scholarly journals Cytogenetics and Cytogenomics Evaluation in Cancer

2019 ◽  
Vol 20 (19) ◽  
pp. 4711 ◽  
Author(s):  
Ilda Patrícia Ribeiro ◽  
Joana Barbosa Melo ◽  
Isabel Marques Carreira
Keyword(s):  
High Throughput ◽  
Cancer Biology ◽  
Large Scale ◽  
Cancer Genomics ◽  
Clonal Evolution ◽  
Chromosomal Rearrangements ◽  
Therapy Resistance ◽  
Cellular Heterogeneity ◽  
Comparative Genomic ◽  
Driver Genes

The availability of cytogenetics and cytogenomics technologies improved the detection and identification of tumor molecular signatures as well as the understanding of cancer initiation and progression. The use of large-scale and high-throughput cytogenomics technologies has led to a fast identification of several cancer candidate biomarkers associated with diagnosis, prognosis, and therapeutics. The advent of array comparative genomic hybridization and next-generation sequencing technologies has significantly improved the knowledge about cancer biology, underlining driver genes to guide targeted therapy development, drug-resistance prediction, and pharmacogenetics. However, few of these candidate biomarkers have made the transition to the clinic with a clear benefit for the patients. Technological progress helped to demonstrate that cellular heterogeneity plays a significant role in tumor progression and resistance/sensitivity to cancer therapies, representing the major challenge of precision cancer therapy. A paradigm shift has been introduced in cancer genomics with the recent advent of single-cell sequencing, since it presents a lot of applications with a clear benefit to oncological patients, namely, detection of intra-tumoral heterogeneity, mapping clonal evolution, monitoring the development of therapy resistance, and detection of rare tumor cell populations. It seems now evident that no single biomarker could provide the whole information necessary to early detect and predict the behavior and prognosis of tumors. The promise of precision medicine is based on the molecular profiling of tumors being vital the continuous progress of high-throughput technologies and the multidisciplinary efforts to catalogue chromosomal rearrangements and genomic alterations of human cancers and to do a good interpretation of the relation genotype—phenotype.

scholarly journals Mutational patterns and clonal evolution from diagnosis to relapse in pediatric acute lymphoblastic leukemia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shumaila Sayyab ◽  
Anders Lundmark ◽  
Malin Larsson ◽  
Markus Ringnér ◽  
Sara Nystedt ◽  
...  
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Large Scale ◽  
Somatic Mutations ◽  
Lymphoblastic Leukemia ◽  
Clonal Evolution ◽  
Point Mutations ◽  
Driver Genes ◽  
Protein Coding ◽  
Pediatric Acute Lymphoblastic Leukemia ◽  
Evolutionary Trajectories

AbstractThe mechanisms driving clonal heterogeneity and evolution in relapsed pediatric acute lymphoblastic leukemia (ALL) are not fully understood. We performed whole genome sequencing of samples collected at diagnosis, relapse(s) and remission from 29 Nordic patients. Somatic point mutations and large-scale structural variants were called using individually matched remission samples as controls, and allelic expression of the mutations was assessed in ALL cells using RNA-sequencing. We observed an increased burden of somatic mutations at relapse, compared to diagnosis, and at second relapse compared to first relapse. In addition to 29 known ALL driver genes, of which nine genes carried recurrent protein-coding mutations in our sample set, we identified putative non-protein coding mutations in regulatory regions of seven additional genes that have not previously been described in ALL. Cluster analysis of hundreds of somatic mutations per sample revealed three distinct evolutionary trajectories during ALL progression from diagnosis to relapse. The evolutionary trajectories provide insight into the mutational mechanisms leading relapse in ALL and could offer biomarkers for improved risk prediction in individual patients.

TMOD-29. STANDARDIZED GENERATION OF TUMOR-ORGANOIDS AS NOVEL DRUG SCREENING MODEL IN MENINGIOMA

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi221-vi222
Author(s):  
Gerhard Jungwirth ◽  
Tao Yu ◽  
Cao Junguo ◽  
Catharina Lotsch ◽  
Andreas Unterberg ◽  
...  
Keyword(s):  
Drug Screening ◽  
Cancer Biology ◽  
Drug Testing ◽  
Large Scale ◽  
Ex Vivo ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Drug Responses ◽  
Novel Drug ◽  
Tumor Organoids

Abstract Tumor-organoids (TOs) are novel, complex three-dimensional ex vivo tissue cultures that under optimal conditions accurately reflect genotype and phenotype of the original tissue with preserved cellular heterogeneity and morphology. They may serve as a new and exciting model for studying cancer biology and directing personalized therapies. The aim of our study was to establish TOs from meningioma (MGM) and to test their usability for large-scale drug screenings. We were capable of forming several hundred TO equal in size by controlled reaggregation of freshly prepared single cell suspension of MGM tissue samples. In total, standardized TOs from 60 patients were formed, including eight grade II and three grade III MGMs. TOs reaggregated within 3 days resulting in a reducted diameter by 50%. Thereafter, TO size remained stable throughout a 14 days observation period. TOs consisted of largely viable cells, whereas dead cells were predominantly found outside of the organoid. H&E stainings confirmed the successful establishment of dense tissue-like structures. Next, we assessed the suitability and reliability of TOs for a robust large-scale drug testing by employing nine highly potent compounds, derived from a drug screening performed on several MGM cell lines. First, we tested if drug responses depend on TO size. Interestingly, drug responses to these drugs remained identical independent of their sizes. Based on a sufficient representation of low abundance cell types such as T-cells and macrophages an overall number of 25.000 cells/TO was selected for further experiments revealing FDA-approved HDAC inhibitors as highly effective drugs in most of the TOs with a mean z-AUC score of -1.33. Taken together, we developed a protocol to generate standardized TO from MGM containing low abundant cell types of the tumor microenvironment in a representative manner. Robust and reliable drug responses suggest patient-derived TOs as a novel drug testing model in meningioma research.

scholarly journals Discovering novel driver mutations from pan-cancer analysis of mutational and gene expression profiles

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242780
Author(s):  
Houriiyah Tegally ◽  
Kevin H. Kensler ◽  
Zahra Mungloo-Dilmohamud ◽  
Anisah W. Ghoorah ◽  
Timothy R. Rebbeck ◽  
...  
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Driver Mutations ◽  
Literature Mining ◽  
Cancer Genes ◽  
Multiple Cancer ◽  
Driver Genes

As the genomic profile across cancers varies from person to person, patient prognosis and treatment may differ based on the mutational signature of each tumour. Thus, it is critical to understand genomic drivers of cancer and identify potential mutational commonalities across tumors originating at diverse anatomical sites. Large-scale cancer genomics initiatives, such as TCGA, ICGC and GENIE have enabled the analysis of thousands of tumour genomes. Our goal was to identify new cancer-causing mutations that may be common across tumour sites using mutational and gene expression profiles. Genomic and transcriptomic data from breast, ovarian, and prostate cancers were aggregated and analysed using differential gene expression methods to identify the effect of specific mutations on the expression of multiple genes. Mutated genes associated with the most differentially expressed genes were considered to be novel candidates for driver mutations, and were validated through literature mining, pathway analysis and clinical data investigation. Our driver selection method successfully identified 116 probable novel cancer-causing genes, with 4 discovered in patients having no alterations in any known driver genes: MXRA5, OBSCN, RYR1, and TG. The candidate genes previously not officially classified as cancer-causing showed enrichment in cancer pathways and in cancer diseases. They also matched expectations pertaining to properties of cancer genes, for instance, showing larger gene and protein lengths, and having mutation patterns suggesting oncogenic or tumor suppressor properties. Our approach allows for the identification of novel putative driver genes that are common across cancer sites using an unbiased approach without any a priori knowledge on pathways or gene interactions and is therefore an agnostic approach to the identification of putative common driver genes acting at multiple cancer sites.

scholarly journals Cancer Stem Cells and Nucleolin as Drivers of Carcinogenesis

2021 ◽  
Vol 14 (1) ◽  
pp. 60
Author(s):  
Laura Sofia Carvalho ◽  
Nélio Gonçalves ◽  
Nuno André Fonseca ◽  
João Nuno Moreira
Keyword(s):  
Stem Cells ◽  
Cancer Stem Cells ◽  
Cancer Cells ◽  
Epithelial To Mesenchymal Transition ◽  
Clonal Evolution ◽  
Therapy Resistance ◽  
Cellular Heterogeneity ◽  
Multifunctional Protein ◽  
Mesenchymal Transition ◽  
Cancer Hallmarks

Cancer, one of the most mortal diseases worldwide, is characterized by the gain of specific features and cellular heterogeneity. Clonal evolution is an established theory to explain heterogeneity, but the discovery of cancer stem cells expanded the concept to include the hierarchical growth and plasticity of cancer cells. The activation of epithelial-to-mesenchymal transition and its molecular players are widely correlated with the presence of cancer stem cells in tumors. Moreover, the acquisition of certain oncological features may be partially attributed to alterations in the levels, location or function of nucleolin, a multifunctional protein involved in several cellular processes. This review aims at integrating the established hallmarks of cancer with the plasticity of cancer cells as an emerging hallmark; responsible for tumor heterogeneity; therapy resistance and relapse. The discussion will contextualize the involvement of nucleolin in the establishment of cancer hallmarks and its application as a marker protein for targeted anticancer therapies

scholarly journals How Cancer Genomics Drives Cancer Biology: Does Synthetic Lethality Explain Mutually Exclusive Oncogenic Mutations?

2016 ◽  
Author(s):  
Harold Varmus ◽  
Arun M. Unni ◽  
William W. Lockwood
Keyword(s):  
Cancer Biology ◽  
Large Scale ◽  
Cancer Genomics ◽  
Synthetic Lethality ◽  
Mutual Exclusivity ◽  
Physiological Basis ◽  
Oncogenic Mutations ◽  
Mutational Pattern ◽  
Cancer Multiple ◽  
Lung Adenocarcinomas

AbstractLarge-scale analyses of cancer genomes are revealing patterns of mutations that suggest biologically significant ideas about many aspects of cancer, including carcinogenesis, classification, and preventive and therapeutic strategies. Among those patterns is “mutual exclusivity”, a phenomenon observed when two or more mutations that are commonly observed in samples of a type of cancer are not found combined in individual tumors. We have been studying a striking example of mutual exclusivity: the absence of co-existing mutations in theKRASandEGFRproto-oncogenes in human lung adenocarcinomas, despite the high individual frequencies of such mutations in this common type of cancer. Multiple lines of evidence suggest that toxic effects of the joint expression ofKRASandEGFRmutant oncogenes, rather than loss of any selective advantages conferred by a second oncogene that operates through the same signaling pathway, are responsible for the observed mutational pattern. We discuss the potential for understanding the physiological basis of such toxicity, for exploiting it therapeutically, and for extending the studies to other examples of mutual exclusivity.

scholarly journals cbpManager: a web application to streamline the integration of clinical and genomic data in cBioPortal to support the Molecular Tumor Board

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Arsenij Ustjanzew ◽  
Alexander Desuki ◽  
Christoph Ritzel ◽  
Alina Corinna Dolezilek ◽  
Daniel-Christoph Wagner ◽  
...  
Keyword(s):  
Clinical Data ◽  
Web Application ◽  
Cancer Biology ◽  
Data Privacy ◽  
Large Scale ◽  
Cancer Genomics ◽  
Tumor Board ◽  
Data Formats ◽  
Tumor Boards ◽  
Data Files

Abstract Background Extensive sequencing of tumor tissues has greatly improved our understanding of cancer biology over the past years. The integration of genomic and clinical data is increasingly used to select personalized therapies in dedicated tumor boards (Molecular Tumor Boards) or to identify patients for basket studies. Genomic alterations and clinical information can be stored, integrated and visualized in the open-access resource cBioPortal for Cancer Genomics. cBioPortal can be run as a local instance enabling storage and analysis of patient data in single institutions, in the respect of data privacy. However, uploading clinical input data and genetic aberrations requires the elaboration of multiple data files and specific data formats, which makes it difficult to integrate this system into clinical practice. To solve this problem, we developed cbpManager. Results cbpManager is an R package providing a web-based interactive graphical user interface intended to facilitate the maintenance of mutations data and clinical data, including patient and sample information, as well as timeline data. cbpManager enables a large spectrum of researchers and physicians, regardless of their informatics skills to intuitively create data files ready for upload in cBioPortal for Cancer Genomics on a daily basis or in batch. Due to its modular structure based on R Shiny, further data formats such as copy number and fusion data can be covered in future versions. Further, we provide cbpManager as a containerized solution, enabling a straightforward large-scale deployment in clinical systems and secure access in combination with ShinyProxy. cbpManager is freely available via the Bioconductor project at https://bioconductor.org/packages/cbpManager/ under the AGPL-3 license. It is already used at six University Hospitals in Germany (Mainz, Gießen, Lübeck, Halle, Freiburg, and Marburg). Conclusion In summary, our package cbpManager is currently a unique software solution in the workflow with cBioPortal for Cancer Genomics, to assist the user in the interactive generation and management of study files suited for the later upload in cBioPortal.

Circulating tumor cell (CTC) genomic signatures of hormone therapy resistance in men with metastatic castration-resistant prostate cancer (mCRPC).

2020 ◽  
Vol 38 (6_suppl) ◽  
pp. 147-147
Author(s):  
Santosh Gupta ◽  
Susan Halabi ◽  
Gabor Kemeny ◽  
Monika Anand ◽  
David M. Nanus ◽  
...  
Keyword(s):  
Clinical Benefit ◽  
Copy Number ◽  
Clonal Evolution ◽  
Circulating Tumor Cell ◽  
Therapy Resistance ◽  
Comparative Genomic ◽  
Copy Number Alterations ◽  
Castration Resistant Prostate Cancer ◽  
Protein Assay ◽  
Poor Outcomes

147 Background: Men with CTC AR-V7 + mCRPC have very poor outcomes when treated with enzalutamide/abiraterone. However, many men lack AR-V7. Here, we determined whether baseline or post-treatment DNA alterations in CTCs from AR-V7 negative mCRPC men could provide clinical utility in predicting outcomes with these hormonal therapies. Methods: We analyzed whole-genome copy number alterations (CNA) using array-comparative genomic hybridization (aCGH) in CTCs from 48 men (45 baseline and 28 progression), and whole-exome sequencing (WES) from 11 mCRPC men treated with abi/enza, longitudinally, and focused exclusively on AR-V7 negative men (N = 40) by the Epic-AR-V7 nuclear protein assay, and comparing those men who benefit from therapy vs. who do not. Results: We observed broad heterogeneity of CNAs between patients; common genomic alterations included gain in KDM6A (44%), FOXA1 (44%), MYCN (32%), and AR (38%), and loss in BRCA1 (30%) and PTEN (25%). Men who had the clinical benefit to abi/enza (n = 23, median PFS 10 mo) were more likely to have CTCs with genomic gains of ATM, HSD17B4, or PTEN, and loss of BRAF, ABL1, or NKX3-1. Likewise, men who did not benefit from abi/enza (n = 14, median PFS 2.6 mo) had CTCs with more copy number alterations than men who had clinical benefit (median 19.5 vs. 14, p = 0.01), and were enriched for gain of BRCA2, APC, KDM5D, SPARC, MYCN, AR, and CYP11B1, and loss of PTEN, CHD1, PHLPP1, and NCOR2. After progression on abi/enza, we observed clonal evolution of CTCs harboring gain of ATM, FOXA1, KDM6A, CYP11B1, MYC, APC, and NCOR2, and loss of NCOR1, ERG, and RUNX2. Several COSMIC-validated non-synonymous pathogenic exome mutations were detected in progressed or non-responding patients’ CTC DNA; TP53 (55 vs 27% at baseline), AKAP9 (36 vs 9%), CDK12, KMT2D, and BRAF (each 36 vs 18%), and BRD4 and SPOP (each 18 vs 0%). Conclusions: We demonstrate that specific CTC genomic profiles associated with TP53, PTEN, WNT, DNA repair, epigenetic, and AR signaling, as well as lineage plasticity pathways are associated with worse clinical outcomes in AR-V7 negative men with mCRPC treated with abi/enza. Further mechanistic and validation studies are warranted. Clinical trial information: NCT02269982.

scholarly journals Androgen receptor and prostate cancer stem cells: biological mechanisms and clinical implications

2015 ◽  
Vol 22 (6) ◽  
pp. T209-T220 ◽  
Author(s):  
Qu Deng ◽  
Dean G Tang
Keyword(s):  
Prostate Cancer ◽  
Stem Cells ◽  
Androgen Receptor ◽  
Cancer Stem Cells ◽  
Clonal Evolution ◽  
Population Level ◽  
Cell Types ◽  
Therapy Resistance ◽  
Cellular Heterogeneity ◽  
Prostate Cancer Stem Cells

Prostate cancer (PCa) contains phenotypically and functionally distinct cells, and this cellular heterogeneity poses clinical challenges as the distinct cell types likely respond differently to various therapies. Clonal evolution, driven by genetic instability, and intraclonal cancer cell diversification, driven by cancer stem cells (CSCs), together create tumor cell heterogeneity. In this review, we first discuss PCa stem cells (PCSCs) and heterogeneity of androgen receptor (AR) expression in primary, metastatic, and treatment-failed PCa. Based on literature reports and our own studies, we hypothesize that, whereas PCSCs in primary and untreated tumors and models are mainly AR−, PCSCs in CRPCs could be either AR+or AR−/lo. We illustrate the potential mechanisms AR+and AR−PCSCs may employ to propagate PCa at the population level, mediate therapy resistance, and metastasize. As a result, targeting AR alone may not achieve long-lasting therapeutic efficacy. Elucidating the roles of AR and PCSCs should provide important clues to designing novel personalized combinatorial therapeutic protocols targeting both AR+and AR−PCa cells.

scholarly journals Simulated Annealing Based Algorithm for Identifying Mutated Driver Pathways in Cancer

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Hai-Tao Li ◽  
Yu-Lang Zhang ◽  
Chun-Hou Zheng ◽  
Hong-Qiang Wang
Keyword(s):  
Large Scale ◽  
Cancer Genomics ◽  
Simulated Data ◽  
Integrative Model ◽  
Driver Mutations ◽  
Driver Genes ◽  
Cancer Proliferation ◽  
Combinatorial Properties ◽  
Sequencing Technologies ◽  
Modified Method

With the development of next-generation DNA sequencing technologies, large-scale cancer genomics projects can be implemented to help researchers to identify driver genes, driver mutations, and driver pathways, which promote cancer proliferation in large numbers of cancer patients. Hence, one of the remaining challenges is to distinguish functional mutations vital for cancer development, and filter out the unfunctional and random “passenger mutations.” In this study, we introduce a modified method to solve the so-called maximum weight submatrix problem which is used to identify mutated driver pathways in cancer. The problem is based on two combinatorial properties, that is, coverage and exclusivity. Particularly, we enhance an integrative model which combines gene mutation and expression data. The experimental results on simulated data show that, compared with the other methods, our method is more efficient. Finally, we apply the proposed method on two real biological datasets. The results show that our proposed method is also applicable in real practice.

Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining

2019 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Mojtaba Haghighatlari ◽  
Sai Prasad Ganesh ◽  
Chong Cheng ◽  
Johannes Hachmann
Keyword(s):  
Data Mining ◽  
Refractive Index ◽  
High Throughput ◽  
First Principles ◽  
High Throughput Screening ◽  
Large Scale ◽  
Computational Study ◽  
High Refractive Index ◽  
Structural Features ◽  
Learning Program

<div>We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an RI prediction protocol based on a combination of first-principles and data modeling developed in previous work, which we employ on a large-scale PI candidate library generated with the ChemLG code. We deploy the virtual screening software ChemHTPS to automate the assessment of this extensive pool of PI structures in order to determine the performance potential of each candidate. This rapid and efficient approach yields a number of highly promising leads compounds. Using the data mining and machine learning program package ChemML, we analyze the top candidates with respect to prevalent structural features and feature combinations that distinguish them from less promising ones. In particular, we explore the utility of various strategies that introduce highly polarizable moieties into the PI backbone to increase its RI yield. The derived insights provide a foundation for rational and targeted design that goes beyond traditional trial-and-error searches.</div>

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