Human cancer cell line experiments are valuable for investigating drug sensitivity biomarkers. The number of biomarkers measured in these experiments is typically on the order of several thousand, whereas the number of samples is often limited to one or at most three replicates for each experimental condition. We have developed an innovative Bayesian approach that efficiently identifies clusters of proteins that exhibit similar patterns of expression. Motivated by the availability of ion mobility mass spectrometry data on cell line experiments in myelodysplastic syndrome and acute myeloid leukemia, our methodology can identify proteins that follow biologically meaningful trends of expression. Extensive simulation studies demonstrate good performance of the proposed method even in the presence of relatively small effects and sample sizes.
QinTYoussefEMJelinekJ, et al.Effect of cytarabine and decitabine in combination in human leukemic cell lines. Clin Cancer Res2007; 13: 4225–4232.
2.
TusherVGTibshiraniRChuG. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A2001; 98: 5116–5121.
3.
RitchieMEPhipsonBWuD, et al.limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acid Res2015; 43: e47–e47.
4.
PhipsonBLeeSMajewskiIJ, et al.Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression. Ann Appl Stat2016; 10: 946–963.
5.
SonesonCDelorenziM. A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinform2013; 14: 1–18.
6.
KvamVMLiuPSiY. A comparison of statistical methods for detecting differentially expressed genes from RNA-seq data. Am J Botany2012; 99: 248–256.
7.
AryeeMJGutiérrez-PabelloJAKramnikI, et al.An improved empirical bayes approach to estimating differential gene expression in microarray time-course data: BETR (Bayesian estimation of temporal regulation). BMC Bioinform2009; 10: 409–409.
8.
TaiYCSpeedTP. A multivariate empirical Bayes statistic for replicated microarray time course data. Ann Statist2006; 34: 2387–2412.
9.
Bar-JosephZGerberGSimonI, et al.Comparing the continuous representation of time-series expression profiles to identify differentially expressed genes. Proc Natl Acad Sci2003; 100: 10146–10151.
10.
StoreyJDXiaoWLeekJT, et al.Significance analysis of time course microarray experiments. Proc Natl Acad Sci USA2005; 102: 12837–12842.
11.
van de SchootRBroereJPerryckK, et al.Analyzing small data sets using Bayesian estimation: the case of posttraumatic stress symptoms following mechanical ventilation in burn survivors. Eur J Psychotraumatol2015; 6: 25216–25216.
12.
WangHHanashS. Mass spectrometry based proteomics for absolute quantification of proteins from tumor cells. Methods2015; 81: 34–40.
13.
KlampferLCammengaJWisniewskiHG, et al.Sodium salicylate activates caspases and induces apoptosis of myeloid leukemia cell lines. Blood1999; 93: 2386–2394.
14.
Garcia-ManeroGYangHFangZ, et al.Anti-Leukemia effect of FF-10501-01, a novel inosine 5’-monophosphate dehydrogenase inhibitor, in advanced Acute Myeloid Leukemia (AML) and Myelodysplastic Syndromes (MDS), including Hypomethylating agent (HMA) failures. Blood2015; 126: 3800–3800.
15.
SlamonDJLeyland-JonesBShakS, et al.Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. New Engl J Med2001; 344: 783–792.
16.
CarvalhoPCHewelJBarbosaVC, et al.Identifying differences in protein expression levels by spectral counting and feature selection. Genet Mol Res: GMR2008; 7: 342–356.
17.
ChoiHFerminDNesvizhskiiAI. Significance analysis of spectral count data in label-free shotgun proteomics. Mol Cell Proteomics2008; 7: 2373–2385.
18.
LoveMIHuberWAndersS. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol2014; 15: 550–550.
19.
McCarthyDJSmythGK. Testing significance relative to a fold-change threshold is a TREAT. Bioinformatics2009; 25: 765–771.
20.
van DykDAParkT. Partially collapsed Gibbs samplers. J Am Stat Assoc2008; 103: 790–796.
21.
StoreyJD. The positive false discovery rate: a Bayesian interpretation and the q -value. Ann Statist2003; 31: 2013–2035.
22.
Storey JD, Leek JT and Bass AJ. edge: Extraction of differential gene expression, 2015, https://github.com/jdstorey/edge (accessed May 2018). R package version 2.6.0..
23.
Tai YC. timecourse: Statistical analysis for developmental microarray time course data. 2007, http://www.bioconductor.org (accessed May 2018). R package version 1.46.0..
24.
SchwarzingerIValentPKllerU, et al.Prognostic significance of surface marker expression on blasts of patients with de novo acute myeloblastic leukemia. J Clin Oncol1990; 8: 423–30.
25.
YoonJ. Acute myeloid leukemia is a disease associated with HLA-C3. Acta Haematol2015; 133: 164–167.
26.
ZhaoXYChangYJXuLP, et al.HLA and KIR genotyping correlates with relapse after T-cell-replete haploidentical transplantation in chronic myeloid leukaemia patients. Br J Cancer2014; 111: 1080–1088.
27.
HorriganSKArbievaZHXieHY, et al.Delineation of a minimal interval and identification of 9 candidates for a tumor suppressor gene in malignant myeloid disorders on 5q31. Blood2000; 95: 2372–2377.
28.
XieHHuZChynaB, et al.Human mortalin (HSPA9): a candidate for the myeloid leukemia tumor suppressor gene on 5q31. Leukemia2000; 14: 2128–2134.
HartiganJAWongMA. Algorithm as 136: a k-means clustering algorithm. J Royal Stat Soc Series C (Appl Stat)1979; 28: 100–108.
31.
Hartigan JA. Clustering algorithms. 99th ed. New York, NY: John Wiley & Sons, Inc., 1975. ISBN 047135645X.
32.
FraleyCRafteryAE. Model-based clustering, discriminant analysis, and density estimation. J Am Stat Assoc2002; 97: 611–631.
33.
TamayoPSlonimDMesirovJ, et al.Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci1999; 96: 2907–2912.
34.
WangXZangMXiaoG. Epigenetic change detection and pattern recognition via Bayesian hierarchical hidden Markov models. Stat Med2013; 32: 2292–2307.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
