Integration of molecular gene signatures with clinicopathological variables improves staging and outcome prediction of oral cancer.
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE84846 | GEO | 2017/08/23
SECONDARY ACCESSION(S): PRJNA335379
REPOSITORIES: GEO
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Project description:PurposeSmall intestinal (SI) neuroendocrine tumors (NETs) have heterogeneous outcomes. The NET societies have recently proposed a TNM staging classification. In this study, we used population-based data to assess the validity of the staging system.Patients and methodsWe identified patients with SI-NETS diagnosed between 1988 and 2009 from the Surveillance, Epidemiology, and End Results registry. We used Kaplan-Meier analysis to assess disease-specific survival according to TNM status. Cox models were constructed to evaluate differences in prognosis after controlling for potential confounders.ResultsWe identified 6,792 patients with SI-NET. Although the current staging system was predictive of prognosis, there was overlap among some groups (stage I/IIA, P = .36; stage IIB/IIIB, P = .70). Additionally, stage IIIB patients had better survival than stage IIIA patients (P < .001). Adjusted analyses showed similar outcomes for T1 versus T2 disease (hazard ratio [HR], 1.02; 95% CI, 0.63 to 1.66). Patients with T3 (HR, 3.60; 95% CI, 2.28 to 5.69) and T4 (HR, 5.50; 95% CI, 3.42 to 8.86) tumors had significantly worse survival than patients with T1 disease. N1 involvement conferred worse survival in T1 (HR, 3.08; 95% CI, 1.75 to 5.44) and T2 disease (HR, 2.73; 95% CI, 1.84 to 4.07) but not in T3 (HR, 0.99; 95% CI, 0.76 to 1.30) or T4 (HR, 0.98; 95% CI, 0.71 to 1.35) disease. A revised classification showed no overlap in survival across groups.ConclusionProgressively more advanced T status is associated with worse SI-NET prognosis. Regional lymph node involvement is a marker of worse survival only among patients with T1 or T2 status. These results suggest that revisions to the current staging classification may be helpful.
| S-EPMC3906571 | biostudies-literature
Project description:BackgroundMetastasized clear cell renal cell carcinoma (ccRCC) is associated with a poor prognosis. Almost one-third of patients with non-metastatic tumors at diagnosis will later progress with metastatic disease. These patients need to be identified already at diagnosis, to undertake closer follow up and/or adjuvant treatment. Today, clinicopathological variables are used to risk classify patients, but molecular biomarkers are needed to improve risk classification to identify the high-risk patients which will benefit most from modern adjuvant therapies. Interestingly, DNA methylation profiling has emerged as a promising prognostic biomarker in ccRCC. This study aimed to derive a model for prediction of tumor progression after nephrectomy in non-metastatic ccRCC by combining DNA methylation profiling with clinicopathological variables.MethodsA novel cluster analysis approach (Directed Cluster Analysis) was used to identify molecular biomarkers from genome-wide methylation array data. These novel DNA methylation biomarkers, together with previously identified CpG-site biomarkers and clinicopathological variables, were used to derive predictive classifiers for tumor progression.ResultsThe "triple classifier" which included both novel and previously identified DNA methylation biomarkers together with clinicopathological variables predicted tumor progression more accurately than the currently used Mayo scoring system, by increasing the specificity from 50% in Mayo to 64% in our triple classifier at 85% fixed sensitivity. The cumulative incidence of progress (pCIP5yr) was 7.5% in low-risk vs 44.7% in high-risk in M0 patients classified by the triple classifier at diagnosis.ConclusionsThe triple classifier panel that combines clinicopathological variables with genome-wide methylation data has the potential to improve specificity in prognosis prediction for patients with non-metastatic ccRCC.
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Project description:Patients with tuberculous meningitis (TBM) in any stage of the British Medical Research Council (BMRC) scale, if requiring mechanical ventilation (MV), are likely to have a poor outcome. We report the usefulness of BMRC, BMRC-MV, and BMRC-hydrocephalus (BMRC-HC) staging, and Haydarpasa Meningitis Severity Index (HAMSI) scoring in predicting the outcome of TBM. One hundred ninety-seven TBM patients were analyzed from a prospectively maintained TBM registry. The severity of meningitis was categorized using BMRC (stages I-III), BMRC-MV (I-IV [MV patients were grouped as stage IV]), and BMRC-HC (I-IV [BMRC stage III patients with hydrocephalus were grouped as stage IV]). Haydarpasa Meningitis Severity Index scoring was categorized as < 6 and ≥ 6. The outcome was defined at 6 months using the modified Rankin Scale (mRS) as death, poor (mRS score > 2), or good (mRS score ≤ 2). Forty-nine (25%) patients died. BMRC-mechanical ventilation stage IV had the highest predictive value for defining death, with a sensitivity of 88% and a specificity of 86%. About 81.7% of surviving patients had a good outcome at 6 months. BMRC-mechanical ventilation stages I-III had the highest predictive value for defining good outcome, with a sensitivity of 93% and a specificity of 61%. In TBM, BMRC-MV staging has the best predictive value for defining death and disability.
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Project description:Accurate staging and outcome prediction is a major problem in clinical management of oral cancer patients, hampering high precision treatment and adjuvant therapy planning. Here, we have built and validated multivariable models that integrate gene signatures with clinical and pathological variables to improve staging and survival prediction of patients with oral squamous cell carcinoma (OSCC). Gene expression profiles from 249 human papillomavirus (HPV)-negative OSCCs were explored to identify a 22-gene lymph node metastasis signature (LNMsig) and a 40-gene overall survival signature (OSsig). To facilitate future clinical implementation and increase performance, these signatures were transferred to quantitative polymerase chain reaction (qPCR) assays and validated in an independent cohort of 125 HPV-negative tumors. When applied in the clinically relevant subgroup of early-stage (cT1-2N0) OSCC, the LNMsig could prevent overtreatment in two-third of the patients. Additionally, the integration of RT-qPCR gene signatures with clinical and pathological variables provided accurate prognostic models for oral cancer, strongly outperforming TNM. Finally, the OSsig gene signature identified a subpopulation of patients, currently considered at low-risk for disease-related survival, who showed an unexpected poor prognosis. These well-validated models will assist in personalizing primary treatment with respect to neck dissection and adjuvant therapies.
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Project description:BackgroundIt remains unknown to what extent consensus molecular subtype (CMS) groups and immune-stromal infiltration patterns improve our ability to predict outcomes over tumor-node-metastasis (TNM) staging and microsatellite instability (MSI) status in early-stage colorectal cancer (CRC).Patients and methodsWe carried out a comprehensive retrospective biomarker analysis of prognostic markers in adjuvant chemotherapy-untreated (N = 1656) and treated (N = 980), stage II (N = 1799) and III (N = 837) CRCs. We defined CMS scores and estimated CD8+ cytotoxic lymphocytes (CytoLym) and cancer-associated fibroblasts (CAF) infiltration scores from bulk tumor tissue transcriptomes (CMSclassifier and MCPcounter R packages); constructed a stratified multivariable Cox model for disease-free survival (DFS); and calculated the relative proportion of explained variation by each marker (clinicopathological [ClinPath], genomics [Gen: MSI, BRAF and KRAS mutations], CMS scores [CMS] and microenvironment cells [MicroCells: CytoLym+CAF]).ResultsIn multivariable models, only ClinPath and MicroCells remained significant prognostic factors, with both CytoLym and CAF infiltration scores improving survival prediction beyond other markers. The explained variation for DFS models of ClinPath, MicroCells, Gen markers and CMS4 scores was 77%, 14%, 5.3% and 3.7%, respectively, in stage II; and 55.9%, 35.1%, 4.1% and 0.9%, respectively, in stage III. Patients whose tumors were CytoLym high/CAF low had better DFS than other strata [HR=0.71 (0.6-0.9); P = 0.004]. Microsatellite stable tumors had the strongest signal for improved outcomes with CytoLym high scores (interaction P = 0.04) and the poor prognosis linked to high CAF scores was limited to stage III disease (interaction P = 0.04).ConclusionsOur results confirm that tumor microenvironment infiltration patterns represent potent determinants of the risk for distant dissemination in early-stage CRC. Multivariable models suggest that the prognostic value of MSI and CMS groups is largely explained by CytoLym and CAF infiltration patterns.
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Project description:Background: Colon cancers with defective DNA mismatch repair (MMR) have a favorable prognosis and may lack benefit from 5-fluorouracil-based adjuvant chemotherapy. The authors developed models to predict MMR deficiency in sporadic colon cancer patients using routine clinical and pathological data.Methods: TNM stage II and III colon carcinomas (n = 982) from 6 5-fluorouracil-based adjuvant therapy trials were analyzed for microsatellite instability and/or MMR protein expression. Tumor-infiltrating lymphocytes (TILs) were quantified (n = 326). Logistic regression and a recursive partitioning and amalgamation analysis were used to identify predictive factors for MMR status.Results: Defective MMR was detected in 147 (15%) cancers. Tumor site and histologic grade were the most important predictors of MMR status. Distal tumors had a low likelihood of defective MMR (3%; 13 of 468); proximal tumors had a greater likelihood (26%; 130 of 506). By using tumor site, grade, and sex, the logistic regression model showed excellent discrimination (c statistic = 0.81). Proximal site, female sex, and poor differentiation showed a positive predictive value (PPV) of 51% for defective MMR. In a patient subset (n = 326), a model including proximal site, TILs (>2/high-power field), and female sex showed even better discrimination (c statistic = 0.86), with a PPV of 81%.Conclusions: Defective MMR is rare in distal, sporadic colon cancers, which should generally not undergo MMR testing. Proximal site, poor differentiation, and female sex detect 51% of tumors with defective MMR; substituting TILs for grade increases the PPV to 81%. These data can increase the efficiency of MMR testing to assist in clinical decisions. Cancer 2010. (c) 2010 American Cancer Society.
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Project description:Metastasis via the lymphatics is a major risk factor in squamous cell carcinoma of the oral cavity (OSCC). We sought to determine whether the presence of metastasis in the regional lymph node could be predicted by a gene expression signature of the primary tumor. A total of 18 OSCCs were characterized for gene expression by hybridizing RNA to Affymetrix U133A gene chips. Genes with differential expression were identified using a permutation technique and verified by quantitative RT-PCR and immunohistochemistry. A predictive rule was built using a support vector machine, and the accuracy of the rule was evaluated using crossvalidation on the original data set and prediction of an independent set of four patients. Metastatic primary tumors could be differentiated from nonmetastatic primary tumors by a signature gene set of 116 genes. This signature gene set correctly predicted the four independent patients as well as associating five lymph node metastases from the original patient set with the metastatic primary tumor group. We concluded that lymph node metastasis could be predicted by gene expression profiles of primary oral cavity squamous cell carcinomas. The presence of a gene expression signature for lymph node metastasis indicates that clinical testing to assess risk for lymph node metastasis should be possible. Keywords = oral carcinoma lymphatic metastasis predict Keywords: repeat sample
2005-02-19 | GSE2280 | GEO
Project description:ObjectivesThe aim of this study was to investigate 3 Tesla multiparametric magnetic resonance imaging (mpMRI)-based predictors for the pretherapeutic T staging of prostate cancer and their accuracy.MethodsConsecutive patients with 3 Tesla mpMRI, positive systematic and MR-targeted biopsy, and subsequent radical prostatectomy (RPE) between 01/2016 and 12/2017 were included. MRI parameters such as measurable extraprostatic extension (EPE) (≥ 3 mm), length of (pseudo)capsular contact (LCC), invasion of neurovascular bundle (NVBI), and/or seminal vesicles lesion contact (SVC) or infiltration (SVI) were assessed and correlated to clinical and histopathological results.Results136 men were included. In 76 cases, a pT2 stage was determined, in 29 cases a pT3a, and in 31 a pT3b stage. The positive and negative predictive values (PPV, NPV) for the detection of T3 by measurable EPE on MRI was 98% (CI 0.88-1) and 81% (CI 0.72-0.87). No visible NVBI was found in pT2 patients (NPV 100%; CI 0.95-1). ROC analysis for T3a prediction with LCC (AUC 0.81) showed a sensitivity of 87% and a specificity of 62% at a threshold of 12.5 mm (J = 0.485) and 93% and 58% at 11 mm (Jmax = 0.512). All patients with pT3a had a LCC > 5 mm. In case of pT3b, 29/31 patients showed a SVC (PPV 76%, CI 0.61-0.87; NPV 98%, CI 0.93-0.99), and 23/31 patients showed a SVI (PPV 100%, CI 0.86-1; NPV 93%, CI 0.87-0.96). EPE (p < 0.01), LCC (p = 0.05), and SVC (p = 0.01) were independent predictors of pT3.ConclusionsMRI-measurable EPE, LCC, and SVC were reliable, independent, preoperative predictors for a histopathological T3 stage. A LCC ≥ 11 mm indicated a pT3a stage, whereas a LCC < 5 mm excluded it. On MRI, visible SVI or even SVC of the PCa lesion was reliable preoperative predictors for a pT3b stage.
| S-EPMC8205913 | biostudies-literature
Project description:Despite wide applications of high-throughput biotechnologies in cancer research, many biomarkers discovered by exploring large-scale omics data do not provide satisfactory performance when used to predict cancer treatment outcomes. This problem is partly due to the overlooking of functional implications of molecular markers. Here, we present a novel computational method that uses evolutionary conservation as prior knowledge to discover bona fide biomarkers. Evolutionary selection at the molecular level is nature's test on functional consequences of genetic elements. By prioritizing genes that show significant statistical association and high functional impact, our new method reduces the chances of including spurious markers in the predictive model. When applied to predicting therapeutic responses for patients with acute myeloid leukemia and to predicting metastasis for patients with prostate cancers, the new method gave rise to evolution-informed models that enjoyed low complexity and high accuracy. The identified genetic markers also have significant implications in tumor progression and embrace potential drug targets. Because evolutionary conservation can be estimated as a gene-specific, position-specific, or allele-specific parameter on the nucleotide level and on the protein level, this new method can be extended to apply to miscellaneous "omics" data to accelerate biomarker discoveries.
| S-EPMC5192825 | biostudies-literature
Project description:Purpose: The current stratification system for acute promyelocytic leukemia (APL) is based on the white blood cell (WBC) and the platelet counts (i.e., Sanz score) over the past two decades. However, the borderlines among different risk groups are sometimes ambiguous, and for some patients, early death and relapse remained challenges. Besides, with the evolving of the treatment strategy from ATRA-chemotherapy to ATRA-ATO-based synergistic targeted therapy, the precise risk stratification with molecular markers is needed. Patients and Methods: This study performed a systematic analysis of APL genomics and transcriptomics to identify genetic abnormalities in 348 patients mainly from the APL2012 trial (NCT01987297) to illustrate the potential molecular background of Sanz score and further optimize it. The least absolute shrinkage and selection operator (LASSO) algorithm was used to analyze the gene expression in 323 cases to establish a scoring system (i.e., APL9 score). Results: Through combining NRAS mutations, APL9 score with WBC, 321 cases can be stratified into two groups with significantly different outcomes. The estimated 5-year overall (P = 0.00031), event-free (P < 0.0001), and disease-free (P = 0.001) survival rates in the revised standard-risk group (95.6%, 93.8%, and 98.1%, respectively) were significantly better than those in the revised high-risk group (82.9%, 77.4%, and 88.4%, respectively), which could be validated using The Cancer Genome Atlas dataset. Conclusions: We have proposed a two-category system improving prognosis in APL patients. Molecular markers identified in this study may also provide genomic insights into the disease mechanism for improved therapy.
2021-04-14 | GSE172057 | GEO