Introduction

Breast cancer is the most common cancer in women [1], with incidence rates rising since the 1990s [2]. Molecular expression profiling of tumors has been effective in allowing for individualized therapy plans in certain types of breast cancer [3]. Expression of three receptors—estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor (HER2)—are routinely used to determine optimal treatment plans for breast cancer patients [4]. PR and ER expression are associated with luminal A and B subtypes of breast cancer, with a lower proliferation index and pathological grade [5]. Disease-free and overall survival is lower in HER2 over-expression and triple negative breast cancers when compared to luminal A and B subtypes [5].

Despite obtaining multiple specimens from percutaneous biopsies as well as analysis of surgical specimens, the temporal and spatial heterogeneity of tumor gene and protein expression cannot be adequately determined [6, 7, 8]. Readily available imaging databases such as The Cancer Imaging Archive (TCIA) are leveraged in order to address the problem of tumor heterogeneity and to predict gene expression and patient responses to therapy based on imaging data [9]. MRI, as well as other modalities, is now used by researchers for extraction of features which correlate with patient responses and gene expression [10-12]. Breast cancer radiomic signatures can potentially predict recurrence when compared with multi-gene assays [13]. Deciphering the associations between imaging features, breast tumor gene/protein expression levels, and patient outcomes holds the potential to guide personalized medicine [12, 14].

High-dimensional variable selection (Supplementary Table S1) is commonly used to analyze relationships between multiple modalities (copy number, expression, etc.) in genomic data. To avoid generating spurious correlations, a number of Bayesian and frequentist approaches have been devised. Bayesian approaches use a sparsity-inducing prior, such as spike-and-slab [15, 16], double-exponential [17], horseshoe [18], horseshoe+[19], or generalized double Pareto prior [20]. Frequentist approaches use penalized regression models: l1 [18], horseshoe+ [19], generalized double Pareto prior [20], L1-norm penalty of the LASSO [21], combined l1/l2 penalty of elastic net [21], or combined L1 and L2-norm penalty of elastic net [22]. These regression models allow us to ignore the loss of statistical efficiency that occurs through correlation structures because they treat all variables as independent [23]. Several approaches to high-dimensional variable selection in highly-correlated datasets have been taken [24-26]. In this study, we used a Bayesian approach to model the correlation structure as previously described [27].

Analyzing a cohort of 82 breast cancer patients included in the TCGA database, we built a model correlating MRI-derived imaging features with proteomics data using a high-dimensional regression approach. Though a previous study of 353 breast cancer patients assessed correlations between 21 imaging traits and mRNA transcript levels [28], to our knowledge our approach has not yet been applied to proteomics data for breast cancer.

Results

Molecules were found to be significantly correlated to each imaging feature (Supplementary Table S2) with the exception of clumped non-mass internal enhancement. These molecules were obtained through high dimensional regression of the RPPA protein expression data on the imaging features set. For example, the axillary lymphadenopathy feature was found to be directly correlated with expression of EIF4EBP1 and PRDX1, and inversely correlated with RAB25, SHC1, XRCC1, and PARK7. Cell surface receptors associated with imaging features are EGFR, KDR, and PDK1.

IPA analysis was implemented to determine the functional implications of the molecules. The IPA software generated p-values and Z-scores for the IPA Canonical Pathways of each feature (Figure 2), as well as scores for the IPA Diseases and Biological Functions of each feature (Figure 3). The Canonical Signaling Pathways most strongly associated with each imaging feature are summarized in Table 2. The results show that the same proteins are found to be correlated with a specific feature, irrespective of whether the data sets were separated into global or primary features.

Sagittal T1 post-contrast MRI of a 48-year-old female patient diagnosed with infiltrating ductal carcinoma (ER-, PR-, HER2-) shows an oval rim enhancing mass

Figure 1. Sagittal T1 post-contrast MRI of a 48-year-old female patient diagnosed with infiltrating ductal carcinoma (ER-, PR-, HER2-) shows an oval rim enhancing mass MRI sequences were obtained from The Cancer Imaging Archive [37].

Representative pattern of associations between BRCA imaging features and IPA Canonical Pathways based on (A) p-values and (B) activation Z-scores

Figure 2. Representative pattern of associations between BRCA imaging features and IPA Canonical Pathways based on (A) p-values and (B) activation Z-scores A subset of the p-values and Z-scores are shown. Values shown are -log(p-value).

Representative pattern of associations between BRCA imaging features and IPA Diseases and Bio-Functions based on (A) p-values and (B) activation Z-scores

Figure 3. Representative pattern of associations between BRCA imaging features and IPA Diseases and Bio-Functions based on (A) p-values and (B) activation Z-scores A subset of the p-values and Z-scores are shown. Values shown are -log(p-value).

Table 1. Patient demographic information

Demographics are given for the 82 patients included in this study.

Statistic
Mean Age at Diagnosis (Range)53.2(29 - 82)
Median Overall Survival (Months)41.72
Median Disease-Free Survival (Months)42.015
Estrogen Receptor (ER) Status (Positive / Negative)67/ 15
Progesterone Receptor (PR) Status (Positive / Negative)59/ 23
Infiltrating Lobular Carcinoma9
Infiltrating Ductal Carcinoma69
Medullary Carcinoma1
Other3

Table 2. Radiological features are associated with unique pathway alterations in breast invasive carcinoma

Lists of molecules (proteins and post-translational modifications) were analyzed in IPA. Top pathways for each feature are shown with the associated –log (p-value) computed by IPA demonstrating the strength of the association of each imaging feature to each pathway.

3 greatest P-values per imaging feature123
T2 Signal IntensityPancreatic Adenocarcinoma Signaling 6.177Melanoma Signaling 4.405Non-Small Cell Lung Cancer Signaling 4.111
T2 HeterogeneityUVB-Induced MAPK Signaling 6.245EGF Signaling 6.206ErbB Signaling 5.725
Skin ThickeningEpithelial Adherens Junction Signaling 8.957Regulation of the Epithelial-Mesenchymal Transition Pathway 8.282Pancreatic Adenocarcinoma Signaling 5.692
Skin Invasion14-3-3-mediated Signaling 10.378Cell Cycle: G2/M DNA Damage Checkpoint Regulation 7.914UVB-Induced MAPK Signaling 7.385
Irregular ShapeUVC-Induced MAPK Signaling 6.845EGF Signaling 6.206STAT3 Pathway 6.112
Rim EnhancementATM Signaling 8.26AMPK Signaling 6.385Cell Cycle: G2/M DNA Damage Checkpoint Regulation 5.037
Pectoral InvasionPI3K/AKT Signaling 12.834Neuregulin Signaling 10.295p70S6K Signaling 9.069
Non-Mass Heterogeneous Internal EnhancementILK Signaling 7.812PI3K/AKT Signaling 6.646Endometrial Cancer Signaling 5.511
Non-Mass Clustered Ring Internal EnhancementATM Signaling 4.078CDK5 Signaling 3.892B Cell Receptor Signaling 3.349
Non-Mass Clumped Internal EnhancementDNA Double-Strand Break Repair by Homologous Recombination 3.181DNA Double-Strand Break Repair by Non-Homologous End Joining 3.181DNA damage-induced 14-3-3σ Signaling 3.049
Regional Non-Mass DistributionAcute Myeloid Leukemia Signaling 3.745Cancer Drug Resistance By Drug Efflux 1.94autophagy 1.853
Multiple Regions Non-Mass DistributionPI3K/AKT Signaling 4.471IL-8 Signaling 4.068CD27 Signaling in Lymphocytes 2.311
Linear Non-Mass DistributionErbB2-ErbB3 Signaling 6.883ErbB Signaling 6.421Relaxin Signaling 5.845
Focal Non-Mass DistributionUVC-Induced MAPK Signaling 11.037Cancer Drug Resistance By Drug Efflux 10.613AMPK Signaling 10.337
Diffuse Non-Mass DistributionDNA Double-Strand Break Repair by Homologous Recombination 5.395Role of BRCA1 in DNA Damage Response 3.879ATM Signaling 3.857
Nipple RetractionPI3K/AKT Signaling 6.112UVB-Induced MAPK Signaling 4.246FLT3 Signaling in Hematopoietic Progenitor Cells 4.025
Nipple InvasionEstrogen-mediated S-phase Entry 2.346Induction of Apoptosis by HIV1 1.949Lymphotoxin β Receptor Signaling 1.901
MarginMolecular Mechanisms of Cancer 3.746DNA damage-induced 14-3-3σ Signaling 2.205GADD45 Signaling 2.205
Lesion SizeHereditary Breast Cancer Signaling 7.625PI3K/AKT Signaling 5.976Insulin Receptor Signaling 5.753
Heterogeneous Enhancement IntensityProlactin Signaling 6.64Th1 Pathway 6.001Th1 and Th2 Activation Pathway 5.588
FibroglandularUVC-Induced MAPK Signaling 13.629UVB-Induced MAPK Signaling 12.176Neuregulin Signaling 11.271
Extent HeterogeneityProstate Cancer Signaling 7.076UVB-Induced MAPK Signaling 4.546FLT3 Signaling in Hematopoietic Progenitor Cells 4.325
Extent - Multi-focalCNTF Signaling 4.587UVB-Induced MAPK Signaling 4.546EGF Signaling 4.52
Extent - Multi-centricHepatic Fibrosis / Hepatic Stellate Cell Activation 3.359Tumoricidal Function of Hepatic Natural Killer Cells 2.346Coagulation System 2.182
EdemaHereditary Breast Cancer Signaling 4.797AMPK Signaling 4.425Endometrial Cancer Signaling 3.607
Dark Internal SeptumHuntington's Disease Signaling 3.418Glucocorticoid Receptor Signaling 3.267Parkinson's Signaling 2.646
BackgroundInsulin Receptor Signaling 5.753Molecular Mechanisms of Cancer 5.539NF-κB Signaling 5.331
Axillary LymphadenopathyERK/MAPK Signaling 4.8EGF Signaling 3.824Erythropoietin Signaling 3.693
Associated Non-Mass EnhancementPancreatic Adenocarcinoma Signaling 7.206UVC-Induced MAPK Signaling 6.399Cancer Drug Resistance By Drug Efflux 6.194

In order to determine which features were most strongly associated with functional alterations to signaling pathways, agglomerative unsupervised hierarchical clustering was performed on the p-values and Z-scores (Figures 2 and 3). This analysis separated the features into groups based on the strength of their correlations with altered pathway activity and disease functions. The most strongly deregulated IPA Diseases and Biological functions featured activation Z-scores between -3.5 and +3.5 (Supplementary Table S3).

Discussion

The strength of the associations between imaging features and protein expression, signaling pathways, and biological functions was computed using a sequential analysis of the protein expression data found through RPPA analysis of MRI scans of the TCGA patients. Correlation coefficients for each possible combination of imaging feature and protein were computed using a high-dimensional regression with a Bayesian selection of covariates. Corrected p-values were computed for each correlation coefficient in order to minimize the false discovery rate (FDR). Only the strongest ten percent of significantly-correlated molecules were analyzed using the standardized Core Analysis workflow in IPA, using correlation coefficients in lieu of gene expression values. The IPA analysis provides associations with pathway activity and pathobiology, allowing for hypotheses regarding the relationship between pathway activity at the cellular level and the manifestations of the alterations at the macroscopic, imaging levels. The activation Z-scores computed from the correlation coefficients indicate whether each pathway (or function) is up- or down-regulated by upstream transcription factor activity. A similar approach integrated breast cancer transcriptomics data with imaging features and extended the interpretation with gene set enrichment analysis to identify metagene signatures such as wound response and hypoxia [28]. Our study extends this approach by leveraging the IPA Knowledge Base to interpret the patterns of protein expression associated with each imaging feature.

In our study, we used a stringent two-step method to select the correlations least likely to result from chance association, overcoming a common issue with high dimensional regression analysis. Despite this, the approach we have described is essentially a hypothesis-generation pipeline, and should be interpreted carefully, following in-vivo perturbation experiments in appropriate model systems.

We found that enhancing rim fraction score, a quantitative MRI feature, was shown to be significantly associated with the expression of the long, non-coding RNA HOTAIR [29]. This expression is known to be associated with breast cancer progression and metastasis [30]. The results of the high dimensional regression method used hints at the molecular underpinnings of macroscopic imaging phenotypes. It is known that MRI features correlate with pathologic stage and lymph node involvement [31]. The results found in this study point to multiple significant associations between molecular expression patterns in the tumor cells and how these manifest as MRI phenotypes [32].

Methods

TCGA patient datasets

Eighty-two patients from multiple institutions with de-identified MRIs and reverse-phase protein array (RPPA) expression data were included in this study. All subject data was de-identified prior to the study through inclusion in The Cancer Genome Atlas (TCGA), and was thus exempt from requiring institutional review board approval, following the terms of the TCGA data use agreement. Imaging data was obtained through The Cancer Imaging Archive (TCIA) database. RPPA protein expression data was obtained from the TCGA through Firehose (https://gdac.broadinstitute.org/).

Scores of twenty-nine MRI semantic features were defined by the TCGA Breast Phenotype Research Group [33]. We used the imaging features as defined by the TCGA group to include mass- and non-mass associated features as shown in Table 3. These feature groups include background features, tumor related features, tumor dimensional features, features associated with the morphology of the non-mass enhancing lesion, and T2-weighted MR acquisition features.

Table 3. List of imaging features

Feature GroupFeatures
BackgroundBackground Enhancement Fibroglandular
Tumor FeaturesIrregular Shape Heterogeneous Enhancement Intensity Dark Internal Septum Rim Enhancement Margin
Tumor DimensionsLesion Size Multicentric Extent Multifocal Extent Heterogeneity Extent
Associated FeaturesPectoral Invasion Nipple Invasion Skin Invasion Axillary Lymphadenopathy Edema Skin Thickening Nipple Retraction
Morphology of Non-Mass Enhancing LesionsAssociated Non-Mass Enhancement Non-mass Clumped Internal Enhancement Non-mass Clustered Ring Internal Enhancement Non-mass Heterogeneous Internal Enhancement Diffuse Non-Mass Distribution Focal Non-Mass Distribution Linear Non-Mass Distribution Multiple Regions Non-Mass Distribution Regional Non-Mass Distribution
Associated with T2 Weighted MR AcquisitionHeterogeneity Signal Intensity

In order to ensure that the effects of each individual feature were appropriately described, the feature set was split into three subsets: one set with only the 8 mass-associated features, one with only the 21 global features, and an aggregate set with all 29 features. The features were isolated in order to determine if there were any significant proteins, associated pathways, or biological functions that appeared in purely global or mass-associated-only feature sets.

Statistical analysis

High dimensional regression

High dimensional regression was done in Matlab using the joint Bayesian selection of covariates developed by Bhadra and Mallick [27]. In this analysis, the independent variables were the imaging features, and the molecules (proteins and phospho-proteins) were the response variables. This arrangement allowed the expression of each protein to be correlated with the expression of many other proteins.

Multiple-testing correction

Multiple testing correction was employed to control the false-discovery rate (FDR) by sequentially designating p-value thresholds [34]. First, the posterior probabilities of the covariates were thresholded at an FDR of 0.25, giving a sparse set of predictors (imaging variables). Second, t-tests were performed using “no-association” as the null hypothesis and “non-zero association” as the alternative hypothesis. The t-tests were computed between each combination of imaging features and molecules in the RPPA dataset. Correlation coefficients with p-values in the 10th percentile and that were less than 0.05 after adjustment for multiple comparisons were considered statistically significant. This approach is similar to that used to discern the relative impact of copy number alterations on messenger RNAs and microRNAs in glioblastoma [30].

Pathway analysis

Pathway analysis was performed on each of the three data sets (based on the image feature subsets) using the “Core Analysis” feature in the IPA software [35]. For the purposes of this analysis, regression correlation coefficients served as expression values. P-values and activation Z-scores were computed internally in IPA as previously described.

Hierarchical clustering

Agglomerative unsupervised hierarchical clustering of p-values and activation Z-scores was carried out the using the “Stats” package in R. Euclidean distance matrices were computed and Ward's method was minimized within-cluster variance [36].

Supplementary Materials

Supplemental File

Author Contributions

Conception and design: A.R, A.B Development of methodology: A.R, A.B, M.L

Acquisition of data: M.L, A.B, S.A, V.R, Y.Z, B.D, E.B, E.S.B, E.M, E.S, G.J.W, J.N, K.B, M.G, M.Z, A.R

Analysis and interpretation of data: M.L, S.A, A.B, A.R,

Writing, review, and/or revision of manuscript: M.L, A.B, S.A, A.R

Administrative, technical, or material support (i.e., reporting or organizing data, constructing database): A.R, E.S, E.M, E.S.B

Acknowledgments and Funding

A.R. was supported by CCSG Bioinformatics Shared Resource P30 CA016672, an Institutional Research Grant from The University of Texas MD Anderson Cancer Center (MD Anderson), CPRIT RP170719, CPRIT RP150578, NCI R01CA214955-01A1, a Career Development Award from the MD Anderson Brain Tumor SPORE, and a Research Scholar Grant from the American Cancer Society (RSG-16-005-01).

A.B. was supported by grant no. DMS-1613063 from the National Science Foundation. This project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. HHSN261200800001E. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.

E.S and E.M were funded in part through the NIH/NCI Cancer Center Support Grant P30 CA008748. The sponsor had no involvement in the study design; the collection, analysis and interpretation of data; the writing of the report; and the decision to submit the article for publication.

Conflicts of Interest

The authors have no conflict of interest to declare.

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