CRLK2 Antibody

What is CRLF2 and why is it significant in hematological research?

CRLF2 (Cytokine receptor-like factor 2) is a cell surface protein that plays a crucial role in hematological malignancies, particularly in B-cell precursor acute lymphoblastic leukemia (BCP-ALL). CRLF2 deregulation occurs in approximately 5-10% of BCP-ALLs . Its significance stems from being overexpressed in approximately half of Philadelphia-like acute lymphoblastic leukemia (Ph-like ALL) cases, where it confers chemoresistance and enhances leukemia cell survival .

CRLF2 functions as both a biomarker and potential therapeutic target. CRLF2 rearrangements (CRLF2-r) are associated with CRLF2 antigen surface overexpression, making it detectable using techniques like multiparametric flow cytometry (MFC) . Furthermore, CRLF2 overexpression frequently correlates with JAK2 mutations, establishing it as an important component in signaling pathways that drive leukemic cell proliferation and survival.

What types of CRLF2 antibodies are currently available for research applications?

Several types of CRLF2 antibodies have been developed for diverse research applications:

• Conventional monoclonal antibodies: Used primarily in flow cytometry applications to identify CRLF2 antigen expression on leukemic cells, these antibodies allow researchers to categorize expression levels and patterns .

• Single-chain variable fragment antibodies with fragment crystallizable regions (CRLF2 scFv-Fc): These specialized antibody constructs have been engineered for targeted therapeutic applications, notably for conjugation to drug-loaded liposomes for targeted therapy of Ph-like ALL .

• Computationally designed antibodies: Recent advances have employed biophysics-informed modeling combined with selection experiments to create antibodies with custom specificity profiles, allowing researchers to design antibodies with precisely defined binding characteristics for CRLF2 .

Each antibody type serves different research purposes, from basic detection and characterization to advanced therapeutic targeting strategies.

How should researchers select appropriate CRLF2 antibodies for specific experimental applications?

When selecting CRLF2 antibodies for specific applications, researchers should consider:

• Application compatibility: Different applications require antibodies with specific characteristics. For flow cytometry, antibodies must recognize native CRLF2 on the cell surface. For western blotting, antibodies must recognize denatured epitopes. The search results indicate antibodies validated for specific applications like western blotting (WB) should be selected accordingly .

• Epitope accessibility: Since CRLF2 is a transmembrane protein, consider whether the epitope is accessible in your experimental system. Antibodies targeting extracellular domains are preferable for flow cytometry, while those targeting intracellular domains may be suitable for western blotting or immunoprecipitation.

• Specificity validation: Review validation data demonstrating specificity, such as experiments comparing wildtype cells to CRLF2 knockdown cells. Ellinghaus et al. employed this approach by comparing antibody binding between Ph-like ALL cell line MHH-CALL-4 and its CRLF2-knockdown counterpart .

• Signal intensity characteristics: For flow cytometry applications, consider mean fluorescence intensity (MFI) characteristics. Research shows significant correlation between intermediate MFI values and CRLF2 rearrangements (p = 0.017) .

• Species reactivity: Ensure the antibody recognizes CRLF2 from your experimental species. Like other antibodies, CRLF2 antibodies may have limited cross-reactivity between species .

What are the optimal parameters for CRLF2 antibody-based multiparametric flow cytometry?

Based on research by Boer et al., the following parameters are critical for optimizing CRLF2 antibody-based multiparametric flow cytometry:

• Positivity threshold: Establish ≥10% CRLF2-positive cells as the threshold for classifying a sample as CRLF2-positive. This threshold showed good correlation with genetic alterations, with 76.7% of cases with CRLF2 rearrangements showing ≥10% positivity .

• Mean Fluorescence Intensity (MFI) classification: Categorize CRLF2 expression into three MFI categories (high, intermediate, low). This classification is essential, as there was a significant correlation between intermediate MFI values and CRLF2 rearrangements (p = 0.017), with 61.5% of cases with CRLF2 rearrangements showing intermediate MFI .

• Pattern recognition: Look for specific patterns in flow cytometry:

  • Strong positivity: Most cells above negative control

  • Moderate positivity: Partial overlap with negative control but with shift to right

  • Various negative patterns: Including complete overlap or small positive populations

• Subclone identification: The protocol should be sensitive enough to detect CRLF2-positive subclones within predominantly negative populations, as these may be clinically significant .

The table below shows the correlation between these parameters and genetic features:

How should researchers validate CRLF2 antibody specificity before experimental use?

Thorough validation of CRLF2 antibodies is essential to ensure reliable experimental results. Researchers should implement these validation strategies:

• Genetic validation: Compare antibody binding between wildtype cells and cells with CRLF2 knockdown or knockout. Ellinghaus et al. demonstrated this approach by comparing antibody binding between the Ph-like ALL cell line MHH-CALL-4 and its lentivirally transduced CRLF2-knockdown counterpart (KD-CALL-4) .

• Correlation with transcript levels: Validate antibody detection results against RT-qPCR measurements of CRLF2 transcript levels. Boer et al. employed this approach using SYBR Green-based RT-qPCR and the 2^-ΔΔCT method to quantify relative gene expression, with high CRLF2 transcript expression defined as values 10-fold above the median .

• Molecular confirmation: Validate antibody results against genetic testing for CRLF2 rearrangements. Research demonstrates a significant correlation between antibody-based detection (particularly with intermediate MFI values) and the presence of CRLF2 rearrangements (p = 0.017) .

• Multiple sample types: Test the antibody across different sample types (cell lines, patient samples, xenografts) to ensure consistent performance. Ellinghaus et al. demonstrated antibody performance across cell lines and patient-derived xenograft cells .

• Specificity controls: Include appropriate negative controls in experiments to establish background levels and confirm specificity, similar to protocols used with other research antibodies .

How can researchers optimize western blot protocols for CRLF2 antibody applications?

Based on the provided search results and general principles for antibody applications, researchers should consider the following for optimizing western blot protocols with CRLF2 antibodies:

• Sample preparation: Since CRLF2 is a transmembrane protein, effective cell lysis and protein extraction are crucial. Use appropriate lysis buffers containing detergents suitable for membrane proteins.

• Appropriate dilutions: Follow manufacturer recommendations for antibody dilutions. For example, search result indicates that anti-CCK2-R antibody requires dilutions of 1:500-1:2,000 for western blotting applications.

• Molecular weight verification: CRLF2 has an expected molecular weight of approximately 50-60 kDa. Verify that detected bands appear at the expected molecular weight, similar to the 50 kDa band observed with the CCK2-R antibody .

• Positive controls: Include positive controls known to express CRLF2, such as Ph-like ALL cell lines (e.g., MHH-CALL-4) mentioned in the research by Ellinghaus et al. .

• Validation with knockdown: Compare western blot results between wildtype and CRLF2 knockdown samples to confirm antibody specificity, similar to the approach used for flow cytometry validation .

• Secondary antibody selection: Choose appropriate secondary antibodies matched to the host species of the primary CRLF2 antibody. For rabbit primary antibodies, suitable secondaries would include goat anti-rabbit IgG antibodies conjugated to appropriate detection systems (HRP, AP, etc.) .

• Testing in different sample types: Validate the protocol across different sample types relevant to your research, such as cell lines, patient samples, or tissue extracts.

How can CRLF2 antibodies be used to identify Philadelphia-like ALL?

CRLF2 antibodies have emerged as valuable tools for identifying Philadelphia-like ALL (Ph-like ALL) through flow cytometry-based approaches. According to research by Boer et al., multiparametric flow cytometry (MFC) using CRLF2 antibodies can efficiently identify cases with CRLF2 overexpression, which is present in approximately 50% of Ph-like ALL cases .

The identification process involves:

• Quantitative assessment of CRLF2 expression: Cases are classified based on the percentage of CRLF2-positive cells (≥10% considered positive) and the mean fluorescence intensity (MFI) values, which are categorized as high, intermediate, or low .

• Pattern recognition: Distinct CRLF2 expression patterns can be observed in flow cytometry, including:

  • Strong positivity pattern: Most cells display fluorescence above the negative control

  • Moderate positivity pattern: Partial overlap with the negative control but with a shift to the right curve

  • Various negative patterns: Including complete overlap with negative control or small proportions of positive cells

• Subclone detection: CRLF2 antibodies can identify subclones of leukemic cells expressing CRLF2, which may be associated with disease relapse .

The research demonstrated that CRLF2 expression evaluated by MFC, particularly when considering both percentage positivity and MFI values, correlates significantly with JAK2 mutations (p < 0.0001) and CRLF2 rearrangements (p = 0.017) . This makes CRLF2 antibody-based flow cytometry a valuable first-line diagnostic tool for identifying potential Ph-like ALL cases before proceeding to more time-consuming and expensive molecular tests.

What is the role of CRLF2 antibodies in developing targeted therapies for B-cell malignancies?

CRLF2 antibodies are playing an increasingly important role in the development of targeted therapies for B-cell malignancies, particularly Ph-like ALL. The research demonstrates innovative approaches:

• Antibody-drug conjugate approach: Ellinghaus et al. developed a CRLF2-targeting antibody fragment (CRLF2 scFv-Fc) conjugated to drug-loaded liposomes (CRLF2-DM1 LIP). This construct contains the cytotoxic agent maytansinoid 1 (DM1) in DOPC liposomes, creating homogeneous CRLF2-targeted liposomes .

• Selective targeting and internalization: The research demonstrated that CRLF2-targeted liposomes showed:

  • Excellent CRLF2-targeting efficiency in Ph-like ALL cell lines

  • Selective association and internalization with patient-derived xenograft (PDX) cells

  • Minimal reactivity with non-target cells

• CRLF2-dependent cytotoxicity: Cell apoptosis assays confirmed the CRLF2-dependent potency of the CRLF2-DM1 LIP construct in Ph-like ALL cell lines, demonstrating the therapeutic potential of this approach .

• Addressing antigen loss: This approach provides an alternative therapeutic strategy for cases that have developed resistance to CD19 or CD22-directed therapies through antigen loss, potentially reducing the likelihood of relapse .

The research concludes: "This study is the first to highlight the therapeutic potential of a CRLF2-directed scFv-Fc-liposomal conjugate for targeting Ph-like ALL" , demonstrating a novel application of CRLF2 antibodies in targeted cancer therapy.

How can researchers use CRLF2 antibodies to identify and track treatment-resistant leukemic subclones?

CRLF2 antibodies provide powerful tools for identifying and tracking treatment-resistant leukemic subclones through several approaches:

• Subclone detection through flow cytometry: As described by Boer et al., "This technique allows for the identification of the CRLF2 antigen in subclones of BCP-ALL associated with disease relapse" . The sensitivity of flow cytometry can detect minor CRLF2-positive populations within predominantly negative samples.

• Pattern analysis for heterogeneity assessment: The research described various CRLF2 expression patterns observable through flow cytometry, including cases where "a small proportion of blast cells with a shift to the right curve" was observed, indicating subclonal positivity .

• Integration with genetic markers: The research demonstrated significant correlations between CRLF2 antibody detection and genetic alterations associated with treatment resistance, including JAK2 mutations and CRLF2 rearrangements . This allows researchers to connect immunophenotypic findings with underlying genetic drivers.

• Longitudinal monitoring: By establishing baseline CRLF2 expression patterns at diagnosis and monitoring changes during treatment, researchers can track the emergence of CRLF2-positive subclones that might indicate evolving resistance.

• Ex vivo testing on patient samples: Ellinghaus et al. demonstrated the ability to use CRLF2 antibodies for "selective association and internalization ex vivo using Ph-like ALL patient-derived xenograft (PDX) cells" , providing a method to evaluate patient-specific responses.

For effective implementation, researchers should:

• Establish clear thresholds for positivity (≥10% recommended)

• Classify MFI patterns (high, intermediate, low)

• Look for distinct expression patterns including subclonal populations

• Correlate findings with genetic analyses for comprehensive characterization

How can researchers distinguish between different binding modes when using CRLF2 antibodies?

Based on principles described in Durand et al. regarding antibody binding modes, researchers can employ several strategies to distinguish between different binding modes when using CRLF2 antibodies :

• Computational modeling approach: The research describes "the identification of different binding modes, each associated with a particular ligand against which the antibodies are either selected or not" . For CRLF2 antibodies, researchers could:

  • Build biophysical models based on antibody-antigen interaction data

  • Use high-throughput sequencing data from selection experiments to identify sequence features associated with different binding modes

  • Apply machine learning methods to disentangle binding modes even when they involve chemically similar epitopes

• Epitope mapping techniques:

  • Alanine scanning mutagenesis: Systematically replace amino acids in the CRLF2 protein with alanine to identify critical binding residues

  • Hydrogen-deuterium exchange mass spectrometry: To identify regions of CRLF2 that are protected from exchange when bound by antibodies

  • X-ray crystallography or cryo-EM: To directly visualize the antibody-CRLF2 complex

• Competitive binding assays:

  • Use a panel of antibodies with known epitopes to perform competitive binding experiments

  • Analyze whether test antibodies compete with reference antibodies for binding to CRLF2

• Kinetic and thermodynamic measurements:

  • Surface plasmon resonance (SPR) to measure binding kinetics

  • Isothermal titration calorimetry (ITC) to measure binding thermodynamics

  • Different binding modes often exhibit distinct kinetic and thermodynamic signatures

As noted in Durand et al., "We demonstrate and validate experimentally the computational design of antibodies with customized specificity profiles, either with specific high affinity for a particular target ligand, or with cross-specificity for multiple target ligands" . This suggests that once binding modes are identified, researchers can leverage this understanding to design antibodies with desired binding characteristics for CRLF2.

How should researchers interpret discrepancies between CRLF2 antibody detection and genetic testing results?

Based on research by Boer et al., researchers may encounter discrepancies between CRLF2 antibody detection and genetic testing results. Here's how to interpret and address such discrepancies :

• Understand the relationship between protein expression and genetic alterations:

  • The study found that while 84.6% of cases with CRLF2 rearrangements showed ≥10% CRLF2-positive cells by flow cytometry, 15.4% did not

  • Conversely, 70.6% of cases without CRLF2 rearrangements also showed ≥10% CRLF2-positive cells

  • This indicates that CRLF2 overexpression can occur through mechanisms other than CRLF2 rearrangements

• Consider MFI values alongside positivity percentage:

  • The research showed a significant correlation between intermediate MFI values and CRLF2 rearrangements (p = 0.017)

  • 61.5% of cases with CRLF2 rearrangements showed intermediate MFI, while only 17.6% of cases without rearrangements showed this pattern

  • This suggests that the quality of expression (MFI) may be as important as the quantity (percentage) in predicting genetic alterations

• Evaluate subclonal heterogeneity:

  • Flow cytometry can detect CRLF2-positive subclones that may be missed by bulk genetic testing

  • The presence of these subclones may have clinical significance even if they don't represent the dominant clone

• Consider transcript level analysis:

  • The study found that CRLF2 transcript overexpression was detected in 16.8% of cases, while CRLF2 antigen positivity was found in 20.6%

  • This discrepancy suggests post-transcriptional regulation may affect protein expression levels

• Assess JAK2 mutation status:

  • All cases with JAK2 mutations showed CRLF2 ≥10% positivity and intermediate or high MFI (p < 0.0001)

  • This strong correlation suggests that in cases with discrepancies, JAK2 mutation status may help resolve the interpretation

Understanding these relationships can help researchers interpret discrepancies in a biologically and clinically meaningful way.

What strategies exist for developing CRLF2 antibodies with customized specificity profiles?

Drawing from the advanced antibody engineering described in Durand et al., several strategies exist for developing CRLF2 antibodies with customized specificity profiles :

• Biophysics-informed computational modeling: The research describes a powerful approach combining experimental selection data with computational modeling:

  • "Using data from phage display experiments, we show that the model successfully disentangles these modes, even when they are associated with chemically very similar ligands"

  • This approach could be applied to design CRLF2 antibodies that distinguish between closely related epitopes or protein isoforms

• Energy function optimization: For CRLF2 antibodies, researchers could apply the described approach:

  • "The generation of new sequences relies on optimizing over s the energy functions E associated with each mode sw w"

  • For cross-specific antibodies: "To obtain cross-specific sequences, we jointly minimize the functions E associated with the desired ligand"

  • For highly specific antibodies: "To obtain specific sequences, we minimize E associated with the desired ligand sw w and maximize the ones associated with undesired ligands"

• CDR engineering: The research utilized a minimal antibody library based on a single human V domain with variations in the CDR3 region:

  • "Four consecutive positions of the third complementary determining region (CDR3) are systematically varied to a large fraction of the 20^4 = 1.6 x 10^5 combinations of amino acids"

  • This targeted engineering approach could be applied to CRLF2 antibodies to modify specificity while maintaining framework stability

• Fragment-based approaches: As demonstrated in Ellinghaus et al., antibody fragments like scFv-Fc constructs offer advantages for certain applications:

  • "We developed a CRLF2-targeting single-chain variable fragment modified by the fragment crystallizable region (CRLF2 scFv-Fc)"

  • These smaller formats may provide better access to certain epitopes while maintaining specificity

The research concludes that "The combination of biophysics-informed modeling and extensive selection experiments holds broad applicability beyond antibodies, offering a powerful toolset for designing proteins with desired physical properties" .

How are CRLF2 antibody fragments being used in liposomal conjugates for targeted therapies?

The research by Ellinghaus et al. demonstrates an innovative approach using CRLF2 antibody fragments in liposomal conjugates for targeted therapy of Philadelphia-like ALL :

• Design of the antibody-liposome conjugate:

  • CRLF2-targeting single-chain variable fragment modified by the fragment crystallizable region (CRLF2 scFv-Fc)

  • Conjugation to drug-loaded liposomes containing maytansinoid 1 (DM1), a potent cytotoxic agent

  • Use of DOPC (dioleoylphosphatidylcholine) as the liposomal carrier

  • Result: homogeneous CRLF2-targeted liposomes (CRLF2-DM1 LIP)

• Validation of targeting efficiency:

  • Cellular association and internalization studies compared the Ph-like ALL cell line MHH-CALL-4 with its CRLF2-knockdown counterpart (KD-CALL-4)

  • Results demonstrated "excellent CRLF2-targeting efficiency" of the CRLF2-DM1 LIP construct

• Selectivity evaluation:

  • Testing with patient-derived xenograft (PDX) cells showed selective association and internalization

  • Minimal reactivity was observed with non-target cells

  • This confirms the specificity of the antibody fragment for CRLF2-expressing cells

• Therapeutic efficacy:

  • Cell apoptosis assays demonstrated the CRLF2-dependent potency of CRLF2-DM1 LIP in Ph-like ALL cell lines

  • This confirms the functional delivery of the cytotoxic payload to target cells

• Significance in the therapeutic landscape:

  • "This study is the first to highlight the therapeutic potential of a CRLF2-directed scFv-Fc-liposomal conjugate for targeting Ph-like ALL"

  • The approach addresses an unmet need for targeting CRLF2 in patients who may relapse due to loss of other targeted antigens like CD19 or CD22

This research represents a significant advance in the application of CRLF2 antibody fragments for targeted therapy, demonstrating both the technical feasibility and biological efficacy of this approach.

What computational methods are being developed to predict CRLF2 antibody specificity?

Drawing from computational approaches described in Durand et al., several advanced methods are being developed that could be applied to predict CRLF2 antibody specificity :

• Biophysics-informed modeling:

  • "Our approach involves the identification of different binding modes, each associated with a particular ligand against which the antibodies are either selected or not"

  • This method could identify specific amino acid patterns in antibody sequences that confer specificity for CRLF2 versus related proteins

• Energy function optimization:

  • "The generation of new sequences relies on optimizing over s the energy functions E associated with each mode sw w"

  • For CRLF2 antibodies, this would involve developing energy functions that accurately model the interaction between antibody residues and CRLF2 epitopes

• Machine learning from selection experiments:

  • "Using data from phage display experiments, we show that the model successfully disentangles these modes, even when they are associated with chemically very similar ligands"

  • This approach combines experimental data from high-throughput sequencing of antibody libraries with computational analysis to identify specificity determinants

• Cross-specificity design:

  • "To obtain cross-specific sequences, we jointly minimize the functions E associated with the desired ligand"

  • This is particularly relevant for designing antibodies that recognize multiple variants or isoforms of CRLF2

• Combined experimental-computational approach:

  • "The combination of biophysics-informed modeling and extensive selection experiments holds broad applicability beyond antibodies, offering a powerful toolset for designing proteins with desired physical properties"

  • This integrated approach leverages both computational predictions and experimental validation to iteratively improve specificity models

These computational methods represent a significant advance over traditional empirical approaches to antibody development, potentially allowing researchers to design CRLF2 antibodies with precisely defined specificity profiles.

How might CRLF2 antibodies contribute to personalized medicine approaches in hematological malignancies?

Based on the research findings, CRLF2 antibodies have significant potential to contribute to personalized medicine approaches in hematological malignancies in several ways:

• Improved diagnostics and risk stratification:

  • CRLF2 antibody-based flow cytometry provides rapid identification of patients with CRLF2 overexpression

  • The study by Boer et al. demonstrates that "identification of the CRLF2 antigen using the MFC, based on the percentage of positivity and MFI values, is a useful tool for predicting JAK2 mutations and CRLF2-r"

  • This allows for early identification of high-risk patients who may benefit from intensified or targeted therapies

• Therapeutic target identification:

  • CRLF2 antibodies can identify patients likely to benefit from CRLF2-targeted therapies

  • This is particularly important for Ph-like ALL patients, who have "high rates of relapse and inferior survival rate"

• Monitoring of minimal residual disease:

  • The ability of CRLF2 antibodies to detect subclones of leukemic cells makes them valuable for monitoring minimal residual disease

  • This can inform decisions about treatment intensity and duration

• Alternative targeting for resistant disease:

  • As noted by Ellinghaus et al., "While targeted treatments against the cell surface proteins CD22 or CD19 have been transformative in the treatment of refractory B-ALL, patients may relapse due to antigen loss, necessitating targeting alternative antigens"

  • CRLF2 antibodies provide an alternative targeting strategy when resistance develops to first-line targeted therapies

• Development of personalized targeted therapies:

  • The CRLF2-targeted liposomal conjugate described by Ellinghaus et al. demonstrates the potential for personalized therapies directed against CRLF2

  • The research showed that this approach was effective specifically in CRLF2-expressing cells, highlighting its potential as a personalized treatment option

As personalized medicine in hematological malignancies continues to evolve, CRLF2 antibodies are likely to play an increasingly important role in both diagnostic and therapeutic strategies, particularly for high-risk subtypes like Ph-like ALL.