DDR1 Recombinant Monoclonal Antibody

What is DDR1 and why is it significant in cancer research?

DDR1 is a 120-140 kDa type I transmembrane glycoprotein belonging to the discoidin-like domain containing subfamily of receptor tyrosine kinases. Also known as CAK, CD167a, RTK6, and TrkE, DDR1 serves as a receptor for collagen and is predominantly expressed on epithelial tissues, activated monocytes, neutrophils, and in several cancer types . DDR1 has been identified as a cancer-associated receptor tyrosine kinase that is highly expressed in several malignancies relative to normal tissues, including lung, breast, esophagus, ovary, and colon cancers . Immunohistochemical analysis of tissue microarrays containing 100 colon cancer specimens revealed that DDR1 was highly expressed in 81% of tumor tissues, with high expression levels associated with poor survival outcomes in patients .

Research methodology: To investigate DDR1's role in cancer, researchers typically perform immunohistochemistry on tissue microarrays, assess expression levels through flow cytometry, and conduct survival analyses to correlate expression with clinical outcomes. Western blotting and RT-PCR may be employed to examine protein and transcript levels, respectively.

How are DDR1 recombinant monoclonal antibodies produced?

The production of DDR1 recombinant monoclonal antibodies follows a multi-step process:

• Immunization and B cell isolation: B cells are isolated from the spleen of animals immunized with recombinant human DDR1 protein (specifically the extracellular domain, amino acids 21-417) .

• RNA extraction and reverse transcription: RNA is extracted from the B cells and converted into cDNA through reverse transcription .

• Gene amplification and vector insertion: Using the cDNA as a template, the gene encoding the DDR1 antibody is amplified using a degenerate primer through PCR and inserted into an expression vector .

• Transfection and expression: The recombinant vector is introduced into host cells (typically HEK293F cells) through transfection, enabling efficient antibody expression .

• Harvesting and purification: The DDR1 recombinant monoclonal antibodies are harvested from the cell culture supernatant and subjected to purification via affinity chromatography, typically using protein G .

• Validation: Rigorous validation, including ELISA testing, is conducted to confirm the antibody's specific reactivity with human DDR1 protein .

Alternative approaches include the mouse hybridoma method, where harvested spleen cells are fused with SP2/0 cells, and hybridoma supernatants are screened by ELISA against the immunogen, followed by flow cytometry to assess binding and internalization abilities .

What are the key applications of DDR1 antibodies in cancer research?

DDR1 antibodies serve multiple research applications:

• Diagnostic marker: DDR1 antibodies can identify DDR1 expression in tumor samples through immunohistochemistry, flow cytometry, and western blotting. This is particularly valuable as DDR1 is overexpressed in various cancer types including colon (81% of specimens), lung, breast, esophagus, and ovarian cancers .

• Therapeutic development: DDR1 antibodies can be engineered into antibody-drug conjugates (ADCs) for targeted cancer therapy. For example, T 4H 11-DM4, an ADC targeting DDR1 carrying the tubulin inhibitor payload DM4, has demonstrated potent anti-proliferative activity in colon cancer cell lines with IC50 values in the nanomolar range .

• Mechanism studies: These antibodies help elucidate DDR1-mediated signaling pathways in tumor progression, including those activated upon collagen binding that lead to sustained receptor phosphorylation .

• Biomarker investigations: DDR1 expression levels can serve as prognostic biomarkers, as high expression correlates with poor survival in colon cancer patients .

When performing these applications, researchers should validate antibody specificity against the target using positive and negative control cell lines with known DDR1 expression levels.

How can DDR1 antibodies be engineered into antibody-drug conjugates for cancer therapy?

Engineering DDR1 antibodies into effective antibody-drug conjugates involves several critical steps:

• Antibody selection: Choose high-affinity anti-DDR1 monoclonal antibodies (such as clone T 4H 11) that demonstrate specific binding to the extracellular domain and efficient internalization upon binding .

• Linker-payload selection: The linking chemistry is crucial for stability and payload release. For example, the T 4H 11-DM4 conjugate utilizes SPDB-DM4 (a disulfide-containing linker with the maytansinoid DM4 payload) .

• Conjugation procedure:

  • Mix the anti-DDR1 mAb with a 10-fold molar excess of the linker-payload (e.g., SPDB-DM4) in conjugation buffer at a concentration of 5 mg·mL⁻¹

  • Perform coupling reactions overnight at 25°C

  • Separate the reaction mixtures by chromatography using a desalting column to yield the conjugates

  • Confirm the drug-antibody ratio (DAR) by LC-MS

• Functional validation:

  • Assess binding affinity to DDR1-expressing cells via flow cytometry

  • Evaluate internalization efficiency using fluorescently labeled antibodies

  • Measure cytotoxicity in DDR1-expressing cancer cell lines

  • Confirm selectivity by testing in DDR1-negative cell lines

In the case of T 4H 11-DM4, this approach produced an ADC that exhibited potent anti-proliferative activity in vitro and achieved complete tumor regression at doses of 5 and 10 mg·kg⁻¹ in HT-29 and HCT116 tumor models in vivo .

What methodologies are most effective for validating DDR1 antibody specificity and functionality?

Comprehensive validation of DDR1 antibodies requires multiple complementary approaches:

• Binding specificity assessment:

  • ELISA: Using recombinant DDR1 protein (extracellular domain, amino acids 21-417) to confirm binding specificity

  • Western blotting: Testing against cell lysates with varying DDR1 expression levels

  • Flow cytometry: Analyzing binding to DDR1-expressing cells versus negative controls

  • Immunoprecipitation: Confirming ability to pull down DDR1 protein

• Cross-reactivity testing:

  • Test against closely related proteins (particularly DDR2)

  • Assess binding to different DDR1 isoforms (DDR1a, DDR1b, etc.)

• Functional validation:

  • Collagen binding inhibition assays: Determine if the antibody blocks DDR1-collagen interaction

  • Phosphorylation studies: Assess impact on DDR1 autophosphorylation using phospho-specific antibodies

  • Signaling pathway analysis: Examine effects on downstream pathways

• Internalization studies:

  • Conjugate antibodies with fluorescent markers (e.g., Cy5.5) to track internalization kinetics

  • Confirm intracellular trafficking using confocal microscopy

• In vivo validation:

  • Biodistribution studies using labeled antibodies

  • Target engagement assessment in tumor models

For therapeutic applications, additional functional assays such as antibody-dependent cellular cytotoxicity (ADCC) or complement-dependent cytotoxicity (CDC) may be relevant depending on the antibody isotype and intended mechanism of action.

How does DDR1 expression correlate with treatment response and what are the implications for patient stratification?

DDR1 expression patterns have significant implications for treatment response and patient stratification:

• Expression correlation with outcomes:

  • High DDR1 expression is associated with poor survival in colon cancer patients

  • This suggests DDR1 may serve as a prognostic biomarker

• Predictive value for targeted therapies:

  • A direct correlation exists between in vivo efficacy of DDR1-targeted ADCs (T 4H 11-DM4) and the levels of DDR1 expression on cell surface

  • Higher expression levels predict better response to DDR1-targeted therapies

• Resistance mechanisms:

  • DDR1-targeted ADCs (T 4H 11-DM4) remain efficacious in oxaliplatin-resistant colon cancer models

  • This indicates potential utility in treatment-refractory patient populations

• Patient stratification methodology:

  • IHC scoring system: Develop standardized scoring for DDR1 expression levels in tumor samples

  • Flow cytometry: Quantify cell surface DDR1 levels on tumor cells

  • Threshold determination: Establish expression thresholds that predict therapeutic response

For optimal patient stratification, researchers should:

• Validate DDR1 expression assessment methods across multiple tumor types

• Correlate expression with response to various therapeutic modalities

• Consider heterogeneity of expression within tumors

• Evaluate DDR1 isoform distribution, as different isoforms may influence therapeutic response

What are the considerations for using DDR1 antibodies in combination with immunotherapeutic approaches?

Integrating DDR1 antibodies with immunotherapies requires careful consideration of several factors:

• Rationale for combination:

  • DDR1 inhibition may modulate the tumor microenvironment, potentially enhancing immunotherapy efficacy

  • Anti-PD-1/PD-L1 agents could complement DDR1-targeted therapies by removing immune suppression while DDR1 antibodies directly target tumor cells

• Preclinical evidence:

  • While specific data on DDR1 antibody combinations with immunotherapy is limited in the provided search results, similar combination approaches have shown promise

  • For example, the addition of anti-PD-1 antibodies to oncolytic virus therapy (MG1) overcame resistance in advanced melanoma models with "immunologically colder" tumor microenvironments

• Sequencing considerations:

  • Determine optimal timing (concurrent vs. sequential administration)

  • Assess whether DDR1 antibody treatment alters immune cell infiltration or activation

• Monitoring parameters:

  • Track changes in immune cell populations following DDR1 antibody treatment

  • Assess alterations in cytokine profiles and immune checkpoint expression

• Safety assessment:

  • Monitor for potential enhanced immune-related adverse events

  • Evaluate overlapping toxicity profiles

A methodological approach would include in vitro co-culture systems with tumor cells and immune components, followed by syngeneic mouse models (if cross-reactive antibodies are available) or humanized mouse models to assess combination efficacy before clinical translation.

What flow cytometry protocols are optimal for assessing DDR1 expression in tumor samples?

Optimized flow cytometry protocols for DDR1 expression assessment in tumor samples should include:

• Sample preparation:

  • For cell lines: Harvest cells using PBS with 2 mmol·L⁻¹ EDTA (not trypsin) to preserve surface receptors

  • For tumor samples: Generate single-cell suspensions using gentle enzymatic digestion with collagenase/dispase mixtures

  • Maintain samples at 4°C to prevent receptor internalization

• Staining procedure:

  • Use 2 × 10⁵ cells per tube

  • Incubate with 5 μg·mL⁻¹ anti-DDR1 primary antibody for 40 minutes at 4°C

  • Wash three times

  • Incubate with fluorophore-conjugated secondary antibody (e.g., Alexa Fluor 488-labeled goat anti-mouse IgG)

  • Include viability dye to exclude dead cells

• Controls:

  • Isotype control antibody at the same concentration

  • FMO (Fluorescence Minus One) controls

  • Positive control (cell line with known high DDR1 expression)

  • Negative control (cell line with no DDR1 expression)

• Multi-parameter considerations:

  • Include markers for specific cell populations (e.g., EpCAM for epithelial tumor cells)

  • Consider intracellular staining for phosphorylated DDR1 following permeabilization

  • Use compensation controls when multiple fluorophores are employed

• Analysis:

  • Gate on single, viable cells first

  • Quantify DDR1 expression as median fluorescence intensity (MFI)

  • Calculate the percentage of DDR1-positive cells using appropriate threshold settings

  • Use software like novoexpress for data processing

This protocol can be adapted for analyzing patient-derived xenografts, clinical samples, or experimental models to ensure consistent and reliable DDR1 quantification across different research applications.

How can researchers optimize DDR1 antibody-based immunohistochemistry for tumor tissue analysis?

Optimizing DDR1 antibody-based immunohistochemistry requires attention to multiple technical parameters:

• Tissue preparation:

  • Fixation: 10% neutral buffered formalin for 24-48 hours

  • Processing: Standard paraffin embedding

  • Sectioning: 4-5 μm thick sections

  • Mounting: Positively charged slides to prevent tissue loss

• Antigen retrieval optimization:

  • Test multiple methods: Heat-induced epitope retrieval (HIER) using citrate buffer (pH 6.0) or EDTA buffer (pH 9.0)

  • Duration: Typically 15-20 minutes in pressure cooker or 30 minutes in water bath at 95-98°C

  • Cool gradually to room temperature before proceeding

• Blocking and antibody parameters:

  • Endogenous peroxidase blocking: 3% hydrogen peroxide for 10 minutes

  • Protein blocking: 5-10% normal serum (species matched to secondary antibody)

  • Primary antibody concentration: Titrate between 1-10 μg/mL

  • Incubation time: Test both short (1-2 hours at room temperature) and overnight (4°C) incubations

  • Detection system: HRP-polymer based for increased sensitivity

• Validation controls:

  • Positive control: Colon cancer tissue (81% express DDR1 at high levels)

  • Negative control: Normal tissue with minimal DDR1 expression

  • Technical negative: Primary antibody omission

• Scoring system development:

  • Establish a 0-3 intensity scale (0=negative, 1=weak, 2=moderate, 3=strong)

  • Calculate H-score (0-300) by multiplying intensity (0-3) by percentage of positive cells

  • Define thresholds for "high" vs "low" expression based on clinical correlation

• Automation considerations:

  • Program standardized protocols on automated platforms

  • Include controls in each run

  • Implement regular quality control assessments

For multiplexing applications, consider fluorescent-based approaches to co-localize DDR1 with other markers of interest, which may provide additional insights into the tumor microenvironment and DDR1's relationship with other signaling pathways.

What factors influence the efficacy of DDR1-targeted antibody-drug conjugates and how can researchers optimize them?

The efficacy of DDR1-targeted ADCs depends on multiple optimization variables:

• Target expression levels:

  • Higher DDR1 expression correlates with better in vivo efficacy

  • Recommended approach: Quantify surface DDR1 levels via flow cytometry before treatment

  • Optimization strategy: Select tumor models with varying DDR1 expression to establish threshold levels needed for response

• Internalization kinetics:

  • DDR1's "swift endocytosis characteristics" make it an ideal ADC target

  • Assessment method: Use fluorescently labeled antibodies (e.g., T 4H 11-Cy5.5) to track internalization rates via confocal microscopy

  • Optimization strategy: Select antibody clones with rapid and efficient internalization

• Linker-payload selection:

  • The SPDB-DM4 linker system used with T 4H 11 proved effective

  • Key considerations:

    • Linker stability in circulation

    • Appropriate payload release mechanism inside target cells

    • Potency of payload against target tumor type

  • Optimization approach: Compare multiple linker-payload combinations (disulfide, peptide, non-cleavable)

• Drug-antibody ratio (DAR):

  • Higher DAR increases potency but may affect pharmacokinetics

  • Analytical method: Confirm DAR via LC-MS

  • Optimization range: Test DAR ranges from 2-8 to identify optimal balance

• Dose and schedule optimization:

  • T 4H 11-DM4 achieved complete tumor regression at doses of 5 and 10 mg·kg⁻¹ in colon cancer models

  • Approach: Test multiple dose levels and schedules (single vs. multiple dosing)

  • Safety assessment: Monitor for toxicities at higher doses (T 4H 11-DM4 exhibited no overt toxicities up to 50 mg·kg⁻¹ in BALB/c mice)

• Resistance mechanisms:

  • While effective in oxaliplatin-resistant models, other resistance mechanisms may emerge

  • Investigation approach: Develop resistant cell lines through chronic exposure

  • Countermeasures: Test combination approaches to overcome resistance

When optimizing DDR1-targeted ADCs, researchers should consider the differential expression of DDR1 isoforms, as these may affect antibody binding, internalization rates, and therapeutic efficacy.

How can researchers address variability in DDR1 antibody performance across different experimental systems?

Addressing variability in DDR1 antibody performance requires systematic troubleshooting:

• Antibody storage and handling:

  • Store according to manufacturer recommendations (typically at -20°C or -80°C)

  • Avoid repeated freeze-thaw cycles (aliquot upon receipt)

  • Maintain cold chain during experiments

  • Validate activity after prolonged storage

• Epitope considerations:

  • DDR1 has five isoforms with variations in structure, though the extracellular domains are highly conserved

  • Solution: Use antibodies targeting conserved regions of the extracellular domain (amino acids 21-417)

  • Verification: Confirm which isoforms are expressed in your experimental system via PCR or western blotting

• Post-translational modifications:

  • DDR1 is a glycoprotein with variable glycosylation patterns

  • Approach: For western blotting, consider deglycosylation treatments

  • Control: Include recombinant DDR1 protein as standard

• Sample preparation impact:

  • Fixation methods may affect epitope accessibility

  • Cell isolation techniques may cleave surface receptors

  • Recommendation: Compare multiple fixation protocols and gentle cell isolation methods

  • For flow cytometry: Use EDTA (2 mmol·L⁻¹) rather than enzymatic methods for cell detachment

• Expression level detection limits:

  • Low expression may require signal amplification

  • Solutions:

    • For IHC: Use polymer-based detection systems

    • For flow cytometry: Consider indirect staining or higher antibody concentrations

    • For western blotting: Increase protein loading or use enhanced chemiluminescence

• Validation across platforms:

  • Create a validation panel of cell lines with known DDR1 expression levels

  • Compare antibody performance across techniques (IHC, flow cytometry, western blotting)

  • Establish standardized protocols for each application

• Batch-to-batch variability:

  • Request certificate of analysis with each lot

  • Test new lots alongside previous lots

  • Consider recombinant antibodies for improved consistency

By systematically addressing these variables, researchers can establish reliable protocols that yield consistent results across different experimental systems and applications.

What considerations are important when developing in vivo imaging applications using DDR1 antibodies?

Developing effective in vivo imaging applications with DDR1 antibodies requires attention to several technical and biological factors:

• Conjugation chemistry:

  • Directly label DDR1 antibodies with appropriate imaging agents:

    • Near-infrared fluorophores (e.g., Cy5.5) for fluorescence imaging

    • Radioisotopes (e.g., 89Zr, 124I) for PET imaging

    • MRI contrast agents (e.g., gadolinium chelates)

  • Methodology: Use amino-based bioconjugation methods similar to those described for T 4H 11-Cy5.5

  • Validation: Confirm that labeling doesn't impair antibody binding or specificity

• Biodistribution optimization:

  • Antibody fragments may provide better tumor penetration and faster clearance

  • Options to consider:

    • Full IgG (longest half-life, slower tumor penetration)

    • F(ab')2 fragments (intermediate properties)

    • Fab fragments (rapid penetration, faster clearance)

  • Assessment approach: Compare biodistribution profiles of different formats at various time points

• Target validation considerations:

  • DDR1 expression in normal tissues (epithelial tissues, monocytes, neutrophils) may cause background signal

  • Control experiments:

    • Include isotype-matched control antibodies (e.g., IgG-Cy5.5)

    • Use DDR1-knockout or knockdown models as negative controls

    • Block with excess unlabeled antibody to demonstrate specificity

• Imaging parameters:

  • Timing: Determine optimal imaging window based on pharmacokinetics

    • Initial dynamic imaging to capture perfusion phase

    • Delayed imaging (24-72 hours post-injection) for optimal tumor-to-background ratio

  • Quantification methods:

    • Region of interest (ROI) analysis

    • Standardized uptake values (SUVs) for PET imaging

    • Tumor-to-background ratios

• Correlation with treatment response:

  • Serial imaging to monitor changes in DDR1 expression during treatment

  • Correlation with ex vivo analysis (IHC, flow cytometry) to validate imaging findings

  • Integration with other imaging modalities (e.g., FDG-PET, MRI) for comprehensive assessment

• Technical challenges and solutions:

  • Autofluorescence: Use near-infrared fluorophores (>700 nm) and spectral unmixing

  • Tissue attenuation: Consider photoacoustic imaging for deeper tissue penetration

  • Signal-to-noise ratio: Optimize antibody dose and imaging time point

When developing these applications, researchers should consider the correlation between DDR1 expression levels and therapeutic response to DDR1-targeted therapies, as this could provide valuable imaging biomarkers for patient selection and treatment monitoring.