Trends in Oncology

With advances in next-generation sequencing (NGS) technologies, gene expression analysis has become an increasingly popular area of research. Two common methods used for this purpose are RNA sequencing (RNAseq) and whole transcriptome sequencing (WTS). Both techniques provide valuable insights into the molecular mechanisms of individual cells and organisms. However, when should one use RNAseq versus WTS for gene expression analysis? In this post, we will explore the advantages and disadvantages of both methods and provide guidance on which one to use.

RNAseq

RNAseq is a method of sequencing RNA molecules and measuring the abundance of each transcript. This method is capable of identifying all types of RNA, including messenger RNA (mRNA), noncoding RNA, and small RNA. It requires high-quality RNA and can detect alternative splicing, fusion genes, and post-transcriptional modifications. RNAseq is particularly useful for identifying novel transcripts and quantifying gene expression levels in low-abundance genes^[1].

However, RNAseq has some disadvantages. Its high sensitivity may lead to high levels of noise when dealing with lowly expressed genes. The method can also be expensive, and the data analysis process can be complex. Additional bioinformatic tools are required to accurately quantify gene expression levels, identify differential expression, and perform pathway analysis^[1].

Whole Transcriptome Sequencing

WTS is a newer approach that can comprehensively analyze the transcriptome of an organism without relying on annotation. This method sequences the entire complement of RNA sequences, including both known and unknown transcripts. WTS can provide greater resolution for splice variants and more accurate gene expression quantification compared to RNAseq^[2].

WTS has several advantages, including the elimination of the need for gene annotation. The technique is also cost-effective since the sequencing depth can be adjusted based on the number of samples analyzed. The method is flexible and can be used in a variety of applications such as identifying regulatory noncoding RNAs and studying alternative splicing events^[2].

However, WTS comes with a few drawbacks. The technique requires higher sequencing depth for accurate gene expression quantification, which can increase the cost of the experiment. Additionally, WTS can detect unexpected transcripts or splice variants, creating new challenges for data analysis and interpretation^[2].

When to use RNAseq versus WTS

Choosing between RNAseq and WTS depends on the goals of the experiment, the type of RNA to be analyzed, and the available resources. Researchers should consider the complexity of their sample and the genotypic variation, as WTS is better suited for samples with a higher degree of variability. RNAseq is a better choice for studies involving differential expression of known protein-coding genes. In contrast, WTS can identify novel transcripts and regulatory noncoding RNA without relying on a predetermined set of annotated transcripts^[3-4].

In conclusion, RNAseq and WTS are both powerful techniques for gene expression analysis. RNAseq is better suited for identifying differentially expressed genes in complex samples with low-abundance genes, while WTS is more effective when searching for novel transcripts and studying alternative splicing events. Both techniques require careful experimental design and bioinformatic analysis for accurate results. Ultimately, researchers should choose the method that aligns with their experimental goals and available resources. Understanding the advantages and disadvantages of each method can help optimize experimental design and result interpretation.

At Champions Oncology, we offer both RNAseq and WTS. We use an enrichment-based RNAseq method when the input RNA is of high quality, while we opt for a depletion-based WTS method when the input RNA quality is lower, with an integrity score as low as 2.0. This approach allows us to utilize different methods in different situations to obtain gene expression results from a broader range of samples.

[1] Martin JA, Wang Z. Next-generation transcriptome assembly. Nat Rev Genet. 2011 Sep 7;12(10):671-82. doi: 10.1038/nrg3068. PMID: 21897427.

[2] Jobanputra V, Wrzeszczynski KO, Buttner R, Caldas C, Cuppen E, Grimmond S, Haferlach T, Mullighan C, Schuh A, Elemento O. Clinical interpretation of whole-genome and whole-transcriptome sequencing for precision oncology. Semin Cancer Biol. 2022 Sep;84:23-31. doi: 10.1016/j.semcancer.2021.07.003. Epub 2021 Jul 10. PMID: 34256129.

[3] Fu X, Fu N, Guo S, Yan Z, Xu Y, Hu H, Menzel C, Chen W, Li Y, Zeng R, Khaitovich P. Estimating accuracy of RNA-Seq and microarrays with proteomics. BMC Genomics. 2009 Apr 16;10:161. doi: 10.1186/1471-2164-10-161. PMID: 19371429; PMCID: PMC2676304.

[4] Ruan M, Liu J, Ren X, Li C, Zhao AZ, Li L, Yang H, Dai Y, Wang Y. Whole transcriptome sequencing analyses of DHA treated glioblastoma cells. J Neurol Sci. 2019 Jan 15;396:247-253. doi: 10.1016/j.jns.2018.11.027. Epub 2018 Nov 22. PMID: 30529802.

Gastrointestinal cancers are a heterogeneous group of cancers that can be caused by H. pylori bacterial or Epstein-Barr virus infection, chronic inflammation, or inherited or spontaneous genetic mutations. Advances in solid tumor immunotherapy for gastrointestinal cancers include the development of methods that improve the screening, evaluation, and development of more precise and robust treatments. Breakthroughs in basic and translational research are leading to better treatment options for gastrointestinal cancer, and CRISPR/Cas9-based (clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9) gene editing technology is at the forefront of these discoveries.

CRISPR/Cas9-based gene editing technology was originally identified in bacteria and archaea as an antiviral defense mechanism^[1]. As a gene editing application, CRISPR/Cas9 uses the Cas9 nuclease to cleave specific sequences in the target DNA and introduces modifications such as knock-in or knock-out mutations^[2]. The DNA sequence is targeted using a single-guide RNA (sgRNA) comprised of CRISPR RNA (crRNA) and trans-activating CRISPR RNA (tracRNA), and these RNAs can be expressed on the same plasmid as the Cas9 nuclease upon delivery into target cells. The crRNA/tracrRNA duplex can bind to the complementary sequence in the genomic DNA and recruit Cas9, which cleaves the target sequence and allows for DNA repair by either homology-directed repair or non-homologous end pairing. This repaired sequence is now a part of the genomic DNA and leaves virtually no trace of gene editing.

CRISPR/Cas9 gene editing has been used for several applications in oncology research and more recently to improve immune-oncology strategies. One of the earliest applications involves knockout screens in which a library of sgRNAs is used to transfect a pool of knockouts that can be used for gain-of-function or small molecule/drug screens. Genome-wide screens of cancer cell lines have been useful in identifying genes that are essential to cancer cell growth but can be prone to false-positive hits if the screens are not correctly designed or analyzed^[3]. Genome-wide screens have also been used to identify genes linked to resistance, and such gene hits have been identified in a melanoma library screen when treating with the inhibitor vemurafenib^[4]. Combinatorial CRISPR/Cas9 screens are now being used to target multiple genes, and this approach can be used for defining genetic or cellular pathways involved in cancer or characterizing drugs that target multiple genes^[5].

CRISPR/Cas9 has moved beyond in vitro cell culture and can be used for human organoid models for studying oncogenesis. ARID1A is one of the most frequently mutated genes in solid tumors, including gastric tumors^[6]. ARID1A encodes a BAF complex subunit that normally targets BAF to AT-rich DNA sequences to regulate transcription. Mouse studies have suggested that ARID1A is involved in gastrointestinal cancer oncogenesis, but this was not validated in humans until a recent study that used CRISPR/Cas9-mediated depletion of ARID1A wild-type human gastric organoids. This ARID1A-knockout organoid system established a forward genetics model of gastric cancer that resembled gastric cancer phenotypically and identified BIRC5/Survivin as a key player in early tumorigenesis. This type of organoid model is not only useful for basic oncogenesis studies but can be used for small molecule inhibitor screens.

CRISPR/Cas9 screens can be used to validate mutations found in patients with gastrointestinal tumors. Activin A receptor type 2A (ACVR2A) is frequently mutated in patients with microsatellite instability-high gastric cancer, and a 2021 study used CRISPR/Cas9 to generate ACVR2A-knockout human gastric cancer cells^[7]. These cells exhibited less aggressive proliferation, migration, and invasion in vitro, and patients with ACVR2A mutations had better prognoses than patients with wild-type ACVR2A. Gastrointestinal stromal tumors (GIST) have been typically characterized by activation mutations in the receptor tyrosine kinase KIT, but a 2022 study using genome-wide CRISPR screening identified the complementary chromatin-modifying complexes MOZ and Menin-MLL as being essential to GIST^[8]. This finding highlights a trend in oncology research elucidating a role for chromatin regulation in tumorigenesis, which has the potential to be targeted therapeutically.

Beyond basic research, CRISPR/Cas9 is starting to be used in applications for clinical trials. A Phase II trial has recently launched that is using CRISPR/Cas9 to delete an intracellular checkpoint molecule called cytokine-induced SH2 (CISH) protein^[9]. Preclinical studies have shown that CRISPR/Cas9-mediated deletion of CISH enhanced antitumor responses, and the Phase II trial is using patient-derived CRISPR-modified T cells to treat individuals with metastatic gastrointestinal tumors.

CAR T-cells (Chimeric Antigen Receptor) have been an incredible progress in personalized therapy targeting cancer.^[10] However, different challenges hampered the availability, efficacy, and safety of this promising cell therapy. CRISPR-Cas9 technology can address many of these limitations. Desired genetic modification can be achieved with cellular precision engineering, leveraging CRISPR-Cas9-based gene editing. In addition, this approach can be exploited to overcome T-cell exhaustion, lack of CAR T-cell persistence, and toxicity related to cytokine release.

In particular, the implementation of CRISPR-Cas9 technology generated universal CAR T Cells resistant to PD1 Inhibition; or enhanced T cell function against bladder cancer disrupting CTLA-4.^[11]  In the oncolytic therapy field, it has been demonstrated that HSV-1-derived CD40L-armed oncolytic therapy by CRISPR-Cas9-based gene editing boosted endogenous immunity in pancreatic ductal adenocarcinoma.^[12]

CRISPR/Cas9 gene editing will continue to transform basic and clinical research, immunotherapy, and cellular therapy providing effective and safe therapeutic strategies. Regulatory agencies are now developing frameworks to allow for the use of this revolutionary tool in a wide range of diseases.

[1] Wiedenheft B, Sternberg SH, Doudna JA. RNA-guided genetic silencing systems in bacteria and archaea. Nature. 2012 Feb 15;482(7385):331-8.

[2] Barrangou R, Doudna JA. Applications of CRISPR technologies in research and beyond. Nat. Biotechnol. 2016;34(9):933-941.

[3] Munoz DM, Cassiani PJ, Li L, Billy E, Korn JM, Jones MD, Golji J, Ruddy DA, Yu K, McAllister G, DeWeck A. CRISPR screens provide a comprehensive assessment of cancer vulnerabilities but generate false-positive hits for highly amplified genomic regions. Cancer Discovery. 2016 Aug 1;6(8):900-13.

[4] Shalem O, Sanjana NE, Hartenian E, Shi X , Scott DA , et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science. 2014. 343:84–87.

[5] Han K, Jeng EE, Hess GT, Morgens DW, Li A, Bassik MC. Synergistic drug combinations for cancer identified in a CRISPR screen for pairwise genetic interactions. Nat. Biotechnol. 2017 May;35(5):463-74.

[6] Lo YH, Kolahi KS, Du Y, Chang CY, Krokhotin A, Nair A, Sobba WD, Karlsson K, Jones SJ, Longacre TA, Mah AT, Tercan B, Sockell A, Xu H, Seoane JA, Chen J, Shmulevich I, Weissman JS, Curtis C, Califano A, Fu H, Crabtree GR, Kuo CJ. A CRISPR/Cas9-Engineered ARID1A-Deficient Human Gastric Cancer Organoid Model Reveals Essential and Nonessential Modes of Oncogenic Transformation. Cancer Discov. 2021 Jun;11(6):1562-1581.

[7] Yuza K, Nagahashi M, Ichikawa H, Hanyu T, Nakajima M, Shimada Y, Ishikawa T, Sakata J, Takeuchi S, Okuda S, Matsuda Y, Abe M, Sakimura K, Takabe K, Wakai T. Activin a Receptor Type 2A Mutation Affects the Tumor Biology of Microsatellite Instability-High Gastric Cancer. J. Gastrointest. Surg. 2021 Jan 8. doi: 10.1007/s11605-020-04889-9. Epub ahead of print.

[8] Hemming ML, Benson MR, Loycano MA, Anderson JA, Andersen JL, Taddei ML, Krivtsov AV, Aubrey BJ, Cutler JA, Hatton C, Sicinska E. MOZ and Menin-MLL Complexes are Complementary Regulators of Chromatin Association and Transcriptional Output in Gastrointestinal Stromal Tumor. Cancer Discovery. 2022 May 2.

[9] https://twin-cities.umn.edu/news-events/university-minnesota-opens-first-its-kind-clinical-trial-treat-metastatic-gi-cancers

[10] Jogalekar MP, Rajendran RL, Khan F, Dmello C, Gangadaran P, Ahn BC.Front Immunol. 2022 Jul 22;13:925985. doi: 10.3389/fimmu.2022.925985. eCollection 2022.

[11] Zhang W, Shi L, Zhao Z, Du P, Ye X, Li D, Cai Z, Han J, Cai J. Disruption of CTLA-4 expression on peripheral blood CD8 + T cell enhances anti-tumor efficacy in bladder cancer. Cancer Chemother Pharmacol. 2019;83:911–20.

[12] Wang R, Chen J, Wang W, Zhao Z, Wang H, Liu S, et al. CD40L-armed oncolytic herpes simplex virus suppresses pancreatic ductal adenocarcinoma by facilitating the tumor microenvironment favorable to cytotoxic T cell response in the syngeneic mouse model. J Immunother Cancer. 2022;10(1):e003809.

The development of new oncology treatments has focused on strategies that alter immune responses. Many of these novel therapies use antibodies that bind to receptors on different immune cell subsets and either activate or suppress their functions depending on the immune response being targeted. Antibody-drug conjugates are also becoming more widely used for improved targeting of drugs to specific cell subsets. Flow cytometry-based receptor occupancy (RO) assays are a valuable tool for quantifying cell surface expression of target molecules and are used to generate pharmacodynamic (PD) data, which can be used with pharmacokinetic (PK) data to guide dosing strategies. Consider these different types of receptor occupancy assays as you develop the most useful tools for screening therapeutic molecules.

1. Total receptor assay: The evaluation of a potential therapeutic target usually begins with an assessment of how many receptors are expressed by a given cell subset. If a receptor is abundantly expressed, it may be a difficult target to use due to potential off-target effects or the requirement of high doses of a therapeutic antibody. Measurement of total receptor involves using a fluorescently labeled antibody that binds to a receptor at a site that is different from the binding site of the therapeutic antibody of interest.
2. Free receptor assay: In this assay, cells are first stained with an unlabeled therapeutic antibody and then stained with a labeled version of the same antibody, which will only bind to the remaining free receptors. Measurement of free receptor levels at different doses is required for generating PK/PD datasets.
3. Drug-occupied receptor assay: This assay labels cells with your antibody of interest (“drug”), and then a labeled secondary antibody binds to a different site on your antibody of interest. This measurement can be used in place of free receptor assays when they are not technically feasible.

Combining data from free and total receptor assays over time is a useful method for measuring the kinetics of antibody binding and calculating potential half-lives of these molecules for therapeutic applications. Consider the type of assay you need for your phase of development. Engage with RO experts who can help you develop validated assays that can be used in preclinical and clinical settings. Flow cytometry-based RO assays will continue to be an essential tool in the development of novel oncology therapeutics.

Recent advances in immunotherapy have led to the development of targeted therapies for treating lymphoma and leukemia. One such therapy is CD19-targeted therapy, which is widely used as a frontline treatment for B cell lymphoma and leukemia. However, despite many patients initially respond well to this therapy relapse remains the major obstacle to be addressed. In this blog post, we will explore the various mechanisms underlying relapse to CD19-targeted therapies in patients with B cell lymphoma and leukemia.

Antigen-positive Relapse:

Early relapse is usually associated with short CAR-T cell persistence. This can be due to poor T cell quality, but, more importantly, it is determined by the costimulatory domain in the CAR. In fact, domains such as 4-1BB have been proven to significantly prolong the persistence of CAR-T cells_[1]. In addition, the gene editing technology used to engineer the T cells can make a difference in CAR-T cell persistence. Combination therapies with checkpoint inhibitors have also been shown to prolong response duration and remission_[1,2].

Immune Evasion Mechanisms:

One of the primary mechanisms of acquired resistance to CD19-targeted therapies is immune evasion. Cancer cells can develop various mechanisms to evade immune surveillance and avoid being targeted by the therapy. For example, cancer cells may downregulate the expression of CD19 on their surface, thereby reducing the effectiveness of the therapy. Alternatively, they may upregulate other immune checkpoint molecules or express inhibitory proteins that prevent immune cells from recognizing and eliminating them_[3].

Antigen Loss Mechanisms:

Another mechanism of acquired resistance is antigen loss. CD19-targeted therapies are designed to bind to and eliminate cancer cells that express the CD19 antigen. However, cancer cells can develop mutations that lead to loss of CD19 expression, rendering the therapy ineffective. This mechanism of resistance has been observed in both lymphoma and leukemia patients, and it is a significant challenge in the development of novel therapies_[4].

Development of Alternative Pathways:

Cancer cells that survive CD19-targeted therapies can develop alternative pathways for survival and growth. These pathways can bypass the effects of the therapy and allow cancer cells to continue to proliferate. Examples of such mechanisms include the activation of alternative signaling pathways and the upregulation of survival factors. Understanding these adaptive mechanisms is crucial in devising new therapeutic strategies to overcome acquired resistance_[1,3].

Tumor Microenvironment:

The tumor microenvironment (TME) plays a critical role in acquired resistance to CD19-targeted therapies. The TME is a complex milieu of immune and stromal cells that interact with cancer cells to promote tumor growth and survival. The TME can also influence the response of cancer cells to therapy. For example, the presence of immunosuppressive cells in the TME can reduce the effectiveness of CD19-targeted therapies_[1,5].

Genetic Factors:

Genetic factors also play a role in acquired resistance to CD19-targeted therapies. For example, mutations in genes involved in DNA repair pathways can increase genomic instability and promote drug resistance. Identifying genetic factors that contribute to resistance can help to personalize therapy and improve outcomes_[6].

Disease relapse and development of acquired resistance to CD19-targeted therapies in patients with B cell lymphoma and leukemia pose a significant clinical challenge. To overcome this challenge, we need to find ways to optimize CAR-T cell design to produce more effective therapies and understand the various mechanisms of resistance to be able to develop new therapeutic strategies that target these mechanisms. Research efforts are underway to identify novel therapeutic targets and personalized treatment approaches that can overcome acquired resistance and improve outcomes for patients_[7].

Continuous improvements in our understanding of acquired resistance and relapse to CD19-targeted therapies will pave the way for the development of more effective and durable treatments for these diseases. To overcome this challenge, access to relevant preclinical models is key to develop alternative or synergistic therapeutic approaches. Champions Oncology offers a large cohort of B-cell lymphoma and leukemia models, including primary samples from patients who progressed after CD19-targeted therapies, to accelerate the development of novel therapeutic approaches to improve B-cell lymphoma and leukemia patients' lives.

1. Shah NN, et al. Mechanisms of resistance to CAR T cell therapy. Nat Rev Clin Oncol. 2019 Jun;16(6):372-385. doi: 10.1038/s41571-019-0184-6. PMID: 30837712; PMCID: PMC8214555.
2. Sterner RC, et al. CAR-T cell therapy: current limitations and potential strategies. Blood Cancer J. 2021 Apr 6;11(4):69. doi: 10.1038/s41408-021-00459-7. PMID: 33824268; PMCID: PMC8024391.
3. Plaks V, et al. CD19 target evasion as a mechanism of relapse in large B-cell lymphoma treated with axicabtagene ciloleucel. Blood. 2021 Sep 23;138(12):1081-1085. doi: 10.1182/blood.2021010930. PMID: 34041526; PMCID: PMC8462361.
4. Sworder BJ, et al. Determinants of resistance to engineered T cell therapies targeting CD19 in large B cell lymphomas. Cancer Cell. 2023 Jan 9;41(1):210-225.e5. doi: 10.1016/j.ccell.2022.12.005. Epub 2022 Dec 29. PMID: 36584673; PMCID: PMC10010070.
5. Frey NV, et al. Optimizing Chimeric Antigen Receptor T-Cell Therapy for Adults With Acute Lymphoblastic Leukemia. J Clin Oncol. 2020 Feb 10;38(5):415-422. doi: 10.1200/JCO.19.01892. Epub 2019 Dec 9. PMID: 31815579; PMCID: PMC8312030.
6. Chen GM, et al. Characterization of Leukemic Resistance to CD19-Targeted CAR T-cell Therapy through Deep Genomic Sequencing. Cancer Immunol Res. 2023 Jan 3;11(1):13-19. doi: 10.1158/2326-6066.CIR-22-0095. PMID: 36255409; PMCID: PMC9808313.
7. Atilla PA, et al. Resistance against anti-CD19 and anti-BCMA CAR T cells: Recent advances and coping strategies. Transl Oncol. 2022 Aug;22:101459. doi: 10.1016/j.tranon.2022.101459. Epub 2022 May 23. PMID: 35617812; PMCID: PMC9136177.

Advances in preclinical oncology research are dependent on gaining insights into tumor biology and applying these insights to the development of novel diagnostics or therapeutics. Next-generation sequencing (NGS) technology has been instrumental in bridging basic immuno-oncology findings and preclinical applications. Here we provide an overview of NGS applications that are transforming preclinical oncology research.

Teasing Apart the Tumor Microenvironment

Solid tumors are now understood to have dynamic and unique tumor microenvironments (TMEs) that influence the immune response as well as tumor progression. Combined with flow cytometry and immunohistochemistry, single-cell NGS methods such as scRNA sequencing (scRNA-seq) and Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-Seq) allow scientists to measure and understand which cells are present in the TME. These findings may characterize how different stromal cells are present and contributing to tumorigenesis[1] and how the TME can create an immunosuppressive environment[2].

Elevating Liquid Biopsies

Liquid biopsies have gained popularity as a method to measure circulating tumor cells (CTCs) related to either solid tumor metastasis or hematological malignancies. NGS methods have greatly improved insights gained from this sampling method[3]. Previous bulk sampling methods for quantifying CTCs were hampered by sample contamination with leukocytes and the extremely low frequency of CTCs. Single-cell NGS methods, including methods to measure single nucleotide variations as well as large-scale mutations such as copy number variations or chromosomal rearrangements, have provided unprecedented information about CTC specimens in different anatomical locations or over time. These methods have also driven the development of liquid biopsy methods that can be used to track treatment efficacy and the emergence of resistance.

Better Biomarkers

Tumor mutation burden (TMB) is a measurement of mutations within a tumor’s genomic sequence and has emerged as a potential biomarker associated with immune checkpoint inhibitor (ICI) efficacy. Numerous studies have now applied NGS methods to measure TMB in relation to ICI efficacy[4], and robust methods are now being validated for use clinically[5]. These findings have consistently found that high TMB correlates with ICI efficacy, and other NGS-based studies have been used to compare TMB with other genomic aberrations like microsatellite instability[6].

NGS data will continue to provide critical insights into preclinical oncology research and is certain to drive the development of better diagnostics and therapeutics.

[1] Lambrechts D, Wauters E, Boeckx B, et al. (2018). Phenotype molding of stromal cells in the lung tumor microenvironment. Nature Medicine, 24(8), 1277-1289.

[2] Devalaraja S, To TK, Folkert IW, et al. (2020). Tumor-derived retinoic acid regulates intratumoral monocyte differentiation to promote immune suppression. Cell.180(6):1098-114.

[3] Lim SB, Di Lee W, Vasudevan J, et al. (2019). Liquid biopsy: one cell at a time. NPJ Precision Oncology. 3(1):1-9.

[4] Goodman AM, Kato S, Bazhenova L, et al. (2017). Tumor Mutational Burden as an Independent Predictor of Response to Immunotherapy in Diverse Cancers. Mol Cancer Ther. 16(11):2598-2608.

[5] Fenizia F, Alborelli I, Costa JL, et al. (2021). Validation of a Targeted Next-Generation Sequencing Panel for Tumor Mutation Burden Analysis: Results from the Onconetwork Immuno-Oncology Consortium. J. Mol. Diagn. S1525-1578(21)00117-3.

[6] Goodman AM, Sokol ES, Frampton GM, et al. Microsatellite-Stable Tumors with High Mutational Burden Benefit from Immunotherapy. (2019). Cancer Immunol. Res. 10:1570-1573.

TumorGraft3D models are Champions’ three-dimensional (3D) ex vivo tissue cultures that mimic the structure and function of tumors in vivo. They are innovative tools for studying tissue physiology, pathophysiology, and drug discovery. These models can be derived from various tissues, such as the brain, liver, intestine, etc., and can self-organize into complex structures resembling their respective tumor locations and are cultured in a matrix-free environment. The establishment and growth of 3D tissue culture models require the models to interact with the myriad of cellular and structural components within the tumor microenvironment. In this blog, we will explore the differences between matrix and matrix-free conditions in 3D tissue cultures.

Traditionally, 3D tissue culture models have been cultured using a matrix-based approach, where the supportive extracellular matrix (ECM) is derived from natural or synthetic sources. Commonly used matrices include Matrigel, laminin, collagen, and hyaluronic acid. Matrigel is the most commonly used matrix for 3D tissue cultures due to its ability to mimic the natural basement membrane of the tissue. However, using matrices can also pose several challenges, such as batch-to-batch variability, limited scalability, high costs, and difficulty in downstream analysis. Additionally, animal-derived matrices, such as Matrigel, can introduce xenobiotic components that may impact the reproducibility and translatability of the results across the different species.

Matrix-free approaches, on the other hand, offer an alternative to matrix-based assays. These approaches allow the self-organization of 3D tissue culture models without the use of an exogenous matrix. Matrix-free approaches are based on the principle that extracellular matrix components can be provided by the models themselves, where the cellular component of the 3D structure secretes and organizes the matrix components in a more physiological way generating a supportive microenvironment. There are several matrix-free approaches available, including hanging drop and rotary culture. These approaches offer several advantages over matrix-based assays, such as scalability, reproducibility, and compatibility with downstream analysis. In addition, this technique overcomes the species-specific differences between the cells and the matrix which will negatively influence the human translatability of the findings.

Hanging drop cultures involve the formation of 3D tissue culture models in a droplet of culture medium suspended upside down. This method provides a low-adhesion environment for the models to organize and secrete their own matrix components. Rotary culture is another matrix-free approach that involves the constant rotation of a culture vessel, allowing the models to grow and form a 3D structure without the need for a supporting matrix.

Both matrix-based and matrix-free assays have pros and cons for 3D tissue cultures. Matrix-based assays mimic the ECM of the tissue providing an artificial microenvironment for model growth. Beyond batch-to-batch variability, limited scalability, and high costs; including matrix in your ex vivo cultures can also represent a biological problem. Given the artificial architecture of the matrix, it imposes unnatural stiffness and pre-defined structures that will influence the hierarchical organization of the cells during 3D structure generation. Matrix-free assays offer an alternative that eliminates the need for exogenous matrix components and provides a low-adhesion environment for the 3D tissue culture growth for a fully human and more physiological microenvironment. While matrix-free approaches are promising, they are not without their challenges. Specifically, there is the need for ad-hoc development of various protocols for each different tumor type, including rigorous procedures for optimization and standardization to ensure results are reproducible.

In summary, the decision to use matrix or matrix-free culture conditions in 3D tissue culture assays can influence the downstream translatability of your assay.  Researchers should evaluate the differences and understand how using a matrix or matrix-free culture conditions can impact their research.  

References:

1) Loewa, A., Feng, J.J. & Hedtrich, S. Human disease models in drug development. Nat Rev Bioeng (2023). https://doi.org/10.1038/s44222-023-00063-3

2) Proietto M, Crippa M, Damiani C, Pasquale V, Sacco E, Vanoni M, Gilardi M. Tumor heterogeneity: preclinical models, emerging technologies, and future applications. Front Oncol. 2023 Apr 28;13:1164535. doi: 10.3389/fonc.2023.1164535. PMID: 37188201; PMCID: PMC10175698.   

3) LeSavage BL, Suhar RA, Broguiere N, Lutolf MP, Heilshorn SC. Next-generation cancer organoids. Nat Mater. 2022 Feb;21(2):143-159. doi: 10.1038/s41563-021-01057-5. Epub 2021 Aug 12. PMID: 34385685.

4) Kozlowski MT, Crook CJ, Ku HT. Towards organoid culture without Matrigel. Commun Biol. 2021 Dec 10;4(1):1387. doi: 10.1038/s42003-021-02910-8. PMID: 34893703; PMCID: PMC8664924.

Advances in precision medicine have transformed treatments for many types of solid tumors, but similar treatment options have been more limited for hematologic oncology. Now, new ex vivo hematologic oncology models are being developed that use patient-derived lymphoma or leukemia cells for screening experimental drugs or biologics.

The lower cost associated with ex vivo drug screening studies compared to in vivo ones makes these platforms ideal for large scale screening of therapeutic agents before progressing to in vivo studies with the most promising drug candidates. In addition, ex vivo hematologic oncology models closely recapitulate the tumor of origin and can provide valuable information about the variability of drug response in a cohort of patients.

In expert hands, these platforms are well-suited to preclinical drug screening and can inform preclinical research decisions as well as clinical care. Some unique aspects of ex vivo hematologic oncology models need to be considered as you advance your research.

1. Viability, Survival, and Proliferation: Patient-derived ex vivo hematologic oncology models may adapt to culture conditions very differently; some cells may die off significantly whereas others proliferate at high rates. It is critical to measure and understand the proliferation and viability parameters of your ex vivo hematologic oncology models to assure you are working with a reliable specimen.
   
   
2. Consider Specific Cell Characteristics: Prolonged culturing of ex vivo hematologic oncology models may introduce unwanted changes to cells that can alter their responses to drugs or antibodies. Be sure to thoroughly characterize the phenotype of specific ex vivo hematologic oncology models before and during ex vivo culture to assure that prolonged culture does not significantly alter the cell phenotype or cause unintended mutations. Short term cultures reduce the risk of such changes happening.
   
   
3. The Power of the Panel: A major advantage of using ex vivo hematologic oncology models is the ability to scale up cultures and screen large panels of experimental antibodies or drugs. This can also be done with multiple patient-derived ex vivo hematologic oncology models being screened in parallel.

Ex vivo hematologic oncology models have advanced significantly as ex vivo drug screening platforms have emerged. With our expertise and our large bank of ex vivo hematologic oncology models, at Champions Oncology, we are well poised to help you advance your preclinical therapeutic screening. Leveraging the multi-omics and multimodal characterization of our ex vivo hematologic oncology models will help you narrow down your drug candidates and speed up the trajectory to clinical investigation.

Clinical flow cytometry is now a standard tool used by clinical researchers in numerous fields, especially immuno-oncology. Consider these “Do’s & Don’ts” of clinical flow cytometry as you decide to use this tool for clinical research.

Do Perfect Your Panel - A staining panel includes the set of antibodies used to identify different cell subsets within a sample and depends on the use of multiple antibodies conjugated with different fluorochromes. These fluorochromes are excited by specific light wavelengths from the cytometer’s lasers, and the emitted light is analyzed and interpreted as different cell types. Researchers take time to develop staining panels that can reasonably distinguish between cell types and avoid aberrations such as spectral overlap.

Do Vigilant Validation - Clinical flow cytometry assay development must satisfy certain criteria, including accuracy, specificity, limit of detection, reproducibility, precision, linearity, and stability. By satisfying these criteria, researchers are able to have confidence in their clinical data, even if experiments were done at different times or in different locations.

Do Maintenance - Reliable flow cytometry data requires a well-designed and validated protocol as well as a properly maintained cytometer. Routine maintenance is carried out by cytometer users to assure that fluidic systems flow properly, lasers are firing correctly, and the emitted light is detected at the expected wavelength. Infrequent, but more involved, maintenance is carried out by technicians and is essential to the long-term functionality of a cytometer.

Don’t Pick the Wrong Test Tube - The very first step of a clinical flow cytometry protocol is collecting a sample, which is typically peripheral blood, cord blood, or tissue biopsy. These samples can be collected in tubes with preservatives or anti-clotting agents, but some of these additives can alter cell viability. It is worthwhile to evaluate different sample tube types to assure that your cells of interest are not being lost or damaged due to test tube effects.

Don’t Lose your Cells - Clinical flow cytometry protocols rely on the acquisition of thousands to millions of cells per sample in order to have a reasonable number of events to analyze. One essential step to acquiring usable data is the inclusion of a viability dye that discriminates between live and dead cells. Viability dyes come in different formats that work with specific flow cytometer configurations, so it is critical to determine which dye will work with your cytometer.

Don’t Screw Up Your Analysis - After you have completed the laborious process of sample staining and data acquisition on a cytometer, it is essential to execute the appropriate data analysis. Be sure you understand how to use the software needed for analysis, and this may mean including certain controls in your staining and acquisition protocols. Errors in analysis may result in your data being inaccurate or undetected.

Bladder cancer is a relatively common form of cancer that is defined as either pre-invasive or invasive, and non-muscle invasive bladder cancer (NMIBC) is the most commonly diagnosed subtype^[1]. NMIBC is typically treated by surgical resection and/or intravesical delivery of chemo- or immunotherapy-based adjuvant treatment, and long-term efficacy is monitored by urine testing or cystoscopy. Muscle-invasive bladder cancer (MIBC) is relatively resistant to current treatment options and occurs more frequently in men. MIBC also has high rates of morbidity and mortality, and novel therapies or combination therapies are being developed to better treat this form of bladder cancer.

Here we highlight recent findings about invasive bladder cancer biology and how these observations are informing the development of new therapies.

Mutation Landscape  

Analysis of MIBC genomes has revealed a high mutation rate in tumors that approaches rates seen in melanoma and lung cancer^[2]. The mutational signature of many MIBC tumors is consistent with mutations associated with APOBEC (apolipoprotein B mRNA-editing enzyme, catalytic polypeptide-like) cytidine deaminase, and many of the mutations are clonal, which suggests that the effects of APOBEC expression in bladder tissue occurs early in tumorigenesis. Non-invasive and invasive bladder tumors are also often classified as being highly heterogeneous, and tumor histology can be extremely variable and includes squamous, plasmacytoid, micropapillary, and small-cell carcinoma. This molecular and histological heterogeneity presents a challenge in subtyping tumors based on gene expression profiles and provides insight into why it can be difficult to identify targeted therapies that are effective against bladder cancers.

Several genes are commonly mutated in both NMIBC and MIBC, but mutation rates for some of these genes, including MLL2 and TP53, are higher in invasive tumors^[3]. TP53 mutations appear to be a critical factor associated with the transition to its invasive form. Mutations have also been identified in genes associated with the sister chromatid cohesion and segregation process (STAG2, ESPL1, FGFR3, and TACC3)^[3] and chromatin remodeling (UTX, MLL-MLL3, CREBBP-EP300, NCOR1, ARID1A, and CHD6)^[4].

Immunotherapy Insights

NIMBC is one of the first forms of cancer treated with an immunotherapy approach in which the attenuated tuberculosis bacterial vaccine, Bacillus Calmette-Guérin (BCG), is delivered intravesically to the bladder and triggers an immune response that inhibits tumor growth^[5]. Recently, immune checkpoint inhibitor-based treatments have been examined as therapeutic options for different bladder cancers. Expression of the PD-L1 immune checkpoint molecule has been detected on both NIMBC and MIBC tumor cells and tumor-infiltrating mononuclear cells. Antibody-based blockade of PD-1/PD-L1 interactions restores anti-tumor responses mediated by T cells. Atezolizumab was the first PD-L1 inhibitor approved for the treatment of locally advanced/ metastatic urothelial carcinoma (LA/mUC) that has progressed or after cisplatin-based chemotherapy[6], and five different PD-L1/PD-1 inhibitors have been approved for this form of bladder cancer. Other checkpoint inhibitors that target different molecules have not shown significant improvements in overall survival or progression-free survival. Clinical trials currently underway are examining the efficacy of combinations of checkpoint inhibitors or the use of these inhibitors as an adjuvant therapy to surgical resection or chemotherapy. In addition, therapies that target specific mutations, such as FGFR, HER2, or chromatin-modifying genes, are also under investigation, and the FGFR inhibitor erdafitinib was recently approved for the treatment of LA/mUC^[7][8].

Bladder cancer treatment outcomes continue to advance because of insights into the basic biology of bladder cancer, and a better understanding of molecular and immunological mechanisms associated with anti-tumor responses. The many preclinical and clinical studies currently underway for bladder cancer will contribute valuable information to new therapeutic options, especially for patients with refractory or invasive forms of bladder cancer.

[1] Knowles MA, Hurst CD. Molecular Biology of Bladder Cancer: New Insights into Pathogenesis and Clinical Diversity. Nat Rev Cancer. 2015;15:25–41.

[2] Robertson AG, Kim J, Al-Ahmadie H, et al. Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer. Cell. 2017;171(3):540-556.e25.

[3] Guo G, Sun X, Chen C, et al. Whole-Genome and Whole-Exome Sequencing of Bladder Cancer Identifies Frequent Alterations in Genes Involved in Sister Chromatid Cohesion and Segregation. Nat Genet. 2013;45(12):1459-1463.

[4] Gui Y, Guo G, Huang Y, et al. Frequent Mutations of Chromatin Remodeling Genes in Transitional Cell Carcinoma of the Bladder. Nat Genet. 2011;43(9):875-878.

[5] Herr HW and Morales A. History of Bacillus Calmette-Guerin and Bladder Cancer: An Immunotherapy Success Story. J Urol. 2008;179.1: 53-56.

[6] Bellmunt J, Powles T, Vogelzang NJ. A Review on the Evolution of PD-1/PD-L1 Immunotherapy for Bladder Cancer: The Future is Now. Cancer Treat. Rev. 2017;54:58-67.

[7] Osterman CK, Milowsky MI. New and Emerging Therapies in the Management of Bladder Cancer. F1000Res. 2020;9:F1000 Faculty Rev-1146. 1

[8] Loriot Y, Necchi A, Park SH, et al. Erdafitinib in Locally Advanced or Metastatic Urothelial Carcinoma. N Engl J Med. 2019 Jul 25;381(4):338-348.

Head and neck squamous cell carcinoma (HNSCC) is one of the most common forms of cancer in the world. This heterogeneous disease is most often seen in men and is closely linked to tobacco usage, which can be enhanced by alcohol usage. Human papillomavirus (HPV) infection is an independent risk factor for HNSCC^[1], and most HPV- HNSCCs occur in the larynx and oral cavity, and HPV+ HNSCCs typically arise in the oropharynx^[2]. Both HPV+ and HPV- HNSCCs are typically diagnosed at advanced disease stages and are often treated with an aggressive combination of surgery, radiotherapy (RT), and chemotherapy^[3]. More recently, cetuximab, an IgG1 monoclonal antibody that targets epidermal growth factor receptor (EGFR) has been used in combination with radiotherapy and has shown some improvements in progression-free survival (PFS) and overall survival (OS)^[4].

Advances in genomic technology have provided critical insights into the pathology of HNSCC subtypes. Some HNSCCs are now known to have high mutational burdens, which is like other cancers associated with carcinogen exposure. Analysis of HPV- and HPV+ HNSCCs in The Cancer Genome Atlas (TGCA) network has been instrumental in identifying genome defects common to both HPV+ and HPV- HNSCCs, as well as defects that are unique to each of these subtypes. Global analysis of all HNSCC subtypes identified TP53 as the most commonly mutated gene, but there tends to be a lower percentage of TP53 mutations in HPV+ tumors versus HPV- tumors, likely related to HPV infection^[5]. Activating mutations in PIK3CA and inactivating mutations in NOTCH1 are associated with both types of HNSCC. HPV- HNSCCs show inactivating mutations in TP53 and CDKN2A cell cycle suppressor genes and amplification of CCND1, which can all contribute to unchecked cell cycle progression. In contrast, HPV+ HNSCCs show expression of the E6 and E7 viral oncogenes, which can inhibit TP53 and RB1, as well as activation of cell cycle regulator E2F1^[6]. Mutations in the MAPK signaling component HRAS have also been identified in a fraction of HNSCC specimens for which treatment options are limited. A recent clinical trial demonstrated encouraging efficacy in patients with recurring or metastatic HNSCCs carrying HRAS mutations^[7] treated with tipifarnib, which is a farnesyltransferase inhibitor that can inhibit farnesylation of HRAS and membrane targeting and downstream signaling.

In addition to targeted therapies, recent studies suggest that PD-1/PD-L1 blockade alone or in combination with chemotherapy can improve overall survival in patients with recurrent or metastatic HNSCC^[8],[9]. PD-1 blockade has shown better survival, toxicity, and response duration compared with cetuximab treatment combined with chemotherapy, but these responses are dependent on patients having HNSCC tumors that are positive for PD-L1. Tumor mutational burden and the expression of inflammatory biomarkers also correlated directly with responses to PD-1 blockade.

Recent insights into genomic variations associated with HNSCC subtypes are providing valuable information with respect to basic tumor biology as well as identifying potential therapeutic targets for preclinical development.

[1] Cognetti DM, Weber RS, Lai SY. Head and neck cancer: an evolving treatment paradigm. Cancer. 113(7 Suppl):1911-1932 (2008).

[2] Mehanna H, Beech T, Nicholson T, El-Hariry I, McConkey C, Paleri V, Roberts S. Prevalence of human papillomavirus in oropharyngeal and nonoropharyngeal head and neck cancer--systematic review and meta-analysis of trends by time and region. Head Neck. 35(5):747–55 (2013).

[3] Pignon J P, le Maître A, Maillard E, Bourhis J. & MACH-NC Collaborative Group. Meta-analysis of chemotherapy in head and neck cancer (MACH-NC): An update on 93 randomized trials and 17,346 patients. Radiother. Oncol. 92(1), 4–14 (2009).

[4] Bonner JA, et al. Radiotherapy plus cetuximab for locoregionally advanced head and neck cancer: 5-year survival data from a phase 3 randomized trial, and the relation between cetuximab-induced rash and survival. Lancet Oncol. 11, 21–28 (2010).

5] Westra, W. H. et al. The inverse relationship between human papillomavirus-16 infection and disruptive p53 gene mutations in squamous cell carcinoma of the head and neck. Clin. Cancer Res. 14, 366–369 (2008)

[6] Beck TN, Golemis EA. Genomic insights into head and neck cancer. Cancers Head Neck 1, 1 (2016).

[7] Ho AL, Brana I, Haddad R. et al. Tipifarnib in Head and Neck Squamous Cell Carcinoma with HRAS Mutations. J. Clin. Oncol. JCO2002903 (2021).

[8] de Sousa LG, Ferrarotto R. Pembrolizumab in the first-line treatment of advanced head and neck cancer. Expt. Rev. Anticanc. Ther. 21.12: 1321-1331 (2021).

[9] Haddad RI, Seiwert TY, Chow LQM et al. Influence of tumor mutational burden, inflammatory gene expression profile, and PD-L1 expression on response to pembrolizumab in head and neck squamous cell carcinoma. J. Immunother. Cancer. 2022 Feb;10(2):e003026.