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

Abstract

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.

A survey on multi-omics-based cancer diagnosis using machine learning with the potential application in gastrointestinal cancer

“…Logistic regression (LR), support vector machine (SVM), random forest (RF) and Xgboost are widely used traditional ML models ( 30 , 31 , 47 ), before feeding the data to them, it generally needs to reduce the dimensionality of high-dimensional features of multiple omics data based on feature extraction methods such as nearest component analysis (NCA) ( 19 , 23 ) and principal component analysis (PCA) ( 21 ), and then concatenate the dimensionality-reduced features and finally feed the concatenated features to the model.…”

“…Furthermore, some biomarkers have been used for cervical cancer including miR-215-5p, miR-192-5p, KAT2B, PCNA, and CD86 ( 29 ). Biomarkers are widely identified by using the methods of feature selection and feature importance ranking in traditional ML ( 10 , 30 , 31 ). When analyzing the contribution of each feature in multiple omics sources, the feature will be set to 0 in turn, and then the performance of the classification or regression model will be calculated, and it will be compared to performance using all features ( 10 ).…”

MALINC1 an Immune-Related Long Non-Coding RNA Associated with Early-Stage Breast Cancer Progression

“…Non-coding RNAs (ncRNA) are of crucial relevance in many important biological processes [ 5 , 6 , 7 ]. In particular, the class of transcripts known as long non-coding RNAs (ncRNAs >200 nt long) have been recognized to be tissue and cell type-specific, playing key roles in regulating chromatin dynamics, gene expression, growth, and differentiation [ 8 , 9 ].…”

Preoperative Breast Immune Prognostic Index as Prognostic Factor Predicts the Clinical Outcomes of Breast Cancer Patients Receiving Neoadjuvant Chemotherapy

“…However, mixed findings are also present in the immunotherapy literature, with the most noticeable one being that substantial heterogeneity in response is observed among different tumors ( 10 ). To address this issue, potential predictive biomarkers such as gene signatures and multi-omics have been used to further evaluate the prognosis of different tumors ( 11 ). However, obtaining and analyzing these biomarkers are often time-consuming, inconvenient, and expensive, which in turn could limit their clinical applications.…”