An unusual cause of acute cardiogenic shock in the operating room

A 51-year-old man with a renal carcinoma with inferior vena cava (IVC) invasion was referred to our hospital for the performance of a radical nephrectomy with IVC thrombus excision. To prevent embolism, an IVC filter was implanted the day before surgery below the suprahepatic veins. On nephrectomy completion, the clinical status of the patient started to deteriorate and an unsuccessful attempt was made to excise the IVC thrombus. The patient developed profound refractory hypotension without significant bleeding and worsening splanchnic stasis was noted. A transesophageal echocardiogram was immediately performed in the operating room, revealing a hemispheric mass protruding from the IVC ostium to the right atrium, completely blocking all venous return. Volume depletion was evident by low left and right atrial volumes and increased septum mobility. No other abnormalities were found that could explain the shock, namely ventricular dysfunction or valvular disease. Cardiac surgery consultation was immediately obtained, ultimately deciding to perform a median sternotomy with direct exploration of right atrium. Under cardiopulmonary bypass, a 6-cm long thrombotic mass was identified, involving the IVC filter, blocking all lower body venous return; the removal of the mass reversed the shock. The patient had an uneventful recovery. Adverse outcomes associated with IVC filters are common. Our case highlights the importance of a team approach to rapid changes in hemodynamic status in the operating room, including the surgeon, the anesthesiologist, and the cardiologist. It also emphasizes the pivotal role of transesophageal echocardiogram in the clinical evaluation of severely unstable patien

The Immune Landscape of Cancer

We performed an extensive immunogenomic anal-ysis of more than 10,000 tumors comprising 33diverse cancer types by utilizing data compiled byTCGA. Across cancer types, we identified six im-mune subtypes\u2014wound healing, IFN-gdominant,inflammatory, lymphocyte depleted, immunologi-cally quiet, and TGF-bdominant\u2014characterized bydifferences in macrophage or lymphocyte signa-tures, Th1:Th2 cell ratio, extent of intratumoral het-erogeneity, aneuploidy, extent of neoantigen load,overall cell proliferation, expression of immunomod-ulatory genes, and prognosis. Specific drivermutations correlated with lower (CTNNB1,NRAS,orIDH1) or higher (BRAF,TP53,orCASP8) leukocytelevels across all cancers. Multiple control modalitiesof the intracellular and extracellular networks (tran-scription, microRNAs, copy number, and epigeneticprocesses) were involved in tumor-immune cell inter-actions, both across and within immune subtypes.Our immunogenomics pipeline to characterize theseheterogeneous tumors and the resulting data areintended to serve as a resource for future targetedstudies to further advance the field

A community effort to create standards for evaluating tumor subclonal reconstruction

Tumor DNA sequencing data can be interpreted by computational methods that analyze genomic heterogeneity to infer evolutionary dynamics. A growing number of studies have used these approaches to link cancer evolution with clinical progression and response to therapy. Although the inference of tumor phylogenies is rapidly becoming standard practice in cancer genome analyses, standards for evaluating them are lacking. To address this need, we systematically assess methods for reconstructing tumor subclonality. First, we elucidate the main algorithmic problems in subclonal reconstruction and develop quantitative metrics for evaluating them. Then we simulate realistic tumor genomes that harbor all known clonal and subclonal mutation types and processes. Finally, we benchmark 580 tumor reconstructions, varying tumor read depth, tumor type and somatic variant detection. Our analysis provides a baseline for the establishment of gold-standard methods to analyze tumor heterogeneity

The Immune Landscape of Cancer

We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes\u2014wound healing, IFN-\u3b3 dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-\u3b2 dominant\u2014characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field. Thorsson et al. present immunogenomics analyses of more than 10,000 tumors, identifying six immune subtypes that encompass multiple cancer types and are hypothesized to define immune response patterns impacting prognosis. This work provides a resource for understanding tumor-immune interactions, with implications for identifying ways to advance research on immunotherapy

The Immune Landscape of Cancer (Immunity (2018) 48 (812–832), (S1074-7613(18)30121-3), (10.1016/j.immuni.2018.03.023))

(Immunity 48, 812–830.e1–e14; April 17, 2018) In the originally published version of this article, the authors neglected to include Younes Mokrab and Aaron M. Newman as co-authors and misspelled the names of authors Charles S. Rabkin and Ilya Shmulevich. The author names have been corrected here and online. In addition, the concluding sentence of the subsection “Immune Signature Compilation” in the Method Details in the original published article was deemed unclear because it did not specify differences among the gene set scoring methods. The concluding sentences now reads “Gene sets from Bindea et al., Senbabaoglu et al., and the MSigDB C7 collection were scored using single-sample gene set enrichment (ssGSEA) analysis (Barbie et al., 2009), as implemented in the GSVA R package (Hänzelmann et al., 2013). All other signatures were scored using methods found in the associated citations.