Conumee 2.0: enhanced copy-number variation analysis from DNA methylation arrays for humans and mice.

MOTIVATION: Copy-number variations (CNVs) are common genetic alterations in cancer and their detection may impact tumor classification and therapeutic decisions. However, detection of clinically relevant large and focal CNVs remains challenging when sample material or resources are limited. This has motivated us to create a software tool to infer CNVs from DNA methylation arrays which are often generated as part of clinical routines and in research settings. RESULTS: We present our R package, conumee 2.0, that combines tangent normalization, an adjustable genomic binning heuristic, and weighted circular binary segmentation to utilize DNA methylation arrays for CNV analysis and mitigate technical biases and batch effects. Segmentation results were validated in a lung squamous cell carcinoma dataset from TCGA (n = 367 samples) by comparison to segmentations derived from genotyping arrays (Pearson's correlation coefficient of 0.91). We further introduce a segmented block bootstrapping approach to detect focal alternations that achieved 60.9% sensitivity and 98.6% specificity for deletions affecting CDKN2A/B (60.0% and 96.9% for RB1, respectively) in a low-grade glioma cohort from TCGA (n = 239 samples). Finally, our tool provides functionality to detect and summarize CNVs across large sample cohorts. AVAILABILITY AND IMPLEMENTATION: Conumee 2.0 is available under open-source license at: https://github.com/hovestadtlab/conumee2.

spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape.

MOTIVATION: Cancer is one of the leading causes of death worldwide. Despite significant improvements in prevention and treatment, mortality remains high for many cancer types. Hence, innovative methods that use molecular data to stratify patients and identify biomarkers are needed. Promising biomarkers can also be inferred from competing endogenous RNA (ceRNA) networks that capture the gene-miRNA gene regulatory landscape. Thus far, the role of these biomarkers could only be studied globally but not in a sample-specific manner. To mitigate this, we introduce spongEffects, a novel method that infers subnetworks (or modules) from ceRNA networks and calculates patient- or sample-specific scores related to their regulatory activity. RESULTS: We show how spongEffects can be used for downstream interpretation and machine learning tasks such as tumor classification and for identifying subtype-specific regulatory interactions. In a concrete example of breast cancer subtype classification, we prioritize modules impacting the biology of the different subtypes. In summary, spongEffects prioritizes ceRNA modules as biomarkers and offers insights into the miRNA regulatory landscape. Notably, these module scores can be inferred from gene expression data alone and can thus be applied to cohorts where miRNA expression information is lacking. AVAILABILITY AND IMPLEMENTATION: https://bioconductor.org/packages/devel/bioc/html/SPONGE.html.

Epigenetic drug screening defines a PRMT5 inhibitor-sensitive pancreatic cancer subtype.

Systemic therapies for pancreatic ductal adenocarcinoma (PDAC) remain unsatisfactory. Clinical prognosis is particularly poor for tumor subtypes with activating aberrations in the MYC pathway, creating an urgent need for novel therapeutic targets. To unbiasedly find MYC-associated epigenetic dependencies, we conducted a drug screen in pancreatic cancer cell lines. Here, we found that protein arginine N-methyltransferase 5 (PRMT5) inhibitors triggered an MYC-associated dependency. In human and murine PDACs, a robust connection of MYC and PRMT5 was detected. By the use of gain- and loss-of-function models, we confirmed the increased efficacy of PRMT5 inhibitors in MYC-deregulated PDACs. Although inhibition of PRMT5 was inducing DNA damage and arresting PDAC cells in the G2/M phase of the cell cycle, apoptotic cell death was executed predominantly in cells with high MYC expression. Experiments in primary patient-derived PDAC models demonstrated the existence of a highly PRMT5 inhibitor-sensitive subtype. Our work suggests developing PRMT5 inhibitor-based therapies for PDAC.

Selective multi-kinase inhibition sensitizes mesenchymal pancreatic cancer to immune checkpoint blockade by remodeling the tumor microenvironment.

KRAS-mutant pancreatic ductal adenocarcinoma (PDAC) is highly immunosuppressive and resistant to targeted and immunotherapies. Among the different PDAC subtypes, basal-like mesenchymal PDAC, which is driven by allelic imbalance, increased gene dosage and subsequent high expression levels of oncogenic KRAS, shows the most aggressive phenotype and strongest therapy resistance. In the present study, we performed a systematic high-throughput combination drug screen and identified a synergistic interaction between the MEK inhibitor trametinib and the multi-kinase inhibitor nintedanib, which targets KRAS-directed oncogenic signaling in mesenchymal PDAC. This combination treatment induces cell-cycle arrest and cell death, and initiates a context-dependent remodeling of the immunosuppressive cancer cell secretome. Using a combination of single-cell RNA-sequencing, CRISPR screens and immunophenotyping, we show that this combination therapy promotes intratumor infiltration of cytotoxic and effector T cells, which sensitizes mesenchymal PDAC to PD-L1 immune checkpoint inhibition. Overall, our results open new avenues to target this aggressive and therapy-refractory mesenchymal PDAC subtype.

Artificial intelligence in early drug discovery enabling precision medicine.

Introduction: Precision medicine is the concept of treating diseases based on environmental factors, lifestyles, and molecular profiles of patients. This approach has been found to increase success rates of clinical trials and accelerate drug approvals. However, current precision medicine applications in early drug discovery use only a handful of molecular biomarkers to make decisions, whilst clinics gear up to capture the full molecular landscape of patients in the near future. This deep multi-omics characterization demands new analysis strategies to identify appropriate treatment regimens, which we envision will be pioneered by artificial intelligence.Areas covered: In this review, the authors discuss the current state of drug discovery in precision medicine and present our vision of how artificial intelligence will impact biomarker discovery and drug design.Expert opinion: Precision medicine is expected to revolutionize modern medicine; however, its traditional form is focusing on a few biomarkers, thus not equipped to leverage the full power of molecular landscapes. For learning how the development of drugs can be tailored to the heterogeneity of patients across their molecular profiles, artificial intelligence algorithms are the next frontier in precision medicine and will enable a fully personalized approach in drug design, and thus ultimately impacting clinical practice.

Genetic Screens Identify a Context-Specific PI3K/p27Kip1 Node Driving Extrahepatic Biliary Cancer.

Biliary tract cancer ranks among the most lethal human malignancies, representing an unmet clinical need. Its abysmal prognosis is tied to an increasing incidence and a fundamental lack of mechanistic knowledge regarding the molecular basis of the disease. Here, we show that the Pdx1-positive extrahepatic biliary epithelium is highly susceptible toward transformation by activated PIK3CAH1047R but refractory to oncogenic KrasG12D. Using genome-wide transposon screens and genetic loss-of-function experiments, we discover context-dependent genetic interactions that drive extrahepatic cholangiocarcinoma (ECC) and show that PI3K signaling output strength and repression of the tumor suppressor p27Kip1 are critical context-specific determinants of tumor formation. This contrasts with the pancreas, where oncogenic Kras in concert with p53 loss is a key cancer driver. Notably, inactivation of p27Kip1 permits KrasG12D-driven ECC development. These studies provide a mechanistic link between PI3K signaling, tissue-specific tumor suppressor barriers, and ECC pathogenesis, and present a novel genetic model of autochthonous ECC and genes driving this highly lethal tumor subtype. SIGNIFICANCE: We used the first genetically engineered mouse model for extrahepatic bile duct carcinoma to identify cancer genes by genome-wide transposon-based mutagenesis screening. Thereby, we show that PI3K signaling output strength and p27Kip1 function are critical determinants for context-specific ECC formation. This article is highlighted in the In This Issue feature, p. 2945.

Xeno-Hybrid Bone Graft Releasing Biomimetic Proteins Promotes Osteogenic Differentiation of hMSCs.

Bone defect is a noteworthy health problem and is the second most transplanted tissue after blood. Numerous bone grafts are designed and applied in clinics. Limitations, however, from different aspects still exist, including limited supply, mechanical strength, and bioactivity. In this study, two biomimetic peptides (P2 and P6) are incorporated into a composite bioactive xeno hybrid bone graft named SmartBonePep®, with the aim to increase the bioactivity of the bone graft. The results, which include cytotoxicity, proliferation rate, confocal microscopy, gene expression, and protein qualification, successfully prove that the SmartBonePep® has multi-modal biological effects on human mesenchymal stem cells from bone marrow. The effective physical entrapment of P6 into a composite xeno-hybrid bone graft, withstanding manufacturing processes including exposure to strong organic solvents and ethylene oxide sterilization, increases the osteogenic potential of the stem cells as well as cell attachment and proliferation. P2 and P6 both show a strong biological potential and may be future candidates for enhancing the clinical performance of bone grafts.