Characterisation of circulating tumour cell phenotypes identifies a partial-EMT sub-population for clinical stratification of pancreatic cancer.

BACKGROUND: Limited accessibility of the tumour precludes longitudinal characterisation for therapy guidance in pancreatic ductal adenocarcinoma (PDAC). METHODS: We utilised dielectrophoresis-field flow fractionation (DEP-FFF) to isolate circulating tumour cells (CTCs) in 272 blood draws from 74 PDAC patients (41 localised, 33 metastatic) to non-invasively monitor disease progression. RESULTS: Analysis using multiplex imaging flow cytometry revealed four distinct sub-populations of CTCs: epithelial (E-CTC), mesenchymal (M-CTC), partial epithelial-mesenchymal transition (pEMT-CTC) and stem cell-like (SC-CTC). Overall, CTC detection rate was 76.8% (209/272 draws) and total CTC counts did not correlate with any clinicopathological variables. However, the proportion of pEMT-CTCs (prop-pEMT) was correlated with advanced disease, worse progression-free and overall survival in all patients, and earlier recurrence after resection. CONCLUSION: Our results underscore the importance of immunophenotyping and quantifying specific CTC sub-populations in PDAC.

System for Quality-Assured Data Analysis: Flexible, reproducible scientific workflows.

The reproducibility of scientific processes is one of the paramount problems of bioinformatics, an engineering problem that must be addressed to perform good research. The System for Quality-Assured Data Analysis (SyQADA), described here, seeks to address reproducibility by managing many of the details of procedural bookkeeping in bioinformatics in as simple and transparent a manner as possible. SyQADA has been used by persons with backgrounds ranging from expert programmer to Unix novice, to perform and repeat dozens of diverse bioinformatics workflows on tens of thousands of samples, consuming over 80 CPU-months of computing on over 300,000 individual tasks of scores of projects on laptops, computer servers, and computing clusters. SyQADA is especially well-suited for paired-sample analyses found in cancer tumor-normal studies. SyQADA executable source code, documentation, tutorial examples, and workflows used in our lab is available from http://scheet.org/software.html.

Adequacy of small biopsy and cytology specimens for comprehensive genomic profiling of patients with non-small-cell lung cancer to determine eligibility for immune checkpoint inhibitor and targeted therapy.

AIMS: In advanced-stage non-small-cell lung cancer (NSCLC), incomplete genotyping for guideline-recommended genomic biomarkers poses a significant challenge to making informed and timely clinical decisions. We report our institution's experience in assessing the adequacy of small specimens for comprehensive genomic profiling for guideline-recommended lung cancer biomarker testing. METHODS: We performed a retrospective evaluation of all image-guided procedures for NSCLC performed in our institution between October 2016 and July 2018, including core needle biopsy (CNB) and fine-needle aspiration (FNA) in patients who had undergone genomic profiling for lung cancer. Lung cancer biomarker adequacy, defined as successful testing of guideline-recommended biomarkers including, epidermal growth factor receptor (EGFR); serine/threonine protein kinase B-Raf (BRAF); anaplastic lymphoma kinase (ALK); proto-oncogene tyrosine protein kinase ROS (ROS1); Rearranged during Transfection (RET); Tyrosine protein kinase Met (MET); and programmed cell death ligand 1 (PD-L1), was evaluated. RESULTS: A total of 865 cases were evaluated in this study, 785 of which included testing of all lung cancer biomarkers. Lung tissue was adequate for biomarker testing in 84% of cases; this rate increased to 87% when biomarker testing was combined with concurrently acquired FNA or CNB specimens. Biomarker testing success correlated strongly with DNA concentration (p<0.0001) and the use of 22G needles in endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) procedures (p=0.0035). Biomarker testing of CNB specimens showed a significantly higher success rate than did biomarker testing of cytology FNA specimens (p=0.0005). The adequacy of EBUS-TBNA samples was not significantly different from that of the transthoracic needle aspiration samples (p=0.40). Variables such as age, gender, lesion size, site, diagnosis and number of needle passes showed no significant correlation with success rates in lung cancer biomarker testing. CONCLUSION: The growing numbers of therapeutic biomarkers in NSCLC requires judicious triage of limited-volume tissue from small specimens. Our study showed that thoracic small tissue specimens can be used successfully to provide prognostic and predictive information for the current guideline-recommended biomarkers for NSCLC in most cases.