Epigenetic profiling for the molecular classification of metastatic brain tumors

DNA Methyltransferase Inhibition Upregulates the Costimulatory Molecule ICAM-1 and the Immunogenic Phenotype of Melanoma Cells

In cutaneous melanoma, epigenetic dysregulation is implicated in drug resistance and tumor immune escape. However, the epigenetic mechanisms that influence immune escape remain poorly understood. To elucidate how epigenetic dysregulation alters the expression of surface proteins that may be involved in drug targeting and immune escape, we performed a 3-dimensional surfaceome screen in primary melanoma cultures and identified the DNA-methyltransferase inhibitor decitabine as significantly upregulating the costimulatory molecule ICAM-1. By analyzing The Cancer Genome Atlas melanoma dataset, we further propose ICAM-1 upregulation on melanoma cells as a biomarker of a proinflammatory and antitumorigenic signature. Specifically, we showed that DNA-methyltransferase inhibitor administration upregulated the expression of the antigen-presenting machinery, HLA class I/II, as well as the secretion of the proinflammatory chemokines CXCL9 and CXCL10. Our in silico analysis on The Cancer Genome Atlas and ex vivo experiments on human primary melanoma samples revealed that increased ICAM-1 expression positively correlated with increased immunogenicity of human melanoma cells and correlated with increased immune cell infiltration. These findings suggest a therapeutic approach to modulate the immunogenic phenotype of melanoma cells, hence supporting the exploration of DNA-methyltransferase inhibitor as a potential inducer of infiltration in immunologically cold tumors.

Rapid brain tumor classification from sparse epigenomic data

Although the intraoperative molecular diagnosis of the approximately 100 known brain tumor entities described to date has been a goal of neuropathology for the past decade, achieving this within a clinically relevant timeframe of under 1 h after biopsy collection remains elusive. Advances in third-generation sequencing have brought this goal closer, but established machine learning techniques rely on computationally intensive methods, making them impractical for live diagnostic workflows in clinical applications. Here we present MethyLYZR, a naive Bayesian framework enabling fully tractable, live classification of cancer epigenomes. For evaluation, we used nanopore sequencing to classify over 200 brain tumor samples, including 10 sequenced in a clinical setting next to the operating room, achieving highly accurate results within 15 min of sequencing. MethyLYZR can be run in parallel with an ongoing nanopore experiment with negligible computational overhead. Therefore, the only limiting factors for even faster time to results are DNA extraction time and the nanopore sequencer's maximum parallel throughput. Although more evidence from prospective studies is needed, our study suggests the potential applicability of MethyLYZR for live molecular classification of nervous system malignancies using nanopore sequencing not only for the neurosurgical intraoperative use case but also for other oncologic indications and the classification of tumors from cell-free DNA in liquid biopsies.

The epigenetic landscape of brain metastasis

Brain metastasis represents a significant challenge in oncology, driven by complex molecular and epigenetic mechanisms that distinguish it from primary tumors. While recent research has focused on identifying genomic mutation drivers with potential clinical utility, these strategies have not pinpointed specific genetic mutations responsible for site-specific metastasis to the brain. It is now clear that successful brain colonization by metastatic cancer cells requires intricate interactions with the brain tumor ecosystem and the acquisition of specialized molecular traits that facilitate their adaptation to this highly selective environment. This is best exemplified by widespread transcriptional adaptation during brain metastasis, resulting in aberrant gene programs that promote extravasation, seeding, and colonization of the brain. Increasing evidence suggests that epigenetic mechanisms play a significant role in shaping these pro-brain metastasis traits. This review explores dysregulated chromatin patterns driven by chromatin remodeling, histone modifications, DNA/RNA methylation, and other epigenetic regulators that underpin brain metastatic seeding, initiation, and outgrowth. We provide novel insights into how these epigenetic modifications arise within both the brain metastatic tumor and the surrounding brain metastatic tumor ecosystem. Finally, we discuss how the inherent plasticity and reversibility of the epigenomic landscape in brain metastases may offer new therapeutic opportunities.

Optimizing carbon source addition to control surplus sludge yield via machine learning-based interpretable ensemble model

Appropriate carbon source addition can save operational costs and reduce surplus sludge yield in the wastewater treatment plant (WWTP). However, the link between carbon source and surplus sludge yield remains neglected although machine learning (ML) has become a powerful tool for WWTP, and is a challenge due to more complex multidimensional pattern recognition. Herein, weighted average ensemble strategy was conducted to assemble multiple diverse basic models to obtain better prediction capability to optimize carbon source addition (Model-1) and further control surplus sludge yield (Model-2). The ensemble models significantly outperformed all single models with MAE of 5.82 g/m3, MSE of 60.59 and R2 value of 0.98 in Model-1 and MAE of 15.09 g/m3, MSE of 449.01 and R2 value of 0.93 in Model-2. The optimal input feature subset was explored to reduce model complexity, indicating that the final ensemble models can predict with high precision using relatively few features with MAE of 6.41 g/m3, MSE of 78.49 and R2 value of 0.97 in Model-1 and MAE of 12.82 g/m3, MSE of 232.71 and R2 value of 0.95 in Model-2. Furthermore, the final models were deployed into an offline web application to facilitate their utility in real-world settings, demonstrating 47.25 % savings in carbon source addition and 15.89 % reductions in surplus sludge yield for an extra month of running. This work offers an efficient approach for the WWTP to optimize carbon source addition and provides new insights into controlling surplus sludge yield.

Autophagy in brain tumors: molecular mechanisms, challenges, and therapeutic opportunities

Autophagy is responsible for maintaining cellular balance and ensuring survival. Autophagy plays a crucial role in the development of diseases, particularly human cancers, with actions that can either promote survival or induce cell death. However, brain tumors contribute to high levels of both mortality and morbidity globally, with resistance to treatments being acquired due to genetic mutations and dysregulation of molecular mechanisms, among other factors. Hence, having knowledge of the role of molecular processes in the advancement of brain tumors is enlightening, and the current review specifically examines the role of autophagy. The discussion would focus on the molecular pathways that control autophagy in brain tumors, and its dual role as a tumor suppressor and a supporter of tumor survival. Autophagy can control the advancement of different types of brain tumors like glioblastoma, glioma, and ependymoma, demonstrating its potential for treatment. Autophagy mechanisms can influence metastasis and drug resistance in glioblastoma, and there is a complex interplay between autophagy and cellular responses to stress like hypoxia and starvation. Autophagy can inhibit the growth of brain tumors by promoting apoptosis. Hence, focusing on autophagy could offer fresh perspectives on creating successful treatments.

Prediction of brain metastasis development with DNA methylation signatures

Brain metastases (BMs) are the most common and among the deadliest brain tumors. Currently, there are no reliable predictors of BM development from primary cancer, which limits early intervention. Lung adenocarcinoma (LUAD) is the most common BM source and here we obtained 402 tumor and plasma samples from a large cohort of patients with LUAD with or without BM (n = 346). LUAD DNA methylation signatures were evaluated to build and validate an accurate model predicting BM development from LUAD, which was integrated with clinical factors to provide comprehensive patient-specific BM risk probabilities in a nomogram. Additionally, immune and cell interaction gene sets were differentially methylated at promoters in BM versus paired primary LUAD and had aligning dysregulation in the proteome. Immune cells were differentially abundant in BM versus LUAD. Finally, liquid biomarkers identified from methylated cell-free DNA sequenced in plasma were used to generate and validate accurate classifiers for early BM detection. Overall, LUAD methylomes can be leveraged to predict and noninvasively identify BM, moving toward improved patient outcomes with personalized treatment.

PathMethy: an interpretable AI framework for cancer origin tracing based on DNA methylation

Despite advanced diagnostics, 3%-5% of cases remain classified as cancer of unknown primary (CUP). DNA methylation, an important epigenetic feature, is essential for determining the origin of metastatic tumors. We presented PathMethy, a novel Transformer model integrated with functional categories and crosstalk of pathways, to accurately trace the origin of tumors in CUP samples based on DNA methylation. PathMethy outperformed seven competing methods in F1-score across nine cancer datasets and predicted accurately the molecular subtypes within nine primary tumor types. It not only excelled at tracing the origins of both primary and metastatic tumors but also demonstrated a high degree of agreement with previously diagnosed sites in cases of CUP. PathMethy provided biological insights by highlighting key pathways, functional categories, and their interactions. Using functional categories of pathways, we gained a global understanding of biological processes. For broader access, a user-friendly web server for researchers and clinicians is available at https://cup.pathmethy.com.

A Fast, Affordable, and Minimally Invasive Diagnostic Test for Cancer of Unknown Primary Using DNA Methylation Profiling

Currently, we cannot provide a conclusive diagnosis for 3% to 5% of people who are confronted with cancer. These patients have cancer of unknown primary (CUP), ie, a metastasized cancer for which the tissue of origin cannot be determined. Studies have shown that the DNA methylation profile is a unique "fingerprint" that can be used to classify tumors. Here we used cell-free reduced representation bisulfite sequencing (cfRRBS), a technique that allows us to identify the methylation profile starting from minimal amounts of highly fragmented DNA, for CUP diagnosis on formalin-fixed paraffin-embedded (FFPE) tissue and liquid biopsies. We collected 80 primary tumor FFPE samples covering 16 tumor entities together with 15 healthy plasma samples to use as a custom cfRRBS reference data set. Entity-specific methylation regions are defined for each entity to build a classifier based on nonnegative least squares deconvolution. This classification framework was tested on 30 FFPE, 19 plasma, and 40 pleural and peritoneal effusion samples of both known metastatic tumors and clinical CUPs for which pathological investigation finally resulted in a cancer diagnosis. Using this framework, 27 of 30 FFPE (all CUPs) and 16 of 19 plasma samples (10/13 CUPs) obtained an accurate diagnosis, with a minimal DNA input of 400 pg. Diagnosis of the 40 pleural and peritoneal effusion samples is possible in 9 of 27 samples with negative/inconclusive cytology (6/13 CUPs), showing that cell-free DNA (cfDNA) methylation profiling could complement routine cytologic analysis. However, a low "cfDNA - high-molecular weight DNA ratio" has a considerable impact on the prediction accuracy. Moreover, the accuracy improves significantly if the predicted tumor percentage is >7%. This proof-of-concept study shows the feasibility of using DNA methylation profiling on FFPE and liquid biopsy samples such as blood, ascites, and pleural effusions in a fast and affordable way. Our novel RRBS-based technique requires minimal DNA input, can be performed in <1 week, and is highly adaptable to specific diagnostic problems as we only use 5 FFPE references per tumor entity. We believe that cfRRBS methylation profiling could be a valuable addition to the pathologist's toolbox in the diagnosis of CUPs.

Beyond the base pairs: comparative genome-wide DNA methylation profiling across sequencing technologies

Deoxyribonucleic acid (DNA) methylation plays a key role in gene regulation and is critical for development and human disease. Techniques such as whole-genome bisulfite sequencing (WGBS) and reduced representation bisulfite sequencing (RRBS) allow DNA methylation analysis at the genome scale, with Illumina NovaSeq 6000 and MGI Tech DNBSEQ-T7 being popular due to their efficiency and affordability. However, detailed comparative studies of their performance are not available. In this study, we constructed 60 WGBS and RRBS libraries for two platforms using different types of clinical samples and generated approximately 2.8 terabases of sequencing data. We systematically compared quality control metrics, genomic coverage, CpG methylation levels, intra- and interplatform correlations, and performance in detecting differentially methylated positions. Our results revealed that the DNBSEQ platform exhibited better raw read quality, although base quality recalibration indicated potential overestimation of base quality. The DNBSEQ platform also showed lower sequencing depth and less coverage uniformity in GC-rich regions than did the NovaSeq platform and tended to enrich methylated regions. Overall, both platforms demonstrated robust intra- and interplatform reproducibility for RRBS and WGBS, with NovaSeq performing better for WGBS, highlighting the importance of considering these factors when selecting a platform for bisulfite sequencing.

Cancer of unknown primary and the «seed and soil» hypothesis

The worldwide incidence rate of cancer of unknown primary (CUP) reaches 5% (Kang et al, 2021; Lee, Sanoff, 2020; Yang et al, 2022). CUP has an alarmingly high mortality rate, with 84% of patients succumbing within the first year following diagnosis (Registration and Service, 2018). Under normal circumstances, tumor cell metastasis follows the «seed and soil» hypothesis, displaying a tissue-specific pattern of cancer cell homing behavior based on the microenvironment composition of secondary organs. In this study, we questioned whether seed and soil concept applies to CUP, and whether the pattern of tumor and metastasis manifestations for cancer of known primary (CKP) can be used to inform diagnostic strategies for CUP. We compared data from metastatic and primary CUP foci to the metastasis patterns observed in CKP. Furthermore, we evaluated several techniques for identifying the tissue-of-origin (TOO) in CUP profiling, including DNA, RNA, and epigenetic TOO techniques.

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.

Brain Tumors: Development, Drug Resistance, and Sensitization - An Epigenetic Approach

In this article, we describe contrasting developmental aspects of paediatric and adult brain tumours. We hypothesize that the formation of cancer progenitor cells, for both paediatric and adult, could be due to epigenetic events. However, the progression of adult brain tumours selectively involves more mutations compared to paediatric tumours. We further discuss epigenetic switches, comprising both histone modifications and DNA methylation, and how they can differentially regulate transcription and expression of oncogenes and tumour suppressor genes. Next, we summarize the currently available therapies for both types of brain tumours, explaining the merits and failures leading to drug resistance. We analyse different mechanisms of drug resistance and the role of epigenetics in this process. We then provide a rationale for combination therapy, which includes epigenetic drugs. In the end, we postulate a concept which describes how a combination therapy could be initiated. The timing, doses, and order of individual drug regimens will depend on the individual case. This type of combination therapy will be part of a personalized medicine which will differ from patient to patient.

EpiGe: A machine-learning strategy for rapid classification of medulloblastoma using PCR-based methyl-genotyping

Molecular classification of medulloblastoma is critical for the treatment of this brain tumor. Array-based DNA methylation profiling has emerged as a powerful approach for brain tumor classification. However, this technology is currently not widely available. We present a machine-learning decision support system (DSS) that enables the classification of the principal molecular groups-WNT, SHH, and non-WNT/non-SHH-directly from quantitative PCR (qPCR) data. We propose a framework where the developed DSS appears as a user-friendly web-application-EpiGe-App-that enables automated interpretation of qPCR methylation data and subsequent molecular group prediction. The basis of our classification strategy is a previously validated six-cytosine signature with subgroup-specific methylation profiles. This reduced set of markers enabled us to develop a methyl-genotyping assay capable of determining the methylation status of cytosines using qPCR instruments. This study provides a comprehensive approach for rapid classification of clinically relevant medulloblastoma groups, using readily accessible equipment and an easy-to-use web-application.t.

Cerebrospinal fluid methylome-based liquid biopsies for accurate malignant brain neoplasm classification

BACKGROUND

Resolving the differential diagnosis between brain metastases (BM), glioblastomas (GBM), and central nervous system lymphomas (CNSL) is an important dilemma for the clinical management of the main three intra-axial brain tumor types. Currently, treatment decisions require invasive diagnostic surgical biopsies that carry risks and morbidity. This study aimed to utilize methylomes from cerebrospinal fluid (CSF), a biofluid proximal to brain tumors, for reliable non-invasive classification that addresses limitations associated with low target abundance in existing approaches.

METHODS

Binomial GLMnet classifiers of tumor type were built, in fifty iterations of 80% discovery sets, using CSF methylomes obtained from 57 BM, GBM, CNSL, and non-neoplastic control patients. Publicly-available tissue methylation profiles (N = 197) on these entities and normal brain parenchyma were used for validation and model optimization.

RESULTS

Models reliably distinguished between BM (area under receiver operating characteristic curve [AUROC] = 0.93, 95% confidence interval [CI]: 0.71-1.0), GBM (AUROC = 0.83, 95% CI: 0.63-1.0), and CNSL (AUROC = 0.91, 95% CI: 0.66-1.0) in independent 20% validation sets. For validation, CSF-based methylome signatures reliably distinguished between tumor types within external tissue samples and tumors from non-neoplastic controls in CSF and tissue. CSF methylome signals were observed to align closely with tissue signatures for each entity. An additional set of optimized CSF-based models, built using tumor-specific features present in tissue data, showed enhanced classification accuracy.

CONCLUSIONS

CSF methylomes are reliable for liquid biopsy-based classification of the major three malignant brain tumor types. We discuss how liquid biopsies may impact brain cancer management in the future by avoiding surgical risks, classifying unbiopsiable tumors, and guiding surgical planning when resection is indicated.

Growth exponents reflect evolutionary processes and treatment response in brain metastases

Tumor growth is the result of the interplay of complex biological processes in huge numbers of individual cells living in changing environments. Effective simple mathematical laws have been shown to describe tumor growth in vitro, or simple animal models with bounded-growth dynamics accurately. However, results for the growth of human cancers in patients are scarce. Our study mined a large dataset of 1133 brain metastases (BMs) with longitudinal imaging follow-up to find growth laws for untreated BMs and recurrent treated BMs. Untreated BMs showed high growth exponents, most likely related to the underlying evolutionary dynamics, with experimental tumors in mice resembling accurately the disease. Recurrent BMs growth exponents were smaller, most probably due to a reduction in tumor heterogeneity after treatment, which may limit the tumor evolutionary capabilities. In silico simulations using a stochastic discrete mesoscopic model with basic evolutionary dynamics led to results in line with the observed data.

Identification of Epigenetically Regulated Genes Distinguishing Intracranial from Extracranial Melanoma Metastases

Despite remarkable advances in treating patients with metastatic melanoma, the management of melanoma brain metastases remains challenging. Recent evidence suggests that epigenetic reprogramming is an important mechanism for the adaptation of melanoma cells to the brain environment. In this study, the methylomes and transcriptomes of a cohort of matched melanoma metastases were evaluated by integrated omics data analysis. The identified 38 candidate genes displayed distinct promoter methylation and corresponding gene expression changes in intracranial compared with extracranial metastases. The 11 most promising genes were validated on protein level in both tumor and surrounding normal tissue using immunohistochemistry. In accordance with the underlying promoter methylation and gene expression changes, a significantly different protein expression was confirmed for STK10, PDXK, WDR24, CSSP1, NMB, RASL11B, phosphorylated PRKCZ, PRKCZ, and phosphorylated GRB10 in the intracranial metastases. The observed changes imply a distinct intracranial phenotype with increased protein kinase B phosphorylation and a higher frequency of proliferating cells. Knockdown of PRKCZ or GRB10 altered the expression of phosphorylated protein kinase B and decreased the viability of a brain-specific melanoma cell line. In summary, epigenetically regulated cancer-relevant alterations were identified that provide insights into the molecular mechanisms that discriminate brain metastases from other organ metastases, which could be exploited by targeting the affected signaling pathways.

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.

Distinguishing Gastric/Esophageal Adenocarcinoma from Pancreatic Adenocarcinoma Using Methylation-Based Droplet Digital PCR

The goal of this study was to develop a methylation-based droplet digital PCR to separate 2 cancer classes that do not have sensitive and specific immunohistochemical stains: gastric/esophageal and pancreatic adenocarcinomas. The assay used methylation-independent primers and methylation-dependent probes to assess a single differentially methylated CpG site; analyses of array data from The Cancer Genome Atlas network showed that high methylation at the cg06118999 probe supports the presence of cells originating from the stomach or esophagus (eg, as in gastric metastasis), whereas low methylation suggests that these cells are rare to absent (eg, pancreatic metastasis). On validation using formalin-fixed paraffin-embedded primary and metastatic samples from our institution, methylation-based droplet digital PCR targeting the corresponding CpG dinucleotide generated evaluable data for 60 of the 62 samples (97%) and correctly classified 50 of the 60 evaluable cases (83.3%), mostly adenocarcinomas from the stomach or pancreas. This ddPCR was created to be easy-to-interpret, rapid, inexpensive, and compatible with existing platforms at many clinical laboratories. We suggest that similarly accessible PCRs could be developed for other differentials in pathology that do not have sensitive and specific immunohistochemical stains.

Assessing the Differential Methylation Analysis Quality for Microarray and NGS Platforms

Differential methylation (DM) is actively recruited in different types of fundamental and translational studies. Currently, microarray- and NGS-based approaches for methylation analysis are the most widely used with multiple statistical models designed to extract differential methylation signatures. The benchmarking of DM models is challenging due to the absence of gold standard data. In this study, we analyze an extensive number of publicly available NGS and microarray datasets with divergent and widely utilized statistical models and apply the recently suggested and validated rank-statistic-based approach Hobotnica to evaluate the quality of their results. Overall, microarray-based methods demonstrate more robust and convergent results, while NGS-based models are highly dissimilar. Tests on the simulated NGS data tend to overestimate the quality of the DM methods and therefore are recommended for use with caution. Evaluation of the top 10 DMC and top 100 DMC in addition to the not-subset signature also shows more stable results for microarray data. Summing up, given the observed heterogeneity in NGS methylation data, the evaluation of newly generated methylation signatures is a crucial step in DM analysis. The Hobotnica metric is coordinated with previously developed quality metrics and provides a robust, sensitive, and informative estimation of methods' performance and DM signatures' quality in the absence of gold standard data solving a long-existing problem in DM analysis.

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.

The Current Landscape of Immune Checkpoint Inhibitor Immunotherapy for Primary and Metastatic Brain Tumors

Antibodies against immune checkpoint inhibitors (ICIs) have revolutionized the treatment of multiple aggressive malignancies, including melanoma and non-small cell lung cancer. ICIs for the treatment of primary and metastatic brain tumors have been used with varying degrees of success. Here, we discuss the available evidence for the use of ICIs in the treatment of primary and metastatic brain tumors, highlighting challenges and opportunities for furthering this type of cancer immunotherapy in neuro-oncology.

Events in CNS Tumor Pathology Post-2016 WHO CNS: cIMPACT-NOW Updates and Other Advancements: A Comprehensive Review Plus a Summary of the Salient Features of 2021 WHO CNS 5

Introduction

The 2016 World Health Organization Classification (WHO) of Tumors of the Central Nervous System (CNS) represented a major change. It recommended an "integrated diagnosis" comprising histologic and molecular information facilitating a more precise diagnosis of specific CNS tumors. Its goal was to provide greater diagnostic precision and reproducibility resulting in more clinical relevance and predictive value, ultimately leading to better patient care. Advances in molecular classification, mostly resulting from DNA methylation array profiling of CNS tumors, were occurring at a very rapid pace and required more rapid integration into clinical practice.

Methods

cIMPACT-NOW updates and other recent papers plus salient features of 2021 WHO CNS5 in this comprehensive write-up were reviewed.

Results

CNS tumor classification needs to be updated at a rapid pace and mechanisms put into place to guide diagnosticians and clinicians in the interim period if major changes in the classification of tumor types came to light. Recognizing the need to integrate these into clinical practice more rapidly and without inordinate delay, the International Society of Neuropathology (ISN) 2016 sponsored an initiative called cIMPACT-NOW.

Discussion and/or Conclusion

Goal of cIMPACT-NOW was to provide clarification regarding contentious issues arising in the wake of the 2016 WHO CNS update as well as report new advancements in molecular classification of CNS tumors and new tumor entities emerging as a result of these advancements. cIMPACT-NOW updates: It thus laid the foundation for the 5th edition of the WHO Classification of CNS tumors (2021 WHO CNS 5). We have discussed cIMPACT updates in detail in this review. In addition, molecular diagnostics including DNA methylation-based classification of CNS tumors and the practical use of molecular classification in the prognostication and treatment of CNS tumors is discussed. Finally, the salient features of the new CNS tumor classification are summarized.

DNA Methylation Analysis Reveals Distinct Patterns in Satellite Cell-Derived Myogenic Progenitor Cells of Subjects with Spastic Cerebral Palsy

Spastic type cerebral palsy (CP) is a complex neuromuscular disorder that involves altered skeletal muscle microanatomy and growth, but little is known about the mechanisms contributing to muscle pathophysiology and dysfunction. Traditional genomic approaches have provided limited insight regarding disease onset and severity, but recent epigenomic studies indicate that DNA methylation patterns can be altered in CP. Here, we examined whether a diagnosis of spastic CP is associated with intrinsic DNA methylation differences in myoblasts and myotubes derived from muscle resident stem cell populations (satellite cells; SCs). Twelve subjects were enrolled (6 CP; 6 control) with informed consent/assent. Skeletal muscle biopsies were obtained during orthopedic surgeries, and SCs were isolated and cultured to establish patient-specific myoblast cell lines capable of proliferation and differentiation in culture. DNA methylation analyses indicated significant differences at 525 individual CpG sites in proliferating SC-derived myoblasts (MB) and 1774 CpG sites in differentiating SC-derived myotubes (MT). Of these, 79 CpG sites were common in both culture types. The distribution of differentially methylated 1 Mbp chromosomal segments indicated distinct regional hypo- and hyper-methylation patterns, and significant enrichment of differentially methylated sites on chromosomes 12, 13, 14, 15, 18, and 20. Average methylation load across 2000 bp regions flanking transcriptional start sites was significantly different in 3 genes in MBs, and 10 genes in MTs. SC derived MBs isolated from study participants with spastic CP exhibited fundamental differences in DNA methylation compared to controls at multiple levels of organization that may reveal new targets for studies of mechanisms contributing to muscle dysregulation in spastic CP.

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.

Comprehensive study of semi-supervised learning for DNA methylation-based supervised classification of central nervous system tumors

BACKGROUND

Precision medicine for cancer treatment relies on an accurate pathological diagnosis. The number of known tumor classes has increased rapidly, and reliance on traditional methods of histopathologic classification alone has become unfeasible. To help reduce variability, validation costs, and standardize the histopathological diagnostic process, supervised machine learning models using DNA-methylation data have been developed for tumor classification. These methods require large labeled training data sets to obtain clinically acceptable classification accuracy. While there is abundant unlabeled epigenetic data across multiple databases, labeling pathology data for machine learning models is time-consuming and resource-intensive, especially for rare tumor types. Semi-supervised learning (SSL) approaches have been used to maximize the utility of labeled and unlabeled data for classification tasks and are effectively applied in genomics. SSL methods have not yet been explored with epigenetic data nor demonstrated beneficial to central nervous system (CNS) tumor classification.

RESULTS

This paper explores the application of semi-supervised machine learning on methylation data to improve the accuracy of supervised learning models in classifying CNS tumors. We comprehensively evaluated 11 SSL methods and developed a novel combination approach that included a self-training with editing using support vector machine (SETRED-SVM) model and an L2-penalized, multinomial logistic regression model to obtain high confidence labels from a few labeled instances. Results across eight random forest and neural net models show that the pseudo-labels derived from our SSL method can significantly increase prediction accuracy for 82 CNS tumors and 9 normal controls.

CONCLUSIONS

The proposed combination of semi-supervised technique and multinomial logistic regression holds the potential to leverage the abundant publicly available unlabeled methylation data effectively. Such an approach is highly beneficial in providing additional training examples, especially for scarce tumor types, to boost the prediction accuracy of supervised models.

Emerging Systemic Treatment Perspectives on Brain Metastases: Moving Toward a Better Outlook for Patients

The diagnosis of brain metastases has historically been a dreaded, end-stage complication of systemic disease. Additionally, with the increasing effectiveness of systemic therapies that prolong life expectancy and improved imaging tools, the incidence of intracranial progression is becoming more common. Within this context, there has been increasing attention directed at understanding the molecular underpinnings of intracranial progression. Exploring the unique features of brain metastases compared with their extracranial counterparts to identify aberrant signaling pathways, which can be targeted pharmacologically, may help lead to new treatments for this patient population. Additionally, critical discoveries outside the sphere of the central nervous system are increasingly being applied to brain metastases with the emergence of immune checkpoint inhibition, becoming a prevalent treatment option for patients with brain metastases across multiple histologies. As novel treatment strategies are considered, they require thoughtful incorporation of agents that can cross the blood-brain barrier and can synergize with pre-existing agents through rational combinations. Lastly, as clinicians and scientists continue to understand key molecular features of these tumors, they will continue to influence the treatment algorithms that are developing for the management of these patients. Due to the complexity of treatment decisions for patients with brain metastases, an emerging tool is the utilization of multidisciplinary brain metastasis tumor boards to ensure optimal treatment decisions are made and that patients are provided access to applicable clinical trials. Looking to the future, the collective effort to understand the various tumor-intrinsic and tumor-extrinsic factors that promote central nervous system seeding and propagation will have the potential to change the clinical trajectory for these patients.

ENPP2 Promoter Methylation Correlates with Decreased Gene Expression in Breast Cancer: Implementation as a Liquid Biopsy Biomarker

Autotaxin (ATX), encoded by the ctonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2) gene, is a key enzyme in lysophosphatidic acid (LPA) synthesis. We have recently described ENPP2 methylation profiles in health and multiple malignancies and demonstrated correlation to its aberrant expression. Here we focus on breast cancer (BrCa), analyzing in silico publicly available BrCa methylome datasets, to identify differentially methylated CpGs (DMCs) and correlate them with expression. Numerous DMCs were identified between BrCa and healthy breast tissues in the gene body and promoter-associated regions (PA). PA DMCs were upregulated in BrCa tissues in relation to normal, in metastatic BrCa in relation to primary, and in stage I BrCa in relation to normal, and this was correlated to decreased mRNA expression. The first exon DMC was also investigated in circulating cell free DNA (ccfDNA) isolated by BrCa patients; methylation was increased in BrCa in relation to ccfDNA from healthy individuals, confirming in silico results. It also differed between patient groups and was correlated to the presence of multiple metastatic sites. Our data indicate that promoter methylation of ENPP2 arrests its transcription in BrCa and introduce first exon methylation as a putative biomarker for diagnosis and monitoring which can be assessed in liquid biopsy.

Glial and myeloid heterogeneity in the brain tumour microenvironment

Brain cancers carry bleak prognoses, with therapeutic advances helping only a minority of patients over the past decade. The brain tumour microenvironment (TME) is highly immunosuppressive and differs from that of other malignancies as a result of the glial, neural and immune cell populations that constitute it. Until recently, the study of the brain TME was limited by the lack of methods to de-convolute this complex system at the single-cell level. However, novel technical approaches have begun to reveal the immunosuppressive and tumour-promoting properties of distinct glial and myeloid cell populations in the TME, identifying new therapeutic opportunities. Here, we discuss the immune modulatory functions of microglia, monocyte-derived macrophages and astrocytes in brain metastases and glioma, highlighting their disease-associated heterogeneity and drawing from the insights gained by studying these malignancies and other neurological disorders. Lastly, we consider potential approaches for the therapeutic modulation of the brain TME.

Machine Learning in Epigenomics: Insights into Cancer Biology and Medicine

The recent deluge of genome-wide technologies for the mapping of the epigenome and resulting data in cancer samples has provided the opportunity for gaining insights into and understanding the roles of epigenetic processes in cancer. However, the complexity, high-dimensionality, sparsity, and noise associated with these data pose challenges for extensive integrative analyses. Machine Learning (ML) algorithms are particularly suited for epigenomic data analyses due to their flexibility and ability to learn underlying hidden structures. We will discuss four overlapping but distinct major categories under ML: dimensionality reduction, unsupervised methods, supervised methods, and deep learning (DL). We review the preferred use cases of these algorithms in analyses of cancer epigenomics data with the hope to provide an overview of how ML approaches can be used to explore fundamental questions on the roles of epigenome in cancer biology and medicine.

DNA methylation-based classifier and gene expression signatures detect BRCAness in osteosarcoma

Although osteosarcoma (OS) is a rare cancer, it is the most common primary malignant bone tumor in children and adolescents. BRCAness is a phenotypical trait in tumors with a defect in homologous recombination repair, resembling tumors with inactivation of BRCA1/2, rendering these tumors sensitive to poly (ADP)-ribose polymerase inhibitors (PARPi). Recently, OS was shown to exhibit molecular features of BRCAness. Our goal was to develop a method complementing existing genomic methods to aid clinical decision making on administering PARPi in OS patients. OS samples with DNA-methylation data were divided to BRCAness-positive and negative groups based on the degree of their genomic instability (n = 41). Methylation probes were ranked according to decreasing variance difference between two groups. The top 2000 probes were selected for training and cross-validation of the random forest algorithm. Two-thirds of available OS RNA-Seq samples (n = 17) from the top and bottom of the sample list ranked according to genome instability score were subjected to differential expression and, subsequently, to gene set enrichment analysis (GSEA). The combined accuracy of trained random forest was 85% and the average area under the ROC curve (AUC) was 0.95. There were 449 upregulated and 1,079 downregulated genes in the BRCAness-positive group (fdr < 0.05). GSEA of upregulated genes detected enrichment of DNA replication and mismatch repair and homologous recombination signatures (FWER < 0.05). Validation of the BRCAness classifier with an independent OS set (n = 20) collected later in the course of study showed AUC of 0.87 with an accuracy of 90%. GSEA signatures computed for this test set were matching the ones observed in the training set enrichment analysis. In conclusion, we developed a new classifier based on DNA-methylation patterns that detects BRCAness in OS samples with high accuracy. GSEA identified genome instability signatures. Machine-learning and gene expression approaches add new epigenomic and transcriptomic aspects to already established genomic methods for evaluation of BRCAness in osteosarcoma and can be extended to cancers characterized by genome instability.

Osteosarcoma (OS) is the most common primary malignant tumor of bone in children and young adults with poor prognosis for patients with refractory or metastatic disease. A common feature, so-called BRCAness, exists in multiple cancers including OS and is characterized by homologous recombination deficiency. Tumors exhibiting BRCAness have been shown to respond to therapy with PARP inhibitors. Currently, BRCAness is mostly assessed by the genomic instability score. This method based on the DNA sequencing requires normal tissue DNA as control and is vulnerable to subjective interpretation of "genomic scarring" events. In this study, we implemented a classifier based on DNA methylation patterns. It is capable of detecting BRCAness in OS samples and does not require control tissue DNA. Therefore, it has the potential to support clinical decision making on administering PARPi in OS patients. We further corroborated the presence of BRCAness in OS by detecting homologous recombination signatures through gene expression analysis.

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.

Neocortical development and epilepsy: insights from focal cortical dysplasia and brain tumours

During the past decade, there have been considerable advances in understanding of the genetic and morphogenic processes underlying cortical malformations and developmental brain tumours. Focal malformations are caused by somatic (postzygotic) variants in genes related to cell growth (ie, in the mTOR pathway in focal cortical dysplasia type 2), which are acquired in neuronal progenitors during neurodevelopment. In comparison, developmental brain tumours result from somatic variants in genes related to cell proliferation (eg, in the MAP-kinase pathway in ganglioglioma), which affect proliferating glioneuronal precursors. The timing of the genetic event and the specific gene involved during neurodevelopment will drive the nature and size of the lesion, whether it is a developmental malformation or a brain tumour. There is also emerging evidence that epigenetic processes underlie a molecular memory in epileptogenesis. This knowledge will together facilitate understanding of why and how patients with these lesions have epilepsy, and could form a basis for a move towards precision medicine for this challenging cohort of patients.

Interleukin enhancer-binding factor 2 promotes cell proliferation and DNA damage response in metastatic melanoma

BACKGROUND

1q21.3 amplification, which is frequently observed in metastatic melanoma, is associated with cancer progression. Interleukin enhancer-binding factor 2 (ILF2) is located in the 1q21.3 amplified region, but its functional role or contribution to tumour aggressiveness in cutaneous melanoma is unknown.

METHODS

In silico analyses were performed using the TCGA SKCM dataset with clinical annotations and three melanoma microarray cohorts from the GEO datasets. RNA in situ hybridisation and immunohistochemistry were utilised to validate the gene expression in melanoma tissues. Four stable melanoma cell lines were established for in vitro ILF2 functional characterisation.

RESULTS

Our results showed that the ILF2 copy number variation (CNV) is positively correlated with ILF2 mRNA expression (r = 0.68, p < .0001). Additionally, ILF2 expression is significantly increased with melanoma progression (p < .0001), and significantly associated with poor overall survival for metastatic melanoma patients (p = .026). The overexpression of ILF2 (ILF2-OV) promotes proliferation in metastatic melanoma cells, whereas ILF2 knockdown decreases proliferation by blocking the cell cycle. Mechanistically, we demonstrated the interaction between ILF2 and the splicing factor U2AF2, whose knockdown reverses the proliferation effects mediated by ILF2-OV. Stage IIIB-C melanoma patients with high ILF2-U2AF2 expression showed significantly shorter overall survival (p = .024). Enhanced ILF2/U2AF2 expression promotes a more efficient DNA-damage repair by increasing RAD50 and ATM mRNA expression. Paradoxically, metastatic melanoma cells with ILF2-OV were more sensitive to ATM inhibitors.

CONCLUSION

Our study uncovered that ILF2 amplification of the 1q21.3 chromosome is associated with melanoma progression and triggers a functional downstream pathway in metastatic melanoma promoting drug resistance.

DNA Methylation-Based Classification of Small B-Cell Lymphomas: A Proof-of-Principle Study

Although most small B-cell lymphomas (SBCLs) can be diagnosed using routine methods, challenges exist. For example, marginal zone lymphomas (MZLs) can be difficult to rule-in, in large part because no widely-used, sensitive, and specific biomarker is available for the marginal zone cell of origin. In this study, it was hypothesized that DNA methylation array profiling can assist with the classification of SBCLs, including MZLs. Extramedullary SBCLs, including challenging cases, were reviewed internally for pathology consensus and profiled. By combining the resulting array data set with data sets from other groups, a set of 26 informative probes was selected and used to train machine learning models to classify 4 common SBCLs: chronic lymphocytic leukemia/small lymphocytic lymphoma, follicular lymphoma, mantle cell lymphoma, and MZL. Prediction probability cutoff was used to separate classifiable from unclassifiable cases, and show that the trained model was able to classify 95% of independent test cases (n = 264/279). The concordance between model predictions and pathology diagnoses was 99.6% (n = 262/263) among classifiable test cases. One validation reference test case was reclassified based on model prediction. The model was also used to predict the diagnoses of two challenging SBCLs. Although the differential examined and data on difficult cases are limited, these results support accurate methylation-based classification of SBCLs. Furthermore, high specificities of predictions suggest that methylation signatures can be used to rule-in MZLs.

The roles of epigenetics in cancer progression and metastasis

Cancer metastasis remains a major clinical challenge for cancer treatment. It is therefore crucial to understand how cancer cells establish and maintain their metastatic traits. However, metastasis-specific genetic mutations have not been identified in most exome or genome sequencing studies. Emerging evidence suggests that key steps of metastasis are controlled by reversible epigenetic mechanisms, which can be targeted to prevent and treat the metastatic disease. A variety of epigenetic mechanisms were identified to regulate metastasis, including the well-studied DNA methylation and histone modifications. In the past few years, large scale chromatin structure alterations including reprogramming of the enhancers and chromatin accessibility to the transcription factors were shown to be potential driving force of cancer metastasis. To dissect the molecular mechanisms and functional output of these epigenetic changes, it is critical to use advanced techniques and alternative animal models for interdisciplinary and translational research on this topic. Here we summarize our current understanding of epigenetic aberrations in cancer progression and metastasis, and their implications in developing new effective metastasis-specific therapies.

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.

Epigenetic Regulation in Melanoma: Facts and Hopes

Cutaneous melanoma is a lethal disease, even when diagnosed in advanced stages. Although recent progress in biology and treatment has dramatically improved survival rates, new therapeutic approaches are still needed. Deregulation of epigenetics, which mainly controls DNA methylation status and chromatin remodeling, is implied not only in cancer initiation and progression, but also in resistance to antitumor drugs. Epigenetics in melanoma has been studied recently in both melanoma preclinical models and patient samples, highlighting its potential role in different phases of melanomagenesis, as well as in resistance to approved drugs such as immune checkpoint inhibitors and MAPK inhibitors. This review summarizes what is currently known about epigenetics in melanoma and dwells on the recognized and potential new targets for testing epigenetic drugs, alone or together with other agents, in advanced melanoma patients.

Pathological Features of Brain Metastases

Metastases are the most common intracranial tumors in adults. Lung cancer, melanoma, renal cell carcinoma, and breast cancer are the most common primary tumors that metastasize to the brain. Improved detection of small metastases by MRI, and improved systemic therapy for primary tumors, resulted in increased incidence of brain metastasis. Advances in neuroanesthesia and neurosurgery have significantly improved the safety of surgical resection of brain metastases. Surgical approach and active management have become applicable for many patients. Subsequently, brain metastases diagnosis no longer equals palliative treatment. Moreover, the demand for diagnosing brain masses has increased with its associated challenges.

A Systems Approach to Brain Tumor Treatment

Brain tumors are among the most lethal tumors. Glioblastoma, the most frequent primary brain tumor in adults, has a median survival time of approximately 15 months after diagnosis or a five-year survival rate of 10%; the recurrence rate is nearly 90%. Unfortunately, this prognosis has not improved for several decades. The lack of progress in the treatment of brain tumors has been attributed to their high rate of primary therapy resistance. Challenges such as pronounced inter-patient variability, intratumoral heterogeneity, and drug delivery across the blood-brain barrier hinder progress. A comprehensive, multiscale understanding of the disease, from the molecular to the whole tumor level, is needed to address the intratumor heterogeneity resulting from the coexistence of a diversity of neoplastic and non-neoplastic cell types in the tumor tissue. By contrast, inter-patient variability must be addressed by subtyping brain tumors to stratify patients and identify the best-matched drug(s) and therapies for a particular patient or cohort of patients. Accomplishing these diverse tasks will require a new framework, one involving a systems perspective in assessing the immense complexity of brain tumors. This would in turn entail a shift in how clinical medicine interfaces with the rapidly advancing high-throughput (HTP) technologies that have enabled the omics-scale profiling of molecular features of brain tumors from the single-cell to the tissue level. However, several gaps must be closed before such a framework can fulfill the promise of precision and personalized medicine for brain tumors. Ultimately, the goal is to integrate seamlessly multiscale systems analyses of patient tumors and clinical medicine. Accomplishing this goal would facilitate the rational design of therapeutic strategies matched to the characteristics of patients and their tumors. Here, we discuss some of the technologies, methodologies, and computational tools that will facilitate the realization of this vision to practice.

DIMEimmune: Robust estimation of infiltrating lymphocytes in CNS tumors from DNA methylation profiles

The interaction of CNS tumors with infiltrating lymphocytes plays an important role in their initiation and progression and might be related to therapeutic responses. Gene expression-based methods have been successfully used to characterize the tumor microenvironment. However, methylation data are now increasingly used for molecular diagnostics and there are currently only few methods to infer information about the microenvironment from this data type. Using an approach based on differential methylation and principal component analysis, we developed DIMEimmune (Differential Methylation Analysis for Immune Cell Estimation) to estimate CD4⁺ and CD8⁺ T cell abundance as well as tumor-infiltrating lymphocytes (TILs) scores from bulk methylation data. Well-established approaches based on gene expression data and immunohistochemistry-based lymphocyte counts were used as benchmarks. The comparison of DIMEimmune to the previously published MethylCIBERSORT and MeTIL algorithms showed an improved correlation with both gene expression-based and immunohistological results across different brain tumor types. Further, we applied our method to large datasets of glioma, medulloblastoma, atypical teratoid/rhabdoid tumors (ATRTs) and ependymoma. High-grade gliomas showed higher scores of tumor-infiltrating lymphocytes than lower-grade gliomas. There were overall only few tumor-infiltrating lymphocytes in medulloblastoma subgroups. ATRTs were highly infiltrated by lymphocytes, most prominently in the MYC subgroup. DIMEimmune-based estimates of TILs were a significant prognostic factor in the overall cohort of gliomas and medulloblastomas, but not within methylation-based diagnostic subgroups. To conclude, DIMEimmune allows for robust estimates of TIL abundance and might contribute to establishing them as a prognostic or predictive factor in future studies of CNS tumors.

Current Triple-Negative Breast Cancer Subtypes: Dissecting the Most Aggressive Form of Breast Cancer

Triple-negative breast cancer (TNBC) is a highly heterogeneous disease defined by the absence of estrogen receptor (ER) and progesterone receptor (PR) expression, and human epidermal growth factor receptor 2 (HER2) overexpression that lacks targeted treatments, leading to dismal clinical outcomes. Thus, better stratification systems that reflect intrinsic and clinically useful differences between TNBC tumors will sharpen the treatment approaches and improve clinical outcomes. The lack of a rational classification system for TNBC also impacts current and emerging therapeutic alternatives. In the past years, several new methodologies to stratify TNBC have arisen thanks to the implementation of microarray technology, high-throughput sequencing, and bioinformatic methods, exponentially increasing the amount of genomic, epigenomic, transcriptomic, and proteomic information available. Thus, new TNBC subtypes are being characterized with the promise to advance the treatment of this challenging disease. However, the diverse nature of the molecular data, the poor integration between the various methods, and the lack of cost-effective methods for systematic classification have hampered the widespread implementation of these promising developments. However, the advent of artificial intelligence applied to translational oncology promises to bring light into definitive TNBC subtypes. This review provides a comprehensive summary of the available classification strategies. It includes evaluating the overlap between the molecular, immunohistochemical, and clinical characteristics between these approaches and a perspective about the increasing applications of artificial intelligence to identify definitive and clinically relevant TNBC subtypes.

Detection and relevance of epigenetic markers on ctDNA: recent advances and future outlook

Liquid biopsy, a minimally invasive approach, is a highly powerful clinical tool for the real-time follow-up of cancer and overcomes many limitations of tissue biopsies. Epigenetic alterations have a high potential to provide a valuable source of innovative biomarkers for cancer, owing to their stability, frequency, and noninvasive accessibility in bodily fluids. Numerous DNA methylation markers are now tested in circulating tumor DNA (ctDNA) as potential biomarkers, in various types of cancer. DNA methylation in combination with liquid biopsy is very powerful in identifying circulating epigenetic biomarkers of clinical importance. Blood-based epigenetic biomarkers have a high potential for early detection of cancer since DNA methylation in plasma can be detected early during cancer pathogenesis. In this review, we summarize the latest findings on DNA methylation markers in ctDNA for early detection, prognosis, minimal residual disease, risk of relapse, treatment selection, and resistance, for breast, prostate, lung, and colorectal cancer.

Molecular Profiles of Brain Metastases: A Focus on Heterogeneity

Simple Summary

Precision cancer medicine depends on the characterization of tumor samples, usually by a single-tumor biopsy, to administer an optimal therapeutic. However, primary tumors and their metastases are often heterogeneous. A metastatic lesion may harbor a completely different genetic makeup to that of its parent tumor, and a single tumor sampling may be ineffective in selecting the most efficient therapy. Brain metastases, due to their low availability and specific microenvironment, pose a particular challenge for precision medicine. In this review, we highlight the genetic landscape of brain metastases, with a particular focus on their heterogeneity. To illustrate this problem, we present phenotypic alterations in brain metastases originating from lung cancer, breast cancer, and melanoma. This article may help clinicians better understand alterations in brain metastases and the relevance of their heterogeneity.

Abstract

Brain metastasis is a common and devastating clinical entity. Intratumor heterogeneity in brain metastases poses a crucial challenge to precision medicine. However, advances in next-generation sequencing, new insight into the pathophysiology of driver mutations, and the creation of novel tumor models have allowed us to gain better insight into the genetic landscapes of brain metastases, their temporal evolution, and their response to various treatments. A plethora of genomic studies have identified the heterogeneous clonal landscape of tumors and, at the same time, introduced potential targets for precision medicine. As an example, we present phenotypic alterations in brain metastases originating from three malignancies with the highest brain metastasis frequency: lung cancer, breast cancer, and melanoma. We discuss the barriers to precision medicine, tumor heterogeneity, the significance of blood-based biomarkers in tracking clonal evolution, the phylogenetic relationship between primary and metastatic tumors, blood–brain barrier heterogeneity, and limitations to ongoing research.

Cancer microenvironment and genomics: evolution in process

Cancer heterogeneity is a result of genetic mutations within the cancer cells. Their proliferation is not only driven by autocrine functions but also under the influence of cancer microenvironment, which consists of normal stromal cells such as infiltrating immune cells, cancer-associated fibroblasts, endothelial cells, pericytes, vascular and lymphatic channels. The relationship between cancer cells and cancer microenvironment is a critical one and we are just on the verge to understand it on a molecular level. Cancer microenvironment may serve as a selective force to modulate cancer cells to allow them to evolve into more aggressive clones with ability to invade the lymphatic or vascular channels to spread to regional lymph nodes and distant sites. It is important to understand these steps of cancer evolution within the cancer microenvironment towards invasion so that therapeutic strategies can be developed to control or stop these processes.

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.

DNA-methylation for the detection and distinction of 19 human malignancies

The contribution of DNA-methylation based gene silencing to carcinogenesis is well established. Increasingly, DNA-methylation is examined using genome-wide techniques, with recent public efforts yielding immense data sets of diverse malignancies representing the vast majority of human cancer related disease burden. Whereas mutation events may group preferentially or in high frequency with a given histology, mutations are poor classifiers of tumour type. Here we examine the hypothesis that cancer-specific DNA-methylation reflects the tissue of origin or carcinogenic risk factor, and these methylation abnormalities may be used to faithfully classify tumours according to histology. We present an analysis of 7427 tumours representing 19 human malignancies and 708 normal samples demonstrating that specific tumour changes in methylation can correctly determine site of origin and tumour histology with 86% overall accuracy. Examination of misclassified tumours reveals underlying shared biology as the source of misclassifications, including common cell of origin or risk factors.

Epigenomic tensor predicts disease subtypes and reveals constrained tumor evolution

Understanding the epigenomic evolution and specificity of disease subtypes from complex patient data remains a major biomedical problem. We here present DeCET (decomposition and classification of epigenomic tensors), an integrative computational approach for simultaneously analyzing hierarchical heterogeneous data, to identify robust epigenomic differences among tissue types, differentiation states, and disease subtypes. Applying DeCET to our own data from 21 uterine benign tumor (leiomyoma) patients identifies distinct epigenomic features discriminating normal myometrium and leiomyoma subtypes. Leiomyomas possess preponderant alterations in distal enhancers and long-range histone modifications confined to chromatin contact domains that constrain the evolution of pathological epigenomes. Moreover, we demonstrate the power and advantage of DeCET on multiple publicly available epigenomic datasets representing different cancers and cellular states. Epigenomic features extracted by DeCET can thus help improve our understanding of disease states, cellular development, and differentiation, thereby facilitating future therapeutic, diagnostic, and prognostic strategies.

Leistico et al. apply tensor decomposition and classification methods to integrate information from hierarchical heterogenous epigenomic datasets and identify histone modification patterns that discriminate disease conditions, tissue types, and differentiation states. Leiomyomas are shown to possess alterations in distal enhancers and large-scale regions confined to chromatin contact domains.