Biology subjects

Gerstner, E.

Publications and source records attributed to Gerstner, E..

3 recordsLinked to original sources

bioRxiv2026

Weak supervision of H&E slides reveals systems-level biology and functional states that govern therapeutic resistance

Precision oncology lacks scalable methods to identify the mechanisms that mediate therapeutic resistance for individual patients. Resistance often arises from focal cellular niches that are obscured by bulk profiling and costly to resolve with multi-omics. Here, we show that deep learning (DL), applied to routine histology, can localize focal tissue regions enriched for therapeutically relevant biology. Using 3111 breast cancer H&E slides with matched bulk transcriptomics, we trained weakly-supervised DL models to infer activities of immune, metabolic, and tumor-intrinsic phenotypes implicated in therapeutic resistance (AUROC>0.80; PCC>0.64). Accurate inference of these phenotypes should identify tissue regions enriched for the corresponding biological signal. Therefore, we validated phenotype inference and spatial localization with complementary analyses. Tissue-matched multiplexed immunofluorescence showed concordance between inferred immune states and corresponding cell fractions (p=0.006-0.106). Across multi-institutional cohorts, model-derived phenotypes recovered expected relationships with therapeutic outcomes (p<0.045). Finally, in a blinded evaluation, pathologists confirmed that model-derived high-attention regions were enriched for phenotype-specific morphology (p<2.408*10-5). Because evaluated phenotypes represent diverse mechanisms of resistance across therapeutic modalities, these findings provide a foundation for resistance-directed localization using therapeutic outcomes as supervision. By directing deep profiling toward model-prioritized regions, this framework could enable scalable nomination of candidate mediators of resistance for subsequent functional validation across real-world patient populations. One sentence summaryWeakly supervised deep learning localizes focal tissue regions enriched for therapeutically relevant biology in routine histology, thus offering a scalable strategy to study therapeutic resistance across large patient populations.

biophysics↗

bioRxiv2025

ESPWA: a deep learning-enabled tool for precision-based use of endocrine therapy in resource-limited settings

Immunohistochemistry for estrogen receptor (ER) expression is often unavailable in low-and-middle-income countries (LMICs), leading to empiric use of endocrine therapy (ET) and unnecessary toxicity in ER-negative patients. To address this unmet need, we developed ESPWA, a deep-learning model trained on 3448 H&E slides and tissue-matched ER status from breast cancer patients treated at Zanmi Lasante (ZL), Haiti. A model trained on The Cancer Genome Atlas (TCGA) exhibited substantial domain shift when applied to the ZL cohort, with AUROCs dropping from 0.846 on TCGA cross-validation to 0.671 on the ZL cohort. In contrast, ESPWA demonstrated improved performance on ZL cross-validation (AUROC=0.790; p=0.005). In an independent test set of 134 Haitian patients with parallel slides prepared and scanned in Mirebalais Hospital (Haiti) and Brigham and Womens Hospital, ESPWA was robust to variations in slide preparation, quality, and scanners, achieving AUROCs of 0.794 on BWH-prepared WSIs and 0.805 on Mirebalais-prepared WSIs. Prospective studies using ESPWA are underway in sub-Saharan Africa to evaluate its utility in informing precision-based use of ET.

cancer biology↗

bioRxiv2024

Identification of a Neuroimmune Circuit that Regulates Allergic Inflammation in the Esophagus

Eosinophilic esophagitis is a chronic food antigen-driven allergic inflammatory disease associated with symptoms involving the nervous system such as refractory pain. Yet, the role of the nervous system in disease pathogenesis has not received much attention. Herein, we demonstrate that allergen exposure evokes pain-like behavior in association with increased nociceptor signaling and transcriptional responses in dorsal root ganglia. NaV1.8+ sensory nerves were found traveling along the length of the esophagus, organized in distinct bundles adjacent to the basal epithelium, with beta III-tubulin+ sensory nerves distributed more distal to the lumen. Targeted deletion of Il4ra in NaV1.8+ neurons impeded allergen-induced increases in nerve innervation density. Furthermore, Il4ra-/-NaV1.8 mice had diminished allergen-induced allergic inflammation in the esophagus including eosinophilia and transcription of pro-inflammatory genes. Translational studies revealed extensive myelinated nerve innervation in the human esophagus, which was increased in patients with eosinophilic esophagitis. Taken together, these data indicate that allergic inflammation is associated with an increase in non-evoked pain, esophageal nerve density, altered sensitivity of sensory neurons, and transcriptional changes in dorsal root ganglia. These finding identify a type 2 neuroimmune circuit that involves the interplay of allergen-induced IL-4 receptor-dependent DRG responses that modify esophageal end-organ inflammatory responses.

immunology↗