Oncotarget最新文献

Histone deacetylase inhibitors (HDACi) can modulate the acetylation status of proteins, influencing the genomic instability exhibited by cancer cells. Poly (ADP ribose) polymerase (PARP) inhibitors (PARPi) have a direct effect on protein poly (ADP-ribosyl)ation, which is important for DNA repair. Decitabine is a nucleoside cytidine analogue, which when phosphorylated gets incorporated into the growing DNA strand, inhibiting methylation and inducing DNA damage by inactivating and trapping DNA methyltransferase on the DNA, thereby activating transcriptionally silenced DNA loci. We explored various combinations of HDACi and PARPi +/- decitabine (hypomethylating agent) in pancreatic cancer cell lines BxPC-3 and PL45 (wild-type BRCA1 and BRCA2) and Capan-1 (mutated BRCA2). The combination of HDACi (panobinostat or vorinostat) with PARPi (talazoparib or olaparib) resulted in synergistic cytotoxicity in all cell lines tested. The addition of decitabine further increased the synergistic cytotoxicity noted with HDACi and PARPi, triggering apoptosis (evidenced by increased cleavage of caspase 3 and PARP1). The 3-drug combination treatments (vorinostat, talazoparib, and decitabine; vorinostat, olaparib, and decitabine; panobinostat, talazoparib, and decitabine; panobinostat, olaparib, and decitabine) induced more DNA damage (increased phosphorylation of histone 2AX) than the individual drugs and impaired the DNA repair pathways (decreased levels of ATM, BRCA1, and ATRX proteins). The 3-drug combinations also altered the epigenetic regulation of gene expression (NuRD complex subunits, reduced levels). This is the first study to demonstrate synergistic interactions between the aforementioned agents in pancreatic cancer cell lines and provides preclinical data to design individualized therapeutic approaches with the potential to improve pancreatic cancer treatment outcomes.

In the landscape of cancer treatments, the efficacy of coadjuvant molecules remains a focus of attention for clinical research with the aim of reducing toxicity and achieving better outcomes. Most of the pathogenetic processes causing tumour development, neoplastic progression, ageing, and increased toxicity involve inflammation. Inflammatory mechanisms can progress through a variety of molecular patterns. As is well known, the ageing process is determined by pathological pathways very similar and often parallel to those that cause cancer development. Among these complex mechanisms, inflammation is currently much studied and is often referred to in the geriatric field as 'inflammaging'. In this context, treatments active in the management of inflammatory mechanisms could play a role as adjuvants to standard therapies. Among these emerging molecules, Silibinin has demonstrated its anti-inflammatory properties in different neoplastic types, also in combination with chemotherapeutic agents. Moreover, this molecule could represent a breakthrough in the management of age-related processes. Thus, Silibinin could be a valuable adjuvant to reduce drug-related toxicity and increase therapeutic potential. For this reason, the main aim of this review is to collect and analyse data presented in the literature on the use of Silibinin, to better understand the mechanisms of the functioning of this molecule and its possible therapeutic role.

GZ17-6.02 has undergone phase I evaluation in patients with solid tumors (NCT03775525). The RP2D is 375 mg PO BID, with an uveal melanoma patient exhibiting a 15% reduction in tumor mass for 5 months at this dose. Studies in this manuscript have defined the biology of GZ17-6.02 in PDX isolates of uveal melanoma cells. GZ17-6.02 killed uveal melanoma cells through multiple convergent signals including enhanced ATM-AMPK-mTORC1 activity, inactivation of YAP/TAZ and inactivation of eIF2α. GZ17-6.02 significantly enhanced the expression of BAP1, predictive to reduce metastasis, and reduced the levels of ERBB family RTKs, predicted to reduce growth. GZ17-6.02 interacted with doxorubicin or ERBB family inhibitors to significantly enhance tumor cell killing which was associated with greater levels of autophagosome formation and autophagic flux. Knock down of Beclin1, ATG5 or eIF2α were more protective than knock down of ATM, AMPKα, CD95 or FADD, however, over-expression of FLIP-s provided greater protection compared to knock down of CD95 or FADD. Expression of activated forms of mTOR and STAT3 significantly reduced tumor cell killing. GZ17-6.02 reduced the expression of PD-L1 in uveal melanoma cells to a similar extent as observed in cutaneous melanoma cells whereas it was less effective at enhancing the levels of MHCA. The components of GZ17-6.02 were detected in tumors using a syngeneic tumor model. Our data support future testing GZ17-6.02 in uveal melanoma as a single agent, in combination with ERBB family inhibitors, in combination with cytotoxic drugs, or with an anti-PD1 immunotherapy.

The diheteroarylamide-based compound 1C8 and the aminothiazole carboxamide-related compound GPS167 inhibit the CLK kinases, and affect the proliferation of a broad range of cancer cell lines. A chemogenomic screen previously performed with GPS167 revealed that the depletion of components associated with mitotic spindle assembly altered sensitivity to GPS167. Here, a similar screen performed with 1C8 also established the impact of components involved in mitotic spindle assembly. Accordingly, transcriptome analyses of cells treated with 1C8 and GPS167 indicated that the expression and RNA splicing of transcripts encoding mitotic spindle assembly components were affected. The functional relevance of the microtubule connection was confirmed by showing that subtoxic concentrations of drugs affecting mitotic spindle assembly increased sensitivity to GPS167. 1C8 and GPS167 impacted the expression and splicing of transcripts in pathways relevant to tumor progression, including MYC targets and the epithelial mesenchymal transition (EMT). Finally, 1C8 and GPS167 altered the expression and alternative splicing of transcripts involved in the antiviral immune response. Consistent with this observation, depleting the double-stranded RNA sensor DHX33 suppressed GPS167-mediated cytotoxicity on HCT116 cells. Our study uncovered molecular mechanisms through which 1C8 and GPS167 affect cancer cell proliferation as well as processes critical for metastasis.

The main goal of the present study was to analyze the expression profile of cyclin D1 in patients with PC, and to determine possible correlations with clinical and histopathological features. A survey was conducted with 100 patients diagnosed with PC, who were treated at two reference hospitals in São Luís, Maranhão, Brazil, between 2013 and 2017. A review of clinical, epidemiological, and histopathological data was performed, Human Papillomavírus (HPV) DNA was detected using polymerase chain reaction (PCR) and cyclin D1 expression analysis was performed using immunohistochemical techniques. The data revealed that the absence of cyclin D1 expression was significantly associated with HPV-positive histological subtypes (p = 0.001), while its expression was associated with high-grade tumors (p = 0.014), histological subtype (p = 0.001), presence of sarcomatoid transformation (p = 0.04), and perineural invasion (p = 0.023). Patients with cyclin D1 expression exhibited lower disease-free survival compared to the cyclin D1-negative group, although the difference was not statistically significant. The results suggest that cyclin D1 may be a potential biomarker for PC, especially for poorer prognosis.

Purpose: Sequential PET/CT studies oncology patients can undergo during their treatment follow-up course is limited by radiation dosage. We propose an artificial intelligence (AI) tool to produce attenuation-corrected PET (AC-PET) images from non-attenuation-corrected PET (NAC-PET) images to reduce need for low-dose CT scans.

Methods: A deep learning algorithm based on 2D Pix-2-Pix generative adversarial network (GAN) architecture was developed from paired AC-PET and NAC-PET images. ¹⁸F-DCFPyL PSMA PET-CT studies from 302 prostate cancer patients, split into training, validation, and testing cohorts (n = 183, 60, 59, respectively). Models were trained with two normalization strategies: Standard Uptake Value (SUV)-based and SUV-Nyul-based. Scan-level performance was evaluated by normalized mean square error (NMSE), mean absolute error (MAE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Lesion-level analysis was performed in regions-of-interest prospectively from nuclear medicine physicians. SUV metrics were evaluated using intraclass correlation coefficient (ICC), repeatability coefficient (RC), and linear mixed-effects modeling.

Results: Median NMSE, MAE, SSIM, and PSNR were 13.26%, 3.59%, 0.891, and 26.82, respectively, in the independent test cohort. ICC for SUV_max and SUV_mean were 0.88 and 0.89, which indicated a high correlation between original and AI-generated quantitative imaging markers. Lesion location, density (Hounsfield units), and lesion uptake were all shown to impact relative error in generated SUV metrics (all p < 0.05).

Conclusion: The Pix-2-Pix GAN model for generating AC-PET demonstrates SUV metrics that highly correlate with original images. AI-generated PET images show clinical potential for reducing the need for CT scans for attenuation correction while preserving quantitative markers and image quality.