Supplementary Methods: IGFBP7 Drives Resistance to Epidermal Growth Factor Receptor Tyrosine Kinase Inhibition in Lung Cancer
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1 S1 of S6 Supplementary Methods: IGFBP7 Drives Resistance to Epidermal Growth Factor Receptor Tyrosine Kinase Inhibition in Lung Cancer Shang-Gin Wu, Tzu-Hua Chang, Meng-Feng Tsai, Yi-Nan Liu, Chia-Lang Hsu, Yih-Leong Chang, Chong-Jen Yu and Jin-Yuan Shih Figure S1. Illustration of the integration of multiple rank lists into a single score to identify TKI resistance-related genes. Figure S2. Gene expression correlation between IGFBP7 and IGFBP5 in lung cancer cell lines from Cancer Cell Line Encyclopedia (CCLE) and lung adenocarcinoma tissue from TCGA-LUAD datasets. A B HCC4006/ER Si-scramble Si-IGFBP7-1 Si-IGFBP7-4 Afatinib (μm) Caspase-7 Cleave-caspase-7 Bim α-tubulin Figure S3. Knock-down IGFBP7 recovers EGFR-TKI sensitivity in EGFR-TKI-resistant cells by increasing apoptosis. (A) EGFR-TKI-resistant cells (HCC4006/ER) were transfected with different IGFBP7 small interfering RNAs (sirnas) (si-igfbp7-1, si-igfbp7-4) or scramble sirna (si-scramble).
2 S2 of S6 The percentage of apoptotic cells was quantified after treatment with 1.0 µm afatinib for 24 h. The columns are the mean of three independent experiments. Error bars show the standard deviations for n = 3 independent experiments (* p < 0.05). (B) HCC4006/ER was exposed to 1.0 µm of afatinib for 24 h. Next, apoptosis markers, including cleaved-caspase-7 and BIM, were assayed by western blotting. A Fold expression (IGFBP7/TBP) IGFBP7 α-tubulin si-scramble si-igfbp7-1 0 HCC827/gefsi-IGFBP7-1 HCC827/gefsi-scramble B C HCC827/gef Si-scramble Si-IGFBP7-1 Gefitinib (nm) PARP Cleave-PARP α-tubulin Figure S4. Knockdown of IGFBP7 expression reversed EGFR-TKI resistance in HCC827/gef cells by enhancing EGFR-TKI-induced cleave-parp expression (A) HCC827/gef cells were transfected with IGFBP7 small interfering RNAs (sirna; si-igfbp7-1) or scramble sirna (si-scramble). The effect of sirnas was evaluated by quantitative RT-PCR (left) and western blot analysis (right). (B) Cellular viability of si-scramble and si-igfbp7 transfectants was determined at different doses of gefitinib for 96 h using MTS assays. Error bars show the standard deviations for n = 3 independent experiments. (*** p < 0.001). (C) HCC827/gef was exposed to 50 and 250 nm of gefitinib for 24 h. Next, the apoptosis marker cleaved-parp was assayed by western blot analysis.
3 S3 of S6 No. Table S1 Gene expression profiles of TKI-sensitive and TKI-resistant human lung cancer cell lines from GEO. Source TKI- Resistant Cells GSM TKI-Resistance Cell Lines EGFR TKI TKI-Sensitive Cells TKI- Sensitive Cell Lines HCC GSE80344 HCC4006-RC2.2-rep1 GSM GSM HCC4006-RC2.2-rep2 GSM HCC GSE80344 GSM HCC827-RA1-rep1 GSM HCC827-RA1-rep2 GSM HCC827 3 GSE80344 GSM HCC827-RA2-rep1 GSM HCC827-RA2-rep2 GSM HCC827 4 GSE80344 GSM HCC827-RB1-rep1 GSM HCC827-RB1-rep2 GSM HCC827 5 GSE80344 GSM HCC827-RB1.1-rep1 GSM HCC827-RB1.1-rep2 GSM HCC827 6 GSE80344 GSM HCC827-RB2-rep1 GSM HCC827-RB2-rep2 GSM HCC827 7 GSE GSM PC9GTR Gefitinib GSM PC9 8 GSE GSM PC9OTR Osimiertinib GSM PC9 9 GSE GSM HCC827GTR Gefitinib GSM HCC GSE GSM HCC827OTR Osimiertinib GSM HCC GSE GSM PC-9-AR Afatinib GSM PC9 12 GSE GSM PC-9-OR Osimiertinib GSM PC9 13 GSE GSM HCC827-AR Afatinib GSM HCC GSE GSM HCC827-OR Osimiertinib GSM HCC GSE95558 GSM HCC827 ZDR3 Gefitinib GSM HCC827 Table S2 IC50 values of EGFR-TKIs examined in this study are summarized. The growth inhibitory effects of EGFR-TKIs were measured using an MTS assay. The cells were treated with increasing doses of gefitinib, erlotinib, and afatinib for 96 h. IC50 values were calculated using SigmaPlot and are shown in the table. Lung cancer cell lines IC50 (μm) Gefitinib (Iressa) (Tarceva) Afatinib (Giotrief) PC <0.001 PC9/gef HCC827 < < < HCC827/gef >10 > HCC <0.002 HCC4006/ER
4 S4 of S6 Table S3 Clinical characteristics of the 102 patients with advanced lung adenocarcinoma administered EGFR-TKIs as first-line treatment. Variable Number of IGFBP7 IHC Patients Positive Negative p-value Total No Age median # range Sex Female Male Smoking Non-/light smoker Smoker EGFR-TKI * gefitinib erlotinib EGFR mutation Del L858R Other β # By Mann-Whitney U; * By Fisher exact test; β Other: 2 G719S, one G719A, one L861Q, one G719C+E790A, one G719S+G709A and one L858R+E709V; IHC: Immunohistochemical staining. Table S4 Clinical characteristics of 75 patients with advanced lung adenocarcinoma administered EGFR-TKIs as first-line treatment (cut-off point was the median value of IGFBP7: ng/ml). Caption Patient No. Serum IGFBP7 level High Low p-value Total No Mean years of age * range Sex Female Male Smoking Nonsmokers Former/current smokers T N EGFR-TKI gefitinib erlotinib by Fisher s Exact test; * by Mann-Whitney U test.
5 S5 of S6 Table S5. List of primers for quantitative RT-PCR. Primers IGFBP7 SPANXA1 HRASLS RNF182 SPANXC LY6D Sequences F: 5 -ACTGGCTGGGTGCTGGTA-3 R: 5 -TGG ATG CAT GGC ACT CAT A-3 F: 5 -AACGAGGCCAACGAGATGAT-3 R: 5 -CTAGTATGGTCGAGGACTCAGATGTT-3 F: 5 -CTGTACTTGGGTGATGGTTACGTTA-3 R: 5 -AGACTTGGCGCTTGTAAAGGAC-3 F: 5 -TCTTAGAGGCAGGACTTGATGA-3 R: 5 -AAGGCCACATGAAGGGTTC-3 F: 5 -CAACGAGGTGAATGAGACGA-3 R: 5 -TGGTCGAGGACTCAGATGTTT-3 F: 5 -CGAACACAGTGGAGCCTCTGA-3 R: 5 -AGCTTCTCATTGCACAGGTCC-3 Table S6. List of antibodies for western blot analysis. Primary antibody Company Dilution IGFBP7 R&D Systems 1:2000 PARP Cell Signaling 1:1000 caspase-3 Cell Signaling 1:1000 caspase-7 Cell Signaling 1:1000 Bim Cell Signaling 1:1000 pigf1r Santa Cruz 1:500 IGF1R GeneTex 1:1000 pakt Cell Signaling 1:1000 Akt Cell Signaling 1:2000 perk Cell Signaling 1:1000 Erk Cell Signaling 1:2000 α-tubulin Millipore 1:3000 β-actin Millipore 1:3000 Supplementary Method Data sources and gene ranking We collected gene the expression profiles of TKI sensitive and TKI-resistant human lung cancer cell lines from the GEO with accession numbers GSE80344, GSE10335, GSE106765, and GSE95558 consisting of 15 comparisons of TKI-resistant to TKI-sensitive cell lines (Table S1). The raw data from GSE were processed using R affy packages and normalized by Frozen Robust Multi-Array Analysis implemented in the frma packages [1]. The raw data from GSE80344 were processed, including background correction, quantile normalization, and summarization, using the limma package in R [2]. The read counts from GSE10335 and GSE95558 were normalized by the trimmed mean of M-values normalization method (TMM) implemented by edger [3]. For each comparison, NOISeq [4] was performed to obtain the logarithm fold-change (logfc) and probability of differential expression (prob) between TKI-resistant and TKI-sensitive cells. The TKI resistance-related score was defined as follows: Score = sign(logfc) prob Where sign () is the sign function. Genes were ranked in descending order based on this score. Rank list fusion The discounted rating system was used to combine these ranking lists into a single score [5]. Briefly, for each rank list, genes were categorized into ten equal-size bins based on their rank positions and assigned ratings ranging from 10 to 1. A higher rating indicates that the gene was more
6 S6 of S6 relevant to TKI resistance. In the next step, the discounted rating for each gene (dri) was calculated using the following formula: ranting dr = log (r +1) Where ratingi and ri are the rating and rank position of the investigated gene in experiment i, respectively. Finally, the combined score for the investigated gene was the mean value of the eight highest discounted ratings of the experiments, and the genes were ranked according to this score. References 1. McCall, M.N.; Jaffee, H.A.; Irizarry, R.A. frma ST: Frozen robust multiarray analysis for Affymetrix Exon and Gene ST arrays. Bioinformatics 2012, 28, Ritchie, M.E.; Phipson, B.; Wu, D.; Hu, Y.; Law, C.W.; Shi, W.; Smyth, G.K. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015, 43, e Robinson, M.D.; McCarthy, D.J.; Smyth, G.K. edger: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010, 26, Tarazona, S.; Furio-Tari, P.; Turra, D.; Pietro, A.D.; Nueda, M.J.; Ferrer, A.; Conesa, A. Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package. Nucleic Acids Res. 2015, 43, e Li, Y.; Patra, J.C. Integration of multiple data sources to prioritize candidate genes using discounted rating system. BMC Bioinformatics 2010, 1, doi: / S1-S by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
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