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Background: Opioids in step III of the WHO analgesic ladder are the standard of care for treating cancer pain. However, a significant minority of patients do not benefit from therapy. Genetics might play a role in predisposing patients to a good or poor response to opioids. Here, we investigated this issue by conducting a genome‐wide association study (GWAS). Methods: We genotyped 2057 European advanced cancer patients treated with morphine, buprenorphine, fentanyl and oxycodone. We carried out a whole‐genome regression model (using REGENIE software) between genotypes and the opioid response phenotype, defined as a numerical score measuring patient pain intensity. Results: The GWAS identified five non‐coding variants on chromosome 20 with a p‐value <5.0 × 10−8. For all of them, the minor allele was associated with lower pain intensity. These variants were intronic to the PCMTD2 gene and were 200 kbp downstream of OPRL1, the opioid related nociceptin receptor 1. Notably according to the eQTLGen database, these variants act as expression quantitative trait loci, modulating the expression mainly of PCMTD2 but also of OPRL1. Variants in the same chromosomal region were recently reported to be significantly associated with pain intensity in a GWAS conducted in subjects with different chronic pain conditions. Conclusions: Our results support the role of genetics in the opioid response in advanced cancer patients. Further functional analyses are needed to understand the biological mechanism underlying the observed association and lead to the development of individualized pain treatment plans, ultimately improving the quality of life for cancer patients. Significance Statement: This genome‐wide association study on European advanced cancer patients treated with opioids identifies novel regulatory variants on chromosome 20 (near PCMTD2 and OPRL1 genes) associated with pain intensity. These findings enhance our understanding of the genetic basis of opioid response, suggesting new potential markers for opioid efficacy. The study is a significant advancement in pharmacogenomics, providing a robust dataset and new insights into the genetic factors influencing pain intensity, which could lead to personalized cancer pain management. [ABSTRACT FROM AUTHOR]

Cancer is one of the leading causes of mortality, with new cases expected to rise. Medical advances increase cure rates and prolong patient lives, but survivorship involves high symptom burden, loss of function and emotional distress. Improving patient-centred care (PCC) and quality of life throughout the care process is essential. Key to this improvement are systematic use of patient-reported outcome measures (PROMs) and patient-centred care pathways (PCCPs). Despite established benefits, current cancer care focuses on tumour-centred care approaches often neglecting the patient perspective. Evidence-based PCC guidelines fail to be routinely incorporated into clinical practices. The Horizon 2020-funded European MyPath project aims to address these gaps by developing, implementing and evaluating digital PCCPs with PROMs. MyPath will be tailored to enhance the organisational contexts of cancer centres across Europe through the application of implementation science strategies. This paper describes the current state of applying PCC in routine cancer care and presents a forward-looking perspective on how the MyPath project can successfully adopt and implement digital PROMs across countries. A literature search was conducted to provide the state-of-the art. [ABSTRACT FROM AUTHOR]

Background: The decision to administer palliative radiotherapy (RT) to patients with bone metastases (BMs), as well as the selection of treatment protocols (dose, fractionation), requires an accurate assessment of survival expectancy. In this study, we aimed to develop three predictive models (PMs) to estimate short‐, intermediate‐, and long‐term overall survival (OS) for patients in this clinical setting. Materials and Methods: This study constitutes a sub‐analysis of the PRAIS trial, a longitudinal observational study collecting data from patients referred to participating centers to receive palliative RT for cancer‐induced bone pain. Our analysis encompassed 567 patients from the PRAIS trial database. The primary objectives were to ascertain the correlation between clinical and laboratory parameters with the OS rates at three distinct time points (short: 3 weeks; intermediate: 24 weeks; prolonged: 52 weeks) and to construct PMs for prognosis. We employed machine learning techniques, comprising the following steps: (i) identification of reliable prognostic variables and training; (ii) validation and testing of the model using the selected variables. The selection of variables was accomplished using the LASSO method (Least Absolute Shrinkage and Selection Operator). The model performance was assessed using receiver operator characteristic curves (ROC) and the area under the curve (AUC). Results: Our analysis demonstrated a significant impact of clinical parameters (primary tumor site, presence of non‐bone metastases, steroids and opioid intake, food intake, and body mass index) and laboratory parameters (interleukin 8 [IL‐8], chloride levels, C‐reactive protein, white blood cell count, and lymphocyte count) on OS. Notably, different factors were associated with the different times for OS with only IL‐8 included both in the PMs for short‐ and long‐term OS. The AUC values for ROC curves for 3‐week, 24‐week, and 52‐week OS were 0.901, 0.767, and 0.806, respectively. Conclusions: We successfully developed three PMs for OS based on easily accessible clinical and laboratory parameters for patients referred to palliative RT for painful BMs. While our findings are promising, it is important to recognize that this was an exploratory trial. The implementation of these tools into clinical practice warrants further investigation and confirmation through subsequent studies with separate databases. [ABSTRACT FROM AUTHOR]

Patients with advanced gastrointestinal cancers often receive chemotherapy near the end of life (EoL), raising concerns about overtreatment. The PALLiON trial, a cluster-randomized trial, assessed the impact of a complex intervention on frequency of EoL treatment; the intervention involved palliative care referrals and the use of PROMs. The present secondary analysis evaluated the prognostic value of baseline performance status (PS), albumin (alb), C-reactive protein (CRP), and body mass index (BMI) for overall survival, comparing pancreatic (PAN, n = 189) vs. other gastrointestinal cancer patients (GI, n = 286). Baseline PS, alb, CRP, mGPS (modified Glasgow prognostic score), and BMI were analyzed using Cox regression. Adjusted for age, sex, and hospital size, PS ≥ 2 and alb < 35 g/L predicted shorter survival in both PAN and GI cancers, while CRP > 10 predicted shorter survival only in GI cancers. In PAN, PS ≥ 2 predicted a 78.4% higher probability of shorter survival, and mGPS 2 predicted a 68.7% higher probability. In GI, mGPS 2 predicted a 70.8% higher probability, whereas PS was not significant. BMI did not improve predictive models. PS ≥ 2 and low albumin are strong predictors of short survival in PAN, whereas increased CRP and low albumin (mGPS 2) are predictors in GI. [ABSTRACT FROM AUTHOR]