794 results on '"Rader, Janet"'
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2. A Phase Ib Study Assessing the Safety, Tolerability, and Efficacy of the First-in-Class Wee1 Inhibitor Adavosertib (AZD1775) as Monotherapy in Patients with Advanced Solid Tumors
3. Student-centered Pipeline to Advance Research in Cancer Careers (SPARCC): Diversifying the Clinical Cancer Research Workforce
4. Analysis of Ugandan cervical carcinomas identifies human papillomavirus clade-specific epigenome and transcriptome landscapes.
5. The Immune Landscape of Cancer
6. GOG 8020/210: Risk stratification of lymph node metastasis, disease progression and survival using single nucleotide polymorphisms in endometrial cancer: An NRG oncology/gynecologic oncology group study
7. Clinicopathologic Characteristics of a Single-institution Cohort of Ovarian Adult Granulosa Cell Tumors, With Biomarker and Therapeutic Implications Utilizing the Detection of Androgen, Estrogen, and Progesterone Hormone Receptor Expression by Immunohistochemistry
8. Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas
9. The Immune Landscape of Cancer
10. A Comprehensive Pan-Cancer Molecular Study of Gynecologic and Breast Cancers
11. RNA-binding protein FXR1 drives cMYC translation by recruiting eIF4F complex to the translation start site
12. Multi-omics mapping of human papillomavirus integration sites illuminates novel cervical cancer target genes
13. Integrated genomic and molecular characterization of cervical cancer
14. Deciphering the divergent transcriptomic landscapes of cervical cancer cells grown in 3D and 2D cell culture systems.
15. Landscape of potential germline pathogenic variants in select cancer susceptibility genes in patients with adult‐type ovarian granulosa cell tumors.
16. An Exploratory Study of Neoadjuvant Cetuximab Followed by Cetuximab and Chemoradiotherapy in Women With Newly Diagnosed Locally Advanced Cervical Cancer
17. Orphan nuclear receptor NR2E3 and its small-molecule agonist induce cancer cell apoptosis through regulating p53, IFNα and MYC pathways
18. HLA and KIR Associations of Cervical Neoplasia
19. ERBB3-induced furin promotes the progression and metastasis of ovarian cancer via the IGF1R/STAT3 signaling axis
20. Identification and validation of a prognostic proteomic signature for cervical cancer
21. Genomic structures and regulation patterns at HPV integration sites in cervical cancer
22. miR-137 is a tumor suppressor in endometrial cancer and is repressed by DNA hypermethylation
23. Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas
24. Human papillomavirus oncogenes reprogram the cervical cancer microenvironment independently of and synergistically with estrogen
25. Prevalence of Defective DNA Mismatch Repair and MSH6 Mutation in an Unselected Series of Endometrial Cancers
26. A stratified randomized double-blind phase II trial of celecoxib for treating patients with cervical intraepithelial neoplasia: The potential predictive value of VEGF serum levels: An NRG Oncology/Gynecologic Oncology Group study
27. Carboplatin, paclitaxel, and pembrolizumab followed by pembrolizumab maintenance for primary treatment of incompletely resected epithelial ovarian cancer.
28. Supplementary Figure S4. Distinct HPV methylation in genic regions between CIN and ICC and CIN3 and SCC from Targeted, Deep Sequencing Reveals Full Methylation Profiles of Multiple HPV Types and Potential Biomarkers for Cervical Cancer Progression
29. Table S2. Sixty-three types of HPV used for Agilent baits from Targeted, Deep Sequencing Reveals Full Methylation Profiles of Multiple HPV Types and Potential Biomarkers for Cervical Cancer Progression
30. Supplementary Data from Oncostatin M Receptor–Targeted Antibodies Suppress STAT3 Signaling and Inhibit Ovarian Cancer Growth
31. Data from Oncostatin M Receptor–Targeted Antibodies Suppress STAT3 Signaling and Inhibit Ovarian Cancer Growth
32. Supplementary Spread Sheet 1 from Peritoneal Spread of Ovarian Cancer Harbors Therapeutic Vulnerabilities Regulated by FOXM1 and EGFR/ERBB2 Signaling
33. Supplementary Figure S3. Distribution of HPV types among CIN and ICC samples from Targeted, Deep Sequencing Reveals Full Methylation Profiles of Multiple HPV Types and Potential Biomarkers for Cervical Cancer Progression
34. Supplementary Spread Sheet 2 from Peritoneal Spread of Ovarian Cancer Harbors Therapeutic Vulnerabilities Regulated by FOXM1 and EGFR/ERBB2 Signaling
35. Supplementary Figure S5. Distinct HPV methylation in CpG sites between CIN and ICC and CIN3 and SCC from Targeted, Deep Sequencing Reveals Full Methylation Profiles of Multiple HPV Types and Potential Biomarkers for Cervical Cancer Progression
36. Supplementary Figure S2. Probability density of proportion of mapped reads in HPV-negative samples from Targeted, Deep Sequencing Reveals Full Methylation Profiles of Multiple HPV Types and Potential Biomarkers for Cervical Cancer Progression
37. Combined Supplementary Materials from Peritoneal Spread of Ovarian Cancer Harbors Therapeutic Vulnerabilities Regulated by FOXM1 and EGFR/ERBB2 Signaling
38. Supplementary Figure S1. HPV capture methodology from Targeted, Deep Sequencing Reveals Full Methylation Profiles of Multiple HPV Types and Potential Biomarkers for Cervical Cancer Progression
39. Data from Peritoneal Spread of Ovarian Cancer Harbors Therapeutic Vulnerabilities Regulated by FOXM1 and EGFR/ERBB2 Signaling
40. Combined Supplementary Data from Oncostatin M Receptor–Targeted Antibodies Suppress STAT3 Signaling and Inhibit Ovarian Cancer Growth
41. Table S4. QC results of 61 BS-converted samples for viral methylation analysis from Targeted, Deep Sequencing Reveals Full Methylation Profiles of Multiple HPV Types and Potential Biomarkers for Cervical Cancer Progression
42. SupplementaryMethods.docx from Oncostatin M Receptor–Targeted Antibodies Suppress STAT3 Signaling and Inhibit Ovarian Cancer Growth
43. Table S1. Subject characteristics for pyrosequencing from Targeted, Deep Sequencing Reveals Full Methylation Profiles of Multiple HPV Types and Potential Biomarkers for Cervical Cancer Progression
44. Supplementary Figure S6. Sensitivity and specificity of methylation as a quantitative classifier of cervical precancer and cancer samples from Targeted, Deep Sequencing Reveals Full Methylation Profiles of Multiple HPV Types and Potential Biomarkers for Cervical Cancer Progression
45. Table S3. Primers used for pyrosequencing of HPV 16-positive samples from Targeted, Deep Sequencing Reveals Full Methylation Profiles of Multiple HPV Types and Potential Biomarkers for Cervical Cancer Progression
46. Table S5. Methylation ratio at each CpG for all samples from Targeted, Deep Sequencing Reveals Full Methylation Profiles of Multiple HPV Types and Potential Biomarkers for Cervical Cancer Progression
47. Supplementary Tables 1-2, Methods and Figures 1-2 from Profiling Microdissected Epithelium and Stroma to Model Genomic Signatures for Cervical Carcinogenesis Accommodating for Covariates
48. Data from A MicroRNA Expression Signature for Cervical Cancer Prognosis
49. Supplementary Table 1 from A MicroRNA Expression Signature for Cervical Cancer Prognosis
50. Data from Profiling Microdissected Epithelium and Stroma to Model Genomic Signatures for Cervical Carcinogenesis Accommodating for Covariates
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