Supplementary Data 6 from The Mutational Landscape of Cancer's Vulnerability to Ionizing Radiation

Supplementary Data 6 from The Mutational Landscape of Cancer's Vulnerability to Ionizing Radiation

<p>Supplementary Data 6: Association between individual metabolites and radiation survival</p>

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Bibliografische gegevens
Hoofdauteur:	Priyanka Gopal (15050862) (author)
Andere auteurs:	Brian D. Yard (15050865) (author), Aaron Petty (15050868) (author), Jessica C. Lal (15050871) (author), Titas K. Bera (15050874) (author), Trung Q. Hoang (15050877) (author), Alexandru D. Buhimschi (15050880) (author), Mohamed E. Abazeed (14936313) (author)
Gepubliceerd in:	2025
Onderwerpen:	Cancer Cancer Detection and Diagnosis Methods and Technology Biomarkers Genetic biomarkers Computational Methods Algorithms Gene Technologies Mutation detection methods Lung Cancer
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Supplementary Data 1 from The Mutational Landscape of Cancer's Vulnerability to Ionizing Radiation
door: Priyanka Gopal (15050862)
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Supplementary Data 2 from The Mutational Landscape of Cancer's Vulnerability to Ionizing Radiation
door: Priyanka Gopal (15050862)
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Supplementary Data 3 from The Mutational Landscape of Cancer's Vulnerability to Ionizing Radiation
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Supplementary Data 4 from The Mutational Landscape of Cancer's Vulnerability to Ionizing Radiation
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Supplementary Data 5 from The Mutational Landscape of Cancer's Vulnerability to Ionizing Radiation
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Supplementary Figure S6 from Elucidating the Genetic Landscape of Oral Leukoplakia to Predict Malignant Transformation
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Supplementary Figure S1 from Elucidating the Genetic Landscape of Oral Leukoplakia to Predict Malignant Transformation
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Supplementary Figure S10 from Elucidating the Genetic Landscape of Oral Leukoplakia to Predict Malignant Transformation
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Supplementary Figure S11 from Elucidating the Genetic Landscape of Oral Leukoplakia to Predict Malignant Transformation
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Supplementary Figure S12 from Elucidating the Genetic Landscape of Oral Leukoplakia to Predict Malignant Transformation
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Supplementary Figure S2 from Elucidating the Genetic Landscape of Oral Leukoplakia to Predict Malignant Transformation
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Supplementary Figure S4 from Elucidating the Genetic Landscape of Oral Leukoplakia to Predict Malignant Transformation
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Supplementary Figure S5 from Elucidating the Genetic Landscape of Oral Leukoplakia to Predict Malignant Transformation
door: Leon J. Wils (15055780)
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Supplementary Figure S7 from Elucidating the Genetic Landscape of Oral Leukoplakia to Predict Malignant Transformation
door: Leon J. Wils (15055780)
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Supplementary Figure S8 from Elucidating the Genetic Landscape of Oral Leukoplakia to Predict Malignant Transformation
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Supplementary Figure S9 from Elucidating the Genetic Landscape of Oral Leukoplakia to Predict Malignant Transformation
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Supplementary Table S1 from Immunostimulatory Cancer-Associated Fibroblast Subpopulations Can Predict Immunotherapy Response in Head and Neck Cancer
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Supplementary Figure S2 from Association of Antifolate Response Signature Status and Clinical Activity of Pemetrexed-Platinum Chemotherapy in Non–Small Cell Lung Cancer: The Piedmont Study
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Supplementary Table S2 from Association of Antifolate Response Signature Status and Clinical Activity of Pemetrexed-Platinum Chemotherapy in Non–Small Cell Lung Cancer: The Piedmont Study
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Figure S1 from <i>De Novo</i> and Histologically Transformed Small-Cell Lung Cancer Is Sensitive to Lurbinectedin Treatment Through the Modulation of EMT and NOTCH Signaling Pathways
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Figure S2 from <i>De Novo</i> and Histologically Transformed Small-Cell Lung Cancer Is Sensitive to Lurbinectedin Treatment Through the Modulation of EMT and NOTCH Signaling Pathways
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Figure S3 from <i>De Novo</i> and Histologically Transformed Small-Cell Lung Cancer Is Sensitive to Lurbinectedin Treatment Through the Modulation of EMT and NOTCH Signaling Pathways
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Supplementary Tables 1-3 from <i>De Novo</i> and Histologically Transformed Small-Cell Lung Cancer Is Sensitive to Lurbinectedin Treatment Through the Modulation of EMT and NOTCH Signaling Pathways
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Figure 6 from Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group
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Figure 1 from Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group
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Figure 2 from Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group
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Figure 3 from Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group
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Figure 4 from Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group
door: David Milewski (15050643)
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Figure 5 from Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group
door: David Milewski (15050643)
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Supplementary Table S1-S3 from Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group
door: David Milewski (15050643)
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door: Tobias Kessler (17079209)
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Supplementary Figure S3 from Prognostic Markers of DNA Methylation and Next-Generation Sequencing in Progressive Glioblastoma from the EORTC-26101 Trial
door: Tobias Kessler (17079209)
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Supplementary Figure S4 from Prognostic Markers of DNA Methylation and Next-Generation Sequencing in Progressive Glioblastoma from the EORTC-26101 Trial
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