Correction: Møller, P.; et al. Causes for Frequent Pathogenic BRCA1 Variants Include Low Penetrance in Fertile Ages, Recurrent De-Novo Mutations and Genetic Drift. Cancers 2019, 11, 132.

The authors wish to make the following corrections to this paper [1]: The authors would like to replace Table 3 in [1]. The corrections are correcting typographical errors when translating our database in BIC format to HGVS nomenclature, and removing four carriers which had zero follow-up time. [...].

Causes for Frequent Pathogenic BRCA1 Variants Include Low Penetrance in Fertile Ages, Recurrent De-Novo Mutations and Genetic Drift.

Background: We have previously demonstrated that the Norwegian frequent pathogenic BRCA1 (path_BRCA1) variants are caused by genetic drift and recurrent de-novo mutations. We here examined the penetrance of frequent path_BRCA1 variants in fertile ages as a surrogate marker for fitness. Material and methods: We conducted an observational prospective study of penetrance for cancer in Norwegian female carriers of frequent path_BRCA1 variants, and compared our observed results to penetrance of infrequent path_BRCA1 variants and to average penetrance of path_BRCA1 variants reported by others. Results: The cumulative risk for breast cancer at 45 years in carriers of frequent path_BRCA1 variants was 20% (94% confidence interval 10⁻30%), compared to 35% (95% confidence interval 22⁻48%) in carriers of infrequent path_BRCA1 variants (p = 0.02), and to the 35% (confidence interval 32⁻39%) average for path_BRCA1 carriers reported by others (p = 0.0001). Discussion and conclusion: Carriers of the most frequent Norwegian path_BRCA1 variants had low incidence of cancer in fertile ages, indicating a low selective disadvantage. This, together with the variant locations being hotspots for de novo mutations and subject to genetic drift, as previously described, may have caused their high prevalence today. Besides being of theoretical interest to explain the phenomenon that a few path_BRCA1 variants are frequent, the later onset of breast cancer associated with the most frequent path_BRCA1 variants may be of interest for carriers who have to decide if and when to select prophylactic mastectomy.

Cancer risk and survival in path_MMR carriers by gene and gender up to 75 years of age: a report from the Prospective Lynch Syndrome Database.

BACKGROUND: Most patients with path_MMR gene variants (Lynch syndrome (LS)) now survive both their first and subsequent cancers, resulting in a growing number of older patients with LS for whom limited information exists with respect to cancer risk and survival. OBJECTIVE AND DESIGN: This observational, international, multicentre study aimed to determine prospectively observed incidences of cancers and survival in path_MMR carriers up to 75 years of age. RESULTS: 3119 patients were followed for a total of 24 475 years. Cumulative incidences at 75 years (risks) for colorectal cancer were 46%, 43% and 15% in path_MLH1, path_MSH2 and path_MSH6 carriers; for endometrial cancer 43%, 57% and 46%; for ovarian cancer 10%, 17% and 13%; for upper gastrointestinal (gastric, duodenal, bile duct or pancreatic) cancers 21%, 10% and 7%; for urinary tract cancers 8%, 25% and 11%; for prostate cancer 17%, 32% and 18%; and for brain tumours 1%, 5% and 1%, respectively. Ovarian cancer occurred mainly premenopausally. By contrast, upper gastrointestinal, urinary tract and prostate cancers occurred predominantly at older ages. Overall 5-year survival for prostate cancer was 100%, urinary bladder 93%, ureter 85%, duodenum 67%, stomach 61%, bile duct 29%, brain 22% and pancreas 0%. Path_PMS2 carriers had lower risk for cancer. CONCLUSION: Carriers of different path_MMR variants exhibit distinct patterns of cancer risk and survival as they age. Risk estimates for counselling and planning of surveillance and treatment should be tailored to each patient's age, gender and path_MMR variant. We have updated our open-access website www.lscarisk.org to facilitate this.

Molecular signatures reflecting microenvironmental metabolism and chemotherapy-induced immunogenic cell death in colorectal liver metastases.

Background: Metastatic colorectal cancer (CRC) is associated with highly variable clinical outcome and response to therapy. The recently identified consensus molecular subtypes (CMS1-4) have prognostic and therapeutic implications in primary CRC, but whether these subtypes are valid for metastatic disease is unclear. We performed multi-level analyses of resectable CRC liver metastases (CLM) to identify molecular characteristics of metastatic disease and evaluate the clinical relevance. Methods: In this ancillary study to the Oslo-CoMet trial, CLM and tumor-adjacent liver tissue from 46 patients were analyzed by profiling mutations (targeted sequencing), genome-wide copy number alteration (CNAs), and gene expression. Results: Somatic mutations and CNAs detected in CLM were similar to reported primary CRC profiles, while CNA profiles of eight metastatic pairs suggested intra-patient divergence. A CMS classifier tool applied to gene expression data, revealed the cohort to be highly enriched for CMS2. Hierarchical clustering of genes with highly variable expression identified two subgroups separated by high or low expression of 55 genes with immune-related and metabolic functions. Importantly, induction of genes and pathways associated with immunogenic cell death (ICD) was identified in metastases exposed to neoadjuvant chemotherapy (NACT). Conclusions: The uniform classification of CLM by CMS subtyping may indicate that novel class discovery approaches need to be explored to uncover clinically useful stratification of CLM. Detected gene expression signatures support the role of metabolism and chemotherapy in shaping the immune microenvironment of CLM. Furthermore, the results point to rational exploration of immune modulating strategies in CLM, particularly by exploiting NACT-induced ICD.

Colorectal cancer incidence in path_MLH1 carriers subjected to different follow-up protocols: a Prospective Lynch Syndrome Database report.

BACKGROUND: We have previously reported a high incidence of colorectal cancer (CRC) in carriers of pathogenic MLH1 variants (path_MLH1) despite follow-up with colonoscopy including polypectomy. METHODS: The cohort included Finnish carriers enrolled in 3-yearly colonoscopy (n = 505; 4625 observation years) and carriers from other countries enrolled in colonoscopy 2-yearly or more frequently (n = 439; 3299 observation years). We examined whether the longer interval between colonoscopies in Finland could explain the high incidence of CRC and whether disease expression correlated with differences in population CRC incidence. RESULTS: Cumulative CRC incidences in carriers of path_MLH1 at 70-years of age were 41% for males and 36% for females in the Finnish series and 58% and 55% in the non-Finnish series, respectively (p > 0.05). Mean time from last colonoscopy to CRC was 32.7 months in the Finnish compared to 31.0 months in the non-Finnish (p > 0.05) and was therefore unaffected by the recommended colonoscopy interval. Differences in population incidence of CRC could not explain the lower point estimates for CRC in the Finnish series. Ten-year overall survival after CRC was similar for the Finnish and non-Finnish series (88% and 91%, respectively; p > 0.05). CONCLUSIONS: The hypothesis that the high incidence of CRC in path_MLH1 carriers was caused by a higher incidence in the Finnish series was not valid. We discuss whether the results were influenced by methodological shortcomings in our study or whether the assumption that a shorter interval between colonoscopies leads to a lower CRC incidence may be wrong. This second possibility is intriguing, because it suggests the dogma that CRC in path_MLH1 carriers develops from polyps that can be detected at colonoscopy and removed to prevent CRC may be erroneous. In view of the excellent 10-year overall survival in the Finnish and non-Finnish series we remain strong advocates of current surveillance practices for those with LS pending studies that will inform new recommendations on the best surveillance interval.

Implementing precision cancer medicine in the public health services of Norway: the diagnostic infrastructure and a cost estimate.

OBJECTIVE: Through the conduct of an individual-based intervention study, the main purpose of this project was to build and evaluate the required infrastructure that may enable routine practice of precision cancer medicine in the public health services of Norway, including modelling of costs. METHODS: An eligible patient had end-stage metastatic disease from a solid tumour. Metastatic tissue was analysed by DNA sequencing, using a 50-gene panel and a study-generated pipeline for analysis of sequence data, supplemented with fluorescence in situ hybridisation to cover relevant biomarkers. Cost estimations compared best supportive care, biomarker-agnostic treatment with a molecularly targeted agent and biomarker-based treatment with such a drug. These included costs for medication, outpatient clinic visits, admission from adverse events and the biomarker-based procedures. RESULTS: The diagnostic procedures, which comprised sampling of metastatic tissue, mutation analysis and data interpretation at the Molecular Tumor Board before integration with clinical data at the Clinical Tumor Board, were completed in median 18 (8-39) days for the 22 study patients. The 23 invasive procedures (12 from liver, 6 from lung, 5 from other sites) caused a single adverse event (pneumothorax). Per patient, 0-5 mutations were detected in metastatic tumours; however, no actionable target case was identified for the current single-agent therapy approach. Based on the cost modelling, the biomarker-based approach was 2.5-fold more costly than best supportive care and 2.5-fold less costly than the biomarker-agnostic option. CONCLUSIONS: The first project phase established a comprehensive diagnostic infrastructure for precision cancer medicine, which enabled expedite and safe mutation profiling of metastatic tumours and data interpretation at multidisciplinary tumour boards for patients with end-stage cancer. Furthermore, it prepared for protocol amendments, recently approved by the designated authorities for the second study phase, allowing more comprehensive mutation analysis and opportunities to define therapy targets.

Serum N-glycome alterations in breast cancer during multimodal treatment and follow-up.

Using our recently developed high-throughput automated platform, N-glycans from all serum glycoproteins from patients with breast cancer were analysed at diagnosis, after neoadjuvant chemotherapy, surgery, radiotherapy and up to 3 years after surgery. Surprisingly, alterations in the serum N-glycome after chemotherapy were pro-inflammatory with an increase in glycan structures associated with cancer. Surgery, on the other hand, induced anti-inflammatory changes in the serum N-glycome, towards a noncancerous phenotype. At the time of first follow-up, glycosylation in patients with affected lymph nodes changed towards a malignant phenotype. C-reactive protein showed a different pattern, increasing after first line of neoadjuvant chemotherapy, then decreasing throughout treatment until 1 year after surgery. This may reflect a switch from acute to chronic inflammation, where chronic inflammation is reflected in the serum after the acute phase response subsides. In conclusion, we here present the first time-course serum N-glycome profiling of patients with breast cancer during and after treatment. We identify significant glycosylation changes with chemotherapy, surgery and follow-up, reflecting the host response to therapy and tumour removal.

The Longitudinal Transcriptional Response to Neoadjuvant Chemotherapy with and without Bevacizumab in Breast Cancer.

Purpose: Chemotherapy-induced alterations to gene expression are due to transcriptional reprogramming of tumor cells or subclonal adaptations to treatment. The effect on whole-transcriptome mRNA expression was investigated in a randomized phase II clinical trial to assess the effect of neoadjuvant chemotherapy with the addition of bevacizumab.Experimental Design: Tumor biopsies and whole-transcriptome mRNA profiles were obtained at three fixed time points with 66 patients in each arm. Altogether, 358 specimens from 132 patients were available, representing the transcriptional state before treatment start, at 12 weeks and after treatment (25 weeks). Pathologic complete response (pCR) in breast and axillary nodes was the primary endpoint.Results: pCR was observed in 15 patients (23%) receiving bevacizumab and chemotherapy and 8 patients (12%) receiving only chemotherapy. In the estrogen receptor-positive patients, 11 of 54 (20%) treated with bevacizumab and chemotherapy achieved pCR, while only 3 of 57 (5%) treated with chemotherapy reached pCR. In patients with estrogen receptor-positive tumors treated with combination therapy, an elevated immune activity was associated with good response. Proliferation was reduced after treatment in both treatment arms and most pronounced in the combination therapy arm, where the reduction in proliferation accelerated during treatment. Transcriptional alterations during therapy were subtype specific, and the effect of adding bevacizumab was most evident for luminal-B tumors.Conclusions: Clinical response and gene expression response differed between patients receiving combination therapy and chemotherapy alone. The results may guide identification of patients likely to benefit from antiangiogenic therapy. Clin Cancer Res; 23(16); 4662-70. ©2017 AACR.

Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome.

BACKGROUND: Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes. METHODS: Tumor tissue from 425 patients with primary breast cancer from the Oslo2 study was cut and blended, and divided into fractions for DNA, RNA and protein isolation and metabolomics, allowing the acquisition of representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a "cluster-of-clusters" approach with consensus clustering. RESULTS: Based on DNA copy number data, tumors were categorized into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and tumors were classified into ten subtypes using integrative clustering. The final consensus clustering that incorporated all aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype; these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significantly different outcomes, and in another a trend was observed. CONCLUSIONS: The six integrated subtypes identified confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing knowledge of the heterogeneity of the luminal A subtype may add pivotal information to guide therapeutic choices, evidently bringing us closer to improved treatment for this largest subgroup of breast cancer.

Cancer incidence and survival in Lynch syndrome patients receiving colonoscopic and gynaecological surveillance: first report from the prospective Lynch syndrome database.

OBJECTIVE: Estimates of cancer risk and the effects of surveillance in Lynch syndrome have been subject to bias, partly through reliance on retrospective studies. We sought to establish more robust estimates in patients undergoing prospective cancer surveillance. DESIGN: We undertook a multicentre study of patients carrying Lynch syndrome-associated mutations affecting MLH1, MSH2, MSH6 or PMS2. Standardised information on surveillance, cancers and outcomes were collated in an Oracle relational database and analysed by age, sex and mutated gene. RESULTS: 1942 mutation carriers without previous cancer had follow-up including colonoscopic surveillance for 13 782 observation years. 314 patients developed cancer, mostly colorectal (n=151), endometrial (n=72) and ovarian (n=19). Cancers were detected from 25 years onwards in MLH1 and MSH2 mutation carriers, and from about 40 years in MSH6 and PMS2 carriers. Among first cancer detected in each patient the colorectal cancer cumulative incidences at 70 years by gene were 46%, 35%, 20% and 10% for MLH1, MSH2, MSH6 and PMS2 mutation carriers, respectively. The equivalent cumulative incidences for endometrial cancer were 34%, 51%, 49% and 24%; and for ovarian cancer 11%, 15%, 0% and 0%. Ten-year crude survival was 87% after any cancer, 91% if the first cancer was colorectal, 98% if endometrial and 89% if ovarian. CONCLUSIONS: The four Lynch syndrome-associated genes had different penetrance and expression. Colorectal cancer occurred frequently despite colonoscopic surveillance but resulted in few deaths. Using our data, a website has been established at http://LScarisk.org enabling calculation of cumulative cancer risks as an aid to genetic counselling in Lynch syndrome.

Incidence of and survival after subsequent cancers in carriers of pathogenic MMR variants with previous cancer: a report from the prospective Lynch syndrome database.

OBJECTIVE: Today most patients with Lynch syndrome (LS) survive their first cancer. There is limited information on the incidences and outcome of subsequent cancers. The present study addresses three questions: (i) what is the cumulative incidence of a subsequent cancer; (ii) in which organs do subsequent cancers occur; and (iii) what is the survival following these cancers? DESIGN: Information was collated on prospectively organised surveillance and prospectively observed outcomes in patients with LS who had cancer prior to inclusion and analysed by age, gender and genetic variants. RESULTS: 1273 patients with LS from 10 countries were followed up for 7753 observation years. 318 patients (25.7%) developed 341 first subsequent cancers, including colorectal (n=147, 43%), upper GI, pancreas or bile duct (n=37, 11%) and urinary tract (n=32, 10%). The cumulative incidences for any subsequent cancer from age 40 to age 70 years were 73% for pathogenic MLH1 (path_MLH1), 76% for path_MSH2 carriers and 52% for path_MSH6 carriers, and for colorectal cancer (CRC) the cumulative incidences were 46%, 48% and 23%, respectively. Crude survival after any subsequent cancer was 82% (95% CI 76% to 87%) and 10-year crude survival after CRC was 91% (95% CI 83% to 95%). CONCLUSIONS: Relative incidence of subsequent cancer compared with incidence of first cancer was slightly but insignificantly higher than cancer incidence in patients with LS without previous cancer (range 0.94-1.49). The favourable survival after subsequent cancers validated continued follow-up to prevent death from cancer. The interactive website http://lscarisk.org was expanded to calculate the risks by gender, genetic variant and age for subsequent cancer for any patient with LS with previous cancer.

Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses.

Removal of, or adjustment for, batch effects or center differences is generally required when such effects are present in data. In particular, when preparing microarray gene expression data from multiple cohorts, array platforms, or batches for later analyses, batch effects can have confounding effects, inducing spurious differences between study groups. Many methods and tools exist for removing batch effects from data. However, when study groups are not evenly distributed across batches, actual group differences may induce apparent batch differences, in which case batch adjustments may bias, usually deflate, group differences. Some tools therefore have the option of preserving the difference between study groups, e.g. using a two-way ANOVA model to simultaneously estimate both group and batch effects. Unfortunately, this approach may systematically induce incorrect group differences in downstream analyses when groups are distributed between the batches in an unbalanced manner. The scientific community seems to be largely unaware of how this approach may lead to false discoveries.

Molecular subtyping for clinically defined breast cancer subgroups.

INTRODUCTION: Breast cancer is commonly classified into intrinsic molecular subtypes. Standard gene centering is routinely done prior to molecular subtyping, but it can produce inaccurate classifications when the distribution of clinicopathological characteristics in the study cohort differs from that of the training cohort used to derive the classifier. METHODS: We propose a subgroup-specific gene-centering method to perform molecular subtyping on a study cohort that has a skewed distribution of clinicopathological characteristics relative to the training cohort. On such a study cohort, we center each gene on a specified percentile, where the percentile is determined from a subgroup of the training cohort with clinicopathological characteristics similar to the study cohort. We demonstrate our method using the PAM50 classifier and its associated University of North Carolina (UNC) training cohort. We considered study cohorts with skewed clinicopathological characteristics, including subgroups composed of a single prototypic subtype of the UNC-PAM50 training cohort (n = 139), an external estrogen receptor (ER)-positive cohort (n = 48) and an external triple-negative cohort (n = 77). RESULTS: Subgroup-specific gene centering improved prediction performance with the accuracies between 77% and 100%, compared to accuracies between 17% and 33% from standard gene centering, when applied to the prototypic tumor subsets of the PAM50 training cohort. It reduced classification error rates on the ER-positive (11% versus 28%; P = 0.0389), the ER-negative (5% versus 41%; P < 0.0001) and the triple-negative (11% versus 56%; P = 0.1336) subgroups of the PAM50 training cohort. In addition, it produced higher accuracy for subtyping study cohorts composed of varying proportions of ER-positive versus ER-negative cases. Finally, it increased the percentage of assigned luminal subtypes on the external ER-positive cohort and basal-like subtype on the external triple-negative cohort. CONCLUSIONS: Gene centering is often necessary to accurately apply a molecular subtype classifier. Compared with standard gene centering, our proposed subgroup-specific gene centering produced more accurate molecular subtype assignments in a study cohort with skewed clinicopathological characteristics relative to the training cohort.

Integrated analysis reveals microRNA networks coordinately expressed with key proteins in breast cancer.

BACKGROUND: The role played by microRNAs in the deregulation of protein expression in breast cancer is only partly understood. To gain insight, the combined effect of microRNA and mRNA expression on protein expression was investigated in three independent data sets. METHODS: Protein expression was modeled as a multilinear function of powers of mRNA and microRNA expression. The model was first applied to mRNA and protein expression for 105 selected cancer-associated genes and to genome-wide microRNA expression from 283 breast tumors. The model considered both the effect of one microRNA at a time and all microRNAs combined. In the latter case the Lasso penalized regression method was applied to detect the simultaneous effect of multiple microRNAs. RESULTS: An interactome map for breast cancer representing all direct and indirect associations between the expression of microRNAs and proteins was derived. A pattern of extensive coordination between microRNA and protein expression in breast cancer emerges, with multiple clusters of microRNAs being associated with multiple clusters of proteins. Results were subsequently validated in two independent breast cancer data sets. A number of the microRNA-protein associations were functionally validated in a breast cancer cell line. CONCLUSIONS: A comprehensive map is derived for the co-expression in breast cancer of microRNAs and 105 proteins with known roles in cancer, after filtering out the in-cis effect of mRNA expression. The analysis suggests that group action by several microRNAs to deregulate the expression of proteins is a common modus operandi in breast cancer.

A statistical model of ChIA-PET data for accurate detection of chromatin 3D interactions.

Identification of three-dimensional (3D) interactions between regulatory elements across the genome is crucial to unravel the complex regulatory machinery that orchestrates proliferation and differentiation of cells. ChIA-PET is a novel method to identify such interactions, where physical contacts between regions bound by a specific protein are quantified using next-generation sequencing. However, determining the significance of the observed interaction frequencies in such datasets is challenging, and few methods have been proposed. Despite the fact that regions that are close in linear genomic distance have a much higher tendency to interact by chance, no methods to date are capable of taking such dependency into account. Here, we propose a statistical model taking into account the genomic distance relationship, as well as the general propensity of anchors to be involved in contacts overall. Using both real and simulated data, we show that the previously proposed statistical test, based on Fisher's exact test, leads to invalid results when data are dependent on genomic distance. We also evaluate our method on previously validated cell-line specific and constitutive 3D interactions, and show that relevant interactions are significant, while avoiding over-estimating the significance of short nearby interactions.

Systematic assessment of prognostic gene signatures for breast cancer shows distinct influence of time and ER status.

BACKGROUND: The aim was to assess and compare prognostic power of nine breast cancer gene signatures (Intrinsic, PAM50, 70-gene, 76-gene, Genomic-Grade-Index, 21-gene-Recurrence-Score, EndoPredict, Wound-Response and Hypoxia) in relation to ER status and follow-up time. METHODS: A gene expression dataset from 947 breast tumors was used to evaluate the signatures for prediction of Distant Metastasis Free Survival (DMFS). A total of 912 patients had available DMFS status. The recently published METABRIC cohort was used as an additional validation set. RESULTS: Survival predictions were fairly concordant across most signatures. Prognostic power declined with follow-up time. During the first 5 years of followup, all signatures except for Hypoxia were predictive for DMFS in ER-positive disease, and 76-gene, Hypoxia and Wound-Response were prognostic in ER-negative disease. After 5 years, the signatures had little prognostic power. Gene signatures provide significant prognostic information beyond tumor size, node status and histological grade. CONCLUSIONS: Generally, these signatures performed better for ER-positive disease, indicating that risk within each ER stratum is driven by distinct underlying biology. Most of the signatures were strong risk predictors for DMFS during the first 5 years of follow-up. Combining gene signatures with histological grade or tumor size, could improve the prognostic power, perhaps also of long-term survival.

Interplay of choline metabolites and genes in patient-derived breast cancer xenografts.

INTRODUCTION: Dysregulated choline metabolism is a well-known feature of breast cancer, but the underlying mechanisms are not fully understood. In this study, the metabolomic and transcriptomic characteristics of a large panel of human breast cancer xenograft models were mapped, with focus on choline metabolism. METHODS: Tumor specimens from 34 patient-derived xenograft models were collected and divided in two. One part was examined using high-resolution magic angle spinning (HR-MAS) MR spectroscopy while another part was analyzed using gene expression microarrays. Expression data of genes encoding proteins in the choline metabolism pathway were analyzed and correlated to the levels of choline (Cho), phosphocholine (PCho) and glycerophosphocholine (GPC) using Pearson's correlation analysis. For comparison purposes, metabolic and gene expression data were collected from human breast tumors belonging to corresponding molecular subgroups. RESULTS: Most of the xenograft models were classified as basal-like (N = 19) or luminal B (N = 7). These two subgroups showed significantly different choline metabolic and gene expression profiles. The luminal B xenografts were characterized by a high PCho/GPC ratio while the basal-like xenografts were characterized by highly variable PCho/GPC ratio. Also, Cho, PCho and GPC levels were correlated to expression of several genes encoding proteins in the choline metabolism pathway, including choline kinase alpha (CHKA) and glycerophosphodiester phosphodiesterase domain containing 5 (GDPD5). These characteristics were similar to those found in human tumor samples. CONCLUSION: The higher PCho/GPC ratio found in luminal B compared with most basal-like breast cancer xenograft models and human tissue samples do not correspond to results observed from in vitro studies. It is likely that microenvironmental factors play a role in the in vivo regulation of choline metabolism. Cho, PCho and GPC were correlated to different choline pathway-encoding genes in luminal B compared with basal-like xenografts, suggesting that regulation of choline metabolism may vary between different breast cancer subgroups. The concordance between the metabolic and gene expression profiles from xenograft models with breast cancer tissue samples from patients indicates that these xenografts are representative models of human breast cancer and represent relevant models to study tumor metabolism in vivo.

Compact representation of k-mer de Bruijn graphs for genome read assembly.

BACKGROUND: Processing of reads from high throughput sequencing is often done in terms of edges in the de Bruijn graph representing all k-mers from the reads. The memory requirements for storing all k-mers in a lookup table can be demanding, even after removal of read errors, but can be alleviated by using a memory efficient data structure. RESULTS: The FM-index, which is based on the Burrows-Wheeler transform, provides an efficient data structure providing a searchable index of all substrings from a set of strings, and is used to compactly represent full genomes for use in mapping reads to a genome: the memory required to store this is in the same order of magnitude as the strings themselves. However, reads from high throughput sequences mostly have high coverage and so contain the same substrings multiple times from different reads. I here present a modification of the FM-index, which I call the kFM-index, for indexing the set of k-mers from the reads. For DNA sequences, this requires 5 bit of information for each vertex of the corresponding de Bruijn subgraph, i.e. for each different k-1-mer, plus some additional overhead, typically 0.5 to 1 bit per vertex, for storing the equivalent of the FM-index for walking the underlying de Bruijn graph and reproducing the actual k-mers efficiently. CONCLUSIONS: The kFM-index could replace more memory demanding data structures for storing the de Bruijn k-mer graph representation of sequence reads. A Java implementation with additional technical documentation is provided which demonstrates the applicability of the data structure (http://folk.uio.no/einarro/Projects/KFM-index/).

Predicting physical interactions between protein complexes.

Protein complexes enact most biochemical functions in the cell. Dynamic interactions between protein complexes are frequent in many cellular processes. As they are often of a transient nature, they may be difficult to detect using current genome-wide screens. Here, we describe a method to computationally predict physical interactions between protein complexes, applied to both humans and yeast. We integrated manually curated protein complexes and physical protein interaction networks, and we designed a statistical method to identify pairs of protein complexes where the number of protein interactions between a complex pair is due to an actual physical interaction between the complexes. An evaluation against manually curated physical complex-complex interactions in yeast revealed that 50% of these interactions could be predicted in this manner. A community network analysis of the highest scoring pairs revealed a biologically sensible organization of physical complex-complex interactions in the cell. Such analyses of proteomes may serve as a guide to the discovery of novel functional cellular relationships.