LUNGBANK: A Novel Biorepository Strategy Tailored for Comprehensive Multi-Omics Analysis and P-Medicine Applications in Lung Cancer

doi:10.21203/rs.3.rs-3816689/v1

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LUNGBANK: A Novel Biorepository Strategy Tailored for Comprehensive Multi-Omics Analysis and P-Medicine Applications in Lung Cancer

https://doi.org/10.21203/rs.3.rs-3816689/v1

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Background: In support of Project LUNGMARK, LUNGBANK was established as a pioneering biorepository dedicated to lung cancer. Designed for streamlined biospecimen handling, it employs high-volume technologies to secure high-quality samples. Fortified with robust data management, LUNGBANK efficiently integrates diverse datasets. This strategic infrastructure positions LUNGBANK as more than a biospecimen repository; it operates as a sophisticated platform for advanced lung cancer research, amplifying the potential for meaningful discoveries.

Methods: In support of Project LUNGMARK, the establishment of LUNGBANK, a pioneering biorepository system dedicated to lung cancer, was undertaken. LUNGBANK is intricately designed to streamline the collection, transfer, storage, and processing of biospecimens, employing high-volume technologies to ensure the acquisition of high-quality biological specimens. The system is fortified with robust data management protocols, facilitating efficient integration of diverse datasets. This strategic infrastructure ensures that LUNGBANK not only serves as a repository of biospecimens but also operates as a sophisticated platform for advanced lung cancer research, enhancing the potential for meaningful discoveries in the field.

Results: LUNGBANK, serving as a groundbreaking biorepository system, plays a pivotal role in furnishing a rich collection of biospecimens essential for unraveling the molecules and mechanisms linked to lung cancer. Rigorous standard operating procedures guided the meticulous collection of biospecimens, subjecting them to stringent quality control measures before inclusion in the study. Moreover, LUNGBANK has proven valuable in the establishment of patient-derived systems. The incorporation of cutting-edge technologies guarantees the acquisition of top-tier data, thereby amplifying the likelihood of substantial breakthroughs in both contemporary translational research and traditional histological evaluations.

Conclusion: The establishment of LUNGBANK marks a significant stride towards addressing the pressing need for effective diagnostic and therapeutic systems in lung cancer research. By facilitating the collection and analysis of high-quality biospecimens, LUNGBANK serves as a valuable resource for unraveling the molecular intricacies of lung cancer initiation and progression. This comprehensive system not only supports Project LUNGMARK but also contributes to the broader landscape of personalized medicine, tailoring interventions to the specific needs of lung cancer patients.

Lung cancer

Biorepository

Biobank

P-medicine

integrative multi-omics

The lung cancer-specific biorepository system consists of a database, dedicated software, and deep/ultra-deep freezer and liquid nitrogen systems.

Ethical guidelines, informed consent, and Institutional Review Board (IRB) approval ensured privacy and data de-identification for the biorepository.

The lung cancer-specific biorepository system is well-suited for integrative multi-omics analysis, personalized medicine applications, and traditional pathology uses.

Rigorous protocols for various sample types maintained their integrity and reliability through well-established Standard Operating Procedures (SOPs).

Lung cancer is one of the most common and deadliest forms of cancer worldwide [1]. Lung cancer can be divided into two main types based on clinical presentation and histological features. Small cell lung cancer (SCLC) tends to grow and spread more quickly. Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, accounting for about 85% of all cases. There are three main subtypes of NSCLC: adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. The prognosis of lung cancer depends on the stage at which it is diagnosed. Although, early-stage lung cancer has a better prognosis approximately 70–75% of patients are diagnosed at an advanced stage when tumor cells have spread, which prevents effective treatment and reduces overall survival rates [2, 3]. There is an urgent need for a comprehensive lung cancer screening and early diagnosis system that includes in-depth molecular profiling at the DNA, RNA, protein, and metabolite levels [4]. In addition, novel and potent therapeutics could be developed by identifying target molecules and pathways.

Biorepositories play a vital role in modern biomedical research by overseeing the acquisition, preservation, handling, and organization of biological specimens and clinical information derived from human participants [5, 6]. They also leverage advanced technologies such as omics data, bioinformatics, AI, and systems biology to enhance their value, ultimately contributing to the expedited understanding of diseases, the advancement of treatments, and the support of precision medicine initiatives, particularly as we approach the era of smart healthcare systems [6–10]. The integration of comprehensive biological profiles is crucial for revealing disease-related patterns, mechanisms, and biomarkers, supporting targeted screening, diagnostics, therapeutics, and early risk evaluation [11–17]. Furthermore, biorepositories with longitudinal data facilitate disease monitoring, treatment assessment, and relapse prediction, while advancements in P-medicine enable the customization of therapies to an individual's biological profile [18, 19].

Project LUNGMARK was initiated to achieve our objective, encompassing the incorporation of an advanced bioinformatics-supported integrative multi-omics approach, alongside the utilization of high-throughput technologies such as next-generation sequencing (NGS) and mass spectrometry coupled with liquid chromatography (LC-MS/MS). The involvement of patient samples was critical, ensuring accurate and meticulous collection, processing, storage, and data management. We designed a lung cancer-specific biorepository system, comprising two essential elements: an extensive sample collection and a comprehensive data management infrastructure to streamline and enhance these efforts. The primary goals of the project encompassed three key aspects; recording comprehensive patient information, maintaining detailed records of collected samples, and documenting both raw and processed data from integrative multi-omics studies. Integrating existing knowledge enhanced the dataset, offering a comprehensive perspective of the disease. The project's goal was to enhance our understanding of the disease and foster improvements in diagnostic, screening, monitoring, and therapeutic methods by establishing connections between the molecular profile and both clinical, histological, and imaging data.

Our advanced biorepository efficiently collected samples from both lung cancer patients and normal donors, meticulously organizing units for collection, processing, storage, and management. A dedicated clinical team skillfully acquired various specimens, including normal lung tissue, primary lung tumors, reactive stroma, lymph nodes, blood/serum/plasma, urine, saliva, sputum, and stool. Solid samples were promptly either snap-frozen or preserved in suitable solutions, depending on their intended application, and then subjected to meticulous processing in accordance with precise protocols. Clinical information was securely stored within a meticulously barcoded database called LUNGBASE, expertly managed by LUNGSOFT. Rigorous assessment of DNA/RNA quality and quantity ensured the provision of high-quality samples for NGS and LC-MS/MS analysis. We also prepared frozen sections and rigorously verified sample identities through Hematoxylin and Eosin (H&E) staining alongside the hospital's pathological assessment of the patients. Moreover, our approach has incorporated patient-derived cell (PDC), patient-derived organoid (PDO), and patient-derived xenograft (PDX) models, and advanced exosome isolation techniques, enhancing the capabilities of personalized lung cancer research.

2.1 Lung cancer-specific biorepository, ethical considerations, IRB, and recruitment

The lung cancer-specific biorepository system integrates various elements, including ethical considerations, patient recruitment protocols, a 96-terabyte data repository, specialized database management software, and sample acquisition, characterization, and quality control (QC) assessments (Fig. 1). We gained valuable insights for establishing the biobank by referencing the BBMRI-ERIC directory on processes employed by European biobanks [20]. We followed various guidelines, emphasizing clear objectives, standards adherence, and privacy protection [21–24]. Privacy was addressed through ethical guidelines like the Declaration of Helsinki and Declaration of Taipei [25–27]. Our ethical recruitment, approved by the İstanbul Medipol University Ethics Committee (approval number: [10840098-01]), involved informed consent, privacy emphasis, and data de-identification. IRB approval and informed consent upheld ethical standards, ensuring privacy [21–24]. Data were sourced from patient records by clinical team ensuring accuracy and minimizing bias, while our analysis considered factors like age, gender, histological subtype, and disease stage/grade for representative and clinically relevant findings. Patients received informed consent forms detailing study objectives, risks, benefits, and rights. We adhered to a rigorous recruitment process, enrolling stage I-IIIA NSCLC patients across all subtypes, excluding those with prior treatments, incomplete medical records, or significant comorbidities.

2.2 Sample collection, processing, and storage procedures

Our commitment to preserving the integrity of the biological specimens collected from lung cancer patients was firm and meticulous. We maintained a close collaboration with healthcare professionals, ensuring the highest standards of quality and reliability in specimen collection, processing, and storage. Snap-frozen solid tissue samples underwent thorough homogenization following strict sterile and RNAse-free protocols, aiming to minimize potential bias in subsequent omics studies (Fig. 2). The same tough standards applied to blood samples, where a rigorous two-step centrifugation process was employed to isolate plasma and serum while preventing hemolysis. We extended this meticulous approach to urine, saliva, sputum, BAL, and stool samples. We carefully preserved samples designated for PDC, PDO, and PDX models by immersing them in a freezing solution (90% FBS + 10% DMSO) and storing then in liquid nitrogen tanks (Fig. 3). Exosomes were isolated with precision, re-suspended in PBS, and stored at -20℃, with continuous temperature monitoring and backup power systems ensuring their preservation. Our storage strategy involved systematic organization, barcoding, and creating multiple aliquots, all designed to minimize the potential impact of freeze-thaw cycles on the specimens.

2.3 QC measurements

Maintaining top-notch QC in our biorepository was crucial, especially for ensuring the suitability of specimens in various omics studies, including whole genome sequencing (WGS), transcriptomics analyses via NGS, and proteomics and metabolomics investigations employing LC-MS/MS. We rigorously implemented a set of stringent QC measures to guarantee the utmost reliability and accuracy of the data derived from our biorepository samples.

2.3.1 Nucleic acids QC measures. QC procedures for nucleic acids were fundamental in safeguarding the integrity and precision of genetic material in our specimens. Three primary instruments for nucleic acid QC were the Thermo Fisher Scientific’s Qubit™ fluorometer and Nanodrop™ spectrophotometer, and Agilent’s Bioanalyzer. The Qubit fluorometer played a vital role in quantifying nucleic acids (DNA and RNA), utilizing specialized Qubit assay kits with fluorescent dyes. Nanodrop spectrophotometry, using UV absorbance, assessed DNA and RNA concentrations, also evaluating purity through ratios. The Bioanalyzer, a microfluidics-based platform, assessed nucleic acid quality and integrity comprehensively. It utilized electrophoresis to examine size distribution and generate electropherograms and gel-like images. This advanced tool excelled in assessing RNA integrity and identifying size variations. Together, these instruments ensured reliable genetic material for the project, enhancing data accuracy while minimizing biases.

2.3.2 Proteomics and metabolomics QC measures. We understood the crucial role of matrix blanks in our workflow, using them to create inclusion/exclusion lists that eliminated background noise and improved annotation precision. Additionally, blank samples were instrumental in identifying potential carry-over during chromatographic separation. We incorporated internal standards to maintain continuous oversight of instrument performance and promptly identify any discrepancies in sample preparation and analysis, enabling us to verify the quality of feature integration through software tools like XCMS or MZmine. Calibration standards with well-defined concentrations played a pivotal role in achieving quantitative accuracy. We consistently analyzed QC samples in order to gauge the LC-MS/MS system's reproducibility. This approach standardized instrument sensitivity across the entire analysis and helped filter out unexpected features.

Enhancing our quantitative proteomics workflow, we incorporated TMT reagents (Thermo Scientific, USA) for improved accuracy and efficiency. This multiplexing method enables simultaneous analysis of four tissue samples from a single patient, enhancing our quantitative proteomics. We also established a systematic QC process for evaluating proteomics data, involving three key stages: 1) examination of raw data, 2) identification, and 3) quantification quality evaluation. These stages encompass various metrics to capture variations arising from sample handling, nanoLC-MS performance, and data processing.

2.4 Histological verification

Supplementing the hospital's pathology report, we introduced a meticulous histological validation procedure to confirm each solid tissue type and provide a visual depiction of their structural integrity and composition. This involved creating histological frozen sections from solid tissue specimens, which then underwent H&E staining. Furthermore, 35 immunohistochemistry and 15 in situ hybridization sections from each tissue were also prepared for the future studies.

In summary, our sample collection, processing, and storage procedures as well as QC measurements adhered to stringent protocols designed to ensure the integrity and quality of biological specimens. These meticulous measures were instrumental in generating reliable data for our lung cancer research, contributing to the credibility and robustness of our findings. All the SOPs provided through Supplementary Data-1.

The biorepository collected a wide range of biological resources from multiple hospitals, which encompassed institutions such as İstanbul Süreyyapaşa Chest Diseases and Chest Surgery Training and Research Hospital, the Department of Thoracic Surgery at İstanbul Bezmialem Vakıf University Medical School, the Department of Thoracic Surgery at İstanbul University-Cerrahpaşa Medical School, the Department of Thoracic Surgery and Department of Internal Medicine at İstanbul Medipol University Medical School, the Department of Thoracic Surgery at Sakarya University Medical School, and the Department of Internal Medicine at İstanbul Medeniyet University Medical School.

3.1 Quantitative data analysis: unveiling the numeric patterns

LUNGBANK was established exclusively for the LUNGMARK project, which aimed to investigate five distinct tissue types sourced from 50 patients diagnosed with NSCLC. A total of 237 lung cancer patients who underwent surgery granted their consent to participate in the project. In order to maintain stringent QC standards, patient enrollment for specimen collection was intentionally increased to more than required number. This measure was implemented due to the study incorporating no fewer than 20 distinct sample and analysis types. If any of these types did not meet the QC standards, the respective patient was excluded from the study. Descriptive statistics, key epidemiological data, histological classifications, and tumor stage/grade categorizations are presented in Fig. 4 and Supplementary Data-2.

3.2 Data collection, integration, and management

Precision in collecting, integrating, and managing data played a pivotal role in the lung cancer project to ensure the commencement of an integrated multi-omics study with uniform starting materials for each individual omics investigation. We established meticulous protocols to access patient health records, clinical assessments, and rich data sources, covering patient demographics, medical histories, and outcomes. We integrated diverse omics data types into a unified database, bridging molecular and clinical aspects.

3.2.1 Database and software. LUNGBASE, an integral part of our research infrastructure, provided a sturdy foundation with an impressive 96 terabytes of storage capacity, as previously detailed [28]. Archiving a comprehensive range of information, LUNGBASE encompassed patient profiles, clinic records, laboratory reports, pathology findings, and detailed staging and grading information. It also securely stored crucial PET-CT imaging data, providing invaluable insights into the disease. Furthermore, LUNGBASE was instrumental in storing comprehensive sample information, ensuring effortless tracking and accessibility for the research team. We seamlessly integrated the user-friendly LUNGSOFT data management platform to boost data management efficiency. This platform supported severe data security measures, ensuring the confidentiality of patient information. Our data management system also incorporated an advanced barcoding system, streamlining organization and retrieval. Each data entry was uniquely tagged with a barcode, enhancing data management. Additionally, a robust data backup system securely stored a duplicate database on a geographically remote server, ensuring data redundancy and preserving our invaluable dataset's integrity and availability.
3.2.2 Data management. A robust data management infrastructure was crucial to safeguard data integrity and security. Our comprehensive dataset resided on the dedicated secure LUNGBASE server equipped with robust access controls and encryption measures to prevent unauthorized access. We regularly conducted automated data backups as a precautionary measure against data loss. A rigorous QC framework was implemented at every stage of data collection and integration in order to maintain data quality.
3.2.3 Radiomics and imaging data. PET-CT (Positron Emission Tomography–Computed Tomography) images were collected alongside biological samples, enriching our biobank's comprehensiveness. The integration of imaging data with biospecimens allowed us to explore the intricate relationship between imaging characteristics and biological attributes, potentially enhancing both diagnosis and treatment strategies for lung cancer.

In summary, our approach to data collection, integration, and management demonstrates a commitment to rigorous standards, transparency, and ethical conduct. By harmonizing diverse data sources and implementing robust data management practices, we unlocked valuable insights into lung cancer, contributing to advancements in the development of screening, diagnosis, monitoring, and therapeutic systems.

3.3 Medical admission, recruitment, and enrollment

We obtained approval from the IRB and participant consent before securing funding for the LUNGMARK project. Our patient management workflow began with recruitment and the collection of medical histories and clinical information, emphasizing informed consent. Collaboration among healthcare experts ensured accurate biospecimens collection and labeling. Detailed records were stored in LUNGBASE. Biospecimens were handled with care, including real-time aliquoting under cold chain conditions, followed by preservation in liquid nitrogen or -20℃/-80℃ freezers. Comprehensive quality assessments involved qualitative and quantitative molecular characterizations, employing advanced techniques like gel electrophoresis, nanodrop, qubit, and ultra-high throughput technologies. Our integrated workflow upheld ethical standards, ensuring data and biospecimens quality.

3.4 Collection, processing, and storage of specimens

SOPs were crafted to ensure the systematic and precise collection of diverse specimen types, as extensively detailed in Supplementary Data-1. All procedures were executed under stringent sterile conditions using sterile and RNAse-free equipment. During surgical procedures, specialized punchers were employed to obtain a minimum of 5 solid tissue samples, each approximately the size of a chickpea. These tissue specimens were subsequently placed into separate cryotubes. Tissue samples earmarked for omics studies were rapidly frozen in liquid nitrogen to maintain their structural integrity and subsequently stored at -80°C in ultra-deep freezers upon being transported to the laboratory on dry ice. Samples intended for histological verification were also collected and preserved in cryotubes on dry ice. Samples were immersed in a suitable tissue culture medium and transported through a controlled cold chain to maintain viability for creating PDC, PDO, and PDX models, as well as for exosome isolation. The tumor tissue was initially cleansed with penicillin/streptomycin in DPBS and then sliced into small fragments using a sterile scalpel. These minced tumor pieces were subsequently preserved in a freezing solution consisting of 90% Fetal Bovine Serum (FBS) and 10% Dimethyl Sulfoxide (DMSO) if not needed immediately. When needed, the stored tumor tissue was thawed and centrifuged to remove the DMSO. The resulting pellet was then resuspended using the Miltenyi Tumor Dissociation Kit, human (130-095-929), following the specific cell isolation protocol outlined in the SOP. A portion of the solid tissue samples underwent dissection into 1.5-3 mm thick pieces, after which they were divided into aliquots in freezing solution and preserved in liquid nitrogen. These preparations were earmarked for future experiments, including the establishment of PDX models and the isolation of exosomes. Liquid samples and stool specimens were collected according to established protocols, while bronchoalveolar lavage fluid was obtained during surgical procedures. Regardless of the sample type, each underwent careful processing and was divided into multiple containers to ensure redundancy and maintain sample quality. Solid tissue samples underwent an additional step, being homogenized into a fine powder using sterile and RNAse-free equipment. These powdered tissue samples were then distributed into a minimum of 5 tubes, each designated for specific omics analysis, and stored at -80℃ until used.

Furthermore, the biorepository incorporates exosomes that harbor valuable data comprising DNA, RNA, proteins, as well as metabolites and lipids [29]. Exosomes play a pivotal role in cell-to-cell communication and have been implicated in cancer progression, including lung cancer [30, 31]. Moreover, our biorepository offers PDC, PDO, and PDX models (Fig. 5). These innovative models enable researchers to culture cells and tissues directly derived from patient samples. PDCs faithfully preserve the genetic and molecular characteristics of the original tumor, rendering them invaluable for drug testing and personalized treatment strategies [32]. PDOs, on the other hand, accurately recapitulate the three-dimensional architecture of tumors and serve as a versatile platform for high-throughput drug screening [33]. These models bridge the gap between traditional cell lines and in vivo animal models, offering a more faithful representation of human lung cancer biology [34]. Samples from our biorepository also serve as a valuable resource for the development of PDX models. PDCs or human tumor tissue is engrafted into immunodeficient mice, effectively replicating the patient's tumor within an in vivo setting, as detailed by Kita [35]. These PDX models offer a powerful platform for testing novel therapeutics and exploring treatment responses, closely mimicking the patient's tumor environment [35]. A total of 50 frozen sections (both for immunohistochemistry (IHC) and in situ hybridization (ISH) were prepared from all solid tissue samples, including normal lung tissue, primary tumor tissue, tumor microenvironment, and lymph node tissue for the confirmation of the tumor and tissue ID provided by pathology reports from the hospital. The resulting images and findings have been documented in Supplementary Data-3 following H&E staining.

3.5 QC and characterization

The DNA and RNA extracted from the powdered aliquots, designated for WGS and RNA-Seq studies, underwent a rigorous and comprehensive characterization procedure. This involved precise quantification using Qubit fluorometry, assessment of purity via Nanodrop spectrophotometry, and evaluation of integrity through Bioanalyzer. These rigorous QC steps ensured that our genetic materials were free from contaminants, accurately quantified, and structurally intact. Descriptive statistics and values for all individual samples were shown in Fig. 6 and supplementary Data-4. Furthermore, appropriate QC measures were applied before proteomics and metabolomics analysis through LC-MS/MS (Data not shown). We assessed raw data quality using various metrics in the initial stage and found that Total Ion Chromatograms exhibited a CV of 5–20%, while in the second stage, the standard filter-aided sample preparation (FASP) method outperformed in-solution digestion methods in terms of peptide and protein group identifications, highlighting the importance of data quality and effective normalization strategies in proteomics workflows.

The establishment of a specialized biorepository dedicated to lung cancer marks a significant milestone in our efforts to unravel the intricate biological complexities of this disease. This repository offers an extraordinary array of sample types, expanding beyond conventional boundaries to encompass not only standard specimens like normal lung tissue, primary tumor tissue but also critical components such as adjacent tumor microenvironment tissue and lymph nodes, essential factors in comprehending cancer metastasis [36]. Additionally, this repository serves a dual role, functioning not just as a storage facility for biological specimens but also as a comprehensive data repository, enriching physical samples with invaluable insights [37, 38]. It systematically compiles pathology data, including highly detailed histological examinations, immunohistochemical assessments, and in situ hybridization analyses [39]. Moreover, the incorporation of radiologic imaging data provides a non-invasive perspective into the structural and functional aspects of lung tumors, enhancing our understanding of disease progression [40]. Lastly, laboratory data delves deep into the molecular details of the samples, exploring gene expression profiles, proteomic landscapes, and metabolomic signatures [41]. Collectively, this comprehensive biorepository equipped us with an unparalleled resource to decode the multifaceted nature of lung cancer.

Our dedication to data management within the biorepository goes beyond clinical boundaries, encompassing an exhaustive catalog of sample data that intricately documents not only sample types and quantities but also provides detailed insights into the specific characteristics within the lung. This precision empowers meticulous and targeted analyses. We curate both raw and analyzed multi-omics data, including epi/genomic, epi/transcriptomic, epi/proteomic, and metabololipidomics data to capture the full spectrum of molecular complexity. Each layer of data provides a unique perspective into the complex biology of lung cancer, enriching our understanding of this intricate disease. This diverse range of sample types and accompanying data provided us with the tools to thoroughly investigate the molecular and genetic diversity present in lung cancer [42].

We have identified a lung cancer-specific biobank with the objective of advancing lung cancer research [43]. Previously, it has been shown that a biobank significantly contribute to the early diagnosis in lung cancer [44]. LUNGBANK, on the other hand, stands distinct from these biobank as it was established for a specific project focused on exploring the intricacies of lung cancer biology through a comprehensive multi-omics approach as reported by Chen [45]. The simultaneous examination of genomics, transcriptomics, proteomics, and metabolomics data derived from identical patient samples constructs a comprehensive panorama of lung cancer biology. This ample approach opens the door to the discovery of complex molecular signatures, innovative biomarkers, and promising therapeutic targets that might otherwise remain concealed when investigating each omics layer in isolation. Therefore, the accumulation of a substantial number of specimens was imperative to facilitate the investigation of various omics and related studies, all originating from identical source materials. This fundamental framework empowered us to scrutinize variations in tumors, the tumor microenvironment, and even potential metastatic processes, all in direct comparison to normal samples from the same patient. Additionally, it allowed for the assessment of these alterations in blood and other liquid biopsy materials [46]. Moreover, the biorepository stands as a cornerstone of personalized medicine [47]. Armed with a wealth of clinical and molecular data, we can pinpoint predictive biomarkers and treatment response indicators that guide personalized therapeutic decisions. This individualized approach holds the promise of optimizing treatment efficacy while minimizing adverse effects [48].

In addition to its advanced capabilities, the biorepository seamlessly complements classical pathology procedures [18]. It effortlessly facilitates the preparation of frozen and paraffin sections, breathing life into the time-honored techniques of examining tissue morphology, histology, immunohistochemistry, and in situ hybridization [24]. These classical methodologies remain indispensable for diagnosing and characterizing lung cancer, continuing to wield profound influence over clinical decisions. Samples from our biorepository serve as a valuable resource for the development of PDC, PDO, and PDX models for the in vitro and in vivo analyses of lung cancer biology as well as personalized therapeutic screening [35].

In summary, this lung cancer-specific biorepository's establishment implies a significant milestone in our ongoing battle against this relentless disease. It provides an exceptional resource in our endeavor to understand the intricacies of lung cancer biology, improve patient care, and develop innovative treatments. We must consistently uphold the highest standards of sample quality, ethical integrity, collaborative teamwork, and adaptability to the evolving landscape of lung cancer research to optimize its potential impact. The future holds the promise of translating discoveries from this biorepository concept into practical clinical applications, ultimately bringing hope to lung cancer patients worldwide.

The establishment of the lung cancer-specific biorepository marks a significant stride in the field of oncology and lung cancer research, poised to catalyze transformative breakthroughs in our understanding and management of this multifaceted disease. Its primary strength lies in an extensive collection of rigorously curated specimens and associated clinical data, covering a wide range of lung cancer tumor subtypes, stages, and patient demographics. This resource provides an invaluable opportunity to explore the molecular drivers, heterogeneity, and underlying mechanisms of lung cancer. Simultaneously, the biorepository lays the foundation for precision medicine in lung cancer through the integration of genomics, transcriptomics, proteomics, metabolomics, and clinical data, enabling the identification of robust biomarkers for early detection and personalized treatment. While challenges related to ethical, logistical, and economical aspects loom, the creation of this biorepository stands as a testament to the collective dedication and vision of the scientific and medical community, fostering hope and healing for those affected by lung cancer through the joint efforts of project LUNGMARK and associated biorepository LUNGBANK.

BBMRI-ERIC

Biobanking and Biomolecular Resources Research Infrastructure – European Research Infrastructure Consortium

DMSO

Dimethyl Sulfoxide

FASP

Filter-aided sample preparation

FBS

Fetal Bovine Serum

H&E

Hematoxylin and Eosin

IHC

Immunohistochemistry

IRB

Institutional Review Board

ISH

In Situ Hybridization

LC-MS/MS

Liquid Chromatography-Mass Spectrometry/Mass Spectrometry

NGS

Next-Generation Sequencing

NSCLC

Non-Small Cell Lung Cancer

PDC

Patient-Derived Cell

PDO

Patient-Derived Organoid

PDX

Patient-Derived Xenograft

PET-CT

Positron Emission Tomography–Computed Tomography

Quality Control

SCLC

Small Cell Lung Cancer

SOP

Standard Operating Procedure

WGS

Whole Genome Sequencing

Acknowledgments

This study (115S113) was funded by TÜBİTAK.

The illustrations created with BioRender.com.

Statement

During the preparation of this work, the author(s) used ChatGPT-3.5 in order to edit the text. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.

Conflict of interest

Authors declare no conflict of interest.

Author contribution

Dilek Çeker: Investigation, Methodology, Validation, Data curation, QC, and Writing-Original draft preparation.

Volkan Baysungur, Serdar Evman, and Abidin Şehitoğulları: Supervision and Methodology, Specimen collection.

Sinem Nalbantoğlu: Conceptualization and Supervision, Specimen collection.

İlker Kolbaş, Abdurrahim Gördebil, Hatice Aslanoğlu, Nilufer Kara, Nilgün Algan, Ayberk Boyacıoğlu, Ömer Soysal, Akif Turna, Mehmet Zeki Günlüoğlu, İhsan Boyacı, Mahmut Gümüş, and Sevda Küçüker: Specimen collection

Yusuf Tambağ: Software development

Ali Osman Çıbıkdiken, Mücahit Büyükyılmaz, İbrahim Berkan Aydilek, Abdulkerim Eneş, and Fatih Karakaya: Database management

Ömer Kaçar^:Frozen IHC and ISH sections,

Ahmet Midi, Nur Büyükpınarbaşılı, and Aslı Çakır: Histological assessment

Betül Karademir-Yılmazand Ali Şahin: PDC and PDO models, Exosome isolation, and Writing-Reviewing and Editing

Hivda Ülbeği-Polat: PDX model

Onur Şenol, Merve Öztuğ, and Evren Saban: QC, and Writing-Reviewing and Editing.

Şaban Tekin: Writing-Reviewing and Editing

Uygar Tazebay: Writing-Reviewing and Editing

Abdullah Karadag: Conceptualization, Visualization, Methodology, Data curation, Supervision, and Writing-Reviewing and Editing.

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SupplementaryData1SOPs.docx
Supplementary Data-1-SOPs
SupplementaryData2Demography.docx
Supplementary Data-2-Demography
SupplementaryData3IHC.docx
Supplementary Data-3-IHC
SupplementaryData4QC.docx
Supplementary Data-4-QC

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LUNGBANK: A Novel Biorepository Strategy Tailored for Comprehensive Multi-Omics Analysis and P-Medicine Applications in Lung Cancer

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Abstract

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Highlights

1 Introduction

2 Materials and methods

2.1 Lung cancer-specific biorepository, ethical considerations, IRB, and recruitment

2.2 Sample collection, processing, and storage procedures

2.3 QC measurements

2.4 Histological verification

3 Results

3.1 Quantitative data analysis: unveiling the numeric patterns

3.2 Data collection, integration, and management

3.3 Medical admission, recruitment, and enrollment

3.4 Collection, processing, and storage of specimens

3.5 QC and characterization

4 Discussion

5 Conclusions

Abbreviations

Declarations

References

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