Kosel, Matthew

Abstract Background Meningiomas lack effective therapies for recurrent or aggressive disease. NF2 inactivation, commonly in meningioma, may sensitize tumors to FAK inhibition and interact with MAPK signaling. This study investigated the therapeutic activity of defactinib (FAK inhibitor) and avutometinib (RAF/MEK inhibitor) alone and in combination in preclinical meningioma models. Methods MAPK-altered meningioma cell lines, IOMM-Lee (NF2 wild-type, BRAF p.V600E) and CH157-MN (NF2 mutant, NRAS p.Q61K), BenMen-1 (NF2 mutant, MAPK wild-type), and three patient-derived NF2 mutant cell lines were cultured in matrigel and treated with defactinib and avutometinib in 3D cell viability assays. In vivo, flank and intracranial xenograft models of IOMM-Lee and CH157-MN were treated with VS-4718 (FAK inhibitor, superior pharmokinetics in mice), avutometinib, or combination. Tumor growth, survival, and pharmacokinetic/pharmacodynamic properties were assessed. Results In vitro, combination therapy showed synergistic or additive effects in CH157-MN and IOMM-Lee cells, respectively. Defactinib alone demonstrated activity in NF2-mutant patient-derived and BenMen-1 cells, while avutometinib did not. In vivo, avutometinib monotherapy suppressed tumor growth and extended survival in both flank and intracranial xenograft models, with evidence of favorable pharmacokinetic properties and target pathway inhibition in intracranial tumors. Combination therapy provided enhanced and sustained control of flank CH157-MN meningioma compared to avutometinib alone but did not significantly improve survival over avutometinib alone in intracranial models. Conclusions Avutometinib demonstrated robust anti-tumor effects in flank and intracranial models of MAPK-altered meningioma. Combination benefits were context dependent. Defactinib activity in NF2-mutant patient-derived cells without targetable MAPK-kinase alterations highlights the importance of genomic biomarkers to guide personalized strategies.

Abstract Glioblastoma, IDH-wildtype (GBM), and primary central nervous system lymphoma (PCNSL) are two aggressive brain tumors with distinct treatment strategies but overlapping imaging features on MRI. Accurate preoperative differentiation remains a clinical challenge, due to their overlapping imaging features, even when advanced MRI techniques are employed. This study explored deep learning approaches leveraging multi-sequence MRI to distinguish GBM from PCNSL. Models were developed utilizing 146 PCNSL cases, which were matched to146 GBM by age, sex and year of diagnosis. A 3D-DenseNet121 convolutional neural network was trained using two analytical strategies: (i) loss-based model selection and (ii) ensemble prediction using five-fold cross-validation. Model performance was evaluated on an independent validation set of 256 GBM and 37 PCNSL from the same institution as well as 36 PCNSL cases from an external institution. All MRIs were preprocessed using the FeTS pipeline. Using the independent validation cohort, area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CI) were computed overall and stratified by demographic characteristics. Using both T1 post-contrast (T1Gd) and T2-weighted images, the loss-based model achieved AUC=0.83 (95% CI: 0.77-0.88) and the ensemble model yielded an AUC=0.82 (95% CI: 0.76-0.89). Model performance was similar across males and females; however, model performance was better for subjects older than 50 years of age versus younger than 50. When using only T1Gd images, the loss-based model dropped to AUC=0.55 (95% CI: 0.48-0.63), whereas the ensemble model had AUC=0.79 (95% CI: 0.72-0.85). Models trained with only T2 images achieved AUCs of 0.67 (95% CI: 0.60-0.73) (loss-based) and 0.75 (95% CI: 0.69-0.82) (ensemble). These results highlight the added value of the multi-sequence input. Our results confirm the feasibility of using automated deep learning models to differentiate GBM from PCNSL. Notably, the model generalized well to external MRI data, emphasizing its potential utility as a diagnostic aid in real-world clinical workflows.

Abstract Background: The four most commonly-mutated genes in clear cell renal cell carcinoma (ccRCC) tumors are BAP1, PBRM1, SETD2 and VHL. And, there are currently 14 known RCC germline variants that have been reproducibly shown to be associated with RCC risk. However, the association of germline genetics with tumor genetics and clinical aggressiveness are unknown.Methods: We analyzed 420 ccRCC patients from The Cancer Genome Atlas (TCGA). Molecular subtype was determined based on acquired mutations in BAP1, PBRM1, SETD2 and VHL. Aggressive subtype was defined clinically using Mayo SSIGN score and molecularly using the ccA/ccB gene expression subtype. Publically-available Hi-C data were used to link germline risk variants with candidate target genes. Results: The 8q24 variant rs35252396 was significantly associated with VHL mutation status (OR=1.6, p=0.0037) and SSIGN score (OR=1.9, p=0.00094), after adjusting for multiple comparisons. We observed that, while some germline variants have interactions with nearby genes, some variants demonstrate long-range interactions with target genes.Conclusions: These data further demonstrate the link between rs35252396, HIF pathway and ccRCC clinical aggressiveness, providing a more comprehensive picture of how germline genetics and tumor genetics interact with respect to tumor development and progression.

Abstract Background The four most commonly-mutated genes in clear cell renal cell carcinoma (ccRCC) tumors are BAP1 , PBRM1 , SETD2 and VHL . And, there are currently 14 known RCC germline variants that have been reproducibly shown to be associated with RCC risk. However, the association of germline genetics with tumor genetics and clinical aggressiveness are unknown. Methods We analyzed 420 ccRCC patients from The Cancer Genome Atlas (TCGA). Molecular subtype was determined based on acquired mutations in BAP1 , PBRM1 , SETD2 and VHL . Aggressive subtype was defined clinically using Mayo SSIGN score and molecularly using the ccA/ccB gene expression subtype. Publically-available Hi-C data were used to link germline risk variants with candidate target genes. Results The 8q24 variant rs35252396 was significantly associated with VHL mutation status (OR=1.6, p=0.0037) and SSIGN score (OR=1.9, p=0.00094), after adjusting for multiple comparisons. We observed that, while some germline variants have interactions with nearby genes, some variants demonstrate long-range interactions with target genes. Conclusions These data further demonstrate the link between rs35252396, HIF pathway and ccRCC clinical aggressiveness, providing a more comprehensive picture of how germline genetics and tumor genetics interact with respect to tumor development and progression.

Abstract Background: The four most commonly-mutated genes in clear cell renal cell carcinoma (ccRCC) tumors are BAP1, PBRM1, SETD2 and VHL. And, there are currently 14 known RCC germline variants that have been reproducibly shown to be associated with RCC risk. However, the association of germline genetics with tumor genetics and clinical aggressiveness are unknown.Methods: We analyzed 420 ccRCC patients from The Cancer Genome Atlas (TCGA). Molecular subtype was determined based on acquired mutations in BAP1, PBRM1, SETD2 and VHL. Aggressive subtype was defined clinically using Mayo SSIGN score and molecularly using the ccA/ccB gene expression subtype. Publically-available Hi-C data were used to link germline risk variants with candidate target genes. Results: The 8q24 variant rs35252396 was significantly associated with VHL mutation status (OR=1.6, p=0.0037) and SSIGN score (OR=1.9, p=0.00094), after adjusting for multiple comparisons. We observed that, while some germline variants have interactions with nearby genes, some variants demonstrate long-range interactions with target genes.Conclusions: These data further demonstrate the link between rs35252396, HIF pathway and ccRCC clinical aggressiveness, providing a more comprehensive picture of how germline genetics and tumor genetics interact with respect to tumor development and progression.