Relevance of systems biological approach in the differential diagnosis of invasive lobular carcinoma &amp; invasive ductal carcinoma

Ragunath, P K; Reddy, B Srikanth; Abhinand, P A; Ahmed, Shiek S. S. J.

doi:10.6026/97320630008359

Cited by 4 publications

(3 citation statements)

References 14 publications

(15 reference statements)

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“…These two sub-types show similarity in certain features such as tumour site, tumour size, stage and grade, but have different metastatic patterns, characteristic histology and malignant calcifications [5], [4]. IDC starts from ducts and spreads to the breast fatty tissue, whereas ILC is restricted to milk producing lobules [6]. These two sub-types are also discriminated at the molecular level with differential expression of gene encoding vimentin, cathespin D, thrombospondin, E-cadherin, vascular endothelial growth factor, cytokeratin 8, and cyclin A.…”

Section: Introductionmentioning

confidence: 99%

“…[4], [7], [8], [9],[10], [11], [12]. The pathological differences between the two sub-types arises as a result of separate gene regulatory networks, which warrants further exploration for the development of appropriate diagnostic and therapeutic treatment strategy [6]. According to reports, 75% cases of invasive breast carcinoma cases are accounted by IDC, however, advanced treatment of IDC patients still remains a challenge due to lack of molecular targets for IDC treatment [13], [14].…”

Section: Introductionmentioning

confidence: 99%

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Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning

Roy

Kumar

Mittal

et al. 2019

Preprint

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Phone: +91 26743007Fax: +91 26742316 Email -dinesh@icgeb.res.in (DG)Early detection of breast cancer and its correct stage determination are important for prognosis and rendering appropriate personalized clinical treatment to breast cancer patients.However, despite considerable efforts and progress, there is a need to identify the specific genomic factors responsible for, or accompanying Invasive Ductal Carcinoma (IDC) progression stages, which can aid the determination of the correct cancer stages. We have developed two-class machine-learning classification models to differentiate the early and late stages of invasive ductal carcinoma. The prediction models are trained with RNA-seq gene expression profiles representing different IDC stages of 610 patients, obtained from The Cancer Genome Atlas (TCGA). Different supervised learning algorithms were trained and evaluated with an enriched model learning, facilitated by different feature selection methods.We also developed a machine-learning classifier trained on the same datasets with training sets reduced data corresponding to IDC driver genes. Based on these two classifiers, we have developed a web-server Duct-BRCA-CSP to predict early stage from late stages of IDC based on input RNA-seq gene expression profiles. The analysis conducted by us also enables deeper insights into the stage-dependent molecular events accompanying breast ductal carcinoma progression. The server is publicly available at http://bioinfo.icgeb.res.in/duct-BRCA-CSP. Key PointsDifferent supervised machine-learning algorithms such as Random Forest, SVM and Naive Bayes were trained with enriched features of the TCGA RNA-seq datasets selected for the study.We have developed two-class classification models, trained with relevant gene expression profiles to efficiently discriminate between the early and late IDC stages.Finally, we also developed a web server using python scikit-learn to provide freely available GUI based access to the machine learning models developed by us. The server is publicly available at http://bioinfo.icgeb.res.in/duct-BRCA-CSP.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning

Roy

Kumar

Mittal

et al. 2019

Preprint

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“…A recent large study of lobular features also categorized several mutations in the PTEN, TBX3, and FOXA1 genes that typify lobular carcinomas 14 . Additionally, ductal tumors often metastasize to the bone, lung and liver, via lymph glands and blood system, while lobular tumors are inclined to metastasize to genital tracts, the gastrointestinal system, and meninges 10 21 22 23 24 25 26 .…”

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confidence: 99%

Molecular Classification of Lobular Carcinoma of the Breast

Zuo

Huang

et al. 2017

Sci Rep

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The morphology of breast tumors is complicated and diagnosis can be difficult. We present here a novel diagnostic model which we validate on both array-based and RNA sequencing platforms which reliably distinguishes this tumor type across multiple cohorts. We also examine how this molecular classification predicts sensitivity to common chemotherapeutics in cell-line based assays. A total of 1845 invasive breast cancer cases in six cohorts were collected, split into discovery and validation cohorts, and a classifier was created and compared to pathological diagnosis, grade and survival. In the validation cohorts the concordance of predicted diagnosis with a pathological diagnosis was 92%, and 97% when inconclusively classified cases were excluded. Tumor-derived cell lines were classified with the model as having predominantly ductal or lobular-like molecular physiologies, and sensitivity of these lines to relevant compounds was analyzed. A diagnostic tool can be created that reliably distinguishes lobular from ductal carcinoma and allows the classification of cell lines on the basis of molecular profiles associated with these tumor types. This tool may assist in improved diagnosis and aid in explorations of the response of lobular type breast tumor models to different compounds.

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Breast cancer stage prediction: a computational approach guided by transcriptome analysis

Athira

Gopakumar²

2022

Mol Genet Genomics

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Relevance of systems biological approach in the differential diagnosis of invasive lobular carcinoma & invasive ductal carcinoma

Cited by 4 publications

References 14 publications

Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning

Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning

Molecular Classification of Lobular Carcinoma of the Breast

Breast cancer stage prediction: a computational approach guided by transcriptome analysis

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