To evaluate the role of oncogenic RAS mutations in pancreatic tumorigenesis, we directed endogenous expression of KRAS(G12D) to progenitor cells of the mouse pancreas. We find that physiological levels of Kras(G12D) induce ductal lesions that recapitulate the full spectrum of human pancreatic intraepithelial neoplasias (PanINs), putative precursors to invasive pancreatic cancer. The PanINs are highly proliferative, show evidence of histological progression, and activate signaling pathways normally quiescent in ductal epithelium, suggesting potential therapeutic and chemopreventive targets for the cognate human condition. At low frequency, these lesions also progress spontaneously to invasive and metastatic adenocarcinomas, establishing PanINs as definitive precursors to the invasive disease. Finally, mice with PanINs have an identifiable serum proteomic signature, suggesting a means of detecting the preinvasive state in patients.

Erratum Preinvasive and invasive ductal pancreatic cancer and its early detection in the mouseIn the article by Hingorani et al. (Cancer Cell 4,, there are several typographical errors in the text regarding the citation of the figures. On page 441, the sentence "In many of the older mice, the pancreata contained extensive ductal lesions, and the acinar parenchyma was largely replaced by an intense stromal, or desmoplastic, reaction comprised of inflammatory cells, fibroblasts, and collagen deposition (Figures 2I-2K)" should instead refer to Figures 2I-2L. Also on page 441, the sentence "Finally, we note that PanINs expressed only low levels of PDX-1, which can nevertheless be discerned when compared to the lack of expression in surrounding acini (Figures 2G and 2H) and to normal ducts in control animals (Figure 2I)" should refer instead to Figure 3, and thus should read "Finally, we note that PanINs expressed only low levels of PDX-1, which can nevertheless be discerned when compared to the lack of expression in surrounding acini (Figures 3G and 3H) and to normal ducts in control animals (Figure 3I)."

SLFN11 sensitizes cancer cells to a broad range of DNA-targeted therapies. Here we show that, in response to replication stress induced by camptothecin, SLFN11 tightly binds chromatin at stressed replication foci via RPA1 together with the replication helicase subunit MCM3. Unlike ATR, SLFN11 neither interferes with the loading of CDC45 and PCNA nor inhibits the initiation of DNA replication but selectively blocks fork progression while inducing chromatin opening across replication initiation sites. The ATPase domain of SLFN11 is required for chromatin opening, replication block, and cell death but not for the tight binding of SLFN11 to chromatin. Replication stress by the CHK1 inhibitor Prexasertib also recruits SLFN11 to nascent replicating DNA together with CDC45 and PCNA. We conclude that SLFN11 is recruited to stressed replication forks carrying extended RPA filaments where it blocks replication by changing chromatin structure across replication sites.

We report a novel mechanism of action of ONC201 as a mitochondria-targeting drug in cancer cells. ONC201 was originally identified as a small molecule that induces transcription of TNF-related apoptosis-inducing ligand (TRAIL) and subsequently kills cancer cells by activating TRAIL death receptors. In this study, we examined ONC201 toxicity on multiple human breast and endometrial cancer cell lines. ONC201 attenuated cell viability in all cancer cell lines tested. Unexpectedly, ONC201 toxicity was not dependent on either TRAIL receptors nor caspases. Time-lapse live cell imaging revealed that ONC201 induces cell membrane ballooning followed by rupture, distinct from the morphology of cells undergoing apoptosis. Further investigation found that ONC201 induces phosphorylation of AMP-dependent kinase and ATP loss. Cytotoxicity and ATP depletion were significantly enhanced in the absence of glucose, suggesting that ONC201 targets mitochondrial respiration. Further analysis indicated that ONC201 indirectly inhibits mitochondrial respiration. Confocal and electron microscopic analysis demonstrated that ONC201 triggers mitochondrial structural damage and functional impairment. Moreover, ONC201 decreased mitochondrial DNA (mtDNA). RNAseq analysis revealed that ONC201 suppresses expression of multiple mtDNA-encoded genes and nuclear-encoded mitochondrial genes involved in oxidative phosphorylation and other mitochondrial functions. Importantly, fumarate hydratase deficient cancer cells and multiple cancer cell lines with reduced amounts of mtDNA were resistant to ONC201. These results indicate that cells not dependent on mitochondrial respiration are ONC201-resistant. Our data demonstrate that ONC201 kills cancer cells by disrupting mitochondrial function and further suggests that cancer cells that are dependent on glycolysis will be resistant to ONC201.

Serum proteomic pattern diagnostics is an emerging paradigm employing low-resolution mass spectrometry (MS) to generate a set of biomarker classifiers. In the present study, we utilized a wellcontrolled ovarian cancer serum study set to compare the sensitivity and specificity of serum proteomic diagnostic patterns acquired using a high-resolution versus a low-resolution MS platform. In blinded testing sets, the high-resolution mass spectral data contained multiple diagnostic signatures that were superior to the low-resolution spectra in terms of sensitivity and specificity ðP < 0:00001Þ throughout the range of modeling conditions. Four mass spectral feature set patterns acquired from data obtained exclusively with the high-resolution mass spectrometer were 100% specific and sensitive in their diagnosis of serum samples as being acquired from either unaffected patients or those suffering from ovarian cancer. Important to the future of proteomic pattern diagnostics is the ability to recognize inferior spectra statistically, so that those resulting from a specific process error are recognized prior to their potentially incorrect (and damaging) diagnosis. To meet this need, we have developed a series of quality-assurance and in-process control procedures to (a) globally evaluate sources of sample variability, (b) identify outlying mass spectra, and (c) develop quality-control release specifications. From these quality-assurance and control (QA/QC) specifications, we identified 32 mass spectra out of the total 248 that showed statistically significant differences from the norm. Hence, 216 of the initial 248 high-resolution mass spectra were determined to be of high quality and were remodeled by pattern-recognition analysis. Again, we obtained four mass spectral feature set patterns that also exhibited 100% sensitivity and specificity in blinded validation tests (68/68 cancer: including 18/18 stage I, and 43/43 healthy). We conclude that (a) the use of high-resolution MS yields superior classification patterns as compared with those obtained with lower resolution instrumentation; (b) although the process error that we discovered did not have a deleterious impact on the present results obtained from proteomic pattern analysis, the major source of spectral variability emanated from mass spectral acquisition, and not bias at the clinical collection site; (c) this variability can be reduced and monitored through the use of QA/QC statistical procedures; (d) multiple and distinct proteomic patterns, comprising low molecular weight biomarkers, detected by high-resolution MS achieve accuracies surpassing individual biomarkers, warranting validation in a large clinical study.

SummaryCellMinerCDB provides a web-based resource (https://discover.nci.nih.gov/cellminercdb/) for integrating multiple forms of pharmacological and genomic analyses, and unifying the richest cancer cell line datasets (the NCI-60, NCI-SCLC, Sanger/MGH GDSC, and Broad CCLE/CTRP). CellMinerCDB enables data queries for genomics and gene regulatory network analyses, and exploration of pharmacogenomic determinants and drug signatures. It leverages overlaps of cell lines and drugs across databases to examine reproducibility and expand pathway analyses. We illustrate the value of CellMinerCDB for elucidating gene expression determinants, such as DNA methylation and copy number variations, and highlight complexities in assessing mutational burden. We demonstrate the value of CellMinerCDB in selecting drugs with reproducible activity, expand on the dominant role of SLFN11 for drug response, and present novel response determinants and genomic signatures for topoisomerase inhibitors and schweinfurthins. We also introduce LIX1L as a gene associated with mesenchymal signature and regulation of cellular migration and invasiveness.

SUMMARY CellMiner-SCLC ( https://discover.nci.nih.gov/SclcCellMinerCDB/ ) integrates drug sensitivity and genomic data, including high-resolution methylome and transcriptome from 118 patient-derived small cell lung cancer (SCLC) cell lines, providing a resource for research into this “recalcitrant cancer.” We demonstrate the reproducibility and stability of data from multiple sources and validate the SCLC consensus nomenclature on the basis of expression of master transcription factors NEUROD1, ASCL1, POU2F3, and YAP1. Our analyses reveal transcription networks linking SCLC subtypes with MYC and its paralogs and the NOTCH and HIPPO pathways. SCLC subsets express specific surface markers, providing potential opportunities for antibody-based targeted therapies. YAP1-driven SCLCs are notable for differential expression of the NOTCH pathway, epithelial-mesenchymal transition (EMT), and antigen-presenting machinery (APM) genes and sensitivity to mTOR and AKT inhibitors. These analyses provide insights into SCLC biology and a framework for future investigations into subtype-specific SCLC vulnerabilities.

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