<scp>ROR</scp>1‐based immunomagnetic protocol allows efficient separation of <scp>CLL</scp> and healthy B cells
Bibliographic record
Abstract
Chronic lymphocytic leukaemia (CLL) is the most common leukaemia among adults in the Western world. This lymphoproliferative disorder is defined by the presence of at least 5 × 106 clonal lymphocytes/ml peripheral blood (Hallek, 2015). The biggest clinical challenge is the fact that CLL eradication by any available therapy, apart from allogeneic stem cell transplantation, fails to cure the disease, inducing only temporary disease remission, i.e., when the disease does not manifest clinically but CLL cells still persist in the body. During this phase a population of CLL cells often slowly re-expands to trigger CLL relapse. The relapse is often based on the poorly understood but clinically important process of therapy-driven selection of more aggressive CLL subclones (Malcikova et al, 2015). There is a growing requirement to physically separate CLL cells from other cell types for both experimental and possible future clinical applications. Positive and negative separation methods, mainly immunomagnetic cell sorting and rosette-forming-based techniques, of the whole B cell population have found a widespread use in routine clinical diagnostics and experimental CLL research (Essakali et al, 2008). These approaches are suitable for samples with high lymphocytosis where CLL cells outnumber healthy B cells but fail to distinguish malignant cells from healthy B cells. Therefore, the isolation and subsequent analysis of CLL cells from patients in disease remission remain limited, mainly due to the fact that healthy B cells can be present in comparable or even higher numbers than malignant cells. Another option for CLL cell separation is fluorescence-activated cell sorting (FACS). However, basic selection using CD19 and CD5 is not efficient because of the variability in CD5 expression among CLL patients (including atypical CLL, lacking CD5) and the presence of CD19 and CD5 on a subpopulation of healthy B cells (Seifert et al, 2012). After therapy, clinicians take advantage of co-staining with additional markers to detect minimal residual disease (MRD) in CLL patients by following a standardized protocol (Rawstron et al, 2013). This multiparametric flow cytometric analysis is based on the screening of CD3, CD5, CD19, CD20, CD22, CD43, CD38, CD45, CD79b, and CD81 (Rawstron et al, 2013; Stehlíková et al, 2014). Apart from MRD monitoring, the Rawstron protocol can be used for FACS separation of residual malignant cells in disease remission. The main limitation of FACS is the need for advanced equipment in specialized flow cytometric facilities to implement multicolour panel cell sorting. To overcome this limitation we tested a methodology to separate residual CLL cells using a surface receptor ROR1 (Receptor Tyrosine Kinase-Like Orphan Receptor-1) as a target for immunomagnetic separation. ROR1 is a unique marker of CLL cells with negligible expression on other peripheral blood cells. ROR1 has been shown to be uniformly expressed on the surface of CLL cells; moreover, its level is not affected by treatment (Baskar et al, 2008; Daneshmanesh et al, 2008; Kaucká et al, 2011). First, we tested and compared ROR1-based positive separation [Anti-ROR1 MicroBead Kit; Whole Blood Anti-ROR1 MicroBead Kit (both Miltenyi Biotec, Bergisch Gladbach, Germany)] with negative, non-B cell depletion-based approaches [RosetteSep™ Human B Cell Enrichment Cocktail (Stem Cell Technologies, Vancouver, Canada); B Cell Isolation Kit II (Miltenyi Biotec); B-CLL Cell Isolation Kit (Miltenyi Biotec); MACSxpress B-CLL cell Isolation Kit (Miltenyi Biotec)] (see details in Fig 1A). All of the anti-ROR1 kits contained monoclonal anti-ROR1 (clone 2A2) antibody, which was reported to induce no apparent toxicity (Baskar et al, 2012), a conclusion supported also by our analyses (data not shown). Blood samples from 10 CLL patients in complete remission were separated in parallel using positive and negative separation protocols. The separated cells from individual fractions were analysed using a 7-colour modified Rawstron protocol (see details in Fig 2). We focused on the quantification of T cells, non-CLL B cells and CLL cells. The obtained data (Fig 1) clearly demonstrated that positive ROR1-based MACS isolation of CLL cells was superior to non-B cell depletion protocols in the samples with low CLL counts. The better efficiency is mainly due to elimination of non-CLL B cells, which are the major contaminant in all four negative separation strategies tested. In particular, the Whole Blood Anti-ROR1 MicroBead Kit can be highly recommended as it enables isolation of CLL cells directly from the whole blood without prior mononuclear cells separation and producing samples with more than 90% CLL cells in a protocol that takes approximately 35 min (compared to 75–120 min in other combinations). Secondly, we took advantage of ROR1 expression on CLL cells and tested two immunomagnetic isolation set-ups for the separation of healthy and malignant B cells (schematized in Fig 2A): (i) the depletion of non-B cells (negative separation) was combined with subsequent ROR1-positive separation (Fig 2A, black arrows), and (ii) direct ROR1-based positive separation was followed by negative depletion of non-B cells (Fig 2A, dashed arrows). In principle, both approaches allow isolation of both CLL and non-CLL B cells from a single patient. As shown in the example of patient in complete remission (Fig. 2C, D), the first two-step separation protocol (negative B cell separation with B Cell Isolation Kit II, followed by ROR1-positive separation with Anti-ROR1 MicroBead Kit) produced pure populations of CLL and non-CLL B cells (Fig 2C, D). Interestingly, the separation efficiency of healthy non-CLL B cells was higher with the second method, when ROR1-positive CLL cells were first removed by anti-ROR1 MACS and the mixture of non-CLL B cells with other cell types was subjected to non-B cell depletion (Fig 2B). This approach would be useful in applications that require back-to-back comparison of healthy and malignant CLL cells from the same patient. In summary, we provide evidence that ROR1-based separation is capable of distinguishing and separating CLL cells from healthy B cells even in patients with low lymphocytosis, e.g. in patients diagnosed in early stage and after treatment and in individuals with monoclonal B lymphocytosis. This methodology represents a unique tool for analysis of CLL progression after treatment and for understanding of the molecular mechanisms driving disease relapse. Supported by a grant from the AZV CR, Ministry of Health, Czech Republic, NR 15-29793A. JK, SPav, PJ and VB were involved in experiment design, data acquisition, data interpretation and manuscript writing. OS designed flow cytometric analysis. IG performed separations. KP, YB, MD and SPos were involved in data analysis and manuscript corrections. All authors read and approved the final manuscript. The authors have no competing interests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".