Literature DB >> 29935311

Molecular pathway activation - New type of biomarkers for tumor morphology and personalized selection of target drugs.

Anton Buzdin1, Maxim Sorokin2, Andrew Garazha3, Marina Sekacheva2, Ella Kim4, Nikolay Zhukov5, Ye Wang6, Xinmin Li7, Souvik Kar8, Christian Hartmann9, Amir Samii8, Alf Giese8, Nicolas Borisov2.   

Abstract

Anticancer target drugs (ATDs) specifically bind and inhibit molecular targets that play important roles in cancer development and progression, being deeply implicated in intracellular signaling pathways. To date, hundreds of different ATDs were approved for clinical use in the different countries. Compared to previous chemotherapy treatments, ATDs often demonstrate reduced side effects and increased efficiency, but also have higher costs. However, the efficiency of ATDs for the advanced stage tumors is still insufficient. Different ATDs have different mechanisms of action and are effective in different cohorts of patients. Personalized approaches are therefore needed to select the best ATD candidates for the individual patients. In this review, we focus on a new generation of biomarkers - molecular pathway activation - and on their applications for predicting individual tumor response to ATDs. The success in high throughput gene expression profiling and emergence of novel bioinformatic tools reinforced quick development of pathway related field of molecular biomedicine. The ability to quantitatively measure degree of a pathway activation using gene expression data has revolutionized this field and made the corresponding analysis quick, robust and inexpensive. This success was further enhanced by using machine learning algorithms for selection of the best biomarkers. We review here the current progress in translating these studies to clinical oncology and patient-oriented adjustment of cancer therapy.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Anticancer target drugs; Big data analytics; Bioinformatics; Biomarkers; Cancer; Epigenetics; Gene expression; Intracellular molecular pathways; Machine learning; Micro RNA; Proteomics; Response to cancer therapy; Systems biology; Transcriptomics; miR

Mesh:

Substances:

Year:  2018        PMID: 29935311     DOI: 10.1016/j.semcancer.2018.06.003

Source DB:  PubMed          Journal:  Semin Cancer Biol        ISSN: 1044-579X            Impact factor:   15.707


  29 in total

1.  Transcriptomics-Guided Personalized Prescription of Targeted Therapeutics for Metastatic ALK-Positive Lung Cancer Case Following Recurrence on ALK Inhibitors.

Authors:  Elena Poddubskaya; Alexey Bondarenko; Alexander Boroda; Evgenia Zotova; Alex Glusker; Svetlana Sletina; Luidmila Makovskaia; Philipp Kopylov; Marina Sekacheva; Alexey Moisseev; Madina Baranova
Journal:  Front Oncol       Date:  2019-10-15       Impact factor: 6.244

2.  Era of the Fourth Industrial Revolution and the Urologists' Journey to Navigating Big Omics Data.

Authors:  Jayoung Kim
Journal:  Int Neurourol J       Date:  2018-07-31       Impact factor: 3.038

3.  Atlas of RNA sequencing profiles for normal human tissues.

Authors:  Maria Suntsova; Nurshat Gaifullin; Daria Allina; Alexey Reshetun; Xinmin Li; Larisa Mendeleeva; Vadim Surin; Anna Sergeeva; Pavel Spirin; Vladimir Prassolov; Alexander Morgan; Andrew Garazha; Maxim Sorokin; Anton Buzdin
Journal:  Sci Data       Date:  2019-04-23       Impact factor: 6.444

4.  Pathway Based Analysis of Mutation Data Is Efficient for Scoring Target Cancer Drugs.

Authors:  Marianna A Zolotovskaia; Maxim I Sorokin; Anna A Emelianova; Nikolay M Borisov; Denis V Kuzmin; Pieter Borger; Andrew V Garazha; Anton A Buzdin
Journal:  Front Pharmacol       Date:  2019-01-23       Impact factor: 5.810

5.  RNA Sequencing in Comparison to Immunohistochemistry for Measuring Cancer Biomarkers in Breast Cancer and Lung Cancer Specimens.

Authors:  Maxim Sorokin; Kirill Ignatev; Elena Poddubskaya; Uliana Vladimirova; Nurshat Gaifullin; Dmitriy Lantsov; Andrew Garazha; Daria Allina; Maria Suntsova; Victoria Barbara; Anton Buzdin
Journal:  Biomedicines       Date:  2020-05-09

6.  Use case driven evaluation of open databases for pediatric cancer research.

Authors:  Fleur Jeanquartier; Claire Jean-Quartier; Andreas Holzinger
Journal:  BioData Min       Date:  2019-01-15       Impact factor: 2.522

7.  Construction of a circRNA-miRNA-mRNA network based on competitive endogenous RNA reveals the function of circRNAs in osteosarcoma.

Authors:  Yu Qiu; Chao Pu; Yanchao Li; Baochuang Qi
Journal:  Cancer Cell Int       Date:  2020-02-10       Impact factor: 5.722

8.  Molecular heterogeneity in breast carcinoma cells with increased invasive capacities.

Authors:  Giulia Negro; Bertram Aschenbrenner; Simona Kranjc Brezar; Maja Cemazar; Andrej Coer; Gorana Gasljevic; Dragana Savic; Maxim Sorokin; Anton Buzdin; Maurizio Callari; Irma Kvitsaridze; Anahid Jewett; Mariela Vasileva-Slaveva; Ute Ganswindt; Ira Skvortsova; Sergej Skvortsov
Journal:  Radiol Oncol       Date:  2020-02-14       Impact factor: 2.991

9.  Mutation Enrichment and Transcriptomic Activation Signatures of 419 Molecular Pathways in Cancer.

Authors:  Marianna A Zolotovskaia; Victor S Tkachev; Alexander P Seryakov; Denis V Kuzmin; Dmitry E Kamashev; Maxim I Sorokin; Sergey A Roumiantsev; Anton A Buzdin
Journal:  Cancers (Basel)       Date:  2020-01-22       Impact factor: 6.639

Review 10.  FDA-Approved and Emerging Next Generation Predictive Biomarkers for Immune Checkpoint Inhibitors in Cancer Patients.

Authors:  Ye Wang; Zhuang Tong; Wenhua Zhang; Weizhen Zhang; Anton Buzdin; Xiaofeng Mu; Qing Yan; Xiaowen Zhao; Hui-Hua Chang; Mark Duhon; Xin Zhou; Gexin Zhao; Hong Chen; Xinmin Li
Journal:  Front Oncol       Date:  2021-06-07       Impact factor: 6.244

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