Internal validation cohort of breast cancers for development of ClinicoMolecular Triad Classification
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ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Wang Dong-Yu
PROVIDER: S-ECPF-GEOD-45725 | biostudies-other |
REPOSITORIES: biostudies-other
Similar Datasets
Project description:In our early study (PMID: 21939527), we have created a ClinicoMolecular Triad Classification (CMTC) to improve prediction and prognostication of breast cancer by using a training cohort contained 161 breast cancer patients (2003 to 2008). Here, a supplemental internal validation cohort contained 340 breast cancer patients was collected (2008 to 2010) for development of the CMTC.
2014-06-19 | GSE45725 | GEO
Project description:In our early study ([GEO Accession: GSE16987], [PMID: 21939527]), we have created a ClinicoMolecular Triad Classification (CMTC) to improve prediction and prognostication of breast cancer by using a training cohort contained 161 breast cancer patients (2003 to 2008). Here, a supplemental internal validation cohort contained 340 breast cancer patients was collected (2008 to 2010) for development of the CMTC. Total 340 fine-needle aspirates (FNA) specimens from breast tumors without replicate were collected and prepared for microarray examination. Finally, 284 invasive breast cancers were used in this study.
2014-06-19 | E-GEOD-45725 | biostudies-arrayexpress
Project description:INTRODUCTION: Using genome-wide expression profiles of a prospective training cohort of breast cancer patients, ClinicoMolecular Triad Classification (CMTC) was recently developed to classify breast cancers into three clinically relevant groups to aid treatment decisions. CMTC was found to be both prognostic and predictive in a large external breast cancer cohort in that study. This study serves to validate the reproducibility of CMTC and its prognostic value using independent patient cohorts. METHODS: An independent internal cohort (n = 284) and a new external cohort (n = 2,181) were used to validate the association of CMTC between clinicopathological factors, 12 known gene signatures, two molecular subtype classifiers, and 19 oncogenic signalling pathway activities, and to reproduce the abilities of CMTC to predict clinical outcomes of breast cancer. In addition, we also updated the outcome data of the original training cohort (n = 147). RESULTS: The original training cohort reached a statistically significant difference (p < 0.05) in disease-free survivals between the three CMTC groups after an additional two years of follow-up (median = 55 months). The prognostic value of the triad classification was reproduced in the second independent internal cohort and the new external validation cohort. CMTC achieved even higher prognostic significance when all available patients were analyzed (n = 4,851). Oncogenic pathways Myc, E2F1, Ras and ?-catenin were again implicated in the high-risk groups. CONCLUSIONS: Both prospective internal cohorts and the independent external cohorts reproduced the triad classification of CMTC and its prognostic significance. CMTC is an independent prognostic predictor, and it outperformed 12 other known prognostic gene signatures, molecular subtype classifications, and all other standard prognostic clinicopathological factors. Our results support further development of CMTC portfolio into a guide for personalized breast cancer treatments.
| S-EPMC4226941 | biostudies-literature
Project description:Purpose This study aims to develop and validate a prediction model for non-operative, epidermal growth factor receptor (EGFR)-positive, locally advanced elderly esophageal cancer (LAEEC). Methods A total of 80 EGFR-positive LAEEC patients were included in the study. All patients underwent radiotherapy, while 41 cases received icotinib concurrent systemic therapy. A nomogram was established using univariable and multivariable Cox analyses. The model’s efficacy was assessed through area under curve (AUC) values, receiver operating characteristic (ROC) curves at different time points, time-dependent AUC (tAUC), calibration curves, and clinical decision curves. Bootstrap resampling and out-of-bag (OOB) cross-validation methods were employed to verify the model’s robustness. Subgroup survival analysis was also conducted. Results Univariable and multivariable Cox analyses revealed that icotinib, stage, and ECOG score were independent prognostic factors for LAEEC patients. The AUCs of model-based prediction scoring (PS) for 1-, 2-, and 3-year overall survival (OS) were 0.852, 0.827, and 0.792, respectively. Calibration curves demonstrated that the predicted mortality was consistent with the actual mortality. The time-dependent AUC of the model exceeded 0.75, and the internal cross-validation calibration curves showed good agreement between predicted and actual mortality. Clinical decision curves indicated that the model had a substantial net clinical benefit within a threshold probability range of 0.2 to 0.8. Model-based risk stratification analysis demonstrated the model’s excellent ability to distinguish survival risk. Further subgroup analyses showed that icotinib significantly improved survival in patients with stage III and ECOG score of 1 (HR 0.122, P<0.001). Conclusions Our nomogram model effectively predicts the overall survival of LAEEC patients, and the benefits of icotinib were found in the clinical stage III population with good ECOG scores.
| S-EPMC10213387 | biostudies-literature
Project description:BACKGROUND:Abundant evidence shows that triple-negative breast cancer (TNBC) is heterogeneous, and many efforts have been devoted to identifying TNBC subtypes on the basis of genomic profiling. However, few studies have explored the classification of TNBC specifically based on immune signatures that may facilitate the optimal stratification of TNBC patients responsive to immunotherapy. METHODS:Using four publicly available TNBC genomics datasets, we classified TNBC on the basis of the immunogenomic profiling of 29 immune signatures. Unsupervised and supervised machine learning methods were used to perform the classification. RESULTS:We identified three TNBC subtypes that we named Immunity High (Immunity_H), Immunity Medium (Immunity_M), and Immunity Low (Immunity_L) and demonstrated that this classification was reliable and predictable by analyzing multiple different datasets. Immunity_H was characterized by greater immune cell infiltration and anti-tumor immune activities, as well as better survival prognosis compared to the other subtypes. Besides the immune signatures, some cancer-associated pathways were hyperactivated in Immunity_H, including apoptosis, calcium signaling, MAPK signaling, PI3K-Akt signaling, and RAS signaling. In contrast, Immunity_L presented depressed immune signatures and increased activation of cell cycle, Hippo signaling, DNA replication, mismatch repair, cell adhesion molecule binding, spliceosome, adherens junction function, pyrimidine metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, and RNA polymerase pathways. Furthermore, we identified a gene co-expression subnetwork centered around five transcription factor (TF) genes (CORO1A, STAT4, BCL11B, ZNF831, and EOMES) specifically significant in the Immunity_H subtype and a subnetwork centered around two TF genes (IRF8 and SPI1) characteristic of the Immunity_L subtype. CONCLUSIONS:The identification of TNBC subtypes based on immune signatures has potential clinical implications for TNBC treatment.
| S-EPMC6310928 | biostudies-literature
Project description:Sample collection and identification of biomarker for personalized therapy in tuberculosis patients
2020-03-30 | GSE147689 | GEO
Project description:Objective: To develop a predictive risk model (PRM) for patient-reported anxiety after treatment completion for early stage breast cancer suitable for use in practice and underpinned by advances in data science and risk prediction.Methods: Secondary analysis of a prospective survey of >?800 women at the end of treatment and again 6?months later using patient reported outcome (PRO) the hospital anxiety and depression scale-anxiety (HADS-A) and?>?20 candidate predictors. Multiple imputation using chained equations (for missing data) and least absolute shrinkage and selection operator (LASSO) were used to select predictors. Final multivariable linear model performance was assessed (R2) and bootstrapped for internal validation.Results: Five predictors of anxiety selected by LASSO were HADS-A (Beta 0.73; 95% CI 0.681, 0.785); HAD-depression (Beta 0.095; 95% CI 0.020, 0.182) and having caring responsibilities (Beta 0.488; 95% CI 0.084, 0.866) increased risk, whereas being older (Beta -?0.010; 95% CI -0.028, 0.004) and owning a home (Beta 0.432; 95% CI -0.954, 0.078) reduced the risk. The final model explained 60% of variance and bias was low (-?0.006 to 0.002).Conclusions: Different modelling approaches are needed to predict rather than explain patient reported outcomes. We developed a parsimonious and pragmatic PRM. External validation is required prior to translation to digital tool and evaluation of clinical implementation. The routine use of PROs and data driven PRM in practice provides a new opportunity to target supportive care and specialist interventions for cancer patients.
| S-EPMC7718350 | biostudies-literature
Project description:Tuberculosis biomarker validation cohort
| PRJNA616117 | ENA
Project description:The purpose of the present study was to compare a new internal mammary irradiation technique with traditional techniques for locally advanced breast cancers in terms of sparing ipsilateral lung and heart and reducing the "cold" and "hot spots"in breast tissue. The new technique uses wide tangential fields for the first eight fractions of treatment. A medial internal mammary field (IMF) of electrons matched with narrowed tangential fields is used for the remaining fractions. Intensity-modulated radiation therapy (IMRT) by means of segmented multileaf collimation (SMLC) is used in the narrowed tangential fields to improve the match between the electron and the photon fields. Treatment planning was performed to compare this technique to a wide-tangential-only technique and to a traditional oblique IMF technique for three patients with differing habitus. Film dosimetry was performed in a solid water phantom to confirm the planning results. For all three patients, the mean doses of the ipsilateral lung and the heart were significantly reduced with the new technique. The lung and the heart volumes were remarkably reduced at lowdose levels (< or =12 Gy) compared to the traditional IMF technique, and significantly reduced at all dose levels compared to the wide tangential technique. The new technique also reduced the "cold" and "hot spots" along the match plane between the IMF and the tangential fields compared to the traditional IMF technique. In conclusion, the new IMF technique shows dosimetric improvement compared to the traditional IMF technique in terms of the critical organ sparing and target dose uniformity.
| S-EPMC5723509 | biostudies-literature
Project description:ObjectiveWe aimed to develop and validate a score to assess inpatient complexity and compare its performance with two currently used but not validated tools to estimate complexity (ie, Charlson Comorbidity Index (CCI), patient clinical complexity level (PCCL)).MethodsConsecutive patients discharged from the department of medicine of a tertiary care hospital were prospectively included into a derivation cohort from 1 October 2016 to 16 February 2017 (n=1407), and a temporal validation cohort from 17 February 2017 to 31 March 2017 (n=482). The physician in charge assessed complexity. Potential predictors comprised 52 parameters from the electronic health record such as health factors and hospital care usage. We fit a logistic regression model with backward selection to develop a prediction model and derive a score. We assessed and compared performance of model and score in internal and external validation using measures of discrimination and calibration.ResultsOverall, 447 of 1407 patients (32%) in the derivation cohort, and 116 of 482 patients (24%) in the validation cohort were identified as complex. Eleven variables independently associated with complexity were included in the score. Using a cut-off of ≥24 score points to define high-risk patients, specificity was 81% and sensitivity 57% in the validation cohort. The score's area under the receiver operating characteristic (AUROC) curve was 0.78 in both the derivation and validation cohort. In comparison, the CCI had an AUROC between 0.58 and 0.61, and the PCCL between 0.64 and 0.69, respectively.ConclusionsWe derived and internally and externally validated a score that reflects patient complexity in the hospital setting, performed better than other tools and could help monitoring complex patients.
| S-EPMC8103941 | biostudies-literature