Building flexible and robust analysis frameworks for molecular subtyping of cancers

Molecular subtyping is essential to infer tumor aggressiveness and predict prognosis. In practice, tumor profiling requires in-depth knowledge of bioinformatics tools involved in the processing and analysis of the generated data. Additionally, data incompatibility (e.g., microarray vs. RNA sequencing data) and technical and uncharacterized biological variance between training and test data can pose challenges in classifying individual samples. In this article, we provide a roadmap for implementing bioinformatics frameworks for molecular profiling of human cancers in a clinical diagnostic setting. We describe a framework for integrating several methods for quality control, normalization, batch correction, classification, and reporting and a use case of the framework in breast cancer.

[1]  Stephen R. Williams,et al.  A single-cell and spatially resolved atlas of human breast cancers , 2021, Nature Genetics.

[2]  Zhiwei Cao,et al.  Rank-in: enabling integrative analysis across microarray and RNA-seq for cancer , 2021, Nucleic acids research.

[3]  S. Paik,et al.  An Improved, Assay Platform Agnostic, Absolute Single Sample Breast Cancer Subtype Classifier , 2020, Cancers.

[4]  Angela N. Brooks,et al.  Visualizing and interpreting cancer genomics data via the Xena platform , 2020, Nature Biotechnology.

[5]  M. Suntsova,et al.  RNA Sequencing in Comparison to Immunohistochemistry for Measuring Cancer Biomarkers in Breast Cancer and Lung Cancer Specimens , 2020, Biomedicines.

[6]  K. Duesing,et al.  DNA Methylation Cancer Biomarkers: Translation to the Clinic , 2019, Front. Genet..

[7]  T. Sørlie,et al.  Re-definition of claudin-low as a breast cancer phenotype , 2019, bioRxiv.

[8]  Wei Wang,et al.  DeepCC: a novel deep learning-based framework for cancer molecular subtype classification , 2019, Oncogenesis.

[9]  Melissa J. Davis,et al.  Single sample scoring of molecular phenotypes , 2018, BMC Bioinformatics.

[10]  L. Olsen,et al.  Using microarray‐based subtyping methods for breast cancer in the era of high‐throughput RNA sequencing , 2018, Molecular oncology.

[11]  Joshua M. Stuart,et al.  Resource Genomic , Pathway Networ k , and Immunologic Features Distinguishing Squamous Carcinomas Graphical , 2018 .

[12]  Christian Brueffer,et al.  Clinical Value of RNA Sequencing–Based Classifiers for Prediction of the Five Conventional Breast Cancer Biomarkers: A Report From the Population-Based Multicenter Sweden Cancerome Analysis Network—Breast Initiative , 2018, JCO precision oncology.

[13]  T. Hansen,et al.  Molecular subtyping of breast cancer improves identification of both high and low risk patients , 2018, Acta oncologica.

[14]  Seokjun Seo,et al.  Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification , 2017, IJCAI.

[15]  Edward F. Chang,et al.  Tumor Evolution of Glioma-Intrinsic Gene Expression Subtypes Associates with Immunological Changes in the Microenvironment. , 2017, Cancer cell.

[16]  Harold L. Moses,et al.  Refinement of Triple-Negative Breast Cancer Molecular Subtypes: Implications for Neoadjuvant Chemotherapy Selection , 2016, PloS one.

[17]  K. Yen,et al.  IDH mutations in cancer and progress toward development of targeted therapeutics. , 2016, Annals of oncology : official journal of the European Society for Medical Oncology.

[18]  Qunhua Li,et al.  A semi-parametric statistical model for integrating gene expression profiles across different platforms , 2016, BMC Bioinformatics.

[19]  A. Arance,et al.  Clinical implications of the intrinsic molecular subtypes of breast cancer. , 2015, Breast.

[20]  E. Mardis,et al.  Development and verification of the PAM50-based Prosigna breast cancer gene signature assay , 2015, BMC Medical Genomics.

[21]  Prasad Patil,et al.  Test set bias affects reproducibility of gene signatures , 2015, Bioinform..

[22]  C. Sotiriou,et al.  Transfer of clinically relevant gene expression signatures in breast cancer: from Affymetrix microarray to Illumina RNA-Sequencing technology , 2014, BMC Genomics.

[23]  M. Hallett,et al.  The prognostic ease and difficulty of invasive breast carcinoma. , 2014, Cell reports.

[24]  J. Pietenpol,et al.  Identification and use of biomarkers in treatment strategies for triple‐negative breast cancer subtypes , 2014, The Journal of pathology.

[25]  G. Getz,et al.  Inferring tumour purity and stromal and immune cell admixture from expression data , 2013, Nature Communications.

[26]  D. Haussler,et al.  The Somatic Genomic Landscape of Glioblastoma , 2013, Cell.

[27]  Joshua M. Stuart,et al.  The Cancer Genome Atlas Pan-Cancer analysis project , 2013, Nature Genetics.

[28]  Jeffrey J Meyer,et al.  Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 2012. (5) , 2013 .

[29]  Ellen T. Gelfand,et al.  The Genotype-Tissue Expression (GTEx) project , 2013, Nature Genetics.

[30]  Sven Laur,et al.  Robust rank aggregation for gene list integration and meta-analysis , 2012, Bioinform..

[31]  F. Bertucci,et al.  A refined molecular taxonomy of breast cancer , 2011, Oncogene.

[32]  Christopher R. Cabanski,et al.  Lung Squamous Cell Carcinoma mRNA Expression Subtypes Are Reproducible, Clinically Important, and Correspond to Normal Cell Types , 2010, Clinical Cancer Research.

[33]  Jason I. Herschkowitz,et al.  Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer , 2010, Breast Cancer Research.

[34]  L. De Cecco,et al.  Impact of biospecimens handling on biomarker research in breast cancer , 2009, BMC Cancer.

[35]  M. J. van de Vijver,et al.  Microarray-Based Determination of Estrogen Receptor, Progesterone Receptor, and HER2 Receptor Status in Breast Cancer , 2009, Clinical Cancer Research.

[36]  Ben S. Wittner,et al.  Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1 , 2009, Nature.

[37]  A. Nobel,et al.  Supervised risk predictor of breast cancer based on intrinsic subtypes. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[38]  R. Tothill,et al.  Novel Molecular Subtypes of Serous and Endometrioid Ovarian Cancer Linked to Clinical Outcome , 2008, Clinical Cancer Research.

[39]  Benjamin M. Bolstad,et al.  affy - analysis of Affymetrix GeneChip data at the probe level , 2004, Bioinform..

[40]  Christian A. Rees,et al.  Molecular portraits of human breast tumours , 2000, Nature.

[41]  M. Hallett,et al.  Absolute assignment of breast cancer intrinsic molecular subtype. , 2015, Journal of the National Cancer Institute.

[42]  E. Kohn,et al.  PARP Inhibitors for BRCA1/2 mutation-associated and BRCA-like malignancies. , 2014, Annals of oncology : official journal of the European Society for Medical Oncology.

[43]  Steven J. M. Jones,et al.  Comprehensive molecular portraits of human breast tumours , 2013 .

[44]  R. Gentleman,et al.  Bioinformatics Applications Note Arrayqualitymetrics—a Bioconductor Package for Quality Assessment of Microarray Data , 2022 .

[45]  Cheng Li,et al.  Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.