Association of 5-FU Therapeutic Drug Monitoring to DPD Phenotype Assessment May Reduce 5-FU Under-Exposure.

In order to limit 5-fluorouracil (5-FU) toxicity, some health agencies recommend evaluating dihydropyrimidine dehydrogenase (DPD) deficiency before any 5-FU treatment introduction. In our study, we investigated relationships between 5-FU clearance and markers of DPD activity such as uracilemia (U), dihydrouracilemia (UH2)/U ratio, or genotype of the gene encoding DPD (DPYD). All patients with gastrointestinal cancers who received 5-FU-based regimens form March 2018 to June 2020 were included in our study. They routinely benefited of a pre-therapeutic DPYD genotyping and phenotyping. During 5-FU infusion, blood samples were collected to measure 5-FU steady-state concentration in order to adapt 5-FU doses at the following cycles. A total of 169 patients were included. Median age was 68 (40-88) years and main primary tumor sites were colorectal (40.8%) and pancreas (31.4%), metastatic in 76.3%. 5-FU was given as part of FOLFIRINOX (44.4%), simplified FOLFOX-6 (26.6%), or docetaxel/FOLFOX-4 (10.6%). Regarding DPD activity, median U and UH2/U were, respectively, 10.8 ng/mL and 10.1, and almost 15% harbored a heterozygous mutation. On the range of measured U and UH2/U, no correlation was observed with 5-FU clearance. Moreover, in patients with U < 16 ng/mL, 5-FU exposure was higher than in other patients, and most of them benefited of dose increase following 5-FU therapeutic drug monitoring (TDM). If recent guidelines recommend decreasing 5-FU dose in patients harboring U ≥ 16 ng/mL, our study highlights that those patients are at risk of under-exposure and that 5-FU TDM should be conducted in order to avoid loss of efficacy.

Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage III colon cancer outcomes in PETACC08 study.

OBJECTIVE: Diagnostic tests, such as Immunoscore, predict prognosis in patients with colon cancer. However, additional prognostic markers could be detected on pathological slides using artificial intelligence tools. DESIGN: We have developed a software to detect colon tumour, healthy mucosa, stroma and immune cells on CD3 and CD8 stained slides. The lymphocyte density and surface area were quantified automatically in the tumour core (TC) and invasive margin (IM). Using a LASSO algorithm, DGMate (DiGital tuMor pArameTErs), we detected digital parameters within the tumour cells related to patient outcomes. RESULTS: Within the dataset of 1018 patients, we observed that a poorer relapse-free survival (RFS) was associated with high IM stromal area (HR 5.65; 95% CI 2.34 to 13.67; p<0.0001) and high DGMate (HR 2.72; 95% CI 1.92 to 3.85; p<0.001). Higher CD3+ TC, CD3+ IM and CD8+ TC densities were significantly associated with a longer RFS. Analysis of variance showed that CD3+ TC yielded a similar prognostic value to the classical CD3/CD8 Immunoscore (p=0.44). A combination of the IM stromal area, DGMate and CD3, designated 'DGMuneS', outperformed Immunoscore when used in estimating patients' prognosis (C-index=0.601 vs 0.578, p=0.04) and was independently associated with patient outcomes following Cox multivariate analysis. A predictive nomogram based on DGMuneS and clinical variables identified a group of patients with less than 10% relapse risk and another group with a 50% relapse risk. CONCLUSION: These findings suggest that artificial intelligence can potentially improve patient care by assisting pathologists in better defining stage III colon cancer patients' prognosis.

Dynamic evaluation of circulating tumour cells in patients with advanced gastric and oesogastric junction adenocarcinoma: Prognostic value and early assessment of therapeutic effects.

BACKGROUND: The identification of dynamic biomarkers in advanced gastric and oesogastric junction adenocarcinoma (GOA) could help to tailor strategies for each patient. Enumeration of circulating tumour cells (CTCs) is approved by the US Food and Drug Administration in breast, colon and prostate cancer but is not in advanced GOA. Our study aims to establish the optimal threshold and the clinical significance of CTC count in advanced GOA before and during treatment. METHODS: One hundred six patients with untreated advanced GOA were included in the ancillary study of the PRODIGE 17-ACCORD 20 trial. CTCs were detected in the peripheral blood using the CellSearch system on day 0 (D0) and day 28 (D28). The prognostic value of CTCs at D0 and D28 was analysed by testing several thresholds. RESULTS: At baseline, median CTC count was 1 (range, 0-415). While CTCs ≥1, 2 or 3 at D0 were all significantly associated with worse overall survival (OS) and progression-free survival (PFS), CTCs ≥2 were the optimal threshold, on D0 or D28. CTCs ≥2 at D28 were also predictive of disease control. Taking into account both D0 and D28 CTC count defined 3 groups (low/low, high/low and low-high/high) with significantly different PFS (p = 0.0002) and OS (p = 0.003). CONCLUSION: Quantification of CTCs at baseline and during treatment may be a useful prognostic tool in advanced GOA, as it is associated with worse PFS and OS. A threshold ≥2 CTCs seems to have the best discriminant value. Change in CTC count between baseline and D28 could help to tailor treatment to each individual patient.

Prognostic value of health-related quality of life for death risk stratification in patients with unresectable glioblastoma.

Glioblastoma is the most common malignant brain tumor in adults. Baseline health-related quality of life (HRQoL) is a major subject of concern for these patients. We aimed to assess the independent prognostic value of HRQoL in unresectable glioblastoma (UGB) patients for death risk stratification. One hundred and thirty-four patients with UGB were enrolled from the TEMAVIR trial. HRQoL was evaluated at baseline using the EORTC QLQ-C30 and BN20 brain cancer module. Clinical and HRQoL parameters were evaluated in univariable and multivariable Cox analysis as prognostic factors for overall survival (OS). Performance assessment and internal validation of the final model were evaluated with Harrel's C-index, calibration plot, and bootstrap sample procedure. Two OS independent predictors were identified: future uncertainty and sensitivity deficit. The final model exhibited good calibration and acceptable discrimination (C statistic = 0.63). The internal validity of the model was verified with robust uncertainties around the hazard ratio. The prognostic score identified three groups of patients with distinctly different risk profiles with median OS estimated at 16.2, 9.2, and 4.5 months. We demonstrated the additional prognostic value of HRQoL in UGB for death risk stratification and provided a score that may help to guide clinical management and stratification in future clinical trials.