Prognostic and predictive clinical factors in patients with metastatic castration-resistant prostate cancer treated with cabazitaxel

Yokom, Daniel; Stewart, John A.; Alimohamed, Nimira; Winquist, Eric; Berry, Scott; Hubay, Stacey; Lattouf, Jean‐Baptiste; Leonard, Helene; Girolametto, Carla; Saad, Fred; Sridhar, Srikala S.

doi:10.5489/cuaj.5108

Cited by 7 publications

(5 citation statements)

References 41 publications

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“…Also, patients with PCa after radical surgery have a high recurrence rate (30%) [ 16 ]. Currently, the treatment protocol for PCa depends on clinical and pathological prognostic biomarkers such as PSA, T staging, and Gleason score [ 17 ]. The National Comprehensive Cancer Network (NCCN) guidelines can stratify the risk of the patient and provide further diagnosis and treatment recommendations.…”

Section: Discussionmentioning

confidence: 99%

Discovering Gene Signature Shared by Prostate Cancer and Neurodegenerative Diseases Based on the Bioinformatics Approach

Dai

Zhang³

et al. 2022

Computational and Mathematical Methods in Medicine

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Background. Prostate cancer (PCa) is one of the highest frequent malignant tumors with very complicated pathogenesis. Genes of neurodegenerative diseases can influence tumor progression. But its role in the progression of PCa remains unclear. The purpose of the present academic work was to identify significant genes with poor outcome and their underlying mechanism. Methods. The GSE70768, GSE88808, and GSE134051 datasets were downloaded to screen the differentially expressed genes (DEGs). The DEG screening criteria were as follows: P < 0.05 and differential fold change logFC ≥ 1 . The common DEGs (co-DEGs) of the three datasets were obtained by the Robust Rank Aggregation (RRA) method. Gene Ontology (GO) function annotation and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis were performed using R software. Protein-protein interaction (PPI) network analysis was performed for co-DEGs using STRING to screen critical genes. Differential expression and prognosis of key genes were analyzed by the online tool Gene Expression Profiling Interactive Analysis 2 (GEPIA2). The intersection gene between key genes and neurodegenerative genes was identified by constructing a Venn diagram. Results. A total of 263 co-DEGs were identified from the three datasets. GO analysis showed that co-DEGs were mainly involved in muscle contraction and blood circulation regulation. The top ten key genes were ACTG2, APOE, F5, CALD1, MYH11, MYL9, MYLK, TPM1, TPM2, and CALM1. GEPIA2 analysis showed that APOE, MYH11, and MYLK differ dramatically between tumor and normal tissues. These key genes are related to disease-free survival (DFS) in PCa. APOE was the intersection gene between key genes and Alzheimer-related genes. Conclusion. The neurodegenerative gene APOE may be a potential prognostic and diagnostic biomarker for PCa.

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Section: Discussionmentioning

confidence: 99%

Discovering Gene Signature Shared by Prostate Cancer and Neurodegenerative Diseases Based on the Bioinformatics Approach

Dai

Zhang³

et al. 2022

Computational and Mathematical Methods in Medicine

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“…One of the most common Grade ≥ 3 adverse events (AEs) associated with the use of cabazitaxel is neutropenia or febrile neutropenia [12,18,19] and, interestingly, there appears to be a correlation between efficacy and rates of Grade ≥ 3 neutropenia [20,21]. Other prognostic factors that have been reported to be associated with clinical outcomes in CRPC patients treated with cabazitaxel include site of metastasis [18,[22][23][24], Eastern Cooperative Oncology Group performance status (ECOG PS) [24,25], number of cabazitaxel treatment cycles [23], prior treatment history [18,26] and several laboratory measures [25,27,28].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive analysis of the associations between clinical factors and outcomes by machine learning, using post marketing surveillance data of cabazitaxel in patients with castration-resistant prostate cancer

et al. 2022

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Background We aimed to evaluate relationships between clinical outcomes and explanatory variables by network clustering analysis using data from a post marketing surveillance (PMS) study of castration-resistant prostate cancer (CRPC) patients. Methods The PMS was a prospective, multicenter, observational study of patients with metastatic, docetaxel-refractory CRPC treated with cabazitaxel in Japan after its launch in 2014. Graphical Markov (GM) model-based simulations and network clustering in ‘R’ package were conducted to identify correlations between clinical factors and outcomes. Factors shown to be associated with overall survival (OS) in the machine learning analysis were confirmed according to the clinical outcomes observed in the PMS. Results Among the 660 patients analyzed, median patient age was 70.0 years, and median OS and time-to-treatment failure (TTF) were 319 and 116 days, respectively. In GM-based simulations, factors associated with OS were liver metastases, performance status (PS), TTF, and neutropenia (threshold 0.05), and liver metastases, PS, and TTF (threshold 0.01). Factors associated with TTF were OS and relative dose intensity (threshold 0.05), and OS (threshold 0.01). In network clustering in ‘R’ package, factors associated with OS were number of treatment cycles, discontinuation due to disease progression, and TTF (threshold 0.05), and liver and lung metastases, PS, discontinuation due to adverse events, and febrile neutropenia (threshold 0.01). Kaplan–Meier analysis of patient subgroups demonstrated that visceral metastases and poor PS at baseline were associated with worse OS, while neutropenia or febrile neutropenia and higher number of cabazitaxel cycles were associated with better OS. Conclusions Neutropenia may be a predictive factor for treatment efficacy in terms of survival. Poor PS and distant metastases to the liver and lungs were shown to be associated with worse outcomes, while factors related to treatment duration were shown to positively correlate with better OS.

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“…Pancreatic cancer is one of the most lethal cancers in the world, with low survival and poor prognosis (5). Several metabolomic studies have led to an enhanced understanding of disease mechanisms, and the discovery of novel diagnostic biomarkers (44,45). In the present study, it was found that 69 metabolites showed significant differences in OCT-embedded groups, and 23 metabolites were significantly different in FFPE groups.…”

Section: Discussionmentioning

confidence: 99%

UPLC‑MS/MS‑based metabolomic characterization and comparison of pancreatic adenocarcinoma tissues using formalin‑fixed, paraffin‑embedded and optimal cutting temperature‑embedded materials

et al. 2019

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The purpose of the present study was to compare metabolites from formalin-fixed and paraffin-embedded (FFPE) pancreatic tissue blocks with those identified in optimal cutting temperature (OCT)-embedded pancreatic tissue blocks. Thus, ultra-performance liquid chromatograph-mass spectrometry/mass spectrometry-based metabolic profiling was performed in paired frozen (n=13) and FFPE (n=13) human pancreatic adenocarcinoma tissue samples, in addition to their benign counterparts. A total of 206 metabolites were identified in both OCT-embedded and FFPE tissue samples. The method feasibility was confirmed through reproducibility and a consistency assessment. Partial least-squares discriminant analysis and heatmap analysis reliably distinguished tumor and normal tissue phenotypes. The expression of 10 compounds, including N-acetylaspartate and creatinine, was significantly different in both OCT-embedded and FFPE tumor samples. These ten compounds may be viable candidate biomarkers of malignant pancreatic tissues. The super-categories to which they belonged exhibited no significant differences between FFPE and OCT-embedded samples. Furthermore, purine, arginine and proline, and pyrimidine metabolism used a shared pathway found in both OCT-embedded and FFPE tissue samples. These results supported the notion that metabolomic data acquired from FFPE pancreatic cancer specimens are reliable for use in retrospective and clinical studies.

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Prognostic and predictive clinical factors in patients with metastatic castration-resistant prostate cancer treated with cabazitaxel

Cited by 7 publications

References 41 publications

Discovering Gene Signature Shared by Prostate Cancer and Neurodegenerative Diseases Based on the Bioinformatics Approach

Discovering Gene Signature Shared by Prostate Cancer and Neurodegenerative Diseases Based on the Bioinformatics Approach

Comprehensive analysis of the associations between clinical factors and outcomes by machine learning, using post marketing surveillance data of cabazitaxel in patients with castration-resistant prostate cancer

UPLC‑MS/MS‑based metabolomic characterization and comparison of pancreatic adenocarcinoma tissues using formalin‑fixed, paraffin‑embedded and optimal cutting temperature‑embedded materials

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