Pat Adams (@PathologyPat) header
Pat Adams (@PathologyPat) profile picture

Product Development New England, Naveris, NavDx, minimal residual disease monitoring in head and neck cancer

Hartford, CT
Joined September 2018
Photo by Pat Adams (@PathologyPat)Photo by Pat Adams (@PathologyPat)Photo by Pat Adams (@PathologyPat)Photo by Pat Adams (@PathologyPat)Photo by Pat Adams (@PathologyPat)Photo by Pat Adams (@PathologyPat)Photo by Pat Adams (@PathologyPat)Photo by Pat Adams (@PathologyPat)
Pat Adams retweeted
T and B cells recognize splicing-derived neutrophil neoantigens in lupus @Nature @SPSchoenberger @ljiresearch @MustelinTomas @UWMedicine nature.com/articles/s41586-0…
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Pat Adams retweeted
In a clinical trial just published in The Lancet Oncology, researchers led by Dr. Saby George reported promising results from combining the targeted cancer drugs bevacizumab (Avastin) and pazopanib (Votrient). Read more: roswellpark.org/newsroom/202…
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Endobronchial ultrasound guided transbronchial needle aspiration of a mediastinal lymph node revealing what organism? Answer: buff.ly/4f9uFpl #PathTwitter #PathArt #Cytology #Cytopathology #IDTwitter
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Pat Adams retweeted
Pathology Grand Rounds today at 12:30 PM ET will feature @GurYaari, PhD, Associate Professor of #Pathology @YaleMed, presenting on, “From Immune Repertoires to Clinical Insight: B Cell and T Cell Receptor Analysis in Human Disease.” tinyurl.com/yv9danh
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Can an EHR model trained at one hospital read another hospital's records? Excited to introduce our new paper, "Discrete Neural Interlingua for Ontology-Agnostic EHR Modeling," accepted at NeurIPS 2026. See you in Sydney 🦘🏖️! Paper: arxiv.org/abs/2610.27047 Code: github.com/mahmoodlab/MEDIAT… Model: huggingface.co/MahmoodLab/ME… Video: See👇 The same clinical concept can be an ICD code, a SNOMED term, a local identifier, or free text, and standard EHR tokenizers treat each one as an unrelated symbol. A model trained on MIMIC-III with a closed vocabulary can use only 13% of eICU events. It isn't only a cross-hospital problem. For example, inside one hospital, "acute kidney injury" can be a billing code, a flowsheet identifier, and free text in a note, and the vocabulary shifts again with every coding transition and EHR migration. The redundancy is there before a model ever leaves the building. MEDIATOR is a discrete, ontology-agnostic tokenizer. Any clinical concept written as text maps to one of 100,000 shared tokens, so "acute kidney failure" (ICD-9), "AKI" (a local code), and "acute kidney injury" (free text) become the same token. It is trained on cross-ontology synonyms from UMLS and guided by LLM-enriched descriptions, code co-occurrence, and a medical relation graph. It is a drop-in token interface for standard EHR Transformers: no manual code mapping, and new code systems can be tokenized without retraining. Main results: • In-domain: +4.0 AUPRC points (8% relative) over SOTA, across 20 MIMIC-III tasks. Merging within-dataset redundancy pays off before you transfer anything. • Zero-shot transfer: on eICU with no vocabulary alignment, MEDIATOR uses all 3.58 million events and improves AUPRC over MedTok by 8.6 points (29% relative) across 14 tasks, up to +21.2 on subarachnoid hemorrhage. Restricted to the same inputs as the closed-vocabulary baselines, it is still the best tokenizer. • Transferable attribution: one shared token space means attributions compare directly across hospitals. For cirrhosis mortality, token importance is rank-correlated between MIMIC-III and eICU at Spearman ρ = 0.92. Tokenizing a dataset's entire vocabulary is a one-time step of about 70 seconds on one GPU. Code and weights are public, and the co-occurrence teacher can be rebuilt from public data with near-identical results. Congradulations to Caiwei Tian Tong Ding and everyone else. #NeurIPS2026 #ClinicalInformatics #EHR #MachineLearning
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This is a very exciting potential new treatment for food allergies! But to appreciate just how remarkable this development is, let me share a personal story about Xolair, the anti-IgE antibody currently used to treat allergies, and how painfully slow biomedical progress has been until now. I believe AI is about to change this dramatically! Back in 1989, 37 years ago, I was a visiting medical student as an intern at Ciba-Geigy immunology laboratories (which later became part of Novartis) in Basel, Switzerland. In the immunology department where I was working, one of the scientists, Christoph Heusser, had proposed developing antibodies against IgE, the molecule responsible for triggering allergic reactions, back in 1986! Around the same time, scientists at a small biotech company called Tanox had independently developed a promising anti-IgE antibody. Ciba-Geigy and Tanox subsequently formed a partnership in 1990 to advance this approach. I still remember the excitement surrounding this research as vividly as if it were yesterday! Imagining the possibility of treating allergies by targeting a single molecule responsible for triggering the entire allergic response was extremely novel back than. I was fortunate enough to witness the early development of this revolutionary idea firsthand, and it was incredibly exciting! But here is where the story becomes rather astonishing. Genentech subsequently developed a similar antibody in 1993, and the three companies eventually joined forces. Their collaboration led to Xolair (omalizumab), which finally received FDA approval in 2003, 17 years after the original concept! And that approval was initially for allergic asthma. It took another 21 years, until 2024, for Xolair to receive approval specifically for food allergies! Think about that. Almost 40 years after those original discoveries, Xolair remains the only FDA-approved anti-IgE antibody for food allergies. That is how painfully slow the traditional process of developing new medicines has been! Now compare that timeline with what Claude and Divergence Labs have accomplished in approximately ONE MONTH! Claude generated roughly 35,000 antibody designs, helping identify promising candidates that could potentially remove IgE already attached to immune cells. This could be a major advance over Xolair, which primarily neutralizes free IgE. Some of these new antibody designs already performed better than Xolair in specific laboratory tests! Of course, these are still early experimental results. The complete therapeutic antibody needs to be developed, validated, and eventually tested in clinical trials. But consider the implications! We are witnessing the beginnings of a fundamental transformation in biomedical research. As AI intelligence continues to advance exponentially, combined with automated laboratories, virtual cells, and eventually patient digital twins, I believe we will compress decades of drug discovery into months or even weeks! Clinical trials and regulatory approval will also become much faster as AI improves our ability to predict efficacy, toxicity, and treatment responses. My prediction is that instead of waiting decades for individual breakthrough medicines, we will eventually be developing hundreds, perhaps thousands, of new treatments in parallel, within just a few years! Having personally witnessed the beginnings of anti-IgE antibody development nearly four decades ago, and now seeing AI begin to revolutionize that very same field, makes this moment extraordinarily exciting for me. I have been waiting for this transformation for most of my scientific career. And I believe we are finally entering the era when the pace of medical discovery will accelerate beyond anything we have experienced in human history!
Today, in collaboration with Anthropic, we’re excited to share rapid initial progress towards an antibody treatment for food allergy, which affects over 200 million people worldwide, discovered with the help of Claude. At Divergence Labs, we believe that AI can unlock the next generation of antibody therapeutics with more effective mechanisms than before. Such mechanisms are urgently needed in food allergy, which involve overcoming one of the tightest affinity protein pairs in immunology, IgE and its receptor FcεRI, on mast cells. Treatment with Xolair—the only approved antibody for food allergy—sequesters circulating IgE, but mast cells remain sensitized for weeks to months due to the extraordinarily slow dissociation of IgE from FcεRI. We envision a treatment that actively pulls IgE off of FcεRI, without waiting for natural dissociation. Like picking a lock, this requires the right combination of geometry and force. Our research team, working with Claude, ideated on mechanisms to accomplish this disruption, and converged on an antibody format with two arms with different functions and epitopes. Within one month, we designed, synthesized, and experimentally confirmed the biophysical function of the two arms, which we are now assembling into full therapeutic formats.
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Thanks for picking our study up! A pleasure !!! @urotoday @OncoAlert
AI allows pre-screening of FGFR3 mutational status using routine histology slides of muscle-invasive bladder cancer. An AI model reading routine H&E slides pre-screened FGFR3 mutations with >93% sensitivity in advanced cases and could cut molecular testing by ~40%. #BladderCancer #AuthorCommentary with @Markuseckstein3 @UniFAU > bit.ly/4zB9Ht6 @NatureComms
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Pat Adams retweeted
Motherhood’s Sentinels: Pregnancy Induces Tissue-Resident Memory T Cells to Curb Breast Cancer Growth—this In the Spotlight commentary highlights a recent Nature Immunology article. doi.org/10.1158/0008-5472.CA… #CD8TCells #TCells #TissueResidentImmunity #BreastCancer
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Yale Pathology has an opening for a Fellowship in Renal/Genitourinary Pathology, beginning July 1, 2027. For more information, visit: medicine.yale.edu/pathology/…
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Mark your calendars to come learn from @MerceJorda1, @drruizcor and many others! This will be an incredibly engaging session with live Q&A. cc: @umiamimedicine
Four challenging tumor cases. One engaging hour of expert discussion. Join the FSP Precision Medicine Academy Phase II on Monday, Nov. 9, at noon ET for its second Virtual Tumor Board. 🔗 Learn more and register today: flpath.org/events/EventDetai…
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Pat Adams retweeted
Join Elena Zoniadis and Danielle Maracaja while they discuss how communicating diagnostic uncertainty is an everyday challenge in pathology practice, particularly for those early in their careers. Register Here: buff.ly/YTurYuX
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Pat Adams retweeted
Adamantinoma-like Ewing Sarcoma: An Underrecognized Mimicker in the Head and Neck With Divergent Clinicopathologic Features from Ewing Sarcoma @ModernPathology Read More: buff.ly/Xs0ZgLi
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I captured this video in my lab to show the power of medicines called T-cell engagers. Left: T cells move in and out of a cancer tumoroid, largely ignoring it. Right: Add a T-cell engager, and the T cells attack. Once they recognize their targets, they tear through the tumor.
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Excited to share CellBridge, our new preprint led by @sevdoche in collaboration with @pafrossard and @DorinaThanou at @EPFL_en! 🔬 Looking at an H&E slide, we can learn a remarkable amount about a tissue: its architecture, the cells within it, and how they interact. But identifying exactly which cells are present can be harder. A lymphocyte may be recognisable, while telling whether it is a T cell or a B cell often requires additional markers. Spatial transcriptomics provides that molecular detail by measuring gene expression in individual cells. But it is much harder to obtain routinely than an H&E slide. Could we use these molecular measurements to teach an image model more about cell identity and help it recognise when appearance alone is not enough? This is the idea behind CellBridge. We trained CellBridge on millions of cells with paired H&E images and Xenium spatial transcriptomic profiles. During training, the model learns to connect each cell’s appearance with its molecular identity. Once trained, it predicts cell types from H&E images alone. Across our benchmarks, CellBridge improved fine-grained cell classification over leading pathology models, with gains of up to 16 percentage points in macro F1. But better classification is only part of the story. Some biologically important distinctions remain difficult to resolve from an image. Our Cell-Ontology-Fallback (COF) module lets the model adjust how specific its answer should be. For example, when the evidence does not support distinguishing a T cell from a B cell, it can report “lymphocyte” rather than commit to an uncertain subtype. COF combines the biological relationships in the Cell Ontology with branch-calibrated conformal prediction to make these decisions. What excites me is the possibility of extracting more cellular detail from routinely available slides while making clearer where additional molecular measurements are still needed. 🎥 Our 1-minute video walks through the idea. Jointly supervised by @pafrossard, @DorinaThanou, and me. A huge thank you to our co-authors: @arpitdm, @AmirhVahidi, Dabin Jeong, @SebastianBirk_, Adam Boxall, @Adib_m_, Marta Rosa Sallese, and Yinshui Chang. 📄 Preprint: biorxiv.org/content/10.64898… 💻 Code: github.com/Lotfollahi-lab/ce… 📓 Tutorial: github.com/Lotfollahi-lab/ce… #ComputationalPathology #SpatialOmics #CellTypeAnnotation #MultimodalLearnin
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A new look at myeloma biology—beyond DNA. A recent Nature Medicine report suggests that baseline RNA-expression signatures may predict early MRD response after Isa-KRd induction. RNA signatures are not new. The new lens: 🧬 DNA tells us what built the clone—and helps define its risk. 〰️ RNA tells us what the clone is doing now—and may reveal treatment sensitivity. Fascinating, but not yet ready to guide treatment selection. Early MRD ≠ sustained MRD, PFS, OS, or cure. nature.com/articles/s41591-0… #MultipleMyeloma #HemeTwitter
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Pathology Grand Rounds on Thursday, Oct. 29 at 12:30 PM ET will feature @GurYaari, PhD, Associate Professor of #Pathology @YaleMed, presenting on, “From Immune Repertoires to Clinical Insight: B Cell and T Cell Receptor Analysis in Human Disease.” tinyurl.com/yv9danh
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