Kwas zoledronowy stosowany przez dwa lata u Chinek z osteoporozą pomenopauzalną zwiększa gęstość mineralną tkanki kostnej i poprawia jakość życia związaną ze stanem zdrowia

Introduction: Osteoporosis is characterised by decreased bone mass and weakened bones, with an increased risk of fractures. Osteoporotic fracture, the most serious complication of osteoporosis, is related not only to lower bone mineral density (BMD), but also falls. Osteoporosis and fractures are associated with a decreased health-related quality of life (HRQL). Zoledronic acid (ZOL) is an intravenous once-yearly bisphosphonate that has been shown to be effective and safe in improving BMD and reducing fracture risk in controlled clinical trials.Material and methods: In this self-controlled, prospective trial, 220 postmenopausal women with osteoporosis (mean age 67 years) received a single infusion of ZOL 5 mg at baseline and month 12. BMD, HRQL and Fall Index (FI) were measured at baseline, and months 12 and 24 (before each use of ZOL). The main outcome measures were the changes in lumbar spine and hip BMD and the changes in HRQL, the Short Form-36 questionnaire (SF-36). Additional comparisons were based on the FI. LSD multiple comparisons were used in the comparisons of BMD, SF-36 domain scores and FI.Results: The patients had significantly higher L1-4, total hip, femoral neck and trochanter BMD (P < 0.05) with improved HRQL (P < 0.05) over two years of treatment of once-yearly ZOL 5mg. FI was reduced (P < 0.05) with oral daily elemental calcium and vitamin D in the treatment course.Conclusions: ZOL improves BMD and HRQL, especially in the physical aspects, over two years of treatment in women with postmenopausal osteoporosis, and can help improve balance ability. (Endokrynol Pol 2014; 65 (2): 96–104)Wstęp: Osteoporoza to schorzenie cechujące się obniżeniem masy kostnej i wytrzymałości mechanicznej kości z towarzyszącym zwiększeniem ryzyka złamań. Złamania osteoporotyczne, będące najpoważniejszym powikłaniem osteoporozy, wiążą się nie tylko z obniżoną gęstością mineralną tkanki kostnej (BMD, bone mineral density) ale też z upadkami. Z osteoporozą i złamaniami wiąże się obniżenie jakości życia związanej ze stanem zdrowia (HRQoL, health-related quality of life). Kwas zoledronowy (ZOL) to bisfosfonian w postaci dożylnej przeznaczony do podawania raz w roku, w przypadku którego w badaniach klinicznych z grupą kontrolną wykazano skuteczność i bezpieczeństwo w zwiększaniu BMD i zmniejszaniu ryzyka złamań.Materiał i metody: Autorzy przeprowadzili samodzielnie kontrolowane, prospektywne badanie z udziałem 220 znajdujących się w wieku pomenopauzalnym kobiet z osteoporozą (średnia wieku 67 lat), które otrzymały jednorazowo roztwór ZOL w dawce 5 mg na początku badania i 12 miesięcy później. Na początku badania, w 12. miesiącu i w 24. miesiącu badania (za każdym razem przed podaniem ZOL) oznaczano BMD, HRQoL i wskaźnik upadków (FI, fall index). Główne punkty końcowe obejmowały zmiany BMD w odcinku lędźwiowym kręgosłupa i BMD w okolicy biodra, a także zmiany HRQoL w kwestionariuszu SF-36. Dodatkowe porównania będą oparte na FI. W porównaniach wartości BMD, liczby punktów w poszczególnych domenach kwestionariusza SF-36 i wartości FI zastosowano metodę wielokrotnych porównań najmniejszej istotnej różnicy.Wyniki: U pacjentek stwierdzono znamiennie większe wartości BMD na poziomie L1–4, BMD w całkowitym obszarze biodra, BMD w obrębie szyjki kości udowej oraz BMD w obrębie krętarza (p < 0,05) oraz znamienną poprawę HRQoL (p < 0,05) w okresie 2 lat leczenia podawanym raz w roku ZOL w dawce 5 mg. Stwierdzono też zmniejszenie FI (p < 0,05) dzięki codziennemu przyjmowaniu wapnia i witaminy D w okresie leczenia.Wnioski: Stosowanie ZOL prowadzi do poprawy BMD i HRQoL, zwłaszcza w aspekcie fizycznym, w okresie 2 lat stosowania u kobiet z osteoporozą pomenopauzalną, i może przyczyniać się do poprawy zdolności utrzymania równowagi. (Endokrynol Pol 2014; 65 (2): 96–104

Integrative analysis based on survival associated co-expression gene modules for predicting Neuroblastoma patients' survival time

BACKGROUND: More than 90% of neuroblastoma patients are cured in the low-risk group while only less than 50% for those with high-risk disease can be cured. Since the high-risk patients still have poor outcomes, we need more accurate stratification to establish an individualized precise treatment plan for the patients to improve the long-term survival rate. RESULTS: We focus on extracting features and providing a workflow to improve survival prediction for neuroblastoma patients. With a workflow for gene co-expression network (GCN) mining in microarray and RNA-Seq datasets, we extracted molecular features from each co-expressed module and summarized them into eigengenes. Then we adopted the lasso-regularized Cox proportional hazards model to select the most informative eigengene features regarding association to the risk of metastasis. Nine eigengenes were selected which show strong association with patient survival prognosis. All of the nine corresponding gene modules also have highly enriched biological functions or cytoband locations. Three of them are unique modules to RNA-Seq data, which complement the modules from microarray data in terms of survival prognosis. We then merged all eigengenes from these unique modules and used an integrative method called Similarity Network Fusion to test the prognostic power of these eigengenes for prognosis. The prognostic accuracies are significantly improved as compared to using all eigengenes, and a subgroup of patients with very poor survival rate was identified. CONCLUSIONS: We first compared GCNs mined from microarray and RNA-seq data. We discovered that each data modality yields unique GCNs, which are enriched with clear biological functions. Then we do module unique analysis and use lasso-cox model to select survival-associated eigengenes. Integration of unique and survival-associated eigengenes from both data types provides complementary information that leads to more accurate survival prognosis

Integrative Analysis of Histopathological Images and Genomic Data Predicts Clear Cell Renal Cell Carcinoma Prognosis

In cancer, both histopathologic images and genomic signatures are used for diagnosis, prognosis, and subtyping. However, combining histopathologic images with genomic data for predicting prognosis, as well as the relationships between them, has rarely been explored. In this study, we present an integrative genomics framework for constructing a prognostic model for clear cell renal cell carcinoma. We used patient data from The Cancer Genome Atlas (n = 410), extracting hundreds of cellular morphologic features from digitized whole-slide images and eigengenes from functional genomics data to predict patient outcome. The risk index generated by our model correlated strongly with survival, outperforming predictions based on considering morphologic features or eigengenes separately. The predicted risk index also effectively stratified patients in early-stage (stage I and stage II) tumors, whereas no significant survival difference was observed using staging alone. The prognostic value of our model was independent of other known clinical and molecular prognostic factors for patients with clear cell renal cell carcinoma. Overall, this workflow and the shared software code provide building blocks for applying similar approaches in other cancers

TPQCI: A topology potential-based method to quantify functional influence of copy number variations

Copy number variation (CNV) is a major type of chromosomal structural variation that play important roles in many diseases including cancers. Due to genome instability, a large number of CNV events can be detected in diseases such as cancer. Therefore, it is important to identify the functionally important CNVs in diseases, which currently still poses a challenge in genomics. One of the critical steps to solve the problem is to define the influence of CNV. In this paper, we provide a topology potential based method, TPQCI, to quantify this kind of influence by integrating statistics, gene regulatory associations, and biological function information. We used this metric to detect functionally enriched genes on genomic segments with CNV in breast cancer and multiple myeloma and discovered biological functions influenced by CNV. Our results demonstrate that, by using our proposed TPQCI metric, we can detect disease-specific genes that are influenced by CNVs. Source codes of TPQCI are provided in Github (https://github.com/usos/TPQCI)

TPSC: a module detection method based on topology potential and spectral clustering in weighted networks and its application in gene co-expression module discovery

Background: Gene co-expression networks are widely studied in the biomedical field, with algorithms such as WGCNA and lmQCM having been developed to detect co-expressed modules. However, these algorithms have limitations such as insufficient granularity and unbalanced module size, which prevent full acquisition of knowledge from data mining. In addition, it is difficult to incorporate prior knowledge in current co-expression module detection algorithms. Results: In this paper, we propose a novel module detection algorithm based on topology potential and spectral clustering algorithm to detect co-expressed modules in gene co-expression networks. By testing on TCGA data, our novel method can provide more complete coverage of genes, more balanced module size and finer granularity than current methods in detecting modules with significant overall survival difference. In addition, the proposed algorithm can identify modules by incorporating prior knowledge. Conclusion: In summary, we developed a method to obtain as much as possible information from networks with increased input coverage and the ability to detect more size-balanced and granular modules. In addition, our method can integrate data from different sources. Our proposed method performs better than current methods with complete coverage of input genes and finer granularity. Moreover, this method is designed not only for gene co-expression networks but can also be applied to any general fully connected weighted network

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

scPADGRN: A preconditioned ADMM approach for reconstructing dynamic gene regulatory network using single-cell RNA sequencing data.

Disease development and cell differentiation both involve dynamic changes; therefore, the reconstruction of dynamic gene regulatory networks (DGRNs) is an important but difficult problem in systems biology. With recent technical advances in single-cell RNA sequencing (scRNA-seq), large volumes of scRNA-seq data are being obtained for various processes. However, most current methods of inferring DGRNs from bulk samples may not be suitable for scRNA-seq data. In this work, we present scPADGRN, a novel DGRN inference method using "time-series" scRNA-seq data. scPADGRN combines the preconditioned alternating direction method of multipliers with cell clustering for DGRN reconstruction. It exhibits advantages in accuracy, robustness and fast convergence. Moreover, a quantitative index called Differentiation Genes' Interaction Enrichment (DGIE) is presented to quantify the interaction enrichment of genes related to differentiation. From the DGIE scores of relevant subnetworks, we infer that the functions of embryonic stem (ES) cells are most active initially and may gradually fade over time. The communication strength of known contributing genes that facilitate cell differentiation increases from ES cells to terminally differentiated cells. We also identify several genes responsible for the changes in the DGIE scores occurring during cell differentiation based on three real single-cell datasets. Our results demonstrate that single-cell analyses based on network inference coupled with quantitative computations can reveal key transcriptional regulators involved in cell differentiation and disease development