QuPath is new bioimage analysis software designed to meet the growing need for a user-friendly, extensible, open-source solution for digital pathology and whole slide image analysis. In addition to offering a comprehensive panel of tumor identification and high-throughput biomarker evaluation tools, QuPath provides researchers with powerful batch-processing and scripting functionality, and an extensible platform with which to develop and share new algorithms to analyze complex tissue images. Furthermore, QuPath’s flexible design makes it suitable for a wide range of additional image analysis applications across biomedical research.
BackgroundRadiotherapy is an effective treatment of intermediate/high-risk locally advanced prostate cancer, however, >30% of patients relapse within 5 years. Clinicopathological parameters currently fail to identify patients prone to systemic relapse and those whom treatment intensification may be beneficial. The purpose of this study was to independently validate the performance of a 70-gene Metastatic Assay in a cohort of diagnostic biopsies from patients treated with radical radiotherapy and androgen deprivation therapy.Patients and methodsA bridging cohort of prostate cancer diagnostic biopsy specimens was profiled to enable optimization of the Metastatic Assay threshold before further independent clinical validation in a cohort of diagnostic biopsies from patients treated with radical radiotherapy and androgen deprivation therapy. Multivariable Cox proportional hazard regression analysis was used to assess assay performance in predicting biochemical failure-free survival (BFFS) and metastasis-free survival (MFS).ResultsGene expression analysis was carried out in 248 patients from the independent validation cohort and the Metastatic Assay applied. Ten-year MFS was 72% for Metastatic Assay positive patients and 94% for Metastatic Assay negative patients [HR = 3.21 (1.35–7.67); P = 0.003]. On multivariable analysis the Metastatic Assay remained predictive for development of distant metastases [HR = 2.71 (1.11–6.63); P = 0.030]. The assay retained independent prognostic performance for MFS when assessed with the Cancer of the Prostate Assessment Score (CAPRA) [HR = 3.23 (1.22–8.59); P = 0.019] whilst CAPRA itself was not significant [HR = 1.88, (0.52–6.77); P = 0.332]. A high concordance [100% (61.5–100)] for the assay result was noted between two separate foci taken from 11 tumours, whilst Gleason score had low concordance.ConclusionsThe Metastatic Assay demonstrated significant prognostic performance in patients treated with radical radiotherapy both alone and independent of standard clinical and pathological variables. The Metastatic Assay could have clinical utility when deciding upon treatment intensification in high-risk patients. Genomic and clinical data are available as a public resource.
Purpose: A number of independent gene expression profiling studies have identified transcriptional subtypes in colorectal cancer with potential diagnostic utility, culminating in publication of a colorectal cancer Consensus Molecular Subtype classification. The worst prognostic subtype has been defined by genes associated with stem-like biology. Recently, it has been shown that the majority of genes associated with this poor prognostic group are stromal derived. We investigated the potential for tumor misclassification into multiple diagnostic subgroups based on tumoral region sampled.Experimental Design: We performed multiregion tissue RNA extraction/transcriptomic analysis using colorectal-specific arrays on invasive front, central tumor, and lymph node regions selected from tissue samples from 25 colorectal cancer patients.Results: We identified a consensus 30-gene list, which represents the intratumoral heterogeneity within a cohort of primary colorectal cancer tumors. Using a series of online datasets, we showed that this gene list displays prognostic potential HR ¼ 2.914 (confidence interval 0.9286-9.162) in stage II/III colorectal cancer patients, but in addition, we demonstrated that these genes are stromal derived, challenging the assumption that poor prognosis tumors with stemlike biology have undergone a widespread epithelial-mesenchymal transition. Most importantly, we showed that patients can be simultaneously classified into multiple diagnostically relevant subgroups based purely on the tumoral region analyzed.Conclusions: Gene expression profiles derived from the nonmalignant stromal region can influence assignment of colorectal cancer transcriptional subtypes, questioning the current molecular classification dogma and highlighting the need to consider pathology sampling region and degree of stromal infiltration when employing transcription-based classifiers to underpin clinical decision making in colorectal cancer.
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