Distribution of segmental chromosomal alterations in neuroblastoma

A Novel Online Nomogram Established with Five Features before Surgical Resection for Predicating Prognosis of Neuroblastoma Children: A Population-Based Study

Background: Neuroblastoma (NB) is the most common childhood cancer, but doctors are unable to predict its overall survival (OS) rate before surgery. We aimed to predict the OS of NB children with some clinical features obtained from biopsy before surgery. Methods: Clinical features of NB children were retrospectively collected from the Therapeutically Applicable Research to Generate Effective Treatments database. The C-index, area under the receiver operating characteristic curve (AUC), calibration curves, and decision curves analysis were used to estimate nomogram models. Results: A total of 488 NB children were evaluated, and the Boruta algorithm was used to detect risk factors. The results showed that artificial neural networks with selected features were able to predict more than 90% of NB children. Five risk factors were used in the construction of the nomogram, including age at diagnosis, MYCN status, ploidy value, histology, and mitosis-karyorrhexis index (MKI). The C-index of the nomogram in training cohort and validation cohort was 0.716 and 0.731. AUC values for 1-, 3-, and 5-years OS predictions were 0.706, 0.755, and 0.762, respectively, and showed good calibrations. Decision curve analysis indicated a better predictability with the nomogram model based on Cox regression compared with one that included all variables and histology only. Also, the Kaplan-Meier curves showed a significantly higher survival probability in the low-risk group (total score <118.34) versus the high-risk group (total score ≥ 118.34) (p < 0.05) using the nomogram model. Conclusions: A web application based on the nomogram model in the present study can be accessed at https://mdzhou.shinyapps.io/DynNomapp/, which could help doctors make accurate clinical decisions about NB children.

Spatial and temporal intratumour heterogeneity has potential consequences for single biopsy-based neuroblastoma treatment decisions

Intratumour heterogeneity is a major cause of treatment failure in cancer. We present in-depth analyses combining transcriptomic and genomic profiling with ultra-deep targeted sequencing of multiregional biopsies in 10 patients with neuroblastoma, a devastating childhood tumour. We observe high spatial and temporal heterogeneity in somatic mutations and somatic copy-number alterations which are reflected on the transcriptomic level. Mutations in some druggable target genes including ALK and FGFR1 are heterogeneous at diagnosis and/or relapse, raising the issue whether current target prioritization and molecular risk stratification procedures in single biopsies are sufficiently reliable for therapy decisions. The genetic heterogeneity in gene mutations and chromosome aberrations observed in deep analyses from patient courses suggest clonal evolution before treatment and under treatment pressure, and support early emergence of metastatic clones and ongoing chromosomal instability during disease evolution. We report continuous clonal evolution on mutational and copy number levels in neuroblastoma, and detail its implications for therapy selection, risk stratification and therapy resistance.

AKR1C1 Contributes to Cervical Cancer Progression via Regulating TWIST1 Expression

Cervical cancer (CC) is a common gynecological malignancy, accounting for 10% of all gynecological cancers. Recently, targeted therapy for CC has shown unprecedented advantages. To improve CC patients' prognosis, there are still urgent needs to develop more promising therapeutic targets. Aldo-keto reductase 1 family member C1 (AKR1C1) is a type of aldosterone reductase and plays a regulatory role in a variety of key metabolic pathways. Several studies indicated that AKR1C1 was highly expressed in a series of tumors, and participated in the progression of these tumors. However, the possible effects of AKR1C1 on CC progression remain unclear. Herein, we revealed AKR1C1 was highly expressed in human CC tissues and correlated with the clinical characteristics of patients with CC. AKR1C1 could regulate the proliferation and invasion of cervical cancer cells in vitro. Further experiments showed that AKR1C1 could regulate TWIST1 expression and AKT pathway. In summary, we confirmed the involvement of AKR1C1 in CC progression, and therefore AKR1C1 may have the potential to be a molecular target for CC treatment.