Despite the established role of Ki67 labeling index in prognostic stratification of adrenocortical carcinomas and its recent integration into treatment flow charts, the reproducibility of the assessment method has not been determined. The aim of this study was to investigate interobserver variability among endocrine pathologists using a web-based virtual microscopy approach. Ki67-stained slides of 76 adrenocortical carcinomas were analyzed independently by 14 observers, each according to their method of preference including eyeballing, formal manual counting, and digital image analysis. The interobserver variation was statistically significant (P<0.001) in the absence of any correlation between the various methods. Subsequently, 61 static images were distributed among 15 observers who were instructed to follow a category-based scoring approach. Low levels of interobserver (F=6.99; Fcrit=1.70; P<0.001) as well as intraobserver concordance (n=11; Cohen κ ranging from -0.057 to 0.361) were detected. To improve harmonization of Ki67 analysis, we tested the utility of an open-source Galaxy virtual machine application, namely Automated Selection of Hotspots, in 61 virtual slides. The software-provided Ki67 values were validated by digital image analysis in identical images, displaying a strong correlation of 0.96 (P<0.0001) and dividing the cases into 3 classes (cutoffs of 0%-15%-30% and/or 0%-10%-20%) with significantly different overall survivals (P<0.05). We conclude that current practices in Ki67 scoring assessment vary greatly, and interobserver variation sets particular limitations to its clinical utility, especially around clinically relevant cutoff values. Novel digital microscopy-enabled methods could provide critical aid in reducing variation, increasing reproducibility, and improving reliability in the clinical setting.

/pdf/an-international-ki67-reproducibility-study-in-adrenal-1qeyldlf44.pdf

An international Ki67 reproducibility study in adrenal cortical Carcinoma

Purpose:
To evaluate safety and characterize anticancer efficacy of hepatic hypoxia-activated intraarterial therapy (HAIAT) with evofosfamide in a rabbit model.

Experimental Design:
VX2-tumor-bearing rabbits were assigned to 4 intraarterial therapy (IAT) groups (n=7/group): 1) saline (control); 2) evofosfamide (Evo); 3) doxorubicin-Lipiodol emulsion followed by embolization with 100-300μm beads (conventional, cTACE); or 4) cTACE and evofosfamide (cTACE+Evo). Blood samples were collected pre-IAT and 1/2/7/14 days post-IAT. A semiquantitative scoring system assessed hepatocellular damage. Tumor volumes were segmented on multidetector CT (baseline, 7/14 days post-IAT). Pathologic tumor necrosis was quantified using manual segmentation on whole slide images. Hypoxic fraction (HF) and compartment (HC) were determined by pimonidazole staining. Tumor DNA damage, apoptosis, cell proliferation, endogenous hypoxia and metabolism were quantified (γ-H2AX/annexin V/caspase-3/Ki-67/HIF1α/VEGF/GAPDH/MCT4 and LDH).

Results:
cTACE+Evo showed a similar profile of liver enzymes elevation and pathologic scores compared to cTACE. Neither hematologic nor renal toxicity were observed. Animals treated with cTACE+Evo demonstrated smaller tumor volumes, lower tumor growth rates and higher necrotic fractions compared to cTACE. cTACE+Evo resulted in a marked reduction in the HF and HC. Correlation was observed between decreases in HF or HC and tumor necrosis. cTACE+Evo promoted antitumor effects as evidenced by increased expression of γ-H2A.X, apoptotic biomarkers and decreased cell proliferation. Increased HIF1α/VEGF expression and tumor glycolysis supported HAIAT.

Conclusions:
HAIAT achieved a promising step towards the locoregional targeting of tumor hypoxia. The favorable toxicity profile and enhanced anticancer effects of evofosfamide in combination with cTACE pave the way towards clinical trials in patients with liver cancer.

/pdf/preclinical-benefit-of-hypoxia-activated-intra-arterial-bu5ku48luk.pdf

Preclinical Benefit of Hypoxia-Activated Intra-arterial Therapy with Evofosfamide in Liver Cancer

Cell or nucleus detection is a fundamental task in microscopy image analysis and has recently achieved state-of-the-art performance by using deep neural networks. However, training supervised deep models such as convolutional neural networks (CNNs) usually requires sufficient annotated image data, which is prohibitively expensive or unavailable in some applications. Additionally, when applying a CNN to new datasets, it is common to annotate individual cells/nuclei in those target datasets for model re-learning, leading to inefficient and low-throughput image analysis. To tackle these problems, we present a bidirectional, adversarial domain adaptation method for nucleus detection on cross-modality microscopy image data. Specifically, the method learns a deep regression model for individual nucleus detection with both source-to-target and target-to-source image translation. In addition, we explicitly extend this unsupervised domain adaptation method to a semi-supervised learning situation and further boost the nucleus detection performance. We evaluate the proposed method on three cross-modality microscopy image datasets, which cover a wide variety of microscopy imaging protocols or modalities, and obtain a significant improvement in nucleus detection compared to reference baseline approaches. In addition, our semi-supervised method is very competitive with recent fully supervised learning models trained with all real target training labels.

Bidirectional Mapping-Based Domain Adaptation for Nucleus Detection in Cross-Modality Microscopy Images

Follicular lymphoma (FL) is an indolent B-cell neoplasm of germinal center origin. Standard treatment regimens consist of anti-CD20 therapy with or without chemotherapy. While high response rates to initial therapy are common, patients ultimately relapse or have progressive disease. Clinical risk factors such as the Follicular Lymphoma International Prognostic Index (FLIPI) have been identified, but there is a need for prognostic and predictive biomarkers. We studied markers of lymphoma cells and tumor microenvironment by immunohistochemistry in tissue samples from patients enrolled in 1 of 4 phase 2 trials of anti-CD20-based biological therapy for previously untreated grades 1 to 2 or 3A FL. Results were correlated with progression-free survival (PFS) and PFS status at 24 months. The 4 trials included 238 patients (51.1% male, median age: 55 y) with stage III, IV, or bulky stage II disease. By FLIPI, 24.6% had low-risk, 56.8% had intermediate-risk, and 18.6% had high-risk disease. The outcome differed significantly for patients treated with lenalidomide and rituximab (CALGB 50803) compared with the other 3 trials (median: PFS not reached vs. 3.0 y, hazard ratio=3.47, 95% confidence interval: 2.11-5.72); therefore, data were stratified by clinical trial (CALGB 50803 vs. all others) and adjusted for FLIPI risk group. Among 154 patients with available tissue, interfollicular BCL6 positivity, interfollicular CD10 positivity, and elevated Ki67 proliferation index ≥30% within neoplastic follicles were each associated with inferior PFS and a high risk of the early event by PFS status at 24 months. We identify promising biomarkers for FL risk stratification that warrant further validation in phase 3 trials.

Biomarkers for Risk Stratification in Patients With Previously Untreated Follicular Lymphoma Receiving Anti-CD20-based Biological Therapy.

Long non-coding RNAs (lncRNAs) comprise a family of non-coding transcripts that are emerging as relevant gene expression regulators of different processes, including tumour development. To determine the possible contribution of lncRNA to the pathogenesis of follicular lymphoma (FL) we performed RNA-sequencing at high depth sequencing in primary FL samples ranging from grade 1-3A to aggressive grade 3B variants using unpurified (n = 16) and purified (n = 12) tumour cell suspensions from nodal samples. FL grade 3B had a significantly higher number of differentially expressed lncRNAs (dif-lncRNAs) with potential target coding genes related to cell cycle regulation. Nine out of the 18 selected dif-lncRNAs were validated by quantitative real time polymerase chain reaction in an independent series (n = 43) of FL. RP4-694A7.2 was identified as the top deregulated lncRNA potentially involved in cell proliferation. RP4-694A7.2 silencing in the WSU-FSCCL FL cell line reduced cell proliferation due to a block in the G1/S phase. The relationship between RP4-694A7.2 and proliferation was confirmed in primary samples as its expression levels positively related to the Ki-67 proliferation index. In summary, lncRNAs are differentially expressed across the clinico-biological spectrum of FL and a subset of them, related to cell cycle, may participate in cell proliferation regulation in these tumours.

/pdf/differential-expression-of-long-non-coding-rnas-are-related-muvs4vm1fp.pdf

Differential expression of long non-coding RNAs are related to proliferation and histological diversity in follicular lymphomas

Objectives: To examine the accuracy of software-assisted measurement of the Ki-67 proliferation index (PI) and its correlation with the grade and clinical progression of follicular lymphoma (FL).

Methods: High-power field equivalents were extracted from H&E- and Ki-67–immunostained slides of FL, and a nuclear quantitation algorithm was used to calculate a PI. Representative fields were manually counted for validation with close agreement.

Results: The PI was significantly higher in World Health Organization grade 3 FL than grade 1 to 2 FL. Disease progression, as defined by subsequent treatment with radiation or cytotoxic chemotherapy, was also significantly associated with elevated PI but not pathologic grade.

Conclusions: These data show that software-automated quantitation of Ki-67 can provide both a useful adjunct to pathologic grade in FL and improved prognostic information for patients.

/pdf/software-automated-counting-of-ki-67-proliferation-index-v32av5musm.pdf

Software-automated counting of Ki-67 proliferation index correlates with pathologic grade and disease progression of follicular lymphomas.

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Software-automated counting of Ki-67 proliferation index correlates with pathologic grade and disease progression of follicular lymphomas.