Relationship Between the DPD and TS mRNA Expression and the Response to S-1-Based Chemotherapy and Prognosis in Patients with Advanced Gastric Cancer

Preoperative prediction of Lauren classification in gastric cancer: a radiomics model based on dual-energy CT iodine map

OBJECTIVE

To investigate the value of a radiomics model based on dual-energy computed tomography (DECT) venous-phase iodine map (IM) and 120 kVp equivalent mixed images (MIX) in predicting the Lauren classification of gastric cancer.

METHODS

A retrospective analysis of 240 patients undergoing preoperative DECT and postoperative pathologically confirmed gastric cancer was done. Training sets (n = 168) and testing sets (n = 72) were randomly assigned with a ratio of 7:3. Patients are divided into intestinal and non-intestinal groups. Traditional features were analyzed by two radiologists, using logistic regression to determine independent predictors for building clinical models. Using the Radiomics software, radiomics features were extracted from the IM and MIX images. ICC and Boruta algorithm were used for dimensionality reduction, and a random forest algorithm was applied to construct the radiomics model. ROC and DCA were used to evaluate the model performance.

RESULTS

Gender and maximum tumor thickness were independent predictors of Lauren classification and were used to build a clinical model. Separately establish IM-radiomics (R-IM), mixed radiomics (R-MIX), and combined IM + MIX image radiomics (R-COMB) models. In the training set, each radiomics model performed better than the clinical model, and the R-COMB model showed the best prediction performance (AUC: 0.855). In the testing set also, the R-COMB model had better prediction performance than the clinical model (AUC: 0.802).

CONCLUSION

The R-COMB radiomics model based on DECT-IM and 120 kVp equivalent MIX images can effectively be used for preoperative noninvasive prediction of the Lauren classification of gastric cancer.

CRITICAL RELEVANCE STATEMENT

The radiomics model based on dual-energy CT can be used for Lauren classification prediction of preoperative gastric cancer and help clinicians formulate individualized treatment plans and assess prognosis.

Molecular Bases of Mechanisms Accounting for Drug Resistance in Gastric Adenocarcinoma

Gastric adenocarcinoma (GAC) is the most common histological type of gastric cancer, the fifth according to the frequency and the third among the deadliest cancers. GAC high mortality is due to a combination of factors, such as silent evolution, late clinical presentation, underlying genetic heterogeneity, and effective mechanisms of chemoresistance (MOCs) that make the available antitumor drugs scarcely useful. MOCs include reduced drug uptake (MOC-1a), enhanced drug efflux (MOC-1b), low proportion of active agents in tumor cells due to impaired pro-drug activation or active drug inactivation (MOC-2), changes in molecular targets sensitive to anticancer drugs (MOC-3), enhanced ability of cancer cells to repair drug-induced DNA damage (MOC-4), decreased function of pro-apoptotic factors versus up-regulation of anti-apoptotic genes (MOC-5), changes in tumor cell microenvironment altering the response to anticancer agents (MOC-6), and phenotypic transformations, including epithelial-mesenchymal transition (EMT) and the appearance of stemness characteristics (MOC-7). This review summarizes updated information regarding the molecular bases accounting for these mechanisms and their impact on the lack of clinical response to the pharmacological treatment currently used in GAC. This knowledge is required to identify novel biomarkers to predict treatment failure and druggable targets, and to develop sensitizing strategies to overcome drug refractoriness in GAC.

Tailored therapy in patients treated with fluoropyrimidines: focus on the role of dihydropyrimidine dehydrogenase

Fluoropyrimidines are widely used in the treatment of solid tumors, mainly gastrointestinal, head and neck and breast cancer. Dihydropyrimidine dehydrogenase (DPD) is the rate-limiting enzyme for catabolism of 5-FU and it is encoded by DPYD gene. To date, many known polymorphisms cause DPD deficiency and subsequent increase of 5-FU toxicity. In addition, reduced inactivation of 5-FU could lead to increased 5-FU intracellular concentration and augmented efficacy of this drugs. Therefore DPD expression, particularly intratumoral, has been investigated as predictive and prognostic marker in 5-FU treated patients. There also seems to be a tendency to support the correlation between DPD expression and response/survival in patients treated with fluoropyrimidine even if definitive conclusions cannot be drawn considering that some studies are conflicting. Therefore, the debate on intratumoral DPD expression as a potential predictor and prognostic marker in patients treated with fluoropyrimidines is still open. Four DPD-polymorphisms are the most relevant for their frequency in population and clinical relevance. Many studies demonstrate that treating a carrier of one of these polymorphisms with a full dose of fluoropyrimidine can expose patient to a severe, even life-threatening, toxicity. Severe toxicity is reduced if this kind of patients received a dose-adjustment after being genotyped. CPIC (Clinical Pharmacogenetics Implementation Consortium) is an International Consortium creating guidelines for facilitating use of pharmacogenetic tests for patient care and helps clinicians ensuring a safer drug delivery to the patient. Using predictive DPD deficiency tests in patients receiving 5FU-based chemotherapy, in particular for colorectal cancer, has proven to be a cost-effective strategy.

The Impact of the Expression Level of Intratumoral Dihydropyrimidine Dehydrogenase on Chemotherapy Sensitivity and Survival of Patients in Gastric Cancer: A Meta-Analysis

The potential impact that the intratumoral expression level of dihydropyrimidine dehydrogenase (DPD) has on chemotherapy sensitivity and long-term survival for gastric cancer (GC) patients remains controversial; therefore, this study seeks to clarify this issue. Our meta-analysis was performed using Review Manager (RevMan) 5.3 software. In vitro drug sensitivity tests, correlation coefficients between sensitivity to 5-fluorouracil (5-FU), and expression levels of intratumoral DPD were used as effective indexes to analyse. Overall survival (OS) and progression-free survival (PFS) were used as endpoints for patient outcome, and hazard ratios (HRs) and 95% confidence intervals (CIs) were noted as measures of effect. There were 15 eligible studies including 1805 patients for the final analysis. The analysis revealed a statistically significant difference between the expression level of intratumoral DPD activity, DPD mRNA levels, and sensitivity to 5-FU in GC patients, with high expression levels of intratumoral DPD resulting in low sensitivity to 5-FU. However, no matter what therapeutic regimens were used, there was no significant difference for patient outcomes between high and low DPD expression groups, either in OS or in PFS. In conclusion, high levels of intratumoral DPD expression have a negative impact on sensitivity to 5-FU in GC patients, but no prognostic value for long-term survival was uncovered.

Phase I Clinical Study of Irinotecan Plus S-1 in Patients With Advanced or Recurrent Cervical Cancer Previously Treated With Platinum-Based Chemotherapy

OBJECTIVES

This study aimed to determine the maximum tolerated dose and acute dose-limiting toxicities (DLTs) of intravenous irinotecan plus oral S-1 in patients with advanced or recurrent uterine cervical cancer.

METHODS

Irinotecan was administered intravenously over the course of 90 minutes on day 1, and S-1 was given orally in 2 divided doses from days 1 to 14 of a 21-day cycle. The dose of S-1 was escalated in a stepwise fashion from 40 (level 1) to 60 mg/m (level 2) and then 80 mg/m (level 3), whereas the dosage of irinotecan remained the same (150 mg/m). The primary end point for the escalation study was acute DLT that occurred within 2 cycles of chemotherapy.

RESULTS

Twelve patients were enrolled and treated over 3 dose levels. Their median age was 47 years (range, 28-48 years). At level 1, one episode of grade 3 anemia and a grade 3 fatigue were observed, but no DLT developed. At level 2, the first patient experienced febrile neutropenia, which was considered to be a DLT. To evaluate the toxicity of this dose level, 5 more patients were evaluated. However, no DLT developed in these patients. At level 3, although grade 1 to 2 hematological and nonhematological toxicities developed, no DLT occurred.

CONCLUSIONS

In women with advanced or recurrent cervical cancer previously treated with platinum-based chemotherapy, S-1 plus irinotecan in a triweekly setting is a reasonable treatment regimen with an acceptable toxicity profile. The recommended doses of S-1 and irinotecan for this regimen are 80 and 150 mg/m, respectively.

HNRNPC as a candidate biomarker for chemoresistance in gastric cancer

Chemoresistance is a major cause of treatment failure and high mortality in advanced gastric cancer (AGC). Currently, the mechanism of chemoresistance remains unclear, and there is no biomarker to accurately predict the efficacy of chemotherapy. In the present study, we established human gastric cancer (GC) cell lines resistant to 5-fluorouracil (5FU), paclitaxel (TA), or cisplatin (DDP) by gradient drug treatment and generated a novel monoclonal antibody 5B2 targeting heterogeneous nuclear ribonucleoproteins C1/C2 (HNRNPC) overexpressed in chemoresistant GC cells. Overexpressing HNRNPC in GC cells promoted chemoresistance, and knockdown of HNRNPC by small interfering RNA (siRNA) reversed chemoresistance. By utilizing available datasets, we demonstrated that high level of HNRNPC transcript indicated poor overall survival (OS) and free of progression (FP). HNRNPC expression was negatively correlated with OS of GC patients treated with 5FU-based drugs and with time to progression (TTP) of GC patients treated with CF regimen. These data suggest the potential usefulness of HNRNPC as a prognostic and therapeutic marker of GC.