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Comprehensive biobanking strategy with clinical impact at the European Cancer Moonshot Lund Center

Oskolas, Henriett LU ; Nogueira, Fábio C N ; Domont, Gilberto B ; Yu, Kun-Hsing ; Semenov, Yevgeniy R ; Sorger, Peter ; Steinfelder, Erik ; Corps, Les ; Schulz, Lesley and Wieslander, Elisabet LU , et al. (2025) In Journal of Proteomics 316.
Abstract

This white paper presents a comprehensive biobanking framework developed at the European Cancer Moonshot Lund Center that merges rigorous sample handling, advanced automation, and multi-omic analyses to accelerate precision oncology. Tumor and blood-based workflows, supported by automated fractionation systems and standardized protocols, ensure the collection of high-quality biospecimens suitable for proteomic, genomic, and metabolic studies. A robust informatics infrastructure, integrating LIMS, barcoding, and REDCap, supports end-to-end traceability and realtime data synchronization, thereby enriching each sample with critical clinical metadata. Proteogenomic integration lies at the core of this initiative, uncovering tumor- and... (More)

This white paper presents a comprehensive biobanking framework developed at the European Cancer Moonshot Lund Center that merges rigorous sample handling, advanced automation, and multi-omic analyses to accelerate precision oncology. Tumor and blood-based workflows, supported by automated fractionation systems and standardized protocols, ensure the collection of high-quality biospecimens suitable for proteomic, genomic, and metabolic studies. A robust informatics infrastructure, integrating LIMS, barcoding, and REDCap, supports end-to-end traceability and realtime data synchronization, thereby enriching each sample with critical clinical metadata. Proteogenomic integration lies at the core of this initiative, uncovering tumor- and blood-based molecular profiles that inform cancer heterogeneity, metastasis, and therapeutic resistance. Machine learning and AI-driven models further enhance these datasets by stratifying patient populations, predicting therapeutic responses, and expediting the discovery of actionable targets and companion biomarkers. This synergy between technology, automation, and high-dimensional data analytics enables individualized treatment strategies in melanoma, lung, and other cancer types. Aligned with international programs such as the Cancer Moonshot and the ICPC, the Lund Center's approach fosters open collaboration and data sharing on a global scale. This scalable, patient-centric biobanking paradigm provides an adaptable model for institutions aiming to unify clinical, molecular, and computational resources for transformative cancer research.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Journal of Proteomics
volume
316
article number
105442
publisher
Elsevier
external identifiers
  • scopus:105002579910
  • pmid:40246065
ISSN
1874-3919
DOI
10.1016/j.jprot.2025.105442
language
English
LU publication?
yes
additional info
Copyright © 2025. Published by Elsevier B.V.
id
98de5ffc-fe1b-4943-be30-3685f77182d3
date added to LUP
2025-05-01 10:02:30
date last changed
2025-05-07 10:46:23
@article{98de5ffc-fe1b-4943-be30-3685f77182d3,
  abstract     = {{<p>This white paper presents a comprehensive biobanking framework developed at the European Cancer Moonshot Lund Center that merges rigorous sample handling, advanced automation, and multi-omic analyses to accelerate precision oncology. Tumor and blood-based workflows, supported by automated fractionation systems and standardized protocols, ensure the collection of high-quality biospecimens suitable for proteomic, genomic, and metabolic studies. A robust informatics infrastructure, integrating LIMS, barcoding, and REDCap, supports end-to-end traceability and realtime data synchronization, thereby enriching each sample with critical clinical metadata. Proteogenomic integration lies at the core of this initiative, uncovering tumor- and blood-based molecular profiles that inform cancer heterogeneity, metastasis, and therapeutic resistance. Machine learning and AI-driven models further enhance these datasets by stratifying patient populations, predicting therapeutic responses, and expediting the discovery of actionable targets and companion biomarkers. This synergy between technology, automation, and high-dimensional data analytics enables individualized treatment strategies in melanoma, lung, and other cancer types. Aligned with international programs such as the Cancer Moonshot and the ICPC, the Lund Center's approach fosters open collaboration and data sharing on a global scale. This scalable, patient-centric biobanking paradigm provides an adaptable model for institutions aiming to unify clinical, molecular, and computational resources for transformative cancer research.</p>}},
  author       = {{Oskolas, Henriett and Nogueira, Fábio C N and Domont, Gilberto B and Yu, Kun-Hsing and Semenov, Yevgeniy R and Sorger, Peter and Steinfelder, Erik and Corps, Les and Schulz, Lesley and Wieslander, Elisabet and Fenyö, David and Kárpáti, Sarolta and Holló, Péter and Kemény, Lajos V and Döme, Balazs and Megyesfalvi, Zsolt and Pawłowski, Krzysztof and Nishimura, Toshihide and Kwon, HoJeong and Encarnación-Guevara, Sergio and Szasz, A Marcell and Veréb, Zoltán and Gyulai, Rolland and Németh, István Balázs and Appelqvist, Roger and Rezeli, Melinda and Baldetorp, Bo and Horvatovich, Peter and Malmström, Johan and Pla, Indira and Sanchez, Aniel and Knudsen, Beatrice and Kiss, András and Malm, Johan and Marko-Varga, György and Gil, Jeovanis}},
  issn         = {{1874-3919}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{Journal of Proteomics}},
  title        = {{Comprehensive biobanking strategy with clinical impact at the European Cancer Moonshot Lund Center}},
  url          = {{http://dx.doi.org/10.1016/j.jprot.2025.105442}},
  doi          = {{10.1016/j.jprot.2025.105442}},
  volume       = {{316}},
  year         = {{2025}},
}