Brain metastasis DNA methylomes, a novel resource for the identification of biological and clinical features

Epigenetic alterations fuel brain metastasis via regulating inflammatory cascade

Brain metastasis (BrM) is a major threat to the survival of melanoma, breast, and lung cancer patients. Circulating tumor cells (CTCs) cross the blood-brain barrier (BBB) and sustain in the brain microenvironment. Genetic mutations and epigenetic modifications have been found to be critical in controlling key aspects of cancer metastasis. Metastasizing cells confront inflammation and gradually adapt in the unique brain microenvironment. Currently, it is one of the major areas that has gained momentum. Researchers are interested in the factors that modulate neuroinflammation during BrM. We review here various epigenetic factors and mechanisms modulating neuroinflammation and how this helps CTCs to adapt and survive in the brain microenvironment. Since epigenetic changes could be modulated by targeting enzymes such as histone/DNA methyltransferase, deacetylases, acetyltransferases, and demethylases, we also summarize our current understanding of potential drugs targeting various aspects of epigenetic regulation in BrM.

Molecular signaling network and therapeutic developments in breast cancer brain metastasis

Breast cancer (BC) is one of the most frequently diagnosed cancers in women worldwide. It has surpassed lung cancer as the leading cause of cancer-related death. Breast cancer brain metastasis (BCBM) is becoming a major clinical concern that is commonly associated with ER-ve and HER2+ve subtypes of BC patients. Metastatic lesions in the brain originate when the cancer cells detach from a primary breast tumor and establish metastatic lesions and infiltrate near and distant organs via systemic blood circulation by traversing the BBB. The colonization of BC cells in the brain involves a complex interplay in the tumor microenvironment (TME), metastatic cells, and brain cells like endothelial cells, microglia, and astrocytes. BCBM is a significant cause of morbidity and mortality and presents a challenge to developing successful cancer therapy. In this review, we discuss the molecular mechanism of BCBM and novel therapeutic strategies for patients with brain metastatic BC.

Epigenetic Rewiring of Metastatic Cancer to the Brain: Focus on Lung and Colon Cancers

Distant metastasis occurs when cancer cells adapt to a tissue microenvironment that is different from the primary organ. This process requires genetic and epigenetic changes in cancer cells and the concomitant modification of the tumor stroma to facilitate invasion by metastatic cells. In this study, we analyzed differences in the epigenome of brain metastasis from the colon (n = 4) and lung (n = 14) cancer and we compared these signatures with those found in primary tumors. Results show that CRC tumors showed a high degree of genome-wide methylation compared to lung cancers. Further, brain metastasis from lung cancer deeply activates neural signatures able to modify the brain microenvironment favoring tumor cells adaptation. At the protein level, brain metastases from lung cancer show expression of the neural/glial marker Nestin. On the other hand, colon brain metastases show activation of metabolic signaling. These signatures are specific for metastatic tumors since primary cancers did not show such epigenetic derangements. In conclusion, our data shed light on the epi/molecular mechanisms that colon and lung cancers adopt to thrive in the brain environment.

Machine Learning-Based Epigenetic Classifiers for Axillary Staging of Patients with ER-Positive Early-Stage Breast Cancer

BACKGROUND

In the era of molecular stratification and effective multimodality therapies, surgical staging of the axilla is becoming less relevant for patients with estrogen receptor (ER)-positive early-stage breast cancer (EBC). Therefore, a nonsurgical method for accurately predicting lymph node disease is the next step in the de-escalation of axillary surgery. This study sought to identify epigenetic signatures in the primary tumor that accurately predict lymph node status.

PATIENTS AND METHODS

We selected a cohort of patients in The Cancer Genome Atlas (TCGA) with ER-positive, HER2-negative invasive ductal carcinomas, and clinically-negative axillae (n = 127). Clinicopathological nomograms from the Memorial Sloan Kettering Cancer Center (MSKCC) and the MD Anderson Cancer Center (MDACC) were calculated. DNA methylation (DNAm) patterns from primary tumor specimens were compared between patients with pN0 and those with > pN0. The cohort was divided into training (n = 85) and validation (n = 42) sets. Random forest was employed to obtain the combinations of DNAm features with the highest accuracy for stratifying patients with > pN0. The most efficient combinations were selected according to the area under the curve (AUC).

RESULTS

Clinicopathological models displayed a modest predictive potential for identifying > pN0 disease (MSKCC AUC 0.76, MDACC AUC 0.69, p = 0.15). Differentially methylated sites (DMS) between patients with pN0 and those with > pN0 were identified (n = 1656). DMS showed a similar performance to the MSKCC model (AUC = 0.76, p = 0.83). Machine learning approaches generated five epigenetic classifiers, which showed higher discriminative potential than the clinicopathological variables tested (AUC > 0.88, p < 0.05).

CONCLUSIONS

Epigenetic classifiers based on primary tumor characteristics can efficiently stratify patients with no lymph node involvement from those with axillary lymph node disease, thereby providing an accurate method of staging the axilla.

PKIB involved in the metastasis and survival of osteosarcoma

Osteosarcoma is frequently metastasized at the time of diagnosis in patients. However, the underlying mechanism of osteosarcoma metastasis remains poorly understood. In this study, we evaluated DNA methylation profiles combined with gene expression profiles of 21 patients with metastatic osteosarcoma and 64 patients with non-metastatic osteosarcoma from TARGET database and identified PKIB and AIM2 as hub genes related to the metastasis of osteosarcoma. To verify the effects of PKIB on migration and invasion of osteosarcoma, we performed wound-healing assay and transwell assay. The results showed that PKIB significantly inhibited the migration and invasion of osteosarcoma cells, and the Western blot experiments showed that the protein level of E-cad was upregulated and of VIM was downregulated in 143-B cell recombinant expression PKIB. These results indicate that PKIB inhibit the metastasis of osteosarcoma. CCK-8 assay results showed that PKIB promote the proliferation of osteosarcoma. In addition, the Western blot results showed that the phosphorylation level of Akt was upregulated in 143-B cells overexpressing PKIB, indicating that PKIB promotes the proliferation of osteosarcoma probably through signaling pathway that Akt involved in. These results give us clues that PKIB was a potential target for osteosarcoma therapy. Furthermore, combined clinical profiles analysis showed that the expression of AIM2- and PKIB- related risk scores was significantly related to the overall survival of patients with osteosarcoma. Thus, we constructed a nomogram based on AIM2 and PKIB expression–related risk scores for osteosarcoma prognostic assessment to predict the 1-, 2-, 3-, and 5-year overall survival rate of patients with metastatic osteosarcoma, assisting clinicians in the diagnosis and treatment of metastatic osteosarcoma.

Emerging Biomarkers for Diagnosis, Prevention and Treatment of Brain Metastases-From Biology to Clinical Utility

Primary malignancies of the lung, skin (melanoma), and breast have higher propensity for metastatic spread to the brain. Advances in molecular tumour profiling have aided the development of targeted therapies, stereotactic radiotherapy, and immunotherapy, which have led to some improvement in patient outcomes; however, the overall prognosis remains poor. Continued research to identify new prognostic and predictive biomarkers is necessary to further impact patient outcomes, as this will enable better risk stratification at the point of primary cancer diagnosis, earlier detection of metastatic deposits (for example, through surveillance), and more effective systemic treatments. Brain metastases exhibit considerable inter- and intratumoural heterogeneity-apart from distinct histology, treatment history and other clinical factors, the metastatic brain tumour microenvironment is incredibly variable both in terms of subclonal diversity and cellular composition. This review discusses emerging biomarkers; specifically, the biological context and potential clinical utility of tumour tissue biomarkers, circulating tumour cells, extracellular vesicles, and circulating tumour DNA.

Development of a hoRizontal data intEgration classifier for NOn-invasive early diAgnosis of breasT cancEr: the RENOVATE study protocol

INTRODUCTION

Standard procedures aimed at the early diagnosis of breast cancer (BC) present suboptimal accuracy and imply the execution of invasive and sometimes unnecessary tissue biopsies. The assessment of circulating biomarkers for diagnostic purposes, together with radiomics, is of great potential in BC management.

METHODS AND ANALYSIS

This is a prospective translational study investigating the accuracy of the combined assessment of multiple circulating analytes together with radiomic variables for early BC diagnosis. Up to 750 patients will be recruited at their presentation at the Diagnostic Senology Unit of Ospedale Policlinico San Martino (Genoa, IT) for the execution of a diagnostic biopsy after the detection of a suspect breast lesion (t0). Each recruited patient will be asked to donate peripheral blood and urine before undergoing breast biopsy. Blood and urine samples will also be collected from a cohort of 100 patients with negative mammography. For cases with histological diagnosis of invasive BC, a second sample of blood and urine will be collected after breast surgery. Circulating tumour DNA, cell-free methylated DNA and circulating proteins will be assessed in samples collected at t0 from patients with stage I-IIA BC at surgery together with those collected from patients with histologically confirmed benign lesions of similar size and from healthy controls with negative mammography. These analyses will be combined with radiomic variables extracted with freeware algorithms applied to cases and matched controls for which digital mammography is available. The overall goal of the present study is to develop a horizontal data integration classifier for the early diagnosis of BC.

ETHICS AND DISSEMINATION

This research protocol has been approved by Regione Liguria Ethics Committee (reference number: 2019/75, study ID: 4452). Patients will be required to provide written informed consent. Results will be published in international peer-reviewed scientific journals.

TRIAL REGISTRATION NUMBER

NCT04781062.

Clinical Biomarkers for Early Identification of Patients with Intracranial Metastatic Disease

Simple Summary

The development of brain metastases, or intracranial metastatic disease (IMD), is a serious and life-altering complication for many patients with cancer. While there have been substantial advancements in the treatments available for IMD and in our understanding of its pathogenesis, conventional methods remain insufficient to detect IMD at an early stage. In this review, we discuss current research on biomarkers specific to IMD. In particular, we highlight biomarkers that can be easily accessed via the bloodstream or cerebrospinal fluid, including circulating tumor cells and DNA, as well as advanced imaging techniques. The continued development of these assays could enable clinicians to detect IMD prior to the development of IMD-associated symptoms and ultimately improve patient prognosis and survival.

Abstract

Nearly 30% of patients with cancer will develop intracranial metastatic disease (IMD), and more than half of these patients will die within a few months following their diagnosis. In light of the profound effect of IMD on survival and quality of life, there is significant interest in identifying biomarkers that could facilitate the early detection of IMD or identify patients with cancer who are at high IMD risk. In this review, we will highlight early efforts to identify biomarkers of IMD and consider avenues for future investigation.

Genomic and Transcriptomic Profiling of Brain Metastases

Simple Summary

Brain metastases (BM) are the most common brain tumors in adults and are the main cause of cancer-associated death. Omics analysis of BM will allow for a better understanding of metastatic progression, prognosis and therapeutic targeting. In this study, BM samples underwent comprehensive molecular profiling with genomics and transcriptomics. Mutational signatures suggested that most mutations were gained prior to metastasis. A novel copy number event centered around the MCL1 gene was found in 75% of all samples. Transcriptomics revealed that melanoma BM formed a distinct cluster in comparison to other subtypes. Poor survival correlated to self-identified black race and absence of radiation treatment but not molecular profiles. These data identify potential new drivers of brain metastatic progression, implicate that melanoma BM are distinctive and likely responsive to unique therapies, and further investigation of sociodemographic and clinical features are needed in BM cohorts.

Abstract

Brain metastases (BM) are the most common brain tumors in adults occurring in up to 40% of all cancer patients. Multi-omics approaches allow for understanding molecular mechanisms and identification of markers with prognostic significance. In this study, we profile 130 BM using genomics and transcriptomics and correlate molecular characteristics to clinical parameters. The most common tumor origins for BM were lung (40%) followed by melanoma (21%) and breast (15%). Melanoma and lung BMs contained more deleterious mutations than other subtypes (p < 0.001). Mutational signatures suggested that the bulk of the mutations were gained before metastasis. A novel copy number event centered around the MCL1 gene was found in 75% of all samples, suggesting a broader role in promoting metastasis. Unsupervised hierarchical cluster analysis of transcriptional signatures available in 65 samples based on the hallmarks of cancer revealed four distinct clusters. Melanoma samples formed a distinctive cluster in comparison to other BM subtypes. Characteristics of molecular profiles did not correlate with survival. However, patients with self-identified black race or those who did not receive radiation correlated with poor survival. These data identify potential new drivers of brain metastatic progression. Our data also suggest further investigation of sociodemographic and clinical features is needed in BM cohorts.

Circular RNA circBCBM1 promotes breast cancer brain metastasis by modulating miR-125a/BRD4 axis

Circular RNAs (circRNAs) play critical roles in tumorigenesis and the progression of various cancers. We previously identified a novel upregulated circRNA, circBCBM1 (hsa_circ_0001944), in the context of breast cancer brain metastasis. However, the potential biological function and molecular mechanism of circBCBM1 in breast cancer brain metastasis remain largely unknown. In this study, we confirmed that circBCBM1 was a stable and cytoplasmic circRNA. Functionally, circBCBM1 promoted the proliferation and migration of 231-BR cells in vitro and growth and brain metastasis in vivo. Mechanistically, circBCBM1 acted as an endogenous miR-125a sponge to inhibit miR-125a activity, resulting in the upregulation of BRD4 (bromodomain containing 4) and subsequent upregulation of MMP9 (matrix metallopeptidase 9) through Sonic hedgehog (SHH) signaling pathway. Importantly, circBCBM1 was markedly upregulated in the breast cancer brain metastasis cells and clinical tissue and plasma samples; besides, circBCBM1 overexpression in primary cancerous tissues was associated with shorter brain metastasis-free survival (BMFS) of breast cancer patients. These findings indicate that circBCBM1 is involved in breast cancer brain metastasis via circBCBM1/miR-125a/BRD4 axis. CircBCBM1 may serve as a novel diagnostic and prognostic biomarker and potential therapeutic target for breast cancer brain metastasis.

iGlioSub: an integrative transcriptomic and epigenomic classifier for glioblastoma molecular subtypes

BACKGROUND

Glioblastoma (GBM) is the most aggressive and prevalent primary brain tumor, with a median survival of 15 months. Advancements in multi-omics profiling combined with computational algorithms have unraveled the existence of three GBM molecular subtypes (Classical, Mesenchymal, and Proneural) with clinical relevance. However, due to the costs of high-throughput profiling techniques, GBM molecular subtyping is not currently employed in clinical settings.

METHODS

Using Random Forest and Nearest Shrunken Centroid algorithms, we constructed transcriptomic, epigenomic, and integrative GBM subtype-specific classifiers. We included gene expression and DNA methylation (DNAm) profiles from 304 GBM patients profiled in the Cancer Genome Atlas (TCGA), the Human Glioblastoma Cell Culture resource (HGCC), and other publicly available databases.

RESULTS

The integrative Glioblastoma Subtype (iGlioSub) classifier shows better performance (mean AUC = 95.9%) stratifying patients than gene expression (mean AUC = 91.9%) and DNAm-based classifiers (AUC = 93.6%). Also, to expand the understanding of the molecular differences between the GBM subtypes, this study shows that each subtype presents unique DNAm patterns and gene pathway activation.

CONCLUSIONS

The iGlioSub classifier provides the basis to design cost-effective strategies to stratify GBM patients in routine pathology laboratories for clinical trials, which will significantly accelerate the discovery of more efficient GBM subtype-specific treatment approaches.

Brain Tumor Biobank Development for Precision Medicine: Role of the Neurosurgeon

Neuro-oncology biobanks are critical for the implementation of a precision medicine program. In this perspective, we review our first year experience of a brain tumor biobank with integrated next generation sequencing. From our experience, we describe the critical role of the neurosurgeon in diagnosis, research, and precision medicine efforts. In the first year of implementation of the biobank, 117 patients (Female: 62; Male: 55) had 125 brain tumor surgeries. 75% of patients had tumors biobanked, and 16% were of minority race/ethnicity. Tumors biobanked were as follows: diffuse gliomas (45%), brain metastases (29%), meningioma (21%), and other (5%). Among biobanked patients, 100% also had next generation sequencing. Eleven patients qualified for targeted therapy based on identification of actionable gene mutations. One patient with a hereditary cancer predisposition syndrome was also identified. An iterative quality improvement process was implemented to streamline the workflow between the operating room, pathology, and the research laboratory. Dedicated tumor bank personnel in the department of neurosurgery greatly improved standard operating procedure. Intraoperative selection and processing of tumor tissue by the neurosurgeon was integral to increasing success with cell culture assays. Currently, our institutional protocol integrates standard histopathological diagnosis, next generation sequencing, and functional assays on surgical specimens to develop precision medicine protocols for our patients. This perspective reviews the critical role of neurosurgeons in brain tumor biobank implementation and success as well as future directions for enhancing precision medicine efforts.

Human methylome variation across Infinium 450K data on the Gene Expression Omnibus

While DNA methylation (DNAm) is the most-studied epigenetic mark, few recent studies probe the breadth of publicly available DNAm array samples. We collectively analyzed 35 360 Illumina Infinium HumanMethylation450K DNAm array samples published on the Gene Expression Omnibus. We learned a controlled vocabulary of sample labels by applying regular expressions to metadata and used existing models to predict various sample properties including epigenetic age. We found approximately two-thirds of samples were from blood, one-quarter were from brain and one-third were from cancer patients. About 19% of samples failed at least one of Illumina's 17 prescribed quality assessments; signal distributions across samples suggest modifying manufacturer-recommended thresholds for failure would make these assessments more informative. We further analyzed DNAm variances in seven tissues (adipose, nasal, blood, brain, buccal, sperm and liver) and characterized specific probes distinguishing them. Finally, we compiled DNAm array data and metadata, including our learned and predicted sample labels, into database files accessible via the recountmethylation R/Bioconductor companion package. Its vignettes walk the user through some analyses contained in this paper.

Minimalist approaches to cancer tissue-of-origin classification by DNA methylation

Classification of cancers by tissue-of-origin is fundamental to diagnostic pathology. While the combination of clinical data, tissue histology, and immunohistochemistry is usually sufficient, there remains a small but not insignificant proportion of difficult-to-classify cases. These challenging cases provide justification for ancillary molecular testing, including high-throughput DNA methylation array profiling, which promises cell-of-origin information and compatibility with formalin-fixed specimens. While diagnostically powerful, methylation profiling platforms are costly and technically challenging to implement, particularly for less well-resourced laboratories. To address this, we simulated the performance of "minimalist" methylation-based tests for cancer classification using publicly-available and internal institutional profiling data. These analyses showed that small and focused sets of the most informative CpG biomarkers from the arrays are sufficient for accurate diagnoses. As an illustrative example, one classifier, using information from just 53 out of about 450,000 available CpG probes, achieved an accuracy of 94.5% on 2575 fresh primary validation cases across 28 cancer types from The Cancer Genome Atlas Network. By training minimalist classifiers on formalin-fixed primary and metastatic cases, generally high accuracies were also achieved on additional datasets. These results support the potential of minimalist methylation testing, possibly via quantitative PCR and targeted next-generation sequencing platforms, in cancer classification.

A database for using machine learning and data mining techniques for coronary artery disease diagnosis

We present the coronary artery disease (CAD) database, a comprehensive resource, comprising 126 papers and 68 datasets relevant to CAD diagnosis, extracted from the scientific literature from 1992 and 2018. These data were collected to help advance research on CAD-related machine learning and data mining algorithms, and hopefully to ultimately advance clinical diagnosis and early treatment. To aid users, we have also built a web application that presents the database through various reports.

Clinicopathological Features of Triple-Negative Breast Cancer Epigenetic Subtypes

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.

Epigenetic Classifiers for Precision Diagnosis of Brain Tumors

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.