Abstract P6-09-12: Breast cancer characteristics and the levonorgestrel intrauterine device. A monocentric retrospective study

OBJECTIVE: The levonorgestrel-intrauterine device (LNG-IUD) is a widely used contraceptive method. It is not clear if LNG-IUD users are more likely to develop breast cancer. Breast cancer growth through the estrogen and/or the human epidermal growth factor receptor 2 (HER2) pathway could be influenced by a continuous low systemic dose of levonorgestrel. In this study, we compare breast cancer characteristics and the receptor expression of estrogen (ER), progesterone (PR) and HER2 in women with and without a LNG- IUD at the time of diagnosis.

METHODS: In this retrospective, observational study, we included 2599 consecutive breast cancer patients who were younger than 55 years at diagnosis and treated between 2000 and 2014 in the University Hospitals Leuven for a primary invasive, non-metastatic tumor. The non LNG-IUD group was matched by age and parity at diagnosis. ER, PR and HER2 status were reported according to ASCO/CAP guidelines. The Chi-square test was used to compare receptor status between groups. All tests were two-sided, and a 5% significance level was assumed. An additional analysis was performed to detect the occurrence of HER 2 expression with or without intake of oral contraception by diagnosis in the control group.

RESULTS: 366 LNG-IUD users and 2233 women without a LNG-IUD were included. Compared to the control group, the LNG-IUD users had a lower Nottingham prognostic index (4.2 vs 4.4; p=0.048), more PR expression (79.2% vs 73.4%; p=0.021) but less HER2 expression (11.6% vs 17.2%; p=0.009). A significant higher rate of ER+PR+HER2- was observed in the LNG-IUD group (63.26 % vs 73.46%; p<0.001). These differences in receptor expression were mainly observed in the age group 45-49 years at diagnosis. Additionally, a trend of more HER2 positivity associated with oral contraceptive use was noticed in the control group.

CONCLUSION: We found in a breast cancer population, matched for age and parity, significant differences in the PR and HER2 expression according to use of LNG-IUD at time of diagnosis. ER positive, PR positive and HER2 negative breast cancers are more frequently seen in LNG-IUD users. There is a trend of less HER 2 positivity in LNG-IUD users and it is more common seen in oral contraception users.

Citation Format: Borowski E, Poppe A, Laenen A, Remmerie C, Van Asten K, Nevelsteen I, Smeets A, Weltens C, Peeters S, Leunen K, Berteloot P, Amant F, Vergote I, Van Limbergen E, Christiaens M-R, Wildiers H, Floris G, Poppe W, Neven P. Breast cancer characteristics and the levonorgestrel intrauterine device. A monocentric retrospective study. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P6-09-12.

Abstract P6-09-11: Independent prognostic value of age depends on breast cancer subtype

Background: Young women present more often with aggressive breast cancer phenotypes and have worse prognosis. It remains controversial whether age is an independent prognostic factor in early stage breast cancer. Arbitrarily chosen age cut-off values have been proposed in different studies. Furthermore, few studies have examined the impact of breast cancer subtypes on the prognostic value of age. This abstract represents an update of a prior analysis (San Antonio Breast Cancer Symposium, December 4-8 2012, P06-07-29).

Methods: We included all primary operable female breast cancer patients from our prospectively managed database in UZ Leuven, Belgium. We assessed the effect of age on locoregional free interval (LRRFI), distant metastasis interval (DMFI) and breast cancer specific survival (BCSS). In univariate analysis, using Cox regression models, we determined the best categorization of age at diagnosis into two or three age groups by considering all possible combinations of cut-off values. Best categorization was obtained with three age groups. We further determined, using multivariate analysis (correcting for phenotype, tumor size, nodal status, adjuvant chemo -, hormone – and radiotherapy, type of surgery and procedure of axillary staging), whether age at diagnosis remains an independent predictor of outcome (LRRFI, DMFI and BCSS). We further explored whether age at diagnosis is an independent predictor of event risk (LRRFI, DMFI and BCSS) in different breast cancer subtypes. Luminal A-like (grade I or II, ER and/or PR positive, HER 2 positive), Luminal B-like (idem but grade III), Luminal HER 2 like (ER and/or PR positive, HER 2 positive), HER 2 like (ER/PR negative, HER 2 positive), triple negative (ER/PR negative, HER 2 negative).

Results: We included 4180 patients with a mean/median age of 58/57 year and with a median follow up of 8.9 year. Multivariate analysis confirmed age as an independent prognostic variable for LRRFI, DMFI and BCSS.

Results multivariable analysis with age in 3 groups (HR (95% CI) P-VALUE) LRRFIDMFIBCSSYoungest versus middle1.61 (1.18-2.18) 0.00251.54 (1.23-1.93) 0.00021.72 (1.26-2.36) 0.0007Youngest versus oldest3.45 (1.85-6.45) 0.00011.25 (0.89-1.77) 0.19821.31 (0.85-2.02) 0.2220Middle versus oldest2.15 (1.22-3.79) 0.00820.81 (0.61-1.09) 0,17060.76 (0.55-1.06) 0.1014

We found optimal cut-off values for LRRFI at 44y and 72y, for DMFI at 47y and 71y and for BCSS at 41y and 70y.

In an exploratory analysis, with age as continuous variable, by subtype we found a significant independent association between age and LRRFI (P=0.0169), DMFI (P=0.0344) in luminal A-like, LRRFI (P=0.0022) in luminal B-like and DMFI (P=0.0010) and BCSS (P=0.0053) in triple negative breast cancer. No significant associations were found in luminal HER2 and HER2 like breast cancers.

Conclusion:

This study has shown that young age is an independent prognostic factor for LRRFI, DMFI and BCSS after correction for the most important clinical prognostic factors. The prognostic effect is most important in luminal A and triple negative subtypes. Additional analyses for subtypes with age as a categorical variable will be performed and optimal cut off values will be defined.

Citation Format: Poppe A, Brouckaert O, Laenen A, Soubry A, Remmerie C, Floris G, Leunen K, Berteloot P, Amant F, Vergote I, Nevelsteen I, Smeets A, Christiaens M-R, Weltens C, Peeters S, Van Limbergen E, Wildiers H, Neven P. Independent prognostic value of age depends on breast cancer subtype. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P6-09-11.

Abstract P6-10-06: The impact of body mass index on age at breast cancer diagnosis and breast cancer phenotype

Background

Evidence suggests that premenopausal obesity decreases and postmenopausal obesity increases breast cancer risk. While it has been hypothesized that carcinogenesis may be accelerated by a disrupted metabolic homeostasis in obese women, it is unclear why this dual relationship is observed. We here study whether body mass index (BMI) affects (a) age at breast cancer diagnosis and (b) the probability of being diagnosed with a specific breast cancer phenotype, taking menopausal status into account.

Patients and methods

All patients with non-metastatic operable breast cancer from UZ Leuven diagnosed between January 1, 2000 and December 31, 2013 were included (n=7020). Luminal A like (= grade 1 or 2, ER and/or PR positive, HER2 negative), Luminal B like (= grade 3 ER and/or PR positive, HER2 negative), Luminal HER2 like (ER and/or PR positive, HER2 positive), HER2 like (ER and PR negative, HER2 positive) and triple negative breast cancer (TNBC = ER and PR and HER2 negative). For statistical analysis, linear models and logistic regression were used to study respectively the association between BMI and age at diagnosis and BMI and breast cancer phenotype by menopausal status.

Results

There was a quadratic relationship between BMI and age at breast cancer diagnosis studying the overall population (p<0.0001). A 5kg/m2 increase in BMI was associated with the following increases in age at diagnosis: +1.8y (95% CI 1.4-2.3y) at BMI=18, +1.2y (95% CI 0.95-1.5y) at BMI=23 and +0.6y (95% CI 0.4-0.9y) at BMI=28 (corrected for menopause). This relationship was independent of the menopausal status, ER or HER2 status, histology and breast cancer phenotype.

We observed a linear relationship between BMI and the probability of being diagnosed with Luminal B like, Luminal HER2 like and HER2 like breast cancer (table 1). This linear relationship interacts with menopausal status for Luminal B like and HER2 like breast cancers (table 1).

Table 1: Probability of being diagnosed with a certain breast cancer phenotype by BMI (linear model) and the impact of menopausal status.Effect of BMI on the probabilityLinear modelof being diagnosed withp-valueeffectLuminal A like0.4430n/aLuminal B like0.0276BMI +5kg/m2 OR 1.07 (95% CI 1.01-1.14)Luminal HER2 like0.0367BMI +5kg/m2 OR 0.91 (95% CI 0.83-1.00)HER2 like0.0219BMI +5kg/m2 OR 0.88 (95% CI 0.78-0.98)TNBC0.5454n/aInteraction with menopausal statusp-valueeffectLuminal A0,2204n/aLuminal B0,0487Premenopausal OR 0,996 (CI 0,974-1,019), p=0,7449; Postmenopausal OR 1,023 (CI 1,008-1,038), p=0.0023Luminal HER20,2571n/aHER2 like0,0031Premenopausal OR 1,020 (CI 0,983-1,059), p=0,2923; Postmenopausal OR 0,947 (CI 0,919-0,976), p=0.0004TNBC0,1638n/a

Conclusion

We could not confirm the hypothesis that increasing BMI decreases (increases) age at diagnosis in postmenopausal (premenopausal) women. Obesity does affect the probability of being diagnosed with certain breast cancer phenotypes, but for certain breast cancer phenotypes an interaction with menopause was observed. We presume a potential biological link through BMI between Luminal B and HER2 like breast cancer that needs further exploration.

Citation Format: Brouckaert O, Poppe A, Laenen A, Floris G, Leunen K, Berteloot P, Amant F, Vergote I, Smeets A, Weltens C, Peeters S, Van Limbergen E, Wildiers H, Christiaens M-R, Neven P. The impact of body mass index on age at breast cancer diagnosis and breast cancer phenotype. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P6-10-06.

ESMO-ESGO-ESTRO Consensus Conference on Endometrial Cancer: diagnosis, treatment and follow-up

The first joint European Society for Medical Oncology (ESMO), European SocieTy for Radiotherapy & Oncology (ESTRO) and European Society of Gynaecological Oncology (ESGO) consensus conference on endometrial cancer was held on 11-13 December 2014 in Milan, Italy, and comprised a multidisciplinary panel of 40 leading experts in the management of endometrial cancer. Before the conference, the expert panel prepared three clinically relevant questions about endometrial cancer relating to the following four areas: prevention and screening, surgery, adjuvant treatment and advanced and recurrent disease. All relevant scientific literature, as identified by the experts, was reviewed in advance. During the consensus conference, the panel developed recommendations for each specific question and a consensus was reached. Results of this consensus conference, together with a summary of evidence supporting each recommendation, are detailed in this article. All participants have approved this final article.

Management of ovarian cysts and cancer in pregnancy

Adnexal masses during pregnancy are not uncommon. Ovarian cysts or masses during pregnancy should be accurately evaluated to identify the patients who need surgical interventions from those where a 'wait-and-see' strategy can be followed. Ultrasound and MRI are safe diagnostic tools to distinguish between benign and malignant lesions. Treatment options (surgical procedures) should be discussed for each patient individually. Both open surgery and laparoscopy can be performed considering the tumour diameter, gestational age and surgical expertise. A multidisciplinary approach is necessary in case of high suspicion of malignancy and preferably patients should be referred to centres with specialized experience.

Optimizing anticancer drug treatment in pregnant cancer patients: pharmacokinetic analysis of gestation-induced changes for doxorubicin, epirubicin, docetaxel and paclitaxel

BACKGROUND

Pregnant patients with cancer are increasingly treated with anticancer drugs, although the specific impact of pregnancy-induced physiological changes on the pharmacokinetics (PK) of anticancer drugs and associated implications for optimal dose regimens remains unclear. Our objectives were to quantify changes in PK during pregnancy for four frequently used anticancer agents doxorubicin, epirubicin, docetaxel and paclitaxel, and to determine associated necessary dose adjustments.

PATIENTS AND METHODS

A pooled analysis of PK data was carried out for pregnant (Pr) and nonpregnant (NPr) patients for doxorubicin (n = 16 Pr/59 NPr), epirubicin (n = 14 Pr/57 NPr), docetaxel (n = 3 Pr/32 NPr) and paclitaxel (n = 5 Pr/105 NPr). Compartmental nonlinear mixed effect models were used to describe the PK and gestational effects. Subsequently, we derived optimized dose regimens aiming to match to the area under the concentration-time curve (AUC) in nonpregnant patients.

RESULTS

The effect of pregnancy on volumes of distribution for doxorubicin, epirubicin, docetaxel and paclitaxel were estimated as fold-change of <1.32, <2.08, <1.37 and <4.21, respectively, with adequate precision [relative standard error (RSE) <37%]. For doxorubicin, no gestational effect could be estimated on clearance (CL). For epirubicin, docetaxel and paclitaxel, a fold-change of 1.1 (RSE 9%), 1.19 (RSE 7%) and 1.92 (RSE 21%) were, respectively, estimated on CL. Calculated dose adjustment requirements for doxorubicin, epirubicin, docetaxel and paclitaxel were +5.5%, +8.0%, +16.9% and +37.8%, respectively. Estimated changes in infusion duration were marginal (<4.2%) except for paclitaxel (-21.4%).

CONCLUSION

Clinicians should be aware of a decrease in drug exposure during pregnancy and should not a priori reduce dose. The decrease in exposure was most apparent for docetaxel and paclitaxel which is supported by known physiological changes during pregnancy. The suggested dose adaptations should only be implemented after conduct of further confirmatory studies of the PK during pregnancy.

Abstract PD2-3: High body mass index (BMI) and worse response to neoadjuvant chemotherapy (NACT) by breast cancer phenotype: Own data and external validation on German breast group (GBG) patients

Introduction

It remains controversial to what extent BMI predicts pathologic complete response (pCR) following NACT. We evaluated pCR by ‘BMI category’ in different breast cancer subtypes and validated our findings in a larger dataset from the GBG.

Patients and methods

A retrospective study from UZL with consecutive breast cancer patients treated between 01-01-2000 and 31-12-2011 with different types of NACT +/- trastuzumab (dose capping BSA≥2m2) followed by surgery. pCR1 (ypT0N0) and pCR2 (ypT0/isN0) were reported in each of the intrinsic breast cancer subtypes based on ER and HER-2 expression. The effect of BMI [BMI 1(≤25kg/m2), BMI 2 (&gt;25-29,9 kg/m2) and BMI 3 (≥ 30 kg/m2)] on both pCR definitions was analyzed in univariate (Chi square test) and multivariate (logistic regression) model. Negative ER and HER-2 status were defined according to ASCO/CAP guidelines (e.g. &lt;1% ER positive tumor cells). Data were validated by GBG using different NACT +/- trastuzumab regimen (no dose capping BSA≥2m2) and similar biomarkers for defining breast cancer subtypes although ER-negative status was defined as &lt;10% ER positive tumor cells.

Results

We included 267 breast cancer cases while the GBG validation was done in 8874 cases. Multivariate model for UZL patients didn't retain ‘BMI category’ as a predictor for any of the pCR definitions. However, univariate analysis found a significant association between BMI and pCR2 (p = 0,048). The tables show the numerical differences for pCR2 in each BMI category by breast cancer subtype for UZL and GBG patients. UZL data suggest a numerical trend with lower pCR2 rate in obese women, which seems more pronounced in ER-negative breast cancer. Results from the much larger GBG dataset with more power to assess ‘BMI category’ for pCR2 in different breast cancer subtypes using a multivariate model are presented in another abstract.

Conclusion

Despite the small cohort of patients, our data suggest a lower pCR rate in obese women. External validation by GBG confirms a significant association between BMI and pCR2 in uni- and multivariate model, which is also significant for luminal A/B breast cancer. The most obvious reason for our observation was dose capping but based on the GBG dataset where dose capping was avoided where possible, we speculate that immunologic factors, the microenvironment or alternative signaling pathways may affect sensitivity to NACT +/- trastuzumab.

Numerical pCR2 differences by BMI category and breast cancer subtype for UZL patients Luminal A/BLuminal HER2HER 2 likeTNBCTotalUZLn = 111n = 40n = 39n = 75n = 267BMI 16.6% (4/61)35.0% (7/20)63.2% (12/19)31.4% (11/35)25.2% (34/135)BMI 23.1% (1/32)50.0% (7/14)56.3% (9/16)27.3% (6/22)27.3% (23/84)BMI 30% (0/18)33.3% (2/6)50.0% (2/4)15.8% (3/19)16.6% (8/48)

Numerical pCR2 differences by BMI category and breast cancer subtype for GBG patients Luminal A/BLuminal HER2HER 2 likeTNBCTotalGBGn = 3250n = 1077n = 806n = 1570n = 8847BMI 111.6%(187/1618)26.7%(147/550)47.3%(191/404)38.9%(300/772)22.5%(982/4358)BMI 210.5%(128/1026)24.9% (81/325)44.2%(111/251)37.4% (187/500)21.2%(596/2813)BMI 37.9% (48/606)22.8%(46/202)41.1%(62/151)31.9%(95/298)18.3%(312/1703)

Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr PD2-3.

Evaluation of the quality of the management of cancer of the corpus uteri--selection of relevant quality indicators and implementation in Belgium

OBJECTIVE

Describe the methodology and selection of quality indicators (QI) to be implemented in the EFFECT (EFFectiveness of Endometrial Cancer Treatment) project. EFFECT aims to monitor the variability in Quality of Care (QoC) of uterine cancer in Belgium, to compare the effectiveness of different treatment strategies to improve the QoC and to check the internal validity of the QI to validate the impact of process indicators on outcome.

METHODS

A QI list was retrieved from literature, recent guidelines and QI databases. The Belgian Healthcare Knowledge Center methodology was used for the selection process and involved an expert's panel rating the QI on 4 criteria. The resulting scores and further discussion resulted in a final QI list. An online EFFECT module was developed by the Belgian Cancer Registry including the list of variables required for measuring the QI. Three test phases were performed to evaluate the relevance, feasibility and understanding of the variables and to test the compatibility of the dataset.

RESULTS

138 QI were considered for further discussion and 82 QI were eligible for rating. Based on the rating scores and consensus among the expert's panel, 41 QI were considered measurable and relevant. Testing of the data collection enabled optimization of the content and the user-friendliness of the dataset and online module.

CONCLUSIONS

This first Belgian initiative for monitoring the QoC of uterine cancer indicates that the previously used QI selection methodology is reproducible for uterine cancer. The QI list could be applied by other research groups for comparison.

[Cancer diagnosis during pregnancy: importance of a national and European registration]

Cancer during pregnancy is relatively rare, but its incidence has been increasing over recent years. A European study has been launched in 2005 by F. Amant (KUL) to register all pregnant patients with a cancer diagnosis with or without treatment during pregnancy (surgery, chemotherapy and/or radiotherapy). All infants exposed to chemotherapy and/or radiotherapy are also followed up by pediatricians, neurologists, cardiologists and psychologists. In Belgium, French- and Dutch- language hospitals are working in close collaboration to follow these pregnant patients. The national results are summarized in this paper.

[Implementation of computerized phisician order entry in a hospital setting: what are the keys to success?]

INTRODUCTION

A software of computerized physician order entry [CPOE] was developed by a data-processing company in collaboration with the Mont-Godinne University Hospital By 2006, parallel to the evolution of the software, the progressive implementation of CPOE was carried out, and currently covers 16 wards, the emergency room, the recovery rooms and the center of medical care [day hospital] as well as the day surgical center

OBJECTIVES

Complete computerization of the drug supply chain, including the regulation by the physician, the pharmaceutical validation, the delivery and the follow-up of stocks by pharmacy, the validation of the administration by the nurse and the tariffing of the drugs.

METHOD AND RESULTS

In 2006, a working group was created in order to validate specifications allowing the development of a software of CPOE, Linked to the computerized medical record. A data-processing company was selected in order to develop this software. Two beds were computerized in the pneumology ward, in order to test and validate the software. From 2007 to 2009, 3 additional wards were computerized [geriatrics, neurosurgery, revalidation]. A steering committee of CPOE, composed of various members (direction, doctors, pharmacists, nurses, data processing specialistsl is created. This committee allows the installation of the means necessary to the deployment of CPOE in the Institution. Structured teams for the deployment are created: medical and nurse coaches. From 2009 to 2012, the deployment of the software is carried out, covering 16 wards, the emergency room, the recovery room and the day-hospitals.

CONCLUSION

The computerization of the drug supply chain is a challenge which concerns the institutional level. The assets of our hospital and our project were: - a strong management committee, making of this project a priority entering the strategical planning of the institution; - a steering committee allowing each type of actor to express his needs, and of prioriser requests; - a closer medical coaching; - teams of nurses coaches, accompanying each ward, during and after the deployment; - a dynamic IT team allowing a relay between the Institution and the data-processing company. These points appeared essential and are as many keys for a successful deployment.