Changes in imatinib plasma trough level during long-term treatment in patients with intermediate- or high-risk gastrointestinal stromal tumors: Relationship between covariates and imatinib plasma trough level

Navigating Ongoing Challenges in GI Stromal Tumors

GI stromal tumors (GISTs) are mesenchymal neoplasms with variable natural histories, originating in the GI tract, most commonly in the stomach. They are frequently characterized by KIT or platelet-derived growth factor receptor alpha (PDGFRA) oncogenic mutations. Surgical resection remains the cornerstone treatment for localized GISTs. Clinical trials have demonstrated the benefits of adjuvant imatinib in patients selected on the basis of recurrence risk and gene mutations, although the optimal duration of therapy is yet to be established. Some data suggest that longer durations of adjuvant imatinib (>3 years) may provide additional benefit, which is being investigated in ongoing trials. Management of imatinib-related adverse effects is essential during treatment, and longitudinal abdominal imaging is mandatory both during and after adjuvant therapy. Once GISTs are more advanced and unresectable, KIT- and PDGFRA-directed tyrosine kinase inhibitors (TKIs) become the key treatment in most patients with KIT mutation. Several TKIs have regulatory approval for advanced GISTs, but in most patients, resistance to TKIs eventually emerges, mainly from secondary resistance mutations in KIT. Each TKI has different coverage of oncogenic KIT mutations, suggested by preclinical and clinical findings, which has given rationale to an ongoing clinical trial that includes molecular selection as eligibility criteria. Furthermore, novel treatment approaches, from TKI combinations to an antibody-drug conjugate, are being investigated. Despite the significant advance in managing GISTs with KIT mutations, those without KIT or PDGFRA mutation, which consists of 10%-15% of patients with GIST, can be a clinical challenge in the advanced setting. These non-KIT/PDGFRA GISTs could be driven by genomic or epigenomic alterations in SDHx, NF1 mutations, and other genomic alterations. Non-KIT/PDGFRA GISTs are less responsive to currently available TKIs than GISTs driven by KIT/PDGFRA mutations, and each subset of non-KIT/PDGFRA GIST has distinctive biology and clinical features. Therefore, individualized, multidisciplinary, biology-based management and consideration for clinical trial enrollment are critical for non-KIT/PDGRFA GISTs.

Genetic and Non-genetic Correlates of Imatinib Pharmacokinetics and Side Effects of Imatinib in Palestinian Patients with Chronic Myeloid Leukemia

Chronic myeloid leukemia (CML) treatment with imatinib mesylate often faces challenges due to inter-patient variability in drug response and adverse effects. Genetic polymorphisms in enzymes involved in imatinib metabolism, such as CYP2C8 and CYP3A4, may contribute to this variability. This study aimed to investigate the distribution of CYP2C8*3, CYP2C8*4, and CYP3A4*22 among CML patients and evaluate their impact on imatinib exposure and side effects. A descriptive cross-sectional study was conducted on Palestinian CML patients receiving 400 mg daily imatinib between June 2021 and August 2022. Genetic analysis and plasma imatinib levels were determined, along with assessment of demographic and clinical parameters. Results revealed significant associations between CYP2C8*3 genotype and lower imatinib trough concentrations, while CYP2C8*4 and CYP3A4*22 variants showed no significant effects. Imatinib C0 levels correlated with treatment duration but not with age, gender, or weight. Notably, higher C0 levels were linked to specific side effects, including fluid retention, diarrhea, myalgia, and anemia. These findings suggest that pharmacogenetic factors, particularly CYP2C8*3 genotype, may influence imatinib exposure and treatment outcomes in CML patients.

Significance of monitoring imatinib plasma concentration in second-line treatment decisions for c-kit 11 gene-mutated gastrointestinal stromal tumors

BACKGROUND

For patients with advanced gastrointestinal stromal tumors (GISTs) carrying the c-kit exon 11 mutation, imatinib (IM) at a standard dosage of 400 mg per day is the preferred first-line treatment. In cases where treatment with IM fails, there is an urgent need for a more precise assessment method to determine whether to switch therapies or escalate the IM dosage. This approach will enhance clinical decision-making and optimize patient outcomes.

AIM

To investigate IM plasma concentration's role in second-line treatment decisions for c-kit 11-mutated advanced GISTs post-IM failure.

METHODS

Patients with advanced GIST harboring c-kit 11 mutation who experienced failure with IM 400 mg per day as first-line treatment at our hospital were retrospectively analyzed. Patients were categorized into a low plasma (LP) concentration group (LP group, < 1100 ng/mL) and high plasma (HP) concentration group (HP group, ≥ 1100 ng/mL). Each group was further subdivided into Group A (dose-escalation group) and Group B (drug-switch group). Baseline characteristics were compared and Kaplan-Meier curves were used to analyze the survival of patients.

RESULTS

Seventy-five patients were included in the analysis. For the LP group (n = 28), Group A (n = 14) had longer overall survival (OS) than Group B (n = 14) (P = 0.02). No differences were observed between the two subgroups in disease control rate (DCR), objective response rate, and progression-free survival (PFS) (P > 0.05). For the HP group (n = 47), Group B (n = 18) had a higher DCR and longer PFS than Group A (n = 29) (P = 0.008 and P = 0.03, respectively). No difference in OS was observed between the two subgroups (P > 0.05).

CONCLUSION

Increasing IM dosage for c-kit 11-mutated advanced GISTs post-IM failure may prolong OS if plasma concentration is < 1100 ng/mL. Switching tyrosine kinase inhibitors may improve DCR and PFS if ≥ 1100 ng/mL.

Advanced gastrointestinal stromal tumor: reliable classification of imatinib plasma trough concentration via machine learning

AIM

Patients with advanced gastrointestinal stromal tumors (GISTs) exhibiting an imatinib plasma trough concentration (IM Cmin) under 1100 ng/ml may show a reduced drug response rate, leading to the suggestion of monitoring for IM Cmin. Consequently, the objective of this research was to create a customized IM Cmin classification model for patients with advanced GISTs from China.

METHODS

Initial data and laboratory indicators from patients with advanced GISTs were gathered, and the above information was segmented into a training set, validation set, and testing set in a 6:2:2 ratio. Key variables associated with IM Cmin were identified to construct the classification model using the least absolute shrinkage and selection operator (LASSO) regression and forward stepwise binary logistic regression. Within the training and validation sets, nine ML classification models were constructed via the resampling method and underwent comparison through the Brier scores, the areas under the receiver-operating characteristic curve (AUROC), the decision curve, and the precision-recall (AUPR) curve to determine the most suitable model for this dataset. Two methods of internal validation were used to assess the most suitable model's classification performance: tenfold cross-validation and random split-sample validation (test set), and the value of the test set AUROC was used to evaluate the model's classification performance.

RESULTS

Six key variables (gender, daily IM dose, metastatic site, red blood cell count, platelet count, and percentage of neutrophils) were ultimately selected to construct the classification model. In the validation set, it is found by comparison that the Extreme Gradient Boosting (XGBoost) model has the largest AUROC, the lowest Brier score, the largest area under the decision curve, and the largest AUPR value. Furthermore, as evaluated via internal verification, it also performed well in the test set (AUROC = 0.725).

CONCLUSION

For patients with advanced GISTs who receive IM, initial data and laboratory indicators could be used to accurately estimate whether the IM Cmin is below 1100 ng/ml. The XGBoost model may stand a chance to assist clinicians in directing the administration of IM.