Death Induced by CD95 or CD95 Ligand Elimination

SummaryCD95 (Fas/APO-1), when bound by its cognate ligand CD95L, induces cells to die by apoptosis. We now show that elimination of CD95 or CD95L results in a form of cell death that is independent of caspase-8, RIPK1/MLKL, and p53, is not inhibited by Bcl-xL expression, and preferentially affects cancer cells. All tumors that formed in mouse models of low-grade serous ovarian cancer or chemically induced liver cancer with tissue-specific deletion of CD95 still expressed CD95, suggesting that cancer cannot form in the absence of CD95. Death induced by CD95R/L elimination (DICE) is characterized by an increase in cell size, production of mitochondrial ROS, and DNA damage. It resembles a necrotic form of mitotic catastrophe. No single drug was found to completely block this form of cell death, and it could also not be blocked by the knockdown of a single gene, making it a promising way to kill cancer cells

Collision tumors revealed by prospectively assessing subtype-defining molecular alterations in 904 individual prostate cancer foci.

BACKGROUNDProstate cancer is multifocal with distinct molecular subtypes. The utility of genomic subtyping has been challenged due to inter- and intrafocal heterogeneity. We sought to characterize the subtype-defining molecular alterations of primary prostate cancer across all tumor foci within radical prostatectomy (RP) specimens and determine the prevalence of collision tumors.METHODSFrom the Early Detection Research Network cohort, we identified 333 prospectively collected RPs from 2010 to 2014 and assessed ETS-related gene (ERG), serine peptidase inhibitor Kazal type 1 (SPINK1), phosphatase and tensin homolog (PTEN), and speckle type BTB/POZ protein (SPOP) molecular status. We utilized dual ERG/SPINK1 immunohistochemistry and fluorescence in situ hybridization to confirm ERG rearrangements and characterize PTEN deletion, as well as high-resolution melting curve analysis and Sanger sequencing to determine SPOP mutation status.RESULTSBased on index focus alone, ERG, SPINK1, PTEN, and SPOP alterations were identified in 47.5%, 10.8%, 14.3%, and 5.1% of RP specimens, respectively. In 233 multifocal RPs with ERG/SPINK1 status in all foci, 139 (59.7%) had discordant molecular alterations between foci. Collision tumors, as defined by discrepant ERG/SPINK1 status within a single focus, were identified in 29 (9.4%) RP specimens.CONCLUSIONInterfocal molecular heterogeneity was identified in about 60% of multifocal RP specimens, and collision tumors were present in about 10%. We present this phenomenon as a model for the intrafocal heterogeneity observed in previous studies and propose that future genomic studies screen for collision tumors to better characterize molecular heterogeneity.FUNDINGEarly Detection Research Network US National Cancer Institute (NCI) 5U01 CA111275-09, Center for Translational Pathology at Weill Cornell Medicine (WCM) Department of Pathology and Laboratory Medicine, US NCI (WCM SPORE in Prostate Cancer, P50CA211024-01), R37CA215040, Damon Runyon Cancer Research Foundation, US MetLife Foundation Family Clinical Investigator Award, Norwegian Cancer Society (grant 208197), and South-Eastern Norway Regional Health Authority (grant 2019016 and 2020063)

Predicting peptide presentation by major histocompatibility complex class I: an improved machine learning approach to the immunopeptidome

Abstract Background To further our understanding of immunopeptidomics, improved tools are needed to identify peptides presented by major histocompatibility complex class I (MHC-I). Many existing tools are limited by their reliance upon chemical affinity data, which is less biologically relevant than sampling by mass spectrometry, and other tools are limited by incomplete exploration of machine learning approaches. Herein, we assemble publicly available data describing human peptides discovered by sampling the MHC-I immunopeptidome with mass spectrometry and use this database to train random forest classifiers (ForestMHC) to predict presentation by MHC-I. Results As measured by precision in the top 1% of predictions, our method outperforms NetMHC and NetMHCpan on test sets, and it outperforms both these methods and MixMHCpred on new data from an ovarian carcinoma cell line. We also find that random forest scores correlate monotonically, but not linearly, with known chemical binding affinities, and an information-based analysis of classifier features shows the importance of anchor positions for our classification. The random-forest approach also outperforms a deep neural network and a convolutional neural network trained on identical data. Finally, we use our large database to confirm that gene expression partially determines peptide presentation. Conclusions ForestMHC is a promising method to identify peptides bound by MHC-I. We have demonstrated the utility of random forest-based approaches in predicting peptide presentation by MHC-I, assembled the largest known database of MS binding data, and mined this database to show the effect of gene expression on peptide presentation. ForestMHC has potential applicability to basic immunology, rational vaccine design, and neoantigen binding prediction for cancer immunotherapy. This method is publicly available for applications and further validation

A 1536-well Fluorescence Polarization Assay to Screen for Modulators of the MUSASHI Family of RNA-Binding Proteins

RNA-binding proteins (RBPs) can act as stem cell modulators and oncogenic drivers, but have been largely ignored by the pharmaceutical industry as potential therapeutic targets for cancer. The MUSASHI (MSI) family has recently been demonstrated to be an attractive clinical target in the most aggressive cancers. Therefore, the discovery and development of small molecule inhibitors could provide a novel therapeutic strategy. In order to find novel compounds with MSI RNA binding inhibitory activity, we have developed a fluorescence polarization (FP) assay and optimized it for high throughput screening (HTS) in a 1536-well microtiter plate format. Using a chemical library of 6,208 compounds, we performed pilot screens, against both MSI1 and MSI2, leading to the identification of 7 molecules for MSI1, 15 for MSI2 and 5 that inhibited both. A secondary FP dose-response screen validated 3 MSI inhibitors with IC50 below 10μM. Out of the 25 compounds retested in the secondary screen only 8 demonstrated optical interference due to high fluorescence. Utilizing a SYBR-based RNA electrophoresis mobility shift assay (EMSA), we further verified MSI inhibition of the top 3 compounds. Surprisingly, even though several aminoglycosides were present in the library, they failed to demonstrate MSI inhibitor activity challenging the concept that these compounds are pan-active against RBPs. In summary, we have developed an in vitro strategy to identify MSI specific inhibitors using an FP HTS platform, which will facilitate novel drug discovery for this class of RBPs

Walk-through experiment results for 78 ATS siRNA duplexes.

<p>Results of walk-through experiments measured at day 6 post transfection with synthetic siRNA duplexes using EGFPB reporter cell line. (<b>A</b>) Clustered heatmap to show % gain in EGFP signal conferred by 78 ATS siRNA duplexes, tested as singles. (<b>B</b>) Clustered heatmap to show % gain in EGFP signal conferred by siRNA duplexes segregated into three pools that of duplexes active as singles, inactive as singles or with all inclusive. Rep stands for replicate, AVG stands for average of the four replicates.</p

Schematics for Alternate Targeting Sequence Generator (ATSG).

<p>Hairpin inside the cell gets cleaved at its theoretical site and silences its target specifically. Inefficiencies in cleavage would lead to ATSG, generating random targeting sequencing which silence alternate targets, making it extremely difficult to comprehend the eventual phenotypic outcomes.</p