MAGEA11 Antibody

What is MAGEA11 and why is it significant in cancer research?

MAGEA11 is a member of the MAGE family of proteins, specifically belonging to the cancer/testis antigen (CTA) category. In humans, the canonical protein consists of 429 amino acid residues with a molecular weight of approximately 48.1 kDa and is primarily localized in the nucleus and cytoplasm . MAGEA11 functions as an androgen receptor coregulator that increases androgen receptor activity by modulating the receptor's interdomain interaction .

The significance of MAGEA11 in cancer research stems from its restricted expression pattern in normal tissues (primarily in placenta and germline cells of the testis) contrasted with its frequent upregulation in various cancer types, including melanoma, head and neck squamous cell carcinoma, lung carcinoma, breast carcinoma, and gastric cancer . Research has demonstrated that MAGEA11 broadly functions as an oncogene by forming a complex with the HUWE1 E3 ubiquitin ligase, which promotes aberrant ubiquitin-dependent proteasomal degradation of PCF11 and subsequent dysregulation of 3' UTR processing of mRNA transcripts encoding core components of oncogenic and tumor suppressor signaling pathways .

What applications are most common for MAGEA11 antibodies in research?

MAGEA11 antibodies are primarily used in the following applications:

• Western Blotting (WB): The most widely used application, typically at dilutions of 1:500-1:1000 . MAGEA11 is commonly detected at 48 kDa and 44 kDa bands .

• Immunohistochemistry (IHC): Used to detect MAGEA11 expression in tissue samples, particularly in tumor tissues versus normal tissues. Typical dilutions range from 1:50-1:100 .

• Immunofluorescence (IF)/Immunocytochemistry (ICC): Used to visualize the subcellular localization of MAGEA11, with recommended dilutions of 1:10-1:100 .

• Enzyme-Linked Immunosorbent Assay (ELISA): Used for quantitative detection of MAGEA11 in various samples .

When selecting between these applications, researchers should consider their specific experimental goals—WB for protein expression levels, IHC for tissue distribution patterns, IF/ICC for subcellular localization, and ELISA for quantitative analysis.

What is the expression profile of MAGEA11 in normal versus cancerous tissues?

MAGEA11 expression in normal human tissues is highly restricted, primarily found in:

• Placenta

• Germline cells of the testis

In contrast, MAGEA11 is frequently upregulated in various cancer types:

• Melanoma

• Head and neck squamous cell carcinoma

• Lung carcinoma

• Breast carcinoma

• Gastric cancer

• Prostate cancer

• Esophageal carcinoma

• Bladder cancer

Research has demonstrated that MAGEA11 mRNA expression in gastric cancer tissues is significantly higher than in normal tissues, with receiver operating characteristic (ROC) curve analysis indicating an area under curve (AUC) value of 0.667 . Moreover, high MAGEA11 expression is significantly associated with poor patient prognosis (HR = 1.43, p = 0.034) . These findings highlight the potential utility of MAGEA11 as a biomarker for cancer diagnosis and prognosis.

What are the key technical considerations when selecting a MAGEA11 antibody?

When selecting a MAGEA11 antibody for research, consider the following technical factors:

• Antibody Type:

  • Monoclonal antibodies: Offer high specificity for a single epitope (e.g., Anti-MAGEA11 antibody [N2N3])

  • Polyclonal antibodies: Recognize multiple epitopes, potentially providing stronger signals but with increased risk of cross-reactivity

• Host Species: Most common are rabbit-derived antibodies, which influences secondary antibody selection

• Reactivity: Confirm that the antibody reacts with your species of interest. Most MAGEA11 antibodies are specific to human MAGEA11, though gene orthologs exist in mouse, rat, and chimpanzee

• Applications: Verify that the antibody has been validated for your specific application:

  • For Western Blot: Typical dilutions of 1:500-1:1000

  • For IHC: Typical dilutions of 1:50-1:100

  • For IF/ICC: Typical dilutions of 1:10-1:100

• Immunogen: Consider the region of MAGEA11 used as immunogen. Some antibodies target specific regions (e.g., middle region, N-terminal region) which may affect detection of different isoforms

• Conjugation: Determine whether you need an unconjugated antibody or one conjugated to a tag for direct detection

• Validation Data: Review available data showing specificity and performance in applications similar to yours

How do researchers validate the specificity of MAGEA11 antibodies?

Validating MAGEA11 antibody specificity is crucial for reliable research outcomes. Recommended validation methods include:

• Positive and Negative Controls:

  • Positive controls: Test antibodies on cell lines known to express MAGEA11 (e.g., A375 cells, SGC-7901 cells)

  • Negative controls: Test on normal tissues with low/no MAGEA11 expression or use knockout/knockdown models

• Western Blot Analysis:

  • Confirm detection of bands at the expected molecular weight (48 kDa, with some isoforms at 44 kDa)

  • Observe absence of bands in negative controls

  • Consider using recombinant MAGEA11 as a positive control

• Immunohistochemistry Validation:

  • Compare staining patterns in cancer tissues versus normal tissues

  • Analyze subcellular distribution (nuclear and cytoplasmic localization expected)

  • Use blocking peptides to confirm specificity

• Knockdown/Knockout Verification:

  • Use MAGEA11 siRNA or CRISPR/Cas9 knockout models to confirm specificity

  • Compare antibody signal between wild-type and MAGEA11-depleted samples

• Cross-Reactivity Assessment:

  • Test against related MAGE family proteins to ensure no cross-reactivity

  • Particularly important when studying multiple MAGE family members simultaneously

• Peptide Competition Assay:

  • Pre-incubate antibody with immunizing peptide before application

  • Should result in abolished or significantly reduced signal

How does MAGEA11 function as an androgen receptor coregulator at the molecular level?

MAGEA11 serves as a key coregulator of androgen receptor (AR) signaling through several molecular mechanisms:

• Interdomain Interaction Modulation:

  • MAGEA11 increases androgen receptor activity by modulating the receptor's interdomain interaction

  • This modulation enhances AR transcriptional activity, which plays a significant role in cellular growth and differentiation, especially in hormone-sensitive cells

• Complex Formation with AR:

  • MAGEA11 forms a physical complex with AR, resulting in enhanced transcriptional activity

  • This complex formation is particularly significant in prostate cancer, where MAGEA11 overexpression promotes cancer development through increased AR signaling

• Interaction with Coactivators:

  • MAGEA11 increases steroid receptor transcriptional activity by interacting with:

    • p300 histone acetyltransferase

    • p160 steroid receptor coactivator

  • These interactions facilitate chromatin remodeling and transcription initiation

• Progesterone Receptor Interaction:

  • Beyond AR, MAGEA11 also acts as a human progesterone receptor-specific coregulator

  • This dual role in steroid hormone receptor regulation expands its influence on hormone-responsive tissues

To investigate these mechanisms, researchers should consider employing co-immunoprecipitation assays to identify MAGEA11-AR protein interactions, chromatin immunoprecipitation (ChIP) to analyze binding to AR target genes, and reporter gene assays to quantify AR transcriptional activity in the presence or absence of MAGEA11.

What methodologies are most effective for studying MAGEA11's role in tumor microenvironment interactions?

The relationship between MAGEA11 expression and the tumor microenvironment can be effectively studied using these methodologies:

• Immune Infiltration Analysis:

  • CIBERSORT algorithm to calculate proportions of 22 types of infiltrating immune cells in tumor samples

  • Correlation analysis between MAGEA11 expression and specific immune cell populations

  • Research has shown significant differences in immune infiltration between high and low MAGEA11 expression groups

• Tumor Microenvironment Scoring:

  • Analysis of Stromalscore, Immunescore, and ESTIMATE scores between high and low MAGEA11 expression groups

  • Studies have shown the score of the MAGEA11 low expression group was significantly higher than the high expression group, indicating strong relationship with tumor microenvironment

• Immune Checkpoint Correlation Analysis:

  • TIMER database analysis to evaluate relationships between MAGEA11 and immune checkpoints

  • The Tumor Immune System Interaction Database (TISIDB) to determine correlations between MAGEA11 expression and tumor-infiltrating lymphocytes (TILs)

• Immunotherapy Response Assessment:

  • Analysis of differential responses to immunotherapy between MAGEA11 high and low expression groups

  • Studies have shown the MAGEA11 low expression group had better effects when receiving immunotherapy than the high expression group

  • Most significant effects observed with combined immunotherapy against PD1 and CTLA4

• Single-cell RNA Sequencing:

  • For high-resolution analysis of cell-type-specific MAGEA11 expression and its impact on the tumor microenvironment

For optimal results, researchers should combine multiple approaches and consider the temporal dynamics of MAGEA11 expression during tumor progression.

How can researchers accurately differentiate between MAGEA11 isoforms in experimental settings?

Differentiating between MAGEA11 isoforms requires a combination of techniques:

• Isoform-Specific PCR:

  • Design primers targeting unique regions of each isoform

  • Use quantitative RT-PCR to measure relative expression levels

  • Validate with sequencing to confirm isoform identity

• Western Blotting with Resolution Optimization:

  • Use gradient gels (e.g., 8-12%) for better separation of closely sized isoforms

  • MAGEA11 is reported to have up to 2 different isoforms

  • Observed molecular weights include 48 kDa (canonical) and 44 kDa variants

  • Extended running times can improve separation of similar-sized isoforms

• Antibody Selection Based on Epitope Location:

  • Choose antibodies targeting regions that differ between isoforms

  • For example, antibodies targeting the N-terminal region (aa 50-150) versus middle regions may detect different isoforms

  • Consider using multiple antibodies recognizing different epitopes

• Mass Spectrometry:

  • For definitive identification of isoforms based on peptide sequences

  • Especially useful for identifying post-translational modifications

• Recombinant Expression of Individual Isoforms:

  • Clone and express individual isoforms as controls

  • Compare migration patterns with endogenous proteins

  • Use for antibody validation and as positive controls

• Isoform-Specific Knockdown:

  • Design siRNAs or shRNAs targeting unique regions of specific isoforms

  • Validate knockdown efficiency with both RNA and protein analysis

When reporting results, clearly document the isoform(s) detected and the methods used for differentiation to ensure reproducibility.

What techniques are recommended for investigating MAGEA11's potential as an immunotherapy target?

Investigating MAGEA11 as an immunotherapy target requires a multifaceted approach:

• Expression Profiling:

  • Confirm restricted expression pattern in normal tissues versus cancer tissues

  • MAGEA11 expression is largely restricted to placenta and germline cells of the testis in normal tissues

  • Validate overexpression in tumor samples using immunohistochemistry and RNA-seq data

• Epitope Identification and TCR Engineering:

  • Identify antigenic epitopes of MAGEA11 presented in the context of class I MHC alleles

  • Research has shown interest in targeting these epitopes using engineered T-cell receptors (TCRs)

  • Use in silico prediction followed by experimental validation of peptide-MHC binding

• Immunogenicity Assessment:

  • Test MAGEA11-derived peptides for ability to stimulate T-cell responses

  • Analyze natural T-cell responses against MAGEA11 in cancer patients

  • Develop functional assays to measure cytotoxicity against MAGEA11-expressing cancer cells

• Combination Therapy Analysis:

  • Evaluate MAGEA11-targeted approaches in combination with checkpoint inhibitors

  • Research has shown MAGEA11 low expression tumors respond better to immunotherapy

  • Most significant effects observed with combined immunotherapy against PD1 and CTLA4

• In Vivo Models:

  • Develop mouse models with humanized immune systems for testing MAGEA11-targeted therapies

  • Evaluate tumor regression, immune infiltration, and potential toxicities

  • Studies have shown tumor volume reductions after MAGEA11 knockdown in nude mice

• Predictive Biomarker Development:

  • Assess whether MAGEA11 expression levels can predict response to immunotherapies

  • Studies have shown correlation between MAGEA11 expression and immunotherapy responses

These approaches should be pursued sequentially, with safety considerations paramount given MAGEA11's expression in some normal tissues.

How should researchers interpret conflicting data on MAGEA11 expression across different cancer types?

When faced with conflicting data regarding MAGEA11 expression across cancer types, researchers should consider these methodological approaches:

• Standardize Detection Methods:

  • Recognize that different antibodies may target different epitopes, leading to discrepancies

  • Compare antibodies used across studies (e.g., polyclonal vs. monoclonal, different target regions)

  • Validate expression using multiple methodologies (IHC, Western blot, qRT-PCR, RNA-seq)

• Consider Tissue and Tumor Heterogeneity:

  • MAGEA11 expression can vary within tumors and across patients

  • Analyze both tumor center and margins

  • Consider using tissue microarrays (TMAs) to assess larger sample numbers

  • Report percentage of positive cells and staining intensity

• Account for Technical Variables:

  • Fixation methods can affect epitope accessibility in IHC

  • RNA degradation may affect transcript detection

  • Establish clear positivity thresholds and scoring systems

• Address Biological Factors:

  • Tumor stage and grade may influence MAGEA11 expression

  • Patient demographics (age, gender) may affect expression patterns

  • Male/female ratios in studies vary (3.6-3.9:1 in some cohorts)

  • Consider hormone receptor status in hormone-sensitive cancers

• Meta-analysis Approach:

  • Synthesize data across multiple studies

  • Weight findings based on sample size and methodology rigor

  • Use forest plots to visualize consistency across studies

• Single-cell Analysis:

  • Determine if apparent conflicts result from bulk tissue averaging

  • Identify specific cell populations expressing MAGEA11

When reporting findings, clearly acknowledge conflicting data in the literature and explain how your methodological choices address potential sources of discrepancy.

What are the experimental considerations when studying MAGEA11's interaction with the HUWE1 E3 ubiquitin ligase complex?

Investigating the MAGEA11-HUWE1 E3 ubiquitin ligase complex requires specific experimental considerations:

• Co-immunoprecipitation Optimization:

  • Use appropriate lysis buffers to preserve protein-protein interactions

  • Consider crosslinking to stabilize transient interactions

  • Include proper controls (IgG, reverse IP)

  • Validate antibody specificity for both MAGEA11 and HUWE1

• Ubiquitination Assays:

  • Research has shown that the MAGEA11-HUWE1 complex promotes aberrant ubiquitin-dependent proteasomal degradation of PCF11

  • Include proteasome inhibitors (e.g., MG132) to stabilize ubiquitinated proteins

  • Perform in vitro ubiquitination assays with purified components

  • Consider using ubiquitin mutants to identify linkage types (K48 vs. K63)

• Functional Analysis of mRNA Processing:

  • The complex leads to dysregulation of 3' UTR processing of mRNA transcripts encoding core components of oncogenic and tumor suppressor signaling pathways

  • Use 3'-RACE to analyze changes in poly(A) site selection

  • RNA-seq with focus on 3' UTR alterations

  • Compare transcriptome changes in MAGEA11 or HUWE1 knockdown/knockout models

• Domain Mapping:

  • Identify specific domains involved in MAGEA11-HUWE1 interaction

  • Generate truncation mutants of both proteins

  • Use yeast two-hybrid or mammalian two-hybrid assays to map interaction domains

• Subcellular Localization Studies:

  • Determine where in the cell the interaction occurs

  • Use confocal microscopy with fluorescently tagged proteins

  • Perform subcellular fractionation followed by co-IP

• Target Identification:

  • Identify additional targets beyond PCF11

  • Use proteomics approaches with quantitative ubiquitin profiling

  • Validate candidates with focused biochemical approaches

These approaches should be pursued in multiple cell types, including both cancer cells with high MAGEA11 expression and normal cells with engineered MAGEA11 expression.

How do researchers isolate the functional collaboration between MAGEA11 and MAGE-A6 in cancer progression studies?

To investigate the functional collaboration between MAGEA11 and MAGE-A6 in cancer progression, researchers should consider these methodological approaches:

• Co-expression Analysis in Clinical Samples:

  • Studies have evaluated the expression pattern and potential clinical significance of MAGEA11 and MAGE-A6 in bladder cancer tissues through immunohistochemistry on tissue microarray slides

  • Design studies that quantify both proteins in the same tissue samples

  • Perform statistical analysis of co-expression patterns

  • Correlate with clinical outcomes to identify synergistic effects

• Sequential and Simultaneous Knockdown/Knockout Studies:

  • Individual knockdown of MAGEA11 or MAGE-A6

  • Double knockdown to identify synergistic effects

  • Rescue experiments with one gene while the other is knocked down

  • Analyze phenotypic changes in proliferation, migration, and other cancer hallmarks

• Protein-Protein Interaction Analysis:

  • Co-immunoprecipitation to determine direct or indirect interactions

  • Proximity ligation assays to visualize interactions in situ

  • Fluorescence resonance energy transfer (FRET) or bimolecular fluorescence complementation (BiFC) for live-cell interaction studies

• Transcriptional Analysis:

  • RNA-seq after individual and combined manipulation of MAGEA11 and MAGE-A6

  • ChIP-seq to identify shared and unique genomic binding sites

  • Identify common downstream targets or pathways

• In Vivo Models with Controlled Expression:

  • Generate xenograft models with:

    • Wild-type expression

    • MAGEA11 overexpression/knockdown

    • MAGE-A6 overexpression/knockdown

    • Combined manipulation

  • Analyze tumor growth, invasion, and metastasis

• Mechanistic Studies of Shared Pathways:

  • Investigate if both proteins affect the same signaling pathways

  • Analyze if they compete for the same binding partners

  • Determine if they have additive or synergistic effects on common targets

When designing these studies, researchers should consider that specific subgroups of MAGE-A members may have functional collaboration to potentiate specific oncogenic functions , which requires careful experimental design to isolate individual and combined effects.