CRISPR Screen to Identify Factors that Render Tumor Cells Sensitive or Resistant to Killing by NK Cells.

Natural killer (NK) cells are an important component of the cancer immune surveillance system. They are regulated by germline-encoded receptors that activate and inhibit their effector function, such as secretion of cytokines and direct lysis of tumor cells and virus-infected cells. Without the need to be primed by prior exposure to tumor antigen, NK cells can detect ligands expressed on tumor cells and selectively kill these cells. NK cells are under strict control by inhibitory receptors that bind to HLA class I on target cells and block early activation signals, thus preventing lysis of target cells. The sensitivity to lysis by NK cells is therefore determined to a large extent by the expression of HLA class I molecules on tumor cells. In addition to receptor-ligand interactions that occur at NK-target cell synapses, many other factors determine the sensitivity of tumor cells to lysis by NK. Intrinsic properties of tumor cells, such as their metabolism and signaling networks establish a threshold above which they will succumb to the death pathways triggered by NK cell attack. Here we provide a protocol for a genome-wide CRISPR screen in tumor cells to identify factors that regulate their sensitivity to primary human NK cells. Tumor cells first transduced for expression of Cas9 are then transduced with a guide RNA (gRNA) library and co-cultured with NK cells. Deep sequencing of the library generated from the genome of tumor cells that survived the selection by NK cells and analysis of the distribution of guide RNAs is performed to identify genes that promote either sensitivity or resistance to NK-mediated killing. The contribution of individual genes to tumor sensitivity can be validated by knockouts using individual gRNAs. The techniques and workflow described here could be applied to primary tumors from cancer patients and reveal tumor-specific points of vulnerability that could be exploited for cancer immunotherapy, such as checkpoint blockade or expression of chimeric antigen receptors specifically designed to activate NK cell cytotoxicity.

Wnt3a Stimulation Promotes Primary Ciliogenesis through ß-Catenin Phosphorylation-Induced Reorganization of Centriolar Satellites.

Primary cilium is an antenna-like microtubule-based cellular sensing structure. Abnormal regulation of the dynamic assembly and disassembly cycle of primary cilia is closely related to ciliopathy and cancer. The Wnt signaling pathway plays a major role in embryonic development and tissue homeostasis, and defects in Wnt signaling are associated with a variety of human diseases, including cancer. In this study, we provide direct evidence of Wnt3a-induced primary ciliogenesis, which includes a continuous pathway showing that the stimulation of Wnt3a, a canonical Wnt ligand, promotes the generation of ß-catenin p-S47 epitope by CK1δ, and these events lead to the reorganization of centriolar satellites resulting in primary ciliogenesis. We have also confirmed the application of our findings in MCF-7/ADR cells, a multidrug-resistant tumor cell model. Thus, our data provide a Wnt3a-induced primary ciliogenesis pathway and may provide a clue on how to overcome multidrug resistance in cancer treatment.

Validation of optimal DCE-MRI perfusion threshold to classify at-risk tumor imaging voxels in heterogeneous cervical cancer for outcome prediction.

PURPOSE: To classify tumor imaging voxels at-risk for treatment failure within the heterogeneous cervical cancer using DCE MRI and determine optimal voxel's DCE threshold values at different treatment time points for early prediction of treatment failure. MATERIAL AND METHOD: DCE-MRI from 102 patients with stage IB2-IVB cervical cancer was obtained at 3 different treatment time points: before (MRI 1) and during treatment (MRI 2 at 2-2.5 weeks and MRI 3 at 4-5 weeks). For each tumor voxel, the plateau signal intensity (SI) was derived from its time-SI curve from the DCE MRI. The optimal SI thresholds to classify the at-risk tumor voxels was determined by the maximal area under the curve using ROC analysis when varies SI value from 1.0 to 3.0 and correlates with treatment outcome. RESULTS: The optimal SI thresholds for MRI 1, 2 and 3 were 2.2, 2.2 and 2.1 for significant differentiation between local recurrence/control, respectively, and 1.8, 2.1 and 2.2 for death/survival, respectively. CONCLUSION: Optimal SI thresholds are clinically validated to quantify at-risk tumor voxels which vary with time. A single universal threshold (SI=1.9) was identified for all 3 treatment time points and remained significant for the early prediction of treatment failure.