Enables Targeted Treatment for Your Patients

Medical report with:

• Variants with potential therapeutic relevance – more information
• Treatment options based on somatic variants
• TMB determination/MSI prediction – more information
• HRD score calculation – more information
• Comprehensive depiction of cancer-relevant pathways – more information
• Detection of copy number variants (CNV) – more information
• Tumor to normal tissue comparison – more information
• A List of all eligible drugs, with EMA and/or FDA approval, for which corresponding biomarkers could be detected in the tumor
• Clear information on approval criteria, restrictions, and drug combinations according to EMA/FDA
• Determination of pharmacogenetically relevant germline variants affecting the metabolism of certain tumor drugs or anesthetics
• Assessment of the evidence for CHIP (Clonal Hematopoiesis of Indeterminate Potential)

Additional Services:

• RNA-based fusion transcript analysis – more information

Our standard sample requirements

Normal tissue:

• 1-2 ml EDTA blood or
• Genomic DNA (1-2 µg)

Tumor tissue: (tumor content at least 20%)

• FFPE tumor block (min. tissue size 5x5x5 mm) or
• FFPE tumor tissue slides (min. 10 slices 4-10 µm, tissue size 5×5 mm) or
• Genomic DNA (> 200 ng) or
• Fresh frozen tumor tissue or
• 3x 10 ml cfDNA tubes for liquid biopsy

Other sample material sources are possible on request. Please note: In case of insufficient sample quality or tumor content the analysis might fail. If you have more than one option of tumor samples, please contact us (tumor@cegat.de) and we will assist you in choosing the optimal sample for your patient. For highest accuracy we require tumor and normal tissue for our somatic tumor diagnostic panel.

The Only Accurate Way to Determine Somatic Variants

Precise information on tumor genetics is needed for correct interpretation. In tumor diagnostics, it is essential to discriminate between variants that are restricted to the tumor (somatic variants) in comparison to the ones also present in the healthy tissue (germline variants). The only accurate way to determine variants in the healthy tissue is to sequence the matching normal tissue together with the tumor tissue. Methods trying to replace normal tissue sequencing by bioinformatics approaches fail to clearly distinguish between germline and somatic variants, especially when the tumor content of the sample is high (Jones et al., 2015; Sun et al., 2018). Therefore we always sequence DNA from the tumor as well as from normal tissue (mostly blood). The sequencing data of both tissues are compared, and thereby truly somatic variants are determined.

Comparing tumors with matching normal tissue is mandatory for obtaining meaningful results. Diagnostic tests that do not analyze tumors and matching normal tissue usually give non-accurate results.We at CeGaT always sequence tumor tissue and matching normal tissue for our CancerPrecision® Diagnostic.

Guidance on Potentially Effective Drugs

For each gene, the somatic change is depicted in detail, and the resulting therapeutic options are stated, including the EMA/FDA approval. These options are the basis for discus-sion in a molecular tumor board (MTB).

At the end of the medical report, in the appendix/supplement, we provide an extensive list of possible therapeutic strategies for each identified somatic changes. This list includes drug classes and names as well as their approval (FDA/EMA) and limiting conditions.

The Basis for Therapeutic Decisions on Immunotherapies with Checkpoint Inhibitors

Tumor mutational burden (TMB ) — the number of somatic mutations per megabase (Mut/Mb) — is a reliable predic-tive biomarker for responses to treatment with immune checkpoint inhibitors. The higher the number of genetic variations within a tumor cell, the more mutated proteins are expressed. These mutated proteins are processed into short fragments (peptides) presented on the surface of tumor cells. Such mutated peptides are called neoan-tigens. Neoantigens are highly immunogenic. This means they are very effectively recognized by immune cells, particularly T cells. T cells are able to eliminate tumor cells upon antigen recognition directly. Therefore, the higher the number of mutations, the higher the chance that neoanti-gens are presented on tumor cells, and thus the more effi-cient is tumor eradication by T cells.

By sequencing the genes of our panel with high sensitivity, we are able to calculate TMB precisely. This metric is used to categorize tumors into low and high mutational load. We list the classification of TMB, as well as the exact mutation rate of the tumor sample. When calculating TMB, the size of the panel is crucial for the precision of the results. With a size of 2.2 Mb, CeGaTs panel is well above the minimum requirement of 1.5 Mb and ensures a robust estimate of TMB (Buchhalter et al., 2019).

MSI (microsatellite instability) is another crucial parameter for response to immune checkpoint blockade. Microsatel-lites are small repetitive sequences of DNA located throug-hout the genome. The size of microsatellites can be altered due to failures of the DNA mismatch repair machinery.

Traditionally, MSI is detected through a comparison of satellite regions in tumor and normal tissue via PCR. However, we at CeGaT can predict the MSI status via NGS. This technique was validated with hundreds of matched normal and tumor sample pairs across various cancer types, testing more than 2,500 target microsatellite foci.

Healthy cells ensure a stable and error-free genome by using different DNA repair mechanisms of which the homologous recombination (HR) pathway is crucial to repair DNA damages that affect both DNA strands (double-strand breaks). In homologous recombination deficiency (HRD), this pathway is defective so that mutations, chromosomal aberrations, and other errors can accumulate in the genome. Through the resulting genomic instability, HRD facilitates tumor development and has been shown to play a role in various cancers, most prominently in breast and ovarian cancer tumorigenesis (Heeke et al., 2018; Nguyen et al., 2020). Efficient therapeutic approaches exploiting synthetic lethality resulting from HRD are available for many HR-deficient tumors and include PARP inhibitors as well as platinum-based drugs. These therapeutics cause DNA breaks, which stress the remaining DNA repair machinery of HR-deficient tumors to such an extent that they undergo cell death while healthy cells survive with minimal side effects. To identify tumors where these medications are applicable, reliable determination of the HRD status is of utmost importance.

HR-deficient tumors are often caused by germline or somatic mutations in BRCA1 or BRCA2. Therefore, this pattern has formerly been referred to as BRCAness. Moreover, mutations in other HR genes such as RAD51C, ATM, PALB2 have been shown to cause HRD. It has to be mentioned that not every genetic defect in HR genes necessarily leads to HRD in the tumor. On the other hand, HRD might be present without a detectable HR gene mutation (e.g., promoter methylation of BRCAness genes has also been reported as a cause of HRD), so a potential HRD remains undetected by trying to detect mutations in BRCAness genes alone. To ensure that HR-deficient tumors are not overlooked, we calculate the HRD score as part of every CancerPrecision® analysis independent of the tumor entity.

The HRD score measures overall genomic instability based on the number of indels, substitutions, and rearrangements occurring on a genome-wide level without needing to identify the exact mutations responsible. This mutation pattern is then used to calculate the HRD score of the tumor sample. The HRD score is calculated from three typical HRD events:

• Loss of heterozygosity (LOH)
• Telomeric allelic imbalance (TAI)
• Large-scale state transition (LST)

LOH is the irreversible loss of a single parental allele, which is especially severe in cases where defective gene versions are retained. LOH regions are defined as larger than 15 Mb but less than the whole chromosome. LST counts the number of transition points between abnormal chromosome regions that generate chromosomal gains or losses larger than 10 Mb. TAI occurs when the telomeric end of a chromosome is severely shortened in one of the two paternal chromosomes, which causes an allelic imbalance in this region. This imbalance occurs because the repetitive DNA sequences in telomere regions are especially sensitive to HRD.

The HRD score is reported in our CancerPrecision® diagnostic report together with any identified somatic mutations and selected fusion genes as well as TMB, MSI, and CNVs to provide a most comprehensive tumor analysis.

Determination of Deletions/Amplifications for the Highest Therapeutic Yield

Cellular processes are tightly regulated. This regulation depends on the correct function of genes. In tumors, the copy number of genes is frequently altered, thus impairing the affected genes’ correct function. Increasing the copy number of a gene can increase its activity while (partial) deletion can result in a loss of function. Therefore, chromosomal aberrations leading to copy number changes can also have therapeutic consequences.

In tumors, copy number variations (CNVs) are frequent due to the overall genomic instability. Here large chromosomal parts are often either deleted or amplified.

Understanding these deletions/amplifications and knowing the genes in the affected region with therapeutic relevance is important. Therefore, deletions and amplifications are detected based on the NGS data obtained.

Deletions and amplifications are listed with the affected genes of therapeutic relevance at the beginning of the report. A complete CNV-profile of the analyzed regions is shown in the report’s appendix

CNVs often play an important role in tumor genetics. Knowing the changes in CNVs assists in choosing the optimal treatment. Therefore CNV analysis is an integral part of CeGaT’s somatic tumor diagnostics.

Identification of fusion transcripts – nothing you want to overlook in your patient

Chromosomal rearrangements frequently occur in all types of cancer. As a result, gene fusions can occur in the cancer genome. Fusions are major drivers of cancer and are therefore most relevant for treatment decisions. Conventional PCR-based methods will not detect a fusion when the other partner is not known (frequently relevant for neutrophic tyrosine kinase, NTRK fusions). Even whole transcriptome analyses are not sensitive enough, especi-ally when the tumor content is low.

To detect all known and previously described as well as novel gene fusions with a therapeutic option, we developed a next-generation targeted enrichment on RNA-basis. The design includes more than 100 genes for novel fusion detec-tion, 85 well-described fusions, and 5 specific transcript variants. This method is superior to DNA-based methods and also to whole RNA-based approaches. We strongly recommend completing the genetic tumor diagnostic by RNA enrichment for fusions for the most complete under-standing of the tumor’s biology.