Topical Collection "Application of Bioinformatics in Cancers"

A topical collection in Cancers (ISSN 2072-6694). This collection belongs to the section "Cancer Informatics and Big Data".

Editor

Dr. J. Chad Brenner
Website
Collection Editor
Department of Otolaryngology—Head and Neck SurgeryDirector, Michigan Otolaryngology and Translational Oncology LaboratoryUniversity of Michigan Health Systems, Ann Arbor, MI 48109, USA
Interests: functional genomic; proteomic and bioinformatics approaches in cancer; sequencing the exomes and transcriptomes of head and neck cancer; drug sensitivities
Special Issues and Collections in MDPI journals

Topical Collection Information

Dear Colleagues,

Bioinformatic applications in cancer have rapidly evolved over the past several years. Ever since its initial implementation, next generation sequencing has altered our understanding of cancer biology, and the approaches to analyzing the more and more complex datasets have also become increasingly complex.  Routine bioinformatics pipelines now range from those that rapidly detect and predict the functional impact of molecular alterations, to those that quantify the changes in the tumor microenvironment. For example, several tools that analyze tumor–immune interactions have been successfully developed so as to assess the tumor infiltrating lymphocyte content, microsatellite instability, total mutational burden, and neoantigen presentation. The further complexity of the integrated omics-based analysis is also now coupled with the emergence of modern machine learning and network-based approaches for analyzing large datasets in the context of publicly available resources, such as the cancer genome atlas.

While much of the focus has so far been on annotating molecular alterations. as well as infiltrating cell types or cell states in ideal sequencing conditions, alternative and application-specific approaches are now emerging that improve on a wide variety of established analysis techniques. These include techniques that range from the improved quantification of the copy number and gene expression from formalin-fixed tissues. as well as applications that require a high sensitivity, such as the quantification of tumor mutations from liquid biopsies (circulating cell free DNA).  Further novel applications attempt to improve the ability to analyze the distribution and molecular impact of complicated genetic features, such as repetitive or transposable endogenous elements (e.g., LINE-1), as well as exogenous genetic elements (e.g., human papilloma virus).

As we develop a better understanding of the limitations of these new informatics approaches, we can ultimately hope to apply these techniques to the existing datasets, and build well-annotated databases of easily accessible information that can be leveraged in multi-variable analysis pipelines. Similar to the success of SIGdb and cBioPortal, this should help yield new diagnostic and prognostic/predictive biomarkers for standard interventional modalities, as well as emerging areas like immuno-oncology, and areas of unmet clinical need. This Special Issue will highlight the current state-of-the-art in bioinformatics applications in cancer biology, and infer future prospects for improving informatics applications through artificial intelligence and machine learning approaches.

Dr. J. Chad Brenner
Collection Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

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Published Papers (28 papers)

2021

Jump to: 2020, 2019

Open AccessReview
Bioinformatic Approaches to Validation and Functional Analysis of 3D Lung Cancer Models
Cancers 2021, 13(4), 701; https://doi.org/10.3390/cancers13040701 - 09 Feb 2021
Viewed by 380
Abstract
3D models of cancer have the potential to improve basic, translational, and clinical studies. Patient-derived xenografts, spheroids, and organoids are broad categories of 3D models of cancer, and to date, these 3D models of cancer have been established for a variety of cancer [...] Read more.
3D models of cancer have the potential to improve basic, translational, and clinical studies. Patient-derived xenografts, spheroids, and organoids are broad categories of 3D models of cancer, and to date, these 3D models of cancer have been established for a variety of cancer types. In lung cancer, for example, 3D models offer a promising new avenue to gain novel insights into lung tumor biology and improve outcomes for patients afflicted with the number one cancer killer worldwide. However, the adoption and utility of these 3D models of cancer vary, and demonstrating the fidelity of these models is a critical first step before seeking meaningful applications. Here, we review use cases of current 3D lung cancer models and bioinformatic approaches to assessing model fidelity. Bioinformatics approaches play a key role in both validating 3D lung cancer models and high dimensional functional analyses to support downstream applications. Full article
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Open AccessArticle
Cancers in Agreement? Exploring the Cross-Talk of Cancer Metabolomic and Transcriptomic Landscapes Using Publicly Available Data
Cancers 2021, 13(3), 393; https://doi.org/10.3390/cancers13030393 - 21 Jan 2021
Viewed by 499
Abstract
One of the major hallmarks of cancer is the derailment of a cell’s metabolism. The multifaceted nature of cancer and different cancer types is transduced by both its transcriptomic and metabolomic landscapes. In this study, we re-purposed the publicly available transcriptomic and metabolomics [...] Read more.
One of the major hallmarks of cancer is the derailment of a cell’s metabolism. The multifaceted nature of cancer and different cancer types is transduced by both its transcriptomic and metabolomic landscapes. In this study, we re-purposed the publicly available transcriptomic and metabolomics data of eight cancer types (breast, lung, gastric, renal, liver, colorectal, prostate, and multiple myeloma) to find and investigate differences and commonalities on a pathway level among different cancer types. Topological analysis of inferred graphical Gaussian association networks showed that cancer was strongly defined in genetic networks, but not in metabolic networks. Using different statistical approaches to find significant differences between cancer and control cases, we highlighted the difficulties of high-level data-merging and in using statistical association networks. Cancer transcriptomics and metabolomics and landscapes were characterized by changed macro-molecule production, however, only major metabolic deregulations with highly impacted pathways were found in liver cancer. Cell cycle was enriched in breast, liver, and colorectal cancer, while breast and lung cancer were distinguished by highly enriched oncogene signaling pathways. A strong inflammatory response was observed in lung cancer and, to some extent, renal cancer. This study highlights the necessity of combining different omics levels to obtain a better description of cancer characteristics. Full article
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Open AccessArticle
The Transcriptomic Landscape of Prostate Cancer Development and Progression: An Integrative Analysis
Cancers 2021, 13(2), 345; https://doi.org/10.3390/cancers13020345 - 19 Jan 2021
Viewed by 450
Abstract
Next-generation sequencing of primary tumors is now standard for transcriptomic studies, but microarray-based data still constitute the majority of available information on other clinically valuable samples, including archive material. Using prostate cancer (PC) as a model, we developed a robust analytical framework to [...] Read more.
Next-generation sequencing of primary tumors is now standard for transcriptomic studies, but microarray-based data still constitute the majority of available information on other clinically valuable samples, including archive material. Using prostate cancer (PC) as a model, we developed a robust analytical framework to integrate data across different technical platforms and disease subtypes to connect distinct disease stages and reveal potentially relevant genes not identifiable from single studies alone. We reconstructed the molecular profile of PC to yield the first comprehensive insight into its development, by tracking changes in mRNA levels from normal prostate to high-grade prostatic intraepithelial neoplasia, and metastatic disease. A total of nine previously unreported stage-specific candidate genes with prognostic significance were also found. Here, we integrate gene expression data from disparate sample types, disease stages and technical platforms into one coherent whole, to give a global view of the expression changes associated with the development and progression of PC from normal tissue through to metastatic disease. Summary and individual data are available online at the Prostate Integrative Expression Database (PIXdb), a user-friendly interface designed for clinicians and laboratory researchers to facilitate translational research. Full article
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2020

Jump to: 2021, 2019

Open AccessArticle
Tumors Widely Express Hundreds of Embryonic Germline Genes
Cancers 2020, 12(12), 3812; https://doi.org/10.3390/cancers12123812 - 17 Dec 2020
Viewed by 527
Abstract
We have recently described a class of 756 genes that are widely expressed in cancers, but are normally restricted to adult germ cells, referred to as germ cell cancer genes (GC genes). We hypothesized that carcinogenesis involves the reactivation of biomolecular processes and [...] Read more.
We have recently described a class of 756 genes that are widely expressed in cancers, but are normally restricted to adult germ cells, referred to as germ cell cancer genes (GC genes). We hypothesized that carcinogenesis involves the reactivation of biomolecular processes and regulatory mechanisms that, under normal circumstances, are restricted to germline development. This would imply that cancer cells share gene expression profiles with primordial germ cells (PGCs). We therefore compared the transcriptomes of human PGCs (hPGCs) and PGC-like cells (PGCLCs) with 17,382 samples from 54 healthy somatic tissues (GTEx) and 11,003 samples from 33 tumor types (TCGA), and identified 672 GC genes, expanding the known GC gene pool by 387 genes (51%). We found that GC genes are expressed in clusters that are often expressed in multiple tumor types. Moreover, the amount of GC gene expression correlates with poor survival in patients with lung adenocarcinoma. As GC genes specific to the embryonic germline are not expressed in any adult tissue, targeting these in cancer treatment may result in fewer side effects than targeting conventional cancer/testis (CT) or GC genes and may preserve fertility. We anticipate that our extended GC dataset enables improved understanding of tumor development and may provide multiple novel targets for cancer treatment development. Full article
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Open AccessArticle
Unsupervised Hierarchical Clustering of Pancreatic Adenocarcinoma Dataset from TCGA Defines a Mucin Expression Profile that Impacts Overall Survival
Cancers 2020, 12(11), 3309; https://doi.org/10.3390/cancers12113309 - 09 Nov 2020
Cited by 1 | Viewed by 902
Abstract
Mucins are commonly associated with pancreatic ductal adenocarcinoma (PDAC) that is a deadly disease because of the lack of early diagnosis and efficient therapies. There are 22 mucin genes encoding large O-glycoproteins divided into two major subgroups: membrane-bound and secreted mucins. We [...] Read more.
Mucins are commonly associated with pancreatic ductal adenocarcinoma (PDAC) that is a deadly disease because of the lack of early diagnosis and efficient therapies. There are 22 mucin genes encoding large O-glycoproteins divided into two major subgroups: membrane-bound and secreted mucins. We investigated mucin expression and their impact on patient survival in the PDAC dataset from The Cancer Genome Atlas (PAAD-TCGA). We observed a statistically significant increased messenger RNA (mRNA) relative level of most of the membrane-bound mucins (MUC1/3A/4/12/13/16/17/20), secreted mucins (MUC5AC/5B), and atypical mucins (MUC14/18) compared to normal pancreas. We show that MUC1/4/5B/14/17/20/21 mRNA levels are associated with poorer survival in the high-expression group compared to the low-expression group. Using unsupervised clustering analysis of mucin gene expression patterns, we identified two major clusters of patients. Cluster #1 harbors a higher expression of MUC15 and atypical MUC14/MUC18, whereas cluster #2 is characterized by a global overexpression of membrane-bound mucins (MUC1/4/16/17/20/21). Cluster #2 is associated with shorter overall survival. The patient stratification appears to be independent of usual clinical features (tumor stage, differentiation grade, lymph node invasion) suggesting that the pattern of membrane-bound mucin expression could be a new prognostic marker for PDAC patients. Full article
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Open AccessArticle
Analysis of Cross-Association between mRNA Expression and RNAi Efficacy for Predictive Target Discovery in Colon Cancers
Cancers 2020, 12(11), 3091; https://doi.org/10.3390/cancers12113091 - 23 Oct 2020
Viewed by 345
Abstract
The availability of large-scale, collateral mRNA expression and RNAi data from diverse cancer cell types provides useful resources for the discovery of anticancer targets for which inhibitory efficacy can be predicted from gene expression. Here, we calculated bidirectional cross-association scores (predictivity and descriptivity) [...] Read more.
The availability of large-scale, collateral mRNA expression and RNAi data from diverse cancer cell types provides useful resources for the discovery of anticancer targets for which inhibitory efficacy can be predicted from gene expression. Here, we calculated bidirectional cross-association scores (predictivity and descriptivity) for each of approximately 18,000 genes identified from mRNA and RNAi (i.e., shRNA and sgRNA) data from colon cancer cell lines. The predictivity score measures the difference in RNAi efficacy between cell lines with high vs. low expression of the target gene, while the descriptivity score measures the differential mRNA expression between groups of cell lines exhibiting high vs. low RNAi efficacy. The mRNA expression of 90 and 74 genes showed significant (p < 0.01) cross-association scores with the shRNA and sgRNA data, respectively. The genes were found to be from diverse molecular classes and have different functions. Cross-association scores for the mRNA expression of six genes (CHAF1B, HNF1B, HTATSF1, IRS2, POLR2B and SATB2) with both shRNA and sgRNA efficacy were significant. These genes were interconnected in cancer-related transcriptional networks. Additional experimental validation confirmed that siHNF1B efficacy is correlated with HNF1B mRNA expression levels in diverse colon cancer cell lines. Furthermore, KIF26A and ZIC2 gene expression, with which shRNA efficacy displayed significant scores, were found to correlate with the survival rate from colon cancer patient data. This study demonstrates that bidirectional predictivity and descriptivity calculations between mRNA and RNAi data serve as useful resources for the discovery of predictive anticancer targets. Full article
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Open AccessArticle
A Methylation-Based Reclassification of Bladder Cancer Based on Immune Cell Genes
Cancers 2020, 12(10), 3054; https://doi.org/10.3390/cancers12103054 - 20 Oct 2020
Cited by 1 | Viewed by 845
Abstract
Background: Bladder cancer is highly related to immune cell infiltration. This study aimed to develop a new classification of BC molecular subtypes based on immune-cell-associated CpG sites. Methods: The genes of 28 types of immune cells were obtained from previous studies. Then, methylation [...] Read more.
Background: Bladder cancer is highly related to immune cell infiltration. This study aimed to develop a new classification of BC molecular subtypes based on immune-cell-associated CpG sites. Methods: The genes of 28 types of immune cells were obtained from previous studies. Then, methylation sites corresponding to immune-cell-associated genes were acquired. Differentially methylated sites (DMSs) were identified between normal samples and bladder cancer samples. Unsupervised clustering analysis of differentially methylated sites was performed to divide the sites into several subtypes. Then, the potential mechanism of different subtypes was explored. Results: Bladder cancer patients were divided into three groups. The cluster 3 subtype had the best prognosis. Cluster 1 had the poorest prognosis. The distribution of immune cells, level of expression of checkpoints, stromal score, immune score, ESTIMATEScore, tumor purity, APC co_inhibition, APC co_stimulation, HLA, MHC class_I, Type I IFN Response, Type II IFN Response, and DNAss presented significant differences among the three subgroups. The distribution of genomic alterations was also different. Conclusions: The proposed classification was accurate and stable. BC patients could be divided into three subtypes based on the immune-cell-associated CpG sites. Specific biological signaling pathways, immune mechanisms, and genomic alterations were varied among the three subgroups. High-level immune infiltration was correlated with high-level methylation. The lower RNAss was associated with higher immune infiltration. The study of the intratumoral immune microenvironment may provide a new perspective for BC therapy. Full article
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Open AccessArticle
Transcriptomics-Based Drug Repurposing Approach Identifies Novel Drugs against Sorafenib-Resistant Hepatocellular Carcinoma
Cancers 2020, 12(10), 2730; https://doi.org/10.3390/cancers12102730 - 23 Sep 2020
Viewed by 664
Abstract
Objective: Hepatocellular carcinoma (HCC) is frequently diagnosed in patients with late-stage disease who are ineligible for curative surgical therapies. The majority of patients become resistant to sorafenib, the only approved first-line therapy for advanced cancer, underscoring the need for newer, more effective drugs. [...] Read more.
Objective: Hepatocellular carcinoma (HCC) is frequently diagnosed in patients with late-stage disease who are ineligible for curative surgical therapies. The majority of patients become resistant to sorafenib, the only approved first-line therapy for advanced cancer, underscoring the need for newer, more effective drugs. The purpose of this study is to expedite identification of novel drugs against sorafenib resistant (SR)-HCC. Methods: We employed a transcriptomics-based drug repurposing method termed connectivity mapping using gene signatures from in vitro-derived SR Huh7 HCC cells. For proof of concept validation, we focused on drugs that were FDA-approved or under clinical investigation and prioritized two anti-neoplastic agents (dasatinib and fostamatinib) with targets associated with HCC. We also prospectively validated predicted gene expression changes in drug-treated SR Huh7 cells as well as identified and validated the targets of Fostamatinib in HCC. Results: Dasatinib specifically reduced the viability of SR-HCC cells that correlated with up-regulated activity of SRC family kinases, its targets, in our SR-HCC model. However, fostamatinib was able to inhibit both parental and SR HCC cells in vitro and in xenograft models. Ingenuity pathway analysis of fostamatinib gene expression signature from LINCS predicted JAK/STAT, PI3K/AKT, ERK/MAPK pathways as potential targets of fostamatinib that were validated by Western blot analysis. Fostamatinib treatment reversed the expression of genes that were deregulated in SR HCC. Conclusion: We provide proof of concept evidence for the validity of this drug repurposing approach for SR-HCC with implications for personalized medicine. Full article
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Open AccessReview
Applications of Genome-Wide Screening and Systems Biology Approaches in Drug Repositioning
Cancers 2020, 12(9), 2694; https://doi.org/10.3390/cancers12092694 - 21 Sep 2020
Cited by 1 | Viewed by 1033
Abstract
Modern drug discovery through de novo drug discovery entails high financial costs, low success rates, and lengthy trial periods. Drug repositioning presents a suitable approach for overcoming these issues by re-evaluating biological targets and modes of action of approved drugs. Coupling high-throughput technologies [...] Read more.
Modern drug discovery through de novo drug discovery entails high financial costs, low success rates, and lengthy trial periods. Drug repositioning presents a suitable approach for overcoming these issues by re-evaluating biological targets and modes of action of approved drugs. Coupling high-throughput technologies with genome-wide essentiality screens, network analysis, genome-scale metabolic modeling, and machine learning techniques enables the proposal of new drug–target signatures and uncovers unanticipated modes of action for available drugs. Here, we discuss the current issues associated with drug repositioning in light of curated high-throughput multi-omic databases, genome-wide screening technologies, and their application in systems biology/medicine approaches. Full article
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Open AccessArticle
CeRNA Network Analysis Representing Characteristics of Different Tumor Environments Based on 1p/19q Codeletion in Oligodendrogliomas
Cancers 2020, 12(9), 2543; https://doi.org/10.3390/cancers12092543 - 07 Sep 2020
Cited by 1 | Viewed by 782 | Correction
Abstract
Oligodendroglioma (OD) is a subtype of glioma occurring in the central nervous system. The 1p/19q codeletion is a prognostic marker of OD with an isocitrate dehydrogenase (IDH) mutation and is associated with a clinically favorable overall survival (OS); however, the exact [...] Read more.
Oligodendroglioma (OD) is a subtype of glioma occurring in the central nervous system. The 1p/19q codeletion is a prognostic marker of OD with an isocitrate dehydrogenase (IDH) mutation and is associated with a clinically favorable overall survival (OS); however, the exact underlying mechanism remains unclear. Long non-coding RNAs (lncRNAs) have recently been suggested to regulate carcinogenesis and prognosis in cancer patients. Here, we performed in silico analyses using low-grade gliomas from datasets obtained from The Cancer Genome Atlas to investigate the effects of ceRNA with 1p/19q codeletion on ODs. Thus, we selected modules of differentially expressed genes that were closely related to 1p/19q codeletion traits using weighted gene co-expression network analysis and constructed 16 coding RNA–miRNA–lncRNA networks. The ceRNA network participated in ion channel activity, insulin secretion, and collagen network and extracellular matrix (ECM) changes. In conclusion, ceRNAs with a 1p/19q codeletion can create different tumor microenvironments via potassium ion channels and ECM composition changes; furthermore, differences in OS may occur. Moreover, if extrapolated to gliomas, our results can provide insights into the consequences of identical gene expression, indicating the possibility of tracking different biological processes in different subtypes of glioma. Full article
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Open AccessArticle
Comprehensive Cohort Analysis of Mutational Spectrum in Early Onset Breast Cancer Patients
Cancers 2020, 12(8), 2089; https://doi.org/10.3390/cancers12082089 - 28 Jul 2020
Viewed by 839
Abstract
Early onset breast cancer (EOBC), diagnosed at age ~40 or younger, is associated with a poorer prognosis and higher mortality rate compared to breast cancer diagnosed at age 50 or older. EOBC poses a serious threat to public health and requires in-depth investigation. [...] Read more.
Early onset breast cancer (EOBC), diagnosed at age ~40 or younger, is associated with a poorer prognosis and higher mortality rate compared to breast cancer diagnosed at age 50 or older. EOBC poses a serious threat to public health and requires in-depth investigation. We studied a cohort comprising 90 Taiwanese female patients, aiming to unravel the underlying mechanisms of EOBC etiopathogenesis. Sequence data generated by whole-exome sequencing (WES) and whole-genome sequencing (WGS) from white blood cell (WBC)–tumor pairs were analyzed to identify somatic missense mutations, copy number variations (CNVs) and germline missense mutations. Similar to regular breast cancer, the key somatic mutation-susceptibility genes of EOBC include TP53 (40% prevalence), PIK3CA (37%), GATA3 (17%) and KMT2C (17%), which are frequently reported in breast cancer; however, the structural protein-coding genes MUC17 (19%), FLG (16%) and NEBL (11%) show a significantly higher prevalence in EOBC. Furthermore, the top 2 genes harboring EOBC germline mutations, MUC16 (19%) and KRT18 (19%), encode structural proteins. Compared to conventional breast cancer, an unexpectedly higher number of EOBC susceptibility genes encode structural proteins. We suspect that mutations in structural proteins may increase physical permeability to environmental hormones and carcinogens and cause breast cancer to occur at a young age. Full article
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Open AccessArticle
Computational Identification of Gene Networks as a Biomarker of Neuroblastoma Risk
Cancers 2020, 12(8), 2086; https://doi.org/10.3390/cancers12082086 - 28 Jul 2020
Cited by 1 | Viewed by 558
Abstract
Neuroblastoma is a common cancer in children, affected by a number of genes that interact with each other through intricate but coordinated networks. Traditional approaches can only reconstruct a single regulatory network that is topologically not informative enough to explain the complexity of [...] Read more.
Neuroblastoma is a common cancer in children, affected by a number of genes that interact with each other through intricate but coordinated networks. Traditional approaches can only reconstruct a single regulatory network that is topologically not informative enough to explain the complexity of neuroblastoma risk. We implemented and modified an advanced model for recovering informative, omnidirectional, dynamic, and personalized networks (idopNetworks) from static gene expression data for neuroblastoma risk. We analyzed 3439 immune genes of neuroblastoma for 217 high-risk patients and 30 low-risk patients by which to reconstruct large patient-specific idopNetworks. By converting these networks into risk-specific representations, we found that the shift in patients from a low to high risk or from a high to low risk might be due to the reciprocal change of hub regulators. By altering the directions of regulation exerted by these hubs, it may be possible to reduce a high risk to a low risk. Results from a holistic, systems-oriented paradigm through idopNetworks can potentially enable oncologists to experimentally identify the biomarkers of neuroblastoma and other cancers. Full article
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Open AccessArticle
A Multiplex Assay for the Stratification of Patients with Primary Central Nervous System Lymphoma Using Targeted Mass Spectrometry
Cancers 2020, 12(7), 1732; https://doi.org/10.3390/cancers12071732 - 29 Jun 2020
Cited by 1 | Viewed by 721
Abstract
Primary central nervous system lymphomas (PCNSL) account for approximately 2% to 3% of all primary brain tumors. Until now, neuropathological tumor tissue analysis, most frequently gained by stereotactic biopsy, is still the diagnostic gold standard. Here, we rigorously analyzed two independent patient cohorts [...] Read more.
Primary central nervous system lymphomas (PCNSL) account for approximately 2% to 3% of all primary brain tumors. Until now, neuropathological tumor tissue analysis, most frequently gained by stereotactic biopsy, is still the diagnostic gold standard. Here, we rigorously analyzed two independent patient cohorts comprising the clinical entities PCNSL (n = 47), secondary central nervous system lymphomas (SCNSL; n = 13), multiple sclerosis (MS, n = 23), glioma (n = 10), other tumors (n = 17) and tumor-free controls (n = 21) by proteomic approaches. In total, we identified more than 1220 proteins in the cerebrospinal fluid (CSF) and validated eight candidate biomarkers by a peptide-centric approach in an independent patient cohort (n = 63). Thus, we obtained excellent diagnostic accuracy for the stratification between PCNSL, MS and glioma patients as well as tumor-free controls for three peptides originating from the three proteins VSIG4, GPNMB4 and APOC2. The combination of all three biomarker candidates resulted in diagnostic accuracy with an area under the curve (AUC) of 0.901 (PCNSL vs. MS), AUC of 0.953 (PCNSL vs. glioma) and AUC 0.850 (PCNSL vs. tumor-free control). In summary, the determination of VSIG4, GPNMB4 and APOC2 in CSF as novel biomarkers for supporting the diagnosis of PCNSL is suggested. Full article
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Open AccessArticle
TP53 Status, Patient Sex, and the Immune Response as Determinants of Lung Cancer Patient Survival
Cancers 2020, 12(6), 1535; https://doi.org/10.3390/cancers12061535 - 11 Jun 2020
Cited by 4 | Viewed by 1124
Abstract
Lung cancer poses the greatest cancer-related death risk and males have poorer outcomes than females, for unknown reasons. Patient sex is not a biological variable considered in lung cancer standard of care. Correlating patient genetics with outcomes is predicted to open avenues for [...] Read more.
Lung cancer poses the greatest cancer-related death risk and males have poorer outcomes than females, for unknown reasons. Patient sex is not a biological variable considered in lung cancer standard of care. Correlating patient genetics with outcomes is predicted to open avenues for improved management. Using a bioinformatics approach across non-small cell lung cancer (NSCLC) subtypes, we identified where patient sex, mutation of the major tumor suppressor gene, Tumour protein P53 (TP53), and immune signatures stratified outcomes in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), among datasets of The Cancer Genome Atlas (TCGA). We exposed sex and TP53 gene mutations as prognostic for LUAD survival. Longest survival in LUAD occurred among females with wild-type (wt) TP53 genes, high levels of immune infiltration and enrichment for pathway signatures of Interferon Gamma (INF-γ), Tumour Necrosis Factor (TNF) and macrophages-monocytes. In contrast, poor survival in men with LUAD and wt TP53 genes corresponded with enrichment of Transforming Growth Factor Beta 1 (TGFB1, hereafter TGF-β) and wound healing signatures. In LUAD with wt TP53 genes, elevated gene expression of immune checkpoint CD274 (hereafter: PD-L1) and also protein 53 (p53) negative-regulators of the Mouse Double Minute (MDM)-family predict novel avenues for combined immunotherapies. LUSC is dominated by male smokers with TP53 gene mutations, while a minor population of TCGA LC patients with wt TP53 genes unexpectedly had the poorest survival, suggestive of a separate etiology. We conclude that advanced approaches to LUAD and LUSC therapy lie in the consideration of patient sex, TP53 gene mutation status and immune signatures. Full article
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Open AccessReview
Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature
Cancers 2020, 12(6), 1387; https://doi.org/10.3390/cancers12061387 - 28 May 2020
Cited by 2 | Viewed by 848
Abstract
Radiomics texture analysis offers objective image information that could otherwise not be obtained by radiologists′ subjective radiological interpretation. We investigated radiomics applications in renal tumor assessment and provide a comprehensive review. A detailed search of original articles was performed using the PubMed-MEDLINE database [...] Read more.
Radiomics texture analysis offers objective image information that could otherwise not be obtained by radiologists′ subjective radiological interpretation. We investigated radiomics applications in renal tumor assessment and provide a comprehensive review. A detailed search of original articles was performed using the PubMed-MEDLINE database until 20 March 2020 to identify English literature relevant to radiomics applications in renal tumor assessment. In total, 42 articles were included in the analysis and divided into four main categories: renal mass differentiation, nuclear grade prediction, gene expression-based molecular signatures, and patient outcome prediction. The main area of research involves accurately differentiating benign and malignant renal masses, specifically between renal cell carcinoma (RCC) subtypes and from angiomyolipoma without visible fat and oncocytoma. Nuclear grade prediction may enhance proper patient selection for risk-stratified treatment. Radiomics-predicted gene mutations may serve as surrogate biomarkers for high-risk disease, while predicting patients’ responses to targeted therapies and their outcomes will help develop personalized treatment algorithms. Studies generally reported the superiority of radiomics over expert radiological interpretation. Radiomics provides an alternative to subjective image interpretation for improving renal tumor diagnostic accuracy. Further incorporation of clinical and imaging data into radiomics algorithms will augment tumor prediction accuracy and enhance individualized medicine. Full article
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Open AccessArticle
Pan-Cancer Analysis of Radiotherapy Benefits and Immune Infiltration in Multiple Human Cancers
Cancers 2020, 12(4), 957; https://doi.org/10.3390/cancers12040957 - 13 Apr 2020
Viewed by 1093
Abstract
Response to radiotherapy (RT) in cancers varies widely among patients. Therefore, it is very important to predict who will benefit from RT before clinical treatment. Consideration of the immune tumor microenvironment (TME) could provide novel insight into tumor treatment options. In this study, [...] Read more.
Response to radiotherapy (RT) in cancers varies widely among patients. Therefore, it is very important to predict who will benefit from RT before clinical treatment. Consideration of the immune tumor microenvironment (TME) could provide novel insight into tumor treatment options. In this study, we investigated the link between immune infiltration status and clinical RT outcome in order to identify certain leukocyte subsets that could potentially influence the clinical RT benefit across cancers. By integrally analyzing the TCGA data across seven cancers, we identified complex associations between immune infiltration and patients RT outcomes. Besides, immune cells showed large differences in their populations in various cancers, and the most abundant cells were resting memory CD4 T cells. Additionally, the proportion of activated CD4 memory T cells and activated mast cells, albeit at low number, were closely related to RT overall survival in multiple cancers. Furthermore, a prognostic model for RT outcomes was established with good performance based on the immune infiltration status. Summarized, immune infiltration was found to be of significant clinical relevance to RT outcomes. These findings may help to shed light on the impact of tumor-associated immune cell infiltration on cancer RT outcomes, and identify biomarkers and therapeutic targets. Full article
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Open AccessArticle
Utilizing Genome-Wide mRNA Profiling to Identify the Cytotoxic Chemotherapeutic Mechanism of Triazoloacridone C-1305 as Direct Microtubule Stabilization
Cancers 2020, 12(4), 864; https://doi.org/10.3390/cancers12040864 - 02 Apr 2020
Viewed by 1001
Abstract
Rational drug design and in vitro pharmacology profiling constitute the gold standard in drug development pipelines. Problems arise, however, because this process is often difficult due to limited information regarding the complete identification of a molecule’s biological activities. The increasing affordability of genome-wide [...] Read more.
Rational drug design and in vitro pharmacology profiling constitute the gold standard in drug development pipelines. Problems arise, however, because this process is often difficult due to limited information regarding the complete identification of a molecule’s biological activities. The increasing affordability of genome-wide next-generation technologies now provides an excellent opportunity to understand a compound’s diverse effects on gene regulation. Here, we used an unbiased approach in lung and colon cancer cell lines to identify the early transcriptomic signatures of C-1305 cytotoxicity that highlight the novel pathways responsible for its biological activity. Our results demonstrate that C-1305 promotes direct microtubule stabilization as a part of its mechanism of action that leads to apoptosis. Furthermore, we show that C-1305 promotes G2 cell cycle arrest by modulating gene expression. The results indicate that C-1305 is the first microtubule stabilizing agent that also is a topoisomerase II inhibitor. This study provides a novel approach and methodology for delineating the antitumor mechanisms of other putative anticancer drug candidates. Full article
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Open AccessReview
Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology
Cancers 2020, 12(4), 797; https://doi.org/10.3390/cancers12040797 - 26 Mar 2020
Cited by 5 | Viewed by 1188
Abstract
Digital Pathology is the process of converting histology glass slides to digital images using sophisticated computerized technology to facilitate acquisition, evaluation, storage, and portability of histologic information. By its nature, digitization of analog histology data renders it amenable to analysis using deep learning/artificial [...] Read more.
Digital Pathology is the process of converting histology glass slides to digital images using sophisticated computerized technology to facilitate acquisition, evaluation, storage, and portability of histologic information. By its nature, digitization of analog histology data renders it amenable to analysis using deep learning/artificial intelligence (DL/AI) techniques. The application of DL/AI to digital pathology data holds promise, even if the scope of use cases and regulatory framework for deploying such applications in the clinical environment remains in the early stages. Recent studies using whole-slide images and DL/AI to detect histologic abnormalities in general and cancer in particular have shown encouraging results. In this review, we focus on these emerging technologies intended for use in diagnostic hematology and the evaluation of lymphoproliferative diseases. Full article
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Open AccessReview
The Application of Deep Learning in Cancer Prognosis Prediction
Cancers 2020, 12(3), 603; https://doi.org/10.3390/cancers12030603 - 05 Mar 2020
Cited by 15 | Viewed by 2958
Abstract
Deep learning has been applied to many areas in health care, including imaging diagnosis, digital pathology, prediction of hospital admission, drug design, classification of cancer and stromal cells, doctor assistance, etc. Cancer prognosis is to estimate the fate of cancer, probabilities of cancer [...] Read more.
Deep learning has been applied to many areas in health care, including imaging diagnosis, digital pathology, prediction of hospital admission, drug design, classification of cancer and stromal cells, doctor assistance, etc. Cancer prognosis is to estimate the fate of cancer, probabilities of cancer recurrence and progression, and to provide survival estimation to the patients. The accuracy of cancer prognosis prediction will greatly benefit clinical management of cancer patients. The improvement of biomedical translational research and the application of advanced statistical analysis and machine learning methods are the driving forces to improve cancer prognosis prediction. Recent years, there is a significant increase of computational power and rapid advancement in the technology of artificial intelligence, particularly in deep learning. In addition, the cost reduction in large scale next-generation sequencing, and the availability of such data through open source databases (e.g., TCGA and GEO databases) offer us opportunities to possibly build more powerful and accurate models to predict cancer prognosis more accurately. In this review, we reviewed the most recent published works that used deep learning to build models for cancer prognosis prediction. Deep learning has been suggested to be a more generic model, requires less data engineering, and achieves more accurate prediction when working with large amounts of data. The application of deep learning in cancer prognosis has been shown to be equivalent or better than current approaches, such as Cox-PH. With the burst of multi-omics data, including genomics data, transcriptomics data and clinical information in cancer studies, we believe that deep learning would potentially improve cancer prognosis. Full article
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Open AccessArticle
The Crossroads of Precision Medicine and Therapeutic Decision-Making: Use of an Analytical Computational Platform to Predict Response to Cancer Treatments
Cancers 2020, 12(1), 166; https://doi.org/10.3390/cancers12010166 - 09 Jan 2020
Cited by 2 | Viewed by 1187
Abstract
Metastatic cancer is a medical challenge that has been historically resistant to treatments. One area of leverage in cancer care is the development of molecularly-driven combination therapies, offering the possibility to overcome resistance. The selection of optimized treatments based on the complex molecular [...] Read more.
Metastatic cancer is a medical challenge that has been historically resistant to treatments. One area of leverage in cancer care is the development of molecularly-driven combination therapies, offering the possibility to overcome resistance. The selection of optimized treatments based on the complex molecular features of a patient’s tumor may be rendered easier by using a computer-assisted program. We used the PreciGENE® platform that uses multi-pathway molecular analysis to identify personalized therapeutic options. These options are ranked using a predictive score reflecting the degree to which a therapy or combination of therapies matches the patient’s biomarker profile. We searched PubMed from February 2010 to June 2017 for all patients described as exceptional responders who also had molecular data available. Altogether, 70 patients with cancer who had received 202 different treatment lines and who had responded (stable disease ≥12 months/partial or complete remission) to ≥1 regimen were curated. We demonstrate that an algorithm reflecting the degree to which patients were matched to the drugs administered correctly ranked the response to the regimens with a sensitivity of 84% and a specificity of 77%. The difference in matching score between successful and unsuccessful treatment lines was significant (median, 65% versus 0%, p-value <0.0001). Full article
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2019

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Open AccessArticle
Parallel Structure Deep Neural Network Using CNN and RNN with an Attention Mechanism for Breast Cancer Histology Image Classification
Cancers 2019, 11(12), 1901; https://doi.org/10.3390/cancers11121901 - 29 Nov 2019
Cited by 8 | Viewed by 1422
Abstract
In this paper, we present a new deep learning model to classify hematoxylin–eosin-stained breast biopsy images into four classes (normal tissues, benign lesions, in situ carcinomas, and invasive carcinomas). Our model uses a parallel structure consist of a convolutional neural network (CNN) and [...] Read more.
In this paper, we present a new deep learning model to classify hematoxylin–eosin-stained breast biopsy images into four classes (normal tissues, benign lesions, in situ carcinomas, and invasive carcinomas). Our model uses a parallel structure consist of a convolutional neural network (CNN) and a recurrent neural network (RNN) for image feature extraction, which is greatly different from the common existed serial method of extracting image features by CNN and then inputting them into RNN. Then, we introduce a special perceptron attention mechanism, which is derived from the natural language processing (NLP) field, to unify the features extracted by the two different neural network structures of the model. In the convolution layer, general batch normalization is replaced by the new switchable normalization method. And the latest regularization technology, targeted dropout, is used to substitute for the general dropout in the last three fully connected layers of the model. In the testing phase, we use the model fusion method and test time augmentation technology on three different datasets of hematoxylin–eosin-stained breast biopsy images. The results demonstrate that our model significantly outperforms state-of-the-art methods. Full article
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Open AccessArticle
Delineation of Tumor Migration Paths by Using a Bayesian Biogeographic Approach
Cancers 2019, 11(12), 1880; https://doi.org/10.3390/cancers11121880 - 27 Nov 2019
Cited by 2 | Viewed by 1089
Abstract
Understanding tumor progression and metastatic potential are important in cancer biology. Metastasis is the migration and colonization of clones in secondary tissues. Here, we posit that clone migration events between tumors resemble the dispersal of individuals between distinct geographic regions. This similarity makes [...] Read more.
Understanding tumor progression and metastatic potential are important in cancer biology. Metastasis is the migration and colonization of clones in secondary tissues. Here, we posit that clone migration events between tumors resemble the dispersal of individuals between distinct geographic regions. This similarity makes Bayesian biogeographic analysis suitable for inferring cancer cell migration paths. We evaluated the accuracy of a Bayesian biogeography method (BBM) in inferring metastatic patterns and compared it with the accuracy of a parsimony-based approach (metastatic and clonal history integrative analysis, MACHINA) that has been specifically developed to infer clone migration patterns among tumors. We used computer-simulated datasets in which simple to complex migration patterns were modeled. BBM and MACHINA were effective in reliably reconstructing simple migration patterns from primary tumors to metastases. However, both of them exhibited a limited ability to accurately infer complex migration paths that involve the migration of clones from one metastatic tumor to another and from metastasis to the primary tumor. Therefore, advanced computational methods are still needed for the biologically realistic tracing of migration paths and to assess the relative preponderance of different types of seeding and reseeding events during cancer progression in patients. Full article
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Open AccessArticle
Targeted-Gene Sequencing to Catch Triple Negative Breast Cancer Heterogeneity before and after Neoadjuvant Chemotherapy
Cancers 2019, 11(11), 1753; https://doi.org/10.3390/cancers11111753 - 08 Nov 2019
Cited by 3 | Viewed by 1191
Abstract
Triple negative breast cancer (TNBC) patients not attaining pathological Complete Response (pCR) after neo-adjuvant chemotherapy (NAC) have poor prognosis. We characterized 19 patients for somatic mutations in primary tumor biopsy and residual disease (RD) at surgery by 409 cancer-related gene sequencing (IonAmpliSeqTM Comprehensive [...] Read more.
Triple negative breast cancer (TNBC) patients not attaining pathological Complete Response (pCR) after neo-adjuvant chemotherapy (NAC) have poor prognosis. We characterized 19 patients for somatic mutations in primary tumor biopsy and residual disease (RD) at surgery by 409 cancer-related gene sequencing (IonAmpliSeqTM Comprehensive Cancer Panel). A median of four (range 1–66) genes was mutated in each primary tumor biopsy, and the most common mutated gene was TP53 followed by a long tail of low frequency mutations. There were no recurrent mutations significantly associated with pCR. However, half of patients with RD had primary tumor biopsy with mutations in genes related to the immune system compared with none of those achieving pCR. Overall, the number of mutations showed a downward trend in post- as compared to pre-NAC samples. PIK3CA was the most common altered gene after NAC. The mutational profile of TNBC during treatment as inferred from patterns of mutant allele frequencies in matched pre-and post-NAC samples showed that RD harbored alterations of cell cycle progression, PI3K/Akt/mTOR, and EGFR tyrosine kinase inhibitor-resistance pathways. Our findings support the use of targeted-gene sequencing for TNBC therapeutic development, as patients without pCR may present mutations of immune-related pathways in their primary tumor biopsy, or actionable targets in the RD. Full article
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Open AccessArticle
Coupled Genome-Wide DNA Methylation and Transcription Analysis Identified Rich Biomarkers and Drug Targets in Triple-Negative Breast Cancer
Cancers 2019, 11(11), 1724; https://doi.org/10.3390/cancers11111724 - 04 Nov 2019
Cited by 2 | Viewed by 1170
Abstract
Triple-negative breast cancer (TNBC) has poor clinical prognosis. Lack of TNBC-specific biomarkers prevents active clinical intervention. We reasoned that TNBC must have its specific signature due to the lack of three key receptors to distinguish TNBC from other types of breast cancer. We [...] Read more.
Triple-negative breast cancer (TNBC) has poor clinical prognosis. Lack of TNBC-specific biomarkers prevents active clinical intervention. We reasoned that TNBC must have its specific signature due to the lack of three key receptors to distinguish TNBC from other types of breast cancer. We also reasoned that coupling methylation and gene expression as a single unit may increase the specificity for the detected TNBC signatures. We further reasoned that choosing the proper controls may be critical to increasing the sensitivity to identify TNBC-specific signatures. Furthermore, we also considered that specific drugs could target the detected TNBC-specific signatures. We developed a system to identify potential TNBC signatures. It consisted of (1) coupling methylation and expression changes in TNBC to identify the methylation-regulated signature genes for TNBC; (2) using TPBC (triple-positive breast cancer) as the control to detect TNBC-specific signature genes; (3) searching in the drug database to identify those targeting TNBC signature genes. Using this system, we identified 114 genes with both altered methylation and expression, and 356 existing drugs targeting 10 of the 114 genes. Through docking and molecular dynamics simulation, we determined the structural basis between sapropterin, a drug used in the treatment of tetrahydrobiopterin deficiency, and PTGS2, a TNBC signature gene involved in the conversion of arachidonic acid to prostaglandins. Our study reveals the existence of rich TNBC-specific signatures, and many can be drug target and biomarker candidates for clinical applications. Full article
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Open AccessArticle
Construction and Validation of an Immune-Related Prognostic Model Based on TP53 Status in Colorectal Cancer
Cancers 2019, 11(11), 1722; https://doi.org/10.3390/cancers11111722 - 04 Nov 2019
Cited by 6 | Viewed by 1585
Abstract
Growing evidence has indicated that prognostic biomarkers have a pivotal role in tumor and immunity biological processes. TP53 mutation can cause a range of changes in immune response, progression, and prognosis of colorectal cancer (CRC). Thus, we aim to build an immunoscore prognostic [...] Read more.
Growing evidence has indicated that prognostic biomarkers have a pivotal role in tumor and immunity biological processes. TP53 mutation can cause a range of changes in immune response, progression, and prognosis of colorectal cancer (CRC). Thus, we aim to build an immunoscore prognostic model that may enhance the prognosis of CRC from an immunological perspective. We estimated the proportion of immune cells in the GSE39582 public dataset using the CIBERSORT (Cell type identification by estimating relative subset of known RNA transcripts) algorithm. Prognostic genes that were used to establish the immunoscore model were generated by the LASSO (Least absolute shrinkage and selection operator) Cox regression model. We established and validated the immunoscore model in GEO (Gene Expression Omnibus) and TCGA (The Cancer Genome Atlas) cohorts, respectively; significant differences of overall survival analysis were found between the low and high immunoscore groups or TP53 subgroups. In the multivariable Cox analysis, we observed that the immunoscore was an independent prognostic factor both in the GEO cohort (HR (Hazard ratio) 1.76, 95% CI (confidence intervals): 1.26–2.46) and the TCGA cohort (HR 1.95, 95% CI: 1.20–3.18). Furthermore, we established a nomogram for clinical application, and the results suggest that the nomogram is a better predictive model for prognosis than immunoscore or TNM staging. Full article
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Open AccessArticle
Mutational Landscape of the BAP1 Locus Reveals an Intrinsic Control to Regulate the miRNA Network and the Binding of Protein Complexes in Uveal Melanoma
Cancers 2019, 11(10), 1600; https://doi.org/10.3390/cancers11101600 - 19 Oct 2019
Cited by 12 | Viewed by 1788
Abstract
The BAP1 (BRCA1-associated protein 1) gene is associated with a variety of human cancers. With its gene product being a nuclear ubiquitin carboxy-terminal hydrolase with deubiquitinase activity, BAP1 acts as a tumor suppressor gene with potential pleiotropic effects in multiple tumor types. Herein, [...] Read more.
The BAP1 (BRCA1-associated protein 1) gene is associated with a variety of human cancers. With its gene product being a nuclear ubiquitin carboxy-terminal hydrolase with deubiquitinase activity, BAP1 acts as a tumor suppressor gene with potential pleiotropic effects in multiple tumor types. Herein, we focused specifically on uveal melanoma (UM) in which BAP1 mutations are associated with a metastasizing phenotype and decreased survival rates. We identified the ubiquitin carboxyl hydrolase (UCH) domain as a major hotspot region for the pathogenic mutations with a high evolutionary action (EA) score. This also includes the mutations at conserved catalytic sites and the ones overlapping with the phosphorylation residues. Computational protein interaction studies revealed that distant BAP1-associated protein complexes (FOXK2, ASXL1, BARD1, BRCA1) could be directly impacted by this mutation paradigm. We also described the conformational transition related to BAP1-BRCA-BARD1 complex, which may pose critical implications for mutations, especially at the docking interfaces of these three proteins. The mutations affect - independent of being somatic or germline - the binding affinity of miRNAs embedded within the BAP1 locus, thereby altering the unique regulatory network. Apart from UM, BAP1 gene expression and survival associations were found to be predictive for the prognosis in several (n = 29) other cancer types. Herein, we suggest that although BAP1 is conceptually a driver gene in UM, it might contribute through its interaction partners and its regulatory miRNA network to various aspects of cancer. Taken together, these findings will pave the way to evaluate BAP1 in a variety of other human cancers with a shared mutational spectrum. Full article
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Open AccessArticle
Next-Generation Sequencing Improves Diagnosis, Prognosis and Clinical Management of Myeloid Neoplasms
Cancers 2019, 11(9), 1364; https://doi.org/10.3390/cancers11091364 - 13 Sep 2019
Cited by 7 | Viewed by 1398
Abstract
Molecular diagnosis of myeloid neoplasms (MN) is based on the detection of multiple genetic alterations using various techniques. Next-generation sequencing (NGS) has been proved as a useful method for analyzing many genes simultaneously. In this context, we analyzed diagnostic samples from 121 patients [...] Read more.
Molecular diagnosis of myeloid neoplasms (MN) is based on the detection of multiple genetic alterations using various techniques. Next-generation sequencing (NGS) has been proved as a useful method for analyzing many genes simultaneously. In this context, we analyzed diagnostic samples from 121 patients affected by MN and ten relapse samples from a subset of acute myeloid leukemia patients using two enrichment-capture NGS gene panels. Pathogenicity classification of variants was enhanced by the development and application of a custom onco-hematology score. A total of 278 pathogenic variants were detected in 84% of patients. For structural alterations, 82% of those identified by cytogenetics were detected by NGS, 25 of 31 copy number variants and three out of three translocations. The detection of variants using NGS changed the diagnosis of seven patients and the prognosis of 15 patients and enabled us to identify 44 suitable candidates for clinical trials. Regarding AML, six of the ten relapsed patients lost or gained variants, comparing with diagnostic samples. In conclusion, the use of NGS panels in MN improves genetic characterization of the disease compared with conventional methods, thus demonstrating its potential clinical utility in routine clinical testing. This approach leads to better-adjusted treatments for each patient. Full article
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Open AccessArticle
In Silico Drug Prescription for Targeting Cancer Patient Heterogeneity and Prediction of Clinical Outcome
Cancers 2019, 11(9), 1361; https://doi.org/10.3390/cancers11091361 - 13 Sep 2019
Cited by 1 | Viewed by 909
Abstract
In silico drug prescription tools for precision cancer medicine can match molecular alterations with tailored candidate treatments. These methodologies require large and well-annotated datasets to systematically evaluate their performance, but this is currently constrained by the lack of complete patient clinicopathological data. Moreover, [...] Read more.
In silico drug prescription tools for precision cancer medicine can match molecular alterations with tailored candidate treatments. These methodologies require large and well-annotated datasets to systematically evaluate their performance, but this is currently constrained by the lack of complete patient clinicopathological data. Moreover, in silico drug prescription performance could be improved by integrating additional tumour information layers like intra-tumour heterogeneity (ITH) which has been related to drug response and tumour progression. PanDrugs is an in silico drug prescription method which prioritizes anticancer drugs combining both biological and clinical evidence. We have systematically evaluated PanDrugs in the Genomic Data Commons repository (GDC). Our results showed that PanDrugs is able to establish an a priori stratification of cancer patients treated with Epidermal Growth Factor Receptor (EGFR) inhibitors. Patients labelled as responders according to PanDrugs predictions showed a significantly increased overall survival (OS) compared to non-responders. PanDrugs was also able to suggest alternative tailored treatments for non-responder patients. Additionally, PanDrugs usefulness was assessed considering spatial and temporal ITH in cancer patients and showed that ITH can be approached therapeutically proposing drugs or combinations potentially capable of targeting the clonal diversity. In summary, this study is a proof of concept where PanDrugs predictions have been correlated to OS and can be useful to manage ITH in patients while increasing therapeutic options and demonstrating its clinical utility. Full article
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