Engineering topics

Stratton, Kelly G. (ORCID:0000000217219688)

Publications and source records attributed to Stratton, Kelly G. (ORCID:0000000217219688).

3 records

DOE OSTI2026

OmicsMLMentor: A Web Application for Guided Machine Learning Analysis of Omics Data

Expression-based omics technologies (e.g. proteomics, metabolomics, transcriptomics, etc.) increasingly rely on supervised and unsupervised machine learning (ML) models to find key biomolecules distinguishing conditions, identify natural groupings in biological data, or generate predictions for outcomes of interest. Fitting ML models to omics data presents several challenges, including handling missing data, selecting a normalization method, choosing a valid model, and optimizing hyperparameters, all requiring statistical programming skills to address these challenges. Thus, the open-source web application SLOPE was designed to lower the barrier to ML modeling for omics data. SLOPE supports the fitting of 15 ML models (10 supervised and 5 unsupervised) tailored to omics datasets, such as proteomics, metabolomics, lipidomics, and transcriptomics. SLOPE offers several omics-specific features, including methods for handling missingness (imputation, conversion, removal), normalization tests, ranking of models based on the structure of a user’s data and user input, and optimal hyperparameter selections using cross-validation splits. By streamlining ML workflows for omics analysis, SLOPE address critical gaps in existing online web tools, facilitating a broader adoption of these models for omics research. Here, SLOPE is applied to data from a lignin exposure study to highlight the workflow for fitting both supervised and unsupervised models to data.

lipidomics↗

DOE OSTI2025

Low Peripheral Blood Counts and Elevated Proinflammatory Cytokines Signal a Poor CD19 Chimeric Antigen Receptor T-cell Response in Acute Lymphoblastic Leukemia

CD19 chimeric antigen receptor T-cell (CAR-T) therapy has significantly improved outcomes for patients with relapsed/refractory B-cell acute lymphoblastic leukemia (R/R B-ALL). However, approximately 20% of patients fail to achieve a complete remission (CR), and some develop severe, life-threatening toxicities. Understanding the biological mechanisms underlying both dysfunctional responses and severe toxicity is essential for optimizing patient management and improving therapeutic efficacy. This study aimed to (1) characterize cytokine profiles associated with dysfunctional responses and severe toxicity following CAR-T infusion, (2) examine the timing and trajectory of cytokine changes in relation to treatment outcomes, and evaluate potential strategies for mitigating toxicity and treatment failure. We conducted a comprehensive analysis of serum cytokine profiles in 86 adult and pediatric patients undergoing autologous CD19 CAR-T therapy for B-ALL. Patients were categorized into three groups: (1) Dysfunctional response—Patients who failed to achieve a minimal residual disease-negative CR (MRD-CR) by Day 63 or who experienced recurrence of CD19+ disease in the setting ongoing CAR-T cell detection before Day 63. (2) Functional response with severe cytokine release syndrome (CRS) and/or neurotoxicity (NTX)—Patients with best response of MRD-CR by Day 63 who experienced grade 3 or higher CRS or NTX. (3) Functional response without severe CRS or NTX—Patients with best response of MRD-CR by Day 63 who did not experience grade =3 CRS or NTX. Cytokine levels were measured during the first-week postinfusion and correlated with treatment efficacy, toxicity outcomes, complete blood counts, and CAR-T expansion dynamics. This analysis aimed to better understand how cytokine profiles relate to patient outcomes and immune responses in CAR-T therapy. Patients with dysfunctional response exhibited decreased neutrophils, platelets, and levels of granulocytic cytokines (suggestive of low bone marrow reserve) alongside elevated pro-inflammatory cytokines by Day 1. Functional response with severe toxicity patients showed a progressive rise in proinflammatory cytokines, reaching similar levels to dysfunctional response patients by Day 7. We observed that high cytokines at both the Day 1 and Day 7 time points were associated with poor survival. These findings remained significant when adjusting for high disease burden, a known predictor of severe inflammatory toxicity and lack of response. Early post-CAR-T infusion inflammation is associated with both dysfunctional response and severe toxicity—even after adjusting for disease burden. This suggests that inflammation, in addition to disease burden, plays a role in determining patient outcome. Therefore, strategies aimed at reducing the pro-inflammatory state prior to or early after CAR-T cell infusion may improve outcomes for R/R B-ALL patients.

Serum cytokines↗

DOE OSTI2025

MODE: A Web Application for Interactive Visualization and Exploration of Omics Data

Studies generating transcriptomics, proteomics, lipidomics, and metabolomics (colloquially referred to as “omics”) data allow researchers to find biomarkers or molecular targets, or understand complex biological structures and functions by identifying changes in biomolecule abundance and expression between experimental conditions. Omics data is multi-dimensional and oftentimes summarization techniques such as principal component analysis (PCA) are used to identify high-level patterns in data. Though useful, these summaries don’t allow exploration of detailed patterns in omics data that may have biological relevance. The use of interactive HTML displays with plots allows researchers to interact with omics data at a detailed level, but building these displays requires significant coding expertise. To overcome this barrier, the software MODE was built to empower users to build their own interactive HTML displays to support scientific discovery. These displays are easily shareable, do not depend on a specific operating system, and allow users to effortlessly sort and filter plots by categorical or numerical variables. MODE allows users to build and share these displays with several options for plot design and meta selection. In conclusion, the MODE web application and its capabilities are presented and then demonstrated on lipidomics data from a leaf wounding study.

lipidomics↗