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A Definition of AGI

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dan Hendrycks · Dawn Song · Christian Szegedy · Honglak Lee · Yarin Gal · Erik Brynjolfsson · Sharon Li · Andy Zou · Lionel Levine · Bo Han · Jie Fu · Ziwei Liu · Jinwoo Shin · Kimin Lee · Mantas Mazeika · Long Phan · George Ingebretsen · Adam Khoja · Cihang Xie · Olawale Salaudeen · Matthias Hein · Kevin Zhao · Alexander Pan · David Duvenaud · Bo Li · Steve Omohundro · Gabriel Alfour · Max Tegmark · Kevin McGrew · Gary Marcus · Jaan Tallinn · Eric Schmidt · Yoshua Bengio
arXiv:2510.18212v3 Announce Type: replace Abstract: The lack of a concrete definition for Artificial General Intelligence (AGI) obscures the gap between today's specialized AI and human-level cognition. This paper introduces a quantifiable framework to address this, defining AGI as matching the cognitive versatility and proficiency of a well-educated adult. To operationalize this, we ground our methodology in Cattell-Horn-Carroll theory, the most empirically validated model of human cognition. The framework dissects general intelligence into ten core cognitive domains-including reasoning, memory, and perception-and adapts established human psychometric batteries to evaluate AI systems. Application of this framework reveals a highly "jagged" cognitive profile in contemporary models. While proficient in knowledge-intensive domains, current AI systems have critical deficits in foundational cognitive machinery, particularly long-term memory storage. The resulting AGI scores (e.g., GPT-4 at 27%, GPT-5 at 57%) concretely quantify both rapid progress and the substantial gap remaining before AGI.
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Scaling Multimodal Search and Recommendation with Small Language Models via Upside-Down Reinforcement Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yu-Chen Lin · Sanat Sharma · Hari Manikandan · Jayant Kumar · Tracy Holloway King · Jing Zheng
arXiv:2502.09854v2 Announce Type: replace-cross Abstract: In this work, we investigate how small language models (SLMs) can be scaled to support multimodal search and recommendation use cases while remaining efficient enough for real-time, resource-constrained deployments. We present a framework that combines upside-down reinforcement learning with synthetic data distillation from a large language model (Llama-3) to train a 100M-parameter GPT-2 model for multitask prompt generation. Despite being up to 80 times smaller than state-of-the-art large language models (LLMs), our SLM achieves relevance and diversity scores within 6% of competitive baselines such as Llama-3 8B, Qwen3 8B, and Ministral 8B. These results demonstrate that SLMs can effectively handle multimodal search and recommendation tasks, while dramatically reducing inference latency and memory overhead. Our study highlights the potential of lightweight models as practical engines for scalable multimodal discovery, bridging the gap between cutting-edge research and real-world multimodal applications such as media recommendations and creative content generation.
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Privacy is All You Need: Revolutionizing Wearable Health Data with Advanced PETs

✇cs.AI, q-bio.NC updates on arXiv.org
By: Karthik Barma
arXiv:2503.03428v2 Announce Type: replace-cross Abstract: In a world where data is the new currency, wearable health devices offer unprecedented insights into daily life, continuously monitoring vital signs and metrics. However, this convenience raises privacy concerns, as these devices collect sensitive data that can be misused or breached. Traditional measures often fail due to real-time data processing needs and limited device power. Users also lack awareness and control over data sharing and usage. We propose a Privacy-Enhancing Technology (PET) framework for wearable devices, integrating federated learning, lightweight cryptographic methods, and selectively deployed blockchain technology. The blockchain acts as a secure ledger triggered only upon data transfer requests, granting users real-time notifications and control. By dismantling data monopolies, this approach returns data sovereignty to individuals. Through real-world applications like secure medical data sharing, privacy-preserving fitness tracking, and continuous health monitoring, our framework reduces privacy risks by up to 70 percent while preserving data utility and performance. This innovation sets a new benchmark for wearable privacy and can scale to broader IoT ecosystems, including smart homes and industry. As data continues to shape our digital landscape, our research underscores the critical need to maintain privacy and user control at the forefront of technological progress.
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Challenges and Limitations of Generative AI in Synthesizing Wearable Sensor Data

✇cs.AI, q-bio.NC updates on arXiv.org
By: Flavio Di Martino · Franca Delmastro
arXiv:2505.14206v2 Announce Type: replace-cross Abstract: The widespread adoption of wearable sensors has the potential to provide massive and heterogeneous time series data, driving the use of Artificial Intelligence in human sensing applications. However, data collection remains limited due to stringent ethical regulations, privacy concerns, and other constraints, hindering progress in the field. Synthetic data generation, particularly through Generative Adversarial Networks and Diffusion Models, has emerged as a promising solution to mitigate both data scarcity and privacy issues. However, these models are often limited to narrow operational scenarios, such as short-term and unimodal signal patterns. To address this gap, we present a systematic evaluation of state-of-the-art generative models for time series data, explicitly assessing their performance in challenging scenarios such as stress and emotion recognition. Our study examines the extent to which these models can jointly handle multi-modality, capture long-range dependencies, and support conditional generation-core requirements for real-world wearable sensor data generation. To enable a fair and rigorous comparison, we also introduce an evaluation framework that evaluates both the intrinsic fidelity of the generated data and their utility in downstream predictive tasks. Our findings reveal critical limitations in the existing approaches, particularly in maintaining cross-modal consistency, preserving temporal coherence, and ensuring robust performance in train-on-synthetic, test-on-real, and data augmentation scenarios. Finally, we present our future research directions to enhance synthetic time series generation and improve the applicability of generative models in the wearable computing domain.
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Harnessing cuproptosis for pancreatic cancer therapy: From molecular insights to clinical prospects

✇(Multiomics OR Omics) AND (Pancreatic)
By: Ali Darzi · Siavash Boroumandi · Pouriya Khajegi · Mohammad Reza Dehdashti · Pedram Abdali · Mohammad Moein Amerinia · Fatemeh Kheirkhah · Mohammad Mahdi Ajalli · Mahla Shokouhfar

Biomed Pharmacother. 2025 Dec 2;193:118852. doi: 10.1016/j.biopha.2025.118852. Online ahead of print.

ABSTRACT

Pancreatic cancer (PC) remains a high-fatality malignancy with limited clinical progress, characterized by aggressive biology, marked resistance to standard therapies, and dismal outcomes. Even with state-of-the-art resection, radiotherapy, and multidrug chemotherapy, median survival benefits are modest, highlighting an urgent need for mechanism-based interventions. Cuproptosis, a newly delineated modality of regulated cell death initiated by intracellular copper accumulation and mitochondrial stress, presents a biologically coherent therapeutic avenue. Distinct from apoptosis, necroptosis, and ferroptosis, cuproptosis is driven by the direct binding of copper to lipoylated enzymes of the tricarboxylic acid (TCA) cycle, resulting in bioenergetic failure, misfolded protein aggregation, and collapse of cytotoxic proteostasis. Converging studies suggest that copper disequilibrium and metabolic reprogramming are recurrent features of PC, potentially contributing to malignant progression, immune evasion, and chemoresistance. These insights motivate two complementary strategies: first, therapeutic manipulation of copper flux, via chelators, ionophores, or transport modulators, to selectively trigger cuproptosis in tumor cells; and second, sensitization of mitochondrial metabolism, through targeting lipoic-acid pathway components, pyruvate utilization, or TCA load, to lower the threshold for cuproptotic killing. In parallel, multi-omic interrogation of cuproptosis-associated genes, proteins, and metabolites may yield prognostic and predictive biomarkers, enabling risk-adapted treatment selection and rational combinations with cytotoxic, targeted, or immunotherapeutic modalities. This review synthesizes recent advances on cuproptosis in PC and outlines its translational potential as both a therapeutic target and a biomarker framework.

PMID:41337879 | DOI:10.1016/j.biopha.2025.118852

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Digital Biometrics in Predicting Risk for Obstructive Sleep Apnea and Hypertension: Decentralized, Prospective Cohort Study

✇Journal of Medical Internet Research
By: Natalia Orendain · Samantha Spierling Bagsic · Edward Ramos · Jay Pandit · Robert L Owens · Stuti Jaiswal
Background: Sleep is an important component of human health and can be measured longitudinally using digital activity trackers. Further, decentralized digital research has the potential to provide a real-world picture of sleep in large populations. Objective: This study examined whether longitudinal sleep patterns from activity trackers could predict risk of obstructive sleep apnea (OSA) and hypertension, as defined the Berlin questionnaire and self report, respectively. Methods: We recruited adults ≥18 years nationwide to join our sleep-focused smartphone-based study, called the Research Framework for Exploring Sleep Health (REFRESH). Our sample of N= 391 adults is comprised predominately of females (68%; n = 247 out of 364) at a mean age of 48 years (standard deviation (SD) = 13.62). Participants were asked to fill out health-related surveys, including the Berlin questionnaire, and the Horne-Ostberg questionnaire for chronotype. Participants were asked to link their own activity tracker to the application to collect longitudinal sleep data. Results: We analyzed sleep data from 391 participants, among a predominately White (65%; n = 231 out of 353) followed by multiracial (17%; n = 61 out of 353) and Hispanic or Latino (6.5%; n = 23 out of 353) cohort. Collinearity testing showed that OSA risk and self-reported hypertension could be considered independently. Holding body mass index (BMI) at fixed value, the odds of having high OSA risk increased by 159% for every one-hour increase in weekday sleep variability (odds ratio (OR) = 2.592, 95% confidence interval (CI) [1.613, 4.400]; P
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Multi-omic profiling provides insights into the heterogeneity, microenvironmental features, and biomarker landscape of small-cell lung cancer

✇Omics In Lung
By: Mingchao Xie · Miljenka Vuko · Shashank Saran · Siyu Liu · Andrew G Chambers · Hana Baakza · Helen K Angell · Felicia Ng · Carl M Gay · Robert J Cardnell · Felix J Segerer · Alma Andoni · Jaime Rodriguez-Canales · Paul M Waring · Markus Schick · J Carl Barrett · Lauren A Byers · Giulia Fabbri

Mol Cancer. 2025 Dec 2. doi: 10.1186/s12943-025-02514-4. Online ahead of print.

ABSTRACT

BACKGROUND: Greater understanding of differential therapeutic sensitivity, specifically to immunotherapy, in small-cell lung cancer (SCLC) is required.

METHODS: We explored SCLC heterogeneity through integrated molecular characterization of tumor tissue samples from 159 treatment-naive patients, utilizing genetic, epigenetic, transcriptional, and proteomic profiling, immunohistochemistry staining for multiple biologically relevant markers including transcriptional subtype-defining proteins, and spatial immune profiling using multiplex immunofluorescence.

RESULTS: Multi-omics analysis confirmed high heterogeneity across/within neuroendocrine and non-neuroendocrine subtypes. Methylomics analysis identified four methylome clusters that may enhance subtype prediction, prognosis, and longitudinal monitoring of subtype evolution. Immunohistochemistry analysis showed high MHC-I expression in non-neuroendocrine subtypes, which have greatest potential benefit from adding immunotherapy to chemotherapy; high DLL3 expression associated with neuroendocrine subtypes and an immune-cold tumor microenvironment. Multiplex immunofluorescence demonstrated associations of MHC-I with spatial arrangement and phenotypic features of immune cells in the tumor microenvironment of high-MHC-I-expressing SCLC, providing mechanistic rationale for MHC-I as a potential biomarker of immunotherapy response.

CONCLUSIONS: This multimodal profiling analysis provides further insights into the biologic complexity of SCLC and highlights potential therapeutic vulnerabilities of distinct disease subtypes.

PMID:41331472 | DOI:10.1186/s12943-025-02514-4

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Integrative Analysis of Multi-Omics Data for Biomarker Discovery

✇Omics in Hepatocellular
By: Rency S Varghese · Xinran Zhang · Sarada Giridharan · Hoi Yan Katharine Chau · Radhika Unnikrishnan · Md Mamunur Rashid · Muhammad Salman Sajid · Habtom W Ressom

Annu Int Conf IEEE Eng Med Biol Soc. 2025 Jul;2025:1-7. doi: 10.1109/EMBC58623.2025.11254134.

ABSTRACT

The complexity of biological systems and the limitations of analyzing individual omics studies for biomarker discovery have raised the need for a holistic approach by multi-omics integration. By integrating data from multiple layers, researchers can gain insights into the entire system rather than just individual components. Also, integrative analysis can help identify molecular signatures that are more accurate in predicting disease onset, progression, and response to treatment, leading to better-targeted therapies and personalized medicine. In this paper, we explored statistical and deep learning methods for integrative analysis of metabolomics, lipidomics, peptidomics, proteomics, and glycoproteomics data acquired by LC-MS/MS analysis of serum samples from 20 hepatocellular carcinoma (HCC) cases and 20 patients with liver cirrhosis (CIRR). The goal is to identify a panel of multi-omics features that distinguish HCC cases from cirrhotic controls. A pathway analysis using these features identified biological pathways such as LXR/RXR Activation and Acute Response signaling as significantly enriched in our multi-omics datasets.

PMID:41336317 | PMC:PMC12694951 | DOI:10.1109/EMBC58623.2025.11254134

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CACARA: Cross-Modal Alignment Leveraging a Text-Centric Approach for Cost-Effective Multimodal and Multilingual Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Diego A. B. Moreira · Alef I. Ferreira · Jhessica Silva · Gabriel O. dos Santos · Gustavo Bonil · Jo\~ao Gondim · Marina dos Santos · Helena Maia · Simone Hashiguti · N\'adia da Silva · Carolina Scarton · Helio Pedrini · Sandra Avila
arXiv:2512.00496v1 Announce Type: cross Abstract: As deep learning models evolve, new applications and challenges are rapidly emerging. Tasks that once relied on a single modality, such as text, images, or audio, are now enriched by seamless interactions between multimodal data. These connections bridge information gaps: an image can visually materialize a text, while audio can add context to an image. Researchers have developed numerous multimodal models, but most rely on resource-intensive training across multiple modalities. Similarly, extending these models to new languages often follows the same resource-heavy training strategy. In this work, we propose a multimodal and multilingual architecture, CACARA, trained through emergent alignment learning, enabling the seamless integration of new modalities into an existing bimodal/multimodal model without requiring full retraining. This work breaks new ground by demonstrating that this emergent alignment paradigm can unlock multilingual capabilities from monolingual training. By fine-tuning the newly incorporated modality only on data aligned with the English language, our model develops support for over 100 languages without explicit multilingual pretraining or tuning of the text encoder. Such emergent multimodal and multilingual properties are gained efficiently, preserving previously learned knowledge at a training cost comparable to that of a monolingual model. Our strategy achieves up to a 14.24 percentage points improvement in R@1 audio-to-text retrieval, outperforming state-of-the-art multimodal models -- all without the heavy computational cost of retraining across every modality and language.
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Deep Learning-Based Computer Vision Models for Early Cancer Detection Using Multimodal Medical Imaging and Radiogenomic Integration Frameworks

✇cs.AI, q-bio.NC updates on arXiv.org
By: Emmanuella Avwerosuoghene Oghenekaro
arXiv:2512.00714v1 Announce Type: cross Abstract: Early cancer detection remains one of the most critical challenges in modern healthcare, where delayed diagnosis significantly reduces survival outcomes. Recent advancements in artificial intelligence, particularly deep learning, have enabled transformative progress in medical imaging analysis. Deep learning-based computer vision models, such as convolutional neural networks (CNNs), transformers, and hybrid attention architectures, can automatically extract complex spatial, morphological, and temporal patterns from multimodal imaging data including MRI, CT, PET, mammography, histopathology, and ultrasound. These models surpass traditional radiological assessment by identifying subtle tissue abnormalities and tumor microenvironment variations invisible to the human eye. At a broader scale, the integration of multimodal imaging with radiogenomics linking quantitative imaging features with genomics, transcriptomics, and epigenetic biomarkers has introduced a new paradigm for personalized oncology. This radiogenomic fusion allows the prediction of tumor genotype, immune response, molecular subtypes, and treatment resistance without invasive biopsies.
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Multi-Modal AI for Remote Patient Monitoring in Cancer Care

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yansong Liu · Ronnie Stafford · Pramit Khetrapal · Huriye Kocadag · Gra\c{c}a Carvalho · Patricia de Winter · Maryam Imran · Amelia Snook · Adamos Hadjivasiliou · D. Vijay Anand · Weining Lin · John Kelly · Yukun Zhou · Ivana Drobnjak
arXiv:2512.00949v1 Announce Type: cross Abstract: For patients undergoing systemic cancer therapy, the time between clinic visits is full of uncertainties and risks of unmonitored side effects. To bridge this gap in care, we developed and prospectively trialed a multi-modal AI framework for remote patient monitoring (RPM). This system integrates multi-modal data from the HALO-X platform, such as demographics, wearable sensors, daily surveys, and clinical events. Our observational trial is one of the largest of its kind and has collected over 2.1 million data points (6,080 patient-days) of monitoring from 84 patients. We developed and adapted a multi-modal AI model to handle the asynchronous and incomplete nature of real-world RPM data, forecasting a continuous risk of future adverse events. The model achieved an accuracy of 83.9% (AUROC=0.70). Notably, the model identified previous treatments, wellness check-ins, and daily maximum heart rate as key predictive features. A case study demonstrated the model's ability to provide early warnings by outputting escalating risk profiles prior to the event. This work establishes the feasibility of multi-modal AI RPM for cancer care and offers a path toward more proactive patient support.(Accepted at Europe NeurIPS 2025 Multimodal Representation Learning for Healthcare Workshop)
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A TinyML Reinforcement Learning Approach for Energy-Efficient Light Control in Low-Cost Greenhouse Systems

✇cs.AI, q-bio.NC updates on arXiv.org
By: Mohamed Abdallah Salem (North Dakota State University) · Manuel Cuevas Perez (North Dakota State University) · Ahmed Harb Rabia (North Dakota State University)
arXiv:2512.01167v1 Announce Type: cross Abstract: This study presents a reinforcement learning (RL)-based control strategy for adaptive lighting regulation in controlled environments using a low-power microcontroller. A model-free Q-learning algorithm was implemented to dynamically adjust the brightness of a Light-Emitting Diode (LED) based on real-time feedback from a light-dependent resistor (LDR) sensor. The system was trained to stabilize at 13 distinct light intensity levels (L1 to L13), with each target corresponding to a specific range within the 64-state space derived from LDR readings. A total of 130 trials were conducted, covering all target levels with 10 episodes each. Performance was evaluated in terms of convergence speed, steps taken, and time required to reach target states. Box plots and histograms were generated to analyze the distribution of training time and learning efficiency across targets. Experimental validation demonstrated that the agent could effectively learn to stabilize at varying light levels with minimal overshooting and smooth convergence, even in the presence of environmental perturbations. This work highlights the feasibility of lightweight, on-device RL for energy-efficient lighting control and sets the groundwork for multi-modal environmental control applications in resource-constrained agricultural systems.
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PhySense: Sensor Placement Optimization for Accurate Physics Sensing

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuezhou Ma · Haixu Wu · Hang Zhou · Huikun Weng · Jianmin Wang · Mingsheng Long
arXiv:2505.18190v4 Announce Type: replace-cross Abstract: Physics sensing plays a central role in many scientific and engineering domains, which inherently involves two coupled tasks: reconstructing dense physical fields from sparse observations and optimizing scattered sensor placements to observe maximum information. While deep learning has made rapid advances in sparse-data reconstruction, existing methods generally omit optimization of sensor placements, leaving the mutual enhancement between reconstruction and placement on the shelf. To change this suboptimal practice, we propose PhySense, a synergistic two-stage framework that learns to jointly reconstruct physical fields and to optimize sensor placements, both aiming for accurate physics sensing. The first stage involves a flow-based generative model enhanced by cross-attention to adaptively fuse sparse observations. Leveraging the reconstruction feedback, the second stage performs sensor placement via projected gradient descent to satisfy spatial constraints. We further prove that the learning objectives of the two stages are consistent with classical variance-minimization principles, providing theoretical guarantees. Extensive experiments across three challenging benchmarks, especially a 3D geometry dataset, indicate PhySense achieves state-of-the-art physics sensing accuracy and discovers informative sensor placements previously unconsidered. Code is available at this repository: https://github.com/thuml/PhySense.
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The AI Productivity Index (APEX)

✇cs.AI, q-bio.NC updates on arXiv.org
By: Bertie Vidgen · Abby Fennelly · Evan Pinnix · Julien Bencheck · Daniyal Khan · Zach Richards · Austin Bridges · Calix Huang · Ben Hunsberger · Isaac Robinson · Akul Datta · Chirag Mahapatra · Dominic Barton · Cass R. Sunstein · Eric Topol · Brendan Foody · Osvald Nitski
arXiv:2509.25721v3 Announce Type: replace-cross Abstract: We present an extended version of the AI Productivity Index (APEX-v1-extended), a benchmark for assessing whether frontier models are capable of performing economically valuable tasks in four jobs: investment banking associate, management consultant, big law associate, and primary care physician (MD). This technical report details the extensions to APEX-v1, including an increase in the held-out evaluation set from n = 50 to n = 100 cases per job (n = 400 total) and updates to the grading methodology. We present a new leaderboard, where GPT5 (Thinking = High) remains the top performing model with a score of 67.0%. APEX-v1-extended shows that frontier models still have substantial limitations when performing typical professional tasks. To support further research, we are open sourcing n = 25 non-benchmark example cases per role (n = 100 total) along with our evaluation harness.
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Exosome-Mediated RUNX3 DNA Delivery for Lung Cancer Therapy

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Jieun Jeon · Hayeon Byun · Myung Chul Lee · Jiseong Kim · Seung Gwa Park · Andrea D Madrigal-Salazar · Montserrat L González · Mariana C Lopez-Pacheco · Wenpeng Liu · Mahmoud L Nasr · Soo-Hong Lee · You-Soub Lee · Luke P Lee · Woo-Jin Kim · Dohun Kim · Su Ryon Shin

ACS Appl Mater Interfaces. 2025 Dec 1. doi: 10.1021/acsami.5c15987. Online ahead of print.

ABSTRACT

Gene therapy represents a promising strategy for treating lung cancer, with the potential to inhibit the proliferation of cancerous cells and induce apoptosis. However, current gene therapy for lung cancer encounters challenges with delivery, targeting, and safety, such as off-target effects, immune responses, and the necessity for better delivery methods. Here, we introduce gene therapy using the key regulator in lung adenocarcinoma, runt-related transcription factor 3 (RUNX3), within exosomes (Exos), which are known for their biocompatibility and ability to selectively target cancer cells. We packaged the RUNX3 plasmid DNA into human exosomes (hExo-Rs), designed to target and induce apoptosis in cancer cells, resulting in a viability decrease to 43.3%. Normal fibroblasts remained viable at 96.0%, confirming the safety of hExo-Rs for future therapies. We delivered hExo-Rs to cancer spheroids, examined their effects, and found that cytokines from treated cells promote M1 macrophage polarization, emphasizing their potential for immunotherapy. We developed a hydrogel platform for the targeted 14-day release of RUNX3 pDNA by attaching hExo-Rs to gelatin using microbial transglutaminase, which enables the selective decrease in cancer cell viability and confirms apoptosis. Our demonstration of RUNX3 gene therapy with Exos presents selective anticancer effectiveness and the promise of clinical use through localized, sustained release using the hydrogel.

PMID:41325015 | DOI:10.1021/acsami.5c15987

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DNA-Based Liquid Biopsy for Evaluating Surgical and Postsurgical Outcomes in Gynecologic Malignancies: A Systematic Review

✇MRD
By: Samaneh Yavari · Sepide Javankiani · Foroozan Yarahmadi · Rojin Sarallah · Behandokht Rezaei · Maryam Maroufi · Mona Khabbazkar Amlashi · Mahshid Imankhan · Mahnaz Marvi · Neda Aslani · Arezou Soltanattar · Zahra Mohammadi · Alireza Azani · Pegah Kavousinia

J Clin Lab Anal. 2025 Dec 1:e70139. doi: 10.1002/jcla.70139. Online ahead of print.

ABSTRACT

INTRODUCTION: DNA-based liquid biopsies, including circulating tumor DNA (ctDNA) and cell-free DNA (cfDNA), are emerging as minimally invasive biomarkers for monitoring surgical and postsurgical outcomes in gynecologic malignancies. These tools offer the potential to guide early intervention, refine risk stratification, and improve prognostic accuracy. This systematic review aimed to assess the clinical utility of DNA-based liquid biopsies in evaluating recurrence, surgical success, and preoperative diagnosis in gynecologic cancers.

METHODS: A systematic review was conducted in accordance with PRISMA guidelines, covering studies published from 2017 to 2025. Literature searches were performed in PubMed, Scopus, and Web of Science. A total of 32 eligible observational studies involving 3210 patients with ovarian, endometrial, uterine, and other gynecologic malignancies were included. Study quality was assessed using the Newcastle-Ottawa Scale (NOS).

RESULTS: The studies showed a broad geographic and methodological diversity, with a median NOS score of 7. CtDNA and cfDNA demonstrated promise in three key areas: (1) Recurrence prediction-postoperative ctDNA positivity was associated with higher relapse rates and reduced disease-free survival; (2) Monitoring surgical outcomes and treatment response-ctDNA dynamics more accurately reflected tumor burden than traditional markers like CA125; (3) Preoperative diagnostic support-cfDNA methylation profiling and cfDNA/CA125 models enhanced malignancy detection and risk stratification. Ovarian and endometrial cancers were most frequently studied.

CONCLUSIONS: DNA-based liquid biopsies show strong potential in perioperative care for gynecologic cancers. Their integration into clinical workflows could improve the detection of minimal residual disease and inform individualized surgical planning.

PMID:41327898 | DOI:10.1002/jcla.70139

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Monitoring of circulating tumor DNA allows early detection of disease relapse in patients with operable breast cancer

✇MRD
By: Kristin Løge Aanestad · Marie Austdal · Oddmund Nordgård · Gunnar Mellgren · Satu Oltedal · Marie L Austbø · Ylva H Vignes · Thomas Helland · Kristin Jonsdottir · Tone H Lende · Emilius A M Janssen · Bjørnar Gilje · Kjersti Tjensvoll · PBCB study group

Mol Oncol. 2025 Nov 27. doi: 10.1002/1878-0261.70170. Online ahead of print.

ABSTRACT

Breast cancer is known for late recurrences, yet current follow-up lacks radiological or blood-based monitoring for systemic relapse. This study evaluated circulating tumor DNA (ctDNA) monitoring for early detection of systemic relapse after curative treatment. In this case-control study of 70 patients with operable breast cancer (35 with relapse and 35 without relapse), blood samples were collected every 6-12 months during a median 8.3-year follow-up. ctDNA was analyzed by targeted DNA sequencing using Oncomine™ Breast cfDNA Research Assay v2, and results were compared to genetic analysis of tumor and metastasis biopsies. ctDNA was detected at relapse in 19 of 35 (54%) patients with disease relapse and preceded clinical or radiological relapse detection in 17, with a median lead time of 10.3 months. In 13 (68%) patients, there was concordance with tumor mutations, and in seven patients, there was also concordance with metastasis. Among the relapse-free patients, seven were ctDNA-positive postsurgery, and only one of them had a match among the tumor variants. These findings suggest serial ctDNA analysis may enable earlier detection of systemic relapse in patients with operable breast cancer.

PMID:41307327 | DOI:10.1002/1878-0261.70170

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Circulating Tumor DNA (ctDNA) in Gastroesophageal Adenocarcinoma (GEA): Evidence and Emerging Applications

✇MRD
By: Oudai Sahwan · Lin Batha · Fares Jamal · Mohamad Bassam Sonbol

Cancers (Basel). 2025 Nov 18;17(22):3692. doi: 10.3390/cancers17223692.

ABSTRACT

The role of circulating tumor DNA (ctDNA) in gastroesophageal adenocarcinoma (GEA) has expanded in recent years. In resectable disease, postoperative ctDNA is able to detect patients at highest risk of recurrence months before scans. Tumor-informed assays provide the best sensitivity and emerging methylation assays are useful when tissue is scarce. In metastatic GEA, baseline ctDNA burden correlates with prognosis, and a decrease in ctDNA level after treatment initiation reflects therapeutic response. It can also uncover actionable targets, including ERBB2, FGFR2, and MSI-H, and detect resistance that can arise after starting treatment. Limitations include variable assay performance, low shedding in some tumors, clonal hematopoiesis confounding, and a lack of randomized data showing that ctDNA-guided changes improve outcomes. Ongoing trials are testing MRD-guided escalation/de-escalation and ctDNA-directed biomarker therapy. In this review, we evaluate the role of ctDNA in GEA cancers over recent years.

PMID:41301057 | PMC:PMC12650754 | DOI:10.3390/cancers17223692

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