DNA methylation of chronic lymphocytic leukemia with differential response to chemotherapy

Methods in DNA methylation array dataset analysis: A review

Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.

Targeting the key players of phenotypic plasticity in cancer cells by phytochemicals

Plasticity of phenotypic traits refers to an organism's ability to change in response to environmental stimuli. As a result, the response may alter an organism's physiological state, morphology, behavior, and phenotype. Phenotypic plasticity in cancer cells describes the considerable ability of cancer cells to transform phenotypes through non-genetic molecular signaling activities that promote therapy evasion and tumor metastasis via amplifying cancer heterogeneity. As a result of metastable phenotypic state transitions, cancer cells can tolerate chemotherapy or develop transient adaptive resistance. Therefore, new findings have paved the road in identifying factors and agents that inhibit or suppress phenotypic plasticity. It has also investigated novel multitargeted agents that may promise new effective strategies in cancer treatment. Despite the efficiency of conventional chemotherapeutic agents, drug toxicity, development of resistance, and high-cost limit their use in cancer therapy. Recent research has shown that small molecules derived from natural sources are capable of suppressing cancer by focusing on the plasticity of phenotypic responses. This systematic, comprehensive, and critical review analyzes the current state of knowledge regarding the ability of phytocompounds to target phenotypic plasticity at both preclinical and clinical levels. Current challenges/pitfalls, limitations, and future perspectives are also discussed.

Tumor cell plasticity in targeted therapy-induced resistance: mechanisms and new strategies

Despite the success of targeted therapies in cancer treatment, therapy-induced resistance remains a major obstacle to a complete cure. Tumor cells evade treatments and relapse via phenotypic switching driven by intrinsic or induced cell plasticity. Several reversible mechanisms have been proposed to circumvent tumor cell plasticity, including epigenetic modifications, regulation of transcription factors, activation or suppression of key signaling pathways, as well as modification of the tumor environment. Epithelial-to-mesenchymal transition, tumor cell and cancer stem cell formation also serve as roads towards tumor cell plasticity. Corresponding treatment strategies have recently been developed that either target plasticity-related mechanisms or employ combination treatments. In this review, we delineate the formation of tumor cell plasticity and its manipulation of tumor evasion from targeted therapy. We discuss the non-genetic mechanisms of targeted drug-induced tumor cell plasticity in various types of tumors and provide insights into the contribution of tumor cell plasticity to acquired drug resistance. New therapeutic strategies such as inhibition or reversal of tumor cell plasticity are also presented. We also discuss the multitude of clinical trials that are ongoing worldwide with the intention of improving clinical outcomes. These advances provide a direction for developing novel therapeutic strategies and combination therapy regimens that target tumor cell plasticity.

Methylation microarray-based detection of clinical copy-number aberrations in CLL benchmarked to standard FISH analysis

Copy-number aberrations (CNAs) are assessed using FISH analysis in diagnostics of chronic lymphocytic leukemia (CLL), but CNAs can also be extrapolated from Illumina BeadChips developed for genome-wide methylation microarray screening. Increasing numbers of microarray data-sets are available from diagnostic samples, making it useful to assess the potential in CNA diagnostics. We benchmarked the limitations of CNA testing from two Illumina BeadChips (EPIC and 450k) and using two common packages for analysis (conumee and ChAMP) to FISH-based assessment of 11q, 13q, and 17p deletions in 202 CLL samples. Overall, the two packages predicted CNAs with similar accuracy regardless of the microarray type, but lower than FISH-based assessment. We showed that the bioinformatics analysis needs to be adjusted to the specific CNA, as no general settings were identified. Altogether, we were able to predict CNAs using methylation microarray data, however, with limited accuracy, making FISH-based assessment of deletions the superior diagnostic choice.

Hobotnica: exploring molecular signature quality

A Molecular Features Set (MFS), is a result of a vast diversity of bioinformatics pipelines. The lack of a “gold standard” for most experimental data modalities makes it difficult to provide valid estimation for a particular MFS's quality. Yet, this goal can partially be achieved by analyzing inner-sample Distance Matrices (DM) and their power to distinguish between phenotypes.

The quality of a DM can be assessed by summarizing its power to quantify the differences of inner-phenotype and outer-phenotype distances. This estimation of the DM quality can be construed as a measure of the MFS's quality. 

Here we propose Hobotnica, an approach to estimate MFSs quality by their ability to stratify data, and assign them significance scores, that allow for collating various signatures and comparing their quality for contrasting groups.

p53-intact cancers escape tumor suppression through loss of long noncoding RNA Dino

Many long noncoding RNA (lncRNA) genes exist near cancer-associated loci, yet evidence connecting lncRNA functions to recurrent genetic alterations in cancer are lacking. Here, we report that DINO, the lncRNA transcribed from the cancer-associated DINO/CDKN1A locus, suppresses tumor formation independent of p21, the protein encoded at the locus. Loss of one or two alleles of Dino impairs p53 signaling and apoptosis, resulting in a haplo-insufficient tumor suppressor phenotype in genetically defined mouse models of tumorigenesis. A discrete region of the DINO/CDKN1A locus is recurrently hypermethylated in human cancers, silencing DINO but not CDKN1A, the gene encoding p21. Hypermethylation silences DINO, impairs p53 signaling pathway in trans, and is mutually exclusive with TP53 alterations, indicating that DINO and TP53 comprise a common tumor suppressor module. Therefore, DINO encodes a lncRNA essential for tumor suppression that is recurrently silenced in human cancers as a mechanism to escape p53-dependent tumor suppression.