Nonparametric pathway-based regression models for analysis of genomic data

Identification of molecular signatures that allow detection of the transition from normal breast epithelial cells to malignant invasive cells is a critical component in the development of diagnostic, therapeutic, and preventative strategies for human breast cancer. Substantial efforts have been devoted to deciphering breast cancer etiology at the genome level, but only a limited number of studies have appeared at the proteome level. In this work, we compared individual in situ proteome profiles of nonpatient matched nine noncancerous, normal breast epithelial (NBE) samples with nine estrogen receptor (ER)-positive (luminal subtype), invasive malignant breast epithelial (MBE) samples by combining laser capture microdissection (LCM) and quantitative shotgun proteomics. A total of 12,970 unique peptides were identified from the 18 samples, and 1623 proteins were selected for quantitative analysis using spectral index (SpI) as a measure of protein abundance. A total of 298 proteins were differentially expressed between NBE and MBE at 95% confidence level, and this differential expression correlated well with immunohistochemistry (IHC) results reported in the Human Protein Atlas (HPA) database. To assess pathway level patterns in the observed expression changes, we developed protein set enrichment analysis (PSEA), a modification of a well-known approach in gene expression analysis, Gene Set Enrichment Analysis (GSEA). Unlike single gene-based functional term enrichment analyses that only examines pathway overrepresentation of proteins above a given significance threshold, PSEA applies a weighted running sum statistic to the entire expression data to discover significantly enriched protein groups. Application of PSEA to the expression data in this study revealed not only well-known ER-dependent and cellular morphology-dependent protein abundance changes, but also significant alterations of downstream targets for multiple transcription factors (TFs), suggesting a role for specific gene regulatory pathways in breast tumorigenesis. A parallel GOMiner analysis revealed both confirmatory and complementary data to PSEA. The combination of the two annotation approaches yielded extensive biological feature mapping for in depth analysis of the quantitative proteomic data.

Macrophages play an important role in breast carcinogenesis. The pathways that mediate the macrophage contribution to breast cancer and the heterogeneity that exists within macrophages are incompletely understood. Macrophage colony-stimulating factor 1 (CSF1) is the primary regulator of tissue macrophages. The purpose of this study was to define a novel CSF1 response signature and to evaluate its clinical and biological significance in breast cancer. We defined the CSF1 response signature by identifying genes overexpressed in tenosynovial giant cell tumor and pigmented villonodular synovitis (tumors composed predominantly of macrophages recruited in response to the overexpression of CSF1) compared with desmoid-type fibromatosis and solitary fibrous tumor. To characterize the CSF1 response signature in breast cancer, we analyzed the expression of CSF1 response signature genes in eight published breast cancer gene expression data sets (n = 982) and did immunohistochemistry and in situ hybridization for CSF1 response genes on a breast cancer tissue microarray (n = 283). In both the gene microarray and tissue microarray analyses, a consistent subset (17-25%) of breast cancers shows the CSF1 response signature. The signature is associated with higher tumor grade, decreased expression of estrogen receptor, decreased expression of progesterone receptor, and increased TP53 mutations (P < 0.001). Our data show that the CSF1 response signature is consistently seen in a subset of breast carcinomas and correlates with biological features of the tumor. Our findings provide insight into macrophage biology and may facilitate the development of personalized therapy for patients most likely to benefit from CSF1-targeted treatments.

Interaction between genes is one driving force that can influence a biological outcome. In a genetic disease such as cancer, understanding genetic interactions may help us elucidate mechanisms sustaining cancer growth. A computational approach is one way to detect genetic interactions in the context of cancer. In this article, we introduce a tool, GenExSt, and its underlying method to study gene interactions. We applied our method to discover gene-pairs whose expressions demonstrate patterns of correlation. For this demonstration, we selected ten breast cancer gene expression data sets from the Genomic Data Commons Data Portal through National Cancer Institute. We focused on genes that suppress genome instability, or instability suppressing genes (GIS), many of which play an important role in cancer. We applied our method to an inter-comparison across data sets. Here we tested statistical normalization approaches derived from the combined expressions of randomly selected, single, housekeeping (HK) genes, and from the calculated mean of three expressions. In addition, our method derives \(R^{2}\) values from linear models in which the expressions of all possible pairs of GIS genes are placed in a linear model to produce heatmaps to indicate probable correlations. We show that results from our method are suited to normalized data, extracted from multiple genes simultaneously, rather than using single gene expression values. GenExSt may be used to study gene expression data in other settings provided that the concept of gene interactions is appropriate in the context.

<title>Abstract</title> Objective(s): 1. The common differential genes of breast cancer and thyroid cancer were identified by Gene Express Omnibus database (GEO). The Cancer Genome Atlas (TCGA) database was used to determine the relationship between the screened differential genes and the clinicopathologic features. Gene set enrichment analysis (GSEA) was used to identify the enrichment pathways of the differential genes in breast and thyroid cancers.2. Retrospective studies were conducted to analyze the relationship between the general characteristics, clinicopathological features, and hormone expression levels in patients with dual cancers of breast cancer combined with thyroid cancer and patients with breast cancer and thyroid cancer alone, and to further analyze the KRT19 expression levels in their tissues. analyze the expression of KRT19 protein in their tissues. Method(s): 1. Download the datasets GSE70947 and GSE3467 from the GEO database, analyze the differential genes of breast cancer, thyroid cancer and normal tissues respectively by using R, and take the intersection of the differential genes of the two tissues to continue the next step of the study. 2. Download the mRNA-seq data of the above differential genes of BRCA and THCA from the TCGA database, and identify the differences in expression of differential genes between normal tissues and tumor tissues by using the above differential gene substitution in R. Gene set enrichment analysis (GSEA) identifies the differences in expression of differential genes enriched in breast and thyroid cancer. Differential gene substitution analysis, and identify the differences in differential gene expression between normal and tumor tissues, Gene set enrichment analysis (GSEA) to identify the pathways of enrichment in breast and thyroid cancers, and then screened out the representative differential gene KRT19 (human cytokeratin 19).3. Collect the mRNA-seq data of BRCA and THCA in the database of TCGA, and use the R language in the substitution analysis of the above differential genes, and identify the differences in differential gene expression between normal and tumor tissues. 3. July 2023 in Zhongshan People's Hospital and puncture or surgical treatment of breast cancer combined with thyroid cancer patient data, a total of 92 cases (experimental group), another randomly collected in the past two years in Zhongshan People's Hospital in the simple breast cancer patients 100 cases (control group 1), thyroid cancer patients, 100 cases (control group 2). 4. the experimental group and the control group 1, 2 of the patient pathology data for Pathological data of patients in experimental and control groups 1 and 2 were retrospectively analyzed, and additional surgical bulk or puncture specimens were subjected to immunohistochemical staining (IHC) to examine the expression of KRT19 protein in the tissues, and to explore whether there was a difference in its expression between experimental and control groups. Result(s): 1. KRT19 mRNA levels were significantly overexpressed in breast and thyroid cancer tissues as analyzed by the GEO database, and KRT19 was associated with clinicopathological features of breast and thyroid cancers as analyzed by the TCGA database, and the GSEA showed that both the breast and thyroid cancer-KRT19 overexpression groups were significantly enriched in the estrogen-responsive pathway.2. In the dual-primary cancer group, of which breast cancer preceded In the dual-primary cancer group, breast cancer preceded thyroid cancer in 86 cases and thyroid cancer preceded in 6 cases, i.e., breast cancer preceded thyroid cancer in the vast majority of patients (&gt; 90%). Compared with the breast cancer group, the dual-primary cancer group was younger at the time of diagnosis, more often in the premenopausal state, with a larger tumor size, and more often positively expressed estrogen receptors (ER) and progesterone receptors (PR), with a statistically significant difference (all <italic>P</italic> &lt; 0.050). Compared with the thyroid cancer group, the odds of tumors occurring bilaterally were increased in the double primary cancer group, and the levels of triiodothyronine (T3) and thyroxine (T4) (both <italic>P</italic> &lt; 0.050) were significantly higher. KRT19 was more frequently positively expressed in breast cancer than in breast cancer alone in dual primary cancers ( <italic>P</italic> = 0.069), and in thyroid cancer than in thyroid cancer alone in dual primary cancers ( <italic>P</italic> &lt; 0.050). Conclusion(s): Pathogenesis correlates between breast and thyroid cancers, estrogen receptor expression is associated with dual carcinogenesis, and KRT19 influences dual carcinogenesis through the estrogen response pathway.

&lt;p style='text-indent:20px;'&gt;Variable selection and parameter estimation are fundamental and important problems in high dimensional data analysis. In this paper, we employ the hard thresholding regularization method [&lt;xref ref-type="bibr" rid="b1"&gt;1&lt;/xref&gt;] to handle these issues under the framework of high-dimensional and sparse linear regression model. Theoretically, we establish a sharp non-asymptotic estimation error for the global solution and further show that the support of the global solution coincides with the target support with high probability. Motivated by the KKT condition, we propose a primal dual active set algorithm (PDAS) to solve the minimization problem, and show that the proposed PDAS algorithm is essentially a generalized Newton method, which guarantees that the proposed PDAS algorithm will converge fast if a good initial value is provided. Furthermore, we propose a sequential version of the PDAS algorithm (SPDAS) with a warm-start strategy to choose the initial value adaptively. The most significant advantage of the proposed procedure is its fast calculation speed. Extensive numerical studies demonstrate that the proposed method performs well on variable selection and estimation accuracy. It has favorable exhibition over the existing methods in terms of computational speed. As an illustration, we apply the proposed method to a breast cancer gene expression data set.&lt;/p&gt;

BackgroundWith the rapid accumulation of genomic data, it has become a challenge issue to annotate and interpret these data. As a representative, Gene set enrichment analysis has been widely used to interpret large molecular datasets generated by biological experiments. The result of gene set enrichment analysis heavily relies on the quality and integrity of gene set annotations. Although several methods were developed to annotate gene sets, there is still a lack of high quality annotation methods. Here, we propose a novel method to improve the annotation accuracy through combining the GO structure and gene expression data.ResultsWe propose a novel approach for optimizing gene set annotations to get more accurate annotation results. The proposed method filters the inconsistent annotations using GO structure information and probabilistic gene set clusters calculated by a range of cluster sizes over multiple bootstrap resampled datasets. The proposed method is employed to analyze p53 cell lines, colon cancer and breast cancer gene expression data. The experimental results show that the proposed method can filter a number of annotations unrelated to experimental data and increase gene set enrichment power and decrease the inconsistent of annotations.ConclusionsA novel gene set annotation optimization approach is proposed to improve the quality of gene annotations. Experimental results indicate that the proposed method effectively improves gene set annotation quality based on the GO structure and gene expression data.

BackgroundAlpha-2-glycoprotein 1, zinc-binding (ZAG), a secreted protein encoded by the AZGP1 gene, is structurally similar to HLA class I. Despite its presumed immunological function, little is known about its role in tumor immunity. In this study, we thus aimed to determine the relationship between the expression of AZGP1/ZAG and the immunological profiles of breast cancer tissues at both the gene and protein level.MethodsUsing a publicly available gene expression dataset from a large-scale breast cancer cohort, we conducted gene set enrichment analysis (GSEA) to screen the biological processes associated with AZGP1. We analyzed the correlation between AZGP1 expression and immune cell composition in breast cancer tissues, estimated using CIBERSORTx. Previously, we evaluated the infiltration of 11 types of immune cells for 45 breast cancer tissues using flow cytometry (FCM). ZAG expression was evaluated by immunohistochemistry on these specimens and analyzed for its relationship with immune cell infiltration. The action of ZAG in M1/M2 polarization models using primary cultures of human peripheral blood mononuclear cells (PBMC)-derived macrophage (Mφ) was analyzed based on the expression of M1/M2 markers (CD86, CD80/CD163, MRC1) and HLA class I/II by FCM.ResultsAZGP1 expression was negatively correlated with multiple immunological processes and specific immune cell infiltration including Mφ M1 using GSEA and CIBERSORTx. ZAG expression was associated with decreased infiltration of monocytes/macrophages, non-classical monocytes, and myeloid-derived suppressor cells in tumor tissues assessed using FCM. In in vitro analyses, ZAG decreased the expression of CD80, CD163, MRC1, and HLA classes I/II in the M1 polarization model and the expression of CD163 and MRC1 in the M2 polarization model.ConclusionZAG is suggested to be a novel immunoregulatory factor affecting the Mφ phenotype in breast cancer tissues.

Background: Breast cancer is the second most common cancer among women in the United States and the second leading cause of cancer death in women. Early and accurate diagnosis is crucial for better treatment and reduction of mortality. Diagnostics based on blood samples are being developed for many diseases including a variety of cancers.Gene expression profiling has been widely used to investigate the mechanisms of tumorigenesis with the goal of developing novel treatment strategies. We have applied microarray technology to study the gene expression signatures in blood samples from patients with invasive breast cancer, with benign breast disease and from disease free (normal) control groups to find potential diagnostic patterns. We have identified differentially expressed genes in each group and investigated the pathways these genes are involved in and how these are regulated.Material and Methods: Human blood samples from 102 invasive breast cancer patients, 57 patients with benign breast disease and 102 normal controls were collected for this study. Patients are enrolled in the Clinical Breast Care Project (CBCP) following HIPAA-compliant IRB approved protocols, with proven breast pathology diagnosis categories. Affymetrix HG-U133 Plus 2 GeneChips were used to investigate the gene expression profile. Microarray experiments were performed following Affymetrix protocols. CEL files were then analyzed with RMA algorithm for the calculation of gene expression matrix. Differentially expressed genes were identified between different groups using Wilcoxon rank sum test. We also applied Gene Set Enrichment Analysis (GSEA) to the entire gene expression profiles to investigate the gene sets or pathways enriched in different groups.Results: We identified about 2,051 differentially expressed genes between normal and invasive groups (p &amp;lt; 0.001 and Fold Change &amp;gt; 1.2). Furthermore, about 445 and 51 genes were identified in normal vs. benign and benign vs. invasive groups respectively. We performed GSEA using 395 gene sets from pathway databases, initially focusing on normal vs. invasive groups. 123 gene sets are highly expressed in the normal group and 272 gene sets are highly expressed in the invasive group. For the normal group, 10 gene sets are significantly enriched and 22 gene sets are significantly enriched in the invasive group (p &amp;lt; 0.05). These results show that some important pathways are down regulated in the invasive group, such as, the B-cell antigen receptor pathway, the BCR signaling pathway, the T-cell signal transduction pathway, the IL4 receptor pathway, the PIP3 pathway, the ERK pathway, etc. The pathways up regulated in the invasive group are oxidative phosphorylation pathway, ATP synthesis, etc.Discussion: Our results suggest that a list of genes differentially expressed in different groups and they may be used to compose cancer marker panels which can be integrated with currently clinical procedures for cancer diagnosis. We also find some crucial pathways enriched in each group. Most of the pathways downregulated in the blood of patients in the invasive group are related to the immune response. In contrast, most of pathways upregulated in the blood of patients in the invasive group are associated with metabolism. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 3024.

IntroductionAge is one of the most important risk factors for human malignancies, including breast cancer; in addition, age at diagnosis has been shown to be an independent indicator of breast cancer prognosis. Except for inherited forms of breast cancer, however, there is little genetic or epigenetic understanding of the biological basis linking aging with sporadic breast cancer incidence and its clinical behavior.MethodsDNA and RNA samples from matched estrogen receptor (ER)-positive sporadic breast cancers diagnosed in either younger (age ≤ 45 years) or older (age ≥ 70 years) Caucasian women were analyzed by array comparative genomic hybridization and by expression microarrays. Array comparative genomic hybridization data were analyzed using hierarchical clustering and supervised age cohort comparisons. Expression microarray data were analyzed using hierarchical clustering and gene set enrichment analysis; differential gene expression was also determined by conditional permutation, and an age signature was derived using prediction analysis of microarrays.ResultsHierarchical clustering of genome-wide copy-number changes in 71 ER-positive DNA samples (27 younger women, 44 older women) demonstrated two age-independent genotypes; one with few genomic changes other than 1q gain/16q loss, and another with amplifications and low-level gains/losses. Age cohort comparisons showed no significant differences in total or site-specific genomic breaks and amplicon frequencies. Hierarchical clustering of 5.1 K genes variably expressed in 101 ER-positive RNA samples (53 younger women, 48 older women) identified six transcriptome subtypes with an apparent age bias (P < 0.05). Samples with higher expression of a poor outcome-associated proliferation signature were predominantly (65%) younger cases. Supervised analysis identified cancer-associated genes differentially expressed between the cohorts; with younger cases expressing more cell cycle genes and more than threefold higher levels of the growth factor amphiregulin (AREG), and with older cases expressing higher levels of four different homeobox (HOX) genes in addition to ER (ESR1). An age signature validated against two other independent breast cancer datasets proved to have >80% accuracy in discerning younger from older ER-positive breast cancer cases with characteristic differences in AREG and ESR1 expression.ConclusionThese findings suggest that epigenetic transcriptome changes, more than genotypic variation, account for age-associated differences in sporadic breast cancer incidence and prognosis.

The analysis of high-volume microarray data faces challenges such as limited sample size, computational complexity, and the risk of inappropriate gene selection. The scarcity of samples hampers computational analysis and classification complexity, while reducing the classification's ability to generalize and predict new samples. Moreover, datasets with a high gene-to-sample ratio raise concerns about the selection of relevant genes for accurate predictive models. Interpreting disease-causing genes becomes intricate as only a subset of genes offers a precise biological insight into the disease. To address these issues, a focus on a smaller set of gene expression data is crucial for a more effective understanding of informative genes. Hence, the primary objective in microarray data analysis is to significantly reduce the number of genes through discriminative gene selection, enhancing the precision of information contained in the data. This article conducts gene expression classification on various cancer types, including colon cancer, breast cancer, leukemia, prostate tumors, and DLBCL. Each cancer type is independently evaluated in the feature selection cycle and classified using varying numbers of features. This approach aims to overcome challenges in microarray data analysis and improve the accuracy and interpretability of gene expression classification.

The gene expression profile of tissue in the colonic mucosa is highly altered in ulcerative colitis (UC) compared with healthy tissue. Analyses of microarray data taken from colonic mucosa biopsies shows upregulation of immune-related genes and may help to indicate molecular pathways that may be targeted for therapeutic intervention. At the same time, genome-wide association studies (GWAS) of ulcerative colitis have identified genetic susceptibility loci highly enriched in immune-related genes as well. We sought to establish the extent of overlap between pathways upregulated in gene expression with genetic susceptibility loci in order to generate a list of core gene sets that can guide functional investigation of UC pathobiology. We conducted a Gene Set Enrichment Analysis (GSEA) in 3 sets of colonic biopsy studies assayed on the Affymetrix U133 Plus 2 array that were publically available through the Gene Expression Omnibus. We also performed a GSEA on genotype data from 8 European-ancestry cohorts consisting of 5584 cases and 11587 controls using a hypergeometric test with the same gene set database of manually-curated pathways from BioCarta, Reactome, and Kyoto Encyclopedia of Genes and Genomes. GSEA revealed that all 3 gene expression datasets share 87 gene sets upregulated in UC lesions and 8 gene sets downregulated (false discovery rate <0.05). The upregulated pathways were dominated by gene sets involved in immune function and signaling, as well as the control of mitosis. GSEA in the GWAS study produced 56 gene sets, 33 of which were concordant with the gene-expression-derived pathways (hypergeometric P = 1.54 × 10−18 for this overlap). The identified pathways were dominated by immune system function, control of mitosis, signaling pathways, oxidative phosphorylation, ubiquitin/proteasome, and extracellular matrix interaction. This study reveals that a concordant set of pathways are altered both at the transcriptome level as well as having risk-conferring genetic variation. These common pathways can serve as a useful guide to prioritizing targets for therapeutic development or establishing personalized treatment of ulcerative colitis.

Background In metastatic triple negative breast cancer (mTNBC), median progression-free survival (PFS) with chemotherapy alone is approximately 2–4 months1 and improvements with single agent checkpoint inhibitors (CI) are limited by...

Background: Many women at high risk for breast cancer will not take tamoxifen or aromatase inhibitors for cancer prevention due to concern of side effects including hot flashes. Further, tamoxifen has detrimental metabolic effects in some overweight/obese women. Duavee®, a tissue-selective complex of bazedoxifene + conjugated estrogen, is FDA-approved for relief of hot-flashes and prevention of osteoporosis. Preclinical studies suggest favorable metabolic effects and potential for breast cancer risk reduction. In a single arm clinical trial, BZA+CE reduced Ki-67 and mammographic density (PMID: 31420361), and this combination is currently being assessed further in a Phase IIB trial of postmenopausal high risk women with vasomotor symptoms. Here, we examined the modifying effects of obesity on response to BZA+CE in a rodent model of obesity and breast cancer risk. Methods: Rats received carcinogen at 7-weeks of age to induce mammary tumors and were fed a high-fat diet (46% kcal fat) to promote obesity. Lean and obese rat were selected based on adiposity at 16 weeks. Separate cohorts of lean and obese ovary-intact or ovariectomized (OVX) rats were randomized to a daily dose of BZA+CE or placebo control for 8 weeks. We assessed tumor development throughout the study and end of study RNA from mammary glands was analysed by gene expression microarray (Affymetrix). We also analyzed body weight/composition, markers of metabolic health (circulating glucose, insulin, adiponectin), as well as changes in the gut microbiome (metagenomic sequencing on DNA isolated from baseline and end-of-study fecal samples) in response to BZA+CE. Results: BZA+CE improved metabolic health in both ovary intact and OVX rats, including reduced body weight and % body fat, particularly in the visceral regions. These effects were greater in obese rats compared to lean. BZA+CE reduced the number of large adipocytes and increased small (insulin sensitive) adipocytes in the mammary adipose, indicating benefical changes in local tumor microenvironment. We found no evidence that Duavee® promoted tumor development or growth in ovary-intact or OVX’d rats. Gene set enrichment analysis (GSEA) of microarray data showed enrichment of cell proliferation pathways in MG from obese rats and these same pathways were downregulated with BZA+CE, consistent with anti-cancer effects. Analysis of the microbiome found that BZA+CE increased proportional abudance of Odoribacter laneus in the gut, regardless of lean/obese or intact/OVX status. Previous studies have demonstrated that this bacterim improves glucose control and reduces inflammatory cytokines when administered to obese mice, suggesting a possible mechanistic link in this study. Conclusions: Unlike traditional SERMs that can have negative metabolic effects, BZA+CE improved whole body and mammary gland metabolic health and reduced expression of cell proliferation pathways, particularly in obese rats. Prelminary data suggest that changes in the gut microbiome could contribute, at least in part, to these effects. Together this supports BZA+CE (Duavee) as an agent with potential beneficial effects on breast cancer risk reduction and improvements in metabolic health in women with obesity. Further analyses will guide assessment of outcomes in an ongoing parallel clinical study in postmenopausal women at high risk for breast cancer. Citation Format: Katherine L. Cook, Ramsey Jenshcke, Karen Corleto, Carol Fabian, Stephen Hursting, Bruce Kimler, Danilo Landrock, Erin Giles. Bazedoxifene plus conjugated estrogen reduces mammary proliferation markers and improves adipocyte size, gut microbiome, and metabolic health: Findings from a preclinical model of obesity and breast cancer risk [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS07-08.

Breast cancer remains a leading cause of mortality among women worldwide, with early and accurate diagnosis being critical for effective treatment. Gene expressionprofiling has emerged as a powerful tool for understanding the molecular mechanisms of cancer and for developing predictive models. However, the high dimensionality and class imbalance inherent in gene expression data pose significant challenges for developing robust predictive models.This study aims to develop and evaluate a predictive model for classifying breast cancer subtypes using high-dimensional gene expression data, addressing the challenges of class imbalance. The objective is to improve the accuracy and reliability of breast cancer subtype prediction to facilitate better diagnostic and treatment strategies.A comprehensive dataset of breast cancer gene expression profiles was utilized, comprising numerous gene expression levels across multiple samples. To address class imbalance, resampling techniques such as Synthetic Minority Over-sampling Technique (SMOTE) and Random Under-sampling, were employed. The machine learning algorithms employed included Support Vector Machines (SVM), Random Forests, and Neural Networks. The algorithms were trained and evaluated using cross-validation to identify the most effective model. The performance of these models was assessed based on metrics such as accuracy, precision, recall, and F1-score. The results indicate that the use of SMOTE in combination with SVM provided the most balanced and accurate predictions, with an F1-score significantly higher than models without resampling. The Random Forest algorithm also showed promising results, particularly in handling the high-dimensionality aspect of the data.The study demonstrates that addressing class imbalance through advanced resampling techniques can significantly enhance the predictive accuracy of models trained on high-dimensional gene expression data. The findings underscore the potential of machine learning models, particularly SVMs and Random Forests, in improving breast cancer subtype classification.

Genomic sequencing studies of breast and other cancers have identified patterns of mutations that have been attributed to the endogenous mutator activity of APOBEC3B (A3B), a member of the AID/APOBEC family of cytidine deaminases. A3B gene expression is increased in many cancers, but its upstream drivers remain undefined. Furthermore, there exists a common germ-line deletion polymorphism (A3B(del)), which has been associated with a paradoxical increase in breast cancer risk. To examine causes and consequences of A3B expression and its constitutive absence in breast cancer, we analyzed two large clinically annotated genomic datasets [The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC)]. We confirmed that A3B expression is associated with aggressive clinicopathologic characteristics and adverse outcomes and show that A3B expression is highly correlated with proliferative features (mitosis and cell cycle-related gene expression) in breast and 15 of 16 other solid tumor types. However, breast cancers arising in homozygous A3B(del) individuals with A3B absent did not differ in these features, indicating that A3B expression is a reflection rather than a direct cause of increased proliferation. Using gene set enrichment analysis (GSEA), we detected a pattern of immune activation in A3B(del) breast cancers, which seems to be related to hypermutation arising in A3B(del) carriers. Together, these results provide an explanation for A3B overexpression and its prognostic effect, giving context to additional study of this mutator as a cancer biomarker or putative drug target. In addition, although immune features of A3B(del) require additional study, these findings nominate the A3B(del) polymorphism as a potential predictor for cancer immunotherapy.