Combination of histoculture drug response assay and qPCR as an effective method to screen biomarkers for personalized chemotherapy in esophageal cancer

Evaluating and comparing immunostaining and computational methods for spatial profiling of drug response in patient-derived explants

Patient-derived explants (PDEs) represent the direct culture of fragments of freshly-resected tumour tissue under conditions that retain the original architecture of the tumour. PDEs have advantages over other preclinical cancer models as platforms for predicting patient-relevant drug responses in that they preserve the tumour microenvironment and tumour heterogeneity. At endpoint, PDEs may either be processed for generation of histological sections or homogenised and processed for 'omic' evaluation of biomarker expression. A significant advantage of spatial profiling is the ability to co-register drug responses with tumour pathology, tumour heterogeneity and changes in the tumour microenvironment. Spatial profiling of PDEs relies on the utilisation of robust immunostaining approaches for validated biomarkers and incorporation of appropriate image analysis methods to quantitatively and qualitatively monitor changes in biomarker expression in response to anti-cancer drugs. Automation of immunostaining and image analysis would provide a significant advantage for the drug discovery pipeline and therefore, here, we have sought to optimise digital pathology approaches. We compare three image analysis software platforms (QuPath, ImmunoRatio and VisioPharm) for evaluating Ki67 as a marker for proliferation, cleaved PARP (cPARP) as a marker for apoptosis and pan-cytokeratin (CK) as a marker for tumour areas and find that all three generate comparable data to the views of a histomorphometrist. We also show that Virtual Double Staining of sequential sections by immunohistochemistry results in imperfect section alignment such that CK-stained tumour areas are over-estimated. Finally, we demonstrate that multi-immunofluorescence combined with digital image analysis is a superior method for monitoring multiple biomarkers simultaneously in tumour and stromal areas in PDEs.

The identification of candidate effective combination regimens for pancreatic cancer using the histoculture drug response assay

The prognosis for patients with pancreatic cancer is extremely poor, as they are resistant to first line chemotherapy. The long-term goal of this study was to identify effective combination chemotherapy for pancreatic cancer using pancreatic cancer surgical specimens in the histoculture drug response assay (HDRA) based on three-dimensional culture of tumour fragments, which maintains nature tumour histology in vitro. From 2015 to 2017, the HDRA was performed with tumour specimens from 52 pancreatic cancer patients from Asan Medical Hospital. First, combination drug regimens showed higher drug efficacy and less patient variation than single drugs. Initially, 5-Fluorouracil(5-FU)/Belotecan/Oxaliplatinum and Tegafur/Gimeracil (TS-1)/Oxaliplatinum/Irinotecan were found to be effective. Second, we were able to correlate the efficacy of some drugs with tumour stage. Third, when designing new combination regimens containing 5-FU or gemcitabine, we could identify more effective drug combinations. This is the first study to demonstrate usefulness of the HDRA for pancreatic cancer. Using this technique, we could identify novel candidate combination drug regimens that should be effective in treating pancreatic cancer.

Patient-derived explants (PDEs) as a powerful preclinical platform for anti-cancer drug and biomarker discovery

Preclinical models that can accurately predict outcomes in the clinic are much sought after in the field of cancer drug discovery and development. Existing models such as organoids and patient-derived xenografts have many advantages, but they suffer from the drawback of not contextually preserving human tumour architecture. This is a particular problem for the preclinical testing of immunotherapies, as these agents require an intact tumour human-specific microenvironment for them to be effective. In this review, we explore the potential of patient-derived explants (PDEs) for fulfilling this need. PDEs involve the ex vivo culture of fragments of freshly resected human tumours that retain the histological features of original tumours. PDE methodology for anti-cancer drug testing has been in existence for many years, but the platform has not been widely adopted in translational research facilities, despite strong evidence for its clinical predictivity. By modifying PDE endpoint analysis to include the spatial profiling of key biomarkers by using multispectral imaging, we argue that PDEs offer many advantages, including the ability to correlate drug responses with tumour pathology, tumour heterogeneity and changes in the tumour microenvironment. As such, PDEs are a powerful model of choice for cancer drug and biomarker discovery programmes.