PIM1 Human

What is the basic structure of human PIM1 kinase?

Human PIM1 is a serine/threonine kinase belonging to the calcium/calmodulin-regulated kinases (CAMK) group. The protein contains a characteristic kinase domain, an ATP anchor, and an active site. In humans, PIM1 exists in two isoforms generated from the same gene through alternative translation initiation: a 44 kD form and a shorter 34 kD form, both containing the functional kinase domain . The production of these isoforms occurs through the use of an alternative upstream CUG initiation codon, with both proteins retaining the catalytic capability to phosphorylate target substrates .

How is PIM1 expression regulated in normal human tissues?

PIM1 expression in normal human tissues follows a widespread but controlled pattern. Expression ranges across the hematopoietic and lymphoid systems, prostate, testis, and oral epithelial cells . The regulation occurs primarily at the transcriptional level, with various cytokines inducing its expression in immune-related contexts. This cytokine-mediated induction has led to PIM1 being characterized as a "booster" for immune responses . At the molecular level, PIM1 expression can be rapidly induced by several external stimuli, particularly cytokines relevant to immune function, suggesting a role in amplifying cellular responses to environmental changes.

What are the primary signaling pathways that involve PIM1?

PIM1 participates in multiple signaling pathways crucial for cellular homeostasis and response to stimuli:

• Cell cycle regulation: PIM1 phosphorylates targets involved in cell cycle progression .

• Apoptotic pathways: PIM1 influences cellular survival by phosphorylating proteins in apoptotic cascades .

• Immune response signaling: PIM1 promotes IFN-β production by enhancing the formation of signaling complexes composed of TRIF, TRAF3, TBK1, and IRF3 .

• AMPK signaling: During glucose deprivation, PIM1 expression is upregulated through AMP-activated protein kinase signaling in colorectal cancer cells .

• Metabolic regulation: PIM1 promotes the Warburg effect in cancer cells, showing positive correlation with hexokinase 2 and lactate dehydrogenase A expression .

What evidence supports PIM1's classification as an oncogene?

PIM1 qualifies as a true oncogene based on substantial experimental and clinical evidence:

• Transformation capability: PIM1 has been demonstrated to promote early transformation of normal cells into malignant phenotypes .

• Tumor progression correlation: Elevated PIM1 expression positively correlates with disease progression in multiple cancer types .

• Clinical outcome association: PIM1 overexpression is associated with aggressive disease subtypes and poor prognosis, particularly in prostate carcinomas and hematopoietic malignancies .

• Functional studies: Knockdown experiments show that PIM1 silencing reduces tumor growth and metastatic potential in experimental models .

• Proliferation promotion: PIM1 enhances cancer cell proliferation in vitro and tumorigenicity in vivo .

These multifaceted lines of evidence establish PIM1 as an oncogene capable of driving malignant transformation and tumor progression across different cancer types.

In which cancer types is PIM1 most frequently implicated?

PIM1 has been particularly implicated in several specific cancer types:

Additional cancer types with 6p21 genomic alterations may also involve PIM1 dysregulation, suggesting its broader oncogenic relevance across multiple malignancies .

How does PIM1 contribute to drug resistance mechanisms in cancer?

PIM1 contributes to drug resistance through several molecular mechanisms:

• EMT promotion: In NSCLC, upregulation of PIM1 promotes epithelial-mesenchymal transition (EMT), a process associated with osimertinib resistance. This occurs through PIM1's suppression of the ubiquitin-proteasome degradation of SNAIL and SLUG by deactivating GSK3β through phosphorylation .

• Metabolic adaptation: PIM1 helps cancer cells adapt to glucose deprivation by promoting the Warburg effect. This metabolic reprogramming provides a survival advantage under nutrient-limited conditions often encountered during cancer therapy .

• Cell survival pathway activation: PIM1 phosphorylates various targets involved in cell survival pathways, potentially counteracting the pro-apoptotic effects of anticancer drugs .

• Hormone insensitivity: In prostate cancer, PIM1 has been implicated in hormone insensitivity, potentially contributing to resistance against hormone-based therapies .

These mechanisms collectively position PIM1 as an important mediator of therapeutic resistance across different cancer types and treatment modalities.

What diagnostic or prognostic value does PIM1 expression offer in clinical oncology?

PIM1 expression demonstrates significant diagnostic and prognostic value in several cancer contexts:

• Prognostic marker: In prostate carcinomas, elevated PIM1 expression serves as a marker of poor prognosis, helping identify high-risk patients who might benefit from more aggressive treatment approaches .

• Aggressive disease identification: PIM1 overexpression is associated with aggressive subgroups of lymphoma, aiding in disease stratification .

• Metabolic activity correlation: In colorectal cancer, PIM1 expression positively correlates with 18F-fluorodeoxyglucose uptake, potentially serving as a metabolic biomarker .

• Treatment resistance prediction: In NSCLC, PIM1 upregulation correlates with EMT-associated osimertinib resistance, potentially helping identify patients less likely to respond to this targeted therapy .

Clinical observations from a study involving 296 colorectal cancer patients showed significant associations between PIM1 expression levels and tumor size (p<0.01), with larger tumors (>4 cm) demonstrating higher PIM1 expression . This suggests potential utility as a biomarker for disease aggressiveness and progression monitoring.

What are the most reliable techniques for measuring PIM1 expression in human tissues?

Several complementary techniques provide reliable assessment of PIM1 expression in human tissues:

• Immunohistochemistry (IHC): This technique allows visualization and semi-quantitative assessment of PIM1 protein expression in tissue sections. For optimal results, researchers should evaluate both cytoplasmic and nuclear staining, using a combined scoring system based on staining intensity (0-3) and extent (0-3) for a total score of 0-6. Cases can be categorized as negative (0-2), weakly positive (3-4), or strongly positive (5-6) .

• Quantitative PCR (qPCR): For reliable mRNA quantification, the 2^-ΔΔCt comparative method using primers such as 5′-CCCGACAGTTTCGTCCTGAT-3′ (forward) and 5′-ACCCGAAGTCGATGAGCTTG-3′ (reverse) for PIM1, with appropriate housekeeping genes like actin for normalization .

• Western blotting: For protein level assessment, using validated antibodies against PIM1 with appropriate positive and negative controls.

• Tissue microarrays (TMA): For high-throughput analysis of multiple patient samples, TMAs combined with IHC provide efficient screening of PIM1 expression across large cohorts .

Each method offers distinct advantages, with IHC providing spatial information about expression patterns, qPCR offering quantitative sensitivity, and western blotting confirming protein size specificity. Combined approaches yield the most comprehensive assessment of PIM1 expression.

What models are most appropriate for studying PIM1 function in cancer biology?

Various experimental models offer complementary insights into PIM1 function in cancer:

• Cell line models: Human cancer cell lines with differential PIM1 expression provide systems for in vitro functional studies. Genetic manipulation through overexpression or knockdown/knockout approaches allows direct assessment of PIM1's role in proliferation, survival, and drug response .

• Patient-derived xenografts (PDX): These models maintain the molecular characteristics of the original tumor and are valuable for studying PIM1's role in a more clinically relevant context, particularly for testing PIM1 inhibitors.

• Genetically engineered mouse models: Transgenic mice with tissue-specific PIM1 overexpression or knockout provide insights into PIM1's role in tumor initiation and progression in vivo.

• Metabolic stress models: Since PIM1 is upregulated in response to glucose deprivation, experimental systems that incorporate nutrient restriction can reveal PIM1's role in metabolic adaptation. Studies have shown that PIM1-silenced cells are more vulnerable to glucose starvation, making this a revealing model context .

The choice of model should align with specific research questions, with consideration for the cancer type, microenvironmental factors, and translational relevance.

What methodological approaches are effective for developing PIM1 inhibitors?

Developing effective PIM1 inhibitors involves several methodological approaches:

• Molecular docking studies: Computer-based methods to predict binding interactions between potential inhibitors and the PIM1 kinase domain. Successful approaches include post-processing docking results that consider different binding modes and interactions with key residues such as Gly45, Pro123, and Asp131 .

• Structure-activity relationship (SAR) analysis: Systematic modification of chemical scaffolds to improve binding affinity and selectivity. This approach has identified that interaction with specific residues correlates with inhibitory potency, with compounds interacting with all four key residues (likely Gly45, Pro123, Asp131, and structural water HOH334) showing enhanced activity .

• Binary logistic regression models: Statistical approaches that can predict compound activity based on binding energy and specific residue interactions. Models using a pIC50 threshold of 5 (corresponding to 10 μM) provide reliable prediction of inhibitory potential .

• In vitro kinase assays: Biochemical assays measuring PIM1 kinase activity inhibition, typically reporting results as IC50 values, with active compounds generally showing activity in the micromolar range or better .

The development of PIM1 inhibitors has faced challenges due to the complexity of PIM1's molecular structure , but structured approaches combining computational methods with experimental validation offer promising pathways forward.

How does PIM1 interact with the innate immune response system?

PIM1 plays a previously underappreciated role in innate immune responses, particularly in antiviral defense mechanisms:

• IFN-β signaling enhancement: PIM1 promotes IFN-β production in macrophages after activation of the Toll-like receptor (TLR) pathway by pathogen-associated molecular patterns (PAMPs). This occurs through enhancement of IRF3 phosphorylation and nuclear translocation .

• Signaling complex formation: Rather than acting through its kinase activity, PIM1 appears to function as a scaffold protein, enhancing the formation of IFN-β signaling complexes composed of TRIF, TRAF3, TBK1, and IRF3 .

• NF-κB-dependent upregulation: PIM1 itself is quickly upregulated in an NF-κB-dependent manner after TLR stimulation with PAMPs, suggesting a positive feedback mechanism in the innate immune response .

• Survival impact: In experimental models, PIM1-deficient mice produced less serum IFN-β and showed decreased survival rates compared to wild-type mice when challenged with Poly (I:C), a viral mimetic .

These findings reveal PIM1 as a novel component of the antiviral innate immune response, specifically in TLR-mediated IFN-β production, offering potential new targets for modulating antiviral immunity.

What metabolic reprogramming does PIM1 induce in cancer cells?

PIM1 drives significant metabolic reprogramming in cancer cells, particularly in response to nutrient stress:

• Warburg effect promotion: PIM1 promotes the Warburg effect, a metabolic shift characterized by increased glucose uptake and lactate production even in the presence of oxygen. This provides cancer cells with metabolic flexibility and supports rapid proliferation .

• Glucose deprivation response: PIM1 expression is significantly upregulated in response to glucose deprivation-induced metabolic stress through AMPK signaling. This upregulation represents an adaptive mechanism enabling cancer cell survival under nutrient-limited conditions .

• Glycolytic enzyme regulation: PIM1 expression positively correlates with hexokinase 2 (HK2) and lactate dehydrogenase A (LDHA) expression, key enzymes in the glycolytic pathway. This relationship suggests PIM1 may regulate these enzymes to enhance glycolytic flux .

• FDG uptake correlation: Clinical observations show that PIM1 expression in colorectal cancer positively correlates with 18F-fluorodeoxyglucose uptake, confirming its role in promoting glucose metabolism in vivo .

• Metabolic vulnerability: PIM1-silenced cancer cells demonstrate increased vulnerability to glucose starvation, and the growth advantage conferred by PIM1 is attenuated when the Warburg effect is blocked, highlighting the functional importance of this metabolic reprogramming .

This metabolic role positions PIM1 as a potential target for therapies aimed at disrupting cancer-specific metabolism, particularly in combination with agents that limit nutrient availability.

What is the relationship between PIM1 and epithelial-mesenchymal transition (EMT) in cancer progression?

PIM1 plays a critical role in promoting EMT, a process central to cancer progression and therapeutic resistance:

• EMT marker correlation: Upregulation of PIM1 is significantly correlated with expression of EMT molecules, suggesting a regulatory relationship .

• GSK3β-mediated mechanism: PIM1 suppresses the ubiquitin-proteasome degradation of key EMT transcription factors SNAIL and SLUG by deactivating GSK3β through phosphorylation. This deactivation of GSK3β represents a critical molecular switch controlling EMT progression .

• EMT transcription factor stabilization: The stability and accumulation of SNAIL and SLUG facilitated by PIM1 directly promotes EMT, enhancing invasive capabilities and drug resistance .

• Osimertinib resistance: In non-small cell lung cancer, PIM1-driven EMT contributes to resistance against osimertinib, a third-generation EGFR tyrosine kinase inhibitor. Treatment with PIM1 inhibitors prevents EMT progression and re-sensitizes resistant cancer cells to osimertinib .

• Clinical correlation: PIM1/GSK3β signaling activation has been observed in clinical samples of osimertinib-resistant NSCLC, confirming the translational relevance of this pathway .

This relationship between PIM1 and EMT offers a mechanistic explanation for PIM1's role in promoting aggressive cancer phenotypes and suggests that targeting PIM1 may reduce metastatic potential and overcome therapeutic resistance.

What combination therapeutic strategies involving PIM1 inhibition show the most promise?

Several combination therapeutic strategies targeting PIM1 show substantial promise:

• EGFR/PIM1 dual blockade: In EGFR-mutant NSCLC, combined inhibition of EGFR and PIM1 demonstrates synergistic effects in reversing osimertinib resistance. This approach directly addresses the PIM1-mediated EMT that contributes to resistance against EGFR-targeted therapies .

• Metabolic targeting combinations: Since PIM1 promotes the Warburg effect, combining PIM1 inhibitors with drugs that target glycolysis or exploit metabolic vulnerabilities may enhance therapeutic efficacy, particularly in tumors with high glucose dependence .

• Immune checkpoint combination: Given PIM1's role in immune signaling, particularly in IFN-β production, combination of PIM1 inhibitors with immune checkpoint inhibitors represents a potential strategy to enhance antitumor immune responses .

• Hormone therapy combinations: In prostate cancer, where PIM1 has been implicated in hormone insensitivity, combining PIM1 inhibitors with androgen-deprivation therapy might overcome resistance mechanisms and improve treatment outcomes .

The rational design of such combination approaches requires careful consideration of molecular contexts and potential synergistic mechanisms. Preclinical data supports pursuing these strategies, particularly the EGFR/PIM1 dual blockade which has demonstrated efficacy in reversing resistance in vivo .

What high-throughput screening methods are most effective for identifying PIM1 inhibitors?

Effective high-throughput screening for PIM1 inhibitors employs multiple complementary approaches:

• Virtual screening with optimized parameters: Computational approaches that incorporate post-processing of docking results to consider various binding modes show enhanced predictive value. Successful models incorporate both binding energy calculations and analysis of specific residue interactions, particularly with Gly45, Pro123, Asp131, and the structural water molecule HOH334 .

• Structure-guided screening: Using crystal structures of PIM1's ATP-binding pocket to design focused libraries that target key structural features. The model utilizing binary logistic regression with a pIC50 threshold of 5 (corresponding to 10 μM) provides reliable prediction of inhibitory potential with a sensitivity of 0.809 .

• Kinase activity assays: Biochemical assays measuring inhibition of PIM1's phosphorylation activity, optimized for miniaturization and automation. When using Class 5 cutoff criteria (probability of a compound having pIC50 > 5), sensitivity can be increased to 0.90 by lowering the calculated Pclass5 value threshold to 0.33 .

• Cellular phenotypic assays: Screening compounds for their ability to reverse PIM1-dependent phenotypes in cancer cells, such as proliferation under glucose-limited conditions or EMT marker expression .

The combination of these approaches, particularly starting with computational screening followed by biochemical and cellular validation, offers an efficient pipeline for identifying promising PIM1 inhibitors with diverse chemical scaffolds.

How can researchers effectively measure PIM1 kinase activity in experimental systems?

Measuring PIM1 kinase activity requires specific methodological approaches:

• In vitro kinase assays: Using purified recombinant PIM1 protein with specific substrates and measuring phosphorylation through techniques such as:

  • Radioactive ATP incorporation

  • Phospho-specific antibody detection

  • Fluorescence-based assays with phospho-specific sensing elements

• Cellular activity assessment: Evaluating PIM1 activity in cellular systems by:

  • Measuring phosphorylation of known PIM1 substrates involved in cell cycle regulation and apoptosis

  • Assessing downstream signaling pathway activation

  • Monitoring cellular phenotypes known to be dependent on PIM1 activity, such as proliferation or metabolic shifts

• Pharmacodynamic markers: When testing PIM1 inhibitors, researchers should monitor:

  • Direct substrate phosphorylation changes

  • Expression changes in genes regulated by PIM1 activity

  • Functional outcomes like reversal of EMT or metabolic reprogramming

• Target engagement assays: Methods to confirm direct interaction between inhibitors and PIM1 in cellular contexts, such as cellular thermal shift assays (CETSA) or related approaches.

To ensure specificity, these approaches should incorporate appropriate controls, including PIM1 knockdown/knockout systems and selective inhibitors as reference standards. Validation across multiple assay formats strengthens confidence in activity measurements.

What advanced imaging techniques are useful for studying PIM1 localization and function?

Advanced imaging techniques offer valuable insights into PIM1 biology:

• Immunofluorescence microscopy with subcellular markers: Co-localization studies with markers for specific organelles (nucleus, mitochondria, endoplasmic reticulum) help determine PIM1's functional compartmentalization. This is particularly important given PIM1's dual nuclear and cytoplasmic localization patterns observed in tissue samples .

• Live-cell imaging with fluorescently tagged PIM1: Using techniques such as:

  • FRAP (Fluorescence Recovery After Photobleaching) to assess PIM1 mobility and binding dynamics

  • FRET (Förster Resonance Energy Transfer) to study PIM1 interactions with binding partners like IRF3

  • Photoactivatable or photoswitchable PIM1 fusions to track protein movement in real-time

• Super-resolution microscopy: Techniques such as STORM, PALM, or STED microscopy can provide nanoscale resolution of PIM1 localization beyond the diffraction limit, particularly valuable for studying PIM1's role in multiprotein signaling complexes like the IFN-β signaling complex .

• Proximity labeling approaches: Methods such as BioID or APEX2 fused to PIM1 can identify proximal proteins in living cells, helping map the PIM1 interactome in different cellular contexts.

• Correlative light and electron microscopy (CLEM): Combining fluorescence imaging of PIM1 with electron microscopy to provide ultrastructural context for PIM1 localization.

These advanced imaging approaches complement biochemical and molecular techniques, providing spatial and temporal information about PIM1 function in intact cellular systems.

How should researchers interpret discrepancies in PIM1 expression across different cancer datasets?

When encountering discrepancies in PIM1 expression across different datasets, researchers should consider several methodological and biological factors:

• Methodological considerations:

  • Detection method variations: Different antibodies in IHC may have varying specificities for PIM1 isoforms (44 kD vs. 34 kD)

  • Scoring system differences: Studies may use different cutoff values for classifying PIM1 expression levels

  • RNA vs. protein detection: Transcriptional measurements may not always correlate with protein expression due to post-transcriptional regulation

• Biological factors:

  • Tumor heterogeneity: PIM1 expression may vary across different regions of the same tumor

  • Cancer subtype differences: Expression patterns may differ between molecular subtypes within the same cancer type

  • Treatment effects: Prior therapies may alter PIM1 expression patterns

  • Microenvironmental influences: Factors like glucose availability can significantly affect PIM1 expression

• Analytical approaches for reconciliation:

  • Meta-analysis techniques incorporating quality assessment of individual studies

  • Stratification by cancer subtype, treatment history, or other relevant clinical parameters

  • Correlation with functional readouts like metabolic activity (18F-FDG uptake)

  • Integration of multiple data types (genomic, transcriptomic, proteomic)

What are the key challenges in translating PIM1 inhibitors from preclinical models to clinical applications?

Translating PIM1 inhibitors to clinical applications faces several important challenges:

• Structural complexity barriers:

  • The molecular structure of PIM1 has posed challenges in developing selective inhibitors with favorable pharmacological properties

  • The distinctive ATP-binding pocket configuration requires specialized inhibitor design approaches

• Target specificity issues:

  • Achieving selectivity against other kinases, particularly other PIM family members

  • Balancing on-target activity with acceptable off-target effects

• Biomarker development needs:

  • Identifying reliable predictive biomarkers for patient selection

  • Developing pharmacodynamic markers to confirm target engagement and biological activity

  • Establishing thresholds for PIM1 expression or activity that predict therapeutic response

• Combination strategy optimization:

  • Determining optimal combination partners for specific cancer contexts

  • Establishing appropriate dosing and scheduling for combination approaches

  • Managing potential combination toxicities

• Clinical trial design considerations:

  • Patient selection strategies based on PIM1 expression or pathway activation

  • Appropriate endpoints to detect PIM1 inhibition benefits

  • Biomarker-driven adaptive trial designs

The complexity of PIM1's biological roles across different tissues and its involvement in both normal and pathological processes necessitates careful consideration of therapeutic windows and potential on-target toxicities in normal tissues expressing PIM1 .

How can researchers best identify patients likely to benefit from PIM1-targeted therapies?

Identifying patients likely to benefit from PIM1-targeted therapies requires multi-faceted biomarker approaches:

• Expression-based biomarkers:

  • IHC assessment of PIM1 protein expression using validated scoring systems (0-6 scale based on intensity and extent)

  • mRNA expression analysis through qPCR or RNA sequencing

  • Consideration of both expression level and subcellular localization patterns

• Functional biomarkers:

  • Metabolic imaging with 18F-FDG PET, given the correlation between PIM1 expression and glucose metabolism

  • Assessment of downstream pathway activation through phosphorylation of PIM1 substrates

  • EMT marker expression in contexts where PIM1 drives EMT-mediated resistance

• Genomic and molecular features:

  • Analysis of 6p21 genomic alterations that may affect PIM1 expression

  • Molecular subtyping to identify cancer subtypes with PIM1 dependency

  • Integration with other oncogenic driver alterations

• Contextual biomarkers:

  • In NSCLC, developed resistance to osimertinib with EMT features suggests potential benefit from PIM1 inhibition

  • In colorectal cancer, tumors with high glycolytic activity or glucose dependence may be more responsive

  • In hematopoietic malignancies, aggressive lymphoma subtypes with high PIM1 expression represent potential candidates

• Resistance mechanism assessment:

  • Evaluation of GSK3β phosphorylation status as an indicator of PIM1-mediated EMT activation

  • Analysis of SNAIL and SLUG stability and expression levels

Multi-parameter biomarker strategies combining these approaches will likely provide the most robust patient selection algorithms for clinical trials and eventual therapeutic applications of PIM1 inhibitors.

What emerging technologies might advance our understanding of PIM1 biology?

Several emerging technologies hold promise for advancing PIM1 research:

• CRISPR-based functional genomics:

  • Genome-wide CRISPR screens to identify synthetic lethal interactions with PIM1 inhibition

  • CRISPR activation/repression systems to modulate PIM1 expression in precise contexts

  • Base editing and prime editing for introducing specific PIM1 mutations to study structure-function relationships

• Single-cell technologies:

  • Single-cell RNA sequencing to characterize PIM1 expression heterogeneity within tumors

  • Single-cell proteomics to assess PIM1 protein levels and activation state at cellular resolution

  • Spatial transcriptomics to map PIM1 expression patterns in relation to tumor microenvironment features

• Proteomics advances:

  • Phosphoproteomics to comprehensively identify PIM1 substrates in different cellular contexts

  • Thermal proteome profiling to identify proteins stabilized by PIM1-mediated phosphorylation

  • Interactome mapping to characterize context-specific PIM1 protein-protein interactions

• Structural biology techniques:

  • Cryo-electron microscopy to visualize PIM1 in complex with regulatory partners

  • Hydrogen-deuterium exchange mass spectrometry to study PIM1 conformational dynamics

  • Fragment-based drug discovery approaches for novel inhibitor development

• In vivo models:

  • Patient-derived organoids to study PIM1 function in more physiologically relevant systems

  • Humanized mouse models for studying PIM1 in immune contexts

  • CRISPR-engineered mouse models with conditional PIM1 manipulation

These technologies will enable more precise dissection of PIM1's context-specific functions and facilitate the development of more effective targeting strategies.

What unexplored therapeutic contexts might benefit from PIM1 inhibition?

Several promising but currently unexplored therapeutic contexts may benefit from PIM1 inhibition:

• Immunotherapy enhancement:

  • Given PIM1's role in IFN-β production and innate immune signaling , PIM1 inhibition or modulation might synergize with immune checkpoint inhibitors

  • Exploring how PIM1 affects tumor-immune interactions could reveal novel immunotherapeutic combinations

• Metabolic vulnerabilities:

  • Since PIM1 promotes the Warburg effect and helps cancer cells adapt to glucose deprivation , combining PIM1 inhibitors with metabolic stress inducers may create synthetic lethality

  • Targeting tumors in metabolically challenging microenvironments where PIM1-dependent adaptations are critical

• Radiation sensitization:

  • Investigating whether PIM1 inhibition might sensitize tumors to radiation therapy by interfering with DNA damage response or metabolic adaptation

• Cancer stem cell targeting:

  • Exploring PIM1's potential role in cancer stem cell maintenance or function, as suggested by its involvement in cell differentiation

• Additional drug resistance contexts:

  • Beyond osimertinib resistance in NSCLC , investigating PIM1's role in resistance to other targeted therapies, chemotherapies, or hormone therapies

  • Particularly promising in contexts where EMT or metabolic adaptation contributes to resistance mechanisms

• Prevention in high-risk contexts:

  • Exploring PIM1 inhibition as a preventive approach in premalignant conditions with elevated PIM1 expression

These contexts represent potential new frontiers for PIM1-targeted therapeutic development, expanding beyond its established roles in hematological malignancies and prostate cancer.

How might systems biology approaches enhance our understanding of PIM1 networks?

Systems biology approaches offer powerful frameworks for understanding PIM1's complex biological roles:

• Network analysis:

  • Integrating PIM1 interactome, phosphoproteome, and transcriptional response data to build comprehensive signaling networks

  • Identifying network hubs and bottlenecks that might represent critical points for therapeutic intervention

  • Mapping context-specific network rewiring in response to different stimuli or in different cancer types

• Multi-omics integration:

  • Combining genomic, transcriptomic, proteomic, and metabolomic data to understand PIM1's impact across biological scales

  • Correlating PIM1 expression with metabolic profiles, given its role in the Warburg effect

  • Identifying emergent properties not evident from single-omics approaches

• Mathematical modeling:

  • Developing quantitative models of PIM1 signaling networks to predict response to inhibition

  • Simulating combination therapy effects to prioritize promising drug combinations

  • Modeling the dynamics of resistance development to PIM1 inhibition

• Evolutionary analysis:

  • Studying how PIM1 networks evolve during cancer progression and treatment

  • Identifying convergent evolution patterns that might indicate essential PIM1 functions

  • Predicting resistance mechanisms to guide preemptive combination strategies

• Computational drug repurposing:

  • Using network-based approaches to identify existing drugs that might modulate PIM1 networks

  • Predicting novel targets within PIM1 networks that might be more druggable than PIM1 itself

These systems approaches will help contextualize PIM1's diverse biological functions within broader cellular networks, potentially revealing unexpected therapeutic opportunities and more effective targeting strategies.