Contrasting DCIS and invasive breast cancer by subtype suggests basal-like DCIS as distinct lesions

Bergholtz, Helga; Lien, Tonje G.; Swanson, David; Frigessi, Arnoldo; Daidone, Maria Grazia; Tost, Jörg; Wärnberg, Fredrik; Sørlie, Thérese

doi:10.1038/s41523-020-0167-x

Cited by 31 publications

(29 citation statements)

References 68 publications

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“…To assess how well DCIS aligned with the PAM50 classification, we evaluated the correlation of each DCIS tumor to the centroid of its assigned subtype and compared the correlations against those observed in 1109 IBCs from The Cancer Genome Atlas (TCGA; Figure 3A ). The median correlation (Spearman’s like DCIS samples was significantly lower than basal-like IBC samples (median IBC = 0.75; median DCIS = 0.38; P Bonferroni = 8.01x10; Wilcoxon rank sum test), as previously shown (Bergholtz et al, 2020). Significantly decreased correlation was also observed for luminal A (median IBC = 0.60; median DCIS = 0.50; P Bonferroni = 3.13x10) and normal-like subtypes (median IBC = 0.60; median DCIS = 0.49; P Bonferroni = 6.21x10).…”

Section: Resultssupporting

confidence: 84%

DCIS genomic signatures define biology and clinical outcome: Human Tumor Atlas Network (HTAN) analysis of TBCRC 038 and RAHBT cohorts

Strand

Rivero-Gutiérrez

Houlahan

et al. 2021

Preprint

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Ductal carcinoma in situ (DCIS) is the most common precursor of invasive breast cancer (IBC), with variable propensity for progression. We have performed the first multiscale, integrated profiling of DCIS with clinical outcomes by analyzing 677 DCIS samples from 481 patients with 7.1 years median follow-up from the Translational Breast Cancer Research Consortium (TBCRC) 038 study and the Resource of Archival Breast Tissue (RAHBT) cohorts. We made observations on DNA, RNA, and protein expression, and generated a de novo clustering scheme for DCIS that represents a fundamental transcriptomic organization at this early stage of breast neoplasia. Distinct stromal expression patterns and immune cell compositions were identified. We found RNA expression patterns that correlate with later events. Our multiscale approach employed in situ methods to generate a spatially resolved atlas of breast precancers, where complementary modalities can be directly compared and correlated with conventional pathology findings, disease states, and clinical outcome.

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Section: Resultssupporting

confidence: 84%

DCIS genomic signatures define biology and clinical outcome: Human Tumor Atlas Network (HTAN) analysis of TBCRC 038 and RAHBT cohorts

Strand

Rivero-Gutiérrez

Houlahan

et al. 2021

Preprint

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“…A major proportion of breast cancer samples are Luminal A, and this is also the case in the training cohort. We have previously shown that Luminal A DCIS and IBC are highly similar at a molecular level, while basal-like DCIS differ substantially from basal-like IBC (Bergholtz et al, 2020). Stratification by subtype prior to creating the models could yield different results and identify genes and biological processes relevant within each subtype, but this approach would, in our high-dimensional analysis, be limited due to rather low sample size of the current cohorts.…”

Section: Discussionmentioning

confidence: 99%

“…As a model training data, we used multi-omics molecular and genomic profiles combined from three patient cohorts, Oslo2, Uppsala, and Milan ( Muggerud et al, 2010 ; Fleischer et al, 2014 ; Lesurf et al, 2016 ; Aure et al, 2017 ; Bergholtz et al, 2020 ). Each patient cohort contains three levels of omics data from gene expression microarrays, DNA methylation, and DNA copy number.…”

Section: Methodsmentioning

confidence: 99%

“…The CpGs were located both inside and outside CpG islands and were enriched in both enhancers and promoters. The second option used gene-level processing, where we calculated a methylation score to represent each protein-coding gene using a principal component analysis (PCA), taking into account the variation of all CpGs mapped to a gene, similarly as before ( Bergholtz et al, 2020 ). The second option leads to gene-level features, whereas in the first option, each gene can be associated with hundreds of CpGs.…”

Section: Methodsmentioning

confidence: 99%

“…It has been shown that the “intrinsic” breast cancer subtypes (luminal A, luminal B, HER2-enriched, and basal-like) have prognostic significance, and a supervised risk predictor was developed based on the intrinsic subtypes and clinical information ( Parker et al, 2009 ). We have also previously performed comparative analyses across the breast cancer subtypes and identified molecular differences between DCIS and IBC for subtype-specific disease progression ( Bergholtz et al, 2020 ). In these subtype-stratified analyses, prominent molecular differences were identified especially for the basal-like DCIS, which was found to be less proliferative and showed a higher degree of differentiation than the basal-like IBC.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Lien

Bergholtz

et al. 2021

Front. Genet.

Self Cite

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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.

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The polytomous discrimination index for prediction involving multistate processes under intermittent observation

Jiang

Cook

2022

Statistics in Medicine

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With the increasing importance of predictive modeling in health research comes the need for methods to rigorously assess predictive accuracy. We consider the problem of evaluating the accuracy of predictive models for nominal outcomes when outcome data are coarsened at random. We first consider the problem in the context of a multinomial response modeled by polytomous logistic regression. Attention is then directed to the motivating setting in which class membership corresponds to the state occupied in a multistate disease process at a time horizon of interest. Here, class (state) membership may be unknown at the time horizon since disease processes are under intermittent observation. We propose a novel extension to the polytomous discrimination index to address this and evaluate the predictive accuracy of an intensity-based model in the context of a study involving patients with arthritis from a registry at the University of Toronto Centre for Prognosis Studies in Rheumatic Diseases.

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Contrasting DCIS and invasive breast cancer by subtype suggests basal-like DCIS as distinct lesions

Cited by 31 publications

References 68 publications

DCIS genomic signatures define biology and clinical outcome: Human Tumor Atlas Network (HTAN) analysis of TBCRC 038 and RAHBT cohorts

DCIS genomic signatures define biology and clinical outcome: Human Tumor Atlas Network (HTAN) analysis of TBCRC 038 and RAHBT cohorts

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

The polytomous discrimination index for prediction involving multistate processes under intermittent observation

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