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Supplementary Data from Differential Survival and Therapy Benefit of Patients with Breast Cancer Are Characterized by Distinct Epithelial and Immune Cell Microenvironments

S1: Image of full size blot for figure 1A; S2: Image of full size blot for figure 1B; S3: SRC3-pS543 and SRC3 chromatin binding patterns in MCF7 cells; S4: Venn diagram of SRC1, SRC2 and SRC3-pS543 enriched binding sites; S5: A. ChIP-QPCR validation of differential enriched chromatin binding sites for total SRC3 and SRC3-pS543; S6: Hormone dependence and phospho-specificity of SRC3-pS543 at sites either or not shared with total SRC3; S7: Overlap of ERïƒ¡ï€ and SRC3 with other publically available datasets; S8: Fulvestrant decreases SRC3-pS543/chromatin interactions at sites shared with ER; S9: Venn diagram, showing the overlap of GO pathways enriched for genes proximal to chromatin binding sites for SRC3-pS543 unique, SRC3 unique or shared regions; S10: 2D graph of normalized ChIP-seq data, illustrating signal for ER, total SRC3, H3K4me1, H3K4me2, H3K4me3 and H3K27Ac binding events at genomic regions occupied by SRC3-pS543; S11: Histological analysis of SRC3 and SRC3-pS543 expression in representative tumour sections; S12: Scatterplot analysis for total SRC3 versus SRC3-pS543 signal in the Nottingham Tenovus Primary Breast Carcinoma Series; S13: Disease Free Survival and breast cancer specific survival in breast cancer patients based on SRC3 protein expression; S14: Disease Free Survival and breast cancer specific survival in patients with HER2 positive and SRC3 high breast cancers compared with all other patients.

Purpose:Extensive work in preclinical models has shown that microenvironmental cells influence many aspects of cancer cell behavior, including metastatic potential and their sensitivity to therapeutics. In the human setting, this behavior is mainly correlated with the presence of immune cells. Here, in addition to T cells, B cells, macrophages, and mast cells, we identified the relevance of nonimmune cell types for breast cancer survival and therapy benefit, including fibroblasts, myoepithelial cells, muscle cells, endothelial cells, and seven distinct epithelial cell types.Experimental Design:Using single-cell sequencing data, we generated reference profiles for all these cell types. We used these reference profiles in deconvolution algorithms to optimally detangle the cellular composition of more than 3,500 primary breast tumors of patients that were enrolled in the SCAN-B and MATADOR clinical trials, and for which bulk mRNA sequencing data were available.Results:This large data set enables us to identify and subsequently validate the cellular composition of microenvironments that distinguish differential survival and treatment benefit for different treatment regimens in patients with primary breast cancer. In addition to immune cells, we have identified that survival and therapy benefit are characterized by various contributions of distinct epithelial cell types.Conclusions:From our study, we conclude that differential survival and therapy benefit of patients with breast cancer are characterized by distinct microenvironments that include specific populations of immune and epithelial cells.

Summary results for all SNPs at the 4q24 locus associated with breast cancer risk at P

Background: A recent association study identified a common variant (rs9790517) at 4q24 to be associated with breast cancer risk. Independent association signals and potential functional variants in this locus have not been explored.Methods: We conducted a fine-mapping analysis in 55,540 breast cancer cases and 51,168 controls from the Breast Cancer Association Consortium.Results: Conditional analyses identified two independent association signals among women of European ancestry, represented by rs9790517 [conditional P = 2.51 × 10−4; OR, 1.04; 95% confidence interval (CI), 1.02–1.07] and rs77928427 (P = 1.86 × 10−4; OR, 1.04; 95% CI, 1.02–1.07). Functional annotation using data from the Encyclopedia of DNA Elements (ENCODE) project revealed two putative functional variants, rs62331150 and rs73838678 in linkage disequilibrium (LD) with rs9790517 (r2 ≥ 0.90) residing in the active promoter or enhancer, respectively, of the nearest gene, TET2. Both variants are located in DNase I hypersensitivity and transcription factor–binding sites. Using data from both The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), we showed that rs62331150 was associated with level of expression of TET2 in breast normal and tumor tissue.Conclusion: Our study identified two independent association signals at 4q24 in relation to breast cancer risk and suggested that observed association in this locus may be mediated through the regulation of TET2.Impact: Fine-mapping study with large sample size warranted for identification of independent loci for breast cancer risk. Cancer Epidemiol Biomarkers Prev; 24(11); 1680–91. ©2015 AACR.

QPCR primer sequences for ChIP validations.

Figure and table legends for supplementary data

Supplementary Table from Differential Survival and Therapy Benefit of Patients with Breast Cancer Are Characterized by Distinct Epithelial and Immune Cell Microenvironments

Purpose: The steroid receptor coactivator SRC3 is essential for the transcriptional activity of estrogen receptor α (ERα). SRC3 is sufficient to cause mammary tumorigenesis, and has also been implicated in endocrine resistance. SRC3 is posttranslationally modified by phosphorylation, but these events have not been investigated with regard to functionality or disease association. Here, we investigate the spatial selectivity of SRC3-pS543/DNA binding over the human genome and its expression in primary human breast cancer in relation with outcome.Experimental Design: Chromatin immunoprecipitation, coupled with sequencing, was used to determine the chromatin binding patterns of SRC3-pS543 in the breast cancer cell line MCF7 and two untreated primary breast cancers. IHC was used to assess the expression of SRC3 and SRC3-pS543 in 1,650 primary breast cancers. The relationship between the expression of SRC3 and SRC3-pS543, disease-free survival (DFS), and breast cancer specific survival (BCSS) was assessed.Results: Although total SRC3 is selectively found at enhancer regions, SRC3-pS543 is recruited to promoters of ERα responsive genes, both in the MCF7 cell line and primary breast tumor specimens. SRC3-pS543 was associated with both improved DFS (P = 0.003) and BCSS (P = 0.001) in tamoxifen untreated high-risk patients, such a correlation was not seen in tamoxifen-treated cases, the interaction was statistically significant (P = 0.001). Multivariate analysis showed SRC3-pS543 to be an independent prognostic factor.Conclusions: Phosphorylation of SRC3 at S543 affects its genomic interactions on a genome-wide level, where SRC3-pS543 is selectively recruited to promoters of ERα-responsive genes. SRC3-pS543 is a prognostic marker, and a predictive marker of response to endocrine therapy. Clin Cancer Res; 22(2); 479–91. ©2015 AACR.

Supplementary Figure from Differential Survival and Therapy Benefit of Patients with Breast Cancer Are Characterized by Distinct Epithelial and Immune Cell Microenvironments

Figure S2. QPCR validation of ChIP-seq data at 45 minutes ligand treatment. (A) Genome browser snapshots of ERα chromatin binding events at the RARA and NRIP1 loci. Cells were ligand treated for 48 hours, prior to fixation. shGFP (blue) and shUSP9X (red) conditions were tested. Tag count and genomic coordinates are shown. (B) ERα ChIP-QPCR validation of the genomic locations, indicated in A. Cells were ligand treated during 45 minutes prior to fixation. shGFP (white) and shUSP9X (black) conditions were tested. Enrichment over negative control region was assessed. Error bar shows SD values from triplicate measurements.

Supplementary figure legends

Patient and tumor characteristics of patients with ERa positive breast cancers

Tamoxifen is one of the most widely used endocrine agents for the treatment of estrogen receptor α (ERα)–positive breast cancer. Although effective in most patients, resistance to tamoxifen is a clinically significant problem and the mechanisms responsible remain elusive. To address this problem, we performed a large scale loss-of-function genetic screen in ZR-75-1 luminal breast cancer cells to identify candidate resistance genes. In this manner, we found that loss of function in the deubiquitinase USP9X prevented proliferation arrest by tamoxifen, but not by the ER downregulator fulvestrant. RNAi-mediated attenuation of USP9X was sufficient to stabilize ERα on chromatin in the presence of tamoxifen, causing a global tamoxifen-driven activation of ERα-responsive genes. Using a gene signature defined by their differential expression after USP9X attenuation in the presence of tamoxifen, we were able to define patients with ERα-positive breast cancer experiencing a poor outcome after adjuvant treatment with tamoxifen. The signature was specific in its lack of correlation with survival in patients with breast cancer who did not receive endocrine therapy. Overall, our findings identify a gene signature as a candidate biomarker of response to tamoxifen in breast cancer. Cancer Res; 74(14); 3810–20. ©2014 AACR.

Data Supplement from USP9X Downregulation Renders Breast Cancer Cells Resistant to Tamoxifen

Purpose: Extensive work in preclinical models has shown that microenvironmental cells influence many aspects of cancer cell behavior, including metastatic potential and their sensitivity to therapeutics. In the human setting, this behavior is mainly correlated with the presence of immune cells. Here, in addition to T cells, B cells, macrophages, and mast cells, we identified the relevance of nonimmune cell types for breast cancer survival and therapy benefit, including fibroblasts, myoepithelial cells, muscle cells, endothelial cells, and seven distinct epithelial cell types. Experimental Design: Using single-cell sequencing data, we generated reference profiles for all these cell types. We used these reference profiles in deconvolution algorithms to optimally detangle the cellular composition of more than 3,500 primary breast tumors of patients that were enrolled in the SCAN-B and MATADOR clinical trials, and for which bulk mRNA sequencing data were available. Results: This large data set enables us to identify and subsequently validate the cellular composition of microenvironments that distinguish differential survival and treatment benefit for different treatment regimens in patients with primary breast cancer. In addition to immune cells, we have identified that survival and therapy benefit are characterized by various contributions of distinct epithelial cell types. Conclusions: From our study, we conclude that differential survival and therapy benefit of patients with breast cancer are characterized by distinct microenvironments that include specific populations of immune and epithelial cells.

Background Breast cancer patients are at significant risk of a second contralateral, breast cancer (CBC). Identification of women at high or low CBC risk could improve patient management decisions. Previous research has shown that breast cancer-associated single nucleotide polymorphisms (SNPs) summarized in a polygenic risk score (PRS) predict the risk of a first breast cancer with an odds ratio (OR) per 1 SD of 1.55 (95% confidence interval (95%CI)=1.52-1.58) (77-SNP PRS). The aim of this study was to evaluate the association between a recently developed PRS and CBC risk. Methods We identified 19 studies from the Breast Cancer Association Consortium (BCAC) with follow-up information on participating patients and at least 10 patients diagnosed with CBC. This included 38,228 females of European ancestry diagnosed with first invasive breast cancer since 1990. Genotyping was done using the iCOGS array or OncoArray, with genotypes for SNPs not on the arrays estimated by imputation. We used a 313-SNP PRS, optimized for prediction of overall (first) breast cancer in the BCAC dataset. Metachronous CBC risk by PRS was quantified using univariable and multivariable Cox regression analyses stratified by country and adjusted for multiple patient, tumor, and treatment characteristics. We assessed PRS interaction with age, family history, adjuvant systemic therapy, and ER-status. Results Median time to develop a CBC (N=1,046) after a first breast cancer was 5.8 years (range 0.3-21.9). Higher PRS was associated with increased CBC risk: hazard ratio (HR) per 1 SD=1.31 (95%CI=1.23-1.39). Patients in the highest and lowest 5% of the PRS had 1.95 fold and 0.67 fold risks of CBC, respectively, compared with patients in the middle quintile. Adjustments for age, year of diagnosis, family history, tumor size, nodal status, ER-status, or treatment (chemotherapy, endocrine therapy, radiotherapy) did not substantially alter these results. We found an interaction with age at first breast cancer diagnosis (Pinteraction=.002); the PRS was associated with an increased CBC risk for patients aged ≥40 years (HR=1.37, 95%CI=1.28-1.47), but not for patients Conclusion The PRS is predictive for the development of CBC in patients ≥40 years at first breast cancer diagnosis. For this group, the PRS could be incorporated in CBC risk prediction models to help define high and low risk patients, and hence optimize screening and treatment strategies. Citation Format: Kramer I, Hooning MJ, Breast Cancer Association Consortium (BCAC), Mavaddat N, Canisius S, Keeman R, van den Broek AJ, Steyerberg E, Hauptmann M, Pharoah PD, Easton DF, Hall P, Schmidt MK. Association between a breast cancer polygenic risk score and contralateral breast cancer risk [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 P1-09-04.

Background Given the high heterogeneity among breast tumors, associations between common germline genetic variants and survival that may exist within specific subgroups could go undetected in an unstratified set of breast cancer patients. Methods We performed genome-wide association analyses within 15 subgroups of breast cancer patients based on prognostic factors, including hormone receptors, tumor grade, age, and type of systemic treatment. Analyses were based on 91,686 female patients of European ancestry from the Breast Cancer Association Consortium, including 7531 breast cancer-specific deaths over a median follow-up of 8.1 years. Cox regression was used to assess associations of common germline variants with 15-year and 5-year breast cancer-specific survival. We assessed the probability of these associations being true positives via the Bayesian false discovery probability (BFDP Results Evidence of associations with breast cancer-specific survival was observed in three patient subgroups, with variant rs5934618 in patients with grade 3 tumors (15-year-hazard ratio (HR) [95% confidence interval (CI)] 1.32 [1.20, 1.45], P = 1.4E−08, BFDP = 0.01, per G allele); variant rs4679741 in patients with ER-positive tumors treated with endocrine therapy (15-year-HR [95% CI] 1.18 [1.11, 1.26], P = 1.6E−07, BFDP = 0.09, per G allele); variants rs1106333 (15-year-HR [95% CI] 1.68 [1.39,2.03], P = 5.6E−08, BFDP = 0.12, per A allele) and rs78754389 (5-year-HR [95% CI] 1.79 [1.46,2.20], P = 1.7E−08, BFDP = 0.07, per A allele), in patients with ER-negative tumors treated with chemotherapy. Conclusions We found evidence of four loci associated with breast cancer-specific survival within three patient subgroups. There was limited evidence for the existence of associations in other patient subgroups. However, the power for many subgroups is limited due to the low number of events. Even so, our results suggest that the impact of common germline genetic variants on breast cancer-specific survival might be limited.

Breast cancer metastasis accounts for most of the deaths from breast cancer. Identification of germline variants associated with survival in aggressive types of breast cancer may inform understanding of breast cancer progression and assist treatment. In this analysis, we studied the associations between germline variants and breast cancer survival for patients with distant metastases at primary breast cancer diagnosis. We used data from the Breast Cancer Association Consortium (BCAC) including 1062 women of European ancestry with metastatic breast cancer, 606 of whom died of breast cancer. We identified two germline variants on chromosome 1, rs138569520 and rs146023652, significantly associated with breast cancer-specific survival (P = 3.19 × 10−8 and 4.42 × 10−8). In silico analysis suggested a potential regulatory effect of the variants on the nearby target genes SDE2 and H3F3A. However, the variants showed no evidence of association in a smaller replication dataset. The validation dataset was obtained from the SNPs to Risk of Metastasis (StoRM) study and included 293 patients with metastatic primary breast cancer at diagnosis. Ultimately, larger replication studies are needed to confirm the identified associations.

Purpose To assess whether contralateral parenchymal enhancement (CPE) on MRI is associated with gene expression pathways in ER+/HER2-breast cancer, and if so, whether such pathways are related to survival. Methods Preoperative breast MRIs were analyzed of early ER+/HER2-breast cancer patients eligible for breast-conserving surgery included in a prospective observational cohort study (MARGINS). The contralateral parenchyma was segmented and CPE was calculated as the average of the top-10% delayed enhancement. Total tumor RNA sequencing was performed and gene set enrichment analysis was used to reveal gene expression pathways associated with CPE (N = 226) and related to overall survival (OS) and invasive disease-free survival (IDFS) in multivariable survival analysis. The latter was also done for the METABRIC cohort (N = 1355). Results CPE was most strongly correlated with proteasome pathways (normalized enrichment statistic = 2.04, false discovery rate = .11). Patients with high CPE showed lower tumor proteasome gene expression. Proteasome gene expression had a hazard ratio (HR) of 1.40 (95% CI = 0.89, 2.16; P = .143) for OS in the MARGINS cohort and 1.53 (95% CI = 1.08, 2.14; P = .017) for IDFS, in METABRIC proteasome gene expression had an HR of 1.09 (95% CI = 1.01, 1.18; P = .020) for OS and 1.10 (95% CI = 1.02, 1.18; P = .012) for IDFS. Conclusion CPE was negatively correlated with tumor proteasome gene expression in early ER+/HER2-breast cancer patients. Low tumor proteasome gene expression was associated with improved survival in the METABRIC data., Highlights • Contralateral parenchymal enhancement on MRI was associated with tumor proteasome gene expression in ER+/HER2-breast cancer. • A high contralateral parenchymal enhancement was associated with a low proteasome gene expression in the breast cancer. • Low proteasome tumor gene expression was associated with improved survival in an independent patient cohort.

Background Observational studies have investigated the association of risk factors with breast cancer prognosis. However, the results have been conflicting and it has been challenging to establish causality due to potential residual confounding. Using a Mendelian randomisation (MR) approach, we aimed to examine the potential causal association between breast cancer-specific survival and nine established risk factors for breast cancer: alcohol consumption, body mass index, height, physical activity, mammographic density, age at menarche or menopause, smoking, and type 2 diabetes mellitus (T2DM). Methods We conducted a two-sample MR analysis on data from the Breast Cancer Association Consortium (BCAC) and risk factor summary estimates from the GWAS Catalog. The BCAC data included 86,627 female patients of European ancestry with 7054 breast cancer-specific deaths during 15 years of follow-up. Of these, 59,378 were estrogen receptor (ER)-positive and 13,692 were ER-negative breast cancer patients. For the significant association, we used sensitivity analyses and a multivariable MR model. All risk factor associations were also examined in a model adjusted by other prognostic factors. Results Increased genetic liability to T2DM was significantly associated with worse breast cancer-specific survival (hazard ratio [HR] = 1.10, 95% confidence interval [CI] = 1.03–1.17, P value [P] = 0.003). There were no significant associations after multiple testing correction for any of the risk factors in the ER-status subtypes. For the reported significant association with T2DM, the sensitivity analyses did not show evidence for violation of the MR assumptions nor that the association was due to increased BMI. The association remained significant when adjusting by other prognostic factors. Conclusions This extensive MR analysis suggests that T2DM may be causally associated with worse breast cancer-specific survival and therefore that treating T2DM may improve prognosis.

Previous research has shown that polygenic risk scores (PRSs) can be used to stratify women according to their risk of developing primary invasive breast cancer. This study aimed to evaluate the association between a recently validated PRS of 313 germline variants (PRS313) and contralateral breast cancer (CBC) risk. We included 56,068 women of European ancestry diagnosed with first invasive breast cancer from 1990 onward with follow-up from the Breast Cancer Association Consortium. Metachronous CBC risk (N = 1,027) according to the distribution of PRS313 was quantified using Cox regression analyses. We assessed PRS313 interaction with age at first diagnosis, family history, morphology, ER status, PR status, and HER2 status, and (neo)adjuvant therapy. In studies of Asian women, with limited follow-up, CBC risk associated with PRS313 was assessed using logistic regression for 340 women with CBC compared with 12,133 women with unilateral breast cancer. Higher PRS313 was associated with increased CBC risk: hazard ratio per standard deviation (SD) = 1.25 (95%CI = 1.18-1.33) for Europeans, and an OR per SD = 1.15 (95%CI = 1.02-1.29) for Asians. The absolute lifetime risks of CBC, accounting for death as competing risk, were 12.4% for European women at the 10th percentile and 20.5% at the 90th percentile of PRS313. We found no evidence of confounding by or interaction with individual characteristics, characteristics of the primary tumor, or treatment. The C-index for the PRS313 alone was 0.563 (95%CI = 0.547-0.586). In conclusion, PRS313 is an independent factor associated with CBC risk and can be incorporated into CBC risk prediction models to help improve stratification and optimize surveillance and treatment strategies. ispartof: AMERICAN JOURNAL OF HUMAN GENETICS vol:107 issue:5 pages:837-848 ispartof: location:United States status: published

The genetically heterogeneous triple-negative breast cancer (TNBC) continues to be an intractable disease, due to lack of effective targeted therapies. Gene amplification is a major event in tumorigenesis. Genes with amplification-dependent expression are being explored as therapeutic targets for cancer treatment. In this study, we have applied Analytical Multi-scale Identification of Recurring Events analysis and transcript quantification in the TNBC genome across 222 TNBC tumors and identified 138 candidate genes with positive correlation in copy number gain (CNG) and gene expression. siRNA-based loss-of-function screen of the candidate genes has validated EGFR, MYC, ASAP1, IRF2BP2, and CCT5 genes as drivers promoting proliferation in different TNBC cells. MYC, ASAP1, IRF2BP2, and CCT5 display frequent CNG and concurrent expression over 2173 breast cancer tumors (cBioPortal dataset). More frequently are MYC and ASAP1 amplified in TNBC tumors (>30%, n = 320). In particular, high expression of ASAP1, the ADP-ribosylation factor GTPase-activating protein, is significantly related to poor metastatic relapse-free survival of TNBC patients (n = 257, bc-GenExMiner). Furthermore, we have revealed that silencing of ASAP1 modulates numerous cytokine and apoptosis signaling components, such as IL1B, TRAF1, AIFM2, and MAP3K11 that are clinically relevant to survival outcomes of TNBC patients. ASAP1 has been reported to promote invasion and metastasis in various cancer cells. Our findings that ASAP1 is an amplification-dependent TNBC driver gene promoting TNBC cell proliferation, functioning upstream apoptosis components, and correlating to clinical outcomes of TNBC patients, support ASAP1 as a potential actionable target for TNBC treatment.

Estrogen receptor alpha (ER alpha) is a key transcriptional regulator in the majority of breast cancers. ER alpha-positive patients are frequently treated with tamoxifen, but resistance is common. In this study, we refined a previously identified 111-gene outcome prediction-classifier, revealing FEN1 as the strongest determining factor in ER alpha-positive patient prognostication. FEN1 levels were predictive of outcome in tamoxifen-treated patients, and FEN1 played a causal role in ER alpha-driven cell growth. FEN1 impacted the transcriptional activity of ER alpha by facilitating coactivator recruitment to the ER alpha transcriptional complex. FEN1 blockade induced proteasome-mediated degradation of activated ER alpha, resulting in loss of ER alpha-driven gene expression and eradicated tumor cell proliferation. Finally, a high-throughput 465,195 compound screen identified a novel FEN1 inhibitor, which effectively blocked ER alpha function and inhibited proliferation of tamoxifen-resistant cell lines as well as ex vivo-cultured ER alpha-positive breast tumors. Collectively, these results provide therapeutic proof of principle for FEN1 blockade in tamoxifen-resistant breast cancer. Significance: These findings show that pharmacologic inhibition of FEN1, which is predictive of outcome in tamoxifen-treated patients, effectively blocks ER alpha function and inhibits proliferation of tamoxifen-resistant tumor cells. The authors thank Ron Kerkhoven from the NKI Genomics Core Facility. The authors thank Sheila Stewart (Department of Cell Biology and Physiology, Washington University, St. Louis, MO) for providing pShuttle-FEN1hWT and pShuttle-D181A, Robert Nicholson (Cardiff School of Pharmacy and Pharmaceutical Science, Cardiff University, United Kingdom) for providing the TAM-R cells, Kenneth Nephew (Indiana University, School of Medicine, Bloomington, IN) for providing MCF7-T cells. The authors thank Hongmao Sun and David Maloney (National Center for Advancing Translational Sciences, NIH, Bethesda, MD) for the additional design and analyses of the FEN1 HTS inhibitor screen and synthesis of required protein and reagents. This work was supported by the Dutch Cancer Society KWF, Netherlands Organization for Scientific Research (NWO), A Sister's Hope, the National Center for Advancing Translational Sciences, National Institutes of Health (R03 MH092154-01), National Institute on Aging, the National Health and Medical Research Council of Australia (ID 1084416; ID 20160711 to W.D. Tilley and T.E. Hickey), the Royal Adelaide Hospital Research Foundation and the Cancer Australia/National Breast Cancer Foundation (ID CA1043497). Zwart, W (corresponding author), Netherlands Canc Inst, Div Oncogen, Amsterdam, Netherlands; Netherlands Canc Inst, Plesmanlaan 121, NL-1066 CX Amsterdam, Netherlands. w.zwart@nki.nl

Background Better understanding of the molecular biology associated with MRI phenotypes may aid in the diagnosis and treatment of breast cancer. Purpose To discover the associations between MRI phenotypes of breast cancer and their underlying molecular biology derived from gene expression data. Materials and Methods This is a secondary analysis of the Multimodality Analysis and Radiologic Guidance in Breast-Conserving Therapy, or MARGINS, study. MARGINS included patients eligible for breast-conserving therapy between November 2000 and December 2008 for preoperative breast MRI. Tumor RNA was collected for sequencing from surgical specimen. Twenty-one computer-generated MRI features of tumors were condensed into seven MRI factors related to tumor size, shape, initial enhancement, late enhancement, smoothness of enhancement, sharpness, and sharpness variation. These factors were associated with gene expression levels from RNA sequencing by using gene set enrichment analysis. Statistical significance of these associations was evaluated by using a sample permutation test and the false discovery rate. Results Gene expression and MRI data were obtained for 295 patients (mean age, 56 years ± 10.3 [standard deviation]). Larger and more irregular tumors showed increased expression of cell cycle and DNA damage checkpoint genes (false discovery rate0.25; normalized enrichment statistic [NES], 2.15). Enhancement and sharpness of the tumor margin were associated with expression of ribosomal proteins (false discovery rate0.25; NES, 1.95). Smoothness of enhancement, tumor size, and tumor shape were associated with expression of genes involved in the extracellular matrix (false discovery rate0.25; NES, 2.25). Conclusion Breast cancer MRI phenotypes were related to their underlying molecular biology revealed by using RNA sequencing. The association between enhancements and sharpness of the tumor margin with the ribosome suggests that these MRI features may be imaging biomarkers for drugs targeting the ribosome. © RSNA, 2020

Purpose To retrospectively explore the relation between parenchymal enhancement of the healthy contralateral breast on dynamic contrast-enhanced magnetic resonance imaging (MRI) and genomic tests for estrogen receptor (ER)-pathway activity in patients with ER-positive/HER2-negative cancer. Methods A subset of 227 consecutively included patients with unilateral invasive ER-positive/HER2-negative breast cancer underwent dynamic contrast-enhanced MRI prior to breast-conserving therapy between 2000 and 2008. Perfusion of the parenchyma in the healthy breast was assessed using a previously reported measure of contralateral parenchymal enhancement (CPE), consisting of the mean of the top-10% late enhancement. ER-pathway activity was assessed from the surgical resection specimen by the previously reported sensitivity to endocrine therapy (SET)-index and ER-factor. The SET-index is a genetic test to estimate survival benefit from endocrine therapy, consisting of genes related to the ESR1 gene. The ER-factor examines other factors as well including protein expression. The relation between CPE and ER-pathway activity was modeled using linear regression. Results Patients had a median age of 59 years. CPE was not significantly associated with the SET-index (R-squared = 0.005) nor the ER-factor (R-squared = 0.0002). The only variable significantly different between low and high CPE was age at diagnosis (P Conclusions Contralateral parenchymal enhancement on dynamic contrast-enhanced MRI was not associated with tumor-derived estrogen receptor pathway activity.

Background: We examined the associations between germline variants and breast cancer mortality using a large meta-analysis of women of European ancestry. Methods: Meta-analyses included summary estimates based on Cox models of twelve datasets using ~10.4 million variants for 96,661 women with breast cancer and 7697 events (breast cancer-specific deaths). Oestrogen receptor (ER)-specific analyses were based on 64,171 ER-positive (4116) and 16,172 ER-negative (2125) patients. We evaluated the probability of a signal to be a true positive using the Bayesian false discovery probability (BFDP). Results: We did not find any variant associated with breast cancer-specific mortality at P < 5 × 10 −8 . For ER-positive disease, the most significantly associated variant was chr7:rs4717568 (BFDP = 7%, P = 1.28 × 10 −7 , hazard ratio [HR] = 0.88, 95% confidence interval [CI] = 0.84–0.92); the closest gene is AUTS2. For ER-negative disease, the most significant variant was chr7:rs67918676 (BFDP = 11%, P = 1.38 × 10 −7 , HR = 1.27, 95% CI = 1.16–1.39); located within a long intergenic non-coding RNA gene (AC004009.3), close to the HOXA gene cluster. Conclusions: We uncovered germline variants on chromosome 7 at BFDP < 15% close to genes for which there is biological evidence related to breast cancer outcome. However, the paucity of variants associated with mortality at genome-wide significance underpins the challenge in providing genetic-based individualised prognostic information for breast cancer patients.

Previous genome-wide association studies among women of European ancestry identified two independent breast cancer susceptibility loci represented by single nucleotide polymorphisms (SNPs) rs13281615 and rs11780156 at 8q24. A fine-mapping study across 2.06 Mb (chr8:127,561,724-129,624,067, hg19) in 55,540 breast cancer cases and 51,168 controls within the Breast Cancer Association Consortium was conducted. Three additional independent association signals in women of European ancestry, represented by rs35961416 (OR = 0.95, 95% CI = 0.93-0.97, conditional p = 5.8 × 10(-6) ), rs7815245 (OR = 0.94, 95% CI = 0.91-0.96, conditional p = 1.1 × 10(-6) ) and rs2033101 (OR = 1.05, 95% CI = 1.02-1.07, conditional p = 1.1 × 10(-4) ) were found. Integrative analysis using functional genomic data from the Roadmap Epigenomics, the Encyclopedia of DNA Elements project, the Cancer Genome Atlas and other public resources implied that SNPs rs7815245 in Signal 3, and rs1121948 in Signal 5 (in linkage disequilibrium with rs11780156, r(2) = 0.77), were putatively functional variants for two of the five independent association signals. The results highlighted multiple 8q24 variants associated with breast cancer susceptibility in women of European ancestry.

Recent studies of the tumor genome seek to identify cancer pathways as groups of genes in which mutations are epistatic with one another or, specifically, “mutually exclusive.” Here, we show that most mutations are mutually exclusive not due to pathway structure but to interactions with disease subtype and tumor mutation load. In particular, many cancer driver genes are mutated preferentially in tumors with few mutations overall, causing mutations in these cancer genes to appear mutually exclusive with numerous others. Researchers should view current epistasis maps with caution until we better understand the multiple cause-and-effect relationships among factors such as tumor subtype, positive selection for mutations, and gross tumor characteristics including mutational signatures and load.

Effective cancer treatment is crucially dependent on the identification of the biological processes that drive a tumor. However, multiple processes may be active simultaneously in a tumor. Clustering is inherently unsuitable to this task as it assigns a tumor to a single cluster. In addition, the wide availability of multiple data types per tumor provides the opportunity to profile the processes driving a tumor more comprehensively. Here we introduce Functional Sparse-Factor Analysis (funcSFA) to address these challenges. FuncSFA integrates multiple data types to define a lower dimensional space capturing the relevant variation. A tailor-made module associates biological processes with these factors. FuncSFA is inspired by iCluster, which we improve in several key aspects. First, we increase the convergence efficiency significantly, allowing the analysis of multiple molecular datasets that have not been pre-matched to contain only concordant features. Second, FuncSFA does not assign tumors to discrete clusters, but identifies the dominant driver processes active in each tumor. This is achieved by a regression of the factors on the RNA expression data followed by a functional enrichment analysis and manual curation step. We apply FuncSFA to the TCGA breast and lung datasets. We identify EMT and Immune processes common to both cancer types. In the breast cancer dataset we recover the known intrinsic subtypes and identify additional processes. These include immune infiltration and EMT, and processes driven by copy number gains on the 8q chromosome arm. In lung cancer we recover the major types (adenocarcinoma and squamous cell carcinoma) and processes active in both of these types. These include EMT, two immune processes, and the activity of the NFE2L2 transcription factor. We validate the breast cancer findings on the METABRIC set and demonstrate the translatability of the TCGA breast cancer factors to METABRIC. In summary, FuncSFA is a robust method to perform discovery of key driver processes in a collection of tumors through unsupervised integration of multiple molecular data types and functional annotation., Author summary In order to select effective cancer treatment, we need to determine which biological processes are active in a tumor. To this end, tumors have been quantified by high dimensional molecular measurements such as RNA sequencing and DNA copy number profiling. In order to support decision making, these measurements need to be condensed into interpretable summaries. Such summaries can be made interpretable by connecting them to biological processes. Biological process activity is continuous and multiple biological processes are taking place in a single tumor. Therefore, the biological processes associated with a tumor are misrepresented by clustering, which tries to put every tumor in a single cluster. In the method introduced in this paper (funcSFA), molecular measurements are summarized into a small number factors. A factor is a continuous value per tumor that aims to represent the activity of a biological process. When applied to breast and lung cancer, funcSFA identifies factors covering well known biology of these tumor types. FuncSFA also finds novel factors covering biology whose importance is not yet widely recognized in these tumor types. Some of the factors suggest treatment opportunities that can be further investigated in cell lines and mice.