Microbiome in Cancer Development and Treatment

Targeting the microbiome, microbiota-derived metabolites, and related pathways represents a significant challenge in oncology. Microbiome analyses have confirmed the negative impact of cancer treatment on gut homeostasis, resulting in acute dysbiosis and severe complications, including massive inflammatory immune response, mucosal barrier disruption, and bacterial translocation across the gut epithelium. Moreover, recent studies revealed the relationship between an imbalance in the gut microbiome and treatment-related toxicity. In this review, we provide current insights into the role of the microbiome in tumor development and the impact of gut and tumor microbiomes on chemo- and immunotherapy efficacy, as well as treatment-induced late effects, including cognitive impairment and cardiotoxicity. As discussed, microbiota modulation via probiotic supplementation and fecal microbiota transplantation represents a new trend in cancer patient care, aiming to increase bacterial diversity, alleviate acute and long-term treatment-induced toxicity, and improve the response to various treatment modalities. However, a more detailed understanding of the complex relationship between the microbiome and host can significantly contribute to integrating a microbiome-based approach into clinical practice. Determination of causal correlations might lead to the identification of clinically relevant diagnostic and prognostic microbial biomarkers. Notably, restoration of intestinal homeostasis could contribute to optimizing treatment efficacy and improving cancer patient outcomes.

Real-Time Fluorescence Imaging Using Indocyanine Green to Assess Therapeutic Effects of Near-Infrared Photoimmunotherapy in Tumor Model Mice

Background:

Near-infrared photoimmunotherapy (NIR-PIT) is a cancer therapy that causes an increase in tumor perfusion, a phenomenon termed the super-enhanced permeability and retention effect. Currently, in vivo treatment efficacy of NIR-PIT is observable days after treatment, but monitoring would be improved by more acute detection of intratumor change. Fluorescence imaging may detect increased tumor perfusion immediately after treatment.

Methods:

In the first experiment, athymic nude mouse models bearing unilateral subcutaneous flank tumors were treated with either NIR-PIT or laser therapy only. In the second experiment, mice bearing bilateral flank tumors were treated with NIR-PIT only on the left-sided tumor. In both groups, immediately after treatment, indocyanine green was injected at different doses intravenously, and mice were monitored with the Shimadzu LIGHTVISION fluorescence imaging system for 1 hour.

Results:

Tumor-to-background ratio of fluorescence intensity increased over the 60 minutes of monitoring in treated mice but did not vary significantly in control mice. Tumor-to-background ratio was highest in the 1 mg kg⁻¹ and 0.3 mg kg⁻¹ doses. In mice with bilateral tumors, tumor-to-untreated tumor ratio increased similarly.

Conclusions:

Acute changes in tumor perfusion after NIR-PIT can be detected by real-time fluorescence imaging.

Prognostic and Predictive Values of Metabolic Parameters of 18F-FDG PET/CT in Patients With Non-Small Cell Lung Cancer Treated With Chemotherapy

Objectives:

Increasing interests have been focused on using artificial intelligence (AI) to extend prognostic value of medical imaging. Feature extraction is a critical step for successful application of AI. The aim of this study was to explore several metabolic parameters measured by ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) as potential AI features in predicting the effectiveness of chemotherapy in patients with non-small cell lung cancer (NSCLC).

Methods:

A set of metabolic parameters of PET/CT and clinical characteristics were detected from 137 patients with NSCLC treated with at least 1 cycle of chemotherapy. Survival receiver–operating characteristic (ROC) analysis was used to define the more significant parameters chosen for the following survival analysis. Patient survival was analyzed by Kaplan-Meier method, log-rank test, and Cox regression.

Results:

Survival ROC showed that maximum standardized uptake value (SUVmax), metabolic tumor volume 50% (MTV50), and total lesion glycolysis 50% (TLG50) had larger area under the curve, and the optimal cutoff values were 11.72, 4.04, and 34.55, respectively. Univariate and multivariate analyses synergistically showed that late PET/CT stage and MTV50 >4.04 were independent factors of poor survival in patients with NSCLC who received chemotherapy.

Conclusions:

Several potential prognostic biomarkers of PET/CT imaging have been extracted for predicting survival and selecting patients with NSCLC who are more likely to benefit from chemotherapy. The identification may accelerate the development of AI methods to improve treatment outcome for NSCLC.