Sofia Khan

Copy Number Analysis Indicates Monoclonal Origin of Lethal Metastatic Prostate Cancer

Many studies have shown that primary prostate cancers are multifocal and are composed of multiple genetically distinct cancer cell clones. Whether or not multiclonal primary prostate cancers typically give rise to multiclonal or monoclonal prostate cancer metastases is largely unknown, although studies at single chromosomal loci are consistent with the latter case. Here we show through a high-resolution genome-wide single nucleotide polymorphism and copy number survey that most, if not all, metastatic prostate cancers have monoclonal origins and maintain a unique signature copy number pattern of the parent cancer cell while also accumulating a variable number of separate subclonally sustained changes. We find no relationship between anatomic site of metastasis and genomic copy number change pattern. Taken together with past animal and cytogenetic studies of metastasis and recent single-locus genetic data in prostate and other metastatic cancers, these data indicate that despite common genomic heterogeneity in primary cancers, most metastatic cancers arise from a single precursor cancer cell. This study establishes that genomic archeology of multiple anatomically separate metastatic cancers in individuals can be used to define the salient genomic features of a parent cancer clone of proven lethal metastatic phenotype.

Exome sequencing identifies FANCM as a susceptibility gene for triple-negative breast cancer

Significance The major portion of hereditary breast cancer still remains unexplained, and many susceptibility loci are yet to be found. Exome sequencing of 24 high-risk familial BRCA1/2-negative breast cancer patients and further genotyping of a large sample set of breast/ovarian cancer cases and controls was used to discover previously unidentified susceptibility alleles and genes. A significant association of a FANCM nonsense mutation with breast cancer, especially triple-negative breast cancer, identifies FANCM as a breast cancer susceptibility gene. Identification of such risk alleles is expected to improve cancer risk assessment for breast cancer patients and families, and may lead to improvements in the prevention, early diagnosis, and treatment of cancer. Inherited predisposition to breast cancer is known to be caused by loss-of-function mutations in BRCA1, BRCA2, PALB2, CHEK2, and other genes involved in DNA repair. However, most families severely affected by breast cancer do not harbor mutations in any of these genes. In Finland, founder mutations have been observed in each of these genes, suggesting that the Finnish population may be an excellent resource for the identification of other such genes. To this end, we carried out exome sequencing of constitutional genomic DNA from 24 breast cancer patients from 11 Finnish breast cancer families. From all rare damaging variants, 22 variants in 21 DNA repair genes were genotyped in 3,166 breast cancer patients, 569 ovarian cancer patients, and 2,090 controls, all from the Helsinki or Tampere regions of Finland. In Fanconi anemia complementation gene M (FANCM), nonsense mutation c.5101C>T (p.Q1701X) was significantly more frequent among breast cancer patients than among controls [odds ratio (OR) = 1.86, 95% CI = 1.26–2.75; P = 0.0018], with particular enrichment among patients with triple-negative breast cancer (TNBC; OR = 3.56, 95% CI = 1.81–6.98, P = 0.0002). In the Helsinki and Tampere regions, respectively, carrier frequencies of FANCM p.Q1701X were 2.9% and 4.0% of breast cancer patients, 5.6% and 6.6% of TNBC patients, 2.2% of ovarian cancer patients (from Helsinki), and 1.4% and 2.5% of controls. These findings identify FANCM as a breast cancer susceptibility gene, mutations in which confer a particularly strong predisposition for TNBC.

Spectrum of disease-causing mutations in protein secondary structures

BackgroundMost genetic disorders are linked to missense mutations as even minor changes in the size or properties of an amino acid can alter or prevent the function of the protein. Further, the effect of a mutation is also dependent on the sequence and structure context of the alteration.ResultsWe investigated the spectrum of disease-causing missense mutations in secondary structure elements in proteins with numerous known mutations and for which an experimentally defined three-dimensional structure is available. We obtained a comprehensive map of the differences in mutation frequencies, location and contact energies, and the changes in residue volume and charge – both in the mutated (original) amino acids and in the mutant amino acids in the different secondary structure types. We collected information for 44 different proteins involved in a large number of diseases. The studied proteins contained a total of 2413 mutations of which 1935 (80%) appeared in secondary structures. Differences in mutation patterns between secondary structures and whole proteins were generally not statistically significant whereas within the secondary structural elements numerous highly significant features were observed.ConclusionNumerous trends in mutated and mutant amino acids are apparent. Among the original residues, arginine clearly has the highest relative mutability. The overall relative mutability among mutant residues is highest for cysteine and tryptophan. The mutability values are higher for mutated residues than for mutant residues. Arginine and glycine are among the most mutated residues in all secondary structures whereas the other amino acids have large variations in mutability between structure types. Statistical analysis was used to reveal trends in different secondary structural elements, residue types as well as for the charge and volume changes.

Identification of inherited genetic variations influencing prognosis in early onset breast cancer

Genome-Wide Association Studies (GWAS) have begun to investigate associations between inherited genetic variations and breast cancer prognosis. Here, we report our findings from a GWAS conducted in 536 patients with early-onset breast cancer aged 40 or less at diagnosis and with a mean follow-up period of 4.1 years (SD = 1.96). Patients were selected from the Prospective Study of Outcomes in Sporadic versus Hereditary breast cancer. A Bonferroni correction for multiple testing determined that a P value of 1.0 × 10(-7) was a statistically significant association signal. Following quality control, we identified 487,496 single nucleotide polymorphisms (SNP) for association tests in stage 1. In stage 2, 35 SNPs with the most significant associations were genotyped in 1,516 independent cases from the same early-onset cohort. In stage 2, 11 SNPs remained associated in the same direction (P ≤ 0.05). Fixed effects meta-analysis models identified one SNP associated at close to genome wide level of significance 556 kb upstream of the ARRDC3 locus [HR = 1.61; 95% confidence interval (CI), 1.33-1.96; P = 9.5 × 10(-7)]. Four further associations at or close to the PBX1, RORα, NTN1, and SYT6 loci also came close to genome-wide significance levels (P = 10(-6)). In the first ever GWAS for the identification of SNPs associated with prognosis in patients with early-onset breast cancer, we report a SNP upstream of the ARRDC3 locus as potentially associated with prognosis (median follow-up time for genotypes: CC = 4 years, CT = 3 years, and TT = 2.7 years; Wilcoxon rank-sum test CC vs. CT, P = 4 × 10(-4) and CT vs. TT, P = 0.76). Four further loci may also be associated with prognosis.

Gene expression profiles in human and mouse primary cells provide new insights into the differential actions of vitamin D3 metabolites.

1α,25-Dihydroxyvitamin D3 (1α,25(OH)2D3) had earlier been regarded as the only active hormone. The newly identified actions of 25-hydroxyvitamin D3 (25(OH)D3) and 24R,25-dihydroxyvitamin D3 (24R,25(OH)2D3) broadened the vitamin D3 endocrine system, however, the current data are fragmented and a systematic understanding is lacking. Here we performed the first systematic study of global gene expression to clarify their similarities and differences. Three metabolites at physiologically comparable levels were utilized to treat human and mouse fibroblasts prior to DNA microarray analyses. Human primary prostate stromal P29SN cells (hP29SN), which convert 25(OH)D3 into 1α,25(OH)2D3 by 1α-hydroxylase (encoded by the gene CYP27B1), displayed regulation of 164, 171, and 175 genes by treatment with 1α,25(OH)2D3, 25(OH)D3, and 24R,25(OH)2D3, respectively. Mouse primary Cyp27b1 knockout fibroblasts (mCyp27b1 −/−), which lack 1α-hydroxylation, displayed regulation of 619, 469, and 66 genes using the same respective treatments. The number of shared genes regulated by two metabolites is much lower in hP29SN than in mCyp27b1 −/−. By using DAVID Functional Annotation Bioinformatics Microarray Analysis tools and Ingenuity Pathways Analysis, we identified the agonistic regulation of calcium homeostasis and bone remodeling between 1α,25(OH)2D3 and 25(OH)D3 and unique non-classical actions of each metabolite in physiological and pathological processes, including cell cycle, keratinocyte differentiation, amyotrophic lateral sclerosis signaling, gene transcription, immunomodulation, epigenetics, cell differentiation, and membrane protein expression. In conclusion, there are three distinct vitamin D3 hormones with clearly different biological activities. This study presents a new conceptual insight into the vitamin D3 endocrine system, which may guide the strategic use of vitamin D3 in disease prevention and treatment.

A Genome Wide Meta-Analysis Study for Identification of Common Variation Associated with Breast Cancer Prognosis

Objective Genome wide association studies (GWAs) of breast cancer mortality have identified few potential associations. The concordance between these studies is unclear. In this study, we used a meta-analysis of two prognostic GWAs and a replication cohort to identify the strongest associations and to evaluate the loci suggested in previous studies. We attempt to identify those SNPs which could impact overall survival irrespective of the age of onset. Methods To facilitate the meta-analysis and to refine the association signals, SNPs were imputed using data from the 1000 genomes project. Cox-proportional hazard models were used to estimate hazard ratios (HR) in 536 patients from the POSH cohort (Prospective study of Outcomes in Sporadic versus Hereditary breast cancer) and 805 patients from the HEBCS cohort (Helsinki Breast Cancer Study). These hazard ratios were combined using a Mantel-Haenszel fixed effects meta-analysis and a p-value threshold of 5×10−8 was used to determine significance. Replication was performed in 1523 additional patients from the POSH study. Results Although no SNPs achieved genome wide significance, three SNPs have significant association in the replication cohort and combined p-values less than 5.6×10−6. These SNPs are; rs421379 which is 556 kb upstream of ARRDC3 (HR = 1.49, 95% confidence interval (CI) = 1.27–1.75, P = 1.1×10−6), rs12358475 which is between ECHDC3 and PROSER2 (HR = 0.75, CI = 0.67–0.85, P = 1.8×10−6), and rs1728400 which is between LINC00917 and FOXF1. Conclusions In a genome wide meta-analysis of two independent cohorts from UK and Finland, we identified potential associations at three distinct loci. Phenotypic heterogeneity and relatively small sample sizes may explain the lack of genome wide significant findings. However, the replication at three SNPs in the validation cohort shows promise for future studies in larger cohorts. We did not find strong evidence for concordance between the few associations highlighted by previous GWAs of breast cancer survival and this study.

Evaluation of Accuracy and Applicability of Protein Models: Retrospective Analysis of Biological and Biomedical Predictions

In order to study protein function and activity structural data is required. Since experimental structures are available for just a small fraction of all known protein sequences, computational methods such as protein modelling can provide useful information. Over the last few decades we have predicted, with homology modelling methods, the structures for numerous proteins. In this study we assess the structural quality and validity of the biological and medical interpretations and predictions made based on the models. All the models had correct scaffolding and were ranked at least as correct or good by numerical evaluators even though the sequence identity with the template was as low as 8%. The biological explanations made based on models were well in line with experimental structures and other experimental studies. Retrospective analysis of homology models indicates the power of protein modelling when made carefully from sequence alignment to model building and refinement. Modelling can be applied to studying and predicting different kinds of biological phenomena and according to our results it can be done so with success.

Abstract 3274: SNP-SNP interaction analyses of NQO1 and NF-κB signaling pathway genes on breast cancer survival and treatment outcome

Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA To improve individualized therapy, new prognostic and predictive markers are needed to identify patients with expected poor breast cancer outcome and patients who profit most from a given treatment. NF-κB pathway plays a pervasive role in the pathogenesis of cancer through its implication in distinct hallmarks of tumorigenesis. Previous studies have indicated the role of NF-κB transcription factor in promoting cell survival and resistance to chemotherapy in breast carcinoma. We have previously found that NQO1-rs1800566 predicts poor survival among breast cancer patients, particularly after anthracycline-based adjuvant chemotherapy and in patients with p53-aberrant tumors. We have also reported the inverse relationship between NQO1 protein expression and NF-κB activation. We have here analyzed the interaction of SNPs residing within the NF-κB signaling pathway genes, and NQO1-rs6499255 (tagging NQO1-rs1800566 with r2> 0.90) on breast cancer patient survival and treatment outcome. The panel of markers included the NF-κB pathway SNPs available in a custom Illumina iSelect genotyping array designed for Collaborative Oncological Gene-Environment Study (iCOGS) as well as NQO1-rs6499255. For the SNP-SNP interaction analysis on patient survival we conducted a likelihood ratio test that compares Coxs regression model with and without an interaction term to evaluate whether the interaction model is a better fit for the prognostic data. The results obtained from this study will be reported. Identification of interactive SNPs/genes contribute to the elucidation of the mechanisms underlying complex diseases such as breast cancer. Citation Format: Maral Jamshidi, Rainer Fagerholm, Sofia Khan, Kristiina Aittomaki, Carl Blomqvist, Marjanka K. Schmidt, Heli Nevanlinna, BCAC: Breast Cancer Association Consortium. SNP-SNP interaction analyses of NQO1 and NF-κB signaling pathway genes on breast cancer survival and treatment outcome. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3274. doi:10.1158/1538-7445.AM2014-3274