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1. The Tumor Profiler Study: integrated, multi-omic, functional tumor profiling for clinical decision support

Author

René Holtackers, Simone Muenst, Martin Zoche, Rudolf Aebersold, Stéphane Chevrier, Tobias Schär, Bettina Sobottka, Marc Zimmermann, Abdullah Kahraman, Lucas Pelkmans, Faisal Alquaddoomi, Philip Jermann, Natascha Santacroce, Andreas Wicki, Norbert Wey, Nora C. Toussaint, Monica-Andreea Drăgan, Mattheus H.E. Wildschut, Ruben Casanova, Shuqing Yu, Markus Tolnay, Marcus Vetter, Mustafa Anil Tuncel, Ximena Bonilla, Stefan Nicolet, Gabriele Gut, Stefan G. Stark, Philipp Markolin, Bruno S. Frey, Ramona Schlenker, Rebekka Wegmann, Walter P. Weber, Lara Bernasconi, Emanuela S. Milani, Viktor H. Koelzer, Christian Rommel, Christian P. Kunze, Sylvia Herter, Cinzia Esposito, Gabriela Senti, Michael Prummer, Katja Eschbach, Bernd Wollscheid, Riccardo Murri, Salvatore Piscuoglio, Mathilde Ritter, Mitchell P. Levesque, Christian Beisel, Tim M. Jaeger, Viola Heinzelmann-Schwarz, Gunnar Rätsch, Severin Schwan, Marina Bacac, Reinhard Dummer, Joanna Ficek, Sandra Goetze, Tatjana Vlajnic, Martin Erkens, Ilaria Alborelli, Ulrike Menzel, Vinko Tosevski, Markus G. Manz, Werner Kuebler, Detlef Günther, Julian M. Metzler, Daniel J. Stekhoven, Christian Kurzeder, Anja Frei, Tamara Huesser, Marta Nowak, Melike Ak, Francis Jacob, Gregor Zuend, Berend Snijder, Martina Haberecker, Pirmin Haeuptle, Anja Irmisch, Maya D'Costa, Linda Grob, Natalia Chicherova, Bernd Bodenmiller, Johanna Ziegler, Per-Olof Attinger, Jack Kuipers, Katharina Jahn, Nicola Miglino, Natalie R. Davidson, Jacobo Sarabia del Castillo, André Fedier, Fabian Wendt, Esther Danenberg, Pedro Ferreira, María Lourdes Rosano-Gonzalez, Sebastian Lugert, Andrea Jacobs, Charlotte K.Y. Ng, Alva Rani James, Tinu M. Thomas, André Kahles, Gerd Maass, Julien Mena, Jonas B. Albinus, Daniel Baumhoer, Sonali Andani, Petra C. Schwalie, Anne Bertolini, Marina Tusup, Franziska Singer, Alexandre Theocharides, Sandra Weber, Beatrice Beck-Schimmer, Sujana Sivapatham, Kjong-Van Lehmann, Stefanie Engler, Holger Moch, Niko Beerenwinkel, Vipin T. Sreedharan, Michael Weller, Audrey Van Drogen, Patrick G. A. Pedrioli, University of Zurich, Rätsch, Gunnar, and Levesque, Mitchell Paul

Subjects
0301 basic medicine, Decision support system, Cancer Research, Computer science, Observational Trial, Clinical Decision-Making, 610 Medicine & health, Computational biology, Clinical decision support system, 1307 Cell Biology, 03 medical and health sciences, 0302 clinical medicine, Neoplasms, 10049 Institute of Pathology and Molecular Pathology, Humans, Profiling (information science), 1306 Cancer Research, Prospective Studies, Precision Medicine, business.industry, Computational Biology, 10177 Dermatology Clinic, 10060 Epidemiology, Biostatistics and Prevention Institute (EBPI), Cell Biology, Decision Support Systems, Clinical, 030104 developmental biology, Oncology, 030220 oncology & carcinogenesis, 10032 Clinic for Oncology and Hematology, 2730 Oncology, Personalized medicine, business, 11493 Department of Quantitative Biomedicine
Abstract

The application and integration of molecular profiling technologies create novel opportunities for personalized medicine. Here, we introduce the Tumor Profiler Study, an observational trial combining a prospective diagnostic approach to assess the relevance of in-depth tumor profiling to support clinical decision-making with an exploratory approach to improve the biological understanding of the disease.

Published
2021
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2. Establishing standardized immune phenotyping of metastatic melanoma by digital pathology

Author

Bettina Sobottka, Marta Nowak, Anja Laura Frei, Martina Haberecker, Samuel Merki, Mitchell P. Levesque, Reinhard Dummer, Holger Moch, Viktor Hendrik Koelzer, Rudolf Aebersold, Melike Ak, Faisal S. Al-Quaddoomi, Jonas Albinus, Ilaria Alborelli, Sonali Andani, Per-Olof Attinger, Marina Bacac, Daniel Baumhoer, Beatrice Beck-Schimmer, Niko Beerenwinkel, Christian Beisel, Lara Bernasconi, Anne Bertolini, Bernd Bodenmiller, Ximena Bonilla, Ruben Casanova, Stéphane Chevrier, Natalia Chicherova, Maya D'Costa, Esther Danenberg, Natalie Davidson, Monica-Andreea Drăganmoch, Stefanie Engler, Martin Erkens, Katja Eschbach, Cinzia Esposito, André Fedier, Pedro Ferreira, Joanna Ficek, Bruno Frey, Sandra Goetze, Linda Grob, Gabriele Gut, Detlef Günther, Pirmin Haeuptle, Viola Heinzelmann-Schwarz, Sylvia Herter, Rene Holtackers, Tamara Huesser, Anja Irmisch, Francis Jacob, Andrea Jacobs, Tim M. Jaeger, Katharina Jahn, Alva R. James, Philip M. Jermann, André Kahles, Abdullah Kahraman, Werner Kuebler, Jack Kuipers, Christian P. Kunze, Christian Kurzeder, Kjong-Van Lehmann, Sebastian Lugert, Gerd Maass, Markus G. Manz, Philipp Markolin, Julien Mena, Ulrike Menzel, Julian M. Metzler, Nicola Miglino, Emanuela S. Milani, Simone Muenst, Riccardo Murri, Charlotte K.Y. Ng, Stefan Nicolet, Patrick G.A. Pedrioli, Lucas Pelkmans, Salvatore Piscuoglio, Michael Prummer, Mathilde Ritter, Christian Rommel, María L. Rosano-González, Gunnar Rätsch, Natascha Santacroce, Jacobo Sarabia del Castillo, Ramona Schlenker, Petra C. Schwalie, Severin Schwan, Tobias Schär, Gabriela Senti, Franziska Singer, Sujana Sivapatham, Berend Snijder, Vipin T. Sreedharan, Stefan Stark, Daniel J. Stekhoven, Alexandre P.A. Theocharides, Tinu M. Thomas, Markus Tolnay, Vinko Tosevski, Nora C. Toussaint, Mustafa A. Tuncel, Marina Tusup, Audrey Van Drogen, Marcus Vetter, Tatjana Vlajnic, Sandra Weber, Walter P. Weber, Rebekka Wegmann, Michael Weller, Fabian Wendt, Norbert Wey, Andreas Wicki, Mattheus HE Wildschut, Bernd Wollscheid, Shuqing Yu, Johanna Ziegler, Marc Zimmermann, Martin Zoche, Gregor Zuend, University of Zurich, Sobottka-Brillout, Bettina, and Koelzer, Viktor Hendrik

Subjects
Pathology, medicine.medical_specialty, Stromal cell, 610 Medicine & health, Disease, Predictive markers, Article, Pathology and Forensic Medicine, 1307 Cell Biology, Immune system, 10049 Institute of Pathology and Molecular Pathology, 1312 Molecular Biology, Medicine, Compartment (pharmacokinetics), Molecular Biology, Melanoma, business.industry, 10177 Dermatology Clinic, Digital pathology, 10060 Epidemiology, Biostatistics and Prevention Institute (EBPI), Cell Biology, Biomarker (cell), 2734 Pathology and Forensic Medicine, 10032 Clinic for Oncology and Hematology, Cohort, Imaging the immune system, business, CD8
Abstract

CD8+ tumor-infiltrating T cells can be regarded as one of the most relevant predictive biomarkers in immune-oncology. Highly infiltrated tumors, referred to as inflamed (clinically “hot”), show the most favorable response to immune checkpoint inhibitors in contrast to tumors with a scarce immune infiltrate called immune desert or excluded (clinically “cold”). Nevertheless, quantitative and reproducible methods examining their prevalence within tumors are lacking. We therefore established a computational diagnostic algorithm to quantitatively measure spatial densities of tumor-infiltrating CD8+ T cells by digital pathology within the three known tumor compartments as recommended by the International Immuno-Oncology Biomarker Working Group in 116 prospective metastatic melanomas of the Swiss Tumor Profiler cohort. Workflow robustness was confirmed in 33 samples of an independent retrospective validation cohort. The introduction of the intratumoral tumor center compartment proved to be most relevant for establishing an immune diagnosis in metastatic disease, independent of metastatic site. Cut-off values for reproducible classification were defined and successfully assigned densities into the respective immune diagnostic category in the validation cohort with high sensitivity, specificity, and precision. We provide a robust diagnostic algorithm based on intratumoral and stromal CD8+ T-cell densities in the tumor center compartment that translates spatial densities of tumor-infiltrating CD8+ T cells into the clinically relevant immune diagnostic categories “inflamed”, “excluded”, and “desert”. The consideration of the intratumoral tumor center compartment allows immune phenotyping in the clinically highly relevant setting of metastatic lesions, even if the invasive margin compartment is not captured in biopsy material., The authors present a robust diagnostic algorithm based on digital pathology and image analysis that quantifies intratumoral and stromal CD8+ T-cell densities in the tumor center and invasive margin compartment in metastatic melanoma. Spatial CD8+ T-cell densities are translated into the clinically relevant immune diagnostic categories “inflamed”, “excluded”, and “desert”. Their approach also allows efficient immune phenotyping of metastatic lesions, on biopsy material or even in the absence of material from the invasive margin.

Published
2021
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