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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Advantages of DRUG-seq in Comparative Analysis

Cost-Effectiveness

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

Sample Efficiency

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

DRUG-seq in Action: Enhancing Oncology Research and Development

Drug Response Profiling

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

Identifying Novel Drug Targets

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

Biomarker Discovery

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

Tumor Heterogeneity

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

Conclusion: The Bright Future of DRUG-seq in Oncology Research

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

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

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.

Next-generation sequencing (NGS) technology has transformed the biomedical research landscape. Only a few years ago, high resolution genome or exome sequencing would be cumbersome and cost-restrictive, but current NGS technology platforms now allow for basic and clinical researchers to include these approaches for routine DNA and RNA sequencing needs. What are the different NGS sequencing approaches and how are they applied to oncology research?

1. Whole Genome Sequencing (WGS): NGS technology can be used for WGS of human genomes and tumor-specific genomes, as well as animal model and microbial genomes. WGS produces high resolution genomic sequences of expressed genomic regions as well as unexpressed regulatory regions. For preclinical oncology research, WGS is critical for characterizing genomic profiles associated with tumor progression or potential responsiveness to targeted drug therapies. WGS can detect single nucleotide variants, copy number variants, and insertions/deletions in tumor cells^[1]. The comprehensive scope of WGS makes it well suited for detecting mutations in both coding and non-coding regions^[2]. WGS is also useful for population level oncology studies that evaluate genetic susceptibility to specific cancers and potential heritability^[3].

2. Whole Exome Sequencing (WES): WES techniques focus on sequencing the exome, which are comprised of protein expressing regions, or exons, within the genome. WES is an appropriate method for identifying genetic mutations that alter protein sequences, and WES data can be used toward measuring the tumor mutational burden (TMB) and predicting treatment efficacy^[4]. WES data can also be used to identify potential new drug targets or mechanisms of drug resistance^[5].

3. Targeted Sequencing: This method focuses on defined gene regions and is typically used in diagnostic applications or for validation of WGS or WES results. Targeted sequencing works well for screening tumor samples for well characterized mutations, such as those associated with BCL2, BRCA-1/2, BRAF, and EGFR, and can be used for identifying appropriate targeted therapies^[6].

4. RNA sequencing: RNA sequencing is now emerging as a powerful tool that complements NGS DNA methods because the transcriptional profile of a single cell can be measured and used to bridge genomic data with cellular phenotypes. Single-cell RNA sequencing (scRNA-seq) has specifically emerged as a powerful method for understanding the heterogeneity of cell populations within a tumor. Together with histological data, scRNA-seq data can be used to distinguish between neoplastic cells, immune cells, and healthy cells from the surrounding tissue, and it can also be used to evaluate how experimental treatments alter the tumor microenvironment^[7].

NGS methods are transforming both basic oncology research and clinical care, from identifying novel mutations to pinpointing personalized cancer therapies. Each method is suited to specific applications, so working with experts in NGS technology is critical to method selection and data analysis.

¹ Bewicke-Copley F et al. Applications and Analysis of Targeted Genomic Sequencing in Cancer Studies. Comput. Struct. Biotechnol. 2019;17: 1348-1359.

² Nakagawa H, Fujita M. Whole Genome Sequencing Analysis for Cancer Genomics and Precision Medicine. Cancer Sci. 2018;109(3):513-522.

³ Rotunno M et al. A Systematic Literature Review of Whole Exome and Genome Sequencing Population Studies of Genetic Susceptibility to Cancer. Cancer Epidemiology and Prevention Biomarkers. 2020;29(8):1519-34.

⁴ Klempner SJ et al. Tumor Mutational Burden as a Predictive Biomarker for Response to Immune Checkpoint Inhibitors: A Review of Current Evidence. Oncologist. 2020 Jan;25(1): e147-e159.

⁵ Beltran H et al. Whole-Exome Sequencing of Metastatic Cancer and Biomarkers of Treatment Response. JAMA Oncol. 2015;1(4):466-474.

⁶ Vestergaard LK et al. Next Generation Sequencing Technology in the Clinic and Its Challenges. Cancers (Basel). 2021;13(8):1751.

⁷ Fan J et al. Single-Cell Transcriptomics in Cancer: Computational Challenges and Opportunities. Exp Mol Med. 2020; (52)1452–1465.