RESUMO
Background: The identification of prognostic biomarkers is crucial for guiding treatment strategies in mesothelioma patients. The Duchenne muscular dystrophy (DMD) gene and its specific transcripts have been associated with patient survival in various tumours. In this study, we aimed to investigate the prognostic potential of DMD gene expression and its transcripts in mesothelioma patients. Methods: We analysed The Cancer Genome Atlas (TCGA) mesothelioma RNAseq, mutation, and clinical data to assess the association between DMD gene expression and its transcripts (Dp427, Dp71 splice variants) and mesothelioma survival. We also evaluated the specific Dp71 transcript as a unique prognostic biomarker across mesothelioma subtypes. Additionally, we performed differential gene expression analysis between high and low DMD gene/transcript expression groups. Results: The analysis included 57 epithelioid, 23 biphasic, two sarcomatoid, and five not otherwise specified (NOS) histological subtypes of mesothelioma samples. Univariate analysis revealed that high expression of the DMD gene and its Dp71 transcript was significantly associated with shorter survival in mesothelioma patients (P=0.003 and P<0.001, respectively). In a multivariate analysis, the association between Dp71 expression and survival remained significant [hazard ratio (HR) 2.29, 95% confidence interval (CI): 1.24-4.23, P=0.008] across all mesothelioma patients, and also among patients with mesotheliomas without deep CDKN2A deletions (HR 3.58, 95% CI: 1.31-9.80, P=0.01). Pathway analysis revealed enrichment of cell cycle (P=3.01×10-4) and homologous recombination (P=0.01) pathways in differentially expressed genes (DEGs) between high and low Dp71 groups. Furthermore, there were correlations between Dp71 transcript expression and tumour microenvironment (TME) cells, including a weak positive correlation with macrophages (R=0.32, P=0.002) specifically M2 macrophages (R=0.34, P=0.001). Conclusions: Our findings indicate that the differential expression of specific DMD transcripts is associated with poor survival in mesothelioma patients. The specific Dp71 transcript can serve as a potential biomarker for predicting patient survival in diverse histological subtypes of mesothelioma. Further studies are needed to understand the role of specific dystrophin transcripts in cancer and TME cells, and their implications in the pathogenesis and progression of mesothelioma. Identifying patients at risk of poor survival based on DMD transcript expression can guide treatment strategies in mesothelioma, informing decisions regarding treatment intensity, follow-up schedules, eligibility for clinical trials, and ultimately, end-of-life care planning.
RESUMO
Introduction: Drug development is systemically inefficient. Research and development costs for novel therapeutics average hundreds of millions to billions of dollars, with the overall likelihood of approval estimated to be as low as 6.7% for oncology drugs. Over half of these failures are due to a lack of drug efficacy. This pervasive and repeated low rate of success exemplifies how preclinical models fail to adequately replicate the complexity and heterogeneity of human cancer. Therefore, new methods of evaluation, early in the development trajectory, are essential both to rule-in and rule-out novel agents with more rigor and speed, but also to spare clinical trial patients from the potentially toxic sequelae (high risk) of testing investigational agents that have a low likelihood of producing a response (low benefit). Methods: The clinical in vivo oncology (CIVO®) platform was designed to change this drug development paradigm. CIVO precisely delivers microdose quantities of up to 8 drugs or combinations directly into patient tumors 4-96 h prior to planned surgical resection. Resected tissue is then analyzed for responses at each site of intratumoral drug exposure. Results: To date, CIVO has been used safely in 6 clinical trials, including 68 subjects, with 5 investigational and 17 approved agents. Resected tissues were analyzed initially using immunohistochemistry and in situ hybridization assays (115 biomarkers). As technology advanced, the platform was paired with spatial biology analysis platforms, to successfully track anti-neoplastic and immune-modulating activity of the injected agents in the intact tumor microenvironment. Discussion: Herein we provide a report of the use of CIVO technology in patients, a depiction of the robust analysis methods enabled by this platform, and a description of the operational and regulatory mechanisms used to deploy this approach in synergistic partnership with pharmaceutical partners. We further detail how use of the CIVO platform is a clinically safe and scientifically precise alternative or complement to preclinical efficacy modeling, with outputs that inform, streamline, and de-risk drug development.