Graphical Abstract Highlights d RTK-RAS-MAPK pathway members score strongly in genome-scale MEKi modifier screens d Depletion of SHOC2 potently sensitizes RAS-driven cells to MEK inhibition d SHOC2 loss impairs RTK-mediated adaptive reactivation of MAPK signaling induced by MEKi d A model of SHOC2 degradation suggests a combination therapeutic strategy with MEKi SUMMARY The mitogen-activated protein kinase (MAPK) pathway is a critical effector of oncogenic RAS signaling, and MAPK pathway inhibition may be an effective combination treatment strategy. We performed genome-scale loss-of-function CRISPR-Cas9 screens in the presence of a MEK1/2 inhibitor (MEKi) in KRAS-mutant pancreatic and lung cancer cell lines and identified genes that cooperate with MEK inhibition. While we observed heterogeneity in genetic modifiers of MEKi sensitivity across cell lines, several recurrent classes of synthetic lethal vulnerabilities emerged at the pathway level. Multiple members of receptor tyrosine kinase (RTK)-RAS-MAPK pathways scored as sensitizers to MEKi. In particular, we demonstrate that knockout, suppression, or degradation of SHOC2, a positive regulator of MAPK signaling, specifically cooperated with MEK inhibition to impair proliferation in RAS-driven cancer cells. The depletion of SHOC2 disrupted survival pathways triggered by feedback RTK signaling in response to MEK inhibition. Thus, these findings nominate SHOC2 as a potential target for combination therapy. 118 Cell Reports 29, 118-134, October 1, 2019 ª 2019 The Author(s). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).(A) Schematic of pooled CRISPR-Cas9 screening strategy. (B-D) Genome-scale screen results in pancreatic cancer, CFPAC-1 (B), and lung cancer lines, A549 (C) and NCI-H23 (D). Red points have FDR < 0.25 (STARS algorithm). Mean trametinib sensitivity (x axis) is calculated as the difference in the log2(fold-change) in sgRNA representation between cells treated with trametinib for 14 days and the initial pool of sgRNAs. Differential sensitivity indicates the difference log2(fold-change) in sgRNA representation between the trametinib-treated and DMSO-treated arms of the screen. Scores represent the average of all guides for a given gene. (E) Venn diagram summarizes the overlap of genes that are depleted in all three screens with an FDR < 0.25.

Pancreatic ductal adenocarcinoma (PDAC) has been classified into classical and basal-like transcriptional subtypes by bulk RNA measurements. However, recent work has uncovered greater complexity to transcriptional subtypes than was initially appreciated using bulk RNA expression profiling. To provide a deeper understanding of PDAC subtypes, we developed a multiplex immunofluorescence (mIF) pipeline that quantifies protein expression of six PDAC subtype markers (CLDN18.2, TFF1, GATA6, KRT17, KRT5, and S100A2) and permits spatially resolved, single-cell interrogation of pancreatic tumors from resection specimens and core needle biopsies. Both primary and metastatic tumors displayed striking intra-tumoral subtype heterogeneity that was associated with patient outcomes, existed at the scale of individual glands, and was significantly reduced in patient-derived organoid cultures. Tumor cells co-expressing classical and basal markers were present in &gt;90% of tumors, existed on a basal-classical polarization continuum, and were enriched in tumors containing a greater admixture of basal and classical cell populations. Cell-cell neighbor analyses within tumor glands further suggested that co-expressor cells may represent an intermediate state between expression subtype poles. The extensive intra-tumoral heterogeneity identified through this clinically applicable mIF pipeline may inform prognosis and treatment selection for patients with PDAC.

Background Many neuronal and glial diseases have been associated with changes in metabolism. Therefore, metabolic reprogramming has become an important area of research to better understand disease at the cellular level, as well as to identify targets for treatment. Model systems are ideal for interrogating metabolic questions in a tissue dependent context. However, while new tools have been developed to study metabolism in cultured cells there has been less progress towards studies in vivo and ex vivo. New Method We have developed a method using newly designed tissue restraints to adapt the Agilent XFe96 metabolic analyzer for whole brain analysis. These restraints create a chamber for Drosophila brains and other small model system tissues to reside undisrupted, while still remaining in the zone for measurements by sensor probes. Results This method generates reproducible oxygen consumption and extracellular acidification rate data for Drosophila larval and adult brains. Single brains are effectively treated with inhibitors and expected metabolic readings are observed. Measuring metabolic changes, such as glycolytic rate, in transgenic larval brains demonstrates the potential for studying how genotype affects metabolism. Comparison with Existing Methods and Conclusions Current methodology either utilizes whole animal chambers to measure respiration, not allowing for targeted tissue analysis, or uses technically challenging MRI technology for in vivo analysis that is not suitable for smaller model systems. This new method allows for novel metabolic investigation of intact brains and other tissues ex vivo in a quick, and simplistic way with the potential for large-scale studies.

<p>Supplementary tables</p>

<p>Supplementary tables</p>