1

1

2

Title: Two members of the TRiC Chaperonin complex, CCT2 and TCP1 are essential for 3

survival of breast cancer cells and are linked to driving oncogenes 4

5

Stephen T. Guest1*, Zachary R. Kratche1, Aliccia Bollig-Fischer2,3, Ramsi Haddad2, and Stephen 6

P. Ethier1 7

8

1Department of Pathology and Laboratory Medicine, Hollings Cancer Center, Medical University 9

of South Carolina, Charleston, SC 29425 USA 10

2 Barbara Ann Karmanos Cancer Institute and 3Department of Oncology, Wayne State 11

University, Detroit, MI 48201 USA 12

*Corresponding author: guests@musc.edu 13

14

The authors have no conflicts of interest to declare. 15

16

17

18

2

Abstract 19

Gene amplification is a common mechanism of oncogene activation in cancer. Several 20

large-scale efforts aimed at identifying the comprehensive set of genomic regions that 21

are recurrently amplified in cancer have been completed. In breast cancer, these 22

studies have identified recurrently amplified regions containing known drivers such as 23

HER2 and CCND1 as well as regions where the driver oncogene is unknown. In this 24

study, we integrated RNAi-based functional genetic data with copy number and 25

expression data to identify genes that are recurrently amplified, overexpressed and also 26

necessary for the growth/survival of breast cancer cells. Further analysis using clinical 27

data from The Cancer Genome Atlas specifically identified candidate genes that play a 28

role in determining patient outcomes. Using this approach, we identified two genes, 29

TCP1 and CCT2, as being recurrently altered in breast cancer, necessary for 30

growth/survival of breast cancer cells in vitro, and determinants of overall survival in 31

breast cancer patients. We also show that expression of TCP1 is regulated by driver 32

oncogene activation of PI3K signaling in breast cancer. Interestingly, the TCP1 and 33

CCT2 genes both encode for components of a multi-protein chaperone complex in the 34

cell known as the TCP1 Containing Ring Complex (TRiC). Our results demonstrate a 35

role for the TRiC subunits TCP1 and CCT2, and potentially the entire TRiC complex, in 36

breast cancer and provide rationale for TRiC as a novel therapeutic target in breast 37

cancer. 38

39

3

Introduction 40

Genetic alterations that lead to the activation of driver oncogenes play a critical role in 41

tumorigenesis and maintaining the transformed phenotype. Alterations that can lead to driver 42

oncogene activation in cancer include point mutation, gene rearrangement, and gene 43

amplification. In the case of gene amplification, increased gene copy number in the cell results 44

in over expression of the gene product and disrupts normal regulation of gene activity. 45

Importantly, targeted inhibition of oncogenes that are activated by amplification has resulted in 46

dramatic improvements in cancer patient outcomes (1,2). One example of this is the well-47

characterized driver oncogene HER2, which is commonly activated by gene amplification in 48

breast cancer (3,4). Treatment of breast cancers that harbor HER2 gene amplification with 49

HER2-specific inhibitors has reduced the recurrence rate and improved overall survival in these 50

patients (1,5,6). More recently, results of a Phase 2 clinical trial in breast cancer patients 51

targeting the amplified driver oncogene Cyclin D1 (CCND1) showed an increase in progression 52

free survival from 7.5 months to 26.1 months (2). These results demonstrate the effectiveness 53

of targeting amplified driver oncogenes and provide a rationale for efforts to identify novel driver 54

oncogenes that are activated by gene amplification. 55

The important role that genetic alterations play in the activation of driver oncogenes, and the 56

success of therapies targeting these oncogenes, has led to large-scale efforts aimed at 57

comprehensively identifying genetic alterations in cancer. These studies have revealed a 58

strikingly complex genetic landscape in which individual tumors commonly contain large 59

numbers of genes that have been altered via point mutation, gene rearrangement and gene 60

amplification/deletion (7). One study that examined copy number alterations in 3,131 cancer 61

samples found that individual tumors contained an average of 24 focal amplification events with 62

each amplified region containing a median of 6.5 genes (8). Analysis across all samples 63

identified a total of 75,700 unique amplification events with 76 of these determined to be 64

4

recurrent at a significant rate. Analysis of the 76 recurrent regions, found that only 25 contained 65

a functionally validated driver oncogene known to be activated by amplification, e.g. MYC, 66

HER2, CCND1, EGFR, etc. For the remaining recurrently amplified regions, the driver or 67

drivers are unknown. 68

Driver oncogenes commonly function to mediate tumor cell proliferation and/or tumor cell 69

survival. Therefore, identifying genes located in recurrently amplified regions that are also 70

responsible for mediating growth/survival of cancer cells is a potential way to identify which 71

gene or genes in an amplicon function as a driver oncogene. RNAi-based screening 72

technologies offer a method for comprehensively identifying the set of genes that are necessary 73

for growth and survival of individual tumor cell lines in vitro (9,10). In this study, we integrated 74

RNAi-based screening data with copy number and expression data to identify genes that are 75

both recurrently altered in breast cancer and also necessary for the growth/survival of breast 76

cancer cells. Further analysis using clinical data specifically identified candidate genes that play 77

a role in determining patient outcomes in breast cancer. 78

Our analysis identified two genes, TCP1 and CCT2, as being recurrently altered in breast 79

cancer, necessary for growth/survival of breast cancer cells in vitro, and determinants of overall 80

survival outcomes in breast cancer patients. Interestingly, both of these genes encode 81

components of the TCP1 Containing Ring Complex (TRiC) (11,12). TRiC is a multi-protein 82

chaperone complex that functions to assist polypeptides in achieving a functional three-83

dimensional configuration. TRiC was originally identified and characterized based on its 84

essential role in folding the highly abundant cytoskeletal proteins actin and tubulin (12-14). 85

Since then, additional TRiC client proteins have been characterized including Cdc20 (15), Cdh1 86

(15), Polo-like kinase 1 (16) cyclin E (17), the von Hippel Lindau tumor suppressor (18) and WD 87

repeat containing family members (19). Our results demonstrate a role for the TRiC subunits 88

5

TCP1 and CCT2, and potentially the entire TRiC complex, in breast cancer and provide 89

rationale for TRiC as a novel therapeutic target in breast cancer. 90

Materials and Methods 91

Reagents 92

The FGFR inhibitor PD173074, PI3K inhibitor BKM-120, mTOR inhibitor Rapamycin, Akt 93

inhibitor MK-2206 and HER2 inhibitor CP724714 were all purchased from Selleckchem. The 94

selective agent puromycin was purchased from InvivoGen (ant-pr-1). Antibodies against CCT2 95

(#3561), total HER2 (D8F12)(#4290) and phospho-HER2 (Tyr1248)(#2247) were purchased 96

from Cell Signaling. The PathScan® antibody cocktail containing antibodies against phospho-97

Akt, phospho-S6 ribosomal protein, phospho-p44/p42 (Erk1/2) and Rab11 was also purchased 98

from Cell Signaling (#5301). Antibodies against TCP1 (ab92587) and histone H3 (ab1791) were 99

purchased from Abcam. Antibody against β-actin (A5441) was purchased from Sigma-Aldrich. 100

SUM breast cancer cell lines were maintained in Hams F-12 cell culture medium as described 101

previously (20-22). All other chemicals were purchased from Sigma-Aldrich. MCF10A cells 102

were a gift from Dr. Herb Soule at the Michigan Cancer Foundation and have been previously 103

published (23). 104

105

Large-scale RNAi-based growth and viability screen 106

The large-scale, RNAi-based growth and viability screen was performed using the Decode 107

annotated genes RNAi viral library screening kit from Thermo Scientific (RHS5339). The 108

Decode library was provided as 3 pools of high titer, ready-to-use viral particles. Each pool 109

contained viral particles generated from 10,000 shRNA expression constructs. Each virus pool 110

was used to transduce 1.6x10^6 SUM-52 cells at a multiplicity of infection of ~.3 in the presence 111

of growth media supplemented with 5µg/ml polybrene. Following transduction, cells were 112

cultured for 3 days to allow expression of the resistance marker. Non-transduced cells were 113

6

eliminated from the culture by the addition of 6 µg/ml puromycin to the growth media. Three 114

days after the addition of puromycin, cells were trypsinized and one-half of the total population 115

was harvested for genomic DNA preparation; this DNA served as the reference time point 116

sample. The remaining cells were plated and cultured, and one-half of the cell population was 117

then harvested on day 19. The remaining cells were plated and cultured until day 29 at which 118

time all cells were harvested. Genomic DNA was isolated from cells harvested at each time 119

point using the Qiagen DNeasy Blood and Tissue kit (#69506). Barcode sequences were PCR 120

amplified from genomic DNA using the GIPZ forward and reverse primers from Thermo 121

Scientific (PRM5340 and PRM5341) in combination with GoTaq Green PCR master mix 122

(Promega, M712). 800µl of PCR reaction for each pool was run on a 3.5% agarose gel and 123

barcode DNA was purified using the E.Z.N.A Gel Extraction kit (Omega Biotek, D2500) 124

according to the manufacturer’s instructions. Barcodes were then further purified using a 125

PureLink Quick PCR Purification Kit (Invitrogen, K310002) according to manufacturer’s 126

instructions. Purified barcodes were labeled with Cy3/Cy5 dyes and hybridized to the custom 127

Agilent Barcode microarrays provided with the Decode screening kit following the instructions 128

provided with the screening kit. Fluorescence intensity for each barcode at the reference time 129

point was compared to the outgrowth samples and used to calculate fold depletion scores for 130

each shRNA. 131

132

RNAi targeting of individual genes 133

Five shRNA expression constructs targeting CCT2 from the MISSION shRNA library were 134

obtained from Sigma (clone IDs TRCN0000029499-TRCN0000029503). In order to prepare 135

lentivirus from shRNA constructs each construct was co-transfected into HEK293 cells with 136

MISSION Lentiviral packaging mix (Sigma, shp-001) and virus was harvested according to the 137

manufacturer’s instructions. For TCP1, five shRNAs from the Thermo Scientific Open 138

Biosystems Expression Arrest GIPZ lentiviral library were obtained (clone IDs V2LHS_192511, 139

7

V2LHS_116665, V2LHS_225554, V3LHS_342912, V3LHS_342911). In order to prepare 140

lentivirus from shRNA constructs each construct was co-transfected with Expression Arrest 141

Translentiviral Packaging Mix (Thermo Scientific, TLP4606) into HEK293 cells and virus was 142

harvested according to manufacturer’s instructions. Target cells were transduced with 143

packaged virus in growth media supplemented with 5µg/ml polybrene. Cells were cultured for 144

three days to allow for expression of the resistance marker. Non-transduced cells were 145

eliminated from the culture by addition of the selection agent puromycin (6µg/ml for SUM-52 146

cells, 1.2µg/ml for MCF10A cells). 147

148

Cell Proliferation Assays 149

SUM-52 or MCF10A cells were seeded at 25,000 cells per well in triplicate into 6-well plates. At 150

each time point cell number was determined by harvesting and counting nuclei on a Z1 Coulter 151

Counter (Beckman Coulter, Brea, CA, USA). To prepare nuclei for counting, cells were washed 152

three times with PBS, incubated on a rocker table with 0.5 ml per well Hepes/MgCl2 buffer (0.01 153

mM HEPES and 0.015 mM MgCl2) for 5 minutes and lysed for 10 minutes with ethyl 154

hexadecyldimethylammonium solution. 155

156

Colony Forming Assays 157

SUM-52 or MCF10A cells were seeded at clonal density in triplicate in 6-well plates. Cells were 158

cultured until colonies in control wells reached a size of ~50-100 cells per colony. For staining, 159

colonies were fixed with 1 mL/well 3.7% paraformaldehyde for 20 min at RT. Colonies were 160

stained with 1 mL/well 0.2% crystal violet for 15 minutes at RT and de-stained with dH2O. 161

Colony counts were generated using a GelCount™ colony counter (Oxford Optronix, 162

Oxfordshire, United Kingdom). 163

Western Blotting 164

8

Cells were lysed in RIPA buffer (Sigma Aldrich, R0278) containing 1mM Na3VO4, and 1x 165

Protease Inhibitor cocktail (Calbiochem, 539131), and protein concentrations were measured by 166

Bradford assay (Bio-Rad). Equal amounts of protein were combined with Laemmli sample 167

buffer (BioRad, 161-0747), boiled for 5 minutes and separated on SDS polyacrylamide gels 168

(BioRad). Proteins were transferred to polyvinylidene difluoride (PVDF) membranes using the 169

Trans-Blot Turbo System (Bio-Rad) and membranes were probed overnight at 4°C with the 170

indicated antibodies: CCT2 (1:2,000), TCP1 (1:5,000), PathScan® (1:500), total HER2 171

(1:1,000), phospho-HER2(1:1,000), β-actin (1:10,000), and histone H3 (1:10,000). 172

173

Results 174

Integrating Functional Genetic, RNAi-Based Growth and Viability Screen Data with 175

Genome-Wide aCGH and Expression Data to Identify Novel Driver Oncogenes in Breast 176

Cancer 177

Copy number amplification is a common mechanism by which driver oncogenes become 178

activated in cancer. In the SUM-52 breast cancer cell line, we have previously identified a high 179

level copy number amplification of the Fibroblast Growth Factor Receptor 2 (FGFR2) gene 180

(20,24), a well characterized driver oncogene in several different cancer types (25,26). Our 181

subsequent characterization of this cell line identified a complete set of genes that, in addition to 182

FGFR2, are both copy number amplified and overexpressed in this cell line. It is likely that this 183

set of genes contains additional driver oncogenes that function to mediate the transformed 184

phenotype of SUM-52 cells. 185

One critical role for driver oncogenes in cancer is to promote the growth and survival of the 186

cancer cell. In order to determine which amplified and overexpressed genes play a role in 187

mediating the growth and survival of SUM-52 breast cancer cells, we performed a large-scale 188

9

RNAi-based growth and viability screen. SUM-52 cells were transduced with the Decode 189

shRNA Lentivirus annotated genes library (Thermo Scientific Open Biosystems). This library 190

contains 3 pools of shRNA expressing lentivirus constructs with each pool being derived from 191

10,000 unique shRNA expression constructs. Following transduction, cells were harvested at 192

day 0, 19 and 29. Genomic DNA was prepared from harvested cells and shRNA associated 193

barcode sequences were PCR amplified from the genomic DNA, purified, fluorescently labeled 194

and used in competitive hybridization assays on a custom Agilent microarray. Fluorescence 195

intensity values from the microarrays were used to calculate a fold-depletion score for each 196

shRNA on days 19 and 29 (Supplementary Table 1). Merging the depletion scores for each 197

shRNA with our genome-wide copy number and expression data identified FGFR2 and 12 198

additional genes that are significantly depleted in the RNAi-based screen (minimum 2 fold 199

depletion at both time points) as well as copy number amplified and overexpressed in this cell 200

line (Table 1). The amplification and overexpression of these genes combined with their role in 201

mediating cell growth/survival indicates that these genes are potentially functioning as driver 202

oncogenes. 203

The CCT2 Gene is Commonly Amplified in Breast Cancer and Located within a Peak 204

Region Predicted to Contain a Novel Driver Oncogene. 205

Copy number analysis of large numbers of clinical cancer specimens has identified regions of 206

the genome that are recurrently altered in cancer. The Tumorscape database contains an 207

analysis of 3,131 cancer samples 243 of which are classified as breast cancer (8). We queried 208

the Tumorscape database with our list of 12 candidate driver oncogenes in order to determine 209

whether any of these candidates are located in recurrently amplified regions in breast cancer. 210

This analysis identified chaperonin containing TCP1 subunit 2 (CCT2) as being located within a 211

region of recurrent amplification in breast cancer (Figure 1A). The CCT2 gene is also located in 212

a sub-region or “peak” of this amplification that contains 13 total genes and is predicted by 213

10

GISTIC analysis to harbor a driver oncogene for breast cancer (Figure 1A). Analysis across all 214

3,131 samples reduced this peak region to two genes, one of which is CCT2. Additionally, data 215

from 1,002 clinical breast cancer specimens in The Caner Genome Atlas (TCGA) database 216

show that CCT2 is amplified and/or overexpressed in ~13% of all breast cancers (Figure 1B) 217

and that expression of CCT2 increases with increased DNA copy number (Figure 1C). Taken 218

together, these data suggest that amplification and overexpression of CCT2 is positively 219

selected for in a subset of breast cancers and that CCT2 is likely a novel driver oncogene in 220

breast cancer. 221

In addition to copy number data, the TCGA database also contains clinical data for all of the 222

breast cancer samples in the database. Integrating this clinical data with the set of 12 candidate 223

genes shown in Table 1 revealed that CCT2 was the only gene whose alteration correlated with 224

decreased overall survival of breast cancer patients. Patients harboring amplification and/or 225

overexpression of CCT2 had significantly worse overall survival compared to patients whose 226

tumors had normal levels of CCT2 expression (p-value .004509; Figure 1D). This data 227

connecting alterations in CCT2 to clinical outcomes supports a role for CCT2 not only as a 228

driver in breast cancer but also as a determinant of the responsiveness of breast tumors to 229

therapy. 230

RNAi-Mediated Knockdown Targeting CCT2 Inhibits Growth and Colony Formation of 231

SUM-52 Breast Cancer Cells 232

Genes identified in large-scale RNAi-based screens have the potential to be false positives (27). 233

In order to confirm the phenotype for CCT2 knockdown observed in the RNAi-based dropout 234

screen, SUM-52 cells were transduced with five unique shRNA vectors targeting CCT2 and 235

plated for proliferation assays. Knocking down CCT2 significantly inhibited SUM-52 cell growth 236

and this effect correlated with the level of CCT2 knockdown (Figure 1E and 1F). These results 237

11

demonstrate that CCT2 is necessary for SUM-52 breast cancer cell growth, confirming the 238

phenotype that was observed in the RNAi-based dropout screen. In order to further 239

characterize the role of CCT2 in SUM-52 cell growth, SUM-52 cells were transduced with CCT2 240

shRNAs and used to seed colony-forming assays. Similar to the effect observed in growth 241

assays, knocking down CCT2 had a significant effect on the ability of SUM-52 cells to form 242

colonies (Figure 1G), providing further evidence that CCT2 plays a role in mediating SUM-52 243

breast cancer cell growth. 244

A role for CCT2 in cell cycle progression has been previously demonstrated in HeLa cells and 245

the colon carcinoma cell line BE (28). In order to determine if CCT2 is generally required for cell 246

cycle progression in all cultured cells, we queried publicly available data from the COLT Cancer 247

initiative which is a large-scale effort that has performed genome-wide RNAi screens in a panel 248

of >70 cancer cell lines (29,30). Data from this project revealed that CCT2 is necessary for 249

growth/survival of 2 additional cell lines, the KPL-1 breast cancer cell line and the SK-OV-3 250

ovarian cancer cell line. This suggests that dependency on CCT2 for cell cycle progression is 251

cell type-specific. 252

The TCP1 Subunit of TRiC is Both Regulated by FGFR2 and Necessary for Cell Growth in 253

SUM-52 Cells 254

As mentioned above, our previous work showed that FGFR2 is significantly amplified in SUM-52 255

cells and functions as a driver oncogene in this cell line (20,24,31). As part of ongoing work 256

aimed at identifying genes that function downstream of FGFR2 to mediate its’ role as a driver 257

oncogene, microarray-based expression analysis has been performed on SUM-52 cells treated 258

with an FGFR inhibitor. To identify which of the FGFR2 regulated genes are also necessary for 259

growth or survival of SUM-52 cells, we merged the expression data obtained following FGFR2 260

inhibition with the hits from the RNAi-based screen. Interestingly, this analysis identified a gene 261

12

known as TCP1, which is a subunit of the same protein complex as CCT2. TCP1 and CCT2 262

both function as part of a multi-protein chaperonin complex known as the TCP1 containing ring 263

complex (TRiC) (32). Figure 2A shows the reduction in mRNA expression levels of TCP1 264

following inhibition of FGFR and Figure 2B shows Western blot analysis demonstrating that this 265

reduction in message leads to significant down-regulation of TCP1 at the protein level. 266

Examining the clinical data available for TCP1 in the TCGA database revealed that, similar to 267

CCT2, amplification and/or overexpression of TCP1 correlates with significantly reduced overall 268

survival of breast cancer patients (p-value <.000611; Figure 2C). 269

In order to confirm the effect of TCP1 knockdown that was observed in the RNAi-based screen, 270

SUM-52 cells were transduced with five unique shRNA vectors and plated for growth assays. 271

Using a GFP reporter system, we observed that all five shRNAs had a significant effect on the 272

growth of SUM-52 cells (Figure 3A). Growth assays using two of these shRNAs showed that 273

shRNAs targeting TCP1 induced efficient knockdown of TCP1 and had a dramatic effect on 274

growth of SUM-52 cells (Figure 3B and 3C). These results demonstrate that TCP1 is necessary 275

for growth of SUM-52 cells and confirm the phenotype observed in the large-scale RNAi-based 276

screen. Taken together, these results suggest that TCP1 functions downstream of FGFR2 to 277

mediate growth/survival of SUM-52 cells and when combined with our findings for CCT2, further 278

highlight a potential role for the TRiC chaperonin in breast cancer. 279

Querying the COLT Cancer database revealed that TCP1 is necessary for growth/survival in 25 280

additional cancer cell lines, 12 of which are breast cancer cell lines. This suggests that 281

maintaining TCP1 expression is a common requirement in a significant portion of breast cancers 282

and that one role of driver oncogene signaling in these cell lines is to mediate TCP1 expression 283

(see below and Figure 6). 284

13

Knocking Down TCP1 Has a Cell Type-Specific Effect on Cell Growth and Colony 285

Forming Capacity in SUM-52 Cells 286

In order to examine the effect of TCP1 knockdown on colony forming ability, SUM-52 cells were 287

transduced with two shRNAs targeting TCP1 and seeded at clonal density. Following colony 288

formation, cells were stained and the number of colonies formed was quantified. Knocking 289

down TCP1 dramatically inhibited colony formation in SUM-52 cells (Figure 4A and 4B). 290

Parallel colony forming assays performed in the non-transformed breast epithelial cell line 291

MCF10A also showed an effect on colony forming ability; however, the effect was markedly less 292

severe in these cells (Figure 4A and 4B). Importantly, the level of TCP1 knockdown in both cell 293

types was consistent throughout the course of the assay (Figure 4C). Analysis of colony size 294

showed that the small percentage of colonies that did form following TCP1 knockdown were the 295

same size as those that formed in control cells (Figure 4D). This is in contrast to what was 296

observed in MCF10A cells, where most of the colonies that formed in TCP1 knockdown cells 297

were significantly smaller than controls. These results indicate that TCP1 knockdown in 298

MCF10A cells causes a growth delay instead of a growth arrest phenotype and suggest that 299

there is a cell type-specific difference in dependency on TCP1 function and that transformed 300

cells may rely more acutely on TCP1 function than non-transformed cells. 301

FGFR2 Signals through PI3K and Akt to Regulate TCP1 Expression 302

Activated FGFR2 has been shown to result in downstream activation of the PI3K signaling 303

pathway (31,33,34). In order to determine if FGFR2 regulates the expression of TCP1 through 304

activation of PI3K signaling, we treated SUM-52 cells with the PI3K inhibitor BKM120. 305

Treatment of cells with BKM120 had a similar effect on TCP1 expression as treatment of cells 306

with the FGFR inhibitor (Figure 5A). Both treatments also resulted in decreased levels of Akt 307

and ribosomal subunit S6 (RPS6) phosphorylation (Figure 5B). Conversely, treatment of the 308

14

cells with an mTOR inhibitor had no effect on TCP1 expression and exhibited only decreased 309

levels of RPS6 phosphorylation (Figure 5A and 5B). These results suggest that FGFR2 signals 310

through PI3K and Akt to regulate TCP1 expression but that this signaling does not require 311

mTOR activity. To confirm the role of AKT in regulating TCP1 downstream of FGFR2, SUM-52 312

cells were treated with an inhibitor of AKT (MK-2206). Western blot analysis showed that Akt 313

inhibition resulted in down-regulation of TCP1 protein as well as reduced levels of Akt and 314

RPS6 phosphorylation (Figure 5C and D). Taken together, these results suggest that FGFR2 315

signals through PI3K and AKT in an mTOR-independent pathway to regulate TCP1 expression 316

in SUM-52 cells. 317

Regulation of TCP1 Expression by Driver Oncogene Signaling in Multiple Breast Cancer 318

Cell Lines 319

To characterize further the connection between driver oncogene signaling and regulation of 320

TCP1 expression, we examined additional breast cancer cell lines that contain a well-321

characterized receptor tyrosine kinase driver oncogene. For these experiments, we used the 322

SUM-185 cell line, which harbors a high level FGFR3 amplification, and the SUM-190 and SUM-323

225 cell lines, which each harbor a HER2 amplification. Each cell line was treated with a small 324

molecule inhibitor of the respective driver oncogene for 72 hours followed by determination of 325

TCP1 levels by Western blot analysis. Driver oncogene inhibition resulted in a significant 326

decrease in TCP1 protein levels in SUM-185 cells, similar to that observed in SUM-52 cells 327

(Figure 6A). SUM-185 cells also showed a decrease in levels of phosphorylated Akt and RPS6 328

protein that was similar to what was observed in SUM-52 cells. In contrast, inhibition of the 329

HER2 driver in SUM-190 cells did not result in significantly decreased levels of TCP1 protein 330

(Figure 6B) even though analysis of HER2 phosphorylation showed that inhibitor treatment was 331

effectively decreasing HER2 activity (Figure 6C). However, we also observed that treating 332

SUM-190 cells with the HER2 inhibitor did not significantly affect the levels of phosphorylated 333

15

Akt and RPS6 (Figure 6B) while it did result in decreased levels of phosphorylated ERK1/2, 334

indicating that HER2 inhibition in SUM-190 cells affects MAPK signaling but not PI3K signaling 335

(Figure 6B). The SUM-190 cell line contains activating mutations in both PIK3CA alleles (35). 336

PIK3CA functions downstream of HER2; therefore, it is not surprising that inhibiting HER2 in this 337

cell line had no effect on the levels of phosphorylated Akt and RPS6. To determine if inhibition 338

of PI3K signaling would affect TCP1 protein expression in this cell line, we treated SUM-190 339

cells with BKM120. This treatment resulted in a significant reduction in TCP1 protein levels as 340

well as in levels of phosphorylated Akt and RPS6 (Figure 6D). These results suggest that PI3K 341

signaling functions independently of HER2 to mediate TCP1 expression in the SUM-190 cell 342

line. In the SUM-225 cell line, treatment with the HER2 inhibitor did not significantly affect TCP1 343

protein expression even though it did reduce levels of Akt and RPS6 protein phosphorylation 344

(Figure 6E). The observed reduction in the levels of Akt and RPS6 phosphorylation in this 345

setting was modest and could explain the lack of an effect on TCP1 protein expression. Taken 346

together, these results suggest that expression of TCP1 is regulated by driver oncogene 347

signaling through the PI3K pathway in multiple breast cancer cell lines and plays an important 348

role in cancer cell survival. 349

Discussion 350

In this study we integrated diverse genomic data sets to identify genes that are commonly 351

altered in breast cancer, important for proliferation/survival of breast cancer cells, and play a 352

role in determining breast cancer patient outcomes. This analysis converged on two genes, 353

TCP1 and CCT2, both of which are members of the protein chaperone complex TRiC. Our 354

results demonstrate a role for TCP1 and CCT2, and potentially the entire TRiC complex, in 355

breast cancer and provide rationale for TRiC as a novel therapeutic target in breast cancer. 356

16

Descriptive genomic data sets such as genome-wide copy number and sequencing data can 357

define gene sets that have been altered in a cancer cell genome. These data sets are useful 358

for identifying putative driver oncogenes in a cancer, but in most cases the number of altered 359

genes is large and contains many more passenger genes than driver genes. Combining these 360

gene lists with functional genetic data generated using large-scale shRNA screening strategies 361

is a powerful approach to highlight genes that are both genomically altered and that play a 362

functional role in the transformed phenotype. This approach has been used effectively to 363

identify several novel driver oncogenes in different cancer subtypes, such as Pax8 in ovarian 364

cancer (29), Cdk12 in Pancreatic Ductal Adenocarcinoma (36) and GNAS in breast cancer (37). 365

One possible limitation to this approach, is that the data are generated in the setting of an in 366

vitro cultured cell line. In this study, we have attempted to overcome this limitation by 367

integrating our RNAi screening data with genomic and clinical data generated from primary 368

breast tumors. This approach expands the relevance of our findings beyond the in vitro model 369

system used in the screen and has allowed us to avoid identifying genes that are artifacts of this 370

system while at the same time focusing on genes that are most clinically relevant. Using this 371

approach we identified CCT2, a gene that has not been previously linked with breast cancer, but 372

which is present in a region that is commonly amplified in breast cancer, but for which the driver 373

oncogene has not been identified (8). 374

An additional advantage of our study was the use of the SUM-52 breast cancer cell line that had 375

been previously extensively characterized in our lab (20,24,38-40). Using this well-376

characterized cell line allowed us to integrate our RNAi-based functional genetic data with 377

additional genome-wide data sets. We showed previously that FGFR2 is a driving oncogene in 378

SUM-52 cells and performed a genome-wide analysis of genes that are regulated in their 379

expression by a small molecule FGFR inhibitor. Combining these data with the RNAi-based 380

functional genetic data allowed us to connect FGFR2 with genes that play a functional role in 381

17

cell proliferation/survival. Again, integrating these results with the TCGA data allowed us to 382

avoid artifacts of the model system and focused our results on genes that are clinically relevant. 383

Interestingly, by using this approach we identified TCP1, which like CCT2, functions as a 384

member of the protein chaperone complex TRiC. The identification of two genes from the same 385

complex as being commonly altered in breast cancer, necessary for breast cancer 386

proliferation/survival, and determinants of overall survival of breast cancer patients suggests an 387

important role for these genes and potentially TRiC in breast cancer. 388

One intriguing finding of this study is the connection between CCT2/TCP1 and poor survival 389

outcomes in breast cancer. Data from the TCGA database, which contains survival outcomes 390

for more than 1000 patients, shows that when either CCT2 or TCP1 is overexpressed, with or 391

without concurrent gene amplification, the survival of this set of breast cancer patients is 392

significantly worse in comparison to patients with wild-type expression. For both genes, the 393

survival curves diverge very early on from patients that have wild-type expression suggesting 394

that patients with CCT2/TCP1 overexpression have more aggressive disease or that these 395

tumors are resistant to current standard therapies. If CCT2/TCP1 play a role in mediating this 396

phenotype, inhibitors targeting these genes or the TRiC complex in general may improve 397

outcomes for the 19% of breast cancer patients that are overexpressing CCT2, TCP1 or both. 398

TRiC is a multi-protein chaperone complex that functions to assist polypeptides in achieving a 399

functional three dimensional configuration. It belongs to a class of chaperones known as the 400

Chaperonins, which form large, barrel-shaped structures in the cell (12). Chaperonins are 401

classified into two groups with Group I being found in bacteria, mitochondria and chloroplasts 402

while Group II is found in the cytosol in eukaryotic cells (41). TRiC is a member of group II and 403

is the most complex of the group II Chaperonins in that it is composed of 8 unique subunits 404

each encoded by an individual gene. TRiC was originally identified and characterized through its 405

essential role in folding the highly abundant cytoskeletal proteins actin and tubulin (12-14). 406

18

Since the discovery of its role in actin and tubulin folding, additional TRiC substrates have been 407

characterized and include cell cycle regulators Cdc20 (15), Cdh1 (15), PLK1 (16) and cyclin E 408

(17), the von Hippel Lindau tumor suppressor (18) and WD repeat containing family members 409

(19). In addition, some studies have estimated that ~5% of all proteins in the eukaryotic cytosol 410

are folding substrates of TRiC (42). 411

Several of the well-characterized TRiC substrates have strong links to breast cancer. Tubulins 412

are the most well-studied TRiC substrate and are the target of taxanes, one of the most 413

commonly prescribed drugs in the treatment of breast cancer. Resistance to taxanes, both 414

inherent and acquired, is a key challenge in the treatment breast cancer. Once patients 415

progress on taxane therapy there are few approved options for the treatment of metastatic 416

disease. The essential role that TRiC plays in tubulin function combined with the poor survival of 417

patients with alterations in the TRiC subunits TCP1 and CCT2 suggests that TRiC may play a 418

critical role in determining resistance to taxanes. Future studies will be aimed at understanding if 419

targeting TRiC can enhance sensitivity of breast cancer cells to taxane therapy and overcome 420

resistance. 421

PLK1, a protein kinase that plays a critical role in mediating several aspects of mitosis, has also 422

been proposed to be a folding substrate of TRiC (16,43). PLK1 has been previously shown to 423

be overexpressed in breast cancer (44) and is a predictor of poor prognosis in breast cancer 424

and other cancer types (45). Interestingly, work in other labs has shown that RNAi targeting 425

PLK1 has a similar, cell-type specific effect in transformed cells versus MCF10A cells as we 426

observed for TCP1 in our study (46). This similarity in phenotypes for TCP1 and PLK1 427

combined with the role of TRiC in folding of PLK1 suggests that knocking down TCP1 may 428

result in reduced PLK1 activity, explaining why targeting either protein results in a similar 429

phenotype. It is possible that inhibiting TRiC activity will provide an additional means for 430

targeting PLK1 in breast cancer. 431

19

The role that TRiC plays in the folding of breast cancer drug targets and the connection 432

between TRiC and poor patient outcomes suggests that TRiC is a strong candidate for the 433

development of anti-breast cancer therapeutics. Inhibitors that target protein chaperones have 434

previously been shown to have anti-cancer activity as single agents and also have been shown 435

to enhance the activity of inhibitors that target their folding substrates (47-50). Inhibitors of the 436

protein chaperone Hsp90 have been shown to enhance the activity of inhibitors that target the 437

Hsp90 substrate HER2 (48). In vitro treatment of cells that are HER-2 positive but have 438

become resistant to anti-HER2 therapies is capable of re-sensitizing these cells to HER2 439

inhibitors (48). In mouse models of HER2 resistance, Hsp90 inhibitors are capable of 440

synergizing with HER2 inhibitors to induce tumor regression (48). Recently, results from a 441

phase II clinical trial testing the combination of an Hsp90 inhibitor with trastuzumab in patients 442

that had progressed on trastuzumab alone demonstrated a 22% response rate and 59% clinical 443

benefit rate, suggesting that Hsp90 inhibitors can re-sensitize resistant tumors in humans (47). 444

These results set a precedent for targeting chaperones that fold driver oncogenes and suggest 445

that inhibitors of TRiC may be active as single agents in breast cancer or in combination with 446

small molecules targeting TRiC folding substrates such as tubulins and PLK1. 447

In summary, we used a combination of correlative and functional genomic data sets to identify 448

genes that are commonly altered in breast cancer and that also play a role in breast cancer cell 449

proliferation/survival. Integrating this analysis with data from the TCGA identified genes that 450

have a significant effect on breast cancer patient outcomes. This analysis identified two genes, 451

TCP1 and CCT2, which are both subunits of the chaperone complex TRiC. Our results suggest 452

that TRiC plays an important role in breast cancer and that it functions in combination with other 453

driver oncogenes to mediate breast cancer cell proliferation and determine breast cancer patient 454

outcomes. 455

456

20

457

Acknowledgements 458

The authors would like to thank the Functional Genomics and Bioinformatics Facility in the 459

Department of Pediatrics, Wayne State University School of Medicine and the Wayne State 460

University School of Medicine C.S. Mott Applied Genomics Technology Center. The authors 461

would like to thank Christiana Kappler for helpful discussions and editing of the manuscript. 462

This work was supported by the American Cancer Society Research Grant #IRG-97-219-14 and 463

by the Herrick Foundation. 464

465

References 466

1. Slamon, D. J., Leyland-Jones, B., Shak, S., Fuchs, H., Paton, V., Bajamonde, A., Fleming, T., 467 Eiermann, W., Wolter, J., Pegram, M., Baselga, J., and Norton, L. (2001) Use of chemotherapy 468 plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. 469 The New England journal of medicine 344, 783-792 470

2. Finn, R. S. (2014) Final results of a randomized phase II study of PD0332991, a cyclin-dependent 471 kinase (CDK)-4/6 inhibitor, in combination with letrozole vs letrozole alone for first-line 472 treatment of ER+/HER2- advanced breast cancer (PALOMA-1; TRIO-18) [abstract]. Proc. Ann. 473 Meeting AACR, CT101 474

3. Slamon, D. J., Godolphin, W., Jones, L. A., Holt, J. A., Wong, S. G., Keith, D. E., Levin, W. J., Stuart, 475 S. G., Udove, J., Ullrich, A., and et al. (1989) Studies of the HER-2/neu proto-oncogene in human 476 breast and ovarian cancer. Science 244, 707-712 477

4. Slamon, D. J., Clark, G. M., Wong, S. G., Levin, W. J., Ullrich, A., and McGuire, W. L. (1987) 478 Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu 479 oncogene. Science 235, 177-182 480

5. Piccart-Gebhart, M. J., Procter, M., Leyland-Jones, B., Goldhirsch, A., Untch, M., Smith, I., Gianni, 481 L., Baselga, J., Bell, R., Jackisch, C., Cameron, D., Dowsett, M., Barrios, C. H., Steger, G., Huang, C. 482 S., Andersson, M., Inbar, M., Lichinitser, M., Lang, I., Nitz, U., Iwata, H., Thomssen, C., Lohrisch, 483 C., Suter, T. M., Ruschoff, J., Suto, T., Greatorex, V., Ward, C., Straehle, C., McFadden, E., Dolci, 484 M. S., Gelber, R. D., and Herceptin Adjuvant Trial Study, T. (2005) Trastuzumab after adjuvant 485 chemotherapy in HER2-positive breast cancer. The New England journal of medicine 353, 1659-486 1672 487

6. Romond, E. H., Perez, E. A., Bryant, J., Suman, V. J., Geyer, C. E., Jr., Davidson, N. E., Tan-Chiu, E., 488 Martino, S., Paik, S., Kaufman, P. A., Swain, S. M., Pisansky, T. M., Fehrenbacher, L., Kutteh, L. A., 489 Vogel, V. G., Visscher, D. W., Yothers, G., Jenkins, R. B., Brown, A. M., Dakhil, S. R., Mamounas, E. 490 P., Lingle, W. L., Klein, P. M., Ingle, J. N., and Wolmark, N. (2005) Trastuzumab plus adjuvant 491 chemotherapy for operable HER2-positive breast cancer. The New England journal of medicine 492 353, 1673-1684 493

7. Cancer Genome Atlas, N. (2012) Comprehensive molecular portraits of human breast tumours. 494 Nature 490, 61-70 495

21

8. Beroukhim, R., Mermel, C. H., Porter, D., Wei, G., Raychaudhuri, S., Donovan, J., Barretina, J., 496 Boehm, J. S., Dobson, J., Urashima, M., Mc Henry, K. T., Pinchback, R. M., Ligon, A. H., Cho, Y. J., 497 Haery, L., Greulich, H., Reich, M., Winckler, W., Lawrence, M. S., Weir, B. A., Tanaka, K. E., 498 Chiang, D. Y., Bass, A. J., Loo, A., Hoffman, C., Prensner, J., Liefeld, T., Gao, Q., Yecies, D., 499 Signoretti, S., Maher, E., Kaye, F. J., Sasaki, H., Tepper, J. E., Fletcher, J. A., Tabernero, J., Baselga, 500 J., Tsao, M. S., Demichelis, F., Rubin, M. A., Janne, P. A., Daly, M. J., Nucera, C., Levine, R. L., 501 Ebert, B. L., Gabriel, S., Rustgi, A. K., Antonescu, C. R., Ladanyi, M., Letai, A., Garraway, L. A., 502 Loda, M., Beer, D. G., True, L. D., Okamoto, A., Pomeroy, S. L., Singer, S., Golub, T. R., Lander, E. 503 S., Getz, G., Sellers, W. R., and Meyerson, M. (2010) The landscape of somatic copy-number 504 alteration across human cancers. Nature 463, 899-905 505

9. Paddison, P. J., Silva, J. M., Conklin, D. S., Schlabach, M., Li, M., Aruleba, S., Balija, V., 506 O'Shaughnessy, A., Gnoj, L., Scobie, K., Chang, K., Westbrook, T., Cleary, M., Sachidanandam, R., 507 McCombie, W. R., Elledge, S. J., and Hannon, G. J. (2004) A resource for large-scale RNA-508 interference-based screens in mammals. Nature 428, 427-431 509

10. Root, D. E., Hacohen, N., Hahn, W. C., Lander, E. S., and Sabatini, D. M. (2006) Genome-scale 510 loss-of-function screening with a lentiviral RNAi library. Nature methods 3, 715-719 511

11. Rommelaere, H., Van Troys, M., Gao, Y., Melki, R., Cowan, N. J., Vandekerckhove, J., and Ampe, 512 C. (1993) Eukaryotic cytosolic chaperonin contains t-complex polypeptide 1 and seven related 513 subunits. Proceedings of the National Academy of Sciences of the United States of America 90, 514 11975-11979 515

12. Frydman, J., Nimmesgern, E., Erdjument-Bromage, H., Wall, J. S., Tempst, P., and Hartl, F. U. 516 (1992) Function in protein folding of TRiC, a cytosolic ring complex containing TCP-1 and 517 structurally related subunits. The EMBO journal 11, 4767-4778 518

13. Ursic, D., and Culbertson, M. R. (1991) The yeast homolog to mouse Tcp-1 affects microtubule-519 mediated processes. Molecular and cellular biology 11, 2629-2640 520

14. Gao, Y., Thomas, J. O., Chow, R. L., Lee, G. H., and Cowan, N. J. (1992) A cytoplasmic chaperonin 521 that catalyzes beta-actin folding. Cell 69, 1043-1050 522

15. Camasses, A., Bogdanova, A., Shevchenko, A., and Zachariae, W. (2003) The CCT chaperonin 523 promotes activation of the anaphase-promoting complex through the generation of functional 524 Cdc20. Molecular cell 12, 87-100 525

16. Liu, X., Lin, C. Y., Lei, M., Yan, S., Zhou, T., and Erikson, R. L. (2005) CCT chaperonin complex is 526 required for the biogenesis of functional Plk1. Molecular and cellular biology 25, 4993-5010 527

17. Won, K. A., Schumacher, R. J., Farr, G. W., Horwich, A. L., and Reed, S. I. (1998) Maturation of 528 human cyclin E requires the function of eukaryotic chaperonin CCT. Molecular and cellular 529 biology 18, 7584-7589 530

18. Feldman, D. E., Thulasiraman, V., Ferreyra, R. G., and Frydman, J. (1999) Formation of the VHL-531 elongin BC tumor suppressor complex is mediated by the chaperonin TRiC. Molecular cell 4, 532 1051-1061 533

19. Craig, E. A. (2003) Eukaryotic chaperonins: lubricating the folding of WD-repeat proteins. Current 534 biology : CB 13, R904-905 535

20. Forozan, F., Veldman, R., Ammerman, C. A., Parsa, N. Z., Kallioniemi, A., Kallioniemi, O. P., and 536 Ethier, S. P. (1999) Molecular cytogenetic analysis of 11 new breast cancer cell lines. British 537 journal of cancer 81, 1328-1334 538

21. Ethier, S. P., Mahacek, M. L., Gullick, W. J., Frank, T. S., and Weber, B. L. (1993) Differential 539 isolation of normal luminal mammary epithelial cells and breast cancer cells from primary and 540 metastatic sites using selective media. Cancer research 53, 627-635 541

22. Ethier, S. P. (1996) Human breast cancer cell lines as models of growth regulation and disease 542 progression. Journal of mammary gland biology and neoplasia 1, 111-121 543

22

23. Tait, L., Soule, H. D., and Russo, J. (1990) Ultrastructural and immunocytochemical 544 characterization of an immortalized human breast epithelial cell line, MCF-10. Cancer research 545 50, 6087-6094 546

24. Tannheimer, S. L., Rehemtulla, A., and Ethier, S. P. (2000) Characterization of fibroblast growth 547 factor receptor 2 overexpression in the human breast cancer cell line SUM-52PE. Breast cancer 548 research : BCR 2, 311-320 549

25. Kelleher, F. C., O'Sullivan, H., Smyth, E., McDermott, R., and Viterbo, A. (2013) Fibroblast growth 550 factor receptors, developmental corruption and malignant disease. Carcinogenesis 34, 2198-551 2205 552

26. Tiong, K. H., Mah, L. Y., and Leong, C. O. (2013) Functional roles of fibroblast growth factor 553 receptors (FGFRs) signaling in human cancers. Apoptosis : an international journal on 554 programmed cell death 18, 1447-1468 555

27. Echeverri, C. J., Beachy, P. A., Baum, B., Boutros, M., Buchholz, F., Chanda, S. K., Downward, J., 556 Ellenberg, J., Fraser, A. G., Hacohen, N., Hahn, W. C., Jackson, A. L., Kiger, A., Linsley, P. S., Lum, 557 L., Ma, Y., Mathey-Prevot, B., Root, D. E., Sabatini, D. M., Taipale, J., Perrimon, N., and Bernards, 558 R. (2006) Minimizing the risk of reporting false positives in large-scale RNAi screens. Nature 559 methods 3, 777-779 560

28. Grantham, J., Brackley, K. I., and Willison, K. R. (2006) Substantial CCT activity is required for cell 561 cycle progression and cytoskeletal organization in mammalian cells. Experimental cell research 562 312, 2309-2324 563

29. Cheung, H. W., Cowley, G. S., Weir, B. A., Boehm, J. S., Rusin, S., Scott, J. A., East, A., Ali, L. D., 564 Lizotte, P. H., Wong, T. C., Jiang, G., Hsiao, J., Mermel, C. H., Getz, G., Barretina, J., Gopal, S., 565 Tamayo, P., Gould, J., Tsherniak, A., Stransky, N., Luo, B., Ren, Y., Drapkin, R., Bhatia, S. N., 566 Mesirov, J. P., Garraway, L. A., Meyerson, M., Lander, E. S., Root, D. E., and Hahn, W. C. (2011) 567 Systematic investigation of genetic vulnerabilities across cancer cell lines reveals lineage-specific 568 dependencies in ovarian cancer. Proceedings of the National Academy of Sciences of the United 569 States of America 108, 12372-12377 570

30. Koh, J. L., Brown, K. R., Sayad, A., Kasimer, D., Ketela, T., and Moffat, J. (2012) COLT-Cancer: 571 functional genetic screening resource for essential genes in human cancer cell lines. Nucleic 572 acids research 40, D957-963 573

31. Moffa, A. B., Tannheimer, S. L., and Ethier, S. P. (2004) Transforming potential of alternatively 574 spliced variants of fibroblast growth factor receptor 2 in human mammary epithelial cells. 575 Molecular cancer research : MCR 2, 643-652 576

32. Spiess, C., Meyer, A. S., Reissmann, S., and Frydman, J. (2004) Mechanism of the eukaryotic 577 chaperonin: protein folding in the chamber of secrets. Trends in cell biology 14, 598-604 578

33. Klint, P., and Claesson-Welsh, L. (1999) Signal transduction by fibroblast growth factor receptors. 579 Frontiers in bioscience : a journal and virtual library 4, D165-177 580

34. Ong, S. H., Hadari, Y. R., Gotoh, N., Guy, G. R., Schlessinger, J., and Lax, I. (2001) Stimulation of 581 phosphatidylinositol 3-kinase by fibroblast growth factor receptors is mediated by coordinated 582 recruitment of multiple docking proteins. Proceedings of the National Academy of Sciences of 583 the United States of America 98, 6074-6079 584

35. Saal, L. H., Holm, K., Maurer, M., Memeo, L., Su, T., Wang, X., Yu, J. S., Malmstrom, P. O., 585 Mansukhani, M., Enoksson, J., Hibshoosh, H., Borg, A., and Parsons, R. (2005) PIK3CA mutations 586 correlate with hormone receptors, node metastasis, and ERBB2, and are mutually exclusive with 587 PTEN loss in human breast carcinoma. Cancer research 65, 2554-2559 588

36. Marcotte, R., Brown, K. R., Suarez, F., Sayad, A., Karamboulas, K., Krzyzanowski, P. M., 589 Sircoulomb, F., Medrano, M., Fedyshyn, Y., Koh, J. L., van Dyk, D., Fedyshyn, B., Luhova, M., 590 Brito, G. C., Vizeacoumar, F. J., Vizeacoumar, F. S., Datti, A., Kasimer, D., Buzina, A., Mero, P., 591

23

Misquitta, C., Normand, J., Haider, M., Ketela, T., Wrana, J. L., Rottapel, R., Neel, B. G., and 592 Moffat, J. (2012) Essential gene profiles in breast, pancreatic, and ovarian cancer cells. Cancer 593 discovery 2, 172-189 594

37. Garcia-Murillas, I., Sharpe, R., Pearson, A., Campbell, J., Natrajan, R., Ashworth, A., and Turner, 595 N. C. (2014) An siRNA screen identifies the GNAS locus as a driver in 20q amplified breast cancer. 596 Oncogene 33, 2478-2486 597

38. Ethier, S. P., Kokeny, K. E., Ridings, J. W., and Dilts, C. A. (1996) erbB family receptor expression 598 and growth regulation in a newly isolated human breast cancer cell line. Cancer research 56, 599 899-907 600

39. Yang, Z. Q., Albertson, D., and Ethier, S. P. (2004) Genomic organization of the 8p11-p12 601 amplicon in three breast cancer cell lines. Cancer genetics and cytogenetics 155, 57-62 602

40. Yang, Z. Q., Moffa, A. B., Haddad, R., Streicher, K. L., and Ethier, S. P. (2007) Transforming 603 properties of TC-1 in human breast cancer: interaction with FGFR2 and beta-catenin signaling 604 pathways. International journal of cancer. Journal international du cancer 121, 1265-1273 605

41. Horwich, A. L., Fenton, W. A., Chapman, E., and Farr, G. W. (2007) Two families of chaperonin: 606 physiology and mechanism. Annual review of cell and developmental biology 23, 115-145 607

42. Spiess, C., Miller, E. J., McClellan, A. J., and Frydman, J. (2006) Identification of the TRiC/CCT 608 substrate binding sites uncovers the function of subunit diversity in eukaryotic chaperonins. 609 Molecular cell 24, 25-37 610

43. Petronczki, M., Lenart, P., and Peters, J. M. (2008) Polo on the Rise-from Mitotic Entry to 611 Cytokinesis with Plk1. Developmental cell 14, 646-659 612

44. Wolf, G., Hildenbrand, R., Schwar, C., Grobholz, R., Kaufmann, M., Stutte, H. J., Strebhardt, K., 613 and Bleyl, U. (2000) Polo-like kinase: a novel marker of proliferation: correlation with estrogen-614 receptor expression in human breast cancer. Pathology, research and practice 196, 753-759 615

45. King, S. I., Purdie, C. A., Bray, S. E., Quinlan, P. R., Jordan, L. B., Thompson, A. M., and Meek, D. 616 W. (2012) Immunohistochemical detection of Polo-like kinase-1 (PLK1) in primary breast cancer 617 is associated with TP53 mutation and poor clinical outcom. Breast cancer research : BCR 14, R40 618

46. Liu, X., Lei, M., and Erikson, R. L. (2006) Normal cells, but not cancer cells, survive severe Plk1 619 depletion. Molecular and cellular biology 26, 2093-2108 620

47. Modi, S., Stopeck, A., Linden, H., Solit, D., Chandarlapaty, S., Rosen, N., D'Andrea, G., Dickler, M., 621 Moynahan, M. E., Sugarman, S., Ma, W., Patil, S., Norton, L., Hannah, A. L., and Hudis, C. (2011) 622 HSP90 inhibition is effective in breast cancer: a phase II trial of tanespimycin (17-AAG) plus 623 trastuzumab in patients with HER2-positive metastatic breast cancer progressing on 624 trastuzumab. Clinical cancer research : an official journal of the American Association for Cancer 625 Research 17, 5132-5139 626

48. Wainberg, Z. A., Anghel, A., Rogers, A. M., Desai, A. J., Kalous, O., Conklin, D., Ayala, R., O'Brien, 627 N. A., Quadt, C., Akimov, M., Slamon, D. J., and Finn, R. S. (2013) Inhibition of HSP90 with 628 AUY922 induces synergy in HER2-amplified trastuzumab-resistant breast and gastric cancer. 629 Molecular cancer therapeutics 12, 509-519 630

49. Fukuyo, Y., Hunt, C. R., and Horikoshi, N. (2010) Geldanamycin and its anti-cancer activities. 631 Cancer letters 290, 24-35 632

50. Soti, C., Nagy, E., Giricz, Z., Vigh, L., Csermely, P., and Ferdinandy, P. (2005) Heat shock proteins 633 as emerging therapeutic targets. British journal of pharmacology 146, 769-780 634

635

636

24

637

Table 1. Genes in the SUM-52 cell line that are gene copy number amplified, over-expressed and ahit in the RNAi-based growth and viability screen

Gene Symbol Amp_Level Location Expression Day 19 fold depletion Day 29 fold depletionSLC12A9 1.361 7q22 1.0330 2.00 2.60

SLC12A9 1.361 7q22 1.0330 2.11 2.20

SMURF1 1.9594 7q22 0.8586 2.50 3.69

SMURF1 1.9594 7q22 0.8586 3.47 4.66

MYST3 0.98 8p11 1.0881 6.00 4.02

CLNS1A 2.1705 11q13 1.7820 6.06 2.09

UVRAG 2.1705 11q13 1.3262 2.51 3.26

CCT2 0.8609 12q15 1.1601 2.64 3.76

CCNB2 1.3395 15q22 0.9262 2.82 2.82

DDX42 1.182 17q23 0.8387 2.30 2.18

PSMC5 1.182 17q23 1.7137 2.04 2.33

EPS8L1 1.4773 19q13 2.7895 5.71 50.00

TFAP2C 0.9925 20q13 3.4313 5.14 4.05

TH1L 0.9925 20q13 1.2969 2.61 3.13

TH1L 0.9925 20q13 1.2969 2.25 2.22

Amp Level = segment mean ratio (log2) compared to cell lines diploid in that regionExpression = fold increase in expression (log2) compared to cell lines diploid in that regionGene Symbols appearing more than once in the table indicate genes with multiple shRNAs that scored as a hit in the screen

25

638

26

639

27

640

28

641

29

642

30

643

31

Titles and legends to figures 644

645

Figure 1. The CCT2 gene is commonly amplified in breast cancer, associated 646

with poor overall survival of breast cancer patients and necessary for breast 647

tumor cell proliferation and colony formation. (A) Summary table from 648

www.tumorscape.com for the CCT2 gene showing that CCT2 is commonly amplified in 649

breast cancer and located within a peak region of amplification. (B) Oncoprint from the 650

TCGA Bioportal showing that CCT2 is amplified and/or over-expressed in ~13% of 651

breast cancers. (C) TCGA Bioportal expression versus copy number analysis showing 652

that CCT2 gene amplification correlates with overexpression of CCT2 message. (D) 653

Overall Survival plots from the TCGA Bioportal for breast cancer patients with (red plot) 654

or without (blue plot) CCT2 altered tumors i.e. CCT2 amplification and/or 655

overexpression. (E) Western blot analysis of CCT2 protein levels in SUM-52 cells 656

transduced with either a non-silencing control shRNA construct or one of five unique 657

shRNA constructs targeting CCT2. (F) Cell proliferation assay of SUM-52 cells 658

transduced with shRNA constructs shown in panel (E). (G) Colony forming assay in 659

SUM-52 cells transduced with a non-silencing control shRNA construct or either of two 660

shRNA constructs targeting CCT2. 661

662

32

Figure 2. The TCP1 subunit of TRiC is regulated by FGFR2, necessary for 663

proliferation of breast cancer cells and associated with poor overall survival of 664

breast cancer patients. (A) Expression level of TCP1 in SUM-52 cells as determined 665

by microarray analysis following treatment of cells with .1uM of the FGFR inhibitor 666

PD173074. (B) Western blot analysis of TCP1 protein levels in SUM-52 cells following 667

treatment with .1uM of the FGFR inhibitor PD173074. (C) Overall Survival plots from 668

the TCGA Bioportal for breast cancer patients with (red plot) or without (blue plot) TCP1 669

altered tumors i.e. TCP1 amplification and/or overexpression. 670

671

33

672

Figure 3. TCP1 is necessary for cell proliferation in the SUM-52 breast cancer cell 673

line. (A) Fluorescent microscopy of SUM-52 cells transduced with a non-silencing 674

control shRNA or one of five shRNA constructs targeting TCP1. GIPZ shRNA vectors 675

co-express a GFP reporter allowing for visualization of transduced cells through 676

visualization of GFP fluorescence. (B) Western blot analysis of TCP1 levels in SUM-52 677

cells transduced with a non-silencing control shRNA construct or one of two shRNA 678

constructs targeting TCP1. (C) Cell proliferation assay of cells transduced with shRNA 679

constructs shown in panel (B). 680

681

682

34

Figure 4. RNAi targeting TCP1 has a cell type-specific effect on cell growth and 683

colony forming ability in SUM-52 cells. (A) Colony forming assay in SUM-52 and 684

MCF10A cells transduced with a non-silencing control shRNA construct or one of two 685

shRNA constructs targeting TCP1. (B) Analysis of the surviving fraction as compared to 686

parental controls for each shRNA targeting TCP1 in SUM-52 and MCF10A cells. (C) 687

Western blot analysis of TCP1 expression in SUM-52 and MCF10A cells that were 688

cultured in parallel with colony forming assay shown in panel (A). Cells were harvested 689

at the time of colony staining. (D) Histogram showing colony size distribution (colony 690

diameter in µm) for SUM-52 and MCF10A cell colony forming assays shown in panel 691

(A). 692

693

694

35

Figure 5. FGFR2 signals through PI3K and Akt to regulate TCP1 expression. (A) 695

Western blot analysis of TCP1 protein levels in SUM-52 cells treated with the indicated 696

inhibitors. Analysis of histone H3 levels was used as a loading control. (B) Western 697

blot analysis of phospho-Akt and phospho-S6 ribosomal protein levels using the 698

PathScan antibody cocktail. An antibody against Rab11 is included in the cocktail and 699

serves as the loading control. (C) and (D) Western blot analysis of TCP1 protein levels 700

and PI3K pathway member protein phosphorylation in SUM-52 cells treated with 3uM of 701

the Akt inhibitor MK-2206. 702

703

704

36

Figure 6. Regulation of TCP1 expression by driver oncogene signaling in multiple 705

breast cancer cell lines. (A) Western blot analysis of TCP1 protein levels in SUM-185 706

cells treated with .1uM of the FGFR inhibitor PD173074. Western blot analysis with an 707

antibody against histone H3 serves as a loading control. Lysates were also analyzed 708

for Akt and ribosomal protein S6 phosphorylation using the PathScan® antibody 709

cocktail. An antibody against Rab11 is included in the cocktail and serves as a loading 710

control. (B) Western blot analysis was performed as in panel (A) for cell lysates 711

prepared from SUM-190 cells treated with 1uM HER2 inhibitor CP724714. (C) Western 712

blot analysis of total HER2 protein and phospho-HER2 (Tyr1248) in SUM-190 cells 713

treated with 1uM HER2 inhibitor CP724714. (D) Western blot analysis of TCP1 protein 714

levels in SUM-190 cells treated with 5uM of the PI3K inhibitor BKM-120. Western blot 715

analysis with an antibody against histone β-actin serves as a loading control. Lysates 716

were also analyzed for Akt and ribosomal protein S6 phosphorylation using the 717

PathScan® antibody cocktail. An antibody against Rab11 is included in the cocktail and 718

serves as a loading control. (E) Western blot analysis performed as in panel (D) on 719

lysates prepared from SUM-225 cells treated with 1uM HER2 inhibitor CP724714. 720