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Vast amounts of molecular data are being collected on tumor samples, which provide unique opportunities for discovering trends within and between cancer subtypes. Such cross-cancer analyses require computational methods that enable intuitive and interactive browsing of thousands of samples based on their molecular similarity. We created a portal called TumorMap to assist in exploration and statistical interrogation of high-dimensional complex “omics” data in an interactive and easily interpretable way. In the TumorMap, samples are arranged on a hexagonal grid based on their similarity to one another in the original genomic space and are rendered with Google's Map technology. While the important feature of this public portal is the ability for the users to build maps from their own data, we pre-built genomic maps from several previously published projects. We demonstrate the utility of this portal by presenting results obtained from The Cancer Genome Atlas project data. Cancer Res; 77(21); e111–4. ©2017 AACR.

OBJECTIVE:The passive leg raising (PLR) maneuver has become standard practice in fluid resuscitation. We aim to investigate the precision and consistency of the PLR for determining fluid responsiveness in critically ill patients and healthy volunteers using bioreactance non-invasive cardiac output monitoring (NiCOM™, Cheetah Medical, Inc., Newton Center, Massachusetts, USA). METHODS:This study is prospective, single-center, observational cohort with repeated measures in critically ill patients admitted to the medical intensive care unit and healthy volunteers at a tertiary academic medical center. Three cycles of PLR were performed, each at 20-30 minutes apart. Fluid responsiveness was defined as a change in stroke volume index (ΔSVI) > 10% with each PLR as determined by NiCOM™. Precision was the variability in ΔSVI after the 3 PLR's, and determined by range, average deviation and standard deviation. Consistency was the same fluid responsiveness determination of "Yes" (ΔSVI > 10%) or "No" (ΔSVI ≤ 10%) for all 3 PLR's. RESULTS:Seventy-five patients and 25 volunteers were enrolled. In patients, the precision was range of 17.2±13.3%, average deviation 6.5±4.0% and standard deviation 9.0±5.2%; and for volunteers, 17.4±10.3%, 6.6±3.8% and 9.0±6.7%, respectively. There was no statistical difference in the precision measurements between patients and volunteers. Forty-nine (65.3%) patients vs. twenty-four (96.0%) volunteers had consistent results, p < 0.01. Among those with consistent results, twenty-four (49.0%) patients and 24 (100%) volunteers were fluid responsive. CONCLUSIONS:The precision and consistency of determining ΔSVI with NiCOM™ after PLR may have clinical implication if ΔSVI > 10% is the absolute cutoff to determine fluid responsiveness.

In this paper, we present Biomedical Multi-Task Deep Neural Network (Bio-MTDNN) on the NLI task of MediQA 2019 challenge. Bio-MTDNN utilizes “transfer learning” based paradigm where not only the source and target domains are different but also the source and target tasks are varied, although related. Further, Bio-MTDNN integrates knowledge from external sources such as clinical databases (UMLS) enhancing its performance on the clinical domain. Our proposed method outperformed the official baseline and other prior models (such as ESIM and Infersent on dev set) by a considerable margin as evident from our experimental results.

Social media-based text mining in healthcare has received special attention in recent times due to the enhanced accessibility of social media sites like Twitter. The increasing trend of spreading important information in distress can help patients reach out to prospective blood donors in a time bound manner. However such manual efforts are mostly inefficient due to the limited network of a user. In a novel step to solve this problem, we present an annotated Emergency Blood Donation Request (EBDR) dataset to classify tweets referring to the necessity of urgent blood donation requirement. Additionally, we also present an automated feature-based SVM classification technique that can help selective EBDR tweets reach relevant personals as well as medical authorities. Our experiments also present a quantitative evidence that linguistic along with handcrafted heuristics can act as the most representative set of signals this task with an accuracy of 97.89%.

SummaryDendritic cells (DCs) are required to initiate and sustain T cell-dependent anti-cancer immunity. However, tumors often evade immune control by crippling normal DC function. The endoplasmic reticulum (ER) stress response factor XBP1 promotes intrinsic tumor growth directly, but whether it also regulates the host anti-tumor immune response is not known. Here we show that constitutive activation of XBP1 in tumor-associated DCs (tDCs) drives ovarian cancer (OvCa) progression by blunting anti-tumor immunity. XBP1 activation, fueled by lipid peroxidation byproducts, induced a triglyceride biosynthetic program in tDCs leading to abnormal lipid accumulation and subsequent inhibition of tDC capacity to support anti-tumor T cells. Accordingly, DC-specific XBP1 deletion or selective nanoparticle-mediated XBP1 silencing in tDCs restored their immunostimulatory activity in situ and extended survival by evoking protective type 1 anti-tumor responses. Targeting the ER stress response should concomitantly inhibit tumor growth and enhance anti-cancer immunity, thus offering a unique approach to cancer immunotherapy.

Tumours evade immune control by creating hostile microenvironments that perturb T cell metabolism and effector function 1?4 . However, it remains unclear how intra-tumoral T cells integrate and interpret metabolic stress signals. Here we report that ovarian cancer?an aggressive malignancy that is refractory to standard treatments and current immunotherapies 5?8 ?induces endoplasmic reticulum stress and activates the IRE1α?XBP1 arm of the unfolded protein response 9,10 in T cells to control their mitochondrial respiration and anti-tumour function. In T cells isolated from specimens collected from patients with ovarian cancer, upregulation of XBP1 was associated with decreased infiltration of T cells into tumours and with reduced IFNG mRNA expression. Malignant ascites fluid obtained from patients with ovarian cancer inhibited glucose uptake and caused N-linked protein glycosylation defects in T cells, which triggered IRE1α?XBP1 activation that suppressed mitochondrial activity and IFNγ production. Mechanistically, induction of XBP1 regulated the abundance of glutamine carriers and thus limited the influx of glutamine that is necessary to sustain mitochondrial respiration in T cells under glucose-deprived conditions. Restoring N-linked protein glycosylation, abrogating IRE1α?XBP1 activation or enforcing expression of glutamine transporters enhanced mitochondrial respiration in human T cells exposed to ovarian cancer ascites. XBP1-deficient T cells in the metastatic ovarian cancer milieu exhibited global transcriptional reprogramming and improved effector capacity. Accordingly, mice that bear ovarian cancer and lack XBP1 selectively in T cells demonstrate superior anti-tumour immunity, delayed malignant progression and increased overall survival. Controlling endoplasmic reticulum stress or targeting IRE1α?XBP1 signalling may help to restore the metabolic fitness and anti-tumour capacity of T cells in cancer hosts. Fil: Song, Minkyung. Weill Cornell Medicine; Estados Unidos Fil: Sandoval, Tito A.. Weill Cornell Medicine; Estados Unidos Fil: Chae, Chang-Suk. Weill Cornell Medicine; Estados Unidos Fil: Chopra, Sahil. Weill Cornell Medicine; Estados Unidos Fil: Tan, Chen. Weill Cornell Medicine; Estados Unidos Fil: Rutkowski, Melanie R.. University of Virginia; Estados Unidos Fil: Raundhal, Mahesh. Dana Farber Cancer Institute; Estados Unidos. Harvard Medical School; Estados Unidos Fil: Chaurio, Ricardo A.. H. Lee Moffitt Cancer Center & Research Institute; Estados Unidos Fil: Payne, Kyle K.. H. Lee Moffitt Cancer Center & Research Institute; Estados Unidos Fil: Konrad, Csaba. Weill Cornell Medicine; Estados Unidos Fil: Bettigole, Sarah E.. Quentis Therapeutics Inc.; Estados Unidos Fil: Shin, Hee Rae. Quentis Therapeutics Inc.; Estados Unidos Fil: Crowley, Michael J. P.. Weill Cornell Graduate School of Medical Sciences; Estados Unidos Fil: Cerliani, Juan Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; Argentina Fil: Kossenkov, Andrew V.. The Wistar Institute; Estados Unidos Fil: Motorykin, Ievgen. Weill Cornell Medicine,; Estados Unidos Fil: Zhang, Sheng. Weill Cornell Medicine,; Estados Unidos Fil: Manfredi, Giovanni. Weill Cornell Medicine,; Estados Unidos Fil: Zamarin, Dmitriy. Memorial Sloan Kettering Cancer Center; Estados Unidos Fil: Holcomb, Kevin. Weill Cornell Medicine,; Estados Unidos Fil: Rodriguez, Paulo C.. H. Lee Moffitt Cancer Center & Research Institute; Estados Unidos Fil: Rabinovich, Gabriel Adrián. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Química Biológica; Argentina Fil: Conejo Garcia, Jose R.. H. Lee Moffitt Cancer Center & Research Institute; Estados Unidos Fil: Glimcher, Laurie H.. Dana Farber Cancer Institute; Estados Unidos. Harvard Medical School; Estados Unidos Fil: Cubillos-Ruiz, Juan R.. Weill Graduate School Of Medical Sciences; Estados Unidos. Weill Graduate School Of Medical Sciences; Estados Unidos

Summary We report the results of a DREAM challenge designed to predict relative genetic essentialities based on a novel dataset testing 98,000 shRNAs against 149 molecularly characterized cancer cell lines. We analyzed the results of over 3,000 submissions over a period of 4 months. We found that algorithms combining essentiality data across multiple genes demonstrated increased accuracy; gene expression was the most informative molecular data type; the identity of the gene being predicted was far more important than the modeling strategy; well-predicted genes and selected molecular features showed enrichment in functional categories; and frequently selected expression features correlated with survival in primary tumors. This study establishes benchmarks for gene essentiality prediction, presents a community resource for future comparison with this benchmark, and provides insights into factors influencing the ability to predict gene essentiality from functional genetic screens. This study also demonstrates the value of releasing pre-publication data publicly to engage the community in an open research collaboration.