WO2021083572A1 - Détermination de la sensibilité de la peau au rayonnement uv - Google Patents

Détermination de la sensibilité de la peau au rayonnement uv Download PDF

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WO2021083572A1
WO2021083572A1 PCT/EP2020/075047 EP2020075047W WO2021083572A1 WO 2021083572 A1 WO2021083572 A1 WO 2021083572A1 EP 2020075047 W EP2020075047 W EP 2020075047W WO 2021083572 A1 WO2021083572 A1 WO 2021083572A1
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genes
cpg sites
subject
features
rna expression
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PCT/EP2020/075047
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English (en)
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Nicholas Holzscheck
Katharina Röck
Marc Winnefeld
Torsten Schläger
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Beiersdorf Aktiengesellschaft
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Priority claimed from EP19206611.6A external-priority patent/EP3816301A1/fr
Application filed by Beiersdorf Aktiengesellschaft filed Critical Beiersdorf Aktiengesellschaft
Publication of WO2021083572A1 publication Critical patent/WO2021083572A1/fr

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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
    • 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
    • C12Q2600/154—Methylation markers
    • 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
    • C12Q2600/158—Expression markers

Definitions

  • the present invention relates to determining the sensitivity of a subject's skin to UV radiation by analysing gene expression or epigenetic markers.
  • the level of RNA expression of particular genes and/or the methylation level of particular CpG sites in a skin sample from the subject are analysed to accurately predict the minimal erythema dose (MED) of a subject's skin, which is an indicator of the UV sensitivity of the subject's skin.
  • Methods of the invention can achieve a mean absolute error (MAE) as low as 7.85 mJ/cm 2 (gene expression) or 4.18 mJ/cm 2 (methylation).
  • UV ultraviolet
  • the sensitivity/tolerance of the skin to UV irradiation can vary widely between individuals. Stratifying individuals by the sensitivity of skin to UV can be useful for assessing their risk of skin damage and cancer, for determining appropriate UV protection strategies, and for determining appropriate doses of therapeutic UV (e.g. PUVA therapy).
  • the Fitzpatrick phototyping scale categorises subjects as phototype l-VI based on their skin's complexion and propensity to tanning and burning in response to UV radiation (Fitzpatrick (1975) Soleil et ashamed, J Med Esthet. (2):33-34; Eilers et al (2013) Accuracy of Self- report in Assessing Fitzpatrick Skin Phototypes I through VI, JAMA Dermatol, 149(11):1289- 1294).
  • This is a semi-quantitative and highly subjective way of predicting an individual's skin's sensitivity to UV and is generally only used when a very fast assessment is required.
  • the sensitivity of the skin to UV radiation can vary significantly even between people with the same phenotypic skin type.
  • MED minimal erythema dose
  • MED minimal Erythema dose
  • One MED corresponds to the lowest UV dose (measured in mJ/cm 2 ), which causes erythema (redness) or oedema (swelling) of the skin 24-48 hours after UV exposure. This is typically determined by irradiating several patches of skin with different doses of UV light and assessing 24 hours later which was the lowest dose causing erythema.
  • This method has several disadvantages: the method requires the skin to be irradiated and burned (risking permanent skin and DNA damage); the precision of the method is low and depends on the doses tested; the method is subjective; the method is slow and requires repeated patient visits to a clinic; the method cannot be performed on skin already exposed to UV radiation.
  • a method for predicting the minimal erythema dose (MED) of a subject's skin comprising: a) i) determining the RNA expression levels of at least two genes selected from the genes in Table 1 in a skin sample obtained from the subject, and ii) predicting the subject's MED based on the determined RNA expression levels using a machine learning model trained on data comprising known MEDs and corresponding known RNA expression levels of the at least two genes; b) i) determining the methylation levels of at least two CpG sites selected from the genes in Table 3 in a skin sample obtained from the subject, and ii) predicting the subject's MED based on the determined methylation levels using a machine learning model trained on data comprising known MEDs and corresponding known methylation levels of the at least two CpG sites; and/or c) i) determining the methylation level(s) and RNA expression level(s) of at least 2 features selected
  • a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the following steps: a) i) inputting the RNA expression levels of at least two genes selected from the genes in Table 1 in a skin sample obtained from the subject; ii) predicting the subject's MED based on the determined RNA expression levels using a machine learning model trained on data comprising known MEDs and corresponding known RNA expression levels of the at least two genes; and iii) outputting the predicted MED; b) i) inputting the methylation levels of at least two CpG sites selected from the genes in Table 3 in a skin sample obtained from the subject; ii) predicting the subject's MED based on the determined methylation levels using a machine learning model trained on data comprising known MEDs and corresponding known methylation levels of the at least two CpG sites; and iii) outputting the predicted MED; and/or c)
  • a method for preventing damage to a subject's skin by UV radiation comprising:
  • a computer-readable medium comprising instructions which, when executed by a computer, cause the computer to carry out the following steps: a) i) inputting the RNA expression levels of at least two genes selected from the genes in Table 1 in a skin sample obtained from the subject; ii) predicting the subject's MED based on the determined RNA expression levels using a machine learning model trained on data comprising known MEDs and corresponding known RNA expression levels of the at least two genes, and iii) outputting the predicted MED; b) i) inputting the methylation levels of at least two CpG sites selected from the genes in Table 3 in a skin sample obtained from the subject; ii) predicting the subject's MED based on the determined methylation levels using a machine learning model trained on data comprising known MEDs and corresponding known methylation levels of the at least two CpG sites; and iii) outputting the predicted MED; and/or c) i) inputting
  • Figure 1 shows the correlation between MED determined using the known, standardised method (x-axis) and predicted MED based on the RNA expression levels of the 20 genes in Table 2, set A (y-axis).
  • the MAE of this model was 7.85 mJ/cm 2 .
  • Figure 2 shows the correlation between MED determined using the known, standardised method (x-axis) and predicted MED (y-axis) based on the methylation levels of the 18 CpG sites in Table 4.
  • the MAE of this model was 4.18 mJ/cm 2 .
  • the present invention is based on the finding that the RNA expression level of particular genes and/or the methylation level of particular CpG sites can be used to determine the sensitivity of a subject's skin to UV radiation by predicting the MED of a subject's skin.
  • the sensitivity of a subject's skin to UV radiation refers to how readily a subject's skin (including DNA) is damaged by UV radiation.
  • the sensitivity of a subject's skin to UV radiation can be represented by a minimal erythema dose (MED).
  • MED minimal erythema dose
  • the “minimal erythema dose” is the minimal dose of UV (measured in mJ/cm 2 ) which results in perceptible erythema (redness) and/or oedema (swelling) of the skin 24 to 48 hours after exposure to UV radiation.
  • the known, standardised method for determining MED is provided in ISO 2444:2010.
  • UV radiation refers to electromagnetic radiation with a wavelength of from about 100 nm to about 400 nm, including UVC radiation (from about 100 to about 280nm), UVB radiation (from about 280 to about 315nm), and UVA radiation (from about 315nm to about 400nm). Subjects and sampling
  • a subject refers to a human subject. In certain embodiments, the subject may be at least 16, 18, or 30 years old.
  • the subject may be phototype l-VI on the Fitzpatrick scale (I meaning always burns, never tans; II meaning burns easily, then develops a light tan; III meaning burns moderately, then develops a light tan; IV meaning burns minimally to rarely, then develops a moderate tan; V meaning never burns, always develops a dark tan; VI meaning never burns, no noticeable change in appearance).
  • the subject may be phototype l-IV on the Fitzpatrick scale.
  • the subject's skin may have previously been exposed to UV radiation and may be tanned or burnt at the time of sampling.
  • the subject had/has not taken anti-histamine or anti-inflammatory drugs within two weeks prior to skin sampling.
  • a skin sample refers to a sample comprising skin cells.
  • the skin cells may have been/may be obtained by harvesting the entire skin sample required from the individual.
  • Harvesting a sample from the individual may be carried out using suction blistering, punch biopsy, shave biopsy or during any surgical procedure such as plastic surgery, lifting, grafting, or the like.
  • the sample may have been/may be obtained by suction blistering.
  • the skin cells may have been/may be obtained by culturing the skin cells using an in vitro method.
  • Skin cells may have been/may be cultured from a small sample of skin cells harvested from an individual.
  • the harvested human skin cells may have been/ may be grown in vitro in a vessel such as a petri dish in a medium or substrate that supplies essential nutrients.
  • the skin samples may have been/may be obtained from the epidermis or dermis.
  • the skin sample may comprise, consist, or consist essentially of epidermal cells and/or dermal cells.
  • the skin sample may comprise, consist, or consist essentially of epidermal cells.
  • the skin cells may comprise a mixture of harvested cells and cultured cells.
  • RNA expression level of particular genes can be determined, for example, by RNA-Seq (e.g. using lllumina's ® TruSeq RNA Library Prep Kit and HiSeq system), RT- qPCR, SAGE, EST sequencing, or hybridisation-based methods such as microarrays.
  • RNA-Seq e.g. using lllumina's ® TruSeq RNA Library Prep Kit and HiSeq system
  • RT- qPCR e.g. using lllumina's ® TruSeq RNA Library Prep Kit and HiSeq system
  • SAGE e.g. RNA sequencing
  • hybridisation-based methods such as microarrays.
  • the RNA expression level of a particular gene can be measured in transcripts per million (TPM). A value of x TPM means that for every 1 million RNA molecules in the sample, x came from the gene of interest.
  • the inventors of the present invention identified 200 genes whose RNA expression levels individually exhibit a strong linear correlation with MED (see Table 1). The inventors of the present invention also identified sets of 18-20 genes whose RNA expression levels can be used to predict MED with high accuracy (see Table 2).
  • RNA expression level of as few as 2 of the genes in Table 1 could be used to accurately predict MED.
  • MAEs of less than about 25 mJ/cm 2 were consistently achieved when using from 2 to 200 genes selected from Table 1 to predict MED.
  • the methods of the present invention may comprise determining the RNA expression level of at least 2 genes selected from Table 1 or 2.
  • the methods of the present invention may comprise a step of determining the RNA expression level of from 2 to 200, 2 to 150, 2 to 100, 2 to 50, or 2 to 20 genes selected from Table 1 or 2.
  • the genes may comprise or consist of: ENSG00000197978 and ENSG00000277060; ENSG00000197978 and ENSG00000100376; ENSG00000197978 and ENSG00000172799; ENSG00000197978 and ENSG00000166670; or ENSG00000172799 and ENSG00000159247.
  • Such methods using at least 2 genes may achieve an absolute error of about 25 mJ/cm 2 or less, about 20 mJ/cm 2 or less, about 15 mJ/cm 2 or less, about 10 mJ/cm 2 or less, or about 5 mJ/cm 2 or less.
  • the methods of the present invention may comprise determining the RNA expression level of at least 5 genes selected from Table 1 or 2. In certain embodiments, the methods of the present invention may comprise a step of determining the RNA expression level of from 5 to 200, 5 to 150, 5 to 100, 5 to 50, or 5 to 20 genes selected from Table 1 or 2. In certain embodiments, the genes may comprise or consist of: ENSG00000197978, ENSG00000277060, ENSG00000100376, ENSG00000166670, and
  • ENSG00000166670 and ENSG00000172799; ENSG00000277060, ENSG00000100376,
  • Such methods using at least 5 genes may achieve an absolute error of about 25 mJ/cm 2 or less, about 20 mJ/cm 2 or less, about 15 mJ/cm 2 or less, about 13 mJ/cm 2 or less, about 10 mJ/cm 2 or less, or about 5 mJ/cm 2 or less.
  • the methods of the present invention may comprise determining the RNA expression level of at least 10 genes selected from Table 1 or 2. In certain embodiments, the methods of the present invention may comprise a step of determining the RNA expression level of from 10 to 200, 10 to 150, 10 to 100, 10 to 50, or 10 to 20 genes selected from Table 1 or 2. In certain embodiments, the genes may comprise or consist of:
  • ENSG00000159247 and ENSG00000260075; ENSG00000197978, ENSG00000277060, ENSG00000166670, ENSG00000172799, ENSG00000106392, ENSG00000159247,
  • Such methods using at least 10 genes may achieve an absolute error of about 25 mJ/cm 2 or less, about 20 mJ/cm 2 or less, about 15 mJ/cm 2 or less, about 10 mJ/cm 2 or less, or about 5 mJ/cm 2 or less.
  • the methods of the present invention may comprise determining the RNA expression level of at least 18 genes selected from Table 1 or 2. In certain embodiments, the methods of the present invention may comprise a step of determining the RNA expression level of from 18 to 200, 18 to 150, 18 to 100, or 18 to 50 genes selected from Table 1 or 2. In certain embodiments, the genes may comprise or consist of 18 genes selected from set A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, or R in Table 2. In one example, the genes may comprise of consist of 18 genes selected from set A in Table 2.
  • Such methods using at least 18 genes may achieve an absolute error of about 25 mJ/cm 2 or less, about 20 mJ/cm 2 or less, about 15 mJ/cm 2 or less, about 10 mJ/cm 2 or less, about 8 mJ/cm 2 or less, or about 5 mJ/cm 2 or less.
  • the methods of the present invention may comprise determining the RNA expression level of at least 20 genes selected from Table 1 or 2. In certain embodiments, the methods of the present invention may comprise a step of determining the RNA expression level of from 20 to 200, 20 to 150, 20 to 100, or 20 to 50 genes selected from Table 1 or 2. In certain embodiments, the genes may comprise or consist of the genes provided in set A, D, G, H, N, O, P, or R in Table 2. In one example, the genes may comprise of consist of the genes provided in set A in Table 2.
  • Such methods using at least 20 genes may achieve an absolute error of about 25 mJ/cm 2 or less, about 20 mJ/cm 2 or less, about 15 mJ/cm 2 or less, about 10 mJ/cm 2 or less, about 8 mJ/cm 2 or less, or about 5 mJ/cm 2 or less.
  • Table 1 - 200 predictive genes :
  • CpG site (also referred to as a CpG dinucleotide) is a cytosine nucleotide immediately followed by a guanine nucleotide in the 5' to 3' direction within a DNA molecule.
  • CpG sites may be in coding or non-coding regions of the genome.
  • CpG sites may be in CpG islands, which are regions having a high density of CpG sites.
  • the cytosine in a CpG site can be methylated by DNA methyltransferases to become 5-methylcytosine. It is known that methylation of CpG sites within a gene can influence the transcriptional regulation and thus expression of the gene (epigenetic regulation).
  • the methylation level of particular CpG sites can be determined, for example, by methylation specific PCR, sequence analysis of bisulfite treated DNA, CHIP-sequencing (lllumina Methylation BeadChip Technology), molecular inversion probe assay, Methyl-CAP- sequencing, Next-Generation-sequencing, COBRA-Assay, methylation specific restriction patterns, or MassARRAY assay.
  • the methylation level of a particular gene can be represented by its M-value, which is the log2 ratio of the intensities of methylated probe versus unmethylated probe.
  • M-value is the log2 ratio of the intensities of methylated probe versus unmethylated probe.
  • the inventors of the present invention identified 200 specific CpG sites whose methylation levels individually exhibit a strong linear correlation with MED (see Table 3).
  • the inventors of the present invention also identified sets of 18-20 CpG sites whose methylation levels can be used to predict MED with high accuracy (see Table 4).
  • the methods of the present invention may comprise determining the methylation level of at least 2 CpG sites selected from Table 3 or 4. In certain embodiments, the methods of the present invention may comprise a step of determining the methylation level of from 2 to 200, 2 to 150, 2 to 100, 2 to 50, or 2 to 18 CpG sites selected from Table 3 or 4.
  • the CpG sites may comprise or consist of: cg06376130 and cg03953789; cg18269134 and cg00613587; cg06376130 and cg01199135; cg03953789 and cg00492074; or cg06376130 cg18269134 (described further in Table 3).
  • Such methods using at least 2 CpG sites may achieve an absolute error of about 20 mJ/cm 2 or less, about 15 mJ/cm 2 or less, about 10 mJ/cm 2 or less, or about 5 mJ/cm 2 or less.
  • the methods of the present invention comprise determining the methylation level of at least 5 CpG sites selected from Table 3 or 4. In certain embodiments, the methods of the present invention comprise a step of determining the methylation level of from 5 to 200, 5, to 150, 5 to 100, 5 to 50, or 5 to 18 CpG sites selected from Table 3 or 4.
  • the CpG sites may comprise or consist of: cg06376130, cg03953789, cg18269134, cg00613587, and cg01199135; cg06376130, eg 18269134, cg00613587, cg01199135, and cg00492074; cg03953789, cg00613587, cg01199135, cg00492074, and cg20271602; cg03953789, cg18269134, cg01199135, cg20707157, and cg22235661 ; or cg06376130, cg03953789, cg01199135, cg00492074, and cg10094916 (described further in Table 3).
  • Such methods using at least 5 CpG sites may achieve an absolute error of about 20 mJ/cm 2 or less, about 15 mJ/cm 2 or less, about 12 mJ/cm 2 or less, about 10 mJ/cm 2 or less, or about 5 mJ/cm 2 or less.
  • the methods of the present invention may comprise determining the methylation level of at least 10 CpG sites selected from T able 3 or 4. In certain embodiments, the methods of the present invention may comprise a step of determining the methylation level of from 10 to 200, 10 to 150, 10 to 100, 10 to 50, or 10 to 18 CpG sites selected from T able 3 or 4.
  • the CpG sites may comprise or consist of: cg06376130, cg03953789, cg00613587, cg20707157, cg09218398, cg26096304, cg09174638, cg00492074, cg20271602, and cg22235661; cg03953789, cg18269134, cg00613587, cg01199135, cg20707157, cg00492074, cg20271602, cg22235661 , cg24688871, and cg10094916; cg06376130, cg03953789, cg01199135, cg20707157, cg09218398, cg00492074, cg20271602, cg22235661 , cg17972013, and cg15224600; or eg 18269134
  • Such methods using at least 10 CpG sites may achieve an absolute error of about 20 mJ/cm 2 or less, about 15 mJ/cm 2 or less, about 10 mJ/cm 2 or less, about 7 mJ/cm 2 or less, or about 5 mJ/cm 2 or less.
  • the methods of the present invention may comprise determining the methylation level of at least 18 CpG sites selected from T able 3 or 4. In certain embodiments, the methods of the present invention may comprise a step of determining the methylation level of from 18 to 200, 18 to 150, 18 to 100, or 18 to 50 CpG sites selected from Table 3 or 4. In certain embodiments, the CpG sites may be selected from the CpG sites provided in set A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, or R in Table 4. In one example, the CpG sites may comprise of consist of the 18 CpG sites provided in set A in Table 4.
  • Such methods using at least 18 CpG sites may achieve an absolute error of about 20 mJ/cm 2 or less, about 15 mJ/cm 2 or less, about 10 mJ/cm 2 or less, or about 5 mJ/cm 2 or less.
  • the methods of the present invention may comprise determining the methylation level of at least 20 CpG sites selected from Table 3 or 4. In certain embodiments, the methods of the present invention may comprise a step of determining the methylation level of from 20 to 200, 20 to 150, 20 to 100, or 20 to 50 CpG sites selected from Table 3 or 4. In certain embodiments, the CpG sites may comprise or consist of the CpG sites provided in set B, C, D, E, G, H, I, J, K, N, O, P, or R in Table 4.
  • Such methods using at least 18 CpG sites may achieve an absolute error of about 20 mJ/cm 2 or less, about 15 mJ/cm 2 or less, about 10 mJ/cm 2 or less, or about 5 mJ/cm 2 or less.
  • Table 3 - 200 predictive CpG sites Table 4 -sets of predictive CpG sites:
  • RNA expression levels of genes and methylation levels of CpG sites can be used to predict MED.
  • feature refers to a gene or CpG site. Accordingly, “features” refers to a plurality of genes and/or CpG sites.
  • the inventors identified 200 features (including genes and CpG sites) whose RNA expression/methylation levels (as appropriate) individually exhibit a strong linear correlation with MED.
  • the inventors also identified sets of 18-20 features (including genes and CpG sites) whose RNA expression/methylation levels can be used to predict MED with high accuracy.
  • RNA expression level of 1 gene in Table 1 and the methylation level of 1 CpG site in Table 3 could be used to predict MED accurately.
  • MAEs of less than about 25 mJ/cm 2 were consistently achieved when MED was predicted using from 2 to 200 features selected from Tables 1 and 3, wherein the features comprise at least one gene and at least one CpG site.
  • the methods of the present invention may comprise determining the RNA expression level of at least one gene selected from Table 1 or 2 and the methylation level of at least one CpG site selected from Table 3 or 4.
  • the methods of the present invention may comprise determining the RNA expression or methylation levels (as appropriate) of at least 2 features selected from Tables 1-4, wherein the features comprise at least one gene and at least one CpG site.
  • the methods of the present invention may comprise a step of determining the RNA expression or methylation levels (as appropriate) of from 2 to 200, 2 to 150, 2 to 100, 2 to 50, 2 to 20, or 2 to 18 features selected from Tables 1-4, wherein the features comprise at least one gene and at least one CpG site.
  • the features may comprise or consist of: ENSG00000197978 and cg06376130; ENSG00000172799 and cg00613587;
  • Such methods using 2 features may achieve an absolute error of about 25 mJ/cm 2 or less, 20 mJ/cm 2 or less, about 15 mJ/cm 2 or less, about 10 mJ/cm 2 or less, or about 5 mJ/cm 2 or less.
  • methods of the present invention may comprise determining the RNA expression or methylation levels (as appropriate) of at least 5 features selected from Tables 1-4, wherein the features comprise at least one gene and at least one CpG site.
  • the methods of the present invention may comprise a step of determining the RNA expression or methylation levels (as appropriate) of from 5 to 200, 5 to 150, 5 to 100, 5 to 50, 5 to 20, or 5 to 18 features selected from Tables 1-4, wherein the features comprise at least one gene and at least one CpG site.
  • the features may comprise or consist of: ENSG00000197978, ENSG00000277060, cg06376130, eg 18269134, and cg00613587; ENSG00000197978, cg06376130, cg00613587, cg03953789, and cg00492074; ENSG00000277060, cg03953789, cg18269134, cg01199135, and cg00492074; ENSG00000159247, ENSG00000100376, ENSG00000172799, cg06376130, and eg 18269134; or ENSG00000100376, cg06376130, cg18269134, cg00613587, and cg01199135.
  • Such methods using 5 features may achieve an absolute error of about 25 mJ/cm 2 or less, 20 mJ/cm 2 or less, about 15 mJ/cm 2 or less, about 10 mJ/cm 2 or less, or about 5 mJ/cm 2 or less.
  • methods of the present invention may comprise determining the RNA expression or methylation levels (as appropriate) of at least 10 features selected from Tables 1-4, wherein the features comprise at least one gene and at least one CpG site.
  • the methods of the present invention may comprise a step of determining the RNA expression or methylation levels (as appropriate) of from 10 to 200, 10 to 150, 10 to 100, 10 to 50, 10 to 20, or 10 to 18 features selected from T ables 1 -4, wherein the features comprise at least one gene and at least one CpG site.
  • the features may comprise or consist of: ENSG00000197978, ENSG00000277060, ENSG00000100376, ENSG00000166670, ENSG00000172799, cg03953789, cg18269134, cg00613587, cg09218398, and cg10094916; ENSG00000197978, ENSG00000166670, cg06376130, cg00613587, cg26096304, cg20707157, cg20271602, cg22235661, cg24688871, and cg09174638; ENSG00000277060, cg06376130, cg03953789, cg18269134, cg01199135, cg20707157, cg09218398, cg00492074, cg20271602, and cg22235661; or ENSG
  • Such methods using 10 features may achieve an absolute error of about 20 mJ/cm 2 or less, about 15 mJ/cm 2 or less, about 10 mJ/cm 2 or less, or about 5 mJ/cm 2 or less, or about 3 mJ/cm 2 or less.
  • methods of the present invention may comprise determining the RNA expression or methylation levels (as appropriate) of at least 18 features selected from Tables 1-4, wherein the features comprise at least one gene and at least one CpG site.
  • the methods of the present invention may comprise a step of determining the RNA expression or methylation levels (as appropriate) of from 18 to 200, 18 to 150, 18 to 100, or 18 to 50 features selected from Tables 1-4, wherein the features comprise at least one gene and at least one CpG site.
  • the CpG sites may comprise or consist of the features provided in sets A, D, E, F, H, or I of Table 5.
  • Such methods using 18 features may achieve an absolute error of about 15 mJ/cm 2 or less, about 10 mJ/cm 2 or less, or about 5 mJ/cm 2 or less, or about 3 mJ/cm 2 or less.
  • methods of the present invention may comprise determining the RNA expression or methylation levels (as appropriate) of at least 20 features selected from Tables 1-4, wherein the features comprise at least one gene and at least one CpG site.
  • the methods of the present invention may comprise a step of determining the RNA expression or methylation levels (as appropriate) of from 20 to 200, 20 to 150, 20 to 100, or 20 to 50 features selected from Tables 1-4, wherein the features comprise at least one gene and at least one CpG site.
  • the CpG sites may comprise or consist of the features provided in sets B, C, G, or J of T able 5. Such methods using 20 features may achieve an absolute error of about 15 mJ/cm 2 or less, about 10 mJ/cm 2 or less, or about 5 mJ/cm 2 or less, or about 3 mJ/cm 2 or less.
  • the method may further comprise a step of predicting the subject's MED based on the determined RNA expression levels using a machine learning model, wherein the machine learning model has been trained on data comprising known MEDs and corresponding known RNA expression levels of the same genes.
  • the method may further comprise a step of predicting the subject's MED based on the determined methylation levels using a machine learning model, wherein the machine learning model has been trained on data comprising known MEDs and corresponding known methylation levels of the same CpG sites.
  • the methods of the present invention may comprise a step of training a machine learning model on data comprising known MEDs and corresponding known methylation levels of the same CpG sites.
  • the method may further comprise a step of predicting the subject's MED based on the determined RNA expression and methylation levels using a machine learning model, wherein the machine learning model has been trained on data comprising known MEDs and corresponding known RNA expression and
  • the methods of the present invention may comprise a step of training a machine learning model on data comprising known MEDs and corresponding known RNA expression and methylation levels of the same features.
  • RNA expression and/or methylation levels and MEDs means RNA expression and/or methylation levels and MEDs determined from the same subject.
  • known means previously determined.
  • a “known MED and corresponding known RNA expression level” means an MED determined on a subject's skin and an RNA expression level determined using a skin sample from the same subject.
  • the known RNA expression levels, the known methylation levels, and/or the known MEDs are derived from at least 5, 10, 15, 20, 30, or 32 subjects.
  • the known RNA expression levels, the known methylation levels, and/or the known MEDs are derived from at least 20 subjects.
  • the known RNA expression levels, the known methylation levels, and/or the known MEDs are derived from at least 32 subjects.
  • Known MEDs may have been determined by any known method for determining MED, for example using the standardised method provided in ISO 2444:2010.
  • Machine learning models may be used to determine predictive feature sets.
  • the machine learning model may be a supervised learning model, for example a support vector machine (SVM).
  • SVM support vector machine
  • the machine learning model may perform sequential backward selection (SBS; also referred to as sequential feature elimination), sequential forward selection (SFS), exhaustive search, random search, or search using genetic algorithms.
  • SBS sequential backward selection
  • FSS sequential forward selection
  • exhaustive search random search, or search using genetic algorithms.
  • the machine learning model may use a regression model, for example a Lasso regression model, a general linear model, a Lasso/ridge regression and elastic nets, decision trees, random forests, gradient boosting, or deep learning and neural networks.
  • a regression model for example a Lasso regression model, a general linear model, a Lasso/ridge regression and elastic nets, decision trees, random forests, gradient boosting, or deep learning and neural networks.
  • absolute error refers to the difference between a subject's MED determined by the known, standardised method and the subject's MED predicted using a method of the invention.
  • MAE mean absolute error
  • the absolute error of a predicted MED may be about 25 mJ/cm 2 or less, about 20 mJ/cm 2 or less, about 15 mJ/cm 2 or less, about 10 mJ/cm 2 or less, or about 5 mJ/cm 2 or less.
  • this information may be used to provide recommendations that are personalised to the subject.
  • the methods of the invention may comprise a step of determining the maximum dose of UV radiation the subject can be exposed to before experiencing negative effects thereof.
  • the negative effects may include skin damage (for example tanning, burning, premature aging) or DNA damage.
  • the methods of the invention may comprise a step of determining which UV protection substances or strategies are appropriate for the subject, for example sunscreens with particular SPFs or sunlight avoidance.
  • the methods of the invention may comprise a step of determining the minimum or optimal dose of UV radiation the subject should be exposed to in order to experience positive effects thereof.
  • the positive effects may include vitamin D synthesis or treatment of a disease or condition.
  • the disease or condition may be selected from the list consisting of: vitamin D deficiency; eczema; acne; psoriasis; graft-versus-host disease; vitiligo; mycosis fungoides; large-plaque parapsoriasis; and cutaneous T-cell lymphoma.
  • the treatment may be in the presence of a psoralen (i.e. PUVA therapy).
  • the methods of the invention may comprise a step of administering an effective amount of a UV protectant to the subject.
  • an effective amount means an amount effective to prevent or reduce damage to the subject's skin by UV radiation, including DNA damage.
  • a “UV protection substance” or “UV protectant” may be a chemical absorber (i.e. an organic chemical compound that absorbs UV light, for example salicalate, cinnimate, or benzophenone) or a physical blocker (i.e. inorganic particulates that reflect, scatter, or absorb UV light, for example Titanium Dioxide or Zinc Oxide).
  • a chemical absorber i.e. an organic chemical compound that absorbs UV light, for example salicalate, cinnimate, or benzophenone
  • a physical blocker i.e. inorganic particulates that reflect, scatter, or absorb UV light, for example Titanium Dioxide or Zinc Oxide
  • a method for predicting the minimal erythema dose (MED) of a subject's skin comprising: i. determining the RNA expression levels of at least 2 genes selected from the genes in Table 1 in a skin sample obtained from the subject, and ii. predicting the subject's MED based on the determined RNA expression levels using a machine learning model trained on data comprising known MEDs and corresponding known RNA expression levels of the at least two genes.
  • MED minimal erythema dose
  • a method for preventing damage to a subject's skin by UV radiation comprising: i. receiving or obtaining a skin sample from a subject; ii. determining the RNA expression levels of at least 2 genes selected from the genes in Table 1 in a skin sample obtained from the subject; iii. predicting the subject's MED based on the determined RNA expression levels using a machine learning model trained on data comprising known MEDs and corresponding known RNA expression levels of the at least two genes; and iv. administering an effective amount of a UV protectant to the subject.
  • genes comprise: i. ENSG00000197978 and ENSG00000277060; ii. ENSG00000197978 and ENSG00000100376; iii. ENSG00000197978 and ENSG00000172799; iv. ENSG00000197978 and ENSG00000166670; or v. ENSG00000172799 and ENSG00000159247.
  • the genes comprise: i. ENSG00000197978, ENSG00000277060, ENSG00000100376, ENSG00000166670, and ENSG00000172799; ii.
  • ENSG00000197978 ENSG00000159247, ENSG00000100376, ENSG00000166670, and ENSG00000172799; iii. ENSG00000277060, ENSG00000100376, ENSG00000166670, ENSG00000172799, and ENSG00000159247; iv. ENSG00000197978, ENSG00000100376, ENSG00000166670, ENSG00000106392, and ENSG00000159247; or v. ENSG00000197978, ENSG00000100376, ENSG00000172799, ENSG00000159247, and ENSG00000260075.
  • ENSG00000277060 ENSG00000166670, ENSG00000106392, ENSG00000159247, ENSG00000100376, ENSG00000260075, ENSG00000169282, ENSG00000126890, ENSG00000176933, and ENSG00000134184.
  • genes comprise the genes in set A, B, C, D, E, F, G, H, I , J, K L, M, N, O, P, Q, or R in Table 2.
  • genes comprise the genes in set A in Table 2. 16. The method of any preceding paragraph, wherein the genes comprise up to 200,150, 100, 50, or 20 genes.
  • the sample comprises, consists, or consists essentially of epidermis cells and/or dermis cells.
  • machine learning model is a support vector machine (SVM).
  • a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the following steps: i. inputting the RNA expression levels of at least two genes selected from the genes in Table 1 in a skin sample obtained from the subject; ii. predicting the subject's MED based on the determined RNA expression levels using a machine learning model trained on data comprising known MEDs and corresponding known RNA expression levels of the at least two genes; and iii. outputting the predicted MED.
  • the at least 2 genes are at least 5 genes.
  • ENSG00000166670 and ENSG00000172799; ii. ENSG00000197978, ENSG00000159247, ENSG00000100376,
  • ENSG00000166670 and ENSG00000172799; iii. ENSG00000277060, ENSG00000100376, ENSG00000166670,
  • ENSG00000106392 and ENSG00000159247; or v. ENSG00000197978, ENSG00000100376, ENSG00000172799,
  • ENSG00000112139 ENSG00000130779, ENSG00000159247, and ENSG00000260075; ii. ENSG00000197978, ENSG00000277060, ENSG00000166670,
  • ENSG00000115602 ENSG00000112139, ENSG00000224472, and ENSG00000233913; or iv. ENSG00000277060, ENSG00000166670, ENSG00000106392,
  • ENSG00000169282 ENSG00000126890, ENSG00000176933, and ENSG00000134184.
  • a computer-readable medium comprising the computer program of any of paragraphs 22-41.
  • a method for predicting the minimal erythema dose (MED) of a subject's skin comprising: i. determining the methylation levels of at least 2 CpG sites selected from the CpG sites in Table 3 in a skin sample obtained from the subject, and ii. predicting the subject's MED based on the determined methylation levels using a machine learning model trained on data comprising known MEDs and corresponding known methylation levels of the at least two CpG sites.
  • MED minimal erythema dose
  • a method for preventing damage to a subject's skin by UV radiation comprising: i. receiving or obtaining a skin sample from a subject; ii. determining the methylation levels of at least 2 CpG sites selected from the CpG sites in Table 3 in a skin sample obtained from the subject; iii. predicting the subject's MED based on the determined methylation levels using a machine learning model trained on data comprising known MEDs and corresponding known methylation levels of the at least two CpG sites; and iv. administering an effective amount of a UV protectant to the subject.
  • cg06376130 cg03953789, cg01199135, cg20707157, cg09218398, cg00492074, cg20271602, cg22235661, cg17972013, and cg15224600; or iv. eg 18269134, cg00613587, cg01199135, cg20707157, cg00492074, cg20271602, cg22235661, cg24688871, and cg10094916.
  • CpG sites comprise up to 200, 150, 100, 50, 20, or 18 CpG sites.
  • the sample comprises, consists, or consists essentially of epidermis cells and/or dermis cells.
  • machine learning model is a support vector machine (SVM).
  • a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the following steps: i. inputting the methylation levels of at least two CpG sites selected from the CpG sites in Table 3 in a skin sample obtained from the subject; ii. predicting the subject's MED based on the determined methylation levels using a machine learning model trained on data comprising known MEDs and corresponding known methylation levels of the at least two CpG sites; and iii. outputting the predicted MED.
  • cg06376130 cg03953789, cg01199135, cg20707157, cg09218398, cg00492074, cg20271602, cg22235661, cg17972013, and cg15224600; or iv. eg 18269134, cg00613587, cg01199135, cg20707157, cg00492074, cg20271602, cg22235661, cg24688871, and cg10094916.
  • a computer-readable medium comprising the computer program of any of paragraphs 66-85.
  • a method for predicting the minimal erythema dose (MED) of a subject's skin comprising: i. determining the methylation level(s) and RNA expression level(s) of at least 2 features selected from the CpG sites in Table 3 and the genes in Table 1 in a skin sample obtained from the subject, wherein the features comprise at least one CpG site in Table 3 and at least one gene in Table 1, and ii. predicting the subject's MED based on the determined methylation level(s) and RNA expression level(s) using a machine learning model trained on data comprising known MEDs and corresponding known methylation level(s) and RNA expression level(s) of the at least 2 features.
  • MED minimal erythema dose
  • a method for preventing damage to a subject's skin by UV radiation comprising: i. receiving or obtaining a skin sample from a subject; ii. determining the methylation level(s) and RNA expression level(s) of at least 2 features selected from the CpG sites in Table 3 and the genes in Table 1 in a skin sample obtained from the subject, wherein the features comprise at least one CpG site in Table 3 and at least one gene in Table 1; iii.
  • ENSG00000159247 ENSG00000100376, ENSG00000172799, cg06376130, and eg 18269134; or v. ENSG00000100376, cg06376130, cg18269134, cg00613587, and cg01199135.
  • ENSG00000166670 ENSG00000172799, cg03953789, cg18269134, cg00613587, cg09218398, and eg 10094916; ii. ENSG00000197978, ENSG00000166670, cg06376130, cg00613587, cg26096304, cg20707157, cg20271602, cg22235661, cg24688871, and cg09174638; iii.
  • ENSG00000277060 cg06376130, cg03953789, cg18269134, cg01199135, cg20707157, cg09218398, cg00492074, cg20271602, and cg22235661; or iv. ENSG00000197978, ENSG00000277060, ENSG00000172799,
  • any of paragraphs 87-106 further comprising: i. determining the maximum dose of UV radiation the subject can be exposed to before experiencing negative effects thereof; ii. determining an appropriate UV protection substance or strategy for the subject; iii. determining the minimum dose of UV radiation the subject should be exposed to in order to experience positive effects thereof; and/or iv. determining the appropriate UV dose for treating a disease or condition susceptible to UV therapy in the subject.
  • a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the following steps: i. inputting the methylation level(s) and RNA expression level(s) of at least 2 features selected from the CpG sites in Table 3 and the genes in Table 1 in a skin sample obtained from the subject, wherein the features comprise at least one CpG site in Table 3 and at least one gene in Table 1, ii. predicting the subject's MED based on the determined methylation level(s) and RNA expression level(s) using a machine learning model trained on data comprising known MEDs and corresponding known methylation level(s) and RNA expression level(s) of the at least 2 features, and iii. outputting the predicted MED.
  • ENSG00000159247 ENSG00000100376, ENSG00000172799, cg06376130, and eg 18269134; or v. ENSG00000100376, cg06376130, cg18269134, cg00613587, and cg01199135.
  • ENSG00000197978 ENSG00000166670, cg06376130, cg00613587, cg26096304, cg20707157, cg20271602, cg22235661, cg24688871, and cg09174638; iii. ENSG00000277060, cg06376130, cg03953789, cg18269134, cg01199135, cg20707157, cg09218398, cg00492074, cg20271602, and cg22235661; or iv.
  • the present invention may comprise any combination of features and/to limitations referred to herein, except for combinations of such features which are mutually exclusive.
  • the foregoing description is directed to particular embodiments of the present invention for the purpose of illustrating it. It will be apparent, however, to one skilled in the art, that many modifications and variations to the embodiments described herein are possible. All such modifications and variations are intended to be within the scope of the present invention, as defined in the appended claims.
  • MED was determined as previously described (Heckman et al (2013) Minimal Erythema Dose (MED) Testing, J Vis Exp. (75): 50175) and outlined below. This method has been standardised as International Standard ISO 2444:2010.
  • the study sites were located on the subjects' lower backs since this area is rarely exposed to sunlight. The sites were split into control and test areas.
  • the first irradiation of the test sites was performed using a SOL 500 full spectrum solar simulator (Honle UV Technology). Intensities were chosen individually to reach 0.9 MED for all subjects (i.e. 90% of the MED in a given test subject).
  • RNA and DNA samples were used for transcriptome sequencing and methylation profiling, respectively, as described below.
  • Transcriptome libraries were prepared using TruSeq Library Prep Kit (lllumina ® ) and sequencing performed at 1x50 bp on lllumina's ® HiSeq system to a final sequencing depth of 100 million reads per sample.
  • Sequencing data was processed using a custom pipeline including Fastqc vO.11.767 for quality control, Trimmomatic v0.3668 for trimming and Salmon v0.8.169 for read mapping and quantification.
  • Methylation profiling was performed using lllumina's ® Infinium Methylation EPIC arrays (Love et al. (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2, Genome Biol. 15:550).
  • Methylation data was processed using the minfi package (Aryee (2014) Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays, Bioinformatics 30:1363-1369) in R. Normalization was carried out using the funnorm normalization method.
  • Sequential backwards selection also referred to as sequential feature elimination was used in a support vector model (SVM) to reduce the sets of 2000 predictive features to sets of fewer features, for example 20 features.
  • SVM support vector model
  • Parameters to be set for SBS are the maximum number of features to retain and a threshold parameter determining the minimal value of improvement needed for a feature to be eliminated from the model.
  • the inventors' models did not use the first parameter, but the second parameter was set to 0.01.
  • Model predictions and accuracy scores were extracted from leave-one-out-cross- validation (LOOCV) to avoid overfitting.
  • the feature selection and training included data from both test and control (irradiated and non-irradiated) samples, in order to allow accurate MED predictions irrespective of previous UV exposure of the sample.
  • This study recruited 32 healthy female subjects belonging to Fitzpatrick phototypes I to IV (12 subjects belonging to phototype I and II, 10 to phototype III; and 10 to phototype IV). The subjects were aged between 30 and 65 years, with homogeneous age distributions in each phototype group.
  • MED values ranged from approximately 50 mJ/cm 2 to approximately 210 mJ/cm 2 . As expected from previously published data, stratification of donors using the Fitzpatrick classification system was an inaccurate predictor of MED. For example, the measured MED values for the subjects of phototype IV varied from 99.7 to 210.4 mJ/cm 2 .
  • a machine learning model was trained on data comprising (i) the MEDs and (ii) the corresponding RNA expression levels of the 200 genes in Table 1, from all 32 subjects. This model was then used to predict MED based on RNA expression levels of the 200 genes in Table 1 in skin samples obtained from subjects.
  • the set of 20 genes in set A in T able 2 achieved a very low MAE of 7.85 mJ/cm 2 (see Figure 1).
  • ENSG00000112139, ENSG00000130779, ENSG00000159247, and ENSG00000260075 achieved an MAE of 9.71 mJ/cm 2 .
  • ENSG00000277060 (NLRP2)
  • ENSG00000100376 (FAM118A)
  • the set of 2 genes ENSG00000197978 and ENSG00000277060 achieved an MAE of 19.62 mJ/cm2.
  • Example 3b - predicting MED using 200 CpG sites [128] A machine learning model was trained on data comprising (i) the MEDs and (ii) the corresponding methylation levels of the 200 CpG sites in Table 3, from all 32 subjects. This model was then used to predict MED based on methylation levels of the 200 CpG sites in Table 3 in skin samples obtained from subjects.
  • the set of 5 CpG sites cg06376130, cg03953789, cg18269134, cg00613587, and cg01199135 achieved an MAE of 11.01 mJ/cm 2 .
  • the set of 2 CpG sites cg06376130 and cg03953789 achieved an MAE of 15.58 mJ/cm 2 .

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Abstract

La présente invention concerne des procédés de prédiction du MED de la peau d'un sujet, comprenant soit la détermination des niveaux de méthylation d'au moins 2 sites CpG sélectionnés parmi certains gènes dans un échantillon de peau obtenu à partir du sujet, et à prédire le MED du sujet sur la base des niveaux de méthylation déterminés à l'aide d'un modèle d'apprentissage automatique entraîné sur des données comprenant des MED connus et des niveaux de méthylation connus correspondants des au moins 2 sites CpG, et/ou la détermination des niveaux d'expression d'ARN d'au moins 2 gènes choisis parmi certains gènes dans un échantillon de peau obtenu à partir du sujet, et la prédiction du MED du sujet sur la base des niveaux d'expression d'ARN déterminés à l'aide d'un modèle d'apprentissage automatique entraîné sur des données comprenant des MED connus et des niveaux d'expression d'ARN connus correspondants des au moins 2 gènes, et/ou la détermination du/des niveau(x) de méthylation et d'un/des niveau(x) d'expression d'ARN d'au moins 2 caractéristiques choisies parmi les sites CpG du tableau 3 et les gènes du tableau 1 dans un échantillon de peau obtenu à partir du sujet, les caractéristiques comprenant au moins un site CpG du tableau 3 et au moins un gène du tableau 1, et prédire le MED du sujet sur la base du/des niveau(x) de méthylation déterminé(s) et un/des niveau(x) d'expression d'ARN à l'aide d'un modèle d'apprentissage automatique entraîné sur des données comprenant des MED connus et un/des niveau(x) de méthylation connu(s) et un/des niveau(x) d'expression d'ARN connu(s) des au moins 2 caractéristiques. L'invention concerne en outre des programmes informatiques permettant de mettre en oeuvre les procédés de l'invention.
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