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

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http://hdl.handle.net/10138/332492

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Xu , H , Lien , T , Bergholtz , H , Fleischer , T , Djerroudi , L , Vincent-Salomon , A , Sorlie , T & Aittokallio , T 2021 , ' Multi-Omics Marker Analysis Enables Early Prediction of Breast Tumor Progression ' , Frontiers in Genetics , vol. 12 , 670749 . https://doi.org/10.3389/fgene.2021.670749

Julkaisun nimi: Multi-Omics Marker Analysis Enables Early Prediction of Breast Tumor Progression
Tekijä: Xu, Haifeng; Lien, Tonje; Bergholtz, Helga; Fleischer, Thomas; Djerroudi, Lounes; Vincent-Salomon, Anne; Sorlie, Therese; Aittokallio, Tero
Tekijän organisaatio: Institute for Molecular Medicine Finland
Helsinki Institute for Information Technology
Bioinformatics
Päiväys: 2021-06-03
Kieli: eng
Sivumäärä: 14
Kuuluu julkaisusarjaan: Frontiers in Genetics
ISSN: 1664-8021
DOI-tunniste: https://doi.org/10.3389/fgene.2021.670749
URI: http://hdl.handle.net/10138/332492
Tiivistelmä: 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.
Avainsanat: risk signature
breast cancer
disease progression
early detection
machine learning
CARCINOMA IN-SITU
DUCTAL CARCINOMA
LOCAL RECURRENCE
FOLLOW-UP
CANCER
EXPRESSION
GRADE
BIOPSY
MODELS
CELLS
1184 Genetics, developmental biology, physiology
Vertaisarvioitu: Kyllä
Tekijänoikeustiedot: cc_by
Pääsyrajoitteet: openAccess
Rinnakkaistallennettu versio: publishedVersion


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