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LETTER TO THE EDITOR

Distinct roles of class I PI3K isoforms in multiple myeloma cell survival and dissemination
Blood Cancer Journal (2014) 4, e204; doi:10.1038/bcj.2014.24; published online 25 April 2014 adhesion properties, with the p110b and p110d knockdown being the most effective (53% reduction and 47% reduction, respectively; Po0.001, Po0.01; Figure 1g). To test the effect of the different p110 isoforms on MM tumor progression in vivo, SCID-Bg mice were injected with MM cells silenced for p110a, b, g and d, and tumor development was monitored by bioluminescence imaging. Scramble-infected cells were used as control. In consistent with in vitro data demonstrating that the most signiﬁcant changes were observed for adhesion of MM cells to BM-MSCs in p110b and p110d knockdown cells, tumor progression was signiﬁcantly lower in p110b- and p110d-knockdown cell-injected mice compared with scramble cell-injected mice (Po0.05); whereas tumor growth observed in p110a- and p110g-knockdown cell-injected mice was similar to control mice (Figures 2a and b). We speculate that this might be due to markedly decreased tumor cell growth triggered by MM cell adhesion to BM-MSCs, as the adhesion of MM cells to BM-MSCs activates many pathways and has a vital role in MM pathogenesis and disease progression.12 We further conﬁrmed that tumor cells showed knockdown for each p110 isoform, as demonstrated ex vivo on tumor cells harvested from each cohort of mice (Figure 2c). Mice were followed until the development of hind limb paralysis or death, and Kaplan–Meier analysis was performed showing prolonged survival in all groups except p110a mice (p110b and p110g, Po0.05; p110d, Po0.001; Figure 2d). Despite similar tumor burden observed between p110g mice and scramble control-injected mice, mice injected with p110g knockdown cells had improved survival compared with control mice. This might be due to the different extent of tumor involvement of various organs13 between the two groups, thus explaining the differences in survival. Interestingly, our data indicate that p110a is not critical for the survival of MM cells in vivo. Unlike most solid tumor malignancies, where PI3KCA (p110a) mutation is the leading cause of activation of this pathway and is the target of many therapeutic agents in development,3 there have been no reports of this speciﬁc mutations in MM.6 Moreover, it was shown that unlike wild-type p110a, overexpression of the wild-type p110b, p110g and p110d is sufﬁcient to induce an oncogenic transformation of ﬁbroblasts in cell culture.14 In this study, p110b was highly expressed in all MM cell lines, whereas only a minor subset expressed p110d at the protein level (Figure 1b), which is consistent with a recent report9 showing expression of p110b in 38 MM cell lines in comparison to the detectable expression of p110d in only 4 cell lines. In addition, another study8 reported similar ﬁndings in cell lines showing lack of p110d expression in most MM cell lines. Of note, we found discrepancies in p110d expression in cell lines between our study and prior published studies but our data was conﬁrmed in the Cancer Cell Line Encyclopedia data at the mRNA level (data not shown).15 Importantly, Ikeda et al.8 evaluated p110d levels in patient samples and detected its expression in all 24 MM patients. This may provide a clinical rationale for targeting p110d despite the lack of expression of p110d in MM cell lines. Overall, our data suggest that, in contrast with solid tumors, MM may be more dependent on PI3K p110b and p110d and less dependent on PI3Ka, and these may be the focus of drug development in this hematological malignancy.

The phosphoinositide 3-kinase (PI3K) pathway has a crucial role in tumor progression and drug resistance, including both conventional chemotherapeutics as well as novel agents.1 Although no mutations have been described in the PI3K/Akt genes in multiple myeloma (MM), it was shown that this pathway is constitutively activated in MM cells and has pleiotropic effects inﬂuencing proliferation, drug resistance, angiogenesis and cell adhesion.2 PI3Ks are divided into three subclasses, and of these, class I PI3Ks—p110a (also known as PIK3CA), p110b (also known as PIK3CB), p110g (also known as PIK3CG) and p110d (also known as PIK3CD)—are well described in terms of their role in cancer development and progression.1,3 PIK3CA is frequently mutated in solid tumors including carcinoma of the prostate, breast colon and endometrium.4,5 However, there have been no reports of cancerspeciﬁc mutations in MM.6 Recently, a number of potential therapeutics targeting speciﬁc PI3K groups or isoforms were developed.3,4 Previous studies have indicated that p110a, p110b and p110d might be potential targets for MM.7–9 Although the basic framework of PI3K signaling has been uncovered, the contribution of the different PI3K isoforms is not well understood.4 In the current study, we investigated the functional role of class I PI3K isoforms in modulating MM cell trafﬁcking in vivo and in vitro. To examine activation of the PI3K/Akt pathway in MM, we ﬁrst performed gene set enrichment analysis10 on the gene-expression data set (Shaughnessy et al. ref. GSE24080) of patients in different International Staging System stages of MM compared with normal donors;11 and found enrichment of genes related to class I PI3Kactivated AKT signaling events. These ﬁndings were observed in stage I, II and III MM patients compared with healthy individuals (Figure 1a). To study the role of each isoform (p110a, b, g, and d) in regulating MM cell survival and trafﬁcking in vivo and in vitro, the expression of PI3K isoforms was examined in a panel of eight MM cell lines showing different levels of expression of PI3K isoforms with only MM.1S expressing all isoforms (Figure 1b). Thus, MM.1SGFP þ /luc þ was infected with lentivirus-mediated small hairpin RNAs targeting the different PI3K isoforms. Stable cell lines were generated, and efﬁciency of knockdown for each isoform was conﬁrmed by reverse transcription quantitative PCR (Figure 1c). Speciﬁcity of knockdown was demonstrated by immunoblotting in cell lines using speciﬁc antibodies against each isoform (Figure 1d). Then, we evaluated the effect of each isoform on PI3K–Akt signaling in MM cells in the context of primary MM bone marrow mesenchymal stromal cells (BM-MSCs) and found inhibition of BM-MSC-dependent induction of phospho(p)-Akt in MM cells with all PI3K isoforms silenced in the tumor clone (Figure 1e). Although p110a, b, and d showed a modest reduction in cell survival in vitro (Figure 1f), cell cycle analysis revealed no signiﬁcant difference on cell cycle distribution patterns (Supplementary Figure 1). We next performed adhesion assay of MM cells to primary MM-derived BM-MSCs; and found that by silencing each of class I PI3K isoforms, MM cells inhibited their

Letter to the Editor

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ISS Stage I vs Healthy Enrichment plot: PID_PI3KCIAKTPATHWAY
0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00 –0.05 –0.10 –0.15 –0.20 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00 –0.05 –0.10 –0.15 Enrichment score (ES) Enrichment score (ES)

ISS Stage II vs Healthy Enrichment plot: PID_PI3KCIAKTPATHWAY
0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00 –0.05 –0.10 –0.15 Enrichment score (ES)

ISS Stage III vs Healthy Enrichment plot: PID_PI3KCIAKTPATHWAY

Ranked list metric (Signal2Noise)

“Stage 1” (positively correlated) 0.5 0.0 –0.5 –1.0 –1.5 0 2,500 5,000 7,500 “Healthy” (negatively correlated) 10,000 12,500 15,000 17,500 20,000 Rank in Ordered Dataset Enrichment profile Hits Ranking metric scores Zero cross at 8697

1.0 “Stage II” (positively correlated) 0.5 0.0 Zero cross at 8868 –0.5 –1.0 –1.5 0 2,500 5,000 7,500 “Healthy” (negatively correlated) 10,000 12,500 15,000 17,500 20,000 Rank in Ordered Dataset Enrichment profile Hits Ranking metric scores

Ranked list metric (Signal2Noise)

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Zero cross at 9894

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p110 1.2 Tubulin p110 Tubulin p110 Tubulin p110 Tubulin Gene Expression (% of Scramble) 1 p110 0.8 p110 0.6 0.4 0.2 Scramble shRNA p110 shRNA 0 1.4 1.2 1 0.8 0.6 0.4 0.2 0 1.4 1.2 1 0.8 0.6 0.4 0.2 0 1.4 1.2 1 0.8 0.6 0.4 0.2 0

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P<0.01 P<0.01 P<0.05 Survival (% of control cells) Adhesion to BMSCs (% of control cells) 120 100 80 60 40 20 0 Scramble p110 p110 p110 p110 P<0.05 120 110 100 90 80 70 60 50 40 30 20 10 0 P<0.01 P<0.05 P<0.001

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Scramble p110 p110 p110 p110
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2.0×108 Tumor Growth (BLI) 1.5×108 1.0×108 5.0×107 9.0×106 6.0×106 6 3.0×10 6.0×105 3.0×105 0 7 21 Days 1.2 1 0.8 p110 p110 0.6 0.4 0.2 Scramble shRNA p110 shRNA Scramble shRNA p110 shRNA 0 1.2 1 0.8 0.6 0.4 0.2 Scramble shRNA p110 shRNA 0 p110 35

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Figure 2.

(Continued)

Figure 1. The role of class I PI3K-mediated Akt signaling in MM. (a) Gene set enrichment analysis software analyzed functionally related genes in class I-mediated Akt activation with statistically signiﬁcant enrichment (false-discovery rate q-values o0.25; o0.25 is considered signiﬁcant), using gene-expression data set (GSE24080). Plots show enrichment results for the gene set (left, stage I MM vs normal subjects; middle, stage II MM vs normal subject; right, stage III MM vs normal subjects). (b) Baseline expression of the different PI3K isoforms (p110a, b, g and d) in MM cell lines was detected by immunoblotting using isoform-speciﬁc antibodies. MM tumor cells (MM.1S-GFP þ /luc þ ) were infected with lentivirus-mediated small hairpin (sh)RNA. Reverse transcription quantitative PCR (c) and immunoblotting (d) were performed to show infection efﬁciency and isoform speciﬁcity, respectively. Scramble and knockdown tumor cells (p110a, b, g and d) were cocultured with BMSCs overnight, and MM cells were then separated from the BMSCs, lysed and whole-cell lysates were subjected to immunoblotting (e) with Akt and P-Akt (Thr308 and Ser473), which shows decreased phosphorylation of Akt in knockdown cells. The effects of inhibition of PI3K isoforms by shRNAs on cell survival were assessed by 3-(4,5-dimethylthiazol-2-yl)2-2-diphenyltetrazolium bromide (MTT) assay (f ). Adhesion assay (g) was performed to show the ability of knockdown cells to adhere to BMSCs after 2 h of incubation.

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Percent survival 100
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Figure 2. Knockdown of PI3K isoforms regulates tumor progression and survival in vivo. MM.1S-GFP þ /luc þ tumor cell lines (Scr, p110a, b, g and d) were injected intravenously into SCID-Bg mice and tumor growth was assessed by in vivo bioluminescence imaging (BLI). (a) Representative BLI of each group in different time points is shown. (b) Quantiﬁcation of BLI signals demonstrated that p110b and d mice showed signiﬁcant reduction in tumor growth (Po0.05) compared with scramble mice. (c) Reverse transcription quantitative PCR was performed on tumor cells that were harvested from hind leg bones of animals by bone marrow ﬂushing. (d) Survival of mice was evaluated until complete hind limb paralysis or death using Kaplan–Meier curves. Compared with scramble mice, all groups except p110a showed prolonged survival (p110b and p110g, Po0.05; p110d, Po0.001).

CONFLICT OF INTEREST
IMG is on the advisory board for Onyx, BMS and Celgene, and receives research lab support from Genzyme and BMS. The remaining authors declare no conﬂict of interest.

REFERENCES
1 Rodon J, Dienstmann R, Serra V, Tabernero J. Development of PI3K inhibitors: lessons learned from early clinical trials. Nat Rev Clin Oncol 2013; 10: 143–153. 2 Harvey RD, Lonial S. PI3 kinase/AKT pathway as a therapeutic target in multiple myeloma. Future Oncol 2007; 3: 639–647. 3 Courtney KD, Corcoran RB, Engelman JA. The PI3K pathway as drug target in human cancer. J Clin Oncol 2010; 28: 1075–1083. 4 Vanhaesebroeck B, Guillermet-Guibert J, Graupera M, Bilanges B. The emerging mechanisms of isoform-speciﬁc PI3K signalling. Nat Rev Mol Cell Biol 2010; 11: 329–341. 5 Zhao L, Vogt PK, Class I. PI3K in oncogenic cellular transformation. Oncogene 2008; 27: 5486–5496. 6 Ismail SI, Mahmoud IS, Msallam MM, Sughayer MA. Hotspot mutations of PIK3CA and AKT1 genes are absent in multiple myeloma. Leuk Res 2010; 34: 824–826. 7 Glauer J, Pletz N, Schon M, Schneider P, Liu N, Ziegelbauer K et al. A novel selective small-molecule PI3K inhibitor is effective against human multiple myeloma in vitro and in vivo. Blood Cancer J 2013; 3: e141. 8 Ikeda H, Hideshima T, Fulciniti M, Perrone G, Miura N, Yasui H et al. PI3K/p110{delta} is a novel therapeutic target in multiple myeloma. Blood 2010; 116: 1460–1468. 9 Munugalavadla V, Mariathasan S, Slaga D, Du C, Berry L, Del Rosario G et al. The PI3K inhibitor GDC-0941 combines with existing clinical regimens for superior activity in multiple myeloma. Oncogene 2013; 33: 316–325.

ACKNOWLEDGEMENTS
This work was supported by R01CA154648.

AUTHOR CONTRIBUTIONS
IS: designed and performed the research, analyzed the data and wrote the manuscript; MM, YM, SVG, BT, FA, YZ, PM, AS, AKA and AMR: performed the research and analyzed the data; IMG: supervised the study and wrote the manuscript.

I Sahin1, M Moschetta1, Y Mishima1, SV Glavey1, B Tsang1, F Azab1,2, S Manier1, Y Zhang1, P Maiso1, A Sacco1, AK Azab1,2, AM Roccaro1 and IM Ghobrial1 1 Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA and 2 Cancer Biology Division, Department of Radiation Oncology, School of Medicine, Washington University in St Louis, St Louis, MO, USA E-mail: irene_ghobrial@dfci.harvard.edu
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10 Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression proﬁles. Proc Natl Acad Sci USA 2005; 102: 15545–15550. 11 Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T et al. PID: the Pathway Interaction Database. Nucleic Acids Res 2009; 37(Database issue): D674–D679. 12 Mahindra A, Hideshima T, Anderson KC. Multiple myeloma: biology of the disease. Blood Rev 2010; 24(Suppl 1): S5–S11. 13 Huang YW, Richardson JA, Tong AW, Zhang BQ, Stone MJ, Vitetta ES. Disseminated growth of a human multiple myeloma cell line in mice with severe combined immunodeﬁciency disease. Cancer Res 1993; 53: 1392–1396. 14 Kang S, Denley A, Vanhaesebroeck B, Vogt PK. Oncogenic transformation induced by the p110beta, -gamma, and -delta isoforms of class I phosphoinositide 3-kinase. Proc Natl Acad Sci USA 2006; 103: 1289–1294. 15 Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 2012; 483: 603–607. This work is licensed under a Creative Commons Attribution 3.0 Unported License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/

Supplementary Information accompanies this paper on Blood Cancer Journal website (http://www.nature.com/bcj)

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