MICA Polymorphism Identified by Whole Genome Array Constitutes a Disease Predisposition Factor in T-Cell Large Granular Lymphocyte Leukemia.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 3304-3304
Author(s):  
Aaron D. Viny ◽  
Hemant Ishwaran ◽  
Andrew Dunbar ◽  
Bartlomiej Przychodzen ◽  
Thomas Loughran ◽  
...  
Keyword(s):  
Statistical Significance ◽  
Snp Array ◽  
Flow Cytometric Analysis ◽  
Large Granular Lymphocyte ◽  
P Value ◽  
Inherited Disease ◽  
Kidney Transplant Recipients ◽  
Training Set ◽  
Test Set ◽  
Large Granular Lymphocyte Leukemia

Abstract Large granular lymphocyte leukemia (LGL) is a disease of semiautonomous proliferation of cytotoxic T-cells (CTL) often accompanied by immune cytopenias, particularly neutropenia. LGL related cytopenias have been attributed to LGL cellular cytotoxicity or proapoptotic cytokines rather than intrinsic properties of the neutrophils. The association of LGL with autoimmunity suggests that genetic predisposition may contribute to disease pathogenesis. We studied 69 patients with LGL leukemia using a case-control approach; control populations included ethnically matched healthy individuals (N=82) and disease controls of aplastic anemia (N=48) and kidney transplant recipients (N=48). Initially, we applied the Illumina 12K non-synonymous SNP array to a subcohort of 36 LGL patients and 54 healthy controls (training set). Results were subjected to independent hypothesis-generating biostatistical algorithms. First, Exemplar automated analysis determined disease prediction based on independent χ2 analysis for each SNP. As expected, no SNP in this underpowered study reached Bonferroni corrected statistical significance, but our analysis allowed for ranking based on p-value. Second, Random Forests, a nonparametric tree method was applied, whereby all SNP information was calculated multivariately to predict disease. In a non-Mendelian inherited disease, this method more closely reflects the biology of complex polygenic traits; remarkably, those SNP identified by Random Forest were among the highest ranking SNP by Exemplar. Our initial hypothesis-generating set identified 1 SNP in unknown gene C8orf31 and 4 SNP within the MHC class I related-chain A (MICA) gene. We focused on MICA, a non-peptide presenting, tightly regulated stress response HLA molecule that could play a role in pathogenesis of neutropenia in LGL. To further substantiate our finding, the initial training set results were subjected to technical validation; fidelity was rechecked by PCR genotyping with 93% concordance. Biological validation was determined by confirmation in an independent test set consisting of 33 LGL patients and additional 28 controls. As only limited numbers of SNP were tested, there was no need for α-error adjustment. MICA SNP rs1063635 was found to have the most predictive value in both the training set (PPV=56%, NPV=89%) and test set (PPV=64%, NPV=86%). Overall, the control frequency of this SNP in homozygous form was 12% vs 60% in LGL (p<.01, OR=9.1). MICA alleles have been implicated in autoimmune diseases and malignancies. Although this SNP may not define a particular MICA genotype, it is possible that it is in linkage disequilibrium with genotype-defining polymorphisms. To study the functional consequences of our findings, flow cytometric analysis using anti-MICA antibodies was performed, which identified higher expression of MICA in neutrophils from patients as compared to controls (p=.04). MICA overexpression decreased after immunosuppressive therapy (p<.01). While the mechanism of MICA induction is unknown, we stipulate that the presence of MICA alleles leads to a persistent stimulatory signal in LGL predisposing to clonal outgrowth. In sum, our findings suggest that MICA polymorphisms may represent a predisposition factor in LGL and/or LGL-associated neutropenia.

Combining Proteomics and Gene Expression Profiling Identifies Proteins/Genes Associated with Short Overall Survival in Multiple Myeloma

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 197-197
Author(s):  
Ricky D Edmondson ◽  
Shweta S. Chavan ◽  
Christoph Heuck ◽  
Bart Barlogie
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Multivariate Analyses ◽  
Rank Test ◽  
P Value ◽  
Newly Diagnosed ◽  
Training Set ◽  
Test Set ◽  
Log Rank Test ◽  
Test Sets

Abstract Abstract 197 We and others have used gene expression profiling to classify multiple myeloma into high and low risk groups; here, we report the first combined GEP and proteomics study of a large number of baseline samples (n=85) of highly enriched tumor cells from patients with newly diagnosed myeloma. Peptide expression levels from MS data on CD138-selected plasma cells from a discovery set of 85 patients with newly diagnosed myeloma were used to identify proteins that were linked to short survival (OS < 3 years vs OS ≥ 3 years). The proteomics dataset consisted of intensity values for 11,006 peptides (representing 2,155 proteins), where intensity is the quantitative measure of peptide abundance; Peptide intensities were normalized by Z score transformation and significance analysis of microarray (SAM) was applied resulting in the identification 24 peptides as differentially expressed between the two groups (OS < 3 years vs OS ≥ 3 years), with fold change ≥1.5 and FDR <5%. The 24 peptides mapped to 19 unique proteins, and all were present at higher levels in the group with shorter overall survival than in the group with longer overall survival. An independent SAM analysis with parameters identical to the proteomics analysis (fold change ≥1.5; FDR <5%) was performed with the Affymetrix U133Plus2 microarray chip based expression data. This analysis identified 151 probe sets that were differentially expressed between the two groups; 144 probe sets were present at higher levels and seven at lower levels in the group with shorter overall survival. Comparing the SAM analyses of proteomics and GEP data, we identified nine probe sets, corresponding to seven genes, with increased levels of both protein and mRNA in the short lived group. In order to validate these findings from the discovery experiment we used GEP data from a randomized subset of the TT3 patient population as a training set for determining the optimal cut-points for each of the nine probe sets. Thus, TT3 population was randomized into two sub-populations for the training set (two-thirds of the population; n=294) and test set (one-third of the population; n=147); the Total Therapy 2 (TT2) patient population was used as an additional test set (n=441). A running log rank test was performed on the training set for each of the nine probe sets to determine its optimal gene expression cut-point. The cut-points derived from the training set were then applied to TT3 and TT2 test sets to investigate survival differences for the groups separated by the optimal cutpoint for each probe. The overall survival of the groups was visualized using the method of Kaplan and Meier, and a P-value was calculated (based on log-rank test) to determine whether there was a statistically significant difference in survival between the two groups (P ≤0.05). We performed univariate regression analysis using Cox proportional hazard model with the nine probe sets as variables on the TT3 test set. To identify which of the genes corresponding to these nine probes had an independent prognostic value, we performed a multivariate stepwise Cox regression analysis. wherein CACYBP, FABP5, and IQGAP2 retained significance after competing with the remaining probe sets in the analysis. CACYBP had the highest hazard ratio (HR 2.70, P-value 0.01). We then performed the univariate and multivariate analyses on the TT2 test set where CACYBP, CORO1A, ENO1, and STMN1 were selected by the multivariate analysis, and CACYBP had the highest hazard ratio (HR 1.93, P-value 0.004). CACYBP was the only gene selected by multivariate analyses of both test sets. Disclosures: No relevant conflicts of interest to declare.

Employing electronic health record data to predict risk of emergency department visits for new patients.

2018 ◽  
Vol 36 (30_suppl) ◽  
pp. 314-314 ◽  
Author(s):  
Robert Michael Daly ◽  
Dmitriy Gorenshteyn ◽  
Lior Gazit ◽  
Stefania Sokolowski ◽  
Kevin Nicholas ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Predictive Value ◽  
Symptom Management ◽  
Predictive Analytics ◽  
Statistical Significance ◽  
Emergency Department Visits ◽  
Multivariate Logistic Regression Model ◽  
Training Set ◽  
Electronic Health Record Data ◽  
Test Set

314 Background: Acute care accounts for half of cancer expenditures and is a measure of poor quality care. Identifying patients at high risk for ED visits enables institutions to target symptom management resources to those most likely to benefit. Risk stratification models developed to date have not been meaningfully employed in oncology, and there is a need for clinically relevant models to improve patient care. Methods: We established a predictive analytics framework for clinical use with attention to the modeling technique, clinician feedback, and application metrics. The model employs EHR data from initial visit to first antineoplastic administration for new patients at our institution from January 2014 to June 2017. The binary dependent variable is occurrence of an ED visit within the first 6 months of treatment. From over 1,400 data features, the model was refined to include 400 clinically relevant ones spanning demographics, pathology, clinician notes, labs, medications, and psychosocial information. Clinician review was performed to confirm EHR data input validity. The final regularized multivariate logistic regression model was chosen based on clinical and statistical significance. Parameter selection and model evaluation utilized the positive predictive value for the top 25% of observations ranked by model-determined risk. The final model was evaluated using a test set containing 20% of randomly held out data. The model was calibrated based on a 5-fold cross-validation scheme over the training set. Results: There are 5,752 antineoplastic starts in our training set, and 1,457 in our test set. The positive predictive value of this model for the top 25% riskiest new start antineoplastic patients is 0.53. The 400 clinically relevant features draw from multiple areas in the EHR. For example, those features found to increase risk include: combination chemotherapy, low albumin, social work needs, and opioid use, whereas those found to decrease risk include stage 1 disease, never smoker status, and oral antineoplastic therapy. Conclusions: We have constructed a framework to build a clinically relevant model. We are now piloting it to identify those likely to benefit from a home-based, digital symptom management intervention.

A surgical predictive risk model of in-hospital mortality for primary resectable lung cancer: Statistical analysis of 8,796 patients from a nationally representative database

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 7677-7677
Author(s):  
P. Falcoz ◽  
L. Brouchet ◽  
M. Conti ◽  
S. Chocron ◽  
M. Puyraveau ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Hospital Mortality ◽  
Risk Model ◽  
Adult Patients ◽  
World Health ◽  
P Value ◽  
Training Set ◽  
Test Set ◽  
Lemeshow Test ◽  
Nationally Representative

7677 Background: The aim of this study was twofold: to identify factors associated with in-hospital mortality among patients operated on for primary resectable lung cancer and to construct a risk model that could be used prospectively to inform decisions and retrospectively to enable comparisons and outcomes. Methods: Data from a nationally representative thoracic surgery database were collected prospectively in 59 hospitals between June 1, 2002 and December 1, 2006. Only adult patients with more than 95% of completed data were selected for the final analysis (n = 8,796 patients). Logistic regression analysis was used to predict the risk of in-hospital mortality. A risk model was developed with a training set of data (50% of patients) and validated on an independent test set (50% of patients). Its fit was assessed by the Hosmer-Lemeshow test (larger p value signifies greater reliability), and predictive accuracy was indicated by the area under the receiver operating characteristic curve (c-index). Results: Of the 8,796 original patients, 255 (2.9%) died during the same hospital admission. Within the data used to develop the model, the factors found to be significantly associated with the occurrence of in-hospital mortality in a multivariate analysis were: age, gender, performance status (World Health Organization) classification, side, class of procedure (lobectomy or wedge resection versus pneumonectomy), tumor histology, TNM stages and presence of co-morbid disease. The model was reliable (Hosmer-Lemeshow test = 8.94; p = 0.35) and accurate: the c-index (95% confidence interval) = 0.83 (0.81 to 0.85) for the training set and 0.82 (0.79 to 0.85) for the test set of data. The correlation between the expected and observed number of deaths was 0.99. Conclusions: The validated multivariate model for risk of in-hospital mortality among adult patients requiring surgery for primary resectable lung cancer described in this report was developed with national data, uses only 8 variables and has good performance characteristics. It would be useful both for calculating the mortality risk of an individual patient and contrasting expected and observed mortality rates for an institution or independent clinician. No significant financial relationships to disclose.

Employing electronic health record data to predict risk of ED visits for new patients.

2018 ◽  
Vol 36 (34_suppl) ◽  
pp. 144-144
Author(s):  
Robert Michael Daly ◽  
Dmitriy Gorenshteyn ◽  
Lior Gazit ◽  
Stefania Sokolowski ◽  
Kevin Nicholas ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Predictive Value ◽  
Symptom Management ◽  
Predictive Analytics ◽  
Statistical Significance ◽  
Multivariate Logistic Regression Model ◽  
Training Set ◽  
Electronic Health Record Data ◽  
Test Set ◽  
Ed Visits

144 Background: Acute care accounts for half of cancer expenditures and is a measure of poor quality care. Identifying patients at high risk for ED visits enables institutions to target symptom management resources to those most likely to benefit. Risk stratification models developed to date have not been meaningfully employed in oncology, and there is a need for clinically relevant models to improve patient care. Methods: We established a predictive analytics framework for clinical use with attention to the modeling technique, clinician feedback, and application metrics. The model employs EHR data from initial visit to first antineoplastic administration for new patients at our institution from January 2014 to June 2017. The binary dependent variable is occurrence of an ED visit within the first 6 months of treatment. From over 1,400 data features, the model was refined to include 400 clinically relevant ones spanning demographics, pathology, clinician notes, labs, medications, and psychosocial information. Clinician review was performed to confirm EHR data input validity. The final regularized multivariate logistic regression model was chosen based on clinical and statistical significance. Parameter selection and model evaluation utilized the positive predictive value for the top 25% of observations ranked by model-determined risk. The final model was evaluated using a test set containing 20% of randomly held out data. The model was calibrated based on a 5-fold cross-validation scheme over the training set. Results: There are 5,752 antineoplastic starts in our training set, and 1,457 in our test set. The positive predictive value of this model for the top 25% riskiest new start antineoplastic patients is 0.53. The 400 clinically relevant features draw from multiple areas in the EHR. For example, those features found to increase risk include: combination chemotherapy, low albumin, social work needs, and opioid use, whereas those found to decrease risk include stage 1 disease, never smoker status, and oral antineoplastic therapy. Conclusions: We have constructed a framework to build a clinically relevant model. We are now piloting it to identify those likely to benefit from a home-based, digital symptom management intervention.

SERUM PROLACTINE LEVEL IN PATIENTS OF PSORIASIS VULGARIS AND ITS CORRELATION WITH DISEASE SEVERITY: A CASE-CONTROL STUDY

2019 ◽  
Vol 3 (6) ◽  
Author(s):  
Pawan Kumar Saini ◽  
Devendra Yadav ◽  
Rozy Badyal ◽  
Suresh Jain ◽  
Arti Singh ◽  
...  
Keyword(s):  
Disease Severity ◽  
Prolactin Level ◽  
Case Control Study ◽  
Statistical Significance ◽  
Case Control ◽  
Serum Prolactin ◽  
P Value ◽  
Serum Prolactin Level ◽  
R Value ◽  
Control Study

Background: Psoriasis is an autoimmune chronic inflammatory disorder affecting the skin mediated by T-lymphocytes resulting in production of cytokines which cause hyperproliferation of keratinocytes.  Several factors and hormones like Prolactin have an action similar to these cytokines in promoting the multiplication of keratinocytes and other cells like lymphocytes and epithelial cells may have a role on the etiopathogenesis of psoriasis. Aim:-The aim of study is to compare the serum Prolactin levels in patients of psoriasis with a control group. Setting and study design: This is a case-control study conducted in the department of Dermatology, Venereology and Leprosy GMC, Kota over a period of 1year from July 2017 to June 2018 Material and method: The study included 100 cases of psoriasis (60 males and 40 females) and 100 controls similar for age and sex. Serum Prolactin levels were measured by ECLIA and results were obtained. Statistical analysis: Mean and standard deviation were calculated for each variable. Statistical significance of the results was analyzed using correlation analysis (Pearson correlation coefficient) and independent samples t-test. Statistical significance was assumed at p value<0.05. Result: Serum Prolactin level was significantly higher in cases of psoriasis compared to controls (p-value <0.001). PASI score and serum Prolactin levels were found to have a positive correlation (r value = 0.337; p-value: 0.001). No significant  correlation was found between serum levels of Prolactin and duration of disease r value= -0.034, P value =0.733). Serum Prolactin level was higher in male patients compared to females patients. Conclusion:- High serum Prolactin may be a biological marker of disease severity in psoriasis and may have a role in the pathogenesis of psoriasis. Further studies with large sample size are required to confirm this hypothesis.

Factors that Influence the Durability of Transplanted Kidneys in South Africa

2019 ◽  
Vol 21 (2) ◽  
Author(s):  
Hillary Ndemera ◽  
Busisiwe R. Bhengu
Keyword(s):  
South Africa ◽  
Kidney Transplant ◽  
Lifestyle Modification ◽  
P Value ◽  
Transplant Recipients ◽  
Kidney Transplant Recipients ◽  
Kidney Allograft ◽  
Factors Influencing ◽  
Allograft Loss ◽  
Transplanted Kidneys

Kidney transplantation is the cornerstone for renal treatment in patients with end-stage renal failure. Despite improvements in short-term outcomes of renal transplantation, kidney allograft loss remains a huge challenge. The aim of the study was to assess factors influencing the durability of transplanted kidneys among transplant recipients in South Africa. A descriptive cross-sectional study design was used. A random sampling was used to select 171 participants. Data were collected through structured face-to-face interviews developed from in-depth consideration of relevant literature. Data were coded and entered into the SPSS software, version 24. The entered data were analysed using descriptive and inferential statistics. The results revealed that the average durability of transplanted kidneys was 9.07 years among selected kidney transplant recipients in South Africa. Factors associated with the durability of transplanted kidneys included age, the sewerage system and strict immunosuppressive adherence, all with a P-value = .000, followed by the mode of transport (P-value = .001) and support system (P-value = .004). Other variables including demographics, the healthcare system, medication and lifestyle modification engagement were not associated with the durability of transplanted kidneys. Understanding the factors influencing the durability of transplanted kidneys among kidney transplant recipients in South Africa is crucial. The study revealed associated factors and gaps which may be contributory factors to kidney allograft loss. This study provides an opportunity to introduce specific interventions to nephrology professionals to promote prolonged graft durability. It is recommended that a specific intervention model be developed, which targets South African kidney recipients taking into account the significant variables in this study and the socio-economic status of the country.

PREDIKSI KUALITAS AIR SUNGAI CILIWUNG DENGAN MENGGUNAKAN ALGORITMA POHON KEPUTUSAN

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Mohammad Haekal ◽  
Henki Bayu Seta ◽  
Mayanda Mega Santoni
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Cross Validation ◽  
Online Monitoring ◽  
Training Set ◽  
Microsoft Excel ◽  
Test Set

Untuk memprediksi kualitas air sungai Ciliwung, telah dilakukan pengolahan data-data hasil pemantauan secara Online Monitoring dengan menggunakan Metode Data Mining. Pada metode ini, pertama-tama data-data hasil pemantauan dibuat dalam bentuk tabel Microsoft Excel, kemudian diolah menjadi bentuk Pohon Keputusan yang disebut Algoritma Pohon Keputusan (Decision Tree) mengunakan aplikasi WEKA. Metode Pohon Keputusan dipilih karena lebih sederhana, mudah dipahami dan mempunyai tingkat akurasi yang sangat tinggi. Jumlah data hasil pemantauan kualitas air sungai Ciliwung yang diolah sebanyak 5.476 data. Hasil klarifikasi dengan Pohon Keputusan, dari 5.476 data ini diperoleh jumlah data yang mengindikasikan sungai Ciliwung Tidak Tercemar sebanyak 1.059 data atau sebesar 19,3242%, dan yang mengindikasikan Tercemar sebanyak 4.417 data atau 80,6758%. Selanjutnya data-data hasil pemantauan ini dievaluasi menggunakan 4 Opsi Tes (Test Option) yaitu dengan Use Training Set, Supplied Test Set, Cross-Validation folds 10, dan Percentage Split 66%. Hasil evaluasi dengan 4 opsi tes yang digunakan ini, semuanya menunjukkan tingkat akurasi yang sangat tinggi, yaitu diatas 99%. Dari data-data hasil peneltian ini dapat diprediksi bahwa sungai Ciliwung terindikasi sebagai sungai tercemar bila mereferensi kepada Peraturan Pemerintah Republik Indonesia nomor 82 tahun 2001 dan diketahui pula bahwa penggunaan aplikasi WEKA dengan Algoritma Pohon Keputusan untuk mengolah data-data hasil pemantauan dengan mengambil tiga parameter (pH, DO dan Nitrat) adalah sangat akuran dan tepat. Kata Kunci : Kualitas air sungai, Data Mining, Algoritma Pohon Keputusan, Aplikasi WEKA.

Awareness of radiation doses and risks among radiology residents, fellows, specialists, consultants and technologists in Jeddah, Saudi Arabia: Cross-Sectional Study. (Preprint)

2019 ◽  
Author(s):  
Bashayer Hassan Shuaib ◽  
Rahaf Hisham Niazi ◽  
Ahmed Haitham Abduljabbar ◽  
Mohammed Abdulraheem Wazzan
Keyword(s):  
Interventional Radiology ◽  
Ionizing Radiation ◽  
Statistical Significance ◽  
Knowledge Score ◽  
P Value ◽  
Radiation Doses ◽  
Cross Sectional ◽  
The Right ◽  
Interventional Radiology Procedures ◽  
Radiology Staff

BACKGROUND Radiology now plays a major role to diagnose, monitoring, and management of several diseases; numerous diagnostic and interventional radiology procedures involve exposure to ionizing radiation. Radiology now plays a major role to diagnose, monitoring, and management of several diseases; numerous diagnostic and interventional radiology procedures involve exposure to ionizing radiation. OBJECTIVE This study aimed to discover and compare the awareness level of radiation doses, protection issues, and risks among radiology staff in Jeddah hospitals. METHODS A cross-sectional survey containing 25 questions on personal information and various aspects of radiation exposure doses and risks was designed using an online survey tool and the link was emailed to all radiology staff in eight tertiary hospitals in Jeddah. The authors were excluded from the study. A P-value of < .05 was used to identify statistical significance. All analyses were performed using SPSS, version 21. RESULTS Out of 156 participants the majority 151 (96.8%) had poor knowledge score, where the mean scores were 2.4±1.3 for doses knowledge, 2.1±1.1for cancer risks knowledge, 2.3±0.6 for general information, and 6.7±1.9 for the total score. Only 34.6% of the participants were aware of the dosage of a single-view chest x-ray, and 9.0% chose the right answer for the approximate effective dose received by a patient in a two-view. 42.9% were able to know the correct dose of CT abdomen single phase. There is a significant underestimation of cancer risk of CT studies especially for CT abdomen where only 23.7% knew the right risk. A p-value of <0.05 was used to identify statistical significance. No significant difference of knowledge score was detected regarding gender (P =.2) or work position (P=.66). CONCLUSIONS Our survey results show considerable inadequate knowledge in all groups without exception. We recommended a conscientious effort to deliver more solid education and obtain more knowledge in these matters and providing periodic training courses to teach how to minimize the dose of radiation and to avoid risk related. CLINICALTRIAL not applicable

scholarly journals rs4143815-PDL1, a New Potential Immunogenetic Biomarker of Biochemical Recurrence in Locally Advanced Prostate Cancer after Radiotherapy

2019 ◽  
Vol 20 (9) ◽  
pp. 2082
Author(s):  
Chiara Zanusso ◽  
Eva Dreussi ◽  
Roberto Bortolus ◽  
Chiara Romualdi ◽  
Sara Gagno ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Immune System ◽  
Biochemical Recurrence ◽  
Advanced Prostate Cancer ◽  
Locally Advanced ◽  
Significant Snps ◽  
P Value ◽  
Training Set ◽  
Locally Advanced Prostate Cancer ◽  
New Biomarkers

Up to 30–50% of patients with locally advanced prostate cancer (PCa) undergoing radiotherapy (RT) experience biochemical recurrence (BCR). The immune system affects the RT response. Immunogenetics could define new biomarkers for personalization of PCa patients’ treatment. The aim of this study is to define the immunogenetic biomarkers of 10 year BCR (primary aim), 10 year overall survival (OS) and 5 year BCR (secondary aims). In this mono-institutional retrospective study, 549 Caucasian patients (a discovery set n = 418; a replication set n = 131) were affected by locally advanced PCa and homogeneously treated with RT. In the training set, associations were made between 447 SNPs in 77 genes of the immune system; and 10 year BCR and 10 year OS were tested through a multivariate Cox proportional hazard model. Significant SNPs (p-value < 0.05, q-value < 0.15) were analyzed in the replication set. Replicated SNPs were tested for 5 year BCR in both sets of patients. A polymorphism in the PDL1 gene (rs4143815) was the unique potential genetic variant of 10 year BCR (training set: p = 0.003, HR (95% CI) = 0.58 (0.41–0.83); replication set: p = 0.063, HR (95% CI) = 0.52 (0.26–1.04)) that was significantly associated with 5 year BCR (training set: p = 0.009, HR (95% CI) = 0.59 (0.40–0.88); replication set: p = 0.036, HR (95% CI) = 0.39 (0.16–0.94)). No biomarkers of OS were replicated. rs4143815-PDL1 arose as a new immunogenetic biomarker of BCR in PCa, giving new insights into the RT/immune system interaction, which could be potentially useful in new approaches using anti-PDL1 therapies for PCa.

scholarly journals QSPR modelling of the octanol/water partition coefficient of organometallic substances by optimal SMILES-based descriptors

2009 ◽  
Vol 7 (4) ◽  
pp. 846-856 ◽  
Author(s):  
Andrey Toropov ◽  
Alla Toropova ◽  
Emilio Benfenati
Keyword(s):  
Partition Coefficient ◽  
Organometallic Compounds ◽  
Applicability Domain ◽  
Training Set ◽  
Input Line ◽  
Test Set ◽  
Water Partition Coefficient ◽  
Definition Of

AbstractUsually, QSPR is not used to model organometallic compounds. We have modeled the octanol/water partition coefficient for organometallic compounds of Na, K, Ca, Cu, Fe, Zn, Ni, As, and Hg by optimal descriptors calculated with simplified molecular input line entry system (SMILES) notations. The best model is characterized by the following statistics: n=54, r2=0.9807, s=0.677, F=2636 (training set); n=26, r2=0.9693, s=0.969, F=759 (test set). Empirical criteria for the definition of the applicability domain for these models are discussed.

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