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Complement protein C1q plays a dual role in a number of inflammatory diseases such as atherosclerosis. While in later stages classical complement pathway activation by C1q exacerbates disease progression, C1q also plays a beneficial role in early disease. Independent of its role in complement activation, we and others have identified a number of potentially beneficial interactions of C1q with phagocytes in vitro, including triggering phagocytosis of cellular and molecular debris and polarizing macrophages toward an anti-inflammatory phenotype. These interactions may also be important in preventing autoimmunity. Here, we characterize variants of recombinant human C1q (rC1q) which no longer initiate complement activation, through mutation of the C1r 2 C1s 2 interaction site. For insight into the structural location of the site of C1q that is important for interaction with phagocytes, we investigated the effect of these mutations on phagocytosis and macrophage inflammatory polarization, as compared to wild-type C1q. Phagocytosis of antibody coated sheep erythrocytes and oxidized LDL was measured in human monocytes and monocyte-derived macrophages (HMDM) respectively that had interacted with rC1q wild-type or variants. Secreted levels of cytokines were also measured in C1q stimulated HMDM. All variants of C1q increased phagocytosis in HMDM compared to controls, similar to native or wild-type rC1q. In addition, levels of certain pro-inflammatory cytokines and chemokines secreted by HMDM were modulated in cells that interacted with C1q variants, similar to wild-type rC1q and native C1q. This includes downregulation of IL-1α, IL-1β, TNFα, MIP-1α, and IL-12p40 by native and rC1q in both resting and M1-polarized HMDM. This suggests that the site responsible for C1q interaction with phagocytes is independent of the C1r 2 C1s 2 interaction site. Further studies with these classical pathway-null variants of C1q should provide greater understanding of the complement-independent role of C1q, and allow for potential therapeutic exploitation., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Background/objective: Triple-negative breast cancer (TNBC) is a heterogeneous collection of breast tumors with numerous differences including morphological characteristics, genetic makeup, immune-cell infiltration, and response to systemic therapy. DNA methylation profiling is a robust tool to accurately identify disease-specific subtypes. We aimed to generate an epigenetic subclassification of TNBC tumors (epitypes) with utility for clinical decision-making., Methods: Genome-wide DNA methylation profiles from TNBC patients generated in the Cancer Genome Atlas project were used to build machine learning-based epigenetic classifiers. Clinical and demographic variables, as well as gene expression and gene mutation data from the same cohort, were integrated to further refine the TNBC epitypes., Results: This analysis indicated the existence of four TNBC epitypes, named as Epi-CL-A, Epi-CL-B, Epi-CL-C, and Epi-CL-D. Patients with Epi-CL-B tumors showed significantly shorter disease-free survival and overall survival [log rank; P = 0.01; hazard ratio (HR) 3.89, 95% confidence interval (CI) 1.3-11.63 and P = 0.003; HR 5.29, 95% CI 1.55-18.18, respectively]. Significant gene expression and mutation differences among the TNBC epitypes suggested alternative pathway activation that could be used as ancillary therapeutic targets. These epigenetic subtypes showed complementarity with the recently described TNBC transcriptomic subtypes., Conclusions: TNBC epigenetic subtypes exhibit significant clinical and molecular differences. The links between genetic make-up, gene expression programs, and epigenetic subtypes open new avenues in the development of laboratory tests to more efficiently stratify TNBC patients, helping optimize tailored treatment approaches.

Background: Pathological response to neoadjuvant chemotherapy (NAC) is critical in prognosis and selection of systemic treatments for patients with triple-negative breast cancer (TNBC). The aim of this study is to identify gene expression-based markers to predict response to NAC., Patients and Methods: A survey of 43 publicly available gene expression datasets was performed. We identified a cohort of TNBC patients treated with NAC (n = 708). Gene expression data from different studies were renormalized, and the differences between pretreatment (pre-NAC), on-treatment (post-C1), and surgical (Sx) specimens were evaluated. Euclidean statistical distances were calculated to estimate changes in gene expression patterns induced by NAC. Hierarchical clustering and pathway enrichment analyses were used to characterize relationships between differentially expressed genes and affected gene pathways. Machine learning was employed to refine a gene expression signature with the potential to predict response to NAC., Results: Forty nine genes consistently affected by NAC were involved in enhanced regulation of wound response, chemokine release, cell division, and decreased programmed cell death in residual invasive disease. The statistical distances between pre-NAC and post-C1 significantly predicted pathological complete response [area under the curve (AUC) = 0.75; p = 0.003; 95% confidence interval (CI) 0.58-0.92]. Finally, the expression of CCND1, a cyclin that forms complexes with CDK4/6 to promote the cell cycle, was the most informative feature in pre-NAC biopsies to predict response to NAC., Conclusions: The results of this study reveal significant transcriptomic changes induced by NAC and suggest that chemotherapy-induced gene expression changes observed early in therapy may be good predictors of response to NAC.

DNA methylation profiling has proven to be a powerful analytical tool, which can accurately identify the tissue of origin of a wide range of benign and malignant neoplasms. Using microarray-based profiling and supervised machine learning algorithms, we and other groups have recently unraveled DNA methylation signatures capable of aiding the histomolecular diagnosis of different tumor types. We have explored the methylomes of metastatic brain tumors from patients with lung cancer, breast cancer, and cutaneous melanoma and primary brain neoplasms to build epigenetic classifiers. Our brain metastasis methylation (BrainMETH) classifier has the ability to determine the type of brain tumor, the origin of the metastases, and the clinical-therapeutic subtype for patients with breast cancer brain metastases. To facilitate the translation of these epigenetic classifiers into clinical practice, we selected and validated the most informative genomic regions utilizing quantitative methylation-specific polymerase chain reaction (qMSP). We believe that the refinement, expansion, integration, and clinical validation of BrainMETH and other recently developed epigenetic classifiers will significantly contribute to the development of more comprehensive and accurate systems for the personalized management of patients with brain metastases., Competing Interests: Declaration of conflicting interests:The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Brain metastases (BM) are one the most lethal and poorly managed clinical complications in cancer patients. These secondary tumors represent the most common intracranial neoplasm in adults, most frequently originating from lung cancer, breast cancer, and cutaneous melanoma. In primary brain tumors, such as gliomas, recent advances in DNA methylation profiling have allowed for a comprehensive molecular classification. Such data provide prognostic information, in addition to helping predict patient response to specific systemic therapies. However, epigenetic alterations of metastatic brain tumors with specific biological and translational relevance still require much further exploration. Using the widely employed Illumina Infinium HumanMethylation 450K platform, we have generated a cohort of genome-wide DNA methylomes from ninety-six needle-dissected BM specimens from patients with lung cancer, breast cancer, and cutaneous melanoma with clinical, pathological, and demographic annotations. This resource offers an unprecedented and unique opportunity to identify novel DNA methylation features influencing the behavior of brain metastasis, and thus accelerate the discovery of BM-specific theranostic epigenetic alterations.

Optimal treatment of brain metastases is often hindered by limitations in diagnostic capabilities. To meet this challenge, here we profile DNA methylomes of the three most frequent types of brain metastases: melanoma, breast, and lung cancers (n = 96). Using supervised machine learning and integration of DNA methylomes from normal, primary, and metastatic tumor specimens (n = 1860), we unravel epigenetic signatures specific to each type of metastatic brain tumor and constructed a three-step DNA methylation-based classifier (BrainMETH) that categorizes brain metastases according to the tissue of origin and therapeutically relevant subtypes. BrainMETH predictions are supported by routine histopathologic evaluation. We further characterize and validate the most predictive genomic regions in a large cohort of brain tumors (n = 165) using quantitative-methylation-specific PCR. Our study highlights the importance of brain tumor-defining epigenetic alterations, which can be utilized to further develop DNA methylation profiling as a critical tool in the histomolecular stratification of patients with brain metastases.

Metastatic cells exhibit an extraordinary phenotypic plasticity, not only in adapting to unfamiliar microenvironments but also in surviving aggressive treatments and immune responses. A major source of phenotypic variability is alternative splicing (AS) of the pre-messenger RNA. This process is catalyzed by one of the most complex pieces of cellular molecular regulatory events, the spliceosome, which is composed of ribonucleoproteins and polypeptides termed spliceosome factors. With strong evidence indicating that AS affects nearly all genes encoded by the human genome, aberrant AS programs have a significant impact on cancer cell development and progression. In this review, we present insights about the genomic and epigenomic factors affecting AS, summarize the most recent findings linking aberrant AS to metastatic progression, and highlight potential prognostic and therapeutic applications.