CancerPrecision®

• Tumor-normal tissue comparison for precise results — learn more
• Full sequencing and analysis of more than 700 tumor-associated genes and fusions in more than 30 genes — learn more
• High average sequencing of 500-1,000x coverage allows detection of therapy-relevant variants in subclones
• Sensitivity: > 97.6%*; Specificity: > 99,9%
• Analysis of tumor mutational burden (TMB), microsatellite instability (MSI), and viral infection (HPV/EBV/MCV/CMV) — essential biomarkers for immunotherapies — learn more
• Analysis of homologous recombination deficiency (HRD) — an essential biomarker for PARP inhibition — learn more
• Analysis of single nucleotide variants (SNVs), insertions and deletions (indels), translocations, and copy number variants (CNVs) — learn more
• Besides therapy-relevant somatic (tumor-specific) mutations also disease causing and therapy-relevant germline variants are reported
• Selected pharmacogenetically relevant germline variants necessary for drug dose adjustment in your patients
• A list of all eligible drugs, with EMA and/or FDA approval, for which corresponding biomarkers could be detected in the tumor — learn more
• Detection of mosaic variants: CHIP (Clonal Hematopoiesis of Indeterminate Potential)

• RNA-based fusion transcript analysis from tumor RNA analyzing > 200 genes (CancerFusionRx®) — learn more
• Transcriptome sequencing of tumor RNA to gain further insights on significant expression changes and their potential therapeutic relevance – learn more

^* Based on a high-quality sample with 20 % tumor content for detecting a somatic heterozygous variant.

We always sequence DNA from both the tumor and normal tissue, usually blood. This is the only way to reliably determine whether a genetic alteration is tumor-specific or inherited. Making this distinction is crucial for accurately assessing genetic alterations and their relevance to potential therapeutic strategies.

Without comparison to normal tissue, there is a risk of misclassifying genetic alterations – hereditary mutations could mistakenly be interpreted as tumor-specific changes, or vice versa. In addition, the presence of an inherited pathogenic alteration does not necessarily mean that it causes the tumor: only the comparison of tumor and normal tissue shows whether a pathogenic hereditary variant is a central driver of the tumor. This is usually confirmed by the detection of an additional tumor-specific genetic alteration (“second hit”) in the same gene that promotes tumor growth.

In up to 20% of all cases, cancer has a hereditary cause – an important diagnosis for patients and family members that should be discussed in genetic counseling. The detection of germline variants also determines eligibility for certain medications. Therefore, it is essential to classify genetic alterations precisely.

An accurate determination of molecular biomarkers such as TMB, MSI, or HRD is only possible by comparing tumor and normal tissue. Without this comparison, for example, the mutational burden may be overestimated, which can lead to the selection of unsuitable therapy options. Patients from population groups underrepresented in genomic databases particularly benefit from the tumor–normal tissue comparison, as it avoids reliance on unreliable bioinformatic estimation models to distinguish between hereditary and tumor-specific genetic alterations.

We analyze more than 700 genes and identify all variants with potential therapeutic relevance. For each relevant genetic alteration, the main section of the medical report provides detailed information on the type of alteration and the expected functional impact on protein structure or function. In addition, the potential therapeutic relevance is assessed. (A) This information forms the basis for discussion in a molecular tumor board (MTB).

In addition, the appendix of our medical report includes a therapy-related overview for each identified alteration, provided approved drugs (FDA/EMA) are available. The tables list specific agents associated with the respective variant – including the tumor entities for which they are approved. Drugs particularly relevant for the patient’s tumor entity are highlighted in color. (B)

This structured overview greatly facilitates the therapeutic classification of the genetic findings and provides a solid starting point for decisions on further treatment strategies.

Sample Report: Exemplary for the BRCA2 variant detected and the resulting therapeutic options in a breast cancer patient. Top panel (A): An excerpt from Table 1 of the findings, listing variants with therapeutic relevance. Lower part (B): An excerpt of the drug listing. In addition to the drugs shown, other drugs are also described.

Tumors arise when the balance between cell growth and cell death is disrupted. During tumor development, both processes increasingly escape control, leading to abnormal cell growth.

Under normal conditions, cellular processes are precisely regulated by a complex network of signaling pathways. In tumors, however, mutations accumulate in genes that play central roles in these pathways. A single variant can already affect multiple signaling pathways – with direct consequences for cell growth and potential drug targets.

In addition to detecting disease-relevant mutations, it is therefore crucial to understand the interplay of the affected signaling pathways. Our medical report provides you with a comprehensive overview of tumor-associated signaling networks – including their molecular key players, relevant genetic alterations, and associated drug classes. It helps to understand the complex interactions between signaling pathways and to address potential tumor escape mechanisms specifically. In this way, combination therapies can also be meaningfully evaluated and integrated into therapy planning.

• Signaling via receptor tyrosine kinases
• Cell cycle
• DNA damage repair
• Hormone pathways
• Wnt pathway

• Hedgehog pathway
• Hippo pathway
• Apoptosis pathway
• Epigenetic regulators

Presentation of tumor cell-derived somatic peptides. Somatic mutations frequently arise in cancer and permanently alter the genomic information. These genetic changes can result in the expression of proteins with altered amino acid sequences. These peptides that carry a somatic change and thus display a particularly strong immunostimulatory potential can be presented on the tumor cell surface and cause an effective anti-tumor immune response.

Tumor mutational burden (TMB), defined as the number of somatic mutations per megabase (Mut/Mb), is an approval-relevant biomarker and predictive of the response to treatment with immune checkpoint inhibitors.

The higher the number of genetic alterations within a tumor cell, the more mutated proteins are formed. These mutated proteins are processed into short fragments (oligopeptides) that are presented on the surface of tumor cells. Such mutated peptides are called neoantigens and are highly immunogenic. This means that they are effectively recognized by immune cells, particularly T cells. Specific T cells are capable of directly eliminating tumor cells after antigen recognition. The higher the number of mutations, the greater the likelihood that neoantigens will be presented on tumor cells, and the more effective tumor elimination by T cells will be. However, T-cell activity can be suppressed by immune checkpoints. Therefore, therapies targeting these checkpoints (so-called immune checkpoint inhibitors) are particularly effective in tumors with high mutational burden.

By comparing tumor and normal tissue, we can calculate the TMB with precision. In our medical report, we provide not only the TMB classification but also the exact mutation value per megabase. This allows for a far more differentiated assessment than the simple categorization into “high” or “low.” Especially in borderline cases, this precise determination enables a more reliable evaluation of whether immunotherapy may be an option.

The accuracy of TMB calculation largely depends on the size of the gene panel analyzed. With 2.96 Mb, CancerPrecision® is well above the recommended minimum size of 1.5 Mb and ensures a robust calculation of mutational burden.

In addition, we determine the MSI status (microsatellite instability), another important marker for immunotherapy response. Microsatellites are short repetitive DNA sequences found throughout the genome. The size of microsatellites can change due to failures in DNA mismatch repair mechanisms when mutations impair the underlying genes. This leads to an accumulation of frameshift mutations, which significantly increase the mutational burden.

Beyond classical PCR methods, prediction in our laboratory is performed fully NGS-based. This enables the analysis of a significantly larger number of microsatellite regions, without requiring a separate laboratory test; the calculation is performed for every sequenced tumor sample. This calculation has been validated with hundreds of matched normal–tumor sample pairs from different tumor types, in which more than 2,500 microsatellite loci were analyzed. Upon request, we also offer the classical PCR-based procedure for MSI determination.

CNVs (copy number variants) 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 our somatic tumor diagnostics.

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, especially 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 currently includes over 200 genes for fusion detection and over 132 exon-exon-specific enrichments with known breakpoints. 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 understanding of the tumor’s biology.