Treatment Response to Preoperative Anthracycline-Based Chemotherapy in Locally Advanced Breast Cancer: The Relevance of Proliferation and Apoptosis Rates

Predictors of Neoadjuvant Chemotherapy Response in Breast Cancer: A Review

Neoadjuvant chemotherapy (NAC) largely increases operative chances and improves prognosis of the local advanced breast cancer patients. However, no specific means have been invented to predict the therapy responses of patients receiving NAC. Therefore, we focus on the alterations of tumor tissue-related microenvironments such as stromal tumor-infiltrating lymphocytes status, cyclin-dependent kinase expression, non-coding RNA transcription or other small molecular changes, in order to detect potentially predicted biomarkers which reflect the therapeutic efficacy of NAC in different subtypes of breast cancer. Further, possible mechanisms are also discussed to discover feasible treatment targets. Thus, these findings will be helpful to promote the prognosis of breast cancer patients who received NAC and summarized in this review.

A Novel Resveratrol-Arsenic Trioxide Combination Treatment Synergistically Induces Apoptosis of Adriamycin-Selected Drug-Resistant Leukemia K562 Cells

Leukemia cells can develop resistance to apoptosis induced by chemotherapeutic agents. Concomitant multidrug resistance of cells remains the greatest clinical obstacle in the effective treatment of blood and solid tumors. Natural products have been identified that possess the capacity to modulate chemotherapeutic resistance and induce apopotosis. In this study, we generated adriamycin-resistant K562 leukemia (K562/RA) cells and compared the responses of sensitive and resistant leukemia cells to the natural products arsenic trioxide (ATO) and resveratrol (Rsv), with a view to determining whether Rsv potentiates the sensitivity of drug-resistant cells to ATO-induced apoptosis and the associated molecular mechanisms. Our results showed that resistance of K562/RA cells induced by adriamycin treatment was significantly higher (115.81-fold) than that of parental K562 cells. Simultaneously, K562/RA cells were cross-resistant to multiple agents, with the exception of ATO. Rsv enhanced the sensitivity of K562/RA cells to ATO and reduced the required dose of ATO as well as associated adverse reactions by promoting the proliferation inhibitory and apoptosis-inducing effects of ATO, which may be associated with reduced expression of the drug resistance genes mdr1/P-gp, mrp1/MRP1 and bcrp/BCRP, as well as the apoptotic inhibitory genes bcl-2, NF-κB and P53, and conversely, activation of caspase-3. Our collective findings indicate that ATO and Rsv synergistically enhance the sensitivity of drug-resistant leukemia cells to apoptosis.

Fractal and Gray Level Cooccurrence Matrix Computational Analysis of Primary Osteosarcoma Magnetic Resonance Images Predicts the Chemotherapy Response

The prediction of induction chemotherapy response at the time of diagnosis may improve outcomes in osteosarcoma by allowing for personalized tailoring of therapy. The aim of this study was thus to investigate the predictive potential of the so far unexploited computational analysis of osteosarcoma magnetic resonance (MR) images. Fractal and gray level cooccurrence matrix (GLCM) algorithms were employed in retrospective analysis of MR images of primary osteosarcoma localized in distal femur prior to the OsteoSa induction chemotherapy. The predicted and actual chemotherapy response outcomes were then compared by means of receiver operating characteristic (ROC) analysis and accuracy calculation. Dbin, Λ, and SCN were the standard fractal and GLCM features which significantly associated with the chemotherapy outcome, but only in one of the analyzed planes. Our newly developed normalized fractal dimension, called the space-filling ratio (SFR) exerted an independent and much better predictive value with the prediction significance accomplished in two of the three imaging planes, with accuracy of 82% and area under the ROC curve of 0.20 (95% confidence interval 0-0.41). In conclusion, SFR as the newly designed fractal coefficient provided superior predictive performance in comparison to standard image analysis features, presumably by compensating for the tumor size variation in MR images.

Glioblastoma multiforme versus pleomorphic xanthoastrocytoma with anaplastic features in the pathological diagnosis: a case report

Background

Pleomorphic xanthoastrocytoma (PXA) with anaplastic features should be strictly distinguished from glioblastoma multiforme (GBM).

Case presentation

A case of PXA that was initially diagnosed as GBM is presented. A 42-year-old man visited our clinic because of right hemiparesis and total aphasia. Head magnetic resonance imaging demonstrated enhanced multiple cystic lesions in the left temporal lobe suggesting an intra-parenchymal brain tumor. The lesion was partially removed and GBM with a Ki-67 index of 20 % was diagnosed by pathological examination of the resected specimen. Despite receiving radiation and chemotherapy, the patient died 6 months after the first admission. At autopsy, the boundary between the tumor and normal brain tissue was clear. Large parts of the tumor demonstrated typical features of PXA, including pleomorphism, clear xanthomatous cells with foamy cytoplasm, positive silver staining, and a Ki-67 index of less than 1 %.

Discussion and conclusions

GBM should be diagnosed only when the majority of the tumor cells are undifferentiated. Although the operative specimen appeared typical GBM histologically, the diagnosis of GBM was subsequently excluded by the autopsy finding that much of the tumor had the characteristic features of a benign PXA. Therefore, the final diagnosis in this case was PXA with anaplastic features. PXA with anaplastic features should be carefully distinguished from GBM to facilitate appropriate decisions concerning treatment.

Multifractal analysis of tumour microscopic images in the prediction of breast cancer chemotherapy response

Due to the individual heterogeneity, highly accurate predictors of chemotherapy response in invasive breast cancer are needed for effective chemotherapeutic management. However, predictive molecular determinants for conventional chemotherapy are only emerging and still incorporate a high degree of predictive variability. Based on such pressing need for predictive performance improvement, we explored the value of pre-therapy tumour histology image analysis to predict chemotherapy response. Fractal analysis was applied to hematoxylin/eosin stained archival tissue of diagnostic biopsies derived from 106 patients diagnosed with invasive breast cancer. The tissue was obtained prior to neoadjuvant anthracycline-based chemotherapy and patients were subsequently divided into three groups according to their actual chemotherapy response: partial pathological response (pPR), pathological complete response (pCR) and progressive/stable disease (PD/SD). It was shown that multifractal analysis of breast tumour tissue prior to chemotherapy indeed has the capacity to distinguish between histological images of the different chemotherapy responder groups with accuracies of 91.4% for pPR, 82.9% for pCR and 82.1% for PD/SD. F(α)max was identified as the most important predictive parameter. It represents the maximum of multifractal spectrum f(α), where α is the Hölder's exponent. This is the first study investigating the predictive value of multifractal analysis as a simple and cost-effective tool to predict the chemotherapy response. Improvements in chemotherapy prediction provide clinical benefit by enabling more optimal chemotherapy decisions, thus directly affecting the quality of life and survival.

The search for optimal cutoff points for apoptosis and proliferation rate in prognostification of early stage breast cancer patients treated with anthracyclines in adjuvant settings

Cancer growth is determined by the proportion of proliferating to dying cells; thus, the aim of the study was to analyze how proliferation rate and apoptosis level affect disease-free survival (DFS) of breast cancer (BC) patients treated with anthracycline-based chemotherapy. For 172 BC, proliferation rate was investigated by Ki-67 labeling index (Ki-67 LI)-assessed immunohistochemically. Apoptosis level was analyzed by apoptotic index (AI) estimated by terminal deoxynucleotidyl transferase dUTP nick end labeling assay. To stratify patients into subgroups with higher and lower DFS and to achieve optimal categorization, optimal cutoff points were searching by minimal P value method. The best separation of DFS curves (P = 0.001) was observed for three categories of AI: (i) AI >1.8 % (DFS = 100 %), (ii) AI ≤0.3 % (DFS = 84.6 %), and (iii) 1.8 % <= AI >0.3 % (DFS = 64.0 %). The highest DFS (86.1 %) was shown for the subgroup with low-proliferating BC (Ki-67 LI ≤18.0 %), intermediate (73.9 %) for patients characterized by Ki-67 LI in the range 18.0-37.0 % and the lowest (60.0 %) for women with fast-proliferating tumors (Ki-67 LI >37.0 %) (P = 0.022). Summarized, minimal P value method allows for optimal separation of survival curves. Apoptosis level and proliferation rate have some prognostic potential for early stage breast cancer patients treated with anthracyclines in adjuvant setting, however, as suggested by multivariate analysis, not as independent prognostic factors.