Genetic determinants of the molecular portraits of epithelial cancers

Prognostic and molecular multi-platform analysis of CALGB 40603 (Alliance) and public triple-negative breast cancer datasets

Triple-negative breast cancer (TNBC) is an aggressive and heterogeneous disease that remains challenging to target with traditional therapies and to predict risk. We provide a comprehensive characterization of 238 stage II-III TNBC tumors with paired RNA and DNA sequencing data from the CALGB 40603 (Alliance) clinical trial, along with 448 stage II-III TNBC tumors with paired RNA and DNA data from three additional datasets. We identify DNA mutations associated with RNA-based subtypes, specific TP53 missense mutations compatible with potential neoantigen activity, and a consistently highly altered copy number landscape. We train exploratory multi-modal elastic net models of TNBC patient overall survival to determine the added impact of DNA-based features to RNA and clinical features. We find that mutations and copy number show little to no prognostic value, while RNA expression features, including signatures of T cell and B cell activity, along with stage, improve stratification of TNBC survival risk.

Defining the regulatory logic of breast cancer using single-cell epigenetic and transcriptome profiling

Annotation of cis-regulatory elements that drive transcriptional dysregulation in cancer cells is critical to understanding tumor biology. Herein, we present matched chromatin accessibility (single-cell assay for transposase-accessible chromatin by sequencing [scATAC-seq]) and transcriptome (single-cell RNA sequencing [scRNA-seq]) profiles at single-cell resolution from human breast tumors and healthy mammary tissues processed immediately following surgical resection. We identify the most likely cell of origin for subtype-specific breast tumors and implement linear mixed-effects modeling to quantify associations between regulatory elements and gene expression in malignant versus normal cells. These data unveil cancer-specific regulatory elements and putative silencer-to-enhancer switching events in cells that lead to the upregulation of clinically relevant oncogenes. In addition, we generate matched scATAC-seq and scRNA-seq profiles for breast cancer cell lines, revealing a conserved oncogenic gene expression program between in vitro and in vivo cells. This work highlights the importance of non-coding regulatory mechanisms that underlie oncogenic processes and the ability of single-cell multi-omics to define the regulatory logic of cancer cells.

Understanding metastasis mixed-treatment responses through genomic analyses

Early-stage and metastatic breast cancers (MBC) can exhibit genomic heterogeneity, even within the same individual. Response to therapy in metastatic breast cancer patients with multiple metastases can also be heterogeneous, with different degrees of responsiveness to the same drug(s) across metastatic sites, termed "mixed response," within the same patient. Whether this treatment response variability is influenced by factors such as intrinsic tumor characteristics of metastatic lesions and/or the microenvironment is unknown. Through genomic analysis of multiple metastases from the same patient, assayed in 6 different patients who had exhibited mixed response on imaging, we identified that higher regulatory T cells (T reg) and CDKN2A gene expression values correlate with non-response, while the KRAS gene, KRAS amplicon, and CD8T cells were associated with response in individual metastases. These genomic features may explain mixed clinical responses and provide valuable insights into intrapatient variations in treatment sensitivity.

Patritumab deruxtecan in HER2-negative breast cancer: part B results of the window-of-opportunity SOLTI-1805 TOT-HER3 trial and biological determinants of early response

Patritumab deruxtecan (HER3-DXd) exhibits promising efficacy in breast cancer, with its activity not directly correlated to baseline ERBB3/HER3 levels. This research investigates the genetic factors affecting HER3-DXd's response in women with early-stage hormone receptor-positive and HER2-negative (HR+/HER2-) breast cancer. In the SOLTI-1805 TOT-HER3 trial, a single HER3-DXd dose was administered to 98 patients across two parts: 78 patients received 6.4 mg/kg (Part A), and 44 received a lower 5.6 mg/kg dose (Part B). The CelTIL score, measuring tumor cellularity and infiltrating lymphocytes from baseline to day 21, was used to assess drug activity. Part A demonstrated increased CelTIL score after one dose of HER3-DXd. Here we report CelTIL score and safety for Part B. In addition, the exploratory analyses of part A involve a comprehensive study of gene expression, somatic mutations, copy-number segments, and DNA-based subtypes, while Part B focuses on validating gene expression. RNA analyses show significant correlations between CelTIL responses, high proliferation genes (e.g., CCNE1, MKI67), and low expression of luminal genes (e.g., NAT1, SLC39A6). DNA findings indicate that CelTIL response is significantly associated with TP53 mutations, proliferation, non-luminal signatures, and a distinct DNA-based subtype (DNADX cluster-3). Critically, low HER2DX ERBB2 mRNA, correlates with increased HER3-DXd activity, which is validated through in vivo patient-derived xenograft  models. This study proposes chemosensitivity determinants, DNA-based subtype classification, and low ERBB2 expression as potential markers for HER3-DXd activity in HER2-negative breast cancer.

Defining the Regulatory Logic of Breast Cancer Using Single-Cell Epigenetic and Transcriptome Profiling

Annotation of the cis-regulatory elements that drive transcriptional dysregulation in cancer cells is critical to improving our understanding of tumor biology. Herein, we present a compendium of matched chromatin accessibility (scATAC-seq) and transcriptome (scRNA-seq) profiles at single-cell resolution from human breast tumors and healthy mammary tissues processed immediately following surgical resection. We identify the most likely cell-of-origin for luminal breast tumors and basal breast tumors and then introduce a novel methodology that implements linear mixed-effects models to systematically quantify associations between regions of chromatin accessibility (i.e. regulatory elements) and gene expression in malignant cells versus normal mammary epithelial cells. These data unveil regulatory elements with that switch from silencers of gene expression in normal cells to enhancers of gene expression in cancer cells, leading to the upregulation of clinically relevant oncogenes. To translate the utility of this dataset into tractable models, we generated matched scATAC-seq and scRNA-seq profiles for breast cancer cell lines, revealing, for each subtype, a conserved oncogenic gene expression program between in vitro and in vivo cells. Together, this work highlights the importance of non-coding regulatory mechanisms that underlie oncogenic processes and the ability of single-cell multi-omics to define the regulatory logic of BC cells at single-cell resolution.

Invention of 3Mint for feature grouping and scoring in multi-omics

Advanced genomic and molecular profiling technologies accelerated the enlightenment of the regulatory mechanisms behind cancer development and progression, and the targeted therapies in patients. Along this line, intense studies with immense amounts of biological information have boosted the discovery of molecular biomarkers. Cancer is one of the leading causes of death around the world in recent years. Elucidation of genomic and epigenetic factors in Breast Cancer (BRCA) can provide a roadmap to uncover the disease mechanisms. Accordingly, unraveling the possible systematic connections between-omics data types and their contribution to BRCA tumor progression is crucial. In this study, we have developed a novel machine learning (ML) based integrative approach for multi-omics data analysis. This integrative approach combines information from gene expression (mRNA), microRNA (miRNA) and methylation data. Due to the complexity of cancer, this integrated data is expected to improve the prediction, diagnosis and treatment of disease through patterns only available from the 3-way interactions between these 3-omics datasets. In addition, the proposed method bridges the interpretation gap between the disease mechanisms that drive onset and progression. Our fundamental contribution is the 3 Multi-omics integrative tool (3Mint). This tool aims to perform grouping and scoring of groups using biological knowledge. Another major goal is improved gene selection via detection of novel groups of cross-omics biomarkers. Performance of 3Mint is assessed using different metrics. Our computational performance evaluations showed that the 3Mint classifies the BRCA molecular subtypes with lower number of genes when compared to the miRcorrNet tool which uses miRNA and mRNA gene expression profiles in terms of similar performance metrics (95% Accuracy). The incorporation of methylation data in 3Mint yields a much more focused analysis. The 3Mint tool and all other supplementary files are available at https://github.com/malikyousef/3Mint/.

Circulating tumor DNA reveals complex biological features with clinical relevance in metastatic breast cancer

Liquid biopsy has proven valuable in identifying individual genetic alterations; however, the ability of plasma ctDNA to capture complex tumor phenotypes with clinical value is unknown. To address this question, we have performed 0.5X shallow whole-genome sequencing in plasma from 459 patients with metastatic breast cancer, including 245 patients treated with endocrine therapy and a CDK4/6 inhibitor (ET + CDK4/6i) from 2 independent cohorts. We demonstrate that machine learning multi-gene signatures, obtained from ctDNA, identify complex biological features, including measures of tumor proliferation and estrogen receptor signaling, similar to what is accomplished using direct tumor tissue DNA or RNA profiling. More importantly, 4 DNA-based subtypes, and a ctDNA-based genomic signature tracking retinoblastoma loss-of-heterozygosity, are significantly associated with poor response and survival outcome following ET + CDK4/6i, independently of plasma tumor fraction. Our approach opens opportunities for the discovery of additional multi-feature genomic predictors coming from ctDNA in breast cancer and other cancer-types.

Multiomics in primary and metastatic breast tumors from the AURORA US network finds microenvironment and epigenetic drivers of metastasis

The AURORA US Metastasis Project was established with the goal to identify molecular features associated with metastasis. We assayed 55 females with metastatic breast cancer (51 primary cancers and 102 metastases) by RNA sequencing, tumor/germline DNA exome and low-pass whole-genome sequencing and global DNA methylation microarrays. Expression subtype changes were observed in ~30% of samples and were coincident with DNA clonality shifts, especially involving HER2. Downregulation of estrogen receptor (ER)-mediated cell-cell adhesion genes through DNA methylation mechanisms was observed in metastases. Microenvironment differences varied according to tumor subtype; the ER+/luminal subtype had lower fibroblast and endothelial content, while triple-negative breast cancer/basal metastases showed a decrease in B and T cells. In 17% of metastases, DNA hypermethylation and/or focal deletions were identified near HLA-A and were associated with reduced expression and lower immune cell infiltrates, especially in brain and liver metastases. These findings could have implications for treating individuals with metastatic breast cancer with immune- and HER2-targeting therapies.

Whole-exome sequencing identifies a set of genes as markers of hepatocellular carcinoma early recurrence

BACKGROUND

Hepatocellular carcinoma (HCC) is characterized by a high recurrence rate and poor prognosis. In recent years, the therapeutic regimen of programmed cell death protein 1 (PD-1) antibody combined with multi-targeted tyrosine kinase inhibitors (mTKIs) has achieved better results in the clinical application of hepatocellular carcinoma. Whole-exome sequencing can reflect the mutational characteristics of patients' exons and guide the clinical selection of molecular targeting drugs more accurately, which is in line with the concept of precision medicine.

METHODS

We performed exome sequencing on 63 patients with HCC treated with radical surgery at our hospital and collected their clinical indexes and postoperative follow-up data. Using machine learning, a prediction model for recurrence within 1 year was constructed and the model was presented in a nomogram. Patients treated with PD-1 antibodies in combination with mTKIs after relapse were grouped by prognosis, and the valuable mutated genes were screened according to whole-exome sequencing data. The tumor tissue immune cells were analyzed using the UCSC Xena database. The expressions of target proteins were verified by Polymerase Chain Reaction (PCR) and Immunohistochemistry (IHC), respectively, on commercial HCC cell lines and pathological specimens of hepatocellular carcinoma collected clinically.

RESULTS

The proportion of patients who relapsed within a year was 41% and the prognosis of those patients was poor. The characteristic exon mutation profile with a high frequency of variants in multiple mucin genes was present in Chinese HCC patients. Multiple nidi and 30 exon variants were brought into the prediction model with an area under the curve (AUC) = 0.94. MUC6 gene mutation was obvious in patients with an early recurrence, and MUC3A and MUC4 gene mutations were evident in patients with poorer responses to PD-1 antibodies combined with mTKIs. Those three mucins were negatively correlated with immune infiltrating cells.

CONCLUSIONS

We depicted the exon characteristics of hepatocellular carcinoma in the Chinese population and established a predictive model for recurrence within 1 year after radical surgical treatment. Moreover, we found that mucins were worthy targets of hepatocellular carcinoma.

Integrated DNA and RNA Sequencing Reveals Drivers of Endocrine Resistance in Estrogen Receptor-Positive Breast Cancer

PURPOSE

Endocrine therapy resistance (ETR) remains the greatest challenge in treating patients with hormone receptor-positive breast cancer. We set out to identify molecular mechanisms underlying ETR through in-depth genomic analysis of breast tumors.

EXPERIMENTAL DESIGN

We collected pre-treatment and sequential on-treatment tumor samples from 35 patients with estrogen receptor-positive breast cancer treated with neoadjuvant then adjuvant endocrine therapy; 3 had intrinsic resistance, 19 acquired resistance, and 13 remained sensitive. Response was determined by changes in tumor volume neoadjuvantly and by monitoring for adjuvant recurrence. Twelve patients received two or more lines of endocrine therapy, with subsequent treatment lines being initiated at the time of development of resistance to the previous endocrine therapy. DNA whole-exome sequencing and RNA sequencing were performed on all samples, totalling 169 unique specimens. DNA mutations, copy-number alterations, and gene expression data were analyzed through unsupervised and supervised analyses to identify molecular features related to ETR.

RESULTS

Mutations enriched in ETR included ESR1 and GATA3. The known ESR1 D538G variant conferring ETR was identified, as was a rarer E380Q variant that confers endocrine hypersensitivity. Resistant tumors which acquired resistance had distinct gene expression profiles compared with paired sensitive tumors, showing elevated pathways including ER, HER2, GATA3, AKT, RAS, and p63 signaling. Integrated analysis in individual patients highlighted the diversity of ETR mechanisms.

CONCLUSIONS

The mechanisms underlying ETR are multiple and characterized by diverse changes in both somatic genetic and transcriptomic profiles; to overcome resistance will require an individualized approach utilizing genomic and genetic biomarkers and drugs tailored to each patient.

Genome-wide profiling of circulatory microRNAs associated with cognition and dementia

INTRODUCTION

MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression. Their role in the pathophysiology of dementia and potential as biomarkers remains undetermined.

METHODS

We conducted a single- (one-by-one) and multi-marker (joint) analysis to identify well-expressed circulating miRNAs in plasma (total = 591) associated with general cognition and incident dementia, for 1615 participants of the population-based Rotterdam Study.

RESULTS

During single-marker analysis, 47 miRNAs were nominally (P ≤ .05) associated with cognition and 18 miRNAs were nominally associated with incident dementia, after adjustment for potential confounders. Three miRNAs were common between cognition and dementia (miR-4539, miR-372-3p, and miR-566), with multi-marker analysis revealing another common miRNA (miR-7106-5p). In silico analysis of these four common miRNAs led to several putative target genes expressed in the brain, highlighting the mitogen-activated protein kinase signaling pathway.

DISCUSSION

We provide population-based evidence on the relationship between circulatory miRNAs with cognition and dementia, including four common miRNAs that may elucidate downstream mechanisms.

HIGHLIGHTS

MicroRNAs (miRNAs) are involved in the (dys)function of the central nervous system. Four circulating miRNAs in plasma are associated with cognition and incident dementia. Several predicted target genes of these four miRNAs are expressed in the brain. These four miRNAs may be linked to pathways underlying dementia. Although miRNAs are promising biomarkers, experimental validation remains essential.

Gene-Level Germline Contributions to Clinical Risk of Recurrence Scores in Black and White Patients with Breast Cancer

Continuous risk of recurrence scores (CRS) based on tumor gene expression are vital prognostic tools for breast cancer. Studies have shown that Black women (BW) have higher CRS than White women (WW). Although systemic injustices contribute substantially to breast cancer disparities, evidence of biological and germline contributions is emerging. In this study, we investigated germline genetic associations with CRS and CRS disparity using approaches modeled after transcriptome-wide association studies (TWAS). In the Carolina Breast Cancer Study, using race-specific predictive models of tumor expression from germline genetics, we performed race-stratified (N = 1,043 WW, 1,083 BW) linear regressions of three CRS (ROR-S: PAM50 subtype score; proliferation score; ROR-P: ROR-S plus proliferation score) on imputed tumor genetically regulated tumor expression (GReX). Bayesian multivariate regression and adaptive shrinkage tested GReX-prioritized genes for associations with tumor PAM50 expression and subtype to elucidate patterns of germline regulation underlying GReX-CRS associations. At FDR-adjusted P < 0.10, 7 and 1 GReX prioritized genes among WW and BW, respectively. Among WW, CRS were positively associated with MCM10, FAM64A, CCNB2, and MMP1 GReX and negatively associated with VAV3, PCSK6, and GNG11 GReX. Among BW, higher MMP1 GReX predicted lower proliferation score and ROR-P. GReX-prioritized gene and PAM50 tumor expression associations highlighted potential mechanisms for GReX-prioritized gene to CRS associations. Among patients with breast cancer, differential germline associations with CRS were found by race, underscoring the need for larger, diverse datasets in molecular studies of breast cancer. These findings also suggest possible germline trans-regulation of PAM50 tumor expression, with potential implications for CRS interpretation in clinical settings. SIGNIFICANCE: This study identifies race-specific genetic associations with breast cancer risk of recurrence scores and suggests mediation of these associations by PAM50 subtype and expression, with implications for clinical interpretation of these scores.

Elastic Net Models Based on DNA Copy Number Variations Predicts Clinical Features, Expression Signatures, and Mutations in Lung Adenocarcinoma

In the precision medicine of lung adenocarcinoma, the identification and prediction of tumor phenotypes for specific biomolecular events are still not studied in depth. Various earlier researches sheds light on the close correlation between genetic expression signatures and DNA copy number variations (CNVs), for which analysis of CNVs provides valuable information about molecular and phenotypic changes in tumorigenesis. In this study, we propose a comprehensive analysis combining genome-wide association analysis and an Elastic Net Regression predictive model, focus on predicting the levels of many gene expression signatures in lung adenocarcinoma, based upon DNA copy number features alone. Additionally, we predicted many other key phenotypes, including clinical features (pathological stage), gene mutations, and protein expressions. These Elastic Net prediction methods can also be applied to other gene sets, thereby facilitating their use as biomarkers in monitoring therapy.

Galaxy and MEAN Stack to Create a User-Friendly Workflow for the Rational Optimization of Cancer Chemotherapy

One aspect of personalized medicine is aiming at identifying specific targets for therapy considering the gene expression profile of each patient individually. The real-world implementation of this approach is better achieved by user-friendly bioinformatics systems for healthcare professionals. In this report, we present an online platform that endows users with an interface designed using MEAN stack supported by a Galaxy pipeline. This pipeline targets connection hubs in the subnetworks formed by the interactions between the proteins of genes that are up-regulated in tumors. This strategy has been proved to be suitable for the inhibition of tumor growth and metastasis in vitro. Therefore, Perl and Python scripts were enclosed in Galaxy for translating RNA-seq data into protein targets suitable for the chemotherapy of solid tumors. Consequently, we validated the process of target diagnosis by (i) reference to subnetwork entropy, (ii) the critical value of density probability of differential gene expression, and (iii) the inhibition of the most relevant targets according to TCGA and GDC data. Finally, the most relevant targets identified by the pipeline are stored in MongoDB and can be accessed through the aforementioned internet portal designed to be compatible with mobile or small devices through Angular libraries.

Multi-Omics Analysis Detects Novel Prognostic Subgroups of Breast Cancer

The unprecedented proliferation of recent large-scale and multi-omics databases of cancers has given us many new insights into genomic and epigenomic deregulation in cancer discovery in general. However, we wonder whether or not there exists a systematic connection between copy number aberrations (CNA) and methylation (MET)? If so, what is the role of this connection in breast cancer (BRCA) tumorigenesis and progression? At the same time, the PAM50 intrinsic subtypes of BRCA have gained the most attention from BRCA experts. However, this classification system manifests its weaknesses including low accuracy as well as a possible lack of association with biological phenotypes, and even further investigations on their clinical utility were still needed. In this study, we performed an integrative analysis of three-omics profiles, CNA, MET, and mRNA expression, in two BRCA patient cohorts (one for discovery and another for validation) – to elucidate those complicated relationships. To this purpose, we first established a set of CNAcor and METcor genes, which had CNA and MET levels significantly correlated (and anti-correlated) with their corresponding expression levels, respectively. Next, to revisit the current classification of BRCA, we performed single and integrated clustering analyses using our clustering method PINSPlus. We then discovered two biologically distinct subgroups that could be an improved and refined classification system for breast cancer patients, which can be validated by a third-party data. Further studies were then performed and realized each-subgroup-specific genes and different interactions between each of the two identified subgroups with the age factor. These findings can show promise as diagnostic and prognostic values in BRCA, and a potential alternative to the PAM50 intrinsic subtypes in the future.

Advances in the Molecular Taxonomy of Breast Cancer

Breast cancers are heterogeneous with variable morphologic features, biologic behavior and response to therapy. Traditional histopathologic features such as size, grade, and lymph node status may be used to provide a general estimate of outcome, stratifying patients into broad prognostic groups with prescribed guidelines for therapy. With this approach however, up to 85% of breast cancer patients are overtreated, and at the other end of the spectrum, 20% of patients succumb to their disease despite receiving maximum therapy. The current routine testing for the Estrogen receptor (ER) and HER2 growth factor receptor (HER2) represents the earliest attempts to provide a targeted approach to breast cancer therapy, based on molecular drivers of the disease. The pioneering works by Perou and Sorlie et al using global gene expression profiling introduced a molecular taxonomy of breast cancer with associated prognostic implications. The Luminal, HER2-enriched, and Basal-like intrinsic subtypes are generally characterized by the presence or absence of ER and HER2. They have been further analyzed and refined using integration of genomic and transcriptomic data made possible by advancements in high throughput molecular techniques and bioinformatics. Indeed, an increased understanding of the genomic landscape of these subtypes, and the molecular basis of breast cancer growth regulation, holds the promise of a more personalized patient selection for specific targeted therapies and improved outcomes.

Past, Present, and Future of Anticancer Nanomedicine

This review aims to summarize the methods that have been used till today, highlight methods that are currently being developed, and predict the future roadmap for anticancer therapy. In the beginning of this review, established approaches for anticancer therapy, such as conventional chemotherapy, hormonal therapy, monoclonal antibodies, and tyrosine kinase inhibitors are summarized. To counteract the side effects of conventional chemotherapy and to increase limited anticancer efficacy, nanodrug- and stem cell-based therapies have been introduced. However, current level of understanding and strategies of nanodrug and stem cell-based therapies have limitations that make them inadequate for clinical application. Subsequently, this manuscript reviews methods with fewer side effects compared to those of the methods mentioned above which are currently being investigated and are already being applied in the clinic. The newer strategies that are already being clinically applied include cancer immunotherapy, especially T cell-mediated therapy and immune checkpoint inhibitors, and strategies that are gaining attention include the manipulation of the tumor microenvironment or the activation of dendritic cells. Tumor-associated macrophage repolarization is another potential strategy for cancer immunotherapy, a method which activates macrophages to immunologically attack malignant cells. At the end of this review, we discuss combination therapies, which are the future of cancer treatment. Nanoparticle-based anticancer immunotherapies seem to be effective, in that they effectively use nanodrugs to elicit a greater immune response. The combination of these therapies with others, such as photothermal or tumor vaccine therapy, can result in a greater anticancer effect. Thus, the future of anticancer therapy aims to increase the effectiveness of therapy using various therapies in a synergistic combination rather than individually.

Somatic genetic aberrations in benign breast disease and the risk of subsequent breast cancer

It is largely unknown how the development of breast cancer (BC) is transduced by somatic genetic alterations in the benign breast. Since benign breast disease is an established risk factor for BC, we established a case-control study of women with a history of benign breast biopsy (BBB). Cases developed BC at least one year after BBB and controls did not develop BC over an average of 17 years following BBB. 135 cases were matched to 69 controls by age and type of benign change: non-proliferative or proliferation without atypia (PDWA). Whole-exome sequencing (WES) was performed for the BBB. Germline DNA (available from n = 26 participants) was utilized to develop a mutation-calling pipeline, to allow differentiation of somatic from germline variants. Among the 204 subjects, two known mutational signatures were identified, along with a currently uncatalogued signature that was significantly associated with triple negative BC (TNBC) (p = 0.007). The uncatalogued mutational signature was validated in 109 TNBCs from TCGA (p = 0.001). Compared to non-proliferative samples, PDWA harbors more abundant mutations at PIK3CA pH1047R (p < 0.001). Among the 26 BBB whose somatic copy number variation could be assessed, deletion of MLH3 is significantly associated with the mismatch repair mutational signature (p < 0.001). Matched BBB-cancer pairs were available for ten cases; several mutations were shared between BBB and cancers. This initial study of WES of BBB shows its potential for the identification of genetic alterations that portend breast oncogenesis. In future larger studies, robust personalized breast cancer risk indicators leading to novel interception paradigms can be assessed.

Bimodal age distribution at diagnosis in breast cancer persists across molecular and genomic classifications

PURPOSE

Female breast cancer demonstrates bimodal age frequency distribution patterns at diagnosis, interpretable as two main etiologic subtypes or groupings of tumors with shared risk factors. While RNA-based methods including PAM50 have identified well-established clinical subtypes, age distribution patterns at diagnosis as a proxy for etiologic subtype are not established for molecular and genomic tumor classifications.

METHODS

We evaluated smoothed age frequency distributions at diagnosis for Carolina Breast Cancer Study cases within immunohistochemistry-based and RNA-based expression categories. Akaike information criterion (AIC) values compared the fit of single density versus two-component mixture models. Two-component mixture models estimated the proportion of early-onset and late-onset categories by immunohistochemistry-based ER (n = 2860), and by RNA-based ESR1 and PAM50 subtype (n = 1965). PAM50 findings were validated using pooled publicly available data (n = 8103).

RESULTS

Breast cancers were best characterized by bimodal age distribution at diagnosis with incidence peaks near 45 and 65 years, regardless of molecular characteristics. However, proportional composition of early-onset and late-onset age distributions varied by molecular and genomic characteristics. Higher ER-protein and ESR1-RNA categories showed a greater proportion of late age-at-onset. Similarly, PAM50 subtypes showed a shifting age-at-onset distribution, with most pronounced early-onset and late-onset peaks found in Basal-like and Luminal A, respectively.

CONCLUSIONS

Bimodal age distribution at diagnosis was detected in the Carolina Breast Cancer Study, similar to national cancer registry data. Our data support two fundamental age-defined etiologic breast cancer subtypes that persist across molecular and genomic characteristics. Better criteria to distinguish etiologic subtypes could improve understanding of breast cancer etiology and contribute to prevention efforts.