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  • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
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  • 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
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  • C12Q2600/00—Oligonucleotides characterized by their use
  • C12Q2600/112—Disease subtyping, staging or classification
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  • C12Q2600/00—Oligonucleotides characterized by their use
  • C12Q2600/154—Methylation markers
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  • C12Q2600/00—Oligonucleotides characterized by their use
  • C12Q2600/156—Polymorphic or mutational markers
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  • C12Q2600/00—Oligonucleotides characterized by their use
  • C12Q2600/158—Expression markers
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  • 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
  • C12Q2600/178—Oligonucleotides characterized by their use miRNA, siRNA or ncRNA

Definitions

• the invention relates to a method for determining which type of cancer an individual has or if the individual has cancer or not, wherein single nucleotide variants, microRNA and DNA methylation are used as markers.
• the invention also relates to uses, methods for treating cancer and methods for providing information.
• Cancer is mostly a manageable disease as long as it is diagnosed before metastasis has begun. In most cases, higher-grade cancer evolves from lower-grade cancer. Therefore, it is important to detect cancer as early as possible by reliable and accessible diagnostic methods.
• Tumor-derived cfDNA harbors somatic mutations originating from the tumor and comprises tissue-specific DNA methylation. Hence organ-specific epigenetic pattern is measurable in the circulation (Lehmann-Werman et al., 2016). Since many tumors originating from different tissues share identical SNVs (Olivier et al., 2010), epigenetic information adds a tissue-specific data layer.
• WO 2016/141169 A1 relates to methods for identifying a treatment associated with cancer.
• the methods are based on the analysis of protein markers, which is relatively complicated. Further, it is not suggested to use the assay for determining which type of cancer an individual has. Overall, the methods could still be improved.
• WO 2019/067092 A 1 relates to methods for identifying a single cancer type based on genetic and protein biomarkers. Since the methods are based on the analysis of protein markers, it is relatively complicated. Overall, the methods could still be improved.
• WO 2016/033114 A1 relates to methods for determining levels of androgen receptor variants in a sample from a prostate cancer patient. The method is specific for a single cancer type and does not relate to distinguishing types of cancer.
• Arora et al, 2020 relates to an outcome-weighted clustering algorithm for integrative molecular stratification focusing on patient survival, which was performed on 18 cancer types across multiple data modalities including somatic mutation, DNA copy number, DNA methylation, and mRNA, miRNA, and protein expression.
• the method is not suitable as a standard diagnostic test, because it is based on a high amount of markers which cannot be determined rapidly and conveniently.
• Hoadley et al, 2018, relates to integrative molecular analyses of tumors 10,000 from specimens and 33 types of cancer, which included chromosome-arm-level aneuploidy, DNA hypermethylation, mRNA, and miRNA expression levels and reverse-phase protein arrays.
• the method is not suitable as a standard diagnostic test, because it is based on a high amount of markers which cannot be determined rapidly and conveniently.
• telomerase reverse transcriptase (TERT) activation and cancer types.
• the known methods for diagnosing cancer or a cancer type have various drawbacks. Most methods relate to the validation of one single cancer type, but do not allow an early distinction between several different cancer types and a reliable prediction if an individual has cancer or not. The known methods are often complicated and cannot be carried out in a fast and cost efficient routine manner. Frequently, they are based on data which is not easily available, such as protein levels. Further, the reliability of known methods could still be improved. There is an ongoing need for simple, efficient and reliable methods for determining a type of cancer.
• the problem underlying the invention is to provide methods for determining the type of cancer of an individual, which overcome the above mentioned problems.
• the method should also be capable of determining whether the individual has cancer or not. It is a problem to provide respective methods which are reliable, but also simple and efficient.
• the method shall provide significant information about a cancer type from a relatively large number of potential cancer types.
• the method shall be based on a relatively low number of markers and should be effective for a probe which is easily accessible.
• the number of reagents and process steps shall be relatively low.
• Subject of the invention is a method for determining which type of cancer an individual has and/or for determining if the individual has cancer or not, comprising
• (b1) at least 2 markers selected from APC (COSM 18561), hsa_miR_133a_3p, hsa_miR_148b_3p, hsa_miR_29c_3p, hsa_miR_20a_5p, hsa_miR_92a_3p, hsa_miR_155_5p, hsa_miR_195_5p, hsa_miR_101_3p, hsa_miR_27a_3p, hsa_miR_26a_5p and hsa_miR_21_5p,
• step (d) comparing the levels to a known standard, and (e) determining, based on the result of step (d), which type of cancer from the group defined in step (b) the individual has and/or if the individual has cancer or not.
• the method is for determining which cancer type an individual has and/or if the individual has cancer at all. Therefore, it is a diagnostic method.
• Cancers involve abnormal cell growth with the potential to invade or spread to other parts of the body. They form a subset of neoplasms (tumors).
• the term “type of cancer” can refer to the body part in which the cancer originates in. However, some body parts contain multiple types of tissue, so that the type of cancer can also be the type of cell that the tumor cells originated from.
• the individual is a mammal, much preferably a human. However, the individual could also be a non-human mammal, such as a farm or domestic animal, such as a horse or dog.
• the user of the method preferably comprises medical professional, such as a physician, laboratory or medical staff. Alternatively, the method may be carried out at least in part by the individual itself or a layman.
• the method may provide the first information in this regard.
• the method can also be used for confirming a type of cancer, for which there has been an indication before by other means.
• the method can also be used for screening or monitoring if an individual, which is not suspected of having cancer, such as a healthy individual, has cancer and which type of cancer.
• the method can also be used for individuals, which are not healthy and where the presence of cancer shall be excluded.
• step (e) it is determined in step (e) which type of cancer an individual has.
• the method determines a type of cancer, which is from a defined group of cancer types.
• the group comprises at least two different cancer types, preferably at least three different types.
• the method is for determining the type of cancer from a group of at least 4, at least 6 or at least 10 different types.
• the method is for determining the type of cancer from a group of 2 to 50, preferably 4 to 30, or 6 to 20 different types of cancer.
• the method is for differentiating between different cancer types. Thereby, the method is different from methods in the art, in which it is only examined or verified if an individual has one single defined type of cancer or not.
• a combination of markers which are described in the following, is especially suitable for distinguishing between different cancer types, although it has not been described in the art that these specific markers would be highly correlated to a single type of cancer.
• the present invention is based on a novel approach for distinguishing between a group of different cancers based on a set of novel markers.
• a group of cancer types is defined in step (b).
• the term “defining” means selecting cancer types, which could be of interest in the diagnosis.
• the defined group could comprise the most common cancer types, such that the result would be as comprehensive as possible.
• the defined group could comprise several cancer types, which are of special interest in view of the symptoms or history of the individual or other circumstances.
• step (e) it is determined which cancer from this group the individual has and/or if the individual has cancer, or at least has a type of cancer from the defined group.
• step (c) a combination of markers is used which provides relevant information regarding the cancer types defined in step (b).
• the method shall predict with high likelihood if the individual has a specific type of cancer.
• the likelihood is at least 70%, more preferably at least 80% or even more than 90% or more than 95%. Based on the result, a medical professional can verify by other means, such as a highly specific assay, if the individual has the single cancer type which was determined in the inventive method.
• a sample from the individual is provided, which is a body liquid or fraction thereof.
• the body liquid is blood, especially in the form of plasma or serum.
• blood plasma especially from venous blood.
• the body liquid may be saliva, urine, sweat, feces or tears.
• the body liquid is not a solid tissue or derived from solid tissue.
• the body liquid is a liquid biopsy sample.
• Liquid biopsy is a minimally invasive technology for detection of molecular biomarkers without the need for invasive procedures.
• a liquid biopsy (fluid biopsy, fluid phase biopsy) is a sample from body fluid, typically blood.
• Liquid biopsy samples may comprise traces of the cancer’s RNA or DNA, which can be identified in the sample as circulating free DNA (cfDNA) or circulating free RNA (cfRNA). Circulating nucleic acids in blood are typically protected by extracellular micro-vesicles, mainly exosomes. With a blood liquid biopsy sample, nucleic acids can be analysed which are released by the tumor or tumor environment into peripheral venous blood.
• the method can be carried out with a body liquid, especially a liquid biopsy sample, which is not invasive and not burdensome for the individual.
• a body liquid especially a liquid biopsy sample
• the method does not require surgical or invasive provision of tissue samples.
• the sample can be obtained easily by medical or unskilled staff or even the individual himself.
• a specific combination of marker levels can provide highly relevant information about a type of cancer from a body liquid, especially a liquid biopsy sample.
• the same combination of markers can provide less relevant or even meaningless results from other samples, especially cancer tissue.
• the method is carried out with body liquid, especially a liquid biopsy sample.
• the sample is blood or a fraction thereof.
• the blood fraction from blood can be serum, plasma or a fraction thereof.
• the sample should comprise circulating free nucleic acids, preferably at relatively high levels.
• the sample should be processed in a manner such that a relatively high level of circulating free nucleic acids is preserved or accumulated.
• the sample may comprise known additives, especially for stabilizing nucleic acids.
• the method takes into account the levels of at least three different marker types, which are single nucleotide variant (SNV), microRNA (miRNA) and DNA methylation. It was found that the specific combination of the markers can provide highly relevant results.
• the marker of which levels are examined in step (c) are those which are compared in step (d) and from which the type of cancer is determined in step (e).
• this set of markers is also referred to as the markers “used” in the method, or on which the method is “based”.
• markers are not considered “used”, if their levels were only determined additionally in step (c), for example because a larger microarray was used, but in which the markers are not compared in step (d) and/or not used for determining the type of cancer in step (e).
• the level of at least one single-nucleotide variant is determined.
• SNV single-nucleotide variant
• An SNV is a variation in a single nucleotide without limitations of frequency. SNVs may arise in somatic cells, for example due to cancer, and thus can be found in low numbers of individuals below or significantly below 1%.
• an SNV is distinct from a single-nucleotide polymorphism (SNP), a substitution of a single nucleotide at a specific position in the genome, that is present in a sufficiently large fraction of the population, such as 1% or more. It is known in the art that various SNV can be associated with specific cancer types.
• SNP single-nucleotide polymorphism
• the level of SNV markers is determined by quantitative PCR.
• determining a level means that it is determined if a mutation is present or not. However, it can also be determined what the ratio and/or amount of the mutation is compared to the wild type gene.
• the SNV marker(s) is or comprises the androgen receptor (AR) mutation AR H875Y [AR_CT_Y_N, COSMIC ID: cosm238555; Genomic coordinates GRCh38, X:67723701..67723701 , CDS mutation; AA mutation: p.H875Y (Substitution - Missense, position 875, H-*Y) (former designation H874Y) ; CDS mutation: c.2623C>T (Substitution, position 2623, C-»T)].
• AR androgen receptor
• the marker can be suitable for determining bladder, colorectal or breast cancers.
• the SNV comprise TP53 (COSM10758) (Cosmic ID 10758, TP53_10758_mu_Y_N; substitution - missense).
• the SNV may also comprise APC (COSM18561) (APC_18561_mu_Y_N, COSMIC ID 18561, Insertion- Frameshift).
• the SNV comprise AR H875Y and/or TP53 (COSM10758).
• the SNV comprise AR H875Y in combination with TP53 (COSM10758) and/or APC (COSM18561). It was found that these specific SNV markers or groups of markers can provide especially relevant results.
• the total number of SNV used in the method is at least 2 or at least 3.
• it is up to 30, up to 20, or as low as up to 10, or up to 5.
• the number of SNV is 1 to 30, especially 2 to 20, or 3 to 10. It is advantageous that relevant results can be obtained based on a relatively low number of SNV markers.
• the level of at least one microRNA is determined.
• a microRNA is a small non-coding RNA molecule, which typically consists of about 22 nucleotides and functions in RNA silencing and post-transcriptional regulation of gene expression.
• the miRNA expression level is determined. Methods for determining miRNA levels in liquid biopsy samples are known in the art (Lan, 2015).
• the at least one miRNA marker comprises hsa_miR_17_5p. This miRNA was found to provide especially relevant results for determining a type of cancer in the method.
• the markers comprise at least one, preferably at least 2 or at least 3 miRNA from the group of hsa_miR_17_5p, hsa_miR_133a_3p, hsa_miR_148b_3p, hsa_miR_29c_3p, hsa_miR_20a_5p, hsa_miR_92a_3p, hsa_miR_155_5p, hsa_miR_195_5p, hsa_miR_101_3p, hsa_miR_27a_3p, hsa_miR_26a_5p and hsa_miR_21_5p.
• the markers comprise 2 to 13, preferably 3 to 13 or 5 to 13 miRNA from this group.
• the markers comprise not more than 10 miRNA, preferably not more than 5 miRNA from this group. These miRNA were found to provide especially relevant results for determining a type of cancer in the method.
• the markers from this group include hsa_miR_17_5p, which can be especially relevant for determining a type of cancer. It is advantageous that relevant results can be obtained based on such a relatively low number of miRNA markers from the defined group.
• the total number of miRNA used in the method is at least 2 or at least 3. Preferably, it is up to 20, up to 10 or even up to 5 only. Preferably, the number of miRNA is 1 to 20, more preferably 2 to 10. It is advantageous that relevant results can be obtained based on a relatively low number of miRNA markers.
• DNA methylation is a biological process in which methyl groups are attached to a DNA molecule. Methylation can change the activity of a DNA segment without changing the sequence.
• the DNA methylation is at a CpG site.
• CpG sites or CG sites are regions of DNA where a cytosine nucleotide is followed by a guanine nucleotide in the linear sequence of bases along its 5' — > 3' direction.
• CpG sites occur with high frequency in genomic regions called CpG islands. Cytosines in CpG dinucleotides can be methylated to form 5-methylcytosines.
• DNA methylation is almost exclusively found in CpG dinucleotides.
• Methods for determining DNA methylation in liquid biopsy samples are known in the art (Gai, 2017). Methylation is typically examined on a gene of interest or region thereof.
• DNA methylation levels are determined by quantitative PCR.
• determining a level means that it is determined if a methylation is present or not. However, it can also be determined what the ratio and/or amount of methylation is compared to corresponding sample from healthy individuals.
• the at least one DNA methylation marker comprises MLH1_meth.
• This marker was found to provide especially relevant results for determining a type of cancer in the method.
• the DNA methylation marker(s) comprise at least one, preferably 2, 3 or all 4 from the group of MLH1_meth, GATA5_meth, Stratifin_meth and MDR1_meth. These DNA methylations were found to provide relevant results for determining a type of cancer in the method.
• the markers from this group include MLH1_meth, which can be especially relevant for determining a type of cancer.
• the total number of DNA methylation markers used in the method is not more than 10, or not more than 6, preferably not more than 4.
• the number of DNA methylation markers is 1 to 6, preferably 1, 2, 3 or 4. It is advantageous that relevant results can be obtained based on such relatively low numbers of DNA methylation markers.
• the markers comprise at least 2 markers, preferably 3 or all 4 from a group (A) of AR H875Y, TP53 (COSM10758), MLH1_meth and hsa_miR_17_5p. It was found that these markers can be especially relevant for determining a type of cancer from a group of multiple types.
• the markers comprise markers from the group (A) and additional markers, which are at least 2, preferably at least 3 or at least 5 from a group (B) consisting of APC (COSM18561), hsa_miR_133a_3p, hsa_miR_148b_3p, hsa_miR_29c_3p, hsa_miR_20a_5p, hsa_miR_92a_3p, hsa_miR_155_5p, hsa_miR_195_5p, hsa_miR_101_3p, hsa_miR_27a_3p, hsa_miR_26a_5p and hsa_miR_21_5p.
• a group (B) consisting of APC (COSM18561), hsa_miR_133a_3p, hsa_miR_148b_3
• the markers comprise the markers from group (A) and (B) as outlined above, and additionally at least one, preferably at least 2 or at least 3, or all 4 markers selected from a group (C) of GATA5_meth, hsa_miR_133a_3p, Stratifin_meth and MDR1_meth.
• the markers comprise
• (b1) at at least 3, preferably at least 5 markers selected from APC (COSM 18561), hsa_miR_133a_3p, hsa_miR_148b_3p, hsa_miR_29c_3p, hsa_miR_20a_5p, hsa_miR_92a_3p, hsa_miR_155_5p, hsa_miR_195_5p, hsa_miR_101_3p, hsa_miR_27a_3p, hsa_miR_26a_5p and hsa_miR_21_5p.
• APC COSM 18561
• the markers comprise
• (b1) at least 5, preferably at least 8 markers selected from APC (COSM18561), hsa_miR_133a_3p, hsa_miR_148b_3p, hsa_miR_29c_3p, hsa_miR_20a_5p, hsa_miR_92a_3p, hsa_miR_155_5p, hsa_miR_195_5p, hsa_miR_101_3p, hsa_miR_27a_3p, hsa_miR_26a_5p and hsa_miR_21_5p, and
• (c1) optionally at least one additional marker selected from GATA5_meth, Stratifin_meth and MDR1_meth.
• a group of at least 2 cancers (cancer types), preferably at least 3, more preferably at least 5, or even at least 8 types is defined.
• the group consists of 2 to 30, or 3 to 25, or 5 to 20 types, especially 6 to 15 types of cancer. Accordingly, it is determined in step (e), which cancer from this pre-determined group the individual has.
• the types of cancer defined in step (b) comprise at least 3, at least 4 or at least 5, more preferably at least 7, at least 8 or all, from the group of bladder, brain, breast, colorectal, lung, ovarian, pancreas, prostate and stomach cancer.
• the types of cancer comprise at least bladder, brain, colorectal and/or stomach cancer, because it was found that the method can identify these types with excellent precision.
• the types of cancer comprise stomach and brain cancer, because it was found that the method can identify these types with especially high precision.
• the types of cancer may comprise other known types, such as kidney, liver, uterine, oesophageal and thyroid cancer.
• the markers comprise:
• - for bladder cancer at least one, preferably at least 2, more preferably all of AR H875Y, TP53 (COSM10758), hsa_miR_17_5p,
• - for brain cancer at least one, preferably at least 2, more preferably all of MLH1_meth, GATA5_meth, hsa_miR_133a_3p,
• - for breast cancer at least one, preferably at least 2, more preferably all of AR H875Y, hsa_miR_17_5p, TP53 (COSM10758), MDR1_meth,
• - for lung cancer at least one, preferably at least 2, more preferably all of hsa_miR_155_5p, TP53 (COSM10758), hsa_miR_92a_3p, hsa_miR_17_5p,
• - for ovarian cancer at least one, preferably at least 2, more preferably all of hsa_miR_148b_3p, hsa_miR_29c_3p, hsa_miR_101_3p, hsa_miR_92a_3p,
• - for pancreas cancer at least one, preferably at least 2, more preferably all of at least one of hsa_miR_148b_3p, hsa_miR_29c_3p, hsa_miR_27a_3p, Stratifin_meth,
• - for prostate cancer at least one, preferably at least 2, more preferably all of AR H875Y, hsa_miR_26a_5p, hsa_miR_17_5p, and/or
• - for stomach cancer at least one, preferably at least 2, more preferably all of hsa_miR_20a_5p, hsa_miR_21_5p, APC (COSM18561).
• the total number of markers used in the method is not more than 50, or not more than 30, or preferably not more than 20.
• the total number is from 5 to 50, especially from 10 to 40, or from 5 to 25. More preferably, the total number is from 10 to 25, especially from 15 to 20.
• a minimum total number of at least 5 or at least 10 markers may be used. It is a special advantage of the method that it can be carried out with a relatively low total number of markers. Thus, the method can be conducted with a relatively simple test kit and/or test tools at comparably low costs. Moreover, it was found that the specificity of the result can be enhanced when the number of markers is reduced.
• step (e) it is preferred that only the levels of markers used for evaluation in step (e) are determined in step (c) and/or compared in step (d), because this simplifies the method and the specificity of the result can be improved.
• step (e) it is also conceivable that a larger data set on marker levels is obtained, although the result in step (e) is obtained only based on a limited set of markers selected in step (c).
• the likelihood that the type of cancer is determined correctly is at least 80%, preferably at least 90%, more preferably at least 95%. Due to the specific selection of markers, it was found that the method allows predictions which such a high accuracy. This provides significant information for the medical professional, which may be used for subsequent verification, for example with other markers or diagnostic tools which are highly specific only for the type of cancer identified in the method.
• the levels of single nucleotide variants (SNV) and DNA methylation are determined with cell free DNA (cfDNA) and/or the levels of the miRNA is determined in cell free miRNA (cfmiRNA).
• the SNV and/or DNA methylation are determined in circulating tumor DNA (ctDNAs). It is known in the art that ctDNA, including tumor specific SNV and DNA methylation, can be examined in liquid biopsy samples, and can be detected in the plasma of cancer patients in the early stages of their disease (Han et al., 2017; Gai, 2019). It is also known in the art that miRNA are potential biomarkers for cancer (Lan, 2015). However, methods for determining a type of cancer based on the marker combination as in the present invention have not been described in the art.
• the levels of the markers are determined by quantitative PCR (qPCR).
• qPCR quantitative PCR
• all marker levels are determined by qPCR. This is advantageous, because all the levels of the different markers SNV, miRNA and DNA methylation can be determined uniformly with this highly specific and relatively simple method.
• microRNA expression can be quantified in a two-step polymerase chain reaction of modified RT-PCR followed by qPCR.
• the inventive method provides significant information based on SNV, miRNA and DNA methylation markers, it is preferred that no other marker types are additionally used. It is especially preferred that no protein marker is used in the method. This is advantageous, because analyzing protein markers is generally more complicated. Moreover, qPCR is not applicable for determining protein marker levels. In a less preferred embodiment, additional other marker types, which are not SNV, miRNA or DNA methylation markers, can be used.
• the method does not comprise a nucleic acid sequencing step. This can be advantageous, because sequencing methods, such as next-generation sequencing, are often more complicated and can be less sensitive.
• the method may comprise nucleic acid sequencing, especially instead of qPCR. In principle, a high sensitivity may also be achieved with a sequencing technique.
• the method is conducted in an automated manner, such as an automated workstation.
• the method can be applied for determining what type of cancer an individual has.
• the result can be that the individual has a cancer type from a defined group. If the result is that the individual does not have a cancer type from the defined group, the result can be that the individual is healthy, or that it may have cancer of another type, which is not part of the defined group.
• the method is for determining a type of cancer from the defined group, or alternatively that the individual does not have cancer at all, or at least not a type of cancer from the defined group.
• the likelihood to determine correctly that the individual does not have cancer is at least 80%, preferably at least 90%, more preferably at least 95%.
• the method is applied for determining if the individual has cancer or not.
• the result will be that the individual has a cancer from the defined group or is healthy.
• This method is of high practical relevance, because it is often necessary in clinical practice to determine rapidly and reliably if an individual has cancer or not, before performing subsequent specific diagnostic method, which are generally more time consuming, complicated and costly.
• the likelihood to determine correctly that the individual has cancer is at least 80%, preferably at least 90%, more preferably at least 95%.
• Subject of the invention is also the use of the single nucleotide variant AR H875Y as a marker for determining from a body liquid or fraction thereof, preferably a liquid biopsy sample, which type of cancer an individual has, wherein the type of cancer is selected from bladder, colorectal, lung, stomach, ovarian or brain cancer, preferably from bladder or colorectal cancer.
• a liquid biopsy sample which type of cancer an individual has, wherein the type of cancer is selected from bladder, colorectal, lung, stomach, ovarian or brain cancer, preferably from bladder or colorectal cancer.
• this SNV could be of special relevance for distinguishing between different types of cancer in liquid biopsy samples.
• the method comprises a final step, in which it is confirmed by other diagnostic means that the type of cancer was identified correctly in step (e).
• the confirmation could be made with one or more highly specific markers for the type of cancer determined, possibly based on a tissue sample.
• the inventive method comprises steps (a) to (e). Whilst steps (a), (c), (d) and (e) are carried out in consecutive order, the definition of the group of types of cancers in step (b) does not necessarily have to be performed between steps (a) and (c).
• the liquid biopsy sample can be taken from the individual, preferably in a non- or minimally invasive method.
• the sample can also be obtained from the individual before step (a), such that the overall method is an in vitro method.
• Marker levels in step (c) can be determined by routine methods of a medical or biochemistry laboratory, preferably qPCR. Respective methods are known and have been described in the art.
• comparing levels in step (d) to a known standard means, that the levels of the markers, which were determined in step (c), are compared to levels which are known from the art or which were determined in advance for the same markers, and for the same cancer types from the group defined in step (b) and for healthy individuals.
• Standard levels of SNVs, miRNA and DNA methylation markers for comparison in step (d) can be obtained from the literature, from this specification or experimentally. Comparative data regarding the marker levels can be obtained experimentally from samples from individuals which are known to have the defined cancer types and/or healthy individuals as a standard control group.
• step (e) the type of cancer from the defined group is determined based on the comparative data from step (d).
• the type of cancer is determined from the comparative data based on a suitable algorithm.
• the algorithm can be selected and adjusted by known methods or as described in detail in the experimental part of this specification.
• the algorithm can be segmented into three modules, which are (i) calculation of a correlation matrix for all variables of each cancer type in contrast to the healthy control group; (ii) ranking all correlation based on their correlation coefficient; (iii) saving the 12 top-ranked variables of each cancer type; (iv) scaling the variable if it’s not a Boolean variable; (v) performing a linear regression with the transformed variables to predict the cancer type and ranking their importance and computing a confusion matrix; (vi) optimization of the classification by computing a score of all possible combinations of the 12 variables to avoid the inclusion of variables only displaying covariances; (vii) predicting the cancer type with the reduced amount of variables analog to step (v).
• top-ranked variables instead of 12 top-ranked variables, it is also possible to select less or more top-ranked variables, such as 8 to 15.
• the most relevant information is if the sample has the AR H875Y mutation or not.
• the samples are initially split into two groups which either have or have not the AR H875Y mutation or not.
• Subject of the invention is also a method for treating cancer, comprising determining in a method of the invention as described above, which type of cancer an individual has, and
• step (f) providing a therapeutic treatment for the individual, which is effective against the type of cancer identified in step (e).
• Subject of the invention is also a method for identifying a therapeutic treatment against cancer for an individual, which comprises determining which type of cancer the individual has by the method as described above, and subsequent identification of the treatment against the type of cancer.
• the therapeutic treatment can be any treatment known in the art for the specific type of cancer, such as provision of an active agent and/or physical treatment, such as radiation or surgical treatment.
• Subject of the invention is also a method for providing information for use in determining which type of cancer an individual has and/or if the individual has cancer or not, comprising (a) providing a sample from the individual, which is a body liquid or more fraction thereof,
• (b1) at least 2 markers selected from APC (COSM 18561), hsa_miR_133a_3p, hsa_miR_148b_3p, hsa_miR_29c_3p, hsa_miR_20a_5p, hsa_miR_92a_3p, hsa_miR_155_5p, hsa_miR_195_5p, hsa_miR_101_3p, hsa_miR_27a_3p, hsa_miR_26a_5p and hsa_miR_21_5p; and
• step (d) comparing the levels to a known standard, and optionally storing the information obtained in step (d) on a storage device.
• This method can provide information in step (d), which can be used later and/or at a different location for determination which type of cancer an individual has.
• the information can be stored and/or transmitted to a user, such as a medical professional.
• the storage device can be digital device, such as a computer or a cloud solution.
• the inventive method solves the problem underlying the invention.
• the method is based on a specific novel combination of markers.
• the markers are generally not those considered most relevant in the art for a single specific type of cancer.
• the specific groups of markers used can distinguish between multiple cancer types.
• the miRNA and DNA methylation can be especially relevant for distinguishing the types of cancer. This was unexpected, because it is generally assumed that SNV are predominantly significant for identifying a specific cancer type.
• the inventive method can be carried out with a low number of markers and provides highly significant information. It is advantageous that it can also be used for determining if an individual is healthy or not. Since all markers can be examined by the same method, especially quantitative PCR, the method can be simple, convenient and cost-efficient.
• the correlation coefficient of each biomarker is plotted on the y axis (“abs(sorted)”).
• An index value (“index”) is assigned to each biomarker according to the value of its correlation coefficient (ranked from highest to lowest value) and plotted on the x axis.
• the 15 biomarkers with the highest correlation coefficients are displayed in the legends of the plots for each cancer type A- 1.
• cfmiRNA, SNVs in cfDNA, and DNA methylation in cfDNA biomarkers can be combined to predict an early cancer setting in a specific tissue under conditions that preserve high specificity for bladder, brain, breast, colorectal, lung, ovarian, prostate, stomach, and pancreatic cancers.
• pan-cancer panel comprising 6 miRNA expression and 2 DNA methylation profiles implicated across 9 tumor tissue types.
• the samples were liquid biopsy samples. The following patient information was documented: Age, weight, height, gender, current infectious diseases, AJCC stage (7th edition), and their family cancer history.
• Blood was sampled using K2-EDTA blood collection tubes. The blood underwent double centrifugation in the first two hours after the blood draw. First, at 2,000 x g for 10 min at 4°C and the resulting plasma was then transferred to a new tube and centrifuged at 16,000 x g for 10 min at 4°C. The cell-free plasma was then stored at -80°C until shipping on dry ice.
• Nucleic acid purification cfDNA was purified from 4 ml plasma on a KingfisherTM Duo Prime purification system using the MagMAXTM Cell-Free DNA Isolation Kit (company ThermoFisher, US). The elution volume for the cfDNA was 80 pl. The DNA concentration was measured with the QubitTM dsDNA HS Assay Kit on a QubitTM 4 fluorometers (ThermoFisher). Total RNA was purified from 100 pl plasma on a KingfisherTM Duo Prime purification system using the MagMAXTM mirVanaTM Total RNA Isolation Kit (ThermoFisher). As a spike-in, C. elegans miRNA 39 was added to the lysis buffer at a concentration of 15 fmol/sample. The elution volume for the total RNA was 50 pl.
• Taq-man PCR assays were designed to cover SNVs playing a significant role in different cancerous diseases.
• the maximal Ct difference between the SNV and corresponding wild-type reference assay is set to 9, reflecting a minimal mutant allele fraction (MAF) of 0.2%. This allows us to detect 0.2 % of mutant alleles in the presence of 99.8 % wild-type alleles.
• BL-WGA Blunt End Ligation-Mediated Whole Genome Amplification
• TaqManTM Mutation Detection Assays are powered by competitive allele-specific TaqManTM PCR (castPCRTM Technology) to detect and measure somatic mutations in genes associated with cancer research.
• the castPCRTM technology is highly specific and sensitive and can detect rare amounts of mutated DNA in a sample that contains large amounts of normal, wild-type DNA.
• PCR utilizes an allele-specific primer for mutant allele detection that competes with an MGB blocker oligonucleotide to suppress the wildtype background. These assays can detect down to 0.1% mutation in a background of wild type DNA.
• the PCR conditions comprised an initial denaturation step of 10 minutes at 95 °C, followed by 5 cycles of 15 sec denaturation at 92 °C and one minute extension at 58 °C. This was followed by 40 cycles of 15 sec denaturation at 92 °C and one minute extension at 60 °C.
• Real-time data were collected during the last 40 cycles of amplification and analyzed using the Mutation DetectorTM software v.2.0 (ThermoFisher). Briefly, the abundance of a SNV is computed by
• TaqMan® Advanced miRNA cDNA Synthesis Kit (ThermoFisher) was used to reverse transcribe miRNAs from 2 pl of the eluted total RNA. We did not change the protocol of the manufacturer. Briefly, 1 :10 diluted pre-amplified cDNA template was mixed with TaqMan® Fast Advanced Master Mix (2X) and nuclease-free water to a final volume of 528 pl. The mixture was dispensed to 48 wells of a pre-spotted 96-well TaqMan Advanced miRNA PCR plate. The reaction volume in each well was 10 pl. The plate was analyzed on a QS3 Real-time PCR system (all ThermoFisher).
• the fast PCR conditions comprised an initial denaturation step of 20 seconds at 95 °C. This was followed by 40 cycles of 1 sec denaturation at 95 °C and 20 sec annealing/extension at 60 °C. Real-time data were collected at the end of each annealing/extension step and analyzed using the relative quantification app, which is part of the ThermoFisher cloud (ThermoFisher).
• the PCR conditions comprised an initial denaturation step of 2 minutes at 95 °C. This was followed by 40 cycles of 3 sec denaturation at 95 °C and 30 sec annealing/extension at 60 °C. Real-time data were collected at the end of each annealing/extension step. A melt curve analysis was performed for quality control. The amount of each region of interest in the depleted and enriched fraction was analyzed with the Quantstudio v 1.4.3 software (ThermoFisher). The methylation of each region of interest was calculated with the following formula:
• SNV marker pool For the initial SNV marker pool, we used the Catalog of Somatic Mutations in Cancer (COSMIC) database to find SNVs that occur in 95 % of each cancer type (bladder, brain, breast, colorectal, lung, ovarian, prostate, stomach, and pancreatic cancer). This list consisting of 203 SNVs in 47 genes, was then reduced to SNVs that are more common for one or more cancer types to fit onto a 96 well PCR plate, including a reference assay for each gene. The final SNV list consists of 75 mutational assays and 21 reference assays. This list was further reduced to include assays that detected at least one positive case in our study population (Fig. 1a, 1b).
• COSMIC Catalog of Somatic Mutations in Cancer
• PCR amplicon is shorter than 120 bp and that the primers are specific for the selected region of interest, which was tested by aligning them against the whole genome using Primer-BLAST (NCBI).
• NCBI Primer-BLAST
• PCR assays for 26 regions in 21 genes were found to meet these criteria and were tested by a SYBR Green qPCR reaction with melt curve analysis.
• the PCR product was also checked on a 2% agarose gel for length confirmation and possible PCR byproduct determination. 12 assays covering CpG rich regions in 12 genes were selected for the study (Fig. 3).
• a subset of features for each cancer type is selected to avoid random associations between features and cancer types.
• the algorithm is segmented into three modules:
• a correlation matrix is calculated for all variables of each cancer type in contrast to the healthy control group to measure the strength of association of each variable with each cancer type. Relevant variables are selected based on significant correlation and high correlation coefficients. Further, some metric variables were dichotomized upon an automatically defined threshold value and used for cancer type classification.
• the correlation data is then used for the classification module.
• the Rpart tree classification performs the classification itself. Modifications made to the default values of the packages:
• This classification by the algorithm gives each variable a score. The higher a score is, the more it is associated with a particular cancer type or cancer itself.
• Each model's predictive accuracy is determined by calculating a confusion matrix, out-of-bag (OOB) estimate of error rate, Mcnemar's Test, and/or Cohen's kappa coefficient.
• OOB out-of-bag
• Mcnemar's Test Mcnemar's Test
• Cohen's kappa coefficient measures the agreement between two raters who classify N items into C mutually exclusive categories. Kappas over 0.75 are regarded as excellent, 0.40 to 0.75 as fair to good, and below 0.40 as inferior (Fleiss, 1973). Kappa values range from 0 (observed allocation is random) to 1 (perfect agreement between prediction and reference).
• the correlation plots for each cancer type are shown in Fig. 5a and 5b.
• the correlation coefficient of each biomarker is plotted on the y axis.
• An index value is assigned to each biomarker according to the value of its correlation coefficient (ranked from highest to lowest value) and plotted on the x axis.
• the 15 biomarkers with the highest correlation coefficients are displayed in the legends of the plots for each cancer type.
• the twelve variables showing the highest scores for each tumor were taken for further optimization. All combinations of these 12 variables were tested on their importance to predict a particular tumor type. This step eliminates variables with redundant information. The exclusion of certain variables has not worsened the predictive power for a particular tumor type.
• Performance metrics can be defined based, e.g., on the confusion matrix such that maximal sensitivity is achieved while allowing a moderate specificity, i.e., false positives can be defined to decrease performance less than false negatives, or false positives can be allowed while strictly limiting the allowed relative number of false negatives.
• the variables/features used for the classification of all cancer types are engineered by varying the number of the features used for each cancer type starting from the most correlated ones, by leaving out (superfluous) features that yield no improvement concerning the performance measure, and by choosing features that are especially useful to discern between cancer types even if their correlations are lower.
• An additional design goal is to limit the total number of features necessary to achieve the desired performance by identifying the most meaningful ones concerning the performance metric, as outlined above. Sought results are statements of the form that a particular tumor type/stage is likely present, that another tumor type/stage is present with lower probability, or that no tumor is present.
• the caret package (short for Classification And REgression Training) is a set of functions that attempt to streamline the process for creating predictive models.
• the package contains tools for data splitting, pre-processing, feature selection, model tuning using resampling, variable importance estimation, as well as other functionality.
• Corrplot A graphical display of a correlation matrix or general matrix. It also contains some algorithms to do matrix reordering.
• Hmisc Contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, importing and annotating datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of R objects to LaTeX and html code, and recoding variables.
• Rpart The rpart code builds classification or regression models of a very general structure using a two-stage procedure; the resulting models can be represented as binary trees.
• the algorithm of the pan-cancer analysis classifies a sample as cancerous or healthy.
• the data is based on 205 cancer patient analysis and 15 healthy cancer-free subjects.
• a group of eight markers is included in the analysis.
• the algorithm is based on a tree model in R, and at each node of a 17-node tree, different thresholds for each marker are applied to split the data into the two bins, cancerous and healthy.
• GATA5_meth GATA5_meth, APC_meth, hsa_miR_126_3p, hsa_miR_143_3p, hsa_miR_148b_3p, hsa_miR_155_5p, hsa_miR_205_5p, hsa_miR_22_3p.
• the algorithm could correctly allocate 96 % of the cancerous cases. Due to the relatively small healthy subject group, the correct allocation of healthy subjects is 60%. When increasing the healthy subject group, it can be expected that the algorithm will enable to classify more healthy subjects as healthy and that the false-negative and false-positive rate will diminish.
• AR H875Y AR_CT_Y_N
• TP53 COSM10758
• hsa_miR_155_5p TP53 (COSM 10758), hsa_miR_92a_3p, hsa_miR_17_5p
• Table 1 Markers used for determining cancer type These markers used can be grouped into three categories as highly relevant, relevant and complementary. Based on the categories and the present study and disclosure, the skilled person can select markers suitable for diagnosing cancer of defined type and patient group. Overall, the relevance of the markers for determining the type of cancer in the specific group can be summarized as follows:
• APC (COSM 18561), hsa_miR_133a_3p, hsa_miR_148b_3p, hsa_miR_29c_3p, hsa_miR_20a_5p, hsa_miR_92a_3p, hsa_miR_155_5p, hsa_miR_195_5p, hsa_miR_101_3p, hsa_miR_27a_3p, hsa_miR_26a_5p, hsa_miR_21_5p.
• AR H875Y was among the most important markers for determining which type of cancer a subject has.
• the distributions of AR H875Y in the current study for a specific cancer type are listed in Table 2.
• the analysis of plasma from 205 cancer patients consisted of 75 mutational assays.
• the assay covering the AR mutation showed that this mutation was present in 102 cancer patients.
• the occurrence of the AR mutation in liquid biopsies of patients who have cancer other than prostate cancer is much higher than reported in the scientific literature. It was found that the AR H875Y is highly suitable for diagnosing bladder, colorectal and breast cancers.
• This inventive method based on a selection of markers which may comprise 3 SNVs, 13 miRNA expression, and 4 DNA methylations from a liquid biopsy, enables a non- invasive diagnosis of nine different solid cancer types.
• the cancer type specific methylation levels for 3 highly relevant cfDNA markers are shown in Fig. 6.
• the cancer type specific expression levels for 11 highly relevant miRNA markers are shown in Fig. 7a and 7b.
• these markers were mostly not considered highly relevant for a specific cancer type. According to the present invention, it was surprisingly found that these markers may not be the most relevant markers for a specific, single cancer type, but can be highly relevant for discriminating between a group of cancers and/or for determining with high accuracy if an individual has cancer or not.
• the SNV detection, miRNA expression, and DNA methylation analysis is based on a quantitative PCR method.
• Methods in the prior art generally focus on just one cancer type, one analyte, and use next-generation sequencing methods for SNV detection, miRNA expression, or DNA methylation analysis.
• qPCR based methods have been found to be more sensitive in the quantification of SNVs and miRNA expression compared to sequencing-based methods (except for ultra-deep sequencing).
• the AR+ group consisted only of tumor samples, since no AR mutation was detected in the control group.
• several discriminant function analyses with a leave-one-out cross-validation were carried out including different sets of biomarkers, not including AR mutation.
• discriminant analysis 1 incorporated all measured targets
• discriminant analysis 2 DA2 only cfDNA mutations
• discriminant analysis 3 DA3 only cfDNA methylation
• discriminant analysis 4 DA4 only miRNAs
• discriminant analysis 5 included the biomarkers with highest correlations identified through the correlation matrixes (Table 3).
• Table 3 Discriminant analysis for classification of AR- samples in healthy and tumor samples The results show that the DA5 model yielded the best results and classified healthy and tumor samples with 95.4% accuracy, 97.9% sensitivity, 80% specificity, and receiver operating characteristic area under the curve (ROC AUC) of 0.884.
• the AR H875Y mutation plays a key role in this model. Androgen receptor alterations have been identified as some of the main drivers of castration-resistant prostate cancer. The AR H875Y mutation has been predominantly found in prostate cancer, but this mutation has been reported also for breast cancer and CRC. However, to our knowledge, this is the first time that AR H875Y mutation is reported for bladder, lung, stomach, ovarian, brain, and pancreas cancer. AR mutations have been predominantly studied in connection to prostate and breast cancer, especially treatment response. We analyzed all predefined targets in all samples, and not only the genes reported to be relevant in the specific cancer type. Besides, we used a qPCR-based method for the detection of cfDNA mutations which is shown to have a better sensitivity to detect low allele fraction variants than sequencing.
• CNAs Circulating nucleic acids

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