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EP-4737590-A1 - METHOD FOR CLASSIFYING A CUTANEOUS MELANOMA PATIENT REGARDING ITS RISK OF DEVELOPING METASTASES

EP4737590A1EP 4737590 A1EP4737590 A1EP 4737590A1EP-4737590-A1

Abstract

The present invention relates to a method ex vivo for classifying a cutaneous melanoma patient, regarding its risk of developing metastases. It also relates to the use of a marker gene which underwent a somatic copy number alteration (SCNA) for classifying a cutaneous melanoma patient regarding its risk of developing metastases. Furthermore, it relates to a method ex vivo for identifying treatment targets in a cutaneous melanoma patient. The invention also relates to a method of selecting a treatment of a cutaneous melanoma patient.

Inventors

  • Röcken, Martin
  • Sinnberg, Tobias
  • Chtziioannou, Eftychia
  • ARMEANU-EBINGER, SORIN
  • SCHROEDER, Christopher
  • RIESS, OLAF
  • Ossowski, Stephan

Assignees

  • Eberhard Karls Universität Tübingen (Medizinische Fakultät)

Dates

Publication Date
20260506
Application Date
20241031

Claims (15)

  1. A method ex vivo for classifying a cutaneous melanoma patient, regarding its risk of developing metastases, comprising the following steps: 1. providing a biological sample of the primary cutaneous melanoma, 2. analyzing the biological sample for the presence of a somatic copy number alteration (SCNA) in at least one marker gene, 3. classifying the cutaneous melanoma patient as a high-risk patient if a SCNA is detected in step 2, wherein the marker gene is selected from the group ("OncoCycle") consisting of: GLI1, IFNE, IFNGR1, JAK2, ARID1B, CDKN2B, CDKN2C, TP53BP1, ERBB3, CDK4, MDM2, PIK3CA, HRAS, CCND3, E2F3, PPM1D, SETD81, CD276 (B7H3), MCL1, LAMA2, and MYC.
  2. The method of any of the preceding claims, wherein the SCNA is an allelic loss and/or a gene amplification.
  3. The method of claim 2, wherein the allelic loss is a homozygous (biallelic) loss.
  4. The method of claim 2 or 3, wherein the gene amplification is a ≥3fold gene amplification.
  5. The method of any of the preceding claims, wherein the following SCNA is detected in the respective marker gene: marker gene SCNA GLI1 gene amplification IFNE biallelic loss IFNGR1 biallelic loss JAK2 biallelic loss ARID1B biallelic loss CDKN2B biallelic loss CDKN2C biallelic loss TP53BP1 biallelic loss ERBB3 gene amplification CDK4 gene amplification MDM2 gene amplification PIK3CA gene amplification HRAS gene amplification CCND3 gene amplification E2F3 gene amplification PPM1D gene amplification SETDB1 gene amplification CD276 (B7H3) gene amplification MCL1 gene amplification LAMA2 biallelic loss MYC gene amplification
  6. The method of any of the preceding claims, wherein at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or all 21 marker genes of the OncoCycle are analyzed.
  7. The method of any of the preceding claims, wherein in step 2 at least the following marker genes are analyzed: - GLI1, IFNE, IFNGR1, CDK4; and/or - IFNE, IFNGR1, JAK2, CDK4; and/or - GLI1, IFNE, JAK2, ARID1B, ERBB3, CDK4, MCL1, MDM2; and/or - GLI1, IFNE, IFNGR1, ARID1B, ERBB3, CDK4, MCL 1, MDM2, ARID1B, SETD81, HRAS, E2F3, CD276; and/or - GLI1, IFNE, IFNGR 1, JAK2, ARID1B, ERBB3, CDK4, MCL 1, MDM2, SETD81, ARID1B, HRAS, E2F3, CD276; and/or - GLI1, IFNE, IFNGR 1, JAK2, CDKN2B, CDKN2C, TP53BP1, ARID1B, ERBB3, CDK4, MDM2, PIK3CA, HRAS, CCND3, E2F3, PPM1D, SETD81, CD276 (B7H3), MCL 1, LAMA2, MYC.
  8. The method of any of the preceding claims, wherein the following additional step(s) is(are) carried out: 2'. analyzing the biological sample for determining the tumor thickness of the primary tumor, and/or 2". analyzing the biological sample for determining the tumor stage, and/or 2‴. analyzing the biological sample for determining the tumor mutational burden (TMB), and/or 2‴ʺ. analyzing the biological sample for determining the copy number variant (CNV) burden.
  9. The method of any of the preceding claims, wherein the following additional step is carried out: 3'. prognosing a reduced progression-free survival (PFS) for the patient if a SCNA is detected in step 2.
  10. The method of any of the preceding claims, wherein in step (2) the biological sample is analyzed via DNA analysis and/or RNA analysis.
  11. The method of any of claims the preceding claims, wherein in step (2) the biological sample is analyzed via Fluorescence in situ hybridization (FISH), and/or via protein analysis.
  12. Use of a marker gene which underwent a somatic copy number alteration (SCNA) for classifying a cutaneous melanoma patient, regarding its risk of developing metastases, wherein the marker gene is selected from the group ("OncoCycle") consisting of: GLI1, IFNE, IFNGR1, JAK2, ARID1B, CDKN2B, CDKN2C, TP53BP1, ERBB3, CDK4, MDM2, PIK3CA, HRAS, CCND3, E2F3, PPM1D, SETD81, CD276 (B7H3), MCL1, LAMA2, and MYC.
  13. A method ex vivo for identifying treatment targets in a cutaneous melanoma patient, comprising the following steps: 1. providing a biological sample of the primary cutaneous melanoma, 2. analyzing the biological sample for the presence of a somatic copy number alteration (SCNA) in at least one marker gene, 3. identifying a marker gene as a treatment target if a SCNA is detected in step 2, wherein the marker gene is selected from the group ("OncoCycle") consisting of: GLI1, IFNE, IFNGR1, JAK2, ARID1B, CDKN2B, CDKN2C, TP53BP1, ERBB3, CDK4, MDM2, PIK3CA, HRAS, CCND3, E2F3, PPM1D, SETD81, CD276 (B7H3), MCL1, LAMA2, and MYC.
  14. A method of selecting a treatment of a cutaneous melanoma (CM) patient, comprising the selection of adjuvant or neo-adjuvant therapy, if, after the patient was subjected to the method of claim 13, a marker gene which underwent SCNA was detected in step 3.
  15. The method of claim 14, wherein the therapy is selected according to the marker gene as follows: Marker gene Therapy GLI1 GLI1 and BET inhibitors, Hedgehog inhibitors, arsenic trioxide IFNE Cytokine therapy and/or cell cycle inhibitors IFNGR1 Cell cycle inhibitors, immune therapy secondary JAK2 Cell cycle inhibitors, immune therapy secondary ARID18 HDAC inhibitors, cell cycle inhibitors, immune checkpoint blockade and/or inhibitors of mTOR, EZH2, histone deacetylases, ATR and/or PARP CDKN2B Cell cycle inhibitors CDKN2C Cell cycle inhibitors TP53BP1 Cell cycle inhibitors and/or p53 activators ERBB3 ERBB family inhibitors CDK4 Cell cycle inhibitors MDM2 Cell cycle inhibitors PIK3CA PI3K pathway inhibitors HRAS RAS inhibitors and/or RAS pathway inhibitors CCND3 Cell cycle inhibitors E2F3 Cell cycle inhibitors PPM1D Cell cycle inhibitors and/or PPM1D inhibitors SETDB1 SETDB1 inhibitors, immune checkpoint blockade and/or inhibitors of mTOR, EZH2, histone deacetylases, ATR and/or PARP CD276 (B7H3) Anti-CD276 MCL1 MCL1 inhibitors and/or apoptosis promoting drugs LAMA2 MYC Cell cycle inhibitors

Description

FIELD OF THE INVENTION The present invention relates to a method ex vivo for classifying a cutaneous melanoma patient, regarding its risk of developing metastases. It also relates to the use of a marker gene which underwent a somatic copy number alteration (SCNA) for classifying a cutaneous melanoma patient regarding its risk of developing metastases. Furthermore, it relates to a method ex vivo for identifying treatment targets in a cutaneous melanoma patient. The invention also relates to a method of selecting a treatment of a cutaneous melanoma patient. BACKGROUND Melanoma is the most dangerous type of skin cancer. It develops from the melanin-producing cells known as melanocytes. It typically occurs in the skin, but may rarely occur in the mouth, intestines, or eye. Melanoma is frequently referred to as malignant melanoma. The primary cause of cutaneous melanoma is ultraviolet light (UV) exposure in those with low levels of the skin pigment melanin. The UV light may be from the sun or other sources, such as tanning devices. Those with many moles, a history of affected family members, and poor immune function are at greater risk. A number of rare genetic conditions, such as xeroderma pigmentosum, also increase the risk. Cutaneous melanoma tends to spread early metastases (secondary tumors) via lymphatic and blood vessels and is the most common fatal skin disease, with a rapidly increasing number of new cases worldwide. Globally, in 2012, it newly occurred in 232,000 people. In 2015, 3.1 million people had active disease, which resulted in 59,800 deaths. The treatment strategy depends primarily on the stage at which the cutaneous melanoma is detected. At an early stage, it is usually sufficient to remove the primary tumor surgically, with a sufficient safety margin, in sano. For more advanced cutaneous melanomas, adjuvant therapy is recommended for those with a higher tumor thickness without further lymph node involvement, as well as after removal of the lymph nodes. In later stages, when the tumor has already metastasized to the skin, lymph nodes and internal organs, the chances of recovery are slim. In these cases, a whole range of alternative therapies are used and tested, which usually only provide temporary improvement but usually have no prospect of recovery. These previously included chemotherapy with DTIC or fotemustine, surgical procedures to reduce the tumor mass, or radiotherapy. New therapeutic approaches are based on the blockade of molecular processes in the signal transduction of the cell, primarily inhibitors of BRAFV600E or immune checkpoint inhibitors. Early detection of cutaneous melanoma is therefore essential for successful treatment. In particular, a treatment option depends on the risk of developing metastases. Current methods for predicting the risk of metastases are largely limited to histological procedures, such as determining tumor thickness or tumor stage. Other methods relate to the determination of ulceration and the mitotic rate or involve sentinel lymph node biopsy. In the latter procedure, the sentinel lymph nodes are removed for subsequent histological examination. Molecular medical methods have been used to supplement these, such as determining the so-called 'tumor mutational burden (TMB)'. These thousands of analyses allow to describe the development of various cancers and the genomic phenotype of metastases. Yet, most importantly until now they did not allow to identify genome signatures in primary tumors that correlate with an increased or decreased risk of developing metastases. Reschke et al., 'Identifying high-risk tumors within AJCC stage IB-III melanomas using a seven-marker immunohistochemical signature, Cancers 2021, 13, 2902, aim to validate a seven-marker immunohistochemical signature, consisting of the genes Bax, Bcl-X, PTEN, COX-2, β-Catenin, MTAP and CD20. According to the authors, the seven-marker signature is able to identify high-risk patients within stages IB-III melanoma patients that have a significantly higher risk of disease recurrence, metastasis, and death. Nunes et al., 'Prognostic genome and transcriptome signatures in colorectal cancers', Nature 2024, Vol. 633, 137ff., disclose a number of genes which were subject to copy number variations (CNVs) or mutations, and which might be associated with metastases in colorectal cancers. These CNVs are mentioned as part of a complex scoring system. The document is, however, silent on melanomas. Corish et al., 'The genomic landscape of 2,023 colorectal cancers', Nature 2024, Vol. 633, 127ff., examined metastatic samples of colorectal cancers and found that almost all of these samples were microsatellite stable (MSS) tumors. They analyzed the role of driver mutations in metastases and identified key genes. Also, this document is silent about melanomas. Zeng et al., 'Biallelic loss of CDKN2A initiates melanoma invasion via BRN2 activation', Cancer Cell 2018, Vol. 34(1), describe that in a mouse model loss