Pathological assessment of Hematoxylin & Eosin (H&E) stained tissue samples is a well-established clinical routine for cancer diagnosis. While providing rich morphological data, it lacks information on protein expression patterns which is crucial for cancer prognosis and treatment recommendations. Imaging Mass Cytometry (IMC) excels in highly multiplexed protein profiling but faces challenges like high operational cost and a restrictive focus on small Regions-of-Interest. Addressing this, we introduce Multi-V-Stain, a novel image-to-image translation method for multiplexed IMC virtual staining. Our method effectively utilizes the rich morphological features from H&E images to predict multiplexed protein expressions at a Whole-Slide Image level. In evaluations using an in-house melanoma dataset, Multi-V-Stain consistently outperforms existing methods in terms of image quality and biological relevance of the generated stains.

V.H.K reports being an invited speaker for Sharing Progress in Cancer Care (SPCC) and Indica Labs; advisory board of Takeda; sponsored research agreements with Roche and IAG, all unrelated to the current study. VHK is a participant of a patent application on the assessment of cancer immunotherapy biomarkers by digital pathology; a patent application on multimodal deep learning for the prediction of recurrence risk in cancer patients, and a patent application on predicting the efficacy of cancer treatment using deep learning. GR is participant of a patient application on matching cells from different measurement modalities which is not directly related to the current work. Moreover, G.R. is cofounder of Computomics GmbH, Germany, and one of its shareholders. B.B. has co-founded Navignostics, a spin-off company of the University of Zurich developing precision oncology diagnostics, and is one of its shareholders and a board member.

We gratefully acknowledge funding from the Tumor Profiler Initiative and the Tumor Profiler Center (to V.H.K., G.R., B.B.). The Tumor Profiler study is jointly funded by a public-private partnership involving F. Hoffmann-La Roche Ltd., ETH Zurich, University of Zurich, University Hospital Zurich, and University Hospital Basel. We also acknowledge funding from the Swiss Federal Institutes of Technology strategic focus area of personalized health and related technologies project 2021-367 (to G.R., V.H.K., S.A.), the ETH AI Center (to G.R. and B.C.), ETH core funding (to G.R.), UZH core funding (to V.H.K) and funding by the Promedica Foundation grant F-87701-41-01 (to V.H.K). B.B. was funded by two SNSF project grants (#310030_205007: Analysis of breast tumor ecosystem properties for precision medicine approaches and #316030_213512: Cellular-resolution high-performance mass spectrometric imaging of biological samples), an NIH grant (UC4 DK108132), the CRUK IMAXT Grand Challenge, and the European Research Council (ERC) under the European Union's Horizon 2020 Program under the ERC grant agreement no. 866074 ("Precision Motifs").

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The ethics committee of the "Swiss Association of Research Ethics Committees" gave ethical approval for the data from the Tumor Profiler Study used in this work. The Tumor Profiler Study is an approved, observational clinical study (BASEC: 2018-02050, 2018-02052, 2019-01326).

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