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.
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.
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.
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.
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]
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]
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 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.
Cholangiocarcinoma is a type of cancer that originates from cholangiocytes, the cells that constitute the bile ducts in the liver, which carry bile from the liver to the small intestine. It is a rare and aggressive cancer that can be categorized into different subtypes based on histological and molecular characteristics.
Histologically, cholangiocarcinoma can be classified as intrahepatic, perihilar, or distal. Intrahepatic cholangiocarcinoma originates within the liver, while perihilar and distal cholangiocarcinoma develop in the ducts outside the liver. These subtypes have distinct clinical presentations and treatment approaches [1,2].
In this blog, discover the diverse nature of cholangiocarcinoma subtypes and learn about the specific clinical care required for each subtype.
Histological Characteristics and Clinical Care
Histological characteristics play a significant role in guiding clinical care for cholangiocarcinoma subtypes. Intrahepatic cholangiocarcinoma often presents as a solitary mass within the liver, while perihilar cholangiocarcinoma typically involves the bifurcation of the bile ducts. Distal cholangiocarcinoma commonly manifests as a tumor in the lower part of the bile duct near the small intestine.
The histological features of each subtype influence the choice of diagnostic tests, surgical interventions, and other treatment modalities. For example, surgical resection is often the primary treatment option for intrahepatic cholangiocarcinoma, while perihilar cholangiocarcinoma may require a combination of surgery and liver transplantation. Distal cholangiocarcinoma may be treated with surgery, radiation therapy, or systemic chemotherapy.
By understanding the histological characteristics of cholangiocarcinoma subtypes, healthcare professionals can provide appropriate clinical care to improve patient outcomes.
Cholangiocarcinoma Subtype-Specific Therapies
Each cholangiocarcinoma subtype requires specific therapies tailored to its unique characteristics. For intrahepatic cholangiocarcinoma, surgical resection is often the primary treatment approach, followed by adjuvant therapy such as chemotherapy or radiation therapy. Liver transplantation may be considered in selected cases.
Perihilar cholangiocarcinoma, on the other hand, often requires more complex surgical interventions, such as liver resection combined with bile duct resection and reconstruction. Liver transplantation may be an option for patients with advanced disease or underlying liver disease.
Distal cholangiocarcinoma may be treated with surgical resection, radiation therapy, or systemic chemotherapy. The choice of treatment depends on the extent of the tumor and the patient's overall health.
By tailoring therapies to the specific cholangiocarcinoma subtype, healthcare professionals can optimize treatment outcomes and improve patient survival rates.
Molecular Profiling and Treatment Strategies
In addition to histological characteristics, molecular profiling has emerged as an essential tool for understanding cholangiocarcinoma subtypes and guiding treatment strategies. Molecular profiling involves analyzing the genetic and molecular alterations in cancer cells to identify potential targets for therapy.
Advancements in molecular profiling techniques have allowed researchers to identify specific biomarkers and genetic mutations associated with cholangiocarcinoma subtypes. This information helps in developing targeted therapies that can effectively inhibit the growth and spread of cancer cells.
Two targeted therapies are currently available for patients presenting tumors with FGFR2 fusions/rearrangements (infigratinib) or IDH1 (ivosidenib) mutation [3,4].
In general, treatment strategies for cholangiocarcinoma subtypes may include targeted therapies, immunotherapy, chemotherapy, and radiation therapy. Molecular profiling enables healthcare professionals to select the most appropriate treatment options based on the molecular characteristics of the tumor and the patient's overall health [5].
Future Directions in Cholangiocarcinoma Care
The field of cholangiocarcinoma care is rapidly evolving, with ongoing research and advancements in treatment options.
Efforts are being made to improve early detection methods for cholangiocarcinoma. Early diagnosis allows for timely intervention and increases the chances of successful treatment. Additionally, advancements in precision medicine and molecular profiling techniques hold promise for tailoring treatment plans based on the unique molecular characteristics of each patient's tumor.
In conclusion, the diverse nature of cholangiocarcinoma subtypes necessitates specific clinical care for optimal treatment outcomes. Through a comprehensive understanding of histological and molecular characteristics, healthcare professionals can provide personalized therapies and contribute to ongoing research efforts aimed at improving cholangiocarcinoma care.
Colorectal cancer (CRC) is a leading cause of cancer deaths globally. Advances in early detection have improved survival rates, but patients diagnosed with metastatic CRC still have stubbornly poor 5-year survival rates.
Standard treatments for CRC include surgery, chemotherapy, and radiotherapy, but alternative immuno-oncology therapies are showing promising results in CRC patients. Here we highlight advances in immuno-oncology therapies that are being used to treat CRC patients or are being pursued in preclinical and clinical studies.
Molecular genetic analysis of CRC tumors is a critical diagnostic tool for classifying and treating this cancer. CRC tumors are typically classified as mismatch repair-deficient/microsatellite instability-high (dMMR—MSI-H) tumors, which have a high overall mutation burden, or mismatch repair-proficient/microsatellite instability-low (pMMR—MSI-L) tumors, which have a lower mutation burden[1]. Defects in MMR are associated with the accumulation of mutations and are caused by defects in mismatch repair proteins, and these defects are typically detected by frame-shift mutations in DNA repeat regions known as microsatellites. Mutations in the BRAF oncogene, particularly the activating V600E mutation, comprise a distinct subset of CRC. BRAF is a component of the mitogen-activated protein kinase (MAPK) pathway that normally functions downstream of the epidermal growth factor receptor to regulate transcription of genes involved in cellular growth and survival, but the BRAF-V600E mutation results in constitutive activation of BRAF and uncontrolled cellular proliferation and tumor growth[2].
Immuno-oncology approaches that target immune checkpoint blockade have proven effective for several cancers, including CRC. T cells can initiate effective anti-tumor responses under ideal conditions, but the immunosuppressive tumor microenvironment of some cancer types inhibits T cell activation, usually through engagement of immune checkpoint molecules like PD-1 and CTLA4. For more than a decade, the development and use of immune checkpoint inhibitors (ICIs) has transformed the treatment of melanoma[3,4] and non-small cell lung cancer[5,6]. The first FDA-approved treatments include a monoclonal antibody (mAb) that targets CTLA4 (ipilimumab) and two mAbs that target PD-1 (pembrolizumab and nivolumab). These early studies suggested that tumors with high mutation burdens respond well to ICI, which may be due in part to the generation and presentation of tumor neoantigens that are recognized as non-self and can be targeted by cytotoxic T cells[7].
CRC tumors with the dMMR—MSI-H signature have a high mutational burden and typically have a high level of CD4+ and CD8+ tumor-infiltrating lymphocytes (TILs). These cells have shown elevated expression of PD-1, PD-L1, and CTLA4, which suggests that they may respond well to ICIs[8]. Indeed, several recent clinical studies have shown improvements in overall survival and progression-free survival in patients treated with individual or combined ICIs that target PD-1. Of note, interim results from a phase II trial that enrolls patients with MMRd locally advanced rectal cancer to receive neoadjuvant dostarlimab, a PD-1 inhibitor, showed sustained complete response 1 year after the end of treatment in all patients[9]. Other current studies are evaluating next-generation PD-1 inhibitors combined with chemotherapy and/or other biologics such as an anti-VEGF and anti-EGFR mAb[10].
In contrast, patients with pMMR–MSI-L have shown poor responses to PD-1 or CTLA4 blockade alone or in combination, which has led to the development of trials that explore different treatment combinations, including inhibitors of the MAPK pathway or angiogenesis[11]. Promising results have been presented in a phase I/II clinical trial (NCT04017650) in which patients with MSI-L and BRAFV600E were treated with BRAF+EGFR inhibitors, to induce a transient MSI-H phenotype, plus anti-PD-1 antibody[12].
Several cutting-edge immuno-oncology therapies are being explored for the treatment of CRC. Beyond combinations of individual antibodies, researchers are engineering bispecific antibodies that bind to tumor cells and T cells simultaneously to enhance anti-tumor T cell responses. One such bispecific antibody, CEA-TCB, is being tested in phase I trials alone or in combination with anti-PD-L1 to treat metastatic CRC[13]. Another novel approach includes adoptive cell therapies like chimeric antigen receptor (CAR) T cells, which are T cells collected from the tumor tissue or peripheral blood of a patient and engineered to bind to tumor antigens and potentiate anti-tumor responses. Oncolytic virus and bacteria-based vaccines are also being studied as potential CRC treatments.
Besides treatments designed on the tumor's intrinsic genetic background, much effort is being put into deciphering the tumor microenvironment, with a particular focus on its interplay with the gut microbiota to modulate inflammation and immune response, known to be involved in the metastatic onset. These studies are leading to the identification of inflammatory/immune signatures that inform the therapeutic agents to be used to target the pro-oncogenic microenvironment and reactivate the immune system[14].
The advancement of immuno-oncology is already transforming the treatment of CRC and will contribute to better outcomes for all types of CRC in the decades to come.
Ex vivo culture systems have been instrumental to preclinical oncology research because they enable researchers to study basic features of tumor cells and carry out large-scale screens of drugs or biologics. Ex vivo cultures do not fully recapitulate physiological conditions, although some advances have been made by developing three-dimensional (3D) culture methods.
Here we highlight the advantages and disadvantages of using ex vivo cultures for preclinical oncology research.
Cell culture: simple, inexpensive, but imperfect
Ex vivo cell cultures of tumors were first developed as adherent two-dimensional (2D) monolayers that were grown in culture flasks or flat dishes. This approach is useful for studying tumors' basic cell biology and carrying out drug screens and preliminary toxicology and pharmacokinetic studies[1]. 2D cultures can be developed rapidly and cultured over longer periods of time using readily available tissue culture reagents. Unfortunately, 2D culture systems are limited in their applications because they do not accurately model the tumor microenvironment, and tumor cell morphology and function change under these culture conditions. Over the last several decades, 3D cultures have advanced significantly[2]. Several different systems now exist for 3D cultures, including liquid suspension cultures passaged on non-adherent plates, cultures grown in semi-solid substrates like soft agar or Matrigel, and cells grown on scaffolds like collagen. 3D culture systems better mimic characteristics of the tumor microenvironment, including cell-cell interactions, hypoxia, and reduced sensitivity to drug treatment[3]. (Visit our dedicated webpage to learn more about Champions' 3D tumor model offer)
3D cultures for drug discovery have become widely adopted over 2D models because they better predict in vivo efficacy and metabolic responses to drug treatments. A major advance in 3D tumor culture systems is the development of 3D spheroid and organoid cultures[4]. Spheroids are clusters of cells derived from tumors or other complex tissues and they cluster together through cell-cell adhesion. Organoids are more complex cell clusters derived from stem cells and can self-assemble and regenerate to form a smaller version of the original tumor or organ tissue. Tumor spheroids are widely used for drug screening and cytotoxicity assays and can be formed from dissociated tumor tissue or circulating tumor cells[5]. (Read our blog "Using 3D Ex Vivo Tumor Models for Oncology Research: An Expert Guide") Tumor organoids are even more accurate reflections of tumors growing under physiological conditions and are now widely used to compare the efficacy of standard-of-care treatments versus targeted therapies in addition to their use in drug screens[6]. Spheroids and organoids can undergo genomic editing as well, and this approach has been instrumental in identifying mutations that drive tumor growth or cause drug resistance[7-8]. (Read our blog "A Step-by-Step Guide to Design a 3D Tumor Model Co-Culture Study")
Ex vivo + in vivo = the most effective strategy
Animal models, especially patient-derived xenograft mouse models, are a critical bridge between preclinical studies and clinical trials. However, due to the lower costs and the compatibility with high throughput analysis, ex vivo tumor cultures are still the preferred method for ranking therapeutic agents, determining drug combination regimens, investigating mechanisms of action, and validating targets. Additionally, ex vivo tumor cultures are essential when studying certain hematological tumors, which are difficult to engraft and cannot be passaged in vivo because of their “liquid” nature.
Both ex vivo and in vivo studies continue to work together to advance oncology research, but each year brings new advances in ex vivo culture systems that enhance their overall impact in predicting treatment responses.
We typically consider viruses as infectious agents or vaccine vectors, which are non-replicating entities that express vaccine antigens. More recently, researchers have been working to develop oncolytic viruses, which are engineered to specifically infect and replicate in tumor cells. This targeted infection results in tumor cell lysis, activation of anti-tumor immune responses, and the release of new oncolytic virus particles that can infect other tumor cells. The concept of oncolytic viruses emerged in the early 20th century when scientists observed that a leukemia patient spontaneously went into remission for a brief time following an influenza virus infection. Early attempts to develop non-specific oncolytic viruses were plagued with complications related to poor efficacy and safety issues. As our understanding of targeted immunotherapy for the treatment of tumors has evolved, oncolytic viruses are gaining traction again and an oncolytic herpesvirus (talimogene laherparepvec or T-VEC) engineered to target metastatic melanoma was approved for clinical use by the FDA in 2015 [1].
Advantages of Oncolytic VirusesOncolytic virus development has expanded significantly in the last two decades. Some of the most widely used viruses that are engineered to target tumors include adenoviruses, alphaviruses, herpes simplex viruses (HSV), rhabdoviruses, and vaccinia viruses.
Viruses can be engineered to target tumor cells for lysis, such as replication-competent HSV-1 deletion mutants (e.g. thymidine kinase or ribonucleotide reductase mutants) that can only replicate in rapidly dividing tumor cells. Similarly, T-VEC is a modified HSV-1 virus with a gene deletion in ICP34.5, which allows for antiviral responses by normal cells but not tumor cells, thus allowing for tumor-specific virus-mediated destruction [2].
Oncolytic viruses can be modified to express immune molecules (e.g. TNF, IL-12, or chemokines) that promote proinflammatory responses and increase recruitment of macrophages and T cells for enhanced antitumor immunity in addition to oncolytic activity [3-4]. Oncolytic viruses can also induce apoptosis of tumor cells or enhance the uptake of chemotherapeutic agents [5].
Currently under pre-clinical investigation is a vaccinia virus carrying a TGFβRII inhibitor that has proved effective in causing tumor regression in mouse tumor models and shown an even greater effect when combined with checkpoint inhibitor therapy [6]. This approach overcomes the difficulties of targeting TGFβ, limiting side effects due to the targeting of non-tumor cells.
Another promising approach currently being evaluated pre-clinically is a therapy using CAR-T and TCR-T cells infected with myxoma virus. This approach induces autosis and adaptive immunity in mouse models of tumors to restrain antigen escape [7]. Some oncolytic viruses can induce long-term immunity to tumors and prevent metastasis or the re-occurrence of these cancers, thus making them attractive therapeutic candidates.
Clinical trials using oncolytic viruses are currently ongoing for numerous solid tumor types including glioblastoma, breast cancer, lung cancer, and bladder cancer [8].
Oncolytic viruses are typically grown in tissue culture systems and are evaluated in vitro using a panel of tumor cell lines, which provides insight into tumor specificity and mode of action. These modified viruses can then be tested in a wide range of animal models, including immunocompetent mice, such as those used for syngeneic mouse tumor models, and immunocompromised mice, which include humanized mice that carry patient-derived tumor xenografts. Certain oncolytic viruses, such as vaccinia virus, require that researchers be vaccinated against this virus before laboratory handling, but in most cases, these viruses can be handled under BSL-2 conditions.
Despite great success in preclinical studies, translating oncolytic virus therapy to the clinic can be quite challenging. One major obstacle is the route of administration, with the oncolytic viruses needing to be injected directly into the tumor. While this can be simpler for superficial tumors like melanoma, it becomes much more complex to reach tumors that grow in deeper body organs.
Many different varieties of oncolytic viruses are currently being evaluated preclinically or in clinical trials and the next decade promises further advances in this cutting-edge field.
Cancer is a complex disease, and developing novel, effective treatments requires testing in preclinical models. Mouse clinical trials represent a critical step in translating promising anti-cancer therapies from bench to bedside. When combined with advanced bioinformatics and analytics tools, mouse clinical trials are a powerful experimental approach to identifying target patient populations, planning cohort extensions, selecting the right combination drug, elucidating the mechanism of resistance, or identifying translational biomarkers [1]. However, conducting a successful mouse clinical trial requires expertise in designing the study, analyzing the data, and interpreting the results. In this blog post, we will guide you through the essential steps of designing a mouse clinical trial and analyzing the resulting data. This guide includes tips on powering the study, preparing the dataset, grouping responders vs non-responders, identifying biomarkers, and integrating proteomics in a multiomics model.
Step 1: Powering the study
In a mouse clinical trial, animals implanted with PDX models are surrogates of the patients whom PDX models were derived from. Before starting a mouse clinical trial, it is essential to determine the sample size required to achieve reliable and statistically significant results. It is important to enroll a large enough number of PDX models (which corresponds to the number of patients we would enroll) and enough animals per PDX model (so as not to lose a patient if we lose a mouse). Power analysis will estimate the minimum number of animals needed to detect a difference in tumor growth inhibition (TGI) between the treatment and control groups. Typically, researchers set the power at 80% and α at 0.05, which means that there is an 80% probability of detecting a significant difference between the two groups if one exists. It is also recommended to power mouse clinical trials according to the study goal and endpoint. For instance, a survival endpoint may require larger group sizes than a TGI endpoint. If the goal of the mouse clinical trial is to identify a novel biomarker of response, a larger number of models will need to be included in the design.
Step 2: Select models
Model selection is a crucial step when designing a mouse clinical trial and needs to be done by taking into account several parameters associated with the clinical history and molecular characteristics of the models so that the PDX model panel recapitulates the targeted patient population. Tumor indication and/or mechanism of action of the drug are usually the two key parameters that guide PDX model enrollment in a mouse clinical trial, and the deeper the information in terms of clinical classification and molecular profiling the more representative will be the PDX model panel [2]. (Read our blog: "How to Use Metadata as Your Model Selection GPS")
Step 3: Preparing the dataset
Once the study is completed, the next step is to prepare the dataset for analysis. This includes checking for outliers and removing/excluding missing data. One critical factor in analyzing mouse clinical trial data is the identification of PDX models with common response profiles. This is usually evaluated by a modified version of the clinical Response Evaluation Criteria in Solid Tumors (mRECIST). The response rate can be calculated by comparing the tumor volume at day 0 either with the volume corresponding to the best response measured on treatment or with the tumor volume measured on the final day of treatment. The threshold value can vary depending on the tumor type and drug mechanism of action. Therefore, it is crucial to validate the threshold on a panel of known drugs.
Step 4: Grouping responders vs non-responders
Once the threshold is established, the next step is grouping responders vs non-responders. Responders are mice that show a reduction in tumor volume or mass greater than or equal to the critical value, and non-responders are mice that show tumor reduction below the critical value. These groups are used to compare gene expression profiles and identify potential biomarkers of response [3].
Step 5: Identifying biomarkers
Bioinformatics analysis can be used to identify biomarkers of response. One widely used approach is differential gene expression analysis (DGEA), which compares the gene expression profiles of responders and non-responders to identify genes that are differentially expressed. Another approach is differential gene set enrichment analysis (DGSE), which identifies functional gene sets that are enriched in the responders and non-responders. Partial least-squares (PLS) regression, a dimensional reduction method part of DIABLO multi-omics integration workflow [4], is commonly used to identify the most influential genes within a multi-omics gene network that can predict response. It is very important that mouse clinical trials are designed accordingly when performing multi-omics analysis as an endpoint. (Watch our webinar: "Identify MOA and Companion Biomarkers in Oncology using Multi-Omic Analyses")
Step 6: Integrating proteomics for improved target validation and biomarker identification
Proteomics approaches, such as mass spectrometry-based proteomics, can provide quantitative information on protein expression levels and post-translational modifications. Given the lack of correlation between protein abundance and RNA expression, integration of proteomics and phospho-proteomics data in a multi-omics model can greatly improve the accuracy of therapeutic target expression for model selection, provide a more comprehensive understanding of the molecular mechanisms underlying drug response, and provide an important additional molecular annotation for biomarker identification via single or multi-omics analysis [5]. (Read our blog: "4D Proteomics: Adding Dimension to Protein Detection")
In this blog post, we have provided a step-by-step guide on how to design and analyze a mouse clinical trial, and shown how a successful mouse clinical trial requires careful planning and expertise in bioinformatics. Lumin Acuity offers tailored bioinformatics and computational biology solutions to support your mouse clinical trial planning and data analysis, accelerating decision-making and driving actionable results. Well-designed and executed mouse clinical trials can be instrumental in developing effective cancer therapies and improving patient outcomes.
Pancreatic ductal adenocarcinoma (PDAC) is one of the most common and aggressive forms of pancreatic cancer that has remained difficult to diagnose early and treat successfully. PDAC has a five-year survival rate below 10% and is one of the leading causes of cancer death. Complete surgical resection is one of the few curative treatment modalities, and chemotherapy protocols have limited efficacy[1].
Unfortunately, most existing immunotherapy-based treatments are also associated with poor response rates[2]. Recent analyses of PDAC samples have provided critical insights into genetic alterations that drive tumorigenesis and have identified potential therapeutic targets. Here we highlight several of these key findings and how they may lead to advances in PDAC treatment.
PDAC arises in the epithelial cells of pancreatic duct, or ductules, and is thought to progress in a manner like other carcinomas, in which the normal epithelial cells transition into pre-invasive pancreatic intraepithelial neoplasia lesions that eventually form invasive PDAC[3]. Most PDAC tumors carry somatic mutations in oncogenes, particularly KRAS, TP53, CDKN2A, and SMAD4[4]. KRAS mutation is the most frequent event in PDAC. The assumption that KRAS is an undruggable target based on extensive drug screens that showed in vitro inhibition but limited or no efficacy in animal models[5] is finally being challenged by the very promising results obtained by administration of Sotoresib (KRAS p.G12C inhibitor) in patients with advanced pancreatic cancer (NCT03600883), and by the identification of small-molecules that inhibit KRAS p.G12D in preclinical studies[6,7].
The need for druggable targets or biomarkers for PDAC has led to more innovative approaches to identify unique molecule attributes. A recent comprehensive proteogenomic characterization of PDAC pancreatic ductal tissues compared against paired normal adjacent tissue, and the findings validated known mutations in oncogenes[8]. This study also defined previously unknown genomic alterations and analyzed differences in protein expression and protein phosphorylation status between tissues. This comprehensive analysis identified a panel of proteins linked to early stage PDAC, and phosphoproteomic analysis identified several signaling pathways downstream of KRAS, including PI3K/AKT/mTOR and MAPK/ERK, that may be targeted by existing kinase inhibitors. The PAK1/PAK2 kinases were also identified as dysregulated in PDAC tissues and have the potential to be targeted therapeutically.
Recent studies have also better characterized the tumor microenvironment (TME) of PDAC and how this may limit treatment efficacy[9]. PDAC tumors tend to be highly heterogenous with a dense stroma and disorganized blood vessels, which impair drug penetration. The TME is enriched for myeloid derived suppressor cells and regulatory T cells and displays a low mutational burden, thus making this a “cold” tumor immunophenotype that is resistant to existing immunotherapies. Current studies are examining immunotherapy or drug-based strategies to turn “cold” PDAC tumors into “hot” tumors that would be receptive to immune checkpoint blockade.
Further advances in PDAC research will require similar extensive proteogenomic studies that better define dysregulated pathways that trigger early events in tumor formation and may function as early biomarkers for disease. The search for novel therapeutic targets or combinations of targets is also essential to improving the currently dismal array of treatment options for PDAC patients. Champions Oncology has 81 highly characterized Pancreatic Cancer Models available for preclinical studies, click below to learn more about how these models can propel your research.
