Jie-Yi Shi, Xiaodong Wang, Guang-Yu Ding, Zhou Dong, Jing Han, Zehui Guan, Li-Jie Ma, Yuxuan Zheng, Lei Zhang, Guan-Zhen Yu, Xiao-Ying Wang, Zhen-Bin Ding, Ai-Wu Ke, Haoqing Yang, Liming Wang, Lirong Ai, Ya Cao, Jian Zhou, Jia Fan, Xiyang Liu, Qiang Gao <<<

OBJECTIVE: Tumour pathology contains rich information, including tissue structure and cell morphology, that reflects disease progression and patient survival. However, phenotypic information is subtle and complex, making the discovery of prognostic indicators from pathological images challenging.DESIGN: An interpretable, weakly supervised deep learning framework incorporating prior knowledge was proposed to analyse hepatocellular carcinoma (HCC) and explore new prognostic phenotypes on pathological whole-slide images (WSIs) from the Zhongshan cohort of 1125 HCC patients (2451 WSIs) and TCGA cohort of 320 HCC patients (320 WSIs). A 'tumour risk score (TRS)' was established to evaluate patient outcomes, and then risk activation mapping (RAM) was applied to visualise the pathological phenotypes of TRS. The multi-omics data of The Cancer Genome Atlas(TCGA) HCC were used to assess the potential pathogenesis underlying TRS.RESULTS: Survival analysis revealed that TRS was an independent prognosticator in both the Zhongshan cohort (p<0.0001) and TCGA cohort (p=0.0003). The predictive ability of TRS was superior to and independent of clinical staging systems, and TRS could evenly stratify patients into up to five groups with significantly different prognoses. Notably, sinusoidal capillarisation, prominent nucleoli and karyotheca, the nucleus/cytoplasm ratio and infiltrating inflammatory cells were identified as the main underlying features of TRS. The multi-omics data of TCGA HCC hint at the relevance of TRS to tumour immune infiltration and genetic alterations such as the FAT3 and RYR2 mutations.CONCLUSION: Our deep learning framework is an effective and labour-saving method for decoding pathological images, providing a valuable means for HCC risk stratification and precise patient treatment. <<<

Anatoly Buchin, Rebecca de Frates, Anirban Nandi, Rusty Mann, Peter Chong, Lindsay Ng, Jeremy Miller, Rebecca Hodge, Brian Kalmbach, Soumita Bose, Ueli Rutishauser, Stephen McConoughey, Ed Lein, Jim Berg, Staci Sorensen, Ryder Gwinn, Christof Koch, Jonathan Ting, Costas A Anastassiou <<<

Temporal lobe epilepsy is the fourth most common neurological disorder, with about 40% of patients not responding to pharmacological treatment. Increased cellular loss is linked to disease severity and pathological phenotypes such as heightened seizure propensity. While the hippocampus is the target of therapeutic interventions, the impact of the disease at the cellular level remains unclear. Here, we show that hippocampal granule cells change with disease progression as measured in living, resected hippocampal tissue excised from patients with epilepsy. We show that granule cells increase excitability and shorten response latency while also enlarging in cellular volume and spine density. Single-nucleus RNA sequencing combined with simulations ascribes the changes to three conductances: BK, Cav2.2, and Kir2.1. In a network model, we show that these changes related to disease progression bring the circuit into a more excitable state, while reversing them produces a less excitable, "early-disease-like" state. <<<

Kevin B Einkauf, Matthew R Osborn, Ce Gao, Weiwei Sun, Xiaoming Sun, Xiaodong Lian, Elizabeth M Parsons, Gregory T Gladkov, Kyra W Seiger, Jane E Blackmer, Chenyang Jiang, Steven A Yukl, Eric S Rosenberg, Xu G Yu, Mathias Lichterfeld <<<

HIV-1-infected cells that persist despite antiretroviral therapy (ART) are frequently considered "transcriptionally silent," but active viral gene expression may occur in some cells, challenging the concept of viral latency. Applying an assay for profiling the transcriptional activity and the chromosomal locations of individual proviruses, we describe a global genomic and epigenetic map of transcriptionally active and silent proviral species and evaluate their longitudinal evolution in persons receiving suppressive ART. Using genome-wide epigenetic reference data, we show that proviral transcriptional activity is associated with activating epigenetic chromatin features in linear proximity of integration sites and in their inter- and intrachromosomal contact regions. Transcriptionally active proviruses were actively selected against during prolonged ART; however, this pattern was violated by large clones of virally infected cells that may outcompete negative selection forces through elevated intrinsic proliferative activity. Our results suggest that transcriptionally active proviruses are dynamically evolving under selection pressure by host factors. <<<

Genomic alterations (GA) in PIK3CA leads to the hyper-activation of the phosphatidylinositol-4, 5-bisphosphate 3-kinase (PI3K) pathway in more than 20% of ovarian cancer (OC) patients. Therefore, PI3K therapies are under clinical evaluation for this subset of patients. Evidently, in clinical trials testing the efficacy of isoform-specific inhibitors of PI3K (PI3Ki), patients having a stable disease eventually relapse, as tumors become resistant to treatment. Hence, there is an urgent clinical need to develop new therapeutic combinations to improve the efficacy of PI3Ki in PIK3CA-driven OC patients. Here we identified the molecular mechanism that limits the efficacy of the beta-sparing PI3Ki, Taselisib (GDC0032), in PIK3CA-mutated OC cell lines (IGROV1 and OAW42) that acquired resistance to GDC0032. By comparing the molecular profile of GDC0032-sensitve and -resistant OC cell lines, we found that AKT/mTOR inhibition is required for GDC0032 efficacy. In resistant cells, the sustained activation of AKT/mTOR was regulated by the upregulation of the insulin growth factor 1 receptor (IGF1R). Knockdown of IGF1R re-sensitized cells to GDC0032 in vitro, and the combination of AEW541, an IGF1R inhibitor, with GDC0032 exhibited potent anti-tumor activity in vitro and in vivo. We further demonstrated that IGF1R regulates tumor cell proliferation in IGROV1 cells, whereas in OAW42, it determines autophagy as well. Overall, our findings suggest that the dual inhibition of PI3K and IGF1R may be considered as a new therapeutic strategy in PIK3CA-driven OC. <<<

Jim Abraham, Amy B Heimberger, John Marshall, Elisabeth Heath, Joseph Drabick, Anthony Helmstetter, Joanne Xiu, Daniel Magee, Phillip Stafford, Chadi Nabhan, Sourabh Antani, Curtis Johnston, Matthew Oberley, Wolfgang Michael Korn, David Spetzler <<<

Cancer of Unknown Primary (CUP) occurs in 3-5% of patients when standard histological diagnostic tests are unable to determine the origin of metastatic cancer. Typically, a CUP diagnosis is treated empirically and has very poor outcomes, with median overall survival less than one year. Gene expression profiling alone has been used to identify the tissue of origin but struggles with low neoplastic percentage in metastatic sites which is where identification is often most needed. MI GPSai, a Genomic Prevalence Score, uses DNA sequencing and whole transcriptome data coupled with machine learning to aid in the diagnosis of cancer. The algorithm trained on genomic data from 34,352 cases and genomic and transcriptomic data from 23,137 cases and was validated on 19,555 cases. MI GPSai predicted the tumor type in the labeled data set with an accuracy of over 94% on 93% of cases while deliberating amongst 21 possible categories of cancer. When also considering the second highest prediction, the accuracy increases to 97%. Additionally, MI GPSai rendered a prediction for 71.7% of CUP cases. Pathologist evaluation of discrepancies between submitted diagnosis and MI GPSai predictions resulted in change of diagnosis in 41.3% of the time. MI GPSai provides clinically meaningful information in a large proportion of CUP cases and inclusion of MI GPSai in clinical routine could improve diagnostic fidelity. Moreover, all genomic markers essential for therapy selection are assessed in this assay, maximizing the clinical utility for patients within a single test. <<<