Ole Christian Lingjærde

Allele-specific copy number analysis of tumors

We present an allele-specific copy number analysis of the in vivo breast cancer genome. We describe a unique bioinformatics approach, ASCAT (allele-specific copy number analysis of tumors), to accurately dissect the allele-specific copy number of solid tumors, simultaneously estimating and adjusting for both tumor ploidy and nonaberrant cell admixture. This allows calculation of “ASCAT profiles” (genome-wide allele-specific copy-number profiles) from which gains, losses, copy number-neutral events, and loss of heterozygosity (LOH) can accurately be determined. In an early-stage breast carcinoma series, we observe aneuploidy (>2.7n) in 45% of the cases and an average nonaberrant cell admixture of 49%. By aggregation of ASCAT profiles across our series, we obtain genomic frequency distributions of gains and losses, as well as genome-wide views of LOH and copy number-neutral events in breast cancer. In addition, the ASCAT profiles reveal differences in aberrant tumor cell fraction, ploidy, gains, losses, LOH, and copy number-neutral events between the five previously identified molecular breast cancer subtypes. Basal-like breast carcinomas have a significantly higher frequency of LOH compared with other subtypes, and their ASCAT profiles show large-scale loss of genomic material during tumor development, followed by a whole-genome duplication, resulting in near-triploid genomes. Finally, from the ASCAT profiles, we construct a genome-wide map of allelic skewness in breast cancer, indicating loci where one allele is preferentially lost, whereas the other allele is preferentially gained. We hypothesize that these alternative alleles have a different influence on breast carcinoma development.

Landscape of somatic mutations in 560 breast cancer whole-genome sequences

We analysed whole genome sequences of 560 breast cancers to advance understanding of the driver mutations conferring clonal advantage and the mutational processes generating somatic mutations. 93 protein-coding cancer genes carried likely driver mutations. Some non-coding regions exhibited high mutation frequencies but most have distinctive structural features probably causing elevated mutation rates and do not harbour driver mutations. Mutational signature analysis was extended to genome rearrangements and revealed 12 base substitution and six rearrangement signatures. Three rearrangement signatures, characterised by tandem duplications or deletions, appear associated with defective homologous recombination based DNA repair: one with deficient BRCA1 function; another with deficient BRCA1 or BRCA2 function; the cause of the third is unknown. This analysis of all classes of somatic mutation across exons, introns and intergenic regions highlights the repertoire of cancer genes and mutational processes operative, and progresses towards a comprehensive account of the somatic genetic basis of breast cancer.

Predicting survival from microarray data—a comparative study

MOTIVATION Survival prediction from gene expression data and other high-dimensional genomic data has been subject to much research during the last years. These kinds of data are associated with the methodological problem of having many more gene expression values than individuals. In addition, the responses are censored survival times. Most of the proposed methods handle this by using Coxs proportional hazards model and obtain parameter estimates by some dimension reduction or parameter shrinkage estimation technique. Using three well-known microarray gene expression data sets, we compare the prediction performance of seven such methods: univariate selection, forward stepwise selection, principal components regression (PCR), supervised principal components regression, partial least squares regression (PLS), ridge regression and the lasso. RESULTS Statistical learning from subsets should be repeated several times in order to get a fair comparison between methods. Methods using coefficient shrinkage or linear combinations of the gene expression values have much better performance than the simple variable selection methods. For our data sets, ridge regression has the overall best performance. AVAILABILITY Matlab and R code for the prediction methods are available at http://www.med.uio.no/imb/stat/bmms/software/microsurv/.

Principles and methods of integrative genomic analyses in cancer

Combined analyses of molecular data, such as DNA copy-number alteration, mRNA and protein expression, point to biological functions and molecular pathways being deregulated in multiple cancers. Genomic, metabolomic and clinical data from various solid cancers and model systems are emerging and can be used to identify novel patient subgroups for tailored therapy and monitoring. The integrative genomics methodologies that are used to interpret these data require expertise in different disciplines, such as biology, medicine, mathematics, statistics and bioinformatics, and they can seem daunting. The objectives, methods and computational tools of integrative genomics that are available to date are reviewed here, as is their implementation in cancer research.

CGH-Explorer: a program for analysis of array-CGH data

SUMMARY CGH-Explorer is a program for visualization and statistical analysis of microarray-based comparative genomic hybridization (array-CGH) data. The program has preprocessing facilities, tools for graphical exploration of individual arrays or groups of arrays, and tools for statistical identification of regions of amplification and deletion.

Genomic architecture characterizes tumor progression paths and fate in breast cancer patients

This study demonstrates the relation among structural genomic alterations, molecular subtype, and clinical behavior and shows that an objective score of genomic complexity can provide independent prognostic information in breast cancer. Form and Malfunction Breast cancer is an iniquitous disease with a panoply of predisposing genetic and environmental causes, the details of which have yet to be fully understood. One of every four women will be diagnosed with breast cancer, hence the early and accurate identification of specific tumor features that may affect overall survival is imperative in achieving an optimal prognosis. A widely appreciated taxonomy in the breast cancer field has enabled the molecular discernment of five pathological subtypes; however, as research dives deeper into the chromosomal underpinnings of the disease, new classifiers are needed to augment what is known with key structural details to create a more vivid tumor landscape. Now, Russnes and colleagues have generated new algorithms that can estimate the specific genomic region as well as the architectural type of rearrangement—gains or losses of chromosome arms. A cohort of breast tumors was scored using this method, and all tumors with complex rearrangements had more whole chromosome arms affected than those without complex rearrangement. Moreover, there was an overlapping correlation with the molecular subtyping features of the tumors, and the score could confer prognostic power. Distinct molecular subtypes of breast carcinomas have been identified, but translation into clinical use has been limited. We have developed two platform-independent algorithms to explore genomic architectural distortion using array comparative genomic hybridization data to measure (i) whole-arm gains and losses [whole-arm aberration index (WAAI)] and (ii) complex rearrangements [complex arm aberration index (CAAI)]. By applying CAAI and WAAI to data from 595 breast cancer patients, we were able to separate the cases into eight subgroups with different distributions of genomic distortion. Within each subgroup data from expression analyses, sequencing and ploidy indicated that progression occurs along separate paths into more complex genotypes. Histological grade had prognostic impact only in the luminal-related groups, whereas the complexity identified by CAAI had an overall independent prognostic power. This study emphasizes the relation among structural genomic alterations, molecular subtype, and clinical behavior and shows that objective score of genomic complexity (CAAI) is an independent prognostic marker in breast cancer.

Presence of bone marrow micrometastasis is associated with different recurrence risk within molecular subtypes of breast cancer

Expression profiles of primary breast tumors were investigated in relation to disseminated tumor cells (DTCs) in bone marrow (BM) in order to increase our understanding of the dissemination process. Tumors were classified into five pre‐defined molecular subtypes, and presence of DTC identified (at median 85 months follow‐up) a subgroup of luminal A patients with particular poor outcome (p=0.008). This was not apparent for other tumor subtypes. Gene expression profiles associated with DTC and with systemic relapse for luminal A patients were identified. This study suggests that DTC in BM differentially distinguishes clinical outcome in patients with luminal A type tumors and that DTC‐associated gene expression analysis may identify genes of potential importance in tumor dissemination.

Glycan gene expression signatures in normal and malignant breast tissue; possible role in diagnosis and progression

Glycosylation is the stepwise procedure of covalent attachment of oligosaccharide chains to proteins or lipids, and alterations in this process have been associated with malignant transformation. Simultaneous analysis of the expression of all glycan‐related genes clearly gives the advantage of enabling a comprehensive view of the genetic background of the glycobiological changes in cancer cells. Studies focusing on the expression of the whole glycome have now become possible, which prompted us to review the present knowledge on glycosylation in relation to breast cancer diagnosis and progression, in the light of available expression data from tumors and breast tissue of healthy individuals. We used various data resources to select a set of 419 functionally relevant genes involved in synthesis, degradation and binding of N‐linked and O‐linked glycans, Lewis antigens, glycosaminoglycans (chondroitin, heparin and keratan sulfate in addition to hyaluronan) and glycosphingolipids. Such glycans are involved in a number of processes relevant to carcinogenesis, including regulation of growth factors/growth factor receptors, cell–cell adhesion and motility as well as immune system modulation. Expression analysis of these glycan‐related genes revealed that mRNA levels for many of them differ significantly between normal and malignant breast tissue. An associative analysis of these genes in the context of current knowledge of their function in protein glycosylation and connection(s) to cancer indicated that synthesis, degradation and adhesion mediated by glycans may be altered drastically in mammary carcinomas. Although further analysis is needed to assess how changes in mRNA levels of glycan genes influence a cells glycome and the precise role that such altered glycan structures play in the pathogenesis of the disease, lessons drawn from this study may help in determining directions for future research in the rapidly‐developing field of glycobiology.

Regularized local linear prediction of chaotic time series

Abstract Local linear prediction, based on the ordinary least squares (OLS) approach, is one of several methods that have been applied to prediction of chaotic time series. Apart from potential linearization errors, a drawback of this approach is the high variance of the predictions under certain conditions. Here, a different set of so-called linear regularization techniques, originally derived to solve ill-posed regression problems, are compared to OLS for chaotic time series corrupted by additive measurement noise. These methods reduce the variance compared to OLS, but introduce more bias. A main tool of analysis is the singular value decomposition (SVD), and a key to successful regularization is to damp the higher order SVD components. Several of the methods achieve improved prediction compared to OLS for synthetic noise-corrupted data from well-known chaotic systems. Similar results were found for real-world data from the R-R intervals of ECG signals. Good results are also obtained for real sunspot data, compared to published predictions using nonlinear techniques.

Plasma microRNAs predicting clinical outcome in metastatic colorectal cancer patients receiving first‐line oxaliplatin‐based treatment

The conventional first‐line chemotherapy for metastatic colorectal cancer (mCRC) consists of fluorouracil (5‐FU) in combination with either oxaliplatin or irinotecan. We have explored microRNAs (miRNAs) in plasma as potential predictive markers to oxaliplatin‐based chemotherapy. The expression of 742 miRNAs was examined in plasma samples from 24 mCRC patients (12 responders and 12 non‐responders) before onset and after four cycles of 5‐FU/oxaliplatin. The top differentially expressed miRNAs between responders and non‐responders were selected for further analysis in a validation cohort of 150 patients. In the validation cohort, there was a significant overrepresentation of miRNAs with higher mean expression in the non‐responder group than in the responder group before treatment (p < 0.002). Moreover, we found three miRNAs (miR‐106a, miR‐484, and miR‐130b) to be significantly differentially expressed before treatment (p = 0.008, 0.008, and 0.008, respectively). All three miRNAs were upregulated in non‐responders. High expression of miR‐27b, miR‐148a, and miR‐326 were associated with decreased progression‐free survival (Hazard ratios (HR) of 1.4 (95% CI 1.1–1.8, p = 0.004), 1.3 (95% CI 1.1–1.6, p = 0.007), and 1.4 (95% CI 1.1–1.8, p = 0.008), respectively). miR‐326 was also associated with decreased overall survival (HR 1.5 (95% CI 1.1–2.0, p = 0.003)). There were no significantly differentially expressed miRNAs in association with clinical outcome after four cycles of chemotherapy. The present study demonstrates that plasma miRNAs analyzed before treatment may serve as non‐invasive markers predicting outcome in mCRC patients treated with 5‐FU and oxaliplatin‐based chemotherapy.