scholarly journals A Death-Related Gene Signature for Prognosis with Osteosarcoma

2021 ◽  
Author(s):  
Bin Xie ◽  
Shiyong Tan ◽  
Chao Li ◽  
Junyang Liang
Keyword(s):  
Regulatory Networks ◽  
Clinical Decision Making ◽  
Prognostic Biomarker ◽  
Survival Rates ◽  
Clinical Decision ◽  
Gene Signature ◽  
Rna Seq ◽  
Survival Group ◽  
Patient Prognosis ◽  
Death Group

Abstract Purpose: Osteosarcoma is one of the most prevalent malignancies, and despite significant advances in its treatment, patient prognosis remains poor and survival rates are low. It is undoubtedly important to explore the possible reasons for the low survival rates of patients and to reveal the differences.Methods and Results: We obtained RNA-Seq (HT seq) and clinical characteristics of osteosarcoma patients from the TCGA database and divided them into survival group and death group. We defined the differentially expressed genes (DEGs) between the two groups as death-related genes (DRGs) and used them to construct a prognostic signature for overall survival of patients with osteosarcoma. The results of the validation demonstrated satisfactory accuracy and predictive prognostic value of the model. In addition, we performed a series of bioinformatic analyses that identified two key genes and the regulatory networks they constituted that may play a role in the progression of osteosarcoma.Conclusion: Our DRGs signature represents a novel and clinically useful prognostic biomarker for patients with osteosarcoma, helping to aid clinical decision-making.

scholarly journals Cancer Grade Model: a multi-gene machine learning-based risk classification for improving prognosis in breast cancer

2021 ◽  
Author(s):  
E. Amiri Souri ◽  
A. Chenoweth ◽  
A. Cheung ◽  
S. N. Karagiannis ◽  
S. Tsoka
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Clinical Decision Making ◽  
Histological Grade ◽  
Tumour Size ◽  
Clinical Decision ◽  
Gene Signature ◽  
Risk Classification ◽  
Breast Cancers ◽  
Grade 3

Abstract Background Prognostic stratification of breast cancers remains a challenge to improve clinical decision making. We employ machine learning on breast cancer transcriptomics from multiple studies to link the expression of specific genes to histological grade and classify tumours into a more or less aggressive prognostic type. Materials and methods Microarray data of 5031 untreated breast tumours spanning 33 published datasets and corresponding clinical data were integrated. A machine learning model based on gradient boosted trees was trained on histological grade-1 and grade-3 samples. The resulting predictive model (Cancer Grade Model, CGM) was applied on samples of grade-2 and unknown-grade (3029) for prognostic risk classification. Results A 70-gene signature for assessing clinical risk was identified and was shown to be 90% accurate when tested on known histological-grade samples. The predictive framework was validated through survival analysis and showed robust prognostic performance. CGM was cross-referenced with existing genomic tests and demonstrated the competitive predictive power of tumour risk. Conclusions CGM is able to classify tumours into better-defined prognostic categories without employing information on tumour size, stage, or subgroups. The model offers means to improve prognosis and support the clinical decision and precision treatments, thereby potentially contributing to preventing underdiagnosis of high-risk tumours and minimising over-treatment of low-risk disease.

Ivy sign: a diagnostic and prognostic biomarker for pediatric moyamoya

2021 ◽  
pp. 1-9
Keyword(s):  
Blood Flow ◽  
Cerebral Artery ◽  
Pediatric Patients ◽  
Clinical Decision Making ◽  
Prognostic Biomarker ◽  
Pediatric Population ◽  
Radiographic Finding ◽  
Clinical Decision ◽  
Stage Iii ◽  
Postoperative Stroke

OBJECTIVE Ivy sign is a radiographic finding on FLAIR MRI sequences and is associated with slow cortical blood flow in moyamoya. Limited data exist on the utility of the ivy sign as a diagnostic and prognostic tool in pediatric patients, particularly outside of Asian populations. The authors aimed to investigate a modified grading scale with which to characterize the prevalence and extent of the ivy sign in children with moyamoya and evaluate its efficacy as a biomarker in predicting postoperative outcomes, including stroke risk. METHODS Pre- and postoperative clinical and radiographic data of all pediatric patients (21 years of age or younger) who underwent surgery for moyamoya disease or moyamoya syndrome at two major tertiary referral centers in the US and Israel, between July 2009 and August 2019, were retrospectively reviewed. Ivy sign scores were correlated to Suzuki stage, Matsushima grade, and postoperative stroke rate to quantify the diagnostic and prognostic utility of ivy sign. RESULTS A total of 171 hemispheres in 107 patients were included. The median age at the time of surgery was 9 years (range 3 months–21 years). The ivy sign was most frequently encountered in association with Suzuki stage III or IV disease in all vascular territories, including the anterior cerebral artery (53.7%), middle cerebral artery (56.3%), and posterior cerebral artery (47.5%) territories. Following surgical revascularization, 85% of hemispheres with Matsushima grade A demonstrated a concomitant, statistically significant reduction in ivy sign scores (OR 5.3, 95% CI 1.4–20.0; p = 0.013). Postoperatively, revascularized hemispheres that exhibited ivy sign score decreases had significantly lower rates of postoperative stroke (3.4%) compared with hemispheres that demonstrated no reversal of the ivy sign (16.1%) (OR 5.5, 95% CI 1.5–21.0; p = 0.008). CONCLUSIONS This is the largest study to date that focuses on the role of the ivy sign in pediatric moyamoya. These data demonstrate that the ivy sign was present in approximately half the pediatric patients with moyamoya with Suzuki stage III or IV disease, when blood flow was most unstable. The authors found that reversal of the ivy sign provided both radiographic and clinical utility as a prognostic biomarker postoperatively, given the statistically significant association with both better Matsushima grades and a fivefold reduction in postoperative stroke rates. These findings can help inform clinical decision-making, and they have particular value in the pediatric population, as the ability to minimize additional radiographic evaluations and tailor radiographic surveillance is requisite.

Identification of High-Risk Patients Among Those Diagnosed With Thin Cutaneous Melanomas

2007 ◽  
Vol 25 (9) ◽  
pp. 1129-1134 ◽  
Author(s):  
Phyllis A. Gimotty ◽  
David E. Elder ◽  
Douglas L. Fraker ◽  
Jeffrey Botbyl ◽  
Kimberly Sellers ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Tumor Cell ◽  
Clinical Decision Making ◽  
Classification Tree ◽  
Survival Rates ◽  
Clinical Decision ◽  
Mitotic Rate ◽  
Lesion Group ◽  
Pigmented Lesion ◽  
Ajcc Staging

Purpose Most patients with melanoma have microscopically thin (≤ 1 mm) primary lesions and are cured with excision. However, some develop metastatic disease that is often fatal. We evaluated established prognostic factors to develop classification schemes with better discrimination than current American Joint Committee on Cancer (AJCC) staging. Patients and Methods We studied patients with thin melanomas from the US population-based Surveillance, Epidemiology, and End Results (SEER) cancer registry (1988 to 2001; n = 26,291) and those seen by the University of Pennsylvania's Pigmented Lesion Group (PLG; 1972 to 2001; n = 2,389; Philadelphia, PA). AJCC prognostic factors were thickness, anatomic level, ulceration, site, sex, and age; PLG prognostic factors also included a set of biologically based candidate prognostic factors. Recursive partitioning was used to develop a SEER-based classification tree that was validated using PLG data. Next, a new PLG-based classification tree was developed using the expanded set of prognostic factors. Results The SEER-based classification tree identified additional criteria to explain survival heterogeneity among patients with thin, nonulcerated lesions; 10-year survival rates ranged from 89.1% to 99%. The new PLG-based tree identified groups using level, tumor cell mitotic rate, and sex. With survival rates from 83.4% to 100%, it had better discrimination. Conclusion Prognostication and related clinical decision making in the majority of patients with melanoma can be improved now using the validated, SEER-based classification. Tumor cell mitotic rate should be incorporated into the next iteration of AJCC staging.

scholarly journals Systematic Review: A Prevention-Based Model of Neuropsychological Assessment for Children With Medical Illness

2017 ◽  
Vol 42 (8) ◽  
pp. 815-822 ◽  
Author(s):  
Kristina K. Hardy ◽  
Katie Olson ◽  
Stephany M. Cox ◽  
Tess Kennedy ◽  
Karin S. Walsh
Keyword(s):  
Clinical Decision Making ◽  
Adaptive Functioning ◽  
Survival Rates ◽  
Clinical Decision ◽  
Medical Illness ◽  
Individual Level ◽  
Care Models ◽  
Neuropsychological Changes ◽  
The Central Nervous System ◽  
The Individual

Abstract Objective Many pediatric chronic illnesses have shown increased survival rates, leading to greater focus on cognitive and psychosocial issues. Neuropsychological services have traditionally been provided only after significant changes in the child’s cognitive or adaptive functioning have occurred. This model of care is at odds with preventative health practice, including early identification and intervention of neuropsychological changes related to medical illness. We propose a tiered model of neuropsychological evaluation aiming to provide a preventative, risk-adapted level of assessment service to individuals with medical conditions impacting the central nervous system based on public health and clinical decision-making care models. Methods Elements of the proposed model have been used successfully in various pediatric medical populations. We summarize these studies in association with the proposed evaluative tiers in our model. Results and Conclusions This model serves to inform interventions through the various levels of assessment, driven by evidence of need at the individual level in real time.

scholarly journals Biologic Drug Survival in Psoriasis: A Systematic Review & Comparative Meta-Analysis

2020 ◽  
Author(s):  
Ahmed I Mourad ◽  
Robert Gniadecki
Keyword(s):  
Real World ◽  
Survival Data ◽  
Clinical Decision Making ◽  
Meta Analysis ◽  
Survival Rates ◽  
Clinical Decision ◽  
Drug Discontinuation ◽  
Large Variability ◽  
Drug Survival ◽  
Biologic Drug

Background: Drug survival studies have been utilized to evaluate the real-world effectiveness of biologics used in psoriasis. However, the increasing volume of drug survival data suffers from large variability due to regional differences in drug availability, patient selection and biologic reimbursement. Objectives: To conduct a meta-analysis of biologic drug survival to determine comparative effectiveness of the biologics in a real-world setting. Methods: Studies reporting drug survival for biologic therapy in psoriasis were identified by a systematic literature search. Hazard ratio data for drug discontinuation were estimated directly from published Kaplan-Meier estimator curves at year 1, 2 and 5 of treatment and compared pairwise for the following biologics: ustekinumab, adalimumab, etanercept, infliximab, secukinumab and ixekizumab. This pooled hazard ratios were used to estimate 2- and 5- year overall drug survival rates. Results: Ustekinumab had the longest persistence at 2 years and 5 years among all biologics included in this meta-analysis. Adalimumab was superior to etanercept and infliximab at 5 years. Pooled 5-year drug survival rates for adalimumab, etanercept, and infliximab were 46.3%, 35.9% and 34.7%, respectively. 2- and 5-year data were not available for anti-IL-17 drugs, but at 1-year ustekinumab outperformed secukinumab, the latter being equal to anti-TNFs. Conclusions: Ustekinumab is characterized by longer drug survival than TNF inhibitors and IL-17 inhibitors. Estimated pooled 2- and 5- year drug survival rates may serve as a useful tool for patient communication and clinical decision-making.

Can we satisfactorily measure the clinical value of new oncology agents with a single summary measure?

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 6606-6606
Author(s):  
Clare Frances Jones ◽  
Giles Monnickendam ◽  
Mingshu Zhu ◽  
Jan McKendrick
Keyword(s):  
Median Survival ◽  
Clinical Benefit ◽  
Survival Benefit ◽  
Clinical Decision Making ◽  
Proportional Hazards ◽  
Survival Rates ◽  
Clinical Decision ◽  
Summary Statistics ◽  
Survival Curves ◽  
Clinical Value

6606 Background: Current value frameworks (VFs) assess clinical value primarily through using clinical trial endpoints as survival metrics (e.g., median and hazard ratio (HR)). But, if key assumptions do not hold, the interpretation of these summary statistics can become problematic and fail to adequately capture the expected benefit to a patient. This has been observed with innovative oncology treatments. As a proof of concept analysis, we reviewed how two VFs (ASCO and ESMO) dealt with cases where the assumption of proportional hazards (PH) does not hold. Methods: Oncology agents approved by the FDA since 2011 were reviewed and three agents were identified with survival profiles where the assumption of PH was found not to hold because, on visual inspection, the survival curves displayed non-standard patterns: Divergence followed by convergence – panobinostat OS in RRMM; Curves initially track together then diverge – nivolumab OS in NSCLC; Curves diverge steadily then a plateau emerged in the active treatment curve – pembrolizumab PFS in refractory melanoma. We evaluated these agents to assess which measures of clinical benefit were most valued under each VF and how the issue of non-PH influenced the outcome. Results: Clinical benefit/value scores varied: ASCO: 14-27 (maximum 100), ESMO: grade 1-3. The ASCO VF uses a hierarchical approach (incorporating HR and median survival benefit, always prioritising the former) adding a bonus for survival benefit in the tail of the distribution. The combination of HR, median survival benefit 2 and 3 year survival rates in the ESMO non-curative VF can potentially capture aspects of clinical benefit in some cases of non-PH. Overall, the ASCO VF appears less flexible to accommodate non-PH than the ESMO VF. Conclusions: Despite VFs using summary statistics which cannot be easily interpreted under conditions of non-PH, the case of non-PH is not explicitly catered for. Additionally, both VFs may miss important interpretation where value is differentiated across patients groups with different response profiles which may underlie non-standard survival curves. In these situations, a more flexible approach to assessing clinical value may render VFs more relevant for clinical decision making.

Discovery and validation of a cross-platform 21-gene prognostic signature in neuroblastoma.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 10035-10035
Author(s):  
Mehul Gupta ◽  
Sunand Nageswaran Kannappan ◽  
Aru Narendran ◽  
Pinaki Bose
Keyword(s):  
Machine Learning ◽  
Risk Stratification ◽  
Clinical Decision Making ◽  
Proportional Hazards ◽  
Risk Model ◽  
Learning Algorithm ◽  
Clinical Decision ◽  
Gene Signature ◽  
Cox Proportional Hazards ◽  
Transcriptomic Signature

10035 Background: Neuroblastoma (NB) is the most common extracranial solid tumor in children. Despite the development of risk stratification tools to improve prognostication, prediction of patient survival outcomes in NB remains poor. In this study we used an unbiased machine-learning algorithm to develop and validate a transcriptomic signature capable of predicting 5-year overall (OS) and event-free survival (EFS) in these patients. Methods: The TARGET-Neuroblastoma dataset (n = 243) was used as the training set. Two independent NB cohorts, E-MTAB-179 (n = 478) and GSE85047 (n = 266) were used as validation sets. Elastic net regression was employed to identify transcripts associated with EFS. Association of the developed signature with EFS and OS was evaluated using univariate Cox proportional hazards (CoxPH), Kaplan-Meier, and 5-year receiver-operator characteristic curves in validation cohorts. Further, the independent prognostic value of the signature was assessed using multivariate CoxPH models with relevant clinicopathologic variables including age, INSS stage, and N-Myc amplification status in both validation sets. Finally, a nomogram was developed to integrate the signature with prognostic clinicopathologic variables to evaluate their combined efficacy for prediction of 5-year EFS and OS. Results: We identified a 21-gene signature that demonstrates significant association with EFS and OS in both E-MTAB-178 and GSE49710 validation cohorts. Moreover, the signature is independent of clinicopathological variables and can be effectively incorporated into a risk model, improving the prognostic performance. Several genes within the signature have been previously implicated in NB, including ECEL1, HOXC9 and ARAF1. Conclusions: To the best of our knowledge, we are the first to use an unbiased machine learning approach to generate a transcriptomic gene signature for neuroblastoma prognosis externally validated in multiple cohorts across platforms. This 21-gene transcriptomic signature significantly associated with EFS and OS in this disease. Combining this signature with current prognostic clinicopathologic variables will improve risk stratification of affected patients and may inform effective clinical decision-making.[Table: see text]

scholarly journals scAmpi - A versatile pipeline for single-cell RNA-seq analysis from basics to clinics

2021 ◽  
Author(s):  
Anne Bertolini ◽  
Michael Prummer ◽  
Mustafa Anil Tuncel ◽  
Ulrike Menzel ◽  
María Lourdes Rosano-González ◽  
...  
Keyword(s):  
Single Cell ◽  
Clinical Decision Making ◽  
Single Cell Analysis ◽  
Clinical Decision ◽  
Relevant Information ◽  
Tumor Board ◽  
Rna Seq ◽  
Candidate Drug ◽  
Sample Composition ◽  
Drug Treatments

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique to decipher tissue composition at the single-cell level and to inform on disease mechanisms, tumor heterogeneity, and the state of the immune microenvironment. Although multiple methods for the computational analysis of scRNA-seq data exist, their application in a clinical setting demands standardized and reproducible workflows, targeted to extract, condense, and display the clinically relevant information. To this end, we designed scAmpi (Single Cell Analysis mRNA pipeline), a workflow that facilitates scRNA-seq analysis from raw read processing to informing on sample composition, clinically relevant gene and pathway alterations, and in silico identification of personalized candidate drug treatments. We demonstrate the value of this workflow for clinical decision making in a molecular tumor board as part of a clinical study.

scholarly journals A Novel Six Metastasis-Related Prognostic Gene Signature for Patients With Osteosarcoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Di Zheng ◽  
Kezhou Xia ◽  
Ling Yu ◽  
Changtian Gong ◽  
Yubo Shi ◽  
...  
Keyword(s):  
Tumor Suppressor ◽  
Epithelial To Mesenchymal Transition ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Gene Signature ◽  
Suppressor Gene ◽  
Migration And Invasion ◽  
Mesenchymal Transition

Osteosarcoma is the most common malignant bone tumor, and although there has been significant progress in its management, metastases often herald incurable disease. Here we defined genes differentially expressed between primary and metastatic osteosarcoma as metastasis-related genes (MRGs) and used them to construct a novel six-MRG prognostic signature for overall survival of patients with osteosarcoma. Validation in internal and external datasets confirmed satisfactory accuracy and generalizability of the prognostic model, and a nomogram based on the signature and clinical variables was constructed to aid clinical decision-making. Of the six MRGs, FHIT is a well-documented tumor suppressor gene that is poorly defined in osteosarcoma. Consistent with tumor suppressor function, FHIT was downregulated in osteosarcoma cells and human osteosarcoma samples. FHIT overexpression inhibited osteosarcoma proliferation, migration, and invasion both in vitro and in vivo. Mechanistically, FHIT overexpression upregulate the epithelial marker E-cadherin while repressing the mesenchymal markers N-cadherin and vimentin. Our six-MRG signature represents a novel and clinically useful prognostic biomarker for patients with osteosarcoma, and FHIT might represent a therapeutic target by reversing epithelial to mesenchymal transition.

Integrative workflows for network analysis

2018 ◽  
Vol 62 (4) ◽  
pp. 549-561
Author(s):  
Faiz M. Khan ◽  
Shailendra K. Gupta ◽  
Olaf Wolkenhauer
Keyword(s):  
Regulatory Networks ◽  
Clinical Decision Making ◽  
Epithelial Mesenchymal Transition ◽  
Therapeutic Targets ◽  
Clinical Decision ◽  
Underlying Disease ◽  
Biological Information ◽  
Dynamical Analysis ◽  
Mesenchymal Transition ◽  
Optimization Function

Due to genetic heterogeneity across patients, the identification of effective disease signatures and therapeutic targets is challenging. Addressing this challenge, we have previously developed a network-based approach, which integrates heterogeneous sources of biological information to identify disease specific core-regulatory networks. In particular, our workflow uses a multi-objective optimization function to calculate a ranking score for network components (e.g. feedback/feedforward loops) based on network properties, biomedical and high-throughput expression data. High ranked network components are merged to identify the core-regulatory network(s) that is then subjected to dynamical analysis using stimulus–response and in silico perturbation experiments for the identification of disease gene signatures and therapeutic targets. In a case study, we implemented our workflow to identify bladder and breast cancer specific core-regulatory networks underlying epithelial–mesenchymal transition from the E2F1 molecular interaction map. In this study, we review our workflow and described how it has developed over time to understand the mechanisms underlying disease progression and prediction of signatures for clinical decision making.

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