CD24 Expression is an Independent Prognostic Marker in Cholangiocarcinoma

CD24 has been described as an adverse prognostic marker in several malignancies. This study evaluates CD24 expression in cholangiocarcinoma and correlates the findings with clinicopathologic data and patient survival. Between 1996 and 2002, 22 consecutive patients with cholangiocarcinoma were treated at our institution. Demographic data, SEER stage, pathologic data, treatment, expression of CD24, mitogen-activated protein kinase (MAPK), phosphorylated MAPK, and survival were analyzed. The majority of the tumors demonstrated CD24 (81.8%) and p-MAPK (87%) expression. A negative association was noted between the expression of CD24 and p-MAPK. Median survival for patients with low expression of CD24 was 36 months and high expression was 8 months. Median survival for patients who received chemotherapy with low CD24 expression was 163 months, and for seven patients with high CD24 expression, it was 17 months (p = 0.04). With the addition of radiation therapy, median survival for patients with low expression of CD24 was 52 months and high expression was 17 months (p = 0.08). On multivariate analysis, the use of chemotherapy (p = 0.0014, hazard ratio 0.069) and the CD24 overexpression (p = 0.02, hazard ratio 7.528) were predictive of survival. CD24 is commonly expressed in cholangiocarcinoma, and overexpression is predictive of poor survival and possibly of lack of response to chemotherapy and radiation therapy. These findings may improve selection of patients for the appropriate treatment modality and the development of CD24-targeted therapy.
Introduction
Malignancies of the biliary tract are uncommon in the Western world. Two-thirds arise in the gallbladder and the remainder in the biliary tree or periampullary region. Cholangiocarcinoma or bile duct cancer is a rare but lethal malignancy with an incidence of 1–2 cases per 100,000 patients in the United States.1 Clinicopathologic factors predictive of survival include curative resection, tumor stage and grade, serum bilirubin level <10 mg/dl, low CA19-9 level, hepatitis viral infection,2–4 lymphovascular or portal vein invasion,5 intrahepatic satellite lesions, inraductal papillary compvonent, tumor angiogenesis,6 and DNA ploidy.7 Reports of molecular markers predictive of survival in cholangiocarcinoma include cluster of differentiation CD24,8 MMP-2, TIMP-2,9 cholinesterase level,10 MUC-4,11 cyclin D1,12 VEGF-C,13 p27,14 p53, and p73.15
Recently, CD24 has been described in a wide variety of malignancies and shown to be a prognostic marker in several solid tumors including colorectal, stomach, lung, prostate, ovarian, and breast.16–21 CD24 is a small, heavily glycosylated, mucin-like, cell-surface protein expressed in developing cells including pre-B cells, keratinocytes, and renal tubular epithelium.22–24 It functions as an alternative ligand of P-selectin, an adhesion receptor expressed on activated endothelial cells and platelets which can enhance the metastatic potential of CD24-expressing tumor cells.25–28 CD24 has apoptotic activity, and its cross-linking induces the sustained activation of p38 MAPK (mitogen-activated protein kinases)—the magnitude of which may determine the survival or death of pre-B cells.29 An improved understanding of the molecular pathways involved in the pathogenesis and progression of cholangiocarcinoma will contribute to the development of targeted therapy.
This study correlates CD24 and MAPK expression with patient survival in cholangiocarcinoma with the objective of identifying a subset of patients who may benefit from targeted molecular therapy.
Patients and Methods
Clinical Data
After obtaining approval of the Institutional Review Board, a review of the tumor registry at Roswell Park Cancer Institute identified 31 consecutive patients with histologically proven cholangiocarcinoma between 1996 and 2002. Twenty-two patients had adequate tissue for further histopathologic studies and constitute the basis of this study. Medical records of these patients were reviewed for demographic data including age; gender; surveillance, epidemiology, and end results (SEER) stage at presentation; treatment; and survival from the time of diagnosis.
Immunohistochemical Staining
For most of the patients, diagnosis was established by examination of conventional hematoxylin and eosin (H&E)-stained slides and, in the remainder diagnosis, was confirmed with ancillary techniques including immunohistochemistry and special histochemistry with mucin and PAS stains. Uniform tissue fixation techniques were used for all patients. For each patient, a representative block containing adequate neoplastic and nonneoplastic tissue was selected. Five-micrometer tissue sections from these blocks were placed on charged slides and dried in a 60°C oven for 1 h. Upon return to room temperature, the slides were deparaffinized in three changes of xylene and rehydrated using graded alcohols. Endogenous peroxidase was quenched with 3% aqueous H2O2 for 15 min and washed with phosphate buffered saline with 0.05% Tween-20 (PBS/T). CD24 primary antibody was obtained from BD Biosciences (clone ML5) and used with the recommended incubation time and antigen retrieval procedures. The primary antibody used for MAPK was obtained from Cell Signaling and for phosphorylated/activated MAPK from Santa Cruz. After a PBS/T wash, 0.03% casein (in PBS/T) was used as a block for 30 min followed by the application of the primary antibody to the slides for an hour or overnight. Another PBS/T wash was followed by exposure to the biotinylated secondary antibody for 30 min. A third PBS/T wash was followed by exposure to the streptavidin–peroxidase complex for 30 min. A PBS/T wash was followed by the application of the chromogen DAB (DAKO, Carpinteria CA, USA) for 5 min. The slides were then counterstained with hematoxylin, rinsed with water, dehydrated, and cleared, and a coverslip was placed. The use of biomarkers, related controls, and interpretation of results using the Histoscore system for quantification of results have been described previously by our group.30 Histoscore was defined as the product of the percentage of positive cells and the intensity of stain. The grade of positive staining depended upon the intensity of staining (0: no staining, 1: weak, 2: moderate, and 3: strong staining) and the percentage of cells stained. The final score was calculated as a sum of each stain intensity multiplied by the percentage of stained cells in the area of interest. For example: if tumor showed 50% weak, 30% moderate, and 20% strong staining, the score assigned was .
Histological Grading
The cholangiocarcinoma tissue specimens were also stained by routine H&E stains. The specimens were graded based on the degree of tumor differentiation using the World Health Organization (WHO) system.
Statistical Analysis
Association between biomarker expressions in tumor tissue was investigated using the Kendall’s tau. Biomarker expressions were classified as high and low based on whether their scores were above or below the median value, and survival between low and high expressions was compared using the log-rank test. The Kaplan–Meier method was also used to estimate the survival curves and median survival. The Cox’s proportional hazards survival analysis was used in the multivariate analysis of survival data to explain the effect of biomarker expressions together with other diagnostic parameters. Patient demographics including age, tumor grade, SEER stage, and treatment received were considered as possible parameters for explanatory variables in the model. All statistical tests were two-sided with statistical significance level at 5%.
Results
Patient Characteristics
Of the 22 patients included in the study, 7 were males and 15 females. The median age was 66.5 years (range: 35–77). SEER staging was local in 1 (4.6%), regional in 14 (63.6%), and distant in 7 (31.8%) patients. Differentiation of the tumor was classified as grade 1 in 3 (13.6%), grade 2 in 9 (40.9%), and grade 3 in 10 (45.5%) patients according to the WHO classification. Treatment for cholangiocarcinoma included surgery only (n = 8), surgery and chemotherapy (n = 5), surgery and radiation (n = 1), chemotherapy only (n = 2), and all three treatment modalities (n = 5), and one patient did not receive any treatment.
Immunohistochemical Staining
Normal bile duct staining was used to set the score intensity. Most of the bile ducts were negative. Occasionally, they demonstrated weak focal and incomplete staining as seen in Fig. 1. The cholangiocarcinoma cells were scored as 1+ when they demonstrated weak expression of CD24, 2+ for moderate expression, and 3+ for strong expression of CD24. Figure 1 depicts cholangiocarcinoma positive for CD24 expression adjacent to normal biliary epithelium. The majority of the tumors demonstrated CD24 (81.8%) and p-MAPK (87%) expression. Immunohistochemical staining for these proteins was higher in malignant tissue in comparison to normal biliary epithelium. The pattern of staining was usually a combination of cytoplasmic and apical, and few specimens demonstrated the apical pattern only.
Figure 1Moderately differentiated cholangiocarcinoma with overexpression of CD24. Normal bile duct (right upper corner) demonstrated weak apical staining, whereas neoplastic cells had strong apical and cytoplasmic staining (20×).
Relationship Between Biomarkers
A negative association was suggested between the expression of CD24 and phosphorylated/activated p-MAPK (Kendall’s τ = −0.32408, p = 0.0501).
Survival
Median survival was 36 months for nine patients with low expression of CD24 and 8 months for 13 patients with high expression of CD24 as shown in Fig. 2. The median survival for five patients who received chemotherapy with low CD24 expression was 163 months, and for seven patients with high CD24 expression, it was 17 months (Fig. 3, p = 0.04). Median survival for four patients treated with radiation in the presence of low CD24 expression was 52 months, and it was 17 months for two patients with overexpression of CD24 (Fig. 4, p = 0.08). Overexpression of CD24 continued to affect survival adversely despite the overall improvement noted with the addition of radiation therapy. Multivariate analysis using the Cox’s proportional hazards survival analysis demonstrated that overexpression of CD24 (p = 0.02, hazard ratio 7.528) and use of chemotherapy (p = 0.0014, hazard ratio 0.069) were predictive of survival (Table 1). There was no significant association noted between survival and patient’s age, sex, SEER stage, grade of the tumor, surgery, radiation therapy, or expression of MAPK.
Figure 2Kaplan–Meier survival curve for patients of cholangiocarcinoma with low and high levels of CD24 expression (n = 22). p = 0.02. Low CD24 (–––––––), high CD24 (- - - - - - - - - - - -).Figure 3Kaplan–Meier survival curve for patients who received chemotherapy with low and high levels of CD24 expression (n = 12). p = 0.04. Low CD24 (–––––––), high CD24 (– - – - – - –).Figure 4Kaplan–Meier survival curve for patients who received radiation therapy with low and high levels of CD24 expression (n = 6). p = 0.08. Low CD24 (–––––––), high CD24 (– - – - – - –).Table 1Prognostic Variables for Survival in 22 Patients with CholangiocarcinomaVariablenMedian survival (months)Survival (p value)Multivariate analysis (p value)Age<681117 (8–36)0.60>681111 (5–52)GenderMale712 (8–163)0.52Female1517 (6–52)Grade1, 21215 (6–52)0.9931011 (8–*)SEER stage1, 21511 (6–52)0.483728 (6–163)ChemotherapyNo106 (4–13)0.00050.0014Yes1252 (17–163)RadiationNo168 (6–28)0.12Yes644 (17–*)SurgeryNo38 (0–28)0.17Yes1917 (7–52)MAPKLow1017 (8–36)0.68High1128 (5–163)P-MAPKLow1013 (8–52)0.34High1136 (4–163)CD24<120936 (13–163)0.100.02>120138 (6–28)*The estimate was not provided because the upper limit of the survival curve had not reached a 50% failure rate.CD denotes cluster of differentiation.p-MAPK denotes phosphorylated form of mitogen-activated protein kinase.
Discussion
The physiologic function of CD24 is incompletely understood but it has been shown to increase tumor proliferation, cell adhesion, motility, invasion, and apoptosis.22–24,31 Selectins are cell adhesion molecules involved in the rolling adhesion of leukocytes to endothelial cells and platelets under the shear forces of circulation, and P-selectin expressed by thrombin-activated platelets and endothelial cells is a major ligand for CD24 on carcinoma cells.26,27 This suggests that CD24-expressing tumor cells can disseminate more readily due to their capacity to form thrombi with activated platelets or to adhere to endothelial cells. Friederichs et al.28 have demonstrated that the carbohydrate sialylLex abundantly expressed on human cancers is essential for CD24-mediated rolling of tumor cells on P-selectin, and in its absence, human adenocarcinoma cells failed to arrest and colonize the lungs. CD24, a metastasis-associated protein, has been recently identified as a downstream target of Ral signaling.32 Ral GTPases are important mediators of transformation, tumorigenesis, and cancer progression. Microarray by immunohistochemistry of a human bladder cancer identified CD24 as a novel Ral-regulated target and a prognostic biological marker.
In this study, 81.8% of patients with cholangiocarcinoma expressed CD24. Median survival for patients with overexpression of CD24 was significantly shorter, and the addition of chemotherapy improved survival. A negative association was noted between the expression of CD24 and p-MAPK. The use of chemotherapy in patients with low expression of CD24 was associated with a median survival of 163 months compared to 17 months in patients with a high CD24 expression (p = 0.04). The use of radiation therapy in patients with low expression of CD24 was also associated with an improved survival than with overexpression of CD24 although the data did not attain statistical significance possibly due to the small number of patients in this series.
It has been reported by Taguchi et al.29 that the cross-linking of CD24 induces apoptosis in Burkitt’s lymphoma enhanced by a B-cell antigen receptor (BCR)-mediated signal. They observed that simultaneous cross-linking of pre-BCR clearly inhibited CD24-mediated apoptosis in pre-B cells. CD24 cross-linking also induces the sustained activation of p38 MAPK, and whether pre-B cells survive or die may be determined by the magnitude of MAPK activation. Consistent with these observations, the present study suggests an inverse association between CD24 and p-MAPK, and eventual cellular proliferation or apoptosis might be a consequence of the dominant effect in a complex interplay of opposing influences.33
Our data indicate that high expression of CD24 remains an adverse prognosticator despite the use of additional therapy. Chemotherapy and radiation were noted to provide maximal survival benefit to low expressors of CD24 although the data for the use of radiation was statistically insignificant probably due to the small number of patients in this study. Furthermore, correlation between CD24 expression and radiation sensitivity has been noted to vary with the cell type as in human small cell lung cancer, and radiation doses required to induce apoptosis of CD24-negative human ALL (acute lymphoblastic leukemia) cells were higher than those required for CD24-positive cells, suggesting that lack of CD24 surface antigen expression is associated with intrinsic radiation resistance.34,35
Hypoxia is a characteristic feature of tumor cells due to the sustained proliferation which progressively results in an acidic, nutrient-deprived, and hypoxic tumor microenvironment. Tumor oxygenation has been identified as an independent prognostic variable for locoregional control and overall survival following definitive irradiation for squamous cell carcinoma of the head, neck, and uterine cervix.36,37 Recent reports have indicated decreased efficacy of chemotherapy under hypoxic conditions in several tumor types including pancreatic cancer and testicular tumors.38,39 Because treatment failure was a consequence of hypoxia, the authors recommend novel treatment strategies aimed at improving tumor oxygenation or enhancing the treatment sensitivity of hypoxic tumor cells. Aimed at identifying potential oxygen-dependent markers in vascular endothelial cells for therapeutic intervention in tumor angiogenesis, Scheurer et al.40 performed a broad-range transcriptomic analysis of selected extracellular matrix protein gene expression levels in human umbilical cord vein endothelial cells exposed in vitro to hypoxia for different time periods. They noted several genes transcriptionally upregulated including CD24 at late times of exposure to hypoxia, indicating that it was a useful marker of hypoxic activation in vascular endothelial cells. In the present series, low expressors of CD24 demonstrated greater survival benefit from chemotherapy and radiation than the high expressors, suggesting that its expression may be a marker for tumor hypoxia and response to therapy. This finding that shows that patients with low expression of CD24 may benefit from chemotherapy or radiation is of importance because it has been previously reported that adjuvant or palliative radiation had no effect on survival in patients with cholangiocarcinoma.41 However, the small number of patients in the present series limits interpretation of data suggesting that CD24 overexpression may be predictive of lack of response to radiation or chemotherapy.
CD24 has been shown to be a prognostic marker for shortened survival and disease progression in several malignancies including colorectal, stomach, lung, prostate, ovarian, and breast cancers.16–21 Weichert et al. report that in colorectal cancer, only the subset of patients with exceptionally strong cytoplasmic CD24 staining comprising 10% of their study group demonstrated a markedly shortened mean survival of 31.5 months compared to 67.5 months for the remaining patients.16 They also reported that cytoplasmic CD24 staining pattern is prognostically more significant than the membranous pattern—the biological significance of which was unclear. Su et al. noted a 51% expression of CD24 by immunohistochemistry in intrahepatic cholangiocarcinoma as compared to the 81.8% in the present series. They reported CD24 expression and tumor stage as independently predictive of survival on multivariate analysis and suggested membrane-bound CD24 protein as a potential target for immunotherapy.8
In conclusion, overexpression of the molecular marker CD24 in cholangiocarcinoma is predictive of poor survival. CD24 overexpressors demonstrated a lack of response to chemotherapy and possibly radiation therapy although these observations were limited by the small sample size. Additional properties of tumor proliferation, invasion, metastasis, and apoptosis make CD24 a potent target for specifically directed molecular therapy and its overexpression a potential criterion in the selection of patients for the appropriate conventional treatment modality.

cd24 expression
cholangiocarcinoma
survival


A cadaveric analysis of contact stress restoration after osteochondral transplantation of a cylindrical cartilage defect

Osteochondral transplantation is a successful treatment for full-thickness cartilage defects, which without treatment would lead to early osteoarthritis. Restoration of surface congruency and stability of the reconstruction may be jeopardized by early mobilization. To investigate the biomechanical effectiveness of osteochondral transplantation, we performed a standardized osteochondral transplantation in eight intact human cadaver knees, using three cylindrical plugs on a full-thickness cartilage defect, bottomed on one condyle, unbottomed on the contralateral condyle. Surface pressure measurements with Tekscan pressure transducers were performed after five conditions. In the presence of a defect the border contact pressure of the articular cartilage defect significantly increased to 192% as compared to the initially intact joint surface. This was partially restored with osteochondral transplantation (mosaicplasty), as the rim stress subsequently decreased to 135% of the preoperative value. Following weight bearing motion two out of eight unbottomed mosaicplasties showed subsidence of the plugs according to Tekscan measurements. This study demonstrates that a three-plug mosaicplasty is effective in restoring the increased border contact pressure of a cartilage defect, which may postpone the development of early osteoarthritis. Unbottomed mosaicplasties may be more susceptible for subsidence below flush level after (unintended) weight bearing motion.
Introduction
Full-thickness cartilage defects may lead to early osteoarthritis [6,13,14]. Osteochondral transplantation is a successful treatment for these defects, but the outcome is dependent on, among other parameters, stability and restoration of surface congruency [9,15,21]. In our previous study about the effect of perfect depth alignment of a transferred plug [8], we evaluated the difference in stability between bottomed plugs (donor plug length and recipient defect depth accurately matched) and unbottomed plugs (donor plug is left shorter than the recipient defect depth). That study demonstrated that bottoming plugs resulted in a significantly more stable situation than unbottoming plugs. Unbottomed plugs leave a cavity at the bottom of the defect and therefore rely exclusively on frictional forces. When performing only a single-plug transfer the whole plug is, in most cases, nicely surrounded by (subchondral) recipient bone, and stability is fairly good, bottomed or not. However, when performing osteochondral transplantation where more than one plug is needed, stability is probably lower because of gaps between the round plugs and their surrounding bone. Kordas et al. [10] reported in agreement with this that the push-in force below flush level for (unbottomed) multiple grafts was significantly lower compared to a single graft. Theoretically, width differences of the cartilage layer between trochlea (donor area) and condyl (defect area) might have an influence on restoration of surface congruency.
Usually a patient is restricted in weight bearing after osteochondral transplantation. Rehabilitation periods of two weeks of non-weight bearing and an additional two to three weeks of partial weight bearing are reported in the literature [6]. This period facilitates good ingrowth of (subchondral) bone [11,15] and thus ensures the preservation of surface congruency as it was intended directly post-operative. Histological research has proven that after transplantation bone resorption takes place on the recipient site and on the graft surface [5,18]. This might be one reason for a gradual loss of the initial press-fit stability postoperatively and a cause of subsiding below flush level of the graft during follow-up. Accordingly, Whiteside et al. [20] reported a reduction in short-term load bearing capacity of a single-plug transplant one week after transplantation. Press-fit mechanisms provide stability up to 4 weeks [6]; subsequently, this is taken over by the integration of the graft and the recipient bone [11].
The objective of the present study was to investigate the biomechanical effectiveness of osteochondral transplantation. More specifically we assessed whether the treatment would decrease peak stresses at the boundaries of the original articular cartilage defect. Subsequently, we analysed if articular stresses were dependent on plug placement (bottomed versus unbottomed) and how this was affected by loads that represented (for instance unintended) early weight bearing.
Materials and methods
Materials
Eight intact human cadaver knees obtained from the anatomical department, from individuals 70–80 years old of unknown gender, were used. Exclusion criteria were severe arthrosis of donor or graft area and malignant processes within the knee. Specimens were freshly frozen and thawed at room temperature overnight before preparation and testing. The K-scan 4000 (Tekscan Inc., Boston, MA) (Fig. 1) was used for measuring surface congruency according to a previously discussed protocol [4]. Before testing, each new Tekscan sensors was pre-loaded and calibrated, as suggested by the Tekscan manual and by Brimacombe et al. [2]. For this loading, a circular plastic plunger, that almost covered the entire width of the sensor, was placed underneath a small loading platform where weights could be placed on top. Each sensor was calibrated separately and calibration files were stored. A new sensor was used for each knee. The sensor measurements were presented as a 22 by 26 pixel matrix with square pixels. For the osteochondral transplantations, a disposable 8 mm Osteochondral Autograft Transfer System (OATS) was used (Arthrex, Somas, Sint Anthonis, The Netherlands).
Fig. 1Tekscan K-4000 measuring device with two thin (0.089 mm) and flexible pressure sensors, which were able to conform to the anatomy of the medial and lateral knee compartments. The sensors consist of printed circuits divided into grids of load-sensing regions. Each sensor measured 28 × 33 mm, with a total of 572 pixels with a surface per pixels of 1.27 mm2
Preparations
Skin, muscles, excess soft tissues, patella and the anterior part of the knee capsule were removed from each knee, whereas collateral ligaments and intra-articular structures were left intact. In this way the tibio-femoral joint area was uncompromised and yet was accessible from the anterior side. Dorsally two small ‘windows’ were created in the capsule, giving access to the posterior part of both condyles. The femur as well as the tibia and fibula were sectioned at ∼18 cm from the joint space. The knee was then placed in a knee-testing device, which was used in earlier experiments [1,19] (Fig. 2). This device provides five degrees of freedom of motion for the tibial side, whereas the femoral side has one degree of freedom and can additionally be manually displaced for flexing or extending movements of the knee (Fig. 2). Weights could be attached to the femur side such that the force may be directed through the longitudinal axis of the femur. To simulate partial weight bearing, we applied 350 N as a tibio-femoral compressive force. Previous research at our institution has shown that a force of 171 N (unbottomed) and 384 N (bottomed) was necessary to push a single-plug-mosaicplasty below flush-level [8].
Fig. 2a Knee-testing device. b Circular plastic cap for positioning of Tekscan sensor. c Tekscan sensor in situ. d Special mould for OATS. e Three-plug mosaicplasty placed flush in full-thickness osteochondral defect
Operation and testing
First, donor sites were marked with a permanent marker on the trochleae or the posterior region of the condyles. Three plugs were harvested with a length of ∼15 mm (to be unbottomed) and three plugs of ∼18 mm length (to be bottomed). After harvesting all six donor plugs, Tekscan sensors were placed inside the tibio-femoral joint space. After positioning the knee in approximately 45° flexion, the sensors were guided alongside the cruciate ligaments (one on each side), and placed between each femoral condyle and tibial plateau. Subsequently, a small circular plastic cap (diameter 10 mm, height 1 mm) was pinned on both condyles, at the location where the mosaicplasty was to be performed (Fig. 2). The compression of both markers on the sensors was clearly visible on the computer screen and assisted in positioning the sensors centrally over the area where the mosaicplasty would be executed. To facilitate repositioning of the sensors between different interventions, the sensors were fixated to the joint by four small metal ‘anchors’ on all four corners of the sensor that could be used to re-attach the sensors to the joint (Fig. 2). In order to confirm validity of this method a test was performed where the sensor was repeatedly fixated and completely removed from the knee and the plastic marker was visualized. Reproducibility of the re-attachement of the sensor was confirmed by five independent measurements of the position of a circular cap, each after removing a re-attachment of the Tekscan sensor. Four out of five measurements showed exactly the same position of the cap within the Tekscan matrix, which proves a high accuracy of re-attachment.
To investigate the consequences of a cartilage defect and subsequent mosaicplasty on the stress distribution under various conditions, Tekscan recordings were performed under five different conditions: (A) preoperatively, intact condition, (B) cartilage defect without any reconstruction, (C) following mosaicplasty, (D) after mosaicplasty and subsequent non-weight bearing motion of the knee and (E) after mosaicplasty with weight bearing motion (E). All surface measurements were performed with the knee in 0° extension, thereby ensuring loading of the (restored) defective sites, and an axial load of 350 N.
The ‘preoperative’ measurement (A) was with intact condyles. After removing the sensors, both on the medial and lateral condyle a standardized subchondral defect, 8 mm deep was created by a circular drill of 16 mm diameter, the center being at exactly the same location as the center of the plastic marker that was measured previously.
The ‘defect’ measurement (B) of the condyle surfaces was performed after creating the osteochondral defect. With the use of a special mould (Fig. 2) and the OATS, these defects were prepared to receive three osteochondral plugs. When the mosaicplasty was to be performed as bottomed, three defects were created, approximately 15 mm deep and the bottoms of the defects were tamped. Following measuring of the depths of these defects, three of the 18-mm donor plugs were matched for these depths by carefully removing some subchondral bone with surgical bone-nibbling pliers according to clinical practice. Plugs were orientated such that the most congruent surface would be achieved. These plugs were tampered in place, until flush level of the cartilage surface with the recipient site was achieved (Fig. 2). At the opposite condyle, which was to be unbottomed, three defects of approximately 20 mm depth were created, and the donor plugs of 15 mm were used. The difference of 5 mm in length between the defects was essential for the unbottomed plugs to be sure to have a cavity at the bottom of the defect and therefore rely exclusively on frictional forces.
The ‘mosaicplasty’ surface measurement (C) was executed after performing both mosaicplasties. The ‘non-weight bearing motion’ measurement (D) was performed following flexing and extending the knee 20 times, while no weight was attached to the test device. The axial load of 350 N was re-applied before the measurement was performed. The final ‘weight bearing motion’ measurement (E), was performed once the knee had been flexed and extended 20 times with 350 N of axial force applied to the knee while performing the flexion-extension movements.
Evaluation of variables
Each of the eight knees provided two paired standardized mosaicplasties of three plugs, one bottomed and one unbottomed. Thus, there were 16 mosaicplasties: eight bottomed and eight unbottomed. Three general groups were created for statistical evaluation: the whole group of all mosaicplasties (bottomed and un-bottomed combined), a group with only bottomed and one with only unbottomed mosaicplasties. The five measurements performed were: (A) preoperative, (B) defect, (C) mosaicplasty, (D) non-weight bearing motion, (E) and weight bearing motion.
Relative to the reconstructed surface, we defined two specific regions of interest: the mosaicplasty area itself and the border of the reconstructed surface. The first region obviously quantified the amount of stress transferred at the mosaic site, whereas the second region was selected to quantify the amount of stress transferred to the border of the defect. The mosaicplasty area (region 1) was a circle with a diameter of 16 mm, for which the best fitting circle representing the standardized defect was obtained on the Tekscan sensor matrix (Fig. 3). This circle was obtained by calculating the lowest surface pressure of a 16-mm diameter circle in the non-reconstructed (defect) case. The border region (region 2) was selected around the first region and had a width of 3 pixels (3.81 mm). To ensure that the two regions were clearly separated a transition zone with a width of 1 pixel was selected and has not included in the measurements (Fig. 3). The total of the pixel values in regions 1 and 2, respectively, were used for further comparative evaluations. To allow for a direct comparison of the pressure values, the pressures on the border and mosaic areas were normalized to the total force on the sensor.
Fig. 3a Typical example of Tekscan report on load distribution for a cartilage defect without any reconstruction. b The mosaicplasty area (region 1) is a circle consisted of square pixels with a diameter of 12 pixels. The border area (region 2) is a ring with a diameter of 3 pixels around the defect circle. A transition zone of 1 pixel around the mosaicplasty area is found between regions 1 and 2
Statistics
Contact pressures were calculated at the boundaries of the cartilage defect (region 2) as well as for the defect/mosaicplasty (region 1) itself. This was done for five different conditions as described before (A–E). These values were expressed for three groups, namely bottomed, unbottomed and combined.
Differences were examined within and among the groups. For statistical analysis, we used SPSS 12.0 for Windows. The linear mixed model was used to evaluate the influence of bottoming or unbottoming the plugs, with Bonferroni correction. P < 0.05 were considered to be statistically significant.
Results
The averaged contact-pressures at the border contact surface are shown in Fig. 4. Overall, there were no significant differences between bottomed and unbottomed. All the data showed the same pattern for the three groups: in presence of a defect (B) the border contact surface pressure was significantly higher compared to the intact (preoperative) situation (A), performing a mosaicplasty made the border pressure decline to approach preoperative values (C), and after motion with and without weight the border contact pressure remained stable (C–E).
Fig. 4Graph showing the border contact pressure for three groups; bottomed (blue line), unbottomed (green line) and the whole group (grey line). In presence of a defect (b) the border contact surface pressure was significantly higher compared to the intact (preoperative) situation (a), performing a mosaicplasty made the border pressure decline to approach preoperative values (c), and after motion with and without weight the border contact pressure remained stable (c–e). Standardized deviation values are presented in Table 1
The pressure pattern of the mosaic contact surface was inverse to the border contact surface pressure (Fig. 5): the mosaicplasty area had almost no contact pressure after creating a defect (B), in the presence of the mosaicplasty the contact surface pressure was regained to some extent (C vs. A) and the contact pressure remained relatively constant after non-weight bearing and weight bearing motions (C–E).
Fig. 5Graph showing the mosaicplasty contact pressure for three groups; bottomed (blue line), unbottomed (green line) and the whole group (grey line). The mosaicplasty area had almost complete lack of contact pressure after creating a defect (b), in presence of the mosaicplasty the contact surface pressure was regained (c vs. a) and the contact pressure remained stable after non-weight bearing and weight bearing motion (c–e). Standardized deviation values are presented in Table 1
Two out of eight unbottomed versus zero bottomed mosaicplasties showed a decreased mosaicplasty pressure and increased rim stress after weight bearing motion on individual Tekscan measurements, indicating that the mosaicplasty subsided below flush level. Five out of eight bottomed plugs were placed on the medial condyle and no significant differences in pressure or rim stress was found.
Statistics
Mean pressure values measured are given in Table 1.
Table 1Results of the surface contact pressure measurements of five conditionsMeasurementWhole group (n = 16)P-valueBottomed (n = 8)Unbottomed (n = 8)BorderA. Preoperative38.7 (7.1)a40.2 (8.3)37.1 (6.0)B. Defect74.3 (5.5)(B vs. A–C–D–E) 0.00075.5 (3.9)73.2 (6.8)C. Mosaicplasty52.1 (9.3)(A vs. C) 0.00154.6 (7.5)49.7 (10.7)D. Motion without weight53.2 (9.8)(A vs. D) 0.00153.9 (8.4)52.4 (11.6)E. Motion with weight53.8 (9.6)(A vs. E) 0.00055.1 (9.0)52.5 (10.6)MosaicA. Preoperative35.7 (7.8)33.6 (9.3)37.9 (5.6)B. Defect4.0 (4.0)(B vs. A–C–D–E) 0.0003.7 (3.8)4.3 (4.4)C. Mosaicplasty23.8 (8.2)(A vs. C) 0.00221.4 (7.4)26.2 (8.9)D. Motion without weight22.8 (8.7)(A vs. D) 0.00122.1 (8.1)23.5 (9.7)E. Motion with weight22.0 (9.1)(A vs. E) 0.00121.0 (8.4)23.0 (10.1)a Values are given as mean (SD), only significant P-values are shown
Effect of defect (A–B): Pressure redistribution occurred in the presence of a defect from the mosaic contact surface to the cartilage surrounding it. The border contact pressure increased to 192% (P = 0.000) compared to the preoperative border contact surface pressure, while the pressure on the defect contact surface decreased to 11% (P = 0.000).
Effect of mosaicplasty (B–C): The mean stress elevation around the defect was partially restored in the presence of a mosaicplasty, as the border contact pressure decreased by 30% compared to the defect measurement. This resulted in a border contact pressure of 135% compared to intact cartilage. The mosaic contact pressure was restored from 11 to 67% (P = 0.000).
Effect of flexion-extension motion (C–D–E): The border contact pressure did not return to preoperative values after performing a mosaicplasty, and did not change after flexion and extension motions. The border contact pressure after non-weight bearing motion’ and ‘weight bearing motion’ remained equal to the mosaicplasty measurement, 138 and 139% (P = 0.001 and 0.000, respectively) compared to intact, preoperative cartilage. For these measurements the mosaic contact pressures remained lower than those of intact cartilage, with pressures 64 and 62%, respectively (respectively P = 0.001 and 0.001).
Discussion
In this biomechanical human cadaver study we clearly demonstrated that an osteochondral cartilage defect severely affects the contact pressure on the remaining intact joint surface. Obviously, there are some additional limitations to our study. The study had only a limited number of knees. During testing osteoarthritic changes in bone were found in some of these elderly knees. Clearly this is different to the bone quality of the typical patient who is relatively young and active. Another limitation was that during the preparations the knee was positioned in such a way that both condyles would be loaded with approximately the same force, which may be different from in vivo loads. This might have had an influence on the absence of medial/lateral differences. Contrary to other experiments we kept all collateral ligaments and intra-articular structures intact, which resembles the anatomical situation more closely and thus gives a greater translational value of pressure transfer. Clearly, no biological effects were taken into account such as the resorption of the plugs (thereby reducing the stability) or bony ingrowth (thereby enhancing stability). We also measured the contact patterns under static conditions with the knee in extension, whereas in reality shear forces at different flexion angles are also applied to the reconstruction. These limitations should be taken into account when interpreting the results.
The results showed that the average border contact pressure almost doubled (increase of 92%) compared to the pressure on an intact congruent joint surface. In the literature, increases in peak pressure between 10 and 30% were found in the presence of a defect with a diameter ranging from 1 to 7 mm [3,9]. Guettler et al. [4] claimed to have found no difference in rim contact pressure for defects below 10 mm diameter, but they found a 64% increase with respect to the healthy situation for all defects above 10 mm. These findings are supported with a recent finite element model, in which it was found that large defects (greater than 0.78 cm2) resulted in significantly increased border contact surface pressures, which may have clinical implications [16].
The relation between increased contact stresses on the joint surface and progressive degenerative changes of the cartilage is well recognized. Lefkoe et al. found a significant decrease in proteoglycan content in the cartilage sampled from the rim of 20-week-old defects [12]. Jackson et al. [7] introduced a ‘zone of influence’, as he found cartilage adjacent to the defect being affected, which may lead to early secondary gonarthrosis. Messner and Maletius [14] reported in a follow-up study radiographic joint space reduction in almost 50% of the patients with severe cartilage damage 14 years earlier. Linden et al. [13] found in a follow-up study of 33 years that 80% of the adults with osteochondritis dissecans developed secondary gonarthrosis. The gonarthrosis seemed to have its onset 10 years earlier (mean age 49 instead of 59 years) in life than primary gonarthrosis. Clearly, the increased potential of degenerative changes in the knee following an articular cartilage defect is of great clinical importance. Especially because this type of cartilage defect frequently occurs in the younger aged population.
In our study we were able to reduce the contact pressure at the boundaries of a large articular cartilage defect by 30% with a osteochondral transplantation. In a study by Raimondi et al. [17] a 16% reduction in peak pressure was found in the presence of a fibrin glue graft. One could question as to what extent this reduction lasts since fibrin glue degrades quite rapidly. Koh et al. [9] reported peak contact pressure reduced to normal when plugs were flush. However, they used only one plug, which does not correspond to clinical practice. It may be more difficult to obtain a smooth cartilage surface when using more plugs, which explains why we did not find a complete normalization of the contact stresses.
Another objective of this study was to investigate the stability of a mosaicplasty during motion (with or without weight). For the whole group (unbottomed and bottomed together), neither motion protocols significantly changed surface congruency; it remained equal to the mosaicplasty directly after surgery. This can be concluded from the fact that the pressure patterns did not change after weight bearing or non weight bearing motion, as the pressure pattern and rim stress remained equal. When comparing the bottomed and unbottomed groups, two unbottomed mosaicplasties showed a pressure pattern according to subsidence below flush level after weight bearing motion. Since the main focus of this study was to measure peak stresses on the articular surface, we did not assess the actual amount of subsidence. Although no significant differences in contact stresses could be detected between bottomed and unbottomed plugs by comparing the total groups; one can at least have some concern about potential subsidence of unbottomed plugs. Nevertheless, most unbottomed plugs remained stable, which is in agreement with a study of Pearce et al. [15], who reported that unbottomed multiple plug mosaicplasties that were placed flush, continued to stay flush after 3 months of weight bearing motion in sheep. However, human bone is much softer and therefore it may be more difficult to obtain a stable reconstruction with unbottomed plugs. In our previous study with human femora we measured the force required to push a single plug below flush level. For an unbottomed plug of 16 mm, 151 N had to be applied in comparison with 294 N for the bottomed plug [8]. For the comparison with the multiple plug mosaicplasty, Kordas et al. [10] reported that application of a mean force of 54 N was enough to push an unbottomed multiple plug mosaicplasty 3 mm below flush level; unfortunately, there was not a bottomed comparison in that study. The applied force of 350 N during the weight bearing motion in our study may have been too low, to destabilize the reconstruction. This load, which is relatively low, was chosen, as it would simulate partial weight-bearing motion during the direct postoperative rehabilitation period and not level walking. The latter is, according to our protocol, permitted after 2–4 weeks, which should allow for in-growth of the plugs [11,15].
Clinical relevance
A cartilage defect results in increased stress levels at the articular cartilage boundaries of the defect. A multiple plug mosaicplasty has a positive effect in reducing these stress elevations, which will reduce the potential of cartilage degeneration, and thus may postpone secondary osteoarthritis. Post-operative non-weight bearing and weight bearing motions did not seem to influence the surface congruency of the mosaicplasty, although the unbottomed mosaicplasties showed a trend of subsidence below flush level after weight bearing motion. It appears feasible to allow limited weight bearing of the knee after osteochondral transplantation, especially when plugs have been bottomed.

transplantation
cartilage
biomechanics
humans
pressure
weight-bearing
articular/pathology
knee joint/surgery
surface properties
autologous


Membrane-seeded autologous chondrocytes: cell viability and characterization at surgery

The implantation of chondrocytes, seeded on matrices such as hyaluronic acid or collagen membranes, is a method that is being widely used for the treatment of chondral defects. The aim of the present study was to evaluate the distribution, viability and phenotype expression of the cells seeded on a collagen membrane just at the time of the implantation. Twelve patients who were suffering from articular cartilage lesions were treated by the MACI® procedure. The residual part of each membrane was tested by colorimetric assay (MTT) and histochemical and ultrastructural analyses were carried out. In all of the samples a large number of viable cells, quite homogenously distributed, was detected. The cells expressed the markers of the differentiated hyaline chondrocytes. These data reassure in that the MACI procedure provides a suitable engineered tissue for cartilage repair, in line with the clinical evidences emerging in the literature.
Introduction
Autologous chondrocyte implantation (ACI) is an established method for the treatment of chondral defects [6]. Some prospective studies have demonstrated subjective and objective improvement in joint function at 12 months after surgery [3, 13]. MRI, second-look surgery and biopsies have shown the formation of nearly normal cartilage in a good number of patients [13, 18, 19]. On the basis of these results, ACI may be considered one of the most effective treatments of articular cartilage lesions. However, this method is not devoid of some problems with regard to, for example, the wide surgical approach, the demanding technique, the presence of periosteal flap and the cell-holding in site [14, 16].
In order to overcome some of these hurdles, new tissue engineering techniques, widely used nowadays, have been developed using chondrocytes seeded on biological matrices such as hyaluronic acid [15] or collagen membranes [7] or atelocollagen gel [17]. Despite the wide diffusion of these methods, there are still some areas that would need better clarification. The presence of numerous, viable and well-differentiated cells at surgery is an essential requisite for the success of all these methods. It has been shown that autologous chondrocytes in suspension before implantation are able to maintain their differentiated phenotype and are capable of proliferating fairly well [9, 20]. On the other hand, little has been reported so far about cell characterization in membrane-seeded ACI techniques.
The aim of the present study was to evaluate the distribution, viability and phenotype expression of the cells seeded on a collagen membrane just at the moment of the implantation.
Materials and methods
In 2003, 12 consecutive patients, 8 males and 4 females, mean age of 34 years, suffering from cartilage lesions of the knee (10 cases) and the ankle (2 cases), underwent a collagenic scaffold-based ACI procedure (MACI®-Verigen, D). Autologous chondrocytes were isolated at the Verigen laboratories from cartilage slices obtained from non-bearing areas of the patients’ joints during a preliminary arthroscopic surgery. Cells were propagated in monolayer cultures in autologous serum for 2 or 3 weeks, according to the cellular growth rate, and were passaged on average 3 times to obtain at least 10 × 106 cells. Cells were then seeded on 20 cm2 type I/III collagen membrane of porcine origin. The cell-seeded membranes were implanted in cartilage defects by means of either arthroscopic or mini-open surgery, using fibrin glue to ensure adhesion. At each implantation, the residual part of the membrane was collected and tested for cell viability, and histochemical and ultrastructural analyses were also performed.
Cell viability analysis
Cell viability was evaluated by MTT (dimethylthiazol-diphenyltetrazol bromide; thiazolil blue) colorimetric assay. MTT (Sigma, Italy) is a water soluble tetrazolium salt that yields a yellowish solution when prepared in medium lacking in phenol red (RTMI 1640, Sigma). Dissolved MTT is converted to an insoluble purple formazan by cleavage of the tetrazolium ring by the active mitochondrial dehydrogenases of living cells. The MTT solution (5 mg MTT/ml medium) was added to three samples (1 cm2) of each membrane, being assayed to equal 1/10 of the original culture medium volume, and incubated for 3 h. The solution was then removed and acidic isopropanol (0.04–0.1 N HCl in absolute isopropanol) was added to solubilize the stain.
The results were evaluated by means of the spectrophotometric assay (570 nm), yielding absorbance as a function of viable cell number.
Histochemical and immunohistochemical analysis
The samples were fixed by immersion in 4% para-formaldehyde in 0.1 M phosphate buffer, pH 7.4, at 4°C and then embedded in paraffin. Specimens were stained with safranin-O. For the immunohistochemistry, non-specific binding was blocked with 3% normal goat serum in a phosphate-buffered saline (PBS), pH 7.4, for 30 min at room temperature; slides were then incubated overnight with primary antibodies at 4°C. Sections were incubated with polyclonal antibodies anti S-100 protein (Dako, Italy), a cytoplasmatic marker of chondrocyte phenotype, diluted at 1:3,000, anti-collagen type I (Monosan, The Netherlands) and II (Calbiochem-Oncogene, CA, USA) at 1:150, and monoclonal antibodies anti chondroitin sulphate (chondroitin-S) (Sigma) at 1:200. Rabbit and mouse immunoglobulins, at the same dilutions as the primary antibodies, were used as controls. After three washes with Tris–HCl (0.05 M, pH 7.6), revelation of the reactions was accomplished by DAKO LSAB + kit, HRP. Stainings were viewed and photographed with a Leica Microscope (Leica Cambridge Ltd., UK).
Ultrastructural analysis
For scanning electron microscopy (SEM), the membranes were fixed in 2% glutaraldeyde in 0.1 M cacodylate buffer (pH 7.4), post-fixed in 1% osmium tetroxide, dehydrated in increasing ethanol concentrations and then CPD-dried. They were mounted on stubs and gold-sputtered. Specimens were observed with a Philips 505 microscope.
Results
Cell viability analysis
The presence of numerous viable cells was observed in all samples, with quite a homogeneous stain distribution, even if some of the areas revealed a greater concentration of converted dye (Fig. 1).
Fig. 1Macroscopic view of human chondrocytes seeded on type I, III collagen membrane after MTT incubation. The converted blue dye shows the presence of numerous viable cells with quite a homogeneous stain distribution
Considerable variability was found in mean cell numbers between the samples from different patients, in relation to the different amounts of cells obtained from cartilage slices and seeded on the membrane. Highest values were observed in samples from the younger patients (Fig. 2, Table 1).Fig. 2The standard curve obtained from each membrane by MTT colorimetric assay (see also Table 1 )Table 1The table shows the mean viable cell number/cm2 obtained from each membrane by MTT colorimetric assayCaseJointAgeSexSeeded cellsViable cells/cm2 (mean ± SD)1 Knee54M19 × 106(7.52 ± 2.79) × 1032 Knee22F17 × 106(12.94 ± 2.54) × 1033Knee50F15.4 × 106(17.82 ± 6.25) × 1034 Knee23M20.3 × 106(100.28 ± 22.14) × 1035Knee29F20.4 × 106(110.07 ± 39.18) × 1036Knee43M18.7 × 106(5.34 ± 3.25) × 1037Knee48M17.5 × 106(4.74 ± 2.37) × 1038Ankle42F18.2 × 106(33.13 ± 25.92) × 1039Knee19M21.6 × 106(99.54 ± 25) × 10310Knee35M20 × 106(103.81 ± 37.26) × 10311Ankle39M19 × 106(79.48 ± 62.57) × 10312Knee13M20.1 × 106(87.88 ± 55.17) × 103 Patients’ characteristics and the number of cells initially seeded on each membrane are also reported (see also Fig. 2)
Histochemical and immunohistochemical analysis
In all of the samples, chondrocytes that were grown on the membrane were arranged in multi-layered sheets, and sometimes invaded deeper into the matrix. The cells appeared to be quite well differentiated, although some had a flattened morphology. They slightly stained metachromatically for safranin O (Fig. 3), and clearly immunoreacted with anti-S-100 protein (Fig. 4), type II collagen (Fig. 5) and chondroitin-S antibodies. Only a few cells stained for type I collagen.
Fig. 3Human chondrocytes seeded on type I, III collagen membrane before the implantation. Numerous cells are located on the membrane and deeper in the matrix forming a multi-layer engineered tissue. (Safranin O stain, ×200)Fig. 4Human chondrocytes seeded on type I, III collagen membrane. A marked cytoplasmic immunoreaction for S-100 protein is evident (×400)Fig. 5Human chondrocytes seeded on type I, III collagen membrane. Cells express a positive immunoreaction for type II collagen, while the membrane remains unstained (×400)
Ultrastructural analysis
The membranes presented a dual appearance: there was a smooth side with tightly packed fibres and a rougher side, a sparse layer, to which the cells were adhered. Numerous cells firmly adhered to the membrane and showed a round shaped morphology and a rough surface (Fig. 6).
Fig. 6Scanning electron micrograph of human chondrocytes seeded on type I, III collagen membrane. Numerous cells (arrows) firmly adhere to the membrane and show a round shaped morphology and a rough surface (×7,500)
Discussion
Cell phenotype and proliferation analysis should be an essential step in the evaluation of all tissue-engineered products because the implantation of dedifferentiated or not-proliferating cells would not justify the therapeutic employment of these biotechnologies.
The chondrocyte culture may undergo a dedifferentiation process, consisting in a fibroblast-like morphology, a reduction of the type II collagen and aggregating proteoglycans synthesis, and an increase of type I collagen expression [1, 4]. Chondrocyte dedifferentiation is common in monolayer cultures, while a long phenotype maintenance is supported by the presence of a tri-dimensional matrix [1, 4, 11]. The chemical and ultrastructural characteristics of the matrix may have great influence on the behaviour of chondrocytes in culture. A Type I collagen membrane appears to be a matrix endowed with such properties which make that ideal and useful for cartilage tissue engineering [8, 10].
MACI is a method for the treatment of articular cartilage defects, that employs autologous chondrocytes expanded in monolayer culture, suspended, and then seeded on type I–III collagen membrane. In a previous study [9], we had observed that autologous chondrocytes in suspension before implantation were of “good cartilaginous quality” and that they possessed an excellent proliferation capacity. Zheng et al. [20] confirmed that cells in ACI procedure maintained chondrocyte phenotype and showed a low apoptotic rate. To our knowledge, there is no previous reference to the analysis of the phenotype and the proliferation activity of human chondrocytes before MACI implantation. Data about cell number and viability, but not about cell phenotype and distribution are provided by Verigen’s laboratory for each implant. It should, however, be remembered that it is quite possible that the presence of a membrane like the one found in the MACI technique could interfere with cell behaviour and modify phenotype expression.
The present study has demonstrated that the autologous chondrocytes that were analysed adhered to the membrane and had quite a homogenous distribution. These cells were viable in all the cases analysed, as shown by the spectrophotometric assay. The differences observed among patients in the number of cells seeded on the membrane and those found to be viable are probably due to several factors. In the first place, the amount of tissue collected from patients and sent to the laboratory for culture was variable. The cellularity of cartilage tissue and the proliferation capacity of chondrocytes in identical culture conditions are also quite variable between subjects, and this variability cannot merely be explained with age, but is more likely to stem from a number of characteristics that define tissue “quality” [5, 12].
We did not investigate whether during monolayer culture chondrocytes underwent dedifferentiation, however, other authors [20] demonstrated that cells grown in monolayer with the same culture conditions maintained a differentiated phenotype. Moreover, we felt it was important to establish the phenotypical expression of chondrocytes seeded on the I/III collagen membrane, since those are the cells that are then implanted, independently of what has taken place in the previous steps of the procedure. The positive immunostaining for S-100 protein, chondroitin-S, type II, but not type I collagen, confirms that cells maintained the characteristics of differentiated hyaline chondrocytes at the moment they were implanted.
These data reassure that the MACI procedure can indeed provide a suitable engineered tissue for cartilage repair, in line with the mid-term clinical results described in the literature so far [2, 7]. Nevertheless, clinical and histological results of this method to attain stable clinical recovery and a hyaline-like cartilaginous scar are auspicated to substantiate these evidences in the long term.

chondrocytes
cells
articular cartilage
tissue engineering
cultured
histocytochemistry


Wine and other alcohol consumption and risk of ovarian cancer in the California Teachers Study cohort

Objective Whether alcohol consumption influences ovarian cancer risk is unclear. Therefore, we investigated the association between alcohol intake at various ages and risk of ovarian cancer.
Introduction
Consistent evidence that moderate alcohol consumption increases the risk of breast cancer, presumably by elevating estrogen and androgen levels [1], suggests that alcohol intake might also influence the development of other hormone-related malignancies, such as ovarian cancer. From a public health standpoint, this hypothesis is attractive since alcohol consumption could represent a readily modifiable ovarian cancer risk factor, whereas other risk factors, particularly reproductive characteristics [2, 3], are generally less amenable to change. Findings from previous studies examining the association between alcohol and ovarian cancer risk have been discrepant, with published reports of mostly null [3–19], some positive [15, 16, 20, 21], and some inverse associations [14, 17, 22–25], including occasional variation in associations by type of alcohol and/or subgroup of ovarian cancer cases. No systematic differences in findings are apparent between case–control and cohort studies.
Although ethanol itself is not a direct ovarian carcinogen, it could influence ovarian cancer risk through effects on steroid hormones, especially estrogens, which are believed to play a primary role in ovarian carcinogenesis [26]. In pre-menopausal women, moderate to high alcohol consumption is associated with elevated levels of total and bioavailable estrogens and androgens [27–29], reduced fertility [30, 31], and decreased menstrual cycle variability and length, resulting in increased cumulative estrogen exposure [32]. In post-menopausal women, moderate alcohol intake markedly increases circulating estrogen levels in hormone therapy (HT) users [33, 34], and it may elevate estrogen and androgen levels in women not using HT [35]. Other mechanisms for alcohol-related carcinogenesis include alteration of gonadotropin levels, promotion of DNA damage, impaired folate metabolism and DNA hypomethylation, inhibition of carcinogen detoxification or clearance, and increased metastatic potential of tumor cells [1].
Given the biologic plausibility of a role of alcohol intake in ovarian cancer etiology, we examined the association between alcohol consumption and risk of ovarian cancer in a prospective cohort in which baseline alcohol consumption was associated with increased breast cancer risk [36, 37]. With data on past and baseline intake of specific alcoholic beverages, as well as information on a variety of demographic and behavioral factors, we were also able to explore the importance of drinking patterns and potential effect modifiers of any association.
Methods
Study population
The California Teachers Study (CTS) cohort includes 133,479 active and retired female public school teachers and administrators who were members of the California State Teachers Retirement System and returned a mailed questionnaire in 1995–1996 [38]. The questionnaire assessed a range of potential cancer risk factors including menstrual and reproductive history, personal and family medical history, physical activity, dietary intake during the previous year (using a food-frequency questionnaire and portion-size assessment [39–42]), alcohol and tobacco use, and other factors. All women provided written informed consent to participate in the study, and the study protocol was approved by the institutional review boards of all participating institutions.
For this analysis, we excluded women (in a hierarchical manner) as follows: those who 1.) lived outside of California at baseline (n = 8,867); 2.) did not provide adequate information on personal history of cancer (n = 662); 3.) consented to participate only in analyses of breast cancer (n = 18); 4.) reported having had ovarian cancer before baseline or were identified by the California Cancer Registry as having been previously diagnosed with ovarian cancer (n = 640); 5.) reported having had a bilateral oophorectomy before baseline (n = 14,422); 6.) were aged 85 years or older at baseline (n = 1,874); 7.) reported never having had a first menstrual period (n = 51); 8.) provided multiple invalid, inconsistent, or blank responses to the dietary questionnaire (n = 2,942); 9.) reported food consumption that was judged to be implausibly low (i.e., < 600 calories/day) or high (i.e., > 5,000 calories/day) (n = 1,565 and 69, respectively); or 10.) provided invalid, missing, or inconsistent data with respect to alcohol intake during the previous year (n = 5,094) or the earlier two periods evaluated (n = 6,904). Of the 90,371 remaining women included in this analysis, 227 were diagnosed with invasive epithelial ovarian cancer and 26 were diagnosed with borderline epithelial ovarian cancer (ICD-O-3 site C569, excluding non-epithelial ovarian cancer cases [morphology codes 8240–8245, 8590–8671, and 9060–9989] [43]) after joining the cohort and on or before 31 December 2003.
Alcohol assessment
Participants reported average weekly consumption of beer, wine/champagne, and cocktails/liquor at ages 18–22 years, at ages 30–35 years, and in the year prior to baseline. Available response categories for average number of drinks per week were none, ≤ 3, 4–10, 11–17, 18–24, and ≥ 25. A typical drink was defined as one bottle, can, or glass of beer; one glass of wine, champagne, or wine cooler; or one cocktail, shot, or mixed drink of liquor. A single drink of beer, wine, or liquor was assumed to contain 13.2, 11.1, or 15.0 grams of alcohol, respectively. Based on these standards, daily intake of grams of alcohol from each type of drink was calculated for each woman during each time period. Alcohol intake in the cohort was reproducible (r = 0.87) and valid compared to multiple 24–hour recalls (r = 0.74) (Pamela Horn-Ross, unpublished data).
Daily consumption of alcohol from beer was categorized as 0, < 13.2, or ≥ 13.2 grams/day; alcohol from wine was categorized as 0, < 11.1, or ≥ 11.1 grams/day; alcohol from liquor was categorized as 0, < 15.0, or ≥ 15.0 grams/day; and total alcohol was categorized as 0, < 10.0, 10.0 to < 20.0, or ≥ 20.0 grams/day. These categories were defined based on the grams of alcohol per standard drink, and on findings from previous analyses of alcohol intake and breast cancer risk in the CTS [36, 37].
Based on their drinking patterns in any two time periods (ages 18–22 years and baseline, ages 30–35 years and baseline, or ages 18–22 years and 30–35 years), women were categorized as having been non-drinkers, moderate drinkers (in the middle category or categories of intake), or heavy drinkers (in the highest category of intake) in both time periods; or decreasing or increasing drinkers if their intake changed between the earlier and the later time period. For each type of alcoholic drink and each time period, women also reported how many days per week they usually had at least one drink. Women were categorized as non-drinkers, as drinking alcohol on 1–4 days/week, or as drinking alcohol on ≥ 5 days/week [37].
Residence-based measures
Based on residential address at entry, women were geocoded to census block groups. To obtain a measure of each cohort member’s relative socioeconomic status (SES), all 1990 census block groups in the state of California were ranked by three measures, according to deciles based on the statewide adult population: percentage of adults over age 25 years who had completed a college degree or higher; median family income; and percentage of adults employed in managerial/professional occupations [44]. A summary SES metric was created by adding the scores (1 through 10) across these attributes; participants were then categorized into deciles or quartiles of the total score. Women were also categorized by median family income in their census block group, based on deciles in the cohort.
Residential census block groups were categorized as rural, town, small city, metropolitan suburban, or metropolitan urban based on population size and density [44]. Women were also classified as residents of the Greater San Francisco Bay Area (Alameda, Contra Costa, Marin, Monterey, San Benito, San Francisco, San Mateo, Santa Clara, and Santa Cruz Counties); the Southern Coastal area (Orange, Los Angeles, and San Diego Counties); or the rest of California [45].
Follow-up
Person-time was accrued from the date of completion of the baseline questionnaire until the date of first diagnosis with borderline or invasive ovarian cancer, relocation out of California, death, or 31 December 2003, whichever occurred earliest. Information on incident ovarian cancer and tumor characteristics was obtained through annual linkage of cohort members to the California Cancer Registry (CCR) based on full name, date of birth, address, and social security number, including manual review of possible matches. The CCR is the population-based cancer registry that covers the entire state of California, has agreements with 13 other states for case-sharing purposes, and maintains high-quality data standards as part of the National Cancer Institute’s Surveillance, Epidemiology, and End Results program. Reporting of new cancer diagnoses to the CCR has been mandated by California state law since 1985, and coverage is estimated to be 99% complete [46], such that all members of the CTS cohort are effectively in active follow-up for cancer outcomes as long as they reside in California.
Date and cause of death are ascertained through linkages with the California state mortality file and the national Social Security Administration death master file, as well as reports from relatives. Address changes are obtained through annual mailed newsletters, notifications by participants, and record linkages with the California Department of Motor Vehicles, the US Postal Service National Change of Address database, and other sources.
Statistical analysis
Multivariable Cox proportional hazards regression analysis was performed to evaluate the association between alcohol consumption and risk of ovarian cancer, using ages at the start and end of follow-up (in days) to define the time scale. Models were adjusted for race (White or non-White), total daily caloric intake (continuous), parity (0, 1–2, or ≥ 3 full-term pregnancies), use of oral contraceptives (never, < 5 years, or ≥ 5 years), average strenuous physical exercise ( < 0.5, 0.5–3.99, or ≥ 4 hours/week during lifetime up to age 54 years), menopausal status/use of HT (pre-menopausal, unknown menopausal status, or peri-/post-menopausal and: never used HT, used combination estrogen-progestin HT, used a mixture of combination and estrogen-only (“mixed”) HT, used estrogen-only HT for ≤ 5 years, used estrogen-only HT for > 5 years, used estrogen-only HT for an unknown duration, or unknown HT use), and an interaction between menopausal status/HT use and the time scale (because menopausal status/HT use violated the proportionality assumption), and were stratified by age at baseline (in years). These potential confounders were chosen based on statistically significant (p-value ≤ 0.05) associations with risk of ovarian cancer, and on prior knowledge of ovarian cancer risk factors. Missing values were coded as dummy variables, none of which were found to be associated with ovarian cancer risk. We tested the assumption of proportional hazards for each alcohol variable using significance tests of interactions with the time scale and visual examination of scaled Schoenfeld residual plots [47], and found no violations of the proportionality assumption.
Hazard rate ratios, presented as relative risks (RR), and corresponding 95% confidence intervals (CI) were estimated for each type of alcoholic beverage, comparing categories of consumption to non-drinkers of that alcohol type as the reference group. Tests for linear trend across exposure categories were conducted using the median of each category coded as an ordinal variable. The median of the highest category of beer, wine, or liquor intake was equal to the lower boundary because most women in those categories reported consuming one drink per day.
Analyses were first performed for all eligible women, and then repeated with restriction to women who were peri- or post-menopausal at baseline. In addition, analyses were performed with restriction to cases of invasive ovarian cancer (excluding borderline cases) or serous ovarian cancer (ICD-O-3 morphology codes 8441–8462 and 9014 [43]; n= 114). Likelihood ratio tests were used to evaluate homogeneity of the estimated RR between strata of women, as well as significant differences in fit between models with and without additional covariates. Tests for non-linearity of trend were based on a likelihood ratio test comparing models with the exposure coded as an ordinal or a categorical variable [48]. Associations with wine drinking were evaluated using multivariate logistic regression, using covariates as defined above. All analyses were performed using SAS Version 9.1 (SAS Institute, Cary, NC).
Results
Distributions of demographic characteristics and ovarian cancer risk factors in the eligible study cohort are shown in Table 1. The median length of follow-up was 2,959 days (8.1 years) and the median age of participants at baseline was 50 years. As shown in Table 2, there was no overall difference in risk of ovarian cancer by total alcohol consumption in the year before baseline, at ages 30–35 years, or at ages 18–22 years. Similarly, consumption of alcohol from beer or liquor during any of the three time periods was not significantly associated with risk of ovarian cancer. In contrast, intake of alcohol from wine during the year before baseline was associated with statistically significantly elevated risk of ovarian cancer. After adjusting for alcohol intake from beer and liquor, as well as ovarian cancer risk factors, women who drank at least 11.1 grams per day of alcohol from wine—the equivalent of one glass per day—at baseline were at 57% higher risk of ovarian cancer, compared to women who did not drink wine (Ptrend = 0.01). Intake of alcohol from wine at ages 30–35 years or ages 18–22 years was not significantly associated with ovarian cancer risk, although the estimated RRs were consistent with those for baseline wine intake.
Table 1Selected baseline characteristics of the California Teachers Study (CTS) cohort included in the present analysis (n= 90,371)Characteristicn(%)Age at baseline (years) <3510,456(11.6%) 35–4418,546(20.5%) 45–5428,275(31.3%) 55–6416,229(18.0%) 65–7411,496(12.7%) 75–845,369(5.9%)Race/ethnicity White78,468(86.8%) Non-white11,266(12.5%) Unknown637(0.7%)Parity (full-term pregnancies) None23,810(26.3%) 1–243,674(48.3%) ≥321,412(23.7%) Unknown1,475(1.6%)Oral contraceptive use (years) None26,643(29.5%) <527,608(30.5%) ≥532,463(35.9%) Unknown3,657(4.0%)Lifetime strenuous physical activity (average hours/week) <0.525,254(27.9%) 0.5–3.949,467(54.7%) ≥4.015,293(16.9%) Unknown357(0.4%)Menopausal status Pre-menopausal42,204(46.7%) Peri-menopausal2,202(2.4%) Post-menopausal39,744(44.0%) Unknown6,221(6.9%)Hormone therapy (HT) use (peri-/post-menopausal women only) None12,968(30.9%) Combination estrogen + progestin HT14,235(33.9%) Estrogen-only HT, ≤5 years4,342(10.4%) Estrogen-only HT, >5 years4,899(11.7%) Estrogen-only HT, unknown duration332(0.8%) Mixed combination and estrogen-only HT4,776(11.4%) Unknown394(0.9%)Region of residence within Californiaa Greater San Francisco Bay Area18,444(20.4%) Southern Coastal/Los Angeles Area35,388(39.2%) Other areas36,483(40.4%) Unknown56(0.1%)Type of residence Rural12,677(14.0%) Town3,136(3.5%) Small city16,135(17.9%) Metropolitan suburban48,278(53.4%) Metropolitan urban9,038(10.0%) Unknown1,107(1.2%)Statewide percentile of socioeconomic status in census block groupa ≤49th18,962(21.0%) 50–59th10,199(11.3%) 60–69th12,620(14.0%) 70–79th14,508(16.1%) 80–89th16,864(18.7%) 90–99th16,076(17.8%) Unknown1,142(1.3%)Smoking history Never60,868(67.4%) Former24,989(27.7%) Current4,428(4.9%) Unknown86(0.1%)Alcohol drinking in the year prior to baseline None31,024(34.3%) Beer only2,222(2.5%) Wine only19,365(21.4%) Liquor only2,868(3.2%) Beer and liquor only1,005(1.1%) Wine and beer/liquor33,887(37.5%)a See Methods for definitionTable 2Relative risks (RRs) and 95% confidence intervals (CIs) for associations between intake of specific types of alcohol at various ages and risk of ovarian cancerAlcohol typeTime periodDaily intake (g/day)Median (g/day)Cases (n)RRa95% CIaRRb95% CIbTotal alcoholYear before baselineNone0.0771.00(reference)—<10.04.5811.04(0.76, 1.42)—10.0– < 20.011.8721.47(1.06, 2.03)—≥20.028.2231.15(0.71, 1.84)—Ptrend = 0.19Ages 30–35 yearscNone0.0671.00(reference)—<10.07.31011.14(0.83, 1.56)—10.0– < 20.011.8471.08(0.74, 1.59)—≥20.029.7160.99(0.56, 1.71)—Ptrend = 0.99Ages 18–22 yearsdNone0.01311.00(reference)—<10.04.5620.76(0.55, 1.03)—10.0– < 20.011.8361.26(0.86, 1.84)—≥20.028.891.00(0.50, 1.99)—Ptrend = 0.63BeerYear before baselineNone0.01991.00(reference)1.00(reference)<13.24.0510.96(0.70, 1.31)0.89(0.64, 1.24)≥13.213.230.58(0.19, 1.84)0.54(0.17, 1.70)Ptrend = 0.40Ptrend = 0.22Ages 30–35 yearscNone0.01801.00(reference)1.00(reference)<13.24.0450.81(0.58, 1.13)0.75(0.53, 1.06)≥13.213.260.72(0.32, 1.64)0.73(0.32, 1.69)Ptrend = 0.20Ptrend = 0.16Ages 18–22 yearsdNone0.01791.00(reference)1.00(reference)<13.24.0460.95(0.68, 1.32)0.93(0.65, 1.33)≥13.213.2131.21(0.68, 2.16)1.30(0.70, 2.39)Ptrend = 0.67Ptrend = 0.57WineYear before baselineNone0.0911.00(reference)1.00(reference)<11.13.3991.08(0.81, 1.43)1.09(0.80, 1.50)≥11.111.1631.50(1.08, 2.09)1.57(1.11, 2.22)Ptrend = 0.01ePtrend = 0.01eAges 30–35 yearscNone0.0901.00(reference)1.00(reference)<11.13.31121.19(0.90, 1.59)1.26(0.92, 1.71)≥11.111.1291.21(0.78, 1.86)1.38(0.87, 2.19)Ptrend = 0.36Ptrend = 0.16Ages 18–22 yearsdNone0.01671.00(reference)1.00(reference)<11.13.3631.11(0.82, 1.50)1.17(0.84, 1.63)≥11.111.181.42(0.69, 2.91)1.63(0.76, 3.50)Ptrend = 0.28Ptrend = 0.15LiquorYear before baselineNone0.01691.00(reference)1.00(reference)<15.04.5681.09(0.82, 1.45)1.06(0.78, 1.44)≥15.015.0160.87(0.52, 1.47)0.82(0.48, 1.39)Ptrend = 0.80Ptrend = 0.56Ages 30–35 yearscNone0.01241.00(reference)1.00(reference)<15.04.5931.07(0.81, 1.41)1.04(0.77, 1.40)≥15.015.0140.77(0.44, 1.35)0.75(0.42, 1.36)Ptrend = 0.52Ptrend = 0.44Ages 18–22 yearsdNone0.01641.00(reference)1.00(reference)<15.04.5690.99(0.74, 1.32)0.94(0.68, 1.29)≥15.015.050.62(0.25, 1.52)0.49(0.19, 1.26)Ptrend = 0.39Ptrend = 0.17a Adjusted for race, total energy intake, parity, oral contraceptive use, strenuous exercise, and menopausal status/hormone therapy use; stratified by age at baselineb Additionally adjusted for consumption of other alcohol types in the same time periodc Among women over age 35 years at baseline with non-missing data on alcohol consumption at ages 30–35 yearsd Among women over age 22 years at baseline with non-missing data on alcohol consumption at ages 18–22 yearsep for non-linearity of trend > 0.05
Intake of specific alcohol types, beyond total alcohol, was associated with ovarian cancer risk, as assessed by comparing a multivariate model with beer, wine, and alcohol consumption in the year before baseline to a model with alcohol consumption alone (p = 0.05, 2 d.f.). Furthermore, the association of wine consumption with ovarian cancer risk differed from that of beer or liquor consumption, as assessed by comparing a multivariate model with beer/liquor and wine consumption to a model with alcohol consumption (p = 0.02, 1 d.f.). Controlling for total alcohol intake did not attenuate the positive association between wine intake at any time period and risk of ovarian cancer (data not shown). Women who drank wine only in the year before baseline (median daily alcohol intake = 11.1 grams), relative to non-drinkers, had an ovarian cancer RR of 1.40 (95% CI 1.01–1.93). In contrast, risk did not vary between non-drinkers and women who drank beer or liquor only (median daily intake of alcohol = 4.5 grams; RR = 1.03 [95% CI 0.58–1.83]) or beer/liquor and wine (median daily alcohol intake = 11.8 grams; RR = 1.08 [95% CI 0.79–1.48]). The multivariate RR among women who exclusively drank at least one glass of wine per day, compared to non-drinkers, was 1.70 (95% CI 1.10–2.62). After simultaneously adjusting for wine drinking during the year before baseline and at ages 18–22 years and 30–35 years, the RR associated with drinking at least one daily glass of wine at baseline was 1.33 (95% CI 0.91–1.96); at ages 30–35 years, RR = 1.01 (95% CI 0.61–1.68); and at ages 18–22 years, RR = 1.28 (95% CI 0.58–2.87).
Women who drank at least one glass per day of wine both in an earlier time period (18–22 years or 30–35 years) and at baseline were at significantly higher risk of ovarian cancer, compared to wine non-drinkers (Table 3). Results were similar when based on intake at ages 18–22 years and 30–35 years (data not shown). Women who were heavy wine drinkers in all three time periods (n= 5 cases) had over four times the risk of ovarian cancer relative to women who never drank wine in any time period (RR = 4.60 [95% CI = 1.76–12.01]), adjusting for beer and liquor drinking patterns. Marginally elevated risk of ovarian cancer was also observed among women who increased or decreased their wine intake between ages 18–22 years and either baseline or ages 30–35 years (latter data not shown). No patterns of total alcohol, beer, or liquor intake were significantly associated with ovarian cancer risk. Furthermore, ovarian cancer risk did not vary according to number of drinking days per week at baseline, whether for total alcohol, beer, wine, or liquor, although the risk of ovarian cancer rose slightly with increasing frequency of wine consumption at baseline (Ptrend = 0.11).
Table 3Relative risks (RRs) and 95% confidence intervals (CIs) for associations between patterns of drinking specific types of alcohol and risk of ovarian cancerAlcohol typeTime periodDrinking patternCases (n)RRa95% CIaRRb95% CIbTotal alcoholAge 30–35 years and the year before baselinecNever491.00(reference)—Steady moderate1181.25(0.89, 1.76)—Decreasing291.06(0.67, 1.68)—Increasing271.10(0.68, 1.77)—Steady heavy81.32(0.62, 2.82)—Age 18–22 years and the year before baselinedNever551.00(reference)—Steady moderate701.07(0.74, 1.53)—Decreasing241.06(0.65, 1.72)—Increasing861.30(0.92, 1.84)—Steady heavy31.87(0.58, 6.04)—Year before baseline0 days/week771.00(reference)—1 to 4 days/week981.25(0.92, 1.69)—5 to 7 days/week, ≤ 20 g/day301.36(0.88, 2.08)—5 to 7 days/week, > 20 g/day181.14(0.68, 1.93)—BeerAge 30–35 years and the year before baselinecNever1631.00(reference)1.00(reference)Steady moderate260.80(0.53, 1.21)0.73(0.47, 1.13)Decreasing250.94(0.62, 1.44)0.88(0.57, 1.38)Increasing171.08(0.65, 1.79)0.99(0.59, 1.66)Steady heavy0——Age 18–22 years and the year before baselinedNever1591.00(reference)1.00(reference)Steady moderate241.06(0.68, 1.64)1.02(0.64, 1.62)Decreasing340.95(0.65, 1.38)0.92(0.61, 1.37)Increasing210.77(0.49, 1.22)0.71(0.44, 1.14)Steady heavy0——Year before baselinee0 days/week1991.00(reference)1.00(reference)1 to 4 days/week400.98(0.70, 1.39)0.79(0.53, 1.16)5 to 7 days.week10.33(0.05, 2.37)0.36(0.05, 2.59)Ptrend = 0.30Ptrend = 0.14WineAge 30–35 years and the year before baselinecNever601.00(reference)1.00(reference)Steady moderate651.15(0.80, 1.64)1.19(0.80, 1.77)Decreasing341.25(0.82, 1.92)1.32(0.82, 2.11)Increasing531.25(0.86, 1.82)1.29(0.86, 1.93)Steady heavy191.54(0.91, 2.62)1.75(1.00, 3.04)Age 18–22 years and the year before baselinedNever711.00(reference)1.00(reference)Steady moderate290.98(0.63, 1.52)1.01(0.62, 1.63)Decreasing201.48(0.89, 2.45)1.73(1.00, 2.99)Increasing1131.29(0.95, 1.74)1.38(0.99, 1.92)Steady heavy52.47(0.99, 6.19)2.76(1.09, 7.00)Year before baselinee0 days/week911.00(reference)1.00(reference)1 to 4 days/week971.19(0.89, 1.60)1.23(0.89, 1.71)5 to 7 days.week371.34(0.91, 1.98)1.45(0.96, 2.20)Ptrend = 0.18Ptrend = 0.11LiquorAge 30–35 years and the year before baselinecNever1101.00(reference)1.00(reference)Steady moderate471.13(0.80, 1.60)1.12(0.76, 1.63)Decreasing501.04(0.74, 1.46)0.97(0.67, 1.41)Increasing211.11(0.69, 1.77)1.04(0.63, 1.70)Steady heavy30.49(0.15, 1.54)0.46(0.15, 1.49)Age 18–22 years and the year before baselinedNever1261.00(reference)1.00(reference)Steady moderate321.12(0.76, 1.66)1.10(0.71, 1.69)Decreasing350.84(0.57, 1.23)0.76(0.50, 1.15)Increasing440.98(0.69, 1.38)0.92(0.64, 1.33)Steady heavy10.80(0.11, 5.77)0.69(0.10, 5.04)Year before baselinee0 days/week1691.00(reference)1.00(reference)1 to 4 days/week571.13(0.83, 1.53)1.10(0.78, 1.55)5 to 7 days.week131.00(0.57, 1.78)0.73(0.35, 1.51)Ptrend = 0.92Ptrend = 0.44a Adjusted for race, total energy intake, parity, oral contraceptive use, strenuous exercise, and menopausal status/hormone therapy use; stratified by age at baselineb Additionally adjusted for drinking patterns of other alcohol types across the same time periodsc Among women over age 35 years at baseline with non-missing data on alcohol consumption at ages 30–35 yearsd Among women over age 22 years at baseline with non-missing data on alcohol consumption at ages 18–22 yearse Excluding women with missing data on number of drinking days per week
The lack of an association with total alcohol, beer, or liquor intake, as well as the persistence of the association between wine intake and risk of ovarian cancer after adjustment for alcohol consumption, suggested that determinants of wine drinking, or ingredients of wine other than alcohol, were responsible for the observed positive association. After mutual adjustment, older age (up to ages 65–69 years), White race, higher total caloric intake, nulliparity, ever-use of oral contraceptives, more physical activity, ever-use of HT, higher SES or median family income, residence in the Greater San Francisco Bay Area, lower body mass index, ever-smoking of cigarettes, and higher intake of coffee and/or tea were significantly associated with drinking at least one daily glass of wine, compared to none (data not shown). However, the positive association between wine consumption and risk of ovarian cancer remained statistically significant even after adjustment for these characteristics (data not shown).
To further explore the relationship between wine consumption and ovarian cancer risk, we stratified the association by various potential effect modifiers, including demographic characteristics and ovarian cancer risk factors (Table 4). There was no statistically significant heterogeneity in the association between intake of alcohol from wine at baseline and risk of ovarian cancer by any of the factors examined. Among peri-/post-menopausal women, we observed no association with wine intake among those who had never used HT, nor among those who used combined estrogen-progestin HT, adjusting for duration of HT use. In contrast, women who used unopposed estrogen HT were at twice the risk of ovarian cancer if they drank one glass of wine per day at baseline, compared to wine non-drinkers. The association was especially strong among women who used estrogen-only HT for over 5 years (RR = 2.39 [95% CI 0.97–5.89], Ptrend = 0.02), whereas there was no such association among women who used combination HT for over 5 years (RR = 1.34 [95% CI 0.51–3.54], Ptrend = 0.29). We also found that the women in the highest quartile of SES statewide were at double the risk of ovarian cancer if they drank at least a glass per day of wine, compared to none, whereas there was no such association among women in the lower three quartiles of SES.
Table 4Stratified relative risks (RRs) and 95% confidence intervals (CIs) for associations between wine intake in the year before baseline and risk of ovarian cancer within participant subgroupsCharacteristic (at baseline)Alcohol consumption from winePtrendPheterogeneity between subgroupsNone < 11.1 g/day≥ 11.1 g/dayCasesRRaCasesRRa(95% CI)aCasesRRa(95% CI)aAge≤Median (50 years)231.00271.07(0.58, 1.99)121.43(0.67, 3.04)0.34>Median681.00721.10(0.76, 1.57)511.62(1.09, 2.39)0.010.95ParityNulliparous181.00261.34(0.69, 2.62)131.56(0.71, 3.40)0.31Parous711.00731.05(0.73, 1.50)481.57(1.06, 2.34)0.020.61Oral contraceptive useNever451.00391.00(0.62, 1.61)291.70(1.02, 2.82)0.03Ever221.00140.76(0.37, 1.58)141.78(0.85, 3.72)0.090.54Lifetime strenuous physical activity≤Median (1.4 hours/week)611.00581.07(0.72, 1.59)401.68(1.09, 2.59)0.01>Median (1.4 hours/week)301.00411.11(0.66, 1.86)231.39(0.77, 2.50)0.260.55Menopausal statusPre-menopausal211.00200.83(0.42, 1.65)101.24(0.55, 2.83)0.53Peri-/Post-menopausal661.00721.16(0.80, 1.66)511.72(1.16, 2.55)0.010.86Hormone therapy (HT) useNoneb211.00221.27(0.64, 2.51)91.20(0.51, 2.78)0.73Combination estrogen + progestin HTb231.00180.69(0.35, 1.37)161.17(0.58, 2.34)0.45Estrogen-only HTb161.00191.27(0.62, 2.61)152.03(0.95, 4.35)0.060.37Region of residenceGreater Bay/Southern Coastal regions451.00621.20(0.79, 1.84)381.69(1.06, 2.71)0.02Other California regions461.00370.94(0.59, 1.52)251.46(0.87, 2.48)0.120.41Type of residenceRural/town/small city351.00341.23(0.74, 2.06)231.77(1.01, 3.11)0.05Metropolitan suburban/urban541.00651.04(0.70, 1.55)401.51(0.97, 2.37)0.050.77Statewide percentile of socioeconomic status in census block groupcLower 75% 571.00501.09(0.72, 1.67)241.35(0.81, 2.27)0.25Upper 25% 321.00491.16(0.72, 1.88)391.96(1.19, 3.24)0.0040.43Body mass index≤Median (23.5 kg/m2)331.00461.31(0.81, 2.11)321.64(0.97, 2.76)0.07>Median531.00520.97(0.63, 1.49)271.48(0.89, 2.45)0.100.69Cigarette smoking statusNever661.00611.05(0.71, 1.56)361.77(1.13, 2.78)0.01Ever251.00381.19(0.69, 2.03)271.42(0.80, 2.50)0.240.57Dietary folate intake≤Median (307.1 μg/day)d231.00210.73(0.37, 1.43)131.15(0.54, 2.44)0.55>Mediand211.00231.16(0.60, 2.24)101.17(0.51, 2.66)0.750.74Total folate intake≤Median (473.0 μg/day)e431.00461.07(0.68, 1.70)251.34(0.78, 2.30)0.27>Mediane411.00481.20(0.77, 1.89)372.07(1.29, 3.35)0.0020.43a Adjusted for race, total energy intake, parity, oral contraceptive use, strenuous exercise, menopausal status/hormone therapy use, and consumption of beer and liquor in the past year; stratified by age at baselineb Including peri-/post-menopausal women onlyc See Methods for definitiond Excluding all multivitamin users (i.e., women consuming supplemental folate)e Excluding short-term multivitamin users (i.e., women consuming supplemental folate for < 2 years)
In secondary analyses, we examined the associations between alcohol consumption and risk of ovarian cancer among only women who were peri- or post-menopausal at baseline (46% of the study population, 75% of cases). In this group, we observed the same lack of a significant association with total alcohol, beer, or liquor intake, along with a significant positive association with baseline wine intake. Likewise, when we restricted the case population to invasive ovarian cancer (90% of cases) or to serous ovarian cancer (45% of cases), baseline intake of alcohol from wine, but not from other sources, was associated with significantly increased risk of ovarian cancer.
Discussion
The lack of association between overall alcohol consumption and risk of ovarian cancer in our study is consistent with most previous studies [3–18], including a pooled analysis of 10 prospective cohorts [19]. Alcohol consumption during ages 18–22 years, ages 30–35 years, or the year before baseline—whether from beer, liquor, or all sources combined—was unrelated to ovarian cancer development. Additionally, changes in beer, liquor, or total alcohol consumption over time, as well as frequency of drinking in the year before baseline, did not affect ovarian cancer risk in this cohort.
In contrast, average consumption of at least one glass per day of wine in the year before baseline was associated with elevated risk of ovarian cancer, while wine intake at ages 30–35 years or 18–22 years was associated with nonsignificantly increased risk. The positive association with baseline wine intake persisted after adjustment for total alcohol intake, suggesting that the apparent effect of wine on ovarian cancer risk was independent of alcohol content. The association was also unchanged by further adjustment for characteristics and behaviors associated with wine drinking in this cohort, and was not significantly modified by reproductive characteristics, demographic factors, or folate intake. However, there was a significant positive association between wine consumption and ovarian cancer risk among peri-/post-menopausal women who used unopposed estrogen HT, whereas there was no such association among peri-/post-menopausal women who did not use HT or used combined estrogen-progestin HT. Wine consumption was also associated with increased ovarian cancer risk among women of high SES, but not among women of relatively low SES.
To our knowledge, no other study of ovarian cancer has examined alcohol consumption at various ages or drinking patterns over time. A positive association between wine consumption and risk of ovarian cancer has previously been detected in two case–control studies [15, 49], although both of those studies reported a positive association with total alcohol intake as well. In contrast, two case–control studies [14, 24] and one cohort study (among women with high dietary folate intake) [17] found an inverse association between wine consumption and ovarian cancer risk. However, there was no association with wine (or total alcohol) consumption in other studies [13, 18, 19].
Few previous studies have examined modification of the association between alcohol consumption and risk of ovarian cancer. A pooled analysis of 529,638 women, including 2,001 incident ovarian cancer cases, found no interaction between alcohol intake and oral contraceptive or HT use, parity, menopausal status, folate intake, BMI, or smoking [19]. In contrast, a case–control study found that an inverse association between wine consumption and ovarian cancer risk was significantly stronger in women who were more highly educated, had never smoked, or had used oral contraceptives—characteristics that describe the majority of CTS cohort members—but found no heterogeneity by menopausal status, BMI, or HT use [24]. Unlike us, others reported an interaction with folate intake [16, 17, 25] or heterogeneity by histologic subtype of ovarian cancer [14, 15], although others did not [3, 12, 19, 24]. We lacked sufficient cases to perform detailed analyses of ovarian cancer histologic subtypes other than the most common serous type.
The restriction of the positive association between wine intake and ovarian cancer risk to peri-/post-menopausal women taking estrogen-only HT suggests a biological mechanism involving estrogen. Likewise, the restriction of the association to women of high SES may reflect reproductive characteristics and/or HT use favoring increased estrogen levels in higher-SES women. A high background level of circulating estrogen, compounded by an upsurge of estrogen resulting from alcohol consumption [1], may promote ovarian carcinogenesis. Furthermore, the lack of an interaction between wine intake and use of estrogen-progestin HT could be explained by a protective effect of progestin against ovarian cancer. Phytochemicals, such as resveratrol, in red wine have been considered as promising cancer preventive agents due to their anti-estrogenic, antioxidant, anti-proliferative, and other anti-carcinogenic effects [50, 51]. However, such phytochemicals have multifarious effects, including pro-estrogenic activity and possible genotoxicity [52–54]; thus, both the potentially beneficial and potentially harmful effects of phytochemicals on cancer development must be considered together. In the present analysis, we were not able to distinguish red from white wine intake.
Our findings should be interpreted in light of some limitations. It is possible that the observed positive association between wine consumption and ovarian cancer risk was due to confounding by unmeasured characteristics beyond those for which we attempted to adjust, or by residual confounding or chance. Our efforts to fully evaluate effect modifiers or confounders of the association between wine consumption and ovarian cancer risk were partly hampered by the lack of heterogeneity in some characteristics among CTS participants. The proportion of women who drank beer or liquor was low, although the range of total alcohol intake was adequate to reveal a significant positive association with risk of breast cancer in an earlier analysis [37]. Even though reporting of alcohol intake in the past year using our questionnaire has been validated, we were not able to assess the reliability or validity of self-reported alcohol consumption at earlier ages, which may be difficult to recall, especially among older women. Misclassification of distant past alcohol intake may explain the absence of a significant association between wine drinking at earlier ages and ovarian cancer risk. Nevertheless, it is unlikely that any exposure misclassification differed systematically between ovarian cancer cases and non-cases, since all information was assessed prospectively.
In summary, given the lack of association between overall alcohol consumption and ovarian cancer risk in our study, further investigations are necessary to determine whether ingredients of wine, but not beer or liquor, foster ovarian cancer development; whether correlates of wine drinking not measured or imperfectly measured in our study population are associated with ovarian cancer risk; or if an association is largely due to the apparent interaction between wine consumption and unopposed estrogen HT use and/or other characteristics of women of high SES. If the observed association between wine drinking and ovarian cancer risk is due to confounding, then there may exist an as-yet unidentified ovarian cancer risk factor that is also associated with wine drinking. On the other hand, if the interactions with estrogen HT use and SES are confirmed, there may be a biological basis for the increased risk of ovarian cancer among wine drinkers with high endogenous and exogenous estrogen levels. However, if alcohol consumption is indeed unrelated to ovarian cancer development but positively associated with the risk of breast cancer, then differences in the hormonal and non-hormonal triggers between these two malignancies may help us understand the carcinogenic effects of alcohol on hormonally responsive tissue. Further understanding of the complex relationships among steroid hormone levels, metabolism of alcohol and wine, and carcinogenesis will help clarify what role, if any, alcohol and wine play in the development of ovarian cancer.

ovarian cancer
cohort studies
alcoholic beverages
women’s health