RNA sequencing for research and diagnostics in clinical oncology

Buzdin, Anton; Sorokin, Maxim; Garazha, Andrew; Glusker, Alexander; Aleshin, Alex; Poddubskaya, Elena; Sekacheva, Marina; Kim, Ella; Гайфуллин, Нуршат; Giese, Alf; Seryakov, Alexander; Румянцев, П. О.; Moshkovskii, Sergei A.; Moiseev, Alexey

doi:10.1016/j.semcancer.2019.07.010

Cited by 69 publications

(58 citation statements)

References 187 publications

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“…Our results also add to the discussion of whether gene expression biomarkers have a potential to replace or add value to DNA mutation biomarkers [69]. When comparing the molecular clustering for the drugs recommendation patterns and for the molecular profiles, we found that the gene expression data reflected drugs usage practice much better that the gene mutation profiles ( Table 3).…”

Section: Discussionsupporting

confidence: 52%

Disparity between Inter-Patient Molecular Heterogeneity and Repertoires of Target Drugs Used for Different Types of Cancer in Clinical Oncology

Zolotovskaia

Sorokin

Petrov³

et al. 2020

IJMS

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Inter-patient molecular heterogeneity is the major declared driver of an expanding variety of anticancer drugs and personalizing their prescriptions. Here, we compared interpatient molecular heterogeneities of tumors and repertoires of drugs or their molecular targets currently in use in clinical oncology. We estimated molecular heterogeneity using genomic (whole exome sequencing) and transcriptomic (RNA sequencing) data for 4890 tumors taken from The Cancer Genome Atlas database. For thirteen major cancer types, we compared heterogeneities at the levels of mutations and gene expression with the repertoires of targeted therapeutics and their molecular targets accepted by the current guidelines in oncology. Totally, 85 drugs were investigated, collectively covering 82 individual molecular targets. For the first time, we showed that the repertoires of molecular targets of accepted drugs did not correlate with molecular heterogeneities of different cancer types. On the other hand, we found that the clinical recommendations for the available cancer drugs were strongly congruent with the gene expression but not gene mutation patterns. We detected the best match among the drugs usage recommendations and molecular patterns for the kidney, stomach, bladder, ovarian and endometrial cancers. In contrast, brain tumors, prostate and colorectal cancers showed the lowest match. These findings provide a theoretical basis for reconsidering usage of targeted therapeutics and intensifying drug repurposing efforts.

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Section: Discussionsupporting

confidence: 52%

Disparity between Inter-Patient Molecular Heterogeneity and Repertoires of Target Drugs Used for Different Types of Cancer in Clinical Oncology

Zolotovskaia

Sorokin

Petrov³

et al. 2020

IJMS

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“…Bulk RNAseq is a cost effective and efficient tool for both cancer research and clinical applications (3)(4)(5). Today, clinical RNAseq is mainly used in novel gene fusion discoveries, panel-based accompanying gene fusion diagnosis, whole transcriptome-based biomarker (signature) discovery, and guidance for therapeutic treatment.…”

Section: Bulk Rnaseqmentioning

confidence: 99%

Changing Technologies of RNA Sequencing and Their Applications in Clinical Oncology

Wang

Mashock²,

Tong

et al. 2020

Front. Oncol.

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RNA sequencing (RNAseq) is one of the most commonly used techniques in life sciences, and has been widely used in cancer research, drug development, and cancer diagnosis and prognosis. Driven by various biological and technical questions, the techniques of RNAseq have progressed rapidly from bulk RNAseq, laser-captured micro-dissected RNAseq, and single-cell RNAseq to digital spatial RNA profiling, spatial transcriptomics, and direct in situ sequencing. These different technologies have their unique strengths, weaknesses, and suitable applications in the field of clinical oncology. To guide cancer researchers to select the most appropriate RNAseq technique for their biological questions, we will discuss each of these technologies, technical features, and clinical applications in cancer. We will help cancer researchers to understand the key differences of these RNAseq technologies and their optimal applications.

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“…A personalized approach in oncology was proven helpful for increasing efficacy of drugs prescription in many cancers [1,2]. Generally, it is based on finding specific biomarkers which can be mutations, protein levels or patterns of gene expression [3].…”

Section: Introductionmentioning

confidence: 99%

Flexible Data Trimming Improves Performance of Global Machine Learning Methods in Omics-Based Personalized Oncology

Tkachev

Sorokin

Borisov

et al. 2020

IJMS

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(1) Background: Machine learning (ML) methods are rarely used for an omics-based prescription of cancer drugs, due to shortage of case histories with clinical outcome supplemented by high-throughput molecular data. This causes overtraining and high vulnerability of most ML methods. Recently, we proposed a hybrid global-local approach to ML termed floating window projective separator (FloWPS) that avoids extrapolation in the feature space. Its core property is data trimming, i.e., sample-specific removal of irrelevant features. (2) Methods: Here, we applied FloWPS to seven popular ML methods, including linear SVM, k nearest neighbors (kNN), random forest (RF), Tikhonov (ridge) regression (RR), binomial naïve Bayes (BNB), adaptive boosting (ADA) and multi-layer perceptron (MLP). (3) Results: We performed computational experiments for 21 high throughput gene expression datasets (41–235 samples per dataset) totally representing 1778 cancer patients with known responses on chemotherapy treatments. FloWPS essentially improved the classifier quality for all global ML methods (SVM, RF, BNB, ADA, MLP), where the area under the receiver-operator curve (ROC AUC) for the treatment response classifiers increased from 0.61–0.88 range to 0.70–0.94. We tested FloWPS-empowered methods for overtraining by interrogating the importance of different features for different ML methods in the same model datasets. (4) Conclusions: We showed that FloWPS increases the correlation of feature importance between the different ML methods, which indicates its robustness to overtraining. For all the datasets tested, the best performance of FloWPS data trimming was observed for the BNB method, which can be valuable for further building of ML classifiers in personalized oncology.

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RNA sequencing for research and diagnostics in clinical oncology

Cited by 69 publications

References 187 publications

Disparity between Inter-Patient Molecular Heterogeneity and Repertoires of Target Drugs Used for Different Types of Cancer in Clinical Oncology

Disparity between Inter-Patient Molecular Heterogeneity and Repertoires of Target Drugs Used for Different Types of Cancer in Clinical Oncology

Changing Technologies of RNA Sequencing and Their Applications in Clinical Oncology

Flexible Data Trimming Improves Performance of Global Machine Learning Methods in Omics-Based Personalized Oncology

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