The limited efficacy of immune checkpoint inhibitor treatment in triple-negative breast cancer (TNBC) patients is attributed to sparse or unresponsive tumor-infiltrating lymphocytes, but the mechanisms that lead to a therapy resistant tumor immune microenvironment are incompletely known. Here we show a strong correlation between MYC expression and loss of immune signatures in human TNBC. In mouse models of TNBC proficient or deficient of breast cancer type 1 susceptibility gene (BRCA1), MYC overexpression dramatically decreases lymphocyte infiltration in tumors, along with immune signature remodelling. MYC-mediated suppression of inflammatory signalling induced by BRCA1/2 inactivation is confirmed in human TNBC cell lines. Moreover, MYC overexpression prevents the recruitment and activation of lymphocytes in both human and mouse TNBC co-culture models. Chromatin-immunoprecipitation-sequencing reveals that MYC, together with its co-repressor MIZ1, directly binds promoters of multiple interferon-signalling genes, resulting in their downregulation. MYC overexpression thus counters tumor growth inhibition by a Stimulator of Interferon Genes (STING) agonist via suppressing induction of interferon signalling. Together, our data reveal that MYC suppresses innate immunity and facilitates tumor immune escape, explaining the poor immunogenicity of MYC-overexpressing TNBCs.

Tumor samples are conserved in clinical practice in formalin-fixed paraffin-embedded (FFPE) blocks. Formalin fixation chemically alters nucleic acids, rendering transcriptomic analysis challenging. RNA-sequencing is usually performed on tumor bulk, without distinction of cell subtypes or location. Here we describe the development of a robust method for RNA extraction and exome-capture RNA-sequencing of lasercapture microdissected tumor cells (TC) and stromal immune cells (TIL) based on their morphology. We applied this method on 7 tumor samples (surgical or core needle biopsy) of triple-negative breast cancer (TNBC) stored in FFPE blocks over 3-10 years.Unsupervised clustering and principal component analysis showed a clear separation between gene-expression profile of TIL and TC. TIL were enriched in markers of B cells (CD79B, PAX5 and BLNK ) and T cells (CD2, CD3D and CD8B) whereas tumor cells expressed epithelial markers (EPCAM, MUC1 and KRT8). Microenvironment cell populations-counter (MCP)-counter deconvolution showed an enrichment in adaptive immune cell signatures in microdissected TIL. Transcripts of immune checkpoints were differentially expressed in TIL and TC. We further validated our results by qRT-PCR and multispectral immunohistochemistry. In conclusion, we showed that combining laser-capture microdissection and RNA-sequencing on archived FFPE blocks is feasible and allows spatial transcriptional characterization of tumor microenvironment.

Background: Mammographic density (MD) has been strongly associated with increased risk of breast cancer (BC). In view of this, we aimed to investigate the predictive value of MD in a large consecutive cohort of BC patients (pts) treated with neoadjuvant chemotherapy (NAC).Methods: Data on NAC treated pts prospectively collected in the registry of Fondazione IRCCS Istituto Nazionale dei Tumori, Milan (May 2009-Aprill 2020) were identified. Diagnostic mammograms were used to evaluate MD, which was categorized by the Breast Imaging-Reporting and Data System (BI-RADS). BI-RADS identify 4 categories of MD in keeping with the masking effect of fibroglandular tissue, as following: A (almost entirely fat), B (scattered areas of fibroglandular density), C (heterogeneously dense), and D (extremely dense). Multivariable logistic regression was used to assess the odds ratios (OR) for pathological complete response (pCR), ie absence of invasive tumor in breast and node surgical specimens, comparing BI-RADS categories with adjustment for patient age, BMI, and tumor characteristics.Results: A total of 442 pts were analyzed, of which 120 (27.1%) attained a pCR. BI-RADS categories A, B, C, and D accounted for 10.0%, 37.8%, 37.1% and 15.2% of cases, respectively, with corresponding pCR rates of 20.5%, 26.9%, 30.5%, 23.9%. At multivariable analysis cases classified as BI-RADS C showed an increased likelihood of pCR as compared to A (odds ratio [OR]¼2.79), B (OR¼1.70), and D (OR¼1.47) independently of age, BMI (OR underweight vs normal¼3.76), clinical N and T (OR T1/ Tx vs T4¼3.87), molecular subtype (HER2 vs luminal¼10.74; triple negative vs luminal¼8.19). In subgroup analyses, the strongest association of MD with pCR was observed in triple negative (