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1. Deep learning for automated scoring of immunohistochemically stained tumour tissue sections – Validation across tumour types based on patient outcomes

2. RAB5A expression is a predictive biomarker for trastuzumab emtansine in breast cancer

3. Atezolizumab plus anthracycline-based chemotherapy in metastatic triple-negative breast cancer: the randomized, double-blind phase 2b ALICE trial

4. Germline HOXB13 mutations p.G84E and p.R217C do not confer an increased breast cancer risk

5. Characterizations of uveal melanoma patients with three additional primary malignancies.

6. Comprehensive multi-omics analysis of breast cancer reveals distinct long-term prognostic subtypes.

7. Ipilimumab and nivolumab combined with anthracycline-based chemotherapy in metastatic hormone receptor-positive breast cancer: a randomized phase 2b trial

10. Abstract 2170: Pre-treatment protein landscape in HER2-negative breast cancer treated with carboplatin in a neoadjuvant chemotherapy setting

11. Data from Toward Personalized Computer Simulation of Breast Cancer Treatment: A Multiscale Pharmacokinetic and Pharmacodynamic Model Informed by Multitype Patient Data

12. Supplementary Text from Toward Personalized Computer Simulation of Breast Cancer Treatment: A Multiscale Pharmacokinetic and Pharmacodynamic Model Informed by Multitype Patient Data

13. Supplementary Tables from The Longitudinal Transcriptional Response to Neoadjuvant Chemotherapy with and without Bevacizumab in Breast Cancer

14. Abstract PD11-11: PD11-11 Results from ALICE – Atezolizumab Combined with Immunogenic Chemotherapy in Patients with Metastatic Triple Negative Breast Cancer, a Randomized Phase IIb Trial

16. Breast cancer quantitative proteome and proteogenomic landscape

17. Time series analysis of neoadjuvant chemotherapy and bevacizumab-treated breast carcinomas reveals a systemic shift in genomic aberrations

18. A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns

19. Combined burden and functional impact tests for cancer driver discovery using DriverPower

20. Integrative pathway enrichment analysis of multivariate omics data

21. Pathway and network analysis of more than 2500 whole cancer genomes

22. Divergent mutational processes distinguish hypoxic and normoxic tumours

23. Genomic footprints of activated telomere maintenance mechanisms in cancer

25. Prognostic significance of S100A4-expression and subcellular localization in early-stage breast cancer

26. LIMT is a novel metastasis inhibiting lncRNA suppressed by EGF and downregulated in aggressive breast cancer

28. Sample Preparation Approach Influences PAM50 Risk of Recurrence Score in Early Breast Cancer

31. Subtype and cell type specific expression of lncRNAs provide insight into breast cancer.

32. Germline HOXB13 mutations p.G84E and p.R217C do not confer an increased breast cancer risk

34. Immune phenotype of tumor microenvironment predicts response to bevacizumab in neoadjuvant treatment ofER‐positive breast cancer

36. Breast cancer metastasis: immune profiling of lymph nodes reveals exhaustion of effector T cells and immunosuppression.

39. Serum levels of inflammation‐related markers and metabolites predict response to neoadjuvant chemotherapy with and without bevacizumab in breast cancers

41. Toward Personalized Computer Simulation of Breast Cancer Treatment: A Multiscale Pharmacokinetic and Pharmacodynamic Model Informed by Multitype Patient Data

42. Inflammation of mammary adipose tissue occurs in overweight and obese patients exhibiting early-stage breast cancer

43. Personalized Computer Simulations of Breast Cancer Tumors Treated with Neoadjuvant Chemotherapy and Bevacizumab

44. Immune phenotype of tumor microenvironment predicts response to bevacizumab in neoadjuvant treatment of ER‐positive breast cancer.

45. Contrasting DCIS and invasive breast cancer by subtype suggests basal-like DCIS as distinct lesions.

46. DNA copy number motifs are strong and independent predictors of survival in breast cancer.

47. Serum levels of inflammation‐related markers and metabolites predict response to neoadjuvant chemotherapy with and without bevacizumab in breast cancers.

48. Towards personalized computer simulation of breast cancer treatment: a multi-scale pharmacokinetic and pharmacodynamic model informed by multi-type patient data

49. Additional file 2: Table S2. of Lipoprotein subfractions by nuclear magnetic resonance are associated with tumor characteristics in breast cancer

50. The Longitudinal Transcriptional Response to Neoadjuvant Chemotherapy with and without Bevacizumab in Breast Cancer

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