Biobanking strategies in clinical trials of novel treatments

Εισαγωγή: Οι βιοτράπεζες είναι εγκαταστάσεις που συλλέγουν, επεξεργάζονται, αποθηκεύουν και διανέμουν βιολογικό υλικό και σχετικά δεδομένα, κυρίως για έρευνα. Αποτελούν έναν κρίσιμο πόρο, υποστηρίζοντας έρευνα σε τομείς όπως η ογκολογία, η γονιδιωματική και η εξατομικευμένη ιατρική, καθώς και η ανάπτυξη διαγνωστικών και θεραπευτικών μεθόδων. Το τμήμα μας αντιμετωπίζει την πρόκληση της αποθήκευσης σύνθετων δειγμάτων όπως πλασματοκύτταρα και Β λεμφοκύτταρα από αναρρόφηση μυελού των οστών. Η οργάνωση των δειγμάτων σε όλη τη ροή εργασίας της τράπεζας μπορεί να συμβάλει σημαντικά στη διατήρηση της λειτουργικής αποτελεσματικότητας και της ιχνηλασιμότητας των δειγμάτων. Μέθοδοι: Προς το παρόν η τράπεζά μας βασίζεται σε μεθόδους χειροκίνητης φόρτωσης και ανάκτησης. Η ροή εργασίας ξεκινά με τη συλλογή δειγμάτων και την επεξεργασία που είναι και οι δύο κρίσιμες κατά τη δημιουργία και την επέκταση μιας τράπεζας. Κατά τη συλλογή και επεξεργασία των δειγμάτων μας, τηρούμε αυστηρά την ορθή κλινική πρακτική (GCP) και την καλή εργαστηριακή πρακτική (GLP) προκειμένου να αποτρέψουμε προβλήματα που θα μπορούσαν να θέσουν σε κίνδυνο πολύτιμα δείγματα και να θέσουν σε κίνδυνο χρόνιας έρευνας. Αποτελέσματα (Η εμπειρία μας): Η βιοτραπεζά μας αποτελείται επί του παρόντος από δείγματα περιφερικού αίματος (πλάσμα και μονοκύτταρα) και αναρρόφησης μυελού των οστών (CD138 +, CD138-, CD19 και πλάσματος) από συγκατάθεση ασθενών με διάφορους τύπους πλασματοκυτταρικών δυσκρασιών, συμπεριλαμβανομένων ασθενών με μυέλωμα, συμπτωματικό ή ασυμμτονατικό, , μακροσφαιριναιμία Waldenstrom, αμυλοείδωση AL. Όποτε είναι δυνατόν, τα δείγματα λαμβάνονται σε διάφορα χρονικά σημεία προκειμένου να ληφθούν διαδοχικά δείγματα στην τράπεζα. Μελλοντικές προοπτικές: Εξετάζουμε τώρα τη διευκόλυνση των προβλεπόμενων ποσοστών ανάκτησης και χωρητικότητας αποθήκευσης μέσω της αυτοματοποιημένης τεχνολογίας παρακολούθησης (bar code). Σε αυτήν την περίπτωση, τα δείγματα θα επισημαίνονται με μόνιμα ανιχνεύσιμα χαρακτηριστικά που επιτρέπουν τη σάρωση και την παρακολούθηση μέσω λογισμικού διαχείρισης δεδομένων (για παράδειγμα συστήματα γραμμωτού κώδικα). Θα ενσωματώσουμε επίσης συστήματα λογισμικού για την αποθήκευση όλων των κλινικών και βιολογικών πληροφοριών που σχετίζονται με τα δείγματά μας με τη χρήση Συστημάτων Διαχείρισης Πληροφοριακών Εργαστηρίων (LIMS). Το LIMS μπορεί να ενσωματωθεί πλήρως με όλα τα όργανα στο εργαστήριο, έτσι ώστε η ροή εργασίας να είναι βελτιωμένη και πιο αποτελεσματική και όλα τα δεδομένα δοκιμών θα συλλέγονται και να αποθηκεύονται ηλεκτρονικά και με ασφάλεια με κάθε δείγμα. Στο μέλλον, ένα συγκεντρωτικό LIMS θα μας επιτρέψει να κλιμακώσουμε καθώς αυξάνεται η ζήτηση, επειδή μπορεί να διαχειριστεί όλες τις τοποθεσίες βιολογικών δειγμάτων, τη διαχείριση αιτημάτων στο διαδίκτυο, τη συμμόρφωση δεδομένων και την ασφάλεια. Συμπέρασμα: Οι βιοτράπεζες γίνονται ένας ουσιαστικός και ολοένα και πιο εξελιγμένος πόρος στη βιοϊατρική έρευνα. Οι τεχνολογικές εξελίξεις όπως ο αυτοματισμός και η μηχανοργάνωση μετασχηματίζουν τη διαχείριση των βιοτραπεζών και επιτρέπουν την εφαρμογή ολοκληρωμένων συστημάτων για τη διαχείριση δειγμάτων, δεδομένων, προσωπικού, πολιτικών και διαδικασιών για τη διανομή βιολογικών δειγμάτων και άλλων υπηρεσιών. Η τάση είναι προς μεγαλύτερες και πιο συγκεντρωτικές βιοτράπεζες, γεγονός που βελτιώνει την οικονομία της επεξεργασίας, αποθήκευσης, διανομής και ανάλυσης δεδομένων. Η ανάπτυξη τυποποιημένων διαδικασιών βασισμένων σε τεκμήρια (SOP) και η υιοθέτηση τεχνικών βέλτιστων πρακτικών, σε συνδυασμό με τη χρήση τεχνολογικών καινοτομιών σε υλικά και εξοπλισμό, μπορεί να υποστηρίξει τη δημιουργία βιοτραπεζών που κατέχουν δείγματα υψηλής ποιότητας που σχετίζονται με καλά χαρακτηρισμένα, αξιόπιστα κλινικά δεδομένα. Η ροή εργασίας από τη συλλογή δειγμάτων έως την αποθήκευση σε μια τράπεζα θα πρέπει να ικανοποιεί την πιθανότητα ότι το δείγμα πιθανότατα θα χρησιμοποιηθεί σε μια ανάλυση που δεν έχουμε ακόμα φανταστεί.Introduction: Biobanks are facilities that collect, process, store and distribute biospecimens and associated data, mainly for biological and medical research. They constitute a crucial resource, supporting cutting-edge investigation in fields such as oncology, genomics and personalised medicine, and the development of diagnostics and therapeutics. Our department is faced with the challenge of storing complex specimens such as plasma cells and B lymphocytes from bone marrow aspirates. Sample organisation throughout the biobank workflow can greatly contribute to the maintenance of operational efficiency and sample traceability. Methods: At the moment our biobank still relies on manual loading and retrieval methods. Workflow starts with sample collection and processing which are both crucial when setting up and expanding a biobank. When collecting and processing our samples, we rigorously adhere to Good Clinical Practice (GCP) and Good Laboratory Practice (GLP) in order to avert problems that could jeopardise valuable specimens and compromise years of research. Results (Our Experience): Our biobank presently consists of peripheral blood (plasma and PBMC’s) and bone marrow aspirate (CD138+, CD138-, CD19 and plasma) samples from consenting patients with various types of plasma cell dyscrasias including patients with myeloma , symptomatic or smoldering, MGUS, Waldenstrom’s macroglobulinemia , AL amyloidosis. Whenever possible, samples are taken at various time points in order to obtain sequential samples in the biobank. Future prospects: We are now looking into facilitating the intended retrieval rates and storage capacity through automated tracking technology. In this case the tubes will be labelled with permanent traceable features that enable scanning and tracking through data management software (for example barcode systems). We will also integrate software systems to store all clinical and biological information associated with our samples with the use of Laboratory Information Management Systems (LIMS). The LIMS can be fully integrated with all instruments in the lab so that workflow is improved and more efficient, and all test data will be electronically and securely compiled and stored with each sample. In the future a centralised LIMS will enable us to scale up as demand increases because it can manage all biospecimen locations, online request management, data compliance and security. Conclusion: Biobanks are becoming an essential and increasingly sophisticated resource in biomedical research. Technological advances such as automation and computerisation are transforming the management of biobanks and enabling the implementation of integrated systems to manage samples, data, personnel, policies and procedures for the distribution of biological specimens and other services. The trend is towards larger and more centralised biobanks, which improves the economics of sample processing, storage, distribution and data analysis. The development of evidence-based standard operation procedures (SOPs) and the adoption of technical best practices, in combination with the use of technological innovations in materials and equipment, can support the generation of biobanks holding high quality samples associated with well-characterised, reliable clinical data. The workflow from sample collection to storage in a biobank should accommodate the possibility that the sample will likely be used downstream in an assay that is currently not even imagined

Personalised progression prediction in patients with monoclonal gammopathy of undetermined significance or smouldering multiple myeloma (PANGEA): a retrospective, multicohort study

BACKGROUND: Patients with precursors to multiple myeloma are dichotomised as having monoclonal gammopathy of undetermined significance or smouldering multiple myeloma on the basis of monoclonal protein concentrations or bone marrow plasma cell percentage. Current risk stratifications use laboratory measurements at diagnosis and do not incorporate time-varying biomarkers. Our goal was to develop a monoclonal gammopathy of undetermined significance and smouldering multiple myeloma stratification algorithm that utilised accessible, time-varying biomarkers to model risk of progression to multiple myeloma. METHODS: In this retrospective, multicohort study, we included patients who were 18 years or older with monoclonal gammopathy of undetermined significance or smouldering multiple myeloma. We evaluated several modelling approaches for predicting disease progression to multiple myeloma using a training cohort (with patients at Dana-Farber Cancer Institute, Boston, MA, USA; annotated from Nov, 13, 2019, to April, 13, 2022). We created the PANGEA models, which used data on biomarkers (monoclonal protein concentration, free light chain ratio, age, creatinine concentration, and bone marrow plasma cell percentage) and haemoglobin trajectories from medical records to predict progression from precursor disease to multiple myeloma. The models were validated in two independent validation cohorts from National and Kapodistrian University of Athens (Athens, Greece; from Jan 26, 2020, to Feb 7, 2022; validation cohort 1), University College London (London, UK; from June 9, 2020, to April 10, 2022; validation cohort 1), and Registry of Monoclonal Gammopathies (Czech Republic, Czech Republic; Jan 5, 2004, to March 10, 2022; validation cohort 2). We compared the PANGEA models (with bone marrow [BM] data and without bone marrow [no BM] data) to current criteria (International Myeloma Working Group [IMWG] monoclonal gammopathy of undetermined significance and 20/2/20 smouldering multiple myeloma risk criteria). FINDINGS: We included 6441 patients, 4931 (77%) with monoclonal gammopathy of undetermined significance and 1510 (23%) with smouldering multiple myeloma. 3430 (53%) of 6441 participants were female. The PANGEA model (BM) improved prediction of progression from smouldering multiple myeloma to multiple myeloma compared with the 20/2/20 model, with a C-statistic increase from 0·533 (0·480-0·709) to 0·756 (0·629-0·785) at patient visit 1 to the clinic, 0·613 (0·504-0·704) to 0·720 (0·592-0·775) at visit 2, and 0·637 (0·386-0·841) to 0·756 (0·547-0·830) at visit three in validation cohort 1. The PANGEA model (no BM) improved prediction of smouldering multiple myeloma progression to multiple myeloma compared with the 20/2/20 model with a C-statistic increase from 0·534 (0·501-0·672) to 0·692 (0·614-0·736) at visit 1, 0·573 (0·518-0·647) to 0·693 (0·605-0·734) at visit 2, and 0·560 (0·497-0·645) to 0·692 (0·570-0·708) at visit 3 in validation cohort 1. The PANGEA models improved prediction of monoclonal gammopathy of undetermined significance progression to multiple myeloma compared with the IMWG rolling model at visit 1 in validation cohort 2, with C-statistics increases from 0·640 (0·518-0·718) to 0·729 (0·643-0·941) for the PANGEA model (BM) and 0·670 (0·523-0·729) to 0·879 (0·586-0·938) for the PANGEA model (no BM). INTERPRETATION: Use of the PANGEA models in clinical practice will allow patients with precursor disease to receive more accurate measures of their risk of progression to multiple myeloma, thus prompting for more appropriate treatment strategies. FUNDING: SU2C Dream Team and Cancer Research UK

Angiogenesis-Related Pathways in the Pathogenesis of Ovarian Cancer

Ovarian Cancer represents the most fatal type of gynecological malignancies. A number of processes are involved in the pathogenesis of ovarian cancer, especially within the tumor microenvironment. Angiogenesis represents a hallmark phenomenon in cancer, and it is responsible for tumor spread and metastasis in ovarian cancer, among other tumor types, as it leads to new blood vessel formation. In recent years angiogenesis has been given considerable attention in order to identify targets for developing effective anti-tumor therapies. Growth factors have been identified to play key roles in driving angiogenesis and, thus, the formation of new blood vessels that assist in feeding cancer. Such molecules include the vascular endothelial growth factor (VEGF), the platelet derived growth factor (PDGF), the fibroblast growth factor (FGF), and the angiopoietin/Tie2 receptor complex. These proteins are key players in complex molecular pathways within the tumor cell and they have been in the spotlight of the development of anti-angiogenic molecules that may act as stand-alone therapeutics, or in concert with standard treatment regimes such as chemotherapy. The pathways involved in angiogenesis and molecules that have been developed in order to combat angiogenesis are described in this paper

Angiogenesis-related pathways in the pathogenesis of ovarian cancer

Ovarian Cancer represents the most fatal type of gynecological malignancies. A number of processes are involved in the pathogenesis of ovarian cancer, especially within the tumor microenvironment. Angiogenesis represents a hallmark phenomenon in cancer, and it is responsible for tumor spread and metastasis in ovarian cancer, among other tumor types, as it leads to new blood vessel formation. In recent years angiogenesis has been given considerable attention in order to identify targets for developing effective anti-tumor therapies. Growth factors have been identified to play key roles in driving angiogenesis and, thus, the formation of new blood vessels that assist in “feeding” cancer. Such molecules include the vascular endothelial growth factor (VEGF), the platelet derived growth factor (PDGF), the fibroblast growth factor (FGF), and the angiopoietin/Tie2 receptor complex. These proteins are key players in complex molecular pathways within the tumor cell and they have been in the spotlight of the development of anti-angiogenic molecules that may act as stand-alone therapeutics, or in concert with standard treatment regimes such as chemotherapy. The pathways involved in angiogenesis and molecules that have been developed in order to combat angiogenesis are described in this paper

Increased apoptosis in the alveolar microenvironment of the healthy human lung

Background. Apoptosis represents a physiological clearance mechanism in human tissues. The role of apoptosis has not been examined in normal lung cell populations, such as alveolar macrophages and polymorphonuclear cells. What is the percentage, as well as the role, of apoptosis in the alveolar microenvironment of the healthy human lung? Patients and methods. Bronchoalveolar lavage was obtained from 21 volunteers without lung disease. The specimens were analyzed using: Annexin V binding, DNA laddering, light microscopy and immunohistochemistry for bcl-2 expression. Results. Apoptosis of the total bronchoalveolar lavage cell population was 51.2%. Both alveolar macrophages and polymorphonuclear cells had a high apoptotic rate (62.1% and 48.3%, respectively) as determined by Annexin V binding. These findings were further confirmed using morphological criteria for apoptosis and gel electrophoresis for DNA fragmentation. In the majority of the individuals examined, (8 out of 21), the bcl-2 gene was expressed in the lymphocyte population mainly. Conclusions. The percentage of apoptosis in lung cells of healthy humans is high. Apoptosis plays a key role in normal lung cell death. It appears to be the mechanism that opposes cell proliferation by eliminating, aged or damaged cells thus facilitating the process of lung remodeling. (C) 2008 Elsevier Inc. All rights reserved