Circulating Tumor DNA and Precision Biomarkers in Colorectal Cancer: Implications for Diagnosis, Monitoring, and Management of Advanced Disease

MRD

By: Vijay Kumar Srinivasalu · Narayana Subramaniam · Mohammed Ibrahim · Manjula Das

18 May 2026 at 18:00

J Gastroenterol Hepatol. 2026 May 18. doi: 10.1111/jgh.70420. Online ahead of print.

ABSTRACT

Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide, with outcomes critically dependent on timely diagnosis, accurate risk stratification, and individualized treatment. Traditional markers such as CEA, KRAS/NRAS, BRAF, and MSI status, while foundational, are insufficient to address the full complexity of CRC biology. Over the past decade, a new generation of biomarkers has emerged spanning liquid biopsy, stool-based methylation, genomics, epigenomics, immune profiling, microbiome analysis, radiomics, patient-derived organoids, and multiomics integration-collectively redefining how CRC is detected, classified, and treated. This narrative review synthesizes evidence from 73 human studies published between 2010 and 2024, identified through structured searches of PubMed, Embase, Web of Science, and the Cochrane Library, and quality-assessed using QUADAS-2 and Newcastle-Ottawa tools. Circulating tumor DNA (ctDNA) emerged as the most clinically validated biomarker, demonstrating superior performance in minimal residual disease (MRD) detection, recurrence prediction, and real-time therapy monitoring. Stool DNA methylation assays showed strong sensitivity for early CRC and advanced adenoma detection. Genomic markers including BRAF V600E, POLE/POLD1, HER2, and KRAS G12C now directly inform targeted therapy selection, while immune biomarkers-MSI-H, TMB, and Immunoscore-guide immunotherapy decisions and stratify prognosis beyond TNM staging. Microbiome signatures, particularly Fusobacterium nucleatum and colibactin-producing Escherichia coli, were associated with chemo resistance and tumor progression. Radiomics and AI-driven imaging models provided noninvasive assessment of nodal involvement and neo-adjuvant therapy response. Patient-derived organoids demonstrated capacity to predict individual drug sensitivity, and multiomic integration enabled refined molecular subtyping. Despite this progress, widespread clinical adoption remains limited by assay variability, lack of prospective multicenter validation, and implementation barriers including cost and infrastructure. As these technologies mature, their integration into standardized, multidisciplinary workflows will be essential to translating biomarker innovation into improved patient outcomes across all stages of CRC care.

PMID:42150752 | DOI:10.1111/jgh.70420

Population-scale repeat expansions elucidate disease risk and brain atrophy

Nature - Issue - nature.com science feeds

By: Vijay Kumar Pounraja · Jae Hoon Sul · Joseph Herman · Sean O’Keeffe · Veera Rajagopal · Xiaodong Bai · Michael D. Kessler · Neelroop Parikshak · Karl Landheer · Xingmin Zhang · Sean Yu · Lance Zhang · Michelle G. LeBlanc · Jennifer Rico-Varela · Frederic Grau · Sarah Wolf · Sriramkumar Sundaramoorthy · Farshid Sepehrband · Eli A. Stahl · Yuda Huo · Mohsin Ahmed · Susan Croll · Adam Buchanan · David J. Carey · Christa L. Martin · Michelle Meyer · Kyle Retterer · David Rolston · James R. Cerhan · Fergus J. Couch · Janet E. Olson · Nicholas B. Larson · Zachary S. Fredericksen · Mine Cicek · Joanna M. Biernacka · Victor M. Karpyak · Prashanthi Vemuri · Vijay K. Ramanan · Owen A. Ross · Mark A. Frye · Jeanette E. Eckel Passow · Robert R. Jenkins · Daniel H. Lachance · Kristen L. Drucker · Paul A. Decker · Matthew L. Kosel · Sarah A. McLaughlin · Kathryn J. Ruddy · Nicholas J. Boddicker · Wenan Chen · Suzette J. Bielinski · John C. Lieske · W. Michael Hooten · Lisa A. Boardman · Richard B. Kennedy · Andrew D. Badley · Sean C. Dowdy · Shariska Harrington · Gretchen E. G

8 April 2026 at 08:00

Nature, Published online: 08 April 2026; doi:10.1038/s41586-026-10345-6

Decreased brain volumes and increased NfL levels can be observed earlier than disease diagnosis in short-tandem-repeat-associated neurological diseases.

Safety Guardrails for LLM-Enabled Robots

cs.AI, q-bio.NC updates on arXiv.org

By: Zachary Ravichandran · Alexander Robey · Vijay Kumar · George J. Pappas · Hamed Hassani

5 March 2026 at 13:00

arXiv:2503.07885v2 Announce Type: replace-cross Abstract: Although the integration of large language models (LLMs) into robotics has unlocked transformative capabilities, it has also introduced significant safety concerns, ranging from average-case LLM errors (e.g., hallucinations) to adversarial jailbreaking attacks, which can produce harmful robot behavior in real-world settings. Traditional robot safety approaches do not address the contextual vulnerabilities of LLMs, and current LLM safety approaches overlook the physical risks posed by robots operating in real-world environments. To ensure the safety of LLM-enabled robots, we propose RoboGuard, a two-stage guardrail architecture. RoboGuard first contextualizes pre-defined safety rules by grounding them in the robot's environment using a root-of-trust LLM. This LLM is shielded from malicious prompts and employs chain-of-thought (CoT) reasoning to generate context-dependent safety specifications, such as temporal logic constraints. RoboGuard then resolves conflicts between these contextual safety specifications and potentially unsafe plans using temporal logic control synthesis, ensuring compliance while minimally violating user preferences. In simulation and real-world experiments that consider worst-case jailbreaking attacks, RoboGuard reduces the execution of unsafe plans from over 92% to below 3% without compromising performance on safe plans. We also demonstrate that RoboGuard is resource-efficient, robust against adaptive attacks, and enhanced by its root-of-trust LLM's CoT reasoning. These results demonstrate the potential of RoboGuard to mitigate the safety risks and enhance the reliability of LLM-enabled robots. We provide additional resources at https://robo-guard.github.io/.

Contextual Safety Reasoning and Grounding for Open-World Robots

cs.AI, q-bio.NC updates on arXiv.org

By: Zachary Ravichadran · David Snyder · Alexander Robey · Hamed Hassani · Vijay Kumar · George J. Pappas

24 February 2026 at 13:00

arXiv:2602.19983v1 Announce Type: cross Abstract: Robots are increasingly operating in open-world environments where safe behavior depends on context: the same hallway may require different navigation strategies when crowded versus empty, or during an emergency versus normal operations. Traditional safety approaches enforce fixed constraints in user-specified contexts, limiting their ability to handle the open-ended contextual variability of real-world deployment. We address this gap via CORE, a safety framework that enables online contextual reasoning, grounding, and enforcement without prior knowledge of the environment (e.g., maps or safety specifications). CORE uses a vision-language model (VLM) to continuously reason about context-dependent safety rules directly from visual observations, grounds these rules in the physical environment, and enforces the resulting spatially-defined safe sets via control barrier functions. We provide probabilistic safety guarantees for CORE that account for perceptual uncertainty, and we demonstrate through simulation and real-world experiments that CORE enforces contextually appropriate behavior in unseen environments, significantly outperforming prior semantic safety methods that lack online contextual reasoning. Ablation studies validate our theoretical guarantees and underscore the importance of both VLM-based reasoning and spatial grounding for enforcing contextual safety in novel settings. We provide additional resources at https://zacravichandran.github.io/CORE.

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