Genetic polymorphisms as predictive and prognostic biomarkers in gynecological cancers: A systematic review

Gynecologic Oncology 124 (2012) 354–365

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Review

Genetic polymorphisms as predictive and prognostic biomarkers in gynecological cancers: A systematic review☆,☆☆ Ivan Diaz-Padilla a, b,⁎, Eitan Amir a, Sharon Marsh c, Geoffrey Liu a, Helen Mackay a a b c

Division of Medical Oncology, Princess Margaret Hospital, University of Toronto, Ontario, Canada Centro Integral Oncologico Clara Campal, Madrid, Spain Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, Alberta, Canada

a r t i c l e

i n f o

Article history: Received 6 October 2011 Accepted 29 October 2011 Available online 6 November 2011 Keywords: Gene polymorphism Ovarian cancer Predictive biomarker Gynecological cancer Vascular endothelial growth factor ERCC1

a b s t r a c t Purpose. Numerous studies have explored the potential role of genetic polymorphisms as predictive or prognostic biomarkers in gynecologic malignancies. A systematic review for all eligible polymorphisms has not yet been reported. The aim of this study was to summarize the current status of the field and provide direction for future research. Design. We searched literature databases (MEDLINE, EMBASE, Cochrane) from 2006 to April 2011 to identify studies evaluating the association between gene polymorphisms and clinical outcome in ovarian, endometrial, cervical, or vulvar cancer. The main outcome measures were overall survival (OS) and progressionfree survival (PFS). Studies reporting relationships between polymorphisms and toxicity were also included. Results. Sixty two studies met the inclusion criteria. The median sample size was 140. Most of the included studies (n = 50, 81%) were conducted in ovarian cancer patients. Almost a third assessed potential predictive associations between gene polymorphism and outcome in ovarian cancer. The most commonly evaluated genes were ERCC1, VEGF, ABCB1 (MDR), and GSTP1. Most studies (n = 44, 71%) were observational caseseries. Only four studies (6%) included a validation arm and patient population ethnicity was explicitly stated only in 27% of included studies. Conclusion. No consistent association between any gene polymorphism and clinical outcome in gynecological cancers has been found across studies. There is incomplete adherence to the REMARK guidelines and inadequate methodology reporting in most studies. Moving forward, analysis of large trial-based clinical samples; adherence to the highest methodological standards, and focus on validation analyses are necessary to identify clinically useful pharmacogenomic biomarkers of outcome. © 2011 Elsevier Inc. All rights reserved.

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Material and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Search strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Study selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data extraction and management . . . . . . . . . . . . . . . . . . . . . . . Assessment of risk of bias in included studies . . . . . . . . . . . . . . . . . . Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gene polymorphisms and ovarian cancer . . . . . . . . . . . . . . . . . . . . Non-VEGF pathway polymorphisms as predictive biomarkers of survival . . . . . Non-VEGF pathway polymorphisms as predictive biomarkers of toxicity in ovarian Non-VEGF pathway polymorphisms as prognostic biomarkers in ovarian cancer .

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☆ Support: I.D.P. is supported by the Programa de Formacion Avanzada en Oncologia at the Asociación Española Contra el Cáncer (AECC), Spain. G.L. is supported by the Alan Brown Chair in Molecular Genomics, the Poslun Family Foundation, Cancer Care Ontario (CCO) Chair in Experimental Therapeutics and Population Studies. ☆☆ Disclaimer: The authors declare no conflict of interest for the present manuscript. This study has not been presented in part or in whole elsewhere. ⁎ Corresponding author at: Division of Medical Oncology and Hematology, Princess Margaret Hospital, 610 University Avenue, Toronto, Ontario, Canada, M5G 2M9. Fax: + 1 416 946 2016. E-mail address: [email protected] (I. Diaz-Padilla). 0090-8258/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.ygyno.2011.10.034

I. Diaz-Padilla et al. / Gynecologic Oncology 124 (2012) 354–365

Polymorphisms of the VEGF pathway and outcome in ovarian cancer . . . Polymorphisms and Outcome in Endometrial, Cervical, and Vulvar Cancers. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conflict of interest statement . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Introduction

Material and methods

Gynecologic malignancies are a major cause of morbidity and mortality. Endometrial and ovarian cancer are the fourth and fifth most common malignancies diagnosed in North American women, while cervical cancer is the second most common worldwide [1]. Despite response rates of over 50% to frontline chemotherapy, relapse rates are high for all gynecologic cancers and recurrent disease incurable [2–4]. Optimizing the use of existing therapies, improving quality of life and the rational and cost-effective incorporation of emerging biological agents are all areas of high priority research. Identification of both prognostic and predictive biomarkers would be highly desirable to help achieve these goals. Validated prognostic biomarkers have the potential to improve risk stratification, thereby allowing more aggressive strategies for patients at risk of a poor outcome or, conversely, the adoption of less aggressive treatment for those whose risk of recurrence is lower. Identification of biomarkers which predict response (or the lack of it) to conventional or targeted treatment will allow patients to avoid toxicity from ineffective therapy and to be fast-tracked into clinical trials. Furthermore, the identification of biomarkers which predict particular drug-related toxicities, for example hypertension associated with anti-angiogenic agents, [5] would allow pro-active preventative measures to be taken in those at risk. Germline polymorphisms are heritable variations in the human genome that typically occur in 1% or greater frequency in the population being studied. Although much of the literature has focused on the association between genetic polymorphisms and the susceptibility of disease (i.e., case–control studies comparing cancer patients to healthy normal individuals), more recently the focus has shifted to include evaluations of the relationships between these variants and clinical outcome after cancer diagnosis [6]. These outcomes include survival parameters (e.g., overall survival [OS], progression or disease free survival [PFS/DFS] and time-to-progression [TTP]), response rates (RR), and acute or chronic toxicity of therapies. Two distinct roles of polymorphism relationships are evaluable: (i) genetic variants as predictive markers of treatment outcome, whereby these biomarkers predict one set of outcomes in patients receiving specific therapies, but have no relationship (or perhaps a completely different relationship) in the absence of receiving such therapies (pharmacogenomics) and (ii) polymorphisms as prognostic markers of outcome, whereby the relationship between the biomarker and clinical outcome is independent of any treatment aspects. In August 2010 the Gynecologic Cancer Intergroup (GCIG) recognized the importance of exploring and promoting this approach and formed the GCIG Ovarian Cancer Pharmacogenomics Working Group. Although numerous pharmacogenomic studies have been reported in gynecologic cancer, most commonly in women with ovarian cancer, to date there has been no systematic review of the published literature to guide future research. Furthermore, as platinum and taxane-based chemotherapy combinations are standard of care for the major gynecologic malignancies, [7–9] any potentially predictive biomarker signature which emerges in one population of women may be relevant for future studies in other gynecologic tumor populations. The goals of this review are three-fold: (i) to summarize the state of the current research field; (ii) to identify the top candidate choices that are either validated, or worthy of ongoing validation; and (iii) to identify gaps and direct the future research in this field.

Search strategy

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Literature searches of MEDLINE (host: OVID), EMBASE (host: OVID) and the Cochrane Central Register of Controlled Trials, from 2006 to April 2011; were performed using keywords and MeSH terms (Appendix 1). The search was restricted to English language articles reporting human studies. Citation and reference lists of retrieved articles were checked to ensure sensitivity of the search strategy. Authors of relevant studies were contacted and asked if they knew of further data which may or may not have been published. Attempts were made to contact and clarify ambiguous result reporting from corresponding authors, where necessary. Study selection Titles and abstracts were retrieved and assessed. Commentaries, single case-reports, editorials, review articles, unrelated articles and duplicates were excluded (authors I.D.P. and G.L.). Full-text articles were obtained for the remainder. The eligibility of retrieved papers was assessed independently by three review authors (I.D.P, G.L., H. M.). English language articles published in peer-reviewed journals that assessed the relationship between germline polymorphic variants and major outcomes of interest (hazard ratio [HR] for survival, odds ratio [OR] for toxicity) were included (Fig. 1). Data extraction and management For included studies, the following data were abstracted: author, year of publication (if published), journal citation, country, inclusion and exclusion criteria, study design and methodology; distribution of clinicopathologic factors (including age, histology, tumor grade, International Federation of Gynecology and Obstetrics [FIGO] stage, extent of residual disease), intervention details (type, timing and outcome of surgery, chemotherapeutic agents, and radiation therapy for non-ovarian studies); duration and completeness of follow-up; genotyping material, methods and completeness; analytical strategy (including whether multivariate analyses were performed) and associations between polymorphisms and outcome data. Relationships with surrogate and unconventional outcomes were excluded. Assessment of risk of bias in included studies The risk of bias in the included randomized clinical trials was assessed using the Cochrane Collaboration's tool and criteria [10]. The risk of bias in non-randomized studies was assessed in accordance with additional criteria: (i) study design; (ii) criteria for assignment of patients to treatment (where provided); (iii) whether or not patients included in analyses were representative of the larger population who would be treated for this condition (external validity); and iv) whether or not bias within the study design and analysis (i.e. internal validity) were appropriately considered. For example, in ovarian cancer, the key prognostic factors such as International Federation of Gynecology and Obstetrics (FIGO) stage, histology, type and extent of surgery, and type of treatment were chosen as minimally acceptable factors required to be considered in analyses.

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Literature Search Databases: Pubmed (2006-April 2011) EMBASE (2006-April 2011) Cochrane Library (2006-April 2011) English -language articles only

Search results combined (n=1462)

292 Duplicates

1170 Manuscripts screened on basis of title and abstract 1108 Manuscripts excluded 780 Articles of unrelated topic 328 Risk/susceptibility studies

62 manuscripts included Fig. 1. Flow diagram of the literature search and selection of included studies.

Three authors (I.D.P, G.L., H.M.) reviewed all papers independently; differences were resolved by consensus. Results A summary of included studies is shown in Table 1. The literature search found 1462 articles. After application of eligibility criteria, 62 studies were identified as evaluating the association between gene

Table 1 Characteristics of included studies. Study characteristics

No. of studies (%)

Study design Case series Case–control Randomized phase III clinical trial Population registry Phase II clinical trial

44 (71) 11 (18) 4 (7) 2 (3) 1 (2)

Studies by tumor type Ovarian Cervical Endometrial Vulvar

50 (81) 8 (13) 3 (5) 1 (2)

Biospecimen source for genotyping Blood leukocytes FFPE tumor Fresh tumor tissue Not specified

40 (64) 14 (23) 5 (8) 3 (5)

Statistical analysis Multivariate Univariate Total no. of included studies

46 (74) 16 (26) 62 (100)

Abbreviations: FFPE, formalin-fixed paraffin-embedded.

polymorphisms and clinical outcome in ovarian, endometrial, cervical, or vulvar cancer (Fig. 1). The majority of these studies were either case series (n = 44, 72%) or cases that formed part of an existing case– control study (n = 11, 18%). Other study designs included cohort observational studies or subset analyses of existing cohort studies (n = 2, 3%), or secondary analyses of randomized clinical trials (n = 4, 7%). Only four studies (6%) included a validation group. Sixteen studies (26%) did not report multivariate analysis in their results. While the majority of studies (n = 40, 64%) performed the genotyping analysis on blood-derived specimens, fourteen studies (23%) performed genotyping on formalin-fixed paraffin-embedded material, five (8%) used frozen tumor tissue, and in three studies (5%) the source for genotyping was not clearly stated. Study populations were predominantly Caucasian (N = 50, 81%), reported or inferred based on the academic affiliation of authors or hospital location. For those studies that explicitly stated the ethnicity of the patient population (n = 17, 27%), no uniform categorization for the non-caucasian cohort (i.e. African American, Hispanic) was found across studies. Twenty five studies (40%) provided little to no information on the treatment their patients received. Though we attempted to contact corresponding authors about missing information, in many cases, no additional information was obtained. For study size assessment, we reported only on the subset of individuals who had genotyping attempted. Study size ranged from 16 to 1499 patients, with a median of 140 (mean of 247). Almost all studies identified themselves as exploratory or in need of validation or replication.

Gene polymorphisms and ovarian cancer Fifty studies evaluated the association between polymorphisms and outcome in ovarian cancer. We separated studies into those that were predictive (Table 2) or prognostic (Table 3 and Supplementary Table 1). Because of the large number of prognostic and

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predictive studies evaluating the vascular endothelial growth factor (VEGF) pathway, we collated all such studies, regardless of whether they had significant associations or not (Table 4). Most studies reported polymorphisms in multiple genes, as well as different polymorphisms within the same gene. For those studies where the same polymorphism was evaluated, the outcome measures were different and/or the hazard ratios were not available, making it difficult to perform a pooled analysis data or to assess for heterogeneity. Non-VEGF pathway polymorphisms as predictive biomarkers of survival A summary of included studies [11–29] is shown in Table 2. Six studies of adjuvant platinum and taxane-based therapy assessed the association between polymorphisms in the P-glycoprotein (PGP) gene, ABCB1 (MDR1), and treatment outcome [13, 14, 16, 19, 22, 25]. Taxanes and other cytotoxic drugs can be extruded from the cell by P-glycoprotein (PGP), an ATP-driven drug export pump. Its overexpression is associated with drug-resistant phenotypes [30]. Results from ovarian cancer clinical studies suggest that increased PGP expression, partly influenced by the ABCB1 polymorphisms 2677G>T/A and 3435C>T, [31] is associated with poorer outcome [32]. Of the three studies that included multivariate analyses, [16, 19, 22] only one study found borderline significant associations with 2677G>T/A, [16] while the largest analysis that used samples from a randomized phase III trial (SCOTROC1) found no association [22]. None reported associations between ABCB1 3435C>T and outcome. Nine studies evaluated the predictive nature of polymorphisms in the nucleotide excision DNA repair pathway with platinum therapy [17–22, 26–28]. Eight studies reported two common polymorphisms of the excision repair cross-complementation group 1 (ERCC1) gene, which codes the protein that repairs cisplatin-induced DNA damage. The ERCC1 codon 118 polymorphism features a C to T transition, but does not change the amino acid, asparagine; the T variant however has been associated with a 50% reduction in transcription, and in an ovarian cancer cell line, has been linked to decreased levels of ERCC1 mRNA, indicating reduced DNA repair capability [33]. In this review, data for the ERCC1 codon 118 polymorphism has been conflicting. Three studies (two case-series studies [17, 27] and one phase III clinical trial [21]) reported the C/C genotype to be associated with higher risk of platinum resistance, disease progression, and death from ovarian cancer; while two analyses of other prospective clinical trials did not confirm such results [20, 22]. The A allele of the second polymorphism, ERCC1 8092C>A, located in the 3′ untranslated region, may affect mRNA stability, and has been associated with more favorable outcomes in other solid tumors [34, 35]. However, ERCC1 8092 A/-genotypes were associated with poorer PFS and OS in two different phase III trials of platinum-based chemotherapy for optimally resected stage III ovarian cancer patients, [20, 21] and in a smaller retrospective study [19]. Other polymorphisms in the nucleotide excision repair pathway (ERCC2 (XPD), XPA, XPC, and XPG), and the base excision pathway (XRCC1) have also been evaluated, with varying associations reported (Table 2) [26]. The glutathione-S-transferase (GST) enzymes catalyze the conjugation of glutathione to platinum, [36] limiting the amount of free platinum drug available for interaction with DNA. Three GST genes, GSTP1, GSTM1 and GSTT1, have common polymorphisms associated with decreased enzymatic activity [37]. GSTP1 105 A>G was the only polymorphism evaluated across multiple studies, with conflicting results. In two studies, the variant G allele was associated with improved outcome, either alone or in combination with another GST polymorphism [11, 24]. This was not confirmed in other studies, [18, 23] while the largest analysis reported no association [22]. Other polymorphisms within genes involved in xenobiotic metabolism have been analyzed in two small studies [15, 29]. Only a polymorphism in the cytochrome P450 complex, namely CYP1A1 Ile462Val, has been associated with platinum resistance [15].

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Non-VEGF pathway polymorphisms as predictive biomarkers of toxicity in ovarian cancer Four studies evaluated the association of non-VEGF pathway polymorphisms with toxicity in ovarian cancer [18, 19, 22, 29]. Three studies assessed the occurrence of grades 3 and 4 toxicities based on pre-specified criteria [19, 22, 29]. One study found borderline associations between polymorphisms (CDKN1A 10971C>T and CYP1B1*3) and increased risk of developing gastrointestinal toxicity when patients were treated with platinum-docetaxel in a training set. This association, however, was not confirmed in an internal validation set [22]. Two univariate analyses of smaller retrospective studies reported associations between polymorphisms in the DNA repair, multi-drug resistance, and drug metabolism pathways and a wide spectrum of toxicities [18, 19]. Kim et al. reported an increased risk of gastrointestinal toxicity for carriers of ABCB1 2677 variants, and a higher risk of hematological toxicity for those patients with a homozygous GSTP1 105 A/A genotype [19]. Khrunin et al. reported that patients with a homozygous GSTM1 deletion (GSTM1 null) experienced fewer adverse events (myelosuppresion, neuropathy) than those who carried functional GSTM1 variants [18]. None of the polymorphism-toxicity relationships has been validated in independent studies. Non-VEGF pathway polymorphisms as prognostic biomarkers in ovarian cancer Twenty five studies evaluated the potential prognostic value of non-VEGF polymorphisms in ovarian cancer. Of these, seven reported no significant associations [38–44] (Supplementary Table 1). Of the remaining studies with at least one positive association [45–62] (Table 3), the significant polymorphisms were from genes involved in a multitude of biological pathways, including tumor suppressor genes, oncogenes, microRNA (miRNA), inflammation, apoptosis, cellular senescence, detoxification, vitamin D, and xenobiotic metabolism. All published studies were hypothesis-generating. Two pathways reported significant associations across at least two studies: p53/cell cycle and hormone (estrogen and/or androgen) pathways. An intact p53 pathway is an important determinant of the response to platinum-based chemotherapy [63]. Polymorphisms and somatic genetic alterations of genes encoding p53 and its regulators have been shown to affect ovarian cancer pathogenesis [64]. Both the MDM2 and the MDM4 genes are critical negative regulators of p53 protein [65, 66]. It has been shown that the G-allele of the promoter MDM2 309T>G gene is associated with the attenuation of the p53 tumor suppressor pathway [65]. MDM2 expression is also regulated by the estrogen receptor (ER) pathway [67]. Three studies evaluated the p53-MDM pathway and found significant associations between clinical outcome and at least one MDM2, MDM4, or p53 polymorphism; in one case, the relationship was limited to ER-negative tumors [45, 58, 62]. Alterations in the androgen hormone homeostasis have been shown to influence the pathogenesis and progression of ovarian cancer either directly or indirectly [68, 69]. The androgen receptor (AR) is frequently overexpressed in ovarian cancer [70]. The AR gene contains a polymorphic cytosine–adenine–guanine (CAG) repeat in exon 1; the length of the CAG repeat sequence inversely correlates with AR function [71]. Two small studies examined the prognostic role of AR gene polymorphisms in ovarian cancer, and both found that the shorter AR alleles (each using different cut-offs) were associated with worse outcome [52, 54]. In contrast, this association was not replicated in a much larger study [55]. Nagle et al. found a significant association between a polymorphism of CYP17C, which codes for an enzyme involved with progesterone metabolism (CYP17 rs743572; 5′UTR T>C), but this association has not been externally validated.

358

Table 2 Ovarian cancer: gene polymorphisms with a predictive association with outcome.

MDR1

Country

N

Johnatty SEa (Population Registry) [16]

Australia

309 Carboplatin Paclitaxel

ABCB1 ABCB 1

1236C>T 2677G>T/A

Green Ha [13] (case series) Sweden

53

Carboplatin Paclitaxel

ABCB 1 ABCB 1

3435C>T 2677G>T/A

Green Ha [14] (case series) Sweden

51

Carboplatin Paclitaxel

Obata H [25] (case series)

Japan

44

Platinum-Based

ABCB 1 ABCB 1 ABCB 1 ABCB 1 MRP1 MRP2 ABCB 1

Marsh Sc [22] (phase III clinical trial)

United Kingdom

914 Platinum Taxane

ABCB 1 ABCB 1 ERCC1 ERCC2 XRCC1

3435C>T 1236C>T 1199G>T/A 1308A>G 2168G>A; 5 more SNPs 3 SNPs 1236C>T, 3435C>T, exon 28 A>G; 2 more SNPs 1236C>T 2677G>T/A, 3435C>T ^118C>T, 8092C>A 751 A>C 399G>A

GSTP1 TP53 ERCC1

Krivak TC [21] (phase III clinical trial)

ERCC1

USA

Treatment

280 Platinum Taxane

Gene(s) Polymorphism(s)

Subset analysis for ABCB 1 2677 and residual disease ≤1 cm: any variant vs GG: HR 0.6 (0.4–0.9) p = 0.01 Analysis was performed on frozen tumor tissue

All OS comparisons; p = NS ABCB1 2677T/T or T/A had greater chance of good response: *RR 1.8 (1.2–2.8); p = 0.04b ABCB1 1199 Carrying at least one A allele was associated with 2 vs 19 months OSb MRP1 2168 A/- vs G/G A/- had greater RR, p = 0.01 All comparisons; p = NS

No multivariate analysis No multivariate analysis No multivariate analysis

ERCC ^118T/- vs CC HR 0.7 (0.5–1.0); p = 0.03

Age FIGO stage Histology Residual disease

ERCC1

8092C>A

OS

NS

RR, OS

NS

PFS

ERCC1 8092 A/- vs C/C HR 1.9 (1.1–3.5); p = 0.03

RR

GSTT1 non-null vs -null, OR 0.3 (0.2–0.7); p = 0.04

OS

HR 1.7 (1.8–3.4)d; p = 0.04

Kim HS [19] (case series)

Korea

118 Platinum Taxane

ERCC1 GSTT1 ERCC2 GSTM1 GSTP1

8092C>A, ^118C>T -null 751 A>C -null 105A>G

ABCB1 XRCC1 ERCC1 XRCC1 XRCC1 GSTP1 GSTA1 ERCC2 TP53 ERCC1

2677G>T/A, 3435C>T R399Q, R194W ^118C>T, 8092C>A 399G>A, 280G>A 194C>T 105A>G, 114C>T -69C>T 312G>A,751 A>C Arg72Pro ^118C>T

103 Platinum-based

FIGO Stage Residual disease (1 cm)

OS

^118C>T

Italy

RR PFS

ABCB1 2677: others vs G/G HR 0.7 (0.5–1.0); p = 0.07

105 A>G, A114V Arg72Pro ^118C>T

ERCC1

Smith S [27] (case series)

PFS (no statistics reported)

Additional comments

Age Tumor grade FIGO stage Histology Residual disease (2 cm) ECOG PS

159 CarboplatinCyclophosphamide

104 Cisplatin Cyclophos-phamide

OS DFI>1 year vs DFIb1 year

Covariates considered

NS

Denmark

Russia

PFS

Reported association

RR, PFS

Steffensen KD [28] (phase III clinical trial)

Khrunin AVe [18] (case series)

Outcome measure

PFS

OS

PFS OS

GSTP1 105A>G A/A had better PFS; p = 0.0001 GSTA1-69C>T T/T had better OS; p = 0.05 ERCC2 312G>A, XPD 751 A>C: Hetero genotypes had Better PFS; p b 0.05 ERCC1 ^118C/C vs T/HR 2.0 (1.1–3.8) p = 0.05 HR 2.0 (1.1–3.8); p = 0.03

Tumor grade FIGO stage Histology Residual disease (1 cm) ERCC1 expression Age Tumor grade FIGO stage Histology Residual disease (1 cm)

Results based on only 2 of 51 patients carrying MDR 1199 A alleles Analysis was performed on formalin-fixed paraffin embedded (FFPE) specimens

ERCC 8092 A/- vs CC: trend toward increased risk of death [HR 1.3 (1.0–1.7); p = 0.08) Analysis was performed on FFPE specimens

No multivariate analysis

No association between genotype and response rates

Age Tumor grade FIGO stage

Combination of C/C+ high ERCC1 mRNA expression HR 3.7 (1.6–8.9); p = 0.03

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MDR1 and ERCC1

Author (study type)

Table 2 (continued)

ERCC1, IP

ERCC2

Author (study type)

Country

N

Treatment

Gene(s) Polymorphism(s)

Kang S [17] (case series)

Korea

60

Carboplatin Paclitaxel

ERCC1

Krivak TC [20] (phase III clinical trial)

USA

Saldivar JS [26] (case series)

USA

Nagle CMg [24] (cases Xenobiotic metabolism from case–control) and p53

Italy

^118C>T Platinum resistance

233 Intraperitoneal (IP) vs Intravenous (IV) Cisplatin, Paclitaxel

ERCC1 ERCC1

^118C>T 8092C>A

125 Carboplatin Paclitaxel

XPA XPG XPC ERCC2 GSTP1

228A>G 1104A>H poly-AT 312G>A; 751A>C GSTP1 105A>G

454 Platinum-based (N = 340, 75%)

215 Platinum-based (N = 101, 47%)

Morari EC [23] (case series)

Brazil

69

Heubner M [15] (case series)

Germany

111 No data reported by authors

Gaducci A [12] (case series)

Italy

46

Takano M [29] (case series)

Japan

24

Platinum-based (N = 29, 42%)

Adjuvant Carboplatin Paclitaxel CisplatinIrinotecanh

All OS comparisons; p = NS

PFS OS

ERCC1 8092 A/- vs C/C, HR 1.4 (1.1–1.9), p = 0.02 HR 1.5 (1.1–2.1), p = 0.02

PFS OS

GSTT1 null GSTM1null GSTM1 null PFS OS

GSTP1 GSTT1

GSTP1 105A>G GSTT1 null

GSTP1 GSTT1 GSTM1 GSTO2 TP53 CYP1A1

GSTP1 105A>G GSTT1 null GSTM1null GSTO2 asn142asp Arg72Pro CYP1A1 Ile462Val

ERCC1 ^118T/- vs C/C OR 0.2 (0.0–0.7); p = 0.02

OS

OS GSTT1 GSTM1 GSTM1

Reported association

PFS OS, RR RR

Platinumresistance

XPA 228G/- vs A/A HR 9.1 (1.1–74); p = NR XPG 1104C/- vs G/G HR 8.9 (1.3–64); p = NR GSTP1 105G/- vs AA: HR 0.8 (0.6–1.0); p = 0.04

GSTM1 null vs non-null HR 0.7, CI 0.4–1.0, p = 0.02 HR 0.7, CI 0.5–1.0, p = 0.06 GSTM1 null/GSTP1 105G/- vs other HR 0.4, CI 0.2–0.8, p = 0.003 NS NS

CYP1A1 462 Val/- is associated with higher rate of platinum resistance OR 5.9 (1.5–23.2), p = 0.005

TP53

Arg72Pro

OS PFS

NS

UGT1A1

UGT1A1*28 UGT1A1*27 UGT1A1*6

RR

NS

Covariates considered

Additional comments

Histology Age Tumor grade

(PFS) Platinum-free interval defined as the interval between treatment commencement and date of relapse

FIGO stage Histology Residual disease (2 cm) Age Tumor grade FIGO stage Histology Age FIGO stage Histology

Residual diseasef GOG PS Ethnicity Treatment arm No significant associations with RR

Age Tumor grade FIGO stage Histology

Carriage of three low function GST genotypesc had trend toward better OS: HR 0.5 (0.2–1.0)

Age Tumor grade FIGO Stage Histology

Analysis was performed on frozen tumor tissue

Not reported by authors

Tumor grade FIGO stage

Analyses were performed on FFPE specimens

Residual disease Platinum resistance No multivariate analysis

Analyses were performed on FFPE specimens

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Beeghly A [11] (case series)

Australia

Outcome measure

No multivariate analysis

359

Studies in each subcategory were listed based on sample size (largest listed first). The first six studies listed evaluated ABCB1 polymorphisms. The fifth through thirteenth studies evaluated DNA Repair Polymorphisms, mainly on ERCC1. The Krivak et al. study was listed at the end because it involved intraperitoneal administration of chemotherapy, while Saldivar et al. did not include ERCC1. The five last studies focused on xenobiotic metabolism and p53 polymorphisms. The Morari et al. study was grouped within the studies evaluating GST polymorphisms. PFS, progression-free survival; OS, overall survival; DFI, disease-free interval; RR, response rate; *RR, Relative Risk; HR, hazard ratio; OR, odds ratio; NS, not significant; SNP, Single Nucleotide Polymorphism; FIGO, International Federation of Obstetrics and Gynecology; ECOG, Eastern Cooperative Oncology Group; PS Performance Status; GOG, Gynecologic Oncology Group; NR, not reported; IP, intraperitoneal. a Baseline population is virtually identical between these two studies. b Not adjusted. c Other polymorphisms evaluated in Marsh et al.: ABCC1 S1334S, ABCC1 IVS19-30C>G, ABCC2 24C>T, ABCC2 IVS12 + 148A>G, ABCC2 V417I, ABCG2 Q141K, CYP2C8 M264I, CYP2C8 R139K, CYP2C8 K399R, CYP3A4*1B, CYP3A4*3, CYP3A5*3C, ERCC1 17677G>T, MAPT P587P, MPO-463G>A, CDKN1A 10971C>T, CYP1B1*3. d The HR is outside of the 95% CI, authors were not able to resolve these contradictory numbers. e Other polymorphisms evaluated in Khrunin et al.: GSTM1 (gene deletion), GSTM3 (intron 6, gene deletion), GSTT1 (gene deletion), TP53 (intron 3, 16-bp dupl), TP53 (intron 6, rs1625895), CYP2E1rs2031920, CYP2E1rs6413432, CYP2E1rs2070676, CYP2E1 (5′flanking, 96-bp insertion). f Gross vs microscopic disease. g Also partially reported in Table 3. Baseline populations are identical between this study and that in Table 3. h Patients had recurrent disease. Also partially reported in Supplementary Table 2. ^ERCC1 codon 118.

360

Table 3 Ovarian cancer: gene polymorphisms (non-VEGF) with at least one positive prognostic association. Country

N

Treatment

Gene(s)

Polymorphism(s)

Outcome measure

Reported positive associations

Covariates considered

Additional comments

BRAF KRAS ERBB2 NMI PI3KCA CYP17 + othersd

BRAF rs6944385 and other 33 TagSNPs were genotypedb

OS

BRAF rs6944385 HR 1.2 (1.0–1.5); p = 0.01

Age Tumor grade FIGO stage Histology

BRAF Haplotype: HR 1.4, CI 1.15–1.8; p = 0.001; Associations not adjusted for multiple testing

CYP17 5′UTR T>C And other 20 SNPs 226 SNPs analyzed

OS

CYP17 5′UTR C/- vs TT HR 1.3 (1.0–1.7); p = 0.04

OS

PDGFC rs1425486 HR 2.7 (1.7–4.3); q = 0.004

Age FIGO stage Tumor grade Histology Age Histology FIGO stage Chemotherapy -adjusted for multiple comparisons Age Ethnicity Tumor grade Chemotherapy FIGO stage CA-125 Histology Place of surgery Residual disease

UK, USA, Quaye L [57] (cases from Denmark case–control) incident, prevalent cases combined

1480 No details

Nagle CMc [55] (cases from case–control) Liang D [53] (cases from case–control)

Australia

454

No details

USA

339

Platinum-based chemotherapy

USA

325

Platinum-taxane

142 miRNA pathway-related genes Multiple pathwaysf 1416 SNPs analyzed

Goode EL [48] (case series)

OS OS OS OS OS OS OS OS

Batra Jg [46] (cases from Australia case–control) Paige AJW [56] (case series) UK

319

No details

KLK15

235

No details

Hefler LA [50] (case series)

Austria

199

No details

WWOX WWOX, WWOX WWOX IL-6

Garg R [47] (case series)

USA

160

No details

IL-6

rs266851 and other 14 TagSNPs were genotypedb 1497T>G, 121C>T, Isnp1 C>T, PFS Isnp8 C>T, Isnp13 A>C. 660 PFS A>G, 1442C>T, Ispn15 A>G OS IL-6 174G>C PFS OS IL-6 174G>C OS

Han CH [49] (case series)

USA

136

Platinum-Taxane

Wynendaele J [62] (case series)

Germany

154

Platinum-based chemotherapy

SULF1 SULF1 SULF1 MDM4

rs2623047 G>A rs13264163, rs6990375, rs3802278, rs3087714 34091 A>C

Six L [60] (case series)

Austria

151

Platinum-based chemotherapy

PFS OS

OS PFS MMP1

− 1607 (G)/GG PFS OS

And for another 23 SNPse VHL rs265318 HR 0.7 (0.4–1); p = 0.04 EIF2B5 rs4912474 HR 0.7 (0.5–0.9); p = 0.004 CCR3 rs4987053 HR 1.6 (1.1–2.4); p = 0.01 IL1B rs1143634 HR 1.4 (1.0–1.8); p = 0.02 IL18 rs5744247 HR 0.5 (0.3–0.8); p = 0.003 IL18 rs11214108 HR 0.5 (0.3–0.9); p = 0.01 CYP1A1 rs2470893 HR 0.7 (CI 0.5–0.9); p = 0.005 KLK15 rs266851 T/- vs CC HR 1.4 (1.0–2.0), p = 0.05 WWOX 1497 GG vs TT: HR 2.1 (1.3–3.4); p = 0.003 GT vs TT: HR 1.6 (1.1–2.3); p = 0.01 P = NS IL-6 174C/- vs G/G was associated with improved PFS (p = 0.003) and OS (p = 0.02) IL-6 174G/G vs others was associated with 131 vs 28 months median OS, p = 0.001 SULF1 rs2623047 G/- was associated with improved PFS (28 vs 12 months, p = 0.02) Subgroup analysis of ER negative tumors MDM4 34091 A/A vs C/HR 5.5 (1.5–20); p = 0.01 HR 4.1 (1.2–14); p = 0.02 MMP1 others vs GG/GG HR 2.1, CI 1.2–3.8; p = 0.01 HR 1.9, CI 1.1–3.4; p = 0.04

Age FIGO stage Tumor grade Histology Age Association not confirmed Tumor grade in SCOTROC1, n = 863 FIGO stage Histology FIGO stage residual disease Lymph node metastases Age Histology Tumor grade Residual disease FIGO stage No multivariate analysis

Residual disease

Tumor grade FIGO stage

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Author (study type) populationa

Santos AM [58] (case series) Portugal

119

Platinum-based chemotherapy

Bartel F [45] (case series)

103

Platinum-based chemotherapy

Germany

TP53

Arg72Pro

PFS

P21 TP53 MDM2

3′UTR C>T Arg72Pro 309T>G

OS OS OS OS

Japan

101

No details

VDR

VDR FokI C>T

OS

Li AJ [52] (case series)

USA

77

No details

AR

CAG repeat length

OS

69

Platinum-based chemotherapy

AR AR FSH FSH PGRG CXCR2 AMg IL8 And morea

CAG repeat length GGN repeat length Ala307Thr Ser680Asn 331G>A 785C>T CA repeat -251T>A

LTBP-1L

202G>C 20 A>C

Ludwig AH [54] (cases from Poland case Control)

Schultheis AM [59] (single arm phase II clinical trial)

USA and Canada

53

CyclophosphamideBevacizumab

Higashi [51] (case series)

Japan

42

No details

PFS PFS OS

RR PFS PFS RR

OS

CXCR2 785T/T vs C/RR 2.1, CI 1.01–4.3; p = 0.03 AML≥14 repeats; S b 14 L/L vs S/S: HR 0.3; p = 0.04 L/S vs S/S: HR 0.5; p = 0.04 IL-8-251T>A: increasing number of A alleles was associated with lower RR (p = 0.006) Haplotype analysis: others vs G/A had longer 5-y OS (p = 0.02)

No multivariate analysis

FIGO stage Residual disease

Analysis were performed on FFPE specimens and not adjusted for multiple comparisons

Age FIGO stage Age Tumor grade FIGO Stage Age Tumor grade FIGO stage Histology

Histology Residual disease Analysis were performed on frozen tumor tissue Residual disease TP53 status Response to CT Platinum sensitivity

No adjustment for multiple comparisons

No multivariate analysis

Analysis were performed on FFPE specimens

Studies were listed from largest sample size to smallest. OS, overall survival; HR, Hazard Ratio; PFS, progression-free survival; q; false discovery rate adjusted p value; SNP, single nucleotide polymorphism; FIGO, International Federation of Gynecology and Obstetrics; NS, non-significant; CA-125, cancer antigen 125; FFPE, formalin-fixed paraffin embedded; CT, chemotherapy; miRNA, microRNA: small, non-coding RNA molecules involved in gene expression regulation; PDGFC, platelet-derived growth factor C; WWOX, WW-domain containing oxidoreductase; ER, estrogen receptor; MMP-1, Matrix metalloproteinase-1; VDR, vitamin D receptor; BMI, Body Mass Index; AR, androgen receptor; FSHR, follicule stimulating hormone receptor; PGRG, progesterone receptor gene; LTBP-1L, latent TGFβ- binding protein. a Other polymorphisms evaluated in Schultheis et al.: NRP1 3′UTR C>T; PGF (placental growth factor) 3′UTR rs8185; KDR 3′UTR T>A (rs12411) and AC repeat + 4422; LEP (Leptin) -2548G>A; CXCR1 2607G>C; CXCR2 785C>T; IGF2 4205G>A; IGFR exon 16 3174G>A; FGFR4 Gly288Arg; F3 (tissue factor) 5′UTR -603A>G; MMP2 -1306C>T; MMP9 -1562C>T; MMP7 -181 A>G; ICAM1 241G>A; EGF 61A>G; EGFR 497G>A; HIF1A 1772C>T; ARNT (HIF1B) rs2228099 G>C; IL6 174G>C; NFKB CA dinucleotide repeat; TNF -308G>A; IL1B -511T>C and 3954C>T; IL1R1 (IL-1Ra) intron 2, 86 bp repeat; MDM2 309T>G; P53 Arg72Pro; COX2 8473T>C; CXCL12 (SDF1) -801G>A. b TagSNPs utilize linkage disequilibrium to choose relatively independently sorting polymorphisms to represent as markers the genetic heritability of each segment of DNA across the chosen gen. c Baseline population is virtually identical between this and a corresponding study reported in Table 2. d Androgen receptor (AR) exon 1 CAG repeat (cutpoint ≥22) was also assessed, but no significant association was found. e The other 23 SNPs did not remain significant after adjusting for multiple testing. f Angiogenesis, inflammation, detoxification, glycosylation, one-carbon transfer, apoptosis, cell cycle regulation, cellular senescence. Angiogenesis polymorphisms are reported in Table 4. g Baseline populations are virtually identical between this and a corresponding study reported in Supplementary Table 1. h For stage III and ER negative tumors.

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Tamez S [61] (case series)

TP53 Arg/Arg was associated with better PFS (p = 0.01) NS TP53 Pro/Pro vs Arg/Arg HR 6.4 (1.1–6.4); global p = 0.03 TP 53 Arg/Pro vs Arg/Arg HR 1.7 (0.8–3.7) MDM2 309G/- vs T/T Median OS: 69 vs 34 mos; p = 0.02h VDR FokI C/C vs T/HR 0.2, CI 0.05–0.6, p = 0.006 Shorter AR (CAG ≤19) HR 3.5 (3.0–4.1); p = 0.02 Median PFS: 6 vs 19 mos; p = 0.0001 Longer AR (CAG ≥23) HR 0.5 (0.2–0.9); p = 0.03 All comparisons, p = NS

361

362

Table 4 Ovarian cancer: VEGF polymorphisms and association with outcome. Author (study type) stage Country Prognostic Hefler LAa [72] (case studies series) all stages

Polterauer Sa [74] (case series) all stages

Goode EL series)

b

[48] (case

Predictive studies

Polymorphism

Out- Reported association come

Covariates Additional comments considered

Austria/ 563 Platinum-taxane Germany

VEGF VEGF

− 634G>C − 1154G>A

OS

Simultaneous carriage of homozygous genotypes (634C/C, -1154G/G, -2578C/C) vs others: HR 2.3 (1.3–4.2); p = 0.006

Austria/ 553 Platinum-taxane Germany

VEGF VEGF VEGF

− 2578C>A 405G>C − 460C>T

OS

NS

OS OS

VEGFC rs17697305 HR 1.9 (1.1–3.1); p = 0.01 VEGFC rs1485766 HR 0.7 (0.6-0.9); p = 0.01

Age Tumor grade FIGO stage Age Tumor grade FIGO stage Age Tumor grade FIGO stage Histology Residual disease Age

Australia

Treatment

325 Platinum-taxane

319 No details

VEGF 936C>T VEGFCVEGFC rs17697305 rs1485766

VEGF

405G>C 3 TagSNPc: rs833068 G>A rs3025033 A>G rs2146323 C>A

VEGF VEGF VEGF VEGF

405G>C − 460C>T

OS

Simultaneous carriage of heterozygous genotypes is associated with better OS: HR 0.5 (0.3-0.7); p = 0.001

− 2578C>A − 1154G>/A

PFS

VEGF Haplotypes All others vs AGCGC: HR 1.9,CI 1.04-3.46; p = 0.04

3′UTR C>T -634G>C 936C>T 3′UTR T>A (rs12411) AC repeat + 4422

PFS PFS RR

− 2578C>A − 1154G>/A -460T>C 405G>C 936C>T

RR, PFS, OS

NRP1 3′UTR C>TT/T vs C/C: HR 2.4; p = 0.05 NRP1 3′UTR C>TC/T vs C/C: HR 2.3; p = 0.05 NRP1 3′UTR C>T P = NS for RR All other comparisons (PFS, RR) for VEGF pathway polymorphisms, p = NS All comparisons, p = NS

VEGF

Smerdel MP [76] (case series) stages II–IV;

Denmark 159 Platinum-taxane

Steffensen KD [77] (case series) all stages

Denmark 143 PlatinumVEGF cyclophosphamide VEGF

d

Schultheis AM [59] (phase II clinical trial) relapsed disease

USA and Canada

Smerdel MP [75] (case series) relapsed disease

Denmark 38

53

VEGF VEGF

405G>C -460T>C

VEGF

936C>T

BevacizumabNRP1 cyclophosphamide VEGF VEGF KDR KDR Single-agent VEGF VEGF Bevacizumab VEGF VEGF VEGF

OS

405 CC vs G/- HR 1.9 (1.1-3.2); p = 0.05 rs833068 AA vs G/- HR 2.1 (1.2-3.8); p = 0.05

OS

Tumor grade FIGO stage Histology Age Tumor grade FIGO stage Histology Residual tumor Age Tumor grade FIGO stage Residual disease (1 cm) Haplotype

No association observed between individual VEGF genotypes and OS

Ethnicity Chemotherapy Presurgical CA-125 Location of surgery

These could not be replicated in two independent datasets No associations were observed either for rs3025033 A>G nor rs2146323 C>A Analyses were performed on formalin-fixed, paraffinembedded tumors

No association observed between individual VEGF genotypes and PFS

Platinum sensitivity

No adjustment for multiple comparisons

Not reported by authors

Haplotype analyses did not show any association either

The present table, we only included those polymorphisms directly associated with VEGF pathways. OS, overall survival; PFS, Progression-free Survival; RR, Response, Rate; HR, Hazard Ratio; FIGO, International Federation of Obstetrics and Gynecology; NS, non-significant; VEGF, vascular endothelial growth factor; NRP1, neuropilin-1; KDR, also known as VEGF receptor 2. a Baseline populations are virtually identical between these two studies. b Multiple pathways. Angiogenesis contributed 130 polymorphisms to the analysis, of which VEGF pathway polymorphisms were included; only significant associations are presented. Non-VEGF pathway polymorphisms are reported in Table 3). c TagSNPs utilize linkage disequilibrium to choose relatively independently sorting polymorphisms to represent as markers the genetic heritability of each segment of DNA across the chosen gene. d Other polymorphisms evaluated in Schultheis et al. are reported in Table 3.

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Lose F [73] (population registry) all stages

Gene

USA

N

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Polymorphisms of the VEGF pathway and outcome in ovarian cancer A summary of included studies [48, 59, 72–77] is shown in Table 4. In two studies, haplotypes, but not individual polymorphisms of VEGF were associated with prognosis, [72, 77] while a third study found significance only combining two VEGF polymorphisms [76]. Only two studies described survival associations involving single VEGF polymorphisms, [48, 73] but these prognostic relationships were not seen in other studies. As for VEGF polymorphisms as predictors of benefit from the anti-VEGF antibody, bevacizumab, both studies had fewer than 55 patients, and only a neuropilin-1 (NRP1) polymorphism reached borderline significance [59, 75]. Polymorphisms and Outcome in Endometrial, Cervical, and Vulvar Cancers A summary of included studies [29, 78–88] is shown in Supplementary Table 2. Endometrial cancer studies were generally larger studies, but none adjusted for clinical prognostic factors [78, 81, 88]. Of the cervical and vulvar studies, only two had a sample size greater than 100 patients [89, 83]. Kim et al. reported that VEGF 405C/C (or an associated haplotype) was associated with worse DFS/OS in 199 cervical cancer patients; [83] this finding has not been confirmed independently. Discussion At the international, co-operative group level, the identification of potentially predictive and/or prognostic genetic polymorphisms has been recognized as a high priority. This is the first systematic review of published studies investigating the potential role of genetic polymorphisms as predictive and prognostic biomarkers in gynecological malignancies. The vast majority of pharmacogenomic studies have been undertaken in ovarian cancer. Despite a steady increase in the number of published reports over the past five years, no compelling or consistent association was identified. Instead, there was an absence of uniform, well-defined, and validated putative polymorphic biomarkers that warranted translation into the clinical setting. Meta-analyses or pooled analyses can provide perspective to the individual results. However, when we attempted to perform a meta-analysis of the polymorphisms of the most commonly evaluated genes (ERCC1, VEGF, ABCB1, GSTP1), additional problems surfaced: (i) different genetic models of inheritance and different combinations of genotypes were evaluated across studies, rendering comparability across studies of the currently published literature of limited value; (ii) although techniques such as inverse weighting can be applied, [90] different outcome measure definitions rendered meta-analysis difficult to interpret; (iii) the sample sizes and number of studies reporting negative results for each polymorphism were generally of greater magnitude than the corresponding studies reporting positive results; and (iv) inability to collect unpublished data from individual investigators. Active efforts are required to prospectively coordinate consortia-based formal meta-analyses and pooled analyses of future studies, using standardized variables, definitions, and analytical models. This systematic review has several limitations. The quality of the studies varied, the vast majority of them being either case series or observational studies, and therefore, exploratory in nature. Many studies were underpowered (32% had fewer than 100 patients) to detect any significant association between the polymorphism(s) analyzed and the clinical outcome. Sixteen studies did not perform multivariate analysis, and therefore did not consider the possibility of confounding by other prognostic factors. At least a third of included studies utilized formalin-fixed paraffin embedded specimens for genotyping, without considering whether somatic changes were frequent in the gene regions of the evaluated polymorphisms. Publication bias

363

to positive studies should also be taken into account when interpreting the results of this review. While case series/observational studies did occasionally include sample sizes over 300, patient treatment and follow-up protocols varied substantially. Furthermore, outcome measures were often not uniformly defined across studies (e.g., progressive disease, specific toxicities). This is particularly relevant in ovarian cancer, where the definition of progressive disease upon completion of the primary treatment may vary depending on the criteria used (radiological, clinical, or based on CA-125 changes). Although case–control studies with secondary outcome collection can lead to reasonable analyses of clinical utility (valuable for general prognosis), such analyses may have limitations in the ability to answer key pharmacogenomic evaluations, particularly of drug toxicity. Future observational studies need to report results according to REMARK guidelines [91]. Only during the systematic review process did we discover examples of significant methodological deviations from guidelines; [91] examples include: (i) publication of two parallel analyses of different sets of VEGF polymorphisms involving virtually identical patient populations, using identical outcomes in two separate journals in the same month; appropriate analysis should have considered all evaluated VEGF polymorphisms in a single analysis; [72, 74] (ii) not reporting or acknowledging that a prognostic association was based on only two individuals in one arm, where the first death occurred perioperatively; [14] and (iii) not reporting significant deviation from Hardy Weinberg Equilibrium, especially when genotyping from fixed tissues, raising questions about technical problems with the genotyping process [86]. Few phase III clinical trials have systematically evaluated the relationship between genetic polymorphisms and clinical outcome in gynecologic cancers [22]. Since the completion of HapMap Project, coupled with a rapid progress in genotyping techniques, researchers are now able to investigate much larger numbers of polymorphisms (i.e., up to 2.5 million) at reasonable costs. Comprehensive discovery approaches (e.g., genome-wide association studies) focused on large phase III clinical trials may be required to move the field forward. Large randomized phase III clinical trials can provide the adequate platform for successful genotype–phenotype association-based discovery, but only when the treatment arms represent reasonable therapeutic options. In addition to discovery studies, equal consideration should be given to the publication of high quality adequately-powered validation studies. There is currently a great opportunity and need in gynecologic oncology to implement well-conducted pharmacogenomic studies. This is particularly apparent in epithelial ovarian cancer. The emergence of many promising agents (e.g. poly-ADP ribose polymerase inhibitors), [92–94] and the recent presentation of three studies [95–97] investigating the addition of the anti-angiogenic agent bevacizumab to standard chemotherapy highlight the need for predictive biomarkers to assist clinicians and patients in their choice of treatment. Since biological agents, such as bevacizumab, may require prolonged administration, often until progression, improving therapeutic ratios by finding effective biomarkers would be both a cost-effective and a clinically effective strategy. This systematic review highlights the need for future studies to adhere to the highest methodological standards. Only with large clinical trial samples or carefully annotated prospective observational studies will we be able to conduct adequately powered pre-defined subgroup analysis. In addition, validation of potential useful biomarkers across studies, and even between tumor types, may be a key in identifying clinically useful pharmacogenomic markers. Supplementary materials related to this article can be found online at doi:10.1016/j.ygyno.2011.10.034. Conflict of interest statement The authors declare no conflict of interest for the present manuscript. This study has not been presented in part or in whole elsewhere.

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