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Evolved resistance to tyrosine kinase inhibitor (TKI)-targeted therapies remains a major clinical challenge. In epidermal growth factor receptor ( EGFR ) mutant non-small-cell lung cancer (NSCLC), failure of EGFR TKIs can result from both genetic and epigenetic mechanisms of acquired drug resistance. Widespread reports of histologic and gene expression changes consistent with an epithelial-to-mesenchymal transition (EMT) have been associated with initially surviving drug-tolerant persister cells, which can seed bona fide genetic mechanisms of resistance to EGFR TKIs. While therapeutic approaches targeting fully resistant cells, such as those harboring an EGFR T790M mutation, have been developed, a clinical strategy for preventing the emergence of persister cells remains elusive. Using mesenchymal cell lines derived from biopsies of patients who progressed on EGFR TKI as surrogates for persister populations, we performed whole-genome CRISPR screening and identified fibroblast growth factor receptor 1 (FGFR1) as the top target promoting survival of mesenchymal EGFR mutant cancers. Although numerous previous reports of FGFR signaling contributing to EGFR TKI resistance in vitro exist, the data have not yet been sufficiently compelling to instigate a clinical trial testing this hypothesis, nor has the role of FGFR in promoting the survival of persister cells been elucidated. In this study, we find that combining EGFR and FGFR inhibitors inhibited the survival and expansion of EGFR mutant drug-tolerant cells over long time periods, preventing the development of fully resistant cancers in multiple vitro models and in vivo. These results suggest that dual EGFR and FGFR blockade may be a promising clinical strategy for both preventing and overcoming EMT-associated acquired drug resistance and provide motivation for the clinical study of combined EGFR and FGFR inhibition in EGFR-mutated NSCLCs.

The authors have developed a barcoded library that enables the high-throughput tracking of tumor cell dynamics and clonal evolution in response to therapy. Resistance to cancer therapies presents a significant clinical challenge. Recent studies have revealed intratumoral heterogeneity as a source of therapeutic resistance. However, it is unclear whether resistance is driven predominantly by pre-existing or de novo alterations, in part because of the resolution limits of next-generation sequencing. To address this, we developed a high-complexity barcode library, ClonTracer, which enables the high-resolution tracking of more than 1 million cancer cells under drug treatment. In two clinically relevant models, ClonTracer studies showed that the majority of resistant clones were part of small, pre-existing subpopulations that selectively escaped under therapeutic challenge. Moreover, the ClonTracer approach enabled quantitative assessment of the ability of combination treatments to suppress resistant clones. These findings suggest that resistant clones are present before treatment, which would make up-front therapeutic combinations that target non-overlapping resistance a preferred approach. Thus, ClonTracer barcoding may be a valuable tool for optimizing therapeutic regimens with the goal of curative combination therapies for cancer.

Steven McCarroll and colleagues report an analytical framework for characterizing genome deletion polymorphism in populations, applied here to the low coverage genome sequences of 168 individuals from the 1000 Genomes Project. Their population-aware analysis enables structural inference with greater accuracy than previous methods. Accurate and complete analysis of genome variation in large populations will be required to understand the role of genome variation in complex disease. We present an analytical framework for characterizing genome deletion polymorphism in populations using sequence data that are distributed across hundreds or thousands of genomes. Our approach uses population-level concepts to reinterpret the technical features of sequence data that often reflect structural variation. In the 1000 Genomes Project pilot, this approach identified deletion polymorphism across 168 genomes (sequenced at 4× average coverage) with sensitivity and specificity unmatched by other algorithms. We also describe a way to determine the allelic state or genotype of each deletion polymorphism in each genome; the 1000 Genomes Project used this approach to type 13,826 deletion polymorphisms (48–995,664 bp) at high accuracy in populations. These methods offer a way to relate genome structural polymorphism to complex disease in populations.

David Altshuler and colleagues describe analysis for integrating genotype calling of SNPs, common copy number polymorphisms and rare CNVs, implemented in a suite of software programs collectively named Birdsuite. Accurate and complete measurement of single nucleotide (SNP) and copy number (CNV) variants, both common and rare, will be required to understand the role of genetic variation in disease. We present Birdsuite, a four-stage analytical framework instantiated in software for deriving integrated and mutually consistent copy number and SNP genotypes. The method sequentially assigns copy number across regions of common copy number polymorphisms (CNPs), calls genotypes of SNPs, identifies rare CNVs via a hidden Markov model (HMM), and generates an integrated sequence and copy number genotype at every locus (for example, including genotypes such as A-null, AAB and BBB in addition to AA, AB and BB calls). Such genotypes more accurately depict the underlying sequence of each individual, reducing the rate of apparent mendelian inconsistencies. The Birdsuite software is applied here to data from the Affymetrix SNP 6.0 array. Additionally, we describe a method, implemented in PLINK, to utilize these combined SNP and CNV genotypes for association testing with a phenotype.

The causes of autism are largely unknown. This study establishes that aberrant dosage of a large genomic segment is associated with autism spectrum disorder. Deletion or duplication of the segment, which encompasses 25 known genes, was present in approximately 1% of case subjects and less than 0.1% of unscreened control subjects. This study establishes that an aberrant dosage of a large genomic segment is associated with autism spectrum disorder. Deletion or duplication of the segment, which encompasses 25 known genes, was present in approximately 1% of case subjects and less than 0.1% of unscreened control subjects. Autism is a pervasive developmental disorder defined by a neurobehavioral phenotype that includes social disability, communication impairment, repetitive behaviors, and restricted interests. The onset is generally before the age of 3 years, and the disorder has a prevalence of 0.6% in the population, affecting many more boys than girls. 1 Results of twin and family studies have shown that the heritability of autism is approximately 90%, making it one of the most heritable complex disorders. 2 In approximately 10% of patients, autism can be explained by genetic syndromes and known chromosomal anomalies (most of which have recognizable features in addition to autism), . . .

David Altshuler and colleagues report the design of a hybrid SNP-CNV genotyping array (Affymetrix SNP 6.0 Array) allowing for integrated SNP and CNV detection. They describe its application to 270 HapMap samples to compile a high-resolution map of over 1,500 copy number polymorphisms, and related population-genetic analyses. Dissecting the genetic basis of disease risk requires measuring all forms of genetic variation, including SNPs and copy number variants (CNVs), and is enabled by accurate maps of their locations, frequencies and population-genetic properties. We designed a hybrid genotyping array (Affymetrix SNP 6.0) to simultaneously measure 906,600 SNPs and copy number at 1.8 million genomic locations. By characterizing 270 HapMap samples, we developed a map of human CNV (at 2-kb breakpoint resolution) informed by integer genotypes for 1,320 copy number polymorphisms (CNPs) that segregate at an allele frequency >1%. More than 80% of the sequence in previously reported CNV regions fell outside our estimated CNV boundaries, indicating that large (>100 kb) CNVs affect much less of the genome than initially reported. Approximately 80% of observed copy number differences between pairs of individuals were due to common CNPs with an allele frequency >5%, and more than 99% derived from inheritance rather than new mutation. Most common, diallelic CNPs were in strong linkage disequilibrium with SNPs, and most low-frequency CNVs segregated on specific SNP haplotypes.

Background Somatic alterations in the cancer genome, some of which are associated with changes in gene expression, have been characterized in multiple studies across diverse cancer types. However, less is known about germline variants that influence tumor biology by shaping the cancer transcriptome. Methods We performed expression quantitative trait loci (eQTL) analyses using multi-dimensional data from The Cancer Genome Atlas to explore the role of germline variation in mediating the cancer transcriptome. After accounting for associations between somatic alterations and gene expression, we determined the contribution of inherited variants to the cancer transcriptome relative to that of somatic variants. Finally, we performed an interaction analysis using estimates of tumor cellularity to identify cell type-restricted eQTLs. Results The proportion of genes with at least one eQTL varied between cancer types, ranging between 0.8% in melanoma to 28.5% in thyroid cancer and was correlated more strongly with intratumor heterogeneity than with somatic alteration rates. Although contributions to variance in gene expression was low for most genes, some eQTLs accounted for more than 30% of expression of proximal genes. We identified cell type-restricted eQTLs in genes known to be cancer drivers including LPP and EZH2 that were associated with disease-specific mortality in TCGA but not associated with disease risk in published GWAS. Together, our results highlight the need to consider germline variation in interpreting cancer biology beyond risk prediction.

Cell autonomous cancer dependencies are now routinely identified using CRISPR loss-of-function viability screens. However, a bias exists that makes it difficult to assess the true essentiality of genes located in amplicons, since the entire amplified region can exhibit lethal scores. These false-positive hits can either be discarded from further analysis, which in cancer models can represent a significant number of hits, or methods can be developed to rescue the true-positives within amplified regions. We propose two methods to rescue true positive hits in amplified regions by correcting for this copy number artefact. The Local Drop Out (LDO) method uses the relative lethality scores within genomic regions to assess true essentiality and does not require additional orthogonal data (e.g. copy number value). LDO is meant to be used in screens covering a dense region of the genome (e.g. a whole chromosome or the whole genome). The General Additive Model (GAM) method models the screening data as a function of the known copy number values and removes the systematic effect from the measured lethality. GAM does not require the same density as LDO, but does require prior knowledge of the copy number values. Both methods have been developed with single sample experiments in mind so that the correction can be applied even in smaller screens. Here we demonstrate the efficacy of both methods at removing the copy number effect and rescuing hits from some of the amplified regions. We estimate a 70-80% decrease of false positive hits with either method in regions of high copy number compared to no correction.

Investigators have linked rare copy number variation (CNVs) to neuropsychiatric diseases, such as schizophrenia. One hypothesis is that CNV events cause disease by affecting genes with specific brain functions. Under these circumstances, we expect that CNV events in cases should impact brain-function genes more frequently than those events in controls. Previous publications have applied \"pathway\" analyses to genes within neuropsychiatric case CNVs to show enrichment for brain-functions. While such analyses have been suggestive, they often have not rigorously compared the rates of CNVs impacting genes with brain function in cases to controls, and therefore do not address important confounders such as the large size of brain genes and overall differences in rates and sizes of CNVs. To demonstrate the potential impact of confounders, we genotyped rare CNV events in 2,415 unaffected controls with Affymetrix 6.0; we then applied standard pathway analyses using four sets of brain-function genes and observed an apparently highly significant enrichment for each set. The enrichment is simply driven by the large size of brain-function genes. Instead, we propose a case-control statistical test, cnv-enrichment-test, to compare the rate of CNVs impacting specific gene sets in cases versus controls. With simulations, we demonstrate that cnv-enrichment-test is robust to case-control differences in CNV size, CNV rate, and systematic differences in gene size. Finally, we apply cnv-enrichment-test to rare CNV events published by the International Schizophrenia Consortium (ISC). This approach reveals nominal evidence of case-association in neuronal-activity and the learning gene sets, but not the other two examined gene sets. The neuronal-activity genes have been associated in a separate set of schizophrenia cases and controls; however, testing in independent samples is necessary to definitively confirm this association. Our method is implemented in the PLINK software package.

The TMPRSS2:ERG gene fusion is common in androgen receptor (AR) positive prostate cancers, yet its function remains poorly understood. From a screen for functionally relevant ERG interactors, we identify the arginine methyltransferase PRMT5. ERG recruits PRMT5 to AR-target genes, where PRMT5 methylates AR on arginine 761. This attenuates AR recruitment and transcription of genes expressed in differentiated prostate epithelium. The AR-inhibitory function of PRMT5 is restricted to TMPRSS2:ERG-positive prostate cancer cells. Mutation of this methylation site on AR results in a transcriptionally hyperactive AR, suggesting that the proliferative effects of ERG and PRMT5 are mediated through attenuating AR’s ability to induce genes normally involved in lineage differentiation. This provides a rationale for targeting PRMT5 in TMPRSS2:ERG positive prostate cancers. Moreover, methylation of AR at arginine 761 highlights a mechanism for how the ERG oncogene may coax AR towards inducing proliferation versus differentiation. Prostate cancers are among the most common types of cancer in men, which, like other cancers, are driven by genetic mutations. Roughly half of all prostate cancers contain a genetic change that incorrectly fuses two genes together, causing the cells to produce abnormally high levels of a protein called ERG. ERG is a transcription factor, a protein that binds to specific sequences of DNA to influence the activity of nearby genes. ERG represses genes that help to prevent prostate cancers from growing, and so promotes prostate cancer development. Like most other transcription factors, ERG is difficult to target with drugs and no therapies that directly prevent the activity of ERG currently exist. Mounir et al. wanted to find out whether ERG cooperates with other proteins to cause prostate cancer cells to grow, with the hope that these proteins could be more easily targeted with a drug. By using various biochemical techniques in human prostate cancer cell lines, Mounir et al. found that ERG interacts with an enzyme called PRMT5. This interaction enables PRMT5 to chemically modify other proteins to change their activity. In the case of prostate cancer cells, PRMT5 inappropriately modifies the androgen receptor, a protein that regulates the growth of normal prostate cells. This abnormal modification contributes to the excessive growth of the cancer cells. Although PRMT5 will be easier to target with drugs than ERG, it also has many other roles besides those described by Mounir et al. Much more work is therefore needed to investigate whether PRMT5 could be safely targeted to treat patients with prostate cancer.