Comparative analysis of the molecular subtype landscape in canine and human mammary gland tumors

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Tailored graphical lasso for data integration in gene network reconstruction.

BACKGROUND: Identifying gene interactions is a topic of great importance in genomics, and approaches based on network models provide a powerful tool for studying these. Assuming a Gaussian graphical model, a gene association network may be estimated from multiomic data based on the non-zero entries of the inverse covariance matrix. Inferring such biological networks is challenging because of the high dimensionality of the problem, making traditional estimators unsuitable. The graphical lasso is constructed for the estimation of sparse inverse covariance matrices in such situations, using [Formula: see text]-penalization on the matrix entries. The weighted graphical lasso is an extension in which prior biological information from other sources is integrated into the model. There are however issues with this approach, as it naïvely forces the prior information into the network estimation, even if it is misleading or does not agree with the data at hand. Further, if an associated network based on other data is used as the prior, the method often fails to utilize the information effectively. RESULTS: We propose a novel graphical lasso approach, the tailored graphical lasso, that aims to handle prior information of unknown accuracy more effectively. We provide an R package implementing the method, tailoredGlasso. Applying the method to both simulated and real multiomic data sets, we find that it outperforms the unweighted and weighted graphical lasso in terms of all performance measures we consider. In fact, the graphical lasso and weighted graphical lasso can be considered special cases of the tailored graphical lasso, and a parameter determined by the data measures the usefulness of the prior information. We also find that among a larger set of methods, the tailored graphical is the most suitable for network inference from high-dimensional data with prior information of unknown accuracy. With our method, mRNA data are demonstrated to provide highly useful prior information for protein-protein interaction networks. CONCLUSIONS: The method we introduce utilizes useful prior information more effectively without involving any risk of loss of accuracy should the prior information be misleading

Multi-Omics Marker Analysis Enables Early Prediction of Breast Tumor Progression

Ductal carcinoma in situ (DCIS) is a preinvasive form of breast cancer with a highly variable potential of becoming invasive and affecting mortality of the patients. Due to the lack of accurate markers of disease progression, many women with detected DCIS are currently overtreated. To distinguish those DCIS cases who are likely to require therapy from those who should be left untreated, there is a need for robust and predictive biomarkers extracted from molecular or genetic profiles. We developed a supervised machine learning approach that implements multi-omics feature selection and model regularization for the identification of biomarker combinations that could be used to distinguish low-risk DCIS lesions from those with a higher likelihood of progression. To investigate the genetic heterogeneity of disease progression, we applied this approach to 40 pure DCIS and 259 invasive breast cancer (IBC) samples profiled with genome-wide transcriptomics, DNA methylation, and DNA copy number variation. Feature selection using the multi-omics Lasso-regularized algorithm identified both known genes involved in breast cancer development, as well as novel markers for early detection. Even though the gene expression-based model features led to the highest classification accuracy alone, methylation data provided a complementary source of features and improved especially the sensitivity of correctly classifying DCIS cases. We also identified a number of repeatedly misclassified DCIS cases when using either the expression or methylation markers. A small panel of 10 gene markers was able to distinguish DCIS and IBC cases with high accuracy in nested cross-validation (AU-ROC = 0.99). The marker panel was not specific to any of the established breast cancer subtypes, suggesting that the 10-gene signature may provide a subtype-agnostic and cost-effective approach for breast cancer detection and patient stratification. We further confirmed high accuracy of the 10-gene signature in an external validation cohort (AU-ROC = 0.95), profiled using distinct transcriptomic assay, hence demonstrating robustness of the risk signature.Peer reviewe

Deciphering molecular heterogeneity and relevance of subtypes in breast cancer progression

Breast cancer affects over 3500 women in Norway every year. Since the introduction of mammographic screening, early stage breast cancers are diagnosed more frequently and many of these are non-invasive with low potential of spreading and affecting mortality. Since there are currently no reliable biomarkers that predict the ability of a non-invasive cancer to invade, many patients are likely overtreated. In this thesis, Helga Bergholtz and colleagues have performed molecular studies of different stages of breast cancer and characterized the heterogeneity of tumor progression. They have analyzed normal breast tissue biopsies from two time-points and studied the transcriptomic changes over time in relation to mammographic density. They have also analyzed gene expression, DNA copy number aberrations and mutations in a chemically induced mammary gland tumor model in mice and show that this may be a suitable model for studying a special type of breast cancer called claudin-low. The thesis has special emphasis on a non-invasive cancer called ductal carcinoma in situ (DCIS). They have performed extensive molecular characterization of DCIS compared to invasive breast tumors using gene expression, DNA copy number and DNA methylation data. Since molecular subtypes have strong clinical relevance in breast cancer, the analyses were performed stratified by subtype. This revealed that basal-like DCIS and basal-like invasive tumors were distinctly different, and that basal-like DCIS might not be precursors of basal-like invasive tumors. Also, they have performed targeted sequencing of different cellular compartments in mixed DCIS/invasive tumors and show a similar mutation spectrum in the two tumor compartments. The findings in this thesis may impact the understanding of tumor progression and contribute to more precise therapy for patients with DCIS

Re-definition of claudin-low as a breast cancer phenotype

The claudin-low breast cancer subtype is defined by gene expression characteristics and encompasses a remarkably diverse range of breast tumors. Here, we investigate genomic, transcriptomic, and clinical features of claudin-low breast tumors. We show that claudin-low is not simply a subtype analogous to the intrinsic subtypes (basal-like, HER2-enriched, luminal A, luminal B and normal-like) as previously portrayed, but is a complex additional phenotype which may permeate breast tumors of various intrinsic subtypes. Claudin-low tumors are distinguished by low genomic instability, mutational burden and proliferation levels, and high levels of immune and stromal cell infiltration. In other aspects, claudin-low tumors reflect characteristics of their intrinsic subtype. Finally, we explore an alternative method for identifying claudin-low tumors and thereby uncover potential weaknesses in the established claudin-low classifier. In sum, these findings elucidate the heterogeneity in claudin-low breast tumors, and substantiate a re-definition of claudin-low as a cancer phenotype

Claudin-low-like mouse mammary tumors show distinct transcriptomic patterns uncoupled from genomic drivers

Background Claudin-low breast cancer is a molecular subtype associated with poor prognosis and without targeted treatment options. The claudin-low subtype is defined by certain biological characteristics, some of which may be clinically actionable, such as high immunogenicity. In mice, the medroxyprogesterone acetate (MPA) and 7,12-dimethylbenzanthracene (DMBA)-induced mammary tumor model yields a heterogeneous set of tumors, a subset of which display claudin-low features. Neither the genomic characteristics of MPA/DMBA-induced claudin-low tumors nor those of human claudin-low breast tumors have been thoroughly explored. Methods The transcriptomic characteristics and subtypes of MPA/DMBA-induced mouse mammary tumors were determined using gene expression microarrays. Somatic mutations and copy number aberrations in MPA/DMBA-induced tumors were identified from whole exome sequencing data. A publicly available dataset was queried to explore the genomic characteristics of human claudin-low breast cancer and to validate findings in the murine tumors. Results Half of MPA/DMBA-induced tumors showed a claudin-low-like subtype. All tumors carried mutations in known driver genes. While the specific genes carrying mutations varied between tumors, there was a consistent mutational signature with an overweight of T>A transversions in TG dinucleotides. Most tumors carried copy number aberrations with a potential oncogenic driver effect. Overall, several genomic events were observed recurrently; however, none accurately delineated claudin-low-like tumors. Human claudin-low breast cancers carried a distinct set of genomic characteristics, in particular a relatively low burden of mutations and copy number aberrations. The gene expression characteristics of claudin-low-like MPA/DMBA-induced tumors accurately reflected those of human claudin-low tumors, including epithelial-mesenchymal transition phenotype, high level of immune activation, and low degree of differentiation. There was an elevated expression of the immunosuppressive genes PTGS2 (encoding COX-2) and CD274 (encoding PD-L1) in human and murine claudin-low tumors. Conclusions Our findings show that the claudin-low breast cancer subtype is not demarcated by specific genomic aberrations, but carries potentially targetable characteristics warranting further research

Claudin-low-like mouse mammary tumors show distinct transcriptomic patterns uncoupled from genomic drivers

Background Claudin-low breast cancer is a molecular subtype associated with poor prognosis and without targeted treatment options. The claudin-low subtype is defined by certain biological characteristics, some of which may be clinically actionable, such as high immunogenicity. In mice, the medroxyprogesterone acetate (MPA) and 7,12-dimethylbenzanthracene (DMBA)-induced mammary tumor model yields a heterogeneous set of tumors, a subset of which display claudin-low features. Neither the genomic characteristics of MPA/DMBA-induced claudin-low tumors nor those of human claudin-low breast tumors have been thoroughly explored. Methods The transcriptomic characteristics and subtypes of MPA/DMBA-induced mouse mammary tumors were determined using gene expression microarrays. Somatic mutations and copy number aberrations in MPA/DMBA-induced tumors were identified from whole exome sequencing data. A publicly available dataset was queried to explore the genomic characteristics of human claudin-low breast cancer and to validate findings in the murine tumors. Results Half of MPA/DMBA-induced tumors showed a claudin-low-like subtype. All tumors carried mutations in known driver genes. While the specific genes carrying mutations varied between tumors, there was a consistent mutational signature with an overweight of T>A transversions in TG dinucleotides. Most tumors carried copy number aberrations with a potential oncogenic driver effect. Overall, several genomic events were observed recurrently; however, none accurately delineated claudin-low-like tumors. Human claudin-low breast cancers carried a distinct set of genomic characteristics, in particular a relatively low burden of mutations and copy number aberrations. The gene expression characteristics of claudin-low-like MPA/DMBA-induced tumors accurately reflected those of human claudin-low tumors, including epithelial-mesenchymal transition phenotype, high level of immune activation, and low degree of differentiation. There was an elevated expression of the immunosuppressive genes PTGS2 (encoding COX-2) and CD274 (encoding PD-L1) in human and murine claudin-low tumors. Conclusions Our findings show that the claudin-low breast cancer subtype is not demarcated by specific genomic aberrations, but carries potentially targetable characteristics warranting further research

Comparable cancer-relevant mutation profiles in synchronous ductal carcinoma in situ and invasive breast cancer

Background: Ductal carcinoma in situ (DCIS) comprises a diverse group of preinvasive lesions in the breast and poses a considerable clinical challenge due to lack of markers of progression. Genomic alterations are to a large extent similar in DCIS and invasive carcinomas, although differences in copy number aberrations, gene expression patterns, and mutations exist. In mixed tumors with synchronous invasive breast cancer (IBC) and DCIS, it is still unclear to what extent invasive tumor cells are directly derived from the DCIS cells. Aim: Our aim was to compare cancer-relevant mutation profiles of different cellular compartments in mixed DCIS/IBC and pure DCIS tumors. Methods and results: We performed targeted sequencing of 50 oncogenes in microdissected tissue from three different epithelial cell compartments (in situ, invasive, and normal adjacent epithelium) from 26 mixed breast carcinomas. In total, 44 tissue samples (19 invasive, 16 in situ, 9 normal) were subjected to sequencing using the Ion Torrent platform and the AmpliSeq Cancer Hotspot Panel v2. For comparison, 10 additional, pure DCIS lesions were sequenced. Across all mixed samples, we detected 23 variants previously described in cancer. The most commonly affected genes were TP53, PIK3CA, and ERBB2. The PIK3CA:p.H1047R variant was found in nine samples from six patients. Most variants detected in invasive compartments were also found in the corresponding in situ cell compartment indicating a clonal relationship between the tumor stages. A lower frequency of variants were observed in pure DCIS lesions. Conclusion: Similar mutation profiles between in situ and invasive cell compartments indicate a similar origin of the two tumor stages in mixed breast tumors. The lower number of potential driver variants found in pure DCIS compared with the in situ cell compartments of mixed tumors may imply that pure DCIS is captured earlier in the path of progression to invasive disease

A Longitudinal Study of the Association between Mammographic Density and Gene Expression in Normal Breast Tissue

High mammographic density (MD) is associated with a 4–6 times increase in breast cancer risk. For post-menopausal women,MD often decreases over time, but little is known about the underlying biological mechanisms. MD reflects breast tissuecomposition, and may be associated with microenvironment subtypes previously identified in tumor-adjacent normal tissue. Currently, these subtypes have not been explored in normal breast tissue. We obtained biopsies from breasts of healthy women at two different time points several years apart and performed microarray gene expression analysis. At time point 1, 65 samples with both MD and gene expression were available. At time point 2, gene expression and MD data were available from 17 women, of which 11 also had gene expression data available from the first time point. We validated findings from our previous study; negative correlation between RBL1 and MD in post-menopausal women, indicating involvement of the TGFβ pathway. We also found that breast tissue samples from women with a large decrease in MD sustained higher expression of genes in the histone family H4. In addition, we explored the previously defined active and inactive microenvironment subtypes and demonstrated that normal breast samples of the active subtype had characteristics similar to the claudin-low breast cancer subtype. Breast biopsies from healthy women are challenging to obtain, but despite a limited sample size, we have identified possible mechanisms relevant for changes in breast biology and MD over time that may be of importance for breast cancer risk and tumor initiation

A Longitudinal Study of the Association between Mammographic Density and Gene Expression in Normal Breast Tissue

High mammographic density (MD) is associated with a 4–6 times increase in breast cancer risk. For post-menopausal women, MD often decreases over time, but little is known about the underlying biological mechanisms. MD reflects breast tissue composition, and may be associated with microenvironment subtypes previously identified in tumor-adjacent normal tissue. Currently, these subtypes have not been explored in normal breast tissue.We obtained biopsies from breasts of healthy women at two different time points several years apart and performed microarray gene expression analysis. At time point 1, 65 samples with both MD and gene expression were available. At time point 2, gene expression and MD data were available from 17 women, of which 11 also had gene expression data available from the first time point. We validated findings from our previous study; negative correlation between RBL1 and MD in post-menopausal women, indicating involvement of the TGFβ pathway. We also found that breast tissue samples from women with a large decrease in MD sustained higher expression of genes in the histone family H4. In addition, we explored the previously defined active and inactive microenvironment subtypes and demonstrated that normal breast samples of the active subtype had characteristics similar to the claudin-low breast cancer subtype. Breast biopsies from healthy women are challenging to obtain, but despite a limited sample size, we have identified possible mechanisms relevant for changes in breast biology and MD over time that may be of importance for breast cancer risk and tumor initiation