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. 2020 Jul 24;3(5):e1267. doi: 10.1002/cnr2.1267

Steroid receptor‐associated and regulated protein is a biomarker in predicting the clinical outcome and treatment response in malignancies

Ali Naderi 1,2,✉
PMCID: PMC7941564  PMID: 32706923

Abstract

Background

Steroid receptor‐associated and regulated protein (SRARP) has recently been identified as a novel tumor suppressor in malignancies of multiple tissue origins. SRARP is located on chromosome 1p36.13 and is widely inactivated by deletions and epigenetic silencing in malignancies. Therefore, additional studies are required to explore SRARP as a potential cancer biomarker.

Aim

This study explores the application of SRARP as a novel biomarker in malignancies of multiple tissue origins using the analysis of large genomic datasets.

Methods and results

A comprehensive genomic analysis of large cancer datasets was carried out to examine the association of SRARP expression and copy‐number with molecular and clinical features in malignancies of multiple tissue origins. This study demonstrated that SRARP under‐expression and copy‐number loss are strongly associated with the loss of other tumor suppressors such as TP53 and NF1 mutations and oncogenic gains, including N‐MYC amplification and ERG rearrangement, suggesting that SRARP inactivation is associated with wider genomic instability in malignancies. Importantly, SRARP under‐expression and copy‐number loss are strong predictors of poor clinical and/or pathological features in breast, colorectal, lung, prostate, gastric, endometrial, cervical, brain, ovarian, bladder, thyroid, and hepatocellular cancers as well as neuroblastoma, uveal melanoma, and acute myeloid leukemia with highly significant odds ratios. Finally, higher SRARP expression and copy‐number predict a better response to several cancer drugs.

Conclusion

This study suggests that the SRARP inactivation presents a robust biomarker in predicting molecular and clinicopathological features, and treatment response in malignancies.

Keywords: biomarker, cancer outcome, SRARP, treatment response

1. INTRODUCTION

Steroid receptor‐associated and regulated protein (SRARP) has been recently identified as a novel tumor suppressor and a corepressor of the androgen receptor (AR). 1 , 2 SRARP and its gene pair, HSPB7, are located 5.2 kb apart on chromosome 1p36.13 and are widely inactivated by deletions and epigenetic silencing in malignancies. 1 Tumor suppressor functions of SRARP and HSPB7 are supported by the fact that the overexpression of these genes markedly suppresses colony formation and cell viability in cancer cell lines of different tissue origins. 1 In addition, these effects are associated with the down‐regulation of Akt and extracellular signal‐regulated kinases (ERK) signaling, and SRARP expression inversely correlates with genes that promote cell proliferation and signal transduction, further supporting its functions as a tumor suppressor. 1 It is also notable that 1p36 is frequently deleted in malignancies and 1p36.1 losses occur in 34% of tumors. 3 However, despite extensive studies, there has been limited success for identifying candidate tumor suppressors on chromosome 1p36. 3 , 4 Therefore, further studies are required to elucidate the biological and prognostic implications of genes located on 1p36 in malignancies.

Furthermore, SRARP expression highly correlates with AR in breast cancer and there is a transcriptional interplay between these two genes. 2 In this process, AR exerts dual regulatory effects on SRARP and although an increased AR activity suppresses SRARP transcription, a minimum level of AR activity is required to maintain baseline SRARP expression in AR+ cancer cells. 1 , 2 SRARP, in turn, interacts with AR as a corepressor and negatively regulates the AR‐mediated induction of prolactin‐induced protein and AR reporter activity. 2 SRARP also has a relatively higher expression in breast tumors that are estrogen receptor positive (ER+), lower grade, and lobular histology. 2 , 5 Other studies have suggested that SRARP is involved in the transcriptional activities of ER in ER+ breast cancer cells. 6 Therefore, while SRARP is broadly inactivated in malignancies and function as a tumor suppressor, when expressed in AR+ cells, this gene carries a transcriptional regulatory function as a corepressor of AR. 1 , 2

Importantly, genome and epigenome‐wide associations of SRARP with survival strongly support its function as a tumor suppressor. 1 In this respect, DNA hypermethylation, lower expression, somatic mutations, and lower copy‐number of SRARP are strongly associated with worse cancer outcome. 1 Moreover, DNA hypermethylation and lower expression of SRARP in normal adjacent tissues predict poor survival, suggesting that SRARP inactivation is an early event in carcinogenesis. 1 Therefore, genomic and epigenomic inactivation of SRARP may provide valuable prognostic markers in malignancies and normal adjacent tissues with translational applications, and additional studies are required to explore the potential applications of SRARP as a biomarker.

Of note, HSPB7 also has tumor suppressor function in several cancer cell lines. 1 , 7 Although HSPB7 copy‐number loss predicts poor survival, HSPB7 expression and DNA methylation levels do not have the same prognostic impact as those of SRARP in malignancies and normal adjacent tissues. 1 These findings have led to a focus on the prognostic implications of SRARP in the current study. Here, a comprehensive analysis of large datasets was conducted to explore SRARP as a biomarker in malignancies of multiple tissue origins.

2. MATERIALS AND METHODS

2.1. Differential analysis for SRARP expression and copy‐number

Differential analysis for SRARP expression and copy‐number was carried out using the datasets available in ONCOMINE database, Research Premium Edition (Life Technologies, Grand Island, New York), (www.oncomine.org). 8 First, differential analysis was conducted to examine the association of SRARP expression and copy‐number with response to cancer therapies. To achieve this, a total of 20 treatment response datasets from brain, breast, colorectal, leukemia, lung, lymphoma, melanoma, multicancer, myeloma, and sarcoma malignancies, including both targeted therapy and chemotherapy studies were analyzed. 9 , 10 , 11 , 12 , 13 Fold change of SRARP expression or copy‐number for each treatment sensitive/resistant group and P value for each fold‐change at a significance level of P < .05 were calculated. A Student's t test was performed to calculate the P value, and fold‐change for the magnitude of differential expression between the two groups was calculated and presented in log2 scale. Biostatistics was carried out using the IBM SPSS Statistics 25 (Armonk, New York).

In addition, to examine whether SRARP expression is induced following treatments, differential expression of SRARP between treatment and control groups was analyzed using datasets for hypoxia in human mammary breast epithelial cells, bortezomib treatment in breast cancer cells, chemotherapy with epirubicin plus cyclophosphamide followed by docetaxel (EC‐D) in breast cancer, and ErbB2 inhibition with pertuzumab and trastuzumab in ovarian cancer. 14 , 15 , 16 , 17 Fold change of SRARP expression for each treatment/control group and P value for each fold change were calculated.

Furthermore, the association of SRARP under‐expression and copy‐number loss with tumor suppressor and oncogenic features, molecular and pathological subtypes, and clinical outcome were assessed in malignancies of multiple tissue origins. In this process, using datasets available in ONCOMINE database, a total of 14 tumor suppressor and oncogenic features, 9 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 and 40 molecular, pathological, and clinical features were analyzed. 9 , 20 , 22 , 25 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 A Student's t test was performed in each dataset to calculate the P value for the significance of differential analysis for each gene between the two groups. Next, genes were ranked based on their P values and the percentile of SRARP under‐expression or copy‐number loss, and the P value and odds ratio (OR) of SRARP differential analysis were measured in each dataset. Moreover, the association of each cancer drug, for which SRARP was found to be a predictor of response, was investigated with the SRARP‐associated tumor suppressor genes or oncogenes using the differential analysis. Fold‐change for each treatment sensitive/resistant group was calculated at a significance level of P < .05.

2.2. Cell culture and treatments

MCF‐7 and T‐47D breast cancer cell lines were obtained from the European Collection of Authenticated Cell Cultures (ECACC) through Sigma‐Aldrich (St. Louis, Missouri). Cell lines were authenticated using STR DNA Profiles and were tested free from mycoplasma contamination. Cell lines were cultured in DMEM/F12 medium (Life Technologies, Grand Island, New York) supplemented with 10% fetal bovine serum (FBS), (Fisher Scientific, Waltham, Massachusetts). Cell line treatments with proteasome inhibitor bortezomib (Selleck Chemicals, Houston, Texas) were performed at 0.8 nM concentration for 48 hours. Treatments with epirubicin (Sigma‐Aldrich, St Louis, Missouri) was carried out at 50 nM concentration for 48 hours. Cell lines treated with vehicle only were applied as control groups.

2.3. RNA extraction and quantitative real time‐polymerase chain reaction

RNA extraction was performed using the RNeasy Mini Kit (Qiagen, Valencia, California). SRARP gene expression was measured by quantitative real time‐polymerase chain reaction (qRT‐PCR). Taqman Gene Expression Assay (Life Technologies) for SRARP (assay ID: Hs00698851_m1) was employed for qRT‐PCR as instructed by the manufacturer. Housekeeping gene RPLP0 (Life Technologies) was used as control. Fold‐change in gene expression is gene expression in treated group/average gene expression in the control group. Experiments were carried out in four replicates. Statistical analysis was carried out using the paired sample t test.

3. RESULTS

3.1. SRARP is a biomarker for better response to cancer therapies

SRARP functions as a tumor suppressor and its inactivation predicts poor clinical outcome in malignancies. 1 Therefore, it was hypothesized that SRARP may be a biomarker for response to cancer therapies. To examine this hypothesis, differential analysis was conducted to examine the association of SRARP expression and copy‐number with the response to cancer therapies in a total of 20 treatment response datasets, including both targeted therapy and chemotherapy studies. Fold‐change of SRARP expression or copy‐number for each treatment sensitive/resistant group and P value for each fold‐change were calculated.

Importantly, higher levels of SRARP expression or copy‐number predicted a significantly better response to cancer therapies in all 20 datasets (P < .05, Table 1 and Figures 1A‐D). Of note, SRARP predicted a better response to the cancer therapies against several targets in the ERK signaling pathway across multiple malignancies (Table 1). The most prominent fold ratio was observed with ErbB2 inhibitor lapatinib in breast cancer in which SRARP expression was 4.7‐fold higher in sensitive compared to resistant samples (P < .05, Table 1). In addition, higher SRARP copy‐number predicted a significantly better response to CHIR‐265, an inhibitor of B‐RAF and RAF1, in brain cancer, myeloma, and a multicancer dataset (P < .05, Table 1 and Figure 1C). Furthermore, higher SRARP copy‐number and expression levels predicted a better response to mTOR inhibitors temsirolimus or sirolimus in brain tumors (DNA), lung cancer (DNA), and sarcoma (RNA), (P < .05, Table 1 and Figure 1B). Moreover, SRARP copy‐number predicted an improved response to IGF1R inhibitor GSK1139710 in a multicancer dataset (P < .05, Table 1).

TABLE 1.

Association of steroid receptor‐associated and regulated protein (SRARP) expression and copy‐number with response to cancer therapies. Cancer type, drug, reference number for the dataset, target of therapies, data type (DNA or RNA), fold change of sensitive/resistant, P value for fold change (P < .05), and sample numbers (No.) are shown

Cancer Drug Target Data Fold P value Sample No.
Brain CHIR‐265 9 B‐RAF, RAF1 DNA 1.21 7.37E‐04 18
Brain Temsirolimus 10 mTOR DNA 1.2 2.20E‐02 11
Breast Lapatinib 9 ErbB2 RNA 4.7 0.044 22
Colorectal Topotecan 9 Topoisomerase I DNA 1.11 0.035 12
Leukemia Compound E 11 γ‐secretase‐notch RNA 1.64 0.004 14
Leukemia Compound E 12 γ‐secretase‐notch RNA 1.3 0.029 20
Lung Purvalanol 13 CDK (1,2,4), Src DNA 1.1 0.026 40
Lung Sirolimus 13 mTOR DNA 1.1 0.044 55
Lymphoma Paclitaxel 9 Microtubules RNA 1.17 0.01 22
Melanoma GSK1070916 10 Aurora kinase DNA 1.13 8.00E‐03 11
Melanoma Topotecan 9 Topoisomerase I RNA 1.11 0.028 16
Multicancer PHA‐665752 9 c‐Met DNA 1.1 0.022 460
Multicancer GSK1139710 10 IGF1R DNA 1.15 0.038 138
Multicancer Panobinostat 9 HDAC (pan) DNA 1.03 0.024 253
Multicancer GSK661637 10 Pan‐PLK DNA 1.1 0.017 163
Multicancer Pazopanib 10 VEGFR1‐3 DNA 1.1 0.005 193
Multicancer CHIR‐265 9 B‐RAF, RAF1 DNA 1.05 3.67E‐04 271
Myeloma CHIR‐265 9 B‐RAF, RAF1 DNA 1.13 0.019 11
Sarcoma Panobinostat 9 HDAC (pan) DNA 1.13 0.021 10
Sarcoma Temsirolimus 10 mTOR RNA 1.17 0.032 12
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FIGURE 1.

FIGURE 1

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The association of steroid receptor‐associated and regulated protein (SRARP) expression and copy‐number with response to cancer treatments and induction of SRARP expression following therapies. A, Differential expression of SRARP in leukemia cell lines treated with compound E. Fold‐change and P value for the magnitude of differential expression between the sensitive and resistant groups were calculated and presented in log2 scale using box plots. B, Differential expression of SRARP in sarcoma cell lines treated with temsirolimus. C, Differential copy‐number of SRARP in brain cancer cell lines treated with CHIR‐265. Fold‐change and P value for the magnitude of differential copy‐number between the sensitive and resistant groups were calculated and presented in log2 scale using box plots. D, Differential copy‐number of SRARP in sarcoma cell lines treated with panobinostat. E, Differential expression of SRARP in human mammary epithelial cells following hypoxia. Fold‐change and P value for the magnitude of differential expression between the treatment and control groups were calculated and presented in log2 scale using box plots. F, Differential expression of SRARP in MCF‐7 breast cancer cell line treated with bortezomib. G, Differential expression of SRARP in breast tumor specimens following treatment with EC‐D chemotherapy. H, Differential expression of SRARP in SK‐OV‐3 ovarian cancer xenograft treated with pertuzumab and trastuzumab

It is notable that higher SRARP copy‐number predicted a significantly better response to other targeted therapies, including to a pan‐HDAC inhibitor panobinostat in sarcoma (Figure 1D), and a multicancer dataset; VEGFR1‐3 inhibitor pazopanib in a multicancer dataset; multikinase inhibitor purvalanol in lung cancer; Aurora Kinase inhibitor GSK1070916 in melanoma; c‐Met inhibitor PHA‐665752 in a multicancer dataset; and pan‐PLK inhibitor GSK661637 in a multicancer dataset (P < .05, Table 1). Furthermore, higher SRARP expression levels predicted an improved response to compound E, an inhibitor of γ‐secretase‐Notch, in two leukemia datasets (P < .05, Figure 1A and Table 1). In addition, higher SRARP copy‐number and expression levels predicted a better response to chemotherapy agent topotecan in colorectal cancer and melanoma, respectively (P < .05, Table 1). Finally, higher SRARP expression was a predictor of an improved response to antimicrotubular chemotherapy with paclitaxel in lymphoma (P < .05).

To examine whether SRARP expression is induced following treatments, differential expression of SRARP between treatment and control groups was analyzed using datasets for hypoxia, proteasome inhibitor bortezomib, chemotherapy with EC‐D, and ErbB2 inhibition with pertuzumab and trastuzumab (Figures 1E‐H). Notably, hypoxia‐induced SRARP expression by 2.45‐fold in human mammary epithelial cells (P = .008, Figure 1E), bortezomib increased SRARP expression by 3.94‐fold in MCF‐7 breast cancer cell line (P = .018, Figure 1F), and EC‐D chemotherapy induced SRARP expression by 2.43‐fold in breast tumor specimens (P = .013, Figure 1G). In addition, treatment with pertuzumab and trastuzumab increased SRARP expression by 1.1‐fold in an ovarian cancer xenograft model (P = .038, Figure 1H).

The effects of bortezomib and epirubicin treatments on the expression of SRARP were further investigated using breast cancer cell lines MCF‐7 and T‐47D. SRARP expression was assessed using qRT‐PCR after cell line treatments and fold changes in gene expression were calculated relative to the control group in each cell line. Bortezomib treatment induced SRARP expression by approximately 3‐ to 4‐fold in MCF‐7 and T‐47D cell lines (P < .01, Figure 2A). In addition, epirubicin treatment increased SRARP expression by about 2‐fold in MCF‐7 and T‐47D cells compared to the vehicle control (P < .01, Figure 2B). These in vitro findings are consistent with the data presents in Figure 1 and suggest that SRARP expression is induced by selective cancer therapies.

FIGURE 2.

FIGURE 2

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The effects of bortezomib and epirubicin treatments on steroid receptor‐associated and regulated protein (SRARP) expression using quantitative real time‐polymerase chain reaction (qRT‐PCR). A, The effect of bortezomib (BOR) treatment on the expression of SRARP in breast cancer cell lines MCF‐7 and T‐47D. SRARP expression was assessed using qRT‐PCR and fold changes in gene expression were calculated relative to the control group in each cell line. *P < .01 was calculated using the paired sample t test. B, The effect of epirubicin (EPI) treatment on the expression of SRARP in breast cancer cell lines MCF‐7 and T‐47D. SRARP expression was assessed using qRT‐PCR. *P < .01 was calculated using the paired sample t test

Collectively, these findings suggest that higher SRARP expression and copy‐number are predictors of better response to several targeted therapies and chemotherapies in malignancies of multiple tissue origins and particularly, SRARP is a biomarker of better response to inhibitors of the ERK signaling pathway. In addition, SRARP is induced following hypoxia in normal mammary cells, and following cancer therapies in malignant breast tissues and cell lines, suggesting that it may be involved in the treatment‐mediated effects on cancer cells.

3.2. SRARP inactivation is associated with the loss of tumor suppressors and oncogenic gains

Carcinogenesis involves a process of inactivation of tumor suppressors and gain in oncogenes. It has recently been shown that SRARP is a tumor suppressor commonly inactivated in malignancies by epigenetic silencing, copy‐number loss, and somatic mutations. 1 To further investigate the involvement of SRARP in carcinogenesis, the association of SRARP expression and copy‐number with 14 tumor suppressor and oncogenic features were examined in cancer datasets. The percentile of SRARP under‐expression or copy‐number loss, P value, and OR were calculated using differential analysis.

Notably, SRARP under‐expression and copy‐number loss were significantly associated with mutations and deletions of tumor suppressors in malignancies (Table 2). Strikingly, SRARP was highly associated with NF1 mutation in sarcoma featuring among the top 5% of copy‐number losses with a highly significant OR of 4429.6, P = 5.54E‐249 (Table 2). In breast cancer, SRARP was in the top 1% of under‐expressed genes associated with TP53 mutation (P = 2.72E‐14, OR: 57.8) and the top 5% of under‐expressed genes associated with BRCA1 mutation (P = 6.63E‐12, OR: 14), (Table 2). In prostate cancer, SRARP featured among the top 1% of under‐expressed genes in ETS2 deletion (P = .0002, OR: 5.3) and top 5% of copy‐number losses in ZFHX3 mutation (P = 1.69E‐76, OR: 23.2), (Table 2). In addition, SRARP was in the top 5% of under‐expressed genes associated with TP53 mutation in hepatocellular carcinoma (P = 1.33E‐04, OR: 1.3) and top 10% of under‐expressed genes associated with APC deletion in lung adenocarcinoma (P = 1.69E‐05, OR: 1.4), (Table 2).

TABLE 2.

Association of steroid receptor‐associated and regulated protein (SRARP) expression and copy‐number with tumor suppressor and oncogenic features in malignancies. For each molecular feature, cancer type, change in SRARP expression or copy‐number, P value, odds ratio, and name of dataset are shown

Cancer type Molecular feature Change in SRARP P value Odds ratio Dataset
Breast TP53 mutation Top 1% under‐expressed 2.72E‐14 57.8 Gluck et al 18
Breast BRCA1 mutation Top 5% under‐expressed 6.63E‐12 14 Waddell et al 19
Cecum K‐RAS mutation Top 10% copy loss 2.94E‐05 1.4 TCGA 20
Hepatocellular TP53 mutation Top 5% under‐expressed 1.33E‐04 1.3 Chiang et al 21
Lung K‐RAS mutation Top 1% copy‐number loss 1.97E‐58 49.3 TCGA 22
Lung adeno APC deletion Top 10% under‐expressed 1.69E‐05 1.4 Ding et al 23
Myeloma IGH‐CCD1 fusion Top 10% under‐expressed 8.48E‐28 2.1 Chapman et al 24
Neuroblastoma ALK amp Top 5% copy‐number loss 7.54E‐82 25.3 Chen et al 25
Neuroblastoma N‐MYC amp Top 1% copy‐number loss 3.79E‐254 946.1 Chen et al 25
Prostate ETS2 deletion Top 1% under‐expressed .0002 5.3 Grasso et al 26
Prostate ETS gene Fusion Top 1% under‐expressed 3.08E‐09 9.1 Grasso et al 26
Prostate ERG rearrang Top 5% under‐expressed 1.76E‐197 186.2 Grasso et al 26
Prostate ZFHX3 mutation Top 5% copy‐number loss 1.69E‐76 23.2 Grasso et al 26
Sarcoma NF1 mutation Top 5% copy‐number loss 5.54E‐249 4429.6 Barretina et al 9
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Note: “amp” is amplification, “rearrang” is rearrangement, and “adeno is adenocarcinoma.

Moreover, SRARP under‐expression and copy‐number loss were also associated with oncogenic gains (Table 2). In neuroblastoma, SRARP was among the top 1% of copy‐number losses associated with N‐MYC amplification at a highly significant OR of 946.1 (P = 3.79E‐254) and top 5% of copy‐number losses associated with ALK amplification (OR: 25.3, 7.54E‐82), (Table 2). In addition, K‐RAS mutation was significantly associated with SRARP copy‐number losses in lung and cecum cancers with OR of 49.3 (P = 1.97E‐58) and 1.4 (P = 2.94E‐05), respectively (Table 2). In prostate cancer, SRARP was in the top 1% of under‐expressed genes associated with ETS gene fusion (P = 3.08E‐09, OR: 9.1) and top 5% of under‐expressed genes associated with ERG rearrangement (P = 1.76E‐197, OR: 186.2), (Table 2). Finally, SRARP was among the top 10% of under‐expressed genes associated with IGH‐CCD1 fusion in myeloma (P = 8.48E‐28, OR: 2.1).

The association of each cancer drug, for which SRARP was found to be a predictor of response (Table 1), was investigated with the SRARP‐associated tumor suppressors or oncogenes using the differential analysis. These analyses indicated that the expression or copy‐number variations of several SRARP‐associated tumor suppressors and oncogenes are predictors of response to cancer therapies like those observed with SRARP (P < .05, Table 3). The significant associations were observed across various cancer types for targeted therapies such as PHA‐665752, CHIR‐265, temsirolimus and Panobinostat as well as for chemotherapy drugs topotecan and paclitaxel (Table 3). Of note, higher copy‐number or expression of tumor suppressor genes (TP53, BRCA1, APC, ETS2, ZFHX3, and NF1) and lower copy‐number or expression of oncogenes (K‐RAS, ALK, and N‐MYC) were associated with a better response to cancer drugs (Table 3). Among the SRARP‐associated cancer genes, TP53 and NF1 showed the greatest number of associations with the response to cancer drugs (Table 3).

TABLE 3.

List of cancer drugs for which steroid receptor‐associated and regulated protein (SRARP) is a predictor of response and are also associated with the expression and copy‐number variations of the SRARP‐associated tumor suppressors or oncogenes. Gene names, cancer drugs, data type (DNA or RNA), fold change of sensitive/resistant, cancer type, and name of dataset are shown

Gene Drug Data Fold Cancer Dataset
TP53 Topotecan DNA 1.2 Colorectal Barretina et al 9
PHA‐665752 RNA 1.35 Multicancer Barretina et al 9
Paclitaxel RNA 1.54 Lymphoma Barretina et al 9
CHIR‐265 RNA 1.25 Multicancer Barretina et al 9
Topotecan RNA 2.13 Melanoma Barretina et al 9
GSK1070916 RNA 1.47 Melanoma Wooster et al 10
GSK661637 RNA 1.3 Multicancer Wooster et al 10
Temsirolimus DNA 1.15 Brain Wooster et al 10
BRCA1 Panobinostat RNA 1.3 Multicancer Barretina et al 9
CHIR‐265 RNA 1.4 Myeloma Barretina et al 9
GSK661637 RNA 1.18 Multicancer Wooster et al 10
K‐RAS Topotecan DNA 0.80 Colorectal Barretina et al 9
Panobinostat DNA 0.75 Sarcoma Barretina et al 9
Panobinostat RNA 0.81 Sarcoma Barretina et al 9
APC CHIR‐265 RNA 2.5 Myeloma Barretina et al 9
ALK Paclitaxel DNA 0.88 Lymphoma Barretina et al 9
CHIR‐265 DNA 0.85 Brain Barretina et al 9
Temsirolimus RNA 0.71 Brain Wooster et al 10
N‐MYC PHA‐665752 DNA 0.83 Multicancer Barretina et al 9
CHIR‐265 DNA 0.88 Brain Barretina et al 9
ETS2 Compound E RNA 1.85 Leukemia Palomero et al 11
ZFHX3 Topotecan RNA 1.2 Melanoma Barretina et al 9
NF1 PHA‐665752 RNA 1.6 Multicancer Barretina et al 9
CHIR‐265 RNA 1.36 Myeloma Barretina et al 9
Topotecan RNA 1.37 Colorectal Barretina et al 9
Topotecan RNA 1.32 Melanoma Barretina et al 9
Compound E RNA 1.34 Leukemia Palomero et al 11
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Note: For each fold change, P value is <.05.

Therefore, SRARP under‐expression and copy‐number loss are highly associated with the loss of other tumor suppressor genes and oncogenic gains, suggesting that SRARP inactivation is associated with wider genomic instability in malignancies.

3.3. SRARP inactivation predicts poor pathological and clinical features in malignancies

The value of SRARP under‐expression and copy‐number loss as a biomarker for molecular, pathological, and clinical features was investigated in malignancies of multiple tissue origins using differential analysis. Importantly, SRARP under‐expression and copy‐number loss were strong predictors of poor clinical outcome and advanced disease in multiple malignancies (Table 4). Notably, SRARP was in the top 5% of copy‐number losses associated with dead at 3 years in neuroblastoma (P = 3.00E‐244, OR: 2185), advanced stage in cervical cancer (P = 5.78E‐244, OR: 2176), dead at 5 years in breast cancer (P = 1.51E‐04, OR: 2.6), dead at 5 years and advanced stage in colon cancer (P = 5.42E‐76, OR: 6.6 and P = 4.16E‐121, OR: 46.4), dead at 1 year in endometrial cancer (P = 4.41E‐52, OR: 14.9), advanced stage and high grade in gastric cancer (P = 1.86E‐10, OR: 4.2 and P = 1.29E‐08, OR: 3.7), recurrence at 3 years in hepatocellular carcinoma (P = 1.20E‐207, OR: 244.2), and metastasis in thyroid cancer (P = 1.31E‐34, OR: 10.1), (Table 4). In addition, SRARP was among the top 1% of copy‐number loss, predicting dead at 1 year in oligodenroglioma (P = 9.05E‐73, OR: 65.5) and advanced nodal stage in rectal cancer (P = 2.34E‐127, OR: 50.6), (Table 4).

TABLE 4.

Association of SRARP under‐expression and copy‐number loss with molecular, pathological, and clinical features in malignancies. For each feature, cancer type, change in SRARP expression or copy‐number, P value, odds ratio, and name of dataset are shown

Cancer type Feature Change in SRARP P value Odds ratio Dataset
Adrenocortical Copy‐number loss Top 5% copy‐number loss 8.61E‐202 216.6 Stephan et al 35
AML Dead at 3 y Top 5% under‐expressed .008 2.1 Heuser et al 36
Astrocytoma Recurrence Top 5% under‐expressed 4.56E‐05 1.9 Phillips et al 38
Bladder Dead at 5 y Top 5% under‐expressed .002 2.3 Lee et al 39
Brain and CNS Copy‐number loss Top 5% copy‐number loss 0.00E+00 39 Neale et al 40
Breast High grade Top 1% under‐expressed 9.96E‐10 62.9 Sotiriou et al 41
Breast Copy‐number loss Top 1% copy‐number loss 5.89E‐163 253.4 TCGA 45
Breast Recurrence (1 y) Top 1% under‐expressed 3.55E‐94 90.3 Esserman et al 42
Breast DCIS High grade Top 1% under‐expressed 4.44E‐15 13 Ma et al 43
Breast lobular Recurrence (3 y) Top 1% under‐expressed 5.96E‐14 12.1 Esserman et al 42
Breast Dead at 5 y Top 5% copy‐number loss 1.51E‐04 2.6 Curtis et al 44
Cervical Advanced stage Top 5% copy‐number loss 5.78E‐244 2176 TCGA 45
Colon Dead at 5 y Top 5% copy‐number loss 5.42E‐76 6.6 TCGA 20
Colon Copy‐number loss Top 5% copy‐number loss 1.55E‐114 41.9 TCGA 20
Colon Advanced stage Top 5% copy‐number loss 4.16E‐121 46.4 TCGA 20
Colon Advanced N Top 10% under‐expressed 3.14E‐05 1.3 TCGA 20
Endometrial Dead at 1 y Top 5% copy‐number loss 4.41E‐52 14.9 TCGA 28
Gastric Advanced Stage Top 5% copy‐number loss 1.86E‐10 4.2 TCGA 29
Gastric High grade Top 5% copy‐number loss 1.29E‐08 3.7 Deng et al 30
GIST Sarcoma type Top 1% under‐expressed 5.12E‐07 3.8 Linn et al 31
Glioblastoma vs normal Top 1% under‐expressed 8.85E‐06 1.9 Bredel et al 32
Head‐neck Hypopharyngeal Top 1% under‐expressed .003 3.8 Slebos et al 33
Hepatocellular Copy‐number loss Top 5% copy‐number loss 7.74E‐148 11.8 Guichard et al 34
Hepatocellular Recurrence (3 y) Top 5% copy‐number loss 1.20E‐207 244.2 Guichard et al 34
Liposarcoma Copy‐number loss Top 5% copy‐number loss 6.89E‐07 1.9 Barretina et al 9
Lung a Dead at 1 y Top 1% under‐expressed 6.75E‐04 4.5 Okayama et al 27
Lung b Copy‐number loss Top 1% copy‐number loss 4.05E‐180 321.2 TCGA 22
Melanoma (U) Metastasis Top 5% under‐expressed 2.40E‐04 2.7 Laurent et al 46
Neuroblastoma Dead at 3 y Top 5% copy‐number loss 3.00E‐244 2185 Chen et al 25
Oligo‐DG Copy‐number loss Top 5% copy‐number loss 5.40E‐116 9.4 TCGA 47
Oligo‐DG Dead at 1 y Top 1% copy‐number loss 9.05E‐73 65.5 Kotliarov et al 48
Ovarian Advanced stage Top 1% under‐expressed .009 3.6 Tothill et al 49
Ovarian High grade Top 5% under‐expressed 2.39E‐04 2.5 Tothill et al 49
Prostate Metastasis Top 5% under‐expressed 5.34E‐06 1.8 Vanaja et al 50
Rectal Copy‐number loss Top 1% copy‐number loss 1.52E‐239 756.7 Firestein et al 51
Rectal Advanced N Top 1% copy‐number loss 2.34E‐127 50.6 TCGA 20
Rectal Advanced stage Top 10% under‐expressed .002 1.3 Bittner et al 52
Renal Copy‐number loss Top 1% copy‐number loss 0.00E+00 3357.8 Beroukhim et al 53
Renal Dead at 3 y Top 5% under‐expressed 3.16E‐04 2.5 TCGA 53
Thyroid Metastasis Top 5% copy‐number loss 1.31E‐34 10.1 TCGA 37
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Note: “Melanoma (U)” is a uveal melanoma, “N” is a nodal stage, and “Ovarian” is a ovarian endometrioid adenocarcinoma.

Abbreviations: AML, acute myeloid leukemia; CNS, central nervous system; DCIS, ductal carcinoma in situ; GIST, gastrointestinal stromal tumor; Oligo‐DR, oligodenroglioma; SRARP, steroid receptor‐associated and regulated protein.

a

Lung adenocarcinoma.

b

Lung adenocarcinoma, mixed subtype‐smoker.

Moreover, SRARP was in the top 1% of under‐expressed genes associated with recurrence at 1 year and high grade in breast cancer (P = 3.55E‐94, OR: 90.3 and P = 9.96E‐10, OR: 62.9), recurrence at 3 years in lobular breast carcinoma (P = 5.96E‐14, OR: 12.1), high grade in breast ductal carcinoma in situ (P = 4.44E‐15, OR: 13), dead at 1 year in lung cancer (P = 6.75E‐04, OR: 4.5), and advanced stage in ovarian cancer (P = .009, OR: 3.6), (Table 4). SRARP was among the top 5% of under‐expressed genes, predicting dead at 3 years in acute myeloid leukemia (P = .008, OR: 2.1), recurrence in astrocytoma (P = 4.56E‐05, OR: 1.9), dead at 5 years in bladder cancer (P = .002, OR: 2.3), metastasis in uveal melanoma (P = 2.40E‐04, OR: 2.7), high grade in ovarian cancer (P = 2.39E‐04, OR: 2.5), metastasis in prostate cancer (P = 5.34E‐06, OR: 1.8), and dead at 3 years in renal cancer (P = 3.16E‐04, OR: 2.5), (Table 4). SRARP was also in the top 10% of under‐expressed genes associated with advanced colon and rectal cancers (P = 3.14E‐05, OR: 1.3 and P = .002, OR: 1.3), (Table 4). In addition, differential analysis revealed that SRARP is among the top 1% to 5% of genes with the highest copy‐number loss in adrenocortical, brain, breast, colon, hepatocellular, liposarcoma, lung adenocarcinoma, rectal and renal cancers (Table 4).

Collectively, these findings strongly suggest that SRARP under‐expression and copy‐number loss are robust biomarkers for poor clinical and pathological features in malignancies of multiple tissue origins.

4. DISCUSSION

SRARP has recently been identified as a tumor suppressor and corepressor of AR that is commonly inactivated by epigenetic silencing, copy‐number loss, and somatic mutations in malignancies. 1 , 2 In addition, SRARP inactivation predicts worse clinical outcome in cancer datasets. 1 Using a genomic approach, this study investigated the application of SRARP as a biomarker for therapeutic response and clinical and pathological features in malignancies.

Consistent with its tumor suppressor function, SRARP inactivation is an early event in carcinogenesis that occurs in normal adjacent tissues and predicts worse clinical outcome. 1 This study demonstrated that SRARP under‐expression and copy‐number loss are strongly associated with the loss of other tumor suppressors and pro‐oncogenic gains (Table 2). In this process, SRARP inactivation is associated with the loss of tumor suppressors TP53, BRCA1, APC, ETS2, ZFHX3, and NF1 in several malignancies. In addition, SRARP loss is associated with pro‐oncogenic gains, including K‐RAS mutation, ALK and N‐MYC amplifications, IGH‐CCD1 fusion, ETS gene fusion, and ERG rearrangements. Furthermore, SRARP is among the genes with the highest copy‐number loss or under‐expression in multiple malignancies such as breast, lung, colorectal, renal, brain, hepatocellular, and hypopharyngeal cancers. Therefore, we can conclude that SRARP is a tumor suppressor that is broadly inactivated across cancer types and SRARP inactivation is associated with wider genomic instability in malignancies.

Importantly, this study suggests that SRARP is a robust biomarker in predicting treatment response, pathological, and clinical features in cancer. In this respect, SRARP under‐expression and copy‐number loss are strong predictors of cancer mortality, advanced stage, recurrence, and poor pathological features in malignancies of multiple tissue origins (Table 4). For instance, SRARP copy‐number loss is a strong predictor of dead at 3 years in neuroblastoma (OR: 2185). This provides another robust association between SRARP loss and neuroblastoma in addition to its associations with N‐MYC and ALK amplifications. It is notable that deletions of the distal short arm of chromosome 1 (1p) were first reported in neuroblastomas in 1977 and 1p36 deletions are present in a broad range of human cancers. 3 , 55 However, despite extensive studies, there has been limited success for identifying candidate tumor suppressors on chromosome 1p36. 3 The current study suggests that the SRARP represents one of the 1p36 genes that its copy‐number loss has prognostic implications as a predictor of clinical outcome in neuroblastoma.

Furthermore, SRARP under‐expression and copy‐number loss are associated with several features of poor outcome in breast cancer, including recurrence at 1 and 3 years, dead at 5 years, and high grade (Tables 4). In addition, SRARP is one of the most deleted genes in breast cancer, which is accompanied by TP53 and BRCA1 mutations and is a predictor of response to lapatinib in this disease. Furthermore, author has previously demonstrated that SRARP overexpression in AR−/ER‐ breast cancer cells results in a strong tumor suppressor activity. 1 Therefore, SRARP is a tumor suppressor in a subset of breast cancers and its loss is accompanied by that of other tumor suppressors, predicting poor clinical features in this disease. In hepatocellular carcinoma, SRARP is one of the most deleted genes and its loss is associated with disease recurrence and TP53 mutations. In addition, SRARP is highly deleted in colorectal cancers and its under‐expression or copy‐number loss predicts cancer mortality, advanced stage, and K‐RAS mutation. Notably, SRARP expression also predicts a better response to DNA damaging agent topotecan in colorectal cancer. Collectively, the findings in this study indicate that SRARP under‐expression and copy‐number loss have a consistent pattern in predicting poor clinical‐pathological features accompanied by pro‐oncogenic changes in multiple malignancies.

Other examples of SRARP application as a biomarker in common malignancies include lung and prostate cancers. In this respect, SRARP is one of the most deleted genes in lung adenocarcinoma (OR: 321) that predicts cancer mortality at 1 year and is accompanied by K‐RAS mutation and APC deletion in this disease. Furthermore, SRARP expression predicts an improved response to targeted therapies with mTOR inhibitor sirolimus and multikinase inhibitor purvalanol in lung cancer. In prostate cancer, SRARP under‐expression predicts metastasis and its copy‐number loss is accompanied by losses in tumor suppressors ETS2 and ZFHX3 in addition to pro‐oncogenic gains of ETS gene fusion and ERG rearrangements (Tables 2 and 4). It is notable that ETS2 is a tumor suppressor gene in prostate cancer and its loss along with other genes within the TMPRSS2‐ERG interstitial region contributes to disease progression partly attributed to activation of MAPK signaling. 56 This presents a potential for cooperation between cancer gene changes associated with SRARP inactivation and regulation of MAPK signaling. Importantly, the associations of SRARP loss with poor clinical and molecular features are consistent with the fact that SRARP overexpression has a potent tumor suppressor function in prostate and lung cancer cells, 1 presenting a clinical application for SRARP as a biomarker in these malignancies.

Moreover, SRARP acts a robust biomarker in several less common malignancies such as predicting advanced stage in cervical cancer (OR: 2176); recurrence and mortality in brain tumors; mortality in acute myeloid leukemia, bladder, renal and endometrial cancers; advanced stage and high grade in gastric and ovarian cancers; and metastasis in uveal melanoma and thyroid cancer (Table 4). These findings are significant since these malignancies are understudied, and there is a need for the identification of novel biomarkers in these diseases.

Finally, SRARP expression predicts a better response to the inhibitors of ERK signaling and other cancer therapies in multiple malignancies, presenting an additional valuable application for SRARP as a cancer biomarker (Table 1). It is notable that the expression or copy‐number variations of several SRARP‐associated tumor suppressors and oncogenes are predictors of response to cancer therapies like those observed with SRARP. Therefore, SRARP prediction of treatment response may not be a direct cause and effect relationship. Instead, the association of SRARP inactivation with broader genomic instability and the tumor suppressor function of this gene are the likely underlying reasons for predicting the response to selective cancer therapies. Altogether, SRARP expression and copy‐number are potential predictive biomarkers for patient stratification in malignancies with diverse applications in prognostication and cancer therapeutics. Future prospective clinical trials are required to further assess the applications of these findings in patient care.

5. CONCLUSIONS

This study suggests that SRARP is a robust biomarker in predicting treatment response, pathological and clinical features in malignancies of multiple tissue origins. Therefore, assessment of SRARP expression and copy‐number may have translational applications as a biomarker in cancer prognostication and management.

CONFLICT OF INTEREST

The author has stated explicitly that there are no conflicts of interest in connection with this article.

AUTHOR CONTRIBUTIONS

Ali Naderi conceived the study, carried out the bioinformatics and biostatistics analyses of datasets, performed the experiments, interpreted the analyzed data, and drafted the manuscript.

ETHICS STATEMENT

This article does not contain any studies with human participants or animals performed by the author.

ACKNOWLEDGMENT

This study was supported by a grant from the Hawaii Cancer Consortium.

Naderi A. Steroid receptor‐associated and regulated protein is a biomarker in predicting the clinical outcome and treatment response in malignancies. Cancer Reports. 2020;3:e1267. 10.1002/cnr2.1267

Funding information Hawaii Cancer Consortium

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available in ONCOMINE database, Research Premium Edition (www.oncomine.org).

REFERENCES

  • 1. Naderi A. SRARP and HSPB7 are epigenetically regulated gene pairs that function as tumor suppressors and predict clinical outcome in malignancies. Mol Oncol. 2018;12:724‐755. 10.1002/1878-0261.12195. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. Naderi A. C1orf64 is a novel androgen receptor target gene and coregulator that interacts with 14–3‐3 protein in breast cancer. Oncotarget. 2017;8:907‐933. 10.18632/oncotarget.17826. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3. Henrich KO, Schwab M, Westermann F. 1p36 tumor suppression‐a matter of dosage? Cancer Res. 2012;72:6079‐6088. 10.1158/0008-5472.CAN-12-2230. [DOI] [PubMed] [Google Scholar]
  • 4. Bagchi A, Mills AA. The quest for the 1p36 tumor suppressor. Cancer Res. 2008;68:2551‐2556. 10.1158/0008-5472.CAN-07-2095. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Su D, Fu X, Fan S, et al. Role of ERRF, a novel ER‐related nuclear factor, in the growth control of er‐positive human breast cancer cells. Am J Pathol. 2012;180:1189‐1201. 10.1016/j.ajpath.2011.11.025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Luo A, Zhang X. ERRF is essential for estrogen‐estrogen receptor alpha signaling pathway in ER positive breast cancer cells. Biochem Biophys Res Commun. 2016;474:400‐405. 10.1016/j.bbrc.2016.04.132. [DOI] [PubMed] [Google Scholar]
  • 7. Lin J, Deng Z, Tanikawa C, et al. Downregulation of the tumor suppressor HSPB7, involved in the p53 pathway, in renal cell carcinoma by hypermethylation. Int J Oncol. 2014;44:1490‐1498. 10.3892/ijo.2014.2314. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. Rhodes DR, Yu J, Shanker K, et al. ONCOMINE: a cancer microarray database and integrated data‐mining platform. Neoplasia. 2004;6:1‐6. 10.1016/S1476-5586(04)80047-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Barretina J, Caponigro G, Stransky N, et al. The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483:603‐607. 10.1038/nature11003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Wooster R.. SNP Profiling of Cancer Cell Line Panel, GlaxoSmithKline. (2008). https://array.nci.nih.gov/caarray/project/details.action?project.id=35.
  • 11. Palomero T, Lim WK, Odom DT, et al. NOTCH1 directly regulates c‐MYC and activates a feed‐forward‐loop transcriptional network promoting leukemic cell growth. Proc Natl Acad Sci. 2006;103:18261‐18266. 10.1073/pnas.0606108103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. Palomero T, Sulis ML, Cortina M, et al. Mutational loss of PTEN induces resistance to NOTCH1 inhibition in T‐cell leukemia. Nat Med. 2007;13:1203‐1210. 10.1038/nm1636. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Sos ML, Michel K, Zander T, et al. Predicting drug susceptibility of non‐small cell lung cancers based on genetic lesions. J Clin Invest. 2009;119:1727‐1740. 10.1172/JCI37127DS1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. Chi J‐T, Wang Z, Nuyten DSA, et al. Gene expression programs in response to hypoxia: cell type specificity and prognostic significance in human cancers. PLoS Med. 2006;3:e47. 10.1371/journal.pmed.0030047. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Nickeleit I, Zender S, Sasse F, et al. Argyrin a reveals a critical role for the tumor suppressor protein p27kip1 in mediating antitumor activities in response to proteasome inhibition. Cancer Cell. 2008;14:23‐35. 10.1016/j.ccr.2008.05.016. [DOI] [PubMed] [Google Scholar]
  • 16. Stickeler E, Pils D, Klar M, et al. Basal‐like molecular subtype and HER4 up‐regulation and response to neoadjuvant chemotherapy in breast cancer. Oncol Rep. 2011;26:1037‐1045. 10.3892/or.2011.1392. [DOI] [PubMed] [Google Scholar]
  • 17. Sims AH, Zweemer AJM, Nagumo Y, et al. Defining the molecular response to trastuzumab, pertuzumab and combination therapy in ovarian cancer. Br J Cancer. 2012;106:1779‐1789. 10.1038/bjc.2012.176. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Glück S, Ross JS, Royce M, et al. TP53 genomics predict higher clinical and pathologic tumor response in operable early‐stage breast cancer treated with docetaxel‐capecitabine ± Trastuzumab. Breast Cancer Res Treat. 2012;132:781‐791. 10.1007/s10549-011-1412-7. [DOI] [PubMed] [Google Scholar]
  • 19. Waddell N, Cocciardi S, Johnson J, et al. Gene expression profiling of formalin‐fixed, paraffin‐embedded familial breast tumours using the whole genome‐DASL assay. J Pathol. 2010;221:452‐461. 10.1002/path.2728. [DOI] [PubMed] [Google Scholar]
  • 20. Cancer Genome Atlas Network . Comprehensive molecular characterization of human colon and rectal cancer. Nature. 2012;487:330‐337. 10.1038/nature11252. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. Chiang DY, Villanueva A, Hoshida Y, et al. Focal gains of VEGFA and molecular classification of hepatocellular carcinoma. Cancer Res. 2008;68:6779‐6788. 10.1158/0008-5472.CAN-08-0742. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Cancer Genome Atlas Research Network , Hammerman PS, Lawrence MS, et al. Comprehensive genomic characterization of squamous cell lung cancers. Nature. 2012;489:519‐525. 10.1038/nature11404. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Ding L, Getz G, Wheeler DA, et al. Somatic mutations affect key pathways in lung adenocarcinoma. Nature. 2008;455:1069‐1075. 10.1038/nature07423. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Chapman MA, Lawrence MS, Keats JJ, et al. Initial genome sequencing and analysis of multiple myeloma. Nature. 2011;471:467‐472. 10.1038/nature09837. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25. Chen Y, Takita J, Choi YL, et al. Oncogenic mutations of ALK kinase in neuroblastoma. Nature. 2008;455:971‐974. 10.1038/nature07399. [DOI] [PubMed] [Google Scholar]
  • 26. Grasso CS, Wu Y‐M, Robinson DR, et al. The mutational landscape of lethal castration‐resistant prostate cancer. Nature. 2012;487:239‐243. 10.1038/nature11125. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27. Okayama H, Kohno T, Ishii Y, et al. Identification of genes upregulated in ALK‐positive and EGFR/KRAS/ALK‐negative lung adenocarcinomas. Cancer Res. 2012;72:100‐111. 10.1158/0008-5472.CAN-11-1403. [DOI] [PubMed] [Google Scholar]
  • 28. Getz G, Gabriel SB, Cibulskis K, et al. Integrated genomic characterization of endometrial carcinoma. Nature. 2013;497:67‐73. 10.1038/nature12113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29. TCGA , The Cancer Genome Atlas. Stomach adenocarcinoma DNA copy number data, Cancer Genome Atlas, Off. Cancer Genomics. National Cancer Institute, National Institute of Health; Bethesda, MD (2013). http://gdac.broadinstitute.org/runs/stddata__2013_05_23/data/.
  • 30. Deng N, Goh LK, Wang H, et al. A comprehensive survey of genomic alterations in gastric cancer reveals systematic patterns of molecular exclusivity and co‐occurrence among distinct therapeutic targets. Gut. 2012;61:673‐684. 10.1136/gutjnl-2011-301839. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Linn SC, West RB, Pollack JR, et al. Gene expression patterns and gene copy number changes in dermatofibrosarcoma protuberans. Am J Pathol. 2003;163:2383‐2395. 10.1016/s0002-9440(10)63593-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Bredel M, Bredel C, Juric D, et al. Functional network analysis reveals extended gliomagenesis pathway maps and three novel MYC‐interacting genes in human gliomas. Cancer Res. 2005;65:8579‐8589. 10.1158/0008-5472.CAN-05-1204. [DOI] [PubMed] [Google Scholar]
  • 33. Slebos RJC, Yi Y, Ely K, et al. Gene expression differences associated with human papillomavirus status in head and neck squamous cell carcinoma. Clin Cancer Res. 2006;12:701‐709. 10.1158/1078-0432.CCR-05-2017. [DOI] [PubMed] [Google Scholar]
  • 34. Guichard C, Amaddeo G, Imbeaud S, et al. Integrated analysis of somatic mutations and focal copy‐number changes identifies key genes and pathways in hepatocellular carcinoma. Nat Genet. 2012;44:694‐698. 10.1038/ng.2256. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35. Stephan EA, Chung T‐H, Grant CS, et al. Adrenocortical carcinoma survival rates correlated to genomic copy number variants. Mol Cancer Ther. 2008;7:425‐431. 10.1158/1535-7163.MCT-07-0267. [DOI] [PubMed] [Google Scholar]
  • 36. Heuser M, Wingen LU, Steinemann D, et al. Gene‐expression profiles and their association with drug resistance in adult acute myeloid leukemia. Haematologica. 2005;90:1484‐1492. 10.1016/J.CCR.2005.03.002. [DOI] [PubMed] [Google Scholar]
  • 37. TCGA , The Cancer Genome Atlas. Thyroid carcinoma DNA copy number data, Cancer Genome Atlas, Off. Cancer Genomics, National Cancer Institute, National Institute of Health. Bethesda, MD. (2013). http://gdac.broadinstitute.org/runs/stddata__2013_05_23/data/.
  • 38. Phillips HS, Kharbanda S, Chen R, et al. Molecular subclasses of high‐grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell. 2006;9:157‐173. 10.1016/j.ccr.2006.02.019. [DOI] [PubMed] [Google Scholar]
  • 39. Lee JS, Leem SH, Lee SY, et al. Expression signature of E2F1 and its associated genes predict superficial to invasive progression of bladder tumors. J Clin Oncol. 2010;28:2660‐2667. 10.1200/JCO.2009.25.0977. [DOI] [PubMed] [Google Scholar]
  • 40. Neale G, Su X, Morton CL, et al. Molecular characterization of the pediatric preclinical testing panel. Clin Cancer Res. 2008;14:4572‐4583. 10.1158/1078-0432.CCR-07-5090. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41. Sotiriou C, Neo S‐Y‐Y, McShane LM, et al. Breast cancer classification and prognosis based on gene expression profiles from a population‐based study. Proc Natl Acad Sci USA. 2003;100:93‐98. 10.1073/pnas.1732912100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42. Esserman LJ, Berry DA, Cheang MCU, et al. Chemotherapy response and recurrence‐free survival in neoadjuvant breast cancer depends on biomarker profiles: results from the I‐SPY 1 TRIAL (CALGB 150007/150012; ACRIN 6657). Breast Cancer Res Treat. 2012;132:1049‐1062. 10.1007/s10549-011-1895-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43. Ma XJ, Wang Z, Ryan PD, et al. A two‐gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen. Cancer Cell. 2004;5:607‐616. 10.1016/j.ccr.2004.05.015. [DOI] [PubMed] [Google Scholar]
  • 44. Curtis C, Shah SP, Chin S‐F, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature. 2012;486:346‐352. 10.1038/nature10983.The. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45. TCGA , The Cancer Genome Atlas. Breast cancer and cervical squamous cell carcinoma DNA copy number data. Cancer Genome Atlas, Off. Cancer Genomics, National Cancer Institute, National Institute of Health. Bethesda, MD. (2013). http://gdac.broadinstitute.org/runs/stddata__2013_09_23/data/.
  • 46. Laurent C, Valet F, Planque N, et al. High PTP4A3 phosphatase expression correlates with metastatic risk in uveal melanoma patients. Cancer Res. 2011;71:666‐674. 10.1158/0008-5472.CAN-10-0605. [DOI] [PubMed] [Google Scholar]
  • 47. TCGA , The Cancer Genome Atlas. Glioblastoma multiforme and brain lower grade glioma DNA copy number data. Cancer Genome Atlas, Off. Cancer Genomics, National Cancer Institute, National Institute of Health. Bethesda, MD. (2013). https://tcga-data.nci.nih.gov/tcga/.
  • 48. Kotliarov Y, Steed ME, Christopher N, et al. High‐resolution global genomic survey of 178 gliomas reveals novel regions of copy number alteration and allelic imbalances. Cancer Res. 2006;66:9428‐9436. 10.1158/0008-5472.CAN-06-1691. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49. Tothill RW, Tinker AV, George J, et al. Novel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome. Clin Cancer Res. 2008;14:5198‐5208. 10.1158/1078-0432.CCR-08-0196. [DOI] [PubMed] [Google Scholar]
  • 50. Vanaja DK, Cheville JC, Iturria SJ, Young CYF. Transcriptional silencing of zinc finger protein 185 identified by expression profiling is associated with prostate cancer progression. Cancer Res. 2003;63:3777‐3782. [PubMed] [Google Scholar]
  • 51. Firestein R, Bass AJ, Kim SY, et al. CDK8 is a colorectal cancer oncogene that regulates β‐catenin activity. Nature. 2008;455:547‐551. 10.1038/nature07179. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52. Bittner M.. Consortia‐pedia. Expression project for oncology‐renal samples, Int. Genomics Consortium. Phoeniz, AZ. (2005).
  • 53. Creighton C, Morgan M, Gunaratne P, et al. Sofia, comprehensive molecular characterization of clear cell renal cell carcinoma. Nature. 2013;499:43‐49. 10.1038/nature12222. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54. Beroukhim R, Brunet JP, Di Napoli A, et al. Patterns of gene expression and copy‐number alterations in von‐Hippel Lindau disease‐associated and sporadic clear cell carcinoma of the kidney. Cancer Res. 2009;69:4674‐4681. 10.1158/0008-5472.CAN-09-0146. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55. Brodeur GM, Sekhon G, Goldstein MN. Chromosomal aberrations in human neuroblastomas. Cancer. 1977;40:2256‐2263. . [DOI] [PubMed] [Google Scholar]
  • 56. Linn DE, Penney KL, Bronson RT, Mucci LA, Li Z. Deletion of interstitial genes between TMPRSS2 and ERG promotes prostate cancer progression. Cancer Res. 2016;76:1869‐1881. 10.1158/0008-5472.CAN-15-1911. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available in ONCOMINE database, Research Premium Edition (www.oncomine.org).

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