Will TMB Guide Future Systemic Treatments in Melanoma?

Will TMB Guide Future Systemic Treatments in Melanoma?

Publication date: Jul 01, 2019

Retrospective analyses show that a high tumor mutation burden (TMB) is associated with an improved response and prolonged survival with immune checkpoint inhibitor (ICI) treatment in several different malignancies, including melanoma.

TMB reflects the total number of somatic mutations in tumor DNA, and is used as a surrogate measure of tumor neoantigen load, which is the spectrum of tumor-specific antigens that can induce reactive T cells.

Despite this promise, expression of the checkpoint regulator programmed death-ligand 1 (PD-L1) – by immunohistochemical assessment – is the only marker that is currently used to select patients for treatment with ICI therapies. There are several challenges to the routine use of TMB to identify melanoma patients for ICI treatment.

To produce a more robust biomarker, assessments of PD-L1 and TMB could be integrated with other tumor alterations, such as indels or copy number changes, and other positive and negative predictors of response to ICIs, such as tumor neoantigen load, T cell infiltration, and inflammatory gene signatures.

Tumor mutational burden (TMB) has been shown to be a predictive biomarker of immune checkpoint inhibitor treatment outcome in several cancers.

TMB is the total of somatic mutations (synonymous and nonsynonymous) in the DNA of tumor cells and is quantified per coding area (megabase, Mb).

This hypothesis further says that tumors with a high neoantigen load have high recognition by antigen-reactive T cells, and thus are likely to respond to immune checkpoint inhibitors.

TMB is a surrogate for tumor neoantigen load, and is therefore a predictor of treatment outcome with immune checkpoint inhibition, Ugurel said.

Malignant melanoma exomes from 64 patients who were treated with CTLA-4 inhibition had their TMB quantified by whole exome sequencing.

Foundation Medicine and Memorial Sloan Kettering have panels that have been tested in translational studies in different cancer entities, showing a correlation between TMB and outcome with immune checkpoint inhibition.

“Quantification of TMB will soon be open for widespread clinical use supported by a switch from whole exome sequencing to targeted gene panel sequencing,” she said.

Although TMB identifies a population with melanoma that has greater benefit from CTLA-4 blockade, the degree of overlap in TMB between patients who derived clinical benefit from treatment and those who did not is very high.

A team of researchers led by Marta Luksza, PhD, of the Institute for Advanced Study in Princeton, New Jersey, constructed a mathematical model showing two determinants of a tumor neoantigen predict for responses to anti-CTA4 therapy in patients with melanoma: Renal cell carcinoma represents another cancer in which tumors with intermediate levels of TMB respond to immune checkpoint blockade.

Concepts Keywords
Amino Acid Trials Tumor
Antigen Synonymous nonsynonymous tumor
Antigens Cancers
Biomarker Show correlation melanoma
Biopsy Melanoma skin cancer
Blockade Tumor melanoma
Blood German melanoma
BRAF Paraffin embedded tumor
Cancer TMB surrogate tumor
Clinical Trials Population melanoma
Coding Region Regions genome cancer
Combination Therapy Malignant melanoma
Congress Somatic mutations tumor
Correlation Hypothesis tumors
CTLA4 Determinants tumor
DNA Checkpoint inhibitor
England Cancer
Essen Melanoma
Exome Immune system
Exomes RTT
Formalin Clinical medicine
Frameshifts Branches of biology
Genome Medicine
Germany Biomarker
Gold Standard Pembrolizumab
ICI Frameshift mutation
Immune Checkpoint Inhibitor
Immunogenicity
Immunohistochemical
Indels
Kettering
Lausanne
Leukemias
Ligand
Lung Cancer
Malignancies
Malignant Melanoma
Megabase
Megabases
Melanoma
Miao
Missense
Monotherapy
Mutation
Neoantigen
Oncology
Paraffin
Pediatric
PhD
Point Mutations
Reproducibility
Selma
Sequencing
Silent Mutations
Skin Cancer
Somatic
Spectrum
Switzerland
Targeted Therapy
TMB
Tobacco
Tumor
Ultraviolet Light

Semantics

Type Source Name
gene UNIPROT SSRP1
drug DRUGBANK Spinosad
disease MESH microsatellite instability
disease DOID renal clear cell carcinoma
drug DRUGBANK Tropicamide
disease MESH lung cancer
disease DOID lung cancer
gene UNIPROT SMIM10L2B
gene UNIPROT SMIM10L2A
disease MESH Renal cell carcinoma
disease DOID Renal cell carcinoma
disease MESH frameshift mutations
disease MESH multiple
gene UNIPROT LARGE1
pathway BSID Renal cell carcinoma
gene UNIPROT PDC
gene UNIPROT BRAF
disease MESH non-small cell lung cancer
disease DOID non-small cell lung cancer
pathway BSID Non-small cell lung cancer
gene UNIPROT TNFSF14
disease MESH skin cancer
disease DOID skin cancer
disease MESH leukemias
drug DRUGBANK Gold
gene UNIPROT CTLA4
gene UNIPROT RPL17
gene UNIPROT PDCD1
drug DRUGBANK Pembrolizumab
disease MESH point mutations
gene UNIPROT MTUS1
gene UNIPROT MTUS2
drug DRUGBANK Formaldehyde
disease DOID cancer
gene UNIPROT BEST1
gene UNIPROT CD274
disease MESH death
disease MESH tumor
pathway BSID Melanoma
disease MESH Melanoma
disease DOID Melanoma

Original Article

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