IGF1R Recombinant Monoclonal Antibody

What is IGF1R and why is it a significant target for monoclonal antibodies?

IGF1R (Insulin-like Growth Factor 1 Receptor) is a transmembrane receptor tyrosine kinase with a molecular weight of approximately 154.8 kDa that plays crucial roles in proliferation, apoptosis, angiogenesis, and tumor invasion . The receptor has emerged as a significant target because its overexpression or hyperactivation is implicated in various cancers, including prostate and breast cancer . Furthermore, IGF1R has been identified as a contributor to resistance mechanisms against several targeted therapies, making it an important focus for developing complementary treatment strategies . When investigating cancer biology, researchers target IGF1R not only to understand fundamental growth signaling but also to potentially reverse therapy resistance, as demonstrated in studies with trastuzumab-resistant ovarian cancer cells (SKOV3-T) .

What applications have been validated for IGF1R recombinant monoclonal antibodies?

IGF1R recombinant monoclonal antibodies have been validated for multiple research applications, each requiring specific optimization:

These applications require method-specific optimization, with antibody concentrations ranging from 1-15 μg/mL depending on the specific application and sample type .

How should IGF1R antibodies be validated before use in critical experiments?

Validating IGF1R antibodies requires a multi-step process to ensure specificity and reliability:

• Positive and negative control cell lines: Compare staining between known IGF1R-positive (e.g., MCF-7, DU-145) and IGF1R-negative (e.g., LNCaP, HDLM) cell lines. Flow cytometry analysis should show distinct binding patterns between these cell types .

• Western blot verification: Confirm target specificity by detecting the correct molecular weight band (~275 kDa for the full receptor under non-reducing conditions) .

• Functional validation: Conduct neutralization assays to verify that the antibody can inhibit IGF1R-mediated cell proliferation. For example, at concentrations of approximately 11 μg/mL, effective antibodies should neutralize 50-75% of the bioactivity induced by 6 ng/mL of recombinant human IGF-I in responsive cell lines like MCF-7 .

• Cross-reactivity assessment: Test against related receptors (particularly insulin receptor) to ensure specificity, especially important when researching metabolic pathways.

• Correlation with genetic manipulation: Compare antibody results with IGF1R knockdown/knockout models to confirm target specificity.

Rigorous validation helps prevent misleading results that could arise from non-specific binding or inadequate inhibition of the receptor's activity.

What are the optimal protocols for using IGF1R antibodies in flow cytometry?

For optimal flow cytometry results with IGF1R antibodies, follow this methodology:

• Cell preparation: Harvest adherent cells (e.g., MCF-7, DU-145) using non-enzymatic dissociation methods when possible to preserve surface epitopes. Prepare single-cell suspensions at 5×106 cells/mL in cold PBS with 1% BSA .

• Antibody concentration: Incubate cells with the primary IGF1R antibody at a concentration of approximately 0.5-1 μg/mL for 30 minutes at room temperature. The precise concentration should be optimized for each antibody clone and cell type .

• Washing steps: Perform three thorough washes with cold PBS to remove unbound antibody and minimize background signal .

• Secondary detection: If using an unconjugated primary antibody, incubate with a fluorochrome-conjugated secondary antibody (e.g., FITC-labeled rabbit anti-mouse IgG at 1 μg/mL) for 30 minutes at room temperature .

• Controls: Always include:

  • Isotype controls (e.g., MAB002 when using mouse monoclonal antibodies)

  • Unstained cells

  • Single-color controls for compensation when using multiple fluorochromes

  • Positive control cell lines (e.g., MCF-7)

  • Negative control cell lines (e.g., HDLM)

• Analysis: Gate appropriately on viable single cells before assessing IGF1R expression. Compare the mean fluorescence intensity between samples and controls to quantify expression levels .

This protocol has been validated in multiple studies and produces consistent results for evaluating membrane-bound IGF1R expression.

How can IGF1R antibodies be effectively deployed for in vivo imaging of tumors?

Developing IGF1R antibodies for in vivo imaging requires specific modifications and validation:

• Conjugation chemistry: For PET imaging applications, conjugate anti-IGF1R antibodies (such as clone 1A2G11) with appropriate chelators like NOTA (1,4,7-triazacyclononane-1,4,7-triacetic acid) that maintain antibody specificity and avidity .

• Radiolabeling protocol: Label the conjugated antibody with positron-emitting radioisotopes such as 64Cu. Optimal labeling conditions yield >50% radiochemical yield and high specific activity (>1 Ci/μmol) .

• Quality control: Verify that radiolabeling does not compromise antibody binding by conducting pre- and post-labeling flow cytometry analysis using IGF1R-positive cell lines .

• Tumor model selection: Choose appropriate xenograft models that represent the IGF1R expression pattern of interest. For example, in prostate cancer research, DU-145 cells serve as IGF1R-positive models while LNCaP cells function as IGF1R-negative controls .

• Imaging timeline: Conduct serial PET imaging at multiple time points (e.g., 4, 24, and 48 hours post-injection) to identify optimal imaging windows. Studies show that maximum tumor uptake in IGF1R-positive models typically occurs at 24-48 hours post-injection .

• Quantification: Measure uptake in terms of percent injected dose per gram (%ID/g) of tissue. For instance, 64Cu-NOTA-1A2G11 demonstrated differential uptake between IGF1R-positive tumors (9.6-10.2 %ID/g at 24-48h) versus IGF1R-negative tumors (<3 %ID/g) .

• Validation: Correlate imaging results with ex vivo histological analysis of IGF1R expression to confirm specificity of signal .

This approach allows for non-invasive assessment of IGF1R expression in tumors and can be valuable for monitoring therapeutic responses in preclinical models.

What mechanisms underlie the ability of IGF1R antibodies to overcome therapy resistance?

IGF1R antibodies can reverse therapy resistance through several interconnected mechanisms:

• Compensatory pathway inhibition: In trastuzumab-resistant cancer cells (such as SKOV3-T), IGF1R is frequently upregulated as a compensatory survival mechanism when HER2 signaling is blocked. Anti-IGF1R antibodies directly inhibit this alternative survival pathway .

• Cell growth and proliferation inhibition: IGF1R antibodies effectively suppress cellular proliferation in resistant cancer cells by blocking the mitogenic signals transmitted through the IGF1R pathway. This has been demonstrated in models where IGF1R expression is induced following development of resistance to HER2-targeted therapies .

• Reduction of invasive potential: IGF1R signaling promotes invasion and migration of cancer cells. Anti-IGF1R antibodies (such as LMAb1) can significantly reduce these capabilities in resistant cell populations .

• Disruption of in vitro colony formation: IGF1R antibodies impair the ability of resistant cancer cells to form colonies, indicating inhibition of clonogenic potential that is essential for tumor growth and recurrence .

• Inhibition of downstream signaling cascades: Effective IGF1R antibodies block phosphorylation events that activate critical downstream pathways including PI3K/Akt and MAPK/ERK, which are often hyperactivated in therapy-resistant cells .

• Synergistic effects with original therapy: When combined with the original therapeutic agent (e.g., trastuzumab in HER2-positive cancers), IGF1R antibodies can restore sensitivity by blocking the escape pathway, potentially leading to enhanced apoptosis through dual receptor inhibition .

This multi-faceted approach explains why IGF1R-targeted strategies show promise for addressing both de novo and acquired resistance to established targeted therapies.

How do different IGF1R antibody clones compare in their binding specificity and functional effects?

Differences between IGF1R antibody clones are critical for selecting the appropriate reagent for specific research applications:

When selecting between these clones, researchers should consider:

• The specific application requirements

• Whether cross-reactivity with mouse IGF1R is desired (important for in vivo studies)

• The need for functional inhibition versus detection only

• The compatibility with conjugation chemistries for specialized applications like imaging

Each clone has distinct binding characteristics that make it more suitable for particular experimental contexts, and these differences should inform antibody selection decisions.

What could cause inconsistent results when using IGF1R antibodies in immunohistochemistry?

Inconsistent immunohistochemistry results with IGF1R antibodies can stem from several methodological factors:

• Fixation variables: Overfixation can mask epitopes while underfixation may compromise tissue morphology. For IGF1R detection in paraffin-embedded sections, perfusion fixation followed by standardized fixation times (as used for mouse heart tissues) produces optimal results .

• Antigen retrieval methods: IGF1R epitopes may require specific retrieval conditions. Research indicates that heat-induced epitope retrieval in citrate buffer (pH 6.0) is often effective, but optimization is necessary for each tissue type and fixation method.

• Antibody concentration gradients: Concentrations that are too high can increase background while too low may yield false negatives. For IGF1R detection in paraffin-embedded sections, concentrations around 15 μg/mL have been validated , but this should be titrated for each specific antibody clone and tissue type.

• Detection system sensitivity: The choice between chromogenic (e.g., DAB) and fluorescent detection impacts sensitivity. For challenging samples, signal amplification using polymer-based detection systems (like Anti-Mouse IgG VisUCyte HRP Polymer) can enhance detection of membrane-bound IGF1R .

• Endogenous peroxidase activity: Inadequate blocking of endogenous peroxidase in tissues rich in this enzyme (like liver) can lead to false-positive results when using HRP-based detection.

• Heterogeneous expression patterns: IGF1R expression can vary significantly within tumors, creating apparent inconsistencies that actually reflect biological heterogeneity rather than technical issues. This biological variation has been observed in multiple tumor types and should be distinguished from technical artifacts.

• Receptor internalization: IGF1R undergoes ligand-induced internalization, potentially altering the subcellular localization pattern. The physiological state of the tissue at collection (fasting vs. fed, treatment status) can influence membrane vs. cytoplasmic staining patterns .

To address these issues, researchers should implement standardized protocols with appropriate controls, including positive control tissues (e.g., MCF-7 xenografts) and negative control tissues (e.g., IGF1R-knockdown samples or naturally low-expressing tissues like HDLM).

How should researchers interpret discrepancies between IGF1R antibody binding data and functional assays?

Discrepancies between IGF1R binding data and functional outcomes require systematic analysis:

• Epitope-specific effects: Different antibody clones bind distinct epitopes that may not equally affect receptor function. For example, antibodies binding the ligand-binding domain may show strong neutralizing activity but moderate receptor downregulation, while those targeting other domains may induce downregulation without directly blocking ligand binding .

• Receptor isoform considerations: IGF1R exists in multiple isoforms, including soluble variants. An antibody might detect total IGF1R without distinguishing functional subtypes. Search results mention a "soluble IGF1R variant 1" that could be detected differently than membrane-bound forms .

• Activation state sensitivity: Some antibodies preferentially bind activated (phosphorylated) versus inactive receptor forms. This differential recognition can lead to apparent discrepancies between abundance and activity measurements.

• Threshold effects: A critical threshold of receptor occupancy (approximately 40-60%) may be required for functional effects. Partial binding may be detected in immunoassays without reaching the threshold needed for functional inhibition.

• Compensatory mechanisms: In cellular systems, compensatory upregulation of alternative signaling pathways (like insulin receptor) may mask functional effects despite confirmed target engagement. This phenomenon has been observed in trastuzumab-resistant cells where IGF1R expression is induced to compensate for HER2 inhibition .

• Technical limitations of binding assays: Flow cytometry measures cell surface receptors, while Western blotting detects total protein regardless of localization. Functional inhibition may correlate better with blocking surface-accessible receptors than total protein levels.

To resolve these discrepancies, researchers should:

• Employ multiple detection methods (flow cytometry, Western blotting, immunoprecipitation)

• Directly measure receptor activation (phosphorylation status)

• Assess downstream signaling effects (AKT/ERK phosphorylation)

• Compare results across multiple antibody clones with defined epitope specificities

Understanding these variables helps explain why an antibody showing strong binding might demonstrate limited functional effects, or why modest binding sometimes produces significant functional outcomes.

What controls are essential when evaluating IGF1R antibody specificity in complex biological systems?

Rigorous control experiments are essential for accurately interpreting IGF1R antibody results:

• Genetic knockdown/knockout controls:

  • siRNA/shRNA knockdown of IGF1R should proportionally reduce antibody signal

  • CRISPR/Cas9 knockout cells provide definitive negative controls

  • Rescue experiments with IGF1R re-expression confirm specificity

• Competing ligand controls:

  • Pre-incubation with excess IGF-1 should block antibody binding if they target overlapping epitopes

  • Dose-dependent competition curves help quantify binding specificity

• Cross-reactivity assessment:

  • Test antibodies against insulin receptor (IR) due to high homology with IGF1R

  • Evaluate hybrid receptors (IGF1R/IR) that may show intermediate binding profiles

  • Confirm selectivity in cells expressing various levels of related receptors

• Cell line panel controls:

  • Include validated positive controls (MCF-7, DU-145) and negative controls (LNCaP, HDLM)

  • Correlate staining intensity with independently measured IGF1R expression levels

• Isotype controls:

  • Include matched isotype controls (e.g., MAB002 for mouse monoclonals)

  • Process identically to experimental samples to assess non-specific binding

• Blocking peptide validation:

  • Pre-absorption with immunizing peptides should abolish specific staining

  • Non-relevant peptides should not affect staining

• Functional validation controls:

  • Compare antibody effects with known IGF1R inhibitors (small molecules, ligand traps)

  • Evaluate inhibition of downstream signaling markers (phospho-AKT, phospho-ERK)

  • Confirm expected biological effects in neutralization assays (e.g., reduced cell proliferation)

• Tissue controls for IHC:

  • Include tissues with known expression patterns (e.g., mouse heart)

  • Serial sections processed with and without primary antibody

  • Gradient of expression across diverse tissues to establish sensitivity range

These controls collectively establish a framework for confidently interpreting IGF1R antibody results and distinguishing true biological effects from technical artifacts.

How might next-generation IGF1R antibodies improve upon current limitations?

Next-generation IGF1R antibodies are being engineered to address several limitations of current antibodies:

• Enhanced specificity through structure-guided design: By leveraging structural data on IGF1R-antibody interactions, researchers can design antibodies that more precisely distinguish between IGF1R and the highly homologous insulin receptor, reducing off-target effects that complicate current research applications.

• Bifunctional/bispecific formats: Developing antibodies that simultaneously target IGF1R and complementary receptors (e.g., HER2, EGFR) would allow investigation of synergistic pathway inhibition, particularly relevant in therapy resistance research where compensatory signaling is common .

• Intracellular delivery capabilities: Engineering antibody formats that can access intracellular compartments would enable targeting of cytoplasmic domains and investigation of nuclear IGF1R functions that remain poorly characterized with current tools.

• Conditional activation mechanisms: Creating antibodies that become active only under specific conditions (pH, protease activity, etc.) would allow more precise spatial and temporal control of IGF1R inhibition in complex biological systems.

• Improved imaging properties: Developing antibodies or antibody fragments with optimized pharmacokinetics for imaging (faster clearance, reduced background) would build upon current success with 64Cu-labeled antibodies while reducing the time needed between injection and imaging .

• Allosteric modulators: Rather than simply blocking ligand binding, next-generation antibodies might selectively modulate receptor conformation to inhibit specific downstream pathways while preserving others, allowing more nuanced study of IGF1R signaling.

• Single-domain antibody platforms: Expanding the use of nanobodies and VHH fragments against IGF1R would provide smaller probes with potentially better tissue penetration for both imaging and functional studies .

These advances would significantly expand the research toolkit for studying IGF1R biology in normal and pathological contexts, potentially revealing new therapeutic opportunities.

What are the critical considerations for combining IGF1R antibodies with other targeted therapies in research models?

When designing combination studies with IGF1R antibodies and other targeted agents, researchers should consider:

• Pathway crosstalk analysis: Map the interconnections between IGF1R and the companion target pathway before designing experiments. The documented crosstalk between IGF1R and HER2 signaling in trastuzumab-resistant models illustrates how one pathway compensates when another is inhibited .

• Sequence-dependent effects: Determine whether simultaneous administration or sequential treatment produces optimal results. Pre-treatment with IGF1R antibodies might sensitize cells to subsequent therapies by blocking a key resistance mechanism.

• Dose rationalization: Establish dose-response relationships for each agent individually before testing combinations. Sub-optimal dosing of either agent can mask potential synergies or antagonisms.

• Appropriate cellular models: Select models with well-characterized expression of both targets. For example, when studying IGF1R and HER2-targeted combinations, use models like SKOV3-T that have documented expression of both receptors .

• Mechanism-based endpoints: Select endpoints that reflect the hypothesized mechanism of interaction. Beyond growth inhibition, assess:

  • Receptor downregulation kinetics

  • Changes in downstream signaling node activation

  • Cell cycle distribution effects

  • Apoptotic pathway engagement

  • Autophagy modulation

• Resistance development monitoring: Design long-term studies to determine whether combined targeting delays or prevents resistance development compared to single-agent approaches.

• In vivo validation strategy: Confirm in vitro findings in appropriate animal models, using the optimized 64Cu-labeled IGF1R antibodies for non-invasive monitoring of receptor engagement alongside traditional measures of efficacy .

• Biomarker identification: Identify predictive biomarkers that might indicate which tumors will respond to the combination therapy, potentially including receptor expression ratios or activation states of downstream signaling nodes.

Carefully designed combination studies can reveal synergistic interactions that might be translated into more effective therapeutic strategies, particularly for overcoming resistance mechanisms in cancer.