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Record W2064547618 · doi:10.1093/humupd/dmn039

Ovulation suppression to protect against chemotherapy-induced ovarian toxicity: helpful or just hopeful?

2008· letter· en· W2064547618 on OpenAlexaff
Edward G. Hughes, M.S. Neal

Bibliographic record

VenueHuman Reproduction Update · 2008
Typeletter
Languageen
FieldMedicine
TopicReproductive Biology and Fertility
Canadian institutionsMcMaster UniversityHamilton Health Sciences
Fundersnot available
KeywordsToxicityOvulationMedicineChemotherapyOncologyInternal medicinePharmacologyHormone

Abstract

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“The whole of medicine depends on the transparent reporting of clinical trials”. Drummond Rennie The options for fertility preservation in female cancer patients currently include ovulation suppression and cryopreservation of reproductive tissue. The most reliable choice remains embryo freezing after conventional IVF. Although the chance of pregnancy is relatively high following this approach, there are several drawbacks. The need for sperm from a donor may create personal, legal and ethical problems for the woman; the time associated with the ovarian stimulation and oocyte recovery procedures may delay the start of cancer treatment; and ovarian stimulation may be harmful in the presence of estrogen sensitive tumours. Oocyte freezing would at least resolve the first of these problems. However, its success is currently limited (Gook and Edgar, 2007). Oocytes are much more vulnerable to cryopreservation damage than sperm. Moreover, a successful oocyte retrieval results in perhaps 10 oocytes, compared with the millions of sperm from a single ejaculate. Cell damage and loss due to freezing are much greater concerns when dealing with female gametes. Nevertheless, the methods of oocyte freezing (both slow-cooling and vitrification) continue to evolve with improving success. The rate of implantation per oocyte retrieved currently ranges from 2% to 5% (Borini et al., 2008). Less is known about the therapeutic value of ovarian tissue freezing, since few women have had frozen tissue replaced, but again this strategy is under scrutiny (Wallace et al., 2004). One advantage with ovarian tissue freezing is its potential for use in pre-pubertal girls. The simplest and most commonly used approach to fertility preservation in women of reproductive age remains suppression of ovulation with GnRHa before and during chemotherapy. There is certainly biological plausibility for this strategy: in women who receive chemotherapy before puberty, ovarian function is more frequently retained in young adulthood. GnRHa may create a ‘pre-pubertal’ ovarian milieu, reducing ovarian vulnerability to cell damage. Despite this rationale, the question remains, does the available evidence demonstrate a protective effect? In their review of this question, Beck-Fruchter et al. (2008) included two randomized controlled trials (RCT), seven case-controlled studies and three case series. They conclude that the evidence is insufficient to claim an effect exists. Blumenfeld and von Wolff (2008) included one RCT and eight human ‘controlled studies’ and reached a different conclusion: that treatment reduces the incidence of premature ovarian failure and the risk of abnormal bleeding associated with the haematological effects of chemotherapy. Both author groups agree that larger RCTs are needed to ‘prove’ a benefit if one exists. How should readers respond to these reviews, which draw somewhat different conclusions? Jadad et al. (1997) suggest that such differences stem from the choice and validity of included studies. In this case, seven studies are common to both reviews, but almost all data are observational, rather than experimental. Important differences in prognostic variables between groups in the observational studies may thus be responsible for apparent treatment effects. Such differences include age at treatment, intensity of chemotherapy and length of follow-up. Imbalance of these and other variables is expected when physicians and patients make choices around who should and should not receive treatment. The choice and measurement of clinically important outcomes is also a key point. Differences in definition of ‘ovarian failure’ and limited data on live birth make studies hard to interpret. In any systematic review combining observational with experimental data, RCT results are worth looking at in isolation. Two trials are available here. However, they include only 47 women and although they show no statistically significant difference in return of menstruation or in live birth, their combined power is insufficient to allow acceptance of the null hypothesis. How should a young female cancer patient respond to this uncertainty? Her primary concern should be the balance of risks, costs and potential benefits of GnRHa co-treatment. The financial cost is significant, common symptoms of estrogen withdrawal uncomfortable and the potential for bone loss important. However, these negatives may be outweighed by the potential for fertility preservation, an emotionally powerful goal, as well as a reduction in abnormal bleeding. Beyond that, she should know as much as possible about her prognosis for fertility resumption post-chemotherapy, without GnRHa. This is very unpredictable. Young age at treatment is a strongly positive prognostic factor and intensity of chemotherapy a negative one, but patients need to know that fertility may be retained, even after aggressive cancer treatments. Attempting pregnancy as soon as possible, once a secure relationship is established, is also pragmatic and should be thoughtfully encouraged by clinicians. Finally, the option of cryopreservation deserves consideration. For pre-pubertal girls and those having gonadal irradiation, ovarian suppression is not a practical option. Tissue or immature oocyte freezing is under study and the costs and benefits of these approaches remain to be determined (Meirow et al., 2005; Kim, 2006). In young women, oocyte freezing and if possible embryo freezing may be the most effective fertility preservation options. Again though, with low success and limited safety information on oocyte freezing, further evaluation is needed. As a community of clinician-scientists, how should we respond to the ovulation suppression data? By supporting well-designed, executed and reported clinical trials. This is the only way to define the true value of GnRHa co-treatment. Only through effective randomization will confounders, known and unknown, be evenly divided between the study groups. Prospective study will also facilitate complete follow-up and measurement of clinically important outcomes, including live birth. At the July 2008 ESHRE annual meeting in Barcelona, preliminary findings of a potentially helpful RCT were presented (Elnasher et al., 2008). The authors reported recruitment of a surprisingly large number of breast cancer patients over a short period of time: 80 women over 2 years. The mean duration of follow-up was only 8 months, but resumption of menses was reported in 33% of unsuppressed women, compared with 90% receiving GnRHa co-treatment. Complete and transparent publication of this study may help resolve many outstanding questions. In summary, two reviews in this issue of HRU agree that definitive RCTs are necessary to resolve with certainty the question of GnRHa co-treatment for fertility preservation. It is both responsible and ethical to offer this treatment within the context of an RCT. As Drummond Rennie succinctly put it, ‘The whole of medicine depends on the transparent reporting of clinical trials’ and as the Oxford Database of Perinatal Trials (Chalmers et al., 1986) showed us, the results of well-done RCTs frequently shatter our long-held ‘beliefs’ in treatment effectiveness. This question is no exception: there are costs and risks to GnRHa co-treatment and we owe women burdened with cancer and other critical illnesses better information on which to base their choices.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.030
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0300.025
Insufficient payload (model declined to judge)0.0070.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.076
GPT teacher head0.325
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations8
Published2008
Admission routes1
Has abstractno

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