Predictive Biomarker Identification

🚨 First genetically guided personalization in multiple sclerosis treatment! 🔬 Towards precision neurology in MS: A new era begins... Proud to share our latest study, now published in eBioMedicine (The Lancet): HLA-A*03:01 as a predictive genetic biomarker for glatiramer acetate treatment response in MS. 📍 Why this matters: This is the first demonstration of a clinically actionable genetic marker guiding platform therapy choice in MS. Patients carrying HLA-A*03:01 experience a significantly improved response to glatiramer acetate (GA) compared to interferon-beta (IFN). GA might not be the latest immune therapy hit on earth, but what we long thought - it acts somewhat like a vaccine, best on certain HLA backgrounds - has now been shown. Clearly it took time, but now its a first example for therapeutic precision neuroimmunology. In five (YES!) independent cohorts, retrospectively analysed, we saw: ✅ Up to 63% reduced relapse risk ✅ Better long-term EDSS trajectories ✅ Reduced MRI and biomarker disease activity 🧠 The biomarker is underpinned by shared T-cell receptor (TRB) expansions post-GA treatment, uniquely in HLA-A*03:01 carriers – a truly robust and reproducible signal across platforms. 📊 This work opens the door to genotype-guided treatment selection in early MS and supports GA as an optimal first-line therapy in this genetically defined subgroup. 🙏 I am grateful to our outstanding collaborators across the German competence network of MS (KKNMS), the MS-EPIC team (UCSF), the French Bionat cohort and numerous European and US centers. A special thanks to co-leads and co-senior authors Tilman Schneider-Hohendorf, Brian Zhang, Nicholas Schwab, Jo Sabatino, and all co-authors, consortial efforts and patients who made this effort possible. 🧬 Its the first example of a meaningful HLA genotyping in MS routine+ care. 👉 https://lnkd.in/erT_vggZ #MultipleSclerosis #Neuroimmunology #PrecisionMedicine #HLA #MSBiomarkers #GlatiramerAcetate #MSResearch #Neurology #PersonalisedMedicine #eBioMedicine

Hot off our recent transformer paper, we're excited to share another AI model for precision medicine! Biological data collected from patients has exploded in recent years, presenting a challenge: how do we decipher that data to understand which patients will benefit most from specific therapies?  We in the Applied Data Science team at AstraZeneca are thrilled to share our paper in Cancer Cell called "AI-Driven Predictive Biomarker Discovery with Contrastive Learning to Improve Clinical Trial Outcomes." Here, we introduce the *Predictive Biomarker Modeling Framework (PBMF)*, a neural network-powered contrastive learning process that: 🔍 Explores vast multimodal datasets to uncover predictive biomarkers in an automated, systematic, and unbiased manner  🧠 Distinguishes predictive biomarkers (which indicate a likely benefit from a specific therapy) from prognostic biomarkers (which indicate general disease outlook)  💡 Distills its outputs into an interpretable decision tree, showing what drives treatment response In our studies, the PBMF:  📊 Surpassed existing methods in finding predictive biomarkers for immunotherapy success across various cancers in clinical trial and real-world data  📈 Discovered a predictive biomarker in an early-stage trial that boosted efficacy by 15% when retrospectively applied to the corresponding phase 3 clinical trial  📈 Discovered predictive biomarkers in single-arm early phase trial data with synthetic control arms, retrospectively improving the efficacy of the corresponding phase 3 trials by at least 10% We believe the PBMF has the potential to improve the way we design clinical trials and match patients to the right therapies. It can integrate with other models like our Clinical Transformer, creating exciting possibilities to someday discover biomarkers of adverse events, dosing strategies, and even to back-translate new drug targets. Read the full paper here: https://lnkd.in/eveAnVRY   Thanks to all the co-authors: Gustavo Arango, Damian Bikiel, Gerald Sun, Elly Kipkogei, Kaitlin Smith, Sebastian Carrasco Pro, Elizabeth Choe #PrecisionMedicine #ClinicalTrials #AIinHealthcare #Biomarkers #Immunotherapy

Clinicians have spent years guessing who will benefit from immunotherapy. But this paper introduces a machine learning-driven scoring system that dramatically improves pan-cancer prediction accuracy. The Problem: - Immunotherapy outcomes, particularly with immune checkpoint blockade (ICB), vary significantly across and within cancer types. - Existing biomarkers like PD-L1 and TMB lack universal predictive value across pan-cancer settings. - Complex tumor immune microenvironments (TIMEs) demand more robust, scalable analytic tools for accurate prediction. What the Authors Did: - Developed the iMLGAM R package, integrating machine learning, gene-pair analysis, and genetic algorithms. - Employed ensemble learning with models like Elastic Net, SVM, KNN, and Random Forest, optimized via genetic algorithms. - Validated predictive performance across multiple independent cohorts using multiomics and immune profiling techniques. Main Findings: - iMLGAM score reliably predicts ICB therapy response across pan-cancer cohorts, with lower scores linked to better outcomes. - Tumors with low iMLGAM scores show stronger immune cell infiltration, increased T cell activity, and favorable mutational signatures. - Notably, CEP55 was identified as a key gene promoting immune evasion; its knockdown reduced tumor aggressiveness and improved T cell function. - In vivo CEP55 suppression combined with anti-PD1 therapy significantly enhanced survival in mouse models. - iMLGAM outperformed 12 existing immunotherapy predictive signatures across multiple datasets. Implications for Cell & Gene Therapy - iMLGAM provides a ready-to-use, accurate scoring system for guiding personalized immunotherapy decisions. - The model's reliance on gene-pair analysis allows for platform-independent application, (enhancing clinical utility). - Integration into clinical workflows could minimize ineffective ICB treatment, reducing costs and avoiding toxicity. - CEP55 could be the next big immunotherapy target, potentially augmenting gene-targeted combination therapies. Kudos to the authors - great job! Anything else you'd add? Drop it in the comments.

Democratizing Biomarker Discovery: omicML Bridges Transcriptomics and Machine Learning Biomarker discovery has long been a technical bottleneck, fragmented across pipelines, programming languages, and statistical tools that demand both coding expertise and computational intuition. A new study describes the omicML framework and attempts to change that paradigm. OmicML integrates bioinformatics and machine learning into a unified, no-code platform for transcriptomic biomarker identification. It enables seamless workflows, from data upload and quality control to differential expression, feature selection, and predictive model generation, through an intuitive graphical interface. Key innovation: omicML merges R and Python workflows under a single GUI, supporting both RNA-seq and microarray data, and automates ML-based feature selection, benchmarking, and validation using nested cross-validation. This empowers biologists to go from raw data to predictive biomarkers without writing a single line of code. Case study highlight: Using multi-platform datasets, the team applied omicML to identify biomarkers of monkeypox virus (MPXV) infection. ▪️ A six-gene diagnostic model, ZNF212, ZNF451, PLAGL1, NFAT5, ICAM5, and RRAD, achieved outstanding diagnostic accuracy (AUROC 0.95; AUPRC 0.92), distinguishing Mpox from SARS-CoV-2, HIV, Ebola, and varicella with remarkable specificity. ▪️ Among these, RRAD emerged as the top-performing single-gene biomarker, offering a potential new diagnostic anchor for viral infections. OmicML exemplifies an advance in translational bioinformatics, lowering the barrier for non-programmers, accelerating multi-omics integration, and providing a reproducible foundation for AI-driven precision medicine. By merging data science rigor with accessibility, it redefines how academic and clinical researchers can translate high-dimensional expression data into actionable diagnostic or therapeutic insights. As machine learning continues to reshape biomedical discovery, tools like omicML will be pivotal in expanding who can contribute to innovation, bridging the gap between bench scientists and computational biology. Website: https://omicml.org Read the preprint: https://lnkd.in/eUxe8yca #Bioinformatics #MachineLearning #Biomarkers #Transcriptomics #PrecisionMedicine

Excited to share a new tutorial, How to Develop Predictive Biomarkers. This guide is intended to fill in a conceptual gap–something I’ve identified when trying to find good resources that allow an experienced computational biologist, with a general understanding of machine learning, to blend their unique skill set and create a biomarker panel that can be used to predict a given outcome, like treatment response in triple negative breast cancer, for example. Part of my inspiration for writing this piece is that most of the resources I've previously come across, or written myself, are about isolated components of ML model development such as data processing, algorithm spot-checking, training a model, evaluating it, and tuning it. However, I've yet to come across a resource that explains the big picture–how all of these things fit together, and then guides you through the thinking process at each step from data acquisition to model deployment. Check it out and let me know what you think! And, if you find it helpful please pass it on. 🧬 https://lnkd.in/ez_vMzEx #compbio #bioinformatics #biomarkers #machinelearning #aixbio #biotech #datascience #proteomcs

Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy, yet most patients fail to achieve durable responses. To better understand the tumor microenvironment (TME), we analyze single-cell RNA-seq (~189 K cells) from 36 metastatic melanoma samples, defining 14 cell types, 55 subtypes, and 15 transcriptional hallmarks of malignant cells. Correlations between cell subtype proportions reveal six distinct clusters, with a mature dendritic cell subtype enriched in immunoregulatory molecules (mregDC) linked to naive T and B cells. Importantly, mregDC abundance predicts progression-free survival (PFS) with ICIs and other therapies, especially when combined with the TCF7 + /– CD8 T cell ratio. Analysis of an independent cohort (n = 318) validates mregDC as a predictive biomarker for anti-CTLA-4 plus anti-PD-1 therapies. Further characterization of mregDCs versus conventional dendritic cells (cDC1/cDC2) highlights their unique transcriptional, epigenetic (single-nucleus ATAC-seq data for cDCs from 14 matched samples), and interaction profiles, offering new insights for improving immunotherapy response and guiding future combination treatments. Paper and research by Jiekun (Jackie) Yang and larger team at Massachusetts Institute of Technology and the Broad Institute of MIT and Harvard. The text above is from the author's manuscript

Deep learning applied to liver MRI scan data can predict the development of cardiovascular disease. That sounds unusual, if not unbelievable, at first glance, doesn't it? In a study published online by JHEP Reports last week (—> https://lnkd.in/eP26rwDs ), Dr. Jakob Nikolas Kather from the Medical Oncology Department of the NCT Heidelberg - Nationales Centrum für Tumorerkrankungen (NCT) Heidelberg and colleagues investigated the application of transformer neural networks using liver MRI data from the U.K.Biobank’s collection to determine their efficacy in cardiovascular risk prediction. Cardiovascular disease is frequently linked to underlying metabolic conditions. Since the liver plays a central role in metabolism, it could serve as a marker for metabolic shifts that precede cardiovascular disease - particularly major adverse cardiac events (MACEs). Developing noninvasive, imaging-based biomarkers to assess cardiovascular risk, especially in individuals who have not yet shown symptoms, could support earlier detection; however, this approach remains difficult to implement. The team used transformer neural networks, a newer, more flexible type of neural network, to develop a liver-MRI foundation model trained through self-supervised learning on 44,672 U.K. Biobank single-slice liver MRIs. Of these scans, those used for the training included a combination of all of those with a recorded occurrence of MACE before the liver MRI exam (974), along with the majority of participants with no history of MACE before the MRI (43,698). An additional 750 (all 214 participants with first-time MACE after the MRI, and 536 randomly selected participants with no history of MACE both before and after the MRI) were used for external validation. In all, there were 45,422 participants. The researchers assessed the predictive ability of the model by comparing predicted risk scores with the actual cardiovascular outcomes. The team evaluated the results of subgroups based on identified risk factors from SCORE2 (e.g., diabetes, cholesterol, systolic blood pressure, sex, and smoking status) within our model’s prediction scores to “provide insight into which cardiovascular risk factors are being captured in a more pronounced way by our model.” The results showed that the model has “significant discriminatory capacity” for predicting MACE and cardiovascular-related mortality, even outperforming methods such as SCORE2. Nevertheless, the authors acknowledged that despite the model’s potential as an imaging-based biomarker for cardiovascular risk, using MRI broadly for screening is unrealistic due to its high cost and limited accessibility. Instead, they recommended its use be focused on high-risk populations or in cases where relevant imaging data already exists such as in patients with known metabolic disorders or those who have undergone liver imaging for other clinical reasons.

Phenomenal #Proteomics #Biomarkers #TherapeuticTargets #Resource now breaking online at Cell Press | UK Biobank plasma yields comprehensive Open-Access #Proteome ↔ #Phenome #Atlas | #health | #disease | #diagnostics | Large-scale proteomics studies can refine our understanding of health and disease and enable precision medicine. Here*, the authors provide a detailed atlas of 2,920 plasma proteins linking to diseases (406 prevalent and 660 incident) and 986 health-related traits in 53,026 individuals (median follow-up: 14.8 years) from the UK Biobank, representing the most comprehensive proteome profiles to date. This atlas revealed 168,100 protein-disease associations and 554,488 protein-trait associations. Over 650 proteins were shared among at least 50 diseases, and over 1,000 showed sex and age heterogeneity. Furthermore, proteins demonstrated promising potential in disease discrimination (area under the curve [AUC] > 0.80 in 183 diseases). Finally, integrating protein quantitative trait locus data determined 474 causal proteins, providing 37 drug-repurposing opportunities and 26 promising targets with favorable safety profiles. These results provide an open-access comprehensive proteome-phenome resource (https://lnkd.in/dXGrTCbD) to help elucidate the biological mechanisms of diseases and accelerate the development of disease biomarkers, prediction models, and therapeutic targets. *https://lnkd.in/dJuTcVMg Celentyx Ltd Professor Nicholas Barnes PhD, FBPhS Omar Qureshi Catherine Brady GRAPHICAL ABSTRACT

I am tremendously excited about the real-world impact of our latest publication on #AI #Biomarkers in Nature Medicine: https://lnkd.in/dv-7aS7Y Even in the US barely half of #lungcancer patients are tested for #EGFR mutations, for which targeted therapies readily exist. We have worked for many, many years now to try to overcome this gap with AI for H&E slides to offer patients a fast and cost-effective solution to get the right treatment. The point of this work is not only that we actually built it, but that Gabriele Campanella and Chad Vanderbilt organized a consortium and created the infrastructure for the first real-world, real-time deployment of a fine-tuned pathology foundation model for lung cancer biomarker detection. 𝙋𝙧𝙤𝙨𝙥𝙚𝙘𝙩𝙞𝙫𝙚𝙡𝙮!   𝐌𝐞𝐞𝐭 𝐄𝐀𝐆𝐋𝐄 (EGFR AI Genomic Lung Evaluation): ✅ 𝟎.𝟖𝟗 𝐀𝐔𝐂 in a 𝐩𝐫𝐨𝐬𝐩𝐞𝐜𝐭𝐢𝐯𝐞 silent trial with clinical-grade performance. 🌍 Generalizes 𝐚𝐜𝐫𝐨𝐬𝐬 𝐡𝐨𝐬𝐩𝐢𝐭𝐚𝐥𝐬 𝐚𝐧𝐝 𝐜𝐨𝐧𝐭𝐢𝐧𝐞𝐧𝐭𝐬 with robustness and reproducibility. 🔬 Validated on 𝐢𝐧𝐭𝐞𝐫𝐧𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐜𝐨𝐡𝐨𝐫𝐭𝐬, 𝐦𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐢𝐧𝐬𝐭𝐢𝐭𝐮𝐭𝐢𝐨𝐧𝐬, 𝐚𝐧𝐝 𝐬𝐜𝐚𝐧𝐧𝐞𝐫𝐬. 🧪 𝟒𝟑% 𝐫𝐞𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐢𝐧 𝐫𝐚𝐩𝐢𝐝 𝐦𝐨𝐥𝐞𝐜𝐮𝐥𝐚𝐫 𝐭𝐞𝐬𝐭𝐬, preserving biopsy tissue for full genomic profiling. ⚡ 𝐃𝐞𝐥𝐢𝐯𝐞𝐫𝐬 𝐫𝐞𝐬𝐮𝐥𝐭𝐬 𝐢𝐧 𝐮𝐧𝐝𝐞𝐫 𝟏 𝐡𝐨𝐮𝐫, compared to 2–3 weeks for NGS. 🚀 A foundational step toward regulatory approval and 𝐀𝐈-𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞𝐝 𝐜𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬.   We have worked on Computational Biomarkers in Pathology continuously for over a decade starting with AI for predicting SPOP in prostate cancer from H&E in 2015, but seeing everything come to fruition at such a scale in 2025 is very humbling. AI, when done right, can give real, tangible help to cancer patients. 𝑰𝒕 𝒊𝒔 𝒐𝒖𝒓 𝒓𝒆𝒔𝒑𝒐𝒏𝒔𝒊𝒃𝒊𝒍𝒊𝒕𝒚 𝒕𝒐 𝒎𝒂𝒌𝒆 𝒊𝒕 𝒂 𝒓𝒆𝒂𝒍𝒊𝒕𝒚! I am deeply grateful to everyone on this most amazing team: Gabriele Campanella, Neeraj Kumar, Ph.D., Swaraj Nanda, Siddharth Singi, Eugene Fluder, Ricky Kwan, Silke Mühlstedt, Nicole  Pfarr, Peter Schüffler, Ida Häggström, Noora Neittaanmäki, Levent Akyürek, Alina Basnet, Tamara Jamaspishvili, Michel Nasr, Matthew Croken, Fred Hirsch, Arielle Elkrief, Helena Yu, Orly Ardon, Greg Goldgof, Meera Hameed, Jane Houldsworth, Maria E. Arcila, Chad Vanderbilt #AI #ComputationalPathology #Biomarkers #AIinHealthcare #DigitalPathology #PrecisionMedicine #LungCancer #EGFR #NatureMedicine #FoundationModels #EAGLEModel #EAGLE #Oncology

Neuroinflammation is increasing recognized as a central driver, rather than a downstream byproduct, of neurodegenerative diseases. Microglia and astrocytes work in unison to regulate an immune response after damage induced by rogue proteins (i.e. amyloid beta, Tau, alpha-synuclein, TDP-43, etc). In short, microglia are neuroprotective to help the brain (i.e. post injury) but under chronic stress from toxic proteins, pro-inflammatory cytokines like IL-1B, IL-6, TNF-alpha are released. This cascade becomes quite deleterious for the brain. Astrocytes are put to work and can be helpful in ‘A2’ states but as inflammation becomes chronic, they can turn ‘A1’ rogue and become neurotoxic and further accelerate neuronal death. This mechanism is one that has really caught my attention. I believe immunology, inflammation, and metabolism represents the next frontier of brain health that will ultimately unlock #precisionmedicine approaches that the field has been craving with improved biomarker detection. That’s why I’m glad to review this publication which sought to better understand the interplay between inflammation and disease state in Alzheimer’s disease (AD). https://lnkd.in/eifQb_5A PET imaging was used for protein pathology detection and translocator protein (TSPO) for an inflammation proxy across 145 individuals with matched controls. The protocol identified two waves of inflammation corresponding to amyloid beta in early disease stages and Tau in later stages. A strong correlation was found between AD disease state and neuroinflammation. Furthermore, amyloid beta burden and high neuroinflammation are correlated to cognitive decline via the CDR-SB rating scale. The localization of inflammation and protein pathology also yielded intriguing results. In the early Tau stage, the associations were predominantly in the temporal cortex and subcortical (hippocampus, amygdala, and lingual gyrus) which shifted to negative correlations in later Tau stages. Neuroinflammation alone did not predict decreases in gray matter density or cognitive decline. However, a synergistic effect was found between Tau burden and inflammation on cognitive decline primarily in the cortical regions. Interestingly, this association can also be used to predict future changes in CDR-SB cognitive decline. The authors note that neuroinflammation can be used as a screening tool to recruit patients in future trials. This can help drug developers accurately stratify patients and enrich results, especially with therapeutic agents targeting a neuroinflammatory pathway. Expect more to come in this disease / therapeutic area as it’s a very interesting mechanism. #sciencesunday