Reproducibility of pharmacogenetics findings for paclitaxel in a heterogeneous population of patients with lung cancer

A systematic review and meta-analysis of the impacts of germline pharmacogenomics on severe toxicity and symptom burden in adult patients with cancer

The clinical application of Pharmacogenomics (PGx) has improved patient safety. However, comprehensive PGx testing has not been widely adopted in clinical practice, and significant opportunities exist to further optimize PGx in cancer care. This systematic review and meta-analysis aim to evaluate the safety outcomes of reported PGx-guided strategies (Analysis 1) and identify well-studied emerging pharmacogenomic variants that predict severe toxicity and symptom burden (Analysis 2) in patients with cancer. We searched MEDLINE, EMBASE, CENTRAL, clinicaltrials.gov, and International Clinical Trials Registry Platform from inception to January 2023 for clinical trials or comparative studies evaluating PGx strategies or unconfirmed pharmacogenomic variants. The primary outcomes were severe adverse events (SAE; ≥ grade 3) or symptom burden with pain and vomiting as defined by trial protocols and assessed by trial investigators. We calculated pooled overall relative risk (RR) and 95% confidence interval (95%CI) using random effects models. PROSPERO, registration number CRD42023421277. Of 6811 records screened, six studies were included for Analysis 1, 55 studies for Analysis 2. Meta-analysis 1 (five trials, 1892 participants) showed a lower absolute incidence of SAEs with PGx-guided strategies compared to usual therapy, 16.1% versus 34.0% (RR = 0.72, 95%CI 0.57-0.91, p = 0.006, I2 = 34%). Meta-analyses 2 identified nine medicine(class)-variant pairs of interest across the TYMS, ABCB1, UGT1A1, HLA-DRB1, and OPRM1 genes. Application of PGx significantly reduced rates of SAEs in patients with cancer. Emergent medicine-variant pairs herald further research into the expansion and optimization of PGx to improve systemic anti-cancer and supportive care medicine safety and efficacy.

Circadian protein expression patterns in healthy young adults

OBJECTIVES

To explore how the blood plasma proteome fluctuates across the 24-hour day and identify a subset of proteins that show endogenous circadian rhythmicity.

METHODS

Plasma samples from 17 healthy adults were collected hourly under controlled conditions designed to unmask endogenous circadian rhythmicity; in a subset of 8 participants, we also collected samples across a day on a typical sleep-wake schedule. A total of 6916 proteins were analyzed from each sample using the SomaScan aptamer-based multiplexed platform. We used differential rhythmicity analysis based on a cosinor model with mixed effects to identify a subset of proteins that showed circadian rhythmicity in their abundance.

RESULTS

One thousand and sixty-three (15%) proteins exhibited significant daily rhythmicity. Of those, 431 (6.2%) proteins displayed consistent endogenous circadian rhythms on both a sleep-wake schedule and under controlled conditions: it included both known and novel proteins. When models were fitted with two harmonics, an additional 259 (3.7%) proteins exhibited significant endogenous circadian rhythmicity, indicating that some rhythmic proteins cannot be solely captured by a simple sinusoidal model. Overall, we found that the largest number of proteins had their peak levels in the late afternoon/evening, with another smaller group peaking in the early morning.

CONCLUSIONS

This study reveals that hundreds of plasma proteins exhibit endogenous circadian rhythmicity in humans. Future analyses will likely reveal novel physiological pathways regulated by circadian clocks and pave the way for improved diagnosis and treatment for patients with circadian disorders and other pathologies. It will also advance efforts to include knowledge about time-of-day, thereby incorporating circadian medicine into personalized medicine.

The Role of TRIP6, ABCC3 and CPS1 Expression in Resistance of Ovarian Cancer to Taxanes

The main problem precluding successful therapy with conventional taxanes is de novo or acquired resistance to taxanes. Therefore, novel experimental taxane derivatives (Stony Brook taxanes; SB-Ts) are synthesized and tested as potential drugs against resistant solid tumors. Recently, we reported alterations in ABCC3, CPS1, and TRIP6 gene expression in a breast cancer cell line resistant to paclitaxel. The present study aimed to investigate gene expression changes of these three candidate molecules in the highly resistant ovarian carcinoma cells in vitro and corresponding in vivo models treated with paclitaxel and new experimental Stony Brook taxanes of the third generation (SB-T-121605 and SB-T-121606). We also addressed their prognostic meaning in ovarian carcinoma patients treated with taxanes. We estimated and observed changes in mRNA and protein profiles of ABCC3, CPS1, and TRIP6 in resistant and sensitive ovarian cancer cells and after the treatment of resistant ovarian cancer models with paclitaxel and Stony Brook taxanes in vitro and in vivo. Combining Stony Brook taxanes with paclitaxel caused downregulation of CPS1 in the paclitaxel-resistant mouse xenograft tumor model in vivo. Moreover, CPS1 overexpression seems to play a role of a prognostic biomarker of epithelial ovarian carcinoma patients’ poor survival. ABCC3 was overexpressed in EOC tumors, but after the treatment with taxanes, its up-regulation disappeared. Based on our results, we can suggest ABCC3 and CPS1 for further investigations as potential therapeutic targets in human cancers.

Pharmacogenomics and functional imaging to predict irinotecan pharmacokinetics and pharmacodynamics: the predict IR study

PURPOSE

Irinotecan (IR) displays significant PK/PD variability. This study evaluated functional hepatic imaging (HNI) and extensive pharmacogenomics (PGs) to explore associations with IR PK and PD (toxicity and response).

METHODS

Eligible patients (pts) suitable for Irinotecan-based therapy. At baseline: (i) PGs: blood analyzed by the Affymetrix-DMET™-Plus-Array (1936 variants: 1931 single nucleotide polymorphisms [SNPs] and 5 copy number variants in 225 genes, including 47 phase I, 80 phase II enzymes, and membrane transporters) and Sanger sequencing (variants in HNF1A, Topo-1, XRCC1, PARP1, TDP, CDC45L, NKFB1, and MTHFR), (ii) HNI: pts given IV 250 MBq-^99mTc-IDA, data derived for hepatic extraction/excretion parameters (CL_HNI, T_1/2-HNI, 1hRET, HEF, T_d1/2). In cycle 1, blood was taken for IR analysis and PK parameters were derived by non-compartmental methods. Associations were evaluated between HNI and PGs, with IR PK, toxicity, objective response rate (ORR) and progression-free survival (PFS).

RESULTS

N = 31 pts. The two most significant associations between PK and PD with gene variants or HNI parameters (P < 0.05) included: (1) PK: SN38-Metabolic Ratio with CL_HNI, 1hRET, (2) Grade 3+ diarrhea with SLC22A2 (rs 316019), GSTM5 (rs 1296954), (3) Grade 3+ neutropenia with CL_HNI, 1hRET, SLC22A2 (rs 316019), CYP4F2 (rs2074900) (4) ORR with ALDH2 (rs 886205), MTHFR (rs 1801133). (5) PFS with T_1/2-HNI, XDH (rs 207440), and ABCB11 (rs 4148777).

CONCLUSIONS

Exploratory associations were observed between Irinotecan PK/PD with hepatic functional imaging and extensive pharmacogenomics. Further work is required to confirm and validate these findings in a larger cohort of patients.

AUSTRALIAN NEW ZEALAND CLINICAL TRIALS REGISTRY (ANZCTR) NUMBER

ACTRN12610000897066, Date registered: 21/10/2010.

GSTM3 Function and Polymorphism in Cancer: Emerging but Promising

Cancer is a major cause of human mortality; however, the molecular mechanisms and proteomic biomarkers that cause tumor progression in malignant tumors are either unknown or only partially revealed. Glutathione S-transferases mu3 (GSTM3), which belongs to a family of xenobiotic detoxifying phase II enzymes, is associated with carcinogen detoxification and the metabolism of exogenous electrophilic substances. It has been reported that GSTM3 has different polymorphisms in various tumor cells and regulates tumorigenesis, cell invasion, metastasis, chemoresistance, and oxidative stress. Deep research into the regulatory mechanisms involved in disorders of GSTM3 expression and the function of GSTM3 in different cancers may facilitate improvements in cancer prevention and targeted therapy. The combination of GSTM3 with other family members can regulate the carcinogenesis and susceptibility to different cancers in humans. GSTM3 also regulates the reactive oxygen species (ROS) and participates in oxidative stress-mediated pathology. Here, we provide a general introduction to GSTM3 in order to better understand the role of GSTM3 in cancer.

Comparison of Eight Technologies to Determine Genotype at the UGT1A1 (TA)n Repeat Polymorphism: Potential Clinical Consequences of Genotyping Errors?

To ensure accuracy of UGT1A1 (TA)_n (rs3064744) genotyping for use in pharmacogenomics-based irinotecan dosing, we tested the concordance of several commonly used genotyping technologies. Heuristic genotype groupings and principal component analysis demonstrated concordance for Illumina sequencing, fragment analysis, and fluorescent PCR. However, Illumina sequencing and fragment analysis returned a range of fragment sizes, likely arising due to PCR "slippage". Direct sequencing was accurate, but this method led to ambiguous electrophoregrams, hampering interpretation of heterozygotes. Gel sizing, pyrosequencing, and array-based technologies were less concordant. Pharmacoscan genotyping was concordant, but it does not ascertain (TA)₈ genotypes that are common in African populations. Method-based genotyping differences were also observed in the publication record (p < 0.0046), although fragment analysis and direct sequencing were concordant (p = 0.11). Genotyping errors can have significant consequences in a clinical setting. At the present time, we recommend that all genotyping for this allele be conducted with fluorescent PCR (fPCR).

Comprehensive analysis of DNA methylation and gene expression profiles in cholangiocarcinoma

BACKGROUND

The incidence of cholangiocarcinoma (CCA) has risen in recent years, and it has become a significant health burden worldwide. However, the mechanisms underlying tumorigenesis and progression of this disease remain largely unknown. An increasing number of studies have demonstrated crucial biological functions of epigenetic modifications, especially DNA methylation, in CCA. The present study aimed to identify and analyze methylation-regulated differentially expressed genes (MeDEGs) involved in CCA tumorigenesis and progression by bioinformatics analysis.

METHODS

The gene expression profiling dataset (GSE119336) and gene methylation profiling dataset (GSE38860) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) were identified using the limma packages of R and GEO2R, respectively. The MeDEGs were obtained by overlapping the DEGs and DMGs. Functional enrichment analyses of these genes were then carried out. Protein-protein interaction (PPI) networks were constructed using STRING and visualized in Cytoscape to determine hub genes. Finally, the results were verified based on The Cancer Genome Atlas (TCGA) database.

RESULTS

We identified 98 hypermethylated, downregulated genes and 93 hypomethylated, upregulated genes after overlapping the DEGs and DMGs. These genes were mainly enriched in the biological processes of the cell cycle, nuclear division, xenobiotic metabolism, drug catabolism, and negative regulation of proteolysis. The top nine hub genes of the PPI network were F2, AHSG, RRM2, AURKB, CCNA2, TOP2A, BIRC5, PLK1, and ASPM. Moreover, the expression and methylation status of the hub genes were significantly altered in TCGA.

CONCLUSIONS

Our study identified novel methylation-regulated differentially expressed genes (MeDEGs) and explored their related pathways and functions in CCA, which may provide novel insights into a further understanding of methylation-mediated regulatory mechanisms in CCA.