KIT mutation

For clinicians managing Klinefelter patients, these findings emphasise the importance of vigilance for EGCTs, particularly in the mediastinum, even though routine testicular cancer surveillance beyond standard care may not be necessary in the absence of other risk factors. Our data do not support a need for intensive TGCT screening in KS at present, given the lack of a statistically significant increase in testicular cancer incidence. Conversely, the significantly elevated incidence of mediastinal GCT in KS raises the question of screening such as periodic chest imaging in adolescent KS patients. While universal screening is debatable due to the rarity of these tumours, a thorough physical exam (looking for mediastinal mass effects) and a low threshold for chest imaging if symptoms arise (e.g., chest pain, dyspnoea) are advisable in KS. In future, larger multi-centre cohort studies and cancer registry linkages (ideally capturing genotype-confirmed KS cases) will be needed to definitively quantify TGCT risk in KS and to explore the molecular underpinnings of the KS-GCT connection.

Clinically significant mutations (KIT: Asp816Val, Ala829Pro; and KRAS: Gln61Leu) suggest potential therapeutic targets for GCT, while MTOR, PIK3CA, and AKT2 emerge as candidates for targeted therapy. These findings provide insights into the genomic alterations of pediatric GCTs and emphasize the potential for targeted therapies.

This perspective highlights key structural insights that elucidate the molecular basis of resistance and its interplay with kinase activation and TKI binding. A comprehensive understanding of these molecular alterations is crucial for guiding future therapeutic strategies to overcome resistance.

Furthermore, decision trees provided interpretability by identifying key textual indicators that LLMs use. This work demonstrates the feasibility of using proprietary LLMs such as GPT 4.0 and Gemini 2.0 "off the shelf" with both limited LLM fine-tuning and limited patient information to evaluate clinical trial eligibility.

P1/2, N=58, Completed, Narodowy Instytut Onkologii Im. Marii Sklodowskiej-Curie Panstwowy Instytut Badawczy | Active, not recruiting --> Completed

The introduction of imatinib, a selective tyrosine kinase inhibitor (TKI), revolutionized the management of advanced GIST...Current approaches emphasize molecular subtype-guided first-line therapy, ctDNA-driven sequencing of subsequent lines, and the use of novel TKIs (e.g., avapritinib, ripretinib, bezuclastinib) tailored to specific resistance mutations. Furthermore, we explore emerging modalities such as boron neutron capture therapy (BNCT), which offers a kinase-independent mechanism to target resistant disease, and combination strategies that integrate immunotherapy, epigenetic modulators, and pathway-specific inhibitors. Advances in liquid biopsy and molecular profiling are enabling real-time adaptation of therapy, moving GIST management toward a dynamic, personalized paradigm aimed at overcoming resistance and improving patient outcomes.

The oncogenic pattern of MUP showed a UV signature suggesting an origin in sun-exposed localisations. OS of MUP is comparable to melanoma of known cutaneous primary.

P2, N=28, Recruiting, Reema A. Patel | Trial completion date: Dec 2025 --> Dec 2027 | Trial primary completion date: Dec 2025 --> Dec 2027

These tumors express diagnostic GIST markers (c-Kit and DOG1) and show significant response to the BRAF inhibitor dabrafenib. This model recapitulates key histopathological and molecular features of human BRAF-mutant GIST and provides a valuable platform for studying tumor initiation, progression, and therapeutic resistance. Importantly, it allows for preclinical testing of targeted therapies in BRAF GIST, offering new insights into treatment strategies.

P2, N=46, Not yet recruiting, Kumquat Biosciences Inc.

For therapeutic categories, performance reached 0.84 for avapritinib sensitivity, 0.81 for imatinib sensitivity. DL applied to WSIs enables prediction of molecular alterations, treatment sensitivity, and RFS in GIST, performing comparably to established risk scores across international cohorts, providing a baseline for future multimodal predictors. Deep learning on histology predicts KIT and PDGFRA mutations in a large international cohort of GISTs from multiple centers Whole-slide image models stratify recurrence-free survival comparable to pathology-based risk scores Prognostic value of deep learning is preserved in adjuvant therapy subgroups, supporting treatment duration decisions.