Immunophenotyping and Transcriptomic Outcomes in PDX-Derived TNBC Tissue

Synthetic Matrix Scaffolds Engineer the In Vivo Tumor Immune Microenvironment for Immunotherapy Screening

Immunotherapy has emerged as one of the most powerful anti-cancer therapies but is stymied by the limits of existing preclinical models with respect to disease latency and reproducibility. Additionally, the influence of differing immune microenvironments within tumors observed clinically and associated with immunotherapeutic resistance cannot be tuned to facilitate drug testing workflows without changing model system or laborious genetic approaches. To address this testing platform gap in the immune oncology drug development pipeline, the authors deploy engineered biomaterials as scaffolds to increase tumor formation rate, decrease disease latency, and diminish variability of immune infiltrates into tumors formed from murine mammary carcinoma cell lines implanted into syngeneic mice. By altering synthetic gel formulations that reshape infiltrating immune cells within the tumor, responsiveness of the same tumor model to varying classes of cancer immunotherapies, including in situ vaccination with a molecular adjuvant and immune checkpoint blockade, diverge. These results demonstrate the significant role the local immune microenvironment plays in immunotherapeutic response. These engineered tumor immune microenvironments therefore improve upon the limitations of current breast tumor models used for immune oncology drug screening to enable immunotherapeutic testing relevant to the variability in tumor immune microenvironments underlying immunotherapeutic resistance seen in human patients.

Type-3 Hyaluronan Synthase Attenuates Tumor Cells Invasion in Human Mammary Parenchymal Tissues

The microenvironment for tumor growth and developing metastasis should be essential. This study demonstrated that the hyaluronic acid synthase 3 (HAS3) protein and its enzymatic product hyaluronic acid (HA) encompassed in the subcutaneous extracellular matrix can attenuate the invasion of human breast tumor cells. Decreased HA levels in subcutaneous Has3-KO mouse tissues promoted orthotopic breast cancer (E0771) cell-derived allograft tumor growth. MDA-MB-231 cells premixed with higher concentration HA attenuate tumor growth in xenografted nude mice. Human patient-derived xenotransplantation (PDX) experiments found that HA selected the highly migratory breast cancer cells with CD44 expression accumulated in the tumor/stroma junction. In conclusion, HAS3 and HA were detected in the stroma breast tissues at a high level attenuates effects for induced breast cancer cell death, and inhibit the cancer cells invasion at the initial stage. However, the highly migratory cancer cells were resistant to the HA-mediated effects with unknown mechanisms.

Ex Vivo Drug Screening Informed Targeted Therapy for Metastatic Parotid Squamous Cell Carcinoma

The purpose of ex vivo drug screening in the context of precision oncology is to serve as a functional diagnostic method for therapy efficacy modeling directly on patient-derived tumor cells. Here, we report a case study using integrated multiomics ex vivo drug screening approach to assess therapy efficacy in a rare metastatic squamous cell carcinoma of the parotid gland. Tumor cells isolated from lymph node metastasis and distal subcutaneous metastasis were used for imaging-based single-cell resolution drug screening and reverse-phase protein array-based drug screening assays to inform the treatment strategy after standard therapeutic options had been exhausted. The drug targets discovered on the basis of the ex vivo measured drug efficacy were validated with histopathology, genomic profiling, and in vitro cell biology methods, and targeted treatments with durable clinical responses were achieved. These results demonstrate the use of serial ex vivo drug screening to inform adjuvant therapy options prior to and during treatment and highlight HER2 as a potential therapy target also in metastatic squamous cell carcinoma of the salivary glands.

In-depth characterization of a new patient-derived xenograft model for metaplastic breast carcinoma to identify viable biologic targets and patterns of matrix evolution within rare tumor types

Metaplastic breast carcinoma (MBC) is a rare breast cancer subtype with rapid growth, high rates of metastasis, recurrence and drug resistance, and diverse molecular and histological heterogeneity. Patient-derived xenografts (PDXs) provide a translational tool and physiologically relevant system to evaluate tumor biology of rare subtypes. Here, we provide an in-depth comprehensive characterization of a new PDX model for MBC, TU-BcX-4IC. TU-BcX-4IC is a clinically aggressive tumor exhibiting rapid growth in vivo, spontaneous metastases, and elevated levels of cell-free DNA and circulating tumor cell DNA. Relative chemosensitivity of primary cells derived from TU-BcX-4IC was performed using the National Cancer Institute (NCI) oncology drug set, crystal violet staining, and cytotoxic live/dead immunofluorescence stains in adherent and organoid culture conditions. We employed novel spheroid/organoid incubation methods (Pu·MA system) to demonstrate that TU-BcX-4IC is resistant to paclitaxel. An innovative physiologically relevant system using human adipose tissue was used to evaluate presence of cancer stem cell-like populations ex vivo. Tissue decellularization, cryogenic-scanning electron microscopy imaging and rheometry revealed consistent matrix architecture and stiffness were consistent despite serial transplantation. Matrix-associated gene pathways were essentially unchanged with serial passages, as determined by qPCR and RNA sequencing, suggesting utility of decellularized PDXs for in vitro screens. We determined type V collagen to be present throughout all serial passage of TU-BcX-4IC tumor, suggesting it is required for tumor maintenance and is a potential viable target for MBC. In this study we introduce an innovative and translational model system to study cell–matrix interactions in rare cancer types using higher passage PDX tissue.

High accuracy gene expression profiling of sorted cell subpopulations from breast cancer PDX model tissue

Intratumor Heterogeneity (ITH) is a functionally important property of tumor tissue and may be involved in drug resistance mechanisms. Although descriptions of ITH can be traced back to very early reports about cancer tissue, mechanistic investigations are still limited by the precision of analysis methods and access to relevant tissue sources. PDX models have provided a reproducible source of tissue with at least a partial representation of naturally occurring ITH. We investigated the properties of phenotypically distinct cell populations by Fluorescence activated cell sorting (FACS) tissue derived cells from multiple tumors from a triple negative breast cancer patient derived xenograft (PDX) model. We subsequently subjected each population to in depth gene expression analysis. Our findings suggest that process related gene expression changes (caused by tissue dissociation and FACS sorting) are restricted to Immediate Early Genes (IEGs). This allowed us to discover highly reproducible gene expression profiles of distinct cellular compartments identifiable by cell surface markers in this particular tumor model. Within the context of data from a previously published model our work suggests that gene expression profiles associated with hypoxia, stemness and drug resistance may reside in tumor subpopulations predictably growing in PDX models. This approach provides a novel opportunity for prospective mechanistic studies of ITH.

Flow based single cell analysis of the immune landscape distinguishes Barrett's esophagus from adjacent normal tissue

Barrett’s esophagus (BE) is metaplasia of the squamous epithelium to a specialized columnar epithelium. BE progresses through low- and high-grade dysplasia before developing into esophageal adenocarcinoma. The BE microenvironment is not well defined. We compare 12 human clinical BE and adjacent normal squamous epithelium biopsies using single cell immunophenotyping by flow cytometry. A cassette of 19 epithelial and immune cell markers was used to detect differences between cellular compartments in normal and BE tissues. We found that the BE microenvironment has an immunological landscape distinct from adjacent normal epithelium. BE has an increased percentage of epithelial cells with a concomitant decrease in the percentage of immune cells, accompanied by a shift in the immune landscape from a predominantly T cell rich microenvironment in normal tissue to a B cell rich landscape in BE tissue. Hierarchical clustering separates BE and normal samples into two discrete groups based upon our 19-marker panel, but also reveals unexpected, shared phenotypes for three patients. Our results suggest that flow based single cell analysis may have the potential for revealing clinically relevant differences between BE and normal adjacent tissue, and that surface immunophenotypes could identify specific subpopulations from dysplastic tissue for further investigation.

Identification of Differentially Expressed Genes between Original Breast Cancer and Xenograft Using Machine Learning Algorithms

Breast cancer is one of the most common malignancies in women. Patient-derived tumor xenograft (PDX) model is a cutting-edge approach for drug research on breast cancer. However, PDX still exhibits differences from original human tumors, thereby challenging the molecular understanding of tumorigenesis. In particular, gene expression changes after tissues are transplanted from human to mouse model. In this study, we propose a novel computational method by incorporating several machine learning algorithms, including Monte Carlo feature selection (MCFS), random forest (RF), and rough set-based rule learning, to identify genes with significant expression differences between PDX and original human tumors. First, 831 breast tumors, including 657 PDX and 174 human tumors, were collected. Based on MCFS and RF, 32 genes were then identified to be informative for the prediction of PDX and human tumors and can be used to construct a prediction model. The prediction model exhibits a Matthews coefficient correlation value of 0.777. Seven interpretable interactions within the informative gene were detected based on the rough set-based rule learning. Furthermore, the seven interpretable interactions can be well supported by previous experimental studies. Our study not only presents a method for identifying informative genes with differential expression but also provides insights into the mechanism through which gene expression changes after being transplanted from human tumor into mouse model. This work would be helpful for research and drug development for breast cancer.

Cancer Immunotherapy: Historical Perspective of a Clinical Revolution and Emerging Preclinical Animal Models

At the turn of the last century, the emerging field of medical oncology chose a cytotoxic approach to cancer therapy over an immune-centered approach at a time when evidence in support of either paradigm did not yet exist. Today, nearly 120 years of data have established that (a) even the best cytotoxic regimens only infrequently cure late-stage malignancy and (b) strategies that supplement and augment existing antitumor immune responses offer the greatest opportunities to potentiate durable remission in cancer. Despite widespread acceptance of these paradigms today, the ability of the immune system to recognize and fight cancer was a highly controversial topic for much of the twentieth century. Why this modern paradigmatic mainstay should have been both dubious and controversial for such an extended period is a topic of considerable interest that merits candid discussion. Herein, we review the literature to identify and describe the watershed events that ultimately led to the acceptance of immunotherapy as a viable regimen for the treatment of neoplastic malignancy. In addition to noting important clinical discoveries, we also focus on research milestones and the development of critical model systems in rodents and dogs including the advanced modeling techniques that allowed development of patient-derived xenografts. Together, their use will further our understanding of cancer biology and tumor immunology, allow for a speedier assessment of the efficacy and safety of novel approaches, and ultimately provide a faster bench to beside transition.