1
|
Garcia-Gaona E, García-Gregorio A, García-Jiménez C, López-Olaiz MA, Mendoza-Ramírez P, Fernandez-Guzman D, Pillado-Sánchez RA, Soto-Pacheco AD, Yareni-Zuñiga L, Sánchez-Parada MG, González-Santiago AE, Román-Pintos LM, Castañeda-Arellano R, Hernández-Ortega LD, Mercado-Sesma AR, Orozco-Luna FDJ, Villa-Angulo C, Villa-Angulo R, Baptista-Rosas RC. mtDNA Single-Nucleotide Variants Associated with Type 2 Diabetes. Curr Issues Mol Biol 2023; 45:8716-8732. [PMID: 37998725 PMCID: PMC10670651 DOI: 10.3390/cimb45110548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 10/18/2023] [Accepted: 10/23/2023] [Indexed: 11/25/2023] Open
Abstract
Type 2 diabetes (T2D) is a chronic systemic disease with a complex etiology, characterized by insulin resistance and mitochondrial dysfunction in various cell tissues. To explore this relationship, we conducted a secondary analysis of complete mtDNA sequences from 1261 T2D patients and 1105 control individuals. Our findings revealed significant associations between certain single-nucleotide polymorphisms (SNPs) and T2D. Notably, the variants m.1438A>G (rs2001030) (controls: 32 [27.6%], T2D: 84 [72.4%]; OR: 2.46; 95%CI: 1.64-3.78; p < 0.001), m.14766C>T (rs193302980) (controls: 498 [36.9%], T2D: 853 [63.1%]; OR: 2.57, 95%CI: 2.18-3.04, p < 0.001), and m.16519T>C (rs3937033) (controls: 363 [43.4%], T2D: 474 [56.6%]; OR: 1.24, 95%CI: 1.05-1.47, p = 0.012) were significantly associated with the likelihood of developing diabetes. The variant m.16189T>C (rs28693675), which has been previously documented in several studies across diverse populations, showed no association with T2D in our analysis (controls: 148 [13.39] T2D: 171 [13.56%]; OR: 1.03; 95%CI: 0.815-1.31; p = 0.83). These results provide evidence suggesting a link between specific mtDNA polymorphisms and T2D, possibly related to association rules, topological patterns, and three-dimensional conformations associated with regions where changes occur, rather than specific point mutations in the sequence.
Collapse
Affiliation(s)
- Enrique Garcia-Gaona
- Facultad de Medicina, Benemérita Universidad Autónoma de Puebla, Puebla 72420, Mexico;
| | - Alhelí García-Gregorio
- Facultad de Enfermería Región Poza Rica-Tuxpan, Universidad Veracruzana, Veracruz 91700, Mexico;
| | - Camila García-Jiménez
- Facultad de Ciencias Médicas y Biológicas “Dr. Ignacio Chávez”, Universidad Michoacana de San Nicolás de Hidalgo, Morelia 58000, Mexico;
| | | | - Paola Mendoza-Ramírez
- Facultad de Ciencias Biológicas, Benemérita Universidad Autónoma de Puebla, Puebla 72420, Mexico;
| | | | | | - Axel David Soto-Pacheco
- Facultad de Medicina Extensión Los Mochis, Universidad Autónoma de Sinaloa, Sinaloa 81223, Mexico; (R.A.P.-S.); (A.D.S.-P.)
| | - Laura Yareni-Zuñiga
- Departamento de Ciencias de la Salud-Enfermedad como Proceso Individual, Centro Universitario de Tonalá, Universidad de Guadalajara, Tonalá 45425, Mexico; (L.Y.-Z.); (L.M.R.-P.); (A.R.M.-S.)
| | - María Guadalupe Sánchez-Parada
- Departamento de Ciencias Biomédicas, Centro Universitario de Tonalá, Universidad de Guadalajara, Tonalá 45425, Mexico; (M.G.S.-P.); (A.E.G.-S.); (R.C.-A.); (L.D.H.-O.)
- Centro de Investigación Multidisciplinaria en Salud, Universidad de Guadalajara, Tonalá 45425, Mexico
| | - Ana Elizabeth González-Santiago
- Departamento de Ciencias Biomédicas, Centro Universitario de Tonalá, Universidad de Guadalajara, Tonalá 45425, Mexico; (M.G.S.-P.); (A.E.G.-S.); (R.C.-A.); (L.D.H.-O.)
- Centro de Investigación Multidisciplinaria en Salud, Universidad de Guadalajara, Tonalá 45425, Mexico
| | - Luis Miguel Román-Pintos
- Departamento de Ciencias de la Salud-Enfermedad como Proceso Individual, Centro Universitario de Tonalá, Universidad de Guadalajara, Tonalá 45425, Mexico; (L.Y.-Z.); (L.M.R.-P.); (A.R.M.-S.)
- Centro de Investigación Multidisciplinaria en Salud, Universidad de Guadalajara, Tonalá 45425, Mexico
| | - Rolando Castañeda-Arellano
- Departamento de Ciencias Biomédicas, Centro Universitario de Tonalá, Universidad de Guadalajara, Tonalá 45425, Mexico; (M.G.S.-P.); (A.E.G.-S.); (R.C.-A.); (L.D.H.-O.)
- Centro de Investigación Multidisciplinaria en Salud, Universidad de Guadalajara, Tonalá 45425, Mexico
| | - Luis Daniel Hernández-Ortega
- Departamento de Ciencias Biomédicas, Centro Universitario de Tonalá, Universidad de Guadalajara, Tonalá 45425, Mexico; (M.G.S.-P.); (A.E.G.-S.); (R.C.-A.); (L.D.H.-O.)
- Centro de Investigación Multidisciplinaria en Salud, Universidad de Guadalajara, Tonalá 45425, Mexico
| | - Arieh Roldán Mercado-Sesma
- Departamento de Ciencias de la Salud-Enfermedad como Proceso Individual, Centro Universitario de Tonalá, Universidad de Guadalajara, Tonalá 45425, Mexico; (L.Y.-Z.); (L.M.R.-P.); (A.R.M.-S.)
- Centro de Investigación Multidisciplinaria en Salud, Universidad de Guadalajara, Tonalá 45425, Mexico
| | | | - Carlos Villa-Angulo
- Laboratorio de Bioinformática y Biofotónica, Instituto de Ingeniería Universidad Autónoma de Baja California, Mexicali 21100, Mexico; (C.V.-A.); (R.V.-A.)
| | - Rafael Villa-Angulo
- Laboratorio de Bioinformática y Biofotónica, Instituto de Ingeniería Universidad Autónoma de Baja California, Mexicali 21100, Mexico; (C.V.-A.); (R.V.-A.)
| | - Raúl C. Baptista-Rosas
- Departamento de Ciencias de la Salud-Enfermedad como Proceso Individual, Centro Universitario de Tonalá, Universidad de Guadalajara, Tonalá 45425, Mexico; (L.Y.-Z.); (L.M.R.-P.); (A.R.M.-S.)
- Centro de Investigación Multidisciplinaria en Salud, Universidad de Guadalajara, Tonalá 45425, Mexico
- Hospital General de Occidente, Secretaría de Salud Jalisco, Zapopan 45170, Mexico
| |
Collapse
|
2
|
Barua A, Hatzikirou H. Cell Decision Making through the Lens of Bayesian Learning. ENTROPY (BASEL, SWITZERLAND) 2023; 25:e25040609. [PMID: 37190396 PMCID: PMC10137733 DOI: 10.3390/e25040609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 03/20/2023] [Accepted: 03/29/2023] [Indexed: 05/17/2023]
Abstract
Cell decision making refers to the process by which cells gather information from their local microenvironment and regulate their internal states to create appropriate responses. Microenvironmental cell sensing plays a key role in this process. Our hypothesis is that cell decision-making regulation is dictated by Bayesian learning. In this article, we explore the implications of this hypothesis for internal state temporal evolution. By using a timescale separation between internal and external variables on the mesoscopic scale, we derive a hierarchical Fokker-Planck equation for cell-microenvironment dynamics. By combining this with the Bayesian learning hypothesis, we find that changes in microenvironmental entropy dominate the cell state probability distribution. Finally, we use these ideas to understand how cell sensing impacts cell decision making. Notably, our formalism allows us to understand cell state dynamics even without exact biochemical information about cell sensing processes by considering a few key parameters.
Collapse
Affiliation(s)
- Arnab Barua
- Departement de Biochimie, Université de Montréal, Montréal, QC H3T 1C5, Canada
- Centre Robert-Cedergren en Bio-Informatique et Génomique, Université de Montréal, Montréal, QC H3C 3J7, Canada
| | - Haralampos Hatzikirou
- Center for Information Services and High Performance Computing, Technische Univesität Dresden, 01062 Dresden, Germany
- Mathematics Department, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates
| |
Collapse
|
3
|
Salmikangas M, Laaksonen M, Edgren H, Salgado M, Suoranta A, Mattila P, Koljonen V, Böhling T, Sihto H. Neurocan expression associates with better survival and viral positivity in Merkel cell carcinoma. PLoS One 2023; 18:e0285524. [PMID: 37146093 PMCID: PMC10162530 DOI: 10.1371/journal.pone.0285524] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 04/25/2023] [Indexed: 05/07/2023] Open
Abstract
Merkel cell carcinoma (MCC) is a rare cutaneous neuroendocrine carcinoma that is frequently divided into Merkel cell polyomavirus negative and positive tumors due their distinct genomic and transcriptomic profiles, and disease outcomes. Although some prognostic factors in MCC are known, tumorigenic pathways, which that explain outcome differences in MCC are not fully understood. We investigated transcriptomes of 110 tissue samples of a formalin-fixed, paraffin-embedded MCC series by RNA sequencing to identify genes showing a bimodal expression pattern and predicting outcome in cancer and that potentially could play a role in tumorigenesis. We discovered 19 genes among which IGHM, IGKC, NCAN, OTOF, and USH2A were associated also with overall survival (all p-values < 0.05). From these genes, NCAN (neurocan) expression was detected in all 144 MCC samples by immunohistochemistry. Increased NCAN expression was associated with presence of Merkel cell polyomavirus DNA (p = 0.001) and viral large T antigen expression in tumor tissue (p = 0.004) and with improved MCC-specific survival (p = 0.027) and overall survival (p = 0.034). We conclude that NCAN expression is common in MCC, and further studies are warranted to investigate its role in MCC tumorigenesis.
Collapse
Affiliation(s)
- Marko Salmikangas
- Department of Pathology, Medicum, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | | | | | - Marco Salgado
- Department of Plastic Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Anu Suoranta
- Institute for Molecular Medicine Finland, Helsinki, Finland
| | - Pirkko Mattila
- Institute for Molecular Medicine Finland, Helsinki, Finland
| | - Virve Koljonen
- Department of Plastic Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Tom Böhling
- Department of Pathology, Medicum, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Harri Sihto
- Department of Pathology, Medicum, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| |
Collapse
|
4
|
Justino JR, Reis CFD, Fonseca AL, Souza SJD, Stransky B. An integrated approach to identify bimodal genes associated with prognosis in câncer. Genet Mol Biol 2021; 44:e20210109. [PMID: 34617951 PMCID: PMC8495773 DOI: 10.1590/1678-4685-gmb-2021-0109] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 07/08/2021] [Indexed: 02/08/2023] Open
Abstract
Bimodal gene expression (where a gene expression distribution has two maxima) is
associated with phenotypic diversity in different biological systems. A critical
issue, thus, is the integration of expression and phenotype data to identify
genuine associations. Here, we developed tools that allow both: i) the
identification of genes with bimodal gene expression and ii) their association
with prognosis in cancer patients from The Cancer Genome Atlas (TCGA).
Bimodality was observed for 554 genes in expression data from 25 tumor types.
Furthermore, 96 of these genes presented different prognosis when patients
belonging to the two expression peaks were compared. The software to execute the
method and the corresponding documentation are available at the Data access
section.
Collapse
Affiliation(s)
- Josivan Ribeiro Justino
- Universidade Federal do Rio Grande do Norte (UFRN), Metrópole Digital, Centro Multiusuário de Bioinformática, Natal, RN, Brazil.,Universidade Federal de Rondônia, Departamento de Matemática e Estatística, Ji-Parana, RO, Brazil
| | - Clovis Ferreira Dos Reis
- Universidade Federal do Rio Grande do Norte (UFRN), Metrópole Digital, Centro Multiusuário de Bioinformática, Natal, RN, Brazil
| | - Andre Luis Fonseca
- Universidade de São Paulo, Departamento de Genética e Biologia Evolutiva, São Paulo, SP, Brazil
| | - Sandro Jose de Souza
- Universidade Federal do Rio Grande do Norte (UFRN), Metrópole Digital, Centro Multiusuário de Bioinformática, Natal, RN, Brazil.,Universidade Federal do Rio Grande do Norte (UFRN), Instituto do Cérebro, Natal, RN, Brazil.,Sichuan University, West China Hospital, Institutes for Systems Genetics, Chengdu, China
| | - Beatriz Stransky
- Universidade Federal do Rio Grande do Norte (UFRN), Metrópole Digital, Centro Multiusuário de Bioinformática, Natal, RN, Brazil.,Universidade Federal do Rio Grande do Norte (UFRN), Centro de Tecnologia, Departamento de Engenharia Biomédica, Natal, RN, Brazil
| |
Collapse
|
5
|
Shohdy KS, Bareja R, Sigouros M, Wilkes DC, Dorsaint P, Manohar J, Bockelman D, Xiang JZ, Kim R, Ohara K, Eng K, Mosquera JM, Elemento O, Sboner A, Alonso A, Faltas BM. Functional comparison of exome capture-based methods for transcriptomic profiling of formalin-fixed paraffin-embedded tumors. NPJ Genom Med 2021; 6:66. [PMID: 34385467 PMCID: PMC8360986 DOI: 10.1038/s41525-021-00231-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 07/26/2021] [Indexed: 11/08/2022] Open
Abstract
The availability of fresh frozen (FF) tissue is a barrier for implementing RNA sequencing (RNA-seq) in the clinic. The majority of clinical samples are stored as formalin-fixed, paraffin-embedded (FFPE) tissues. Exome capture platforms have been developed for RNA-seq from FFPE samples. However, these methods have not been systematically compared. We performed transcriptomic analysis of 32 FFPE tumor samples from 11 patients using three exome capture-based methods: Agilent SureSelect V6, TWIST NGS Exome, and IDT XGen Exome Research Panel. We compared these methods to the TruSeq RNA-seq of fresh frozen (FF-TruSeq) tumor samples from the same patients. We assessed the recovery of clinically relevant biological features. The Spearman's correlation coefficients between the global expression profiles of the three capture-based methods from FFPE and matched FF-TruSeq were high (rho = 0.72-0.9, p < 0.05). A significant correlation between the expression of key immune genes between individual capture-based methods and FF-TruSeq (rho = 0.76-0.88, p < 0.05) was observed. All exome capture-based methods reliably detected outlier expression of actionable gene transcripts, including ERBB2, MET, NTRK1, and PPARG. In urothelial cancer samples, the Agilent assay was associated with the highest molecular subtype concordance with FF-TruSeq (Cohen's k = 0.7, p < 0.01). The Agilent and IDT assays detected all the clinically relevant fusions that were initially identified in FF-TruSeq. All FFPE exome capture-based methods had comparable performance and concordance with FF-TruSeq. Our findings will enable the implementation of RNA-seq in the clinic to guide precision oncology approaches.
Collapse
Affiliation(s)
- Kyrillus S Shohdy
- Department of Medicine, Division of Hematology and Medical Oncology, Weill Cornell Medicine, New York, NY, USA
- Department of Clinical Oncology, Kasr Alainy School of Medicine, Cairo University, Cairo, Egypt
| | - Rohan Bareja
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Michael Sigouros
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - David C Wilkes
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Princesca Dorsaint
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Jyothi Manohar
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Daniel Bockelman
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Jenny Z Xiang
- Genomic Resources Core Facility, Weill Cornell Medicine, New York, NY, USA
| | - Rob Kim
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Kentaro Ohara
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Kenneth Eng
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Juan Miguel Mosquera
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Olivier Elemento
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Andrea Sboner
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Alicia Alonso
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Bishoy M Faltas
- Department of Medicine, Division of Hematology and Medical Oncology, Weill Cornell Medicine, New York, NY, USA.
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA.
- Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA.
- Department of Cell and Developmental Biology, Weill Cornell Medicine, New York, NY, USA.
| |
Collapse
|
6
|
A comprehensive characterization of the cell-free transcriptome reveals tissue- and subtype-specific biomarkers for cancer detection. Nat Commun 2021; 12:2357. [PMID: 33883548 PMCID: PMC8060291 DOI: 10.1038/s41467-021-22444-1] [Citation(s) in RCA: 95] [Impact Index Per Article: 31.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 03/10/2021] [Indexed: 12/12/2022] Open
Abstract
Cell-free RNA (cfRNA) is a promising analyte for cancer detection. However, a comprehensive assessment of cfRNA in individuals with and without cancer has not been conducted. We perform the first transcriptome-wide characterization of cfRNA in cancer (stage III breast [n = 46], lung [n = 30]) and non-cancer (n = 89) participants from the Circulating Cell-free Genome Atlas (NCT02889978). Of 57,820 annotated genes, 39,564 (68%) are not detected in cfRNA from non-cancer individuals. Within these low-noise regions, we identify tissue- and cancer-specific genes, defined as “dark channel biomarker” (DCB) genes, that are recurrently detected in individuals with cancer. DCB levels in plasma correlate with tumor shedding rate and RNA expression in matched tissue, suggesting that DCBs with high expression in tumor tissue could enhance cancer detection in patients with low levels of circulating tumor DNA. Overall, cfRNA provides a unique opportunity to detect cancer, predict the tumor tissue of origin, and determine the cancer subtype. Cell-free RNA (cfRNA) is a promising analyte for cancer diagnosis. Here, the authors determine the baseline cell-free transcriptome in the absence of cancer and identify tissue- and subtype-specific cfRNA biomarkers in breast and lung cancer patients.
Collapse
|
7
|
Winkler C, Armenia J, Jones GN, Tobalina L, Sale MJ, Petreus T, Baird T, Serra V, Wang AT, Lau A, Garnett MJ, Jaaks P, Coker EA, Pierce AJ, O'Connor MJ, Leo E. SLFN11 informs on standard of care and novel treatments in a wide range of cancer models. Br J Cancer 2021; 124:951-962. [PMID: 33339894 PMCID: PMC7921667 DOI: 10.1038/s41416-020-01199-4] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 10/06/2020] [Accepted: 11/11/2020] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND Schlafen 11 (SLFN11) has been linked with response to DNA-damaging agents (DDA) and PARP inhibitors. An in-depth understanding of several aspects of its role as a biomarker in cancer is missing, as is a comprehensive analysis of the clinical significance of SLFN11 as a predictive biomarker to DDA and/or DNA damage-response inhibitor (DDRi) therapies. METHODS We used a multidisciplinary effort combining specific immunohistochemistry, pharmacology tests, anticancer combination therapies and mechanistic studies to assess SLFN11 as a potential biomarker for stratification of patients treated with several DDA and/or DDRi in the preclinical and clinical setting. RESULTS SLFN11 protein associated with both preclinical and patient treatment response to DDA, but not to non-DDA or DDRi therapies, such as WEE1 inhibitor or olaparib in breast cancer. SLFN11-low/absent cancers were identified across different tumour types tested. Combinations of DDA with DDRi targeting the replication-stress response (ATR, CHK1 and WEE1) could re-sensitise SLFN11-absent/low cancer models to the DDA treatment and were effective in upper gastrointestinal and genitourinary malignancies. CONCLUSION SLFN11 informs on the standard of care chemotherapy based on DDA and the effect of selected combinations with ATR, WEE1 or CHK1 inhibitor in a wide range of cancer types and models.
Collapse
Affiliation(s)
| | - Joshua Armenia
- Bioinformatics and Data Science, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Gemma N Jones
- Translational Medicine, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Luis Tobalina
- Bioinformatics and Data Science, Research and Early Development, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Matthew J Sale
- Signalling Programme, The Babraham Institute, Babraham Research Campus, Cambridge, CB22 3AT, Cambridge, UK
| | - Tudor Petreus
- Bioscience, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Tarrion Baird
- Translational Medicine, Oncology R&D, AstraZeneca, Cambridge, UK
| | - Violeta Serra
- Experimental Therapeutics Group, Vall d' Hebron Institute of Oncology, Barcelona, Spain
| | | | - Alan Lau
- Bioscience, Oncology R&D, AstraZeneca, Cambridge, UK
| | | | | | | | - Andrew J Pierce
- Translational Medicine, Oncology R&D, AstraZeneca, Cambridge, UK
| | | | | |
Collapse
|
8
|
Källberg D, Vidman L, Rydén P. Comparison of Methods for Feature Selection in Clustering of High-Dimensional RNA-Sequencing Data to Identify Cancer Subtypes. Front Genet 2021; 12:632620. [PMID: 33719342 PMCID: PMC7943624 DOI: 10.3389/fgene.2021.632620] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 02/03/2021] [Indexed: 11/13/2022] Open
Abstract
Cancer subtype identification is important to facilitate cancer diagnosis and select effective treatments. Clustering of cancer patients based on high-dimensional RNA-sequencing data can be used to detect novel subtypes, but only a subset of the features (e.g., genes) contains information related to the cancer subtype. Therefore, it is reasonable to assume that the clustering should be based on a set of carefully selected features rather than all features. Several feature selection methods have been proposed, but how and when to use these methods are still poorly understood. Thirteen feature selection methods were evaluated on four human cancer data sets, all with known subtypes (gold standards), which were only used for evaluation. The methods were characterized by considering mean expression and standard deviation (SD) of the selected genes, the overlap with other methods and their clustering performance, obtained comparing the clustering result with the gold standard using the adjusted Rand index (ARI). The results were compared to a supervised approach as a positive control and two negative controls in which either a random selection of genes or all genes were included. For all data sets, the best feature selection approach outperformed the negative control and for two data sets the gain was substantial with ARI increasing from (-0.01, 0.39) to (0.66, 0.72), respectively. No feature selection method completely outperformed the others but using the dip-rest statistic to select 1000 genes was overall a good choice. The commonly used approach, where genes with the highest SDs are selected, did not perform well in our study.
Collapse
Affiliation(s)
- David Källberg
- Department of Statistics, USBE, Umeå University, Umeå, Sweden
- Department of Mathematics and Mathematical Statistics, Umeå University, Umeå, Sweden
| | - Linda Vidman
- Department of Mathematics and Mathematical Statistics, Umeå University, Umeå, Sweden
- Department of Radiation Sciences, Oncology, Umeå University, Umeå, Sweden
| | - Patrik Rydén
- Department of Mathematics and Mathematical Statistics, Umeå University, Umeå, Sweden
| |
Collapse
|
9
|
Nishimura T, Nakamura H, Yachie A, Hase T, Fujii K, Koizumi H, Naruki S, Takagi M, Matsuoka Y, Furuya N, Kato H, Saji H. Disease-related cellular protein networks differentially affected under different EGFR mutations in lung adenocarcinoma. Sci Rep 2020; 10:10881. [PMID: 32616892 PMCID: PMC7331587 DOI: 10.1038/s41598-020-67894-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2019] [Accepted: 05/28/2020] [Indexed: 12/21/2022] Open
Abstract
It is unclear how epidermal growth factor receptor EGFR major driver mutations (L858R or Ex19del) affect downstream molecular networks and pathways. This study aimed to provide information on the influences of these mutations. The study assessed 36 protein expression profiles of lung adenocarcinoma (Ex19del, nine; L858R, nine; no Ex19del/L858R, 18). Weighted gene co-expression network analysis together with analysis of variance-based screening identified 13 co-expressed modules and their eigen proteins. Pathway enrichment analysis for the Ex19del mutation demonstrated involvement of SUMOylation, epithelial and mesenchymal transition, ERK/mitogen-activated protein kinase signalling via phosphorylation and Hippo signalling. Additionally, analysis for the L858R mutation identified various pathways related to cancer cell survival and death. With regard to the Ex19del mutation, ROCK, RPS6KA1, ARF1, IL2RA and several ErbB pathways were upregulated, whereas AURK and GSKIP were downregulated. With regard to the L858R mutation, RB1, TSC22D3 and DOCK1 were downregulated, whereas various networks, including VEGFA, were moderately upregulated. In all mutation types, CD80/CD86 (B7), MHC, CIITA and IFGN were activated, whereas CD37 and SAFB were inhibited. Costimulatory immune-checkpoint pathways by B7/CD28 were mainly activated, whereas those by PD-1/PD-L1 were inhibited. Our findings may help identify potential therapeutic targets and develop therapeutic strategies to improve patient outcomes.
Collapse
Affiliation(s)
- Toshihide Nishimura
- Department of Translational Medicine Informatics, St. Marianna University School of Medicine, Kawasaki, Kanagawa, 216-8511, Japan.
| | - Haruhiko Nakamura
- Department of Translational Medicine Informatics, St. Marianna University School of Medicine, Kawasaki, Kanagawa, 216-8511, Japan
- Department of Chest Surgery, St. Marianna University School of Medicine, Kawasaki, Kanagawa, 216-8511, Japan
| | - Ayako Yachie
- The Systems Biology Institute, Tokyo, 141-0022, Japan
| | - Takeshi Hase
- The Systems Biology Institute, Tokyo, 141-0022, Japan
| | - Kiyonaga Fujii
- Department of Translational Medicine Informatics, St. Marianna University School of Medicine, Kawasaki, Kanagawa, 216-8511, Japan
| | - Hirotaka Koizumi
- Department of Pathology, St. Marianna University Hospital, Kawasaki, Kanagawa, 216-8511, Japan
| | - Saeko Naruki
- Department of Pathology, St. Marianna University Hospital, Kawasaki, Kanagawa, 216-8511, Japan
| | - Masayuki Takagi
- Department of Pathology, St. Marianna University Hospital, Kawasaki, Kanagawa, 216-8511, Japan
| | | | - Naoki Furuya
- Division of Respiratory Medicine, Department of Internal Medicine, St. Marianna University School of Medicine, Kawasaki, Kanagawa, 216-8511, Japan
| | - Harubumi Kato
- Tokyo Medical University, Tokyo, 160-0023, Japan
- International University of Health and Welfare, Tokyo, 107-8402, Japan
| | - Hisashi Saji
- Department of Chest Surgery, St. Marianna University School of Medicine, Kawasaki, Kanagawa, 216-8511, Japan
| |
Collapse
|