Neutrophil to lymphocyte ratio in myelofibrosis patients treated with ruxolitinib may predict prognosis and rate of discontinuation.

BACKGROUND: Myelofibrosis (MF) is a clonal Philadelphia chromosome negative myeloproliferative neoplasm (Ph-MPN). MF is featured by an inflammatory condition that can also drive the progression of disease. Ruxolitinib (ruxo) is the-first-in-class Jak1/2 inhibitor approved for treatment of MF, proved to reduce spleen volume and decrease symptom burden. In various malignancies neutrophil-to-lymphocyte ratio (NLR) has been indicated as predictor of progression free survival (PFS) and overall survival (OS). NLR might reflect the balance between systemic inflammation and immunity and is emerging as a prognostic biomarker in several neoplasms, including the hematological ones. METHODS: We analyzed a cohort of 140 MF patients treated with ruxo to validate baseline NLR (as a continuous variable and as a cut-off 2) as predictor of OS and of ruxo treatment discontinuation. RESULTS: We found that both baseline NLR as a continuous variable [HR 0.8 (95% CI: 0.7-0.9) (p = .006)] and NLR (<2 vs. ≥2) [HR 3.4 (95% CI: 1.6-7.0) (p = .001)] were significantly associated with OS. Censoring for patients undergone allotransplant, baseline NLR <2 was predictive of an earlier ruxo any-other-cause discontinuation [HR 3.7 (95%CI 1.7-8.3) (p < .001)]. CONCLUSIONS: NLR before starting ruxo treatment may be used as a simple and early predictor of OS and earlier ruxo discontinuation in clinical practice.

Novel therapeutic agents for myelofibrosis after failure or suboptimal response to JAK2 inhbitors.

PURPOSE OF REVIEW: JAK2 inhibitors have changed the therapeutic strategies for the management of primary and secondary myelofibrosis. Ruxolitinib, the first available agent, improved disease-related symptoms, spleen volume, and overall survival compared to conventional chemotherapy. It has been revealed that after 3 years of treatment, about 50% of patients discontinued ruxolitinib for resistance and/or intolerance and should be candidate to a second line of treatment. RECENT FINDINGS: Second-generation tyrosine kinase inhibitors have been tested in this setting, but all these new drugs do not significantly impact on disease progression. Novel agents are in developments that target on different pathways, alone or in combination with JAK2 inhibitors. SUMMARY: In this review, we summarize all the clinical efficacy and safety data of these drugs providing a vision of the possible future.

Discovery of a first-in-class EZH2 selective degrader.

The enhancer of zeste homolog 2 (EZH2) is the main enzymatic subunit of the PRC2 complex, which catalyzes trimethylation of histone H3 lysine 27 (H3K27me3) to promote transcriptional silencing. EZH2 is overexpressed in multiple types of cancer including triple-negative breast cancer (TNBC), and high expression levels correlate with poor prognosis. Several EZH2 inhibitors, which inhibit the methyltransferase activity of EZH2, have shown promise in treating sarcoma and follicular lymphoma in clinics. However, EZH2 inhibitors are ineffective at blocking proliferation of TNBC cells, even though they effectively reduce the H3K27me3 mark. Using a hydrophobic tagging approach, we generated MS1943, a first-in-class EZH2 selective degrader that effectively reduces EZH2 levels in cells. Importantly, MS1943 has a profound cytotoxic effect in multiple TNBC cells, while sparing normal cells, and is efficacious in vivo, suggesting that pharmacologic degradation of EZH2 can be advantageous for treating the cancers that are dependent on EZH2.

The Architecture of a Precision Oncology Platform.

Precision oncology is a novel research field and approach to cancer care which leverages high-throughput sequencing technologies and bioinformatics pipelines to determine diagnosis, prognosis, and treatment of patients in a personalized manner. This chapter provides an overview of a typical precision oncology software platform, from raw data to patient reports. Standard and advanced analytical components are described and discussed, along with their strengths and limitations, in general and in the context of a precision oncology application for advanced cancer patients.

Computational Approaches for the Investigation of Intra-tumor Heterogeneity and Clonal Evolution from Bulk Sequencing Data in Precision Oncology Applications.

While the clonal model of cancer evolution was first proposed over 40 years ago, only recently next-generation sequencing has allowed a more precise and quantitative assessment of tumor clonal and subclonal landscape. Consequently, a plethora of computational approaches and tools have been developed to analyze this data with the goal of inferring the clonal landscape of a tumor and characterize its temporal or spatial evolution. This chapter introduces intra-tumor heterogeneity (ITH) in the context of precision oncology applications and provides an overview of the basic concepts, algorithms, and tools for the dissection, analysis, and visualization of ITH from bulk DNA sequencing.

Software Workflows and Infrastructures for Precision Oncology.

Precision oncology mainly relies on genetic and molecular patient profiling from high-throughput sequencing data. The necessity to process and analyze large volumes of data has led to the development of robust computational tools and methods. The most challenging aspect in the implementation of a precision oncology workflow involves proper handling of large volume of data, while ensuring the results are reproducible and replicable. In this chapter, we provide a detailed description of the various tools available for the design and implementation of a precision oncology pipeline along with the technical considerations to make to utilize these tools effectively. We then provide a guide to the development of a precision oncology pipeline, with a specific emphasis on the software workflows and infrastructure needed.

Artificial Intelligence for Precision Oncology.

Precision oncology is an innovative approach to cancer care in which diagnosis, prognosis, and treatment are informed by the individual patient's genetic and molecular profile. The rapid development of novel high-throughput omics technologies in recent years has led to the generation of massive amount of complex patient data, which in turn has prompted the development of novel computational infrastructures, platforms, and tools to store, retrieve, and analyze this data efficiently. Artificial intelligence (AI), and in particular its subfield of machine learning, is ideal for deciphering patterns in large datasets and offers unique opportunities for advancing precision oncology. In this chapter, we provide an overview of the various public data resources and applications of AI in precision oncology and cancer research, from subtype identification to drug prioritization, using multi-omics datasets. We also discuss the impact of AI-powered medical image analysis in oncology and present the first diagnostic FDA-approved AI-powered tools.

Single-Cell Sequencing Technologies in Precision Oncology.

Single-cell sequencing technologies are revolutionizing cancer research and are poised to become the standard for translational cancer studies. Rapidly decreasing costs and increasing throughput and resolution are paving the way for the adoption of single-cell technologies in clinical settings for personalized medicine applications. In this chapter, we review the state of the art of single-cell DNA and RNA sequencing technologies, the computational tools to analyze the data, and their potential application to precision oncology. We also discuss the advantages of single-cell over bulk sequencing for the dissection of intra-tumor heterogeneity and the characterization of subclonal cell populations, the implementation of targeted drug repurposing approaches, and describe advanced methodologies for multi-omics data integration and to assess cell signaling at single-cell resolution.

Multi-Omics Profiling of the Tumor Microenvironment.

All solid tumors and many hematological malignancies grow and proliferate in a tumor microenvironment (TME), a spectrum of continuous and highly dynamic interactions with different immune and stromal cells. This ecosystem contributes to the extensive heterogeneity that exists between and within cancer patients. Understanding the characteristics of this intricate network could significantly improve cancer prognosis, as was demonstrated already for a subset of patients by the advent of immunotherapies (including monoclonal antibodies, bispecific antibodies, and chimeric antigen receptor (CAR) T cells. The development of multimodal omics technologies has allowed researchers to document and characterize the TME at single-cell resolution, which provides an unprecedent opportunity to understand the full complexity of the tumor microenvironment. In this chapter, we highlight the paradigm shift that has brought the TME to the forefront of cancer research and discuss its composition. In addition, we summarize the available multimodal single-cell omics methods that allow studying the TME from different angles, as well as their advantages and limitations. We discuss computational analysis tools, data integration, and methods to specifically study crosstalk between TME components. Finally, we touch upon the implications of studying the TME for ongoing or future clinical studies and how these can lead to more effective treatments for cancer patients.

miRandola 2017: a curated knowledge base of non-invasive biomarkers.

miRandola (http://mirandola.iit.cnr.it/) is a database of extracellular non-coding RNAs (ncRNAs) that was initially published in 2012, foreseeing the relevance of ncRNAs as non-invasive biomarkers. An increasing amount of experimental evidence shows that ncRNAs are frequently dysregulated in diseases. Further, ncRNAs have been discovered in different extracellular forms, such as exosomes, which circulate in human body fluids. Thus, miRandola 2017 is an effort to update and collect the accumulating information on extracellular ncRNAs that is spread across scientific publications and different databases. Data are manually curated from 314 articles that describe miRNAs, long non-coding RNAs and circular RNAs. Fourteen organisms are now included in the database, and associations of ncRNAs with 25 drugs, 47 sample types and 197 diseases. miRandola also classifies extracellular RNAs based on their extracellular form: Argonaute2 protein, exosome, microvesicle, microparticle, membrane vesicle, high density lipoprotein and circulating. We also implemented a new web interface to improve the user experience.

Selective targeting of point-mutated KRAS through artificial microRNAs.

Mutated protein-coding genes drive the molecular pathogenesis of many diseases, including cancer. Specifically, mutated KRAS is a documented driver for malignant transformation, occurring early during the pathogenesis of cancers such as lung and pancreatic adenocarcinomas. Therapeutically, the indiscriminate targeting of wild-type and point-mutated transcripts represents an important limitation. Here, we leveraged on the design of miRNA-like artificial molecules (amiRNAs) to specifically target point-mutated genes, such as KRAS, without affecting their wild-type counterparts. Compared with an siRNA-like approach, the requirement of perfect complementarity of the microRNA seed region to a given target sequence in the microRNA/target model has proven to be a more efficient strategy, accomplishing the selective targeting of point-mutated KRAS in vitro and in vivo.

miR-579-3p controls melanoma progression and resistance to target therapy.

Therapy of melanoma patients harboring activating mutations in the BRAF (V-raf murine sarcoma viral oncogene homolog B1) oncogene with a combination of BRAF and MEK inhibitors is plagued by the development of drug resistance. Mutational events, as well as adaptive mechanisms, contribute to the development of drug resistance. In this context we uncover here the role of a miRNA, miR-579-3p. We first show that low expression of miR-579-3p is a negative prognostic factor correlating with poor survival. Expression levels of miR-579-3p decrease from nevi to stage III/IV melanoma samples and even further in cell lines resistant to BRAF/MEK inhibitors. Mechanistically, we demonstrate that miR-579-3p acts as an oncosuppressor by targeting the 3'UTR of two oncoproteins: BRAF and an E3 ubiquitin protein ligase, MDM2. Moreover miR-579-3p ectopic expression impairs the establishment of drug resistance in human melanoma cells. Finally, miR-579-3p is strongly down-regulated in matched tumor samples from patients before and after the development of resistance to targeted therapies.

microRNA editing in seed region aligns with cellular changes in hypoxic conditions.

RNA editing is a finely tuned, dynamic mechanism for post-transcriptional gene regulation that has been thoroughly investigated in the last decade. Nevertheless, RNA editing in non-coding RNA, such as microRNA (miRNA), have caused great debate and have called for deeper investigation. Until recently, in fact, inadequate methodologies and experimental contexts have been unable to provide detailed insights for further elucidation of RNA editing affecting miRNAs, especially in cancer.In this work, we leverage on recent innovative bioinformatics approaches applied to a more informative experimental context in order to analyze the variations in miRNA seed region editing activity during a time course of a hypoxia-exposed breast cancer cell line. By investigating its behavior in a dynamic context, we found that miRNA editing events in the seed region are not depended on miRNA expression, unprecedentedly providing insights on the targetome shifts derived from these modifications. This reveals that miRNA editing acts under the influence of environmentally induced stimuli.Our results show a miRNA editing activity trend aligning with cellular pathways closely associated to hypoxia, such as the VEGF and PI3K/Akt pathways, providing important novel insights on this poorly elucidated phenomenon.

MicroRNA-148a reduces tumorigenesis and increases TRAIL-induced apoptosis in NSCLC.

Nonsmall cell lung cancer (NSCLC) is one of the leading causes of death worldwide. TNF-related apoptosis-inducing ligand (TRAIL) has been shown to induce apoptosis in malignant cells without inducing significant toxicity in normal cells. However, several carcinomas, including lung cancer, remain resistant to TRAIL. MicroRNAs (miRNAs) are small noncoding RNAs of ∼ 24 nt that block mRNA translation and/or negatively regulate its stability. They are often aberrantly expressed in cancer and have been implicated in increasing susceptibility or resistance to TRAIL-induced apoptosis by inhibiting key functional proteins. Here we show that miR-148a is down-regulated in cells with acquired TRAIL-resistance compared with TRAIL-sensitive cells. Enforced expression of miR-148a sensitized cells to TRAIL and reduced lung tumorigenesis in vitro and in vivo through the down-modulation of matrix metalloproteinase 15 (MMP15) and Rho-associated kinase 1 (ROCK1). These findings suggest that miR-148a acts as a tumor suppressor and might have therapeutic application in the treatment of NSCLC.

A set of NF-κB-regulated microRNAs induces acquired TRAIL resistance in lung cancer.

TRAIL (TNF-related apoptosis-inducing ligand) is a promising anticancer agent that can be potentially used as an alternative or complementary therapy because of its specific antitumor activity. However, TRAIL can also stimulate the proliferation of cancer cells through the activation of NF-κB, but the exact mechanism is still poorly understood. In this study, we show that chronic exposure to subtoxic concentrations of TRAIL results in acquired resistance. This resistance is associated with the increase in miR-21, miR-30c, and miR-100 expression, which target tumor-suppressor genes fundamental in the response to TRAIL. Importantly, down-regulation of caspase-8 by miR-21 blocks receptor interacting protein-1 cleavage and induces the activation of NF-κB, which regulates these miRNAs. Thus, TRAIL activates a positive feedback loop that sustains the acquired resistance and causes an aggressive phenotype. Finally, we prove that combinatory treatment of NF-κB inhibitors and TRAIL is able to revert resistance and reduce tumor growth, with important consequences for the clinical practice.

Noncoding RNA: Current Deep Sequencing Data Analysis Approaches and Challenges.

One of the most significant biological discoveries of the last decade is represented by the reality that the vast majority of the transcribed genomic output comprises diverse classes of noncoding RNAs (ncRNAs) that may play key roles and/or be affected by many biochemical cellular processes (i.e., RNA editing), with implications in human health and disease. With 90% of the human genome being transcribed and novel classes of ncRNA emerging (tRNA-derived small RNAs and circular RNAs among others), the great majority of the human transcriptome suggests that many important ncRNA functions/processes are yet to be discovered. An approach to filling such vast void of knowledge has been recently provided by the increasing application of next-generation sequencing (NGS), offering the unprecedented opportunity to obtain a more accurate profiling with higher resolution, increased throughput, sequencing depth, and low experimental complexity, concurrently posing an increasing challenge in terms of efficiency, accuracy, and usability of data analysis software. This review provides an overview of ncRNAs, NGS technology, and the most recent/popular computational approaches and the challenges they attempt to solve, which are essential to a more sensitive and comprehensive ncRNA annotation capable of furthering our understanding of this still vastly uncharted genomic territory.