41P Total tumor burden and radiomics to evaluate response in dose escalation studies: Roginolisib (IOA-244), a highly selective PI3Kδ inhibitor in metastatic uveal melanoma patients

tumour types (melanoma, renal cell carcinoma, bladder cancer and triple-negative breast cancer), where predicting treatment outcomes and toxicities remains an unmet need. Results:We have access to longitudinal samples of >3,000 patients across 10 reported studies.Through partner NHS sites, we aim to prospectively collect and profile samples (blood, stool and tumour) from w3,000 patients over 3 years.A tiered approach will implement workflows for high-volume biomarker discovery (Tier 1).Indepth profiling (Tiers 2&3) will further characterize tumours using peripheral immune profiling including high-dimensional flow cytometry, liquid biopsy (cfDNA, immune methylation profiling), spatial tissue image-profiling and molecular profiling (WES, bulk&long-read RNAseq, TCR&BCRseq).For selected patients, we will apply Representative Sequencing (RepSeq), to overcome sampling bias in solid tumours and conduct drug-sensitivity screening through patient-derived tumour fragments.Conclusions: Machine learning will be applied to derive uni-and multi-modal biomarkers of response and toxicity, and the master databases will be available for ongoing academic and industry research.

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Publication Details

Journal
Immuno-Oncology Technology
Published
2024-12-01
DOI
https://doi.org/10.1016/j.iotech.2024.101001
Primary Topic
Ocular Oncology and Treatments
Type
article
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article

41P Total tumor burden and radiomics to evaluate response in dose escalation studies: Roginolisib (IOA-244), a highly selective PI3Kδ inhibitor in metastatic uveal melanoma patients

Nikhil Sindhwani, Wim Vos, Aria Rasooli, C.C. Meca et al.
Immuno-Oncology Technology
Ocular Oncology and Treatments
article

41P Total tumor burden and radiomics to evaluate response in dose escalation studies: Roginolisib (IOA-244), a highly selective PI3Kδ inhibitor in metastatic uveal melanoma patients

Nikhil Sindhwani, Wim Vos, Aria Rasooli, C.C. Meca, Michael Lahn, Marco Di Maio, A. Santoro, Muhammad Asif Nawaz, Andrea Corsi, Flore Belmans, Anna Maria Di Giacomo, T. R. J. Evans
article en

Abstract

tumour types (melanoma, renal cell carcinoma, bladder cancer and triple-negative breast cancer), where predicting treatment outcomes and toxicities remains an unmet need. Results:We have access to longitudinal samples of >3,000 patients across 10 reported studies.Through partner NHS sites, we aim to prospectively collect and profile samples (blood, stool and tumour) from w3,000 patients over 3 years.A tiered approach will implement workflows for high-volume biomarker discovery (Tier 1).Indepth profiling (Tiers 2&3) will further characterize tumours using peripheral immune profiling including high-dimensional flow cytometry, liquid biopsy (cfDNA, immune methylation profiling), spatial tissue image-profiling and molecular profiling (WES, bulk&long-read RNAseq, TCR&BCRseq).For selected patients, we will apply Representative Sequencing (RepSeq), to overcome sampling bias in solid tumours and conduct drug-sensitivity screening through patient-derived tumour fragments.Conclusions: Machine learning will be applied to derive uni-and multi-modal biomarkers of response and toxicity, and the master databases will be available for ongoing academic and industry research.

Immuno-Oncology TechnologyVol. 24
University of Siena (IT), University of Liège (BE), IRCCS Humanitas Research Hospital (IT), University of Glasgow (GB)
Good health and well-being
Openalex Percentile: Top 63%
Ocular Oncology and Treatments
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