I translate complex multi-omics and clinical trial data into actionable insights for immuno-oncology research, from single-cell sequencing to biomarker strategy.
5+
Years Experience
10+
Projects Led
9
Publications
I am a computational biologist specializing in cancer immunology and multi-omics data science, with hands-on experience across the full data lifecycle: from wet-lab experimental design to data creation, multi-omics integration, and biologically grounded interpretation.
In my current role at the University Hospital of Basel, I build pan-cancer immune atlases and act as scientific lead across 10+ multi-omics projects, integrating single-cell transcriptomics, immune repertoire profiling, and perturbation data from longitudinal clinical and pre-clinical studies. Beyond executing analyses, I scope projects with clinicians and experimental teams, increasingly using LLM-assisted and agentic workflows to move from raw data to interpretation, and communicate results in decision-oriented formats that directly inform biomarker prioritization and R&D direction.
At Novartis, working in GMP-regulated cell therapy manufacturing taught me that scientific rigor must align with business needs and regulatory constraints, a mindset that stays with me in every collaborative setting. My formal training at ETH Zurich (MSc Chemistry) and LMU Munich (BSc Chemistry & Biochemistry) gave me a mechanistic perspective I bring to every omics dataset.
My work sits at the intersection of scientific depth, stakeholder interaction, and real-world clinical impact, across biopharma R&D, translational research, and scientific consulting.
Capabilities that emerge from working at the intersection of biology, computation, and clinical research, and that are rarely found combined in a single person.
Integrating computational analysis with biological interpretation: every model output is evaluated in the context of what is biologically plausible.
Bridging clinicians, wet-lab scientists, and bioinformaticians, translating between domains so that every stakeholder understands what the data mean for their decisions.
Translating high-dimensional multi-omics datasets into decision-ready results, moving from terabytes of sequencing data to a concrete, actionable biological conclusion.
Understanding how experimental data are generated and where artifacts arise, enabling more accurate interpretation and preventing misleading computational conclusions.
Recognizing meaningful biological signals within complex datasets, distinguishing true biology from technical noise, batch effects, and experimental confounders.
Selected research projects combining multi-omics, clinical trial data, and computational methods to advance cancer immunotherapy.
Longitudinal single-cell RNA and TCR sequencing across five time points (baseline tumor, two-phase ex vivo expansion, and post-infusion blood and tumor biopsies) in seven melanoma patients, resolving both the CD8+ and CD4+ compartments. Tumor-reactive CD8+ T cells are reinvigorated from exhaustion into HLA-II-high or KLF2-high states, while the CD4+ compartment recovers in a lineage-dependent way: follicular helper T cells adopt an effector state whereas exhausted CD4+ T cells retain dysfunction. Across three cohorts, co-transferred type 17 T cells and de novo regulatory T cell expansion after transfer associate with treatment failure.
Resolved NK cell heterogeneity in non-small cell lung cancer using matched snRNA-seq and snATAC-seq. Trajectory and regulon analysis revealed an inflammation-driven transition from early GZMK+ NK cells toward an ENTPD1+ (CD39+) effector state defined by interferon-stimulated gene programs, alongside ITGAE+ and ITGA1+ tissue-resident subsets. Functional profiling established CD39+ tumor-associated NK cells as the dominant cytotoxic population, with killing capacity further potentiated by NKG2A blockade, nominating a targetable effector population for NK-directed immunotherapy.
Investigated how sialylated glycans on cancer cell surfaces drive immune suppression in the tumor microenvironment. Siglec receptor engagement on myeloid-derived suppressor cells induced CCL2 secretion, suppressing T cell infiltration. CRISPR knockout of Siglec-E in myeloid cells improved tumor control in vivo, identifying the glycan–Siglec axis as a tractable immunotherapy target.
University Hospital of Basel (Zippelius & Dixon Lab) · Basel, CH
University Hospital of Basel (Läubli Lab) · Basel, CH
University Hospital of Basel (Läubli Lab) · Basel, CH
Novartis Pharma Stein AG · Stein, CH
University of Basel · Basel, CH
Swiss Federal Institute of Technology (ETH Zurich) · Zurich, CH
Ludwig Maximilian University (LMU) · Munich, DE
* These authors contributed equally to this work.
Sandholzer, M. T., et al. Single-cell tracking reveals tumor-reactive T cell plasticity during melanoma TIL therapy. Cell Rep Med, 103051 (2026).
Nimmerfroh, J., Heiligensetzer, D., Sandholzer, M., et al. α2,3-sialylation on human naïve T cells restrains bispecific engager-mediated anti-tumor immunity. bioRxiv, 2026.08.02.742298 (2026).
Serger, C., Rebuffet, L.*, Sandholzer, M. T.*, et al. Cytotoxic CD39+ tumor-associated NK cells respond to NKG2A blockade in lung cancer. Sci Immunol, 11, eaeb6645 (2026).
Rodrigues Mantuano, N., Sandholzer, M. T., et al. Local glycan engineering induces systemic antitumor immune reactions via antigen cross-presentation. bioRxiv, 2026.05.04.720097 (2026).
Fusi, I., Serger, C., Herzig, P., Germann, M., Sandholzer, M. T., et al. PD-1–targeted cis-delivery of an IL-2 variant induces a multifaceted antitumoral T cell response in human lung cancer. Sci Transl Med, 17, eadr3718 (2025).
König, D.*, Sandholzer, M. T.*, et al. Melanoma clonal heterogeneity leads to secondary resistance after adoptive cell therapy with tumor-infiltrating lymphocytes. Cancer Immunol Res, 12, 814–821 (2024).
Wieboldt, R., Sandholzer, M., et al. Engagement of sialylated glycans with Siglec receptors on suppressive myeloid cells inhibits anti-cancer immunity via CCL2. Cell Mol Immunol, 21, 495–509 (2024).
König, D., Kasenda, B., Sandholzer, M., et al. Adoptive cell therapy with tumor-infiltrating lymphocytes in combination with nivolumab in patients with advanced melanoma. Immunooncol Technol, 24, 100728 (2024).
Pellegrino, C., Favalli, N., Sandholzer, M., et al. Impact of Ligand Size and Conjugation Chemistry on the Performance of Universal Chimeric Antigen Receptor T-Cells for Tumor Killing. Bioconjug Chem (2020).
Professional references from academic, clinical, and industry supervisors are available upon request. Please reach out via email or LinkedIn.
mt.sandholzer@gmail.com
linkedin.com/in/sandholzer
GitHub
github.com/Sandholzer
Google Scholar
scholar.google.com
ORCID
0009-0005-4842-6013
Curriculum Vitae
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Open to opportunities
I am interested in roles at the intersection of data science, precision medicine, and biomedical research: in biopharma R&D, scientific consulting, bioinformatics platforms, or translational research teams. Feel free to reach out.