BMC Med Res Methodol. 2026 Mar 9. doi: 10.1186/s12874-026-02821-4. Online ahead of print.
NO ABSTRACT
PMID:41803732 | DOI:10.1186/s12874-026-02821-4
Cancer Treat Res Commun. 2026 Mar 2;47:101162. doi: 10.1016/j.ctarc.2026.101162. Online ahead of print.
ABSTRACT
BACKGROUND: The collection of clinical data and patient tumor specimens in institutional repositories is essential to accelerate translational research in lung cancer, linking laboratory findings with patient outcomes. These resources allow investigators to explore tumor heterogeneity, analyze therapeutic profiles and, more recently, generate patient-derived models of cancer. Given the plethora of therapies in clinical use or under investigation, it is critical to establish tissue collection programs that support the identification of predictive biomarkers of drug sensitivity to define patient subgroups that may benefit from tailored therapeutic strategies. However, access to high-quality viable specimens remains limited.
METHODS: We established a multidisciplinary program -the Lung Cancer Tissue Collection (LCTC) study- to prospectively collect viable human specimens and clinical data. Samples can be collected post-diagnosis and at multiple treatment time points, preserving material for future studies.
RESULTS: In the first 24 months of the LCTC study, we enrolled 158 patients and collected over 700 specimens from patients with lung cancer. EGFR and KRAS mutations were the most frequently identified oncogenic drivers, mirroring frequencies reported in public datasets. We achieved a 60 % success rate in cryopreservation -measured by the proportion of patient-derived organoids growing after tissue thawing and processing- highlighting the feasibility of our program.
CONCLUSIONS: The LCTC biobank captures the molecular and clinical diversity of lung cancer, providing a clinically annotated resource of viable tissue and longitudinal blood specimens. This platform enables patient-derived modeling and multi-omic and functional studies to investigate tumor biology, treatment response, and resistance, supporting biomarker discovery and precision medicine.
PMID:41797251 | DOI:10.1016/j.ctarc.2026.101162
Int J Clin Oncol. 2026 Mar 6. doi: 10.1007/s10147-026-03001-6. Online ahead of print.
ABSTRACT
BACKGROUND: Early detection of cancer and precise recurrence monitoring remain major unmet needs in oncology. Conventional screening is limited to a few cancer types, leaving nearly half of cancers without established programs. Multi-cancer early detection (MCED) tests based on circulating tumor biomarkers have shown promise, but sensitivity for early-stage remains a challenge. In parallel, detection of molecular residual disease (MRD) using circulating tumor DNA (ctDNA) has emerged as a powerful prognostic and predictive tool, though current assays remain limited in sensitivity and specificity. This study aims to integrate multi-omics data to develop more refined and highly sensitive MCED and MRD assays.
METHODS: This study leverages clinical information and biospecimens from patients with cancer and cancer-naïve individuals. Samples from patients with cancers will be derived from the MONSTAR-SCREEN-3 study, while those from cancer-naïve individuals will be obtained from the Tohoku Medical Megabank Project. Comprehensive analyses will include whole-genome sequencing (WGS), whole-exome sequencing (WES), whole-transcriptome sequencing (WTS), proteomics, metabolomics, and microbiome profiling using stool and saliva. Artificial intelligence (AI)-based multi-omics integration will be performed to develop novel MCED and MRD assays and to evaluate their clinical performance. The primary endpoints are the sensitivity and specificity of MCED and MRD assays.
DISCUSSION: This is the first large-scale study to integrate comprehensive multi-omics profiling with AI for MCED and MRD assay development. The findings are expected to advance precision oncology by improving early diagnosis and recurrence monitoring.
TRIAL REGISTRATION: UMIN000053815, approved by the Institutional Review Board of the National Cancer Center Hospital East.
PMID:41790338 | DOI:10.1007/s10147-026-03001-6
Last year, the Department of Health and Human Services published a sweeping document that described the agency’s approach to real-world data. Historically, health and biomedical data has been intentionally manufactured, the output of carefully designed clinical trials. But in a digitized world, it can instead be mined — and patients’ interactions with the health care system are the natural resource.
The Living HHS Open Data Plan, published in July, proposed treating data more like we do other natural resources. “At the core” of the plan, it reads, “lies the concept that data is a ‘public utility’ for good that powers scientific advancement, innovation, and progress.” Patients should have access to that utility, HHS argued, but it should also be easier to leverage for research, safety monitoring, and other uses in the public interest.
On Thursday, a group of researchers, former agency officials, and health data companies continued that call in a policy forum published in Science. If health data is to be treated like a public utility, they write, it should be similarly governed. Like electricity, the system would have to involve customers, local distribution companies, transmission companies, generators, and the government.
Continue to STAT+ to read the full story…
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MedComm (2020). 2026 Mar 2;7(3):e70656. doi: 10.1002/mco2.70656. eCollection 2026 Mar.
ABSTRACT
Chemotherapy is the mainstay in the treatment of advanced gastric cancer (GC); yet, GC showed diverse responses to first-line chemotherapy regimens and the underlying molecular basis is still not clear. Here, we established a system that combined organoid-based chemotherapy regimen screening and transcriptome-based evaluation to identify underlying molecular signatures of different responses to chemotherapy. We generated 19 GC patient-derived organoids (PDOs) from surgically resected specimens with corresponding histological characteristics of parent tumors and tested all of the five most commonly used first-line chemotherapy regimens. Based on the treatment responses, PDOs were classified into double-sensitive, single-sensitive, and not-sensitive groups. PDOs that responded well to chemotherapy presented high expression levels of the P53 pathway genes and low expression levels of cell proliferative activity genes. Furthermore, the chemotherapy-based tumor classification of GC was established. The GC tumor classification was verified by multi-omics features from the TCGA dataset and public drug response datasets. In conclusion, this study systematically evaluated clinical chemotherapy regimens for GC and identified chemotherapy response-associated molecular signatures based on human GC organoids, which are beneficial to the precise treatments of GC.
PMID:41782964 | PMC:PMC12954136 | DOI:10.1002/mco2.70656
Adidas engineers describe shifting from a centralized Infrastructure-as-Code model to a decentralized one. Five teams autonomously deployed over 81 new infrastructure stacks in two months, using layered IaC modules, automated pipelines, and shared frameworks. The redesign illustrates how to scale infrastructure delivery while maintaining governance at scale.
By Leela Kumili
Artificial intelligence is no longer just powering defensive cybersecurity tools, it is reshaping the entire threat landscape. AI is accelerating reconnaissance, improving the realism of phishing, automating malware mutation, and enabling adaptive attack techniques. At the same time, enterprises are embedding AI agents, copilots, and generative AI tools into everyday workflows.
That dual dynamic has created a new category: AI security.
AI security platforms focus on three primary challenges in 2026:
Below are five of the strongest AI security solutions in 2026.
Check Point integrates AI security into its broader Infinity platform, covering network, cloud, endpoint, and AI usage in a unified architecture.
The core of the platform is ThreatCloud AI, which leverages more than 50 AI engines and intelligence from over 150,000 connected networks. Compromise indicators propagate across the platform within seconds, enabling coordinated defense across domains.
The platform addresses AI risk at multiple layers. GenAI Protect monitors employee interactions with generative AI tools, semantically analysing prompts to enforce data loss prevention policies in real time. This approach focuses on contextual classification rather than simple keyword matching.
Check Point also secures AI infrastructure and enhances security operations through Infinity AI Copilot. Independent testing has shown high efficacy against zero-day malware, and the platform has consistently ranked highly in hybrid firewall evaluations.
Best for: Enterprises seeking unified AI security across infrastructure, AI usage, and security operations.
CrowdStrike extends its Falcon platform into AI protection by integrating telemetry from endpoints, identities, cloud workloads, and AI agent activity.
Falcon AIDR focuses specifically on defending against prompt injection and malicious manipulation of AI agents. It is designed to identify known prompt injection techniques while maintaining low latency, which is critical in production AI environments.
CrowdStrike also integrates AI assistants directly into security operations. Charlotte AI supports natural language threat investigation and automated triage, reinforcing the company’s vision of an AI-augmented SOC.
The approach is particularly strong for organisations already standardised on the Falcon ecosystem, allowing AI security capabilities to extend existing endpoint and cloud telemetry.
Best for: Organisations seeking integrated AI threat detection within an established endpoint-centric security architecture.
Cisco approaches AI security from a network-centric vantage point. Because it operates at the network layer, Cisco can inspect AI-related traffic across enterprise environments, including API calls and model interactions that may not be visible at the endpoint level.
Cisco AI Defense integrates into the broader Security Service Edge architecture. Recent enhancements include AI Bills of Materials to map dependencies within AI ecosystems, real-time guardrails for agentic systems, and red teaming simulations against AI workflows.
Cisco aligns its controls with established frameworks such as NIST AI Risk Management Framework and MITRE ATLAS. This emphasis on governance makes it attractive to enterprises operating in regulated industries.
Best for: Enterprises with strong Cisco network infrastructure seeking AI security embedded at the traffic and control layer.
Microsoft’s AI security advantage lies in scale. The company processes tens of trillions of security signals daily across its global infrastructure.
Security Copilot functions as an AI assistant embedded within Defender, Entra, Intune, and Purview. It automates alert triage, assists with natural language threat investigation, and orchestrates remediation actions.
Microsoft has also expanded AI security posture management to include multi-cloud environments, including AWS and Google Cloud AI services. This is particularly important for enterprises building AI models outside Azure.
For organisations already invested in Microsoft 365 enterprise licensing, AI-enhanced security capabilities can be layered into existing subscriptions without introducing additional vendor complexity.
Best for: Enterprises deeply aligned with Microsoft 365 and Defender ecosystems.
As AI agents proliferate, identity becomes a primary attack surface. Many AI systems operate with high levels of privilege and autonomy.
Okta focuses specifically on identity governance in AI environments. Its architecture treats AI agents as first-class identities, applying authentication, authorisation, and lifecycle governance controls similar to those applied to human users.
Identity Security Posture Management identifies over-privileged accounts, including non-human identities, and surfaces risk in real time. The company also promotes open standards for managing AI-to-application connectivity through extended OAuth mechanisms.
For enterprises rapidly deploying AI agents internally, identity-centric AI security becomes essential.
Best for: Organisations deploying AI agents at scale that require identity governance for non-human actors.
| Vendor | Core strength | Ideal buyer |
| Check Point | Unified AI security across infrastructure and usage | Large enterprises seeking platform consolidation |
| CrowdStrike | Endpoint-integrated AI threat detection | Falcon-centric organisations |
| Cisco | Network-layer AI traffic visibility | Cisco ecosystem enterprises |
| Microsoft | Signal scale and Copilot integration | Microsoft 365-heavy environments |
| Okta | AI identity governance | Organisations deploying AI agents broadly |
Selecting the right AI security platform depends on architecture and maturity.
Organisations building AI internally should prioritise infrastructure protection and identity governance. Enterprises concerned with employee generative AI usage should evaluate prompt monitoring and DLP integration. Security teams overwhelmed by alert volume may prioritise AI-augmented SOC automation.
AI security is not a separate silo. It intersects with network security, identity management, cloud governance, and incident response.
The platforms above represent different strategic entry points into AI risk management. The best solution is the one aligned with your existing ecosystem and operational model.
In 2026, AI is both a tool and a target. Enterprises that treat AI security as an integrated part of their security architecture will be better positioned to manage evolving threats.
Image source: Pixabay
The post Best AI security solutions 2026: Top enterprise platforms compared appeared first on AI News.
Cancer Lett. 2026 Feb 28;645:218391. doi: 10.1016/j.canlet.2026.218391. Online ahead of print.
ABSTRACT
Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal cancers, characterized by pronounced cellular heterogeneity, a dense desmoplastic stroma, and a highly immunosuppressive tumor microenvironment (TME). Recent advances in single-cell RNA sequencing (scRNA-seq) have reshaped our understanding of PDAC by characterizing its cellular composition at single-cell resolution. These studies have uncovered complex TME networks involving T cells, myeloid populations, fibroblasts, and malignant epithelial cells, and have provided mechanistic insights into immune evasion, metastatic progression, and therapeutic resistance. Collectively, these findings depict PDAC as a dynamic and interactive ecosystem driven by cellular interactions. In this review, we systematically summarize recent scRNA-seq-based studies addressing PDAC heterogeneity, tumorigenesis, immune remodeling, therapeutic resistance and biomarker discovery. We further discuss integrative single-cell and spatial multi-omics approaches to map the TME of PDAC, providing a framework for understanding PDAC biology at single-cell and spatial resolution.
PMID:41771343 | DOI:10.1016/j.canlet.2026.218391
Front Immunol. 2026 Feb 13;17:1743187. doi: 10.3389/fimmu.2026.1743187. eCollection 2026.
ABSTRACT
BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC), an aggressive cancer with poor prognosis, poses major challenges owing to late diagnosis and limited response to current therapies. However, the identification of candidate drugs through multi-omics analyses and therapeutic peptides targeting key molecular pathways may provide improved outcomes. Although lactate metabolism is a critical factor in tumor progression, affecting cell proliferation, metastasis, and immune evasion, its role in PDAC-particularly within the tumor microenvironment, remains underexplored.
OBJECTIVES: This study investigated lactate metabolism in PDAC using high-throughput transcriptomic sequencing and single-cell transcriptomic analysis.
METHODS: Lactate metabolism-related gene expression was analyzed in tumor cells and their microenvironment, and correlations with patient prognosis were determined. Additionally, a machine learning-based prognostic model was established to identify lactate metabolism biomarkers for early diagnosis and personalized therapy.
RESULTS: Lactate metabolism significantly impacted the survival of patients with PDAC (n = 92; log-rank test, p < 0.05). Single-cell RNA and spatial transcriptomics analyses of 50, 795 cells from 8 PDAC samples revealed that 521 malignant cells exhibited hyperactive lactate metabolism (AUCell score comparison, p < 0.001). A prognostic model constructed from lactate metabolism-related genes using ensemble machine learning (StepCox + Enet, α = 0.5) effectively stratified patients into high- and low-risk groups across multiple cohorts (ICGC: n = 92; GSE28735: n = 45; GSE62452: n = 69; GSE183795: n = 139; all log-rank p < 0.05). Key prognostic genes identified included lysozyme (LYZ) and polymeric immunoglobulin receptor, which were significantly associated with patient survival (univariate Cox regression, p < 0.05). These genes may serve as clinical biomarkers of PDAC.
CONCLUSIONS: This study provides insights into PDAC metabolic features and highlights lactate metabolism as a potential therapeutic target. The identified biomarkers could facilitate early diagnosis and improve treatment strategies, ultimately enhancing patient outcomes.
PMID:41766855 | PMC:PMC12946077 | DOI:10.3389/fimmu.2026.1743187
Nature Biotechnology, Published online: 02 March 2026; doi:10.1038/s41587-026-03056-w
Establishing a commercial solution for extremely rare genetic diseases
Diagnostics (Basel). 2026 Feb 9;16(4):523. doi: 10.3390/diagnostics16040523.
ABSTRACT
Background: Liquid biopsy, particularly through the analysis of circulating tumor DNA (ctDNA), represents a significant advancement in oncology. Unlike traditional tissue biopsies, ctDNA offers a minimally invasive, real-time approach to cancer management. It has demonstrated considerable potential in early cancer detection, monitoring of therapeutic responses, and assessing minimal residual disease (MRD) to predict recurrence. By enabling comprehensive molecular profiling through a simple blood test, ctDNA supports the core principles of precision oncology, facilitating more personalized and adaptive treatment strategies. Methods: In the following article we describe the recent developments focused on refining ctDNA detection assays to improve sensitivity and specificity. Advanced technologies, including next-generation sequencing (NGS) and digital PCR, are commonly employed. The integration of artificial intelligence (AI) and multi-omics approaches-such as combining genomic, epigenomic, and transcriptomic data-has further enhanced the analytical power of ctDNA assays. Results: Emerging evidence shows that ctDNA-based liquid biopsy enables dynamic, real-time tracking of tumor evolution and therapeutic resistance. Clinical studies have demonstrated its efficacy in detecting early-stage cancers, guiding treatment selection, and predicting relapse with higher accuracy than some conventional methods. Moreover, AI-enhanced algorithms have improved signal detection, allowing for more precise and earlier identification of actionable mutations and MRD. Conclusions: ctDNA analysis via liquid biopsy is poised to revolutionize cancer care by offering a non-invasive, precise, and adaptive tool for tumor characterization and monitoring. Although obstacles remain-particularly regarding assay sensitivity, standardization, and economic feasibility-ongoing technological innovations and multi-omics integration are rapidly advancing its clinical viability. With continued progress, ctDNA-based liquid biopsy is likely to become a cornerstone of routine oncology practice.
PMID:41750672 | PMC:PMC12938931 | DOI:10.3390/diagnostics16040523
bioRxiv [Preprint]. 2026 Feb 18:2026.02.18.706515. doi: 10.64898/2026.02.18.706515.
ABSTRACT
The recurrence rate following complete surgical resection of primary non-small cell lung cancer is as high as 55%, yet no approach currently exists to evaluate the risk of local recurrence. The premetastatic paradigm is the recognition that metastasis is preceded by reprogramming naïve tissues to prime a microenvironment for tumor cell survival and subsequent reactivation. Identification of biomarkers of the pre-metastatic niche would allow us to evaluate a patient's risk of local relapse in the normal lung parenchyma surrounding the resected tumor. We designed a workflow incorporating in vivo modelling, radiology, and deep learning-guided three-dimensional (3D) imaging, spatial proteomics, and transcriptomics to identify previously unreported signals associated with the early transformation of the lung parenchyma announcing regional metastasis. We curated biorepository spanning timepoints before and after resection of primary Lewis Lung Carcinoma (LLC) tumors. Using radiology and cellular resolution 3D histology, we calculated the number and distribution of metastases in mouse lungs and developed an algorithm to guide placement of spatial proteomics and transcriptomics to regions containing early micro-metastases and the pre-metastatic microenvironment. Molecular and tissue features associated with presence, size, and location of metastases guided the identification of both myeloid (F4/80) and senescent (p16/p21) cell signatures in the premetastatic and metastatic environments. Finally, multiparametric flow cytometry of metastatic lungs in a senescence reporter GEMM (tdTomato-p16 INKA mice) resolved senescent cells including alveolar macrophages as the cellular phenotypes associated with these early premetastatic signatures. Altogether, this work highlights a novel AI-assisted approach for detection of biomarkers of tissue remodeling during lung cancer invasion.
PMID:41756853 | PMC:PMC12934922 | DOI:10.64898/2026.02.18.706515
Innovation (Camb). 2025 Aug 6;7(1):101076. doi: 10.1016/j.xinn.2025.101076. eCollection 2026 Jan 5.
ABSTRACT
Although circulating cell-free DNA (cfDNA) methylation has emerged as the mainstream approach in multi-cancer detection blood tests (MCDBTs), the potential of integrating proteins and mutations, to enhance its performance remains unclear. The PROMISE study (NCT04972201) was conducted to investigate the feasibility of a multi-omics integration strategy in MCDBTs across nine types of cancers in head and neck (excluding nasopharynx), esophagus, lung, stomach, liver, biliary tract, pancreas, colorectum, and ovary. Blood samples were prospectively collected from 1,706 participants (840 non-cancer; 866 cancer) and then randomly divided into training and validation sets. The complementarity between various omics were investigated, and specific omics features were carefully selected for further multimodal model construction. The methylation-based classifier outperformed both the mutation-based and protein-based classifiers. As 95.0% of cancer cases detected by the mutation-based classifier were simultaneously identified by the methylation-based classifier, while 14.0% of the protein-positive samples were missed, protein markers may provide complementary value to the methylation-based classifier. Compared with the methylation-based classifier, the multimodal classifier combining methylation and protein features exhibited an improved sensitivity of 75.1% (95% confidence interval [CI], 69.3%-80.3%) at the same specificity of 98.8% with the accuracy of top predicted origin (TPO1) of 73.1% (95% CI, 66.2%-79.2%). Notably, the TPO1 accuracy reached 100% in liver and ovarian cancers with negative results of the methylation-based classifier. Collectively, these data suggest that the integration of protein markers in the multimodal classifier can offer additional benefits to the methylation-based classifier, particularly in identifying liver and ovarian cancers.
PMID:41737326 | PMC:PMC12925926 | DOI:10.1016/j.xinn.2025.101076
Mol Aspects Med. 2026 Feb 23;108:101461. doi: 10.1016/j.mam.2026.101461. Online ahead of print.
ABSTRACT
Diabetes mellitus (DM) is a significant risk factor for several cancers, particularly cancers of the liver, pancreas, and endometrium. This review aims to understand the connections between diabetic pathophysiology and cancer biology. We synthesize how core metabolic disturbances-hyperinsulinemia, hyperglycemia, and inflammation-promote tumorigenesis by dysregulating canonical oncogenic pathways such as IGF-1 signaling, DNA damage repair, and immunometabolism. Subsequently, we focus on how key molecular integrators-such as p38 MAPK, Wnt/β-catenin, and the AGEs-RAGE axis-mediate metabolic stress to confer proliferative and invasive advantages to tumor cells. However, a direct translational application of these mechanisms, particularly in the context of repurposing antidiabetic drugs for cancer therapy, remains challenging due to inconsistent clinical outcomes. To address this gap, we suggest that a fundamental shift in approach is required. We propose that future research must move beyond simple pathway categorization and instead utilize spatial analysis techniques to reveal how diabetic metabolites reshape the tumor microenvironment (TME). By integrating single-cell and spatial omics technologies, the field can begin to map the precise cellular niches within tumors where diabetic metabolites exacerbate malignant progression and foster treatment resistance. This perspective is essential for developing targeted strategies to mitigate cancer risk and improve outcomes for the expanding population of patients with DM and cancer.
PMID:41734405 | DOI:10.1016/j.mam.2026.101461
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