Google Scholar link for full list
N. Chaudhary, …, and M. Hafner, Real-world data reveal increased ESR1 mutation prevalence and altered genomic landscape in HR+/HER2- breast cancer tumors treated with CDK4/6 inhibitors, npj Breast Cancer (2024), vol. 10, 15.
T. J. Hagenbeek*, J. R. Zbieg*, M. Hafner*, et al., An allosteric pan-TEAD inhibitor blocks oncogenic YAP/TAZ signaling and overcomes KRAS G12C inhibitor resistance, Nat Cancer (2023), vol. 4: 812–828.
R. Diegmiller, L. Salphati, … , M. Hafner, Growth-rate model predicts in vivo tumor response from in vitro data. CPT: Pharmacometrics & Systems Pharmacology (2022), vol. 11 (9): 1183-1193.
K. Song*, K. Edgar*, E. Hanan, M. Hafner, et al. RTK-dependent inducible degradation of mutant PI3Kα drives GDC-0077 (Inavolisib) efficacy. Cancer Disc (2022), vol. 12 (1): 204–219.
J. Liang, …, M. Hafner, C. Metcalfe, Giredestrant counters a progesterone hypersensitivity program driven by estrogen receptor mutations in breast cancer, Sci Transl Med (2022), vol. 14 (663).
J Ma, SH Fong, …, M. Hafner, et al., Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients. Nat Cancer (2021), vol. 2(2), 233-244.
J. Guan*, W. Zhou*, M. Hafner, et al., Therapeutic Ligands Antagonize Estrogen Receptor Function by Impairing Its Mobility, Cell (2019), vol. 178(4), 949-963.
S.M. Bronner, …, M. Hafner, et al.. Design of a brain-penetrant CDK4/6 inhibitor for glioblastoma. Bioorganic & Medicinal Chemistry Letters (2019), vol. 29(16), 2294-2301.
*equal contribution
C. E. Mills*, K. Subramanian*, M. Hafner*, et al. Multiplexed and reproducible high content screening of live and fixed cells using the Dye Drop method. Nat Comm (2022), vol. 13 (1), 1-18.
M. Hafner*, C.E. Mills*, … , D. Juric#, and P.K. Sorger#, Multiomics Profiling Establishes the Polypharmacology of FDA-Approved CDK4/6 Inhibitors and the Potential for Differential Clinical Activity. Cell Chem Biol (2019), vol. 26(8), 1067–1080.
M. Niepel*, M. Hafner*, C. E. Mills*, K. Subramanian*, et al.. A Multi-center Study on the Reproducibility of Drug-Response Assays in Mammalian Cell Lines. Cell Systems (2019), vol. 9(1), 35-48.
LINCS consortium. The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations. Cell Systems (2018), vol. 6(1), 13-24
M. Hafner, M. Niepel, and P.K. Sorger, Alternative drug sensitivity metrics improve preclinical cancer pharmacogenomics. Nat Biotech (2017), vol. 35(6), 500-2.
M. Hafner*, M. Niepel*, K. Subramanian*, and P.K. Sorger, Designing drug response experiments and quantifying their results. Curr Protoc Chem Biol (2017), vol. 9(2), 96–116.
M. Niepel*, M. Hafner*, M. Chung, and P.K. Sorger, Measuring cancer drug sensitivity and resistance in cultured cells. Curr Protoc Chem Biol (2017), vol. 9(2), 55–74.
N. Clark*, M. Hafner*, M. Kouril, E.H. Williams, et al., GRcalculator: an online tool for calculating and mining dose-response data. BMC Cancer (2017), vol. 17, 698.
M. Niepel*, M. Hafner*, Q. Duan, E. Paull, J. Stuart, A. Subramanian, A. Ma’ayan, and P. Sorger, Common and cell-type specific responses to anti-cancer drugs revealed by high throughput transcript profiling, Nat Commun (2017), vol. 8, 1186.
M. Hafner*, L. Heiser*, M. Niepel, E.H. Williams, J.W. Gray, and P.K. Sorger, Quantification of sensitivity and resistance of breast cancer cell lines to anti-cancer drugs using GR metrics, Sci Data (2017), vol. 4, 170166.
M. Hafner*, M. Niepel*, M. Chung, and P.K. Sorger, Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs. Nat Methods (2016), vol. 13, 521–7.
J. Roux*, M. Hafner*, S. Bandara, J. J. Sims, D. Chai, and P. K. Sorger, Cell-to-cell variability in overcoming a caspase activity threshold and fractional killing by TRAIL. Mol Syst Biol (2015) 11:803.
M. Niepel*, M. Hafner*, E. Pace*, M. Chung, D. H. Chai, L. Zhou, B. Schoeberl, and P. K. Sorger, Molecular determinants of growth factor signaling in genetically diverse breast cancer lines. BMC Biology (2014), vol. 12:20.
M. Niepel*, M. Hafner*, E. Pace*, M. Chung, D. H. Chai, L. Zhou, B. Schoeberl, and P. K. Sorger, Basal and induced receptor profiles predict drug response in breast cancer lines. Sci Signal (2013), vol. 6 (294), ra84.
*equal contribution
H. Koeppl, M. Hafner and J. Lu, Mapping behavioral specifications to model parameters in synthetic biology. BMC Bioinformatics (2013), vol. 14(Suppl 10), S9.
M. Hafner, H. Koeppl, and D. Gonze, Effect of network architecture on synchronization and entrainment properties of the circadian oscillations in the suprachiasmatic nucleus. PLoS Comput Biol (2012), 8(3), e1002419.
M. Miller*, M. Hafner*, E. Sontag, N. Davidsohn, S. Subramanian, P. Purnick, D. Lauffenburger, and R. Weiss, Modular Design of Artificial Tissue Homeostasis: Robust Control through Synthetic Cellular Heterogeneity. in PLoS Comput Biol (2012), 8(7), e1002579.
Commented in Nature Chem Biol (2012) 8(9), p. 738.
P. De Los Rios, M. Hafner, and A. Pastore, Explaining the length threshold of polyglutamine aggregation. Journal of Physics: Condensed Matter (2012), vol. 24(24), 244105.
H. Koeppl*, M. Hafner*, A. Ganguly, and A. Mehrotra, Deterministic characterization of phase noise in biomolecular oscillators. Physical Biology (2011), vol. 8(5), 055008.
M. Hafner, P. Sacré, L. Symul, R. Sepulchre, and H. Koeppl, Multiple feedback loops in circadian cycles: Robustness and entrainment as selection criteria. Proceedings of the Seventh Workshop on Computational Systems Biology (2010), pp. 43-46.
M. Hafner, H. Koeppl, M. Hasler, and A. Wagner, ‘Glocal’ robustness analysis and model discrimination for circadian oscillators. PLoS Comput Biol (2009), vol. 5(10), e1000534.
M. Hafner, H. Koeppl and A. Wagner, Evolution of feedback loops in oscillatory systems in Proceedings of the Third International Conference on Foundations of Systems Biology in Engineering (2009), pp. 157-160, http://arxiv.org/abs/1003.1231.
*equal contribution