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

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 identified 812 genes associated with ipsilateral recurrence within 5 years from treatment and developed a classifier that was predictive of DCIS or IBC recurrence in both cohorts. Pathways associated with recurrence include proliferation, immune response, and metabolism. Distinct stromal expression patterns and immune cell compositions were identified. 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. HIGHLIGHTS ⍰ Development of a new classifier for DCIS recurrence or progression ⍰ Outcome associated pathways identified across multiple data types and compartments ⍰ Four stroma-specific signatures identified ⍰ CNAs characterize DCIS subgroups associated with high risk invasive cancers

[1]  P. Kristel,et al.  Genomic profiling defines variable clonal relatedness between invasive breast cancer and primary ductal carcinoma in situ , 2021, medRxiv.

[2]  K. Chin,et al.  Genomic Alterations during the In Situ to Invasive Ductal Breast Carcinoma Transition Shaped by the Immune System , 2020, Molecular Cancer Research.

[3]  Tonje G. Lien,et al.  Contrasting DCIS and invasive breast cancer by subtype suggests basal-like DCIS as distinct lesions , 2020, npj Breast Cancer.

[4]  K. Polyak,et al.  Immune Escape during Breast Tumor Progression , 2020, Cancer Immunology Research.

[5]  J. Reis-Filho,et al.  Whole-Exome Sequencing Analysis of the Progression from Non–Low-Grade Ductal Carcinoma In Situ to Invasive Ductal Carcinoma , 2020, Clinical Cancer Research.

[6]  Yul Ri Chung,et al.  Immune microenvironment in ductal carcinoma in situ: a comparison with invasive carcinoma of the breast , 2020, Breast Cancer Research.

[7]  S. Killcoyne,et al.  Genomic copy number predicts esophageal cancer years before transformation , 2020, bioRxiv.

[8]  Yuan Wang,et al.  Single-Cell Map of Diverse Immune Phenotypes in the Metastatic Brain Tumor Microenvironment of Non Small Cell Lung Cancer , 2019, bioRxiv.

[9]  Nicholas J. Wang,et al.  Genomic landscape of ductal carcinoma in situ and association with progression , 2019, Breast Cancer Research and Treatment.

[10]  Ruud H. Brakenhoff,et al.  ACE: absolute copy number estimation from low-coverage whole-genome sequencing data , 2019, Bioinform..

[11]  L. Shevde,et al.  The Tumor Microenvironment Innately Modulates Cancer Progression. , 2019, Cancer research.

[12]  Bin Liu,et al.  Dynamics of breast cancer relapse reveal late recurring ER-positive genomic subgroups , 2019, Nature.

[13]  S. Srivastava,et al.  HER2-mediated GLI2 stabilization promotes anoikis resistance and metastasis of breast cancer cells. , 2019, Cancer letters.

[14]  J. Jonkers,et al.  Cancer-associated fibroblasts as key regulators of the breast cancer tumor microenvironment , 2018, Cancer and Metastasis Reviews.

[15]  Arnoldo Frigessi,et al.  A Bayesian Two-Way Latent Structure Model for Genomic Data Integration Reveals Few Pan-Genomic Cluster Subtypes in a Breast Cancer Cohort , 2018, bioRxiv.

[16]  Ambrose J. Carr,et al.  Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment , 2018, Cell.

[17]  Jesper Eisfeldt,et al.  Sarek: A portable workflow for whole-genome sequencing analysis of germline and somatic variants , 2018, bioRxiv.

[18]  Adrian V. Lee,et al.  An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics , 2018, Cell.

[19]  Phuong Dao,et al.  Single-cell Map of Diverse Immune Phenotypes Driven by the Tumor Microenvironment , 2018 .

[20]  Mary E. Edgerton,et al.  Multiclonal Invasion in Breast Tumors Identified by Topographic Single Cell Sequencing , 2018, Cell.

[21]  R. West,et al.  Gene expression profiling of single cells from archival tissue with laser-capture microdissection and Smart-3SEQ , 2017, Genome Research.

[22]  T. Sørlie,et al.  Immune Escape in Breast Cancer During In Situ to Invasive Carcinoma Transition. , 2017, Cancer discovery.

[23]  G. Parmigiani,et al.  Stromal and epithelial transcriptional map of initiation progression and metastatic potential of human prostate cancer , 2017, Nature Communications.

[24]  K. Yoon-Flannery,et al.  Evaluating the Risk of Upstaging HER2-Positive DCIS to Invasive Breast Cancer , 2017, Annals of Surgical Oncology.

[25]  Jianxin Wang,et al.  BPG: Seamless, automated and interactive visualization of scientific data , 2019, BMC Bioinformatics.

[26]  Kylie L. Gorringe,et al.  Relationship of the Breast Ductal Carcinoma In Situ Immune Microenvironment with Clinicopathological and Genetic Features , 2017, Clinical Cancer Research.

[27]  Francesca Martella,et al.  Impact of hormonal status on outcome of ductal carcinoma in situ treated with breast-conserving surgery plus radiotherapy: Long-term experience from two large-institutional series. , 2017, Breast.

[28]  L. Villani,et al.  The Relationships between HER2 Overexpression and DCIS Characteristics , 2017, The breast journal.

[29]  E. Rutgers,et al.  Finding the balance between over- and under-treatment of ductal carcinoma in situ (DCIS). , 2017, Breast.

[30]  R. Kalluri The biology and function of fibroblasts in cancer , 2016, Nature Reviews Cancer.

[31]  T. Sørlie,et al.  Molecular Features of Subtype-Specific Progression from Ductal Carcinoma In Situ to Invasive Breast Cancer. , 2016, Cell reports.

[32]  Joel S. Parker,et al.  Genefu: an R/Bioconductor package for computation of gene expression-based signatures in breast cancer , 2016, Bioinform..

[33]  L. Esserman,et al.  Characterizing the immune microenvironment in high-risk ductal carcinoma in situ of the breast , 2016, Breast Cancer Research and Treatment.

[34]  Jaime Rodriguez-Canales,et al.  A Patient-Derived, Pan-Cancer EMT Signature Identifies Global Molecular Alterations and Immune Target Enrichment Following Epithelial-to-Mesenchymal Transition , 2015, Clinical Cancer Research.

[35]  A. Sahin,et al.  A Molecular Portrait of High-Grade Ductal Carcinoma In Situ. , 2015, Cancer research.

[36]  Kylie L. Gorringe,et al.  Copy number analysis of ductal carcinoma in situ with and without recurrence , 2015, Modern Pathology.

[37]  Piet Demeester,et al.  FlowSOM: Using self‐organizing maps for visualization and interpretation of cytometry data , 2015, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[38]  W. Huber,et al.  Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.

[39]  E. Batlle,et al.  TGF-beta in CAF-mediated tumor growth and metastasis. , 2014, Seminars in cancer biology.

[40]  M. Reginato,et al.  The Oncogene HER2/neu (ERBB2) Requires the Hypoxia-inducible Factor HIF-1 for Mammary Tumor Growth and Anoikis Resistance* , 2013, The Journal of Biological Chemistry.

[41]  Serafim Batzoglou,et al.  Genome evolution during progression to breast cancer , 2013, Genome research.

[42]  Justin Guinney,et al.  GSVA: gene set variation analysis for microarray and RNA-Seq data , 2013, BMC Bioinformatics.

[43]  A. Schäffer,et al.  Single-cell genetic analysis of ductal carcinoma in situ and invasive breast cancer reveals enormous tumor heterogeneity yet conserved genomic imbalances and gain of MYC during progression. , 2012, The American journal of pathology.

[44]  F. Markowetz,et al.  The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups , 2012, Nature.

[45]  Ian G. Campbell,et al.  Identification of copy number alterations associated with the progression of DCIS to invasive ductal carcinoma , 2012, Hereditary Cancer in Clinical Practice.

[46]  David Venet,et al.  Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome , 2011, PLoS Comput. Biol..

[47]  G. Getz,et al.  GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers , 2011, Genome Biology.

[48]  D. Allred,et al.  Ductal carcinoma in situ: terminology, classification, and natural history. , 2010, Journal of the National Cancer Institute. Monographs.

[49]  M. DePristo,et al.  The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. , 2010, Genome research.

[50]  A. Krasnitz,et al.  Genomic Architecture Characterizes Tumor Progression Paths and Fate in Breast Cancer Patients , 2010, Science Translational Medicine.

[51]  Karla Kerlikowske,et al.  Biomarker expression and risk of subsequent tumors after initial ductal carcinoma in situ diagnosis. , 2010, Journal of the National Cancer Institute.

[52]  F. Pépin,et al.  Stromal gene expression predicts clinical outcome in breast cancer , 2008, Nature Medicine.

[53]  J. Thiery,et al.  Integrated Genomic and Transcriptomic Analysis of Ductal Carcinoma In situ of the Breast , 2008, Clinical Cancer Research.

[54]  A. Vincent-Salomon,et al.  Bone marrow micrometastasis in breast cancer: review of detection methods, prognostic impact and biological issues , 2007, Journal of Clinical Pathology.

[55]  Wessel N. van Wieringen,et al.  CGHcall: calling aberrations for array CGH tumor profiles , 2007, Bioinform..

[56]  M. Hussein,et al.  Analysis of the mononuclear inflammatory cell infiltrate in the normal breast, benign proliferative breast disease, in situ and infiltrating ductal breast carcinomas: preliminary observations , 2006, Journal of Clinical Pathology.

[57]  Jun Yao,et al.  Combined cDNA array comparative genomic hybridization and serial analysis of gene expression analysis of breast tumor progression. , 2006, Cancer research.

[58]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[59]  S. Devries,et al.  Patterns of Chromosomal Alterations in Breast Ductal Carcinoma In situ , 2004, Clinical Cancer Research.

[60]  Rameen Beroukhim,et al.  Molecular characterization of the tumor microenvironment in breast cancer. , 2004, Cancer cell.

[61]  T. Hagemann,et al.  A Role for Endothelin-2 and Its Receptors in Breast Tumor Cell Invasion , 2004, Cancer Research.

[62]  Pablo Tamayo,et al.  Metagenes and molecular pattern discovery using matrix factorization , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[63]  R. Salunga,et al.  Gene expression profiles of human breast cancer progression , 2003, Proceedings of the National Academy of Sciences of the United States of America.

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

[65]  C. Osborne,et al.  HER-2/neu in node-negative breast cancer: prognostic significance of overexpression influenced by the presence of in situ carcinoma. , 1992, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.