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METABRIC

Solid tumors are complex tissues composed of a mixture of cancer and normal cells, which complicates the interpretation of their molecular profiles. Normal cell contamination can dilute cancer cell information and tissue architecture is generally not reflected in molecular assays. To address these challenges, we developed a computational approach based on standard Haematoxylin and Eosin-stained sections and demonstrated its power in a discovery cohort of 323 breast tumors and an independent validation cohort of 241 tumors. First, to deconvolute cellular heterogeneity and detect subtle genomic aberrations, we introduced an algorithm based on tumor cellularity to increase the comparability of copy-number profiles between samples. Second, we demonstrated that a predictor for survival integrating image-based and molecular features significantly outperforms classifiers based on single data types. Third, we described and validated a novel, independent prognostic factor based on quantitative analysis of spatial patterns between stromal cells, which are not detectable by molecular assays. Our quantitative methods refine and complement molecular assays of tumor samples and could benefit all large-scale cancer studies.First, to deconvolute cellular heterogeneity and detect subtle genomic aberrations, we introduced an algorithm based on tumor cellularity to increase the comparability of copy-number profiles between samples. Second, we demonstrated that a predictor for survival integrating image-based and molecular features significantly outperforms classifiers based on single data types. Third, we described and validated a novel, independent prognostic factor based on quantitative analysis of spatial patterns between stromal cells, which are not detectable by molecular assays. Our quantitative methods refine and complement molecular assays of tumor samples and could benefit all large-scale cancer studies.

Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data

Dataset ID Description Technology Samples
EGAD00010000266 Affymetrix SNP 6.0 543
EGAD00010000268 Illumina HT 12 543
EGAD00010000270 Aperio image - H&E stained tissue_section 564
Publications Citations
Modelling the spatial heterogeneity and molecular correlates of lymphocytic infiltration in triple-negative breast cancer.
J R Soc Interface 12: 2015 20141153
39
An mRNA Gene Expression-Based Signature to Identify FGFR1-Amplified Estrogen Receptor-Positive Breast Tumors.
J Mol Diagn 19: 2017 147-161
6
Co-expression of nuclear P38 and hormone receptors is prognostic of good long-term clinical outcome in primary breast cancer and is linked to upregulation of DNA repair.
BMC Cancer 18: 2018 1027
3
Cadherin 11 Inhibition Downregulates β-catenin, Deactivates the Canonical WNT Signalling Pathway and Suppresses the Cancer Stem Cell-Like Phenotype of Triple Negative Breast Cancer.
J Clin Med 8: 2019 E148
29
Glucose-6-phosphate dehydrogenase blockade potentiates tyrosine kinase inhibitor effect on breast cancer cells through autophagy perturbation.
J Exp Clin Cancer Res 38: 2019 160
44
Immuno-subtyping of breast cancer reveals distinct myeloid cell profiles and immunotherapy resistance mechanisms.
Nat Cell Biol 21: 2019 1113-1126
153
CD47 blockade augmentation of trastuzumab antitumor efficacy dependent on antibody-dependent cellular phagocytosis.
JCI Insight 4: 2019 131882
60
Dopamine and cAMP-regulated phosphoprotein 32 kDa (DARPP-32) and survival in breast cancer: a retrospective analysis of protein and mRNA expression.
Sci Rep 9: 2019 16987
8
Targetable ERBB2 mutation status is an independent marker of adverse prognosis in estrogen receptor positive, ERBB2 non-amplified primary lobular breast carcinoma: a retrospective in silico analysis of public datasets.
Breast Cancer Res 22: 2020 85
21
Immunological Differences Between Immune-Rich Estrogen Receptor-Positive and Immune-Rich Triple-Negative Breast Cancers.
JCO Precis Oncol 4: 2020 PO.19.00350
19
Survival time prediction by integrating cox proportional hazards network and distribution function network.
BMC Bioinformatics 22: 2021 192
7
PP1, PKA and DARPP-32 in breast cancer: A retrospective assessment of protein and mRNA expression.
J Cell Mol Med 25: 2021 5015-5024
6
Mitochondrial-nuclear epistasis underlying phenotypic variation in breast cancer pathology.
Sci Rep 12: 2022 1393
4
Analyzing biomarker discovery: Estimating the reproducibility of biomarker sets.
PLoS One 17: 2022 e0252697
3
Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction.
Diagnostics (Basel) 13: 2023 161
9
A comparative analysis of clinicopathological features and survival between pre and postmenopausal breast cancer from an Indian cohort.
Sci Rep 13: 2023 3938
1
Development and testing of a polygenic risk score for breast cancer aggressiveness.
NPJ Precis Oncol 7: 2023 42
1
In Silico Identification of a BRCA1:miR-29:DNMT3 Axis Involved in the Control of Hormone Receptors in BRCA1-Associated Breast Cancers.
Int J Mol Sci 24: 2023 9916
0
Unravelling transcriptomic complexity in breast cancer through modulation of DARPP-32 expression and signalling pathways.
Sci Rep 13: 2023 21163
0