scholarly journals BIOM-52. A PROGNOSTIC GENE EXPRESSION RISK SCORE FOR MENINGIOMA

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii12-ii13
Author(s):  
William Chen ◽  
Harish N Vasudevan ◽  
Abrar Choudhury ◽  
Calixto-Hope G Lucas ◽  
Stephen Magill ◽  
...  

Abstract BACKGROUND Clinical biomarkers for identifying patients at risk for recurrence after resection of meningioma are lacking and are needed for guiding adjuvant therapy. The aim of this study was to identify a prognostic gene expression signature for meningioma. METHODS Targeted gene expression analysis was performed on a discovery dataset of 96 meningiomas with suitable tissue identified from a retrospective institutional biorepository. Recurrence was dichotomized based on the median time to local recurrence (TTR). With median follow-up of 6.4 years, the discovery dataset was enriched for clinical endpoints of local recurrence (58%), mortality (42%), and disease-specific mortality (49% of deaths). A 266 gene expression panel was used to interrogate the discovery dataset, and a prognostic gene signature and risk score was generated using prediction analysis for microarrays (PAM) and elastic net regression. The risk score was validated using gene expression data (GSE58037) from 56 meningiomas resected at an independent institution (20% local recurrence, 18% mortality, median follow-up 5.4 years). RESULTS A 36-gene signature was identified achieving an AUC of 0.86 for TTR faster than the median in the discovery cohort. A risk score between 0 and 1 based on this signature was strongly associated with shorter TTR (F-test, P< 0.0001), and on multivariate Cox regression (MVA), was independently associated with recurrence (RR 1.56 per 0.1 increase, 95% CI 1.30–1.90, P< 0.0001) and mortality (RR 1.32 per 0.1 increase, 1.07–1.64, P=0.01) after adjusting for WHO grade, age, extent of resection, and sex. Similarly, in the validation dataset, the gene risk score was correlated with shorter TTR (P=0.002) and associated with mortality on MVA (RR 1.86 per 0.1 increase, 1.19–2.88, P=0.005) after adjustment for WHO grade. CONCLUSIONS The prognostic meningioma gene expression risk score presented here could be useful in identifying patients at higher risk of progression after resection.

Neurosurgery ◽  
2020 ◽  
Vol 88 (1) ◽  
pp. 202-210 ◽  
Author(s):  
William C Chen ◽  
Harish N Vasudevan ◽  
Abrar Choudhury ◽  
Melike Pekmezci ◽  
Calixto-Hope G Lucas ◽  
...  

Abstract BACKGROUND Prognostic markers for meningioma are needed to risk-stratify patients and guide postoperative surveillance and adjuvant therapy. OBJECTIVE To identify a prognostic gene signature for meningioma recurrence and mortality after resection using targeted gene-expression analysis. METHODS Targeted gene-expression analysis was used to interrogate a discovery cohort of 96 meningiomas and an independent validation cohort of 56 meningiomas with comprehensive clinical follow-up data from separate institutions. Bioinformatic analysis was used to identify prognostic genes and generate a gene-signature risk score between 0 and 1 for local recurrence. RESULTS We identified a 36-gene signature of meningioma recurrence after resection that achieved an area under the curve of 0.86 in identifying tumors at risk for adverse clinical outcomes. The gene-signature risk score compared favorably to World Health Organization (WHO) grade in stratifying cases by local freedom from recurrence (LFFR, P < .001 vs .09, log-rank test), shorter time to failure (TTF, F-test, P < .0001), and overall survival (OS, P < .0001 vs .07) and was independently associated with worse LFFR (relative risk [RR] 1.56, 95% CI 1.30-1.90) and OS (RR 1.32, 95% CI 1.07-1.64), after adjusting for clinical covariates. When tested on an independent validation cohort, the gene-signature risk score remained associated with shorter TTF (F-test, P = .002), compared favorably to WHO grade in stratifying cases by OS (P = .003 vs P = .10), and was significantly associated with worse OS (RR 1.86, 95% CI 1.19-2.88) on multivariate analysis. CONCLUSION The prognostic meningioma gene-expression signature and risk score presented may be useful for identifying patients at risk for recurrence.


2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Wei Hu ◽  
Mingyue Li ◽  
Qi Zhang ◽  
Chuan Liu ◽  
Xinmei Wang ◽  
...  

Abstract Background Copy number variation (CNVs) is a key factor in breast cancer development. This study determined prognostic molecular characteristics to predict breast cancer through performing a comprehensive analysis of copy number and gene expression data. Methods Breast cancer expression profiles, CNV and complete information from The Cancer Genome Atlas (TCGA) dataset were collected. Gene Expression Omnibus (GEO) chip data sets (GSE20685 and GSE31448) containing breast cancer samples were used as external validation sets. Univariate survival COX analysis, multivariate survival COX analysis, least absolute shrinkage and selection operator (LASSO), Chi square, Kaplan-Meier (KM) survival curve and receiver operating characteristic (ROC) analysis were applied to build a gene signature model and assess its performance. Results A total of 649 CNV related-differentially expressed gene obtained from TCGA-breast cancer dataset were related to several cancer pathways and functions. A prognostic gene sets with 9 genes were developed to stratify patients into high-risk and low-risk groups, and its prognostic performance was verified in two independent patient cohorts (n = 327, 246). The result uncovered that 9-gene signature could independently predict breast cancer prognosis. Lower mutation of PIK3CA and higher mutation of TP53 and CDH1 were found in samples with high-risk score compared with samples with low-risk score. Patients in the high-risk group showed higher immune score, malignant clinical features than those in the low-risk group. The 9-gene signature developed in this study achieved a higher AUC. Conclusion The current research established a 5-CNV gene signature to evaluate prognosis of breast cancer patients, which may innovate clinical application of prognostic assessment.


2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi19-vi20
Author(s):  
William Chen ◽  
Abrar Choudhury ◽  
Harish Vasudevan ◽  
Calixto Lucas ◽  
Minh Nguyen ◽  
...  

Abstract BACKGROUND Surgery is the mainstay of meningioma treatment, but improvements in meningioma risk stratification are needed and indications for postoperative radiotherapy are controversial. DNA methylation profiling, copy number variants (CNVs), exome sequencing, and RNA sequencing have improved understanding of meningioma biology, but have not superseded histologic grading, or revealed biomarkers for radiotherapy responses. To address these unmet needs, we optimized and validated a targeted gene expression biomarker predicting meningioma outcomes and responses to radiotherapy. METHODS Targeted gene expression profiling was performed on a discovery cohort of 173 meningiomas (median follow-up 8.1 years) and a validation cohort of 331 meningiomas (median follow-up 6.1 years) treated with surgery (n=504) and postoperative radiotherapy (n=73) at independent, international institutions (70% WHO grade 1, 24% WHO grade 2, 6% WHO grade 3). Optimized targeted gene expression models predicting clinical outcomes (34 genes) or radiotherapy responses (12 genes) were developed from the discovery cohort, and compared to histologic and molecular classification systems by performing DNA methylation profiling, CNV analysis, exome sequencing, and RNA sequencing on the same meningiomas. RESULTS Targeted gene expression profiling achieved a concordance-index of 0.75 ± 0.03 (SEM) for local freedom from recurrence (LFFR) and 0.72 ± 0.03 for overall survival (OS) in the validation cohort, outperforming WHO grade (5-year LFFR delta-AUC 0.15, 95% CI 0.076-0.229, p=0.001) and DNA methylation grouping (delta-AUC 0.075, 95% CI 0.006-0.130, p=0.01) for LFFR, disease-specific survival, and OS. The biomarker was independently prognostic after accounting for WHO grade, extent of resection, primary versus recurrent presentation, CNV status, DNA methylation group, and Ki67 labeling index, and identified meningiomas benefiting from radiotherapy (interaction p-value=0.0008), suggesting postoperative radiotherapy could be refined in 30.2% of cases. CONCLUSIONS Targeted gene expression profiling of 504 meningiomas improves discrimination of meningioma local recurrence, disease-specific survival, and overall survival, and predicts radiotherapy responses.


2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi157-vi157
Author(s):  
Christine Jungk ◽  
Mara Gluszak ◽  
Philip Dao Trong ◽  
Andreas von Deimling ◽  
Christel Herold-Mende ◽  
...  

Abstract Until now, the Pignatti risk score has been used to guide treatment decisions after histological diagnosis of diffuse glioma WHO grade 2. However, its prognostic value was derived from a historic cohort that has been diagnosed by morphologic rather than molecular criteria. We re-challenged the Pignatti score in a contemporary, molecularly characterized cohort. From our institutional cohort of 422 diffuse gliomas WHO grade 2, 202 patients were identified for whom IDH mutation status was known and 1p/19q co-deletion or loss of ATRX expression unambiguously classified tumors into astrocytoma or oligodendroglioma. Patients with IDH wildtype astrocytoma (n=9), multifocal lesions or brainstem involvement were excluded. Potential prognostic factors including the individual items of the Pignatti score (astrocytoma; age ≥40 years; neurologic deficit; maximum tumor diameter ≥6cm; tumor crossing midline) were correlated with progression-free survival (PFS) by univariate log-rank und multivariate Cox regression analysis. 165 patients with astrocytoma or oligodendroglioma were analysed of whom 109 (66%) did not receive adjuvant radio- or chemotherapy. 94 untreated patients with a minimum follow-up of 24 months entered survival analysis. These patients were classified as “high-risk” (Pignatti 3-5) and “low-risk” (Pignatti 0-2) in 15% and 85% and did not differ with regard to potential prognostic factors (gender; resection vs. biopsy; tumor recurrence) other than the individual Pignatti score items. Diameter ≥6 cm (p=0.006; HR=2.18) and midline crossing (p=0.003; HR=3.54) were identified as independent prognostic factors of PFS. Noteworthy, prognostic factors coincided when all patients (n=144) with a minimum follow-up of 24 months, regardless of adjuvant treatment, were analysed. In IDH mutant, molecularly characterized diffuse gliomas WHO grade 2, the Pignatti risk score as a whole no longer seems to be of prognostic relevance. Instead, outcome seems to be determined by tumor burden.


Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 3275-3275 ◽  
Author(s):  
Ryan van Laar ◽  
Phillip Farmer ◽  
Richard A Bender ◽  
Aga Zielinski ◽  
Kenton Leigh ◽  
...  

Abstract Background: The 70-gene prognostic risk score (MyPRS), originally developed by the University of Arkansas for Medical Sciences, is the most validated genomic assay for prediction of event free and overall survival in asymptomatic, newly diagnosed and relapsed multiple myeloma. Gene expression profiling was performed on CD138+ plasma cells obtained from the bone marrow of individuals with the precursor condition, monoclonal gammopathy of undetermined significance (MGUS), who later progressed to MM. Analysis of the 70 gene risk score vs. the probability of progression to MM requiring therapy was performed. Method and Results: Between 2011 and 2015, MyPRS gene expression profiling of 266 individuals who initially presented with MGUS was performed. The mean length of time between MGUS diagnosis and disease progression or last follow-up was 6.9 years (standard deviation = 4.0 years). The mean length of time between MGUS gene expression profiling and either disease progression or date of last follow-up was 4.8 years (standard deviation = 2.9 years). Disease progression was defined as the development of CRAB criteria or bone marrow plasmacytosis exceeding 60%. 28 patients developed MM requiring therapy within two years of their MGUS GEP. Twelve individuals (5%) were classified as high risk using the previously established threshold for AMG (GEP70 >37.2 = high risk). Four high risk patients (33%) progressed to active MM within 2 years. 24/255 (9%) patients who were classified as low risk progressed to MM within the same length of time. A risk score histogram and binary fitted line plot of risk score vs. probability of progression to MM within 2 years were generated. Conclusion: Performing MyPRS gene expression profiling on patients diagnosed with MGUS provides personalized information on the individuals' risk of progression to MM requiring treatment. While the overall rate of progression is low, approximately 5% of individuals are at higher risk and may benefit from increased monitoring. The 70-gene signature appears useful for identifying high risk behavior in MGUS patients thereby allowing early intervention and possible inclusion in clinical trials. MyPRS provides a risk assessment at a single point in time unlike recently reported metrics (ASCO Abs. # 8004) which measure a change in Hgb and M protein over time, along with bone marrow plasmacytosis, in order to determine the risk of progression. Figure 1 Figure 1. Figure 2 Figure 2. Disclosures van Laar: Signal Genetics, Inc.: Employment. Farmer:University of Arkansas: Employment. Bender:Signal Genetics, Inc.: Employment. Zielinski:Signal Genetics, Inc.: Employment. Leigh:Signal Genetics, Inc.: Employment. Brown:Signal Genetics, Inc.: Employment. Barlogie:Mount Sinai Hospital: Employment. Morgan:Univ of AR for Medical Sciences: Employment; Bristol Meyers: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Janssen: Research Funding; Celgene: Consultancy, Honoraria, Research Funding.


Author(s):  
Yael Haberman ◽  
Phillip Minar ◽  
Rebekah Karns ◽  
Phillip J Dexheimer ◽  
Sudhir Ghandikota ◽  
...  

Abstract Background and Aims Ileal strictures are the major indication for resective surgery in Crohn’s disease [CD]. We aimed to define ileal gene programmes present at diagnosis and linked with future stricturing behaviour during 5-year follow-up, and to identify potential small molecules to reverse these gene signatures. Methods Antimicrobial serologies and pre-treatment ileal gene expression were assessed in a representative subset of 249 CD patients within the RISK multicentre paediatric CD inception cohort study, including 113 that are unique to this report. These data were used to define genes associated with stricturing behaviour and for model testing to predict stricturing behaviour. A bioinformatics approach to define small molecules which may reverse the stricturing gene signature was applied. Results A total of 19 of the 249 patients developed isolated B2 stricturing behaviour during follow-up, while 218 remained B1 inflammatory. Using deeper RNA sequencing than in our previous report, we have now defined an inflammatory gene signature including an oncostatin M co-expression signature, tightly associated with extra-cellular matrix [ECM] gene expression, in those who developed stricturing complications. We further computationally prioritise small molecules targeting macrophage and fibroblast activation and angiogenesis which may reverse the stricturing gene signature. A model containing ASCA and CBir1 serologies and a refined eight ECM gene set was significantly associated with stricturing development by Year 5 after diagnosis {AUC (area under the curve) (95th CI [confidence interval]) = 0.82 [0.7–0.94)}. Conclusions An ileal gene programme for macrophage and fibroblast activation is linked to stricturing complications in treatment of naïve pediatric CD, and may inform novel small molecule therapeutic approaches.


2020 ◽  
Author(s):  
Guanbao Zhou ◽  
Genjie Lu ◽  
Liang Yang ◽  
Yangfang Lu

Abstract Background: Hepatocellular carcinoma (HCC) is the most common type of liver cancer with relatively poor prognosis. Thus, we aimed to identify novel molecular biomarkers to effectively predict the prognosis of HCC patients and eventually guide treatment. Methods: Prognosis-associated genes were determined by Kaplan-Meier and multivariate Cox regression analyses using the expression and clinical data of 373 HCC patients from The Cancer Genome Atlas (TCGA) database and validated in an independent Gene Expression Omnibus (GEO) dataset. The classification of AML was performed by unsupervised hierarchical clustering of ten gene expression levels. A prognostic risk score was established based on a linear combination of ten gene expression levels using the regression coefficients derived from the multivariate Cox regression models. Results: A total of 183 genes were significantly associated with prognosis in HCC. SLC25A15, RAB8A, GOT2, SORBS2, IL18RAP were top five protective genes, while FHL3, AMD1, DCAF13, UBE2E1, PTDSS2 were top five risk genes in HCC. SLC25A15, GOT2, IL18RAP were significantly down-regulated and DCAF13, PTDSS2 and SORBS2 were significantly up-regulated in the HCC samples and these genes exhibited high accuracy in differentiating HCC tissues from normal liver tissues. Hierarchical clustering analysis of the ten genes discovered three clusters of HCC patients. HCC tumors of cluster1 and 2 were significantly associated with more favourable OS than those of cluster3, cluster2 tumors showed higher pathologic stage than cluster3 tumors. The risk score was predictive of increased mortality rate in HCC patients. Conclusions: The ten-gene signature and the risk score may turn out to be novel molecular biomarkers and stratification of HCC patients to considerably ameliorate the prognostic prediction.


2021 ◽  
Vol 11 ◽  
Author(s):  
Qiu Lin ◽  
Li Luo ◽  
Hua Wang

Numerous colon cancer cases are resistant to chemotherapy based on oxaliplatin and suffer from relapse. A number of survival- and prognosis-related biomarkers have been identified based on database mining for patients who develop drug resistance, but the single individual gene biomarker cannot attain high specificity and sensitivity in prognosis prediction. This work was conducted aiming to establish a new gene signature using oxaliplatin resistance-related genes to predict the prognosis for colon cancer. To this end, we downloaded gene expression profile data of cell lines that are resistant and not resistant to oxaliplatin from the Gene Expression Omnibus (GEO) database. Altogether, 495 oxaliplatin resistance-related genes were searched by weighted gene co-expression network analysis (WGCNA) and differential expression analysis. As suggested by functional analysis, the above genes were mostly enriched into cell adhesion and immune processes. Besides, a signature was built based on four oxaliplatin resistance-related genes selected from the training set to predict the overall survival (OS) by stepwise regression and least absolute shrinkage and selection operator (LASSO) Cox analysis. Relative to the low risk score group, the high risk score group had dismal OS (P < 0.0001). Moreover, the area under the curve (AUC) value regarding the 5-year OS was 0.72, indicating that the risk score was accurate in the prediction of OS for colon cancer patients (AUC >0.7). Additionally, multivariate Cox regression suggested that the signature constructed based on four oxaliplatin resistance-related genes predicted the prognosis for colon cancer cases [hazard ratio (HR), 2.77; 95% CI, 2.03–3.78; P < 0.001]. Finally, external test sets were utilized to further validate the stability and accuracy of oxaliplatin resistance-related gene signature for prognosis of colon cancer patients. To sum up, this study establishes a signature based on four oxaliplatin resistance-related genes for predicting the survival of colon cancer patients, which sheds more light on the mechanisms of oxaliplatin resistance and helps identify colon cancer cases with a dismal prognostic outcome.


2020 ◽  
Author(s):  
Jelena Lukovic ◽  
Melania Pintilie ◽  
Jeffrey P. Bruce ◽  
Rob Cairns ◽  
Naz Chaudary ◽  
...  

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 3507-3507 ◽  
Author(s):  
Vivian G. Oehler ◽  
KaYee Yeung ◽  
Ailin Zhang ◽  
Theodore A. Gooley ◽  
Jerald P. Radich

Abstract Abstract 3507 Disease phase, transplant donor type, donor recipient match, age, and interval from diagnosis to transplantation (the EBMT risk score) are recognized variables that affect transplant outcomes for chronic myeloid leukemia (CML), but do not entirely account for the heterogeneity in outcomes. We have previously applied a probabilistic method to a large CML microarray gene expression dataset, and found a 6-gene signature of disease phase that discriminated between early and late chronic phase (CP). The combined expression of all 6 genes could be represented as a probability score where values closer to 0 are more similar to CP and values closer to 1 are more similar to blast crisis (BC). Moreover, in 17 accelerated phase (AP) CML patients, the 6-gene probability score was associated with outcomes after transplantation. We thus hypothesized that genes predictive of CML progression could be used to predict outcomes after transplantation regardless of disease phase. We derived 6 additional models (i.e. gene sets) from our CML microarray data, each consisting of 6–10 genes (total of 35 genes), that are also highly predictive of CML progression. These gene sets were derived using a novel network-driven approach aimed to identify genes that are functionally related to genes in pathways that are known to be associated with CML. We then examined expression of the genes in these models using quantitative PCR in bone marrow samples from 213 patients (176 CP, 23 AP, and 14 BC remission patients) prior to myeloablative allogeneic transplantation. GUSB was used as an endogenous control to correct for RNA integrity. Transplants occurred between 1993 and 2007 and a majority of patients did not receive prior tyrosine kinase inhibitor therapy. For CP patients, gene expression for all genes and models was independent of white blood cell and blast count. Among 176 CP CML patients, 45 patients died and 24 patients relapsed by last contact, leading to 1-year and 5-year estimates of overall survival of 85% and 78%, respectively, and 1-year and 5-year estimates of relapse of 7% and 12%, respectively. In CP patients we found not only that the expression of the original six-gene model (NOB1, DDX47, CD101, LTB4R, SCARB1, SLC25A3) was associated with a trend towards increased relapse, but that another model (RALGDS, LASP1, G6PD, ADRBKI, LRPPRC, PSMA1) was statistically significantly associated with an increased risk of relapse. In CP patients we found that an increase of 0.2 in the 6-gene probability score correlated with an increase in relapse of 46% (HR=1.46 (1.06-2.02, p=.02)) after adjustment for EBMT risk score (Figure 1a). Lastly, we also found that, individually, several of our progression-associated genes were statistically significantly associated with overall survival (G6PD and CAMK1D (Figure 1b)), relapse (RAC2 and ADRBK1), and non-relapse mortality (G6PD, CIQBP, and CAMK1D). In conclusion, these data suggest that gene expression prior to therapy is associated with treatment outcomes even after considering the contribution from known risk factors. These data provide evidence that a molecular signature associated with disease progression when detected in CP patients drives outcomes after transplantation. Given that all treatment outcomes are dependent on phase, it is possible that the expression of these genes prior to tyrosine kinase inhibitor therapy may also predict response. Figure 1. After adjustment for EBMT risk score, the probability of expression (Prob Score) of a 6-gene signature (RALGDS, LASP1, G6PD, ADRBKI, LRPPRC, PSMA1) correlates with relapse (Figure 1a) and CAMK1D expression correlates with overall survival (Figure 1b) after allogeneic transplantation in CP CML patients. Figure 1. After adjustment for EBMT risk score, the probability of expression (Prob Score) of a 6-gene signature (RALGDS, LASP1, G6PD, ADRBKI, LRPPRC, PSMA1) correlates with relapse (Figure 1a) and CAMK1D expression correlates with overall survival (Figure 1b) after allogeneic transplantation in CP CML patients. Disclosures: Oehler: Pfizer: Research Funding. Radich:Novartis: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria.


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