Genomic analysis defines clonal relationships of ductal carcinoma in situ and recurrent invasive breast cancer

Ductal carcinoma in situ (DCIS) is the most common form of preinvasive breast cancer and, despite treatment, a small fraction (5–10%) of DCIS patients develop subsequent invasive disease. A fundamental biologic question is whether the invasive disease arises from tumor cells in the initial DCIS or represents new unrelated disease. To address this question, we performed genomic analyses on the initial DCIS lesion and paired invasive recurrent tumors in 95 patients together with single-cell DNA sequencing in a subset of cases. Our data show that in 75% of cases the invasive recurrence was clonally related to the initial DCIS, suggesting that tumor cells were not eliminated during the initial treatment. Surprisingly, however, 18% were clonally unrelated to the DCIS, representing new independent lineages and 7% of cases were ambiguous. This knowledge is essential for accurate risk evaluation of DCIS, treatment de-escalation strategies and the identification of predictive biomarkers.Pattern Recognition and Bioinformatic

Comprehensive characterization of pre- and post-treatment samples of breast cancer reveal potential mechanisms of chemotherapy resistance

When locally advanced breast cancer is treated with neoadjuvant chemotherapy, the recurrence risk is significantly higher if no complete pathologic response is achieved. Identification of the underlying resistance mechanisms is essential to select treatments with maximal efficacy and minimal toxicity. Here we employed gene expression profiles derived from 317 HER2-negative treatment-naïve breast cancer biopsies of patients who underwent neoadjuvant chemotherapy, deep whole exome, and RNA-sequencing profiles of 22 matched pre- and post-treatment tumors, and treatment outcome data to identify biomarkers of response and resistance mechanisms. Molecular profiling of treatment-naïve breast cancer samples revealed that expression levels of proliferation, immune response, and extracellular matrix (ECM) organization combined predict response to chemotherapy. Triple negative patients with high proliferation, high immune response and low ECM expression had a significantly better treatment response and survival benefit (HR 0.29, 95% CI 0.10–0.85; p = 0.02), while in ER+ patients the opposite was seen (HR 4.73, 95% CI 1.51–14.8; p = 0.008). The characterization of paired pre-and post-treatment samples revealed that aberrations of known cancer genes were either only present in the pre-treatment sample (CDKN1B) or in the post-treatment sample (TP53, APC, CTNNB1). Proliferation-associated genes were frequently down-regulated in post-treatment ER+ tumors, but not in triple negative tumors. Genes involved in ECM were upregulated in the majority of post-chemotherapy samples. Genomic and transcriptomic differences between pre- and post-chemotherapy samples are common and may reveal potential mechanisms of therapy resistance. Our results show a wide range of distinct, but related mechanisms, with a prominent role for proliferation- and ECM-related genes.Pattern Recognition and Bioinformatic

Ovarian Cancer-Specific BRCA-like Copy-Number Aberration Classifiers Detect Mutations Associated with Homologous Recombination Deficiency in the AGO-TR1 Trial

Purpose: Previously, we developed breast cancer BRCA1-like and BRCA2-like copy-number profile shrunken centroid classifiers predictive for mutation status and response to therapy, targeting homologous recombination deficiency (HRD). Therefore, we investigated BRCA1- and BRCA2-like classification in ovarian cancer, aiming to acquire classifiers with similar properties as those in breast cancer. Experimental Design: We analyzed DNA copy-number profiles of germline BRCA1- and BRCA2-mutant ovarian cancers and control tumors and observed that existing breast cancer classifiers did not sufficiently predict mutation status. Hence, we trained new shrunken centroid classifiers on this set and validated them in the independent The Cancer Genome Atlas dataset. Subsequently, we assessed BRCA1/2-like classification and obtained germline and tumor mutation and methylation status of cancer predisposition genes, among them several involved in HR repair, of 300 ovarian cancer samples derived from the consecutive cohort trial AGO-TR1 (NCT02222883). Results: The detection rate of the BRCA1-like classifier for BRCA1 mutations and promoter hypermethylation was 95.6%. The BRCA2-like classifier performed less accurately, likely due to a smaller training set. Furthermore, three quarters of the BRCA1/ 2-like tumors could be explained by (epi)genetic alterations in BRCA1/2, germline RAD51C mutations and alterations in other genes involved in HR. Around half of the non-BRCA-mutated ovarian cancer cases displayed a BRCA-like phenotype. Conclusions: The newly trained classifiers detected most BRCAmutated and methylated cancers and all tumors harboring a RAD51C germline mutations. Beyond that, we found an additional substantial proportion of ovarian cancers to be BRCA-like. _2021 The Authors; Published by the American Association for Cancer Research.Pattern Recognition and Bioinformatic

Correction To: Tumour-educated platelets for breast cancer detection: biological and technical insights (British Journal of Cancer, (2023), 128, 8, (1572-1581), 10.1038/s41416-023-02174-5)

In this article the data availability statement was incorrectly given. “EGAS0000100682” should have been “EGAS00001006821”. The correct statement as follows. The datasets generated during and/or analysed during the current study are deposited at the European Genome-phenome Archive (EGA) under the accession numbers EGAS00001006821 and EGAD00001009790. The original article has been corrected.Pattern Recognition and Bioinformatic

Tumour-educated platelets for breast cancer detection: biological and technical insights

Background: Studies have shown that blood platelets contain tumour-specific mRNA profiles tumour-educated platelets (TEPs). Here, we aim to train a TEP-based breast cancer detection classifier. Methods: Platelet mRNA was sequenced from 266 women with stage I–IV breast cancer and 212 female controls from 6 hospitals. A particle swarm optimised support vector machine (PSO-SVM) and an elastic net-based classifier (EN) were trained on 71% of the study population. Classifier performance was evaluated in the remainder (29%) of the population, followed by validation in an independent set (37 cases and 36 controls). Potential confounding was assessed in post hoc analyses. Results: Both classifiers reached an area under the curve (AUC) of 0.85 upon internal validation. Reproducibility in the independent validation set was poor with an AUC of 0.55 and 0.54 for the PSO-SVM and EN classifier, respectively. Post hoc analyses indicated that 19% of the variance in gene expression was associated with hospital. Genes related to platelet activity were differentially expressed between hospitals. Conclusions: We could not validate two TEP-based breast cancer classifiers in an independent validation cohort. The TEP protocol is sensitive to within-protocol variation and revision might be necessary before TEPs can be reconsidered for breast cancer detection.Pattern Recognition and Bioinformatic

Mammary tumor-derived CCL2 enhances pro-metastatic systemic inflammation through upregulation of IL1β in tumor-associated macrophages

Patients with primary solid malignancies frequently exhibit signs of systemic inflammation. Notably, elevated levels of neutrophils and their associated soluble mediators are regularly observed in cancer patients, and correlate with reduced survival and increased metastasis formation. Recently, we demonstrated a mechanistic link between mammary tumor-induced IL17-producing γδ T cells, systemic expansion of immunosuppressive neutrophils and metastasis formation in a genetically engineered mouse model for invasive breast cancer. How tumors orchestrate this systemic inflammatory cascade to facilitate dissemination remains unclear. Here we show that activation of this cascade relies on CCL2-mediated induction of IL1β in tumor-associated macrophages. In line with these findings, expression of CCL2 positively correlates with IL1Β and macrophage markers in human breast tumors. We demonstrate that blockade of CCL2 in mammary tumor-bearing mice results in reduced IL17 production by γδ T cells, decreased neutrophil expansion and enhanced CD8+ T cell activity. These results highlight a new role for CCL2 in facilitating the breast cancer-induced pro-metastatic systemic inflammatory γδ T cell–IL17–neutrophil axis.Pattern Recognition and Bioinformatic