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. 2013 Sep 17;109(6):1599-608.
doi: 10.1038/bjc.2013.452. Epub 2013 Sep 3.

Gene-expression data integration to squamous cell lung cancer subtypes reveals drug sensitivity

D Wu  1 , Y Pang, M D Wilkerson, D Wang, P S Hammerman, J S Liu
Affiliations

Gene-expression data integration to squamous cell lung cancer subtypes reveals drug sensitivity

D Wu et al. Br J Cancer. .

Abstract

Background: Squamous cell lung cancer (SqCC) is the second most common type of lung cancer in the United States. Previous studies have used gene-expression data to classify SqCC samples into four subtypes, including the primitive, classical, secretory and basal subtypes. These subtypes have different survival outcomes, although it is unknown whether these molecular subtypes predict response to therapy.

Methods: Here, we analysed RNAseq data of 178 SqCC tumour samples and characterised the features of the different SqCC subtypes to define signature genes and pathway alterations specific to each subtype. Further, we compared the gene-expression features of each molecular subtype to specific time points in models of airway development. We also classified SqCC-derived cell lines and their reported therapeutic vulnerabilities.

Results: We found that the primitive subtype may come from a later stage of differentiation, whereas the basal subtype may be from an early time. Most SqCC cell lines responded to one of five anticancer drugs (Panobinostat, 17-AAG, Irinotecan, Topotecan and Paclitaxel), whereas the basal-type cell line EBC-1 was sensitive to three other drugs (PF2341066, AZD6244 and PD-0325901).

Conclusion: Compared with the other three subtypes of cell lines, the secretory-type cell lines were significantly less sensitive to the five most effective drugs, possibly because of their low proliferation activity. We provide a bioinformatics framework to explore drug repurposing for cancer subtypes based on the available genomic profiles of tumour samples, normal cell types, cancer cell lines and data of drug sensitivity in cell lines.

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Figures

Figure 1
Figure 1
Differential expression analysis results of the 178 SqCC tumour samples. (A) Multidimensional scaling plot of the normalised data for the 178 SqCC subtype samples. (B) Venn diagram of the DE analysis of the comparisons among six groups. The numbers represent the number of DE genes in each comparison (red for up and blue for down). The total genes in each diagram is the number of DE genes including up and down in any of the three comparisons involving that subtype. Regarding the subtypes, b for basal, c for classical, p for primitive and s for secretory. The overlap of the three comparison results for that subtype defines the signature gene set for each of the four SqCC subtypes. (C) Heatmap to shown the expression pattern of the signature gene sets (including up and down diretions) of the four SqCC subtypes in the 178 tumour samples. No clustering method was used in this heatmap. Rows for genes, columns for samples.
Figure 2
Figure 2
Sample relationship in the normal airway time-course data. (A) Multidimensional scaling plot of the normal airway time-course data. The first dimension represents the HBEC-ALIC time points well. It suggests that the samples can be clustered into three stages of early, middle and late. (B) Heatmap of the bronchial time-course data based on the hierarchical clustering. Five hundred genes with the largest variability across samples were used. Columns are for samples and rows are for genes. The label for x axis is the days in culture. This plot further supports to cluster the 11 tHBEC-ALIC time points into three clusters. Days 0, 1, 2, 4 are the early stage; days 8, 10, 12 are the middle stage and days 14, 17, 21, 28 are the late stage.
Figure 3
Figure 3
Signature scores of the SqCC subtype signature genes in the normal bronchial time-course data. x-axis represents the days. The higher the scores are, the more similar the subtype and the time-course samples become.
Figure 4
Figure 4
Cell-line ranks of signature scores of the SqCC subtype signature genes in the 28 SqCC cell lines. x-axis represents the different SqCC cell lines. y-axisrepresents the ranks of the signature scores in each SqCC subtype.
Figure 5
Figure 5
Drug sensitivity to SqCC subtypes through CCLE. (A) Heatmap of the activity area score for 17 SqCC cell lines (four basal, four classical, three primitive, six secretory and SqCC cell lines based on ClaNC) and the 24 drugs. The white block in the plot is for missing data due to the lack of some drug treatments to the cell lines. The rows are for the SqCC cell lines and the columns are for the 24 drugs. Both dimensions have been clustered by hierarchical clustering. The ClaNC results of SqCC subtype classification were shown for each cell line. (B) Scatter plot of the activity area score for 17 cell lines and 24 drugs. Colours represent the four subtypes. (C) On the left panel, proliferation scores for secretory SqCC samples or other SqCC samples (P-value 7.5e-05). On the right panel, the activity area score of all 24 drugs for secretory cell lines or others (P-value 0.014). Secretory SqCC subtype has lower proliferation scores and lower activity area scores of drug treatment. Two-sided P-value was obtained by Wilcoxon Rank sum test (P-value 7.5e-05 on the left, 0.068 on the right). (D) Focusing on the five drugs (Panobinostat, 17-AAG, Irinotecan, Topotecan and Paclitaxel), this shows the area scores for secretory cell lines are significantly different to the scores in each of the other three SqCC types of cell lines. Two-sided Wilcoxon mean rank test was used (secretory vs basal P-value 0.002, vs classical P-value 0.006 and vs primitive P-value 0.071).
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References

    1. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, Wilson CJ, Lehar J, Kryukov GV, Sonkin D, Reddy A, Liu M, Murray L, Berger MF, Monahan JE, Morais P, Meltzer J, Korejwa A, Jane-Valbuena J, Mapa FA, Thibault J, Bric-Furlong E, Raman P, Shipway A, Engels IH, Cheng J, Yu GK, Yu J, Aspesi P., Jr, de Silva M, Jagtap K, Jones MD, Wang L, Hatton C, Palescandolo E, Gupta S, Mahan S, Sougnez C, Onofrio RC, Liefeld T, MacConaill L, Winckler W, Reich M, Li N, Mesirov JP, Gabriel SB, Getz G, Ardlie K, Chan V, Myer VE, Weber BL, Porter J, Warmuth M, Finan P, Harris JL, Meyerson M, Golub TR, Morrissey MP, Sellers WR, Schlegel R, Garraway LA. The cancer cell line encyclopaedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483:603–607. - PMC - PubMed
    1. Beadsmoore CJ, Screaton NJ. Classification, staging and prognosis of lung cancer. Eur J Radiol. 2003;45:8–17. - PubMed
    1. Chen Z, Cheng K, Walton Z, Wang Y, Ebi H, Shimamura T, Liu Y, Tupper T, Ouyang J, Li J, Gao P, Woo MS, Xu C, Yanagita M, Altabef A, Wang S, Lee C, Nakada Y, Pena CG, Sun Y, Franchetti Y, Yao C, Saur A, Cameron MD, Nishino M, Hayes DN, Wilkerson MD, Roberts PJ, Lee CB, Bardeesy N, Butaney M, Chirieac LR, Costa DB, Jackman D, Sharpless NE, Castrillon DH, Demetri GD, Janne PA, Pandolfi PP, Cantley LC, Kung AL, Engelman JA, Wong KK. A murine lung cancer co-clinical trial identifies genetic modifiers of therapeutic response. Nature. 2012;483:613–617. - PMC - PubMed
    1. Collins FS. Reengineering translational science: the time is right. Sci Transl Med. 2011;3:90cm17. - PMC - PubMed
    1. Dabney AR. Clanc: point-and-click software for classifying microarrays to nearest centroids. Bioinformatics. 2006;22:122–123. - PubMed

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