All Stories

  1. Navigating discrepancies: The assessment of residual lymphovascular invasion in breast carcinoma after neoadjuvant treatment
  2. Data from Global Transcriptional Complexity of Estrogen Receptor–Low Positive Breast Cancers in the Prospective Swedish Population–Based SCAN-B Cohort
  3. Supplementary Figure S1 from Global Transcriptional Complexity of Estrogen Receptor–Low Positive Breast Cancers in the Prospective Swedish Population–Based SCAN-B Cohort
  4. Supplementary Figure S10 from Global Transcriptional Complexity of Estrogen Receptor–Low Positive Breast Cancers in the Prospective Swedish Population–Based SCAN-B Cohort
  5. Supplementary Figure S2 from Global Transcriptional Complexity of Estrogen Receptor–Low Positive Breast Cancers in the Prospective Swedish Population–Based SCAN-B Cohort
  6. Supplementary Figure S3 from Global Transcriptional Complexity of Estrogen Receptor–Low Positive Breast Cancers in the Prospective Swedish Population–Based SCAN-B Cohort
  7. Supplementary Figure S4 from Global Transcriptional Complexity of Estrogen Receptor–Low Positive Breast Cancers in the Prospective Swedish Population–Based SCAN-B Cohort
  8. Supplementary Figure S5 from Global Transcriptional Complexity of Estrogen Receptor–Low Positive Breast Cancers in the Prospective Swedish Population–Based SCAN-B Cohort
  9. Supplementary Figure S6 from Global Transcriptional Complexity of Estrogen Receptor–Low Positive Breast Cancers in the Prospective Swedish Population–Based SCAN-B Cohort
  10. Supplementary Figure S7 from Global Transcriptional Complexity of Estrogen Receptor–Low Positive Breast Cancers in the Prospective Swedish Population–Based SCAN-B Cohort
  11. Supplementary Figure S8 from Global Transcriptional Complexity of Estrogen Receptor–Low Positive Breast Cancers in the Prospective Swedish Population–Based SCAN-B Cohort
  12. Supplementary Figure S9 from Global Transcriptional Complexity of Estrogen Receptor–Low Positive Breast Cancers in the Prospective Swedish Population–Based SCAN-B Cohort
  13. Supplementary Methods 1 from Global Transcriptional Complexity of Estrogen Receptor–Low Positive Breast Cancers in the Prospective Swedish Population–Based SCAN-B Cohort
  14. Supplementary Tables 1 from Global Transcriptional Complexity of Estrogen Receptor–Low Positive Breast Cancers in the Prospective Swedish Population–Based SCAN-B Cohort
  15. Global Transcriptional Complexity of Estrogen Receptor–Low Positive Breast Cancers in the Prospective Swedish Population–Based SCAN-B Cohort
  16. A bottom-up initiated digital external quality assessment scheme for the state-of-the-art pathology in Sweden: reduced variability between pathology departments
  17. Evaluation of alternative prognostic thresholds for SP142 and 22C3 immunohistochemical PD-L1 expression in triple-negative breast cancer: results from a population-based cohort
  18. The analytical and clinical validity of AI algorithms to score TILs in TNBC: can we use different machine learning models interchangeably?
  19. Reproducible Reporting of the Collection and Evaluation of Annotations for Artificial Intelligence Models
  20. Training pathologists to assess stromal tumour‐infiltrating lymphocytes in breast cancer synergises efforts in clinical care and scientific research
  21. Digital PCR quantification of ultrahigh ERBB2 copy number identifies poor breast cancer survival after trastuzumab
  22. Comparison of SP142 and 22C3 PD-L1 assays in a population-based cohort of triple-negative breast cancer patients in the context of their clinically established scoring algorithms
  23. Appendix S1 from Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort
  24. Appendix S1 from Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort
  25. Data from An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
  26. Data from An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
  27. Data from Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort
  28. Data from Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort
  29. Figure S1 from An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
  30. Figure S1 from An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
  31. Figure S1 from Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort
  32. Figure S1 from Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort
  33. Figure S2 from An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
  34. Figure S2 from An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
  35. Figure S2 from Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort
  36. Figure S2 from Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort
  37. Figure S3 from An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
  38. Figure S3 from An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
  39. Figure S3 from Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort
  40. Figure S3 from Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort
  41. Figure S4 from An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
  42. Figure S4 from An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
  43. Figure S5 from An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
  44. Figure S5 from An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
  45. Figure S6 from An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
  46. Figure S6 from An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
  47. Supplementary Legend from Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort
  48. Supplementary Legend from Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort
  49. Table S1 from An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
  50. Table S1 from An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
  51. Table S1 from Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort
  52. Table S1 from Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort
  53. Table S2 from An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
  54. Table S2 from An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
  55. Table S2 from Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort
  56. Table S2 from Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort
  57. Table S3 from An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
  58. Table S3 from An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
  59. Table S3 from Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort
  60. Table S3 from Prediction of Lymph Node Metastasis in Breast Cancer by Gene Expression and Clinicopathological Models: Development and Validation within a Population-Based Cohort
  61. Table S4 from An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
  62. Table S4 from An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
  63. Systematically higher Ki67 scores on core biopsy samples compared to corresponding resection specimen in breast cancer: a multi-operator and multi-institutional study
  64. How Reliable Are Gene Expression-Based and Immunohistochemical Biomarkers Assessed on a Core-Needle Biopsy? A Study of Paired Core-Needle Biopsies and Surgical Specimens in Early Breast Cancer
  65. RNA sequencing-based single sample predictors of molecular subtype and risk of recurrence for clinical assessment of early-stage breast cancer
  66. Pilot study to evaluate tools to collect pathologist annotations for validating machine learning algorithms
  67. Development of Training Materials for Pathologists to Provide Machine Learning Validation Data of Tumor-Infiltrating Lymphocytes in Breast Cancer
  68. 52P RNA sequencing-based single sample predictors of molecular subtype and risk of recurrence for clinical assessment of early-stage breast cancer
  69. The Prognostic Role of Intratumoral Stromal Content in Lobular Breast Cancer
  70. The tale of TILs in breast cancer: A report from The International Immuno-Oncology Biomarker Working Group
  71. Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions
  72. An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
  73. Preexisting Somatic Mutations of Estrogen Receptor Alpha (ESR1) in Early-Stage Primary Breast Cancer
  74. Variability in Breast Cancer Biomarker Assessment and the Effect on Oncological Treatment Decisions: A Nationwide 5-Year Population-Based Study
  75. Distinct mechanisms of resistance to fulvestrant treatment dictate level of ER independence and selective response to CDK inhibitors in metastatic breast cancer
  76. Molecular analyses of triple-negative breast cancer in the young and elderly
  77. Difficulties in diagnostics of lung tumours in biopsies: an interpathologist concordance study evaluating the international diagnostic guidelines
  78. Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples
  79. The mutational landscape of the SCAN ‐B real‐world primary breast cancer transcriptome
  80. Sex differences in oncogenic mutational processes
  81. Abstract CT074: Pre-existing ESR1 mutations in early-stage primary breast cancer predict failure of endocrine therapy and poor survival
  82. Comprehensive molecular comparison of BRCA1 hypermethylated and BRCA1 mutated triple negative breast cancers
  83. Spatial Deconvolution of HER2-positive Breast Tumors Reveals Novel Intercellular Relationships
  84. Prognostic implications of the expression levels of different immunoglobulin heavy chain-encoding RNAs in early breast cancer
  85. Defining the mutational landscape of 3,217 primary breast cancer transcriptomes through large-scale RNA-seq within the Sweden Cancerome Analysis Network: Breast Project (SCAN-B; NCT03430492).
  86. Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials
  87. Pitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer
  88. Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group
  89. The path to a better biomarker: application of a risk management framework for the implementation of PD‐L1 and TILs as immuno‐oncology biomarkers in breast cancer clinical trials and daily practice
  90. Abstract P5-02-01: Analytical validation and prognostic potential of an automated digital scoring protocol for Ki67: An International Ki67 Working Group study
  91. UK Interdisciplinary Breast Cancer Symposium 2020
  92. Pan-cancer analysis of whole genomes
  93. The Mutational Landscape of the SCAN-B Real-World Primary Breast Cancer Transcriptome
  94. Expression of HIF-1α is related to a poor prognosis and tamoxifen resistance in contralateral breast cancer
  95. Whole-genome sequencing of triple-negative breast cancers in a population-based clinical study
  96. Cross comparison and prognostic assessment of breast cancer multigene signatures in a large population-based contemporary clinical series
  97. Agreement between molecular subtyping and surrogate subtype classification: a contemporary population-based study of ER-positive/HER2-negative primary breast cancer
  98. Prediction of lymph node metastasis in breast cancer by gene expression and clinicopathological models: Development and validation within a population based cohort
  99. Refinement of breast cancer molecular classification by miRNA expression profiles
  100. Analytical validation of a standardized scoring protocol for Ki67 immunohistochemistry on breast cancer excision whole sections: an international multicenter collaboration
  101. Stability of oestrogen and progesterone receptor antigenicity in formalin-fixed paraffin-embedded breast cancer tissue over time
  102. Multidimensional transcriptomics provides detailed information about immune cell distribution and identity in HER2+ breast tumors
  103. Clinical Value of RNA Sequencing–Based Classifiers for Prediction of the Five Conventional Breast Cancer Biomarkers: A Report From the Population-Based Multicenter Sweden Cancerome Analysis Network—Breast Initiative
  104. Abstract P1-06-01: Putting multigene signatures to the test: Prognostic assessment in population-based contemporary clinical breast cancer
  105. Abstract P2-02-09: Breast cancer subtype distribution and circulating tumor DNA in response to neoadjuvant chemotherapy: Experiences from a preoperative cohort within SCAN-B
  106. Abstract P2-03-01: Analytical validation of a standardized scoring protocol for Ki67 assessed on breast excision whole sections: An international multicenter collaboration
  107. Abstract P3-02-02: Concordance between immunohistochemical and gene-expression based subtyping of early breast cancer using core needle biopsies and surgical specimens - experices from SCAN-B
  108. Abstract P4-09-03: On the development and clinical value of RNA-sequencing-based classifiers for prediction of the five conventional breast cancer biomarkers: A report from the population-based multicenter SCAN-B study
  109. Minimizing inequality in access to precision medicine in breast cancer by real-time population-based molecular analysis in the SCAN-B initiative
  110. Abstracts
  111. Abstract P1-07-17: The SCAN-B study: 5-year summary of a large-scale population-based prospective breast cancer translational genomics platform covering a wide geography of Sweden (NCT02306096)
  112. Histological grade provides significant prognostic information in addition to breast cancer subtypes defined according to St Gallen 2013
  113. Prior Adjuvant Tamoxifen Treatment in Breast Cancer Is Linked to Increased AIB1 and HER2 Expression in Metachronous Contralateral Breast Cancer
  114. Prognosis, stage and oestrogen receptor status of contralateral breast cancer in relation to characteristics of the first tumour, prior endocrine treatment and radiotherapy
  115. Contralateral breast cancer can represent a metastatic spread of the first primary tumor: determination of clonal relationship between contralateral breast cancers using next-generation whole genome sequencing
  116. Frequent somatic transfer of mitochondrial DNA into the nuclear genome of human cancer cells
  117. Abstract P6-08-43: Histological grade provides significant prognostic information in the discrimination between luminal A-like and luminal B-like HER-2 normal subtypes of breast cancer according to St Gallen 2013
  118. The Sweden Cancerome Analysis Network - Breast (SCAN-B) Initiative: a large-scale multicenter infrastructure towards implementation of breast cancer genomic analyses in the clinical routine
  119. Origins and functional consequences of somatic mitochondrial DNA mutations in human cancer
  120. Extensive transduction of nonrepetitive DNA mediated by L1 retrotransposition in cancer genomes
  121. Processed pseudogenes acquired somatically during cancer development
  122. Acute pancreatitis evoked by small-cell lung carcinoma metastases and detected by endoscopic ultrasound
  123. The G protein-coupled estrogen receptor 1 (GPER/GPR30) does not predict survival in patients with ovarian cancer
  124. G protein-coupled estrogen receptor 1 (GPER, GPR 30) in normal human endometrium and early pregnancy decidua
  125. Changing clinical presentation of angiosarcomas after breast cancer: from late tumors in edematous arms to earlier tumors on the thoracic wall
  126. Histamine uptake by human endometrial cells expressing the organic cation transporter EMT and the vesicular monoamine transporter-2
  127. Differential localization and expression of urokinase plasminogen activator (uPA), its receptor (uPAR), and its inhibitor (PAI-1) mRNA and protein in endometrial tissue during the menstrual cycle
  128. Paracrine Stimulation of Capillary Endothelial Cell Migration by Endometrial Tissue Involves Epidermal Growth Factor and Is Mediated Via Up-Regulation of the Urokinase Plasminogen Activator Receptor
  129. Breast metastases from pancreatic and ovarian carcinoma