TY - JOUR
T1 - Comprehensive biobanking strategy with clinical impact at the European Cancer Moonshot Lund Center
AU - Oskolas, Henriett
AU - Nogueira, Fábio C N
AU - Domont, Gilberto B
AU - Yu, Kun-Hsing
AU - Semenov, Yevgeniy R
AU - Sorger, Peter
AU - Steinfelder, Erik
AU - Corps, Les
AU - Schulz, Lesley
AU - Wieslander, Elisabet
AU - Fenyö, David
AU - Kárpáti, Sarolta
AU - Holló, Péter
AU - Kemény, Lajos V
AU - Döme, Balazs
AU - Megyesfalvi, Zsolt
AU - Pawłowski, Krzysztof
AU - Nishimura, Toshihide
AU - Kwon, HoJeong
AU - Encarnación-Guevara, Sergio
AU - Szasz, A Marcell
AU - Veréb, Zoltán
AU - Gyulai, Rolland
AU - Németh, István Balázs
AU - Appelqvist, Roger
AU - Rezeli, Melinda
AU - Baldetorp, Bo
AU - Horvatovich, Peter
AU - Malmström, Johan
AU - Pla, Indira
AU - Sanchez, Aniel
AU - Knudsen, Beatrice
AU - Kiss, András
AU - Malm, Johan
AU - Marko-Varga, György
AU - Gil, Jeovanis
N1 - Copyright © 2025. Published by Elsevier B.V.
PY - 2025/5/30
Y1 - 2025/5/30
N2 - 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.
AB - 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.
U2 - 10.1016/j.jprot.2025.105442
DO - 10.1016/j.jprot.2025.105442
M3 - Review article
C2 - 40246065
SN - 1874-3919
VL - 316
JO - Journal of proteomics
JF - Journal of proteomics
M1 - 105442
ER -