Breast Cancer Driver Genes Research Articles

Genomic and transcriptomic profiling of pre- and postneoadjuvant chemotherapy triple negative breast cancer tumors.

Our understanding of neoadjuvant treatment with microtubule inhibitors (MTIs) for triple negative breast cancer (TNBC) remains limited. To advance our understanding of the role of breast cancer driver genes' mutational status with pathological complete response (pCR; ypT0/isypN0) prediction and to identify distinct gene sets for MTIs like eribulin and paclitaxel, we carried out targeted genomic (n = 50) and whole transcriptomic profiling (n = 64) of TNBC tumor samples from the Japan Breast Cancer Research Group 22 (JBCRG-22) clinical trial. Lower PIK3CA, PTEN, and HRAS mutations were found in homologous recombination deficiency (HRD)-high (HRD score ≥ 42) tumors with higher pCR rates. When HRD-high tumors were stratified by tumor BRCA mutation status, the pCR rates in BRCA2-mutated tumors were higher (83% vs. 36%). Transcriptomic profiling of TP53-positive tumors identified downregulation of FGFR2 (false discovery rate p value = 2.07e-7), which was also the only common gene between HRD-high and -low tumors with pCR/quasi-pCR treated with paclitaxel and eribulin combined with carboplatin, respectively. Differential enrichment analysis of the HRD-high group posttreatment tumors revealed significant correlation (p = 0.006) of the glycan degradation pathway. FGFR2 expression and the differentially enriched pathways play a role in the response and resistance to MTIs containing carboplatin treatment in TNBC patients.

Drop the MYC: An expansion of triple-negative breast cancer driver genes

Drop the MYC: An expansion of triple-negative breast cancer driver genes

PhenoDriver: interpretable framework for studying personalized phenotype-associated driver genes in breast cancer.

Identifying personalized cancer driver genes and further revealing their oncogenic mechanisms is critical for understanding the mechanisms of cell transformation and aiding clinical diagnosis. Almost all existing methods primarily focus on identifying driver genes at the cohort or individual level but fail to further uncover their underlying oncogenic mechanisms. To fill this gap, we present an interpretable framework, PhenoDriver, to identify personalized cancer driver genes, elucidate their roles in cancer development and uncover the association between driver genes and clinical phenotypic alterations. By analyzing 988 breast cancer patients, we demonstrate the outstanding performance of PhenoDriver in identifying breast cancer driver genes at the cohort level compared to other state-of-the-art methods. Otherwise, our PhenoDriver can also effectively identify driver genes with both recurrent and rare mutations in individual patients. We further explore and reveal the oncogenic mechanisms of some known and unknown breast cancer driver genes (e.g. TP53, MAP3K1, HTT, etc.) identified by PhenoDriver, and construct their subnetworks for regulating clinical abnormal phenotypes. Notably, most of our findings are consistent with existing biological knowledge. Based on the personalized driver profiles, we discover two existing and one unreported breast cancer subtypes and uncover their molecular mechanisms. These results intensify our understanding for breast cancer mechanisms, guide therapeutic decisions and assist in the development of targeted anticancer therapies.

Efforts to Grow Genomic Research in Ancestrally Diverse and Admixed Populations.

Recent initiatives by the research community to characterize the genomic and molecular landscapes of tumors in ancestrally diverse and admixed populations, including the publication by Ding and colleagues in this issue of Cancer Research, represent important efforts to improve our understanding of the entire spectrum of cancer genomic variation with potential clinical consequences. Ding and colleagues confirmed a similar prevalence of mutations in established breast cancer driver genes including PIK3CA, TP53, GATA3, MAP3K1, CDH1, CBFB, PTEN, and RUNX1 and recurrent amplifications in breast cancer drivers including MYC, FGFR1, CCND1, and ERBB2 in tumors from Hispanic/Latina women as compared with non-Hispanic White women. Importantly, they also identified Catalogue of Somatic Mutations in Cancer (COSMIC) signature 16 in a significant fraction of tumors from Hispanic/Latina women and a novel recurrent amplification on 17q11.2. This study highlights the potential for inclusion of participants from diverse populations to accelerate discoveries and advance equity in genomic medicine, as well as the need for even larger collaborative initiatives. See related article by Ding et al., p. 2600.

The Analysis of Relevant Gene Networks Based on Driver Genes in Breast Cancer.

The occurrence and development of breast cancer has a strong correlation with a person's genetics. Therefore, it is important to analyze the genetic factors of breast cancer for future development of potential targeted therapies from the genetic level. In this study, we complete an analysis of the relevant protein-protein interaction network relating to breast cancer. This includes three steps, which are breast cancer-relevant genes selection using mutual information method, protein-protein interaction network reconstruction based on the STRING database, and vital genes calculating by nodes centrality analysis. The 230 breast cancer-relevant genes were chosen in gene selection to reconstruct the protein-protein interaction network and some vital genes were calculated by node centrality analyses. Node centrality analyses conducted with the top 10 and top 20 values of each metric found 19 and 39 statistically vital genes, respectively. In order to prove the biological significance of these vital genes, we carried out the survival analysis and DNA methylation analysis, inquired about the prognosis in other cancer tissues and the RNA expression level in breast cancer. The results all proved the validity of the selected genes. These genes could provide a valuable reference in clinical treatment among breast cancer patients.

Heritable genomic diversity in breast cancer driver genes and associations with risk in a Chilean population

BackgroundDriver mutations are the genetic components responsible for tumor initiation and progression. These variants, which may be inherited, influence cancer risk and therefore underlie many familial cancers. The present study examines the potential association between SNPs in driver genes SF3B1 (rs4685), TBX3 (rs12366395, rs8853, and rs1061651) and MAP3K1 (rs72758040) and BC in BRCA1/2-negative Chilean families.MethodsThe SNPs were genotyped in 486 BC cases and 1258 controls by TaqMan Assay.ResultsOur data do not support an association between rs4685:C > T, rs8853:T > C, or rs1061651:T > C and BC risk. However, the rs12366395-G allele (A/G + G/G) was associated with risk in families with a strong history of BC (OR = 1.2 [95% CI 1.0–1.6] p = 0.02 and OR = 1.5 [95% CI 1.0–2.2] p = 0.02, respectively). Moreover, rs72758040-C was associated with increased risk in cases with a moderate-to-strong family history of BC (OR = 1.3 [95% CI 1.0–1.7] p = 0.02 and OR = 1.3 [95% CI 1.0–1.8] p = 0.03 respectively). Finally, risk was significantly higher in homozygous C/C cases from families with a moderate-to-strong BC history (OR = 1.8 [95% CI 1.0–3.1] p = 0.03 and OR = 1.9 [95% CI 1.1–3.4] p = 0.01, respectively). We also evaluated the combined impact of rs12366395-G and rs72758040-C. Familial BC risk increased in a dose-dependent manner with risk allele count, reflecting an additive effect (p-trend = 0.0002).ConclusionsOur study suggests that germline variants in driver genes TBX3 (rs12366395) and MAP3K1 (rs72758040) may influence BC risk in BRCA1/2-negative Chilean families. Moreover, the presence of rs12366395-G and rs72758040-C could increase BC risk in a Chilean population.

Utilizing patient information to identify subtype heterogeneity of cancer driver genes.

Identifying cancer driver genes is essential for understanding the mechanisms of carcinogenesis and designing therapeutic strategies. Although driver genes have been identified for many cancer types, it is still not clear whether the selection pressure of driver genes is homogeneous across cancer subtypes. We propose a statistical framework MutScot to improve the identification of driver genes and to investigate the heterogeneity of driver genes across cancer subtypes. Through simulation studies, we show that MutScot properly controls the type I error in detecting driver genes. In addition, we demonstrate that MutScot can identify subtype heterogeneity of driver genes. Applications to three studies in The Cancer Genome Atlas (TCGA) project showcase that MutScot has a desirable sensitivity for detecting driver genes and that MutScot identifies subtype heterogeneity of driver genes in breast cancer and lung cancer with regards to the status of hormone receptor and to the smoking status, respectively.

Comprehensive analysis of the cancer driver genes in breast cancer demonstrates their roles in cancer prognosis and tumor microenvironment

BackgroundBreast cancer is the most common malignancy in women. Cancer driver gene-mediated alterations in the tumor microenvironment are critical factors affecting the biological behavior of breast cancer. The purpose of this study was to identify the expression characteristics and prognostic value of cancer driver genes in breast cancer.MethodsThe Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets are used as the training and test sets. Classified according to cancer and paracancerous tissues, we identified differentially expressed cancer driver genes. We further screened prognosis-associated genes, and candidate genes were submitted for the construction of a risk signature. Functional enrichment analysis and transcriptional regulatory networks were performed to search for possible mechanisms by which cancer driver genes affect breast cancer prognosis.ResultsWe identified more than 200 differentially expressed driver genes and 27 prognosis-related genes. High-risk group patients had a lower survival rate compared to the low-risk group (P<0.05), and risk signature showed high specificity and sensitivity in predicting the patient prognosis (AUC 0.790). Multivariate regression analysis suggested that risk scores can independently predict patient prognosis. Further, we found differences in PD-1 expression, immune score, and stromal score among different risk groups.ConclusionOur study confirms the critical prognosis role of cancer driver genes in breast cancer. The cancer driver gene risk signature may provide a novel biomarker for clinical treatment strategy and survival prediction of breast cancer.

Uncovering the Subtype-Specific Molecular Characteristics of Breast Cancer by Multiomics Analysis of Prognosis-Associated Genes, Driver Genes, Signaling Pathways, and Immune Activity.

Breast cancer is a heterogeneous malignant disease with different prognoses and has been divided into four molecular subtypes. It is believed that molecular events occurring in breast stem/progenitor cells contribute to the carcinogenesis and development of different breast cancer subtypes. However, these subtype-specific molecular characteristics are largely unknown. In this study, we employed 1217 breast cancer samples from The Cancer Genome Atlas (TCGA) database for a multiomics analysis of the molecular characteristics of different breast cancer subtypes based on PAM50 algorithms. We detected the expression changes of subtype-specific genes and revealed that the expression of particular subtype-specific genes significantly affected prognosis. We also investigated the mutations and copy number variations (CNVs) of breast cancer driver genes and the representative genes of ten signaling pathways in different subtypes and revealed several subtype-specifically altered genes. Moreover, we detected the infiltration of various immune cells in different subtypes of breast cancer and showed that the infiltration levels of major immune cell types are different among these subtypes. Additionally, we investigated the factors affecting the immune infiltration level and the immune cytolytic activity in different breast cancer subtypes, namely, the mutation burden, genome instability and cancer-associated fibroblast (CAF) infiltration. This study may shed light on the molecular events contributing to carcinogenesis and development and provide potential markers and targets for the clinical diagnosis and treatment of different breast cancer subtypes.

Abstract 2176: Mutational profiling of premalignant breast microbiopsies

Abstract Ductal carcinoma in situ (DCIS) comprises ~20% of all breast cancer diagnoses in the United States. Nearly all DCIS are treated to prevent progression to potentially lethal invasive cancer, yet it is estimated about half of DCIS progress within 10 years making the systematic treatment controversial. This highlights an urgent need for reliable molecular markers to stratify patients by risk. Small, degraded biopsies have made molecular profiling historically challenging; furthermore, a single biopsy can have several regions varying in nuclear grade, hormone receptor status, and histological architecture requiring laser-capture microdissection. Recent advances in whole-exome sequencing now enable mutational profiling from limited quantities (&amp;lt;10ng) of damaged, formalin-fixed, paraffin-embedded (FFPE) DNA, and present an opportunity to sequence individual regions and relate the phenotypic heterogeneity with genetic alterations. Here, we apply these advances to obtain mutational profiles from pure DCIS microbiopsies, enabling a comprehensive molecular profile of pure DCIS, direct measurement of genetic heterogeneity, and associations between molecular and histopathological features. Whole-exome sequencing was performed on 59 regions across 31 pure DCIS cases varying in nuclear grade (including 19% low-grade), and histological architecture (52% cribriform, 39% solid). Driver analysis revealed breast cancer-like driver alterations in 30/31 cases, and evidence for multiple putative drivers in 26/31, even in low-grade DCIS. The most frequently mutated genes across patients were common breast cancer driver genes: PIK3CA (37%), TP53 (22%), and GATA3 (11%). Phylogenetic trees constructed on multi-region biopsies revealed that regions of the same biopsy shared 12-336 alterations, and all pathogenic driver point mutations. Nevertheless, 1-22 region-specific alterations were found in each biopsy, some of which were likely driver CNA. We identified several histological associations with genetic alterations including high grade (p=2x10-4) and solid DCIS (p=3x10-4) associated with high copy number burden, and a non-significant increase in GATA3 (13% vs 0%) and PIK3CA (50% vs 28%) pathogenic mutations in cribriform vs solid architecture. Lastly, the most genetically diverse DCIS were HER2+/ER- (p=0.07) and those displaying necrosis (p=0.006). Overall, using novel methodology we performed detailed multi-region mutational profiling on difficult to sequence precancer lesions, revealing varying genetic diversity within a biopsy and novel associations between histology and underlying genetic landscape. We illustrate multiple driver genetic alterations and diversity present even in low-grade, pure DCIS lesions, providing key insight into early breast carcinogenesis and representing a critical step towards the development of prognostic markers of progression. Citation Format: Daniela Nachmanson, Mark F. Evans, Joseph Steward, Adam Officer, Huazhen Yao, Thomas J. O'Keefe, Farnaz Hasteh, Gary S. Stein, Kristen Jepsen, Donald L. Weaver, Gillian L. Hirst, Brian L. Sprague, Laura J. Esserman, Alexander Borowsky, Janet L. Stein, Olivier Harismendy. Mutational profiling of premalignant breast microbiopsies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2176.

Abstract PS19-05: Functional importance of long-tail mutations in breast cancer

Abstract Background: Cancer driver genes are characterized by frequently recurrent mutations, substantial functional impact on in vitro and in vivo cancer growth, and several of the represent clinically important therapeutic targets. Whole exome and genome sequencing studies identified mutations in at least one cancer driver gene in about 70% breast cancers. These studies also revealed that each cancer also harbors hundreds to thousands of additional non-recurrent mutations which follow a long-tail distribution. Historically, these long tail “passenger” mutations are considered random due to genomic instability and not expected to contribute to the biology of the disease. We hypothesize that “long-tail” mutations could also have functional importance and contribute to the unique biology of a given cancer. The goal of the current analysis was to identify the long-tail mutations in breast cancer, and estimate their overall functional importance. Methods: We obtained somatic mutations from the breast cancer TCGA cohort (N=1076) and calculated the somatic mutation frequency for each gene. The dNdScv algorithm was used to estimate significantly mutated genes. A gene was considered to be in the long tail of mutations if its somatic mutation frequency was 1-3%, and the dNdScv p-value was &amp;lt; 0.05. We performed pathway enrichment analysis with 21 cancer pathways assembled by NanoString Technologies (Seattle, WA) for the long-tail genes using Fisher’s exact test and obtained gene dependency scores (DS) from The Cancer Dependency Map (DepMap) project which performed genome-wide pooled loss of function screening for 17,634 human genes using and CRISPR-Cas9-mediated (CRISPR) gene editing to estimate tumor cell viability after gene silencing in 563 cell lines. The more negative (i.e. lower) the dependency score the more important the gene is to sustain cell viability. We compared the DS of the long tail genes with known cancer driver genes and other human genes excluding known breast cancer driver genes. We also compared the dependency score of genes with long-tail mutations between breast cancer and other cancer types using the Mann-Whitney U test. We estimated the trend of average dependency score across genesets using the Jonckheere Terpstra test and using Kendall's tau (τ) coefficient to show the increasing (positive value) or decreasing (negative value) trend. Results: Seventy percent of breast cancers (n=763) carried long-tail mutations in 115 different genes. The average number of long tail mutations was N=3 (range from 1 to 118). Genes with long-tail mutations were enriched in epithelial-mesenchymal transition, extracellular matrix, angiogenesis, adaptive immunity, MAPK, innate immunity, inflammation, and RAS pathways (FDR&amp;lt;0.035). Genes with long-tail mutations showed the lowest average dependency score in breast cancer cell lines (n=28) with median DS= -0.080, the range of median DS was -0.079 to -0.056 in other cancer cell lines (P=0.70). In the breast cancer cell lines, known breast cancer driver genes (N=11) had the lowest DS (median=-0.352), followed by the long-tail genes (median=-0.0801), followed by all other human genes (median DS=-0.0528). An increasing trend (τ=1.78, P=0.024) of dependency score was observed across known cancer driver genes, long-tail genes, and other human genes which indicate that long-tail genes are important in cancer cell viability. Conclusions: Long-tail mutations are seen in most breast cancers in unique combinations. They primarily affect genes involved in key cancer pathways. Long tail genes have negative dependency scores across 563 cancer cell lines indicating functional importance in sustaining cancer viability. These results suggest that long-tail genes with mutations could contribute to the biology of breast cancers and might explain the broad range of differences in clinical behavior or morphologically similar cancers. Citation Format: Tao Qing, Hussein Mohsen, Mariya Rozenblit, Michal Marczyk, Julia Foldi, Kim Blenman, Vignesh Gunasekharan, Lajos Pusztai. Functional importance of long-tail mutations in breast cancer [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS19-05.

Abstract B37: Understanding DCIS initiation and progression using PDX and genetically engineered mouse and rat models

Abstract Ductal carcinoma in situ (DCIS) is a starting lesion in the milk duct of the breast, which accounts for 25% of all “breast cancers” detected. DCIS is usually treated by surgery combined with radiotherapy, which can have a large impact on the life of patients. However, there is little to no evidence that treatment of low- and intermediate-grade DCIS reduces mortality, while women diagnosed with DCIS do perceive their risk of dying to be the same as patients with invasive disease. To reduce the negative perception and the overtreatment of DCIS, but assure proper treatment for high-risk DCIS, it is crucial to understand the biology underlying DCIS. To model DCIS and the progression to invasive ductal carcinoma (IDC), we took two approaches. 1) We established patient-derived xenograft (PDX) models by intraductally injecting fresh patient DCIS material into the mammary ducts of female immunocompromised mice. To date, 100 human patient DCIS samples were engrafted and incubated in vivo for a period of 12 months. The first 25 of these PDX models had a 72% take rate and show both indolent and invasive tumor growth. Histology and molecular subtyping by PAM50 classification are well preserved in the outgrown PDX lesions compared to the original human counterpart, ensuring that our models are a good representation of the patient disease. On top of this, we profiled copy numbers for both PDX and original patient lesions. Finally, using whole mount imaging, we were able to create 3D sections of the injected mammary glands extracted from our PDX models, showing two distinct growth patterns correlated with invasion. 2) We generated genetically engineered mouse and rat models by intraductal injection of lentiviral vectors (over)expressing known breast cancer driver genes and mutations, or combinations thereof, which are suspected to play a role in DCIS initiation and/or its progression. We were able to successfully and selectively transduce the mammary epithelial cell layer in both mice and rats and are therefore able to somatically engineer the mammary gland in vivo. DCIS formation presented in mice with overexpression of Ccnd1 and Myc only. However, the in situ stage could be observed in rat models with overexpression of the latter genes, as well as oncogenic point mutations in Pik3ca and p53 and all combinations of the tested genetic perturbations. These models will eventually allow us to study the biologic processes underlying DCIS progression. This knowledge could then be used to predict DCIS evolution and distinguish patients with high-risk DCIS from those with low-risk DCIS, which will aid in better care. Citation Format: Stefan Hutten, Catrin Lutz, Colinda Scheele, Madelon Badoux, Timo Eijkman, Stefano Annunziatio, Jelle Wesseling, Jos Jonkers. Understanding DCIS initiation and progression using PDX and genetically engineered mouse and rat models [abstract]. In: Proceedings of the AACR Special Conference on the Evolving Landscape of Cancer Modeling; 2020 Mar 2-5; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2020;80(11 Suppl):Abstract nr B37.

Model-Based Integration Analysis Revealed Presence of Novel Prognostic miRNA Targets and Important Cancer Driver Genes in Triple-Negative Breast Cancers.

Background: miRNAs (microRNAs) play a key role in triple-negative breast cancer (TNBC) progression, and its heterogeneity at the expression, pathological and clinical levels. Stratification of breast cancer subtypes on the basis of genomics and transcriptomics profiling, along with the known biomarkers’ receptor status, has revealed the existence of subgroups known to have diverse clinical outcomes. Recently, several studies have analysed expression profiles of matched mRNA and miRNA to investigate the underlying heterogeneity of TNBC and the potential role of miRNA as a biomarker within cancers. However, the miRNA-mRNA regulatory network within TNBC has yet to be understood. Results and Findings: We performed model-based integrated analysis of miRNA and mRNA expression profiles on breast cancer, primarily focusing on triple-negative, to identify subtype-specific signatures involved in oncogenic pathways and their potential role in patient survival outcome. Using univariate and multivariate Cox analysis, we identified 25 unique miRNAs associated with the prognosis of overall survival (OS) and distant metastases-free survival (DMFS) with “risky” and “protective” outcomes. The association of these prognostic miRNAs with subtype-specific mRNA genes was established to investigate their potential regulatory role in the canonical pathways using anti-correlation analysis. The analysis showed that miRNAs contribute to the positive regulation of known breast cancer driver genes as well as the activation of respective oncogenic pathway during disease formation. Further analysis on the “risk associated” miRNAs group revealed significant regulation of critical pathways such as cell growth, voltage-gated ion channel function, ion transport and cell-to-cell signalling. Conclusion: The study findings provide new insights into the potential role of miRNAs in TNBC disease progression through the activation of key oncogenic pathways. The results showed previously unreported subtype-specific prognostic miRNAs associated with clinical outcome that may be used for further clinical evaluation.

Abstract 751: Ductal carcinoma in situ of the breast: Cancer precursor or not

Abstract Background. Ductal carcinoma in situ (DCIS) is a potential precursor of invasive breast cancer (IBC). However, the natural course of a particular DCIS lesion is unknown, because almost all women with DCIS are treated. Furthermore, most studies are biased because these comprise DCIS adjacent to IBC, also known as synchronous DCIS and IBC, indicating that such DCIS lesions already have the capacity to progress to IBC. It is still unknown which proportion and type of subsequent ipsilateral IBCs (iIBC) are related to the initial primary “pure” DCIS lesion. Therefore, we performed an extensive molecular characterization of DCIS and matched subsequent iIBC, to better understand the natural course of DCIS. Patients and methods. We used a unique series of 78 women diagnosed with DCIS and treated by breast conserving surgery (BCS) alone, which subsequently developed iIBC. Mean time to iIBC event was 6.3 years (range 0.5-17.0). These 78 women are a representative sample of a case-control series, nested in a nation-wide, population-based cohort including all patients diagnosed with DCIS between 1989 and 2005 in the Netherlands (Visser, et al Clin Can Res 2018). Data on tumor location (ICD-10) was available for all lesions. DNA and RNA was simultaneously extracted for 78 DCIS lesions and 78 matched subsequent iIBC (DNA &amp;gt;20ng; RNA &amp;gt;100ng), and RNA sequencing (RNAseq) and low coverage whole genome sequencing (CNVseq) was performed. Panel sequencing (PanelSeq), using a custom panel of 53 breast cancer driver genes, was performed with the remaining DNA of 42 DCIS and iIBC matched pairs. We determined if the iIBC lesion and DCIS lesion were related, by comparing tumor location and genomic features. Results. Based on tumor location and histological grade, &amp;gt;95% of the subsequent iIBC reflected outgrowth of residual disease. Based on RNAseq data, 77% of all DCIS and IBC lesions classified into the same PAM50 subtypes. The CNVseq data showed that the DCIS lesions contained copy number aberrations on typical breast cancer-associated loci, such as 1q gain, 8q gain, 16q loss, 20q gain. However, when we compared DCIS with their matched iIBC, we observed in 41% of the cases very distinct copy number profiles, indicating either outgrow of a different tumor (minority) subclone or a second primary tumor. Analysis of PanelSeq mutation data supported this clonal or independent origin of the subsequent iIBC. Conclusion. To our knowledge, our study is the first to investigate if subsequent iIBC is likely to originate from the initial primary “pure” DCIS in a large series with long-term follow-up. Surprisingly, our CNVseq and PanelSeq results indicate that more than one third of the subsequent iIBCs after BCS alone treated primary DCIS are likely to be second primary tumors or represent selective outgrowth of a minority DCIS subclone. Citation Format: Lindy Visser, Marlous Hoogstraat, Tycho Bismeijer, Lotte Elshof, Koen van de Vijver, Emilie Groen, Mathilde Almekinders, Joyce Sanders, Carolien Bierman, Dennis Peters, Ingrid Hofland, Frank Nieboer, Michiel de Maaker, Petra Kristel, Lennart Mulder, Annegien Broeks, Michael Schaapveld, Marjanka Schmidt, Lodewyk Wessels, Esther Lips, Jelle Wesseling, On behalf of the PRECISION team. Ductal carcinoma in situ of the breast: Cancer precursor or not [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 751.

Combined Tumor Sequencing and Case-Control Analyses of RAD51C in Breast Cancer.

Loss-of-function variants in RAD51C are associated with familial ovarian cancer, but its role in hereditary breast cancer remains unclear. The aim of this study was to couple breast tumor sequencing with case-control data to clarify the contribution of RAD51C to hereditary breast cancer. RAD51C was sequenced in 3080 breast cancer index cases that were negative in BRCA1/2 clinical tests and 4840 population-matched cancer-free controls. Pedigree and pathology data were analyzed. Nine breast cancers and one ovarian cancer from RAD51C variant carriers were sequenced to identify biallelic inactivation of RAD51C, copy number variation, mutational signatures, and the spectrum of somatic mutations in breast cancer driver genes. The promoter of RAD51C was analyzed for DNA methylation. A statistically significant excess of loss-of-function variants was identified in 3080 cases (0.4%) compared with 2 among 4840 controls (0.04%; odds ratio = 8.67, 95% confidence interval = 1.89 to 80.52, P< .001), with more than half of the carriers having no personal or family history of ovarian cancer. In addition, the association was highly statistically significant among cases with estrogen-negative (P <. 001) ortriple-negative cancer (P < .001), but not in estrogen-positive cases. Tumor sequencing from carriers confirmed bi-allelic inactivation in all the triple-negative cases and was associated with high homologous recombination deficiency scores and mutational signature 3 indicating homologous recombination repair deficiency. This study provides evidence that germline loss-of-function variants of RAD51C are associated with hereditary breast cancer, particularly triple-negative type. RAD51C-null breast cancers possess similar genomic and clinical features to BRCA1-null cancers and may also be vulnerable to DNA double-strand break inducing chemotherapies and poly ADP-ribose polymerase inhibitors.

Abstract P3-05-03: Identification of epigenetically silenced breast cancer driver genes

Abstract Breast cancer is clinically and molecularly complex disease driven by aberrant genetic and epigenetic alterations. Epigenetic alterations in particular DNA methylation changes are one of the most important events involved in breast cancer initiation and progression. Previous reports identified many aberrant DNA methylation signatures associated with molecular subtypes of breast cancer and over 100 candidate genes with promoter hypermethylation in breast cancer. However, it remains elusive which of these genes with promoter hypermethylation play “driver” role in tumorigenesis. In previous studies, the average gain of DNA methylation across all cancer samples compared to the average DNA methylation in normal samples has been the criterion to select for potential targets. However, known tumor suppressor driver genes regulated by methylation are relatively infrequently altered in target cancers. Therefore, we propose the paradoxical hypothesis that identifying hypermethylated cancer drivers require focusing on infrequent rather than frequent events. Hence, to identify these potential driver genes, we developed an algorithm with two unique properties. First, unlike previous studies we focused on targets that gained DNA methylation relatively infrequently (10-40%) and that lost expression in breast cancer. Second, using this algorithm, we distinguished cancer dependent gain of DNA methylation from age-dependent gain of methylation. To discern age dependent and independent DNA methylation changes, we generated DNA methylation sequencing data on 29 normal purified breast epithelium (age range 33-82 years old). Furthermore, to study the biological effects of the overexpression or downregulation of these genes, we generated DNA methylation sequencing data on 6 breast cancer cell lines. We also used DNA methylation and RNA expression datasets (675 cancer, 100 normal) available through the TCGA. Using our algorithm, we identified 53 genes with age independent promoter hypermethylation and loss of expression in TCGA tumor samples. To begin testing the biological effects of these driver genes, we performed canonical pathway enrichment analyses using Ingenuity Pathway Analysis software. We also investigated the mutational status of these genes and their molecular subtype enrichment. Based on these analyses, we picked 12 genes (C10orf125, RUNX3, YOD1, FXYD5, SMOC1, SLC16A5, RNLS, DKK1, PNPLA3, FZD10, RND2, and PLCB1) for further study. We stably overexpressed these potential driver genes in different breast cancer cell lines. Twelve genes out of the 12 tested, slowed cell proliferation and 9 decreased anchorage independent growth. We further validated these driver genes by knocking them out in normal human mammary epithelial cells using CRISPR/Cas9 tool. The loss of these genes, increased cell proliferation rate in normal human mammary epithelial cells compared to the control cells. In conclusion, based on our preliminary data, using bioinformatics tools as well as functional assays, we identified epigenetically altered breast cancer driver genes. Identifying and deciphering true epigenetic cancer drivers could potentially lead to the development of therapeutic drugs targeting these genes and/or targeting pathway dependence. Citation Format: Panjarian S, Madzo J, Slater C, Jelinek J, Chen X, Issa J-P. Identification of epigenetically silenced breast cancer driver genes [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P3-05-03.

Revealing clonality and subclonality of driver genes for clinical survival benefits in breast cancer.

Genomic studies have revealed that genomic aberrations play important roles in the progression of this disease. The aim of this study was to evaluate the associations between clinical survival outcomes of the clonality and subclonality status of driver genes in breast cancer. We performed an integrated analysis to infer the clonal status of 55 driver genes in breast cancer data from TCGA. We used the chi-squared test to assess the relations between clonality of driver gene mutations and clinicopathological factors. The Kaplan-Meier method was performed for the visualization and the differences between survival curves were calculated by log-rank test. Univariate and multivariate Cox proportional hazards regression models were used to adjust for clinicopathological factors. We identified a high proportion of clonal mutations in these driver genes. Among them, there were 17 genes showing significant associations between their clonality and multiple clinicopathologic factors. Performing survival analysis on BRCA patients with clonal or subclonal driver gene mutations, we found that clonal ERBB2, FOXA1, and KMT2C mutations and subclonal GATA3 and RB1 mutations predicted shorter overall survival compared with those with wild type. Furthermore, clonal ERBB2 and FOXA1 mutations and subclonal GATA3 and RB1 mutations independently predicted for shorter overall survival after adjusting for clinicopathological factors. By longitudinal analysis, the clonality of ERBB2, FOXA1, GATA3, and RB1 significantly predicted patients' outcome within some specific BRCA tumor stages and histological subtypes. In summary, these clonal or subclonal mutations of driver genes have implications for diagnosis, prognosis, and treatment with BRCA patients.

Dynamic genome and transcriptional network-based biomarkers and drugs: precision in breast cancer therapy.

Despite remarkable progress in medium-term overall survival benefit in the adjuvant, neoadjuvant and metastatic settings, with multiple recent targeted drug approvals, acquired resistance, late relapse, and cancer-related death rates remain challenging. Integrated technological systems have been developed to overcome these unmet needs. The characterization of structural and functional noncoding genome elements through next-generation sequencing (NGS) systems, Hi-C and CRISPR/Cas9, as well as computational models, allows for whole genome and transcriptome analysis. Rapid progress in large-scale single-biopsy genome analysis has identified several novel breast cancer driver genes and oncotargets. The exploration of spatiotemporal tumor evolution has returned exciting while inconclusive data on dynamic intratumor heterogeneity (ITH) through multiregional NGS and single-cell DNA/RNA sequencing and circulating genomic subclones (cGSs) by serial circulating cell-free DNA NGS to predict and overcome intrinsic and acquired therapeutic resistance. This review discusses reliable breast cancer genome analysis data and focuses on two major crucial perspectives. The validation of ITH, cGSs, and intrapatient genetic/genomic heterogeneity as predictive biomarkers, as well as the valid discovery of novel oncotargets within patient-centric genomic trials, encouraging early drug development, could optimize primary and secondary therapeutic decision-making. A longer-term goal is to identify the individualized landscape of both coding and noncoding key mutations. This progress will enable the understanding of molecular mechanisms perturbating regulatory networks, shaping the pharmaceutical controllability of deregulated transcriptional biocircuits.

Molecular analysis of PALB2-associated breast cancers.

PALB2 is established as the most clinically important moderate to high penetrance breast cancer predisposition gene after BRCA1 and BRCA2. Mutations in classical familial cancer predisposition genes are presumed to be recessive at the cellular level and therefore a second inactivating somatic mutation is required in the tumour tissue. However, from the limited data that exist, PALB2 may be an example of a cancer predisposition gene that does not conform to Knudson's 'two hit' paradigm. We conducted genome-wide copy number analysis and targeted sequencing of PALB2 and other breast cancer driver genes in 15 invasive breast cancers from individuals carrying pathogenic germline mutations in PALB2. The majority of cancers showed clear evidence of bi-allelic inactivation of PALB2 (10/15) either as loss of heterozygosity involving the wild-type allele (six tumours) or as somatic point mutations (four tumours). All PALB2-null cancers had high homologous recombination deficiency (HRD) scores consistent with a homologous recombination repair deficiency. Interestingly, all but one of the PALB2 heterozygous cancers also had high HRD scores, suggesting that alternative mechanisms of PALB2 functional loss might be operating in these cancers. Our findings demonstrate that PALB2 does undergo bi-allelic inactivation in the majority of breast cancers from PALB2 germline mutation carriers. This feature has implications for the discovery of new moderate to high penetrance breast cancer predisposition genes as it supports using the existence of a 'second hit' and mutation signatures as important search criteria. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Abstract ES10-1: Genetic dissection of cancer development, therapy response, and resistance in mouse models of breast cancer

Abstract Genetically engineered mouse models (GEMMs) and patient-derived xenograft (PDX) models of breast cancer have proven invaluable for basic and translational cancer research. These preclinical models have been useful for in vivo analysis of breast cancer initiation and progression, as well as for investigating anti-tumor efficacy of targeted drugs and immunotherapeutics. However, the establishment of GEMMs and PDX models involves cost and time limitations that represent serious bottlenecks. To keep pace with the growing number of hypotheses that warrant preclinical in vivo testing, continuous refinement of current breast cancer models is crucial. Novel technologies, such as CRISPR/Cas9-based in vivo gene editing, non-germline mouse models of breast cancer, and breast cancer-derived organoids enable accelerated and more refined in vivo and ex vivo modeling of human breast cancer. The resulting preclinical models can be used to identify and validate breast cancer driver genes and candidate drug targets, evaluate anticancer drug efficacy, and identify novel mechanisms of drug resistance. Citation Format: Jonkers J. Genetic dissection of cancer development, therapy response, and resistance in mouse models of breast cancer [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr ES10-1.