Search bioRxiv⌕ Search

Biology subjects

Conejo-Garcia, J. R.

Publications and source records attributed to Conejo-Garcia, J. R..

2 recordsLinked to original sources

A Novel Humanized Immune Stroma PDX Cancer Model for Therapeutic Studies

Standard preclinical human tumor models lack a human tumor stroma. However, as stroma contributes to therapeutic resistance, the lack of human stroma may make current models less stringent for testing new therapies. To address this, using patient-derived tumor cells, patient derived cancer-associated mesenchymal stem/progenitor cells, and human endothelial cells, we created a Human Stroma-Patient Derived Xenograft (HS-PDX) tumor model. HS-PDX, compared to the standard PDX model, demonstrate greater resistance to targeted therapy and chemotherapy, and better reflect patient response to therapy. Furthermore, HS-PDX can be grown in mice with humanized bone marrow to create humanized immune stroma patient-derived xenograft (HIS-PDX) models. The HIS-PDX model contains human connective tissues, vascular and immune cell infiltrates. RNA sequencing analysis demonstrated a 94-96% correlation with primary human tumor. Using this model, we demonstrate the impact of human tumor stroma on response to CAR-T cell therapy and immune checkpoint inhibitor therapy. We show an immunosuppressive role for human tumor stroma and that this model can be used to identify immunotherapeutic combinations to overcome stromally mediated immunosuppression. Combined, our data confirm a critical role for human stoma in therapeutic response and indicate that HIS-PDX can be an important tool for preclinical drug testing. Statement of SignificanceWe developed a tumor model with human stromal, vascular, and immune cells. This model mirrors patient response to chemotherapy, targeted therapy, and immunotherapy, and can be used to study therapy resistance.

cancer biology↗

VALIS: Virtual Alignment of pathoLogy Image Series

Spatial analyses can reveal important interactions between and among cells and their microenvironment. However, most existing staining methods are limited to a handful of markers per slice, thereby limiting the number of interactions that can be studied. This limitation is frequently overcome by registering multiple images to create a single composite image containing many markers. While there are several existing image registration methods for whole slide images (WSI), most have specific use cases. Here, we present the Virtual Alignment of pathoLogy Image Series (VALIS), a fully automated pipeline that opens, registers (rigid and/or non-rigid), and saves aligned slides in the ome.tiff format. VALIS has been tested with 273 immunohistochemistry (IHC) samples and 340 immunofluorescence (IF) samples, each of which contained between 2-69 images per sample. The registered WSI tend to have low error and are completed within a matter of minutes. In addition to registering slides, VALIS can also using the registration parameters to warp point data, such as cell centroids previously determined via cell segmentation and phenotyping. VALIS is written in Python and requires only few lines of code for execution. VALIS therefore provides a free, opensource, flexible, and simple pipeline for rigid and non-rigid registration of IF and/or IHC that can facilitate spatial analyses of WSI from novel and existing datasets.

cancer biology↗