The Center for Cancer Systems Pharmacology

Research

Research Reproducibility

The LSP is strongly committed to making our research findings FAIR (Findable, Accessible, Interoperable and Reusable). 

LSP investigators thoughtfully consider the factors that influence research reproducibility and advocate for solutions. Whenever feasible, we make our software open-source and release our data available under Creative Commons licenses. We co-host a seminar series that features speakers at the cutting edge of data and knowledge management. 

LSP investigators have studied the irreproducibility of preclinical drug response and pharmacodynamic data in detail (Niepel, 2019) and developed multiple methods to address the problem (Hafner, 2016; Mills, 2022). We have also commented on the importance of public data release for reproducibility (AlQuraishi, 2016) and developed methods to make survival data from clinical trials available to the community (cancertrials.io, Plana, 2022).

LSP investigators have also created open-source software and standards for the emerging field of multiplexed imaging. These include MCMICRO, an open-source data processing pipeline that increases the reliability of complex data analysis (Schapiro, 2022a), MITI, a metadata scheme for tissue images (Schapiro, 2022b), and Cylinter, the first quality control software for high-plex imaging data (Baker, 2024). 

In addition, we developed Minerva, a lightweight software that makes it possible to view whole-slide multiplex images online without download, which can be a barrier to sharing large image files (Hoffer, 2020; Rashid, 2022). We've partnered with external organizations like cBioPortal (Wala, 2024) and the Data Coordinating Center of the Human Tumor Atlas Network (HTAN) (De Bruijn, 2024) to incorporate the Minerva image viewer into existing data repositories.

The Minerva image viewer
A sample of metastatic melanoma as viewed through Minerva. A subset of channels are shown. See https://www.cycif.org/data/MIP-CHTN to browse the image.

Related News

October 31, 2024
Innovative assays for drug discovery and development

Featured Publications

Cancer patient survival can be parametrized to improve trial precision and reveal time-dependent therapeutic effects

Plana et al.
Nature Communications. 2022 Feb 15. PMCID: PMC8847344
Journal
PubMed

MITI minimum information guidelines for highly multiplexed tissue images

Schapiro et al.
Nat Methods. 2022 Mar. PMCID: PMC9009186
Journal
PubMed

Related Publications

de Bruijn, I., Nikolov, M., Lau, C., Clayton, A., Gibbs, D. L., Mitraka, E., Pozhidayeva, D., Lash, A., Sumer, S. O., Altreuter, J., Anton, K., DeFelice, M., Li, X., Lisman, A., Longabaugh, W. J. R., Muhlich, J., Santagata, S., Nandakumar, S., Sorger, P. K., … Eddy, J. A. (2025). Sharing data from the Human Tumor Atlas Network through standards, infrastructure and community engagement. Nature Methods, 22(4), 664–671. https://doi.org/10.1038/s41592-025-02643-0
Baker, G. J., Novikov, E., Zhao, Z., Vallius, T., Davis, J. A., Lin, J.-R., Muhlich, J. L., Mittendorf, E. A., Santagata, S., Guerriero, J. L., & Sorger, P. K. (2024). Quality control for single-cell analysis of high-plex tissue profiles using CyLinter. Nature Methods, 21(12), 2248–2259. https://doi.org/10.1038/s41592-024-02328-0
Wala, J., de Bruijn, I., Coy, S., Gagné, A., Chan, S., Chen, Y.-A., Hoffer, J., Muhlich, J., Schultz, N., Santagata, S., & Sorger, P. K. (2024). Integrating spatial profiles and cancer genomics to identify immune-infiltrated mismatch repair proficient colorectal cancers [Preprint]. bioRxiv. https://doi.org/10.1101/2024.09.24.614701
Mills, C. E., Subramanian, K., Hafner, M., Niepel, M., Gerosa, L., Chung, M., Victor, C., Gaudio, B., Yapp, C., Nirmal, A. J., Clark, N., & Sorger, P. K. (2022). Multiplexed and reproducible high content screening of live and fixed cells using Dye Drop. Nature Communications, 13(1), 6918. https://doi.org/10.1038/s41467-022-34536-7
Rashid, R., Chen, Y.-A., Hoffer, J., Muhlich, J. L., Lin, J.-R., Krueger, R., Pfister, H., Mitchell, R., Santagata, S., & Sorger, P. K. (2022). Narrative online guides for the interpretation of digital-pathology images and tissue-atlas data. Nature Biomedical Engineering, 6(5), 515–526. https://doi.org/10.1038/s41551-021-00789-8
Schapiro, D., Yapp, C., Sokolov, A., Reynolds, S. M., Chen, Y.-A., Sudar, D., Xie, Y., Muhlich, J., Arias-Camison, R., Arena, S., Taylor, A. J., Nikolov, M., Tyler, M., Lin, J.-R., Burlingame, E. A., Human Tumor Atlas Network, Chang, Y. H., Farhi, S. L., Thorsson, V., … Sorger, P. K. (2022). MITI minimum information guidelines for highly multiplexed tissue images. Nature Methods, 19(3), 262–267. https://doi.org/10.1038/s41592-022-01415-4
Schapiro, D., Sokolov, A., Yapp, C., Chen, Y.-A., Muhlich, J. L., Hess, J., Creason, A. L., Nirmal, A. J., Baker, G. J., Nariya, M. K., Lin, J.-R., Maliga, Z., Jacobson, C. A., Hodgman, M. W., Ruokonen, J., Farhi, S. L., Abbondanza, D., McKinley, E. T., Persson, D., … Sorger, P. K. (2022). MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging. Nature Methods, 19(3), 311–315. https://doi.org/10.1038/s41592-021-01308-y
Plana, D., Fell, G., Alexander, B. M., Palmer, A. C., & Sorger, P. K. (2022). Cancer patient survival can be parametrized to improve trial precision and reveal time-dependent therapeutic effects. Nature Communications, 13(1), 873. https://doi.org/10.1038/s41467-022-28410-9
Hoffer, J., Rashid, R., Muhlich, J. L., Chen, Y.-A., Russell, D. P. W., Ruokonen, J., Krueger, R., Pfister, H., Santagata, S., & Sorger, P. K. (2020). Minerva: a light-weight, narrative image browser for multiplexed tissue images. Journal of Open Source Software, 5(54), 2579. https://doi.org/10.21105/joss.02579
Niepel, M., Hafner, M., Mills, C. E., Subramanian, K., Williams, E. H., Chung, M., Gaudio, B., Barrette, A. M., Stern, A. D., Hu, B., Korkola, J. E., LINCS Consortium, Gray, J. W., Birtwistle, M. R., Heiser, L. M., & Sorger, P. K. (2019). A multi-center study on the reproducibility of drug-response assays in mammalian cell lines. Cell Systems, 9(1), 35-48.e5. https://doi.org/10.1016/j.cels.2019.06.005
Hafner, M., Niepel, M., Chung, M., & Sorger, P. K. (2016). Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs. Nature Methods, 13(6), 521–527. https://doi.org/10.1038/nmeth.3853
AlQuraishi, M., & Sorger, P. K. (2016). Reproducibility will only come with data liberation. Science Translational Medicine, 8(339), 339ed7. https://doi.org/10.1126/scitranslmed.aaf0968

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