Trends in Oncology

Did you know that Chronic Lymphocytic Leukemia (CLL) is a relentless adversary in the realm of hematologic malignancies? Characterized by the excessive accumulation of abnormal B lymphocytes, CLL presents a unique set of challenges for oncologists, hematologists, and cancer researchers. Despite advancements in targeted therapies and immunotherapies, achieving sustained remission remains elusive for many patients.

In this blog, we will explore the critical role preclinical CLL models play in the development of novel therapies. From understanding the disease's biology to facilitating the development of breakthrough treatments, these models are indispensable tools in our fight against CLL.

The Role of Preclinical CLL Models in Cancer Research

Preclinical CLL models are the unsung heroes of CLL research, providing a crucial bridge between laboratory discoveries and clinical applications. These models enable researchers to study the complex biology of CLL in a controlled environment, offering insights that are impossible to glean from human studies alone.

By replicating the disease in animals or in culture with preclinical CLL models, scientists can test the efficacy and safety of potential therapies before they reach clinical trials. This accelerates the drug development process and enhances our understanding of disease mechanisms. In essence, preclinical CLL models are the bedrock upon which modern CLL cancer therapies are built.

Overview of Current Preclinical CLL Models

The landscape of preclinical CLL models is diverse and continually evolving. Each model offers unique advantages and limitations, making choosing the right one for specific research objectives essential.

Cell line-derived Xenograft (CDX) Models
Xenograft models involve transplanting human CLL cells into immunodeficient mice. These preclinical CLL models allow for the study of human-specific disease characteristics and the evaluation of human-targeted therapies. However, the lack of a fully functional immune system in these mice limits the study of immune-based treatments.

Genetically Engineered Mouse Models (GEMMs)
GEMMs are designed to mimic the genetic aberrations found in human CLL. These preclinical CLL models provide a more accurate representation of the disease's progression and response to therapies. They are particularly valuable for studying the genetic and epigenetic factors driving CLL.

Patient-Derived Xenografts (PDXs)
PDXs involve implanting primary CLL cells from patients into mice and serially passaging to obtain stable in vivo models[1]. These preclinical CLL models retain genetic features of the original tumor, making them predictive of clinical outcomes, although the lack of a proficient immune system needs to be considered. 

Using Preclinical CLL Models in Oncology Research 

The latest therapies approved for CLL are a testament to the power of preclinical CLL models in developing revolutionary therapies. Pirtobrutinib (approved by the FDA at the end of 2023[2]) and lisocabtagene maraleucel (approved by the FDA for use in CLL in 2024[3]) were meticulously tested in preclinical CLL models before their clinical debut, ensuring their safety and efficacy in targeting CLL cells.

Pirtobrutinib is a highly selective, noncovalent Bruton tyrosine kinase inhibitor (BTKi). Preclinical testing of pirtobrutinib was conducted in several preclinical CLL models. CLL cell lines were used to assess target engagement, potency, cellular phosphorylation, and other cellular activity of the inhibitor[4, 5, 6]. Further studies in primary CLL cells and xenograft models confirmed pirtobrutinib's ability to kill CLL cells and reduce tumor burden[4, 6].

Lisocabtagene maraleucel is a CD19-targeted CAR-T cell therapy. Preclinical studies of lisocabtagene maraleucel involved in vitro and xenograft models to evaluate its ability to target and eliminate CLL cells[7, 8]. These preclinical CLL models provided crucial data on the therapy's potency, specificity, and potential side effects.

The success of these preclinical trials paved the way for pirtobrutinib and lisocabtagene maraleucel approval and use in clinical settings. These therapies are now transforming the treatment landscape for CLL and other hematologic malignancies.

The Impact and Future of Preclinical CLL Models in Developing Novel Therapies

The impact of preclinical CLL models on the development of new therapies cannot be overstated. They have accelerated the discovery of novel treatments, reduced the risk of adverse effects, and improved patient outcomes. However, the field is far from static.

Advancements in Model Precision
The future of preclinical CLL models lies in their ability to reproduce clinical characteristics and support personalized medicine. The use of primary patient-derived CLL models by direct injection of patients’ cells in mice without additional passages in the animals allows better preservation of tumor heterogeneity and patient population diversity[9], Patient-derived CLL models can be used in a preclinical trial format as well as for the testing of therapies on individual patient tumors, enabling tailored treatment strategies. 

The development of such models in humanized mice, which possess a reconstituted human immune system, will further improve the robustness of these models as patients’ surrogates by enabling a deeper understanding of how therapies interact with the human immune system, leading to more effective treatments.

Integration of Computational Models
Integrating computational models with preclinical studies is another promising avenue. By simulating disease progression and treatment responses in silico, researchers can optimize experimental designs and predict outcomes more accurately. This synergy between computational and experimental approaches is poised to accelerate the development of next-generation CLL therapies.

The Importance of Continued Research and Innovation

Preclinical CLL models are the linchpin of CLL therapy development, providing invaluable insights and accelerating the transition from bench to bedside. As we continue to refine these models and integrate new technologies, the future of CLL treatment looks increasingly promising.

Champions Oncology's bank of preclinical CLL models includes primary models derived from pretreated and naive patients. With deep multi-omic and multimodal characterization and comprehensive clinical annotations, we strive to make our preclinical CLL models the best tool to accelerate your drug pipeline through reliable data and extensive expertise in the hematologic malignancies field. Contact us to speak with one of our experts. 

Did you know that 3D organ-like tumor models are biomimetic and yield superior results in drug screening? These models more accurately mimic cell-cell signaling and physiological conditions, providing a superior representation of human tumors outside the body. This blog is dedicated to answering frequently asked questions about Champion's advanced TumorGraft3D platform and assisting scientists in choosing the right platform for their drug screening endeavors.
Champions Oncology offers the multiclonal TumorGraft3D drug screening platform, a biologically relevant platform with 3D organoid models derived from our superior, well-characterized patient-derived xenografts (PDXs) for ex vivo drug testing.

1)    What is TumorGraft3D and what differentiates them from conventional 3D models?

TumorGraft3D models are self-organizing three-dimensional PDX-derived cell clusters that mimic parental human tumor's morphological and molecular phenotype, thereby rendering themselves clinically relevant models for drug discovery.

2)    What types of TumorGraft3D models are currently available?

Our TumorGraft3D biobank is constantly expanding and currently includes over 150 off-the-shelf models across 16 different tumor types for ex vivo studies. However, our entire PDX bank is available for TumorGraft3D generation, only requiring a short pre-study development phase before becoming available for clients’ studies. 

3)    What are the advantages of using TumorGraft3D models compared to cell lines or in vivo PDX studies?

TumorGraft3D models are generated from our deeply characterized and clinically annotated patient-derived xenograft models and maintain the parent model’s characteristics. They represent the heterogeneity of the patient population and have drug response profiles comparable to the parent PDX model. This makes them a great resource for ex vivo drug screening, with better clinical correlation than cell lines, and have a faster turnaround time and higher throughput than in vivo studies.   

4)    Are TumorGraft3D models a close representation of patients’ tumors? How do you ensure that patients' characteristics are not lost?

TumorGraft3D models are derived from our PDX models without intermediate processing steps and are maintained at low passages to avoid genetic drift and preserve the parent PDX model characteristics. Moreover, IHC, NGS, and drug response analysis are conducted to characterize the TumorGraft3D model and verify that this maintains the parent PDX model’s molecular and pharmacological profile. 

5)    Why do you use a matrix-free assay? What are the advantages and disadvantages of it?

Traditionally, 3D tissue models have been cultured using an extracellular matrix-dependent approach, where the supportive extracellular matrix is derived from natural or synthetic sources. Matrices can pose several challenges such as sourcing of materials, interactions with test agents, and interference in imaging-relevant readouts. Matrix-free approaches allow the end user to observe the self-organization of 3D tissue models without using an exogenous matrix, circumventing all the above challenges. A matrix-free environment allows homogeneous distribution of therapeutic agents and easy access to tumor and immune cells. Additionally, the absence of confounding factors simplifies the assay readout and reduces variability. The use of an exogenous matrix can mimic the physical characteristics of the TME but can also influence tumor cell behavior and it interferes with flow cytometry readouts.

6)    How can I use TumorGraft3D to study the TME and agents targeting/engaging the TME?

TumorGraft3D models are a well-defined versatile platform that lends itself to various co-culture options and readouts that can generate a complete picture of the TME response to test agents. At Champions, we offer co-culture assays with autologous and allogeneic NK cells, PBMCs, TILs, and other immune cells, allowing for testing of several classes of therapeutic agents such as T cell and NK cell engagers, BiTEs, ADCC drugs, immune checkpoint inhibitors, small molecules, gene therapy, cell therapy, and combinatorial therapy. 

7)    What are the applications of TumorGraft3D? How can I use this platform to advance my oncology research program?

TumorGraft3D models' versatility and clinical relevance, along with their molecular characterization, make them the ideal ex vivo models to measure agent efficacy and on-target effect, unravel the complexities of tumor biology and the crucial interactions within the tumor microenvironment, and evaluate synergistic combination therapies to de-risk in vivo studies through data-driven decisions.

8)    How does Champions Oncology ensure the quality of your 3D tumor models?

At Champions, we strive to provide the highest quality services. Our TumorGraft3D models are carefully developed following cutting-edge guidelines for organoid development and culture. Our models are:
•    maintained at low passages to retain molecular and phenotypic characteristics,
•    qualified for assays using predefined SOC compounds,
•    verified for true 3D structure formation and viability throughout the length of the assay. 

9)    What is unique about the TumorGraft3D platform? 

Specially tailored for integration with various immune cell types, TumorGraft3D models incorporate the unparalleled advantage of Champions’ proprietary autologous systems. Our proprietary autologous co-culture systems proficiently examine the potency of therapeutic agents alongside the intricate interactions between a patient's tumor and their own immune system. This advancement eliminates the inconsistencies commonly seen with the use of allogeneic donors, directing the focus onto patient-specific responses. Our clinically relevant models, coupled with advanced high-content imaging and flow cytometry immunophenotyping make our platform a rich source of insights.

With unprecedented precision, scientists can now:
•    monitor the influence of test agents on the interaction between tumors and their microenvironment,
•    decrypt their mechanisms of action,
•    meticulously track the infiltration of immune cells,
•    and craft potent combination therapies tailored for IO applications.

Clinical specialty testing laboratories, like Champions Oncology, are expected to adhere to stringent standards to ensure accuracy and reliability of test results which can have life-altering implications for patients. Regulatory compliance is not a mere bureaucratic hoop but a foundational element that guarantees the integrity of laboratory operations.

Navigating through the complex landscape of clinical specialty testing and its regulatory environment is crucial for the success of each clinical trial. Regulatory compliance within each clinical trial is vital to ensure data validity and also ensures each laboratory’s commitment to patients’ safety.  In this blog post, we’ll explore the intricacies of adhering to Good Clinical Laboratory Practice (GCLP), Clinical Laboratory Improvement Amendments (CLIA), and College of American Pathologists (CAP) standards, compare regulatory frameworks in the United States (US) versus the European Union (EU) and underscore why meticulous regulatory compliance is a non-negotiable for each clinical trial.

Clinical specialty testing laboratories, like Champions Oncology, are expected to adhere to stringent standards to ensure accuracy and reliability of test results which can have life-altering implications for patients. Regulatory compliance is not a mere bureaucratic hoop but a foundational element that guarantees the integrity of laboratory operations.

Good Clinical Laboratory Practice (GCLP)
GCLP is a quality system that ensures laboratories conducting clinical trial testing provide data of consistent quality. It bridges the gap between the guidelines provided by Good Laboratory Practice (GLP) and Good Clinical Practice (GCP), focusing on pre-analytical, analytical, and post-analytical processes.

Clinical Laboratory Improvement Amendments (CLIA)
In the United States, CLIA regulations pertain to laboratory testing and require labs to be certified by the federal government. They establish standards for test performance, personnel qualifications, quality control, and proficiency testing for each specific assay performed at the specialty testing laboratory.

College of American Pathologists (CAP)
The CAP accreditation is an internationally recognized program that provides a framework for clinical labs to achieve excellence in patient care and ensure compliance with statutory and regulatory requirements. CAP takes a peer-reviewed approach to help maintain the highest standard of care.

While there may be considerable overlap in what these regulations and standards aim to achieve, there are nuanced differences in their requirements and scopes. GCLP is broader and more flexible in its application, potentially accommodating international guidelines. CLIA is prescriptive and specific to the United States, focusing significantly on the analytical phase of testing. CAP, albeit a US-based program, aligns with many international standards and offers a comprehensive accreditation process that envelopes all aspects of lab operations.

Comparatively, the European Union (EU) takes a different approach to laboratory oversight. The EU mandates that each company ensures the quality and safety of its laboratories, but it does not impose a uniform set of standards. Instead of an EU-wide equivalent to CLIA, countries may have their own regulatory frameworks or adhere to international standards like those of the International Organization for Standardization (ISO).

Regulatory standards are the pillars that support the validity of clinical trial data. They are key to ensuring that the specialty tests upon which clinical decisions are based are reliable and reproducible. Compliance ensures patient safety, the validity of data submitted to regulatory authorities, and ultimately the success of a clinical trial. Failures in compliance can lead to serious legal consequences and ethical breaches, undermining public trust. Every clinical scientist must understand that regulatory compliance is not simply about following rules; it's about upholding the scientific rigor and ethical duty inherent in clinical research. Each standard, whether it be GCLP, CLIA, or CAP, serves as a QA/QC mechanism to this end.

By mastering these regulatory frameworks and recognizing their importance in every aspect of a clinical trial, we safeguard the integrity of clinical research, protect patient welfare, and contribute to the greater good of advancing scientific clinical research.

The landscape of oncology research and the quest for better treatments have intensified with the advent of precision medicine introducing more targeted agents with higher efficacy and lower toxicity compared to traditional chemotherapy. In personalized medicine, where tailored treatments are revolutionizing patient care, understanding drug resistance mechanisms is key to developing more effective therapies. This blog takes a deep dive into pre-treated patient-derived xenograft (PDX) models, explaining what they are and how they are employed in preclinical studies to combat resistance to standard of care drugs, ultimately leading to better patient outcomes.

Refining Precision: PDX Models in a Nutshell

PDX models involve the transplantation of human cancer tissue into immunodeficient mice for in vivo growth and expansion. The resultant 'model' is a living tumor system that retains molecular and cellular characteristics closely resembling those of the original patient tumor.

Compared to traditional cancer cell line models, PDX models are superior avatars as they capture the heterogeneity and microenvironmental cues found in human tumors. This fidelity allows for a more accurate predictive platform to assess drug responses in preclinical oncology research.

Unveiling Resistance: The Power of Pre-treated PDX Models

In the quest to outwit cancer, the central challenge has always been predicting and overcoming therapeutic resistance, which often arises after initial treatment with standard of care drugs. Pre-treated PDX models are established from tumors from patients that have previously been exposed to treatments, thus embodying resistant tumor phenotypes that can mimic clinical presentations.

The use of PDX models after exposure to specific lines of chemotherapy, hormone therapy, or targeted drug regimens reveals a wealth of insights into how these tumors adapt, evolve, and evade the therapeutic effects. This critical approach allows researchers to identify the genetic and molecular determinants of resistance and devise strategies to combat them.

Pre-treated PDX Models at Champions Oncology

Champions’ highly characterized biobank includes over 1,000 tumor models across 60+ tumor types derived from advanced stage primary and metastatic patient tumors pre-treated with a plethora of therapies including the latest generations of targeted antibodies and small molecules, immune checkpoint inhibitors, ADCs, bispecific antibodies, and even experimental therapies currently undergoing clinical trial.

Our pre-treated models can be easily identified in Lumin, where you can select models of interest to you based on clinical and molecular characteristics, and draft a PDX cohort for your next study.

With extensive longitudinal clinical annotations and industry-leading multi-omic and multi-modal characterization, Champions' pre-treated tumor models allow for testing therapeutic agents in systems that closely mirror the clinical trial patient population significantly derisking the drug development process.

Transforming Resistance to Resilience in Research

Pre-treated PDX models have already catalyzed groundbreaking advancements in oncological research. For instance, studies have utilized these models to identify secondary oncogenic events that drive drug resistance and to develop combinatorial therapies that prevent or even reverse drug insensitivity.

One of these studies is highlighted in our case study "Using Champions’ patient-derived xenograft (PDX) models for preclinical validation of HER2-specific small molecule inhibitors" where tucatinib is used as a single agent or in combination with trastuzumab in a selection of HER2-amplified PDX models, both pre-treated and naïve, showing tumor inhibition in trastuzumab-resistant models.

The data derived from pre-treated PDX models can be integral in informing personalized treatment strategies for cancer patients.

The Future of Research and Care in Oncology

The implications of utilizing pre-treated PDX models are not just confined to the benches of research labs. These models have the potential to significantly impact the course of clinical trials, where they can serve as powerful tools to validate therapeutic responses and study mechanisms of drug failure in a more controlled environment.

Investigating drug resistance in PDX models allows for a more systematic and accelerated approach to drug development. New compounds can be tested on pre-treated PDX models to evaluate their potential benefits, potential interactions with existing therapies, and their ability to overcome resistance, ensuring a fast track to clinical trial.

By providing a more accurate representation of human tumors and treatment responses, pre-treated PDX models bridge the gap between bench-side discoveries and clinical applications. This synergy is crucial in ensuring that the most promising treatments reach patients in a timely and effective manner.

Gene copy number in a tumor cell is a significant indicator of the implication of a given gene in several oncogenic processes such as uncontrolled proliferation, elusion of programmed cell death, and resistance to treatments.

At Champions Oncology, gene copy number analysis is performed by using the EXCAVATOR2^[1] tool on whole exome sequencing (WES) data generated to characterize our patient-derived xenograft (PDX) models. This tool allows for classifying each segmented region into five qualitative genomic states (two-copy deletion, one-copy deletion, normal, one-copy duplication, and multiple-copy amplification) and quantifying the number of chromosomal copies.

All our model characterization data can be explored in Lumin, a unique solution integrating Champions’ tumor model multi-omic data and public datasets in one accessible platform for model selection and data interpretation.

In Lumin, gene copy number analysis results are presented in the format shown in the example below:

Here we answer the most common questions about CNV data reporting to help you navigate WES data in our Lumin platform as well as interpret your own study data.

Q: What does Log2R mean and why sometimes is it marked as NA?

A: The Log2R (Log2 ratio) value represents the number of copies relative to the normal reference sample (NA12878). The EXCAVATOR2 algorithm used to calculate CNV uses a median normalization approach, with the log-transformed ratio (Log2R) being calculated from the window mean read count (WMRC) values of the test sample compared to the normal reference. When Log2R value is marked as NA, no significant copy number alteration was detected.

Q: What are the Call values and how are they defined?

A: The Call value is calculated using the FastCall algorithm^[2] and classifies each segmented region as one of five possible states: 2 copy deletion= -2; one copy deletion= -1; normal= 0; one copy duplication= 1; and multiple copy amplification= 2.

Q: Are the copy number values the absolute or the relative copy numbers detected?

A: Copy number values represent the absolute copy number detected, which is derived from the Copy Number Fraction and is rounded to the nearest integer.

Q: Do “Amp” and "Del" always mean that there is a gain or loss in copy number? Or is this only the case if the “Alteration” column says “Gain” or “HomoDel”/ “Hetloss”?

A: The "amp_del" column definitions are derived from the Call values, whereas the "Alteration" column classifies the alteration based on the copy number detected. In some instances, there may be discordance between the copy number and Call (as shown for FAM231C gene in the example table above), as the two values are derived by approximation of continuous values. In this specific example, the conflict between the two annotations is indicative of the tumor gene copy number being between 1 and 2, suggesting the presence of both cells with 1 and cells with 2 copies of that genomic region. For additional investigation, we recommend looking at the continuous values in the raw data.

Q: What columns should I consider when I want to search for a model with an amplification/deletion for a certain gene?

A: We would first recommend using the "CopyNumber" column to identify models with an amplification or deletion of a specific gene. Once you have filtered the models based on this, you can then use the “Alteration” column to verify whether Call, copy number, and the “amp_del” column values are concordant.

In the realm of oncology, few words instill as much uncertainty and trepidation as "triple negative breast cancer" (TNBC). With its resistance to many standard forms of therapy, TNBC demands a new generation of treatment innovation. Enter pembrolizumab—an immunotherapy designed to engage the body’s immune system in the fight against cancer. Its recent breakthroughs in clinical trials have charted a promising new course in the treatment of this aggressive form of breast cancer.

The TNBC challenge

Conventionally, breast cancer treatment plans are designed around the presence or absence of three receptors: estrogen, progesterone, and HER2/neu. TNBC, characterized by the absence of these receptors, compels a more bespoke approach, given the limitations it imposes on targeted treatments available for other forms of breast cancer. The lack of defined therapeutic targets has historically left TNBC patients with fewer options and a greater risk of disease progression and poor prognosis.^[1]

The arrival of pembrolizumab: a precision tool in the TNBC arsenal

Pembrolizumab operates on a fundamentally different principle than traditional treatments. It is an immune checkpoint inhibitor that effectively releases the brakes on the immune system, allowing it to identify and combat cancer cells in a manner that is highly specific to a patient's tumor profile. This tailored approach has been nothing short of revolutionary in cancers with high mutational loads, such as TNBC, where the potential for an immune system response is significant.^[2]

Groundbreaking trials: pembrolizumab's journey to TNBC approval

KEYNOTE-355

The KEYNOTE-355 study burst into the scientific limelight with its findings on the efficacy and safety of pembrolizumab in combination with chemotherapy in the treatment of locally recurrent inoperable or metastatic TNBC. The trial's incorporation of a diverse patient population, coupled with the comprehensive analysis of the drug's performance set a new precedent for the depth and breadth of oncology research.^[3]

This landmark study, along with other trials such as KEYNOTE-012 and KEYNOTE-086, has served as evidence in the FDA's approval of pembrolizumab in combination with chemotherapy for the treatment of advanced TNBC expressing high levels of PD-L1. These studies have demonstrated not only the drug's ability to improve patient outcomes but also its potential to revolutionize the treatment landscape for this challenging form of breast cancer.^[4]

The KEYNOTE-355 study has been a beacon of hope for those combating triple-negative breast cancer, illuminating the path forward with its groundbreaking results. Central to its findings is the remarkable improvement in overall survival (OS) rates for patients treated with pembrolizumab in conjunction with chemotherapy. Specifically, the study delineates a median OS of 23 months for these patients, significantly surpassing the 16.1 months median observed in those receiving chemotherapy alone. This stark contrast not only emphasizes pembrolizumab's efficacy but also marks a tangible advancement in extending the lives of individuals facing this formidable adversary.^[4,5]

KEYNOTE-522

Similarly, the KEYNOTE-522 trial, which investigated pembrolizumab in the neoadjuvant setting, demonstrated a significant pathologic complete response (pCR) rate. This paradigm-shifting evidence supported the FDA's approval of pembrolizumab for high-risk early-stage TNBC, leading to a pivotal shift in the narrative around treating this formidable adversary.^[6]

The results from KEYNOTE-522 have been nothing short of revolutionary, illustrating a marked improvement in both pCR (7.5% higher than in the control arm) and event-free survival (EFS). Patients on the pembrolizumab arm experienced EFS benefit regardless of tumor PD-L1 status.^[6,7]

What makes these results particularly compelling is the implication for long-term survival outcomes. Early indications suggest that the increase in pathologic complete response rates correlates with longer overall survival, offering new hope that pembrolizumab could extend lives in a population historically challenged by high relapse and mortality rates. These outcomes underscore the importance of pembrolizumab in the TNBC treatment paradigm, highlighting its potential to significantly alter the prognostic outlook for patients facing this aggressive form of breast cancer.^[6,7]

The future of the TNBC treatment landscape

The integration of pembrolizumab into TNBC treatment protocols is a landmark event that underscores the ongoing refinement of personalized oncology care. While the successes in clinical trials are incredibly encouraging, the implementation of these new standards into broader clinical practice requires deliberate consideration of patient selection, administration protocols, and the management of potential immune-related adverse events.^[8]

Researchers and clinicians are now tasked with harnessing the full potential of this breakthrough treatment as well as identifying novel strategies to extend patient survival and improve quality of life. The future of TNBC treatment lies in leveraging emerging technologies and collaborative frameworks to uncover even more effective therapeutic solutions.

Renal cell carcinoma (RCC) is a common cancer of the genitourinary tract that has very poor survival outcomes if metastatic. RCC is now understood to be composed of several different types of cancer with different genetic features and varied clinical responses. Histological diagnosis has been the primary method to diagnose RCC and has been used to define three major RCC subtypes, including the most common subtype, clear cell renal cell carcinoma (ccRCC), papillary renal cell carcinoma (PRCC; further divided into two subtypes), and chromophobe renal cell carcinoma (ChRCC)^[1]. More recent comparative genomic and phenotypic analysis has identified mutations and epigenetic modifications associated with different histological subtypes^[2]. Across all subtypes, increased DNA hypermethylation and gene alterations in CDKN2A were associated with a poor prognosis as was an increased Th2 immune gene signature. For ccRCC, increased levels of mRNA transcripts associated with ribose metabolism and the immune response were associated with poor survival. ccRCC is also defined by the early loss of chromosome 3p, which in turn causes a loss of heterozygosity for the VHL, PBRM1, SETD2, and BAP1 tumor suppressor genes and subsequent mutation of these genes that leads to tumorigenesis^[3]. There is also a subset of ChRCC with a unique metabolic expression pattern that is associated with extremely poor survival^[2]. PRCC can be classified as type 1, for which PBRM1 mutations are linked to poor survival but type 2 PRCC has increased expression of glycolysis and metabolism-related mRNA transcripts^[2].

ccRCC tumors with VHL mutations show overexpression of vascular endothelial growth factor (VEGF) and hypoxia-inducible factors (HIFs) can contribute to angiogenesis and cancer progression. Similarly, some RCCs show hyperactivation of the serine/threonine kinase mammalian target of rapamycin (mTOR), which can lead to the overproduction of VEGF. VEGFR inhibitors and anti-VEGF antibodies have been tested as therapies for RCC, and the anti-VEGF antibody bevacizumab has been approved for use in combination with IFN-α for metastatic RCC^[4]. The mTOR inhibitors everolimus and temsirolimus have also been approved for the treatment of RCC, typically in combination with tyrosine kinase inhibitors (TKIs)^[5]. Combination therapies that target VEGF and mTOR are considered more effective since they work in concert to target tumor growth and vascularization, whereas sequential treatments are typically associated with a greater likelihood of tumors developing resistance^[6]. Unfortunately, these combination therapies are associated with undesirable toxicities and have not been linked with durable responses^[7]. Notably, an HIF-2α inhibitor, belzutifan,  has shown good overall response rate and duration of response and has been approved by the FDA in 2021 for use in adults with VHL-associated RCC and in patients with advanced RCC who have been treated with anti-VEGF therapy and anti-PD-1/PD-L1 therapy^[8]. Combination therapies with belzutifan and other targeted agents or ICB are currently being evaluated in clinical trials^[9].

Advances in immunotherapy have led to transformative treatment options for RCC. Immune checkpoint blockade (ICB) has been one promising treatment strategy, and the FDA approved the use of anti-PD-1/PD-L1 (nivolumab) for the treatment of advanced ccRCC in 2015^[10]. A follow-up study showed that anti-PD-1 was associated with the best clinical benefit in ccRCC carrying loss-of-function mutations in PBRM1, which appears to affect tumor expression profiles in such a way to maintain responsiveness to checkpoint blockade^[11]. Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) is another checkpoint molecule targeted for checkpoint blockade with the monoclonal antibody ipilimumab. Similar to PD-L1 blockade, CTLA-4 blockade was associated with a partial response against metastatic RCC. Combination therapies have shown much more durable responses in patients with advanced RCC, and the FDA approved the use of nivolumab plus ipilimumab for the treatment of intermediate or high-risk metastatic RCC in 2018^[12]. ICB in combination with TKI have also been approved for advanced disease ^{[13,14,15,16]}. Unfortunately, PD-1/PD-L1 blockade has been less successful for PRCC^[17] and ChRCC^[18], and further studies are needed to identify therapeutic targets in these forms of RCC. Clinical studies with other checkpoint blockade targets are currently underway and are likely to provide new treatment options to RCC patients^[19].

The future of RCC therapies relies on the identification of new molecules or pathways in tumor cells that can be targeted therapeutically without causing toxicity or promoting resistance. Advances in single-cell omics are leading the way in terms of target identification and understanding how different forms of RCC progress.

Mouse models have been the workhorses of preclinical immuno-oncology (IO) research, and advances in mouse model development have expanded to applications for nearly all types of solid tumors and hematological malignancies. Preclinical evaluation of experimental immunotherapies has been advanced by syngeneic and humanized mouse models.

Syngeneic mice are one of the most established types of models used in cancer research, whereas humanized mice are a contemporary mouse model that has been critical to the screening of immunotherapeutic agents. Here we highlight features of syngeneic and humanized mouse models and define which models are most relevant to different phases of preclinical IO research.

Syngeneic Tumor Models

Syngeneic tumor models are created by transplantation of tumor cell lines into immunocompetent mice with the same genetic background as the cell line.^[1] Tumors can be transplanted intravenously or subcutaneously into mice and typically grow rapidly over several weeks. Different types of tumor cell lines can be used in this type of model, including spontaneous, transgenic, or carcinogen-induced tumor cell lines. Syngeneic mouse models are best suited for screening novel IO agents or gaining insight into anti-tumor responses in the context of an intact immune system. Given the rapid growth of tumors in syngeneic mice, these models are less well suited to studying early events in tumor growth associated with cancer stem cells or understanding the contributions of heterogeneous tumor microenvironments, and these models typically do not recapitulate the mutational heterogeneity observed in human tumors.^[2]

Humanized Tumor Models

Humanized tumor models are a more recent addition to preclinical IO research that provide valuable insight into how individual tumors from patients (xenografts) respond to experimental therapies. Prior to the development of humanized mouse models, human xenograft models were used for screening cytotoxic or immunotherapeutic agents like chimeric antigen receptor (CAR) T cells^[3], and these models use human tumor cell lines or patient-derived specimens transplanted into immunocompromised host mice. Different immunocompromised models can be used, including athymic mice that lack T cells or severe combined immunodeficiency (SCID) models that lack all adaptive immune responses. Humanized mice have been engineered from immunocompromised mouse strains that include genetic mutations in other adaptive immune functions that allow for the engraftment of human hematopoietic cells. The NOD/SCID IL2rγ chain knockout (NSG) mouse (NOD.Cg-Prkdc^scid Il2rg^tm1Wjl/SzJ) is one of the most used combined immunodeficiency models that can be engrafted with human hematopoietic cells and primary human tumors.^[4] These patient-derived xenograft (PDX) models are useful for evaluating experimental IO therapies in the context of the human immune system and can use human immune cells from the same or different donor as the tumor source. PDX models are suited to evaluating experimental therapies in the context of a genetically heterogeneous tumor and better recapitulate aspects of the tumor microenvironment. Tumors can be grafted either orthotopically or subcutaneously and this also impacts how tumors grow and respond to experimental treatments.^[5] Given the heavily modified nature of the NSG immune system, these models do not always reflect responses observed in humans during clinical trials. Nonetheless, NSG mice and similarly modified humanized mice offer valuable insights into the efficacy of IO candidates.

Mouse models are constantly being refined and improved to better reflect human physiology. Both syngeneic and humanized mouse models serve as valuable tools for preclinical IO research and accelerate the screening and evaluation of novel therapeutics.

Mantle cell lymphoma (MCL) is a rare and aggressive non-Hodgkin’s lymphoma (NHL) that originates in B cells and occurs in secondary follicles of the lymph nodes.¹ MCL accounts for roughly 6% of all lymphomas diagnosed annually, and despite its relatively low incidence, the aggressive course of MCL means it can quickly become a life-threatening disease. Understanding the origin and progression of MCL is crucial to identify new targets for drug development studies.

Understanding Mantle Cell Lymphoma Pathology

Over the last decade, our understanding of MCL pathogenesis has evolved from a single definition to a consensus that MCL development and progression is related to a range of molecular events. Early on MCL was defined as a pathognomonic chromosomal translocation t(11;14)(q13;q32) causing a mutation in the CCND1 gene, resulting in the over-expression of Cyclin D1.^{1, 2}

Under normal conditions, Cyclin D1 is heavily regulated and modulates cell cycle transition from G1 phase to S phase.¹ However, overexpression of Cyclin D1 activates cyclin-dependent kinases (CDK) 4 and 6 which later deactivate the retinoblastoma protein (Rb), a cell cycle inhibitor. This series of events accelerates cell cycle progression from G1 to S phase in many cell types, including B-cells.³ The progression of this process induces uncontrolled B-cell proliferation causing an enlargement of lymph nodes and immune system dysfunction that eventually spreads to critical organs.

Fewer Mutations Raise More Questions

Interestingly, Cyclin D1 mutations are not found in all MCL cells, suggesting other molecular actors, such as transcription factors, are involved. In 90% of MCL patients, transcription factor SOX11 is over-expressed in MCL cells with and without mutated CCND1. While SOX11’s role in MCL is not well understood, studies suggest that PAX-5, a transcription factor that regulates B-cell development and differentiation, is activated by SOX11.⁴ The overexpression of SOX11 that is commonly seen in MCL patients can therefore lead to reduced B-cell differentiation and increased B-cell antigen signaling.^{3, 5}

Identifying Treatment Targets in Mantle Cell Lymphoma

B-cell proliferation and differentiation rely on B-cell receptor (BCR) signaling and are activated in response to antigen binding.⁶ Using the framework for CLL pathogenesis, preliminary MCL-BCR research has found that BCR signaling is highly active in MCL patients and intentional BCR activation resulted in an increase of BCR signaling in MCL cells.⁷ Additionally, murine studies have found SOX11-overexpressing B-cells have high levels of BTK, a key enzyme involved with BCR signaling, resulting in proliferation which suggests SOX11’s deeper role in MCL.⁷

Initial MCL treatment includes R-CHOP therapy, however, many MCL patients are refractory or become resistant creating a need for additional therapies. As with CLL, BCR signaling plays a critical role in MCL progression shifting treatment protocols towards BTK inhibitors (BTKi) like ibrutinib, approved in 2013.^{1, 3} Ibrutinib is metabolized in the liver via CYP3A and CYPRD6 and irreversibly binds to cysteine residue 481 found on the active site of BTK.

While this targeting strategy effectively blocks BTK signaling limiting MCL cell development, ibrutinib has significant off-target consequences, namely IL-2 inducible kinase (ITK), which sparked the development of zanubrutinib.⁸ Zanubrutinib was approved for use in MCL patients and though it has a similar mechanism of action to ibrutinib, its reduced affinity for ITK, a major T-cell and natural killer (NK) cell regulator,⁹ makes it a highly selective and potent BTKi with improved overall response rate relative to ibrutinib.⁸

Resistant Mantle Cell Lymphoma and Future Therapeutic Strategies

Unfortunately, 32% of MCL patients become resistant to BTKi due to BTK mutations.^{10, 11} As a result, combination therapies such as ibrutinib combined with cirmtuzumab (a novel anti-ROR1 monoclonal antibody) or with obinutuzumab and venetoclax have been used to treat resistant MCL patients. ¹¹ These therapeutic combinations have shown excellent remission results paving the way for other combination therapies, such as acalabrutinib, rituximab, and bendamustine. ^{11, 12} Notably, a phase 1 study investigating acalabrutinib, rituximab, and bendamustine achieved an 85% overall response rate which has led to a larger, ongoing Phase 2 study (NCT04115631). Additionally, novel MCL-1 inhibitors are under development in preclinical studies.¹³

Recently, the FDA has approved the first CAR T cell therapy for relapsed/refractory MCL, brexucabtagene autoleucel (Tecartus).¹⁴ Tecartus has shown durable response at the 3-year follow-up even in high-risk patients (NCT02601313), with sustained survival and a 67% complete response rate.¹⁵

Understanding the pathophysiology of naïve and BTKi resistant MCL is crucial to developing curative MCL therapies. While standard of care treatments like ibrutinib and zanubrutinib have had some success, additional studies investigating strategic combination therapies are underway providing hope for patients battling this disease.

Champions supports your in vivo preclinical studies with low passaged MCL PDX models available for subcutaneous modeling and fully characterized with NGS data in 4 models CTG-3771, CTG-3772, CTG-3776 (shown to be rituximab and ibrutinib resistant) and CTG-3808. Preclinical hematological scientists need to evaluate their pipeline of therapeutic candidates in robust hematological screening platforms; Champions' VitroScreen platform can advance potential next-generation therapies into the clinic, generating additional options for MCL patients.   

Oncology research is a field that continuously demands cutting-edge technologies to unravel the complexities of cancer. Among these innovations, RNA sequencing (RNA-seq) has emerged as a vital tool, enabling us to study the transcriptome with unprecedented depth. However, a new star is on the rise in the realm of RNA-seq — DRUG-seq. This application dazzles oncology researchers and genomics professionals with its cost-effective approach, reduced bias, and high sample efficiency, bringing with it a promise to redefine how we investigate cancer at a molecular level.^[1]

Champ-seq is Champions's DRUG-seq platform. In this comprehensive exploration, we'll dissect the advantages of DRUG-seq, and map out its pivotal role in oncology research, shedding light on its unique applications and use cases in the fight against cancer.

Unveiling DRUG-seq: The New Era in RNA-Seq

RNA-seq, widely employed for quantifying gene expression, has been a game-changer in understanding the genetic mechanisms driving cancer. However, DRUG-seq takes this a step further, providing a powerful lens through which to examine the transcriptome under a spectrum of conditions, particularly those relevant to drug treatment responses.

While traditional RNA-seq methods offer insights into the steady state of gene expression, DRUG-seq provides a dynamic view that captures the immediate and prolonged gene expression changes following drug administration. This level of detail is especially critical in oncology, where the intricate interplay of genetics, environmental factors, and treatment response paints a highly complex picture of disease progression and therapy outcomes.

By enabling high-throughput profiling of the transcriptional response to drug compounds, DRUG-seq stands out as a catalyst for precision medicine, biomarker discovery, and the personalization of cancer treatment strategies.^[1]

The Advantages of DRUG-seq in Comparative Analysis

Cost-Effectiveness

One of DRUG-seq's most touted benefits is its cost-effectiveness. The methodology uses a smaller number of sequences to cover the transcriptome, thanks to its selective enrichment of pre-existing reference indices. This focus on specific gene regions, associated with drug response or otherwise, lowers the overall sequencing cost per sample, making large-scale comparative studies feasible within more restrained budgets.

Sample Efficiency

With oncology often facing constraints of sample availability, the high efficiency of DRUG-seq is a game-changer. It requires smaller amounts of starting material, which not only conserves precious samples but also aligns with the trend toward microsampling in emerging clinical research practices.

DRUG-seq in Action: Enhancing Oncology Research and Development

Drug Response Profiling

An immediate application of DRUG-seq is in profiling the response of cancer cells to different compounds. By comparing the expression profiles pre- and post-treatment, researchers gain a comprehensive view of how drugs affect gene networks. This deep understanding underpins the development of more effective therapies by identifying compounds that selectively target critical pathways in specific cancer types.^[2]

Identifying Novel Drug Targets

DRUG-seq empowers the hunt for new targets by revealing unsuspected links between gene expression patterns and drug effects. This insight into the cellular response can lead to the discovery of novel molecular targets that modulate sensitivity or resistance to treatment, providing fertile ground for the next generation of anti-cancer compounds.

Biomarker Discovery

Precision oncology heavily relies on the discovery and validation of biomarkers to predict patient outcomes and guide therapeutic decisions. DRUG-seq, with its ability to uncover gene signatures indicative of treatment response, plays a pivotal role in biomarker discovery, potentially leading to tests that can stratify patients for tailored treatment interventions.^[1]

Tumor Heterogeneity

Cancer is not a single disease but a collection of disorders, each with its own molecular profile and behavior. DRUG-seq's power to unravel drug response within this context is invaluable, as it allows researchers to study how different cell populations within a tumor respond to treatment. This understanding of tumor heterogeneity can inform the development of combination therapies that target multiple facets of the disease.

Conclusion: The Bright Future of high-throughput transcriptional profiling with Champ-seq in Oncology Research

From cost-effectiveness and reduced bias to sample efficiency and rich data, Champ-seq provides a valuable addition to the oncologist's toolkit. Its applications span across drug and biomarker discovery, as well as understanding tumor heterogeneity, which is pivotal in the era of personalized medicine.

As we have navigated the many advantages, it's clear that Champ-seq isn't just a new trend; it's a technological leap that can redefine the standard for RNA-seq applications in oncology research. By harnessing this tool, researchers can explore cancer treatments with unprecedented precision and efficacy, ultimately leading to improved patient outcomes and a more comprehensive arsenal against this formidable foe.