Description of the condition
Worldwide, nearly 225,000 women are diagnosed with ovarian cancer each year making it the eighth most common cancer in women. The estimated risk of getting the disease is approximately 1% in developed countries (Europe, Northern America, Australia/New Zealand and Japan) and 0.5% in the rest of the world (GLOBOCAN 2008).
Ovarian cancer usually has a silent onset and, initially, inconspicuous progression. Symptoms are characteristically non-specific and include abdominal swelling and pain, early satiety and increased urinary urgency and frequency (NICE 2011). This absence of a clear clinical profile results in most women being diagnosed with advanced-stage disease (FIGO III and IV; FIGO 2009) making ovarian cancer the deadliest of all gynaecological cancers with five-year relative survival rates of only 37% and 54% in Europe and America, respectively (EUROCARE-4 2009; SEER 2007). Importantly, however, if women are diagnosed at FIGO stage I, they have a 90% chance of surviving the following five years (SEER 2007).
The need to improve the detection of early disease was recognised over 30 years ago and led to the development of screening protocols using vaginal examination, measurement of serum CA125 levels and ultrasonography (Campbell 1989; Jacobs 1988). However, while studies using refined diagnostic techniques, such as transvaginal ultrasound, are still underway (Menon 2009), it is not yet clear if the implementation of a screening programme would lower mortality rates and benefit affected women. Around 90% of ovarian malignancies are epithelial ovarian cancers and other types include germ cell, stromal cell and Müllerian tumours (SEER 2007). Primary diagnostic tests for ovarian cancer are the measurement of serum CA125 levels and ultrasonography. If disease is suspected women undergo explorative laparotomy, when the tumour is histologically classified and staged and all macroscopic disease is removed (NICE 2011).
Further standard treatment is disease-dependent. Women presenting with low-grade stage I tumours might not undergo chemotherapy, while high-grade stage I and all other stages receive intravenous platinum-based chemotherapy in combination with paclitaxel (NICE 2011). Carboplatin is usually favoured over cisplatin because it is less toxic but has equivalent efficacy (NICE 2003). Surgery and standard adjuvant chemotherapy have a response rate of 70% to 80%, however, 55% to 75% of responders relapse within two years of treatment completion (NICE 2003).
Description of the intervention
Doxorubicin hydrochloride is a cytotoxic drug that has been available since the 1960s and belongs to the anthracycline family (EMA 2010). It was originally used in the first-line treatment of ovarian cancer in the 1970s, when in vitro experiments showed a dose-response relationship in ovarian cancer cell lines; activity against epithelial ovarian cancer was subsequently proven in clinical trials (A'Hern 1995; OCMP 1991; Ozols 1980). Despite its potent antineoplastic activity, the clinical use of doxorubicin has been limited by its associated side effects, in particular haematological toxicity and irreversible cardiac damage. Pegylated liposomal doxorubicin (PLD) is a formulation of liposomal doxorubicin that is coated in polyethylene glycol (PEG), which reduces the rate at which the active drug is broken down (Gabizon 2001) and makes it less toxic to heart muscle.
In the UK, PLD is currently licensed for the treatment of advanced ovarian cancer in women for whom platinum-based chemotherapy has failed (EMA 2010). In these women, PLD may be given intravenously at a dose of 50 mg/m² once every four weeks for six cycles if tolerated and if the disease does not progress (EMA 2010). However, recent trials have employed lower doses (e.g. CALYPSO 2010; 30 mg/m² every four weeks) to reduce drug-related adverse effects. The main toxicities associated with PLD are nausea, palmar-plantar erythema (redness and soreness of palms of hands and soles of feet), stomatitis and myelosuppression (Janssen-Cilag 2011).
Although single-agent PLD treatment is usually employed in platinum-resistant disease (NICE 2011), PLD in combination with platinum (CALYPSO 2010; HeCOG 2010) has been shown to be a good alternative to carboplatin/paclitaxel treatment in platinum-sensitive relapsed disease. Compared with standard carboplatin/paclitaxel treatment, the carboplatin/PLD regimen may be better tolerated: in CALYPSO 2010, significantly more women experienced complete hair loss, hypersensitivity reactions and neuropathies in the paclitaxel arm compared with the PLD arm, and women in the paclitaxel arm were more likely to discontinue treatment. Since PLD in combination with carboplatin is a good alternative to carboplatin/paclitaxel for relapsed ovarian cancer, carboplatin/PLD may represent a good alternative to the current standard carboplatin/paclitaxel for first-line chemotherapy.
How the intervention might work
Anthracyclines function by interacting with DNA, adversely affecting all cell functions that rely on DNA. Furthermore, they interact with cell membranes, altering their functions and generating hydrogen peroxide and hydroxy radicals which are highly destructive to cells (Zunino 2002). The special PEG coating of PLD represents a hydrophilic barrier, protecting the liposomes from detection by the reticuloendothelial system and increasing the time that the active drug remains in circulation (Gabizon 1997; Gabizon 2001). Moreover, the size of the liposomes prevents them from entering tissues with tight capillary junctions, such as the heart and gastrointestinal tract, reducing toxicity, while leading to accumulation and increased drug concentrations in the tumour (Waterhouse 2001), resulting in increased efficacy.
Why it is important to do this review
PLD is a novel formulation of a proven chemotherapeutic agent, with an improved efficacy and safety profile. There is good evidence to support its use in the treatment of relapsed ovarian cancer in combination with carboplatin in platinum-sensitive disease, and as a single agent in platinum-resistant disease. This represents a strong rationale for testing PLD in the first-line setting. Following the completion of a large randomised controlled trial (RCT) of PLD/carboplatin for first-line treatment of ovarian cancer (MITO-2 2011), we consider it important to review the evidence relating to PLD administration, before or after primary cytoreductive surgery, as initial chemotherapy for newly diagnosed ovarian cancer.
Our aim is to conduct a comprehensive systematic review of the literature to clarify the role of pegylated liposomal doxorubicin, either alone or in combination, in the first-line treatment of epithelial ovarian cancer.
Criteria for considering studies for this review
Types of studies
Types of participants
Women with epithelial ovarian cancer who may or may not have undergone primary cytoreductive surgery.
Types of interventions
PLD alone or in combination with another agent/s(e.g. carboplatin) versus other agent/s.
Types of outcome measures
- Progression-free survival (PFS)
- Overall survival (OS)
- Severe adverse events, classified according to CTCAE 2006, including specific haematological, gastrointestinal, genitourinary, dermatological, neurological, pulmonary and other severe adverse events
- Symptom control(e.g. haemopoietic growth factors, transfusions, anti-emetics, dose delays and reductions)
- Quality of life (QoL)
Search methods for identification of studies
We will search the following electronic databases (also see Cochrane Gynaecological Cancer Group methods used in reviews):
- The Cochrane Gynaecological Cancer Group's Trial Register
- Cochrane Central Register of Controlled Trials (CENTRAL)
The MEDLINE, EMBASE and CENTRAL search strategies, based on terms related to the review topic, are presented in Appendix 1; Appendix 2; Appendix 3 respectively. As PLD has been recently developed, we will search databases from 1990 onwards.
Searching other resources
We will search the metaRegister of Controlled Trials (mRCT) (www.controlled-trials.com), www.clinicaltrials.gov and the Physicians Data Query (PDQ) (www.cancer.gov/clinicaltrials) for ongoing trials. We will also search the abstracts of relevant scientific meetings from 2000 onwards, including the American Society of Clinical Oncologists (ASCO), the European Society of Medical Oncologists (ESMO) and the European Society of Gynaecologic Oncologists (ESGO) Annual Meetings, using the zetoc.mimas.ac.uk website. Where necessary, we will contact the main investigators of relevant ongoing trials for further information. In addition, we will check the citation lists of included studies to identify other relevant reports/studies.
Data collection and analysis
Selection of studies
We will download all titles and abstracts retrieved by electronic searching to the reference management database (Reference Manager version 10) and remove duplicates. Two review authors (Tess Lawrie (TL) and Clemens Thoma (CT)) will review the remaining records independently to identify potentially relevant trials. We will exclude studies that clearly do not meet the inclusion criteria and obtain the full text of potentially relevant trials. Two review authors (CT and Roy Rabbie (RR)) will independently assess these identified trials for eligibility. Where there are any disagreements, we will involve a third review author (TL) in the process. For excluded studies, we will document the reasons for exclusion.
Data extraction and management
We will design and pilot a data extraction form for the review. Two review authors (RR, CT) will independently extract data from included studies. Where there is disagreement between these authors, a third author will be involved (TL or JM) to resolve it.
For included studies, we will abstract the following data where possible:
- Author, year of publication and journal citation
- Inclusion and exclusion criteria
- Study design, methodology
- Duration of follow-up
- Study population
- Total number enrolled
- Patient characteristics
- Ovarian cancer details at diagnosis
- FIGO stage
- Histological cell type
- Tumour grade
- Performance status
- Extent of disease
- Total number of intervention groups
- Intervention details
- Details of PLD including dose, regime, frequency and the number of cycles
- Comparison details including type of control and dose, regime, frequency and number of cycles
- Proportion of participants who received all/part/none of the intended treatment
- Delays in treatment
- Risk of bias in study (see Assessment of risk of bias in included studies)
- Outcomes – progression-free survival, overall survival, QoL, symptom control and adverse events
- For each outcome: outcome definition (with diagnostic criteria if relevant)
- Unit of measurement (if relevant)
- For scales: upper and lower limits, and whether high or low score is good
- Results: number of participants allocated to each intervention group
- For each outcome of interest: sample size; missing participants
Assessment of risk of bias in included studies
We will assess the risk of bias in included RCTs using The Cochrane Collaboration's tool and the criteria specified in chapter 8 of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). This includes assessment of the following domains.
- Selection bias:
- Random sequence generation
- Allocation concealment
- Performance bias
- Blinding of participants and personnel (patients and treatment providers)
- Detection bias
- Blinding of outcome assessment
- Attrition bias
- Incomplete outcome data: we will record the proportion of participants whose outcomes are not reported at the end of the study and consider > 20% attrition to be at a high risk of bias
- Reporting bias
- Selective reporting of outcomes
- Other possible sources of bias
Two review authors (RR and CT) will apply the 'Risk of bias' tool (Appendix 4) independently and differences will be resolved by discussion or by appeal to a third review author (TL or JM). We will present results in a 'Risk of bias' summary graph and interpret the results of the meta-analyses in light of the findings with respect to risk of bias.
Measures of treatment effect
We will use the following measures of the effect of treatment:
- For time-to-event data, we will use hazard ratios (HR). If these are not reported, we will attempt to estimate them from other reported statistics using the methods of Parmar 1998 (e.g. number of events in each arm and log-rank P value comparing the relevant outcomes in each arm). If it is not possible to estimate the HR, we will extract the number of patients in each treatment arm who experienced the outcome of interest at a specific time point, in order to estimate a risk ratio (RR).
- For dichotomous outcomes (e.g. adverse events), we will extract the number of patients in each treatment arm who experienced the outcome of interest and the number of patients assessed at endpoint, in order to estimate a RR.
- For continuous outcomes (e.g. QoL measures), we will extract the mean difference (MD) and standard deviation (SD) between the final value of the outcome measure in each treatment arm at the end of follow-up. If standard deviations of final values are not available, we will use change scores if their SDs are available. If no SDs are available, we will omit these trials from the analyses.
Where possible, we will extract data relevant to an intention-to-treat analysis (ITT), in which participants are analysed in the groups to which they are assigned. Where time-to-event outcomes are assessed by more than one method, i.e. independent radiology review, investigator assessment or independent oncology review, we will use the independent radiology review data and note any differences in effect size and direction, compared with the other methods, in the text.
Where data from several time points are reported, we will use the data from the last assessment in our meta-analyses, if appropriate. If a trial evaluates the same drug in two or more different doses versus PLD, we will extract the combined data and the individual data for the most efficacious dose/regimen versus PLD.
Unit of analysis issues
The unit of analysis will usually be the individual participant; however, where data are presented per treatment cycle (e.g. for dose delays) we will also extract these data.
Dealing with missing data
We will not impute missing data.
Assessment of heterogeneity
We will assess heterogeneity between trials by visual inspection of forest plots, by estimation of the percentage heterogeneity between trials which cannot be ascribed to sampling variation (Higgins 2003) and by a formal statistical test of the significance of the heterogeneity (Deeks 2001). In each meta-analysis, we will regard heterogeneity as substantial if I² is greater than 50% and either T² is greater than zero, or there is a low P value (less than 0.10) in the Chi² test. If there is evidence of substantial heterogeneity, we will investigate the possible reasons for this and report it.
Assessment of reporting biases
If there are more than 10 studies in a meta-analysis, we will use funnel plots to evaluate the potential for publication bias due to small study effects.
When sufficient clinically similar trials are available, we will pool their results in meta-analyses:
- For time-to-event data, we will pool HRs using the generic inverse variance facility of RevMan 2012.
- For any dichotomous outcomes, we will pool the RRs.
- For continuous outcomes, we will pool the mean differences (MDs) between the treatment arms at the end of follow-up if all trials measure the outcome on the same scale, otherwise we will pool standardised mean differences (SMDs).
We will use random-effects models with inverse variance weighting for all meta-analyses (DerSimonian 1986).
Subgroup analysis and investigation of heterogeneity
Where possible, for the primary outcome, we will endeavour to perform subgroup analyses according to:
- stage of disease: early (FIGO 1a, Ib, IIa) and advanced (FIGO IIb, III, IV);
- optimal staging (residual disease less than 1 cm), suboptimal staging (residual disease 1 cm or more) and no surgery;
- age < 70 and ≥ 70 years.
Where there is significant heterogeneity between the types of chemotherapy agents employed, we will consider whether pooling data is clinically meaningful and, if not, we will subgroup these data.
We will perform sensitivity analyses for primary outcomes by excluding trials which are at a high risk of bias.
We thank the Managing Editors, Gail Quinn and Clare Jess, of the Cochrane Gynaecological Cancer Group for their administrative support, and the library staff at the Royal United Hospital for their assistance with the sourcing of reference material.
Appendix 1. MEDLINE search strategy
- exp Ovarian Neoplasms/
- (ovar* adj5 (cancer* or neoplas* or tumor* or tumour* or carcinoma* or malignan*)).mp.
- 1 or 2
- exp Doxorubicin/
- 4 or 5 or 6 or 7 or 8
- 3 and 9
- randomized controlled trial.pt.
- controlled clinical trial.pt.
- clinical trials as topic.sh.
- 11 or 12 or 13 or 14 or 15 or 16 or 17
- 10 and 18
- exp animals/ not humans.sh.
- 19 not 20
key: mp = protocol supplementary concept, rare disease supplementary concept, title, original title, abstract, name of substance word, subject heading word, unique identifier, pt = publication type, ab = abstract, ti = title, sh = subject heading
Appendix 2. EMBASE search strategy
- exp ovary tumor/
- (ovar* adj5 (cancer* or neoplas* or tumor* or tumour* or carcinoma* or malignan*)).mp.
- 1 or 2
- exp doxorubicin/
- 4 or 5 or 6 or 7 or 8
- 3 and 9
- crossover procedure/
- randomized controlled trial/
- single blind procedure/
- (crossover* or cross over* or cross-over).mp.
- (doubl* adj blind*).mp.
- (singl* adj blind*).mp.
- 11 or 12 or 13 or 14 or 15 or 16 or 17 or 18 or 19 or 20 or 21 or 22
- 10 and 23
key: mp = title, abstract, subject headings, heading word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, keyword
Appendix 3. CENTRAL search strategy
- MeSH descriptor Ovarian Neoplasms explode all trees
- ovar* near/5 (cancer* or neoplas* or tumor* or tumour* or carcinoma* or malignan*)
- (#1 OR #2)
- MeSH descriptor Doxorubicin explode all trees
- (#4 OR #5 OR #6 OR #7)
- (#3 AND #8)
Appendix 4. 'Risk of bias' tool
We will apply this tool to included studies to assess the risk of bias:
1. Random sequence generation
- Low risk of bias e.g. participants assigned to treatments on basis of a computer-generated random sequence or a table of random numbers.
- High risk of bias e.g. participants assigned to treatments on basis of date of birth, clinic ID number or surname, or no attempt to randomise participants.
- Unclear risk of bias e.g. not reported, information not available.
2. Allocation concealment
- Low risk of bias e.g. where the allocation sequence could not be foretold.
- High risk of bias e.g. allocation sequence could be foretold by patients, investigators or treatment providers.
- Unclear risk of bias e.g. not reported.
3. Blinding of participants and personnel
- Low risk of bias if participants and personnel were adequately blinded.
- High risk of bias if participants were not blinded to the intervention that the participant received.
- Unclear risk of bias if this was not reported or unclear.
4. Blinding of outcomes assessors
- Low risk of bias if outcome assessors were adequately blinded.
- High risk of bias if outcome assessors were not blinded to the intervention that the participant received.
- Unclear risk of bias if this was not reported or unclear.
5. Incomplete outcome data
- Low risk of bias, if fewer than 20% of patients were lost to follow-up and reasons for loss to follow-up were similar in both treatment arms.
- High risk of bias, if more than 20% of patients were lost to follow-up or reasons for loss to follow-up differed between treatment arms.
- Unclear risk of bias if loss to follow-up was not reported.
6. Selective reporting of outcomes
- Low risk of bias e.g. reports all outcomes specified in the protocol.
- High risk of bias e.g. it is suspected that outcomes have been selectively reported.
- Unclear if it is unclear whether outcomes have been selectively reported.
7. Other bias
- Low risk of bias if you do not suspect any other source of bias and the trial appears to be methodologically sound.
- High risk of bias if you suspect that the trial was prone to an additional bias.
- Unclear risk of bias if you are uncertain whether an additional bias may have been present.
Contributions of authors
TL, CT and RR wrote the protocol; JM reviewed it.
Declarations of interest
Sources of support
- No sources of support supplied
- Department of Health, UK.NHS Cochrane Collaboration Programme Grant Scheme CPG-10/4001/12