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The number of osteoclasts in a biopsy specimen can predict the efficacy of neoadjuvant chemotherapy for primary osteosarcoma.

ABSTRACT: Osteosarcoma is the most common primary malignant bone tumor, and its standard treatment is a combination of surgery and chemotherapy. A poor response to chemotherapy causes unfavorable oncological outcomes. We investigated the correlation between osteoclast differentiation in biopsy specimens and the efficacy of neoadjuvant chemotherapy in resected specimens. Forty-nine patients who underwent neoadjuvant chemotherapy and subsequent surgical treatment at our institution between 1999 and 2018 were enrolled. Using medical records, we investigated the age, sex, tumor size, location, subtype, staging, chemotherapy agents (doxorubicin, cisplatin, ifosfamide, and methotrexate), number of neoadjuvant chemotherapy courses, number of osteoclasts in biopsy specimens, and efficacy of neoadjuvant chemotherapy according to the Rosen and Huvos classification (Grade I-IV) in resected specimens. Univariate and multivariate analyses were performed to identify factors predictive of a good response in resected specimens after neoadjuvant chemotherapy. A good response (Grade III/IV) was detected in 25, while a poor response (Grade I/II) was detected in 24. According to the multivariate analysis, ≥ 46 years old (odds ratio [OR], 0.05; 95% confidence interval [CI], 0.01-0.45; p < 0.01) and ≥ 5 mature osteoclasts in a biopsy specimen (OR, 36.9; 95% CI, 6.03-225; p < 0.01) were significantly associated with the neoadjuvant chemotherapy efficacy. The accuracy for predicting a good response to chemotherapy based on ≥ 5 osteoclasts in a biopsy specimen in patients < 46 years old was 85%. The number of mature osteoclasts in biopsy specimens is a simple factor for predicting the efficacy of chemotherapy before treatment, although further studies will be required to determine the underlying mechanism.

SUBMITTER: Araki Y

PROVIDER: S-EPMC7820005 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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The number of osteoclasts in a biopsy specimen can predict the efficacy of neoadjuvant chemotherapy for primary osteosarcoma.

Araki Yoshihiro Y Yamamoto Norio N Hayashi Katsuhiro K Takeuchi Akihiko A Miwa Shinji S Igarashi Kentaro K Higuchi Takashi T Abe Kensaku K Taniguchi Yuta Y Yonezawa Hirotaka H Morinaga Sei S Asano Yohei Y Ikeda Hiroko H Nojima Takayuki T Tsuchiya Hiroyuki H

Scientific reports 20210121 1

Osteosarcoma is the most common primary malignant bone tumor, and its standard treatment is a combination of surgery and chemotherapy. A poor response to chemotherapy causes unfavorable oncological outcomes. We investigated the correlation between osteoclast differentiation in biopsy specimens and the efficacy of neoadjuvant chemotherapy in resected specimens. Forty-nine patients who underwent neoadjuvant chemotherapy and subsequent surgical treatment at our institution between 1999 and 2018 wer ...[more]

PMID: 33479294

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Project description:BackgroundThis study sought to predict the early responses to neoadjuvant chemotherapy (NACT) of patients with primary conventional osteosarcoma (COS) using the apparent diffusion coefficient (ADC) and to evaluate the factors affecting the tumor necrosis rate (TNR).MethodsThe data of 41 patients who underwent magnetic resonance imaging (MRI) and diffusion-weighted imaging sequence scans before NACT, 5 days after the end of the first phase of NACT, after the end of the whole course of chemotherapy, were prospectively collected. ADC1 refers to the ADC before chemotherapy, ADC2 refers to the ADC after the first phase of chemotherapy, and ADC3 refers to the ADC before surgery. The change in values before and after the first phase of chemotherapy was calculated as follows: ADC2-1 = ADC2 - ADC1. The change in values before and after the last phase of chemotherapy was calculated as follows: ADC3-1 = ADC3 - ADC1. The change in values after the first phase and the last phase of chemotherapy was calculated as follows: ADC3-2 = ADC3 - ADC2. We recorded the patient characteristics, including age, gender, pulmonary metastasis, alkaline phosphatase (ALP), and lactate dehydrogenase (LDH). The patients were divided into the following 2 groups based on their histological TNR after postoperative: (I) the good-response group (≥90% necrosis, n=13) and (II) the poor-response group (<90% necrosis, n=28). Changes in the ADCs were compared between the good-response and poor-response groups. The different ADCs between the 2 groups were compared, and a receiver operating characteristic analysis was performed. A correlation analysis was performed to assess the correlations of the clinical features, laboratory features, and different ADCs with patients' histopathological responses to NACT.ResultsThe ADC2 (P<0.001), ADC3 (P=0.004), ADC3-1 (P=0.008), ADC3-2 (P=0.047), and ALP before NACT (P=0.019) were significantly higher in the good-response group than in the poor-response group. The ADC2 [area under the curve (AUC) =0.723; P=0.023], ADC3 (AUC =0.747; P=0.012), and ADC3-1 (AUC =0.761; P=0.008) showed good diagnostic performance. Based on the univariate binary logistic regression analysis, the ADC2 (P=0.022), ADC3 (P=0.009), ADC2-1 (P=0.041), and ADC3-1 (P=0.014) were correlated with the TNR. However, based on the multivariate analysis, these parameters were not significantly correlated with the TNR.ConclusionsIn patients with COS who are undergoing neoadjuvant chemotherapy, the ADC2 is a promisingindicator for predicting tumor response to chemotherapy in early.

Project description:ImportanceImage-guided breast biopsy of a residual imaging abnormality or tumor bed after neoadjuvant chemotherapy (NACT) is increasingly used to assess residual cancer, facilitate risk-adaptive surgery, and potentially identify exceptional responders in whom local therapy may be de-escalated.ObjectiveTo further assess the accuracy of post-NACT image-guided biopsy to predict residual cancer in the breast.Design, setting, and participantsThis diagnostic study analyzed multicenter patient-level data of patients with breast cancer who were treated with NACT and underwent image-guided biopsy before surgery at Royal Marsden Hospital in London, UK; Seoul National University Hospital in Seoul, South Korea; and MD Anderson Cancer Center in Houston, Texas. Data were analyzed from June to July 2019.Main outcomes and measuresDiagnostic accuracy of post-NACT image-guided biopsy. Final surgical pathology was used as reference standard.ResultsData from 166 women were analyzed. The median (range) age was 49 (25-76) years. The median (range) tumor size on pretreatment and posttreatment imaging was 33.5 (12-100) mm and 10 (0-100) mm, respectively. The overall pathologic complete response rate was 51.2% (n = 85) (16.1% [5 of 31] for hormone receptor-positive/ERBB2 (formerly HER2)-negative; 44.7% [21 of 47] for hormone receptor-positive/ERBB2-positive; 69% [20 of 29] for hormone receptor-negative/ERBB2-positive; and 66.1% [39 of 59] for triple negative). The majority (143 [86.1%]) underwent image-guided vacuum-assisted biopsy (VAB), and 23 had core-cut biopsy. The median (range) needle gauge was 10 (7-14), and the median (range) number of samples was 6 (2-18). When image-guided biopsy (VAB and core-cut biopsy) was representative (159 [95.8%]), the false-negative rate across the whole cohort was 18.7% (95% CI, 10.6%-29.3%). Subgroup analysis of patients with a complete/partial clinical response and residual imaging abnormality of 2 cm or smaller with at least 6 VABs taken (76 [45.8%]) demonstrated a false-negative rate of 3.2% (95% CI, 0.1%-16.7%), a negative predictive value of 97.4% (95% CI, 86.5%-99.9%), and an overall accuracy of 89.5% (95% CI, 80.3%-95.3%).Conclusions and relevanceThis large multicenter pooled data analysis suggests that a standardized protocol using image-guided VAB of a tumor bed measuring 2 cm or smaller with 6 or more representative samples allows reliable prediction of residual disease. These results could inform the design of de-escalation trials in NACT exceptional responders testing the safety of eliminating surgery.

Project description:BackgroundDoxorubicin and cisplatin are both first-line chemotherapeutics for osteosarcoma (OS) treatment. However, the efficacy of doxorubicin/cisplatin chemotherapy varies considerably. Thus, identifying an efficient diagnostic biomarker to distinguish patients with good and poor responses to doxorubicin/cisplatin chemotherapy is of paramount importance.MethodsTo predict the efficacy of doxorubicin/cisplatin chemotherapy, we analyzed the differentially expressed proteins in 37 resected OS samples, which were categorized into the primary group (PG), the recurrent group (RG) and the metastatic group (MG). The characteristics of the enriched differentially expressed proteins were assessed via GO and KEGG analyses. Protein‒protein interactions were identified to determine the relationships among the differentially expressed proteins. Receiver operating characteristic (ROC) curve analyses were performed to explore the clinical significance of the differentially expressed proteins. Parallel reaction monitoring (PRM) was used to validate the candidate proteins. Immunohistochemical (IHC) staining was performed to confirm the expression of cathepsin (CTSG) in patients with good and poor response to doxorubicin/cisplatin.ResultsA total of 9458 proteins were identified and quantified, among which 143 and 208 exhibited significant changes (|log2FC|>1, p < 0.05) in the RG and MG compared with the PG, respectively. GO and KEGG enrichment led to the identification of neutrophil extracellular traps (NETs). ROC curve analyses revealed 74 and 86 proteins with areas under the curve greater than 0.7 in the RG and MG, respectively. PRM validation revealed the statistical significance of CTSG, which is involved in NET formation, at the protein level in both the RG and MG. IHC staining of another cohort revealed that CTSG was prominently upregulated in the poor response group after treatment with doxorubicin/cisplatin.ConclusionCTSG and its associated NETs are potential biomarkers with which the efficacy of doxorubicin/cisplatin chemotherapy could be predicted in OS patients.

Project description:BACKGROUND:To propose a personalized therapeutic approach in osteosarcoma treatment, we assessed whether sequential [18F]FDG PET/CT (PET/CT) could predict the outcome of patients with osteosarcoma of the extremities after one cycle and two cycles of neoadjuvant chemotherapy. METHODS:A total of 73 patients with AJCC stage II extremity osteosarcoma treated with 2 cycles of neoadjuvant chemotherapy, surgery, and adjuvant chemotherapy were retrospectively analyzed in this study. All patients underwent PET/CT before (PET0), after 1 cycle (PET1), and after the completion of neoadjuvant chemotherapy (PET2), respectively. Maximum standardized uptake value (SUVmax) (corrected for body weight) and the % changes of SUVmax were calculated, and histological responses were evaluated after surgery. Receiver-operating characteristic (ROC) curve analyses and the Cox proportional hazards models were used to analyze whether imaging and clinicopathologic parameters could predict event-free survival (EFS). RESULTS:A total of 36 patients (49.3%) exhibited a poor histologic response and 17 patients (23.3%) showed events (metastasis in 15 and local recurrence in 2). SUVmax on PET2 (SUV2), the percentage change of SUVmax between PET0 and PET1 (Δ%SUV01), and between PET0 and PET2 (Δ%SUV02) most accurately predicted events using the ROC curve analysis. SUV2 (relative risk, 8.86; 95% CI, 2.25-34.93), Δ%SUV01 (relative risk, 5.97; 95% CI, 1.47-24.25), and Δ%SUV02 (relative risk, 6.00; 95% CI, 1.16-30.91) were independent predicting factors for EFS with multivariate analysis. Patients with SUV2 over 5.9 or Δ%SUV01 over - 39.8% or Δ%SUV02 over - 54.1% showed worse EFS rates than others (p < 0.05). CONCLUSIONS:PET evaluation after 1 cycle of presurgical chemotherapy can predict the clinical outcome of extremity osteosarcoma. [18F]FDG PET, which shows a potential role in the early evaluation of the modification of timing of local control, can be a useful modality for early response monitoring of neoadjuvant chemotherapy.

Project description:ObjectivesThis study aims to evaluate the value of machine learning-based dynamic contrast-enhanced MRI (DCE-MRI) radiomics nomogram in prediction treatment response of neoadjuvant chemotherapy (NAC) in patients with osteosarcoma.MethodsA total of 102 patients with osteosarcoma and who underwent NAC were enrolled in this study. All patients received a DCE-MRI scan before NAC. The Response Evaluation Criteria in Solid Tumors was used as the standard to evaluate the NAC response with complete remission and partial remission in the effective group, stable disease, and progressive disease in the ineffective group. The following semi-quantitative parameters of DCE-MRI were calculated: early dynamic enhancement wash-in slope (Slope), time to peak (TTP), and enhancement rate (R). The acquired data is randomly divided into 70% for training and 30% for testing. Variance threshold, univariate feature selection, and least absolute shrinkage and selection operator were used to select the optimal features. Three classifiers (K-nearest neighbor, KNN; support vector machine, SVM; and logistic regression, LR) were implemented for model establishment. The performance of different classifiers and conventional semi-quantitative parameters was evaluated by confusion matrix and receiver operating characteristic curves. Furthermore, clinically relevant risk factors including age, tumor size and site, pathological fracture, and surgical staging were collected to evaluate their predictive values for the efficacy of NAC. The selected clinical features and imaging features were combined to establish the model and the nomogram, and then the predictive efficacy was evaluated.ResultsThe clinical relevance risk factor analysis demonstrates that only surgical stage was an independent predictor of NAC. A total of seven radiomic features were selected, and three machine learning models (KNN, SVM, and LR) were established based on such features. The prediction accuracy (ACC) of these three models was 0.89, 0.84, and 0.84, respectively. The area under the subject curve (AUC) of these three models was 0.86, 0.92, and 0.93, respectively. As for Slope, TTP, and R parameters, the prediction ACC was 0.91, 0.89, and 0.81, respectively, while the AUC was 0.87, 0.85, and 0.83, respectively. In both the training and testing sets, the ACC and AUC of the combined model were higher than those of the radiomics models (ACC = 0.91 and AUC = 0.95), which indicate an outstanding performance of our proposed model.ConclusionsThe radiomics nomogram demonstrates satisfactory predictive results for the treatment response of patients with osteosarcoma before NAC. This finding may provide a new decision basis to improve the treatment plan.

Dataset Information

The number of osteoclasts in a biopsy specimen can predict the efficacy of neoadjuvant chemotherapy for primary osteosarcoma.

Publications

The number of osteoclasts in a biopsy specimen can predict the efficacy of neoadjuvant chemotherapy for primary osteosarcoma.

Similar Datasets

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