PARP7 negatively regulates the type I interferon response in cancer cells and its inhibition triggers antitumor immunity.

PARP7 is a monoPARP that catalyzes the transfer of single units of ADP-ribose onto substrates to change their function. Here, we identify PARP7 as a negative regulator of nucleic acid sensing in tumor cells. Inhibition of PARP7 restores type I interferon (IFN) signaling responses to nucleic acids in tumor models. Restored signaling can directly inhibit cell proliferation and activate the immune system, both of which contribute to tumor regression. Oral dosing of the PARP7 small-molecule inhibitor, RBN-2397, results in complete tumor regression in a lung cancer xenograft and induces tumor-specific adaptive immune memory in an immunocompetent mouse cancer model, dependent on inducing type I IFN signaling in tumor cells. PARP7 is a therapeutic target whose inhibition induces both cancer cell-autonomous and immune stimulatory effects via enhanced IFN signaling. These data support the targeting of a monoPARP in cancer and introduce a potent and selective PARP7 inhibitor to enter clinical development.

Institutional implementation of clinical tumor profiling on an unselected cancer population.

BACKGROUND. Comprehensive genomic profiling of a patient's cancer can be used to diagnose, monitor, and recommend treatment. Clinical implementation of tumor profiling in an enterprise-wide, unselected cancer patient population has yet to be reported. METHODS. We deployed a hybrid-capture and massively parallel sequencing assay (OncoPanel) for all adult and pediatric patients at our combined cancer centers. Results were categorized by pathologists based on actionability. We report the results for the first 3,727 patients tested. RESULTS. Our cohort consists of cancer patients unrestricted by disease site or stage. Across all consented patients, half had sufficient and available (>20% tumor) material for profiling; once specimens were received in the laboratory for pathology review, 73% were scored as adequate for genomic testing. When sufficient DNA was obtained, OncoPanel yielded a result in 96% of cases. 73% of patients harbored an actionable or informative alteration; only 19% of these represented a current standard of care for therapeutic stratification. The findings recapitulate those of previous studies of common cancers but also identify alterations, including in AXL and EGFR, associated with response to targeted therapies. In rare cancers, potentially actionable alterations suggest the utility of a "cancer-agnostic" approach in genomic profiling. Retrospective analyses uncovered contextual genomic features that may inform therapeutic response and examples where diagnoses revised by genomic profiling markedly changed clinical management. CONCLUSIONS. Broad sequencing-based testing deployed across an unselected cancer cohort is feasible. Genomic results may alter management in diverse scenarios; however, additional barriers must be overcome to enable precision cancer medicine on a large scale. FUNDING. This work was supported by DFCI, BWH, and the National Cancer Institute (5R33CA155554 and 5K23CA157631).

Diffuse large B-cell lymphoma patient-derived xenograft models capture the molecular and biological heterogeneity of the disease.

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease defined by transcriptional classifications, specific signaling and survival pathways, and multiple low-frequency genetic alterations. Preclinical model systems that capture the genetic and functional heterogeneity of DLBCL are urgently needed. Here, we generated and characterized a panel of large B-cell lymphoma (LBCL) patient-derived xenograft (PDX) models, including 8 that reflect the immunophenotypic, transcriptional, genetic, and functional heterogeneity of primary DLBCL and 1 that is a plasmablastic lymphoma. All LBCL PDX models were subjected to whole-transcriptome sequencing to classify cell of origin and consensus clustering classification (CCC) subtypes. Mutations and chromosomal rearrangements were evaluated by whole-exome sequencing with an extended bait set. Six of the 8 DLBCL models were activated B-cell (ABC)-type tumors that exhibited ABC-associated mutations such as MYD88, CD79B, CARD11, and PIM1. The remaining 2 DLBCL models were germinal B-cell type, with characteristic alterations of GNA13, CREBBP, and EZH2, and chromosomal translocations involving IgH and either BCL2 or MYC Only 25% of the DLBCL PDX models harbored inactivating TP53 mutations, whereas 75% exhibited copy number alterations of TP53 or its upstream modifier, CDKN2A, consistent with the reported incidence and type of p53 pathway alterations in primary DLBCL. By CCC criteria, 6 of 8 DLBCL PDX models were B-cell receptor (BCR)-type tumors that exhibited selective surface immunoglobulin expression and sensitivity to entospletinib, a recently developed spleen tyrosine kinase inhibitor. In summary, we have established and characterized faithful PDX models of DLBCL and demonstrated their usefulness in functional analyses of proximal BCR pathway inhibition.

Metformin pharmacogenomics: a genome-wide association study to identify genetic and epigenetic biomarkers involved in metformin anticancer response using human lymphoblastoid cell lines.

Metformin is currently considered as a promising anticancer agent in addition to its anti-diabetic effect. To better individualize metformin therapy and explore novel molecular mechanisms in cancer treatment, we conducted a pharmacogenomic study using 266 lymphoblastoid cell lines (LCLs). Metformin cytotoxicity assay was performed using the MTS assay. Genome-wide association (GWA) analyses were performed in LCLs using 1.3 million SNPs, 485k DNA methylation probes, 54k mRNA expression probe sets, and metformin cytotoxicity (IC50s). Top candidate genes were functionally validated using siRNA screening, followed by MTS assay in breast cancer cell lines. Further study of one top candidate, STUB1, was performed to elucidate the mechanisms by which STUB1 might contribute to metformin action. GWA analyses in LCLs identified 198 mRNA expression probe sets, 12 SNP loci, and 5 DNA methylation loci associated with metformin IC50 with P-values <10−4 or <10−5. Integrated SNP/methylation loci-expression-IC50 analyses found 3 SNP loci or 5 DNA methylation loci associated with metformin IC50 through trans-regulation of expression of 11 or 26 genes with P-value <10−4. Functional validation of top 61 candidate genes in 4 IPA networks indicated down regulation of 14 genes significantly altered metformin sensitivity in two breast cancer cell lines. Mechanistic studies revealed that the E3 ubiquitin ligase, STUB1, could influence metformin response by facilitating proteasome-mediated degradation of cyclin A. GWAS using a genomic data-enriched LCL model system, together with functional and mechanistic studies using cancer cell lines, help us to identify novel genetic and epigenetic biomarkers involved in metformin anticancer response.

Targetable genetic features of primary testicular and primary central nervous system lymphomas.

Primary central nervous system lymphomas (PCNSLs) and primary testicular lymphomas (PTLs) are extranodal large B-cell lymphomas (LBCLs) with inferior responses to current empiric treatment regimens. To identify targetable genetic features of PCNSL and PTL, we characterized their recurrent somatic mutations, chromosomal rearrangements, copy number alterations (CNAs), and associated driver genes, and compared these comprehensive genetic signatures to those of diffuse LBCL and primary mediastinal large B-cell lymphoma (PMBL). These studies identify unique combinations of genetic alterations in discrete LBCL subtypes and subtype-selective bases for targeted therapy. PCNSLs and PTLs frequently exhibit genomic instability, and near-uniform, often biallelic, CDKN2A loss with rare TP53 mutations. PCNSLs and PTLs also use multiple genetic mechanisms to target key genes and pathways and exhibit near-uniform oncogenic Toll-like receptor signaling as a result of MYD88 mutation and/or NFKBIZ amplification, frequent concurrent B-cell receptor pathway activation, and deregulation of BCL6. Of great interest, PCNSLs and PTLs also have frequent 9p24.1/PD-L1/PD-L2 CNAs and additional translocations of these loci, structural bases of immune evasion that are shared with PMBL.

Myeloid neoplasm demonstrating a STAT5B-RARA rearrangement and genetic alterations associated with all-trans retinoic acid resistance identified by a custom next-generation sequencing assay.

We describe the case of a patient presenting with several weeks of symptoms related to pancytopenia associated with a maturation arrest at the late promyelocyte/early myelocyte stage of granulocyte differentiation. A diagnosis of acute promyelocytic leukemia was considered, but the morphologic features were atypical for this entity and conventional tests for the presence of a PML-RARA fusion gene were negative. Additional analysis using a custom next-generation sequencing assay revealed a rearrangement producing a STAT5B-RARA fusion gene, which was confirmed by reverse transcription polymerase chain reaction (RT-PCR) and supplementary cytogenetic studies, allowing the diagnosis of a morphologically atypical form of acute promyelocytic leukemia to be made. Analysis of the sequencing data permitted characterization of both chromosomal breakpoints and revealed two additional alterations, a small deletion in RARA exon 9 and a RARA R276W substitution, that have been linked to resistance to all-trans retinoic acid. This case highlights how next-generation sequencing can augment currently standard testing to establish diagnoses in difficult cases, and in doing so help guide selection of therapy.

BreaKmer: detection of structural variation in targeted massively parallel sequencing data using kmers.

Genomic structural variation (SV), a common hallmark of cancer, has important predictive and therapeutic implications. However, accurately detecting SV using high-throughput sequencing data remains challenging, especially for 'targeted' resequencing efforts. This is critically important in the clinical setting where targeted resequencing is frequently being applied to rapidly assess clinically actionable mutations in tumor biopsies in a cost-effective manner. We present BreaKmer, a novel approach that uses a 'kmer' strategy to assemble misaligned sequence reads for predicting insertions, deletions, inversions, tandem duplications and translocations at base-pair resolution in targeted resequencing data. Variants are predicted by realigning an assembled consensus sequence created from sequence reads that were abnormally aligned to the reference genome. Using targeted resequencing data from tumor specimens with orthogonally validated SV, non-tumor samples and whole-genome sequencing data, BreaKmer had a 97.4% overall sensitivity for known events and predicted 17 positively validated, novel variants. Relative to four publically available algorithms, BreaKmer detected SV with increased sensitivity and limited calls in non-tumor samples, key features for variant analysis of tumor specimens in both the clinical and research settings.

Multiplexed DNA repair assays for multiple lesions and multiple doses via transcription inhibition and transcriptional mutagenesis.

The capacity to repair different types of DNA damage varies among individuals, making them more or less susceptible to the detrimental health consequences of damage exposures. Current methods for measuring DNA repair capacity (DRC) are relatively labor intensive, often indirect, and usually limited to a single repair pathway. Here, we describe a fluorescence-based multiplex flow-cytometric host cell reactivation assay (FM-HCR) that measures the ability of human cells to repair plasmid reporters, each bearing a different type of DNA damage or different doses of the same type of DNA damage. FM-HCR simultaneously measures repair capacity in any four of the following pathways: nucleotide excision repair, mismatch repair, base excision repair, nonhomologous end joining, homologous recombination, and methylguanine methyltransferase. We show that FM-HCR can measure interindividual DRC differences in a panel of 24 cell lines derived from genetically diverse, apparently healthy individuals, and we show that FM-HCR may be used to identify inhibitors or enhancers of DRC. We further develop a next-generation sequencing-based HCR assay (HCR-Seq) that detects rare transcriptional mutagenesis events due to lesion bypass by RNA polymerase, providing an added dimension to DRC measurements. FM-HCR and HCR-Seq provide powerful tools for exploring relationships among global DRC, disease susceptibility, and optimal treatment.

Discovery of genetic biomarkers contributing to variation in drug response of cytidine analogues using human lymphoblastoid cell lines.

BACKGROUND: Two cytidine analogues, gemcitabine and cytosine arabinoside (AraC), are widely used in the treatment of a variety of cancers with a large individual variation in response. To identify potential genetic biomarkers associated with response to these two drugs, we used a human lymphoblastoid cell line (LCL) model system with extensive genomic data, including 1.3 million SNPs and 54,000 basal expression probesets to perform genome-wide association studies (GWAS) with gemcitabine and AraC IC50 values. RESULTS: We identified 11 and 27 SNP loci significantly associated with gemcitabine and AraC IC50 values, respectively. Eleven candidate genes were functionally validated using siRNA knockdown approach in multiple cancer cell lines. We also characterized the potential mechanisms of genes by determining their influence on the activity of 10 cancer-related signaling pathways using reporter gene assays. Most SNPs regulated gene expression in a trans manner, except 7 SNPs in the PIGB gene that were significantly associated with both the expression of PIGB and gemcitabine cytotoxicity. CONCLUSION: These results suggest that genetic variation might contribute to drug response via either cis- or trans- regulation of gene expression. GWAS analysis followed by functional pharmacogenomics studies might help identify novel biomarkers contributing to variation in response to these two drugs and enhance our understanding of underlying mechanisms of drug action.

Host plant peptides elicit a transcriptional response to control the Sinorhizobium meliloti cell cycle during symbiosis.

The α-proteobacterium Sinorhizobium meliloti establishes a chronic intracellular infection during the symbiosis with its legume hosts. Within specialized host cells, S. meliloti differentiates into highly polyploid, enlarged nitrogen-fixing bacteroids. This differentiation is driven by host cells through the production of defensin-like peptides called "nodule-specific cysteine-rich" (NCR) peptides. Recent research has shown that synthesized NCR peptides exhibit antimicrobial activity at high concentrations but cause bacterial endoreduplication at sublethal concentrations. We leveraged synchronized S. meliloti populations to determine how treatment with a sublethal NCR peptide affects the cell cycle and physiology of bacteria at the molecular level. We found that at sublethal levels a representative NCR peptide specifically blocks cell division and antagonizes Z-ring function. Gene-expression profiling revealed that the cell division block was produced, in part, through the substantial transcriptional response elicited by sublethal NCR treatment that affected ∼15% of the genome. Expression of critical cell-cycle regulators, including ctrA, and cell division genes, including genes required for Z-ring function, were greatly attenuated in NCR-treated cells. In addition, our experiments identified important symbiosis functions and stress responses that are induced by sublethal levels of NCR peptides and other antimicrobial peptides. Several of these stress-response pathways also are found in related α-proteobacterial pathogens and might be used by S. meliloti to sense host cues during infection. Our data suggest a model in which, in addition to provoking stress responses, NCR peptides target intracellular regulatory pathways to drive S. meliloti endoreduplication and differentiation during symbiosis.

Global analysis of cell cycle gene expression of the legume symbiont Sinorhizobium meliloti.

In α-proteobacteria, strict regulation of cell cycle progression is necessary for the specific cellular differentiation required for adaptation to diverse environmental niches. The symbiotic lifestyle of Sinorhizobium meliloti requires a drastic cellular differentiation that includes genome amplification. To achieve polyploidy, the S. meliloti cell cycle program must be altered to uncouple DNA replication from cell division. In the α-proteobacterium Caulobacter crescentus, cell cycle-regulated transcription plays an important role in the control of cell cycle progression but this has not been demonstrated in other α-proteobacteria. Here we describe a robust method for synchronizing cell growth that enabled global analysis of S. meliloti cell cycle-regulated gene expression. This analysis identified 462 genes with cell cycle-regulated transcripts, including several key cell cycle regulators, and genes involved in motility, attachment, and cell division. Only 28% of the 462 S. meliloti cell cycle-regulated genes were also transcriptionally cell cycle-regulated in C. crescentus. Furthermore, CtrA- and DnaA-binding motif analysis revealed little overlap between the cell cycle-dependent regulons of CtrA and DnaA in S. meliloti and C. crescentus. The predicted S. meliloti cell cycle regulon of CtrA, but not that of DnaA, was strongly conserved in more closely related α-proteobacteria with similar ecological niches as S. meliloti, suggesting that the CtrA cell cycle regulatory network may control functions of central importance to the specific lifestyles of α-proteobacteria.

Genome-wide association study for biomarker identification of Rapamycin and Everolimus using a lymphoblastoid cell line system.

The mammalian target of rapamycin (mTOR) inhibitors, a set of promising potential anti-cancer agents, has shown response variability among individuals. This study aimed to identify novel biomarkers and mechanisms that might influence the response to Rapamycin and Everolimus. Genome-wide association (GWA) analyses involving single nucleotide polymorphisms (SNPs), mRNA, and microRNAs microarray data were assessed for association with area under the cytotoxicity dose response curve (AUC) of two mTOR inhibitors in 272 human lymphoblastoid cell lines (LCLs). Integrated analysis among SNPs, expression data, microRNA data and AUC values were also performed to help select candidate genes for further functional characterization. Functional validation of candidate genes using siRNA screening in multiple cell lines followed by MTS assays for the two mTOR inhibitors were performed. We found that 16 expression probe sets (genes) that overlapped between the two drugs were associated with AUC values of two mTOR inhibitors. One hundred and twenty seven and one hundred SNPs had P < 10(-4), while 8 and 10 SNPs had P < 10(-5) with Rapamycin and Everolimus AUC, respectively. Functional studies indicated that 13 genes significantly altered cell sensitivity to either one or both drugs in at least one cell line. Additionally, one microRNA, miR-10a, was significantly associated with AUC values for both drugs and was shown to repress expression of genes that were associated with AUC and desensitize cells to both drugs. In summary, this study identified genes and a microRNA that might contribute to response to mTOR inhibitors.

Genetic association with overall survival of taxane-treated lung cancer patients - a genome-wide association study in human lymphoblastoid cell lines followed by a clinical association study.

BACKGROUND: Taxane is one of the first line treatments of lung cancer. In order to identify novel single nucleotide polymorphisms (SNPs) that might contribute to taxane response, we performed a genome-wide association study (GWAS) for two taxanes, paclitaxel and docetaxel, using 276 lymphoblastoid cell lines (LCLs), followed by genotyping of top candidate SNPs in 874 lung cancer patient samples treated with paclitaxel. METHODS: GWAS was performed using 1.3 million SNPs and taxane cytotoxicity IC50 values for 276 LCLs. The association of selected SNPs with overall survival in 76 small or 798 non-small cell lung cancer (SCLC, NSCLC) patients were analyzed by Cox regression model, followed by integrated SNP-microRNA-expression association analysis in LCLs and siRNA screening of candidate genes in SCLC (H196) and NSCLC (A549) cell lines. RESULTS: 147 and 180 SNPs were associated with paclitaxel or docetaxel IC50s with p-values <10-4 in the LCLs, respectively. Genotyping of 153 candidate SNPs in 874 lung cancer patient samples identified 8 SNPs (p-value < 0.05) associated with either SCLC or NSCLC patient overall survival. Knockdown of PIP4K2A, CCT5, CMBL, EXO1, KMO and OPN3, genes within 200 kb up-/downstream of the 3 SNPs that were associated with SCLC overall survival (rs1778335, rs2662411 and rs7519667), significantly desensitized H196 to paclitaxel. SNPs rs2662411 and rs1778335 were associated with mRNA expression of CMBL or PIP4K2A through microRNA (miRNA) hsa-miR-584 or hsa-miR-1468. CONCLUSIONS: GWAS in an LCL model system, joined with clinical translational and functional studies, might help us identify genetic variations associated with overall survival of lung cancer patients treated paclitaxel.

Shared genomic segment analysis: the power to find rare disease variants.

Shared genomic segment (SGS) analysis uses dense single nucleotide polymorphism genotyping in high-risk (HR) pedigrees to identify regions of sharing between cases. Here, we illustrate the power of SGS to identify dominant rare risk variants. Using simulated pedigrees, we consider 12 disease models based on disease prevalence, minor allele frequency and penetrance to represent disease loci that explain 0.2-99.8% of total disease risk. Pedigrees were required to contain ≥ 15 meioses between all cases and to be HR based on significant excess of disease (P < 0.001 or P < 0.00001). Across these scenarios, the power for a single pedigree ranged widely. Nonetheless, fewer than 10 pedigrees were sufficient for excellent power in the majority of models. Power increased with the risk attributable to the disease locus, penetrance and the excess of disease in the pedigree. Sharing allowing for one sporadic case was uniformly more powerful than sharing using all cases. Furthermore, an SGS analysis using a large attenuated familial adenomatous polyposis pedigree identified a 1.96 Mb region containing the known causal APC gene with genome-wide significance. SGS is a powerful method for detecting rare variants and a valuable complement to genome-wide association studies and linkage analysis.

Human liver methionine cycle: MAT1A and GNMT gene resequencing, functional genomics, and hepatic genotype-phenotype correlation.

The "methionine cycle" plays a critical role in the regulation of concentrations of (S)-adenosylmethionine (AdoMet), the major biological methyl donor. We set out to study sequence variation in genes encoding the enzyme that synthesizes AdoMet in liver, methionine adenosyltransferase 1A (MAT1A) and the major hepatic AdoMet using enzyme, glycine N-methyltransferase (GNMT), as well as functional implications of that variation. We resequenced MAT1A and GNMT using DNA from 288 subjects of three ethnicities, followed by functional genomic and genotype-phenotype correlation studies performed with 268 hepatic biopsy samples. We identified 44 and 42 polymorphisms in MAT1A and GNMT, respectively. Quantitative Western blot analyses for the human liver samples showed large individual variation in MAT1A and GNMT protein expression. Genotype-phenotype correlation identified two genotyped single-nucleotide polymorphisms (SNPs), reference SNP (rs) 9471976 (corrected p = 3.9 × 10(-10)) and rs11752813 (corrected p = 1.8 × 10(-5)), and 42 imputed SNPs surrounding GNMT that were significantly associated with hepatic GNMT protein levels (corrected p values < 0.01). Reporter gene studies showed that variant alleles for both genotyped SNPs resulted in decreased transcriptional activity. Correlation analyses among hepatic protein levels for methionine cycle enzymes showed significant correlations between GNMT and MAT1A (p = 1.5 × 10(-3)) and between GNMT and betaine homocysteine methyltransferase (p = 1.6 × 10(-7)). Our discovery of SNPs that are highly associated with hepatic GNMT protein expression as well as the "coordinate regulation" of methionine cycle enzyme protein levels provide novel insight into the regulation of this important human liver biochemical pathway.

Serine hydroxymethyltransferase 1 and 2: gene sequence variation and functional genomic characterization.

Serine hydroxymethyltransferase (SHMT) catalyzes the transfer of a ß-carbon from serine to tetrahydrofolate to form glycine and 5,10-methylene-tetrahydrofolate. This reaction plays an important role in neurotransmitter synthesis and metabolism. We set out to resequence SHMT1 and SHMT2, followed by functional genomic studies. We identified 87 and 60 polymorphisms in SHMT1 and SHMT2, respectively. We observed no significant functional effect of the 13 non-synonymous single-nucleotide polymorphism (SNPs) in these genes, either on catalytic activity or protein quantity. We imputed additional variants across the two genes using '1000 Genomes' data, and identified 14 variants that were significantly associated (p<1.0E-10) with SHMT1 messenger RNA expression in lymphoblastoid cell lines. Many of these SNPs were also significantly correlated with basal SHMT1 protein expression in 268 human liver biopsy samples. Reporter gene assays suggested that the SHMT1 promoter SNP, rs669340, contributed to this variation. Finally, SHMT1 and SHMT2 expression were significantly correlated with those of other Folate and Methionine Cycle genes at both the messenger RNA and protein levels. These experiments represent a comprehensive study of SHMT1 and SHMT2 gene sequence variation and its functional implications. In addition, we obtained preliminary indications that these genes may be co-regulated with other Folate and Methionine Cycle genes.

Gemcitabine metabolic pathway genetic polymorphisms and response in patients with non-small cell lung cancer.

BACKGROUND AND OBJECTIVE: Gemcitabine is widely used to treat non-small cell lung cancer (NSCLC). The aim of this study was to assess the pharmacogenomic effects of the entire gemcitabine metabolic pathway, we genotyped single nucleotide polymorphisms (SNPs) within the 17 pathway genes using DNA samples from patients with NSCLC treated with gemcitabine to determine the effect of genetic variants within gemcitabine pathway genes on overall survival (OS) of patients with NSCLC after treatment of gemcitabine. METHODS: Eight of the 17 pathway genes were resequenced with DNA samples from Coriell lymphoblastoid cell lines (LCLs) using Sanger sequencing for all exons, exon-intron junctions, and 5'-, 3'-UTRs. A total of 107 tagging SNPs were selected on the basis of the resequencing data for the eight genes and on HapMap data for the remaining nine genes, followed by successful genotyping of 394 NSCLC patient DNA samples. Association of SNPs/haplotypes with OS was performed using the Cox regression model, followed by functional studies performed with LCLs and NSCLC cell lines. RESULTS: Five SNPs in four genes (CDA, NT5C2, RRM1, and SLC29A1) showed associations with OS of those patients with NSCLC, as well as nine haplotypes in four genes (RRM1, RRM2, SLC28A3, and SLC29A1) with a P value of less than 0.05. Genotype imputation using the LCLs was performed for a region of 200 kb surrounding those SNPs, followed by association studies with gemcitabine cytotoxicity. Functional studies demonstrated that downregulation of SLC29A1, NT5C2, and RRM1 in NSCLC cell lines altered cell susceptibility to gemcitabine. CONCLUSION: These studies help in identifying biomarkers to predict gemcitabine response in NSCLC, a step toward the individualized chemotherapy of lung cancer.

Genetic variation predicting cisplatin cytotoxicity associated with overall survival in lung cancer patients receiving platinum-based chemotherapy.

PURPOSE: Inherited variability in the prognosis of lung cancer patients treated with platinum-based chemotherapy has been widely investigated. However, the overall contribution of genetic variation to platinum response is not well established. To identify novel candidate single nucleotide polymorphisms (SNP)/genes, we carried out a genome-wide association study (GWAS) for cisplatin cytotoxicity by using lymphoblastoid cell lines (LCL), followed by an association study of selected SNPs from the GWAS with overall survival (OS) in lung cancer patients. EXPERIMENTAL DESIGN: A GWAS for cisplatin was conducted with 283 ethnically diverse LCLs. A total of 168 top SNPs were genotyped in 222 small cell lung cancer (SCLC) and 961 non-SCLC (NSCLC) patients treated with platinum-based therapy. Association of the SNPs with OS was determined by using the Cox regression model. Selected candidate genes were functionally validated by siRNA knockdown in human lung cancer cells. RESULTS: Among 157 successfully genotyped SNPs, 9 and 10 SNPs were top SNPs associated with OS for patients with NSCLC and SCLC, respectively, although they were not significant after adjusting for multiple testing. Fifteen genes, including 7 located within 200 kb up or downstream of the 4 top SNPs and 8 genes for which expression was correlated with 3 SNPs in LCLs were selected for siRNA screening. Knockdown of DAPK3 and METTL6, for which expression levels were correlated with the rs11169748 and rs2440915 SNPs, significantly decreased cisplatin sensitivity in lung cancer cells. CONCLUSIONS: This series of clinical and complementary laboratory-based functional studies identified several candidate genes/SNPs that might help predict treatment outcomes for platinum-based therapy of lung cancer.

Pedigree association: assigning individual weights to pedigree members for genetic association analysis.

OBJECTIVE: Methods exist to appropriately perform association analyses in pedigrees. However, for genome-wide association analysis, these methods are computationally impractical. It is therefore important to determine alternate methods that can be efficiently used genome-wide. Here, we introduce a new algorithm that considers all relationships simultaneously in arbitrary-structured pedigrees and assigns weights to pedigree members that can be used in subsequent analyses to address relatedness. We compare this new method with an existing weighting algorithm, a naïve analysis (relatedness is ignored), and an empirical method that appropriately accounts for all relationships (the gold standard). METHODS: Framingham Heart Study Genetic Analysis Workshop 16 Problem 2 data were used with a dichotomous phenotype based on high-density lipoprotein cholesterol level (1,611 cases and 4,043 controls). New and existing algorithms for calculating weights were used. Cochran-Armitage trend tests were performed for 17,333 single-nucleotide polymorphisms on chromosome 8 using both weighting systems and the naïve approach; a subset of 500 single-nucleotide polymorphisms were tested empirically. Correlations of p-values from each method were determined. RESULTS: Results from the two weighting methods were strongly correlated (r = 0.96). Our new weighting method performed better than the existing weighting method (r = 0.89 vs. r = 0.83), which is due to a more moderate down-weighting. The naive analysis obtained the best correlation with the empirical gold standard results (r = 0.99). CONCLUSION: Our results suggest that weighting methods do not accurately represent tests that account for familial relationships in genetic association analyses and are inferior to the naïve method as an efficient initial genome-wide screening tool.

Evaluation of genetic risk scores for lipid levels using genome-wide markers in the Framingham Heart Study.

BACKGROUND: Multiple single-nucleotide polymorphisms have been associated with low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) levels. In this paper, we evaluate a weighted and an unweighted approach for estimating the combined effect of multiple markers (using genotypes and haplotypes) on lipid levels for a given individual. METHODS: Using data from the Framingham Heart Study SHARe genome-wide association study, we tested genome-wide genotypes and haplotypes for association with lipid levels and constructed genetic risk scores (GRS) based on multiple markers that were weighted according to their estimated effects on LDL-C, HDL-C, and TG. These scores (GRS-LDL, GRS-HDL, and GRS-TG) were then evaluated for associations with LDL-C, HDL-C, and TG, and compared with results of an unweighted method based on risk-allele counts. For comparability of metrics, GRS variables were divided into quartiles. RESULTS: GRS-LDL quartiles were associated with LDL-C levels (p = 2.1 x 10-24), GRS-HDL quartiles with HDL-C (p = 5.9 x 10-22), and GRS-TG quartiles with TG (p = 5.4 x 10-25). In comparison, these p-values were considerably lower than those for the associations of the unweighted GRS quartiles for LDL-C (p = 3.6 x 10-7), HDL-C (p = 6.4 x 10-16), and TG (p = 4.1 x 10-10). CONCLUSION: GRS variables were highly predictive of LDL-C, HDL-C, and TG measurements, especially when weighted based on each marker's individual association with those intermediate risk phenotypes. The allele-count GRS approach that does not weight the GRS by individual marker associations was considerably less predictive of lipid and lipoprotein measures when the same genetic markers were utilized, suggesting that substantially more risk-associated genetic marker information is encapsulated by the weighted GRS variables.