Gut microbiome-derived bacterial extracellular vesicles in patients with solid tumours.

INTRODUCTION: Gut microbiome-derived nanoparticles, known as bacterial extracellular vesicles (bEVs), have garnered interest as promising tools for studying the link between the gut microbiome and human health. The diverse composition of bEVs, including their proteins, mRNAs, metabolites, and lipids, makes them useful for investigating diseases such as cancer. However, conventional approaches for studying gut microbiome composition alone may not be accurate in deciphering host-gut microbiome communication. In clinical microbiome research, there is a gap in the knowledge on the role of bEVs in solid tumor patients. OBJECTIVES: Analyzing the functionality of bEVs using (meta)genomics and proteomics could highlight the unique aspects of host-gut microbiome interactions in solid tumor patients. Therefore, we performed a comparative analysis of the proteome and microbiota composition of gut microbiome-derived bEVs isolated from patients with solid tumors and healthy controls. METHODS: After isolating bEVs from the feces of solid tumor patients and healthy controls, we performed spectrometry analysis of their proteomes and next-generation sequencing (NGS) of the 16S gene. We also investigated the gut microbiomes of feces from patients and controls using 16S sequencing and used machine learning to classify the samples into patients and controls based on their bEVs and fecal microbiomes. RESULTS: Solid tumor patients showed decreased microbiota richness and diversity in both the bEVs and feces. However, the bEV proteomes were more diverse in patients than in the controls and were enriched with proteins associated with the metabolism of amino acids and carbohydrates, nucleotide binding, and oxidoreductase activity. Metadata classification of samples was more accurate using fecal bEVs (100%) compared with fecal samples (93%). CONCLUSION: Our findings suggest that bEVs are unique functional entities. There is a need to explore bEVs together with conventional gut microbiome analysis in functional cancer research to decipher the potential of bEVs as cancer diagnostic or therapeutic biomarkers.

Radium-223 dichloride treatment in metastatic castration-resistant prostate cancer in Finland: A real-world evidence multicenter study.

BACKGROUND: Radium-233 dichloride is an alpha emitter that specifically targets bone metastases in prostate cancer. Results of a previously reported phase III randomized trial showed survival benefit for radium-223 compared to best supportive care in castration-resistant prostate cancer (CRPC) with bone metastases. However, real-world data are also needed with wider inclusion criteria. METHODS: We report results of a retrospective multicenter study including all patients with metastatic CRPC treated with radium-223 in all five university hospitals in Finland since the introduction of the treatment. We identified 160 patients who had received radium-223 in Finland in 2014-2019. RESULTS: The median overall survival (OS) was 13.8 months (range 0.5-57 months), and the median real-world progression-free survival (rwPFS) was 4.9 months (range 0.5-29.8 months). Alkaline phosphatase (ALP) values within the normal range before and during the radium-223 treatment or the reduction of elevated ALP to normal range during treatment were associated with better OS when compared to elevated ALP values before and during treatment (p < 0.0001). High prostate-specific antigen (PSA) level (≥100 µg/L) before radium-223 treatment was associated with poor OS compared to low PSA level (<20 µg/L) (p = 0.0001). Most patients (57%) experienced pain relief. Pain relief indicated better OS (p = 0.002). Radium-223 treatment was well tolerated. Toxicity was mostly low grade. Only 12.5% of the patients had grade III-IV adverse events, most commonly anemia, neutropenia, leucopenia, and thrombocytopenia. CONCLUSION: Radium-223 was well tolerated in routine clinical practice, and most patients achieved pain relief. Pain relief, ALP normalization, lower baseline PSA, and PSA decrease during radium-223 treatment were prognostic for better survival. The efficacy of radium-223 in mCRPC as estimated using OS was comparable to earlier randomized trial in this retrospective real-world study. Our results support using radium-223 for mCRPC patients with symptomatic bone metastases even in the era of new-generation androgen receptor-targeted agents.

A Deep Learning-Based Automated CT Segmentation of Prostate Cancer Anatomy for Radiation Therapy Planning-A Retrospective Multicenter Study.

A commercial deep learning (DL)-based automated segmentation tool (AST) for computed tomography (CT) is evaluated for accuracy and efficiency gain within prostate cancer patients. Thirty patients from six clinics were reviewed with manual- (MC), automated- (AC) and automated and edited (AEC) contouring methods. In the AEC group, created contours (prostate, seminal vesicles, bladder, rectum, femoral heads and penile bulb) were edited, whereas the MC group included empty datasets for MC. In one clinic, lymph node CTV delineations were evaluated for interobserver variability. Compared to MC, the mean time saved using the AST was 12 min for the whole data set (46%) and 12 min for the lymph node CTV (60%), respectively. The delineation consistency between MC and AEC groups according to the Dice similarity coefficient (DSC) improved from 0.78 to 0.94 for the whole data set and from 0.76 to 0.91 for the lymph nodes. The mean DSCs between MC and AC for all six clinics were 0.82 for prostate, 0.72 for seminal vesicles, 0.93 for bladder, 0.84 for rectum, 0.69 for femoral heads and 0.51 for penile bulb. This study proves that using a general DL-based AST for CT images saves time and improves consistency.