Enables Targeted Treatment for Your Patients
There is no ‘one-size-fits-all’ in cancer medicine, as every patient and every tumor is unique. Thus, it is vital to understand the disease history and each tumor in the best possible way. As every cancer results from genetic changes in the tumor genome (somatic mutations), cancer is considered a genetic disease. On top of that, in every fifth patient, cancer predisposition is inherited. Identifying inherited variants and genetic changes within the tumor provides valuable information for choosing the most efficient treatment for each patient. We at CeGaT have fully committed ourselves to provide the best possible diagnostic approach. With our long-term experience in genetic testing, we have brought our tumor diagnostic tools to the next level. We aim to identify every possible therapeutically relevant change in the tumor. We have optimized target enrichment and complement by sequencing on NovaSeq6000, the latest and most reliable sequencing technology. With our interdisciplinary team of experts, we uncover and interpret genetic variants responsible for tumor growth, drug resistance, treatment efficacy, and highlight potential pharmaceutical toxicity.
By using next-generation sequencing (NGS) technology, we analyze a panel of 766 tumor-associated genes and selected therapy relevant fusions in 31 genes. Variations in these genes are known to have a significant impact on tumor pathogenesis, progression and metastasis. Concerning immunotherapies we determine tumor mutational burden (TMB) and microsatellite instability (MSI). Targeted RNA-based fusion analysis allows the detection of fusion transcripts with de-novo and known partners in 106 genes. The generated data is summarized in a comprehensive report supporting the treating physician in finding efficient treatment for each patient.
Our somatic tumor panel CancerPrecision is the first choice genetic diagnostics for cancer patients.
- Large panel approach: Full sequencing and analysis of 766 genes and fusions in 31 genes (2,2 MB)
- High average sequencing coverage to detect subclonal variants: 500-1,000x
- Sensitivity: >99.9%1; Specificity: >99.9%
- Targeted RNA-based fusion transcript analysis possible
1Based on a high-quality sample for detection of a somatic heterozygous variant.
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Processing time: 3-4 weeks after sample receipt
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Service Details
Medical report with:
- Variants with potential therapeutic relevance – more information
- TMB determination/MSI prediction – 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
Additional Services:
- Extended reporting option – more information
- 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.
Process for Diagnostics
Step 1:
Test selection.
We are happy to assist in choosing the suitable diagnostic strategy
Step 2:
Counseling.
The patient receives genetic counseling and signs the order and consent form. Patient samples are retrieved and, together with the order form, send to CeGaT.
Step 3:
Analysis.
CeGaT performs the requested analysis and issues the medical report.
Step 4:
Counseling.
Results are discussed with the patient.
Step 5:
Implementation.
The results obtained from genetic testing are integrated into patient care.
Variants with Potential Therapeutic Relevance
Guidance on Potentially Effective Drugs
An increasing number of tumor therapies is available or currently tested in clinical trials. Many of these treatments address specific genetic mutations or effected pathways of the tumor cells. Thus, identification of genetic mutations or effected pathways is an important factor for personalizing the patient’s treatment and for finding new treatment options.
The goal of our medical report is to assist the treating physician with choosing the best treatment. Therefore, we suggest treatment strategies based on the genetic variants in the patient’s tumor. This information is listed in a comprehensive table. In addition, for each genetic variant the effect on the respective protein is listed. Also, the subsequently effected signaling pathway, in which the mutated protein plays a role, is highlighted.
NAF: Novel allele frequency, the frequency with which the mutated allele occurs in the sequencing (1 is 100%). The observed frequencies are influenced by the tumor content and do not correlate directly to the variant frequency in the tumor. The somatic alterations were classified with respect to their functional effect on protein level in the categories: inactivating/activating/function altered, likely inactivating/activating/function altered, unknown and benign details in the methods section). Predicted response: represents the predicted response considering known interferences and pathway crosstalks. Level of evidence: for legend see supplement
TMB/MSI Determination
The Basis for Therapeutic Decisions on Immunotherapies with Checkpoint Inhibitors
Recent clinical studies have identified the tumor mutational burden (TMB) as reliable predictive biomarker for responses to treatment with immune checkpoint blockade (Hellmann et al., 2018). TMB is defined as somatic mutations per megabase (mut/MB).
The higher the numbers of genetic variations within a tumor cell, the more mutated proteins are expressed. These mutated proteins are processed into short fragments (peptides) which are presented on the cell surface of tumor cells. Such mutated peptides are called neoantigens. Neoantigens are highly immunogenic. This means that they are very effectively recognized by immune cells, particularly by T cells. T cells are able to directly eliminate tumor cells upon antigen recognition. Therefore, the higher the numbers of mutations, the higher the chance that neoantigens are presented on tumor cells and thus the more efficient is tumor eradication by T cells.
By sequencing the genes of our panel with high sensitivity, we are able to calculate the TMB. This metric is used to classify tumors into groups with low, medium and high mutational load. Calculation of TMB is part of our medical report. We list the classification of the TMB, as well as the exact mutation rate of the tumor sample. When calculating the 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.
MSI (microsatellite instability) is another important parameter for response to immune checkpoint blockade. Microsatellites are small repetitive sequences of DNA located throughout the genome. The size of microsatellites can be altered (microsatellite instability, MSI) due to failures of the DNA mismatch repair machinery.
Presentation of tumor cell-derived somatic peptides. Somatic mutations arise frequently in cancer and permanently alter the genomic information. These genetic changes can result in expression of proteins with altered amino acid sequence. These, peptides which 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.
Pathway Illustration
For a Detailed Understanding of Altered Signaling
Cancer arises as a consequence of aberrant cell behavior with respect to cell growth and survival. Both processes become uncontrollable in the course of tumor development. Typically, all cellular processes are strongly regulated and controlled by a complex network of signaling pathways.
Tumors are characterized by an accumulation of mutations in genes that fulfill key roles in this complex signaling machinery. Mutations in specific genes lead to pathologic and cancer-promoting signaling. Moreover, a single genetic alteration can affect multiple pathways. Thus, next to the detection of disease-associated mutations, it is crucial to understand the interplay of signaling pathways, which are affected by the genetic variants.
This approach is necessary in order to identify possible bypass strategies of a given tumor. By doing so, all possible therapeutic options, including effective combination therapies, can be considered. Therefore, our comprehensive somatic tumor panel covers important signaling pathways that are known to be frequently genetically dysregulated in different cancer types.
Moreover, our medical report delivers a comprehensive depiction of relevant cancer-related pathways and their molecular “key players”. All relevant genetic alterations and available drug classes are highlighted in the signaling pathway depiction for each patient. This strategy facilitates the best possible treatment decision support.
Considered signaling pathways
- Signaling via receptor tyrosine kinases
- Cell cycle
- DNA damage repair
- Hormone pathways
- Wnt pathway
- Hedgehog pathway
- Hippo pathway
- Apoptosis pathway
- Epigenetic regulators
CNV 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.
It is important to understand these deletions/amplifications and know the genes in the affected region with therapeutic relevance. Based on the NGS data obtained, deletions and amplifications are detected.
Together with the affected genes of therapeutic relevance, deletions and amplifications are listed at the beginning of the report.
Tumor to Normal Tissue Comparison
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. This is why 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.
In addition, the separate sequencing of the germline allows us to determine treatment relevant germline variants, including pharmacogenetic variants, as well as germline variants causative for the patient’s tumor. This additional information can be used to plan healthcare further and opens diagnostic options for family members.
Extended Reporting Option
Understanding the biological background for the treatment options
Besides the regular somatic tumor report, we offer an extended report option. Here, we provide information on the biological function and prevalence of the detected variants as well as a detailed description of possible treatment options, combination therapies, and possible resistance mechanisms. The report is supplemented by list of eligible FDA- and EMA-approved drugs.
Next Generation Fusion Transcript Analysis – RNA based
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 methods that are PCR-based will not detect a fusion when the other partner is not known (frequently relevant for 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 includes 106 genes for novel fusion detection, 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 suggest completing the genetic tumor diagnostic by RNA enrichment for fusions for the most complete understanding of the tumor biology.
Gene Directory
Gene list for DNA-based anaylsis
AAK1, ABCB1, ABCG2, ABL1, ABL2, ABRAXAS1, ACD, ACVR1, ADGRA2, ADRB1, ADRB2, AIP, AIRE, AJUBA, AKT1, AKT2, AKT3, ALK, ALOX12B, AMER1, ANKRD26, APC, APLNR, APOBEC3A, APOBEC3B, AR, ARAF, ARHGAP35, ARID1A, ARID1B, ARID2, ARID5B, ASXL1, ASXL2, ATM, ATP1A1, ATR, ATRX, AURKA, AURKB, AURKC, AXIN1, AXIN2, AXL, B2M, BAP1, BARD1, BAX, BCHE, BCL10, BCL11A, BCL11B, BCL2, BCL3, BCL6, BCL9, BCL9L, BCOR, BCORL1, BCR, BIRC2, BIRC3, BIRC5, BLM, BMI1, BMPR1A, BRAF, BRCA1, BRCA2, BRD3, BRD4, BRD7, BRIP1, BTK, BUB1B, CALR, CAMK2G, CARD11, CASP8, CBFB, CBL, CBLB, CBLC, CCDC6, CCND1, CCND2, CCND3, CCNE1, CD274, CD79A, CD79B, CD82, CDC73, CDH1, CDH11, CDH2, CDH5, CDK1, CDK12, CDK4, CDK5, CDK6, CDK8, CDKN1A, CDKN1B, CDKN1C, CDKN2A, CDKN2B, CDKN2C, CEBPA, CENPA, CEP57, CFTR, CHD1, CHD2, CHD4, CHEK1, CHEK2, CIC, CIITA, CKS1B, CNKSR1, COL1A1, COMT, COQ2, CREB1, CREBBP, CRKL, CRLF2, CRTC1, CRTC2, CSF1R, CSF3R, CSMD1, CSNK1A1, CTCF, CTLA4, CTNNA1, CTNNB1, CTRC, CUX1, CXCR4, CYLD, CYP1A2, CYP2A7, CYP2B6, CYP2C19, CYP2C8, CYP2C9, CYP2D6, CYP3A4, CYP3A5, CYP4F2, DAXX, DCC, DDB2, DDR1, DDR2, DDX11, DDX3X, DDX41, DEK, DHFR, DICER1, DIS3L2, DNMT1, DNMT3A, DOT1L, DPYD, E2F3, EBP, EED, EFL1, EGFR, EGLN1, EGLN2, EIF1AX, ELAC2, ELF3, EME1, EML4, EMSY, EP300, EPAS1, EPCAM, EPHA2, EPHA3, EPHA4, EPHB4, EPHB6, ERBB2, ERBB3, ERBB4, ERCC1, ERCC2, ERCC3, ERCC4, ERCC5, ERG, ERRFI1, ESR1, ESR2, ETNK1, ETS1, ETV1, ETV4, ETV5, ETV6, EWSR1, EXO1, EXT1, EXT2, EZH1, EZH2, FAN1, FANCA, FANCB, FANCC, FANCD2, FANCE, FANCF, FANCG, FANCI, FANCL, FANCM, FAS, FAT1, FBXO11, FBXW7, FEN1, FES, FGF10, FGF14, FGF19, FGF2, FGF23, FGF3, FGF4, FGF5, FGF6, FGF9, FGFBP1, FGFR1, FGFR2, FGFR3, FGFR4, FH, FLCN, FLI1, FLT1, FLT3, FLT4, FOXA1, FOXA2, FOXE1, FOXL2, FOXO1, FOXO3, FOXP1, FOXQ1, FRK, FRS2, FUBP1, FUS, FYN, G6PD, GALNT12, GATA1, GATA2, GATA3, GATA4, GATA6, GGT1, GLI1, GLI2, GLI3, GNA11, GNA13, GNAQ, GNAS, GNB3, GPC3, GPER1, GREM1, GRIN2A, GRM3, GSK3A, GSK3B, GSTP1, H3-3A, H3-3B, H3C2, HABP2, HCK, HDAC1, HDAC2, HDAC6, HGF, HIF1A, HLA-A, HLA-B, HLA-C, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1, HLA-DRA, HLA-DRB1, HMGA2, HMGCR, HMGN1, HNF1A, HNF1B, HOXB13, HRAS, HSD3B1, HSP90AA1, HSP90AB1, HTR2A, ID3, IDH1, IDH2, IDO1, IFNGR1, IFNGR2, IGF1R, IGF2, IGF2R, IKBKB, IKBKE, IKZF1, IKZF3, IL1B, IL1RN, ING4, INPP4A, INPP4B, INPPL1, INSR, IRF1, IRF2, IRS1, IRS2, IRS4, ITPA, JAK1, JAK2, JAK3, JUN, KAT6A, KDM5A, KDM5C, KDM6A, KDR, KEAP1, KIAA1549, KIF1B, KIT, KLF2, KLF4, KLHL6, KLLN, KMT2A, KMT2B, KMT2C, KMT2D, KNSTRN, KRAS, KSR1, LATS1, LATS2, LCK, LIG4, LIMK2, LRP1B, LRRK2, LTK, LYN, LZTR1, MAD2L2, MAF, MAGI1, MAGI2, MAML1, MAP2K1, MAP2K2, MAP2K3, MAP2K4, MAP2K5, MAP2K6, MAP2K7, MAP3K1, MAP3K13, MAP3K14, MAP3K3, MAP3K4, MAP3K6, MAP3K8, MAPK1, MAPK11, MAPK12, MAPK14, MAPK3, MAX, MBD1, MC1R, MCL1, MDC1, MDH2, MDM2, MDM4, MECOM, MED12, MEF2B, MEN1, MERTK, MET, MGA, MGMT, MITF, MLH1, MLH3, MLLT10, MLLT3, MN1, MPL, MRE11, MS4A1, MSH2, MSH3, MSH4, MSH5, MSH6, MSR1, MST1R, MTAP, MTHFR, MTOR, MT-RNR1, MTRR, MUC1, MUTYH, MXI1, MYB, MYC, MYCL, MYCN, MYD88, MYH11, MYH9, NAT2, NBN, NCOA1, NCOA3, NCOR1, NF1, NF2, NFE2L2, NFKB1, NFKB2, NFKBIA, NFKBIE, NIN, NKX2-1, NLRC5, NOTCH1, NOTCH2, NOTCH3, NOTCH4, NPM1, NQO1, NR1I3, NRAS, NRG1, NRG2, NSD1, NSD2, NSD3, NT5C2, NT5E, NTHL1, NTRK1, NTRK2, NTRK3, NUMA1, NUP98, NUTM1, OPRM1, PAK1, PAK3, PAK4, PAK5, PALB2, PALLD, PARP1, PARP2, PARP4, PAX3, PAX5, PAX7, PBK, PBRM1, PBX1, PDCD1, PDCD1LG2, PDGFA, PDGFB, PDGFC, PDGFD, PDGFRA, PDGFRB, PDIA3, PDK1, PDPK1, PGR, PHF6, PHOX2B, PIGA, PIK3C2A, PIK3C2B, PIK3C2G, PIK3CA, PIK3CB, PIK3CD, PIK3CG, PIK3R1, PIK3R2, PIK3R3, PIM1, PKHD1, PLCG1, PLCG2, PLK1, PML, PMS1, PMS2, POLD1, POLE, POLH, POLQ, POT1, PPM1D, PPP2R1A, PPP2R2A, PRDM1, PREX2, PRKAR1A, PRKCA, PRKCI, PRKD1, PRKDC, PRKN, PRMT5, PRSS1, PSMB1, PSMB10, PSMB2, PSMB5, PSMB8, PSMB9, PSMC3IP, PSME1, PSME2, PSME3, PSPH, PTCH1, PTCH2, PTEN, PTGS2, PTK2, PTK6, PTK7, PTPN11, PTPN12, PTPRC, PTPRD, PTPRS, PTPRT, RABL3, RAC1, RAC2, RAD21, RAD50, RAD51, RAD51B, RAD51C, RAD51D, RAD54B, RAD54L, RAF1, RALGDS, RARA, RASA1, RASAL1, RB1, RBM10, RECQL4, RET, RFC2, RFWD3, RFX5, RFXANK, RFXAP, RHBDF2, RHEB, RHOA, RICTOR, RINT1, RIPK1, RIT1, RNASEL, RNF43, ROS1, RPS20, RPS6KB1, RPS6KB2, RPTOR, RSF1, RUNX1, RYR1, SAMHD1, SAV1, SBDS, SCG5, SDHA, SDHAF2, SDHB, SDHC, SDHD, SEC23B, SERPINB9, SETBP1, SETD2, SETDB1, SF3B1, SGK1, SH2B1, SH2B3, SHH, SIK2, SIN3A, SKP2, SLC19A1, SLC26A3, SLCO1B1, SLIT2, SLX4, SMAD3, SMAD4, SMARCA4, SMARCB1, SMARCD1, SMARCE1, SMC1A, SMC3, SMO, SOCS1, SOX11, SOX2, SOX9, SPEN, SPINK1, SPOP, SPRED1, SPTA1, SRC, SRD5A2, SRGAP1, SRSF2, SSTR1, SSTR2, SSX1, STAG1, STAG2, STAT1, STAT3, STAT5A, STAT5B, STK11, SUFU, SUZ12, SYK, TAF1, TAF15, TAP1, TAP2, TAPBP, TBK1, TBL1XR1, TBX3, TCF3, TCF4, TCF7L2, TCL1A, TEK, TENT5C, TERC, TERF2IP, TERT, TET1, TET2, TFE3, TGFB1, TGFBR2, TLR4, TLX1, TMEM127, TMPRSS2, TNFAIP3, TNFRSF11A, TNFRSF13B, TNFRSF14, TNFRSF8, TNFSF11, TNK2, TOP1, TOP2A, TP53, TP53BP1, TP63, TPMT, TPX2, TRAF2, TRAF3, TRAF5, TRAF6, TRAF7, TRRAP, TSC1, TSC2, TSHR, TTK, TUBB, TYMS, U2AF1, UBE2T, UBR5, UGT1A1, UGT2B15, UGT2B7, UIMC1, UNG, USP34, USP9X, VEGFA, VEGFB, VHL, VKORC1, WRN, WT1, XIAP, XPA, XPC, XPO1, XRCC1, XRCC2, XRCC3, XRCC5, XRCC6, YAP1, YES1, ZFHX3, ZNF217, ZNF703, ZNRF3, ZRSR2
DNA-based detection of selected fusions in these genes
ALK, BCL2, BCR, BRAF, BRD4, EGFR, ERG, ETV4, ETV6, EWSR1, FGFR1, FGFR2, FGFR3, FUS, MET, MYB, MYC, NOTCH2, NTRK1, PAX3, PDGFB, RAF1, RARA, RET, ROS1, SSX1, SUZ12, TAF15, TCF3, TFE3, TMPRSS2
RNA-based fusion transcript analysis option
Gene list for de-novo fusion detection:
ABL1, AFAP1, AGK, AKAP12, AKAP4, AKAP9, AKT2, AKT3, ALK, ASPSCR1, BAG4, BCL2, BCORL1, BCR, BICC1, BRAF, BRD3, BRD4, CCAR2, CCDC6, CD74, CIC, CLTC, CNTRL, COL1A1, CRTC1, DDIT3, EGFR, EML4, ERBB2, ERBB4, ERG, ESR1, ETV1, ETV4, ETV5, ETV6, EWSR1, EZR, FGFR1, FGFR2, FGFR3, FLI1, FN1, FUS, GOPC, JAZF1, KIAA1549, KIF5B, MAGI3, MAML1, MET, MGA, MYB, MYC, NAB2, NCOA4, NFIB, NOTCH2, NPM1, NRG1, NSD3, NTRK1, NTRK2, NTRK3, NUTM1, PAX3, PAX7, PAX8, PDGFB, PDGFRB, PIK3CA, PLAG1, PML, POU5F1, PRKAR1A, QKI, RAF1, RARA, RET, ROS1, SDC4, SHTN1, SLC34A2, SND1, SQSTM1, SS18, SSX1, STAT6, STRN, SUZ12, TACC1, TACC3, TAF15, TFE3, TFG, THADA, TMPRSS2, TPM3, TPR, TRIM24, TRIM33, WT1, YAP1, ZMYM2, ZNF703
Gene list for selected break points in these fusion genes:
TRIM24-BRAF, KIAA1549-BRAF, SND1-BRAF, EML4-ALK, CLTC-ALK, NPM1-ALK,
TPM3-ALK, KIF5B-ALK, ETV6-NTRK3, EWSR1-ERG, EWSR1-FLI1, FGFR3-TACC3, FGFR2-BICC1, FGFR2-TACC3, FGFR1-TACC1, TMPRSS2-ERG, TPM3-NTRK1,TPR-NTRK1, TRIM24-NTRK2, AFAP1-NTRK2, QKI-NTRK2, ETV6-NTRK2, KIF5B-RET, CCDC6-RET, NCOA4-RET, PRKAR1A-RET, TRIM33-RET, CD74-ROS1, EZR-ROS1, SLC34A2-ROS1, TPM3-ROS1, SDC4-ROS1, BRD4-NUTM1, BRD3-NUTM1, MAG-NUTM1, NSD3-NUTM1, NAB2-STAT6
List for specific transcript variants:
EGFR del ex25-26, EGFR del ex25-27, EGFR VII, EGFR VIII, MET ex14 skipping
