Germline polymorphisms in model organisms and humans influence susceptibility to complex trait diseases such as inflammation and cancer. Mice of the Mus spretus species are resistant to tumour development, and crosses between M. spretus and susceptible Mus musculus strains have been used to map locations of genetic variants that contribute to skin cancer susceptibility. We have integrated germline polymorphisms with gene expression in normal skin from a M. musculus x M. spretus backcross to generate a network view of the gene expression architecture of mouse skin. Here we demonstrate how this approach identifies expression motifs that contribute to tissue organization and biological functions related to inflammation, haematopoiesis, cell cycle control and tumour susceptibility. Motifs associated with inflammation, epidermal barrier function and proliferation are differentially regulated in backcross mice susceptible or resistant to tumour development. The intestinal stem cell marker Lgr5 is identified as a candidate master regulator of the hair follicle, and the vitamin D receptor (Vdr) is linked to coordinated control of epidermal barrier function, inflammation and tumour susceptibility.

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We are now in an era of molecular medicine, where specific DNA alterations can be used to identify patients who will respond to specific drugs. However, there are only a handful of clinically used predictive biomarkers in oncology. Herein, we describe an approach utilizing in vitro DNA and RNA sequencing and drug response data to create TreAtment Response Generalized Elastic-neT Signatures (TARGETS). We trained TARGETS drug response models using Elastic-Net regression in the publicly available Genomics of Drug Sensitivity in Cancer (GDSC) database. Models were then validated on additional in-vitro data from the Cancer Cell Line Encyclopedia (CCLE), and on clinical samples from The Cancer Genome Atlas (TCGA) and Stand Up to Cancer/Prostate Cancer Foundation West Coast Prostate Cancer Dream Team (WCDT). First, we demonstrated that all TARGETS models successfully predicted treatment response in the separate in-vitro CCLE treatment response dataset. Next, we evaluated all FDA-approved biomarker-based cancer drug indications in TCGA and demonstrated that TARGETS predictions were concordant with established clinical indications. Finally, we performed independent clinical validation in the WCDT and found that the TARGETS AR signaling inhibitors (ARSI) signature successfully predicted clinical treatment response in metastatic castration-resistant prostate cancer with a statistically significant interaction between the TARGETS score and PSA response (p = 0.0252). TARGETS represents a pan-cancer, platform-independent approach to predict response to oncologic therapies and could be used as a tool to better select patients for existing therapies as well as identify new indications for testing in prospective clinical trials.

Metastatic castration-resistant prostate cancer (mCRPC) is a lethal form of prostate cancer. Although long-noncoding RNAs (lncRNAs) have been implicated in mCRPC, past studies have relied on bulk sequencing methods with low depth and lack of single-cell resolution. Hence, we performed a lncRNA-focused analysis of single-cell RNA-sequencing data (n = 14) from mCRPC biopsies followed by integration with bulk multi-omic datasets. This yielded 389 cell-enriched lncRNAs in prostate cancer cells and the tumor microenvironment (TME). These lncRNAs demonstrated enrichment with regulatory elements and exhibited alterations during prostate cancer progression. Prostate-lncRNAs were correlated with AR mutational status and response to treatment with enzalutamide, while TME-lncRNAs were associated with RB1 deletions and poor prognosis. Finally, lncRNAs identified between prostate adenocarcinomas and neuroendocrine tumors exhibited distinct expression and methylation profiles. Our findings demonstrate the ability of single-cell analysis to refine our understanding of lncRNAs in mCRPC and serve as a resource for future mechanistic studies.

Next-generation sequencing of human tumours has refined our understanding of the mutational processes operative in cancer initiation and progression, yet major questions remain regarding the factors that induce driver mutations and the processes that shape mutation selection during tumorigenesis. Here we performed whole-exome sequencing on adenomas from three mouse models of non-small-cell lung cancer, which were induced either by exposure to carcinogens (methyl-nitrosourea (MNU) and urethane) or by genetic activation of Kras (Kras(LA2)). Although the MNU-induced tumours carried exactly the same initiating mutation in Kras as seen in the Kras(LA2) model (G12D), MNU tumours had an average of 192 non-synonymous, somatic single-nucleotide variants, compared with only six in tumours from the Kras(LA2) model. By contrast, the Kras(LA2) tumours exhibited a significantly higher level of aneuploidy and copy number alterations compared with the carcinogen-induced tumours, suggesting that carcinogen-induced and genetically engineered models lead to tumour development through different routes. The wild-type allele of Kras has been shown to act as a tumour suppressor in mouse models of non-small-cell lung cancer. We demonstrate that urethane-induced tumours from wild-type mice carry mostly (94%) Kras Q61R mutations, whereas those from Kras heterozygous animals carry mostly (92%) Kras Q61L mutations, indicating a major role for germline Kras status in mutation selection during initiation. The exome-wide mutation spectra in carcinogen-induced tumours overwhelmingly display signatures of the initiating carcinogen, while adenocarcinomas acquire additional C > T mutations at CpG sites. These data provide a basis for understanding results from human tumour genome sequencing, which has identified two broad categories of tumours based on the relative frequency of single-nucleotide variations and copy number alterations, and underline the importance of carcinogen models for understanding the complex mutation spectra seen in human cancers.

HHT shows clinical variability within and between families. Organ site and prevalence of arteriovenous malformations (AVMs) depend on the HHT causative gene and on environmental and genetic modifiers. We tested whether variation in the functional ENG allele, inherited from the unaffected parent, alters risk for pulmonary AVM in HHT1 mutation carriers who are ENG haploinsufficient. Genetic association was found between rs10987746 of the wild type ENG allele and presence of pulmonary AVM [relative risk = 1.3 (1.0018-1.7424)]. The rs10987746-C at-risk allele associated with lower expression of ENG RNA in a panel of human lymphoblastoid cell lines (P = 0.004). Moreover, in angiogenically active human lung adenocarcinoma tissue, but not in uninvolved quiescent lung, rs10987746-C was correlated with expression of PTPN14 (P = 0.004), another modifier of HHT. Quantitative TAQMAN expression analysis in a panel of normal lung tissues from 69 genetically heterogeneous inter-specific backcross mice, demonstrated strong correlation between expression levels of Eng, Acvrl1, and Ptpn14 (r2 = 0.75-0.9, P < 1 × 10(-12)), further suggesting a direct or indirect interaction between these three genes in lung in vivo. Our data indicate that genetic variation within the single functional ENG gene influences quantitative and/or qualitative differences in ENG expression that contribute to risk of pulmonary AVM in HHT1, and provide correlative support for PTPN14 involvement in endoglin/ALK1 lung biology in vivo. PTPN14 has been shown to be a negative regulator of Yap/Taz signaling, which is implicated in mechanotransduction, providing a possible molecular link between endoglin/ALK1 signaling and mechanical stress. EMILIN2, which showed suggestive genetic association with pulmonary AVM, is also reported to interact with Taz in angiogenesis. Elucidation of the molecular mechanisms regulating these interactions in endothelial cells may ultimately provide more rational choices for HHT therapy.

We have performed an in-depth single-cell phenotypic characterization of high-grade serous ovarian cancer (HGSOC) by multiparametric mass cytometry (CyTOF). Using a CyTOF antibody panel to interrogate features of HGSOC biology, combined with unsupervised computational analysis, we identified noteworthy cell types co-occurring across the tumors. In addition to a dominant cell subset, each tumor harbored rarer cell phenotypes. One such group co-expressed E-cadherin and vimentin (EV), suggesting their potential role in epithelial mesenchymal transition, which was substantiated by pairwise correlation analyses. Furthermore, tumors from patients with poorer outcome had an increased frequency of another rare cell type that co-expressed vimentin, HE4, and cMyc. These poorer-outcome tumors also populated more cell phenotypes, as quantified by Simpson's diversity index. Thus, despite the recognized genomic complexity of the disease, the specific cell phenotypes uncovered here offer a focus for therapeutic intervention and disease monitoring.

The androgen receptor (AR) antagonist enzalutamide is one of the principal treatments for men with castration-resistant prostate cancer (CRPC). However, not all patients respond, and resistance mechanisms are largely unknown. We hypothesized that genomic and transcriptional features from metastatic CRPC biopsies prior to treatment would be predictive of de novo treatment resistance. To this end, we conducted a phase II trial of enzalutamide treatment (160 mg/d) in 36 men with metastatic CRPC. Thirty-four patients were evaluable for the primary end point of a prostate-specific antigen (PSA)50 response (PSA decline ≥50% at 12 wk vs. baseline). Nine patients were classified as nonresponders (PSA decline <50%), and 25 patients were classified as responders (PSA decline ≥50%). Failure to achieve a PSA50 was associated with shorter progression-free survival, time on treatment, and overall survival, demonstrating PSA50's utility. Targeted DNA-sequencing was performed on 26 of 36 biopsies, and RNA-sequencing was performed on 25 of 36 biopsies that contained sufficient material. Using computational methods, we measured AR transcriptional function and performed gene set enrichment analysis (GSEA) to identify pathways whose activity state correlated with de novo resistance. TP53 gene alterations were more common in nonresponders, although this did not reach statistical significance (P = 0.055). AR gene alterations and AR expression were similar between groups. Importantly, however, transcriptional measurements demonstrated that specific gene sets-including those linked to low AR transcriptional activity and a stemness program-were activated in nonresponders. Our results suggest that patients whose tumors harbor this program should be considered for clinical trials testing rational agents to overcome de novo enzalutamide resistance.

Although DNA methylation is a key regulator of gene expression, the comprehensive methylation landscape of metastatic cancer has never been defined. Through whole-genome bisulfite sequencing paired with deep whole-genome and transcriptome sequencing of 100 castration-resistant prostate metastases, we discovered alterations affecting driver genes that were detectable only with integrated whole-genome approaches. Notably, we observed that 22% of tumors exhibited a novel epigenomic subtype associated with hypermethylation and somatic mutations in TET2, DNMT3B, IDH1 and BRAF. We also identified intergenic regions where methylation is associated with RNA expression of the oncogenic driver genes AR, MYC and ERG. Finally, we showed that differential methylation during progression preferentially occurs at somatic mutational hotspots and putative regulatory regions. This study is a large integrated study of whole-genome, whole-methylome and whole-transcriptome sequencing in metastatic cancer that provides a comprehensive overview of the important regulatory role of methylation in metastatic castration-resistant prostate cancer.

Body mass index (BMI) has been implicated as a primary factor influencing cancer development. However, understanding the relationship between these two complex traits has been confounded by both environmental and genetic heterogeneity.

Inherited germline polymorphisms can cause gene expression levels in normal tissues to differ substantially between individuals. We present an analysis of the genetic architecture of normal adult skin from 470 genetically unique mice, demonstrating the effect of germline variants, skin tissue location, and perturbation by exogenous inflammation or tumorigenesis on gene signaling pathways. Gene networks related to specific cell types and signaling pathways, including sonic hedgehog (Shh), Wnt, Lgr family stem cell markers, and keratins, differed at these tissue sites, suggesting mechanisms for the differential susceptibility of dorsal and tail skin to development of skin diseases and tumorigenesis. The Pten tumor suppressor gene network is rewired in premalignant tumors compared to normal tissue, but this response to perturbation is lost during malignant progression. We present a software package for expression quantitative trait loci (eQTL) network analysis and demonstrate how network analysis of whole tissues provides insights into interactions between cell compartments and signaling molecules.

Polymorphisms in the PER3 gene have been associated with several human disease phenotypes, including sleep disorders and cancer. In particular, the long allele of a variable number of tandem repeat (VNTR) polymorphism has been previously linked to an increased risk of breast cancer. Here we carried out a combined germline and somatic genetic analysis of the role of the PER3VNRT polymorphism in breast cancer. The combined data from 8284 individuals showed a non-significant trend towards increased breast cancer risk in the 5-repeat allele homozygous carriers (OR = 1.17, 95% CI: 0.97-1.42). We observed allelic imbalance at the PER3 locus in matched blood and tumor DNA samples, showing a significant retention of the long variant (risk) allele in tumor samples, and a preferential loss of the short repetition allele (p = 0.0005). Gene co-expression analysis in healthy and tumoral breast tissue samples uncovered significant associations between PER3 expression levels with those from genes which belong to several cancer-associated pathways. Finally, relapse-free survival (RFS) analysis showed that low expression levels of PER3 were linked to a significant lower RSF in luminal A (p = 3 × 10-12) but not in the rest of breast cancer subtypes.

Germline polymorphisms can influence gene expression networks in normal mammalian tissues and can affect disease susceptibility. We and others have shown that analysis of this genetic architecture can identify single genes and whole pathways that influence complex traits, including inflammation and cancer susceptibility. Whether germline variants affect gene expression in tumors that have undergone somatic alterations, and the extent to which these variants influence tumor progression, is unknown.

Chronic inflammation promotes breast tumor growth and invasion by accelerating angiogenesis and tissue remodeling in the tumor microenvironment. There is a complex relationship between inflammation and estrogen, which drives the growth of 70 percent of breast tumors. While low levels of estrogen exposure stimulate macrophages and other inflammatory cell populations, very high levels are immune suppressive. Breast tumor incidence is increased by obesity and age, which interact to influence inflammatory cell populations in normal breast tissue. To characterize the impact of these factors on tumors and the tumor microenvironment, we measured gene expression in 195 breast adenocarcinomas and matched adjacent normal breast tissue samples collected at Akershus University Hospital (AHUS). Age and Body Mass Index (BMI) were independently associated with inflammation in adjacent normal tissue but not tumors. Estrogen Receptor (ER)-negative tumors had elevated macrophage expression compared with matched normal tissue, but ER-positive tumors showed an unexpected decrease in macrophage expression. We found an inverse relationship between the increase in tumor estrogen pathway expression compared with adjacent normal tissue and tumor macrophage score. We validated this finding in 126 breast tumor-normal pairs from the previously published METABRIC cohort. We developed a novel statistic, the Rewiring Coefficient, to quantify the rewiring of gene co-expression networks at the level of individual genes. Differential correlation analysis demonstrated distinct pathways were rewired during tumorigenesis. Our data support an immune suppressive effect of high doses of estrogen signaling in breast tumor microenvironment, suggesting that this effect contributes to the greater presence of prognostic and therapeutically relevant immune cells in ER-negative tumors.

Small nucleolar RNAs (snoRNAs) are a diverse group of non-coding RNAs that direct chemical modifications at specific residues on other RNA molecules, primarily on ribosomal RNA (rRNA). SnoRNAs are altered in several cancers; however, their role in cell homeostasis as well as in cellular transformation remains poorly explored. Here, we show that specific subsets of snoRNAs are differentially regulated during the earliest cellular response to oncogenic RASG12V expression. We describe a novel function for one H/ACA snoRNA, SNORA24, which guides two pseudouridine modifications within the small ribosomal subunit, in RAS-induced senescence in vivo. We find that in mouse models, loss of Snora24 cooperates with RASG12V to promote the development of liver cancer that closely resembles human steatohepatitic hepatocellular carcinoma (HCC). From a clinical perspective, we further show that human HCCs with low SNORA24 expression display increased lipid content and are associated with poor patient survival. We next asked whether ribosomes lacking SNORA24-guided pseudouridine modifications on 18S rRNA have alterations in their biophysical properties. Single-molecule Fluorescence Resonance Energy Transfer (FRET) analyses revealed that these ribosomes exhibit perturbations in aminoacyl-transfer RNA (aa-tRNA) selection and altered pre-translocation ribosome complex dynamics. Furthermore, we find that HCC cells lacking SNORA24-guided pseudouridine modifications have increased translational miscoding and stop codon readthrough frequencies. These findings highlight a role for specific snoRNAs in safeguarding against oncogenic insult and demonstrate a functional link between H/ACA snoRNAs regulated by RAS and the biophysical properties of ribosomes in cancer.

Endometrioid ovarian carcinoma (EOVC) is an uncommon subtype of epithelial ovarian carcinoma and its molecular characteristics have been incompletely described. Prior sequencing investigations have been limited to targeted gene panels. We performed whole-exome sequencing to build an unbiased genetic profile of molecular alterations in endometrioid ovarian tumors with a goal to better understand this disease in the context of epithelial ovarian cancer and endometrioid uterine cancers.

One approach to identifying cancer-specific vulnerabilities and therapeutic targets is to profile genetic dependencies in cancer cell lines. Here, we describe data from a series of siRNA screens that identify the kinase genetic dependencies in 117 cancer cell lines from ten cancer types. By integrating the siRNA screen data with molecular profiling data, including exome sequencing data, we show how vulnerabilities/genetic dependencies that are associated with mutations in specific cancer driver genes can be identified. By integrating additional data sets into this analysis, including protein-protein interaction data, we also demonstrate that the genetic dependencies associated with many cancer driver genes form dense connections on functional interaction networks. We demonstrate the utility of this resource by using it to predict the drug sensitivity of genetically or histologically defined subsets of tumor cell lines, including an increased sensitivity of osteosarcoma cell lines to FGFR inhibitors and SMAD4 mutant tumor cells to mitotic inhibitors.

The development of targeted therapeutics for neuroblastoma, the third most common tumor in children, has been limited by a poor understanding of growth signaling mechanisms unique to the peripheral nerve precursors from which tumors arise. In this study, we combined genetics with gene-expression analysis in the peripheral sympathetic nervous system to implicate arginase 1 and GABA signaling in tumor formation in vivo. In human neuroblastoma cells, either blockade of ARG1 or benzodiazepine-mediated activation of GABA-A receptors induced apoptosis and inhibited mitogenic signaling through AKT and MAPK. These results suggest that ARG1 and GABA influence both neural development and neuroblastoma and that benzodiazepines in clinical use may have potential applications for neuroblastoma therapy.

Oil-secreting sebaceous glands (SGs) are critical for proper skin function; however, it remains unclear how different factors act together to modulate SG stem cells. Here, we provide functional evidence that each SG lobe is serviced by its own dedicated stem cell population. Upon ablating Notch signaling in different skin subcompartments, we find that this pathway exerts dual counteracting effects on SGs. Suppressing Notch in SG progenitors traps them in a hybrid state where stem and differentiation features become intermingled. In contrast, ablating Notch outside of the SG stem cell compartment indirectly drives SG expansion. Finally, we report that a K14:K5→K14:K79 keratin shift occurs during SG differentiation. Deleting K79 destabilizes K14 in sebocytes, and attenuates SGs and eyelid meibomian glands, leading to corneal ulceration. Altogether, our findings demonstrate that SGs integrate diverse signals from different niches and suggest that mutations incurred within one stem cell compartment can indirectly influence another.

Resistance to two classes of FDA-approved therapies that target DNA repair-deficient tumors is caused by mutations that restore the tumor cell's DNA repair function. Identifying these "reversion" mutations currently requires manual annotation of patient tumor sequence data. Here we present AARDVARK, an R package that automatically identifies reversion mutations from DNA sequence data.