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. 2012 Feb 21;109(8):2724-9.
doi: 10.1073/pnas.1018854108. Epub 2011 Oct 14.

Subtype and pathway specific responses to anticancer compounds in breast cancer

Laura M Heiser  1 , Anguraj Sadanandam, Wen-Lin Kuo, Stephen C Benz, Theodore C Goldstein, Sam Ng, William J Gibb, Nicholas J Wang, Safiyyah Ziyad, Frances Tong, Nora Bayani, Zhi Hu, Jessica I Billig, Andrea Dueregger, Sophia Lewis, Lakshmi Jakkula, James E Korkola, Steffen Durinck, François Pepin, Yinghui Guan, Elizabeth Purdom, Pierre Neuvial, Henrik Bengtsson, Kenneth W Wood, Peter G Smith, Lyubomir T Vassilev, Bryan T Hennessy, Joel Greshock, Kurtis E Bachman, Mary Ann Hardwicke, John W Park, Laurence J Marton, Denise M Wolf, Eric A Collisson, Richard M Neve, Gordon B Mills, Terence P Speed, Heidi S Feiler, Richard F Wooster, David Haussler, Joshua M Stuart, Joe W Gray, Paul T Spellman
Affiliations

Subtype and pathway specific responses to anticancer compounds in breast cancer

Laura M Heiser et al. Proc Natl Acad Sci U S A. .

Abstract

Breast cancers are comprised of molecularly distinct subtypes that may respond differently to pathway-targeted therapies now under development. Collections of breast cancer cell lines mirror many of the molecular subtypes and pathways found in tumors, suggesting that treatment of cell lines with candidate therapeutic compounds can guide identification of associations between molecular subtypes, pathways, and drug response. In a test of 77 therapeutic compounds, nearly all drugs showed differential responses across these cell lines, and approximately one third showed subtype-, pathway-, and/or genomic aberration-specific responses. These observations suggest mechanisms of response and resistance and may inform efforts to develop molecular assays that predict clinical response.

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Conflict of interest statement

Conflict of interest statement: K.W.W. is an employee at Cytokinetics. P.G.S. is an employee at Millenium Pharmaceuticals. L.T.V. is an employee at Hoffman-LaRoche. J.G., K.E.B. M.A.H. and R.F.W., are employees and stockholders of GlaxoSmithKline. L.J.M. is an employee at Progen.

Figures

Fig. 1.
Fig. 1.
Cell lines show a broad range of responses to therapeutic compounds. (A) Luminal and ERBB2AMP cell lines respond preferentially to AKT inhibition. Each bar represents the response of a single breast cancer cell line to the Sigma AKT1-2 inhibitor and is colored according to subtype. Cell lines are ordered by decreasing sensitivity [−log10(GI50)]. (B) Drug-response profiles for compounds with similar mechanisms and targets are highly correlated. Heatmap shows hierarchical clustering of pairwise correlations between responses of breast cancer cell lines treated with one of eight compounds. Red indicates positively correlated sensitivity across the panel of cell lines. Green indicates anticorrelated drug-response profiles. (C) Many compounds are significantly associated with subtype. Each column represents one cell line, and each row represents the median-centered −log10(GI50) for a particular compound. Both rows and columns are clustered hierarchically. Red represents sensitivity, green represents resistance, and gray represents missing values. Colored boxes below the dendogram identify sample subtype. Overall, cell lines of similar subtype tend to cluster together, as do compounds with similar targets or mechanisms. (D) CNAs are associated with compound response. Boxplots show distribution of response sensitivity for cell lines with amplified (A) and normal (N) copy number at the noted genomic locus. (a) 20q13 (STK15/AURKA) amplification is associated with GSK1070916 response (A = 7; N = 26 samples). (b) Amplification at 11q13 (CCND1) is associated with response to carboplatin (A = 9; N = 28 samples). (c) 17q12 (ERBB2) amplification is associated with sensitivity to BIBW2992 (A = 6; N = 19 samples), 17-AAG (A = 7; N = 27 samples), and gefitinib (A = 7; N = 18 samples), as well as resistance to NU6102 (A = 6; N = 21 samples).
Fig. 2.
Fig. 2.
Cell-line subtypes have unique SuperPathway network features. In all panels, each node represents a pathway “concept” corresponding to a protein (circle), a multimeric complex (hexagon), or an abstract cellular process (square). Node sizes are drawn in proportion to the DA score; larger nodes correspond to concepts more correlated with a particular subtype than with all other subtypes. Color indicates whether the concept is correlated positively (red) or negatively (blue) with the subtype of interest. Lines represent interactions, including protein–protein interactions (dashed lines) and transcriptional interactions (solid lines). Interactions are included if they connect concepts whose absolute level of DA is higher than the mean absolute level. Labels on some nodes are omitted for clarity. (A) An ERK1/2 subnet preferentially activated in basal breast cancer cell lines. (B) A MYC/MAX network activated in claudin-low cell lines. (C) A FOXA1/FOXA2 network up-regulated in the luminal subtype. (D) A CTNNB1 subnet down-regulated in the ERBB2AMP subtype. APOB, apolipoprotein B [including Ag(x) antigen]; BCAT1, branched chain amino-acid transaminase 1, cytosolic; BDH1, 3-hydroxybutyrate dehydrogenase, type 1; BIRC5, baculoviral IAP repeat containing 5; CAMK4, calcium/calmodulin-dependent protein kinase IV; CAPN2, calpain 2, (m/II) large subunit; CCND2, cyclin D2; CDCA7, cell division cycle associated 7; DDX18, DEAD (Asp-Glu-Ala-Asp) box polypeptide 18; DLK1, delta-like 1 homolog (Drosophila); DUSP1, dual specificity phosphatase 1; DUSP6, dual specificity phosphatase 6; E2F3, E2F transcription factor 3; EIF4A1, eukaryotic translation initiation factor 4A1; ERK1-2, mitogen-activated protein kinase 3-1; GCG, glucagon; JUN, jun proto-oncogene; KLK3, kallikrein-related peptidase 3; LEF1, lymphoid enhancer-binding factor 1; MAP2K1, mitogen-activated protein kinase kinase 1; MAPK9, mitogen-activated protein kinase 9; MSH2, mutS homolog 2, colon cancer, nonpolyposis type 1 (E. coli); MSK1-2, ribosomal protein S6 kinase, 90kDa, polypeptide 5; MTA1, metastasis associated 1; PISD, phosphatidylserine decarboxylase; PITX2, paired-like homeodomain 2; POLR3D, polymerase (RNA) III (DNA directed) polypeptide D, 44kDa; PTGS2, prostaglandin-endoperoxide synthase 2 (prostaglandin G/H synthase and cyclooxygenase); RANBP3, RAN binding protein 3; RUNX2, runt-related transcription factor 2; SCGB1A1, secretoglobin, family 1A, member 1 (uteroglobin); SKP2, S-phase kinase-associated protein 2 (p45); SMAD3, SMAD family member 3; SOD1, superoxide dismutase 1, soluble; SSH, slingshot homolog; TCF1E, HNF homeobox A; TCF4E, transcription factor 4; TFF1, trefoil factor 1; TP53, tumor protein p53; VCAN, versican; VTN, vitronectin; XBP1, X-box binding protein 1; ZFP36, zinc finger protein 36, C3H type, homolog (mouse).
Fig. 3.
Fig. 3.
Pathway diagrams can be used to predict response to therapies. (A) (Left) Basal breast cancer cell lines respond preferentially to the DNA-damaging agent cisplatin. Each boxplot represents the distribution of drug response data for basal (right) and non-basal (left) cell lines. (Right) Basal cell lines show enhanced pathway levels in a subnetwork associated with the DNA-damage response, providing a possible mechanism by which cisplatin acts in these cell lines. (B) (Left) ERBB2AMP cell lines are sensitive to the HSP90 inhibitor geldanamycin. (Right) The ERBB2–HSP90 network is up-regulated in ERBBP2AMP cell lines. Conventions are as in Fig. 2. BCL6, B-cell CLL/lymphoma 6; CASP1, caspase 1, apoptosis-related cysteine peptidase (interleukin 1, beta, convertase); CASP6, caspase 6, apoptosis-related cysteine peptidase; CHEK2, CHK2 checkpoint homolog (S. pombe); DOCK7, dedicator of cytokinesis 7; ERBB3, v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 (avian); ERBB4, v-erb-a erythroblastic leukemia viral oncogene homolog 4 (avian); EREG, epiregulin; FAS, Fas (TNF receptor superfamily, member 6); GADD45A, growth arrest and DNA-damage-inducible, alpha; NRG1B, neuregulin 1; NRG2, neuregulin 2; PLK3, polo-like kinase 3; TP53, tumor protein p53; TP63, tumor protein p63.
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