Biology subjects

Landua, J. D.

Publications and source records attributed to Landua, J. D..

4 recordsLinked to original sources

bioRxiv2024

Patient-Derived Xenografts of Triple-Negative Breast Cancer Enable Deconvolution and Prediction of Chemotherapy Responses

Chemotherapy regimens for triple-negative breast cancer (TNBC) combine agents without knowing which agents drive response. Consequently, predictors derived from multi-agent regimens cannot be assumed to generalize to individual drugs or other regimens, motivating development of treatment-matched predictors. Here, we used TNBC patient-derived xenografts (PDXs) treated with carboplatin, docetaxel, or the combination to deconvolute drug-specific responses and identify associated molecular features. Combination treatment rarely improved upon the best single agent, with enhanced responses in only 13% of PDXs and antagonism in a comparable fraction. Proteogenomic analyses identified high cytokeratin-5 (KRT5) as a general marker of chemotherapy responsiveness and KRT5 immunohistochemistry discriminated responsive PDXs (AUROC, 0.83). To train treatment-specific predictors, we integrated these data with independent PDX and clinical cohorts with responses assessed after anthracycline-free platinum, taxane, or platinum-taxane therapy, ensuring response corresponded to the modeled treatment. Four feature selection strategies yielded largely nonoverlapping biomarker panels converging on treatment-relevant pathways. On independent test data, RNA-based predictors of complete response (CR) to platinum-based (carboplatin or cisplatin) and taxane-based (docetaxel or paclitaxel) chemotherapy achieved AUROCs of 0.80 and 0.86, respectively. For platinum-taxane regimens (carboplatin plus docetaxel or paclitaxel), proteomic-guided feature selection generated a 10-biomarker, protein- informed RNA predictor of pathologic complete response (pCR) that outperformed RNA-only feature selection and achieved an AUROC of 0.85 in an independent clinical cohort, while retaining practicality as an RNA-based assay. Treatment-matched integration of multi-omic PDX and clinical datasets provides a framework for chemotherapy-specific predictors with clinically relevant performance, supporting biomarker-guided precision selection and treatment optimization for patients with TNBC. Statement of significanceIntegration of multi-omic data from patient-derived xenografts with treatment-matched clinical cohorts yielded three retrospectively validated predictors of platinum, taxane, and platinum+taxane response, enabling biomarker-guided chemotherapy selection for patients with triple-negative breast cancer.

cancer biology↗

bioRxiv2023

Interferon-Induced Bone Marrow Stromal Antigen 2 (BST2) Is A Functional Tumor-Initiating Cell Marker In Triple-Negative Breast Cancer

A tumor cell subpopulation of tumor-initiating cells (TIC), or "cancer stem cells", are associated with therapeutic resistance, as well as both local and distant recurrence. Enriched populations of TIC are identified by markers including aldehyde dehydrogenase (ALDH1) activity, the cell surface marker combination CD44+/CD24-, or fluorescent reporters for signaling pathways that regulate TIC function. We showed previously that Signal Transducer and Activator of Transcription (STAT)-mediated transcription allows enrichment for TIC in claudin-low models of human triple-negative breast cancer using a STAT-responsive reporter. However, the molecular phenotypes of STAT TIC are not well understood, and there is no existing method to lineage-trace TIC as they undergo cell state changes. Using a new STAT-responsive lineage-tracing (LT) system in conjunction with our original reporter, we enriched for cells with enhanced mammosphere-forming potential in some, but not all, basal-like triple-negative breast cancer (TNBC) xenograft models (TNBC) indicating TIC-related and TIC-independent functions for STAT signaling. Single-cell RNA sequencing (scRNAseq) of reporter-tagged xenografts and clinical samples identified a common interferon (IFN)/STAT1-associated transcriptional state, previously linked to inflammation and macrophage differentiation, in TIC. Surprisingly, most of the genes we identified are not present in previously published TIC signatures derived using bulk RNA sequencing. Finally, we demonstrated that bone marrow stromal cell antigen 2 (BST2), is a cell surface marker of this state, and that it functionally regulates TIC frequency. These results suggest TIC may exploit the IFN/STAT1 signaling axis to promote their activity, and that targeting this pathway may help eliminate TIC. SignificanceTIC differentially express interferon response genes, which were not previously reported in bulk RNA sequencing-derived TIC signatures, highlighting the importance of coupling single-cell transcriptomics with enrichment to derive TIC signatures.

cancer biology↗

bioRxiv2023

BCM PDX Portal: An Intuitive Web-based Tool for Patient-Derived Xenograft Collection Management, as well as Visual Integration of Clinical and Omics Data

ObjectiveMouse Patient-Derived Xenograft (PDX) models are essential tools for evaluating experimental therapeutics. Baylor College of Medicine (BCM) established a PDX Core to provide technical support and infrastructure for PDX-based research. To manage PDX collections effectively, de-identified patient clinical and omics data, as well as PDX-related information and omics data, must be curated and stored. Data must then be analyzed and visualized for each case. To enhance PDX collection management and data dissemination, the BCM Biomedical Informatics Core created the BCM PDX Portal (https://pdxportal.research.bcm.edu/). Materials and MethodsPatient clinical data are abstracted from medical records for each PDX and stored in a central database. Annotations are reviewed by a clinician and de-identified. PDX development method and biomarker expression are annotated. DNAseq, RNAseq, and proteomics data are processed through standardized pipelines and stored. PDX gene expression (mRNA/protein), copy number alterations, and mutations can be searched in combination with clinical markers to identify models potentially useful as a PDX cohort. ResultsPDX collection management and PDX selection of models for drug evaluation are facilitated using the PDX Portal. DiscussionTo improve the translational effectiveness of PDX models, it is beneficial to use a tool that captures and displays multiple features of the patient clinical and molecular data. Selection of models for studies should be representative of the patient cohort from which they originated. ConclusionThe BCM PDX Portal is a highly effective PDX collection management tool allowing data access in a visual, intuitive manner thereby enhancing the utility of PDX collections.

cancer biology↗

bioRxiv2022

A pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis

Patient-derived xenografts (PDXs) model human intra-tumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histological imaging via hematoxylin and eosin (H&E) staining is performed on PDX samples for routine assessment and, in principle, captures the complex interplay between tumor and stromal cells. Deep learning (DL)-based analysis of large human H&E image repositories has extracted inter-cellular and morphological signals correlated with disease phenotype and therapeutic response. Here, we present an extensive, pan-cancer repository of nearly 1,000 PDX and paired human progenitor H&E images. These images, curated from the PDXNet consortium, are associated with genomic and transcriptomic data, clinical metadata, pathological assessment of cell composition, and, in several cases, detailed pathological annotation of tumor, stroma, and necrotic regions. We demonstrate that DL can be applied to these images to classify tumor regions and to predict xenograft-transplant lymphoproliferative disorder, the unintended outgrowth of human lymphocytes at the transplantation site. This repository enables PDX-specific, investigations of cancer biology through histopathological analysis and contributes important model system data that expand on existing human histology repositories. We expect the PDXNet Image Repository to be valuable for controlled digital pathology analysis, both for the evaluation of technical issues such as stain normalization and for development of novel computational methods based on spatial behaviors within cancer tissues.

bioinformatics↗