Biology subjects

Nomie, K.

Publications and source records attributed to Nomie, K..

4 recordsLinked to original sources

bioRxiv2026

AI-based separation of malignant cell- and microenvironment-specific gene expression from bulk RNA sequencing enhances biomarker interpretation

Bulk RNA sequencing (RNA-seq)-based gene expression analysis is a promising tool for personalized cancer diagnostics, disease monitoring, and treatment decision-making. However, its clinical utility is limited by interference from non-malignant tumor microenvironment cells, which can dominate transcript data in low-purity tumors. While cell deconvolution methods like Kassandra can predict digital cell percentages from bulk RNA-seq, approaches for delineating the gene expression contribution of tumor compartments remain limited. To overcome this limitation, we developed Helenus, a machine-learning-based tool that separates gene expression between malignant and non-malignant cells. Trained on over 200 million synthetic RNA profiles representing diverse tumor types and purities, Helenus demonstrated high accuracy in separating gene expression origin. Helenus also uncovered true genomic-RNA correlations such as copy number alterations and the expression of therapeutic antibody-drug conjugate targets specifically on tumor cells. Helenus provides critical insights into tumor biology and immunotherapy response by precisely identifying biomarker expressions, paving the way for more effective personalized cancer care. SignificanceHelenus extracts gene expression profiles of cancerous and non-cancerous compartments of tumor biopsies from bulk RNA-seq data, enabling the determination of how the expression of specific genes affects malignancy and tumor immunity.

cancer biology↗

bioRxiv2025

A simple circuit to sustain intact tumor microenvironments for complex drug interrogations

Deep learning and large language models can integrate complex datasets to uncover biological insights that are often undetectable through conventional analyses. With application to translational cancer research, these computational tools have positioned 3D patient-derived tumor avatars front and center as crucial data input sources. However, a major challenge remains: the lack of standardization in media composition in 3D patient-derived tumor models unpredictably affects cell behavior and limit the utility beyond predicting treatment responses. To address this unmet need, we developed a simple, reproducible perfusion circuit system to approximate in vivo physiology using autologous patient plasma. With peritoneal metastases and core needle biopsies across multiple tumor histologies, we demonstrate preservation of the tumor microenvironment for up to 48 hours using multi-modal interrogation techniques. With proof-of-concept experiments, we display the systems ability to unveil complex drug-dependent biology within this time window. Standardizable, physiologically relevant platforms for 3D patient-derived tumor avatars will yield unprecedented insights through the integration of data from broad groups of patients and the use of an expanding armamentarium of artificial intelligence capabilities.

cancer biology↗

bioRxiv2022

A Multi-scale, Multiomic Atlas of Human Normal and Follicular Lymphoma Lymph Nodes

Reference atlases, molecular and spatial maps of mammalian tissues, are critical resources for discovery efforts and translational research. Their utility is dependent on operationalizing the resulting data by identifying cell types, histological patterns, and predictive biomarkers underlying health and disease. The human lymph node (LN) offers a compelling use case because of its importance in immunity, structural and cellular diversity, and neoplastic involvement. One hematological malignancy, follicular lymphoma (FL), evolves from developmentally blocked germinal center B cells residing in and trafficking through these tissues. To promote survival and immune escape, tumor B cells undergo significant genetic changes and extensively remodel the lymphoid microenvironment. Here, we present an integrated portrait of healthy and FL LNs using multiple genomic and advanced imaging technologies. By leveraging the strengths of each platform, we identified several tumor-specific features and microenvironmental patterns enriched in individuals who experience early relapse, the most high-risk of FL patients.

cancer biology↗

bioRxiv2021

Immune microenvironment subtypes and association with tumor cell mutations and antigen expression in follicular lymphoma.

Follicular lymphoma (FL) is a B-cell lymphoma with a complex tumor microenvironment that is rich in non-malignant immune cells. We applied single-cell RNA-sequencing to characterize the diverse tumor and immune cell populations of FL and identified major phenotypic subsets of FL T-cells including a novel cytotoxic CD4 T-cell population. Their relative proportions of T-cells defined four major FL subtypes, characterized by differential representation or relative depletion of distinct T-cell subsets. By integrating exome sequencing, we observed that somatic mutations are associated with, but not definitive for, reduced antigen presentation on FL cells. In turn, expression of MHC class II genes by FL cells was associated with significant differences in the proportions and targetable immunophenotypic characteristics. This provides a classification framework of the FL microenvironment, their association with FL genotypes and antigen presentation, and informs different potential immunotherapeutic strategies based upon tumor cell MHC class II expression. Statement of significanceWe have characterized the FL-infiltrating T-cells, identified cytotoxic CD4 T-cells as an important component, showed that the abundance of these T-cell populations is associated with tumor-cell-intrinsic characteristics, and identified sets of targetable immune checkpoints on T-cells that differed between FLs with normal versus low antigen presentation.

cancer biology↗