Aberrant JAK-STAT signaling-mediated chromatin remodeling impairs the sensitivity of NK/T-cell lymphoma to chidamide.

BACKGROUND: Natural killer/T-cell lymphoma (NKTL) is a rare type of aggressive and heterogeneous non-Hodgkin's lymphoma (NHL) with a poor prognosis and limited therapeutic options. Therefore, there is an urgent need to exploit potential novel therapeutic targets for the treatment of NKTL. Histone deacetylase (HDAC) inhibitor chidamide was recently approved for treating relapsed/refractory peripheral T-cell lymphoma (PTCL) patients. However, its therapeutic efficacy in NKTL remains unclear. METHODS: We performed a phase II clinical trial to evaluate the efficacy of chidamide in 28 relapsed/refractory NKTL patients. Integrative transcriptomic, chromatin profiling analysis and functional studies were performed to identify potential predictive biomarkers and unravel the mechanisms of resistance to chidamide. Immunohistochemistry (IHC) was used to validate the predictive biomarkers in tumors from the clinical trial. RESULTS: We demonstrated that chidamide is effective in treating relapsed/refractory NKTL patients, achieving an overall response and complete response rate of 39 and 18%, respectively. In vitro studies showed that hyperactivity of JAK-STAT signaling in NKTL cell lines was associated with the resistance to chidamide. Mechanistically, our results revealed that aberrant JAK-STAT signaling remodels the chromatin and confers resistance to chidamide. Subsequently, inhibition of JAK-STAT activity could overcome resistance to chidamide by reprogramming the chromatin from a resistant to sensitive state, leading to synergistic anti-tumor effect in vitro and in vivo. More importantly, our clinical data demonstrated that combinatorial therapy with chidamide and JAK inhibitor ruxolitinib is effective against chidamide-resistant NKTL. In addition, we identified TNFRSF8 (CD30), a downstream target of the JAK-STAT pathway, as a potential biomarker that could predict NKTL sensitivity to chidamide. CONCLUSIONS: Our study suggests that chidamide, in combination with JAK-STAT inhibitors, can be a novel targeted therapy in the standard of care for NKTL. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02878278. Registered 25 August 2016, https://clinicaltrials.gov/ct2/show/NCT02878278.

A promising deep learning-assistive algorithm for histopathological screening of colorectal cancer.

Colorectal cancer is one of the most common cancers worldwide, accounting for an annual estimated 1.8 million incident cases. With the increasing number of colonoscopies being performed, colorectal biopsies make up a large proportion of any histopathology laboratory workload. We trained and validated a unique artificial intelligence (AI) deep learning model as an assistive tool to screen for colonic malignancies in colorectal specimens, in order to improve cancer detection and classification; enabling busy pathologists to focus on higher order decision-making tasks. The study cohort consists of Whole Slide Images (WSI) obtained from 294 colorectal specimens. Qritive's unique composite algorithm comprises both a deep learning model based on a Faster Region Based Convolutional Neural Network (Faster-RCNN) architecture for instance segmentation with a ResNet-101 feature extraction backbone that provides glandular segmentation, and a classical machine learning classifier. The initial training used pathologists' annotations on a cohort of 66,191 image tiles extracted from 39 WSIs. A subsequent application of a classical machine learning-based slide classifier sorted the WSIs into 'low risk' (benign, inflammation) and 'high risk' (dysplasia, malignancy) categories. We further trained the composite AI-model's performance on a larger cohort of 105 resections WSIs and then validated our findings on a cohort of 150 biopsies WSIs against the classifications of two independently blinded pathologists. We evaluated the area under the receiver-operator characteristic curve (AUC) and other performance metrics. The AI model achieved an AUC of 0.917 in the validation cohort, with excellent sensitivity (97.4%) in detection of high risk features of dysplasia and malignancy. We demonstrate an unique composite AI-model incorporating both a glandular segmentation deep learning model and a classical machine learning classifier, with excellent sensitivity in picking up high risk colorectal features. As such, AI plays a role as a potential screening tool in assisting busy pathologists by outlining the dysplastic and malignant glands.