A novel machine learning prediction model for metastasis in breast cancer

BACKGROUND

Breast cancer (BC) metastasis is the common cause of high mortality. Conventional prognostic criteria cannot accurately predict the BC metastasis risk. The machine learning technologies can overcome the disadvantage of conventional models.

AIM

We developed a model to predict BC metastasis using the random survival forest (RSF) method.

METHODS

Based on demographic data and routine clinical data, we used RSF-recursive feature elimination to identify the predictive variables and developed a model to predict metastasis using RSF method. The area under the receiver operating characteristic curve (AUROC) and Kaplan-Meier survival (KM) analyses were plotted to validate the predictive effect when C-index was plotted to assess the discrimination and Brier scores was plotted to assess the calibration of the predictive model.

RESULTS

We developed a metastasis prediction model comprising three variables (pathological stage, aspartate aminotransferase, and neutrophil count) selected by RSF-recursive feature elimination. The model was reliable and stable when assessed by the AUROC (0.932 in training set and 0.905 in validation set) and KM survival analyses (p < .0001). The C-indexes (0.959) and Brier score (0.097) also validated the good predictive ability of this model.

CONCLUSIONS

This model relies on routine data and examination indicators in real-time clinical practice and exhibits an accurate prediction performance without increasing the cost for patients. Using this model, clinicians can facilitate risk communication and provide precise and efficient individualized therapy to patients with breast cancer.

Structure-Based Inhibitor Discovery of Class I Histone Deacetylases (HDACs)

Class I histone deacetylases (HDACs) are promising targets for epigenetic therapies for a range of diseases such as cancers, inflammations, infections and neurological diseases. Although six HDAC inhibitors are now licensed for clinical treatments, they are all pan-inhibitors with little or no HDAC isoform selectivity, exhibiting undesirable side effects. A major issue with the currently available HDAC inhibitors is that they have limited specificity and target multiple deacetylases. Except for HDAC8, Class I HDACs (1, 2 and 3) are recruited to large multiprotein complexes to function. Therefore, there are rising needs to develop new, hopefully, therapeutically efficacious HDAC inhibitors with isoform or complex selectivity. Here, upon the introduction of the structures of Class I HDACs and their complexes, we provide an up-to-date overview of the structure-based discovery of Class I HDAC inhibitors, including pan-, isoform-selective and complex-specific inhibitors, aiming to provide an insight into the discovery of additional HDAC inhibitors with greater selectivity, specificity and therapeutic utility.

Differential regulatory network-based quantification and prioritization of key genes underlying cancer drug resistance based on time-course RNA-seq data

Drug resistance is a major cause for the failure of cancer chemotherapy or targeted therapy. However, the molecular regulatory mechanisms controlling the dynamic evolvement of drug resistance remain poorly understood. Thus, it is important to develop methods for identifying key gene regulatory mechanisms of the resistance to specific drugs. In this study, we developed a data-driven computational framework, DryNetMC, using a differential regulatory network-based modeling and characterization strategy to quantify and prioritize key genes underlying cancer drug resistance. The DryNetMC does not only infer gene regulatory networks (GRNs) via an integrated approach, but also characterizes and quantifies dynamical network properties for measuring node importance. We used time-course RNA-seq data from glioma cells treated with dbcAMP (a cAMP activator) as a realistic case to reconstruct the GRNs for sensitive and resistant cells. Based on a novel node importance index that comprehensively quantifies network topology, network entropy and expression dynamics, the top ranked genes were verified to be predictive of the drug sensitivities of different glioma cell lines, in comparison with other existing methods. The proposed method provides a quantitative approach to gain insights into the dynamic adaptation and regulatory mechanisms of cancer drug resistance and sheds light on the design of novel biomarkers or targets for predicting or overcoming drug resistance.

Clinicopathological findings and imaging features of intraductal papillary neoplasm of the bile duct: comparison between contrast-enhanced ultrasound and contrast-enhanced computed tomography

PURPOSE

Intraductal papillary neoplasms of the bile duct (IPNBs) are a group of rare lesions with uncertain clinical findings and imaging features. We aim to investigate the clinicopathological features and imaging findings of IPNBs on contrast-enhanced ultrasound (CEUS) and contrast-enhanced computed tomography (CECT).

METHODS

From February 2005 to March 2018, 30 patients with pathologically confirmed IPNBs were retrospectively identified in our hospital. Demographic, clinical, and pathological data, CEUS and CECT features and surgical strategies were analyzed.

RESULTS

The most common clinical manifestations were abdominal pain (53.3%), jaundice (23.3%), and acute cholangitis (10.0%). Among all lesions, 5/30 (16.7%) lesions presented as dilated bile ducts only, while 13/30 (43.3%) lesions presented as dilated bile ducts with intraductal papillary masses, and 12/30 (40.0%) presented as solid masses with dilated bile ducts. For the 20 patients who underwent both CEUS and CECT, 18 lesions were hyperenhanced on CEUS, and 17 lesions were hyperenhanced on CECT in the arterial phase. In total, 16 and 18 lesions showed washout in the portal and late phases on CEUS, while the corresponding number of lesions that showed washout in the portal and late phases on CECT were 11 and 13. Twelve lesions (40.0%) showed atypical hyperplasia, while 16/30 (53.3%) lesions underwent malignant transformations.

CONCLUSIONS

There are 3 major forms of IPNBs on grayscale ultrasound, including diffusely dilated bile ducts without visible mass; focal dilated bile duct with intraductal papillary masses; and solid mass surrounded by dilated bile ducts. The enhancement patterns of IPNBs on CEUS and on CECT were consistent. IPNB has a high malignant potential, and patients should be treated with surgical resection after the diagnosis is established.