Prof. Carlos Caldas
Email: carlos.caldas@mail.huji.ac.il
Tumor ecosystems- from biology to clinical application
All malignant tumors are complex and evolving cellular ecosystems formed by cancer cells and normal cells of stromal, vascular and immune origin. This
has profound implications for both cancer biology and for potential clinical application.
My laboratory over the past 20 years focused on the functional genomics of breast cancer and its biological and clinical implications. We redefined the
molecular taxonomy of breast cancer and its phenogenomic landscapes and showed that this determines the clinical trajectories of patients. We led the studies that established ctDNA as a monitoring biomarker and as a liquid biopsy in breast cancer and pioneered the use of patient-derived tumor explants as in vivo cancer models. In a recent landmark paper published in
Nature, using multi-omics and machine learning, we showed the biology of the breast tumor ecosystems predicts response to therapy. Our new laboratory at the Lautenberg Center in the Faculty of Medicine of Hebrew University, grounded on the foundations we built at the University of Cambridge, will have two main areas of integrated research:
1. Modelling cancer ecosystems in space and time using patient-derived tumor xenografts (PDTXs) to study the regulation of cell fate and cell state, both in the malignant compartment and in the TME.
2. Developing novel cancer biomarkers and therapeutics using multi- omics, dynamic monitoring, and data integration and interpretation with machine learning and AI.
Osnat Hazan, PhD, Lab Manager
Lab Alumni
Selected Publications
2025
Nguyen LV, Eyal-Lubling Y, Guerrero-Romero D, et al. Fitness and transcriptional plasticity of human breast cancer single-cell-derived clones. Cell Rep. 2025 May 27;44(5):115699. DOI: 10.1016/j.celrep.2025.115699.
Tu KJ, Guerrero-Romero D, Eason K, et al. The tumor microenvironment of 14,837 breast cancers is associated with clinical outcome independently of genomic subtypes. Cell Rep Med. 2025 Nov 18;6(11):102450. DOI: 10.1016/j.xcrm.2025.102450.
Beddowes EJ, Ortega Duran M, et al. A large-scale retrospective study in metastatic breast cancer patients using circulating tumour DNA and machine learning to predict treatment outcome and progression-free survival. Mol Oncol. 2025 Apr 15. DOI: 10.1002/1878-0261.70015.
Shea A, Eyal-Lubling Y, Guerrero-Romero D, et al. Modeling Drug Responses and Evolutionary dynamics using Patient-Derived Xenografts reveals precision medicine strategies for triple-negative breast cancer. Cancer Res 2025;85(3):567-584. DOI: 10.1158/0008-5472.CAN-24-1703.
2024
Sammut SJ, Galson JD, Minter R, et al. Predictability of B cell clonal persistence and immunosurveillance in breast cancer. Nat Immunol 2024;25(5):916-924. DOI: 10.1038/s41590-024-01821-0.
Masina R, Caldas C. Precision Cancer Medicine 2.0-Oncology in the postgenomic era. Mol Oncol. 2024 Sep;18(9):2065-2069. DOI: 10.1002/1878-0261.13707.
Lewis MT, Caldas C. The Power and Promise of Patient-Derived Xenografts of Human Breast Cancer. Cold Spring Harb Perspect Med 2024;14(4). DOI: 10.1101/cshperspect.a041329.
Hoang DT, Dinstag G, Hermida LC, et al. Prediction of cancer treatment response from histopathology images through imputed transcriptomics. Nat Cancer. 2024 Sep;5(9):1305-1317. DOI: 10.1038/s43018-024-00793-2.
2023
Cannell IG, Sawicka K, Pearsall I, et al. FOXC2 promotes vasculogenic mimicry and resistance to anti-angiogenic therapy. Cell Rep. 2023 Aug 29;42(8):112791. DOI: 10.1016/j.celrep.2023.112791.
Kreuzaler P, Inglese P, Ghanate A, et al. Vitamin B5 supports MYC oncogenic metabolism and tumor progression in breast cancer. Nat Metab. 2023 Nov;5(11):1870-1886. DOI: 10.1038/s42255-023-00915-7
2022
Sammut SJ, Crispin-Ortuzar M, Chin SF, et al. Multi-omic machine learning predictor of breast cancer therapy response. Nature 2022;601(7894):623-629. DOI: 10.1038/s41586-021-04278-5.
Danenberg E, Bardwell H, Zanotelli VRT, et al. Breast tumor microenvironment structures are associated with genomic features and clinical outcome. Nat Genet 2022;54(5):660-669. DOI: 10.1038/s41588-022-01041-y.
2021
Georgopoulou D, Callari M, Rueda OM, et al. Landscapes of cellular phenotypic diversity in breast cancer xenografts and their impact on drug response. Nat Commun 2021;12(1):1998. DOI: 10.1038/s41467-021-22303-z.
Batra RN, Lifshitz A, Vidakovic AT, et al. DNA methylation landscapes of 1538 breast cancers reveal a replication-linked clock, epigenomic instability and cis-regulation. Nat Commun 2021;12(1):5406. DOI: 10.1038/s41467-021-25661-w.
2020
Ali HR, Jackson HW, Zanotelli VRT, et al. Imaging mass cytometry and multiplatform genomics define the phenogenomic landscape of breast cancer. Nat Cancer 2020;1(2):163-175. DOI: 10.1038/s43018-020-0026-6.
Ros S, Wright AJ, D’Santos P, et al. Metabolic Imaging Detects Resistance to PI3Kalpha Inhibition Mediated by Persistent FOXM1 Expression in ER(+) Breast Cancer. Cancer Cell 2020;38(4):516-533 e9. DOI: 10.1016/j.ccell.2020.08.016.
2012-2019
Curtis C, Shah SP, Chin SF, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 2012;486(7403):346-52. DOI: 10.1038/nature10983.
Dawson SJ, Rueda OM, Aparicio S, Caldas C. A new genome-driven integrated classification of breast cancer and its implications. EMBO J 2013;32(5):617-28. DOI: 10.1038/emboj.2013.19.
Aparicio S, Caldas C. The implications of clonal genome evolution for cancer medicine. N Engl J Med 2013;368(9):842-51. DOI: 10.1056/NEJMra1204892.
Dvinge H, Git A, Graf S, et al. The shaping and functional consequences of the microRNA landscape in breast cancer. Nature 2013;497(7449):378-82. DOI: 10.1038/nature12108.
Dawson SJ, Tsui DW, Murtaza M, et al. Analysis of circulating tumor DNA to monitor metastatic breast cancer. N Engl J Med. 2013 Mar 28;368(13):1199-209. DOI: 10.1056/NEJMoa1213261.
Murtaza M, Dawson SJ, Tsui DW, et al. Non-invasive analysis of acquired resistance to cancer therapy by sequencing of plasma DNA. Nature. 2013 May 2;497(7447):108-12. DOI: 10.1038/nature12065.
Ali HR, Rueda OM, Chin SF, et al. Genome-driven integrated classification of breast cancer validated in over 7,500 samples. Genome Biol 2014;15(8):431. DOI: 10.1186/s13059-014-0431-1.
Pereira B, Chin SF, Rueda OM, et al. The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes. Nat Commun 2016;7:11479. DOI: 10.1038/ncomms11479.
Bruna A, Rueda OM, Greenwood W, et al. A Biobank of Breast Cancer Explants with Preserved Intra-tumor Heterogeneity to Screen Anticancer Compounds. Cell 2016;167(1):260-274 e22. DOI: 10.1016/j.cell.2016.08.041.
De Mattos-Arruda L, Sammut SJ, Ross EM, et al. The Genomic and Immune Landscapes of Lethal Metastatic Breast Cancer. Cell Rep 2019;27(9):2690-2708.e10. DOI: 10.1016/j.celrep.2019.04.098.
Rueda OM, Sammut SJ, Seoane JA, et al. Dynamics of breast-cancer relapse reveal late-recurring ER-positive genomic subgroups. Nature 2019;567(7748):399-404. DOI: 10.1038/s41586-019-1007-8.
AT HUJI and HADASSAH
Prof Eli Pikarsky [Molecular pathology]
Prof Shani Paluch Shimon, Prof Tanir Allweis and Dr Yael Berner-Wygoda [Breast cancer]
Prof Michal Lotem [Melanoma]
Prof Ori Wald and Dr Yoni Arnon [Lung cancer]
Prof Aron Popovitzer [Head&Neck cancer]
Dr Mor Nitzan [Computer Science]
IN ISRAEL
Prof Yardena Samuels and Dr Leeat Keren, Weizmann Institute
ABROAD
Prof Sam Aparicio, BCCA, Vancouver, Canada
Prof Paul Pharoah and Prof Eytan Ruppin, Cedars Sinai, Los Angeles, USA
Dr Oscar Rueda, University of Cambridge
Dr Stephen-John Sammut, ICR, London
