Trends in Oncology

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

Antibody-drug conjugates (ADCs) continue to generate momentum in oncology, offering the promise of targeted cytotoxic delivery with greater specificity and reduced systemic toxicity. However, the complexity of these molecules, from antigen binding and internalization to payload release and clearance, makes them notoriously difficult to optimize. Many ADC programs stall not because the payload is ineffective, but because developers lack insight into whether the antibody is reaching and engaging the target in vivo. Radiotracers offer a unique opportunity to bridge this knowledge gap. By radiolabeling antibodies, , or ADC-like constructs with radioisotopes, researchers can evaluate in vivo biodistribution, tumor targeting, and off-target accumulation long before clinical trials. These tools provide a non-invasive, high-resolution view of compound behavior that traditional pharmacokinetic assays or histology cannot capture alone. In this article, we explore how radiotracers techniques, when combined with clinically relevant preclinical models, are helping ADC developers de-risk critical decisions earlier in the development cycle. From model selection to target validation to biodistribution profiling, radiotracers are becoming an indispensable part of the ADC toolkit. PDX Models + Radiopharmaceuticals = Translational Power Understanding Risk in ADC Development The appeal of ADCs lies in their elegant concept: deliver a cytotoxic payload directly to cancer cells via a highly specific antibody, sparing healthy tissue. In practice, however, ADC development is fraught with failure points. Many candidates show limited efficacy or unacceptable toxicity — not due to poor payload design, but because of incomplete understanding of how the construct behaves in vivo. Key risks include: Heterogeneous or insufficient target expression, which leads to poor tumor uptake Off-target accumulation in antigen-expressing normal tissues Suboptimal pharmacokinetics, including premature clearance or payload release Lack of internalization or poor intracellular trafficking, reducing payload delivery These risks are difficult to detect with traditional in vitro methods alone. Even when early pharmacology data appear favorable, translational failures often emerge when the compound enters more physiologically complex systems or encounters unexpected biological variability. What ADC developers need is a translational lens into antibody behavior — one that captures how the full construct distributes across tumor and normal tissue, how long it persists at the target site, and how that behavior varies across models. That’s where radiotracers come in. Radiolabeling ADCs to Visualize Biodistribution and Target Engagement Radiolabeling ADCs with radioisotopes enables developers to evaluate compound behavior with far greater granularity than traditional methods. By tagging the antibody or the full ADC construct with an isotope such as Zirconium-89 (Zr-89), Lutetium-177 (Lu-177) or Indium-111 (In-111), researchers can track distribution, uptake, and retention across tissues and tumor models in real time. This technique serves multiple functions: Biodistribution profiling: Understand where the antibody accumulates, and how much it localizes to the tumor versus healthy organs Target engagement assessment: Confirm that the biologic is reaching antigen-positive tumors with sufficient intensity and duration In vivo tumor targeting characterization in well characterized PDX models: Understand ADC distribution variability across PDX with preserved inter- and intra-tumoral heterogeneity found in human cancers. Off-target surveillance: Identify unintended uptake in antigen-expressing normal tissues (e.g., liver, spleen, bone marrow) early in development (depending on cross reactivity) Comparative evaluation: Screen multiple antibodies, formats, or linkers to determine which offers the best tumor-to-background ratio Radiolabeled ADC constructs can be studied across multiple timepoints to assess dynamic distribution and clearance profiles. When paired with well-characterized preclinical models—particularly those that reflect human heterogeneity in target expression, this approach allows for an evidence-based refinement of lead selection and study design. In addition to guiding compound optimization, radiolabeled compounds generate data that supports IND-enabling work by illustrating tumor specificity and helping predict potential toxicity risks related to off-target delivery. Using PDX Models to Reflect Real-World Variability in Antigen Expression While radiolabeling provides a powerful tool for visualizing distribution, the choice of model system ultimately determines how meaningful those insights will be. Cell line–derived xenografts (CDX), though commonly used, often overexpress the target antigen in a uniform and artificial manner. This can mask important limitations in targeting specificity and distribution, leading to false confidence in a compound’s performance. In contrast, patient-derived xenograft (PDX) models preserve the inter- and intra-tumoral heterogeneity found in human cancers. Differences in antigen density, vascularization, stromal composition, and tumor architecture all impact how an ADC or radiolabeled construct will behave in vivo. Testing compounds across a panel of PDX models allows developers to assess performance across a range of real-world tumor phenotypes, gaining visibility into variability that may influence clinical response. Champions Oncology’s Lumin platform includes hundreds of PDX models annotated with genomic, phenotypic, and treatment-response data. These models can be screened in advance using tissue microarrays (TMAs) to identify tumors with varying levels of antigen expression, enabling strategic model selection and rational study design. In the context of radiopharmaceuticals or radiolabeled ADCs, this means developers can: Evaluate targeting across low, mid, and high-expressing tumors Identify models most likely to mirror patient response Explore relationships between uptake and known molecular drivers By integrating radiolabeling with clinically annotated, heterogenous models, developers gain a more complete picture of how a compound is likely to perform across the clinical population —and avoid late-stage surprises. From Imaging to Strategy: De-Risking ADC Development Earlier in the Pipeline The high cost and complexity of ADC development demand early, informed decision-making. Traditional pharmacology and histology provide critical insights, but they don’t tell the whole story, especially when it comes to understanding how a biologic behaves in real biological systems over time. Radiotracers tools fill that gap by enabling non-invasive, temporal, and quantitative evaluation of ADC behavior across diverse, clinically relevant models. When used early in development, radio labeling and biodistribution studies can help developers: Select better antibody constructs or formats based on real in vivo performance Prioritize linker-payload combinations with favorable pharmacokinetics and tumor retention Predict potential toxicity or dosing issues from off-target accumulation Justify model selection and dose rationale in regulatory submissions By integrating these insights into the design phase — not as a retrospective check — developers can refine their therapeutic strategy while reducing attrition risk later in the pipeline. Radiotracers not only illuminate compound behavior but also serve as a translational bridge that connects target biology, delivery, and clinical feasibility. In a field where timelines are long, investment is high, and failure is costly, this approach offers a pragmatic and data-rich path forward: study smarter, screen earlier, and develop with confidence. The Only CRO Pairing PDX Models with Radiopharma

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

Imagine a world where doctors can predict how a tumor will respond to treatment before a patient starts therapy. For decades, cancer researchers have relied on The Cancer Genome Atlas (TCGA), a massive dataset of 20,000 samples across 33 cancer types, to decode the molecular secrets of tumors. But TCGA has a blind spot: it mostly studies untreated, primary tumors, leaving critical questions about advanced cancers and treatment responses unanswered. Enter the Champions Oncology TumorGraft® platform—a game-changer in cancer research. With 1,500 patient-derived tumor models from over 50 cancer types, TumorGraft® captures the real-world complexity of advanced, metastatic, and heavily treated tumors. By combining molecular data with detailed pretreatment histories and treatment response insights, it’s opening new doors for predicting drug effectiveness, uncovering resistance mechanisms, and designing smarter therapies. In this post, we’ll explore how TumorGraft® complements TCGA, using a fascinating case study on mutational signatures to show its power. TCGA: The Gold Standard with Limits TCGA is a cornerstone of cancer research. It's publicly accessible, high-quality data, spanning DNA, RNA, and proteins, has fueled countless discoveries about how tumors develop. Researchers use it to characterize tumors, improve diagnoses, and identify molecular drivers of cancer. But TCGA isn’t perfect. Most of its samples come from primary, untreated tumors, with only a small fraction (about 42 patients) having received neoadjuvant treatment. This makes TCGA ideal for studying cancer’s early stages but less useful for advanced, metastatic, or post-treatment tumors. It also underrepresents certain populations and cancer stages, lacks pretreatment histories, and doesn’t allow access to physical samples for follow-up experiments. If you’re studying treatment resistance or real-world patient outcomes, TCGA’s data can only take you so far. TumorGraft®: A Window into Advanced Cancers The TumorGraft® platform, developed by Champions Oncology, flips the script. Its 1,500 patient-derived xenografts (PDXs)—tumors grown in mice to mimic human cancer—represent over 50 cancer types, focusing on advanced-stage, metastatic, and pre-treated tumors. These models reflect the diversity and complexity of patients seen in clinics, where cancers often evolve under the pressure of multiple therapies. Unlike TCGA, TumorGraft® includes detailed pretreatment information, capturing the therapies patients received before their tumors were sampled. This data, which can be further mined, offers a window into how prior treatments shape tumor biology, enabling researchers to study real-world clinical scenarios. Combined with treatment response data from in vivo experiments, where Champions Oncology has tested drugs representing multiple standards of care and measured Tumor Growth Inhibition (TGI)—how much a drug slows tumor growth—TumorGraft® unlocks use cases TCGA can’t touch: • Predicting Treatment Responses: See how a tumor’s molecular profile and pretreatment history predict its reaction to specific drugs. • Discovering Biomarkers: Identify markers that signal whether a treatment will work, informed by prior therapies. • Understanding Resistance: Study why some tumors resist therapy and find ways to overcome it, leveraging pretreatment data. • Improving Combination Therapies: Test drug combinations to find the most effective mixes, considering treatment histories. • Finding New Drug Targets: Link molecular features and pretreatment patterns to treatment outcomes to uncover novel therapies. Mutational Signatures: A Shared Language To prove TumorGraft’s® reliability, researchers compared it to TCGA using mutational signatures—DNA damage patterns that act like fingerprints, revealing what caused a tumor, like UV light or faulty DNA repair. These signatures, cataloged in the COSMIC database, help identify vulnerabilities in tumors and guide treatment strategies. The analysis began by curating COSMIC signatures and calculating their exposure and frequency in both TCGA and TumorGraft® datasets. Figure 1: Diagram showing calculated exposure/frequency of tumors in TumorGraft® (left) and TCGA (right). The results were striking. Both datasets flagged similar patterns. For example, SBS7a—a signature tied to UV light exposure (COSMIC SBS7a)—was strongly linked to melanoma in both TumorGraft’s® PDX models and TCGA’s samples. Analysis of 1,155 PDXs revealed SBS7a as a hallmark of melanoma, mirroring TCGA’s findings. This alignment shows that TumorGraft’s® data is as biologically accurate as TCGA’s, despite its focus on advanced, treated tumors. Figure 2: Chart of multi-omic data analysis from 1,155 TumorGraft® PDXs, highlighting trends in mutational signatures across tumor types. Figure 3: Bar chart showing SBS7a mutational signature prevalence in melanoma PDXs from TumorGraft®, with high association compared to other cancer types. Researchers analyzed signatures using different motifs—single base substitutions (SBS96), double base substitutions (DBS78), and insertions/deletions (IND83). All signatures found in TumorGraft® appeared in TCGA, with TCGA’s larger sample size revealing a few extra patterns due to its scale. Figure 4: Comparison of mutational signatures across SBS96, DBS78, and IND83 motifs in TumorGraft® and TCGA datasets. A UMAP plot, a visual tool that maps data similarity, confirmed that tumors cluster by type (e.g., melanoma, lung), not by whether they came from TCGA or TumorGraft®. This suggests that biology, not the data source, drives the differences—a green light for using TumorGraft® alongside TCGA. Figure 5: UMAP scatterplot showing tumors clustered by type, not data source, confirming TumorGraft’s® biological consistency with TCGA. Figure 6: Dot plot showing associations between mutational signatures and metadata, with dot size indicating event frequency and color representing p-value. Notably, tumors with signatures SBS6, SBS15, and SBS10b showed resistance to alkylating agents and platinum-based chemotherapies, exhibiting lower TGI. According to COSMIC, SBS6 and SBS15 are linked to defective DNA mismatch repair, common in microsatellite-unstable tumors, while SBS10b is tied to mutations in DNA polymerase epsilon, often seen in hypermutator tumors. By mining pretreatment data, researchers can explore how prior therapies influence these resistance patterns, offering clues to personalize treatments and avoid ineffective drugs. Figure 7: Graph showing correlation between SBS6, SBS15, and SBS10b signatures and resistance to alkylating agents/platinum in TumorGraft® PDXs, with lower tumor growth inhibition (TGI). This is just the beginning. TumorGraft’s® pretreatment data, combined with patient treatment histories and in vivo responses to thousands of standard-of-care drugs, is a goldmine for studying how prior therapies shape tumor evolution and treatment outcomes. Why TumorGraft® Matters The similarities between TCGA and TumorGraft’s® mutational signatures prove that TumorGraft® is a reliable partner to the gold standard. But its focus on advanced, treated tumors, detailed pretreatment information, and treatment response data takes cancer research to new heights. The ability to mine pretreatment histories—unavailable in TCGA—enables researchers to uncover how past therapies influence tumor biology, paving the way for personalized medicine and better patient outcomes. Whether you’re developing new drugs, tackling treatment resistance, or designing combination therapies, TumorGraft® provides insights that TCGA can’t. Looking ahead, researchers can dig deeper with TumorGraft® by exploring copy number signatures, RNA sequencing, or proteomics. Mining pretreatment data alongside these analyses could reveal even more about how tumors evolve under therapeutic pressure, driving breakthroughs in cancer care. Ready to Transform Cancer Research? The TumorGraft® platform is more than a dataset—it’s a bridge to personalized medicine. By combining the molecular depth of TCGA with TumorGraft’s® real-world treatment and pretreatment insights, researchers can unlock answers that bring us closer to curing cancer. Want to explore TumorGraft® for your next study? Learn how our platform can power your research today. Note: Data sourced from Champions Oncology and validated against TCGA’s data using the mutational motifs in COSMIC.

Antibody-drug conjugates (ADCs) represent a cutting-edge advancement in cancer therapy. These unique biopharmaceuticals act as "smart bombs," combining monoclonal antibodies specifically targeting cancer cells with potent cytotoxic drugs delivered directly to the tumor site. This precise targeting reduces collateral damage to healthy cells, minimizing adverse effects. Given the growing adoption of ADCs in clinical oncology, predicting their efficacy has become a critical challenge. Factors such as tumor heterogeneity, antigen expression, and individual patient differences underscore the need for precise biomarkers and advanced tools to determine patient suitability. The integration of immunohistochemistry (IHC) and next-generation sequencing (NGS) has emerged as a powerful approach to refining this prediction process. This blog explores the challenges of predicting ADC efficacy, the roles of IHC and NGS, and how these technologies are shaping the future of ADC-based therapy. The Importance of Predicting ADC Efficacy Why Predicting ADC Outcomes is Crucial? The therapeutic landscape of ADCs continues to evolve, with several FDA-approved ADCs and many others progressing through clinical trials. However, not all patients with cancer respond to these therapies, making the prediction of ADC efficacy vital to ensuring optimal outcomes. Key considerations include: • Target Antigen Expression: ADCs' performance depends on the presence and density of specific antigens on tumor cells. • Tumor Heterogeneity: Variability in antigen expression within and between tumors can impact ADC penetration and effectiveness. • Resistance Mechanisms: Both primary and acquired resistance to ADCs challenge their sustained efficacy. The Role of Immunohistochemistry in Predicting ADC Efficacy How IHC Works in ADC Therapy? Immunohistochemistry (IHC) is a gold standard for detecting protein expression within tumors. By applying antigen-specific antibodies to tissue samples, IHC enables visualization and quantification of target antigens. For ADCs, this method is highly valuable in determining whether a patient’s tumor expresses the antigen necessary for ADC binding and delivery. Benefits of Using IHC for ADC Target Assessment • Direct Visualization: Precise localization of target antigens not only confirms presence but also identifies antigen distribution within the tumor. • Threshold Analysis: IHC enables clinicians to set expression thresholds for ADC targeting, ensuring that only eligible patients receive therapy. • Readily Available Tool: IHC is widely accessible across pathology labs, making it a practical option for many cancer centers. Challenges in IHC Analysis Quantification of protein expression in IHC is typically assessed with H-scores, calculated by pathologists based on the identification of the percentage of cancer cells expressing the target and its level of intensity. The subjectivity of the methodology is an inherent risk of inconsistency for inter- intra- assay, for this reason, the H-score is usually calculated as the result of the independent IHC data analysis from at least two pathologists. Also, despite its benefits, IHC has limitations in predicting ADC efficacy, as for known ADC targets such as HER2 and TROP2, the correlation between target expression-related IHC scores and ADC efficacy, is not always strong. Next-Generation Sequencing (NGS) Contributions to ADC Accuracy What is NGS and How it enhances ADC Targeting? Next-generation sequencing (NGS) analyzes DNA, RNA, and gene expression at unprecedented speed and precision. By providing data-rich insights, NGS enables oncology researchers to evaluate molecular profiles in addition to traditional methods like IHC. In particular, NGS data can help researchers with the identification of biomarkers that may predict ADC responses. Recent studies have demonstrated NGS's advantages in ADC biomarker identification. For example, RNA sequencing correlations with IHC staining (e.g., TROP2, HER2) highlight strong alignment in certain targets, offering the potential for RNA-based cutoffs to complement IHC in ADC prediction. Variability, however, remains for some antigens, underscoring the need for continuing refinement. Innovations in Predicting ADC Efficacy Beyond IHC and NGS Emerging technologies and methodologies are refining ADC efficacy prediction even further. Key innovations include: • Multivariate Biomarkers: Next-gen tools like ADC Treatment Response Scores (ADC-TRS) evaluate gene expression alongside additional factors (e.g., adhesion, proliferation markers), significantly enhancing response prediction. • AI-Powered Pathology: Artificial intelligence in cancer pathology is enabling automated image and molecular data analysis, providing deeper insights into tumor heterogeneity and antigen expression thresholds. • Molecular Imaging: Imaging technologies are being integrated with NGS and IHC to provide real-time visualization of ADC biodistribution within patient tumors. Future Potential • Predictive Precision: Enhanced tools and algorithms will improve patient stratification, leading to better survival outcomes and fewer treatment-related toxicities. • Adaptive Therapies: With the ability to monitor antigen dynamics over time, clinicians can tailor ADC therapies to evolving tumor characteristics. Accurate Predictions Mean Better Outcomes for Patients Antibody-drug conjugates are paving the way for highly targeted and effective cancer treatments. However, maximizing their potential hinges on the ability to accurately predict suitable candidates through methods like IHC and NGS. By leveraging the latest advancements in predictive biomarkers and sequencing technologies, scientists and oncologists can improve patient outcomes, advancing precision medicine to new heights. As the field evolves, innovations will continue to refine ADC efficacy predictions, enabling personalized treatment strategies that benefit patients across diverse cancer types. Reach out to Champions Oncology to learn more about how we can help you develop your ADCs with our cutting-edge ex vivo and in vivo platforms and predictive tools that drive innovation. [1] Katrini J, Boldrini L, Santoro C, Valenza C, Trapani D, Curigliano G. Biomarkers for Antibody-Drug Conjugates in Solid Tumors. Mol Cancer Ther. 2024 Apr 2;23(4):436-446. doi: 10.1158/1535-7163.MCT-23-0482. PMID: 38363729. [2] Sachdev P. Thomas, Laurel A. Habel, Jennifer Marie Suga, Ninah Achacoso, Josh Nugent, Katarina M. Robinson, Ryan White, and Scott A. Tomlins. Evaluation of a predictive biomarker for antibody drug conjugates (ADCs). Journal of Clinical Oncology, Volume 42, Number 16_suppl. doi.org/10.1200/JCO.2024.42.16_suppl.3140 [3] Makawita S, Meric-Bernstam F. Antibody-Drug Conjugates: Patient and Treatment Selection. Am Soc Clin Oncol Educ Book. 2020 Mar;40:1-10. doi: 10.1200/EDBK_280775. PMID: 32213087. [4] Kushnarev V, Stupichev D, Kryukov K, et al143 Correlating RNA-seq detection and IHC staining of potential antibody-drug conjugate (ADC) targets: HER3, HER2, TROP2, Nectin4, and aFLRJournal for ImmunoTherapy of Cancer 2023;11:doi: 10.1136/jitc-2023-SITC2023.0143 [5] Ascione L, Crimini E, Trapani D, Marra A, Criscitiello C, Curigliano G. Predicting Response to Antibody Drug Conjugates: A Focus on Antigens' Targetability. Oncologist. 2023 Nov 2;28(11):944-960. doi: 10.1093/oncolo/oyad246. PMID: 37665782; PMCID: PMC10628585. [6] Paolo F. Caimi, Mehdi Hamadani, Carmelo Carlo-Stella, Masoud Nickaeen, Eric Jordie, Kiersten Utsey, Tim Knab, Francesca Zammarchi, Serafino Pantano, Karin Havenith, Ying Wang, Joseph Boni; CD19 Expression by IHC Alone Is Not a Predictor of Response to Loncastuximab Tesirine: Results from the LOTIS-2 Clinical Trial and Quantitative Systems Pharmacology Modeling. Blood 2022; 140 (Supplement 1): 9548–9550. doi: https://doi.org/10.1182/blood-2022-159626