Trends in Oncology

The case for measuring the surface directly Biologics and antibody drug conjugates operate at the cell's surface, yet many programs still guide patient selection using RNA expression or whole cell protein abundance. That practice can misclassify candidates because transcripts and total protein often do not correlate, due to post-transcriptional and post-translational mechanisms. Moreover, cellular localization and receptor density can’t be quantified by using whole cell bulk proteomics, therefore not guaranteeing that a receptor is exposed on the exterior membrane above drug relevant thresholds. In 2024, investigators profiled the surfaceome of 100 genetically diverse, primary human AML specimens and resolved antigen patterns on primitive and stem like cells with limited expression in essential normal tissues. The work demonstrated that surface level heterogeneity is real, clinically meaningful, and different from what one would infer from bulk RNA or whole cell proteomics alone. Cell+1 Calls to better map the surfaceome are growing. A 2024 Cancer Discovery commentary noted that only a few dozen cell surface targets currently anchor FDA or EMA approved therapies, despite the surface being the most accessible compartment for antibodies, CAR T cells, and radiopharmaceuticals. The authors argued for intensified efforts to chart the universe of surface proteins across cancers, which would accelerate target discovery and improve translational relevance for modalities that require true surface accessibility. AACR Journals+1 Solving the Surfaceome Problem Why a surface resolved view changes development plans Modern reviews describe how advances in mass spectrometry, fractionation, and enrichment are making it feasible to survey the cancer surfaceome at scale. These approaches improve the identification of drug accessible proteins that whole cell proteomics can miss, because abundant intracellular proteins dominate unfractionated measurements and mask low abundance, surface localized receptors. The reviews also outline practical ways to mitigate technical challenges such as low copy number, hydrophobic domains, and contamination by intracellular compartments, for example through careful membrane preparation, parallel intracellular and whole cell fractions, and stringent enrichment and quality control thresholds. The program level implication is straightforward. When a therapy acts at the surface, the biomarker strategy should resolve surface localization directly rather than infer it from RNA or whole cell protein alone. PubMed+1 A second implication concerns internalization and trafficking. For ADCs and some bispecifics, internalization kinetics and routing to lysosomes affect payload delivery and potency. Surface resolved workflows can be coupled to orthogonal assays, for example flow cytometry with ligand induced internalization or live cell imaging, to determine whether a receptor is not only present at the surface but also behaves in a way that supports the intended mechanism. This behavioral dimension rarely appears in transcript based selection but influences both efficacy and safety. From heterogeneity to practical enrichment Turning surface heterogeneity into clinical signal requires a sequence of disciplined steps. First, measure what matters. Use fractionated surface proteomics or validated surface specific immunohistochemistry and flow assays that distinguish the exterior membrane from total abundance. Second, define actionable thresholds that tie expression to benefit and that can be reproduced across sites. Educational content from the ASCO Educational Book emphasizes that quantitative cutoffs and standardized assays are central to patient selection for antibody drug conjugates, because target accessibility and abundance determine benefit, and because inconsistent thresholds erode the interpretability of early phase studies. Third, map prevalence by histology and by clinically relevant subgroups, since an attractive target with low prevalence may not support enrollment or may require a targeted site strategy. When these elements are present, enrichment reflects how the drug actually works and not a proxy signal. ASCO Publications+1 Surface level diversity can also sharpen indication strategy. If a receptor is highly enriched at the surface in a subset of colorectal cancer but not in pancreatic cancer, a program can prioritize the former for first in human evaluation, even if RNA levels are similar in both. This is particularly relevant where surface and whole cell abundances diverge. The AML study noted above showed precisely this phenomenon, where antigen exposure on stem like compartments could be quantified and connected to therapeutic concepts that require surface binding. Cell Selectivity and safety as design constraints Surface heterogeneity intersects with safety because many candidate antigens have some expression in normal tissues. A 2024 review in Trends in Pharmacological Sciences surveyed strategies that increase antibody selectivity in oncology, including superselectivity through avidity and multivalency, conditional or pH sensitive binding, dual targeting that requires co expression to achieve high affinity, and engineering designs that leverage tissue context. These concepts translate directly into earlier program decisions. Targets that can be paired with selective engineering approaches should score higher than those that would require unrealistic discrimination from a conventional binder. A transparent scoring framework, for example one that penalizes normal tissue expression in essential organs and rewards tumor specific co expression patterns, makes those choices auditable during governance reviews. Cell+1 The safety argument is not theoretical. ADCs can cause off tumor toxicities when a payload is delivered to normal tissues that express the target at modest levels. A biomarker plan that quantifies true surface exposure in disease and screens for surface exposure in a curated panel of normal tissues improves the chance of a workable therapeutic window. Reviews that focus on ADC biomarkers, together with trial design guidance, point in the same direction, namely that selection should be quantitative and assayable, not an exploratory cutpoint chosen after the fact. ASCO Publications Concrete examples of surfaceome driven discovery Evidence that surface resolved discovery translates into programs is accumulating. In late 2024, a Cancer Cell study used an integrative proteogenomic surfaceome approach to credential DLK1 as an immunotherapeutic target in neuroblastoma. The authors combined mass spectrometry based surface profiling with genomic and transcriptomic context to demonstrate surface exposure, tumor specificity, and functional relevance, then moved to validation experiments that supported drug development. This work illustrates how surface level datasets, anchored in clinical material, can identify and qualify targets that a transcript only screen might underrate. PMC+3Cell+3PubMed+3 Surfaceome maps are also becoming more accessible, which will help teams generalize findings across institutions. The AML dataset is public in GEO, enabling independent inspection of QC criteria, antigen lists, and statistical methods. Commentary from Cancer Discovery underscores the systemic opportunity, arguing that better cartography of the surfaceome across cancers is likely to grow the small set of currently actionable surface targets. Together, public data and field level commentary support a move from opportunistic target picking toward systematic, population informed discovery. NCBI+1 TALK Decoding the Cell Surface to Accelerate Discovery Program design, trial execution, and measurement Programs that embrace surface resolved selection can operationalize three habits that improve downstream signal. First, connect discovery assays to clinical screening early. If discovery uses a fractionated surface proteomics threshold, define the clinical assay that will mirror that readout, for example an IHC score or a validated flow protocol, and harmonize cutoffs before first patient in. Second, pre specify enrichment rules in the protocol. Enrolling only the top percentiles of surface expressors may seem restrictive, but it increases the chance of a pharmacodynamic signal that validates the mechanism and informs dose expansion. Third, measure what happens when selection criteria are varied in sensitivity analyses, and share these details in publications and regulatory interactions. This transparency builds cumulative knowledge that benefits the field and informs the next iteration of thresholds. There is also value in linking surface metrics to pharmacology. If a surface antigen is abundant but shows slow internalization, a payload with a bystander effect may be preferable to a payload that requires rapid lysosomal routing. If surface expression is heterogeneous at the lesion level, a radionuclide therapy that can exploit crossfire may offer advantages over a conventional ADC. These are not general rules, they are examples of how a surface resolved view can shape the choice of modality and payload in a way that reflects the physical constraints of the target. Where Champions Oncology contributes Champions Oncology generates surface resolved datasets from low passage, clinically relevant tumor models and integrates them with deep multi omic profiles and in vivo pharmacology. This enables teams to quantify true surface positivity, to set and test actionable thresholds, and to understand prevalence by indication before committing to costly trials. The same datasets support orthogonal validation and method standardization so that discovery assays translate into clinical screening. The approach is designed to be non promotional and data first, the objective is to clarify risk and to help programs make better decisions earlier.

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.

High dimensional omics datasets often include many features that contribute little to downstream analysis. This can blur structure in unsupervised tasks, slow computation, and complicate model training. The MADVAR study introduces two simple, data driven procedures that set feature selection thresholds from the distribution of the data itself, rather than relying on fixed heuristics. The first procedure, madvar, computes a variance cutoff using the median plus a multiple of the median absolute deviation. The second, intersect Distributions, fits a two component Gaussian mixture to the variance or another continuous score, and uses the intersection point between components as the cutoff. Both methods are implemented in an R package and were evaluated across public datasets that include TCGA gene expression, GTEx proteomics, and CPTAC phosphoproteomics. The paper reports improvements in unsupervised clustering quality and competitive supervised performance with fewer features, while keeping runtime and memory use modest. What the Paper Tested The benchmarking examined unsupervised structure and supervised classification. For unsupervised analysis, the authors applied filtering and then assessed cluster quality with connectivity, the Dunn index, and the Biological Homogeneity Index. Across datasets, the variance based approaches produced tighter or more homogeneous clusters on these metrics. For supervised analysis, they trained random forest models with repeated runs. Both approaches produced low out of bag error rates. Retaining more features sometimes improved accuracy, but MADVAR often matched the mixture based approach while selecting fewer features. The paper also documents practical defaults, such as Ward.D for hierarchical clustering with Euclidean distance, and explains how to pass either a raw matrix or a precomputed variance vector into the functions. Source code and documentation are available on GitHub. When These Methods are a Good Fit These procedures are particularly well suited for rapid, large-scale preprocessing, when analyses require a quick, efficient, and transparent approach to feature selection prior to dimensionality reduction, clustering, or model fitting. They also integrate naturally into interpretability-oriented pipelines, since thresholds based on medians or mixture-model intersections are simple to explain and justify to collaborators. Because the logic operates on continuous feature scores, the same framework can be applied seamlessly to any quantitative data type that can be summarized by variability Things to keep in mind. Variance is a proxy for informativeness, not a guarantee. Low variance does not always mean a feature is uninformative. Some biomarkers remain stable yet become predictive in combination with others. If domain knowledge indicates a feature should be preserved, the package allows must keep lists. The mixture based method assumes the variance distribution resembles a two-component mixture. If the fit is poor, the intersection may not be meaningful, so density plots are worth inspecting before adopting the cutoff. Downstream metric choice also matters. Gains in the Biological Homogeneity Index or Dunn index describe cluster characteristics, which may not translate to improvements on other endpoints such as survival modeling or dose response prediction. Finally, supervised performance can depend on class imbalance and sample size. If your data are skewed, tune the learner and validate with a scheme that reflects your use case. Read the Full Paper How to Apply, a Straightforward Workflow A practical workflow starts by exploring the variance distribution. Plot the density (using the madvar flag `plot_density = TRUE`), confirm whether there is a near zero peak, and decide whether a MAD based threshold or a two-component mixture are appropriate. Set a conservative first pass using the default MAD multiplier (`mads = 2`) and adjust if another stringency level is preferred, or, if you prefer the mixture approach, verify the intersection visually before you commit to the cutoff. Preserve domain critical features by whitelisting known markers or controls that should not be dropped. Re run the planned clustering or model fitting, compare structure and error rates before and after filtering, and record any change in feature counts and compute time so the impact is transparent to collaborators. Reproducibility and availability The R implementation and documentation are available on GitHub, as referenced in the paper. The evaluations draw on TCGA gene expression, GTEx proteomics, and CPTAC phosphoproteomics, with figures that show density plots, clustering metrics, and classification results. The article appears as an Application Note in Bioinformatics Advances and is available for open access. Summary MADVAR provides two transparent, variance-based rules for feature selection, enabling the removal of near invariant features from large omics matrices. In the reported benchmarks, these procedures improved or maintained clustering quality and supervised accuracy while substantially reducing feature counts and computational load. The approach is easy to inspect, easy to explain, and simple to integrate into existing R workflows. As with any filter, final value depends on the analysis goal, so it is worth validating its effects on the endpoints that matter for your study. Silberberg G. “MADVAR, a lightweight, data driven tool for automated feature selection in omics data.” Bioinformatics Advances 2025, vbaf211. doi, 10.1093/bioadv/vbaf211. Explore our Data Ecosystem

In oncology drug development, cell line-derived xenograft (CDX) models remain one of the cornerstone in vivo tools for evaluating new therapies in the preclinical ecosystem. They offer a balance of biological relevance, reproducibility, and speed that makes them ideal for early phase hypothesis testing. Nevertheless, while CDX models are indispensable for initial target screening and validation, they also have well-understood limitations that can upset downstream progress if not properly accounted for. As drug developers increasingly rely on CDX-based systems to screen and prioritize compounds before moving into clinically relevant, albeit more expensive, patient-derived xenografts (PDX), the need for appropriate model selection has become critical. The right CDX program can deliver early translational clarity and strategic focus, while the wrong one can generate noise that obscures the true efficacy of an otherwise promising therapeutic, potentially derailing its continued development. Balancing Speed and Fidelity CDX studies are often designed for speed. They enable large-scale screening of novel agents, as both mono- and combination therapies, and dosing regimens in a fraction of the time and cost required for PDX studies. This makes them especially valuable at the earliest stages of decision-making, where timelines are incredibly compressed and attrition risk is high. But that same speed exacts a cost. Many CDX models originate from long established cell lines that have been maintained in culture for decades. Over time, these lines lose virtually all the critical genomic and cellular heterogeneity, stromal interactions, and microenvironmental complexity characteristic of a bona fide tumor. Whilst they grow predictably, the biological features they retain may no longer reflect that of tumors seen in clinical patients. However, the key is not to abandon CDX models, but to recognize where they fit in the development pipeline and to acknowledge and mitigate the limitations they have. Used prudently, CDX models are an efficient and scientifically powerful system to rank compounds, explore mechanisms, and refine hypotheses before moving into PDX for deeper translational validation. Defining a High Value CDX Model The quality of a CDX model is defined by its biological fidelity and characterization depth. Models derived from contemporary, clinically annotated cell lines are more likely to capture the genomic and phenotypic diversity relevant to modern oncology therapeutics. When models reflect the intrinsic complexity of current patient populations, such as the plethora of KRAS G12 mutations or the legion mechanisms by which EGFR becomes hyperactivated, they offer drug developers meaningful results and mechanism-linked insights that can inform clinical strategy. Biological and “omic” characterization matters as much, if not more, than cellular/tissue origin in CDX models, particularly as clinical oncology continues to diverge from tissue-based therapy to therapeutic roadmaps grounded in the molecular features of each patient tumor. A high value CDX model is supported by multi-omic profiling that includes genomic, transcriptomic, and proteomic annotation. This data allows researchers to interpret observed drug effects through the lens of pathway activation, resistance mechanisms, and biomarker expression. In contrast, models lacking such characterization risk generating results that are descriptive rather than explanatory. Drive Proof of Concept and Target Validation with Champions’ CDX Models Mechanistic Clarity Through Multi-Omic Integration The rise of precision oncology has shown that pharmacology and data science are inextricably linked. Researchers now expect and rely on preclinical models to yield mechanistic understanding and insight, not just tumor regression rates. Integrating omic datasets into CDX studies transforms them from mere screening tools into translational resources capable of generating biological comprehension and preclinical direction. For instance, RNA sequencing of treated and untreated xenografts can reveal transcriptional signatures underpinning positive response outcomes, potentially allowing clinical partitioning of patients likely to receive the most benefit from a therapy. As another example, phosphoproteomic profiling can identify compensatory signaling cascades mediating adaptive drug resistance, permitting de novo combination therapies to be trialed to preempt such resistance before it evolves. This approach enables drug developers to anticipate how tumors might evade inhibition, long before clinical exposure. Moreover, omic integration provides a framework for cross platform alignment. Data from CDX models can be mapped against PDX datasets, public repositories, or patient trial cohorts, accelerating the feedback loop between preclinical findings and clinical validation. Modeling Resistance and Tumor Evolution One of the most powerful applications of CDX technologies is in modeling acquired resistance. By exposing tumor-bearing mice to sustained drug pressure, scientists can select for resistant clones that mimic clinical relapse. Comparative molecular profiling between parental and resistant CDX lines may illuminate the pathways that drive therapeutic escape, whether through secondary genomic changes, activation of bypass signaling cascades, or metabolic rewiring. This approach supports the rational design of next-generation inhibitors or combination strategies aimed at delaying or overcoming resistance. It also informs biomarker development by revealing the early molecular changes that forecast reduced drug sensitivity, enabling the design of clinical trials with built-in resistance monitoring. Bridging the Gap to PDX Despite their wide-ranging utility and flexibility as experimental tools, CDX models are at best a starting technology in the developmental pipeline. Translationally-minded organizations deploy CDX models as a filter for promising candidates in an experimental continuum that leads naturally into PDX. PDX models, established directly from patient tumors, preserve the architecture, stromal components, and molecular heterogeneity of the original cancer. They capture biological features that CDXs inherently lack, including contextualized immune responses in humanized systems, evolution and expansion of tumor sub-clones, and the influence of the microenvironment on cancer progression. For these reasons, PDX validation remains an important next step, if not a critical one, once a compound demonstrates clear activity in CDX. The most efficient development pipelines are those where CDX and PDX models are in biological alignment, where both originate from well characterized sources and overlap genomically and/or phenotypically. In this context, compound evaluation flows smoothly from screening to translational evaluation. A consistent molecular linkage between model systems strengthens the predictive bridge and ensures that early results translate more faithfully into clinical outcomes. Speed Without Sacrificing Relevance The enduring appeal of CDX model systems lies in the speed with which large quantities of data can be generated to reinforce or oppose development of individual drug compounds, or indeed entire drug programs. Studies can be initiated quickly, with timelines to results measured in weeks rather than months. Moreover, CDX models can support the simultaneous exploration of multiple therapeutic hypotheses. Where delays mean patients remain beset with therapeutic inadequacies and can cost developers millions in lost opportunity, this speed is a major competitive advantage. But whilst speed is a necessary component of drug development, it is insufficient for success. The most effective drug pipelines are designed with translational intent incorporated into CDX programs from the outset. CDX model selection is based on mechanistic alignment derived from deep omics comprehensive characterization, and the clinical transition to PDX model systems is a crucial element of the sequence rather than an unintegrated effort. This approach ensures CDX models are deployed as value-enhancing study tools,leveraging the efficiency of CDXs to inform smarter, faster progression into the PDX models best approximating patient clinical features and responses. Designing a Modern CDX Strategy For biotechnology and pharmaceutical teams, a modern CDX strategy balances three principles:speed, depth, and connectivity. Speed means using CDX models to quickly triage candidate molecules, confirm on-target effects, and eliminate ineffective compounds prior to larger resource investment. Depth refers to multi-layered omics characterization to uncover mechanistic drivers of response or resistance. And finally, connectivity means designing CDX studies with the downstream transition to PDX models in mind, ensuring molecular alignment and continuity between the different systems. When these principles are applied judiciously, CDX models become a strategic asset that accelerates development timelines without compromising scientific integrity. The Future of Translational Modeling As oncology continues to evolve toward precision medicine, the most impactful preclinical programs will be those that connect fast data generation with deep omics characterization, using rapid CDX screening to guide more complex, patient-relevant studies. Emerging approaches such as multi-omic analytics, AI-driven model selection, and ex vivo/organoid validation are expanding how CDX data can inform clinical decision-making. Taken in concert, these all suggest a future where the value of a model is defined not only by how fast it can be employed to produce data, but also by how that same data can be used to map the necessary downstream steps to ensure successful drug development and patient application. Choosing the Right Partner In an increasingly competitive preclinical landscape, the distinction between a vendor and a scientific partner has never been clearer. The most valuable CDX programs combine biological relevance, data transparency, and translational foresight. When evaluating potential collaborators, sponsors should ask not only what models are available but how those models were developed, characterized, and validated. The answers will reveal whether a platform can deliver more than results, whether it can deliver understanding. Cell Line Select Tool A smarter way to explore Champions’ cell line models.

When Inhibition Isn’t Enough: How Dual-Mechanism AR Degraders Are Redefining Resistance Modeling For over a decade, androgen receptor (AR) pathway inhibitors such as enzalutamide and abiraterone have formed the foundation of treatment for advanced prostate cancer. Yet the same story continues to unfold patients initially respond, then relapse. Despite continued suppression of androgen signaling, tumors adapt reactivating the AR axis through overexpression, gene amplification, or ligand-binding domain mutations. What follows is a return of disease activity that current inhibitors can no longer control. This persistent pattern highlights a central truth in oncology drug development: inhibition alone is not enough. Over time, cells find ways to reengage the same signaling pathways that were once silenced. A new class of therapies is changing that. Bristol Myers Squibb recently published a landmark study in Clinical Cancer Research describing BMS-986365, a first-in-class dual AR degrader and antagonist. By combining proteasomal degradation with receptor antagonism, BMS-986365 achieves a more profound and sustained blockade of AR activity than traditional inhibitors. The drug not only shuts down signaling but also removes the receptor protein responsible for resistance and relapse. Learn more about our Prostate Models Champions Oncology’s patient-derived xenograft (PDX) models CTG-2440 and CTG-2441 were instrumental in this discovery. These models were derived from the same patient before and after abiraterone therapy, creating a unique, clinically matched system for studying adaptive resistance. In both models, BMS-986365 was tested alongside enzalutamide to evaluate its impact on AR signaling in tumors that had already progressed on prior therapy. While the model developed before the patient received abiraterone showed modest increased sensitivity to BMS-986365 compared to Enzalutamide, BMS-986365 outperformed Enzalutamide when administered to the model developed after the patient progressed under abiraterone. Both drugs increased AR mRNA expression in treated tumors, q a typical feedback response to pathway inhibition but only BMS-986365, by virtue of its degrader activity, maintained low AR protein levels and continued to suppress AR target gene activity. This finding underscores the importance of degradation: while transcriptional upregulation persisted, the protein was degraded before it could restore signaling. The biological feedback loop driving adaptive resistance was disrupted during treatment. These insights speak to a broader principle in translational research: resistance is dynamic, not static. It doesn’t exist as a single genetic event, but as a continuum of cellular responses that evolve under therapeutic pressure. Capturing this dynamic behavior requires preclinical models that replicate the complexity of human disease including treatment history, adaptive feedback mechanisms, and the heterogeneous signaling environments of late-line tumors. For drug developers, this means that testing next-generation degraders and dual-mechanism agents cannot rely on traditional cell lines or simplified, outdated in vivo models. The field now demands functional, clinically grounded resistance models that measure more than endpoint response they must reveal how and why tumors adapt, and how new modalities can overcome that adaptation. Champions Oncology’s portfolio of pretreated and resistance-matched PDX models was built precisely for this purpose. By recreating clinical resistance within the same biological context in which it arises, these models provide a high-fidelity platform to evaluate degrader pharmacodynamics, durability of response, and combination potential. The BMS-986365 study offers a clear demonstration of their value: real-world resistance biology, translated into preclinical discovery that informed a novel therapeutic strategy. The success of BMS-986365 represents more than a promising drug—it marks a shift in how we define preclinical relevance. By pairing innovative therapeutics with equally advanced resistance models, the field is beginning to close the translational gap that has long limited success in late-stage prostate cancer. Discover Pretreated and Resistance-Matched PDX Models

Clinical attrition has been oncology’s oldest problem and, in many ways, still its biggest. The pattern is painfully familiar. A promising therapy emerges with encouraging preclinical data, advances through IND-enabling studies, shows early signals of activity in Phase I, and then fails in Phase II or Phase III. The financial costs of these failures are staggering, billions of dollars are lost globally each year. But the greater cost is measured in time and opportunity, years of development work invested, only to leave patients still waiting for new therapies. Despite decades of innovation, attrition rates in oncology haven’t shifted as much as the industry hoped. Better trial design and precision medicine strategies have helped in some areas, but the fundamental problem remains: the data we use to make early decisions often doesn’t capture the full reality of patient biology. Decoding the Cell Surface to Accelerate Discovery Why attrition remains so stubborn To understand why attrition persists, it’s worth looking at the foundation. Much of oncology R&D still relies on models and datasets that, while powerful, were never meant to carry the full burden of translational decision-making. Genomics is a prime example. Sequencing technologies have revolutionized how we classify tumors and identify potential targets. But tumors are not defined by their mutations alone. Transcriptional programs, proteomic signaling networks, post-translational modifications, and dynamic adaptations under treatment all contribute to how a tumor grows, evades therapy, and eventually resists intervention. A therapeutic strategy built solely on genetic alterations may miss the downstream biology that ultimately determines clinical outcome. Cell lines are another example. They are convenient, reproducible, and cost-effective, which is why they remain a staple of preclinical research. But they lack the heterogeneity and clinical context of patient tumors. They rarely reflect the complexity of pretreated, metastatic disease — exactly the patient populations that new oncology drugs are tested in. When early models don’t reflect the biology of the intended clinical population, it is not surprising that translation breaks down. Even when multi-omic data is available, it is often sparse, fragmented, or drawn from public repositories that were never built for translational research. These datasets may be useful for generating hypotheses, but they are rarely robust enough to support critical go/no-go decisions. And yet, in the absence of better resources, they are often asked to do just that. The gap between data and patients The result of this reliance on incomplete models is a gap between what we believe about a therapy and what happens when it is tested in patients. That gap is where attrition lives. It’s the difference between a drug that looks compelling in preclinical settings and one that can’t demonstrate sufficient efficacy or durability in the clinic. One concrete example comes from RNA and protein data. In acute myeloid leukemia (AML), large-scale analyses have shown that only about 17% of genes have a positive correlation between RNA expression and protein expression. That means if you are relying on transcriptomics alone to predict biology, you’re often looking at signals that don’t translate to the level where drugs actually act. This divergence isn’t unique to AML — it’s a reminder that single-omic views can give an incomplete or even misleading picture of tumor biology. Another example is in resistance biology. In pretreated patient-derived xenografts (PDX), resistance pathways are often “baked in” from the start, reflecting real-world clinical histories. These mechanisms are invisible in naïve cell lines, which haven’t experienced therapy. By working with tumors that already carry resistance features, researchers can anticipate escape mechanisms before they derail late-stage trials. What better data could look like If we accept that the root of the problem lies in the misalignment between early data and patient biology, then the question becomes: what would better data look like? First, it would need to come from models that are closer to the clinic. Patient-derived tumors, especially those from pretreated and metastatic populations, preserve the genetic complexity, phenotypic heterogeneity, and resistance mechanisms that cell lines cannot replicate. Studying these tumors allows us to see not just what cancer looks like in theory, but how it behaves in practice. Second, it would need to move beyond genomics into multi-omic depth. Genes matter, but so do the transcripts they produce, the proteins they encode, the phosphorylation states that regulate those proteins, and the cell surface markers that mediate interactions with the immune system or targeted therapies. Each of these layers adds context. And critically, each reveals discrepancies that can’t be seen in isolation. Take cell surface proteomics as an example. Traditional workflows for mapping the “surfacome” are plagued by noise and misclassification, which can lead to wasted effort on false targets. By capturing both plasma membrane and intracellular fractions, newer approaches provide cleaner enrichment and reduce false positives. The result is surface protein datasets that can actually be used to prioritize antibody, ADC, or CAR-T targets with confidence. That’s not a small improvement — it’s the difference between pursuing targets that work in patients and chasing dead ends. Third, it would need to incorporate functional context. Static descriptions of tumors, no matter how deep, tell us what’s there, but not how the tumor behaves under pressure. Functional assays that perturb tumors directly, whether through gene knockdowns or compound exposure, provide causal insights that correlation alone cannot. They show us how pathways respond, how resistance emerges, and how biology adapts. For example, siRNA knockdown studies in 3D PDX models can reveal dependencies that aren’t obvious from genomics alone. When combined with high-resolution transcriptomic profiling (what we call FunctionalSeq), these experiments identify pathways that are not only present but functionally essential. That’s the kind of information that can distinguish a biomarker from a true therapeutic target. What this means for pharma decision-making For pharma R&D leaders, the implications of this kind of data are significant. Instead of evaluating a candidate on a narrow slice of biology, you can assess it in the context of real patient tumors, profiled across multiple dimensions. You can compare across cohorts, understand potential resistance pathways earlier, and align therapeutic strategies with the biology most likely to be encountered in the clinic. Consider the decision to advance an asset into IND-enabling studies. In many organizations, this call is based primarily on genomic alignment, preliminary efficacy signals, and a limited view of resistance. Adding multi-omic and functional data changes the conversation. It allows teams to say, “Yes, the target is present at the DNA level, but the protein expression isn’t concordant,” or, “The mechanism looks strong in cell lines, but resistance emerges rapidly in pretreated PDX.” These insights don’t just inform science — they directly affect which assets receive investment and how development strategies are shaped. A future with fewer blind spots Attrition will always be a risk in oncology. Biology is unpredictable, and even the most carefully designed program may fail in the clinic. But the scale of today’s attrition, and the cost it imposes does not have to be inevitable. By aligning our early data more closely with patient reality, we can reduce blind spots, strengthen translational confidence, and make smarter decisions about which programs deserve to move forward. For pharma leaders, the payoff is not just fewer late-stage failures. It’s a more rational, efficient, and patient-centered pipeline. And for patients, it’s a better chance that the therapies entering trials are the ones with the greatest likelihood of success. That is the promise of stronger data and the reason it should be the starting point for oncology R&D. This isn’t just data. It’s a foundation for discovery.

As radiopharmaceutical therapies move from proof-of-concept to clinical investment, preclinical efficacy data plays an outsized role in shaping go/no-go decisions. But not all efficacy studies are created equal. Traditional in vivo models often fall short in capturing the biological variability that influences drug performance in patients leading to overly optimistic interpretations and costly setbacks downstream. That’s where patient-derived xenograft (PDX) models provide a distinct advantage. Unlike cell line–based systems, PDX models preserve the molecular, phenotypic, and histological heterogeneity found in human tumors, enabling more realistic evaluations of how radiopharmaceuticals behave across diverse tumor types. In this post, we explore how tumor diversity impacts radiopharmaceutical efficacy readouts, why it matters for translational success, and how the right PDX strategy can strengthen early decisions on compound prioritization, dose optimization, and biomarker alignment. PDX Models + Radiopharmaceuticals = Translational Power The Challenge with Cell Line–Based Tumors in Preclinical Efficacy? Conventional xenograft models, particularly those directly derived from established cell lines (CDX), have been a mainstay of preclinical oncology research. Their reproducibility and ease of use make them convenient, but their biological homogeneity is also their greatest limitation, especially for evaluating targeted therapies such as radiopharmaceuticals. CDX models typically originate from immortalized cell lines propagated in vitro for years. As a result, they display homogeneous antigen expression, clonal architecture, simplified stromal and vascular features, and a lack of microenvironmental complexity. These characteristics often inflate perceived efficacy. Uniform antigen presentation can lead to idealized tumor uptake, while consistent growth kinetics and structure can overstate the durability and magnitude of response. Drugs that appear highly potent in CDX models often underperform when tested against the biological heterogeneity of patient tumors. Because CDX models fail to capture inter-patient and intra-tumoral variability, they offer little insight into how a drug might behave across subsets of patients. That gap matters, since therapeutic index, antigen density, and radiosensitivity all vary widely in the clinic. The “clean” signals that CDX models produce may look promising on paper but can be misleading. For radiopharmaceutical programs, where tumor retention, dose-response, and antigen heterogeneity shape success, more clinically faithful models are essential. How Tumor Diversity Affects Radiopharmaceutical Response Radiopharmaceutical efficacy depends heavily on biological context. Antigen density, vascular accessibility, tumor perfusion, and radiosensitivity all differ not just from patient to patient, but even across different regions of the same tumor. This variability shapes how radiopharmaceuticals behave in vivo. Heterogeneous antigen expression may result in only partial tumor coverage, which reduces therapeutic effect. Vascular differences and interstitial pressure can limit isotope delivery and retention. Radiosensitivity, influenced by DNA repair pathways, hypoxia, and tumor subtype, alters how readily tumors undergo radiation-induced cell death. Models that reflect this complexity are critical for generating data that predicts what happens in the clinic. PDX models, derived directly from treatment-naïve or pretreated patient tumors, retain the diversity of native tumor architecture. Studying drug response across panels of heterogeneous PDX models helps developers see which tumor types or biologically distinct variants within a disease are more likely to respond. It also reveals whether efficacy correlates with specific biomarkers, what dose ranges perform consistently across variable biology, and where resistance mechanisms are likely to emerge. Tumor diversity is not noise to be eliminated, it is a vital translational signal. Recognizing it early allows developers to refine compound selection, optimize dosing strategies, and build preclinical hypotheses that stand a better chance of holding true in patients. PDX Models as a Tool for High-Resolution Efficacy Readouts Patient-derived xenograft models offer a far more realistic view of radiopharmaceutical performance. Because they maintain heterogeneity, microenvironmental features, and in many cases prior treatment history, they give researchers a nuanced way to evaluate efficacy. For radiopharmaceuticals, PDX models deliver several advantages. They present clinically relevant antigen variability that allows researchers to assess uptake and response across a realistic spectrum of tumors. They preserve human-like tumor morphology, which helps predict intratumoral diffusion and retention. Their fidelity across passages ensures histology and molecular markers remain stable. And because study design can be adapted across tumor subtypes, expression levels, and therapeutic contexts, they enable head-to-head comparisons that are both versatile and reliable. Incorporating PDX models into efficacy studies unlocks more than traditional tumor growth inhibition curves or survival metrics. It allows teams to analyze dose-response relationships across diverse biology, track differences in duration of response, map model-specific radiosensitivity trends, and explore correlations with genomic or phenotypic biomarkers. Champions Oncology’s PDX platform builds on this by linking PDX models to clinical, genomic, and treatment response data. Tumors can even be pre-screened with tissue microarrays to identify expression-positive cases for targeted agents, streamlining model selection and aligning studies with clinical goals. For developers, the outcome is clear: studies that reflect the diversity of the patient population rather than a single optimized tumor line. Translating Efficacy Insights into Clinical Strategy Radiopharmaceuticals combine targeted delivery with localized radiation, but their success depends on early validation in models that matter. PDX-based efficacy studies don’t just generate more realistic data, they provide strategic guidance that shapes clinical development. Testing compounds across representative tumor panels reveals which patients are most likely to respond. It helps optimize dose selection and fractionation strategies by showing how retention and radiosensitivity vary across models. It uncovers resistance patterns tied to tumor phenotype, guiding combination strategies. And it strengthens IND-enabling packages by grounding them in data that reflects real-world heterogeneity. Crucially, these insights can differentiate active agents from niche responders, making trial design sharper and reducing the risk of failure from overgeneralized assumptions. Combined with biomarker data, they can even inform companion diagnostic strategies, connecting uptake and efficacy to measurable markers of patient eligibility. Conclusion: Tumor Diversity Is Not a Variable - It’s a Vital Input The path to effective, targeted radiopharmaceuticals depends on more than clever chemistry or potent payloads. It requires a clear understanding of how these agents behave across the complex biological spectrum seen in patients. PDX models, with their preserved heterogeneity and clinical relevance, offer a translational advantage that’s hard to ignore. By designing radiopharmacology studies that embrace, rather than eliminate tumor diversity, radiopharmaceutical developers can make earlier, smarter decisions that de-risk development, sharpen clinical strategy, and ultimately improve the odds of success. If you're committed to building radiopharmaceuticals that perform where it matters mos. In patients, it’s time to elevate how you evaluate efficacy. The Only CRO Pairing PDX Models with Radiopharma

In antibody–drug conjugate (ADC) development, knowing where your drug goes and whether it’s doing what you designed it to do can make the difference between success and costly setbacks. Radiochemistry offers a powerful way to generate that insight, using radioactive isotopes to “tag” antibodies, payloads, or both so their journey through the body can be tracked with precision. In this guide, we’ll break down the basics of radiochemistry for ADC teams, explain key concepts in radioisotope selection, and share practical tips to avoid common pitfalls. What Is Radioactivity and Why It Matters for ADCs Radioactivity is the process by which an unstable atomic nucleus releases energy (decay) to become more stable. For ADC developers, understanding these fundamentals is critical: Half-life (t½): The time it takes for half of the atoms to decay. This must align with your ADC’s pharmacokinetics (PK) so the isotope’s signal lasts long enough to track distribution and clearance. Decay type: Determines the detection method. For example, PET (Positron Emission Tomography) uses isotopes that emit positrons, which allows scientists to create very detailed, 3D images of how a tracer moves and accumulates in the body. SPECT (Single Photon Emission Computed Tomography) uses isotopes that emit gamma rays, producing images that show biological activity and how a drug or tracer behaves over time. Specific activity: Radioactivity per unit mass; higher values mean you can label with very little isotope, minimizing interference with binding or PK. Decay Modes and Detection Methods Different isotopes release different types of radiation, which affects how they’re used: Alpha (α): Heavy, short-range particles, mainly for therapeutic applications (e.g., Ac-225). Beta minus (β–): Electrons; common in therapeutic isotopes like Lu-177 and I-131. Beta plus (β+) / positrons: Produce PET photons for high-resolution imaging (e.g., Zr-89, Cu-64). Gamma (γ): Photons detected in SPECT imaging (e.g., In-111). Tip: Match decay type to your goal — positron emitters for imaging, beta/alpha for therapy, gamma for SPECT tracking. The Only CRO Pairing PDX Models with Radiopharma Key Concepts in Isotope Selection for ADCs Match half-life to PK: Zr-89 for long-lived antibodies; In-111 for mid-timescale studies; I-131/Lu-177 for longer courses or therapy-linked readouts. Generally, the payload should be radiolabeled with short half-life isotopes. Preserve function: Choose high-specific-activity materials and gentle conjugation chemistry. Regulatory fit & supply: Pick isotopes with robust supply chains and established handling/documentation. Imaging vs therapy: Imaging isotopes maximize detectability; therapeutic isotopes are chosen for their cytotoxic radiation. Labeling Strategies for ADCs Direct labeling (iodination) Attaches iodine isotopes (I-123/124/125/131) directly to tyrosines / histidines in the antibody. Fast and efficient but in vivo radio deiodination can occur. Residualizing tags may be used to avoid in vivo radio deiodination Indirect labeling (chelation) Uses a bifunctional chelator (e.g., DFO for Zr-89; DOTA for Lu-177) conjugated to the antibody before loading the isotope. Offers higher in vivo stability; chelator choice depends on isotope chemistry. However, although not usual, the chelators could change the PK and/or biological activity of the antibody. Payload labeling Isotope is attached to the cytotoxic payload to monitor release and clearance. Can be combined with antibody labeling (dual-label) to differentiate intact ADC from free payload. Common Pitfalls and How to Avoid Them Label instability: Choose isotope–chelator pairs with proven in vivo stability. Biological alteration: Avoid harsh labeling conditions that can impair binding or PK. PK mismatch: Don’t use a half-life that’s too short to capture late-phase distribution or clearance. Quick Reference: Isotopes for ADC Applications Isotope Half-life Emission Type Imaging Modality / Typical Use Typical Tracking Window Notes Zr-89 78.4 h (~3.3 d) β+ (positron) PET imaging – ideal for long-lived antibodies Up to 7–10 days Matches antibody PK; provides high-resolution PET images for extended studies Lu-177 6.65 d β– (beta) Therapeutic payload; can also support imaging Days to 1–2 weeks Dual-use radionuclide (therapy + imaging); strong track record in radiopharma I-131 8.02 d β–, γ (beta and gamma) Therapy and imaging for antibody/payload ADME 1–2 weeks Widely used in radioimmunotherapy; dual imaging/therapy capacity In-111 2.8 d γ (gamma) SPECT imaging – mid-timescale biodistribution 1–5 days typical (up to ~10 with cut-and-count) Best suited for 1–5 day studies; imaging resolution optimal in shorter window Ac-225 ~10 d α (alpha) Targeted alpha therapy Days to weeks (therapy-focused) Very high linear energy transfer (LET); highly cytotoxic, therapeutic only Cu-67 2.6 d β– (beta) Therapy; theranostic partner with Cu-64 Several days Can be paired with Cu-64 PET (same chemistry) for theranostic workflows Using Radiotracers in PDX and CDX Models Radiotracer studies can be performed in a range of preclinical models, but model selection directly affects how translatable your data will be. The two most common approaches for ADC biodistribution are patient-derived xenograft (PDX) models and cell line-derived xenograft (CDX) models. PDX Models What they are: Tumors from actual patients implanted into immunodeficient mice, retaining original histology and molecular characteristics. Strengths: Closely mimic human tumor biology, heterogeneity, and target expression; often more predictive of clinical outcomes. Weaknesses: More variable growth rates, higher cost, and sometimes limited availability for rare targets. CDX Models What they are: Tumors grown from established cancer cell lines implanted into mice. Strengths: Easier to grow, faster to establish, and highly reproducible; good for early proof-of-concept and method development. Weaknesses: Less heterogeneity and may not fully recapitulate the target expression or microenvironment seen in patients. Choosing the Right Model For ADC radiotracer studies, CDX models can be a cost-effective starting point to validate isotope choice and labeling chemistry, while PDX models are best for confirming biodistribution and target engagement in a clinically relevant setting before moving to the clinic. Many developers use both — starting in CDX for feasibility and scaling into PDX for translational validation. Study Design Checklist for ADC Radiolabeling Define your primary question: target engagement, linker stability, payload distribution, or therapy evaluation. Select isotope(s) to match PK and imaging/therapy needs. Choose labeling chemistry that preserves ADC function. Plan imaging modality and sampling timepoints to capture both early and late phases. Combine imaging with ex vivo biodistribution for quantitative confirmation. Include mass-dose escalation to determine receptor saturation. Radiochemistry isn’t just attaching an isotope, it’s matching the right isotope, chemistry, and study design to your ADC’s biology.

Navigating New FDA Guidance for Radiopharmaceuticals Radiopharmaceuticals are emerging as one of the most promising frontiers in oncology, offering the ability to deliver targeted radiation directly to cancer cells while sparing healthy tissue. While the concept of using radiation in cancer therapy dates back almost a century with external beam radiotherapy, today’s systemically delivered radiopharmaceuticals represent an entirely new wave of innovation. With this promise comes new complexity, and the FDA recently released new draft guidance, Oncology Therapeutic Radiopharmaceuticals: Dosage Optimization During Clinical Development (2025), to raise the standards for how these therapies are developed and evaluated. Michael Ritchie, Chief Commercial Officer at Champions Oncology, has seen this evolution firsthand. With over a decade at Champions leading commercial operations and contributing to R&D strategy, he recognizes both the opportunity and the challenges of this emerging space. Raising the Bar: What the FDA Wants The FDA’s new guidance makes clear that more is expected of sponsors developing radiopharmaceuticals. Beyond simply demonstrating efficacy, companies must now generate robust data on pharmacodynamics, therapeutic windows, and toxicity. Regulators want to see meaningful insights into dosimetry, acute toxicities, and long-term safety profiles. They are also encouraging developers to test different doses and schedules to better define the boundaries of both safety and effectiveness. In short, the FDA is signaling that radiopharmaceuticals must be studied with the same rigor applied to other advanced oncology therapeutics, while also accounting for the unique properties of radioactive isotopes. PDX Models + Radiopharmaceuticals = Translational Power The Scientific Unknowns Despite the excitement surrounding radiopharmaceuticals, there is still much we do not know. Unlike antibody-drug conjugates, where decades of research have built a strong understanding of organ tolerability and patient management, radiopharmaceuticals remain an open field. Key questions persist: Which tumor types respond best? What are the predictable toxicities? What makes for the most effective construct from a pharmaceutical sciences perspective? Answering these questions is essential if these therapies are to fulfill their clinical potential. The Logistical Hurdles Beyond the science, logistics present another layer of challenge. Radiopharmaceuticals operate in a highly regulated space tied to nuclear medicine. Isotopes are not always easy to source, and many clinical sites lack the infrastructure or certification to administer them. This constrains patient enrollment and trial execution. Compounding the issue, isotopes have a short half-life and shelf life. Unlike traditional drugs that can be stored for months, radiopharmaceuticals must be manufactured and administered almost immediately, requiring precise planning and coordination. Charting the Path Forward The future of radiopharmaceuticals is undeniably bright, but realizing their full potential will require navigating both scientific unknowns and logistical barriers. The FDA’s Oncology Therapeutic Radiopharmaceuticals guidance provides structure, but it also highlights the complexity of the journey ahead. Success will depend on deeper biological insights, smarter trial design, and operational excellence in manufacturing and delivery. The Only CRO Pairing PDX Models with Radiopharma At Champions Oncology, we are committed to partnering with pharma and biotech innovators to address these challenges head-on. By leveraging our advanced preclinical models and translational platforms, we help generate the data and confidence needed to accelerate radiopharmaceutical development and bring these groundbreaking therapies closer to patients.

In a recent video interview with Pharmaceutical Technology, Mike Ritchie, Chief Commercial Officer at Champions Oncology, shared his perspective on the fast-evolving field of radiopharmaceuticals. Drawing on his 20 years of experience in cancer research and leadership roles at Pfizer and Champions, Mike highlighted what makes radiopharmaceuticals unique, the challenges they present, and how advanced models and real-world data are shaping their development. Q: What is unique about the use of radiopharmaceuticals to treat cancer? Radiopharmaceuticals have actually been around for nearly a century. In the early days, radiation was directed externally using large machines to treat tumors. Over time, the technology evolved to address internal tumors as well. What makes today different is the transformation inspired by antibody-drug conjugates (ADCs). ADCs showed us that toxic payloads could be delivered precisely to tumors, sparing healthy tissue. After decades of learning how to design and manufacture ADCs effectively, we now understand what makes a targeted therapy work. Radiopharmaceuticals are taking a similar approach. By linking radioactive isotopes—what Mike calls “radio ligands”—to antibodies, scientists can deliver radiation directly to cancer cells. This creates radio-drug conjugates, an exciting new mechanism of action that could address unmet needs, expand the therapeutic index, and potentially offer safer treatment options for patients. PDX Models + Radiopharmaceuticals = Translational Power Q: What are the challenges associated with developing radiopharmaceuticals? The hurdles fall into two categories: drug development and tumor biology. From a development standpoint, researchers are still learning how to design radiopharmaceuticals that are stable, manufacturable at scale, and behave predictably in patients. Linking a radioligand to an antibody is a complex chemistry challenge, and improving these linkers remains a key opportunity for the field. Biologically, we are just beginning to understand which tumor types and molecular subgroups are most responsive. As with other therapies, some cancers will respond while others develop resistance. Identifying the right patient cohorts will be critical as these drugs advance through clinical development. Q: How are PDX models used in radiopharmaceutical testing? Patient-derived xenograft (PDX) models play a central role in this area. They allow researchers to test radiopharmaceuticals in tumor models that more closely mirror the clinical setting. Patients in clinical trials are often heavily pretreated, with metastatic disease across multiple organs. These advanced lesions are the ones that ultimately determine patient outcomes, so therapies must be tested against representative biology. At Champions, large PDX libraries are used to simulate clinical trials in animals, testing drugs across diverse tumors. The results not only reveal where a therapy works, but also highlight the molecular features linked to response or resistance. This helps guide patient selection strategies for clinical trials. Q: What role does real-world data play in radiopharmaceutical development? Real-world data provides critical context: how patients are treated, how they respond, the stage and metastatic nature of their disease. But Mike emphasizes that pairing this clinical information with deep molecular data is where the real value lies. Champions is unique in combining these two layers. Their datasets link real-world patient outcomes with rich tumor profiling, including whole-exome sequencing, RNA-seq, proteomics, phospho-proteomics, and cell surface proteomics. This combination allows researchers to uncover vulnerabilities in tumors, predict resistance mechanisms, and design smarter clinical trials. When companies run simulated trials at Champions, they can leverage this integrated dataset to refine predictions and improve translational relevance. Closing Thoughts As radiopharmaceuticals move from concept to clinic, success will depend on solving both development and biology challenges. The lessons learned from ADCs are accelerating progress, but identifying the right patients and optimizing drug design remain key hurdles. By combining advanced preclinical models with integrated real-world and molecular data, Champions Oncology is helping researchers de-risk development and bring this promising new class of therapies closer to patients who need them most. The Only CRO Pairing PDX Models with Radiopharma

Antibody-drug conjugates (ADCs) have transformed oncology by combining targeted delivery with potent cytotoxic payloads. But traditional preclinical studies, focused on tumor growth inhibition, often leave blind spots in understanding how these complex molecules behave in the body. That’s where radiolabeling changes the game. In our recent webinar, Radiotracers and ADC Targeting: Scientific Insights for In Vivo Validation and Clinical Transition, experts Dr. Denis R. Beckford-Vera (Champions Oncology) and Dr. Shankar Vallabhajosula (Weill Cornell Medicine, Convergent Therapeutics) unpacked the science and strategy behind using radiotracers to generate decision-driving data for ADC development. WEBINAR Radiotracers and ADC Targeting: Scientific Insights for In Vivo Validation and Clinical Transition Why Radiolabeling Matters for ADCs Unlike unconjugated antibodies, ADCs can vary significantly in pharmacokinetics, biodistribution, and biotransformation depending on conjugation methods, linker chemistry, and payload properties. These factors directly influence efficacy and safety. Radiolabeling both the antibody and/or payload enables you to: Quantify tissue distribution across the whole body Assess target engagement in tumors versus off-target organs Predict efficacy earlier in development Measure payload release and clearance for linker optimization Key Takeaways from the Webinar 1. Dual Radiolabeling Reveals the Full Picture By attaching one isotope to the antibody (e.g., Zr-89) and another to the payload (e.g., I-131), researchers can simultaneously track both components without signal interference. This approach reveals where the conjugate stays intact and where the payload is released, a critical insight for linker stability and safety profiling. 2. Imaging as a Predictor of Response Molecular imaging with Zr-89–labeled ADCs has shown that tumor uptake patterns can predict which preclinical models respond to therapy. In responsive models, tumor-to-blood ratios are higher and sustained, while non-responsive models show uptake similar to non-specific controls. 3. Dual-Mechanism Therapeutic Potential Pairing a radiolabeled antibody with a chemotherapeutic payload in a single ADC can produce additive or even synergistic anti-tumor effects, as demonstrated with Lu-177 + MMAE combinations. 4. Translating Preclinical Insight to the Clinic In a HER3-targeting immuno-PET study, Zr-89–labeled antibodies were used to measure dose-dependent target engagement in patients. Imaging revealed saturable tumor uptake at higher mass doses, confirming target specificity and informing dose optimization strategies—insights not possible from biopsy alone. The Champions Oncology Advantage Champions Oncology integrates radiochemistry, in vivo imaging, biodistribution, and PDX model expertise into a streamlined workflow. This enables ADC developers to: Validate tumor targeting in clinically relevant models Quantify on-target and off-target uptake with precision Generate high-quality biodistribution and PK data to support IND submissions and partner discussions Ensure secure isotope supply for uninterrupted studies Watch the Full Webinar The blog you’re reading is just the highlights. In the full webinar, Radiotracers and ADC Targeting: Scientific Insights for In Vivo Validation and Clinical Transition, Dr. Beckford-Vera and Dr. Vallabhajosula dive into: Radiochemistry techniques for antibodies and payloads Case studies from approved and experimental ADCs Isotope selection and chelation strategies Translational use cases bridging preclinical and clinical studies Watch the full recording to see how radiolabeling can de-risk your ADC program, accelerate timelines, and improve the odds of clinical success. Watch the Webinar: Radiotracers and ADC Targeting