Ex Vivo Drug Testing in Patient-derived Papillary Renal Cancer Cells Reveals EGFR and the BCL2 Family as Therapeutic Targets.

BACKGROUND: Immune checkpoint inhibitors and antiangiogenic agents are used for first-line treatment of advanced papillary renal cell carcinoma (pRCC) but pRCC response rates to these therapies are low. OBJECTIVE: To generate and characterise a functional ex vivo model to identify novel treatment options in advanced pRCC. DESIGN, SETTING, AND PARTICIPANTS: We established patient-derived cell cultures (PDCs) from seven pRCC samples from patients and characterised them via genomic analysis and drug profiling. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Comprehensive molecular characterisation in terms of copy number analysis and whole-exome sequencing confirmed the concordance of pRCC PDCs with the original tumours. We evaluated their sensitivity to novel drugs by generating drug scores for each PDC. RESULTS AND LIMITATIONS: PDCs confirmed pRCC-specific copy number variations such as gains in chromosomes 7, 16, and 17. Whole-exome sequencing revealed that PDCs retained mutations in pRCC-specific driver genes. We performed drug screening with 526 novel and oncological compounds. Whereas exposure to conventional drugs showed low efficacy, the results highlighted EGFR and BCL2 family inhibition as the most effective targets in our pRCC PDCs. CONCLUSIONS: High-throughput drug testing on newly established pRCC PDCs revealed that inhibition of EGFR and BCL2 family members could be a therapeutic strategy in pRCC. PATIENT SUMMARY: We used a new approach to generate patient-derived cells from a specific type of kidney cancer. We showed that these cells have the same genetic background as the original tumour and can be used as models to study novel treatment options for this type of kidney cancer.

Cancer Sample Biobanking at the Next Level: Combining Tissue With Living Cell Repositories to Promote Precision Medicine.

Biorespositories of formalin-fixed and paraffin-embedded (FFPE) or fresh frozen human tissues from malignant diseases generated as integral part of the diagnostic workup in many pathology departments have been pivotal resources for translational cancer studies. However, such tissue biobanks have traditionally contained only non-viable specimens and thus cannot enable functional assays for the discovery and validation of therapeutic targets or the assessment of drug responses and resistance to treatment. To overcome these limitations, we have developed a next-generation comprehensive biobanking platform that includes the generation of patient-derived in vitro cell models from colorectal, pancreatic and kidney cancers among others. As such patient-derived cell (PDC) models retain important features of the original human tumors, they have emerged as relevant tools for more dynamic clinical and experimental analyses of cancer. Here, we describe details of the complex processes of acquisition and processing of patient-derived samples, propagation, annotation, characterization and distribution of resulting cell models and emphasize the requirements of quality assurance, organizational considerations and investment into resources. Taken together, we show how clinical tissue collections can be taken to the next level thus promising major new opportunities for understanding and treating cancer in the context of precision medicine.

Early transcriptional events in the udder and teat after intra-mammary Escherichia coli and Staphylococcus aureus challenge.

Intra-mammary bacterial infections can result in harmful clinical mastitis or subclinical mastitis with persistent infections. Research during the last decades closely examined the pathophysiology of inflamed udders. Initial events after pathogen perception but before the onset of mastitis have not been examined in vivo The objective of this study was to develop a mastitis model in cows by monitoring initial transcriptional pathogen-specific host response before clinical signs occur. We applied a short-term infection model to analyse transcripts encoding chemokines, cytokines and antimicrobial molecules in the teat cistern (TC) and lobulo-alveolar parenchyma (LP) up to 3 h after challenge with E and Staphylococcus aureus Both pathogens elicited an immune reaction by 1 h after challenge. Escherichia coli induced all analysed factors (CCL20, CXCL8, TNF, IL6, IL12B, IL10, LAP, S100A9); however, S. aureus failed to induce IL12B, IL10, LAP and S100A9 expression. The E. coli-induced up-regulation was 25-105 times greater than that after S. aureus challenge. Almost all the responses were restricted to the TC. The short-term mastitis model demonstrates that a divergent pathogen-specific response is generated during the first h. It confirms that the first transcripts are generated in the TC prior to a response in the LP.

A mammalian cell based FACS-panning platform for the selection of HIV-1 envelopes for vaccine development.

An increasing number of broadly neutralizing monoclonal antibodies (bnMAb) against the HIV-1 envelope (Env) protein has been discovered recently. Despite this progress, vaccination efforts with the aim to re-elicit bnMAbs that provide protective immunity have failed so far. Herein, we describe the development of a mammalian cell based FACS-panning method in which bnMAbs are used as tools to select surface-exposed envelope variants according to their binding affinity. For that purpose, an HIV-1 derived lentiviral vector was developed to infect HEK293T cells at low multiplicity of infection (MOI) in order to link Env phenotype and genotype. For proof of principle, a gp145 Env model-library was established in which the complete V3 domain was substituted by five strain specific V3 loop sequences with known binding affinities to nMAb 447-52D, respectively. Env genes were recovered from selected cells by PCR, subcloned into a lentiviral vector (i) to determine and quantify the enrichment nMAb binders and (ii) to generate a new batch of transduction competent particles. After 2 selection cycles the Env variant with highest affinity was enriched 20-fold and represented 80% of the remaining Env population. Exploiting the recently described bnMAbs, this procedure might prove useful in selecting Env proteins from large Env libraries with the potential to elicit bnMAbs when used as vaccine candidates.