Evaluating a Novel GCN2 Inhibitor for Acute Myeloid Leukemia (AML) Treatment Using Patient-Derived Models

The quality of data received from clinical specialty testing labs is largely dependent on the integrity of the samples tested. One critical aspect of this is the proper isolation of PBMCs - peripheral blood mononuclear cells - often used in high-complexity flow cytometry research. To get the best quality results, researchers must use the most optimal tubes to isolate these cells. Three main types of tubes are commonly used: SepMate tubes, CPT tubes, and Manual Ficoll Gradient tubes. Each of these tubes has advantages and disadvantages that researchers must consider carefully. In this blog post, we will look at the pros and cons of each tube type and what you should consider before selecting one for your clinical trial. SepMate Tubes: SepMate tubes come in different sizes that enable researchers to isolate PBMCs from different numbers of tubes at once. These tubes work by using an inserted plastic separation technology that helps remove unwanted clumps of cells. One of the most significant advantages of these tubes is that they can be used for small to medium-sized samples with ease. The technology also separates cells significantly, making it easy to measure the concentration of cells after the separation procedure accurately. Furthermore, the tube also allows for rapid and consistent layering and easy collection of PBMCs, making it ideal for research. However, obtaining a high percentage of neutrophils is not possible, as they would be trapped below the barrier inside the tube. SepMate tubes are disposable, which makes them less financially sensible in the long run. CPT Tubes: CPT tubes are anticoagulant-based layered tubes that allow for the collection directly to cell separation without any additional processing. CPT tubes simplify the separation process and reduce the risk of contamination. They offer a more robust PBMC isolation process, and specific manufacturers produce four types to suit different research needs regarding testing human cells. The tubes generate high PBMC yields with high cell viability, making them an optimal option where quality is paramount. The primary disadvantage of CPT tubes is that they are quite expensive when compared to other tube types for PBMC isolation. Manual Ficoll Gradient Tubes: Manual Ficoll Gradient tubes require manual handling and processing, so they aren't automated. Due to this, the individual processing of Ficoll gradient tubes can cause variability in the manual layering steps, leading to variation in the final cell yield. The biggest issue with this type of isolation tube is that it takes longer to obtain the actual PBMCs than using SepMate or CPT tubes, which may create more significant delays in your research. Additionally, due to the manual nature of this type of PBMC isolation, final PBMC recovery rates are usually dependent on the experience of the researcher. In conclusion, the right tube type will depend on your primary requirements for your research. Regardless of your choice, there is still a need to make sure you select a tube type that meets the technical requirements of your studies and delivers high-quality results. Champions Oncology has expertise in GCLP-compliant PBMC isolation using SepMate, CPT, and manual Ficoll Gradient tube types and can advise the best choice for your upcoming clinical trial.

Radiopharmaceuticals represent a rapidly advancing class of targeted oncology therapeutics, leveraging radionuclide-labeled molecules to deliver ionizing radiation directly to tumor cells. Despite the promising clinical potential of alpha- and beta-emitting radiopharmaceuticals, achieving translational success remains challenging. Robust, well-characterized preclinical models are essential to increase confidence in compound performance before entering the clinic. Where biodistribution, receptor heterogeneity, and tumor penetration critically influence therapeutic index and patient selection strategies, traditional preclinical models often fall short. Cell line–derived xenografts (CDX), in particular, offer limited predictive value due to their clonal homogeneity, uniform tumor architecture, and lack of biological diversity, factors that can lead to inaccurate assessments of targeting performance, distribution, and treatment efficacy. Patient-derived xenograft (PDX) models offer a superior alternative, retaining the histological architecture, molecular diversity, and intra, and inter-tumoral heterogeneity of the donor patient’s tumor. These attributes enable more physiologically relevant assessment of targeting efficacy, radiotracer distribution, and therapeutic response—key metrics in determining compound viability prior to clinical translation. In this article, we examine the limitations of traditional models, the biological advantages of PDX platforms, and the specific ways in which PDX enhances radiopharmaceutical study design. We also highlight how access to large, clinically annotated model libraries—such as Champions Oncology’s Lumin platform can support more informed, data-driven decisions during preclinical development. PDX Models + Radiopharmaceuticals = Translational Power The Limits of Traditional Preclinical Models Despite their ubiquity in oncology research, traditional preclinical models—particularly cell line–derived xenografts (CDX)—present significant limitations for targeted drug development, including radiopharmaceuticals. CDX models are generated by implanting immortalized cancer cell lines into immunodeficient mice. While they offer logistical advantages such as rapid tumor growth and reproducibility, these models are inherently reductionist. Their clonal architecture lacks the genomic and phenotypic heterogeneity observed in primary tumors, which can lead to misleading conclusions regarding target accessibility, tumor penetration, and intratumoral uptake of radiolabeled compounds. Moreover, CDX models typically fail to recapitulate the complex tumor microenvironment (TME), including stromal interactions, vasculature, and immune contexture—all of which are known to influence radiopharmaceutical distribution and efficacy. In addition, receptor expression in cell lines is often artificially uniform or overexpressed, providing an inaccurate representation of clinical target variability. For radiopharmaceuticals — where therapeutic performance depends heavily on fine, tuned targeting, localized retention, and clearance kinetics, these simplifications are not benign. Data generated from CDX models may overestimate therapeutic potential or fail to predict safety liabilities, contributing to a translational gap between preclinical validation and clinical outcomes. What Makes PDX Models Different Patient-derived xenograft (PDX) models are established by implanting primary tumor tissue directly from oncology patients into immunodeficient mice, preserving the cellular heterogeneity, stromal components, and histopathological architecture of the original tumor. Unlike CDX systems, PDX models retain critical aspects of human tumor biology across multiple passages. This biological fidelity translates into substantial advantages for radiopharmaceutical development. First, PDX models capture both intratumoral and intertumoral heterogeneity, a key determinant of response variability in radiolabeled therapies. Differences in antigen density, receptor expression, vascularization, and stromal composition can significantly affect radiotracer uptake and therapeutic distribution—elements that are often uniform or absent in traditional systems. Second, because PDX tumors grow in vivo without prior dissociation or in vitro manipulation, their tumor microenvironments more accurately reflect the spatial and structural complexity of human malignancies. This includes irregular vasculature, hypoxic regions, and heterogeneous interstitial pressure—factors that influence compound diffusion, radiation deposition, and biological effects. PDX models have demonstrated greater predictive validity than CDX systems across multiple drug classes, with treatment responses that more closely reflect clinical outcomes. This makes them particularly valuable for de-risking radiopharmaceutical assets in the early stages of development, providing insight into variability in target engagement and therapeutic effect. While tumor-specific uptake can be assessed in a human-relevant context, off-target distribution in preclinical models may not fully reflect human cross-reactivity due to interspecies differences in antigen expression. In short, PDX models offer a translational bridge between mechanistic discovery and clinical decision making, one that is especially important when developing complex, spatially dependent therapies like radiopharmaceuticals. How PDX Enhances Radiopharmaceutical Testing The evaluation of radiopharmaceuticals requires more than evidence of cytotoxicity; it demands a nuanced understanding of how a radiolabeled compound distributes within and interacts with—a tumor and its microenvironment. PDX models provide the translational resolution needed to interrogate these complex dynamics. One of the primary advantages of using PDX models in this context is the ability to model inter-patient variability in target expression. Radiopharmaceuticals often rely on the presence of specific cell surface antigens or receptors for tumor localization. In a clinical setting, these markers are rarely expressed uniformly across patient populations. By leveraging a library of diverse PDX models—each with distinct molecular and phenotypic profiles—researchers can assess how differences in target expression influence uptake, specificity, and efficacy. Additionally, PDX models enable realistic biodistribution analysis in tumors that replicate human heterogeneity in vascular density, stromal content, and perfusion. These factors play a significant role in modulating the intratumoral deposition of radiolabeled compounds, Preclinical studies in PDX therefore allow developers to anticipate challenges related to tracer penetration, off, target accumulation, and clearance kinetics, challenges that CDX models routinely obscure. Efficacy evaluation is another area where PDX models offer substantial value. Because these tumors respond to treatment in ways that reflect clinical patterns, including partial response, acquired resistance, and heterogeneous regression, they offer a more realistic basis for determining therapeutic window, optimal dosing, and potential biomarkers of response. When used systematically, PDX models allow radiopharmaceutical developers to move beyond binary efficacy readouts and instead generate layered, clinically relevant insights into compound behavior—insights that inform both development decisions and regulatory discussions. The Lumin Advantage While the value of PDX models in radiopharmaceutical development is clear, the ability to scale these insights depends on access to a diverse, well-characterized model library. Champions Oncology’s PDX platform is the most deeply annotated and clinically relevant PDX collections available globally, enabling sponsors to tailor studies with unprecedented precision. The library encompasses thousands of PDX models derived from a wide range of solid tumors, each backed by comprehensive clinical, histological, and molecular data. This includes mutational profiles, gene expression signatures, prior treatment history, and, critically for radiopharmaceutical programs, data on target expression heterogeneity across tumor types. To accelerate the design of rational studies, complementary tissue microarrays (TMAs) prepared from the PDX collection are also available. These arrays allow researchers to screen panels of models for antigen expression or biomarker prevalence prior to initiating in vivo work, enabling efficient model selection, improving study design, and reducing downstream variability. In radiopharmaceutical testing, where variability in receptor density or antigen availability can dramatically influence tracer uptake and therapeutic effect, this level of pre-screening and data integration is a strategic advantage. It allows developers to assess compound performance across diverse biological backgrounds and identify model subsets most likely to inform clinical translation. Combined with Champions’ in-house imaging, conjugation, and radiolabeling capabilities, Lumin platform offers a comprehensive ecosystem for generating radiopharmaceutical data that’s not only robust—but truly relevant to human disease. The Only CRO Pairing PDX Models with Radiopharma

Why a functional view changes predictions Genomics and transcriptomics remain foundational for precision oncology, but they do not fully represent the functional state that determines how tumors respond to therapy. Proteins and phosphoproteins capture activity at the level where drugs actually engage, for example receptor density and localization, complex assembly, and pathway signaling. That distinction is not academic. In a 2024 pan-cancer analysis from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), investigators integrated proteogenomic data from 1,043 patients across 10 tumor types, surveyed 2,863 druggable proteins, and quantified biological factors that weaken mRNA to protein correlation, making the case for models that learn directly from protein and phosphoprotein context rather than inferring from transcripts alone. Cell From data to models that travel The practical bottleneck has been access to harmonized, well-annotated cohorts that support training, testing, and independent validation. In August 2023, the National Cancer Institute announced a standardized pan-cancer proteogenomic dataset that aligns genomics, proteomics, imaging, and clinical data for more than 1,000 tumors across 10 cancer types, explicitly to enable reproducible discovery and model benchmarking. The Proteomic Data Commons (PDC) now serves these resources in a way that supports programmatic access and cross-study comparisons, a requirement if machine-learning outputs are going to generalize beyond a single study. National Cancer Institute+1 Solving the Surfaceome Problem What the evidence shows when proteins are included Two Cell papers from 2024 illustrate why adding protein-level information changes conclusions. An immune-landscape analysis derived distinct immune subtypes by integrating genomic, epigenomic, transcriptomic, and proteomic features and connected oncogenic drivers to downstream protein states that influence immune surveillance and evasion. A companion pan-cancer study expanded the landscape of therapeutic opportunities by evaluating thousands of druggable proteins across tissues and documenting where mRNA is a poor proxy for protein, especially in pathways relevant to therapy response. Together, they show that multi-omic modeling, including protein and phosphoprotein features, improves biological interpretability and exposes actionable biology that single-omic approaches overlook. Cell+1 A broader signal from the field The trend is not confined to CPTAC. The Pan-Cancer Proteome Atlas (TPCPA), published in Cancer Cell in 2025, quantified 9,670 proteins across 999 primary tumors representing 22 cancer types using DIA-MS. The atlas offers a tissue-based substrate for target nomination, biomarker discovery, and external validation, and has been highlighted in the trade press for its global availability and immediate relevance to oncology research. Such atlases are valuable because they capture proteomic variability directly in clinical material, not only in cell lines, providing realistic distributions for features that ML models attempt to learn. Cell+2PubMed+2 Why proteogenomic ML improves prediction Integrating proteins and phosphoproteins adds information that is both mechanistic and measurable. First, pathway activity is reflected in phosphorylation states, which function as on–off or rheostat-like controls for signaling. Second, receptor exposure and complex formation at the protein level determine whether a therapy can bind or disrupt a process. Third, protein degradation and post-translational regulation often decouple mRNA abundance from target availability, which explains why transcript-only biomarkers can fail at the bedside. When these features are engineered into models, performance gains are not just numeric; they tend to be more interpretable, mapping to drug-actionable pathways and receptors that clinicians recognize. The 2024 CPTAC studies provide concrete examples. Immune subtypes defined by proteogenomic features correlate with differences in antigen presentation, cytokine signaling, and interferon responses, features with obvious translational implications. The survey of druggable proteins shows wide variation in abundance and localization across tumors and details the contexts where transcript and protein diverge, arguing for protein-aware rules when nominating targets or stratifying patients. Cell+1 What good practice looks like in model building There is a growing consensus on practical guardrails. Independent validation across cohorts is essential to avoid overfitting, and the infrastructure now exists to support that step through the PDC and related CPTAC resources. Feature construction should prioritize pathway-level signals that aggregate individual phospho-sites into kinase or pathway activity because these are more stable across cohorts and easier to interpret for clinical decision making. Finally, clinically annotated samples, including treatment history and outcomes, are indispensable if models are expected to inform responder enrichment and mechanism-of-resistance hypotheses rather than only classify molecular subtypes. National Cancer Institute Translational payoffs, with appropriate caution When executed with these guardrails, proteogenomic ML offers tangible benefits. Programs can generate earlier responder and non-responder hypotheses and test them prospectively in preclinical systems before committing costly clinical designs. Resistance pathways inferred from phospho-proteomic features can motivate combination strategies, for example pairing an antibody-drug conjugate with a kinase inhibitor when signaling indicates a plausible escape route. Educational content from ASCO has emphasized the centrality of quantitative thresholds and validated assays for patient selection, particularly for ADCs where surface accessibility and abundance determine benefit. The lesson is consistent across modalities. Predictive features must be connected to assays that can be deployed consistently in trials, and thresholds should be defined in a way that anticipates real-world variability. PubMed Where limitations still matter Several limitations deserve explicit mention. Proteomic and phospho-proteomic data remain technically variable across platforms and laboratories. Although CPTAC and PDC mitigate this through standardization, modelers should evaluate batch effects and apply normalization strategies suited to proteomic data. Coverage of kinase–substrate relationships and post-translational networks is incomplete, which constrains inference. Tumor heterogeneity adds another layer, particularly when bulk tissue averages mask subclonal or microenvironmental signals. These caveats do not negate the value of proteogenomic ML, but they do argue for conservative claims, orthogonal validation, and a bias toward features that can be measured reproducibly in clinical settings. TALK Decoding the Cell Surface to Accelerate Discovery Implications for trial design and portfolio focus The immediate implication is a more disciplined approach to enrichment. If protein-level features identify a subgroup with plausible sensitivity, early designs can incorporate eligibility criteria and stratification based on validated assays rather than exploratory cutpoints. Conversely, if pathway-level features suggest multiple escape routes, it may be more efficient to prioritize combinations earlier instead of iterating single-agent studies. At a portfolio level, proteogenomic evidence can help prioritize programs with a mechanistic rationale supported by functional data, not only by mutation prevalence or gene expression. How Champions Oncology contributes Champions Oncology builds models on tumor-derived systems that preserve patient biology and heterogeneity. Our datasets link genomics and transcriptomics with proteomics, phospho-proteomics, and cell surface proteomics, and they are annotated with pharmacologic phenotypes. This combination supports models that tie features to functional biology and drug accessibility, making it possible to move from correlation to causal relation and from causal relation to druggable targets.