Genome and transcriptome delineation of two major oncogenic pathways governing invasive ductal breast cancer development

The clinical and genetic features in patients coexisting primary breast and thyroid cancers

Background

We attempted to examine the clinical characteristics in patients with breast cancer (BC) and thyroid cancer (TC); explore the potential mechanisms of tumorigenesis and progression.

Methods

Using the Surveillance, Epidemiology, and End Result Program-9 (SEER-9) database, a retrospective study (1975-2017) was conducted on patients with BC and TC. We identified the common differentially expressed genes involved in BC and TC using the Gene Expression Omnibus database (GEO). Immunohistochemical staining (IHC) was performed to verify the expression of the hit gene in patients with co-occurrence of BC and TC. Using The Cancer Genome Atlas (TCGA) database, the relationship between gene expression and clinicopathological characters was determined. Gene set enrichment analysis (GSEA) was used to identify the pathways enriched in BC and TC.

Results

BC patients had a higher predisposition to develop TC (standardized incidence ratio, SIR: 1.29) and vice-versa (SIR: 1.12). Most of these patients were differentiated thyroid carcinoma (DTC) and hormone receptor (HR) - positive BC. The mRNA expression of COMP (Cartilage oligomeric matrix protein) was significantly overexpressed in BC and TC by analyzing the GEO database. The protein expression of COMP was increased in both BC and TC tissues obtained from the same patients validated by IHC. COMP was correlated with worse OS in BC (stage II-IV) and TC; it was the independent factor for prognosis of BC. GSEA indicated that the estrogen response and epithelial-mesenchymal transition (EMT) pathways were significantly enriched in both TC- and BC- COMP overexpressed groups.

Conclusion

The co-occurrence risk of BC and TC in the same individual is higher than in the general population. Overexpression of COMP could promote oncogenesis and progression in patients with BC and TC through estrogen signaling and EMT pathways.

Understanding Breast Cancers through Spatial and High-Resolution Visualization Using Imaging Technologies

Breast cancer is the most common cancer affecting women worldwide. Although many analyses and treatments have traditionally targeted the breast cancer cells themselves, recent studies have focused on investigating entire cancer tissues, including breast cancer cells. To understand the structure of breast cancer tissues, including breast cancer cells, it is necessary to investigate the three-dimensional location of the cells and/or proteins comprising the tissues and to clarify the relationship between the three-dimensional structure and malignant transformation or metastasis of breast cancers. In this review, we aim to summarize the methods for analyzing the three-dimensional structure of breast cancer tissue, paying particular attention to the recent technological advances in the combination of the tissue-clearing method and optical three-dimensional imaging. We also aimed to identify the latest methods for exploring the relationship between the three-dimensional cell arrangement in breast cancer tissues and the gene expression of each cell. Finally, we aimed to describe the three-dimensional imaging features of breast cancer tissues using noninvasive photoacoustic imaging methods.

Identification of Recurrent Chromosome Breaks Underlying Structural Rearrangements in Mammary Cancer Cell Lines

Cancer genomes are characterized by the accumulation of small-scale somatic mutations as well as large-scale chromosomal deletions, amplifications, and complex structural rearrangements. This characteristic is at least partially dependent on the ability of cancer cells to undergo recurrent chromosome breakage. In order to address the extent to which chromosomal structural rearrangement breakpoints correlate with recurrent DNA double-strand breaks (DSBs), we simultaneously mapped chromosome structural variation breakpoints (using whole-genome DNA-seq) and spontaneous DSB formation (using Break-seq) in the estrogen receptor (ER)-positive breast cancer cell line MCF-7 and a non-cancer control breast epithelium cell line MCF-10A. We identified concurrent DSBs and structural variation breakpoints almost exclusively in the pericentromeric region of chromosome 16q in MCF-7 cells. We fine-tuned the identification of copy number variation breakpoints on 16q. In addition, we detected recurrent DSBs that occurred in both MCF-7 and MCF-10A. We propose a model for DSB-driven chromosome rearrangements that lead to the translocation of 16q, likely with 10q, and the eventual 16q loss that does not involve the pericentromere of 16q. We present evidence from RNA-seq data that select genes, including SHCBP1, ORC6, and MYLK3, which are immediately downstream from the 16q pericentromere, show heightened expression in MCF-7 cell line compared to the control. Data published by The Cancer Genome Atlas show that all three genes have increased expression in breast tumor samples. We found that SHCBP1 and ORC6 are both strong poor prognosis and treatment outcome markers in the ER-positive breast cancer cohort. We suggest that these genes are potential oncogenes for breast cancer progression. The search for tumor suppressor loss that accompanies the 16q loss ought to be augmented by the identification of potential oncogenes that gained expression during chromosomal rearrangements.

ABI1-based expression signature predicts breast cancer metastasis and survival

Despite the current standard of care, breast cancer remains one of the leading causes of mortality in women worldwide, thus emphasizing the need for better predictive and therapeutic targets. ABI1 is associated with poor survival and an aggressive breast cancer phenotype, although its role in tumorigenesis, metastasis, and the disease outcome remains to be elucidated. Here, we define the ABI1-based seven-gene prognostic signature that predicts survival of metastatic breast cancer patients; ABI1 is an essential component of the signature. Genetic disruption of Abi1 in primary breast cancer tumors of PyMT mice led to significant reduction of the number and size of lung metastases in a gene dose-dependent manner. The disruption of Abi1 resulted in deregulation of the WAVE complex at the mRNA and protein levels in mouse tumors. In conclusion, ABI1 is a prognostic metastatic biomarker in breast cancer. We demonstrate, for the first time, that lung metastasis is associated with an Abi1 gene dose and specific gene expression aberrations in primary breast cancer tumors. These results indicate that targeting ABI1 may provide a therapeutic advantage in breast cancer patients.

Decision Theory versus Conventional Statistics for Personalized Therapy of Breast Cancer

Estrogen and progesterone receptors being present or not represents one of the most important biomarkers for therapy selection in breast cancer patients. Conventional measurement by immunohistochemistry (IHC) involves errors, and numerous attempts have been made to increase precision by additional information from gene expression. This raises the question of how to fuse information, in particular, if there is disagreement. It is the primary domain of Dempster–Shafer decision theory (DST) to deal with contradicting evidence on the same item (here: receptor status), obtained through different techniques. DST is widely used in technical settings, such as self-driving cars and aviation, and is also promising to deliver significant advantages in medicine. Using data from breast cancer patients already presented in previous work, we focus on comparing DST with classical statistics in this work, to pave the way for its application in medicine. First, we explain how DST not only considers probabilities (a single number per sample), but also incorporates uncertainty in a concept of ‘evidence’ (two numbers per sample). This allows for very powerful displays of patient data in so-called ternary plots, a novel and crucial advantage for medical interpretation. Results are obtained according to conventional statistics (ODDS) and, in parallel, according to DST. Agreement and differences are evaluated, and the particular merits of DST discussed. The presented application demonstrates how decision theory introduces new levels of confidence in diagnoses derived from medical data.

Identification of genes and pathways related to breast cancer metastasis in an integrated cohort

BACKGROUND

Breast cancer is the most common malignant disease in women. Metastasis is the most common cause of death from this cancer. Screening genes related to breast cancer metastasis may help elucidate the mechanisms governing metastasis and identify molecular targets for antimetastatic therapy. The development of advanced algorithms enables us to perform cross-study analysis to improve the robustness of the results.

MATERIALS AND METHODS

Ten data sets meeting our criteria for differential expression analyses were obtained from the Gene Expression Omnibus (GEO) database. Among these data sets, five based on the same platform were formed into a large cohort using the XPN algorithm. Differentially expressed genes (DEGs) associated with breast cancer metastasis were identified using the differential expression via distance synthesis (DEDS) algorithm. A cross-platform method was employed to verify these DEGs in all ten selected data sets. The top 50 validated DEGs are represented with heat maps. Based on the validated DEGs, Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Protein interaction (PPI) networks were constructed to further illustrate the direct and indirect associations among the DEGs. Survival analysis was performed to explore whether these genes can affect breast cancer patient prognosis.

RESULTS

A total of 817 DEGs were identified using the DEDS algorithm. Of these DEGs, 450 genes were validated by the second algorithm. Enriched KEGG pathway terms demonstrated that these 450 DEGs may be involved in the cell cycle and oocyte meiosis in addition to their functions in ECM-receptor interaction and protein digestion and absorption. PPI network analysis for the proteins encoded by the DEGs indicated that these genes may be primarily involved in the cell cycle and extracellular matrix. In particular, several genes played roles in multiple signalling pathways and were related to patient survival. These genes were also observed to be targetable in the CTD2 database.

CONCLUSIONS

Our study analysed multiple cross-platform data sets using two different algorithms, helping elucidate the molecular mechanisms and identify several potential therapeutic targets of metastatic breast cancer. In addition, several genes exhibited promise for applications in targeted therapy against metastasis in future research.

Decision theory for precision therapy of breast cancer

Correctly estimating the hormone receptor status for estrogen (ER) and progesterone (PGR) is crucial for precision therapy of breast cancer. It is known that conventional diagnostics (immunohistochemistry, IHC) yields a significant rate of wrongly diagnosed receptor status. Here we demonstrate how Dempster Shafer decision Theory (DST) enhances diagnostic precision by adding information from gene expression. We downloaded data of 3753 breast cancer patients from Gene Expression Omnibus. Information from IHC and gene expression was fused according to DST, and the clinical criterion for receptor positivity was re-modelled along DST. Receptor status predicted according to DST was compared with conventional assessment via IHC and gene-expression, and deviations were flagged as questionable. The survival of questionable cases turned out significantly worse (Kaplan Meier p < 1%) than for patients with receptor status confirmed by DST, indicating a substantial enhancement of diagnostic precision via DST. This study is not only relevant for precision medicine but also paves the way for introducing decision theory into OMICS data science.

Lipidomics investigations into the tissue phospholipidomic landscape of invasive ductal carcinoma of the breast

The need of identifying alternative therapeutic targets for invasive ductal carcinoma (IDC) of the breast with high specificity and sensitivity for effective therapeutic intervention is crucial for lowering the risk of fatality. Lipidomics has emerged as a key area for the discovery of potential candidates owing to its several shared pathways between cancer cell proliferation and survival. In the current study, we performed comparative phospholipidomic analysis of IDC, benign and control tissue samples of the breast to identify the significant lipid alterations associated with malignant transformation. A total of 33 each age-matched tissue samples from malignant, benign and control were analyzed to identify the altered phospholipids by using liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM/MS). A combination of univariate and multivariate statistical approaches was used to select the phospholipid species with the highest contribution in group segregation. Furthermore, these altered phospholipids were structurally confirmed by tandem mass spectrometry. A total of 244 phospholipids were detected consistently at quantifiable levels, out of which 32 were significantly altered in IDC of the breast. Moreover, in pairwise comparison of IDC against benign and control samples, 11 phospholipids were found to be significantly differentially expressed. Particularly, LPI 20:3, PE (22:1/22:2), LPE 20:0 and PC (20:4/22:4) were observed to be most significantly associated with IDC tissue samples. Apart from that, we also identified that long-chain unsaturated fatty acids were enriched in the IDC tissue samples as compared to benign and control samples, indicating its possible association with the invasive phenotype.

Elevated mRNA expression levels of DLGAP5 are associated with poor prognosis in breast cancer

Breast cancer is the most commonly diagnosed type of cancer and one of the leading causes of cancer-associated mortality in women. In addition, the underlying molecular mechanisms of the occurrence and development of breast cancer requires further investigation. In the present study, bioinformatics analysis was performed to identify differentially expressed genes (DEGs) between breast cancer and normal breast tissues to investigate the underlying molecular mechanisms. In addition, reverse transcription-quantitative PCR and immunohistochemistry (IHC) were performed to investigate the protein and mRNA expression levels of a specific DEG, discs large-associated protein 5 (DLGAP5). A Cell Counting Kit-8 assay and flow cytometry analysis were used to assess the effects of DLGAP5 on cell proliferation. In total, 85 DEGs were identified in the three Gene Expression Omnibus datasets, including 40 upregulated and 45 downregulated genes. In addition, 30 hub genes were identified following the construction of a protein-protein interaction network, and 28 of the 30 hub genes were established to be indicators of breast cancer prognosis. DLGAP5 was highly expressed in breast cancer specimens, and its expression levels were correlated with clinical stage and lymph node status. In addition, downregulation of DLGAP5 repressed the proliferation of breast cancer MDA-MB-231 cells and induced cell cycle arrest. Additionally, DLGAP5 was identified to be localized in the mitochondria, and the presence of a conserved microtubule-associated proteins 1A/1B light chain 3B-interacting region motif suggested that DLGAP5 may serve a role in mitophagy. The present results demonstrated an association between DLGAP5 expression levels and the clinicopathological characteristics of patients with breast cancer using IHC. In conclusion, DLGAP5 may be a promising target in the diagnosis and treatment of breast cancer.

A qualitative transcriptional signature to reclassify histological grade of ER-positive breast cancer patients

BACKGROUND

Histological grade (HG) is commonly adopted as a prognostic factor for ER-positive breast cancer patients. However, HG evaluation methods, such as the pathological Nottingham grading system, are highly subjective with only 50-85% inter-observer agreements. Specifically, the subjectivity in the pathological assignment of the intermediate grade (HG2) breast cancers, comprising of about half of breast cancer cases, results in uncertain disease outcomes prediction. Here, we developed a qualitative transcriptional signature, based on within-sample relative expression orderings (REOs) of gene pairs, to define HG1 and HG3 and reclassify pathologically-determined HG2 (denoted as pHG2) breast cancer patients.

RESULTS

From the gene pairs with significantly stable REOs in pathologically-determined HG1 (denoted as pHG1) samples and reversely stable REOs in pathologically-determined HG3 (denoted as pHG3) samples, concordantly identified from seven datasets, we extracted a signature which could determine the HG state of samples through evaluating whether the within-sample REOs match with the patterns of the pHG1 REOs or pHG3 REOs. A sample was classified into the HG3 group if at least a half of the REOs of the 10 gene pairs signature within this sample voted for HG3; otherwise, HG1. Using four datasets including samples of early stage (I-II) ER-positive breast cancer patients who accepted surgery only, we validated that this signature was able to reclassify pHG2 patients into HG1 and HG3 groups with significantly different survival time. For the original pHG1 and pHG3 patients, the signature could also more accurately and objectively stratify them into distinct prognostic groups. And the up-regulated and down down-regulated genes in HG1 compared with HG3 involved in cell proliferation and extracellular signal transduction pathways respectively. By comparing with existing signatures, 10-GPS was with prognostic significance and was more aligned with survival of patients especially for pHG2 samples.

CONCLUSIONS

The transcriptional qualitative signature can provide an objective assessment of HG states of ER-positive breast cancer patients, especially for reclassifying patients with pHG2, to assist decision making on clinical therapy.

Screening of core genes and pathways in breast cancer development via comprehensive analysis of multi gene expression datasets

Breast cancer has been the leading cause of cancer-associated mortality in women worldwide. Perturbation of oncogene and tumor suppressor gene expression is generally considered as the fundamental cause of cancer initiation and progression. In the present study, three gene expression datasets containing information of breast cancer and adjacent normal tissues that were detected using traditional gene microarrays were downloaded and batch effects were removed with R programming software. The differentially expressed genes between breast cancer and normal tissue groups were closely associated with cancer development pathways. Interestingly, five pathways, including ‘extracellular matrix-receptor interaction’, ‘peroxisome proliferator-activated receptors signaling pathway’, ‘propanoate metabolism’, ‘pyruvate metabolism’ and ‘regulation of lipolysis in adipocytes’, were thoroughly connected by 10 genes. Patients with upregulation of six of these hub genes (acetyl-CoA carboxylase β, acyl-CoA dehydrogenase medium chain, adiponectin, C1Q and collagen domain containing, acyl-CoA synthetase short chain family member 2, phosphoenolpyruvate carboxykinase 1 and perilipin 1) exhibited improved breast cancer prognosis. Additionally, breast cancer-specific network analysis identified several gene-gene interaction modules. These gene clusters had strong interactions according to the scoring in the whole network, which may be important to the development of breast cancer. In conclusion, the present study may improve the understanding of the mechanisms of breast cancer and provide several valuable prognosis and treatment signatures.

Decreased methylation in the SNAI2 and ADAM23 genes associated with de-differentiation and haematogenous dissemination in breast cancers

BACKGROUND

In breast cancer (BC), deregulation of DNA methylation leads to aberrant expressions and functions of key regulatory genes. In our study, we investigated the relationship between the methylation profiles of genes associated with cancer invasivity and clinico-pathological parameters. In detail, we studied differences in the methylation levels between BC patients with haematogenous and lymphogenous cancer dissemination.

METHODS

We analysed samples of primary tumours (PTs), lymph node metastases (LNMs) and peripheral blood cells (PBCs) from 59 patients with sporadic disseminated BC. Evaluation of the DNA methylation levels of six genes related to invasivity, ADAM23, uPA, CXCL12, TWIST1, SNAI1 and SNAI2, was performed by pyrosequencing.

RESULTS

Among the cancer-specific methylated genes, we found lower methylation levels of the SNAI2 gene in histologic grade 3 tumours (OR = 0.61; 95% CI, 0.39-0.97; P = 0.038) than in fully or moderately differentiated cancers. We also evaluated the methylation profiles in patients with different cancer cell dissemination statuses (positivity for circulating tumour cells (CTCs) and/or LNMs). We detected the significant association between reduced DNA methylation of ADAM23 in PTs and presence of CTCs in the peripheral blood of patients (OR = 0.45; 95% CI, 0.23-0.90; P = 0.023).

CONCLUSION

The relationships between the decreased methylation levels of the SNAI2 and ADAM23 genes and cancer de-differentiation and haematogenous dissemination, respectively, indicate novel functions of those genes in the invasive processes. After experimental validation of the association between the lower values of SNAI2 and ADAM23 methylation and clinical features of aggressive BCs, these methylation profiles could improve the management of metastatic disease.

Comprehensive integrated analysis of gene expression datasets identifies key anti-cancer targets in different stages of breast cancer

Breast cancer is one of the primary threats to women's health worldwide. However, the molecular mechanisms underlying the development of breast cancer remain to be fully elucidated. The present study aimed to investigate specific target gene expression profiles in breast cancer tissues in general and in different breast cancer stages, as well as to explore their functions in tumor development. For integrated analysis, a total of 5 gene expression profiling datasets for 3 different stages of breast cancer (stages I-III) were downloaded from the Gene Expression Omnibus of the National Center for Biotechnology Information. Pre-processing of these datasets was performed using the Robust Multi-array Average algorithm and global renormalization was performed for all studies. Differentially expressed genes between breast cancer patients and controls were estimated using the empirical Bayes algorithm. The Database for Annotation, Visualization and Integrated Discovery web server was used for analyzing the enrichment of the differentially expressed genes in Gene Ontology terms of the category biological process and in Kyoto Encyclopedia of Genes and Genomes pathways. Furthermore, breast cancer target genes were downloaded from the Thomson Reuters Integrity Database. We merged these target genes with the genes in breast cancer datasets. Analysis of anti-breast cancer gene networks was performed using the Genome-scale Integrated Analysis of Gene Networks in Tissues web server. The results demonstrated that the normal functions of the cell cycle, cell migration and cell adhesion were altered in all stages of breast cancer. Furthermore, 12 anti-breast cancer genes were identified to be dysregulated in at least one of the three stages. Among all of these genes, ribonucleotide reductase regulatory subunit M2 (RRM2) exhibited the highest degree of interaction with other interacting genes. Analysis of the network interactions revealed that the transcription factor of RRM2 is crucial for cancer development. Other genes, including mucin 1, progesterone receptor and cyclin-dependent kinase 5 regulatory subunit associated protein 3, also exhibited a high degree of interaction with the associated genes. In conclusion, several key anti-breast cancer genes identified in the present study are mainly associated with the regulation of the cell cycle, cell migration, cell adhesion and other cancer-associated cell functions, particularly RRM2.

Comprehensive tissue-specific gene set enrichment analysis and transcription factor analysis of breast cancer by integrating 14 gene expression datasets

Breast cancer is the most commonly diagnosed malignancy in women. Several key genes and pathways have been proven to correlate with breast cancer pathology. This study sought to explore the differences in key transcription factors (TFs), transcriptional regulation networks and dysregulated pathways in different tissues in breast cancer. We employed 14 breast cancer datasets from NCBI-GEO and performed an integrated analysis in three different tissues including breast, blood and saliva. The results showed that there were eight genes (CEBPD, EGR1, EGR2, EGR3, FOS, FOSB, ID1 and NFIL3) down-regulated in breast tissue but up-regulated in blood tissue. Furthermore, we identified several unreported tissue-specific TFs that may contribute to breast cancer, including ATOH8, DMRT2, TBX15 and ZNF367. The dysregulation of these TFs damaged lipid metabolism, development, cell adhesion, proliferation, differentiation and metastasis processes. Among these pathways, the breast tissue showed the most serious impairment and the blood tissue showed a relatively moderate damage, whereas the saliva tissue was almost unaffected. This study could be helpful for future biomarker discovery, drug design, and therapeutic and predictive applications in breast cancers.

The WYL Domain of the PIF1 Helicase from the Thermophilic Bacterium Thermotoga elfii is an Accessory Single-Stranded DNA Binding Module

PIF1 family helicases are conserved from bacteria to man. With the exception of the well-studied yeast PIF1 helicases (e.g., ScPif1 and ScRrm3), however, very little is known about how these enzymes help maintain genome stability. Indeed, we lack a basic understanding of the protein domains found N- and C-terminal to the characteristic central PIF1 helicase domain in these proteins. Here, using chimeric constructs, we show that the ScPif1 and ScRrm3 helicase domains are interchangeable and that the N-terminus of ScRrm3 is important for its function in vivo. This suggests that PIF1 family helicases evolved functional modules fused to a generic motor domain. To investigate this hypothesis, we characterized the biochemical activities of the PIF1 helicase from the thermophilic bacterium Thermotoga elfii (TePif1), which contains a C-terminal WYL domain of unknown function. Like helicases from other thermophiles, recombinant TePif1 was easily prepared, thermostable in vitro, and displayed activities similar to its eukaryotic homologues. We also found that the WYL domain was necessary for high-affinity single-stranded DNA (ssDNA) binding and affected both ATPase and helicase activities. Deleting the WYL domain from TePif1 or mutating conserved residues in the predicted ssDNA binding site uncoupled ATPase activity and DNA unwinding, leading to higher rates of ATP hydrolysis but less efficient DNA helicase activity. Our findings suggest that the domains of unknown function found in eukaryotic PIF1 helicases may also confer functional specificity and additional activities to these enzymes, which should be investigated in future work.

Gene expression information improves reliability of receptor status in breast cancer patients

Immunohistochemical (IHC) determination of receptor status in breast cancer patients is frequently inaccurate. Since it directs the choice of systemic therapy, it is essential to increase its reliability.

We increase the validity of IHC receptor expression by additionally considering gene expression (GE) measurements. Crisp therapeutic decisions are based on IHC estimates, even if they are borderline reliable. We further improve decision quality by a responsibility function, defining a critical domain for gene expression. Refined normalization is devised to file any newly diagnosed patient into existing data bases. Our approach renders receptor estimates more reliable by identifying patients with questionable receptor status. The approach is also more efficient since the rate of conclusive samples is increased. We have curated and evaluated gene expression data, together with clinical information, from 2880 breast cancer patients. Combining IHC with gene expression information yields a method more reliable and also more efficient as compared to common practice up to now.

Several types of possibly suboptimal treatment allocations, based on IHC receptor status alone, are enumerated. A ‘therapy allocation check’ identifies patients possibly miss-classified. Estrogen: false negative 8%, false positive 6%. Progesterone: false negative 14%, false positive 11%. HER2: false negative 2%, false positive 50%. Possible implications are discussed.

We propose an ‘expression look-up-plot’, allowing for a significant potential to improve the quality of precision medicine.

Methods are developed and exemplified here for breast cancer patients, but they may readily be transferred to diagnostic data relevant for therapeutic decisions in other fields of oncology.

Transposon insertional mutagenesis in mice identifies human breast cancer susceptibility genes and signatures for stratification

Despite concerted efforts to identify causal genes that drive breast cancer (BC) initiation and progression, we have yet to establish robust signatures to stratify patient risk. Here we used in vivo transposon-based forward genetic screening to identify potentially relevant BC driver genes. Integrating this approach with survival prediction analysis, we identified six gene pairs that could prognose human BC subtypes into high-, intermediate-, and low-risk groups with high confidence and reproducibility. Furthermore, we identified susceptibility gene sets for basal and claudin-low subtypes (21 and 16 genes, respectively) that stratify patients into three relative risk subgroups. These signatures offer valuable prognostic insight into the genetic basis of BC and allow further exploration of the interconnectedness of BC driver genes during disease progression.

Robust prognostic gene signatures and therapeutic targets are difficult to derive from expression profiling because of the significant heterogeneity within breast cancer (BC) subtypes. Here, we performed forward genetic screening in mice using Sleeping Beauty transposon mutagenesis to identify candidate BC driver genes in an unbiased manner, using a stabilized N-terminal truncated β-catenin gene as a sensitizer. We identified 134 mouse susceptibility genes from 129 common insertion sites within 34 mammary tumors. Of these, 126 genes were orthologous to protein-coding genes in the human genome (hereafter, human BC susceptibility genes, hBCSGs), 70% of which are previously reported cancer-associated genes, and ∼16% are known BC suppressor genes. Network analysis revealed a gene hub consisting of E1A binding protein P300 (EP300), CD44 molecule (CD44), neurofibromin (NF1) and phosphatase and tensin homolog (PTEN), which are linked to a significant number of mutated hBCSGs. From our survival prediction analysis of the expression of human BC genes in 2,333 BC cases, we isolated a six-gene-pair classifier that stratifies BC patients with high confidence into prognostically distinct low-, moderate-, and high-risk subgroups. Furthermore, we proposed prognostic classifiers identifying three basal and three claudin-low tumor subgroups. Intriguingly, our hBCSGs are mostly unrelated to cell cycle/mitosis genes and are distinct from the prognostic signatures currently used for stratifying BC patients. Our findings illustrate the strength and validity of integrating functional mutagenesis screens in mice with human cancer transcriptomic data to identify highly prognostic BC subtyping biomarkers.