Machine Learning Applicability for Classification of PAD/VCD Chemotherapy Response Using 53 Multiple Myeloma RNA Sequencing Profiles

Borisov, Nicolas; Sergeeva, Anna; Suntsova, Maria; Raevskiy, Mikhail; Гайфуллин, Нуршат; Mendeleeva, Larisa; Gudkov, Alexander; Nareiko, Maria; Garazha, Andrew; Tkachev, Victor; Li, Xinmin; Sorokin, Maxim; Surin, V. L.; Buzdin, Anton

doi:10.3389/fonc.2021.652063

Cited by 16 publications

(16 citation statements)

References 96 publications

(49 reference statements)

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“…The proliferation-associated gene signature for MM described by Hose et alwas used as reference for transcripts upregulated in quickly replicating MM 38 . In the GSE159426 dataset (RNAseq data from 53 MM patients), multivariate analysis compared the expression of Itga4 relative to all of the 17 proliferation-signature transcripts 39 . Controls were chosen among MM markers, such as b2 microglobulin and CS1, and established housekeeping genes, such as tubulin or RPS18.…”

Section: Resultsmentioning

confidence: 99%

Ablation of VLA4 in multiple myeloma cells redirects tumor spread and prolongs survival

Hathi

Chanswangphuwana

Cho

et al. 2022

Sci Rep

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Multiple myeloma (MM) is a cancer of bone marrow (BM) plasma cells, which is increasingly treatable but still incurable. In 90% of MM patients, severe osteolysis results from pathological interactions between MM cells and the bone microenvironment. Delineating specific molecules and pathways for their role in cancer supportive interactions in the BM is vital for developing new therapies. Very Late Antigen 4 (VLA4, integrin α4β1) is a key player in cell–cell adhesion and signaling between MM and BM cells. We evaluated a VLA4 selective near infrared fluorescent probe, LLP2A-Cy5, for in vitro and in vivo optical imaging of VLA4. Furthermore, two VLA4-null murine 5TGM1 MM cell (KO) clones were generated by CRISPR/Cas9 knockout of the Itga4 (α4) subunit, which induced significant alterations in the transcriptome. In contrast to the VLA4+ 5TGM1 parental cells, C57Bl/KaLwRij immunocompetent syngeneic mice inoculated with the VLA4-null clones showed prolonged survival, reduced medullary disease, and increased extramedullary disease burden. The KO tumor foci showed significantly reduced uptake of LLP2A-Cy5, confirming in vivo specificity of this imaging agent. This work provides new insights into the pathogenic role of VLA4 in MM, and evaluates an optical tool to measure its expression in preclinical models.

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Section: Resultsmentioning

confidence: 99%

Ablation of VLA4 in multiple myeloma cells redirects tumor spread and prolongs survival

Hathi

Chanswangphuwana

Cho

et al. 2022

Sci Rep

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“…Additionally, it should be noted that in both successful results, FloWPS dynamic data trimming method was used as a practical approach to transforming data from a high-dimensional space into a low-dimensional space. Furthermore, the RNAseq and microarray datasets results implied that in both groups of good and poor responders, five genes, including FGFR3, MAF, IGHA2, IGHV1-69, and GRB14 were overexpressed (Borisov et al, 2021).…”

Section: In Multi-omics Data Analyses Of Cancermentioning

confidence: 98%

Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer

et al. 2022

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Cancer is defined as a large group of diseases that is associated with abnormal cell growth, uncontrollable cell division, and may tend to impinge on other tissues of the body by different mechanisms through metastasis. What makes cancer so important is that the cancer incidence rate is growing worldwide which can have major health, economic, and even social impacts on both patients and the governments. Thereby, the early cancer prognosis, diagnosis, and treatment can play a crucial role at the front line of combating cancer. The onset and progression of cancer can occur under the influence of complicated mechanisms and some alterations in the level of genome, proteome, transcriptome, metabolome etc. Consequently, the advent of omics science and its broad research branches (such as genomics, proteomics, transcriptomics, metabolomics, and so forth) as revolutionary biological approaches have opened new doors to the comprehensive perception of the cancer landscape. Due to the complexities of the formation and development of cancer, the study of mechanisms underlying cancer has gone beyond just one field of the omics arena. Therefore, making a connection between the resultant data from different branches of omics science and examining them in a multi-omics field can pave the way for facilitating the discovery of novel prognostic, diagnostic, and therapeutic approaches. As the volume and complexity of data from the omics studies in cancer are increasing dramatically, the use of leading-edge technologies such as machine learning can have a promising role in the assessments of cancer research resultant data. Machine learning is categorized as a subset of artificial intelligence which aims to data parsing, classification, and data pattern identification by applying statistical methods and algorithms. This acquired knowledge subsequently allows computers to learn and improve accurate predictions through experiences from data processing. In this context, the application of machine learning, as a novel computational technology offers new opportunities for achieving in-depth knowledge of cancer by analysis of resultant data from multi-omics studies. Therefore, it can be concluded that the use of artificial intelligence technologies such as machine learning can have revolutionary roles in the fight against cancer.

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“…The apparent technical heterogeneity of gene expression data essentially reduces our ability to compare multiple datasets at once, both at the cross-platform and at the intraplatform levels (Bartlett, Dhruva, Shah, Ryan, & Ross, 2019;Berger et al, 2017;. The latter is mostly due to the so-called batch effect, which frequently arises even within the same platform and reagent settings; however, similar effects can be seen with the comparison of different sets of experimental data (Aliper et al, 2017;Borisov et al, 2017Borisov et al, , 2021Demetrashvili, Kron, Pethe, Bapat, & Briollais, 2010).…”

Section: Shambhala-2mentioning

confidence: 99%

“…Shambhala‐1 allows one‐by‐one adding of new harmonized expression samples to a common pre‐calculated pool, and no recalculation is needed for the whole set of samples. This feature can dramatically reduce calculation time and costs, which is especially sensitive considering next‐level gene expression metrics such as molecular pathway activation levels (Aliper et al., 2017; Borisov et al., 2017; Borisov, Sorokin, Garazha, & Buzdin, 2020; Buzdin, Prassolov, Zhavoronkov, & Borisov, 2017; Buzdin et al., 2014, 2018), individual drug sensitivity estimates (Poddubskaya et al., 2019; Tkachev, Sorokin, Garazha et al., 2020; Zolotovskaia et al., 2019), machine learning models (Borisov & Buzdin, 2019; Borisov et al., 2018, 2021; Tkachev et al., 2019; Tkachev, Sorokin, Borisov, et al., 2020), and more. Technically, the Shambhala‐1 method utilizes a piecewise linear normalization method (XPN) for building a universal gene expression matrix (Shabalin et al., 2008).…”

Section: Introductionmentioning

confidence: 99%

Shambhala‐2: A Protocol for Uniformly Shaped Harmonization of Gene Expression Profiles of Various Formats

et al. 2022

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Uniformly shaped harmonization of gene expression profiles is central for the simultaneous comparison of multiple gene expression datasets. It is expected to operate with the gene expression data obtained using various experimental methods and equipment, and to return harmonized profiles in a uniform shape. Such uniformly shaped expression profiles from different initial datasets can be further compared directly. However, current harmonization techniques have strong limitations that prevent their broad use for bioinformatic applications. They can either operate with only up to two datasets/platforms or return data in a dynamic format that will be different for every comparison under analysis. This also does not allow for adding new data to the previously harmonized dataset(s), which complicates the analysis and increases calculation costs. We propose here a new method termed Shambhala‐2 that can transform multi‐platform expression data into a universal format that is identical for all harmonizations made using this technique. Shambhala‐2 is based on sample‐by‐sample cubic conversion of the initial expression dataset into a preselected shape of the reference definitive dataset. Using 8390 samples of 12 healthy human tissue types and 4086 samples of colorectal, kidney, and lung cancer tissues, we verified Shambhala‐2's capacity in restoring tissue‐specific expression patterns for seven microarray and three RNA sequencing platforms. Shambhala‐2 performed well for all tested combinations of RNAseq and microarray profiles, and retained gene‐expression ranks, as evidenced by high correlations between different single‐ or aggregated gene expression metrics in pre‐ and post‐Shambhalized samples, including preserving cancer‐specific gene expression and pathway activation features. © 2022 Wiley Periodicals LLC. Basic Protocol: Shambhala‐2 harmonizer Alternate Protocol 1: Linear Shambhala/Shambhala‐1 Alternate Protocol 2: Alternative (flexible‐format and uniformly shaped) normalization methods Support Protocol 1: Watermelon multisection (WM) Support Protocol 2: Calculation of cancer‐to‐normal log‐fold‐change (LFC) and pathway activation level (PAL)

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Machine Learning Applicability for Classification of PAD/VCD Chemotherapy Response Using 53 Multiple Myeloma RNA Sequencing Profiles

Cited by 16 publications

References 96 publications

Ablation of VLA4 in multiple myeloma cells redirects tumor spread and prolongs survival

Ablation of VLA4 in multiple myeloma cells redirects tumor spread and prolongs survival

Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer

Shambhala‐2: A Protocol for Uniformly Shaped Harmonization of Gene Expression Profiles of Various Formats

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