CCNB1 Monoclonal Antibody

What is CCNB1 and why is it important in cancer research?

CCNB1 (Cyclin B1) is a regulatory protein involved in mitosis that complexes with p34(cdc2) to form the maturation-promoting factor (MPF). The protein exists in two alternative transcripts: a constitutively expressed transcript and a cell cycle-regulated transcript predominantly expressed during G2/M phase . CCNB1 has emerged as an important biomarker in cancer research, particularly breast cancer, where high expression correlates with aggressive tumor behavior, presence of lymphovascular invasion (LVI), larger tumor size, higher histological grade, hormonal receptor negativity, and HER2 positivity . These correlations make CCNB1 a valuable target for understanding cancer progression mechanisms and identifying potential therapeutic targets.

Recent research has established CCNB1 as an independent predictor of shorter breast cancer-specific survival (HR = 1.3; 95% CI 1.2–1.5; p = 0.010), highlighting its prognostic value . Given these associations, CCNB1 monoclonal antibodies serve as essential tools for investigating cell cycle regulation abnormalities in cancer cells and assessing patient prognosis.

What are the optimal protocols for immunohistochemistry using CCNB1 antibodies?

When performing immunohistochemistry with CCNB1 monoclonal antibodies, researchers should consider the following optimized protocol based on published research:

• Sample preparation: Use formalin-fixed, paraffin-embedded tissue sections (4-5 μm thickness). For tissue microarrays (TMAs), ensure cores contain at least 15% invasive tumor area .

• Antibody selection: Choose validated antibodies with demonstrated specificity, such as mouse monoclonal anti-CCNB1 antibody (ab72, Abcam) used in published research .

• Antibody validation: Confirm specificity using western blot prior to IHC staining using appropriate breast cancer cell lines (e.g., MCF-7, SK-BR-3, MB-MDA-231) .

• Dilution optimization: For immunofluorescence, a dilution of 0.5-1 μg/mL is recommended as a starting point . The optimal working dilution should be determined empirically for each experimental system.

• Scoring system: Use the H-score method, which combines staining intensity (negative=0, weak=1, moderate=2, strong=3) and percentage of positive cells (0-100%), resulting in a score range of 0-300 .

• Concordance assessment: For reliable results, scoring should be performed by at least two observers (e.g., a researcher and a pathologist) with concordance evaluation (aim for ICC ≥ 0.9) .

• Cut-off determination: Establish a cut-off for CCNB1 positivity based on the cohort's median H-score (e.g., 100 H-score in breast cancer studies) .

This protocol has been successfully applied in large breast cancer cohorts (n=2480) with long-term outcome data, demonstrating its reliability for clinical research applications .

How do you validate the specificity of a CCNB1 monoclonal antibody?

Validating CCNB1 monoclonal antibody specificity requires a multi-faceted approach:

• Western blot validation:

  • Use multiple relevant cell lines (e.g., MCF-7, SK-BR-3, MB-MDA-231 for breast cancer research)

  • Apply primary antibody at 1:1000 dilution and appropriate secondary antibody (e.g., IRDye 700CW Donkey anti-mouse at 1:15,000)

  • Confirm a specific band at the expected molecular weight (40-62 kDa for CCNB1)

  • Include endogenous control (e.g., GAPDH with anti-GAPDH primary antibody at 1:5000)

• Positive and negative controls:

  • Include known positive tissues/cells with high CCNB1 expression (e.g., highly proliferative tumors)

  • Use appropriate negative controls (antibody diluent only) on serial sections

  • Consider siRNA knockdown of CCNB1 in cell lines as an additional specificity control

• Cross-reactivity assessment:

  • Evaluate antibody reactivity across species if working with non-human models (verified reactivity across human, mouse, and hamster has been documented for some antibodies)

  • Check for cross-reactivity with other cyclins using immunoprecipitation followed by mass spectrometry

• Functional validation:

  • Confirm the antibody can precipitate active CDK1/cyclin B1 complexes (1-2 μg antibody per 500 μg protein)

  • Verify expected cellular localization patterns during different cell cycle phases

Proper validation ensures experimental results accurately reflect CCNB1 biology and avoids misinterpretation of data due to non-specific antibody binding.

How can CCNB1 monoclonal antibodies be used to study lymphovascular invasion mechanisms in breast cancer?

Studying CCNB1's role in lymphovascular invasion (LVI) requires sophisticated experimental approaches:

• Transcriptomic-proteomic correlation:

  • Combine mRNA expression analysis from large cohorts (e.g., METABRIC n=1980, TCGA n=854) with protein expression using tissue microarrays (n=2480)

  • Assess correlation between mRNA and protein levels (note: previous studies found weak correlation r=0.136)

  • Compare expression between LVI-positive and LVI-negative tumors using appropriate statistical tests

• Co-expression analysis with LVI-related biomarkers:

  • Investigate associations between CCNB1 and established LVI markers including N-cadherin, P-cadherin, and TWIST2

  • Perform multiplex immunofluorescence to visualize co-localization in tumor sections

  • Quantify co-expression using digital pathology platforms

• Functional studies in model systems:

  • Modulate CCNB1 expression in breast cancer cell lines using overexpression or knockdown approaches

  • Assess effects on invasion capabilities using transwell invasion assays

  • Evaluate impact on LVI-related biomarkers at protein and mRNA levels

• Patient-derived xenograft models:

  • Create PDX models from tumors with varying CCNB1 expression levels

  • Monitor LVI development using specialized staining methods

  • Test targeted interventions against CCNB1 or downstream pathways

• Multivariate analysis for clinical significance:

  • Perform Cox regression analysis adjusting for confounding factors

  • Calculate hazard ratios for association between CCNB1 expression and LVI

  • Develop and validate predictive models incorporating CCNB1 and other biomarkers

This integrated approach has revealed that high CCNB1 expression is significantly associated with LVI presence (p<0.0001) and serves as an independent predictor of shorter breast cancer-specific survival (HR=1.3; 95% CI 1.2–1.5; p=0.010) .

What methodologies are effective for studying CCNB1 nuclear translocation?

CCNB1 nuclear translocation is a critical regulatory event in cell cycle progression. Research strategies to study this process include:

• Cellular fractionation with western blotting:

  • Separate nuclear and cytoplasmic fractions using established protocols

  • Perform western blotting with anti-CCNB1 antibodies

  • Use appropriate loading controls (e.g., PCNA for nuclear fraction)

  • Quantify relative distribution between compartments over time

• Live-cell imaging with fluorescently tagged CCNB1:

  • Create fluorescent protein fusions with CCNB1

  • Perform time-lapse confocal microscopy during cell cycle progression

  • Quantify nuclear/cytoplasmic signal intensity ratios

  • Correlate with cell cycle phase markers

• Fixed-cell immunofluorescence:

  • Use CCNB1 monoclonal antibodies at 0.5-1 μg/mL concentration

  • Counterstain with DAPI for nuclear visualization

  • Acquire z-stack images to ensure accurate localization

  • Analyze nuclear versus cytoplasmic staining intensity

• Modulation of transport mechanisms:

  • Investigate the effects of factors like circ-CCNB1 that have been shown to inhibit CCNB1 nuclear translocation

  • Study interaction partners like SIAH1 that may regulate localization

  • Apply inhibitors of nuclear import/export machinery

• Proximity ligation assays:

  • Detect interactions between CCNB1 and nuclear transport factors

  • Visualize spatial relationships at specific cell cycle stages

  • Quantify interaction frequency under different conditions

This methodological framework has been successfully applied to demonstrate that circ-CCNB1 suppresses CCNB1 nuclear expression in trophoblast cells, providing insights into regulatory mechanisms .

How can researchers optimize co-immunoprecipitation protocols for CCNB1 and its binding partners?

Optimizing co-immunoprecipitation (co-IP) protocols for CCNB1 interactions requires careful consideration of experimental conditions:

• Antibody selection and amount:

  • Use antibodies specifically validated for immunoprecipitation

  • Apply 1-2 μg antibody per 500 μg protein lysate

  • Select antibodies that recognize epitopes not involved in protein-protein interactions

• Lysis buffer optimization:

  • For stable interactions (e.g., CCNB1-CDK1): use RIPA buffer with protease/phosphatase inhibitors

  • For transient interactions: use milder NP-40 or digitonin-based buffers

  • Avoid harsh detergents that may disrupt protein complexes

• Cross-linking considerations:

  • For weak interactions: incorporate reversible cross-linkers (e.g., DSP)

  • Optimize cross-linking time and concentration

  • Include appropriate controls to confirm specificity

• Cell synchronization:

  • Synchronize cells at G2/M phase to maximize CCNB1-complex formation

  • Verify synchronization efficiency by flow cytometry

  • Compare interaction profiles across cell cycle phases

• Validation approaches:

  • Perform reverse co-IP (precipitate with partner antibody, detect CCNB1)

  • Include IgG control and CCNB1-depleted lysate controls

  • Confirm functional relevance of interactions with kinase assays

Example protocol for CCNB1-CDK1 complex isolation:

• Harvest synchronized cells in mid-M phase

• Lyse in buffer containing 0.5% NP-40, 150mM NaCl, 50mM Tris pH 7.5, protease/phosphatase inhibitors

• Clear lysate by centrifugation (14,000g, 10 min, 4°C)

• Pre-clear with protein A/G beads (1 hour, 4°C)

• Incubate cleared lysate with 1-2 μg anti-CCNB1 antibody overnight at 4°C

• Add protein A/G beads for 2 hours

• Wash 4-5 times with lysis buffer

• Elute in Laemmli buffer and analyze by western blot

This approach has been successfully used to precipitate active CDK1/cyclin B1 complexes in research applications .

What are the key considerations when designing flow cytometry experiments with CCNB1 antibodies?

Flow cytometry using CCNB1 antibodies requires careful experimental design to generate reliable data:

• Sample preparation optimization:

  • For cell lines: gentle fixation with 4% paraformaldehyde followed by permeabilization with 0.1-0.5% Triton X-100

  • For primary tissues: optimize dissociation protocols to maintain epitope integrity

  • Use appropriate blocking (5-10% serum from secondary antibody host species)

• Antibody titration and controls:

  • Perform titration experiments (starting with 0.5-1 μg/million cells)

  • Include isotype controls matched to primary antibody (IgG1, kappa for many CCNB1 antibodies)

  • Use known positive and negative controls to establish gating strategy

• Multi-parameter analysis:

  • Combine with DNA content measurement (PI, DAPI) to correlate with cell cycle phase

  • Include markers of proliferation (Ki-67) or mitosis (phospho-histone H3)

  • Consider co-staining for CDK1 to evaluate complex formation

• Signal amplification considerations:

  • For low abundance detection: consider secondary antibody signal amplification

  • Evaluate fluorochrome brightness relative to expected expression level

  • Optimize signal-to-noise ratio through careful titration

• Data analysis approaches:

  • Gate on single cells using FSC-H vs. FSC-A

  • Analyze CCNB1 expression relative to cell cycle phase

  • Consider bivariate analysis of CCNB1 vs. DNA content

Example gating strategy for cell cycle-specific CCNB1 expression:

This methodical approach allows researchers to accurately quantify cell cycle-dependent expression patterns of CCNB1 and correlate them with other cellular parameters.

How do researchers interpret discordant results between CCNB1 mRNA and protein expression?

Discordant results between CCNB1 mRNA and protein expression levels are commonly observed in research. A systematic approach to understanding these differences includes:

• Statistical evaluation of correlation:

  • Calculate correlation coefficients between mRNA and protein expression

  • Published research shows weak correlation between CCNB1 mRNA and protein (r=0.136)

  • Consider non-linear relationships through scatter plot visualization

• Biological explanations for discordance:

  • Post-transcriptional regulation: miRNAs targeting CCNB1 mRNA

  • Post-translational modifications affecting protein stability

  • Protein compartmentalization (nuclear vs. cytoplasmic localization)

  • Alternative splicing generating different protein isoforms

• Technical considerations:

  • Different detection thresholds between RNA sequencing and IHC

  • Tissue heterogeneity in bulk samples vs. cellular resolution in IHC

  • Antibody specificity to certain isoforms or modified forms

  • RNA quality and degradation effects

• Validation strategies:

  • Single-cell approaches to correlate mRNA and protein in individual cells

  • Pulse-chase experiments to assess protein turnover rates

  • Investigation of regulatory factors using CCNB1 reporter constructs

  • Targeted inhibition of degradation pathways

• Functional significance assessment:

  • Determine whether mRNA or protein better predicts biological outcomes

  • Evaluate which measure correlates more strongly with clinical parameters

  • Develop integrated biomarker signatures combining both parameters

Suggested workflow for resolving discordant expression patterns:

This systematic approach helps researchers interpret seemingly contradictory results and gain deeper insights into CCNB1 biology and its regulatory mechanisms.

What are common challenges in CCNB1 immunohistochemistry and how can they be resolved?

Researchers frequently encounter several challenges when performing CCNB1 immunohistochemistry that can affect result interpretation:

• Variable staining intensity:

  • Problem: Inconsistent staining between samples or within the same section

  • Solution: Standardize fixation time (24-48 hours), use automated staining platforms, and ensure consistent antigen retrieval conditions (buffer pH and temperature)

  • Validation: Include control tissues on each slide to monitor staining consistency

• Background staining:

  • Problem: Non-specific staining obscuring true CCNB1 signal

  • Solution: Optimize blocking (5-10% serum, 1 hour), extend washing steps, and titrate primary antibody concentration

  • Validation: Include isotype control and secondary-only control

• Epitope masking:

  • Problem: Inability to detect CCNB1 despite known expression

  • Solution: Test multiple antigen retrieval methods (citrate pH 6.0 vs. EDTA pH 9.0), optimize retrieval time (15-30 minutes), and consider alternative fixatives

  • Validation: Use multiple antibodies targeting different epitopes

• Scoring reproducibility:

  • Problem: Subjective interpretation leading to scorer variability

  • Solution: Implement H-score system combining intensity and percentage, use digital pathology platforms, and require independent scoring by multiple observers

  • Validation: Calculate inter-observer concordance (ICC ≥ 0.9 as target)

• Cut-off determination:

  • Problem: Arbitrary thresholds affecting clinical correlations

  • Solution: Use data-driven approaches based on median or ROC curve analysis

  • Validation: Apply multiple cut-offs and compare with clinical outcomes

When troubleshooting challenging samples, implement a systematic approach testing one variable at a time and documenting all protocol modifications to ensure reproducibility.

How can researchers distinguish between active and inactive forms of CCNB1 using monoclonal antibodies?

Distinguishing between active and inactive CCNB1 forms is crucial for understanding its functional state in cell cycle regulation:

• Phosphorylation-specific antibodies:

  • Approach: Use antibodies specifically recognizing phosphorylated residues on CCNB1 that indicate activation

  • Key sites: Ser126, Ser128, Ser133, Ser147 (activating phosphorylations)

  • Validation: Treatment with phosphatase to confirm specificity

• CDK1-CCNB1 complex detection:

  • Approach: Proximity ligation assay (PLA) to visualize CCNB1-CDK1 interaction

  • Advantage: Provides spatial information about complex formation

  • Controls: Serum-starved cells (negative) vs. mitotic cells (positive)

• Subcellular localization analysis:

  • Approach: Immunofluorescence to track nuclear translocation, which correlates with activation

  • Method: Co-staining with DAPI and quantification of nuclear/cytoplasmic ratio

  • Consideration: Factors like circ-CCNB1 can inhibit nuclear translocation

• Activity-based probes:

  • Approach: Use fluorescent substrates that are modified by active CDK1-CCNB1 complex

  • Readout: Flow cytometry or live-cell imaging to measure substrate modification

  • Advantages: Direct measurement of functional activity

• Immunoprecipitation of active complexes:

  • Approach: Use antibodies that specifically recognize the conformational epitope present in active CDK1-CCNB1 complex

  • Validation: Kinase assay with precipitated complexes using histone H1 as substrate

  • Recommended usage: 1-2 μg antibody per 500 μg protein for immunoprecipitation

By combining these approaches, researchers can distinguish between total CCNB1 levels and the functionally relevant active pool, providing more meaningful insights into cell cycle regulation and dysregulation in disease states.

How does CCNB1 expression correlate with molecular subtypes of breast cancer?

CCNB1 expression varies significantly across breast cancer molecular subtypes, providing important insights into tumor biology and potential therapeutic strategies:

• Expression patterns across PAM50 subtypes:

  • Highest expression: Basal-like and HER2-enriched subtypes

  • Intermediate expression: Luminal B subtype

  • Lowest expression: Luminal A and normal-like subtypes

  • Statistical significance: p<0.0001 for differences between subtypes

• Correlation with receptor status:

  • High CCNB1 expression significantly associates with:

    • Hormonal receptor negativity (ER/PR negative)

    • HER2 positivity

    • p<0.0001 for these associations in large cohorts

• Relationship with tumor grade and proliferation:

  • Strong positive correlation with high histological grade

  • Association with proliferation markers

  • Significantly higher in aggressive tumor phenotypes

• Multivariate analysis findings:

  • CCNB1 remains an independent prognostic factor after adjusting for standard clinicopathological parameters

  • Hazard ratio for breast cancer-specific survival: HR=1.3; 95% CI 1.2–1.5; p=0.010

CCNB1 expression across breast cancer molecular subtypes:

This pattern of expression suggests that CCNB1 may play a particularly important role in more aggressive breast cancer subtypes and could serve as a potential therapeutic target in these difficult-to-treat tumors.

What is the relationship between CCNB1 and circular RNAs in disease progression?

The relationship between CCNB1 and circular RNAs (circRNAs) represents an emerging area of research with implications for disease understanding:

• Circ-CCNB1 regulatory functions:

  • Circ-CCNB1 has been found upregulated in villous tissues from patients with spontaneous abortion (SA)

  • Acts as a modulator of trophoblast cell function

  • Inhibits CCNB1 nuclear translocation in HTR-8/SVneo cells

• Molecular mechanisms:

  • Enhances cytoplasmic expression of SIAH1

  • Suppresses CCNB1 nuclear protein expression

  • Does not significantly alter total CCNB1 protein levels

  • Influences subcellular localization rather than expression levels

• Functional consequences:

  • Affects trophoblast proliferation and invasion

  • May contribute to pathological processes in pregnancy complications

  • Creates a regulatory feedback loop affecting CCNB1 activity

• Experimental approaches for studying circ-CCNB1/CCNB1 interactions:

  • Subcellular fractionation to analyze protein distribution

  • Immunofluorescence to visualize localization changes

  • Use of PCNA as internal control for nuclear proteins

  • Targeted manipulation of circ-CCNB1 expression to assess effects on CCNB1 function

• Broader implications:

  • Suggests a novel layer of post-transcriptional regulation

  • Indicates circRNAs may regulate protein function through localization control

  • Opens potential therapeutic avenues targeting circular RNA-protein interactions

This research area demonstrates how circRNAs like circ-CCNB1 can regulate CCNB1 function not by changing expression levels but by affecting subcellular localization, representing an important regulatory mechanism with potential implications for various pathological conditions including reproductive disorders and cancer.

How can CCNB1 monoclonal antibodies be used in developing prognostic models for cancer?

CCNB1 monoclonal antibodies can contribute significantly to the development of prognostic models in cancer through several methodological approaches:

• Tissue microarray (TMA) analysis:

  • Methodology: Immunohistochemical staining of TMAs from large patient cohorts (n>2000)

  • Scoring system: H-score combining intensity (0-3) and percentage (0-100%)

  • Validation: Requires blinded scoring by multiple observers with high concordance (ICC≥0.9)

  • Cut-off determination: Data-driven approaches using median or outcome-based optimization

• Integration with molecular data:

  • Approach: Combine protein expression data with transcriptomic profiles

  • Cohorts: Use established datasets like METABRIC (n=1980) and TCGA (n=854)

  • Analysis: Multivariate models incorporating molecular subtypes and other biomarkers

  • Validation: Independent cohort testing to confirm prognostic value

• Multi-marker prognostic panels:

  • Strategy: Combine CCNB1 with other cell cycle and LVI-related biomarkers

  • Examples: N-cadherin, P-cadherin, TWIST2

  • Statistical methods: Regularized regression (LASSO) or machine learning approaches

  • Output: Risk score calculation for patient stratification

• Quantitative image analysis:

  • Technology: Digital pathology platforms for automated quantification

  • Advantages: Reduces subjectivity, increases throughput

  • Features: Nuclear/cytoplasmic ratio, staining heterogeneity, spatial patterns

  • Integration: Deep learning algorithms to identify subtle expression patterns

• Clinical implementation considerations:

  • Standardization: Protocol harmonization across laboratories

  • Quality control: Regular proficiency testing

  • Reporting: Standardized scoring and interpretation guidelines

  • Clinical validation: Prospective studies showing impact on patient management

Example prognostic model incorporating CCNB1:

Based on published research, this integrated approach has demonstrated that CCNB1 provides independent prognostic information beyond standard clinicopathological parameters in breast cancer .

What emerging techniques are enhancing CCNB1 protein analysis in single cells?

Single-cell analysis of CCNB1 protein expression is advancing rapidly with several innovative methodologies:

• Mass cytometry (CyTOF):

  • Technology: Metal-tagged antibodies for deep phenotyping

  • Advantage: Minimal spectral overlap allowing 40+ parameters simultaneously

  • Application: Correlate CCNB1 with multiple cell cycle regulators and signaling pathways

  • Challenge: Antibody validation for metal conjugation

• Single-cell Western blotting:

  • Approach: Microfluidic platforms for protein separation from individual cells

  • Benefit: Confirms antibody specificity at single-cell level

  • Application: Heterogeneity analysis in tumor samples

  • Advancement: Multiplexed detection of CCNB1 along with binding partners

• Imaging mass cytometry:

  • Methodology: Laser ablation of tissue sections stained with metal-tagged antibodies

  • Advantage: Spatial information preserved with subcellular resolution

  • Application: Tumor microenvironment context for CCNB1 expression

  • Analysis: Machine learning algorithms for pattern recognition

• In situ protein sequencing:

  • Technology: Antibody-based detection with DNA barcodes for spatial mapping

  • Benefit: Highly multiplexed protein detection in intact tissue

  • Application: Relationship between CCNB1 and microenvironmental factors

  • Development stage: Emerging technique with growing applications

• Live-cell reporters:

  • Approach: CRISPR knock-in of fluorescent tags to endogenous CCNB1

  • Advantage: Real-time monitoring of expression and localization

  • Application: Cell cycle dynamics in living tumor cells

  • Extension: Combined with optogenetic tools for functional manipulation

These emerging technologies are transforming our understanding of CCNB1 biology by revealing cell-to-cell variability, spatial context, and temporal dynamics that are masked in bulk analyses, potentially leading to more precise prognostic indicators and therapeutic strategies.

How can researchers integrate CCNB1 antibody data with multi-omics approaches?

Integrating CCNB1 antibody-derived data with multi-omics approaches creates powerful research frameworks:

• Transcriptomic-proteomic integration:

  • Methodology: Correlate CCNB1 protein levels (IHC) with mRNA expression (RNA-seq)

  • Insight: Identify post-transcriptional regulatory mechanisms

  • Challenge: Weak correlation observed (r=0.136) between mRNA and protein levels

  • Approach: Use machine learning to identify factors explaining discordance

• Epigenomic-proteomic correlation:

  • Technique: Integrate CCNB1 protein data with DNA methylation and histone modification profiles

  • Application: Identify epigenetic regulators of CCNB1 expression

  • Analysis: Multivariate regression models linking epigenetic patterns to protein levels

  • Outcome: Potential epigenetic biomarkers predicting CCNB1 activity

• Proteogenomic integration:

  • Framework: Combine genomic alterations, transcriptomics, and CCNB1 protein data

  • Advantage: Comprehensive view of regulatory mechanisms

  • Approach: Pathway analysis incorporating genetic variants affecting CCNB1 network

  • Clinical relevance: Identify patient subgroups for targeted therapies

• Spatial multi-omics:

  • Technology: Spatial transcriptomics combined with multiplexed protein imaging

  • Application: Map CCNB1 protein expression to spatially resolved transcriptomes

  • Benefit: Context-dependent understanding of CCNB1 regulation

  • Analysis: Spatial statistics and neighborhood analyses

• Network-based integration:

  • Methodology: Construct protein-protein interaction networks centered on CCNB1

  • Data sources: Antibody-based interactome data, phosphoproteomics, transcriptomics

  • Analysis: Network perturbation algorithms to identify key regulators

  • Outcome: System-level understanding of CCNB1 function

Multi-omics workflow for CCNB1 research: