Biology subjects

Miller, L. D.

Publications and source records attributed to Miller, L. D..

4 recordsLinked to original sources

bioRxiv2026

Enhancing Patient Lymphocyte Response to Peritoneal Malignancies Using a Personalized Immunocompetent Microfluidic Co-Culture Platform

Harnessing patient immune cells via adoptive cellular therapy is a promising cancer therapeutic strategy. However, major challenges remain for advanced solid malignancies, including difficulty isolating sufficient tumor infiltrating lymphocytes (TILs) and limited targeting of diverse neoantigens in heterogenous tumors. To address this, we have developed a tumor-on-a-chip platform with co-cultured patient-derived tumor cells, autologous peripheral blood mononuclear cells (PBMCs), and lymphoid tissue-derived antigen presenting cells. This approach generates organoid interacting lymphocytes (OILs) with enhanced anti-tumor activity. In peritoneal malignancies, OIL-induced cytotoxicity of patient-matched tumor cells surpasses both TILs and static-expanded PBMCs. We find that this improved performance was linked to increased CD8+ T and NK cells among OILs, and increased effector cytokine polyfunctionality, particularly Granzyme A. Our platform represents a versatile and scalable approach to generate patient-specific therapeutic lymphocytes even when TILs are insufficient, offering a promising avenue to treat diverse solid tumors associated with poor outcomes under current immunotherapies. TeaserA tumor-on-a-chip device primes patient immune cells with tumor recognition for personalized immunotherapy applications.

cancer biology↗

bioRxiv2022

A Community Challenge to Predict Clinical Outcomes After Immune Checkpoint Blockade in Non-Small Cell Lung Cancer

PurposePredictive biomarkers of immune checkpoint inhibitors (ICIs) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. MethodsParticipants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. ResultsA total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The bestperforming models showed improved predictive power over reference variables, including TMB or PD-L1. ConclusionThis DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICIs clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICIs efficacy. Context summaryO_ST_ABSKey objectiveC_ST_ABSNot all patients with non-small cell lung cancer (NSCLC) eligible for immune checkpoint inhibitor (ICIs) respond to treatment, but accurate predictive biomarkers of ICIs clinical outcomes are currently lacking. This crowdsourced initiative enabled the robust assessment of predictive models using data from two randomized clinical trials of first-line ICI in metastatic NSCLC. Knowledge generatedModels submitted indicate that a combination of programmed death ligand 1 (PD-L1), tumor mutational burden (TMB), and immune gene signatures might be able to identify patients more likely to respond to ICIs. TMB and PD-L1 seemed important to predict progression-free survival and overall survival. Mechanisms including apoptosis, T-cell crosstalk, and adaptive immune resistance appeared essential to predict response. Relevance

cancer biology↗

bioRxiv2021

Kidney pathological changes alter cell clustering features in single-cell RNA sequencing

Single cell RNA sequencing (scRNA-Seq) is useful to classify cell-specific gene expression profiles in kidney tissue. As viable cells are required, we report an optimized cell dissociation methodology and the necessity of screening tissue histology prior to scRNA-Seq. We demonstrate that glomerular injury can selectively reduce the appearance of groups of cells during analysis of cell clustering and we confirmed reductions in cell-specific markers among injured cells on kidney sections with fluorescence microscopy. Interpretation of scRNA-Seq results may be refined based upon these considerations.

cell biology↗

bioRxiv2020

scLM: automatic detection of consensus gene clusters across multiple single-cell datasets

In gene expression profiling studies, including single-cell RNA-seq (scRNAseq) analyses, the identification and characterization of co-expressed genes provides critical information on cell identity and function. Gene co-expression clustering in scRNA-seq data presents certain challenges. We show that commonly used methods for single cell data are not capable of identifying co-expressed genes accurately, and produce results that substantially limit biological expectations of co-expressed genes. Herein, we present scLM, a gene co-clustering algorithm tailored to single cell data that performs well at detecting gene clusters with significant biologic context. scLM can simultaneously cluster multiple single-cell datasets, i.e. consensus clustering, enabling users to leverage single cell data from multiple sources for novel comparative analysis. scLM takes raw count data as input and preserves biological variations without being influenced by batch effects from multiple datasets. Results from both simulation data and experimental data demonstrate that scLM outperforms the existing methods with considerably improved accuracy. To illustrate the biological insights of scLM, we apply it to our in-house and public experimental scRNA-seq datasets. scLM identifies novel functional gene modules and refines cell states, which facilitates mechanism discovery and understanding of complex biosystems such as cancers. A user-friendly R package with all the key features of the scLM method is available at https://github.com/QSong-WF/scLM.

bioinformatics↗