Machine learning: a powerful tool for identifying key microbial agents associated with specific cancer types.

Machine learning (ML) includes a broad class of computer programs that improve with experience and shows unique strengths in performing tasks such as clustering, classification and regression. Over the past decade, microbial communities have been implicated in influencing the onset, progression, metastasis, and therapeutic response of multiple cancers. Host-microbe interaction may be a physiological pathway contributing to cancer development. With the accumulation of a large number of high-throughput data, ML has been successfully applied to the study of human cancer microbiomics in an attempt to reveal the complex mechanism behind cancer. In this review, we begin with a brief overview of the data sources included in cancer microbiomics studies. Then, the characteristics of the ML algorithm are briefly introduced. Secondly, the application progress of ML in cancer microbiomics is also reviewed. Finally, we highlight the challenges and future prospects facing ML in cancer microbiomics. On this basis, we conclude that the development of cancer microbiomics can not be achieved without ML, and that ML can be used to develop tumor-targeting microbial therapies, ultimately contributing to personalized and precision medicine.

Radiomics analysis of pretreatment MRI in predicting tumor response and outcome in hepatocellular carcinoma with transarterial chemoembolization: a two-center collaborative study.

BACKGROUND AND OBJECTIVE: To develop a machine-learning model by integrating clinical and imaging modalities for predicting tumor response and survival of hepatocellular carcinoma (HCC) with transarterial chemoembolization (TACE). METHODS: 140 HCC patients with TACE were retrospectively included from two centers. Tumor response were evaluated using modified Response Evaluation Criteria in Solid Tumors (mRECIST) criteria. Response-related radiomics scores (Rad-scores) were constructed on T2-weighted images (T2WI) and dynamic contrast-enhanced (DCE) imaging separately, and then integrated with conventional clinic-radiological variables into a logistic regression (LR) model for predicting tumor response. LR model was trained in 94 patients in center 1 and independently tested in 46 patients in center 2. RESULTS: Among 4 MRI sequences, T2WI achieved better performance than DCE (area under the curve [AUC] 0.754 vs 0.602 to 0.752). LR model by combining Rad-score on T2WI with Barcelona Clinic Liver Cancer (BCLC) stage and albumin-bilirubin (ALBI) grade resulted in an AUC of 0.813 in training and 0.781 in test for predicting tumor response. In survival analysis, progression-free survival (PFS) and overall survival (OS) presented significant difference between LR-predicted responders and non-responders. The ALBI grade and BCLC stage were independent predictors of PFS; and LR-predicted response, ALBI grade, satellite node, and BCLC stage were independent predictors of OS. The resulting Cox model produced concordance-indexes of 0.705 and 0.736 for predicting PFS and OS, respectively. CONCLUSIONS: The model combined MRI radiomics with clinical factors demonstrated favorable performance for predicting tumor response and clinical outcomes, thus may help personalized clinical management.

The safety and efficacy of TACE combined with apatinib on patients with advanced hepatocellular carcinoma: a retrospective study.

As a novel vascular endothelial growth factor receptor-2 tyrosine kinase inhibitor (VEGFR2-TKI), apatinib has a certain anti-tumor effect for a variety of solid tumors. The present study evaluates its efficacy and safety in advanced hepatocellular carcinoma (HCC). In this study, 47 patients with advanced HCC were included. TACE monotherapy group included 22 patients that responded to TACE, while the group that received TACE and apatinib included 25 patients that progressed on TACE and were able to receive apatinib off label. Median overall survival (OS) was significantly improved in the apatinib plus TACE group compared with the TACE group. Similarly, apatinib in combination with TACE significantly prolonged median progression-free survival (PFS) compared with TACE monotherapy. Furthermore, there was a significant difference between combination therapy and monotherapy in both Barcelona clinic liver cancer (BCLC) B and BCLC C group. The combination therapy had a dramatic effect on OS and PFS for patients at both BCLC B and BCLC C level. The most common clinically adverse events of apatinib plus TACE group were fatigue, weight loss, hypertension, hand-foot syndrome and anorexia, which were manageable and tolerable. The efficacy analysis showed that there was no significant association of survival benefit with age, gender, Eastern Cooperative Oncology Group (ECOG) performance status, hypertension and hand-foot syndrome. Patients with macrovascular invasion and extrahepatic invasion showed worse survival benefits. In conclusion, apatinib combined with TACE revealed certain survival benefits for HCC patients who experienced progression following TACE, which can provide a promising strategy for HCC treatment.