CRTAC1 Antibody

What is CRTAC1 and what cellular functions have been identified?

CRTAC1 (cartilage acidic protein 1) is a protein encoded by the CRTAC1 gene with a calculated molecular weight of 71 kDa (661 amino acids) and observed molecular weight of 68-72 kDa in Western blot analyses . Recent research has identified CRTAC1 as a pyroptosis-related gene that functions as a protective factor in several cancer types including gastric adenocarcinoma and bladder cancer .

In bladder cancer, CRTAC1 has been shown to inhibit cell viability, proliferation, migration, invasion, and the epithelial-mesenchymal transition (EMT) process . CRTAC1 also plays a significant role in non-small cell lung cancer (NSCLC) where it enhances chemosensitivity to cisplatin treatment by promoting calcium-dependent Akt1 degradation and subsequent apoptosis .

What are the standard applications for CRTAC1 antibodies in research?

CRTAC1 antibodies, such as the 13001-1-AP from Proteintech, are validated for multiple research applications:

Positive WB detection has been confirmed in human brain tissue, while positive IHC detection has been observed in mouse lung tissue, mouse brain tissue, and rat lung tissue . For optimal results, it is recommended to titrate the antibody concentration for each specific testing system and sample type .

How is CRTAC1 expression altered in pathological states?

CRTAC1 expression shows significant alterations in several pathological conditions:

In cancer:

• CRTAC1 mRNA expression is significantly downregulated in lung adenocarcinoma (LUAD) tissues compared to normal lung tissue (p < 0.05)

• Lower CRTAC1 expression is associated with poor prognosis in LUAD patients

• Similarly, CRTAC1 is downregulated in bladder cancer tissues and cell lines

• Both mRNA and protein levels of CRTAC1 are reduced in tumor tissue compared to adjacent normal tissue in LUAD

In degenerative conditions:

• CRTAC1+ cell populations show altered expression patterns in degenerative spinal ligaments

• A higher number of CRTAC1+ cells express CD44 in degenerative ligaments compared to normal tissues

These expression patterns suggest that CRTAC1 may function as a tumor suppressor in certain cancers, with its downregulation contributing to pathogenesis and poor outcomes.

How should researchers design experiments to investigate CRTAC1's role in chemosensitivity?

Based on established methodologies in NSCLC research , a comprehensive experimental design should include:

In vitro studies:

• Generate stable cell lines:

  • Overexpress CRTAC1 in cancer cell lines with high chemotherapeutic IC50 values

  • Create vector control cell lines for comparison

  • Validate CRTAC1 expression by Western blotting

• Perform knockdown experiments:

  • Use siRNAs targeting CRTAC1 in cell lines with naturally lower IC50 values

  • Verify knockdown efficiency by Western blotting

  • Include appropriate scramble controls

• Assess chemosensitivity:

  • Conduct ATP assays to determine IC50 values for chemotherapeutic agents

  • Perform dose-response curves across multiple cell lines

  • Measure apoptosis using Annexin V/7-AAD flow cytometry

  • Quantify cell death markers (cleaved caspase-3, PARP) by Western blotting

In vivo studies:

• Establish xenograft models:

  • Inject CRTAC1-overexpressing cells and vector control cells into immunodeficient mice

  • Allow tumors to form (approximately 7 days)

  • Randomize mice into treatment and control groups

• Administer treatment:

  • Treat with chemotherapeutic agent (e.g., cisplatin 3 mg/kg every 3 days)

  • Treat control groups with vehicle solution (e.g., PBS)

• Monitor outcomes:

  • Measure tumor volume throughout the study period

  • Weigh tumors at study endpoint

  • Perform immunohistochemical staining for proliferation (Ki67) and apoptosis (cleaved caspase-3) markers

• Analyze molecular mechanisms:

  • Examine downstream signaling pathways (e.g., Ca²⁺-NFAT-STUB1-Akt1 pathway)

  • Verify protein expression and activation states in tumor samples

What methodological approaches are most effective for studying CRTAC1's interaction with signaling pathways?

To investigate CRTAC1's interactions with signaling pathways, researchers should implement the following methodological approaches:

• Protein-protein interaction studies:

  • Co-immunoprecipitation (Co-IP) to identify direct binding partners

  • Proximity ligation assay (PLA) for visualizing protein interactions in situ

  • Immunofluorescence co-localization studies (as used for CRTAC1 and YY1 in bladder cancer research)

• Transcriptional regulation analysis:

  • Chromatin immunoprecipitation (ChIP) to identify DNA binding interactions

  • Luciferase reporter assays to assess effects on transcriptional activity

  • qRT-PCR to measure changes in target gene expression

• Pathway activity assessment:

  • Western blotting for pathway component activation (phosphorylation states)

  • Calcium imaging to monitor intracellular Ca²⁺ levels when studying CRTAC1's role in calcium signaling

  • Inhibitor/activator studies to validate pathway connections

• Mechanistic validation:

  • Protein degradation analysis using cycloheximide (CHX) chase assays (as performed for Akt1 degradation studies)

  • Proteasome inhibition experiments using MG132 to confirm ubiquitin-proteasome involvement

  • Rescue experiments by simultaneously modulating CRTAC1 and pathway components

For example, researchers studying CRTAC1 in NSCLC demonstrated that it promotes NFAT transcriptional activation by increasing intracellular Ca²⁺, inducing STUB1 expression, which in turn accelerates Akt1 protein degradation and enhances cisplatin-induced apoptosis . In bladder cancer, researchers showed that CRTAC1 inactivates the TGF-β pathway by downregulating YY1 expression .

How can single-cell approaches be leveraged to study CRTAC1+ cell populations in complex tissues?

Single-cell approaches offer powerful tools for investigating CRTAC1+ cell populations in complex tissues:

• Single-cell RNA sequencing (scRNA-seq):

  • Identify and characterize CRTAC1+ cell subpopulations

  • Reveal heterogeneity within CRTAC1-expressing cells

  • Discover co-expression patterns with other markers (e.g., CD44 in degenerative ligaments)

  • Map differentiation trajectories and lineage relationships

• Spatial transcriptomics:

  • Maintain tissue context while obtaining single-cell resolution data

  • Map spatial distribution of CRTAC1+ cells within tissue microenvironments

  • Correlate CRTAC1 expression with anatomical features or pathological changes

  • Identify spatial relationships with other cell types

• CyTOF (mass cytometry):

  • Simultaneously analyze >40 protein markers at single-cell resolution

  • Create high-dimensional phenotypic profiles of CRTAC1+ cells

  • Identify rare CRTAC1+ subpopulations in heterogeneous samples

  • Correlate CRTAC1 with activation/functional markers

• Single-cell functional assays:

  • Isolate CRTAC1+ cells by FACS for downstream functional analyses

  • Perform single-cell cloning to assess functional heterogeneity

  • Conduct single-cell secretome analysis to identify paracrine factors

• Integrated analysis approaches:

  • Combine multiple single-cell modalities for comprehensive characterization

  • Integrate with bulk -omics data for validation

  • Apply computational methods to infer cell-cell communication networks

  • Use trajectory inference to understand developmental relationships

These approaches would be particularly valuable for understanding the role of CRTAC1+ cells in conditions like spinal ligament degeneration, where single-cell analysis has already revealed intriguing patterns of co-expression with markers like CD44 .

How should researchers interpret contradictory CRTAC1 expression data across different cancer types?

When confronted with seemingly contradictory CRTAC1 expression data across different cancer types, researchers should consider several analytical frameworks:

• Tissue-specific baseline expression:

  • Normal CRTAC1 expression varies across tissue types (detected in brain and lung by WB/IHC)

  • Compare cancer samples against tissue-matched normal controls

  • Use tissue-specific thresholds for defining "high" versus "low" expression

• Cancer subtype heterogeneity:

  • Different molecular subtypes within a cancer type may show distinct CRTAC1 expression patterns

  • Stratify samples by established molecular classifications

  • Consider histological subtypes as potential sources of variation

• Methodological considerations:

  • Compare detection methods (RNA-seq, qPCR, Western blot, IHC) used across studies

  • Account for antibody differences (epitope recognition, sensitivity)

  • Consider sample preparation variations (fixation methods, processing protocols)

• Biological context:

  • Evaluate CRTAC1 in relation to specific signaling pathways active in each cancer type

  • Consider interaction partners (e.g., YY1 in bladder cancer ) that may differ between tissues

  • Assess cell-type specific expression patterns within tumors

• Disease stage and progression:

  • Temporal dynamics may differ across cancer types

  • Early vs. late-stage expression patterns may reveal context-dependent roles

  • Primary vs. metastatic lesions may show different expression profiles

For example, while CRTAC1 is consistently downregulated in both lung adenocarcinoma and bladder cancer , the downstream mechanisms and pathways affected may differ (Ca²⁺-NFAT-STUB1-Akt1 in NSCLC vs. YY1-TGF-β in bladder cancer ), explaining potential functional differences despite similar expression trends.

What statistical approaches should be used when analyzing CRTAC1 as a biomarker for cancer prognosis?

When analyzing CRTAC1 as a biomarker for cancer prognosis, researchers should employ these statistical approaches:

• Survival analysis:

  • Kaplan-Meier curves stratified by CRTAC1 expression levels

  • Log-rank tests to assess statistical significance of survival differences

  • Cox proportional hazards regression for multivariable analysis

  • Calculation of hazard ratios with confidence intervals

• Expression threshold determination:

  • ROC curve analysis to identify optimal cutoff values

  • X-tile analysis for unbiased cut-point selection

  • Consideration of quartiles or median split for categorical classification

  • Sensitivity analyses using multiple thresholds to ensure robustness

• Multivariate modeling:

  • Include established clinicopathological parameters (stage, grade, age, etc.)

  • Test for independence of CRTAC1's prognostic value

  • Develop nomograms or other visual prediction tools

  • Calculate concordance indices (C-index) to assess predictive accuracy

• Subgroup analyses:

  • Stratification by treatment regimen (particularly for chemotherapy response)

  • Analysis within specific molecular subtypes

  • Stage-specific prognostic value assessment

  • Evaluation in primary versus metastatic disease

• Validation approaches:

  • Internal validation using bootstrapping or cross-validation

  • External validation in independent cohorts

  • Comparison with established prognostic biomarkers

  • Meta-analysis across multiple datasets when available

• Integration with other biomarkers:

  • Development of combined prognostic indices

  • Assessment of complementarity with other markers

  • Network-based approaches incorporating pathway information

  • Machine learning methods for complex pattern recognition

For example, in lung adenocarcinoma research, reduced CRTAC1 expression has been associated with poor prognosis , suggesting its utility as a positive prognostic factor. Statistical robustness of such findings should be ensured through multivariable analysis controlling for established prognostic factors.

How can proteomics data be integrated with transcriptomic findings to better understand CRTAC1 regulation?

Integration of proteomics with transcriptomics provides a comprehensive understanding of CRTAC1 regulation:

• Correlation analysis:

  • Compare CRTAC1 mRNA and protein expression levels across samples

  • Calculate Pearson or Spearman correlation coefficients

  • Identify discordant cases suggesting post-transcriptional regulation

  • Analyze temporal dynamics if time-series data are available

• Post-translational modification (PTM) characterization:

  • Use mass spectrometry to identify PTMs on CRTAC1 protein

  • Map modifications to functional domains

  • Correlate PTM patterns with activity or stability

  • Develop targeted assays for key regulatory modifications

• Protein-protein interaction network analysis:

  • Perform immunoprecipitation-mass spectrometry (IP-MS) to identify CRTAC1 interactors

  • Construct interaction networks integrating proteomic and transcriptomic data

  • Identify hub proteins that may regulate CRTAC1

  • Validate key interactions through orthogonal methods

• Proteogenomic integration methods:

  • Apply computational frameworks specifically designed for multi-omics integration

  • Use dimensionality reduction techniques to identify patterns across data types

  • Employ network-based approaches to identify regulatory modules

  • Implement machine learning algorithms to predict functional relationships

• Pathway enrichment analysis:

  • Conduct integrative pathway analysis using both protein and transcript data

  • Identify pathways consistently altered at both levels

  • Discover potential regulatory mechanisms within pathway context

  • Focus on signaling pathways previously linked to CRTAC1 (e.g., Ca²⁺ signaling , TGF-β )

• Protein degradation assessment:

  • Study protein half-life using pulse-chase experiments

  • Investigate ubiquitination patterns of CRTAC1

  • Connect to observed mechanisms like CRTAC1's role in Akt1 degradation

  • Identify E3 ligases potentially regulating CRTAC1 stability

This integrative approach is particularly relevant for CRTAC1, as research has demonstrated important post-transcriptional regulatory mechanisms, such as the protein degradation pathway involving STUB1-mediated Akt1 degradation in NSCLC .

What are the critical factors for successful Western blotting of CRTAC1?

Successful Western blotting of CRTAC1 requires attention to several critical factors:

• Sample preparation:

  • Use appropriate lysis buffers containing protease inhibitors

  • Human brain tissue serves as a reliable positive control

  • Ensure adequate protein loading (typically 20-50 μg total protein)

  • Optimize extraction conditions for your specific tissue/cell type

• Antibody selection and optimization:

  • Use validated antibodies (e.g., 13001-1-AP) with confirmed specificity

  • Titrate antibody concentrations (recommended dilution 1:500-1:1000)

  • Optimize primary antibody incubation (typically overnight at 4°C)

  • Select appropriate secondary antibody (anti-rabbit IgG for 13001-1-AP)

• Detection parameters:

  • Look for bands at 68-72 kDa (observed molecular weight of CRTAC1)

  • Ensure adequate exposure time without saturation

  • Consider enhanced chemiluminescence for low-abundance detection

  • Use appropriate molecular weight markers spanning the 60-80 kDa range

• Common issues and solutions:

  • For weak signal: Increase protein loading, reduce antibody dilution, extend exposure time

  • For high background: Increase blocking time, use more stringent washing, reduce secondary antibody concentration

  • For multiple bands: Validate with positive controls, consider negative controls (knockdown samples)

  • For inconsistent results: Standardize protein extraction and quantification methods

• Protocol optimization:

  • Follow manufacturer's specific WB protocol for CRTAC1 antibody

  • Consider gradient gels for optimal resolution around the target molecular weight

  • Adjust transfer conditions based on protein size (wet transfer often preferred for >50 kDa proteins)

  • Optimize blocking conditions (typically 5% non-fat milk or BSA in TBST)

Researchers studying CRTAC1 in clinical samples should be particularly attentive to sample preservation methods, as protein degradation can significantly impact detection of this biomarker.

What are the key considerations for optimizing immunohistochemistry protocols for CRTAC1 detection?

Optimizing immunohistochemistry (IHC) protocols for CRTAC1 detection requires attention to these key considerations:

• Tissue fixation and processing:

  • Standardize fixation time (typically 24-48 hours in 10% neutral buffered formalin)

  • Use consistent processing protocols to ensure reproducibility

  • Consider tissue-specific requirements (lung and brain tissues show reliable CRTAC1 detection)

  • Optimize section thickness (typically 4-5 μm for FFPE tissues)

• Antigen retrieval optimization:

  • Recommended method: TE buffer at pH 9.0

  • Alternative method: Citrate buffer at pH 6.0

  • Compare heat-induced epitope retrieval methods (microwave, pressure cooker, water bath)

  • Optimize retrieval time based on tissue type and fixation parameters

• Antibody dilution and incubation:

  • Test a range of dilutions within recommended range (1:50-1:500 for IHC)

  • Optimize primary antibody incubation time and temperature

  • Consider signal amplification systems for low-abundance detection

  • Use automated staining platforms if available for consistency

• Detection system selection:

  • Choose between chromogenic (DAB, AEC) and fluorescent detection based on research needs

  • For multiplex IHC, ensure compatibility with other antibodies

  • Select detection systems with appropriate sensitivity for expression level

  • Consider tyramide signal amplification for weak signals

• Controls and validation:

  • Include positive tissue controls (mouse lung tissue, mouse brain tissue, rat lung tissue)

  • Use negative controls (primary antibody omission, isotype controls)

  • Consider dual staining to confirm cellular localization

  • Validate with orthogonal methods (WB, IF) for critical findings

• Quantification approaches:

  • Define clear scoring methods (H-score, percentage positive cells, intensity scale)

  • Consider digital image analysis for objective quantification

  • Use multiple independent observers for manual scoring

  • Document representative images of scoring categories

Following these optimization steps will enable reliable detection of CRTAC1 in tissue specimens, facilitating its use as a diagnostic or prognostic biomarker in cancer research.

How should researchers approach the validation of CRTAC1 knockdown or overexpression models?

Comprehensive validation of CRTAC1 knockdown or overexpression models requires multi-level verification:

• Genetic/transcript level validation:

  • Quantify CRTAC1 mRNA levels using qRT-PCR with validated primers

  • Design primers spanning exon-exon junctions to avoid genomic DNA amplification

  • Use multiple reference genes for normalization

  • For knockdown models, measure efficiency relative to control (scramble siRNA)

• Protein level confirmation:

  • Perform Western blotting to verify changes in CRTAC1 protein levels

  • Use validated antibodies with appropriate controls

  • Quantify band intensity using densitometry relative to loading controls

  • Consider immunofluorescence or IHC to assess spatial distribution changes

• Functional validation:

  • Verify phenotypic changes consistent with CRTAC1's known functions

  • For cancer models, assess proliferation, migration, invasion, and apoptosis

  • Measure changes in known downstream pathways (e.g., Ca²⁺-NFAT-STUB1-Akt1 in NSCLC )

  • Assess chemosensitivity changes using appropriate drug response assays

• Specificity controls:

  • Include rescue experiments (re-express CRTAC1 in knockdown models)

  • Use multiple siRNA/shRNA sequences targeting different regions

  • For CRISPR-based knockout, sequence verify the edited region

  • Test for potential off-target effects on related genes

• Stability assessment:

  • Verify persistence of knockdown/overexpression over experimental timeframe

  • For stable cell lines, test expression after multiple passages

  • For inducible systems, characterize kinetics of induction/reversal

  • Consider clonal variation in stable cell lines

• Documentation standards:

  • Maintain detailed records of construct sequences, cell line origins, and passage numbers

  • Record complete transfection/transduction protocols

  • Archive original validation data (unprocessed blot images, qPCR raw data)

  • Validate phenotypes in multiple cell lines when possible

For example, in NSCLC research, CRTAC1 overexpression was validated by Western blotting and functionally confirmed by demonstrating increased chemosensitivity to cisplatin in multiple cell lines (H1299, HCC827, and H226) . Similarly, knockdown efficiency was verified before conducting functional assays in A549 and H1975 cell lines .

How can CRTAC1 be leveraged as a predictive biomarker for chemotherapy response?

CRTAC1 shows significant potential as a predictive biomarker for chemotherapy response, particularly for platinum-based treatments:

• Clinical implementation strategies:

  • Develop standardized assays for CRTAC1 detection in tumor samples

  • Establish validated cutoff values for "high" versus "low" expression

  • Create predictive algorithms incorporating CRTAC1 with other biomarkers

  • Design prospective clinical trials to validate predictive accuracy

• Mechanistic basis for predictive value:

  • CRTAC1 overexpression increases sensitivity to cisplatin in NSCLC through:

    • Promotion of NFAT transcriptional activation via increased intracellular Ca²⁺

    • Induction of STUB1 expression

    • Acceleration of Akt1 protein degradation

    • Enhancement of cisplatin-induced apoptosis

  • These mechanisms provide a biological rationale for CRTAC1's predictive potential

• Multi-drug response prediction:

  • Higher CRTAC1 expression correlates with increased drug sensitivity beyond platinum agents

  • Investigate predictive value for other chemotherapeutics with distinct mechanisms

  • Develop drug-specific prediction models incorporating CRTAC1

  • Consider combination therapy prediction models

• Integration with companion diagnostics:

  • Explore CRTAC1 testing as a companion diagnostic for specific therapies

  • Develop point-of-care testing methods for rapid assessment

  • Integrate with existing biomarker panels for enhanced prediction

  • Correlate with imaging-based response assessment methods

• Resistance mechanism identification:

  • Study CRTAC1 expression changes in acquired resistance settings

  • Identify bypass mechanisms in CRTAC1-high non-responders

  • Develop strategies to overcome resistance in CRTAC1-low patients

  • Investigate combination approaches targeting CRTAC1-related pathways

• Personalized dosing strategies:

  • Use CRTAC1 levels to guide chemotherapy dosing decisions

  • Investigate relationship between CRTAC1 expression and optimal drug scheduling

  • Develop adaptive therapy approaches based on CRTAC1 dynamics

  • Correlate with pharmacokinetic parameters for optimized exposure

The established relationship between CRTAC1 expression and cisplatin sensitivity in NSCLC provides a strong foundation for developing CRTAC1 as a clinically useful predictive biomarker.

What emerging technologies can enhance CRTAC1 detection and functional characterization?

Several emerging technologies offer promising approaches for enhanced CRTAC1 detection and functional characterization:

• Digital spatial profiling:

  • Combines high-plex protein or RNA detection with spatial resolution

  • Maps CRTAC1 expression within tissue context alongside numerous other markers

  • Correlates CRTAC1 with microenvironmental features and cell types

  • Enables region-of-interest selection for focused analysis

• Single-molecule imaging techniques:

  • Super-resolution microscopy (STORM, PALM, STED) for nanoscale localization

  • Single-molecule FISH for detecting low-abundance CRTAC1 transcripts

  • Live-cell single-particle tracking to monitor CRTAC1 dynamics

  • Visualizes CRTAC1 interactions with signaling partners at molecular scale

• CRISPR-based functional genomics:

  • CRISPR activation (CRISPRa) to upregulate endogenous CRTAC1

  • CRISPR interference (CRISPRi) for precise transcriptional repression

  • CRISPR screening to identify synthetic lethal interactions with CRTAC1

  • Base editors or prime editors for introducing specific CRTAC1 variants

• Proximity labeling proteomics:

  • BioID or APEX2 fusion proteins to identify proximal proteins in living cells

  • Maps the CRTAC1 protein interaction network with spatial resolution

  • Identifies context-specific interactors in different cellular compartments

  • Reveals previously unknown functional associations

• Organoid and patient-derived xenograft models:

  • Tests CRTAC1 function in physiologically relevant 3D systems

  • Preserves tissue architecture and cellular heterogeneity

  • Enables long-term studies of CRTAC1 in complex environments

  • Facilitates personalized medicine approaches for CRTAC1-based therapies

• Liquid biopsy approaches:

  • Detects CRTAC1 expression in circulating tumor cells

  • Monitors tumor-derived exosomes for CRTAC1 protein or mRNA

  • Enables longitudinal non-invasive monitoring of CRTAC1 status

  • Correlates with treatment response or disease progression

Application of these technologies would significantly advance our understanding of CRTAC1's role in cancer biology and other pathological conditions, potentially accelerating its clinical translation as both a biomarker and therapeutic target.

How might CRTAC1-targeting therapeutic strategies be developed for cancer treatment?

Development of CRTAC1-targeting therapeutic strategies for cancer treatment could proceed along several promising avenues:

• Expression restoration approaches:

  • Since CRTAC1 is downregulated in multiple cancers , restoring expression may have therapeutic benefits

  • Delivery methods could include:

    • Non-viral gene therapy using lipid nanoparticles

    • Viral vectors (AAV, lentivirus) for CRTAC1 gene delivery

    • mRNA therapeutics for transient expression

    • Small molecules that upregulate endogenous CRTAC1 expression

• Pathway modulation strategies:

  • Target the signaling pathways regulated by CRTAC1:

    • Ca²⁺ signaling modulators to mimic CRTAC1's effect on NFAT activation

    • STUB1 inducers to promote Akt1 degradation

    • YY1 inhibitors to suppress TGF-β pathway activation in bladder cancer

    • Combination approaches targeting multiple nodes in CRTAC1-regulated pathways

• Combinatorial approaches with chemotherapy:

  • Given CRTAC1's role in chemosensitivity , develop rational combinations:

    • CRTAC1 inducers plus cisplatin for enhanced efficacy

    • Sequential therapy designs (pathway priming followed by cytotoxic agents)

    • Dosage optimization based on CRTAC1 expression levels

    • Biomarker-guided patient selection for combination approaches

• Immunotherapy integration:

  • Leverage CRTAC1's association with immune cell infiltration :

    • Evaluate combination with immune checkpoint inhibitors

    • Explore CRTAC1's influence on tumor microenvironment

    • Develop cell therapies with engineered CRTAC1 expression

    • Target CRTAC1+ cells with immune-recruiting bispecific antibodies

• Drug delivery innovations:

  • Develop targeted delivery systems for CRTAC1-based therapies:

    • Antibody-drug conjugates targeting cancer-specific markers

    • Tumor-homing peptides for selective delivery

    • Stimuli-responsive nanocarriers for tumor-specific release

    • Local delivery systems for accessible tumors

• Companion diagnostics development:

  • Create paired diagnostic/therapeutic strategies:

    • CRTAC1 expression assays to guide therapy selection

    • Pharmacodynamic biomarkers to monitor target engagement

    • Resistance mechanism profiling for adaptive treatment

    • Early response indicators for timely intervention adjustment

While these therapeutic strategies are largely conceptual at present, they are grounded in the emerging understanding of CRTAC1's biology in cancer. Particularly promising is the prospect of enhancing chemosensitivity in NSCLC through CRTAC1-targeted approaches, building on established mechanistic insights linking CRTAC1 to cisplatin response .