CancerNeo

Identify neoantigens for personalized cancer vaccine design

Personalized cancer vaccines are a powerful tool for boosting the immune system’s response to cancer cells. Such patient-individualized vaccines require an in-depth understanding of all mutations of the tumor and the body’s MHC types. CeGaT’s CancerNeo provides the insights required for the design of vaccines: The analysis of a patient’s tumor exome data detects tumor-specific mutations and predicts neoepitopes, which are important targets for tumor elimination. The expression of these neoantigens is confirmed by whole RNA sequencing (transcriptome) from the same FFPE tissue.

Neoepitopes are abnormal peptides, which are little fragments of proteins. Body cells constantly present peptides on their surface, indicating the current state of the cell. Immune cells can recognize if a cell presents normal peptides or abnormal ones and use this information to determine and eliminate defective cells. Neoepitopes are also produced by tumor mutations and represent targets for tumor elimination. To strengthen the neoepitope recognition through vaccination hence supports cancer treatment.

Treating physicians receive two comprehensive medical reports. One report highlights the treatment options based on variants identified in cancer-relevant genes. The second report gives a selection of neoantigens that can be used to create a personalized vaccination.

Our service:

Processing time: 4-6 weeks after sample receipt
Highest confidentiality and quality standards
Our customer service accompanies you at each single step

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Service Details

Tumor Neoantigen Diagnostics

Tumor-/normal tissue whole exome sequencing using CeGaT ExomeXtra
Detailed assessment of treatment relevant variants detected in 766 tumorrelevant
Medical report with
- Validated list of variants with potential therapeutic relevance
- Treatment options based on somatic variants
- TMB determination/MSI prediction
- Comprehensive depiction of cancer-relevant pathways
- Detection of copy number variants (CNVs)
Tumor Whole RNA sequencing with rRNA depletion
HLA class I typing
Prediction of HLA class I restricted peptide epitopes (neoepitopes) spanning tumor-specific variants from sequencing data
Selection of most relevant HLA class I and HLA class II restricted peptides
Second medical report with selected peptides for formulation of a vaccine

Optional Services

Additional panel sequencing – more information
- The medical report of 766 tumor-relevant genes including selected translocations in 31 genes is assessed based on TUM01 panel sequencing. This does not alter the report but provides much higher coverage allowing to detect subclonal variants
Immunohistochemical (IHC) analyses – 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.

Sample Medical Report

Beispielbefund_-Peptidepred2.0_CeGaT_EN_mit-RNA_2021-02

Download sample medical report (PDF)

Process for diagnostics

Step 1:

Test selection.
We are happy to assist in choosing the suitable diagnostic strategy

Step 2:

Counseling.
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 medical report.

Step 4:

Counseling.
Results are discussed with the patient.

Step 5:

Implementation.
The results obtained from genetic testing are integrated in patient care.

Scientific Background: Neoantigen Prediction

Neoepitopes can be recognized by CD8⁺ cytotoxic T cells (CTL) and by CD4⁺ T helper cells. Neoepitopes recognized by CD8⁺ T cells are typically 8-10 amino acids long and presented by HLA class I molecules, while neoepitopes recognized by CD4⁺ T helper cells are usually longer (13-18 amino acids in length) and presented by HLA class II molecules. For successful immunotherapy, neoantigen selection should include short and long peptides to activate both, CD4⁺ and CD8⁺ T cells. Immune responses of each individual patient can be monitored during anti-cancer vaccination by flow cytometry-based analysis of neoepitope-specific T cell activation.

Our cancer immunology experts use in silico algorithms based on exome sequencing and HLA typing to predict and select up to 12 eligible neoantigen epitopes individualized for each patient. The selected peptides are predicted to activate not only cytotoxic T cells but also T helper cells. Therefore, in addition to short peptides (8-12 amino acids) potentially binding to HLA class I molecules also long peptides (~17 amino acids) potentially binding to HLA class II molecules are included. To confirm the expression of the selected neoepitopes, transcriptome data by RNA sequencing of the tumor sample is done in parallel. In cases of no availability of RNA, information about neoepitope expression is retrieved from protein expression databases.

Additional panel sequencing

The basis of analysis for CancerNeo is whole-exome sequencing, as it is necessary to understand all somatic mutations for the prediction of neoantigens. Our specialized somatic tumor panel enrichment, the basis for our CancerPrecision diagnostic service, focused the analysis on tumor-associated genes and selected translocations. These are therefore sequenced in much higher resolution. The additional panel sequencing option bases the medical report for treatment decision support (CancerPrecision) on the sequencing data from the specialized panel enrichment while the medical report for predicted neoantigens usable for a personalized vaccination approach is based on the whole exome sequencing data. The CancerPrecision report benefits from the higher sequencing resolution and the possibility of detecting the selected translocations enriched by the panel sequencing.

Immunohistochemistry (IHC) analyses

To complete our genetic diagnostics, we offer to organize immunohistochemistry analysis on the tumor sample in cooperation with partner laboratories. We forward the pathological examination reports upon completion.

PD-L1

Detection of expression in the tumor tissue of the Programmed death-ligand 1 (PD-L1) is important for selecting patients which may benefit (the most) from immunotherapy, such as pembrolizumab.

MGMT promotor methylation analysis

Detection of MGMT promotor methylation in the tumor tissue is important for glioma patients since it is a potential biomarker of sensitivity to alkylating chemotherapy, including temozolomide (TMZ).

HLA Class I and II

HLA molecules present tumor antigens to T cells. Expression analysis of HLA-molecules is a helpful tool to tailor your immune-therapeutic strategy.

CAR T cell panel (GD2, EGFR, IL13Ralpha, CD276, HER2, PSMA, ROR1, CD47)

The CAR T cell panel detects eight different target antigens of CAR-T Cell therapies in clinical or preclinical development.

Next Generation Fusion Transcript Analysis – RNA based

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 NTRKfusions). 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 103 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.