxygen-breathing life on Earth. But MIT geobiologists and colleagues have found evidence that some early forms of life evolved the ability to use oxygen hundreds of millions of years before that.

By mapping enzyme sequences from several thousand modern organisms onto an evolutionary tree of life, the researchers traced the origins of an enzyme that enables organisms to use oxygen to the Mesoarchean period, 3.2 to 2.8 billion years ago.

The team’s results may help explain a longstanding puzzle in Earth’s history: Given that the first oxygen-­producing microbes likely emerged before the Mesoarchean, why didn’t oxygen build up in the atmosphere until hundreds of millions of years later? Having evolved the key enzyme, organisms living near those microbes, called cyanobacteria, may have gobbled up the small amounts of oxygen they produced.

“This does dramatically change the story of aerobic respiration,” says Fatima Husain, SM ’18, PhD ’25, a research scientist in MIT’s Department of Earth, Atmospheric, and Planetary Sciences (EAPS) and a coauthor with Gregory Fournier, an associate professor of geobiology, of a paper on the research. “It shows us how incredibly innovative life is at all periods in Earth’s history.” 

This tool could show how consciousness works

How does the physical matter in our brains translate into thoughts, sensations, and emotions? It’s hard to explore that question without neurosurgery. But in a recent paper, MIT philosopher Matthias Michel, Lincoln Lab researcher Daniel Freeman, and colleagues outline a strategy for doing so with an emerging tool called transcranial focused ultrasound.

This noninvasive technology reaches deeper into the brain, with greater resolution, than techniques such as EEG and MRI. It works by sending acoustic waves through the skull to focus on an area of a few millimeters, allowing specific brain structures to be stimulated so the effects can be studied.

The researchers lay out an experimental approach that would use the tool to help test two competing conceptions of consciousness. The “cognitivist” concept holds that brain activity generating conscious experience must involve higher-level processes such as reasoning or self-reflection, likely using the frontal cortex. The “non-­cognitivist” idea is that specific patterns of neural activity—more localized in subcortical structures or at the back of the cortex—give rise to subjective experiences directly.

“This is a tool that’s not just useful for medicine, or even basic science, but could also help address the hard problem of consciousness,” Freeman says. “It can probe where in the brain are the neural circuits that generate a sense of pain, a sense of vision, or even something as complex as human thought.” 

A natural protein may protect the GI tract from infection

Embedded in the body’s mucosal surfaces, proteins called lectins bind to sugars found on cell surfaces. A team led by MIT chemistry professor Laura Kiessling has found that one such protein, intelectin-2, both helps fortify the mucosal barrier and offers broad-spectrum protection against harmful bacteria found in the GI tract. 

Intelectin-2 binds to a sugar molecule called galactose that is found on bacterial membranes, the team found, trapping the bacteria and hindering their growth; the trapped microbes eventually disintegrate, suggesting that the protein is able to kill them by disrupting their cell membranes. It also helps strengthen the intestine’s protective lining by binding to the galactose in the mucins that make up mucus.

“What’s remarkable is that intelectin-2 operates in two complementary ways. It helps stabilize the mucus layer, and if that barrier is compromised, it can directly neutralize or restrain bacteria that begin to escape,” says Kiessling, who conducted the study with colleagues including Amanda Dugan, a former MIT postdoc and research scientist, and Deepsing Syangtan, PhD ’24.

Because intelectin-2 can neutralize or eliminate pathogens such as Staphylococcus aureus and Klebsiella pneumoniae, which are often difficult to treat with antibiotics, it could someday be adapted as an antimicrobial agent, the researchers say. Restoring desirable levels of intelectin-2 could also help people with disorders such as inflammatory bowel disease, who may have either too little of it (potentially weakening the mucus barrier) or too much (killing off beneficial gut bacteria).

“Harnessing human lectins as tools to combat antimicrobial resistance opens up a fundamentally new strategy that draws on our own innate immune defenses,” Kiessling says. “Taking advantage of proteins that the body already uses to protect itself against pathogens is compelling and a direction that we are pursuing.” 

The new word in home construction could be “plastics”

Single-use plastics are a persistent source of environmental pollution, and the need to house a growing global population puts increasing pressure on resources such as timber. MIT engineers have an idea that could make a dent in both problems at once.

In a recent study, a team led by mechanical engineering professor David Hardt, SM ’74, PhD ’79, and lecturer and research scientist AJ Perez ’13, MEng ’14, PhD ’23, laid out a plan for using recycled plastic to 3D-print construction-grade beams, trusses, and other structures that could one day offer lighter, more sustainable alternatives to traditional wood-based framing. Although some companies are working on using large-scale additive manufacturing to create walls, they’re mainly using concrete or clay, whose production typically has a large negative environmental impact. These engineers are among the first to explore printing structural framing elements—and to do so using recycled plastic.

The design they came up with is similar in shape to the traditional wooden trusses that support flooring, with beams that connect in a pattern resembling a ladder with diagonal rungs. To test it, they obtained pellets made of recycled PET polymers and glass fibers from an aerospace materials company and fed them into a room-size 3D printer as “ink.” When they printed four long trusses with this material and configured them into a conventional plywood-topped floor frame, the result had a load-bearing capacity of over 4,000 pounds, far exceeding key building standards set by the US Department of Housing and Urban Development.

The plastic-printed trusses weigh about 13 pounds each, light enough to transport without a flatbed truck. An industrial printer can crank one out in under 13 minutes. Crucially, the researchers are developing the process to work with “dirty” plastic that hasn’t been cleaned or preprocessed. In addition to floor trusses, they are working on printing other elements and combining them into a full frame for a modest-size house.

“We’ve estimated that the world needs about 1 billion new homes by 2050. If we try to make that many homes using wood, we would need to clear-cut the equivalent of the Amazon rainforest three times over,” says Perez. “The key here is: We recycle dirty plastic into building products for homes that are lighter, more durable, and sustainable.”

The researchers envision that one day, trash like used bottles and food containers could be sent directly into a shredder, turned into pellets, and fed into a large-scale additive manufacturing machine to become structural composite construction components. At the construction site, the elements could be quickly fitted into a lightweight yet sturdy home frame.

“The idea is to bring shipping containers close to where you know you’ll have a lot of plastic, like next to a football stadium,” Perez says. “Then you could use off-the-shelf shredding technology and feed that dirty shredded plastic into a large-scale additive manufacturing system, which could exist in micro-factories, just like bottling centers, around the world. You could print the parts for entire buildings that would be light enough to transport on a moped or pickup truck to where homes are most needed.” 

STAT+: Key GOP senators push back on Trump’s plan to cut NIH, reorganize HHS

WASHINGTON — Both Democratic and Republican senators who oversee federal spending seemed skeptical of proposed cuts to health research and public health in the White House’s budget, potentially teeing up a congressional package that ignores many of the administration’s most dramatic proposals for a second year. 

During a Senate appropriations health subcommittee hearing Tuesday, lawmakers questioned health secretary Robert F. Kennedy Jr. on how his department could tackle chronic disease, smoking cessation, and cancer research with a proposed 2027 fiscal budget that would cut the department by 12%. 

The request, which is broadly similar to what was proposed last year, includes deep cuts to the National Institutes of Health, the elimination of a health research agency, and the creation of a new agency devoted to chronic diseases called the Administration for a Healthy America.

Continue to STAT+ to read the full story…

Integrating Mobile Text Messaging Pre-Exposure Prophylaxis Navigation Services Into a Home HIV and Sexually Transmitted Infection Self-Testing Program in the United States: Formative Work and Pilot Implementation Study

Background: HIV testing is the gateway to the HIV prevention continuum and offers an important opportunity to provide HIV prevention services. TakeMeHome.org is an online program that enables state and local health departments to offer free in-home HIV and sexually transmitted infection self-testing. As few TakeMeHome users have used pre-exposure prophylaxis (PrEP), there is an opportunity to link TakeMeHome users to PrEP information and services. Objective: The aim of this study is to develop an implementation strategy to link HIV or sexually transmitted infection self-testers from online orders to PrEP services via direct digital linkage to a novel SMS text messaging navigation program. Methods: PrEPmate is an evidence-based bidirectional text-messaging platform that has demonstrated increased PrEP retention and adherence. We developed a novel program to link TakeMeHome testers to mobile SMS text messaging PrEP navigation via PrEPmate. We conducted focus groups among TakeMeHome users to elicit preferences for linkage from TakeMeHome to PrEPmate. Based on these focus groups, we revised the content and functionality of this linkage intervention. In October 2023, we launched a pilot implementation study in 2 US Ending the HIV Epidemic jurisdictions: Sacramento, California, and Tarrant, Texas. Results: Thirteen TakeMeHome users participated in 4 focus groups (mean age 31.5 years; n=4, 31% Latinx, n=2, 15% Black; n=9, 69% never used PrEP). When shown wireframes of the TakeMeHome or PrEPmate linkage, most thought they were easy to navigate and user-friendly. They liked the privacy of connecting with a PrEP navigator using SMS text messaging. Participants recommended providing a clear description of PrEP and PrEPmate services and indicating that PrEP is low or no cost on the TakeMeHome website. On the PrEPmate landing page, they recommended adding language on confidentiality and the partnership with TakeMeHome to show that both services are connected. Once enrolled, they recommended weekly or biweekly check-ins to assist with PrEP navigation. Overall, 92% (12/13) of focus group participants were likely to use PrEPmate to learn more about PrEP and/or link to PrEP services. From October 2023 to May 2024, among 537 individuals who ordered test kits and were not on PrEP, 169 (31%) were linked to the PrEPmate page, and 86 (16%) enrolled in PrEPmate. PrEP navigation was provided via SMS text messaging or phone, with 46 (53%) receiving PrEP education and 26 (30%) in various stages of starting PrEP. In exit interviews, participants found the intervention easy to use and appreciated being connected with an experienced PrEP navigator who helped them access PrEP. Conclusions: Through user-centered design, we successfully developed a program to link TakeMeHome testers to PrEP navigation via PrEPmate, with high feasibility and acceptability of the intervention and a substantial number of clients starting PrEP. The next steps will involve evaluating the effectiveness of this program on a larger scale and, if successful, expanding PrEPmate navigation to all Ending the HIV Epidemic jurisdictions using TakeMeHome.
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Promoting Family Communication for Cascade Cancer Genetic Testing With Relational Agent Role-Play: Quasi-Experimental Study

Background: If a patient with cancer is identified as having a pathogenic variant, at-risk relatives are eligible for genetic testing, known as cascade testing. However, in the United States, the patient is responsible for informing their family members, and only about 30% of these family members are ultimately informed and complete testing. There is a need to train patients with cancer to communicate risk information and motivate their family members to obtain genetic testing. Objective: This study evaluates “GRACE,” an online relational agent that trains patients with cancer to talk to their family about cancer risk, including role-play simulations that enable patients to practice communication skills. Methods: A quasi-experimental study was conducted with 30 crowd workers with cancer. Primary measures included 5-point pre-post self-reported intent, importance, comfort, and confidence to share genetic test information with family members, as well as knowledge of cancer genetics (KnowGene), satisfaction with (10-item satisfaction measure), and usability of (SUS) the relational agent system. Results: Likelihood of sharing genetic test information increased significantly pre-post from 4.43 (SD 1.04) to 4.67 (SD .66), Wilcoxon (Z=2.07, =.04). Importance of sharing genetic test information increased significantly pre-post from 4.47 (SD .82) to 4.77 (SD .50), Wilcoxon (Z=2.46, =.01). Comfort sharing genetic test information increased pre-post from 4.33 (SD 0.99) to 4.57 (SD 0.90), Wilcoxon (Z=1.811, =.07). Confidence to share genetic test information increased significantly pre-post from 4.33 (SD 0.994) to 4.63 (SD 0.765), Wilcoxon (Z=2.23, =.03). Knowledge of cancer genetics did not increase significantly (mean 13.27, range 1.911 to 13.7, SD 1.932, paired t=1.245, =.22). Participants gave high scores for usability (SUS score=71%) and satisfaction (6.09 SD 0.96 out of 7.0), significantly greater than neutral, t=13.445, <.001) with the relational agent system. Conclusions: GRACE provides communication skills training and information better enabling patients with cancer to reach out to their families, and our preliminary study indicates a potential for future impact. While results were generally positive, these findings should be interpreted with caution due to limitations in the population included in the pilot, the quasi-experimental design and small sample size. Future development should focus on larger-scale evaluation and in-depth follow-up of family communication dynamics following the use of GRACE.

Multimodal Sentiment and Emotion Analysis Framework for Personalized Health Coaching Messages: Proof-of-Concept Study

Background: Text generation approaches in health care communication have evolved along 2 major paths. The first path involves generative adversarial networks, progressing from basic architectures to specialized variants like Text-to-Text Generative Adversarial Network (TT-GAN) and Time and Frequency Domain-Based Generative Adversarial Network (TF-GAN), which address challenges in discrete text generation through techniques such as Gumbel-Softmax and reinforcement learning. The second path emerges from transformer-based architectures, particularly Generative Pretrained Transformer-2 (GPT-2), which uses extensive pretraining and self-attention mechanisms to generate contextually appropriate text. GPT-2’s transformer architecture enhances persuasive health communication by generating personalized messages using various strategies like task support, dialogue support, and social support for effective health interventions. Objective: This study aimed to use GPT-2 as a generative method to construct persuasive text in a dataset and compare the performance of sentiment analysis and emotion detection analysis. Methods: We combined sentiment analysis tools (VADER [Valence Aware Dictionary and Sentiment Reasoner] and TextBlob) with emotion detection methods (Text2Emotion and NRCLex [National Research Council Lexicon]) to analyze health coaching messages across different persuasive types: reminder, reward, suggestion, and praise. Results: TextBlob and VADER achieved accuracies of 57% and 69%, respectively, while RoBERTa (robustly optimized BERT approach)-sentiment outperformed them with an accuracy of 88%. Emotion detection showed a high prevalence of “joy” and “happy” labels (93.69% positive skew). While transformers excel in accuracy, lexicon-based models like VADER offer a better performance-efficiency balance for real-time health communication systems. For emotion detection, all categories showed perfect accuracy (1.0), while trust showed mixed results, with precision, recall, and -score values ranging from 0.81 to 0.96. The emotion detection analysis revealed varying success rates across different emotions, with some categories, such as anger and neutral, showing reasonable performance and others, such as trust, showing mixed performance. Conclusions: This research contributes to understanding the emotional dynamics of persuasive health communication and highlights both the capabilities and limitations of current natural language processing tools in analyzing health-related persuasive messaging. This proof-of-concept study using synthetically generated data establishes a methodological framework for multimodal sentiment and emotion analysis. The findings require validation with real-world health coaching messages before clinical deployment.
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