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Prediction of resistance to chemotherapy in ovarian cancer: a systematic review

BMC Cancer201515:117

https://doi.org/10.1186/s12885-015-1101-8

Received: 8 August 2014

Accepted: 20 February 2015

Published: 11 March 2015

Abstract

Background

Patient response to chemotherapy for ovarian cancer is extremely heterogeneous and there are currently no tools to aid the prediction of sensitivity or resistance to chemotherapy and allow treatment stratification. Such a tool could greatly improve patient survival by identifying the most appropriate treatment on a patient-specific basis.

Methods

PubMed was searched for studies predicting response or resistance to chemotherapy using gene expression measurements of human tissue in ovarian cancer.

Results

42 studies were identified and both the data collection and modelling methods were compared. The majority of studies utilised fresh-frozen or formalin-fixed paraffin-embedded tissue. Modelling techniques varied, the most popular being Cox proportional hazards regression and hierarchical clustering which were used by 17 and 11 studies respectively. The gene signatures identified by the various studies were not consistent, with very few genes being identified by more than two studies. Patient cohorts were often noted to be heterogeneous with respect to chemotherapy treatment undergone by patients.

Conclusions

A clinically applicable gene signature capable of predicting patient response to chemotherapy has not yet been identified. Research into a predictive, as opposed to prognostic, model could be highly beneficial and aid the identification of the most suitable treatment for patients.

Keywords

Ovarian cancerChemoresistancePredictive modelStatistical modelling

Background

Ovarian cancer is the fifth most common cancer in women in the UK and accounted for 4% of cancer diagnoses in women between 2008 and 2010 [1]. Worryingly, it was also responsible for 6% of cancer-related deaths in women over the same time period [1] and the five-year survival of women diagnosed with ovarian cancer between 2005 and 2009 was 42% [2]. It has been observed that although 40%-60% of patients achieve complete clinical response to first-line chemotherapy treatment [3], around 50% of these patients relapse within 5 years [4] and only 10%-15% of patients presenting with advanced stage disease achieve long-term remission [5]. It is thought that the high relapse rate is at least in part due to resistance to chemotherapy, which may be inherent or acquired by altered gene expression [6].

For ovarian cancer in the UK, the standard of care for first-line chemotherapy treatment recommended by the National Institute for Health and Care Excellence is ‘paclitaxel in combination with a platinum-based compound or platinum-based therapy alone’ [7]. This uniform approach ignores the complexity of ovarian cancer histologic types, particularly as there is evidence to suggest differences in response [8]. Winter et al. [9] investigated the survival of patients following paclitaxel and platinum chemotherapy and found histology to be a significant predictor of overall survival in multivariate Cox proportional hazards regression.

Improvement in survival has also been poor in ovarian cancer. Between 1971 and 2007 there was a 38% increase in relative 10-year survival in breast cancer, whereas the increase in ovarian cancer was 17% [10]. This difference in progress is likely to be due, at least in part, to the lack of tools with which to predict chemotherapy response in ovarian cancer.

Gene expression based tools for the prediction of patient prognosis after surgery or chemotherapy are currently available for some cancers. For example, MammaPrint®; uses the expression of 70 genes to predict the likelihood of metastasis in breast cancer [11]. Similarly, the Oncotype DX®; assay uses the expression of a panel of 21 genes to predict recurrence after treatment of breast cancer [12]. The Oncotype DX assay is also available for colon [13] and prostate cancers [14]. The development of a similar tool for ovarian cancer could greatly improve patient prognosis and quality of life by guiding chemotherapy choices. The prediction of cancer prognosis using gene signatures is a popular research field, within which a wide variety of approaches have been considered. Popular RNA or protein expression measurement techniques include cDNA hybridisation microarrays, end-point and quantitative reverse transcription PCR, and immunohistochemistry approaches.

Another variable aspect of studies predicting chemotherapy response is the computational and statistical approaches utilised. One of most popular methods for survival analysis is Cox proportional hazards regression. This model assumes that the hazard of death is proportional to the exponential of a linear predictor formed of the explanatory variables. This model has the advantage that, unlike many other regression techniques, it can appropriately deal with right-censored data such as that found in medical studies where patients leave before the end of the study period [15].

Other popular modelling techniques include linear models, support vector machines, hierarchical clustering, principal components analysis and the formation of a scoring algorithm. When dealing with data sets of varying sizes it is important to consider the number of samples and the amount of data per patient when choosing a modelling method. If the number of patients is large it is clear that a model will be better informed about the population from which the patient sample was drawn, and hence is likely to generalise more effectively to independent data sets. As the number of measurements per patient increases, the dimensionality and hence the flexibility of the model may increase. However, it is also important that the number of patients is sufficiently large to supply enough information about the factors being considered. Of the models identified here, linear models are relatively restrictive as the relationship between any factor and the outcome is assumed to be linear and so are suitable for smaller data sets. Conversely, hierarchical clustering simply finds groups of similar samples and there are minimal assumptions concerning the relationship between factors and outcome.

Classification models are used to predict which of a number of groups an individual falls into and are used for categorical variables, such as tumour grade and having or not having a disease. For visualisation and the assessment of classification model predictive power, a Kaplan-Meier plot is often combined with the log-rank test to investigate significance. It is worth noting that this method does not compare predictions with measurements, it simply considers the difference in survival between groups.

Many of the studies identified by this review involved developing a model using one set of samples, a training set, followed by testing of the model carried out on an independent set of samples, the test or validation set. This partitioning of samples is important as it allows the generalisability of the model to be assessed, and hence guards against over-fitting. If this check is not carried out, the true predictive ability of the model will not be known.

The aim of this review is to investigate the literature surrounding the prediction of chemotherapy response in ovarian cancer using gene expression. It has been observed, for example by Gillet et al. [16], that gene signatures obtained from cancer cell lines are not always relevant to in vivo studies, and that cell lines are inaccurate models of chemosensitivity [17]. The search was therefore restricted to studies involving human tissue in order to ensure that the resulting gene signatures are applicable in a clinical setting. It was also specified that the study must involve patients who have undergone chemotherapy treatment, so that the effects of resistance may be investigated.

Methods

Search methodology

The aim of this review is to investigate the literature on the prediction of chemoresistance in patients with ovarian cancer. Therefore, the six most important requirements identified were:
  • Concerned with (specifically) ovarian cancer

  • Patients were treated with chemotherapy

  • Gene expression was measured for use in predictions

  • Predictions are related to a measure of chemoresistance (e.g. response rates, progression-free survival)

  • Measurements were taken on human tissue (not cell lines)

  • The research aim is to develop a diagnostic tool or predict response

A PubMed search was carried out on 6th August 2014 to identify studies fulfilling the above requirements. The search terms may be found in Additional file 1. This search resulted in 78 papers.

Filtering

The search results were filtered twice, once based on abstracts and once based on full texts, by KL. An overview of the filtering process may be found in Figure 1. For the abstract-based filtering, papers were excluded if the six essential criteria were not all met, if the paper was a review article or if the paper was non-English language. This resulted in 48 papers remaining. For the full-text-based filtering, exclusion was due to not fulfilling the search criteria or papers that were not available. 42 papers were remaining after full-text-based filtering.
Figure 1
Figure 1

PRISMA search filtering flow diagram. The initial search results were filtered using titles and abstracts and, later, the full text to ensure the search criteria were fulfilled. Following filtering the number of papers included reduced from 78 to 42.

Data extraction

Data was extracted using a pre-defined table created for the purpose. Extraction was carried out in duplicate by a single author (KL) with a wash-out period of 3 months to avoid bias. Variables extracted were: author, year, journal, number of samples, number of genes measured, study end-point, tissue source, percentage cancerous tissue, gene or protein expression measurement technique, sample histological types and stages, patient prior chemotherapy, modelling techniques applied, whether the model accounts for heterogeneity in patient chemotherapy, whether the model was prognostic or predictive, whether the model was validated, model predictive ability including any metrics or statistics, and the genes found to be predictive.

Bias analysis

Bias in the studies selected for the systematic review was assessed according to QUADAS-2 [18], a tool for the quality assessment of diagnostic accuracy studies. Levels of evidence were also assessed according to the CEBM 2011 Levels of Evidence [19]. Results of these analyses may be found in Additional files 2 and 3. Briefly, the majority of studies were considered to be low risk, with six studies judged to have unclear risk for at least one domain and seven studies judged to be high risk for at least one domain. Thirty-six studies where judged to have evidence of level 2, with the remaining six having evidence of level 3. These levels of risk and evidence suggest that the majority of conclusions drawn from these studies are representative and applicable to the review question.

Gene set enrichment

Gene set enrichment analysis was applied to the gene sets reported by the studies selected for this review. Analysis was performed using the R package HTSanalyseR [20]. Where reported, gene sets were extracted and combined according to the chemotherapy treatments applied to patients in each study. The two groups assessed were those studies where all patients were treated with platinum and taxane in combination, and those studies where patients were given treatments other than platinum and taxane. The second group includes those given platinum as a single agent. Any studies reporting treatments from both groups were excluded, as were studies that did not report the chemotherapy treatments used. Kyoto Encyclopedia of Genes and Genomes (KEGG) terms were identified for each gene and gene set collection analysis was carried out, which applies hypergeometric tests and gene set enrichment analysis. A p-value cut-off of 0.0001 was used. Enrichment maps were then plotted, using the 30 most significant KEGG terms. P-values were adjusted using the ‘BH’ correction [21].

Ethics statement

Ethical approval was not required for this systematic review, which deals exclusively with previously published data.

Results

Tables 1, 2, 3, 4, 5 and 6 detail some key information regarding the studies included in the review. Table 1 contains the number of samples analysed, the number of genes considered for the model, and the resulting genes retained as the predictive gene signature. Table 2 provides information about the tissue used for gene expression measurements and whether the studies assessed the percent neoplastic tissue before measurement, and Table 3 details the gene expression measurement techniques used. Table 4 contains the reported histological types and stages of the samples processed by each study. Table 5 provides information on chemotherapy treatments undergone by patients, whether the model was prognostic or predictive, and whether the model was validated using either an independent set of samples or cross validation. Table 6 lists the outcome to be predicted, the modelling techniques applied, and the predictive ability of the resulting model.
Table 1

Journal and study information of papers included in the systematic review

Study

Journal

No. samples

No. genes in study

No. genes in signature

Jeong et al. [22]

Anticancer Res.

487

612

388, 612

Lisowska et al. [23]

Front. Oncol.

127

>47000

0

Roque et al. [24]

Clin. Exp. Metastasis

48

1

1

Li et al. [3]

Oncol. Rep.

44

1

1

Schwede et al. [25]

PLoS ONE

663

2632

51

Verhaak et al. [26]

J. Clin. Invest.

1368

11861

100

Obermayr et al. [27]

Gynecol. Oncol.

255

29098

12

Han et al. [28]

PLoS ONE

322

12042

349, 18

Hsu et al. [29]

BMC Genomics

168

12042

134

Lui et al. [30]

PLoS ONE

737

NS

227

Kang et al. [31]

J. Nat. Cancer Inst.

558

151

23

Gillet et al. [32]

Clin. Cancer Res.

80

356

11

Ferriss et al. [33]

PLos ONE

341

NS

251, 125

Brun et al. [34]

Oncol. Rep.

69

6

0

Skirnisdottir and Seidal [35]

Oncol. Rep.

105

3

2

Brenne et al. [36]

Hum. Pathol.

140

1

1

Sabatier et al. [37]

Br. J. Cancer

401

NS

7

Gillet et al. [38]

Mol. Pharmeceutics

32

350

18, 10, 6

Chao et al. [39]

BMC Med. Genomics

6

8173

NS

Schlumbrecht et al. [40]

Mod. Pathol.

83

7

2

Glaysher et al. [41]

Br. J. Cancer

31

91

10, 4, 3, 5, 5, 11, 6, 6

Yan et al. [42]

Cancer Res.

42

2

1

Yoshihara et al. [43]

PLoS ONE

197

18176

88

Williams et al. [44]

Cancer Res.

242

NS

15 to 95

Denkert et al. [45]

J. Pathol

198

NS

300

Matsumura et al. [46]

Mol. Cancer Res.

157

22215

250

Crijns et al. [47]

PLoS Medicine

275

15909

86

Mendiola et al. [48]

PLoS ONE

61

82

34

Gevaert et al. [49]

BMC Cancer

69

24000

3000

Bachvarov et al. [50]

Int. J. Oncol.

42

20174

155, 43

Netinatsunthorn et al. [51]

BMC Cancer

99

1

1

De Smet et al. [52]

Int. J. Gynecol. Cancer

20

21372

3000

Helleman et al. [53]

Int. J. Cancer

96

NS

9

Spentzos et al. [54]

J. Clin. Oncol.

60

NS

93

Jazaeri et al. [55]

Clin. Cancer Res.

40

40033, 7585

85, 178

Raspollini et al. [56]

Int. J. Gynecol. Cancer

52

2

2

Hartmann et al. [57]

Clin. Cancer Res.

79

30721

14

Spentzos et al. [58]

J. Clin. Oncol.

68

12625

115

Selvanayagam et al. [59]

Cancer Genet. Cytogenet.

8

10692

NS

Iba et al. [60]

Cancer Sci.

118

4

1

Kamazawa et al. [61]

Gynecol. Oncol.

27

3

1

Vogt et al. [62]

Acta Biochim. Pol.

17

3

0

If more than one value is given, the study used multiple different starting gene-sets or found multiple gene signatures. NS: Not Specified.

Table 2

Tissue information of papers included in systematic review

Study

Tissue source

% Cancerous tissue

Jeong et al. [22]

  

Lisowska et al. [23]

Fresh-frozen

NS

Roque et al. [24]

FFPE, Fresh-frozen

min. 70%

Li et al. [3]

FFPE

NS

Schwede et al. [25]

  

Verhaak et al. [26]

  

Obermayr et al. [27]

Fresh-frozen, Blood

NS

Han et al. [28]

  

Hsu et al. [29]

  

Lui et al. [30]

  

Kang et al. [31]

  

Gillet et al. [32]

Fresh-frozen

min. 75%

Ferriss et al. [33]

FFPE

min. 70%

Brun et al. [34]

FFPE

NS

Skirnisdottir and Seidal [35]

FFPE

NS

Brenne et al. [36]

Fresh-frozen effusion, Fresh-frozen

min. 50%

Sabatier et al. [37]

Fresh-frozen

min. 60%

Gillet et al. [38]

Fresh-frozen effusion

NS

Chao et al. [39]

  

Schlumbrecht et al. [40]

Fresh-frozen

min. 70%

Glaysher et al. [41]

FFPE, Fresh

min. 80%

Yan et al. [42]

Fresh-frozen

NS

Yoshihara et al. [43]

Fresh-frozen

min. 80%

Williams et al. [44]

  

Denkert et al. [45]

Fresh-frozen

NS

Matsumura et al. [46]

Fresh-frozen

NS

Crijns et al. [47]

Fresh-frozen

median = 70%

Mendiola et al. [48]

FFPE

min. 80%

Gevaert et al. [49]

Fresh-frozen

NS

Bachvarov et al. [50]

Fresh-frozen

min. 70%

Netinatsunthorn et al. [51]

FFPE

NS

De Smet et al. [52]

Not specified

NS

Helleman et al. [53]

Fresh-frozen

median = 64%

Spentzos et al. [54]

Fresh-frozen

NS

Jazaeri et al. [55]

FFPE, Fresh-frozen

NS

Raspollini et al. [56]

FFPE

NS

Hartmann et al. [57]

Fresh-frozen

min. 70%

Spentzos et al. [58]

Fresh-frozen

NS

Selvanayagam et al. [59]

Fresh-frozen

min. 70%

Iba et al. [60]

FFPE, Fresh-frozen

NS

Kamazawa et al. [61]

FFPE, Fresh-frozen

NS

Vogt et al. [62]

None specified

NS

If more than one value is given, the study used tissue from multiple sources. NS: Not Specified.

Table 3

Gene expression measurement techique information of papers included in systematic review

Study

Immunohistochemistry

TaqMan array

q-RT-PCR

Commercial microarray

Custom microarray

RT-PCR

Jeong et al. [22]

Lisowska et al. [23]

Roque et al. [24]

Li et al. [3]

Schwede et al. [25]

Verhaak et al. [26]

Obermayr et al. [27]

Han et al. [28]

Hsu et al. [29]

Lui et al. [30]

Kang et al. [31]

Gillet et al. [32]

Ferriss et al. [33]

Brun et al. [34]

Skirnisdottir and Seidal [35]

Brenne et al. [36]

Sabatier et al. [37]

Gillet et al. [38]

Chao et al. [39]

Schlumbrecht et al. [40]

Glaysher et al. [41]

Yan et al. [42]

Yoshihara et al. [43]

Williams et al. [44]

Denkert et al. [45]

Matsumura et al. [46]

Crijns et al. [47]

Mendiola et al. [48]

Gevaert et al. [49]

Bachvarov et al. [50]

Netinatsunthorn et al. [51]

De Smet et al. [52]

Helleman et al. [53]

Spentzos et al. [54]

Jazaeri et al. [55]

Raspollini et al. [56]

Hartmann et al. [57]

Spentzos et al. [58]

Selvanayagam et al. [59]

Iba et al. [60]

Kamazawa et al. [61]

Vogt et al. [62]

Table 4

Histology information of papers included in systematic review

Study

Sub-type

Stage

Jeong et al. [22]

Serous, Endometrioid, Adenocarcinoma

I, II, III, IV

Lisowska et al. [23]

Serous, Endometrioid, Clear cell, Undifferentiated

II, III, IV

Roque et al. [24]

Serous, Endometrioid, Clear cell, Undifferentiated, Mixed

IIIC, IV

Li et al. [3]

Serous, Endometrioid, Clear cell, Mucinous, Transitional

II, III, IV

Schwede et al. [25]

Serous, Endometrioid, Clear cell, Mucinous, Adenocarcinoma, OSE

I, II, III, IV

Verhaak et al. [26]

NS

II, III, IV

Obermayr et al. [27]

Serous, Non-serous

II, III, IV

Han et al. [28]

Serous, Endometrioid, Clear cell, Mucinous, Mixed, Poorly differentiated

II, III, IV

Hsu et al. [29]

NS

III, IV

Lui et al. [30]

Serous

II, III, IV

Kang et al. [31]

Serous

I, II, III, IV

Gillet et al. [32]

Serous

III, IV

Ferriss et al. [33]

Serous, Clear cell, Other

III, IV

Brun et al. [34]

Serous, Endometrioid, Clear cell, Mucinous, Other

III, IV

Skirnisdottir and Seidal [35]

Serous, Endometrioid, Clear cell, Mucinous, Anaplastic

I, II

Brenne et al. [36]

Serous, Endometrioid, Clear cell, Undifferentiated, Mixed

II, III, IV

Sabatier et al. [37]

Serous, Endometrioid, Clear cell, Mucinous, Undifferentiated, Mixed

I, II, III, IV

Gillet et al. [38]

Serous

III, IV, NS

Chao et al. [39]

NS

NS

Schlumbrecht et al. [40]

Serous

III, IV

Glaysher et al. [41]

Serous, Endometrioid, Clear cell, Mucinous, Mixed, Poorly differentiated

IIIC, IV

Yan et al. [42]

Serous, Endometrioid, Clear cell, Mucinous, Transitional

II, III, IV

Yoshihara et al. [43]

Serous

III, IV

Williams et al. [44]

Serous, Endometrioid, Undifferentiated

III, IV

Denkert et al. [45]

Serous, Non-serous, Undifferentiated

I, II, III, IV

Matsumura et al. [46]

Serous

I, II, III, IV

Crijns et al. [47]

Serous

III, IV

Mendiola et al. [48]

Serous, Non-serous

III, IV

Gevaert et al. [49]

Serous, Endometrioid, Mucinous, Mixed

I, III, IV

Bachvarov et al. [50]

Serous, Endometrioid, Clear cell

II, III, IV

Netinatsunthorn et al. [51]

Serous

III, IV

De Smet et al. [52]

Serous, Endometrioid, Mucinous, Mixed

I, III, IV

Helleman et al. [53]

Serous, Endometrioid, Clear cell, Mucinous, Mixed, Poorly differentiated

I/II, III/IV

Spentzos et al. [54]

Serous, Endometrioid, Clear cell, Mixed

I, II, III, IV

Jazaeri et al. [55]

Serous, Endometrioid, Clear cell, Mixed, Undifferentiated, Carcinoma

II, III, IV

Raspollini et al. [56]

Serous

IIIC

Hartmann et al. [57]

Serous, Endometrioid, Mixed

II, III, IV

Spentzos et al. [58]

Serous, Endometrioid, Clear cell, Mixed

I, II, III, IV

Selvanayagam et al. [59]

Serous, Endometrioid, Clear cell, Undifferentiated

III, IV

Iba et al. [60]

Serous, Endometrioid, Clear cell, Mixed

I, II, III, IV

Kamazawa et al. [61]

Serous, Endometrioid, Clear cell

III, IV

Vogt et al. [62]

NS

NS

Entries in bold indicate that the study data set was comprised of at least 80% this type. NS: Not Specified.

Table 5

Basic modelling and patient information of papers included in systematic review

Study

Patient prior chemotherapy treatment

Model accounts for the different chemotherapies?

Prognostic or predictive?

Model validated?

Jeong et al. [22]

Platinum-based

Predictive

Lisowska et al. [23]

Platinum/Cyclophosphamide, Platinum/Taxane

Prognostic

Roque et al. [24]

NS

Prognostic

Li et al. [3]

Platinum/Cyclophosphamide, Platinum/Taxane

Prognostic

Schwede et al. [25]

NS

Prognostic

Verhaak et al. [26]

NS

Prognostic

Obermayr et al. [27]

Platinum-based

Prognostic

Han et al. [28]

Platinum/Paclitaxel

 

Prognostic

Hsu et al. [29]

Platinum/Paclitaxel

   
 

+ additional treatments

Prognostic

Lui et al. [30]

NS

Prognostic

Kang et al. [31]

Platinum/Taxane

 

Prognostic

Gillet et al. [32]

Carboplatin/Paclitaxel

 

Prognostic

Ferriss et al. [33]

Platinum-based

Predictive

Brun et al. [34]

NS

Prognostic

Skirnisdottir and Seidal [35]

Carboplatin/Paclitaxel

 

Prognostic

Brenne et al. [36]

NS

Prognostic

Sabatier et al. [37]

Platinum-based

Prognostic

Gillet et al. [38]

NS

Prognostic

Chao et al. [39]

NS

Prognostic

Schlumbrecht et al. [40]

Platinum/Taxane

 

Prognostic

Glaysher et al. [41]

Platinum, Platinum/Paclitaxel

Predictive

Yan et al. [42]

Platinum-based

Prognostic

Yoshihara et al. [43]

Platinum/Taxane

 

Prognostic

Williams et al. [44]

NS

Predictive

Denkert et al. [45]

Carboplatin/Paclitaxel

 

Prognostic

Matsumura et al. [46]

Platinum-based

Predictive

Crijns et al. [47]

Platinum, Platinum/

   
 

Cyclophosphamide, Platinum/Paclitaxel

Prognostic

Mendiola et al. [48]

Platinum/Taxane

 

Prognostic

Gevaert et al. [49]

NS

Prognostic

Bachvarov et al. [50]

Carboplatin/Paclitaxel,

   
 

Carboplatin/Cyclophosphamide, Cisplatin/Paclitaxel

Prognostic

Netinatsunthorn et al. [51]

Platinum/Cyclophosphamide

 

Prognostic

De Smet et al. [52]

Platinum/Cyclophosphamide, Platinum/Paclitaxel

Prognostic

Helleman et al. [53]

Platinum/Cyclophosphamide, Platinum-based

Prognostic

Spentzos et al. [54]

Platinum/Taxane

 

Prognostic

Jazaeri et al. [55]

Carboplatin/Paclitaxel, Cisplatin/Cyclophosphamide, Carboplatin/Docetaxel, Carboplatin

Prognostic

Raspollini et al. [56]

Cisplatin/Cyclophosphamide, Carboplatin/Cyclophosphamide, Carboplatin/Paclitaxel

Prognostic

Hartmann et al. [57]

Cisplatin/Paclitaxel, Carboplatin/Paclitaxel

Prognostic

Spentzos et al. [58]

Platinum/Taxane

 

Prognostic

Selvanayagam et al. [59]

Cisplatin/Cyclophosphamide, Carboplatin/Cyclophosphamide, Cisplatin/Paclitaxel

Prognostic

Iba et al. [60]

Carboplatin/Paclitaxel

 

Prognostic

Kamazawa et al. [61]

Carboplatin/Paclitaxel

 

Prognostic

Vogt et al. [62]

Etoposide, Paclitaxel/Epirubicin, Carboplatin/Paclitaxel

Predictive

If more than one value is given, the study included patients treated with different treatments. NS: Not Specified.

Table 6

Basic modelling information of papers included in systematic review

Study

Prediction

Prediction method

Predictive ability

Jeong et al. [22]

Overall Survival

Student’s T test, Hierarchical clustering, Compound covariate predictor algorithm, Cox proportional hazards regression, Kaplan-Meier curves, Log-rank test, ROC analysis

‘Taxane-based treatment significantly affected OS for patients in the YA subgroup (3 year rate: 74.4% with taxane vs. 37.9% without taxane, p=0.005 by log-rank test)’, ‘estimated hazard ratio for death after taxane-based treatment in the YA subgroup was 0.5 (95% CI=0.31−−0.82,p=0.005)’

Lisowska et al. [23]

Chemoresponse, Disease-Free Survival, Overall Survival

Support vector machines, Kaplan-Meier curves, Log-rank test

No genes found to be significant in the training set were significant in the test set, for chemoresponse, DFS or OS

Roque et al. [24]

Overall Survival

Kaplan-Meier curves, Log-rank test, Student’s T test

‘OS was predicted by increased class III β-tubulin staining by both tumor (HR3.66, 96%CI=1.11–12.1, p=0.03) and stroma (HR4.53, 95%CI=1.28–16.1, p=0.02)’

Li et al. [3]

Chemoresponse (chemoresistant vs. chemosensitive)

Correlation of p-CFL1 staining and chemoresponse

‘immunostaining of p-CFL1 was positive in 77.3% of chemosensitive and in 95.9% of the chemoresistant’ (p=0.014, U=157.5)

Schwede et al. [25]

Stem cell-like subtype, Disease-Free Survival, Overall Survival

ISIS unsupervised bipartitioning, Diagonal linear discriminant analysis, Gaussian mixture modelling, Kaplan-Meier curves, Log-rank test

OS (p values): Dressman =0.0354, Crijns =0.021, Tothill =4.4E−7

Verhaak et al. [26]

Poor Prognosis vs. Good Prognosis

Significance analysis of microarrays, Single sample gene set enrichment analysis, Kaplan-Meier curves, Log-rank test

Good or Poor prognosis, likelihood ratio =44.63

Obermayr et al. [27]

Disease-Free Survival, Overall Survival

Kaplan-Meier curves, Cox proportional hazards regression, χ2 test

‘The presence of CTCs six months after completion of the adjuvant chemotherapy indicated relapse within the following six months with 41% sensitivity, and relapse within the entire observation period with 22% sensitivity (85% specificity)’

Han et al. [28]

Complete Response or Progressive Disease

Supervised principal component method

349 gene signature: ROC AUC =0.702, p=0.022. 18 gene: ROC AUC =0.614, p=0.197.

Hsu et al. [29]

Progression-Dree Survival

Semi-supervised hierarchical clustering

Good Response vs. Poor Response, p=0.021

Lui et al. [30]

Chemosensitivity, Overall Survival, Progression-Dree Survival

Predictive score using weighted voting algorithm, Kaplan-Meier curves, Log-rank Test, Cox proportional hazards regression

Response of 26 of 35 patients in an independent data set was correctly predicted, patients in the low-scoring group exhibited poorer PFS (HR=0.43, p=0.04), ROC AUC = 0.90(0.86–0.95)

Kang et al. [31]

Overall Survival, Progression-Free Survival, Recurrence-Free Survival

Kaplan-Meier curves, Log-rank test, Cox proportional hazards regression, Pearson correlation coefficient

Berchuck dataset: HR=0.33, 95%CI=0.13–0.86, p=0.013; Tothill dataset: HR=0.61, 95%CI=0.36–0.99, p=0.044

Gillet et al. [32]

Overall Survival, Progression-Free Survival

Supervised principle components method, Cox proportional hazards regression, Kaplan-Meier curves, Log-rank test

‘An 11-gene signature whose measured expression significantly improves the power of the covariates to predict poor survival’(p<0.003)

Ferriss et al. [33]

Overall Survival

COXEN coefficient, Mann-Whitney U test, ROC analysis, Unsupervised Hierarchical Clustering

Carboplatin: sensitivity = 0.906, specificity = 0.174, PPV = 60%, NPV = 57% (UVA-55 validation set)

Brun et al. [34]

2-year Disease-Free Survival

Student’s T test, Principal component analysis, Concordance index, Kaplen-Meier curves, Log-rank test

No genes were found to have prognostic value

Skirnisdottir and Seidal [35]

Recurrence, Disease-Free Survival

χ2 test, Kaplan-Meier curves, Log-rank test, Logistic regression, Cox proportional hazards regression

p53-status (OR=4.123, p=0.009; HR=2.447, p=0.019) was a significant and independent factor for tumor recurrence and DFS.

Brenne et al. [36]

OC or MM, Progression-Free Survival, Overall Survival

Mann-Whitney U test, Kaplan-Meier curves, Log-rank test, Cox proportional hazards regression

Cox Multivariate Analysis: EHF mRNA expression in pre-chemotherapy effusions was an independent predictor of PFS (p=0.033, relative risk=4.528)

Sabatier et al. [37]

Progression-Free Survival, Overall Survival

Cox proportional hazards regression, Pearson’s coefficient correlation score

Favourable vs. Unfavourable: ‘sensitivity = 61.6%, specificity = 62.4%, OR=2.7, 95%CI=1.7–4.2; p=6.1×10−06, Fisher’s exact test’

Gillet et al. [38]

Overall Survival, Progression-Free Survival, Treatment Response

Linear regression, Hierarchical clustering, Kaplan-Meier curves, Log-rank test

‘6 gene signature alone can effectively predict the progression-free survival of women with ovarian serous carcinoma (log-rank p=0.002)’

Chao et al. [39]

Chemoresistance

Interaction and expression networks for pathway identification, pathway intersections, betweenness and degree centrality, Student’s T test

No statistical measure available. Many genes identified have previously been found experimentally

Schlumbrecht et al. [40]

Overall Survival, Recurrence-Free Survival

Linear regression, Logistic regression, Cox proportional hazards regression, Kaplan-Meier curves, Unsupervised cluster analysis, Log-rank test, Mann-Whitney U test, χ2 test

‘Greater EIG121 expression was associated with shorter time to recurrence (HR=1.13 (CI=1.02–1.26), p=0.021)’, ‘Increased expression of EIG121 demonstrated a statistically significant association with worse OS (HR=1.21 (CI1.09–1.35), p<0.001)’

Glaysher et al. [41]

Chemosensitivity

AIC gene selection, Multiple linear regression

Cisplatin: \(R^{2}_{\textit {adj}} = 0.836\), p<0.001

Yan et al. [42]

Chemosensitivity

ANOVA, Student’s T test, Mann-Whitney U test

‘Immunostaining scores [Annexin A3] are significantly higher in platinum-resistant tumors (p=0.035)’

Yoshihara et al. [43]

Progression-Free Survival

Cox proportional hazards regression, Ridge regression, Prognostic index, ROC analysis, Kaplan-Meier curves, Log-rank test

‘Prognostic index was an independent prognostic factor for PFS time (HR=1.64, p=0.0001)’, sensitivity = 64.4%, specificity = 69.2%

Williams et al. [44]

Overall Survival

COXEN score, Kaplan-Meier curves, Student’s T test, ROC analysis, Spearman’s rank correlation coefficient, Logistic regression, Log-rank test

Carboplatin and Taxol: sensitivity = 77%, specificity = 56%, PPV=71%, NPV=78%

Denkert et al. [45]

Overall Survival

Semi-supervised analysis via Cox scoring, Principal components analysis, Kaplan-Meier curves, Log-rank test, Cox proportional hazards regression

Duke et al.: ‘clinical outcome is significantly different depending on the OPI (p=0.021), with an HR of 1.7 (CI 1.1–2.6)’

Matsumura et al. [46]

Taxane sensitivity, Overall Survival

Hierarchical clustering, Kaplan-Meier curves, Log-rank test

‘Patients in the YY1-High cluster who were treated with paclitaxel showed improved survival compared with the other groups (p=0.010)’

Crijns et al. [47]

Overall Survival

Supervised principal components method, Cox proportional hazards regression, Kaplan-Meier curves, Log-rank test, χ2 test

OSP: (High-risk vs. low-risk) HR=1.940, CI=1.190–3.163, p=0.008

Mendiola et al. [48]

Progression-Free Survival, Overall Survival

Kaplan-Meier curves, Log-rank test, AIC-based model selection, ROC curves, Cox proportional hazards regression

OS: sensitivity = 87.2%, specificity = 86.4%

Gevaert et al. [49]

Platin Resistance/Sensitivity, Stage

Principal component analysis, Least squares support vector machines

Platin-Resistance/Sensitivity: sensitivity = 67%, specificity = 40%, accuracy = 51.11%

Bachvarov et al. [50]

Chemoresistance

Hierarchical Clustering, Support vector machines

No prediction metric applied

Netinatsunthorn et al. [51]

Overall Survival, Recurrence-Free Survival

Kaplan-Meier curves, Cox proportional hazards regression

OS: HR=1.98, 95%CI=1.28–3.79, p=0.0138 ; RFS: HR=3.36, 95%CI=1.60–7.03, p=0.0017

De Smet et al. [52]

Stage I vs. Advanced stage, Platin-sensistive vs. Platin-resistant

Principal component analysis, Least squares support vector machines

Estimated Classification Accuracy: Stage I vs Advanced Stage =100%, Platin-sensitive vs. Platin-resistant =76.9%

Helleman et al. [53]

Chemoresponse (responder vs. non-responder)

Class prediction, Hierarchical clustering, Principal component analysis

Test set: PPV=24%, NPV=97%, sensitivity =89%, specificity =59%

Spentzos et al. [54]

Chemoresponse (pathological-CR or PD), Disease-Free survival, Overall Survival

Class prediction analysis, Compound covariate algorithm, Average linkage hierarchical clustering, Kaplan-Meier curves, Log-rank test, Cox proportional hazards regression

Cox PH (resistant vs. sensitive): Recurrence HR=2.7 (95%CI=1.2–6.1), Death HR=3.9 (95%CI=3.1–11.4)

Jazaeri et al. [55]

Clinical response

Class prediction

9 most significantly differentially expressed genes, primary chemoresistant vs. primary chemosensitive: accuracy =77.8%

Raspollini et al. [56]

Overall Survival (high vs. low)

Univariate logistic regression, χ2 test

COX-2: OR=0.23, 95%CI=0.06–0.77, p=0.017; MDR1: OR=0.01, 95%CI=0.002–0.09, p=<0.0005

Hartmann et al. [57]

Time To Relapse (early vs.late)

Support vector machine, Kaplan-Meier curves, Log-rank test, average linkage clustering

Accuracy =86%, PPV=95%, NPV=67%

Spentzos et al. [58]

Disease-Free Survival, Overall Survival

Supervised pattern recognition/class prediction, Kaplan-Meier curves, Log-rank test, Cox proportional hazards regression

Unfavourable vs. Favourable OS : (CPH) HR=4.6, 95%CI=2.0–10.7, p=0.0001

Selvanayagam et al. [59]

Chemoresistance (chemoresistant vs. chemosensitive)

Supervised voice-pattern recognition algorithm (clustering)

PPV=1, NPV=1

Iba et al. [60]

Chemoresponse, Overall Survival

Kaplan-Meier curves, Log-rank test, Cox propotionate hazards regression, ROC analysis, χ2 test, Student’s T test, Mann-Whitney U test

‘Patients with c-myc expression of over 200 showed a significantly better 5-year survival rate (69.8% vs. 43.5%)’, p<0.05

Kamazawa et al. [61]

Chemoresponse (CR or PR vs. NC or PD)

Defined threshold expressionto divide responders and non-responders

MDR-1 (all samples): specificity =95%, sensitivity = 100%, predictive value =96%

Vogt et al. [62]

Chemoresistance

Correlation of AUC from in-vitro ATP-CVA and gene expression

All p values for correlation of drugs and genes were >0.05

If more than one value is given, the study used multiple different prediction methods or predicted more than one endpoint.

Tissue source

For studies involving RNA extraction the tissue source is an important consideration, as RNA degradation and fragmentation could affect the results of techniques involving amplification. This is a notable issue in formalin fixed paraffin embedded (FFPE) tissue, due to the cross-linking of genetic material and proteins [63]. Of the 42 papers included in this review, the majority used fresh-frozen biopsy tissue. The numbers of each tissue source may be found in Table 7, and the tissue source used by individual papers may be found in Table 2. Nine papers did not use an RNA source directly as secondary data was used. Data sources were mostly other studies or data repositories, such as the TCGA dataset. Two studies did not specify the source tissue though extraction and expression measurement methods were detailed.
Table 7

Numbers of studies using various mRNA sources

mRNA source

Number of studies

FFPE tissue

12

Fresh-frozen tissue

22

Fresh-frozen effusion

2

Fresh tissue

1

Blood

1

Not used

9

Not specified

2

The majority of papers in this review used fresh-frozen tissue. This choice was likely made to minimise RNA degradation and hence improve measurement accuracy. Due to the risk of RNA degradation because of long storage times and the fixing process applied to FFPE tissue, it is often expected that FFPE tissue will be irreversibly cross-linked and fragmented. However, following investigation into RNA integrity when extracted from paired FFPE and fresh-frozen tissue, Rentoft et al. [64] found that for most samples up- and down-regulation of four genes was found to be the same whether measured in FFPE or fresh-frozen tissue. They concluded that, if samples were screened to ensure RNA quality, FFPE material can successfully provide RNA for gene expression measurement.

The use of fresh-frozen tissue in a research setting is not unusual, as can be seen from the fact that this tissue type was most popular in this review. However, for translational research expected to lead to a clinical test, this is not as reasonable. FFPE tissue is much more readily available, due to simpler acquisition and storage, and tissue is already taken for histological analysis. Therefore a model capable of using data obtained from FFPE tissue is much more likely to be applicable in a clinical setting.

Another important consideration is the proportion of neoplastic cells in the sample. For each paper the reported proportion may be seen in Table 2. Of the 42 papers, 14 reported that the proportion of cancerous cells was measured. This was usually done using hematoxylin and eosin stained histologic slides. It is important for the gene expression measurement that the tissue used contains a high proportion of neoplastic cells, and hence it is important that this pre-analytical variable is controlled. Of the studies in this review, those reporting the percentage cancerous cells were evenly distributed between FFPE and fresh-frozen tissues.

Gene or protein expression quantification

Of the studies highlighted by this review, there were four main techniques applied for gene or protein expression measurement: Probe-target hybridization microarrays, quantitative PCR, reverse transcription end-point-PCR, and immunohistochemical staining. Of these methods only immunohistochemistry measures protein expression, via classification of the level of staining, and the other methods quantify gene expression via measurement of mRNA copy number.

Methods involving probe-target hybridization are available commercially, and 19 of the 42 studies utilised these. For example the Affymetrix®; Human U133A 2.0 GeneChip and the Agilent®; Whole Human Genome Oligo Microarray were both used by multiple studies. Additionally, 7 studies used custom-made probe-target hybridization arrays. Probe-target hybridisation arrays generally measure thousands of genes and hence can provide a wealth data per sample. TaqMan®; microfluidic arrays or quantitative-PCR were used by 16 studies. These techniques are typically used for smaller panels of genes. The TaqMan®; arrays for example may contain up to 384 genes per array. These methods are more targeted and hence the price per sample is usually lower.

Immunohistochemistry is a more labour-intensive technique, requiring staining for each gene considered, and hence was mostly only used by studies using small numbers of genes. This technique, which is semi-quantitative due to the scoring systems employed, also suffers from a lack of standardisation of procedures. Of the 11 papers using this technique, the maximum number of genes analysed was seven, and the mean number of genes assessed was 2.8. Although these studies provide useful information regarding the correlation of particular genes with outcome, the small numbers of genes is likely to result in an incomplete gene signature and low predictive power.

Several of the papers utilising quantifiable techniques used an alternative method or replicates to obtain a measure of the assay variability. Five papers involving commercial or custom microarrays also used reverse transcription PCR (RT-PCR) to measure the expression of a small number of genes for comparison and one study used samples run in duplicate to calculate the coefficient of variation. Of the studies using TaqMan microfluidic arrays, two used samples run in duplicate to obtain the coefficient of variation. However, even fewer papers reported a metric representing the level of variability found. Two studies reported a coefficient of variation; Glaysher et al. [41] reported CoV=2%=0.02 for TaqMan arrays and Hartmann et al. [57] reported CoV=0.2 for their custom microarray. Another two reported Spearman’s or Pearson’s r coefficients of correlation between microarray and RT-PCR results. Yoshihara et al. [43] gave Pearson r values ranging from 0.5 to 0.8, and Crijns et al. [47] gave Spearman’s r values between -0.6 and -0.9.

Histology

Table 4 details the histology (types and stages) of the patient samples used by each study. As may be seen, the majority of studies were heterogeneous with respect to the types of cancer included. However, 23 of the 42 studies used at least 80% serous samples, suggesting that the majority of information contributed to the gene signatures of these studies is related to the mechanisms and pathways in serous cancer. In the authors’ opinion it is important to identify the histologies of patient samples: although treatment is currently the same across types, response to chemotherapy has been found to vary [9,65,66]. It therefore may be advisable for future studies to include histological information when developing models predicting chemotherapy response.

Chemotherapy

Table 5 lists the chemotherapy treatments undergone by patients in each study. The 10 papers labelled NS did not specify the regimen applied, though the patients did have chemotherapy. These cohorts cannot therefore be assumed to be homogeneous with respect to patient chemotherapy treatment. All studies that specified the chemotherapy regimen undergone by patients noted at least one platinum-based treatment. Of these, 24 included patients treated with a platinum-taxane combination and 10 with a cyclophosphamide-platinum combination. It is important to note that 19 of the 42 papers stated the population was heterogeneous with regards to chemotherapy treatments and, of those that did, only 8 included patient treatment history as a feature of the study. The aims of the majority of the studies were to identify genes of which the expression may be used to predict survival time, or prognosis. As already noted, the presence of resistance to the chemotherapy agent administered will dramatically affect the survival of a patient. It is therefore reasonable to expect the gene signatures identified to include genes responsible for chemoresistance, which will depend on the mechanism of action of the drug. Using a heterogeneous cohort in terms of chemotherapy treatment may then be causing problems with the identification of a minimal predictive gene set.

End-point to be predicted

As may be expected, there was variation between the end-point chosen by studies for prediction. Popular end-points include overall survival, progression-free survival and response to chemotherapy. The endpoints considered by each study may be found in Table 6. Of these some are clinical endpoints, such as overall survival, others use non-clinical endpoints, such as response to chemotherapy, many of which are considered to be surrogates for overall survival. For cancer studies, overall survival is considered to be the most reliable and is the variable that is of most interest when considering the effect of an intervention.

Model development

Within this review, many different modelling techniques were used to identify an explanatory gene signature to predict patient outcome. The most popular was Cox proportional hazards regression, which was applied by 17 studies. This was closely followed by hierarchical clustering, which was used by 11 studies. All other methods were used by 8 or fewer studies. In total 24 different types of modelling techniques were applied, ranging from statistical tests such as Student’s T test and Mann-Whitney U test, to logistic regression, to ridge regression. Table 8 lists the modelling techniques identified and the number of studies that employed them. It is of interest that most of the techniques applied are forms of classification. These methods result in samples being assigned to groups, such as ‘good prognosis’ and ‘poor prognosis’. Whilst this may be useful in some settings, for a clinically-applicable tool a regression technique may be more appropriate as it will provide a value, such as a likelihood of relapse, rather than simply a class. Techniques in Table 8 capable of a numeric prediction include logistic and linear regression, Cox proportional hazards regression, and ridge regression.
Table 8

Key modelling techniques applied by studies in the review

Technique

Number of papers

Cox proportional hazards regression

17

Hierarchical clustering

11

Principal components analysis

8

Student’s T test

7

Scoring algorithm

6

Support Vector Machines

5

Correlation coefficients

5

Mann-Whitney U test

5

χ2 test

5

ROC analysis

5

Class prediction

4

Logistic regression

3

Linear regression

3

AIC gene selection

2

Concordance index

1

Pathway interaction networks

1

ANOVA

1

Expression threshold identified

1

Gene set enrichment analysis

1

Linear discriminant analysis

1

ISIS bipartitoning

1

Gaussian mixture modelling

1

Significance analysis of microarrays

1

Ridge regression

1

Jointly with the modelling methods identified above, 23 of the 42 studies implemented Kaplan-Meier curves to visualise the survival of the patient classes identified by the models. This enables the difference in survival between classes, for example ‘good prognosis’ and ‘poor prognosis’, to be seen and assessed. The application of a log-rank test assesses the separation of the curves and identifies whether there is a statistically significant difference in survival distribution between the classes. It should be noted that, although this gives an idea of separation of classes achieved by the model, the model results must still be compared with known outcomes to check positive and negative predictive power. This step was missing in several papers, such as Gillet et al. [38], where the p value returned by the log-rank test is given as the measure of model success.

It is important to highlight the difference between prognostic and predictive models. A prognostic model is one capable of predicting prognosis, such as survival time, using patient information and biomarkers and does not vary between different treatment options. In contrast, a predictive model is one able to predict the effect of a treatment on patient prognosis [67,68]. It is therefore clear that, although prognostic models may be useful for research purposes and when one treatment option is available (such as the standard platinum-taxane combination), predictive models have a much greater part to play in stratified medicine where the aim is to identify the most appropriate treatment on a patient-by-patient basis. In order for a model to be predictive, the effects of multiple treatments must be considered and the response compared with the biomarker status. Classification of the studies as prognostic or predictive may be seen in Table 5. Of the papers identified by this review, only a minority considered the effects of chemotherapy treatment on the predicted outcome and hence could be considered predictive. Glaysher et al. [41] and Vogt et al. [62] produced separate models for various treatments, allowing the effects of different drugs and combinations to be compared. Both studies applied drugs in vitro to cultured tissue to measure response to chemotherapy. This was combined with gene expression measurements to form the model training data set. In this way the same patient samples may be used to create a set of models predicting response to a variety of drugs. These models are therefore predictive rather than prognostic. Alternatively, models may be trained on sets of patients split by treatments undergone, which would lead to treatment-specific models predicting response to the particular drug. This method was used by Jeong et al. [22], Ferriss et al. [33], Williams et al. [44] and Matsumura et al. [46]. Additionally, the use of a model variable specifying patient treatment history could allow these models to be combined onto one using a single training set of all patients. The model may then be passed a variable specifying the drug of interest for resistance prediction. A simple version of this method was implemented by Crijns et al. [47], who included a feature for whether a patient was treated with paclitaxel. It is clear that the integration of patient chemotherapy treatment into these models is underused, and it is likely to be beneficial for this to be incorporated into future research.

Genes identified

Of the 42 papers in this review, 32 provided full or partial lists of the genes identified by their models. Of the remainder, it was common that the gene sets were large or that the genes were not explicitly identified by the model, as is the case with modelling techniques such as principal components analysis.

In total across the papers, 1298 unique genes were selected by models and of these 93.53% were found by only one paper. The most commonly chosen gene was selected by only four papers. Table 9 shows the numbers and percentages of genes chosen by one to four papers.
Table 9

Numbers and percentages of genes featured in the gene sets of various numbers of papers

Number of papers

Number of genes

Percent of genes

identifying a gene

  

1

1214

93.53%

2

78

6.01%

3

5

0.385%

4

1

0.08%

A list of the genes identified by the papers in the review may be found in Table 10.
Table 10

List of genes reported by studies included in this review

A1BG

CHPF2

FSCN1

LRRC16B

PKD1

SOBP

A2M

CHRDL1

FXYD6

LRRC17

PKHD1

SORBS3

AADAC

CHRNE

FZD4

LRRC59

PLA2G7

SOS1

AAK1

CHST6

FZD5

LRSAM1

PLAA

SOX12

ABCA13

CHTOP

G0S2

LSAMP

PLAU

SOX21

ABCA4

CIAPIN1

G3BP1

LSM14A

PLAUR

SPANXD

ABCB1

CIB1

GABRP

LSM3

PLCB3

SPATA13

ABCB10

CIB2

GAD1

LSM7

PLEC

SPATA18

ABCB11

CIITA

GALNT10

LSM8

PLEK

SPATA4

ABCB7

CILP

GAP43

LTA4H

PLIN2

SPC25

ABCC3

CITED2

GART

LTB

PLS1

SPDEF

ABCC5

CKLF

GATAD2A

LTK

PMM1

SPEN

ABCD2

CLCA1

GCH1

LUC7L2

PMP22

SPHK2

ABCG2

CLCNKB

GCHFR

LY6K

PMVK

SPOCK2

ABLIM1

CLDN10

GCM1

LY96

PNLDC1

SPTBN2

ACADVL

CLIP1

GDF6

LZTFL1

PNLIPRP2

SRC

ACAT2

CNDP1

GFRA1

MAB21L2

PNMA5

SREBF2

ACKR2

CNKSR3

GGCT

MAD2L2

POFUT2

SRF

ACKR3

CNN2

GGT1

MAGEE2

POLH

SRRM1

ACO2

CNOT8

GJB1

MAGEF1

POLR3K

SRSF3

ACOT13

CNTFR

GLRX

MAK

POMP

SSR1

ACP1

cofilin1

GMFB

MAMLD1

POU2AF1

SSR2

ACRV1

COL10A1

GMPR

MANF

POU5F1

SSUH2

ACSM1

COL21A1

GNA11

MAP6D1

PPAP2B

SSX2IP

ACSS3

COL3A1

GNAO1

MAPK1

PPAT

ST6GALNAC1

ACTA2

COL4A4

GNAZ

MAPK1IP1L

PPCDC

STC2

ACTB

COL4A6

GNG4

MAPK3

PPCS

STK38

ACTBL3

COL6A1

GNG7

MAPK8IP3

PPFIA3

STX12

ACTG2

COL7A1

GNL2

MAPK9

PPIC

STX1B

ACTR3B

COX8A

GNMT

MAPKAP1

PPIE

STX7

ACTR6

CPD

GNPDA1

MAPKAPK2

PPP1R1A

STXBP2

ADAMDEC1

CPE

GOLPH3

MARCKS

PPP1R1B

STXBP6

ADAMTS5

CPEB1

GPIHBP1

MARK4

PPP1R2

SUB1

ADIPOR2

CRCT1

GPM6B

MATK

PPP1R26

SULT1C2

ADK

CREB5

GPR137

MB

PPP2R3C

SULT2B1

AEBP1

CRYAB

GPT2

MBOAT7

PPP2R5C

SUPT5H

AF050199

CRYBB1

GPX2

MCF2L

PPP2R5D

SUSD4

AF052172

CRYL1

GPX3

MCL1

PPP4R4

SUV420H1

AFM

CRYM

GPX8

MCM3

PPP6R1

SV2C

AFTPH

CSE1L

GRAMD1B

MDC1

PRAP1

SYNM

AGFG1

CSPP1

GRB2

MDFI

PRELP

SYT1

AGR2

CSRP1

GRK6

MDK

PRKAB1

SYT11

AGT

CSRP3

GRM2

MDR-1

PRKCH

SYT13

AIPL1

CST6

GRPEL1

MEA1

PRKCI

TAC3

AKAP12

CST9L

GRSF1

MEAF6

PRKD3

TAP1

AKR1A1

CT45A6

GSPT1

MECOM

PROC

TASP1

AKR1C1

CTA-246H3.1

GSTM2

MEF2B

PROK1

TBCC

AKT1

CTNNBL1

GSTT1

MEGF11

PRPF31

TBP

AKT2

CTSD

GTF2E1

MEST

PRRX1

TCF15

ALCAM

CUTA

GTF2F2

METRN

PRSS16

TCF7L2

ALDH5A1

CX3CL1

GTF2H5

METTL13

PRSS22

TENM3

ALDH9A1

CXCL1

GTPBP4

METTL4

PRSS3

TEX30

ALG5

CXCL10

GUCY1B3

MFAP2

PRSS36

TFF1

ALMS1

CXCL12

GYG1

MFSD7

PSAT1

TFF3

AMPD1

CXCL13

GYPC

MGMT

PSMB5

TFPI2

ANKHD1

CXCR4

GZMB

MINOS1

PSMB9

TGFB1

ANKRD27

CYB5B

GZMK

MKRN1

PSMC4

THBS4

ANXA3

CYBRD1

H2AFX

MLF2

PSMD1

TIAM1

ANXA4

CYP27A1

H3F3A

MLH1

PSMD12

TIMM10B

AOC1

CYP2E1

HAP1

MLX

PSMD14

TIMM17B

AP2A2

CYP3A7

HBG2

MMP1

PSME4

TIMP1

APC

CYP4X1

HDAC1

MMP10

PTBP1

TIMP2

API5

CYP4Z1

HDAC2

MMP12

PTCH2

TIMP3

APOE

CYP51A1

HECTD4

MMP13

PTEN

TKTL1

AQP10

CYSTM1

HES1

MMP16

PTGDS

TLE2

AQP5

CYTH3

HEY1

MMP17

PTGS2

TM9SF2

AQP6

D4S234E

HHIPL2

MMP3

PTP4A1

TM9SF3

AQP9

DAP

HIF1A

MMP7

PTP4A2

TMCC1

ARAF

DAPL1

HIP1R

MMP9

PTPRN2

TMED5

ARAP1

DBI

HIPK1

MPZL1

PTPRS

TMEM139

AREG

DCBLD2

HIST1H1C

MRPL2

PWP2

TMEM14B

ARFGEF2

DCHS1

HK2

MRPL35

QPRT

TMEM150A

ARHGAP29

DCK

HLAA

MRPL49

R3HDM2

TMEM161A

ARHGDIA

DCTN5

HLADMB

MRPS12

RAB26

TMEM259

ARL14

DCTPP1

HLADOB

MRPS17

RAB27B

TMEM260

ARL6IP4

DCUN1D4

HMBOX1

MRPS24

RAB40B

TMEM45A

ARMC1

DCUN1D5

HMGCS1

MRPS9

RAB5B

TMEM50A

ARNT2

DDB1

HMGCS2

MRS2

RAB5C

TMPRSS3

ARPC4

DDB2

HMGN1

MSH2

RABIF

TMSB15B

ASAP1

DDR1

HMOX2

MSL1

RAC1

TMTC1

ASAP3

DDX23

HNRNPA1

MSMO1

RAC3

TMX2

ASF1A

DDX49

HNRNPUL2

MST1

RAD23A

TNFRSF17

ASIP

DEFB132

HOPX

MT1G

RAD51

TNS1

ASPA

DERL1

HOXA5

MTCP1

RAD51AP1

TOMM40

ASPHD1

DFNB31

HOXB6

MTMR11

RANBP1

TONSL

ASS1

DHCR7

HPN

MTMR2

RANGAP1

TOP1

ASUN

DHRS11

HRASLS

MTPAP

RARRES2

TOP2A

ATM

DHRS9

Hs.120332

MTUS1

RB1

TOX3

ATP1B3

DHX15

HS3ST1

MTX1

RBBP7

TP53

ATP5D

DHX29

HS3ST5

MUS81

RBFA

TP53TG5

ATP5F1

DIAPH3

HSD11B2

MUTYH

RBM11

TP73

ATP5L

DICER1

HSD17B11

MXD1

RBM39

TPD52

ATP6V0E1

DIRC1

HSPA1L

MXI1

RCHY1

TPM2

ATP7B

DKK1

HSPA4

MYBPC1

RER1

TPP2

ATP8A2

DLAT

HSPA8

MYC

RFC3

TPPP

AUP1

DLEU2

HSPB7

MYCBP

RGL2

TPRKB

AURKA

DLG1

HSPD1

MYL9

RGP1

TRA

AURKC

DLG3

HTATIP2

MYO1D

RGS19

TRAF3IP2

AVIL

DLGAP4

HTN1

MYOM1

RHOT1

TRAM1

B3GALNT1

DLGAP5

HTR3A

NANOS1

RHPN2

TRAPPC4

B3GNT2

DMRT3

ICAM1

NASP

RIIAD1

TRAPPC9

B4GALT5

DNAH2

ICAM5

NBEA

RIN1

TREML1

BAG3

DNAH7

ID1

NBL1

RIT1

TREML2

BAIAP2L1

DNAJB12

ID4

NBN

RNF10

TRIAP1

BAK1

DNAJB5

IDI1

NCAM1

RNF13

TRIM27

BASP1

DNAJC16

IFIT1

NCAPD2

RNF14

TRIM49

BAX

DNASE1L3

IGF1R

NCAPG

RNF148

TRIM58

BCHE

DOCK3

IGFBP2

NCAPH

RNF34

TRIML2

BCL2A1

DPH2

IGFBP5

NCKAP5

RNF6

TRIT1

BCL2L11

DPM1

IGHM

NCOA1

RNF7

TRMT1L

BCL2L12

DPP7

IGKC

NCOR2

RNF8

TRO

BCR-ABL

DPYSL2

IGKV1-5

NCR2

RNGTT

TRPV4

BEAN

DRD4

IHH

NCSTN

RNPEPL1

TRPV6

BEST4

DTYMK

IKZF4

NDRG2

ROBO1

TSPAN3

BFSP1

DUSP2

IL11RA

NDST1

ROR1

TSPAN4

BFSP2

DUSP4

IL15

NDUFA12

ROR2

TSPAN6

BGN

DUX3

IL17RB

NDUFA9

RP13-347D8.3

TSPAN7

BHLHE40

DYNLT1

IL1B

NDUFAB1

RP13-36C9.6

TSR1

BIN1

DYRK3

IL23A

NDUFAF4

RPA3

TTC31

BIRC5

E2F2

IL27

NDUFB4

RPL23

TTLL6

BIRC6

ECH1

IL6

NDUFS5

RPL29P17

TTPAL

BLCAP

EDF1

IL8

NEBL

RPL31

TTYH1

BLMH

EDN1

IMPA2

NETO2

RPL36

TUBB3

BMP8B

EDNRA

ING3

NEUROD2

RPP30

TUBB4A

BMPR1A

EDNRB

INHBA

NFE2

RPS15

TUBB4Q

BNIP3

EEF1A2

INPP5A

NFE2L3

RPS16

TUSC3

BOLA3

EFCAB14

INPP5B

NFIB

RPS19BP1

UBD

BPTF

EFEMP2

INSR

NFKBIB

RPS24

UBE2I

BRCA1

EFNB2

INTS12

NFS1

RPS28

UBE2K

BRCA2

EGF

INTS9

NID1

RPS4Y1

UBE2L3

BRSK1

EGFR

IRF2BP1

NIT1

RPS6KA2

UBE4B

BTN3A3

EHD1

ISCA1

NKIRAS2

RPSA

UBR5

BTNL9

EHF

ISG20

NKX31

RRAGC

UGT2B17

C11orf16

EI24

ITGAE

NKX62

RRBP1

UGT8

C11orf74

EIF1

ITGB2

NLGN1

RRN3

UHRF1BP1

C12orf5

EIF2AK2

ITGB6

NOP5/58

RSL24D1

UMOD

C16orf89

EIF3K

ITGB7

NOS3

RSU1

UPK1A

C17orf45

EIF4E2

ITLN1

NOTCH4

RTN4R

UPK1B

C17orf53

EIF5

ITM2A

NOV

RXRB

UQCRC2

C17orf70

ELF3

ITM2C

NOX1

RYBP

URI1

C1orf109

ELF5

ITPR2

NPAS3

RYR3

USP14

C1orf115

EML4

ITPRIP

NPR1

S100A10

USP18

C1orf159

ENC1

JAG2

NPR3

S100A4

USP21

C1orf198

ENOPH1

JAK2

NPTX2

S100P

UST

C1orf27

ENSA

JAKMIP2

NPTXR

SAMD4B

UTP11L

C1orf68

ENTPD4

KCNB1

NPY

SASH1

UTP20

C1QTNF3

EPB41L4A

KCNE3

NRBP2

SCAMP3

UVRAG

C20orf199

EPCAM

KCNH2

NRG4

SCARF1

VDR

C2orf72

EPHB2

KCNJ16

NRP1

SCG2

VEGFA

C4A

EPHB3

KCNN1

NSFL1C

SCGB1C1

VEGFB

C4BPA

EPHB4

KCNN3

NSL1

SCGB3A1

VEZF1

C6orf120

EPOR

KCTD1

NSMCE4A

SCNM1

VPS39

C6orf124

ERBB3

KCTD5

NT5C3A

SCO2

VPS52

C9orf3

ERCC8

KDELC1

NTAN1

SCUBE2

VPS72

C9orf47

ERMP1

KDELR1

NTF4

SDF2L1

VTCN1

CA13

ESF1

KDELR2

NUDT21

SEC14L2

VTI1B

CACNA1B

ESM1

KDM4A

NUDT9

SELT

WBP2

CACNG6

ESR1

Ki67

NUS1

SEMA3A

WBP4

CADM1

ESRP2

KIAA0125

OAS3

SENP3

WDR12

CALML3

ESYT1

KIAA0141

OASL

SENP6

WDR45B

CAMK2B

ETS1

KIAA0226

ODF4

SEPN1

WDR7

CAMK2N1

ETV1

KIAA0368

OGFOD3

SERPINB6

WDR77

CANX

EVA1A

KIAA1009

OGN

SERPIND1

WIT1

CAP1

EXOC6B

KIAA1033

OPA3

SERPINF1

WIZ

CAP2

EXTL1

KIAA1324

OR10A3

SERTAD4

WNK4

CAPN13

EYA2

KIAA1551

OR2AG1

SETBP1

WNT16

CAPN5

F2R

KIAA2022

OR4C15

SF3A3

WT1

CASC3

FAAH

KIAA4146

OR51B5

SF3B4

WTAP

CASP9

FABP1

KIF3A

OR51I1

SGCB

WWOX

CASS4

FABP7

KIFC3

OR6F1

SGCG

XBP1

CATSPERD

FADS1

KIT

OR9G9

SGPP1

XPA

CC2D1A

FADS2

KLF12

OSGEPL1

SH3PXD2A

XPO4

CCBL1

FAM133A

KLF5

OSGIN2

SHFM1

XYLT1

CCDC130

FAM135A

KLHDC3

OSM

SHOX

Y09846

CCDC135

FAM155B

KLHL7

OXTR

SIDT1

YBX1

CCDC147

FAM174B

KLK10

P2RX4

SIGLEC8

YIPF3

CCDC167

FAM19A4

KLK6

PABPC4

SIRT5

YIPF6

CCDC19

FAM211B

KPNA3

PAGR1

SIRT6

YLPM1

CCDC53

FAM217B

KPNA6

PAH

SIVA1

YWHAE

CCDC9

FAM49B

KRT10

PAK4

SIX2

YWHAZ

CCL13

FAM8A1

KRT12

PALB2

SKA3

ZBTB11

CCL2

FANCB

KYNU

PARD6B

SLAMF7

ZBTB16

CCL28

FANCE

L1TD1

PAX6

SLC12A2

ZBTB8A

CCM2L

FANCF

LAMB1

PBK

SLC12A4

ZC3H13

CCNA2

FANCG

LAMTOR5

PBX2

SLC14A1

ZCCHC8

CCNG2

FANCI

LARP4

PBXIP1

SLC15A2

ZEB2

CCT6A

FARP1

LAX1

PCF11

SLC1A1

ZFHX4

CCZ1

FAS

LAYN

PCGF3

SLC1A3

ZFP91

CD34

FASLG

LBR

PCK1

SLC22A5

ZFR2

CD38

FBXL18

LCMT2

PCNA

SLC25A37

ZKSCAN7

CD44

FCGBP

LCTL

PCNXL2

SLC25A41

ZMYND11

CD46

FCGR3B

LDB1

PCOLCE

SLC25A5

ZNF106

CD70

FEN1

LDHB

PCSK6

SLC26A9

ZNF12

CD97

FEZ1

LGALS4

PDCD2

SLC27A6

ZNF124

CDC42EP4

FGF2

LGR5

PDE3A

SLC29A1

ZNF148

CDCA2

FGFBP1

LHB

PDGFA

SLC2A1

ZNF155

CDH12

FGFR1OP

LHX1

PDGFRA

SLC2A5

ZNF180

CDH19

FGFR1OP2

LIN28A

PDGFRB

SLC37A4

ZNF200

CDH3

FGFR2

LINGO1

PDP1

SLC39A2

ZNF292

CDH4

FHL2

LIPA

PDSS1

SLC4A11

ZNF337

CDH5

FILIP1

LIPC

PDZK1

SLC5A1

ZNF432

CDK17

FJX1

LIPG

PEBP1

SLC5A3

ZNF467

CDK20

FKBP11

LMO3

PEX11A

SLC5A5

ZNF48

CDK5R1

FKBP1B

LMO4

PEX6

SLC6A3

ZNF503

CDK8

FKBP7

LOC100129250

PFAS

SLC7A2

ZNF521

CDKN1A

FLII

LOC149018

PGAM1

SMAD2

ZNF569

CDY1

FLJ41501

LOC1720

PHF3

SMC4

ZNF644

CDYL2

FLNC

LOC389677

PHGDH

SMG1

ZNF71

CEACAM5

FLOT2

LOC642236

PHKA1

SMPD2

ZNF711

CEACAM6

FLT1

LOC646808

PHKA2

SNIP1

ZNF74

CEACAM7

FMN2

LOC90925

PI3

SNRPA1

ZNF76

CEP55

FMO1

LPAR6

PIC3CD

SNRPC

ZNF780B

CES1

FN1

LPCAT2

PIGC

SNRPD3

ZYG11A

CES2

FOXA2

LPCAT4

PIGR

SNX13

 

CFI

FOXD4L2

LPHN2

PIK3CG

SNX19

 

CH25H

FOXJ1

LRIG1

PIP5K1B

SNX7

 

CHIT1

FOXO3

LRIT1

PITRM1

SOAT2

 

Gene names have been standardised. Genes in bold were selected by more than two studies.

It is clear that the gene sets selected by the studies are very different and there is very little overlap. The genes chosen by two or more studies may be seen in Table 11. Many of these genes are known to have links to cancer, which may suggest that these genes are therefore implicated in ovarian cancer. It is possible that, although the genes selected varied, they in fact represent similar mechanisms. This could occur if there are large sets of highly covariate genes representing particular cellular processes and the genes in the signatures were simply random selections from these gene sets. The same gene being selected by multiple papers would then be unlikely, although the same information contribution would be made. It may then be more informative to assess and compare the mechanisms controlled by the genes chosen as part of the models.
Table 11

Genes chosen most commonly by studies in review

Gene symbol

Number of studies

Function

Expression links to cancer in literature

AGR2

4

Cell migration and growth

Prostate, breast, ovarian, pancreatic

MUTYH

3

Oxidative DNA damage repair

Colorectal

AKAP12

3

Subcellular compartmentation of PKA

Colorectal, lung, prostate

TP53

3

Cell cycle regulation

Breast

TOP2A

3

Required for DNA replication

Breast, prostate, ovarian

FOXA2

3

Liver-specific transcription factor

Lung, prostate

SRC

2

Regulation of cell growth

Colon, liver, lung, breast, pancreatic

SIVA1

2

Pro-apoptotic protein

Many cancers

ALDH9A1

2

Aldehyde dehydrogenase

Many cancers

LGR5

2

Associated with stem cells

Cancer stem cells

EHF

2

Epithelial differentiation and proliferation

Prostate

BAX

2

Apoptotic activator

Colon, breast, prostate, gastric, leukaemia

CES2

2

Intestine drug clearance

Colorectal

CPE

2

Synthesis of hormones and neurotransmitters

 

FGFBP1

2

Cell proliferation, differentiation and migration

Colorectal, pancreatic

TUBB4A

2

Component of microtubules

 

ZNF12

2

Transcription regulation

 

RBM39

2

Steroid hormone receptor-mediated transcription

 

RFC3

2

Required for DNA replication

 

GNPDA1

2

Triggers calcium oscillations in mammalian eggs

 

ANXA3

2

Regulation of cellular growth

Prostate, ovarian

NFIB

2

Activates transcription and replication

Breast

ACTR3B

2

Actin cyctoskeleton organisation

Lung

YWHAE

2

Mediates signal transduction

Lung, endometrial

CYP51A1

2

Drug metabolism and lipid synthesis

 

HMGCS1

2

Cholesterol synthesis and ketogenesis

 

ZMYND11

2

Transcriptional repressor

 

FADS2

2

Regulates unsaturation of fatty acids

 

SNX7

2

Family involved in intracellular trafficking

 

ARHGDIA

2

Regulates the GDP/GTP exchange reaction of the Rho proteins

Prostate, lung,

NDST1

2

Inflammatory response

Prostate, breast

AOC1

2

Catalyses degredation of such as histamine and spermidine

 

DAP

2

Positive mediator of programmed cell death

 

ERCC8

2

Transcription-coupled nucleotide excision repair

 

GUCY1B3

2

Catalyzes conversion of GTP to the second messenger cGMP

 

HDAC1

2

Control of cell proliferation and differentiation

Prostate, breast, colorectal, gastric

HDAC2

2

Transcriptional regulation and cell cycle progression

Cervical, gastric, colorectal

IGFBP5

2

Cell proliferation, differentiation, survival, and motility

Breast

IL6

2

Transcriptional inflammatory response, B cell maturation

Many cancers

LSAMP

2

Neuronal surface glycoprotein

Osteosarcoma

MDK

2

Cell growth, migration, angiogenesis

Many cancers

MYCBP

2

Stimulates the activation of E box-dependent transcription

 

S100A10

2

Transport of neurotransmitters

Colorectal, lung, breast

SLC1A3

2

Glutamate transporter

 

NCOA1

2

Stimulates hormone-dependent transcription

Breast, prostate

TIAM1

2

Modulates the activity of Rho GTP-binding proteins

Many cancers

VEGFA

2

Angiogenesis, cell growth, cell migration, apoptosis

Many cancers

RPL36

2

Component of ribosomal 60S subunit

 

LBR

2

Anchors lamina and heterochromatin to the nuclear membrane

 

ABCB1

2

ATP-dependent drug efflux pump for xenobiotic compounds

Many cancers

FASLG

2

Required for triggering apoptosis in some cell types

Many cancers

TIMP1

2

Extracellular matrix, proliferation, apoptosis

Many cancers

FN1

2

Cell adhesion, motility, migration processes

Many cancers

TGFB1

2

Proliferation, differentiation, adhesion, migration

Prostate, breast, colon, lung, bladder

XPA

2

DNA excision repair

Many cancers

ABCB10

2

Mitochondrial ATP-binding cassette transporter

 

POLH

2

Polymerase capable of replicating UV-damaged DNA for repair

 

ITGAE

2

Adhesion, intestinal intraepithelial lymphocyte activation

 

ZNF200

2

Zinc finger protein

 

COL3A1

2

Collagen type III, occurring in most soft connective tissues

 

ACKR3

2

G-protein coupled receptor

 

EPHB3

2

Mediates developmental processes

Lung, colorectal

NBN

2

Double-strand DNA repair, cell cycle control

 

PCF11

2

May be involved in Pol II release following polymerisation

 

DFNB31

2

Sterocilia elongation, actin cystoskeletal assembly

 

BRCA2

2

Double-strand DNA repair

Breast, ovarian

AADAC

2

Arylacetamide deacetylase

 

CD38

2

Glucose-induced insulin secretion

Leukaemia

CHIT1

2

Involved in degradation of chitin-containing pathogens

 

CXCR4

2

Receptor specific for stromal-derived-factor-1

Breast, glioma, kidney, prostate

EFNB2

2

Mediates developmental processes

 

MECOM

2

Apoptosis, development, cell differentiation, proliferation

Leukaemia

FILIP1

2

Controls neocortical cell migration

Ovarian

HSPB7

2

Heat shock protein

 

LRIG1

2

Regulator of signaling by receptor tyrosine kinases

Glioma

MMP1

2

Breakdown of extracellular matrix

Gastric, breast

PSAT1

2

Phosphoserine aminotransferase

 

SDF2L1

2

Part of endoplasmic reticulum chaperone complex

 

TCF15

2

Regulation of patterning of the mesoderm

 

EPHB2

2

Contact-dependent bidirectional signaling between cells

Colorectal

ETS1

2

Involved in stem cell development, cell senescence and death

Many cancers

TRIM27

2

Male germ cell differentiation

Ovarian, endometrial, prostate

MARK4

2

Mitosis, cell cycle control

Glioma

B4GALT5

2

Biosynthesis of glycoconjugates and saccharides

 

Genes listed by number of papers selecting each gene. Gene function and links to cancer obtained via cursory literature search.

Gene set enrichment

The gene sets reported by the studies identified in this review were assessed to identify whether certain biological pathways and mechanisms featured more prominently according to the genes selected. Studies were split by chemotherapy treatments recieved by the patients, and the groups identified were platinum and taxane, and other treatments (such as platinum, cyclophosphamide and combinations). Studies that did not specify the chemotherapy treatments used were excluded. Studies falling into the platinum and taxane group were Han et al. [28], Kang et al. [31], Gillet et al. [32], Skirnisdottir and Seidal [35], Schlumbrecht et al. [40], Yoshihara et al. [43], Denkert et al. [45], Hartmann et al. [57], Iba et al. [60], and Kamazawa et al. [61]. Studies falling into the other treatments group were Obermayr et al. [27], Sabatier et al. [27], Yan et al. [42], Netinatsunthorn et al. [51], and Helleman et al. [53]. The results of the gene set enrichment using the KEGG system may be seen in Figures 2 and 3. From the plots, it may be seen that both groups identify several cancer-related pathways relevant to the drug mechanisms of action.
Figure 2
Figure 2

Gene set enrichment networks for studies assessing ovarian cancer patients treated with platinum and taxane. Network maps of the 30 most enriched KEGG pathways. Node marker size signifies the number of genes in this category, and the thickness of edges indicate the Jaccard similarity coefficient between categories. Node markers are coloured according to adjusted p value as reported by the hypergeometric test, where darker red denotes more highly significant.

Figure 3
Figure 3

Gene set enrichment networks for studies assessing ovarian cancer patients treated with treatments other than platinum and taxane. Network maps of the 30 most enriched KEGG pathways. Node marker size signifies the number of genes in this category, and the thickness of edges indicate the Jaccard similarity coefficient between categories. Node markers are coloured according to adjusted p value as reported by the hypergeometric test, where darker red denotes more highly significant.

It is informative to consider the KEGG terms in the context of the mechanisms of action of the chemotherapy drugs applied. Both groups contain patients treated with platinum single agents or platinum-containing combinations. It should therefore be expected that processes associated with the mechanism of action of platinum will be enriched. Once activated, the platinum binds to DNA and results in the formation of monoadducts, intra-strand crosslinking, inter-strand crosslinking and protein crosslinking. This DNA structure change affects the ability of the DNA to be unwound and replicated, resulting in the triggering of the G2-M DNA damage checkpoint and cell cycle arrest. The affected cell will attempt DNA repair and, if unsuccessful, undergo apoptosis [69]. Expected KEGG terms therefore include those relating to apoptosis and DNA damage.

From Figure 2, KEGG pathways highlighted for this group of studies include ten cancer-specific terms and six cancer-related terms. Here italics denote a KEGG term. The ErbB signalling pathway has been found to influence in proliferation, migration, differentiation and apoptosis in cancer [70] and overexpression of ERBB1 and ERBB2 have been implicated in head and neck and breast cancers. The neurotrophin signalling pathway is known to trigger MAPK and PI3K signalling, affecting differentiation, proliferation and development, and survival, growth, motility and angiogenesis respectively [71]. Altered expression of genes in this pathway has been found to correlate with poorer survival in colon, breast, lung and prostate cancers. Changes in expression of genes relating to focal adhesion, which is responsible for attachment of cells to the extracellular matrix, have been implicated in cancer migration, invasion, survival and growth [72]. The TGF-beta signalling pathway also regulates many cellular processes, including proliferation, cellular adhesion and motility, coregulation of telomerase function, regulation of apoptosis, angiogenesis, immunosuppression and DNA repair [73]. The p53 signalling pathway has many varied links to cancer. This pathway many be triggered by various stress signals and can result in several responses, including cell cycle arrest, apoptosis, the inhibition of angiogenesis and metastasis, and DNA repair [74]. Finally, nucleotide excision repair is known to promote cancer development when both up and down regulated. Down-regulation correlates is thought to increases susceptibility to mutation formation and hence the formation of cancer [75], whereas up-regulation has been found to correlate with resistance to platinum as the DNA damage caused by the chemotherapy agent is repaired [76].

The first group of studies considered patients treated with taxanes in addition to platinum. Taxanes act by stabilising tubulin, preventing the microtubule structure formation required for mitosis. This results in cell cycle arrest at the G2/M DNA damage checkpoint and apoptosis. Mechanisms for taxane resistance are, however, not well understood. Two suggested mechanisms include the increased expression of multidrug transporters, and changes in the expression of the β-tubulin isoforms [77]. Neither of these mechanisms seem to be enriched in the platinum and taxol group. In addition to the single-agent effects of platinum and taxanes, there is an additional synergistic effect [78]. However, this effect is also not well studied and hence the mechanisms by which this occurs are not clear.

The second group, as seen in Figure 3, was composed of studies applying chemotherapy treatments other than platinum and taxanes. This group is heterogeneous with respect to chemotherapy treatment, and mainly consists of studies reporting treatment as ‘platinum-based’. The other drug explicitly mentioned by studies in this group is cyclophosphamide. This drug is an alkylating agent and acts to form adducts in DNA [79]. This DNA damage triggers the G2/M DNA damage checkpoint, resulting in DNA repair or apoptosis. This suggests that the same DNA repair mechanisms related to platinum treatment are also relevant to cyclophosphamide. For this group, the KEGG pathway analysis shows that the gene set is enriched with 14 pathways related to cancer, in addition to two general cancer-related terms. The mTOR signalling pathway is downstream to the PI3K/AKT pathway and regulates growth, proliferation and survival [80]. The MAPK signalling pathway controls the cell cycle, and has been found to contribute to the control of proliferation, differentiation, apoptosis, migration and inflammation in cancer [81]. The chemokine signalling pathway has been found to regulate growth, survival and migration in addition to its role in inflammation [82]. Angiogenesis and vasculogenesis are known to be regulated by the VEGF signalling pathway [83], which is already the target of treatments such as bevacizumab. Purine metabolism is required for the production and recycling of adenine and guanine, and hence is required for DNA replication. This process is the target of chemotherapies such as methotrexate. The term drug metabolism – other enzymes is partially cancer related; this term refers to five drugs: azathioprine, 6-mercaptopurine, irinotecan, fluorouracil and isoniazid. Of these, two are chemotherapy treatments; irinotecan is a topoisomerase-I inhibitor and fluorouracil acts as a purine analogue. Also featuring in Figure 3 are apoptosis, ErbB signalling pathway, focal adhesion, neurotrophin signalling pathway, B cell receptor signalling pathway and Jak-STAT signalling pathway, all of which are known to be related to cancer.

Overall, the gene sets appear to be enriched for cancer-related resistance mechanisms [84]. However, when combined there is little evidence from this analysis to suggest that the signatures are capturing chemotherapy-specific mechanisms in addition to more general survival pathways. The DNA repair terms may suggest a response to platinum-based treatment, though the down-regulation of these mechanisms is also related to cancer development and resistance in general [85]. It is likely that, due to the varying reliability suggested by the bias analysis and the reported model development techniques, the signal-to-noise ratio of informative genes is low when the gene signatures are combined, preventing the identification of processes of interest.

Model predictive ability

Sensitivity and specificity

The comparison of the success of the various models is difficult, particularly due to the fact that many papers report different metrics as measures of model accuracy. Many of these are also incomplete, not providing enough information to fully describe the model. Ideally, models should be applied to an independent set of samples with known outcomes and performance measures on this data set reported. For classification models an informative set of measures would be positive predictive value, negative predictive value, specificity and sensitivity:
$$\begin{aligned} \text{Sensitivity} &= \frac{n_{\text{true positive}}}{n_{\text{true positive}}+n_{\text{false negative}}}\\ \text{Specificity} &= \frac{n_{\text{true negative}}}{n_{\text{true negative}}+n_{\text{false positive}}}\\ \text{PPV} &= \frac{n_{\text{true positive}}}{n_{\text{true positive}}+n_{\text{false positive}}}\\ \text{NPV} &= \frac{n_{\text{true negative}}}{n_{\text{true negative}}+n_{\text{false negative}}} \end{aligned} $$
where ntrue positive is the number of true positive predictions, nfalse positive is the number of false positive predictions, ntrue negative is the number of true negative predictions and nfalse negative is the number of false negative predictions.

Together these provide information on true positive and negative rates as well as false positive and false negative rates, all of which are important when assessing the performance of a model.

Using the sensitivity and specificity the positive and negative likelihood ratios may be calculated and, using the prevalence of the condition in the test population, the probability of a patient having the condition based on the test results may be found, as in the equations below.
$$\begin{aligned} \text{LR}_{\text{+ve}} &= \frac{\text{sensitivity}}{1-\text{specificity}}\\ \text{LR}_{\text{-ve}}& = \frac{1-\text{sensitivity}}{\text{specificity}}\\ P(\text{Condition}+|\text{Test}+) &= \frac{\frac{P(\text{Condition}+)}{1-P(\text{Condition}+)}\cdot\text{LR}_{\text{+ve}}}{\frac{P(\text{Condition}+)}{1-P(\text{ Condition}+)}\cdot\text{LR}_{\text{+ve}}+1}\\ P(\text{Condition}+|\text{Test}-) &= \frac{\frac{P(\text{Condition}-)}{1-P(\text{Condition}-)}\cdot\text{LR}_{\text{-ve}}}{\frac{P(\text{Condition}-)}{1-P(\text{Condition}-)}\cdot\text{LR}_{\text{-ve}}+1} \end{aligned} $$

These post-test probabilities are much easier to interpret and incorporate the prevalence of the condition. It should be noted that in order for the test to be applied in a clinical situation the pre-test probabilities used, P(Condition+) and P(Condition−), should be correct for the population of patients to whom the test will be applied. Here the sample prevalence from each study was used for convenience. However, it would be informative to recalculate P(Condition+|Test+) and P(Condition+|Test−) for the general population of ovarian cancer patients, as this would provide a better comparison between models.

Table 12 details the post-test probabilities of patients having a condition based on a positive or negative test result from the models developed by studies in this review. The papers appearing here are those that supplied sensitivity and specificity and the numbers of patients with and with without the condition, or alternative information allowing these to be calculated such as numbers of true and false positives and negatives.
Table 12

Prediction metrics for studies reporting sensitivity and specificity

Study

Prediction

Sensitivity

Specificity

LR+ve

LR-ve

P(C+)

P(C−)

P(C+|T+)

P(C+|T−)

Li et al. [3]

Chemoresistance

0.96*

0.23*

1.24

0.18

\(\frac {22}{44}\)

\(\frac {22}{44}\)

0.55

0.15

Obermayr et al. [27]

RFS

0.22*

0.85*

1.47

0.92

\(\frac {46}{216}\)

\(\frac {170}{216}\)

0.28

0.77

Ferriss et al. [33]

Chemoresponse

0.94*

0.29*

1.33

0.20

\(\frac {85}{119}\)

\(\frac {34}{119}\)

0.77

0.07

Sabatier et al. [37]

Prognosis

0.62*

0.62*

1.64

0.62

\(\frac {194}{366}\)

\(\frac {172}{366}\)

0.65

0.35

Yoshihara et al. [43]

PFS

0.64*

0.69*

2.06

0.52

\(\frac {45}{87}\)

\(\frac {39}{87}\)

0.69

0.30

Williams et al. [44]

Prognosis

0.77*

0.56*

1.75

0.41

\(\frac {97}{143}\)

\(\frac {46}{143}\)

0.79

0.16

Gevaert et al. [49]

Chemoresistance

0.67*

0.40*

1.12

0.82

\(\frac {15}{45}\)

\(\frac {30}{45}\)

0.36

0.62

Helleman et al. [53]

Chemoresistance

0.89*

0.56*

2.02

0.20

\(\frac {9}{72}\)

\(\frac {63}{72}\)

0.22

0.58

De Smet et al. [52]

Chemoresistance

0.71

0.83

4.29

0.34

\(\frac {6}{13}\)

\(\frac {7}{13}\)

0.79

0.29

Raspollini et al. [56]

Prognosis

0.79

0.46

1.45

0.47

\(\frac {28}{52}\)

\(\frac {24}{52}\)

0.63

0.29

Hartmann et al. [57]

Prognosis

0.86*

0.86*

6.14

0.16

\(\frac {21}{28}\)

\(\frac {7}{28}\)

0.95

0.05

Selvanayagam et al. [59]

Chemoresistance

1.00

1.00

0.00

\(\frac {4}{8}\)

\(\frac {4}{8}\)

1.00

0.00

Kamazawa et al. [61]

Chemoresponse

1.00*

0.83

6.00

0.00

\(\frac {21}{27}\)

\(\frac {5}{27}\)

0.95

0.00

*Value stated in reference.

Value calculated.

C: condition presence.

T: test result.

RFS: Relapse Free Survival.

PFS: Progression Free Survival.

From the table it may be seen that there is a great variety between the success of the models. For example, Kamazawa et al. [61] and Hartmann et al. [57] both achieved P(Condition+|Test+)=0.95 on their respective samples of the population. This means that if a patient tests positive, there is a 95% probability that they are positive for the condition in question, which in these cases are ‘responding to chemotherapy’ and ‘poor prognosis’ respectively. In contrast, Obermayr et al. [27], Helleman et al. [53] and Gevaert et al. [49] only achieved P(Condition+|Test+) of between 0.20 and 0.40. These results suggest that the tests are not able to predict the outcome of a patient any better than a random choice, and in the case of tests in the region of 0.20 it is likely that most patients are simply assigned to the same class.

The ability of tests to not commit type II errors and give false negatives is also important. Ferriss et al. [33] and Hartmann et al. [57] both achieved well in this regard, with P(Condition+|Test−)=0.07 and P(Condition+|Test−)=0.05 respectively. Several studies, by contrast, had very poor probabilities of false negatives; Obermayr et al. [27], Helleman et al. [53] and Gevaert et al. [49] all have P(Condition+|Test−)>0.5, which suggests that these models give a false negative more often than a random assignment.

Kamazawa et al. [61] and Selvanayagam et al. [59] both achieved extremely impressive prediction abilities, as may be seen by the very large P(Condition+|Test+) and very small P(Condition+|Test−) values. However, these studies exemplify why care must be taken in assessing the predictive ability of models. Both studies calculated sensitivity and specificity based on only training set results and hence there is no way to judge the generalisability of the models. There is a tendency for models to perform better on the training set than any following independent data set to which it is subsequently applied. Secondly, the training set used by Selvanayagam et al. [59] is extremely small at eight patients and has a 50 : 50 ratio of chemoresistant to chemosensitive patients. This sample is not representative of the population and hence the values of P(Condition+|Test+) and P(Condition+|Test−) will be skewed by unrepresentative P(Condition+) and P(Condition−).

Overall, the most successful model of this group is that by Hartmann et al. [57] as it makes predictions with good reliability and has been validated on an independent data set. The least successful models were Obermayr et al. [27], Helleman et al. [53] and Gevaert et al. [49]. These studies suffered from low ability to identify true positives and high probability of false positives, resulting in poor predictive ability.

Hazard ratios

It is common for studies of survival to quote hazard ratios comparing the results of clusters identified by classification models or relative-risk models such as Cox proportional hazards regression. These ratios represent the ratio of the probability of an event occurring to a patient in each of the two groups. The event is often death, but could also be recurrence for example. The studies listed in Table 13 supplied hazard ratios as measures of predictive ability. The hazard ratios vary from 0.23 to 4.6 with the majority around 2 to 3. A hazard ratio that is not equal to 1 suggests that the variable has predictive ability, and a ratio of 4, for example, suggests that a member of the high-risk group is 4 times as likely to die within the study period than a member of the low-risk group. The study with the highest hazard ratio is Spentzos et al. [58], with HR=4.6. This is closely followed by Raspollini [56] with HR=0.23 and Skirnisdottir and Seidal [35] with HR=4.12. The confidence intervals on the hazard ratios of all the studies are large and, with the exception of Spentzos et al. [58], at the lowest edge the hazard ratio is very close to 1. This suggests that, although all these hazard ratios were found to be significant, some were close to not reaching the arbitrary 5% level. Most notable are Roque et al. [24], Schlumbrecht and Seidal[40], and Denkert et al. [45]. These models would need further investigation to determine their predictive ability. Of the papers in this group, Spentzos et al. [58] appears to have the best predictive ability when classifying patients into two clusters with significantly different survival times.
Table 13

Prediction metrics for studies reporting hazard ratios

Study

Prediction

Classes

HR

95% CI

Median survival

P value

Jeong et al. [22]

OS

YA subgroup vs. YI subgroup

0.5

0.31−0.82

 

0.005

Roque et al. [24]

OS

High vs. low TUBB3 staining

3.66

1.11−12.05

707 days vs. not reached

0.03

Kang et al. [31]

OS

High vs. low score

0.33

0.13−0.86

1.8 years vs. 2.9 years

<0.001

Skirnisdottir and Seidal [35]

Recurrence

p53 -ve vs. +ve

4.12

1.41−12.03

 

0.009

Schlumbrecht et al. [40]

RFS

EIG121 high vs. low

1.13

1.02−1.26

 

0.021

Yoshihara et al. [43]

PFS

High vs. low score

1.64

1.27−2.13

 

0.0001

Denkert et al. [45]

OS

Low vs. high score

1.7

1.1−2.6

 

0.021

Crijns et. al [47]

OS

 

1.94

1.19−3.16

 

0.008

Netinatsunthorn et al. [51]

RFS

Yes vs. no WT1 staining

3.36

1.60−7.03

 

0.0017

Spentzos et al. [54]

OS

Resistant vs. sensitive

3.9

1.3−11.4

41 months vs. not reached

<0.001

Raspollini et al. [56]

OS

No vs. yes COX-2 staining

0.23

0.06−0.77

 

0.017

Spentzos et al. [58]

OS

High vs. low score

4.6

2.0−10.7

30 months vs. not reached

0.0001

Calculated value.

HR: Hazard Ratio.

OS: Overall Survival.

RFS: Relapse Free Survival.

PFS: Progression Free Survival.

CI: Confidence Interval.

Linear regression

Two papers reported the success of model assessed using linear regression: Glaysher et al. [41] and Kang et al. [31]. These studies plotted the predicted values or model score against the measured values and applied linear regression to obtain a line of best fit. The R2 or \(R^{2}_{\text {adj}}\) of this line is then calculated to assess the discrimination of the model. Glaysher et al. [41] achieved R2=0.901 (\(R^{2}_{\text {adj}}=0.836\)) for a model predicting resistance to cisplatin via cross-validation and Kang et al. [31] achieved R2=0.84 for a model predicting recurrence-free survival in the data set on which it was derived. These values suggest a good level of predictive ability, both in terms of calibration and discrimination, with the model by Glaysher et al. [41] achieving the better predictions.

Cox proportional hazards models

When studies identified by this review applied the Cox proportional hazards model to predict patient outcome, it was common for the main analysis of the model to be assessing whether the gene signature was found to be significant and whether the signature was an independent predictor. However, the application of this model to an independent data set was much less common. As may be seen from Table 6, the success of many models was judged using the significance of covariates including the gene signature in the model. It is likely that this model was not applied to external data sets due to subtleties in what the model predicts when compared to methods such as linear regression. Whereas in linear regression the survival times are predicted directly, Cox proportional hazards regression predicts hazard ratios. Royston and Altman [86] developed techniques for the external validation of Cox proportional hazards models by application to an independent data set. These rely on having at least the weights of the variables included in the linear predictor, and ideally the baseline survival function. The first allows the assessment of the discriminatory power of a model, whereas the second is also required to allow the calibration of the model to be assessed. Royston and Altman [86] are of the opinion that the inclusion of a log-rank test p-value is not informative due to the irrelevance of the null hypothesis being tested, and hence this should not be considered when judging model performance. An alternative to the log-rank test to compare survival between groups would be time-dependent ROC curves [87].

Failure to predict

Of the studies identified by this review, some models failed to achieve significant predictive ability. These include Lisowska et al. [23], Vogt et al. [62] and Brun et al. [34]. Of these papers, Vogt et al. [62] and Brun et al. [34] both considered small numbers of genes when constructing their models. It is possible then that these models failed because no informative genes were considered. Conversely, Lisowska [23] applied their modelling technique to over 47000 genes using 127 patients. It is therefore a possibility that genes were selected by their model purely by chance rather than due to true explanatory ability. This model was tested using an independent data. When the model was applied to this data set it performed poorly, suggesting that the genes chosen did not generalise to the second cohort of patients. Neither Vogt et al. [62] nor Brun et al. [34] reported measuring the precision or accuracy of the gene expression measurements. Lisowska et al. [23] used RT-PCR to measure the expression of 18 genes from the microarray, but the RT-PCR measurements were carried out on a separate set of samples and hence are not useful when considering accuracy. It is therefore unknown whether the gene expression measurement techniques applied by these studies were sufficiently accurate.

Discussion

The papers identified as part of this review tackled the important issue of chemoresistance and survival prediction in ovarian cancer via gene or protein expression. The concept of identifying gene signatures is popular, but requires careful handling to extract the information required for this to be successful. It was observed that of the many different tissue preservation techniques applied, the most common were fresh-frozen and formalin fixed, paraffin embedded tissue. It is our opinion that, due to the high quality expression measurements that may now be achieved with FFPE tissue, this is the most appropriate choice for research intended to translate into a clinical setting.

It was found that the majority of the studies included in this review were heterogeneous with respect to the histological type of the patient cohort. This suggests that, due to the differing response of different types of ovarian cancer to chemotherapy, the gene signatures may be identifying different pathways and mechanisms. However, it should also be noted that although 27 of the 42 studies were heterogeneous, 12 of these consisted of greater than 80% serous samples. Therefore, for these studies the inclusion of multiple histological types is likely to have less effect on the gene signature and mechanisms highlighted could be expected to occur in serous ovarian cancer. It would be advisable for future studies to include histological type and grade as model features.

The majority of studies identified by this review attempt to classify patients into groups with different characteristics, for example ‘poor prognosis’ and ‘good prognosis’ or ‘chemosensitive’ and ‘chemoresistant’. However, variables such as response to chemotherapy and prognosis are rarely so well separated into classes; they are by nature continuous variables. Altman and Royston [88] are clear that dichotomising continuous variables into categories (such as high-risk vs. low-risk) should be avoided, as it results in loss of information and may lead to underestimation of variation and the masking of non-linearity. Arbitrary choices of cutoff values may further obscure the situation, when the original continuous variable could serve the same purpose in many models. In terms of a clinical test it therefore may be more appropriate to apply alternative techniques, such as various types of regression, to obtain a real valued prediction of patient outcome.

It was noted that the metrics reported as measures of predictive ability vary between studies. These vary in the amount of information conveyed and hence care should be taken to use metrics that fully describe the model. Sensitivity and specificity are commonly reported for classification techniques and, together with the numbers of patients in each class in the data set, allows the probabilities of a patient having the condition of interest given that they have tested positive or negative. It is the ultimate aim of most classification studies to obtain these probabilities, as it allows the predictive ability of the test to be assessed and the applicability of the test to be evaluated. Of the studies reporting sensitivity, specificity and related information, the best predictive ability was achieved by Hartmann et al. [57] and the worst by Helleman et al. [53]. It is important to note that from the sensitivity and specificity the model by Helleman et al. [53] does not appear to be any worse than some of the others, but these probabilities incorporate the prevalence of the condition of interest in the test population. It would therefore be highly informative to recalculate these probabilities using the prevalence of the condition in the population of ovarian cancer patients. Since some of the test populations were not representative of the overall population (having so called ‘spectrum bias’), this would give a much more reliable indication of the predictive ability of the models in a clinical setting.

One of the main aims of the studies identified was to obtain a ‘gene signature’, the expression of which can explain and predict the response in the patient. To this end, the majority of the papers (32 of 42) provided full or partial list of the genes selected by the modelling process. An analysis of these gene signatures resulted in the conclusion that the signatures were very dissimilar, with the most commonly selected gene appearing in only four papers. 93.53% of genes were selected by only one paper. This seems to indicate that the gene signatures identified were not based on underlying cellular processes, or at least that the processes being highlighted were not the same across the papers. It should be noted that many of the studies used cohorts of patients who were heterogeneous in terms of chemotherapy treatment and, due to the development of resistance to chemotherapy via gene expression changes, this may affect the genes found to be explanatory. It may be that several gene signatures from sub-populations of patients treated with different drugs are combining and hence reducing the predictive ability of the models.

In order to assess the biological relevance of the genes selected for the gene signatures, gene set enrichment analysis was carried out. This technique is used to highlight processes and pathways that are over-represented in the gene signature compared to the set of all genes. For the purposes of this review, two groups of studies were considered: those where the patients were treated with platinum and taxane, and those where the patients were treated with other platinum based treatments. These groups were selected due to the low numbers of studies using a single treatment option. For example, there were no studies considering platinum, taxane or cyclophosphamide as single agents. Following the analysis, 30 KEGG terms were returned for each group. Of these, each list comprised of approximately half cancer related terms. Of these the majority were processes often up- or down-regulated in cancer cells, such as proliferation, apoptosis, and motility and metastasis [89]. It is unclear whether the change in regulation of these processes is further altered in response to specific chemotherapy treatments. However, one process worthy of additional consideration is DNA repair. DNA repair is known to be an important mechanism in cancer both though cancer development when down-regulated or mutated [75] and resistance to DNA damaging chemotherapy when up-regulated [76]. Therefore, the strong presence of DNA repair terms may suggest the presence of platinum resistance pathways in the gene signatures. It is the authors’ opinion that, although the combined gene signatures appear not to include predictive chemotherapy-specific information, they may be capable of providing prognostic information. It is also thought that some studies, such as Glaysher et al., may include genes relevant to additional chemotherapy-specific processes which are ‘drowned out’ when combined with other signatures.

Conclusion

It is clear that the prediction of response to chemotherapy in ovarian cancer is an ongoing research problem that has been attracting attention for many years. However, although many studies have been published, a clinical tool is still not available. It is our belief that, although not yet accomplished, progress within the field suggests that the development of a predictive model is possible. There is great variability between the approaches and success of existing studies in the literature, and there have been very high levels of variation in the genes identified as explanatory. It is the authors’ opinion that, if more care is taken when selecting the patients for inclusion to control for treatment history, these gene signatures may be simplified and models able to predict response to treatment may be developed.

Declarations

Acknowledgements

KLL acknowledges support from an EPSRC PhD studentship (though MOAC DTC, EP/F500378/1). RSS acknowledges support from an MRC Biostatistics Fellowship.

Authors’ Affiliations

(1)
MOAC DTC, University of Warwick, Coventry, UK
(2)
Warwick Medical School, University of Warwick, Coventry, UK
(3)
Systems Biology Centre, University of Warwick, Coventry, UK

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