Biology subjects

Harmon, S.

Publications and source records attributed to Harmon, S..

4 recordsLinked to original sources

bioRxiv2026

An AI-powered cloud biofoundry for autonomous biological research

Autonomous experimentation, where an automated system designs, performs, analyzes, and iteratively improves experiments with minimal human intervention represents a transformative scientific research paradigm. However, it remains hampered by the disconnect between computational design and experimental execution. Here we present iCloudBiofoundry, a cloud-accessible, scalable, and self-evolving platform that bridges this divide. A scientific multi-agent system, iBioGenie, plans research workflows by converting natural language research objectives into testable hypotheses and experimentally grounded protocols using literature, databases, and computational tools. A physical AI layer, BiofoundryAI, translates them into executable workflows that run autonomously on distributed robotic biofoundries, and collects the data for iterative design. We demonstrate the end-to-end capabilities of iCloudBiofoundry across three representative case studies spanning enzyme retrieval, protein engineering, and metabolic engineering. By integrating agentic AI with physical AI, iCloudBiofoundry provides a cloud accessible platform that unifies computational design and robotic execution, enabling autonomous biological research.

synthetic biology↗

bioRxiv2026

OncoBERT: Context-Aware Modeling of Somatic Mutations for Precision Oncology

Somatic mutation profiling is central to cancer diagnosis and treatment selection. However, most studies focus on individual actionable mutations, overlooking the broader mutational context that shapes tumor evolution and treatment response. Here, we introduce OncoBERT, a language model that learns contextual representations of somatic mutations from large-scale clinical sequencing data spanning >210,000 patients, 113 cancer types and 20 institutions. OncoBERT uncovers robust patient-specific mutational subtypes across diverse cohorts and targeted sequencing panels, revealing clinically meaningful mutation patterns that are associated with differential response to chemotherapy, targeted therapies, and immunotherapy. Importantly, integrating OncoBERTs contextual representations with clinically approved biomarkers of immunotherapy response, such as tumor mutational burden (TMB) and microsatellite instability (MSI), significantly improved prediction of clinical benefit. By further incorporating matched tumor transcriptomic profiles, we linked OncoBERT-defined mutational subtypes to distinct cancer hallmark programs and tumor microenvironment states. Together, OncoBERT provides a scalable framework for deciphering somatic mutational landscapes, enabling improved patient stratification and advancing precision oncology.

genomics↗

bioRxiv2025

Towards interpretable molecular and spatial analysis of the tumor microenvironment from digital histopathology images with HistoTME-v2

The tumor microenvironment (TME) is a critical focus for biomarker discovery and therapeutic targeting in cancer. However, widespread clinical adoption of TME profiling is hindered by the high cost and technical complexity of current platforms such as spatial transcriptomics and proteomics. Artificial Intelligence (AI)-based analysis of the TME from routine Hematoxylin & Eosin (H&E)-stained pathology slides presents a promising alternative. Yet, most existing deep learning approaches depend on extensive high-quality single-cell or patch-level annotations, which are labor-intensive and costly to generate. To address these limitations, we previously introduced HistoTME, a weakly supervised deep learning framework that predicts the activity of cell type-specific transcriptomic signatures directly from whole slide H&E images of non-small cell lung cancer. This enables rapid, high throughput analysis of the TME composition from whole slide H&E images (WSI) without the need for segmenting and classifying individual cells. In this work, we present HistoTME-v2, a pan-cancer extension of HistoTME, applied across 25 solid tumor types, substantially broadening the scope of prior efforts. HistoTME-v2 demonstrates high accuracy for predicting cell type-specific transcriptomic signature activity from H&E images, achieving a median Pearson correlation of 0.61 with ground truth measurements in internal cross- validation on The Cancer Genome Atlas (TCGA), encompassing 7,586 WSIs, 6,901 patients, and 24 cancer types, and a median Pearson correlation of 0.53 on external validation datasets spanning 5,657 WSIs, 1,775 patients and 9 cancer types. Furthermore, HistoTME- v2 resolves the spatial distribution of key immune and stromal cell types, exhibiting strong spatial concordance with single-cell measurements derived from multiplex imaging (CODEX, IHC) as well as Visium spatial transcriptomics, spanning 259 WSI, 154 patients, and 7 cancer types. Overall, across both bulk and spatial settings, HistoTME-v2 significantly outperforms baselines, positioning it as a robust, interpretable and cost-efficient tool for TME profiling and advancing the integration of spatial biology into routine pathology workflows.

pathology↗

bioRxiv2023

Large Scale Comparative Deconvolution Analysis of the Canine and Human Osteosarcoma Tumor Microenvironment Uncovers Conserved Clinically Relevant Subtypes

Osteosarcoma is a relatively rare but aggressive cancer of the bones with a shortage of effective biomarkers. Although less common in humans, Osteosarcomas are fairly common in adult pet dogs and have been shown to share many similarities with their human analogs. In this work, we analyze bulk transcriptomic data of 213 primary and 100 metastatic Osteosarcoma samples from 210 pet dogs enrolled in nation-wide clinical trials to uncover three Tumor Microenvironment (TME)-based subtypes: Immune Enriched (IE), Immune Enriched Dense Extra-Cellular Matrix-like (IE-ECM) and Immune Desert (ID) with distinct cell type compositions, oncogenic pathway activity and chromosomal instability. Furthermore, leveraging bulk transcriptomic data of canine primary tumors and their matched metastases from different sites, we characterize how the Osteosarcoma TME evolves from primary to metastatic disease in a standard of care clinical setting and assess its overall impact on clinical outcomes of canines. Most importantly, we find that TME-based subtypes of canine Osteosarcomas are conserved in humans and predictive of progression free survival outcomes of human patients, independently of known prognostic biomarkers such as presence of metastatic disease at diagnosis and percent necrosis following chemotherapy. In summary, these results demonstrate the power of using canines to model the human Osteosarcoma TME and discover novel biomarkers for clinical translation.

cancer biology↗