The origin and evolution of mutations in acute myeloid leukemia.

Most mutations in cancer genomes are thought to be acquired after the initiating event, which may cause genomic instability and drive clonal evolution. However, for acute myeloid leukemia (AML), normal karyotypes are common, and genomic instability is unusual. To better understand clonal evolution in AML, we sequenced the genomes of M3-AML samples with a known initiating event (PML-RARA) versus the genomes of normal karyotype M1-AML samples and the exomes of hematopoietic stem/progenitor cells (HSPCs) from healthy people. Collectively, the data suggest that most of the mutations found in AML genomes are actually random events that occurred in HSPCs before they acquired the initiating mutation; the mutational history of that cell is "captured" as the clone expands. In many cases, only one or two additional, cooperating mutations are needed to generate the malignant founding clone. Cells from the founding clone can acquire additional cooperating mutations, yielding subclones that can contribute to disease progression and/or relapse.

Persistent gut microbiota immaturity in malnourished Bangladeshi children.

Therapeutic food interventions have reduced mortality in children with severe acute malnutrition (SAM), but incomplete restoration of healthy growth remains a major problem. The relationships between the type of nutritional intervention, the gut microbiota, and therapeutic responses are unclear. In the current study, bacterial species whose proportional representation define a healthy gut microbiota as it assembles during the first two postnatal years were identified by applying a machine-learning-based approach to 16S ribosomal RNA data sets generated from monthly faecal samples obtained from birth onwards in a cohort of children living in an urban slum of Dhaka, Bangladesh, who exhibited consistently healthy growth. These age-discriminatory bacterial species were incorporated into a model that computes a 'relative microbiota maturity index' and 'microbiota-for-age Z-score' that compare postnatal assembly (defined here as maturation) of a child's faecal microbiota relative to healthy children of similar chronologic age. The model was applied to twins and triplets (to test for associations of these indices with genetic and environmental factors, including diarrhoea), children with SAM enrolled in a randomized trial of two food interventions, and children with moderate acute malnutrition. Our results indicate that SAM is associated with significant relative microbiota immaturity that is only partially ameliorated following two widely used nutritional interventions. Immaturity is also evident in less severe forms of malnutrition and correlates with anthropometric measurements. Microbiota maturity indices provide a microbial measure of human postnatal development, a way of classifying malnourished states, and a parameter for judging therapeutic efficacy. More prolonged interventions with existing or new therapeutic foods and/or addition of gut microbes may be needed to achieve enduring repair of gut microbiota immaturity in childhood malnutrition and improve clinical outcomes.

Mutational landscape and significance across 12 major cancer types.

The Cancer Genome Atlas (TCGA) has used the latest sequencing and analysis methods to identify somatic variants across thousands of tumours. Here we present data and analytical results for point mutations and small insertions/deletions from 3,281 tumours across 12 tumour types as part of the TCGA Pan-Cancer effort. We illustrate the distributions of mutation frequencies, types and contexts across tumour types, and establish their links to tissues of origin, environmental/carcinogen influences, and DNA repair defects. Using the integrated data sets, we identified 127 significantly mutated genes from well-known (for example, mitogen-activated protein kinase, phosphatidylinositol-3-OH kinase, Wnt/ß-catenin and receptor tyrosine kinase signalling pathways, and cell cycle control) and emerging (for example, histone, histone modification, splicing, metabolism and proteolysis) cellular processes in cancer. The average number of mutations in these significantly mutated genes varies across tumour types; most tumours have two to six, indicating that the number of driver mutations required during oncogenesis is relatively small. Mutations in transcriptional factors/regulators show tissue specificity, whereas histone modifiers are often mutated across several cancer types. Clinical association analysis identifies genes having a significant effect on survival, and investigations of mutations with respect to clonal/subclonal architecture delineate their temporal orders during tumorigenesis. Taken together, these results lay the groundwork for developing new diagnostics and individualizing cancer treatment.

Identification of functional variants for cleft lip with or without cleft palate in or near PAX7, FGFR2, and NOG by targeted sequencing of GWAS loci.

Although genome-wide association studies (GWASs) for nonsyndromic orofacial clefts have identified multiple strongly associated regions, the causal variants are unknown. To address this, we selected 13 regions from GWASs and other studies, performed targeted sequencing in 1,409 Asian and European trios, and carried out a series of statistical and functional analyses. Within a cluster of strongly associated common variants near NOG, we found that one, rs227727, disrupts enhancer activity. We furthermore identified significant clusters of non-coding rare variants near NTN1 and NOG and found several rare coding variants likely to affect protein function, including four nonsense variants in ARHGAP29. We confirmed 48 de novo mutations and, based on best biological evidence available, chose two of these for functional assays. One mutation in PAX7 disrupted the DNA binding of the encoded transcription factor in an in vitro assay. The second, a non-coding mutation, disrupted the activity of a neural crest enhancer downstream of FGFR2 both in vitro and in vivo. This targeted sequencing study provides strong functional evidence implicating several specific variants as primary contributory risk alleles for nonsyndromic clefting in humans.

Whole-exome sequencing identifies rare and low-frequency coding variants associated with LDL cholesterol.

Elevated low-density lipoprotein cholesterol (LDL-C) is a treatable, heritable risk factor for cardiovascular disease. Genome-wide association studies (GWASs) have identified 157 variants associated with lipid levels but are not well suited to assess the impact of rare and low-frequency variants. To determine whether rare or low-frequency coding variants are associated with LDL-C, we exome sequenced 2,005 individuals, including 554 individuals selected for extreme LDL-C (>98(th) or <2(nd) percentile). Follow-up analyses included sequencing of 1,302 additional individuals and genotype-based analysis of 52,221 individuals. We observed significant evidence of association between LDL-C and the burden of rare or low-frequency variants in PNPLA5, encoding a phospholipase-domain-containing protein, and both known and previously unidentified variants in PCSK9, LDLR and APOB, three known lipid-related genes. The effect sizes for the burden of rare variants for each associated gene were substantially higher than those observed for individual SNPs identified from GWASs. We replicated the PNPLA5 signal in an independent large-scale sequencing study of 2,084 individuals. In conclusion, this large whole-exome-sequencing study for LDL-C identified a gene not known to be implicated in LDL-C and provides unique insight into the design and analysis of similar experiments.

Outcomes of Lung Transplantation for Infants and Children with Genetic Disorders of Surfactant Metabolism.

OBJECTIVE: To compare outcomes of infants and children who underwent lung transplantation for genetic disorders of surfactant metabolism (SFTPB, SFTPC, ABCA3, and NKX2-1) over 2 epochs (1993-2003 and 2004-2015) at St Louis Children's Hospital. STUDY DESIGN: We retrospectively reviewed clinical characteristics, mortality, and short- and long-term morbidities of infants (transplanted at <1 year; n = 28) and children (transplanted >1 year; n = 16) and compared outcomes by age at transplantation (infants vs children) and by epoch of transplantation. RESULTS: Infants underwent transplantation more frequently for surfactant protein-B deficiency, whereas children underwent transplantation more frequently for SFTPC mutations. Both infants and children underwent transplantation for ABCA3 deficiency. Compared with children, infants experienced shorter times from listing to transplantation (P = .014), were more likely to be mechanically ventilated at the time of transplantation (P < .0001), were less likely to develop bronchiolitis obliterans post-transplantation (P = .021), and were more likely to have speech and motor delays (P ≤ .0001). Despite advances in genetic diagnosis, immunosuppressive therapies, and supportive respiratory and nutritional therapies, mortality did not differ between infants and children (P = .076) or between epochs. Kaplan-Meier analyses demonstrated that children transplanted in epoch 1 (1993-2003) were more likely to develop systemic hypertension (P = .049) and less likely to develop post-transplantation lymphoproliferative disorder compared with children transplanted in epoch 2 (2004-2015) (P = .051). CONCLUSION: Post-lung transplantation morbidities and mortality remain substantial for infants and children with genetic disorders of surfactant metabolism.

Re-sequencing expands our understanding of the phenotypic impact of variants at GWAS loci.

Genome-wide association studies (GWAS) have identified >500 common variants associated with quantitative metabolic traits, but in aggregate such variants explain at most 20-30% of the heritable component of population variation in these traits. To further investigate the impact of genotypic variation on metabolic traits, we conducted re-sequencing studies in >6,000 members of a Finnish population cohort (The Northern Finland Birth Cohort of 1966 [NFBC]) and a type 2 diabetes case-control sample (The Finland-United States Investigation of NIDDM Genetics [FUSION] study). By sequencing the coding sequence and 5' and 3' untranslated regions of 78 genes at 17 GWAS loci associated with one or more of six metabolic traits (serum levels of fasting HDL-C, LDL-C, total cholesterol, triglycerides, plasma glucose, and insulin), and conducting both single-variant and gene-level association tests, we obtained a more complete understanding of phenotype-genotype associations at eight of these loci. At all eight of these loci, the identification of new associations provides significant evidence for multiple genetic signals to one or more phenotypes, and at two loci, in the genes ABCA1 and CETP, we found significant gene-level evidence of association to non-synonymous variants with MAF<1%. Additionally, two potentially deleterious variants that demonstrated significant associations (rs138726309, a missense variant in G6PC2, and rs28933094, a missense variant in LIPC) were considerably more common in these Finnish samples than in European reference populations, supporting our prior hypothesis that deleterious variants could attain high frequencies in this isolated population, likely due to the effects of population bottlenecks. Our results highlight the value of large, well-phenotyped samples for rare-variant association analysis, and the challenge of evaluating the phenotypic impact of such variants.

Selection of models for the analysis of risk-factor trees: leveraging biological knowledge to mine large sets of risk factors with application to microbiome data.

MOTIVATION: Establishment of a statistical association between microbiome features and clinical outcomes is of growing interest because of the potential for yielding insights into biological mechanisms and pathogenesis. Extracting microbiome features that are relevant for a disease is challenging and existing variable selection methods are limited due to large number of risk factor variables from microbiome sequence data and their complex biological structure. RESULTS: We propose a tree-based scanning method, Selection of Models for the Analysis of Risk factor Trees (referred to as SMART-scan), for identifying taxonomic groups that are associated with a disease or trait. SMART-scan is a model selection technique that uses a predefined taxonomy to organize the large pool of possible predictors into optimized groups, and hierarchically searches and determines variable groups for association test. We investigate the statistical properties of SMART-scan through simulations, in comparison to a regular single-variable analysis and three commonly-used variable selection methods, stepwise regression, least absolute shrinkage and selection operator (LASSO) and classification and regression tree (CART). When there are taxonomic group effects in the data, SMART-scan can significantly increase power by using bacterial taxonomic information to split large numbers of variables into groups. Through an application to microbiome data from a vervet monkey diet experiment, we demonstrate that SMART-scan can identify important phenotype-associated taxonomic features missed by single-variable analysis, stepwise regression, LASSO and CART.

Genetic Factors Contribute to Risk for Neonatal Respiratory Distress Syndrome among Moderately Preterm, Late Preterm, and Term Infants.

OBJECTIVE: To determine the genetic contribution to risk for respiratory distress syndrome (RDS) among moderately preterm, late preterm, and term infants (estimated gestational age ≥32 weeks) of African- and European-descent. STUDY DESIGN: We reviewed clinical records for 524 consecutive twin pairs ≥32 weeks gestation. We identified pairs in which at least 1 twin had RDS (n = 225) and compared the concordance of RDS between monozygotic and dizygotic twins. Using mixed-effects logistic regression, we identified covariates that increased disease risk. We performed additive genetic, common environmental, and residual effects modeling to estimate genetic variance and used the ratio of genetic variance to total variance to estimate genetic contribution to RDS disease risk. RESULTS: Monozygotic twins were more concordant for RDS than dizygotic twins (P = .0040). Estimated gestational age, European-descent, male sex, delivery by cesarean, and 5-minute Apgar score each independently increased risk for RDS. After adjusting for these covariates, genetic effects accounted for 58% (P = .0002) of the RDS disease risk variance for all twin pairs. CONCLUSIONS: In addition to environmental factors, genetic factors may contribute to RDS risk among moderately preterm, late preterm, and term infants. Discovery of risk alleles may be important for prediction and management of RDS risk.

Genome remodelling in a basal-like breast cancer metastasis and xenograft.

Massively parallel DNA sequencing technologies provide an unprecedented ability to screen entire genomes for genetic changes associated with tumour progression. Here we describe the genomic analyses of four DNA samples from an African-American patient with basal-like breast cancer: peripheral blood, the primary tumour, a brain metastasis and a xenograft derived from the primary tumour. The metastasis contained two de novo mutations and a large deletion not present in the primary tumour, and was significantly enriched for 20 shared mutations. The xenograft retained all primary tumour mutations and displayed a mutation enrichment pattern that resembled the metastasis. Two overlapping large deletions, encompassing CTNNA1, were present in all three tumour samples. The differential mutation frequencies and structural variation patterns in metastasis and xenograft compared with the primary tumour indicate that secondary tumours may arise from a minority of cells within the primary tumour.

Candidate gene resequencing to identify rare, pedigree-specific variants influencing healthy aging phenotypes in the long life family study.

BACKGROUND: The Long Life Family Study (LLFS) is an international study to identify the genetic components of various healthy aging phenotypes. We hypothesized that pedigree-specific rare variants at longevity-associated genes could have a similar functional impact on healthy phenotypes. METHODS: We performed custom hybridization capture sequencing to identify the functional variants in 464 candidate genes for longevity or the major diseases of aging in 615 pedigrees (4,953 individuals) from the LLFS, using a multiplexed, custom hybridization capture. Variants were analyzed individually or as a group across an entire gene for association to aging phenotypes using family based tests. RESULTS: We found significant associations to three genes and nine single variants. Most notably, we found a novel variant significantly associated with exceptional survival in the 3' UTR OBFC1 in 13 individuals from six pedigrees. OBFC1 (chromosome 10) is involved in telomere maintenance, and falls within a linkage peak recently reported from an analysis of telomere length in LLFS families. Two different algorithms for single gene associations identified three genes with an enrichment of variation that was significantly associated with three phenotypes (GSK3B with the Healthy Aging Index, NOTCH1 with diastolic blood pressure and TP53 with serum HDL). CONCLUSIONS: Sequencing analysis of family-based associations for age-related phenotypes can identify rare or novel variants.

Estimating and testing pleiotropy of single genetic variant for two quantitative traits.

Along with the accumulated data of genetic variants and biomedical phenotypes in the genome era, statistical identification of pleiotropy is of growing interest for dissecting and understanding genetic correlations between complex traits. We proposed a novel method for estimating and testing pleiotropic effect of a genetic variant on two quantitative traits. Based on a covariance decomposition and estimation, our method quantifies pleiotropy as the portion of between-trait correlation explained by the same genetic variant. Unlike most multiple-trait methods that assess potential pleiotropy (i.e., whether a variant contributes to at least one trait), our method formulates a statistic that tests exact pleiotropy (i.e., whether a variant contributes to both of two traits). We developed two approaches (a regression approach and a bootstrapping approach) for such test and investigated their statistical properties, in comparison with other potential pleiotropy test methods. Our simulation shows that the regression approach produces correct P-values under both the complete null (i.e., a variant has no effect on both two traits) and the incomplete null (i.e., a variant has effect on only one of two traits), but requires large sample sizes to achieve a good power, when the bootstrapping approach has a better power and produces conservative P-values under the complete null. We demonstrate our method for detecting exact pleiotropy using a real GWAS dataset. Our method provides an easy-to-implement tool for measuring, testing, and understanding the pleiotropic effect of a single variant on the correlation architecture of two complex traits.

Adjusting family relatedness in data-driven burden test of rare variants.

Family data represent a rich resource for detecting association between rare variants (RVs) and human traits. However, most RV association analysis methods developed in recent years are data-driven burden tests which can adaptively learn weights from data but require permutation to evaluate significance, thus are not readily applicable to family data, because random permutation will destroy family structure. Direct application of these methods to family data may result in a significant inflation of false positives. To overcome this issue, we have developed a generalized, weighted sum mixed model (WSMM), and corresponding computational techniques that can incorporate family information into data-driven burden tests, and allow adaptive and efficient permutation test in family data. Using simulated and real datasets, we demonstrate that the WSMM method can be used to appropriately adjust for genetic relatedness among family members and has a good control for the inflation of false positives. We compare WSMM with a nondata-driven, family-based Sequence Kernel Association Test (famSKAT), showing that WSMM has significantly higher power in some cases. WSMM provides a generalized, flexible framework for adapting different data-driven burden tests to analyze data with any family structures, and it can be extended to binary and time-to-onset traits, with or without covariates.

VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing.

Cancer is a disease driven by genetic variation and mutation. Exome sequencing can be utilized for discovering these variants and mutations across hundreds of tumors. Here we present an analysis tool, VarScan 2, for the detection of somatic mutations and copy number alterations (CNAs) in exome data from tumor-normal pairs. Unlike most current approaches, our algorithm reads data from both samples simultaneously; a heuristic and statistical algorithm detects sequence variants and classifies them by somatic status (germline, somatic, or LOH); while a comparison of normalized read depth delineates relative copy number changes. We apply these methods to the analysis of exome sequence data from 151 high-grade ovarian tumors characterized as part of the Cancer Genome Atlas (TCGA). We validated some 7790 somatic coding mutations, achieving 93% sensitivity and 85% precision for single nucleotide variant (SNV) detection. Exome-based CNA analysis identified 29 large-scale alterations and 619 focal events per tumor on average. As in our previous analysis of these data, we observed frequent amplification of oncogenes (e.g., CCNE1, MYC) and deletion of tumor suppressors (NF1, PTEN, and CDKN2A). We searched for additional recurrent focal CNAs using the correlation matrix diagonal segmentation (CMDS) algorithm, which identified 424 significant events affecting 582 genes. Taken together, our results demonstrate the robust performance of VarScan 2 for somatic mutation and CNA detection and shed new light on the landscape of genetic alterations in ovarian cancer.

MuSiC: identifying mutational significance in cancer genomes.

Massively parallel sequencing technology and the associated rapidly decreasing sequencing costs have enabled systemic analyses of somatic mutations in large cohorts of cancer cases. Here we introduce a comprehensive mutational analysis pipeline that uses standardized sequence-based inputs along with multiple types of clinical data to establish correlations among mutation sites, affected genes and pathways, and to ultimately separate the commonly abundant passenger mutations from the truly significant events. In other words, we aim to determine the Mutational Significance in Cancer (MuSiC) for these large data sets. The integration of analytical operations in the MuSiC framework is widely applicable to a broad set of tumor types and offers the benefits of automation as well as standardization. Herein, we describe the computational structure and statistical underpinnings of the MuSiC pipeline and demonstrate its performance using 316 ovarian cancer samples from the TCGA ovarian cancer project. MuSiC correctly confirms many expected results, and identifies several potentially novel avenues for discovery.

MSIsensor: microsatellite instability detection using paired tumor-normal sequence data.

MOTIVATION: Microsatellite instability (MSI) is an important indicator of larger genome instability and has been linked to many genetic diseases, including Lynch syndrome. MSI status is also an independent prognostic factor for favorable survival in multiple cancer types, such as colorectal and endometrial. It also informs the choice of chemotherapeutic agents. However, the current PCR-electrophoresis-based detection procedure is laborious and time-consuming, often requiring visual inspection to categorize samples. We developed MSIsensor, a C++ program for automatically detecting somatic microsatellite changes. It computes length distributions of microsatellites per site in paired tumor and normal sequence data, subsequently using these to statistically compare observed distributions in both samples. Comprehensive testing indicates MSIsensor is an efficient and effective tool for deriving MSI status from standard tumor-normal paired sequence data. AVAILABILITY AND IMPLEMENTATION: https://github.com/ding-lab/msisensor

Admixture mapping of coronary artery calcification in African Americans from the NHLBI family heart study.

BACKGROUND: Coronary artery calcification (CAC) is an imaging biomarker of coronary atherosclerosis. In European Americans, genome-wide association studies (GWAS) have identified several regions associated with coronary artery disease. However, few large studies have been conducted in African Americans. The largest meta-analysis of CAC in African Americans failed to identify genome-wide significant variants despite being powered to detect effects comparable to effects identified in European Americans. Because CAC is different in prevalence and severity in African Americans and European Americans, admixture mapping is a useful approach to identify loci missed by GWAS. RESULTS: We applied admixture mapping to the African American cohort of the Family Heart Study and identified one genome-wide significant region on chromosome 12 and three potential regions on chromosomes 6, 15, and 19 that are associated with CAC. Follow-up studies using previously reported GWAS meta-analysis data suggest that the regions identified on chromosome 6 and 15 contain variants that are possibly associated with CAC. The associated region on chromosome 6 contains the gene for BMP-6, which is expressed in vascular calcific lesions. CONCLUSIONS: Our results suggest that admixture mapping can be a useful hypothesis-generating tool to identify genomic regions that contribute to complex diseases in genetically admixed populations.

Atg16L1 deficiency confers protection from uropathogenic Escherichia coli infection in vivo.

Urinary tract infection (UTI), a frequent and important disease in humans, is primarily caused by uropathogenic Escherichia coli (UPEC). UPEC forms acute cytoplasmic biofilms within superficial urothelial cells and can persist by establishing membrane-enclosed latent reservoirs to seed recurrent UTI. The host responds with an influx of innate immune cells and shedding of infected epithelial cells. The autophagy gene ATG16L1 has a commonly occurring mutation that is associated with inflammatory disease and intestinal cell abnormalities in mice and humans. Here, we show that Atg16L1-deficient mice (Atg16L1(HM)) cleared bacteriuria more rapidly and thoroughly than controls and showed rapid epithelial recovery. Atg16L1 deficiency was associated with a potent proinflammatory cytokine response with increased recruitment of monocytes and neutrophils to infected bladders. Chimeric and genetic studies showed that Atg16L1(HM) hematopoietic cells alone could increase clearance and that Atg16L1-deficient innate immune cells were required and sufficient for enhanced bacteriuric clearance. We also show that Atg16L1-deficient mice exhibit cell-autonomous architectural aberrations of superficial urothelial cells, including increases in multivesicular bodies, lysosomes, and expression of the UPEC receptor Up1a. Finally, we show that Atg16L1(HM) epithelial cells contained a significantly reduced number of latent reservoirs. Together, our results show that Atg16L1 deficiency confers protection in vivo to the host against both acute and latent UPEC infection, suggest that deficiency in a key autophagy protein can be protective against infection in an animal model of one of the most common diseases of women worldwide, and may have significant clinical implications for understanding the etiology of recurrent UTIs.

Human regulatory T cells induce T-lymphocyte senescence.

Regulatory T (Treg) cells have broad suppressive activity on host immunity, but the fate and function of suppressed responder T cells remains largely unknown. In the present study, we report that human Treg cells can induce senescence in responder naive and effector T cells in vitro and in vivo. Senescent responder T cells induced by human Treg cells changed their phenotypes and cytokine profiles and had potent suppressive function. Furthermore, Treg-mediated molecular control of senescence in responder T cells was associated with selective modulation of p38 and ERK1/2 signaling and cell-cycle-regulatory molecules p16, p21, and p53. We further revealed that human Treg-induced senescence and suppressor function could be blocked by TLR8 signaling and/or by specific ERK1/2 and p38 inhibition in vitro and in vivo in animal models. The results of the present study identify a novel mechanism of human Treg cell suppression that induces targeted responder T-cell senescence and provide new insights relevant for the development of strategies capable of preventing and/or reversing Treg-induced immune suppression.

Tumor-infiltrating γδ T lymphocytes predict clinical outcome in human breast cancer.

Understanding and dissecting the role of different subsets of regulatory tumor-infiltrating lymphocytes (TILs) in the immunopathogenesis of individual cancer is a challenge for anti-tumor immunotherapy. High levels of γδ regulatory T cells have been discovered in breast TILs. However, the clinical relevance of these intratumoral γδ T cells is unknown. In this study, γδ T cell populations were analyzed by performing immunohistochemical staining in primary breast cancer tissues from patients with different stages of cancer progression. Retrospective multivariate analyses of the correlations between γδ T cell levels and other prognostic factors and clinical outcomes were completed. We found that γδ T cell infiltration and accumulation in breast tumor sites was a general feature in breast cancer patients. Intratumoral γδ T cell numbers were positively correlated with advanced tumor stages, HER2 expression status, and high lymph node metastasis but inversely correlated with relapse-free survival and overall survival of breast cancer patients. Multivariate and univariate analyses of tumor-infiltrating γδ T cells and other prognostic factors further suggested that intratumoral γδ T cells represented the most significant independent prognostic factor for assessing severity of breast cancer compared with the other known factors. Intratumoral γδ T cells were positively correlated with FOXP3(+) cells and CD4(+) T cells but negatively correlated with CD8(+) T cells in breast cancer tissues. These findings suggest that intratumoral γδ T cells may serve as a valuable and independent prognostic biomarker, as well as a potential therapeutic target for human breast cancer.