Fran Martínez-Jiménez
@franmj88.bsky.social
Group Leader of Computational Immunogenomics group at VHIO |
Data Mining lead at HartwigMedicalFoundation | Personal account | Advocate for social and environmental justice 🌍
https://www.franmartinezlab.com/
Data Mining lead at HartwigMedicalFoundation | Personal account | Advocate for social and environmental justice 🌍
https://www.franmartinezlab.com/
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As genomic profiling expands, such approaches are vital to balance patient benefit, cost, and sustainability. But will require systematic data generation and sharing to achieve real-world impact. End.
As genomic profiling expands, such approaches are vital to balance patient benefit, cost, and sustainability. But will require systematic data generation and sharing to achieve real-world impact. End.
October 8, 2025 at 6:06 AM
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As genomic profiling expands, such approaches are vital to balance patient benefit, cost, and sustainability. But will require systematic data generation and sharing to achieve real-world impact. End.
As genomic profiling expands, such approaches are vital to balance patient benefit, cost, and sustainability. But will require systematic data generation and sharing to achieve real-world impact. End.
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We also model the statistical power needed for clinical implementation of such biomarkers, offering a roadmap for translating non-response biomarkers into clinical practice. Our analysis revealed that we are still underpowered to implement it with existing data
We also model the statistical power needed for clinical implementation of such biomarkers, offering a roadmap for translating non-response biomarkers into clinical practice. Our analysis revealed that we are still underpowered to implement it with existing data
October 8, 2025 at 6:06 AM
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We also model the statistical power needed for clinical implementation of such biomarkers, offering a roadmap for translating non-response biomarkers into clinical practice. Our analysis revealed that we are still underpowered to implement it with existing data
We also model the statistical power needed for clinical implementation of such biomarkers, offering a roadmap for translating non-response biomarkers into clinical practice. Our analysis revealed that we are still underpowered to implement it with existing data
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Under stringent analytical control, we found several known and novel biomarkers, many aligned with resistance mechanisms (e.g., KRASG12X in colorectal cancer treated with comb. chemotherapy agents, immune escape events in melanoma patients treated with ICIs). See full list in Supp. Tables
Under stringent analytical control, we found several known and novel biomarkers, many aligned with resistance mechanisms (e.g., KRASG12X in colorectal cancer treated with comb. chemotherapy agents, immune escape events in melanoma patients treated with ICIs). See full list in Supp. Tables
October 8, 2025 at 6:06 AM
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Under stringent analytical control, we found several known and novel biomarkers, many aligned with resistance mechanisms (e.g., KRASG12X in colorectal cancer treated with comb. chemotherapy agents, immune escape events in melanoma patients treated with ICIs). See full list in Supp. Tables
Under stringent analytical control, we found several known and novel biomarkers, many aligned with resistance mechanisms (e.g., KRASG12X in colorectal cancer treated with comb. chemotherapy agents, immune escape events in melanoma patients treated with ICIs). See full list in Supp. Tables
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We analyzed data from >2,500 patients with WGS/WTS and matched clinical outcomes — the largest dataset of its kind — to systematically identify non-response biomarkers.
We analyzed data from >2,500 patients with WGS/WTS and matched clinical outcomes — the largest dataset of its kind — to systematically identify non-response biomarkers.
October 8, 2025 at 6:06 AM
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We analyzed data from >2,500 patients with WGS/WTS and matched clinical outcomes — the largest dataset of its kind — to systematically identify non-response biomarkers.
We analyzed data from >2,500 patients with WGS/WTS and matched clinical outcomes — the largest dataset of its kind — to systematically identify non-response biomarkers.