Purpose The recent increase in the incidence of ductal carcinoma in situ (DCIS) has sparked debate over the classification and treatment of this disease. Although DCIS is considered a precursor lesion to invasive breast cancer, some DCIS may have more or less risk than is realized. In this study, we characterized the immune microenvironment in DCIS to determine if immune infiltrates are predictive of recurrence. Methods Fifty-two cases of high-grade DCIS (HG-DCIS), enriched for large lesions and a history of recurrence, were age matched with 65 cases of non-high-grade DCIS (nHG-DCIS). Immune infiltrates were characterized by single- or dual-color staining of FFPE sections for the following antigens: CD4, CD8, CD20, FoxP3, CD68, CD115, Mac387, MRC1, HLA-DR, and PCNA. Nuance multispectral imaging software was used for image acquisition. Protocols for automated image analysis were developed using CellProfiler. Immune cell populations associated with risk of recurrence were identified using classification and regression tree analysis. Results HG-DCIS had significantly higher percentages of FoxP3+ cells, CD68+ and CD68+PCNA+ macrophages, HLA-DR+ cells, CD4+ T cells, CD20+ B cells, and total tumor infiltrating lymphocytes (TILs) compared to nHG-DCIS. A classification tree, generated from 16 immune cell populations and 8 clinical parameters, identified three immune cell populations associated with risk of recurrence: CD8+HLADR+ T cells, CD8+HLADR− T cells, and CD115+ cells. Conclusion These findings suggest that the tumor immune microenvironment is an important factor in identifying DCIS cases with the highest risk for recurrence and that manipulating the immune microenvironment may be an efficacious strategy to alter or prevent disease progression.

Purpose: African American (AA) patients with triple-negative breast cancer (TNBC) are less likely to achieve pathologic complete response from neoadjuvant chemotherapy and have poorer prognosis than Caucasian patients with TNBC, suggesting potential biological differences by race. Immune infiltration is the most consistent predictive marker for chemotherapy response and improved prognosis in TNBC. In this study, we test the hypothesis that the immune microenvironment differs between AA and Caucasian patients. Methods: RNA-seq expression data were obtained from The Cancer Genome Atlas (TCGA) database for 162 AA and 697 Caucasian breast cancers. Estrogen receptor (ER)-positive, human epidermal growth factor receptor-2 (HER2)-positive, and TNBC subtypes were included in the analyses. Tumor infiltrating lymphocyte (TIL) counts, immunomodulatory scores, and molecular subtypes were obtained from prior publications for a subset of the TNBC cases. Differences in immune cell distributions and immune functions, measured through gene expression and TIL counts, as well as neoantigen, somatic mutation, amplification and deletion loads, were compared by race and tumor subtype. Results: Immune metagene analysis demonstrated marginal immune attenuation in AA TNBC relative to Caucasian TNBC that did not reach statistical significance. The distributions of immune cell populations, lymphocyte infiltration, molecular subtypes, and genomic aberrations between AA and Caucasian subtypes were also not significantly different. The MHC1 metagene demonstrated increased expression in AA ER-positive cancers relative to Caucasian ER-positive cancers.

Cancers harbor many somatic mutations and germline variants, we hypothesized that the combined effect of germline variants that alter the structure, expression, or function of protein-coding regions of cancer-biology related genes (gHFI) determines which and how many somatic mutations (sM) must occur for malignant transformation. We show that gHFI and sM affect overlapping genes and the average number of gHFI in cancer hallmark genes is higher in patients who develop cancer at a younger age (r = −0.77, P = 0.0051), while the average number of sM increases in increasing age groups (r = 0.92, P = 0.000073). A strong negative correlation exists between average gHFI and average sM burden in increasing age groups (r = −0.70, P = 0.017). In early-onset cancers, the larger gHFI burden in cancer genes suggests a greater contribution of germline alterations to the transformation process while late-onset cancers are more driven by somatic mutations.

Tumor mutational burden (TMB) is a promising tool to help define patients with triple-negative breast cancer (TNBC) most likely to benefit from immune checkpoint blockade (ICB) therapies. Roughly reflecting the degree of neo-antigens that tumors present to immune cells, TMB associates with multiple measures of tumoral immunogenicity and has proven clinically useful in cancers with relatively high mutation burden. TNBC carries higher TMB than other breast cancer subtypes, and recent data suggest that high-TMB TNBC cases may derive particular benefit from ICB in combination with chemotherapy (GeparNuevo, IMpassion130) or even ICB alone (KEYNOTE-119, TAPUR). Given the recent approval of pembrolizumab and atezolizumab in combination with chemotherapy for PD-L1-positive, metastatic TNBC, standardizing TMB calculation methods and cut-off values is of critical importance to deploy this clinical biomarker.

PURPOSE A subset of estrogen receptor–positive (ER-positive) breast cancer (BC) contains high levels of tumor-infiltrating lymphocytes (TILs), similar to triple-negative BC (TNBC). The majority of immuno-oncology trials target TNBCs because of the greater proportion of TIL-rich TNBCs. The extent to which the immune microenvironments of immune-rich ER-positive BC and TNBC differ is unknown. PATIENTS AND METHODS RNA sequencing data from The Cancer Genome Atlas (TCGA; n = 697 ER-positive BCs; n = 191 TNBCs) were used for discovery; microarray expression data from Molecular Taxonomy of Breast Cancer International Consortium (METABRIC; n = 1,186 ER-positive BCs; n = 297 TNBCs) was used for validation. Patients in the top 25th percentile of a previously published total TIL metagene score distribution were considered immune rich. We compared expression of immune cell markers, immune function metagenes, and immuno-oncology therapeutic targets among immune-rich subtypes. RESULTS Relative fractions of resting mast cells (TCGA Padj = .009; METABRIC Padj = 4.09E-15), CD8+ T cells (TCGA Padj = .015; METABRIC Padj = 0.390), and M2-like macrophages (TCGA Padj= 4.68E-05; METABRIC Padj = .435) were higher in immune-rich ER-positive BCs, but M0-like macrophages (TCGA Padj = 0.015; METABRIC Padj = .004) and M1-like macrophages (TCGA Padj = 9.39E-08; METABRIC Padj = 6.24E-11) were higher in immune-rich TNBCs. Ninety-one immune-related genes (eg, CXCL14, CSF3R, TGF-B3, LRRC32/GARP, TGFB-R2) and a transforming growth factor β (TGF-β) response metagene were significantly overexpressed in immune-rich ER-positive BCs, whereas 41 immune-related genes (eg, IFNG, PD-L1, CTLA4, MAGEA4) were overexpressed in immune-rich TNBCs in both discovery and validation data sets. TGF-β pathway member genes correlated negatively with expression of immune activation markers ( IFNG, granzyme-B, perforin) and positively with M2-like macrophages ( IL4, IL10, and MMP9) and regulatory T-cell ( FOXP3) markers in both subtypes. CONCLUSION Different immunotherapy strategies may be optimal in immune-rich ER-positive BC and TNBC. Drugs targeting the TGF-β pathway and M2-like macrophages are promising strategies in immune-rich ER-positive BCs to augment antitumor immunity.

Immune checkpoint inhibitors (ICIs) have minimal therapeutic effect in hormone receptor-positive (HR+ ) breast cancer. We present final overall survival (OS) results (n = 88) from a randomized phase 2 trial of eribulin ± pembrolizumab for patients with metastatic HR+ breast cancer, computationally dissect genomic and/or transcriptomic data from pre-treatment tumors (n = 52) for molecular associations with efficacy, and identify cytokine changes differentiating response and ICI-related toxicity (n = 58). Despite no improvement in OS with combination therapy (hazard ratio 0.95, 95% CI 0.59–1.55, p = 0.84), immune infiltration and antigen presentation distinguished responding tumors, while tumor heterogeneity and estrogen signaling independently associated with resistance. Moreover, patients with ICI-related toxicity had lower levels of immunoregulatory cytokines. Broadly, we establish a framework for ICI response in HR+ breast cancer that warrants diagnostic and therapeutic validation. ClinicalTrials.gov Registration: NCT03051659.

Purpose Age is one of the strongest risk factors for the development of breast cancer, however, the underlying etiology linking age and breast cancer remains unclear. We have previously observed links between epigenetic aging signatures in breast/tumor tissue and breast cancer risk/prevalence. However, these DNA methylation-based aging biomarkers capture diverse epigenetic phenomena and it is not known to what degree they relate to breast cancer risk, and/or progression. Methods Using six epigenetic clocks, we analyzed whether they distinguish normal breast tissue adjacent to tumor (cases) vs normal breast tissue from healthy controls (controls). Results The Levine (p = 0.0037) and Yang clocks (p = 0.023) showed significant epigenetic age acceleration in cases vs controls in breast tissue. We observed that much of the difference between cases and controls is driven by CpGs associated with polycomb-related genes. Thus, we developed a new score utilizing only CpGs associated with polycomb-related genes and demonstrated that it robustly captured epigenetic age acceleration in cases vs controls (p = 0.00012). Finally, we tested whether this same signal could be seen in peripheral blood. We observed no difference in cases vs. controls and no correlation between matched tissue/blood samples, suggesting that peripheral blood is not a good surrogate marker for epigenetic age acceleration. Conclusions Moving forward, it will be critical for studies to elucidate whether epigenetic age acceleration in breast tissue precedes breast cancer diagnosis and whether methylation changes at CpGs associated with polycomb-related genes can be used to assess the risk of developing breast cancer among unaffected individuals.

No abstract