WO2020201742A1 - Biomarqueurs et leurs utilisations pour classifier des patients atteints de mélanome - Google Patents

Biomarqueurs et leurs utilisations pour classifier des patients atteints de mélanome Download PDF

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WO2020201742A1
WO2020201742A1 PCT/GB2020/050860 GB2020050860W WO2020201742A1 WO 2020201742 A1 WO2020201742 A1 WO 2020201742A1 GB 2020050860 W GB2020050860 W GB 2020050860W WO 2020201742 A1 WO2020201742 A1 WO 2020201742A1
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melanoma
patients
mutations
immunotherapy
genes
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PCT/GB2020/050860
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English (en)
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Amaya VIRÓS USANDIZAGA
Stephen Paul Smith
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The University Of Manchester
Cambridge Enterprise Limited
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/112—Disease subtyping, staging or classification
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/156—Polymorphic or mutational markers

Definitions

  • the present invention relates to methods for stratifying melanoma patients and methods for identifying melanoma patients likely to respond to immunotherapy.
  • Melanoma is a type of skin cancer associated with poor patient survival rates.
  • the incidence and mortality of melanoma increases with age 1 ’ 2 3 and approximately 80% of melanoma deaths affect patients who are older than 59 4 5
  • Older patients more frequently present with high-risk, thicker primary tumours, (AJCC stage IIB- IIC) 4 ’ 6 and additional characteristics of poor prognosis, such as ulceration, elevated mitotic rate, and early visceral metastasis are also more common in the elderly 4 .
  • age is the most important independent marker of adverse outcome together with primary tumour thickness 1 7 .
  • Novel immunotherapies offer potential treatment options for some patients and such therapies are known to be effective in early and late stages of disease. Encouragingly, recent analysis of immunotherapy and targeted therapy response stratified by age has shown elderly patients clearly stand to benefit from both treatment regimens, and aged patients may present better response rates to checkpoint inhibitors 8-13 . These preliminary observations suggest the elderly melanoma population, who is at highest risk of death, may gain the most from immunotherapy. Importantly, these novel agents are currently being tested in the adjuvant setting to treat early stage disease 14 15 , and a significant challenge is to identify the subset of the melanoma population that is both at highest risk of progression and most likely to respond to therapy.
  • SLNB sentinel lymph node biopsies
  • a method of stratifying and/or determining patient prognosis of a melanoma patient aged over 55 comprising the identification of one or more mutations in one or more of the following genes: NRAS, OR11 H12, POTEM, IDH1 , HIST1 H3D, C15orf23, ACD, OXA1 L, NPPA, BRAF, C9orf171 , NR5A1 , ABCD3, TROAP, GAGE2A. NAA11 , WNT5A and/or CDKN2A.
  • There method of stratifying and/or determining patient prognosis of a melanoma patient may be in a patient aged 56 or over, 57 or over or 59 or over.
  • the method of stratifying and/or determining patient prognosis of a melanoma patient is in a patient aged 60 or over.
  • the genes may comprise one or more of any of the above 18 genes, or alternatively combinations of a smaller number of these genes can be used to predict outcome in the elderly.
  • the presence of a single gene mutation in one or more of the following combination: BRAF, NRAS, IDH1 and/or CDKN2A have been determined by the inventors to be advantageously representative, powerful combination of genes that can be used to predict outcome.
  • the present inventors have found that these four genes are strongly associated with poor outcome in older patients in localised and metastatic melanoma.
  • the 4 gene driver mutation signature can provide a robust and simple analysis which can be easily incorporated into clinical practice to identify patients at genetic high risk of disease progression.
  • a method of stratifying and/or determining patient prognosis of a melanoma patient aged 55 or less comprising the identification of one or more mutations in one or more of the following genes: BRAF, NRAS, CDK4, VN1 R4, IGLL5, KRTAP1 Cl 8, SAG, BEGAIN, ZFAND2A, TIGD4 and/or RAC1. All these genes have been identified by the inventors as associated with a better patient outcome when found mutated singly or mutated in combination with one another.
  • the present inventors have identified an association between patient prognosis with driver mutations in 11 key driver genes in younger patients.
  • the genetic signature can stratified the aged melanoma patient at any stage of melanoma according to AJCC criteria.
  • the melanoma patient may have a primary or a late-stage melanoma.
  • the melanoma patient may have a melanoma of any invasive Breslow.
  • the gene mutation signatures identified by the present inventors can predict poor disease specific outcomes in melanoma patients. Identifying the mutation status of the genes at any stage of the disease offers a greater prognostic power than current staging guidelines. Patients who are genetically predisposed to progression can therefore be distinguished. The method can be incorporated into clinical practice to identify patients at genetic high risk of progression and thus select suitable candidates for therapy and specific types of therapy.
  • the method of either aspect may further comprise stratification of the melanoma patient into disease progression risk groups. For instance if a mutation of any of the specified genes is identified the patient may be stratified into a prognosis group.
  • a method of identifying whether a melanoma patient aged over 55 (and preferably 60 or over) is likely to respond to immunotherapy comprising identification of a high tumour mutation burden in combination with the identification of one or more mutations in one or more of the following genes: BRAF, NRAS, IDH1 and/or CDKN2A.
  • the present inventors have shown that identifying this particular combination powerfully predicted those patients most likely to respond to immunotherapy. In particular those who would respond to immune checkpoint inhibitor therapies. It has been shown that although TMB is a predictor of survival in immunotherapy-treated patients across all cancer types it is not a useful predictor of immunotherapy response in melanoma when used alone.
  • a method of selecting a melanoma patient aged 60 or over for immunotherapy treatment comprising identification of a high tumour mutation burden in combination with the identification of one or more mutations in one or more of the following genes: BRAF, NRAS, IDH1 and/or CDKN2A.
  • the genes may be BRAF, NRAS, IDH1 and CDKN2A.
  • the melanoma patient may have a tumour mutation burden in the highest 30% centile or top tercile of the melanoma patient population in any given test.
  • the immunotherapy treatment may be one or more of adjuvant, neoadjuvant and/or late stage immunotherapy.
  • the immunotherapy treatment may be immune checkpoint inhibitor treatment.
  • the method can guide the selection of immunotherapy. Integrating tumour mutation burden with the four-gene signature identifies melanoma patients with a better response to immune checkpoint inhibitors.
  • the four gene signature and tumour mutation burden is a powerful tool to improve outcome prediction and is a step towards personalised therapy in melanoma.
  • the kit may comprise use in the identification of an individual who would be likely to respond to immunotherapy treatment, the kit comprising:
  • Figures 1A and 1 B show overall (1A) and progression-free (1 B) survival of patients with metastatic melanoma from TCGA, stratified by age;
  • Figure 2 shows the mutational signature decomposition for old and young melanoma samples from TCGA. The vast majority of the mutational contribution comes from UV damage, with minor contributions that differ between the two groups;
  • Figures 3A, 3B and 3C show the molecular characterisation of metastatic melanoma (TCGA).
  • TCGA metastatic melanoma
  • 3A Top 2 panels show rainfall plots of total mutations across the genome (x) by the distance from the previous mutation (y) illustrating the greater density of mutations in the elderly (left panel). Black arrows on the x axis indicate points of hypermutation.
  • 3B Results of the oncodriveCLUST algorithm, which identifies 18 driver genes in elderly melanoma, brackets indicate the cluster of mutation for that gene.
  • Figure 5 shows OncodriveCLUST analysis of metastatic melanoma in age ⁇ 55 showing 11 driver genes and their clusters in brackets;
  • Figure 6 shows the overall survival in metastatic melanoma in TCGA.
  • Upper plot shows the predictive effect of mutations in any of the 18 identified driver mutations in the elderly, the lower plot show the same for the 11 driver mutations in young melanoma;
  • Figure 7 shows overall survival in melanoma patients of age >59 from high- risk primary melanomas (French cohort) and from early stage melanomas (Spanish cohort);
  • Figure 8 shows the correlation between age and TMB across the TCGA and MSK-IMPACT cohorts.
  • Figure 9 shows overall survival in immunotherapy treated metastatic melanoma from the MSK-IMPACT study stratified by age and the 4 gene driver signature.
  • This study reveals the molecular drivers of old age melanoma. It identifies the genetic changes of early and late stage melanoma that predict patient outcome in international, independent cohorts. The genetic predictors of prognosis are additionally able to identify immune checkpoint inhibitor responders in the aged melanoma population with a high tumour mutation burden.
  • the genetic signature of poor outcome stratifies patient prognosis more accurately than the current staging guidelines, making it immediately relevant to clinical practice.
  • the use of age-specific markers can guide the selection of immunotherapy in old patients at late metastatic stages.
  • Metastatic melanoma samples were analysed to establish the effect of age on prognosis and treatment response. Age-specific molecular differences and driver genes linked to outcome and to response to immunotherapy in early and late stage international independent cohorts were identified.
  • the study defines the driver mutations of old age melanoma that are powerful predictors of poor outcome at all stages of disease. This signature stratifies patients more accurately than the current staging guidelines, making it relevant to immediate clinical practice. Furthermore, integrating the molecular signature with tumour mutation burden identifies the subset of aged patients with a higher rate of response to immune checkpoint inhibitors. A robust tool to distinguish patients who are genetically predisposed to progression and the subset of patients with a higher rate of immunotherapy response is provided.
  • the French cohort comprises 197 patients with clinical outcome data and targeted deep sequencing information. 109 patients were older than 59 at the time of diagnosis.
  • the Spanish cohort comprises 72 patients with similar clinical and exome sequencing data, 30 of whom were older than 59 at diagnosis (Table 1 below). Relevant institutional review boards and ethics committees at both institutions approved molecular research. All human participants gave written informed consent.
  • Table 1 (A) Demographic and clinical characteristics of patients in the 4 tested cohorts, and (B) Results of multivariate regression analysis of TCGA patient survival.
  • Age, Breslow mean + sd. Exact ages and staging information not given in MSK- IMPACT. Breslow thickness and stage not recorded in MSK-IMPACT. Mutation count: all mutations in TCGA, non-synonymous variants from selected gene panel in MSK-IMPACT.
  • the melanoma cohort included 320 patients, 181 of whom were older than 59 at diagnosis, all of whom had been treated with immunotherapy agents and whose sequencing data included the mutation status of the four driver genes.
  • the centile groupings within the melanoma group were investigated and the third decile (ie those in the highest 30% tumour mutation burden) used as high.
  • the effect of varying the decile threshold described in the original publication was reproduced 17 , and minimal differences between deciles 1 -3 in predictive effect (consistent with the original findings) were found so the threshold which included as many patients as possible was used.
  • TMB Tumour mutation burden
  • Mutation density and kataegis (hypermutation) analysis was calculated by assessing the average distance between consecutive mutations across all series of six mutations, with hypermutation loci defined as those with six consecutive mutations with an average distance of 1000 kb or less between them 21 .
  • Driver gene analysis incorporating background mutation rates and gene length used the oncodriveCLUST algorithm 22 implemented in maftools.
  • TMB When TMB was considered as in previous work a binary variable (high or low) with a cut-off value of 130 mutations, it was associated with improved survival 23 ; however when analysed as a continuous variable in multivariate regression (logarithmic or unchanged), TMB was no longer a significant predictor of survival and only age predicted outcome in the full model (Table 1 ).
  • a core set of four genes were identified ⁇ BRAF, NRAS, IDH1 and CDKN2A) that when mutated singly or in combination powerfully predicts poor outcome in the TCGA cohort (Figure 3C).
  • the mutational signature identified increases in frequency as the melanoma population ages, and it is in the elderly subgroup where its strongest predictive power is demonstrated.
  • a set of alternative genes can more accurately predict outcome in the young, with BRAF the primary driver of outcome in that group ( Figure 6).
  • the core four-gene driver mutation signature identified represents a robust, simple set of analyses that can be easily incorporated into clinical practice to identify patients at genetic high risk of progression.
  • a predictive signature should be applicable to all stages of disease, including early stage primary melanomas from an unselected group of patients.
  • two additional multinational cohorts of primary melanoma including patients aged >59 with available sequencing information were interrogated. It was confirmed that the signature significantly and powerfully predicted poor disease- specific outcomes in older patients with primary melanoma in both cohorts including primary melanomas >1 mm diagnosed in France and primary melanomas (Breslow >0mm) diagnosed in Spain ( Figure 7).
  • Untreated aged melanoma patients have a significantly worse outcome. These analyses delineate the molecular changes that identify aged patients at highest risk of death. Intriguingly, it is precisely this subset of patients who will derive the most benefit from checkpoint inhibitor treatment in the metastatic setting.
  • the core four gene driver mutation signature represents a robust, simple set of analyses that can be easily incorporated into clinical practice to identify patients at genetic high risk of progression, and when combined with TMB, can identify the patients who are most likely to respond to immunotherapy.
  • the four gene signature and TMB is a powerful tool to improve outcome prediction and a genuine step towards personalised therapy in melanoma.
  • tumour immune-state ICI response predictors of poor outcome in melanoma that are more prevalent in the young 33 , further supporting the premise that age classifies melanoma into distinct clinical and therapeutic categories, possibly underpinned by unique pathophysiological drivers.
  • young patients present lower levels of TMB with an immune phenotype that renders them more immune evasive; and on the other, old patients with high TMB have an immune microenvironment and tumour profile more propitious to ICI.

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Abstract

La présente invention concerne des méthodes de classification de patients âgés de plus de 55 ans ou de 55 ans ou moins de 55 ans et atteints d'un mélanome, le procédé comprenant l'identification positive de mutations dans un ou plusieurs gènes spécifiques. La présente invention concerne également une méthode permettant d'établir si un patient âgé de plus de 55 ans et atteint d'un mélanome est susceptible de répondre à une immunothérapie, le procédé comprenant l'identification d'une charge de mutation tumorale élevée en combinaison avec l'identification de mutations dans un ou plusieurs gènes spécifiques. La présente invention concerne également une méthode de sélection d'un patient âgé de plus de 55 ans et atteint d'un mélanome pour l'immunothérapie. La présente invention concerne également des trousses pour la mise en œuvre des méthodes selon l'invention.
PCT/GB2020/050860 2019-04-01 2020-03-31 Biomarqueurs et leurs utilisations pour classifier des patients atteints de mélanome WO2020201742A1 (fr)

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Citations (2)

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