A diagnostic challenge of KIT p.V559D and BRAF p.G469A mutations in a paragastric mass.

A patient with gastrointestinal stroma tumor (GIST) and KIT p.V559D and BRAF p.G469A alterations was referred to our institutional molecular tumor board (MTB) to discuss therapeutic implications. The patient had been diagnosed with B-cell chronic lymphocytic leukemia (CLL) years prior to the MTB presentation. GIST had been diagnosed 1 month earlier. After structured clinical annotation of the molecular alterations and interdisciplinary discussion, we considered BRAF/KIT co-mutation unlikely in a treatment-naïve GIST. Discordant variant allele frequencies furthermore suggested a second malignancy. NGS of a CLL sample revealed the identical class 2 BRAF alteration, thus supporting admixture of CLL cells in the paragastric mass, leading to the detection of 2 alterations. Following the MTB recommendation, the patient received imatinib and had a radiographic response. Structured annotation and interdisciplinary discussion in specialized tumor boards facilitate the clinical management of complex molecular findings. Coexisting malignancies and clonal hematopoiesis warrant consideration in case of complex and uncommon molecular findings.

Target Selection for T-Cell Therapy in Epithelial Ovarian Cancer: Systematic Prioritization of Self-Antigens.

Adoptive T cell-receptor therapy (ACT) could represent a promising approach in the targeted treatment of epithelial ovarian cancer (EOC). However, the identification of suitable tumor-associated antigens (TAAs) as targets is challenging. We identified and prioritized TAAs for ACT and other immunotherapeutic interventions in EOC. A comprehensive list of pre-described TAAs was created and candidates were prioritized, using predefined weighted criteria. Highly ranked TAAs were immunohistochemically stained in a tissue microarray of 58 EOC samples to identify associations of TAA expression with grade, stage, response to platinum, and prognosis. Preselection based on expression data resulted in 38 TAAs, which were prioritized. Along with already published Cyclin A1, the TAAs KIF20A, CT45, and LY6K emerged as most promising targets, with high expression in EOC samples and several identified peptides in ligandome analysis. Expression of these TAAs showed prognostic relevance independent of molecular subtypes. By using a systematic vetting algorithm, we identified KIF20A, CT45, and LY6K to be promising candidates for immunotherapy in EOC. Results are supported by IHC and HLA-ligandome data. The described method might be helpful for the prioritization of TAAs in other tumor entities.

Leveraging Large Language Models for Decision Support in Personalized Oncology.

Importance: Clinical interpretation of complex biomarkers for precision oncology currently requires manual investigations of previous studies and databases. Conversational large language models (LLMs) might be beneficial as automated tools for assisting clinical decision-making. Objective: To assess performance and define their role using 4 recent LLMs as support tools for precision oncology. Design, Setting, and Participants: This diagnostic study examined 10 fictional cases of patients with advanced cancer with genetic alterations. Each case was submitted to 4 different LLMs (ChatGPT, Galactica, Perplexity, and BioMedLM) and 1 expert physician to identify personalized treatment options in 2023. Treatment options were masked and presented to a molecular tumor board (MTB), whose members rated the likelihood of a treatment option coming from an LLM on a scale from 0 to 10 (0, extremely unlikely; 10, extremely likely) and decided whether the treatment option was clinically useful. Main Outcomes and Measures: Number of treatment options, precision, recall, F1 score of LLMs compared with human experts, recognizability, and usefulness of recommendations. Results: For 10 fictional cancer patients (4 with lung cancer, 6 with other; median [IQR] 3.5 [3.0-4.8] molecular alterations per patient), a median (IQR) number of 4.0 (4.0-4.0) compared with 3.0 (3.0-5.0), 7.5 (4.3-9.8), 11.5 (7.8-13.0), and 13.0 (11.3-21.5) treatment options each was identified by the human expert and 4 LLMs, respectively. When considering the expert as a criterion standard, LLM-proposed treatment options reached F1 scores of 0.04, 0.17, 0.14, and 0.19 across all patients combined. Combining treatment options from different LLMs allowed a precision of 0.29 and a recall of 0.29 for an F1 score of 0.29. LLM-generated treatment options were recognized as AI-generated with a median (IQR) 7.5 (5.3-9.0) points in contrast to 2.0 (1.0-3.0) points for manually annotated cases. A crucial reason for identifying AI-generated treatment options was insufficient accompanying evidence. For each patient, at least 1 LLM generated a treatment option that was considered helpful by MTB members. Two unique useful treatment options (including 1 unique treatment strategy) were identified only by LLM. Conclusions and Relevance: In this diagnostic study, treatment options of LLMs in precision oncology did not reach the quality and credibility of human experts; however, they generated helpful ideas that might have complemented established procedures. Considering technological progress, LLMs could play an increasingly important role in assisting with screening and selecting relevant biomedical literature to support evidence-based, personalized treatment decisions.

Effects of RAL signal transduction in KRAS- and BRAF-mutated cells and prognostic potential of the RAL signature in colorectal cancer.

Our understanding of oncogenic signaling pathways has strongly fostered current concepts for targeted therapies in metastatic colorectal cancer. The RALA pathway is novel candidate due to its independent role in controlling expression of genes downstream of RAS.We compared RALA GTPase activities in three colorectal cancer cell lines by GTPase pull-down assay and analyzed the transcriptional and phenotypic effects of transient RALA silencing. Knocking-down RALA expression strongly diminished the active GTP-bound form of the protein. Proliferation of KRAS mutated cell lines was significantly reduced, while BRAF mutated cells were mostly unaffected. By microarray analysis we identified common genes showing altered expression upon RALA silencing in all cell lines. None of these genes were affected when the RAF/MAPK or PI3K pathways were blocked.To investigate the potential clinical relevance of the RALA pathway and its associated transcriptome, we performed a meta-analysis interrogating progression-free survival of colorectal cancer patients of five independent data sets using Cox regression. In each dataset, the RALA-responsive signature correlated with worse outcome.In summary, we uncovered the impact of the RAL signal transduction on genetic program and growth control in KRAS- and BRAF-mutated colorectal cells and demonstrated prognostic potential of the pathway-responsive gene signature in cancer patients.