Trends in Oncology

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.

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.