Molecular and Morphological Data Improve the Classification of Plantagineae (Lamiales).
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ABSTRACT:
SUBMITTER: Shipunov A
PROVIDER: S-EPMC8625185 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Identification of specific leukemia subtypes is a key to successful risk-directed therapy in childhood acute lymphoblastic leukemia (ALL). Although RNA sequencing (RNA-seq) is the best approach to identify virtually all specific leukemia subtypes, the routine use of this method is too costly for patients in resource-limited countries. This study enrolled 295 patients with pediatric ALL from 2010 to 2020. Routine screening could identify major cytogenetic alterations in approximately 69% of B-cell ALL (B-ALL) cases by RT-PCR, DNA index, and multiplex ligation-dependent probe amplification. STIL-TAL1 was present in 33% of T-cell ALL (T-ALL) cases. The remaining samples were submitted for RNA-seq. More than 96% of B-ALL cases and 74% of T-ALL cases could be identified based on the current molecular classification using this sequential approach. Patients with Philadelphia chromosome-like ALL constituted only 2.4% of the entire cohort, a rate even lower than those with ZNF384-rearranged (4.8%), DUX4-rearranged (6%), and Philadelphia chromosome-positive (4.4%) ALL. Patients with ETV6-RUNX1, high hyperdiploidy, PAX5 alteration, and DUX4 rearrangement had favorable prognosis, whereas those with hypodiploid and KMT2A and MEF2D rearrangement ALL had unfavorable outcomes. With the use of multiplex ligation-dependent probe amplification, DNA index, and RT-PCR in B-ALL and RT-PCR in T-ALL followed by RNA-seq, childhood ALL can be better classified to improve clinical assessments.
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Project description:BackgroundData artifacts due to variations in experimental handling are ubiquitous in microarray studies, and they can lead to biased and irreproducible findings. A popular approach to correct for such artifacts is through post hoc data adjustment such as data normalization. Statistical methods for data normalization have been developed and evaluated primarily for the discovery of individual molecular biomarkers. Their performance has rarely been studied for the development of multi-marker molecular classifiers-an increasingly important application of microarrays in the era of personalized medicine.MethodsIn this study, we set out to evaluate the performance of three commonly used methods for data normalization in the context of molecular classification, using extensive simulations based on re-sampling from a unique pair of microRNA microarray datasets for the same set of samples. The data and code for our simulations are freely available as R packages at GitHub.ResultsIn the presence of confounding handling effects, all three normalization methods tended to improve the accuracy of the classifier when evaluated in an independent test data. The level of improvement and the relative performance among the normalization methods depended on the relative level of molecular signal, the distributional pattern of handling effects (e.g., location shift vs scale change), and the statistical method used for building the classifier. In addition, cross-validation was associated with biased estimation of classification accuracy in the over-optimistic direction for all three normalization methods.ConclusionNormalization may improve the accuracy of molecular classification for data with confounding handling effects; however, it cannot circumvent the over-optimistic findings associated with cross-validation for assessing classification accuracy.
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Project description:BackgroundNasopharyngeal carcinoma (NPC) treatment is largely based on a 'one-drug-fits-all' strategy in patients with similar pathological characteristics. However, given its biological heterogeneity, patients at the same clinical stage or similar therapies exhibit significant clinical differences. Thus, novel molecular subgroups based on these characteristics may better therapeutic outcomes.MethodsHerein, 192 treatment-naïve NPC samples with corresponding clinicopathological information were obtained from Fujian Cancer Hospital between January 2015 and January 2018. The gene expression profiles of the samples were obtained by RNA sequencing. Molecular subtypes were identified by consensus clustering. External NPC cohorts were used as the validation sets.ResultsPatients with NPC were classified into immune, metabolic, and proliferative molecular subtypes with distinct clinical features. Additionally, this classification was repeatable and predictable as validated by the external NPC cohorts. Metabolomics has shown that arachidonic acid metabolites were associated with NPC malignancy. We also identified several key genes in each subtype using a weighted correlation network analysis. Furthermore, a prognostic risk model based on these key genes was developed and was significantly associated with disease-free survival (hazard ratio, 1.11; 95% CI, 1.07-1.16; P < 0.0001), which was further validated by an external NPC cohort (hazard ratio, 7.71; 95% CI, 1.39-42.73; P < 0.0001). Moreover, the 1-, 3-, and 5-year areas under the curve were 0.84 (95% CI, 0.74-0.94), 0.81 (95% CI, 0.73-0.89), and 0.82 (95% CI, 0.73-0.90), respectively, demonstrating a high predictive value.ConclusionsOverall, we defined a novel classification of nasopharyngeal carcinoma (immune, metabolism, and proliferation subtypes). Among these subtypes, metabolism and proliferation subtypes were associated with advanced stage and poor prognosis of NPC patients, whereas the immune subtype was linked to early stage and favorable prognosis.
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