A combined tissue‐engineered/in silico signature tool patient stratification in lung cancer

Abstract

Patient‐tailored therapy based on tumor drivers is promising for lung cancer treatment. For this, we combined in vitro tissue models with in silico analyses. Using individual cell lines with specific mutations, we demonstrate a generic and rapid stratification pipeline for targeted tumor therapy. We improve in vitro models of tissue conditions by a biological matrix‐based three‐dimensional (3D) tissue culture that allows in vitro drug testing: It correctly shows a strong drug response upon gefitinib (Gef) treatment in a cell line harboring an EGFR‐activating mutation (HCC827), but no clear drug response upon treatment with the HSP90 inhibitor 17AAG in two cell lines with KRAS mutations (H441, A549). In contrast, 2D testing implies wrongly KRAS as a biomarker for HSP90 inhibitor treatment, although this fails in clinical studies. Signaling analysis by phospho‐arrays showed similar effects of EGFR inhibition by Gef in HCC827 cells, under both 2D and 3D conditions. Western blot analysis confirmed that for 3D conditions, HSP90 inhibitor treatment implies different p53 regulation and decreased MET inhibition in HCC827 and H441 cells. Using in vitro data (western, phospho‐kinase array, proliferation, and apoptosis), we generated cell line‐specific in silico topologies and condition‐specific (2D, 3D) simulations of signaling correctly mirroring in vitro treatment responses. Networks predict drug targets considering key interactions and individual cell line mutations using the Human Protein Reference Database and the COSMIC database. A signature of potential biomarkers and matching drugs improve stratification and treatment in KRAS‐mutated tumors. In silico screening and dynamic simulation of drug actions resulted in individual therapeutic suggestions, that is, targeting HIF1A in H441 and LKB1 in A549 cells. In conclusion, our in vitro tumor tissue model combined with an in silico tool improves drug effect prediction and patient stratification. Our tool is used in our comprehensive cancer center and is made now publicly available for targeted therapy decisions.

Connecting Cancer Pathways to Tumor Engines: A Stratification Tool for Colorectal Cancer Combining Human In Vitro Tissue Models with Boolean In Silico Models

“…The preserved basal membrane structure enables physiological anchorage of epithelial cells and the analysis of invasive processes. In lung cancer, we observed better predictivity compared to 2D as we could, for example, mirror the clinical attrition of an anti-HSP90 therapy in patients with a KRAS mutation that was suggested by other preclinical models [19,35]. Moreover, these 3D models were combined with an in silico tool for the development of signaling-and mutational-based signatures for therapy predictions in stratified patient groups [18,35].…”

“…These cell lines, HROC24 and HROC87 (Hansestadt ROstock-Colon), harbor a BRAF mutation with further co-mutations: HROC24 with APC mutation and HROC87 with p53 mutation [27][28][29]. For our CRC models, we use an avascular part of a decellularized porcine gut [30][31][32][33]-called small intestine submucosa with preserved mucosa (SISmuc) [34], that is also applied for lung cancer and breast cancer models [18,19,[35][36][37]. The preserved basal membrane structure enables physiological anchorage of epithelial cells and the analysis of invasive processes.…”

How to Steer and Control ERK and the ERK Signaling Cascade Exemplified by Looking at Cardiac Insufficiency

“…This can also easily be investigated within our framework. In such situations, ERK is part of the signaling cascade while the constitutive activation may either stem from an activating, oncogenic mutation of a key receptor such as Epidermal Growth Factor Receptor (EGFR, usually treated by Gefitinib [19]) or by a kinase mutation (most well known are B-Raf and Ras mutations, however, in some aggressive cancers this can also be ERK mutations).…”

ROR1-CAR T cells are effective against lung and breast cancer in advanced microphysiologic 3D tumor models

“…in previous studies that tumor cells established on our 3D scaffold have a more realistic proliferation rate and differentiation state, drug sensitivity, and response compared with tumor cells in conventional 2D culture (13,15). In our dynamic 3D culture models with A549 cells and MDA-MB-231 cells, there were multiple layers of tumor cells and tumor cell aggregates in former crypts that provided a substantially greater challenge to CAR T cells for conferring their antitumor reactivity compared with singularized tumor cells in 2D culture.…”

“…The BioVaSc is derived from decellularized porcine jejunum and used as a collagen scaffold with intact basement membrane (BM) that permits the engraftment and propagation of normal and cancerous epithelial tissues in 3D culture. We have recently demonstrated the development of a 3D lung tumor from A549 non-small cell lung cancer (NSCLC) using the BioVaSc technology (13)(14)(15). The A549 tumor model uses the nonvascular part of the BioVaSc -the small intestinal submucosa and mucosa (SISmuc) -for cell engraftment.…”