Biology subjects

Hilsenbeck, S. G.

Publications and source records attributed to Hilsenbeck, S. G..

7 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↗

bioRxiv2024

Nasal microbionts differentially colonize and elicit cytokines in human nasal epithelial organoids

Nasal colonization by Staphylococcus aureus or Streptococcus pneumoniae is associated with an increased risk of infection by these pathobionts, whereas nasal colonization by Dolosigranulum species is associated with health. Human nasal epithelial organoids (HNOs) physiologically recapitulate human nasal respiratory epithelium with a robust mucociliary blanket. We reproducibly monocolonized HNOs with these three bacteria for up to 48 hours with varying kinetics across species. HNOs tolerated bacterial monocolonization with localization of bacteria to the mucus layer and with minimal cytotoxicity compared to uncolonized HNOs. Human nasal epithelium exhibited both species-specific and general cytokine responses, without induction of type I interferons, consistent with colonization rather than infection. Only live S. aureus colonization robustly induced IL-1 family cytokines, suggestive of inflammasome signaling. D. pigrum and live S. aureus decreased CXCL10, whereas S. pneumoniae increased CXCL11, chemokines involved in antimicrobial responses to both viruses and bacteria. Overall, HNOs are a compelling model system to reveal host-microbe dynamics at the human nasal mucosa. IMPORTANCEHuman nasal microbiota often includes highly pathogenic members, many of which are antimicrobial resistance threats, e.g., methicillin-resistant Staphylococcus aureus and antibiotic-resistant Streptococcus pneumoniae. Preventing colonization by nasal pathobionts decreases infections and transmission. In contrast, nasal microbiome studies identify candidate beneficial bacteria that might resist pathobiont colonization, e.g., Dolosigranulum pigrum. Discovering how these microbionts colonize the human nasal passages and means to reduce pathobiont colonization is limited by previous models. This creates an urgent need for human-based models that exemplify bacterial nasal colonization. We addressed this need by developing human nasal epithelial organoids (HNOs) as a new model system of bacterial nasal colonization. HNOs accurately represent the mucosal surface of the human nasal passages enabling exploration of bacterial-epithelial interactions, which is crucial since the epithelium instigates the initial innate immune response to bacteria. Here, we identified differential epithelial cytokine responses to these three bacteria setting the stage for future research.

microbiology↗

bioRxiv2024

A Comprehensive Atlas of AAV Tropism in the Mouse

Gene therapy with Adeno-Associated Viral (AAV) vectors requires knowledge of their tropism within the body. Here we analyze the tropism of ten naturally occurring AAV serotypes (AAV3B, AAV4, AAV5, AAV6, AAV7, AAV8, AAV9, AAVrh8, AAVrh10 and AAVrh74) following systemic delivery into male and female mice. A transgene expressing ZsGreen and Cre recombinase was used to identify transduction in a cell-dependent manner based on fluorescence. Cre-driven activation of tdTomato fluorescence offered superior sensitivity for transduced cells. All serotypes except AAV3B and AAV4 had high liver tropism. Fluorescence activation revealed transduction of unexpected tissues, including adrenals, testes and ovaries. Rare transduced cells within tissues were also readily visualized. Biodistribution of AAV genomes correlated with fluorescence, except in immune tissues. AAV4 was found to have a pan-endothelial tropism while also targeting pancreatic beta cells. This public resource enables selection of the best AAV serotypes for basic science and preclinical applications in mice.

molecular 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↗

bioRxiv2021

A Network Approach to Identify Biomarkers of Differential Chemotherapy Response Using Patient-Derived Xenografts of Triple-Negative Breast Cancer

Triple negative breast cancer (TNBC) is a highly heterogeneous set of diseases that has, until recently, lacked any FDA-approved, molecularly targeted therapeutics. Thus, systemic chemotherapy regimens remain the standard of care for many. Unfortunately, even combination chemotherapy is ineffective for many TNBC patients, and side-effects can be severe or lethal. Identification of predictive biomarkers for chemotherapy response would allow for the prospective selection of responsive patients, thereby maximizing efficacy and minimizing unwanted toxicities. Here, we leverage a cohort of TNBC PDX models with responses to single-agent docetaxel or carboplatin to identify biomarkers predictive for differential response to these two drugs. To demonstrate their ability to function as a preclinical cohort, PDX were molecularly characterized using whole-exome DNA sequencing, RNAseq transcriptomics, and mass spectrometry-based total proteomics to show proteogenomic consistency with TCGA and CPTAC clinical samples. Focusing first on the transcriptome, we describe a network-based computational approach to identify candidate epithelial and stromal biomarkers of response to carboplatin (MSI1, TMSB15A, ARHGDIB, GGT1, SV2A, SEC14L2, SERPINI1, ADAMTS20, DGKQ) and docetaxel (ITGA7, MAGED4, CERS1, ST8SIA2, KIF24, PARPBP). Biomarker panels are predictive in PDX expression datasets (RNAseq and Affymetrix) for both taxane (docetaxel or paclitaxel) and platinum-based (carboplatin or cisplatin) response, thereby demonstrating both cross expression platform and cross drug class robustness. Biomarker panels were also predictive in clinical datasets with response to cisplatin or paclitaxel, thus demonstrating translational potential of PDX-based preclinical trials. This network-based approach is highly adaptable and can be used to evaluate biomarkers of response to other agents.

cancer biology↗

bioRxiv2021

Tumor suppressor PLK2 may serve as a biomarker in triple-negative breast cancer for improved response to PLK1 therapeutics

Polo-like kinase (PLK) family members play important roles in cell cycle regulation. The founding member PLK1 is oncogenic and preclinically validated as a cancer therapeutic target. Paradoxically, PLK2 (chromosome 5q11.2) is frequently deleted in human breast cancers, preferentially in basal-like and triple-negative breast cancer subtypes. Here, we found that PLK2 was tumor suppressive in breast cancer and knockdown of PLK1 rescued phenotypes induced by PLK2-loss both in vitro and in vivo. We also demonstrated that PLK2 directly interacted with PLK1 at prometaphase and that mutations in the kinase domain of PLK2, but not polo-box binding domains, changed their interaction pattern. Furthermore, treatment of syngeneic transplantation mouse tumor models and patient-derived xenografts using the PLK1 inhibitor volasertib alone, or in combination with carboplatin, indicated that PLK2-low breast tumors had a significantly better response to these drugs. Re-expression of PLK2 in an inducible PLK2-null mouse model reduced the therapeutic efficacy of volasertib. Taken together, our data suggest PLK2 loss may serve as a biomarker to predict response to PLK1 therapeutics, alone and in combination with chemotherapy. SignificanceThe tumor suppressive role of PLK2, and its relationship with the oncogene PLK1, provide a mechanistic rationalization to use PLK1 inhibitors in combination with chemotherapy to treat PLK2 low/deleted tumors. TNBC, and other cancers with low PLK2 expression, are such candidates to leverage precision medicine to identify patients who might benefit from treatment with these inhibitors.

cancer biology↗

bioRxiv2018

Bacteria-to-human protein networks reveal origins of endogenous DNA damage

DNA damage provokes mutations and cancer, and results from external carcinogens or endogenous cellular processes. Yet, the intrinsic instigators of DNA damage are poorly understood. Here we identify proteins that promote endogenous DNA damage when overproduced: the DNA-damaging proteins (DDPs). We discover a large network of DDPs in Escherichia coli and deconvolute them into six DNA-damage-causing function clusters, demonstrating DDP mechanisms in three: reactive-oxygen increase by transmembrane transporters, chromosome loss by replisome binding, and replication stalling by transcription factors. Their 284 human homologs are over-represented among known cancer drivers, and their expression in tumors predicts heavy mutagenesis and poor prognosis. Half of tested human homologs, when overproduced in human cells, promote DNA damage and mutation, with DNA-damaging mechanisms like those in E. coli. Together, our work reveals DDP networks that provoke endogenous DNA damage and may indicate functions of many human known and newly implicated cancer-promoting proteins.

cancer biology↗