Material and Methods: This retrospective study included 32 GCTB patients. Clinical, radiological, and pathological data were reviewed. Histomorphological features, including surgical margins, bone cortex invasion, soft tissue invasion, mitotic count, vascular invasion, percentage of spindle cell areas, and presence of tumor-associated lymphocytes (TALs), along with tumor size and demographic features, were evaluated. Tissue sections were stained with p63, RANK, RANKL, and VEGF. The relationship between these parameters and post-surgical recurrence or metastasis was analyzed.
Results: A higher percentage of spindled pattern was significantly associated with a lower frequency of recurrence. A larger tumor diameter at diagnosis was associated with the development of metastasis. Other histomorphological parameters and the expression of p63, RANK, RANKL, and VEGF were not significantly associated with recurrence or metastasis.
Conclusion: Percentage of spindled pattern and primary tumor diameter are potential prognostic factors for recurrence and metastasis, respectively, in GCTB.
Recurrence or metastasis in GCTB cases has been associated with histomorphological and clonal characteristics of mononuclear cells[8-11]. However, no clearly defined histomorphological features associated with recurrence and metastasis have been reported to date.
p63 protein is expressed in a subset of the mononuclear cells in GCTBs and is considered to constitute the group of cells that transform into osteoblasts[12]. It has been reported that the mean p63 expression rate in recurrent GCTB cases was significantly higher compared to non-recurrent cases[9].
Overexpression of RANKL by neoplastic mononuclear cells stimulates RANK-expressing monocytic cells to form osteoclastic giant cells. This increased RANK/RANKL activity leads to increased bone matrix erosion and release of growth factors from the matrix that stimulate cell proliferation in GCTB. Ultimately, this cycle of increased bone remodeling and cell proliferation may increase the risk of recurrence and metastasis[10,11,13].
VEGF is a growth factor and an important regulator involved in angiogenesis[14]. GCTBs are hypervascularized tumors, and there are opinions that VEGF expression increases in cases showing a clinically aggressive course. It was suggested that VEGF was responsible not only for angiogenesis but also for the migration of mononuclear cells within GCTB. Evaluating the relationship between VEGF and GCTB aggressiveness may aid early detection of metastases[14-16].
As a general principle in tumor immunology, tumor-associated lymphocytes (TAL) may respond inappropriately to conventional stimuli or not respond at all[17-19]. It was reported that the inflammatory tumor microenvironment promoted angiogenesis, tumor proliferation, and metastasis[20].
We aimed to evaluate the pathophysiological mechanisms described above in GCTB cases with and without recurrence/ metastasis, and to identify histomorphological and immunohistochemical parameters that predict the risk of recurrence/metastasis.
During the pathological evaluation of cases, H3.3G34W immunohistochemistry-stained slides from the archive were examined; H3.3G34W staining was also applied to cases for which no slide was available for this marker. After determining the areas where the tumor was best represented, immunohistochemical examination was performed by applying RANK, RANKL, p63, and VEGF markers.
For all IHC protocols (Table I), 2 μm thick sections taken from formalin-fixed paraffin-embedded tissues were placed on electrostatically charged slides and dried at 72°C for 1 hour. All procedures, including deparaffinization and antigen retrieval, were performed using the ROCHE/Ventana BenchMark XT IHC Stainer or Ventana BenchMark Ultra automation systems. A ready-made kit containing biotin-free, HRP (horseradish peroxidase) multimer-based, hydrogen peroxide substrate, and 3,3`-diaminobenzidine tetrahydroxychloride (DAB) chromogen (UltraviewTM, Universal DAB Detection Kit, Catalog Number 760-500, Ventana Medical Systems, Tucson, AZ) was used for the procedure. The process was finalized by manually performing counterstaining with hematoxylin and bluing reagent, dehydrating the sections in the staining device, clearing with xylene, and covering with a coverslip.
Table I: Summary of antibodies and protocols used for immunohistochemistry.
H&E-stained slides were evaluated for surgical margin, bone cortex invasion, soft tissue invasion, total number of mitoses in 10 high-power fields (HPF) at the hot-spot, vascular invasion, percentage of spindle cell areas (Figure 1), and presence of TALs. The assessment of vascular invasion was made with consideration of the possibility of false positives due to the curettage method used to obtain the tissue samples. Only frank endothelial invasion through the vascular lumen and tumor thrombus in the vascular lumen were counted as vascular invasion. TALs were quantified by summing the total number of lymphocytes in 10 HPFs, selected from the most densely infiltrated areas among neoplastic cells. Hemorrhage areas were excluded to prevent possible misinterpretations.
Figure 1: Spindle cell morphologic pattern
Bone cortex and soft tissue invasion were determined based on radiological and/or histopathological findings. Any disruption of cortical bone integrity by a tumor was considered cortical invasion. Tumor cells that make frankly invasive clusters within connective tissue were considered as soft tissue invasion. Radiological evidence of surrounding soft tissue invasion was also regarded as soft tissue invasion.
p63 staining was evaluated by manually counting positive neoplastic mononuclear cells as a percentage of the total neoplastic mononuclear cell population in 10 HPFs at hot-spot areas. Neoplastic cells in the highest H3.3G34Wstained regions that were chosen for immunohistochemical evaluation were carefully identified and distinguished from the reactive non-neoplastic cells based on their distinct morphology.
Regarding high variability in staining density of RANK and RANKL antibodies, the strength of staining was scored between 0 and 3 (0: none, 1: weak, 2: moderate, 3: strong). The percentages of staining for each strength level were calculated. Each staining strength score was multiplied by the percentage of staining at that strength level to express staining intensity. The results were summed to determine the final RANK (Figure 2) and RANKL (Figure 3) scores.
Figure 2: RANK expression at varying intensities
Figure 3: RANKL expression at varying intensities
For VEGF IHC examination, cytoplasmic staining in neoplastic mononuclear cells was evaluated. The percentage of cytoplasmic staining in mononuclear cells was determined by visual estimation across the entire tumor area of the tissue section.
The IBM Statistical Package for the Social Sciences (IBM SPSS version 26.0) program was used for statistical analysis to evaluate the findings obtained in the study. Descriptive analyses were calculated with median values for continuous data when they did not show normal distribution, and with numerical and percentage values for categorical data. The Shapiro-Wilk test was performed to determine whether the continuous data were suitable for a normal distribution. The Mann-Whitney U test was used as the continuous data did not conform to the normal distribution. Fisher`s Exact Test was used in pairs for the comparison of categorical data. Spearman`s Rank Correlation Test was used in pairs for the comparison of continuous data. Cox regression analysis method was used for disease-free survival analysis and multivariate analysis. A value of p<0.05 was accepted for statistical significance.
The initial diagnostic biopsy was a curettage in all cases except 1 case. Subsequently, en bloc resection was performed in 2 cases (18.1%), and excisional biopsy in 2 cases (18.1%). Patients did not get any additional medical therapy.
Cases that received a GCTB diagnosis at the exact tumor location at least 1 month after the initial diagnosis were considered as recurrence. Clinical, radiological, and/or pathological recurrence was detected in 11 (34.3%) of the 32 cases during follow-up. Among the recurrent cases, the mean time until recurrence was 16 months. The shortest period was 1 month, and the longest was 60 months. The disease recurred only once, in one case. Distant metastasis was observed in 3 cases (9.3%). The location of distant metastasis was the lung in all three cases. Two of the metastatic cases also showed recurrence. In cases with distant metastasis, the interval between resection and metastasis ranged from 16 to 135 months, with a median of 25.3 months. When all cases were considered together, the mean disease-free survival time was 51.5 months.
No significant relationship was found between recurrence/ metastasis and gender, age, tumor location, or surgical method. When tumor diameter, mitosis count, spindle cell ratio, TALs, bone cortex, and soft tissue invasion were compared with recurrence and metastasis, a significant relationship was found between the tumor diameter at the time of diagnosis and metastasis, with the risk of metastasis increasing as the tumor diameter increased (Table II). The spindle cell pattern ratio was evaluated as the percentage of spindle cell pattern areas observed in the tumor parenchyma, and the average ratio was 27.8%. The analysis showed that a higher spindle pattern ratio was related to a lower risk of recurrence. Recurrence was more frequent, especially in cases with a spindle pattern percentage below 20% (95% CI: 20.2-44.7). It was found that an increase in the spindle pattern percentage reduced the probability of recurrence by 1.3 times. There was no significant relationship between the ratio of spindle cell pattern and metastasis. No statistically significant relationship was detected in other parameters (Table II).
Associations of bone cortex, soft tissue, and vascular invasion with recurrence and metastasis are summarized in Table III. Statistical analysis revealed no significant relationship among these variables.
Evaluation of surgical margins was limited because tissues were obtained by curettage, and materials were highly fragmented in most cases. No recurrence was observed among cases that underwent en bloc resection. The number of cases was insufficient to evaluate the significance of surgical margins statistically.
When immunohistochemical markers were evaluated, no statistically significant associations were found between p63, VEGF, RANK, and RANKL and recurrence or metastasis (Table IV). Comparison of demographic and clinical data against immunohistochemical and histomorphological findings revealed no statistically significant relationships.
Table IV: Comparison of immunohistochemical markers by recurrence and metastasis
A significant relationship was observed between spindle pattern percentage and the RANK marker (p=0.03). It was determined that as the spindle pattern percentage increased, the staining rate with the RANK marker decreased. No statistically significant relationship was detected between the other immunohistochemical markers and the assessed histomorphological features.
Comparing immunohistochemical markers with each other showed that the RANK marker staining rate significantly correlated with the staining rates of both RANKL (p=0.02) and p63 (p=0.03). Higher staining amounts for the RANK marker were associated with increased staining for p63 and RANKL. No significant relationship was found in the comparison of other markers.
In the multivariate analysis, no parameter was identified as an independent risk factor for recurrence.
Curettage was the most common treatment in our cases, accounting for 96.8% (31 cases). This approach parallels current literature data[2,22,26]. Due to the high frequency of curettage in our cohort, the association between surgical excision method and recurrence or metastasis could not be evaluated. Consequently, surgical margin assessment could not be performed. Studies with a higher number of en bloc resections would be useful for this purpose.
Although tumor size is often cited as a risk factor for recurrence[2,8,27], our analysis revealed no statistically significant association between tumor size and recurrence. This finding mirrors that of Zhou et al.[28], who also reported no significant association between tumor size and recurrence. However, we observed a significant association between tumor diameter at diagnosis and distant metastasis; larger tumors were more likely to metastasize. This finding suggests that the large diameter may reflect aggressive tumor behavior, including metastatic potential.
We observed high rates of bone cortex invasion and soft tissue invasion. However, neither factor showed a statistically significant association with recurrence/metastasis in our study. This finding contrasts with a conflicted literature: Zhou et al.[28] linked cortical thinning, but not soft tissue invasion, to recurrence, whereas Balke et al.[21] and Chan et al.[23] found that both parameters were significantly associated with recurrence and metastasis. Additional studies offer mixed results[15,22,29]. The high frequency of both cortical bone and soft-tissue invasion in our patients without recurrence/metastasis likely explains the lack of a statistically significant association between these variables in our study.
We observed an average spindle pattern ratio of 27.8%, and found that recurrence frequency increased 1.3 times in cases with a spindle pattern below 20% (p=0.04). Although there are studies suggesting spindle cells are associated with reactive bone formation and collagen synthesis[30,31], there is no study examining the percentage of spindle pattern as a recurrence risk factor. Our findings imply that a higher spindle cell component may facilitate total resection by limiting tumor invasion with desmoplasia-related stromal changes. Although the relationship between spindle cell percentage and recurrence was not found to be an independent variable in the multivariate analysis (p=0.08), it was close to the significance threshold.
In the study by Quattrini et al., the frequency of metastasis was higher in cases with high RANK and RANKL expression. In the same study, no relationship was defined between recurrence and RANK and RANKL expression[10]. RANK and RANKL expression did not correlate with recurrence and/or metastasis in our study. Our findings suggest that, despite the well-known effects of RANK and RANKL on bone matrix destruction, their expression levels in tumor cells do not predict recurrence or metastasis. Our statistical analyses revealed significant relationships between RANK expression and RANKL expression levels, as well as between RANK expression and p63 expression levels. High RANKL score in cases with high RANK score is expected and can be attributed to the receptor-ligand relationship of these molecules. We found higher RANK levels with increasing p63 levels. This correlation suggests that p63 stimulates neoplastic cells to show osteoblastic activity, as previously reported[12]. We have also observed a significant negative correlation between the RANK score and the ratio of the spindle cell pattern. It was observed that RANK scores decreased as the spindle pattern percentage increased. This finding suggests that the spindle cell pattern is associated with desmoplastic host response; therefore, as the proportion of the spindle cell pattern increases, RANK expression, osteoclastic activity, and recurrence rate decrease.
A positive correlation was found between tumor diameter at the time of diagnosis and the frequency of metastasis, suggesting that tumors with larger sizes are biologically more aggressive and metastasize more frequently. Additionally, a negative correlation was observed between the spindle pattern ratio and both RANK score and tumor recurrence rate, suggesting that the spindle pattern may be associated with a tumor-limiting desmoplastic response mechanism and can be evaluated as a prognostic parameter.
GCTB is a rare tumor and exhibits known local aggressive behavior. However, its recurrence and metastatic potential cannot yet be clearly estimated. The limited sample size of our study may reduce the statistical power of the results. Furthermore, previous studies evaluating prognostic parameters for estimating the biological behavior of this condition have yielded inconsistent or conflicting findings. These combined limitations underscore the necessity for larger, multicenter series to comprehensively evaluate these parameters and establish definitive predictive factors for the biological behavior of GCTB.
Conflict of Interest
The authors report there are no competing interests to declare.
Funding
This study was financially supported by the Marmara University
Scientific Research Project Unit (Project ID: 10914, 12.03.2023)
Ethical Approval
This study, approved by the Clinical Research Ethics Committee of
Marmara University (issue date 16/10/2022, number 09.2022.1133),
was conducted in accordance with the 1964 Helsinki declaration and
its later amend-ments or comparable ethical standards.
Authorship Contributions
Concept: CBT, HKT, Design: CBT, HKT, Data collection and/or
processing: CBT, HKT, OS, BE, Analysis and/or interpretation:
CBT, HKT, Literature search: CBT, Writing: CBT, HKT, Approval:
CBT, HKT, OS, BE.
1) Jaffe HL, Lichtenstein L, Partis RB. Giant cell tumor of bone. Its
pathologic appearance, grading, supposed variants and treatment.
Arch Path. 1940;30:993-1031.
2) Siegal GPHMB JL, Cates JMM: WHO Classification of Tumors
5th Edition Soft Tissue and Bone Tumors. 5th edition, 2020.
3) Presneau N, Baumhoer D, Behjati S, Pillay N, Tarpey P, Campbell
PJ, Jundt G, Hamoudi R, Wedge DC, Loo PV, Hassan AB, Khatri
B, Ye H, Tirabosco R, Amary MF, Flanagan AM. Diagnostic value
of H3F3A mutations in giant cell tumour of bone compared to
osteoclast-rich mimics. J Pathol Clin Res. 2015;1:113-23.
4) Behjati S, Tarpey PS, Presneau N, Scheipl S, Pillay N, Van Loo P,
Wedge DC, Cooke SL, Gundem G, Davies H, Nik-Zainal S, Martin
S, McLaren S, Goodie V, Robinson B, Butler A, Teague JW,
Halai D, Khatri B, Myklebost O, Baumhoer D, Jundt G, Hamoudi
R, Tirabosco R, Amary MF, Futreal PA, Stratton MR, Campbell
PJ, Flanagan AM. Distinct H3F3A and H3F3B driver mutations
define chondroblastoma and giant cell tumor of bone. Nat Genet.
2013;45:1479-82.
5) Amary F, Berisha F, Ye H, Gupta M, Gutteridge A, Baumhoer
D, Gibbons R, Tirabosco R, O'Donnell P, Flanagan AM. H3F3A
(Histone 3.3) G34W Immunohistochemistry: A Reliable Marker
Defining Benign and Malignant Giant Cell Tumor of Bone. Am J
Surg Pathol. 2017;41:1059-68.
6) Yoshida A: Bone and Soft Tissue Pathology. 2nd edition, Elsevier,
Inc., 2022
7) Turcotte RE. Giant cell tumor of bone. Orthop Clin North Am.
2006;37:35-51.
8) Lin JL, Wu YH, Shi YF, Lin H, Nisar M, Meftah Z, Xu C, Chen JX,
Wang XY. Survival and prognosis in malignant giant cell tumor
of bone: A population-based analysis from 1984 to 2013. J Bone
Oncol. 2019;19:100260.
9) Yanagisawa M, Kakizaki H, Okada K, Torigoe T, Kusumi T. p63 as
a prognostic marker for giant cell tumor of bone. Ups J Med Sci.
2013;118:23-8.
10) Quattrini I, Pollino S, Pazzaglia L, Conti A, Novello C, Ferrari C,
Pignotti E, Picci P, Benassi MS. Prognostic role of nuclear factor/
IB and bone remodeling proteins in metastatic giant cell tumor of
bone: A retrospective study. J Orthop Res. 2015;33:1205-11.
11) Atsawaphidsawat N, Ungarreevittaya P, Sumananont C: Prognostic
Role of RANK and RANKL Expression in Recurrent Giant
Cell Tumor of Bone: A Retrospective Study. Journal of Molecular
Biomarkers & Diagnosis 2018;9:1-5.
12) Curtis KM, Aenlle KK, Frisch RN, Howard GA. TAp63γ and
ΔNp63β promote osteoblastic differentiation of human mesenchymal
stem cells: regulation by vitamin D3 Metabolites. PLoS
One. 2015;10:e0123642.
13) Wu PF, Tang JY, Li KH. RANK pathway in giant cell tumor of
bone: pathogenesis and therapeutic aspects. Tumour Biol.
2015;36:495-501.
14) Kumta SM, Huang L, Cheng YY, Chow LT, Lee KM, Zheng MH.
Expression of VEGF and MMP-9 in giant cell tumor of bone and
other osteolytic lesions. Life Sci. 2003;73:1427-36.
15) Lin X, Liu J, Xu M. The prognosis of giant cell tumor of bone
and the vital risk factors that affect its postoperative recurrence: a
meta-analysis. Transl Cancer Res. 2021;10:1712-22.
16) Zhang J, Dong J, Yang Z, Ma X, Zhang J, Li M, Chen Y, Ding
Y, Li K, Zhang Z. Expression of ezrin, CD44, and VEGF in giant
cell tumor of bone and its significance. World J Surg Oncol.
2015;13:168.
17) Elinav E, Nowarski R, Thaiss CA, Hu B, Jin C, Flavell RA. Inflammation-
induced cancer: crosstalk between tumours, immune
cells and microorganisms. Nat Rev Cancer. 2013;13:759-71.
18) O'Callaghan DS, O'Donnell D, O'Connell F, O'Byrne KJ. The role
of inflammation in the pathogenesis of non-small cell lung cancer.
J Thorac Oncol. 2010;5:2024-36.
19) Whiteside TL. 22. Immune responses to malignancies. J Allergy
Clin Immunol. 2003;111:S677-86.
20) Schäfer M, Werner S. Cancer as an overhealing wound: an old
hypothesis revisited. Nat Rev Mol Cell Biol. 2008;9:628-38.
21) Balke M, Schremper L, Gebert C, Ahrens H, Streitbuerger A,
Koehler G, Hardes J, Gosheger G. Giant cell tumor of bone:
treatment and outcome of 214 cases. J Cancer Res Clin Oncol.
2008;134:969-78.
22) Klenke FM, Wenger DE, Inwards CY, Rose PS, Sim FH. Giant cell
tumor of bone: risk factors for recurrence. Clin Orthop Relat Res.
2011;469:591-9.
23) Chan CM, Adler Z, Reith JD, Gibbs CP Jr. Risk factors for pulmonary
metastases from giant cell tumor of bone. J Bone Joint Surg
Am. 2015;97:420-8.
24) Liede A, Hernandez RK, Tang ET, Li C, Bennett B, Wong SS, Jandial
D. Epidemiology of benign giant cell tumor of bone in the
Chinese population. J Bone Oncol. 2018;12:96-100.
25) He Y, Zhang J, Ding X. Prognosis of local recurrence in giant cell
tumour of bone: what can we do? Radiol Med. 2017;122:505-19.
26) McGough RL, Rutledge J, Lewis VO, Lin PP, Yasko AW. Impact
severity of local recurrence in giant cell tumor of bone. Clin Orthop
Relat Res. 2005;438:116-22.
27) Murphey MD, Nomikos GC, Flemming DJ, Gannon FH, Temple
HT, Kransdorf MJ. From the archives of AFIP. Imaging of giant
cell tumor and giant cell reparative granuloma of bone: radiologic-
pathologic correlation. Radiographics. 2001;21:1283-309.
28) Zhou L, Lin S, Jin H, Zhang Z, Zhang C, Yuan T. Preoperative
CT for prediction of local recurrence after curettage of giant cell
tumor of bone. J Bone Oncol. 2021;29:100366.
29) Errani C, Ruggieri P, Asenzio MA, Toscano A, Colangeli S, Rimondi
E, Rossi G, Longhi A, Mercuri M. Giant cell tumor of the
extremity: A review of 349 cases from a single institution. Cancer
Treat Rev. 2010;36:1-7.