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    Broad Institute Inc tcga human methylation 450k array
    a To illustrate the feature ranking methods, consider a hypothetical differential consisting of five cancer types (T1 through T5). The X vs. all approach seeks to identify the markers that distinguish each tumor type from all other tumor types. In this illustration, the corresponding analyses result in five feature rank lists that prioritize probes for training X vs. all classifiers. The pairwise differential approach aims to identify the best markers for differentiating each possible tumor pair (e.g., T1 vs. T2). These analyses result in ten prioritized lists, which are used in the training of pairwise classifiers. One disadvantage of the pairwise approach is that the numbers of analyses increase dramatically with numbers of cancer classes (i.e., n = # cancer classes, # analyses = n(n - 1)/2). Hybrid classifiers start with X vs. all lists, and depending on classifier performance, specific pairwise lists corresponding to difficult differentials (red; in this example, T3 vs. T4) are subsequently added—ideally, striking a balance between minimalism and accuracy. The ranked lists can also be combined in various other combinations, depending on the specific differential being considered; Table 1 shows examples of various minimalist classifiers in this study, all developed based on highly-ranked probes from <t>TCGA</t> analyses. b Example of a highly informative CpG marker (cg24727122, OSM, chr22: 30662972) from the X vs. all analysis that accurately separates acute myeloid leukemias (n = 135, red circle) from the other 27 TCGA cancer types (n = 5827; AUC = 1.00). c Heat map for the AUCs of the top-ranked CpG biomarker identified in the 378 pairwise differential analyses; the top AUCs were above 0.98 for most pairwise differentials in TCGA training cases (see Supplementary Table 8 for the numerical values). ACC adrenocortical carcinoma, BLCA bladder carcinoma, BRCA breast invasive carcinoma, CESC cervical and endocervical cancers, CHOL cholangiocarcinoma, CORE colorectal adenocarcinoma, DLBC diffuse large B-cell lymphoma, ESCC esophageal squamous cell carcinoma, GBMLGG glioma (glioblastoma and low-grade glioma), GEAD gastric and esophageal carcinoma, HNSC head and neck squamous cell carcinoma, KIPAN pan-kidney cohort (clear cell, chromophobe, and papillary renal cell carcinoma), LAML acute myeloid leukemia, LIHC liver hepatocellular carcinoma, LUAD lung adenocarcinoma, LUSC lung squamous cell carcinoma, MESO mesothelioma, PAAD pancreatic adenocarcinoma, PCPG pheochromocytoma and paraganglioma, PRAD prostate adenocarcinoma, SARC sarcoma, SKCM skin cutaneous melanoma, TGCT testicular germ cell tumor, THCA thyroid carcinoma, THYM thymoma, UCEC uterine corpus endometrial carcinoma, UCS uterine carcinosarcoma, UVM uveal melanoma.
    Tcga Human Methylation 450k Array, supplied by Broad Institute Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    1) Product Images from "Minimalist approaches to cancer tissue-of-origin classification by DNA methylation"

    Article Title: Minimalist approaches to cancer tissue-of-origin classification by DNA methylation

    Journal: Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc

    doi: 10.1038/s41379-020-0547-7

    a To illustrate the feature ranking methods, consider a hypothetical differential consisting of five cancer types (T1 through T5). The X vs. all approach seeks to identify the markers that distinguish each tumor type from all other tumor types. In this illustration, the corresponding analyses result in five feature rank lists that prioritize probes for training X vs. all classifiers. The pairwise differential approach aims to identify the best markers for differentiating each possible tumor pair (e.g., T1 vs. T2). These analyses result in ten prioritized lists, which are used in the training of pairwise classifiers. One disadvantage of the pairwise approach is that the numbers of analyses increase dramatically with numbers of cancer classes (i.e., n = # cancer classes, # analyses = n(n - 1)/2). Hybrid classifiers start with X vs. all lists, and depending on classifier performance, specific pairwise lists corresponding to difficult differentials (red; in this example, T3 vs. T4) are subsequently added—ideally, striking a balance between minimalism and accuracy. The ranked lists can also be combined in various other combinations, depending on the specific differential being considered; Table 1 shows examples of various minimalist classifiers in this study, all developed based on highly-ranked probes from TCGA analyses. b Example of a highly informative CpG marker (cg24727122, OSM, chr22: 30662972) from the X vs. all analysis that accurately separates acute myeloid leukemias (n = 135, red circle) from the other 27 TCGA cancer types (n = 5827; AUC = 1.00). c Heat map for the AUCs of the top-ranked CpG biomarker identified in the 378 pairwise differential analyses; the top AUCs were above 0.98 for most pairwise differentials in TCGA training cases (see Supplementary Table 8 for the numerical values). ACC adrenocortical carcinoma, BLCA bladder carcinoma, BRCA breast invasive carcinoma, CESC cervical and endocervical cancers, CHOL cholangiocarcinoma, CORE colorectal adenocarcinoma, DLBC diffuse large B-cell lymphoma, ESCC esophageal squamous cell carcinoma, GBMLGG glioma (glioblastoma and low-grade glioma), GEAD gastric and esophageal carcinoma, HNSC head and neck squamous cell carcinoma, KIPAN pan-kidney cohort (clear cell, chromophobe, and papillary renal cell carcinoma), LAML acute myeloid leukemia, LIHC liver hepatocellular carcinoma, LUAD lung adenocarcinoma, LUSC lung squamous cell carcinoma, MESO mesothelioma, PAAD pancreatic adenocarcinoma, PCPG pheochromocytoma and paraganglioma, PRAD prostate adenocarcinoma, SARC sarcoma, SKCM skin cutaneous melanoma, TGCT testicular germ cell tumor, THCA thyroid carcinoma, THYM thymoma, UCEC uterine corpus endometrial carcinoma, UCS uterine carcinosarcoma, UVM uveal melanoma.
    Figure Legend Snippet: a To illustrate the feature ranking methods, consider a hypothetical differential consisting of five cancer types (T1 through T5). The X vs. all approach seeks to identify the markers that distinguish each tumor type from all other tumor types. In this illustration, the corresponding analyses result in five feature rank lists that prioritize probes for training X vs. all classifiers. The pairwise differential approach aims to identify the best markers for differentiating each possible tumor pair (e.g., T1 vs. T2). These analyses result in ten prioritized lists, which are used in the training of pairwise classifiers. One disadvantage of the pairwise approach is that the numbers of analyses increase dramatically with numbers of cancer classes (i.e., n = # cancer classes, # analyses = n(n - 1)/2). Hybrid classifiers start with X vs. all lists, and depending on classifier performance, specific pairwise lists corresponding to difficult differentials (red; in this example, T3 vs. T4) are subsequently added—ideally, striking a balance between minimalism and accuracy. The ranked lists can also be combined in various other combinations, depending on the specific differential being considered; Table 1 shows examples of various minimalist classifiers in this study, all developed based on highly-ranked probes from TCGA analyses. b Example of a highly informative CpG marker (cg24727122, OSM, chr22: 30662972) from the X vs. all analysis that accurately separates acute myeloid leukemias (n = 135, red circle) from the other 27 TCGA cancer types (n = 5827; AUC = 1.00). c Heat map for the AUCs of the top-ranked CpG biomarker identified in the 378 pairwise differential analyses; the top AUCs were above 0.98 for most pairwise differentials in TCGA training cases (see Supplementary Table 8 for the numerical values). ACC adrenocortical carcinoma, BLCA bladder carcinoma, BRCA breast invasive carcinoma, CESC cervical and endocervical cancers, CHOL cholangiocarcinoma, CORE colorectal adenocarcinoma, DLBC diffuse large B-cell lymphoma, ESCC esophageal squamous cell carcinoma, GBMLGG glioma (glioblastoma and low-grade glioma), GEAD gastric and esophageal carcinoma, HNSC head and neck squamous cell carcinoma, KIPAN pan-kidney cohort (clear cell, chromophobe, and papillary renal cell carcinoma), LAML acute myeloid leukemia, LIHC liver hepatocellular carcinoma, LUAD lung adenocarcinoma, LUSC lung squamous cell carcinoma, MESO mesothelioma, PAAD pancreatic adenocarcinoma, PCPG pheochromocytoma and paraganglioma, PRAD prostate adenocarcinoma, SARC sarcoma, SKCM skin cutaneous melanoma, TGCT testicular germ cell tumor, THCA thyroid carcinoma, THYM thymoma, UCEC uterine corpus endometrial carcinoma, UCS uterine carcinosarcoma, UVM uveal melanoma.

    Techniques Used: Marker, Biomarker Discovery

    a t-distributed stochastic neighbor embedding (t-SNE) plot for the smallest hybrid model based on information from 53 unique CpG sites shows excellent separation of cancer classes. b Heat map for the confusion matrix for the smallest hybrid model; see Supplementary Table 12 for the numbers of cases in each cell. c The relationship between classifier confidence and accuracy: the numbers of cases/percentages of validation set/accuracies for the high, moderate, and low confidence groups are 994 cases/39% of validation cases/100% accuracy, 1147/45% of cases/98% accuracy, and 434/17% of cases/73% accuracy, respectively. d Correctly classified cases have statistically higher tumor purities compared with incorrectly classified cases (Wilcoxon test p value = 2.8 × 10−4), although the difference in the distributions is modest. e Density scatter plot showing a direct correlation between purity and prediction confidence (Spearman rho = 0.19); many TCGA cases have fairly high purities (>50%) and many have high confidence predictions. Conceivably, these 53 probes could be quantitatively evaluated via next-generation sequencing. ACC adrenocortical carcinoma, BLCA bladder carcinoma, BRCA breast invasive carcinoma, CESC cervical and endocervical cancers, CHOL cholangiocarcinoma, CORE colorectal adenocarcinoma, DLBC diffuse large B-cell lymphoma, ESCC esophageal squamous cell carcinoma, GBMLGG glioma (glioblastoma and low-grade glioma), GEAD gastric and esophageal carcinoma, HNSC head and neck squamous cell carcinoma, KIPAN pan-kidney cohort (clear cell, chromophobe, and papillary renal cell carcinoma), LAML acute myeloid leukemia, LIHC liver hepatocellular carcinoma, LUAD lung adenocarcinoma, LUSC lung squamous cell carcinoma, MESO mesothelioma, PAAD pancreatic adenocarcinoma, PCPG pheochromocytoma and paraganglioma, PRAD prostate adenocarcinoma, SARC sarcoma, SKCM skin cutaneous melanoma, TGCT testicular germ cell tumor, THCA thyroid carcinoma, THYM thymoma, UCEC uterine corpus endometrial carcinoma, UCS uterine carcinosarcoma, UVM uveal melanoma.
    Figure Legend Snippet: a t-distributed stochastic neighbor embedding (t-SNE) plot for the smallest hybrid model based on information from 53 unique CpG sites shows excellent separation of cancer classes. b Heat map for the confusion matrix for the smallest hybrid model; see Supplementary Table 12 for the numbers of cases in each cell. c The relationship between classifier confidence and accuracy: the numbers of cases/percentages of validation set/accuracies for the high, moderate, and low confidence groups are 994 cases/39% of validation cases/100% accuracy, 1147/45% of cases/98% accuracy, and 434/17% of cases/73% accuracy, respectively. d Correctly classified cases have statistically higher tumor purities compared with incorrectly classified cases (Wilcoxon test p value = 2.8 × 10−4), although the difference in the distributions is modest. e Density scatter plot showing a direct correlation between purity and prediction confidence (Spearman rho = 0.19); many TCGA cases have fairly high purities (>50%) and many have high confidence predictions. Conceivably, these 53 probes could be quantitatively evaluated via next-generation sequencing. ACC adrenocortical carcinoma, BLCA bladder carcinoma, BRCA breast invasive carcinoma, CESC cervical and endocervical cancers, CHOL cholangiocarcinoma, CORE colorectal adenocarcinoma, DLBC diffuse large B-cell lymphoma, ESCC esophageal squamous cell carcinoma, GBMLGG glioma (glioblastoma and low-grade glioma), GEAD gastric and esophageal carcinoma, HNSC head and neck squamous cell carcinoma, KIPAN pan-kidney cohort (clear cell, chromophobe, and papillary renal cell carcinoma), LAML acute myeloid leukemia, LIHC liver hepatocellular carcinoma, LUAD lung adenocarcinoma, LUSC lung squamous cell carcinoma, MESO mesothelioma, PAAD pancreatic adenocarcinoma, PCPG pheochromocytoma and paraganglioma, PRAD prostate adenocarcinoma, SARC sarcoma, SKCM skin cutaneous melanoma, TGCT testicular germ cell tumor, THCA thyroid carcinoma, THYM thymoma, UCEC uterine corpus endometrial carcinoma, UCS uterine carcinosarcoma, UVM uveal melanoma.

    Techniques Used: Biomarker Discovery, Next-Generation Sequencing

    For FFPE primary cases (a, b; n=339, 12 cancer types), the model trained on TCGA fresh primary data (a) was far less accurate (74.6%vs. 97.6%, left panels) and less confident (right panels) on independent validation of FFPE primary cancers than the model trained on combined TCGA and randomly selected FFPE primary cases (b). Since the two models used the same 55 probes, a, b suggest that probes identified via TCGA analyses are robust—although classifiers may need to be retrained on new data. For FFPE brain metastasis (c, d; n=45, including three CUPs, four cancer types): c) t-SNE plot shows that FFPE brain metastasis cases (Ys, n=42) are well-separated, and cluster with their fresh primary counterparts (dots); CUPs are indicated by black open circles. d The heat map/confusion matrix for the classifier trained on FFPE brain metastases, validated on the 39 FFPE brain metastases of known origins (six unique probes, 95.2% accuracy); CUPs are not shown here. e A single probe (cg22280705) accurately separated all nine lymph node metastases and 98.5% of 32 melanoma and 235 breast carcinoma primaries on validation. BLCA bladder carcinoma, BRCA breast invasive carcinoma, CORE colorectal adenocarcinoma, DLBC diffuse large B-cell lymphoma, GBMLGG glioma (glioblastoma and low-grade glioma), GCT germ cell tumor (intracranial), HNSC head and neck squamous cell carcinoma, KIPAN pan-kidney cohort (clear cell, chromophobe, and papillary renal cell carcinoma), LUAD lung adenocarcinoma, LUSC lung squamous cell carcinoma, PRAD prostate adenocarcinoma, SKCM skin cutaneous melanoma, UCEC uterine corpus endometrial carcinoma.
    Figure Legend Snippet: For FFPE primary cases (a, b; n=339, 12 cancer types), the model trained on TCGA fresh primary data (a) was far less accurate (74.6%vs. 97.6%, left panels) and less confident (right panels) on independent validation of FFPE primary cancers than the model trained on combined TCGA and randomly selected FFPE primary cases (b). Since the two models used the same 55 probes, a, b suggest that probes identified via TCGA analyses are robust—although classifiers may need to be retrained on new data. For FFPE brain metastasis (c, d; n=45, including three CUPs, four cancer types): c) t-SNE plot shows that FFPE brain metastasis cases (Ys, n=42) are well-separated, and cluster with their fresh primary counterparts (dots); CUPs are indicated by black open circles. d The heat map/confusion matrix for the classifier trained on FFPE brain metastases, validated on the 39 FFPE brain metastases of known origins (six unique probes, 95.2% accuracy); CUPs are not shown here. e A single probe (cg22280705) accurately separated all nine lymph node metastases and 98.5% of 32 melanoma and 235 breast carcinoma primaries on validation. BLCA bladder carcinoma, BRCA breast invasive carcinoma, CORE colorectal adenocarcinoma, DLBC diffuse large B-cell lymphoma, GBMLGG glioma (glioblastoma and low-grade glioma), GCT germ cell tumor (intracranial), HNSC head and neck squamous cell carcinoma, KIPAN pan-kidney cohort (clear cell, chromophobe, and papillary renal cell carcinoma), LUAD lung adenocarcinoma, LUSC lung squamous cell carcinoma, PRAD prostate adenocarcinoma, SKCM skin cutaneous melanoma, UCEC uterine corpus endometrial carcinoma.

    Techniques Used: Biomarker Discovery

    The distributions of methylation values for pancreatic and gastric cancers are similar in ICGC/GEO and TCGA validation datasets. Conceivably, analysis of single CpG sites can be carried out via quantitative PCR. ICGC International Cancer Genome Consortium, GEAD gastric/esophageal adenocarcinoma, GEO Gene Expression Omnibus, PAAD pancreatic adenocarcinoma, STAD gastric adenocarcinoma.
    Figure Legend Snippet: The distributions of methylation values for pancreatic and gastric cancers are similar in ICGC/GEO and TCGA validation datasets. Conceivably, analysis of single CpG sites can be carried out via quantitative PCR. ICGC International Cancer Genome Consortium, GEAD gastric/esophageal adenocarcinoma, GEO Gene Expression Omnibus, PAAD pancreatic adenocarcinoma, STAD gastric adenocarcinoma.

    Techniques Used: Methylation, Biomarker Discovery, Real-time Polymerase Chain Reaction, Gene Expression

    We trained a classifier using data from TCGA and FFPE methylation array cases. This classifier was tested on 15 institutional FFPE primary cases, for which both methyl-seq (triangles) and EPIC array (circles) data were available (top panel). The reference cancer type is color coded in the sample name labels on the X-axis and the predictions of the classifier are color coded (by cancer type) and plotted with respect to their levels of confidence. The classifier correctly predicted 11 out of 15 diagnoses (73.3%; of which, 10 of 15 had medium or high levels of confidence) based on methyl-seq data, and 14 out of 15 diagnoses (93.3%) based on array data. Below each sample, the correlations between methylation levels across the 24 probes for the two platforms are shown (bottom panel). BLCA bladder carcinoma, CESC cervical and endocervical cancers, CORE colorectal adenocarcinoma, LUAD lung adenocarcinoma, SKCM skin cutaneous melanoma, UCEC uterine corpus endometrial carcinoma.
    Figure Legend Snippet: We trained a classifier using data from TCGA and FFPE methylation array cases. This classifier was tested on 15 institutional FFPE primary cases, for which both methyl-seq (triangles) and EPIC array (circles) data were available (top panel). The reference cancer type is color coded in the sample name labels on the X-axis and the predictions of the classifier are color coded (by cancer type) and plotted with respect to their levels of confidence. The classifier correctly predicted 11 out of 15 diagnoses (73.3%; of which, 10 of 15 had medium or high levels of confidence) based on methyl-seq data, and 14 out of 15 diagnoses (93.3%) based on array data. Below each sample, the correlations between methylation levels across the 24 probes for the two platforms are shown (bottom panel). BLCA bladder carcinoma, CESC cervical and endocervical cancers, CORE colorectal adenocarcinoma, LUAD lung adenocarcinoma, SKCM skin cutaneous melanoma, UCEC uterine corpus endometrial carcinoma.

    Techniques Used: Methylation

    Comparison of methylation platforms for research and clinical practice.
    Figure Legend Snippet: Comparison of methylation platforms for research and clinical practice.

    Techniques Used: Comparison, Methylation, Mutagenesis, DNA Methylation Assay, Sequencing, Genome Wide, Biomarker Discovery

    Related Articles

    DNA Methylation Assay:

    Article Title: Minimalist approaches to cancer tissue-of-origin classification by DNA methylation
    Article Snippet: .. Gathering of publicly-available DNA methylation array data TCGA Human Methylation 450K array data from fresh primary cases were downloaded from the Broad Institute GDAC website ( https://gdac.broadinstitute.org ). ..

    Article Title: Minimalist approaches to cancer tissue-of-origin classification by DNA methylation.
    Article Snippet: .. Gathering of publicly-available DNA methylation array data TCGA Human Methylation 450K array data from fresh primary cases were downloaded from the Broad Institute GDAC website (https://gdac.broadinstitute.org). ..

    Methylation:

    Article Title: Minimalist approaches to cancer tissue-of-origin classification by DNA methylation
    Article Snippet: .. Gathering of publicly-available DNA methylation array data TCGA Human Methylation 450K array data from fresh primary cases were downloaded from the Broad Institute GDAC website ( https://gdac.broadinstitute.org ). ..

    Article Title: Minimalist approaches to cancer tissue-of-origin classification by DNA methylation
    Article Snippet: .. TCGA Human Methylation 450K array data from fresh primary cases were downloaded from the Broad Institute GDAC website ( https://gdac.broadinstitute.org ). ..

    Article Title: Minimalist approaches to cancer tissue-of-origin classification by DNA methylation.
    Article Snippet: .. Gathering of publicly-available DNA methylation array data TCGA Human Methylation 450K array data from fresh primary cases were downloaded from the Broad Institute GDAC website (https://gdac.broadinstitute.org). ..



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    tcga human methylation 450k array  (Broad Institute Inc)
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    Broad Institute Inc tcga human methylation 450k array
    a To illustrate the feature ranking methods, consider a hypothetical differential consisting of five cancer types (T1 through T5). The X vs. all approach seeks to identify the markers that distinguish each tumor type from all other tumor types. In this illustration, the corresponding analyses result in five feature rank lists that prioritize probes for training X vs. all classifiers. The pairwise differential approach aims to identify the best markers for differentiating each possible tumor pair (e.g., T1 vs. T2). These analyses result in ten prioritized lists, which are used in the training of pairwise classifiers. One disadvantage of the pairwise approach is that the numbers of analyses increase dramatically with numbers of cancer classes (i.e., n = # cancer classes, # analyses = n(n - 1)/2). Hybrid classifiers start with X vs. all lists, and depending on classifier performance, specific pairwise lists corresponding to difficult differentials (red; in this example, T3 vs. T4) are subsequently added—ideally, striking a balance between minimalism and accuracy. The ranked lists can also be combined in various other combinations, depending on the specific differential being considered; Table 1 shows examples of various minimalist classifiers in this study, all developed based on highly-ranked probes from <t>TCGA</t> analyses. b Example of a highly informative CpG marker (cg24727122, OSM, chr22: 30662972) from the X vs. all analysis that accurately separates acute myeloid leukemias (n = 135, red circle) from the other 27 TCGA cancer types (n = 5827; AUC = 1.00). c Heat map for the AUCs of the top-ranked CpG biomarker identified in the 378 pairwise differential analyses; the top AUCs were above 0.98 for most pairwise differentials in TCGA training cases (see Supplementary Table 8 for the numerical values). ACC adrenocortical carcinoma, BLCA bladder carcinoma, BRCA breast invasive carcinoma, CESC cervical and endocervical cancers, CHOL cholangiocarcinoma, CORE colorectal adenocarcinoma, DLBC diffuse large B-cell lymphoma, ESCC esophageal squamous cell carcinoma, GBMLGG glioma (glioblastoma and low-grade glioma), GEAD gastric and esophageal carcinoma, HNSC head and neck squamous cell carcinoma, KIPAN pan-kidney cohort (clear cell, chromophobe, and papillary renal cell carcinoma), LAML acute myeloid leukemia, LIHC liver hepatocellular carcinoma, LUAD lung adenocarcinoma, LUSC lung squamous cell carcinoma, MESO mesothelioma, PAAD pancreatic adenocarcinoma, PCPG pheochromocytoma and paraganglioma, PRAD prostate adenocarcinoma, SARC sarcoma, SKCM skin cutaneous melanoma, TGCT testicular germ cell tumor, THCA thyroid carcinoma, THYM thymoma, UCEC uterine corpus endometrial carcinoma, UCS uterine carcinosarcoma, UVM uveal melanoma.
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    tcga infinium human methylation 450k arrays  (INFINIUM Inc)
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    TSKU expression levels in different cancer types. ( A ) Elevated or decreased TSKU expression in data sets of different cancers compared with normal tissues in the Oncomine database. ( B ) TSKU mRNA levels in multiple tumor types from the <t>TCGA</t> database were analyzed by TIMER. (* P < 0.05, ** P <0.01, *** P < 0.001).
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    a To illustrate the feature ranking methods, consider a hypothetical differential consisting of five cancer types (T1 through T5). The X vs. all approach seeks to identify the markers that distinguish each tumor type from all other tumor types. In this illustration, the corresponding analyses result in five feature rank lists that prioritize probes for training X vs. all classifiers. The pairwise differential approach aims to identify the best markers for differentiating each possible tumor pair (e.g., T1 vs. T2). These analyses result in ten prioritized lists, which are used in the training of pairwise classifiers. One disadvantage of the pairwise approach is that the numbers of analyses increase dramatically with numbers of cancer classes (i.e., n = # cancer classes, # analyses = n(n - 1)/2). Hybrid classifiers start with X vs. all lists, and depending on classifier performance, specific pairwise lists corresponding to difficult differentials (red; in this example, T3 vs. T4) are subsequently added—ideally, striking a balance between minimalism and accuracy. The ranked lists can also be combined in various other combinations, depending on the specific differential being considered; Table 1 shows examples of various minimalist classifiers in this study, all developed based on highly-ranked probes from TCGA analyses. b Example of a highly informative CpG marker (cg24727122, OSM, chr22: 30662972) from the X vs. all analysis that accurately separates acute myeloid leukemias (n = 135, red circle) from the other 27 TCGA cancer types (n = 5827; AUC = 1.00). c Heat map for the AUCs of the top-ranked CpG biomarker identified in the 378 pairwise differential analyses; the top AUCs were above 0.98 for most pairwise differentials in TCGA training cases (see Supplementary Table 8 for the numerical values). ACC adrenocortical carcinoma, BLCA bladder carcinoma, BRCA breast invasive carcinoma, CESC cervical and endocervical cancers, CHOL cholangiocarcinoma, CORE colorectal adenocarcinoma, DLBC diffuse large B-cell lymphoma, ESCC esophageal squamous cell carcinoma, GBMLGG glioma (glioblastoma and low-grade glioma), GEAD gastric and esophageal carcinoma, HNSC head and neck squamous cell carcinoma, KIPAN pan-kidney cohort (clear cell, chromophobe, and papillary renal cell carcinoma), LAML acute myeloid leukemia, LIHC liver hepatocellular carcinoma, LUAD lung adenocarcinoma, LUSC lung squamous cell carcinoma, MESO mesothelioma, PAAD pancreatic adenocarcinoma, PCPG pheochromocytoma and paraganglioma, PRAD prostate adenocarcinoma, SARC sarcoma, SKCM skin cutaneous melanoma, TGCT testicular germ cell tumor, THCA thyroid carcinoma, THYM thymoma, UCEC uterine corpus endometrial carcinoma, UCS uterine carcinosarcoma, UVM uveal melanoma.

    Journal: Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc

    Article Title: Minimalist approaches to cancer tissue-of-origin classification by DNA methylation

    doi: 10.1038/s41379-020-0547-7

    Figure Lengend Snippet: a To illustrate the feature ranking methods, consider a hypothetical differential consisting of five cancer types (T1 through T5). The X vs. all approach seeks to identify the markers that distinguish each tumor type from all other tumor types. In this illustration, the corresponding analyses result in five feature rank lists that prioritize probes for training X vs. all classifiers. The pairwise differential approach aims to identify the best markers for differentiating each possible tumor pair (e.g., T1 vs. T2). These analyses result in ten prioritized lists, which are used in the training of pairwise classifiers. One disadvantage of the pairwise approach is that the numbers of analyses increase dramatically with numbers of cancer classes (i.e., n = # cancer classes, # analyses = n(n - 1)/2). Hybrid classifiers start with X vs. all lists, and depending on classifier performance, specific pairwise lists corresponding to difficult differentials (red; in this example, T3 vs. T4) are subsequently added—ideally, striking a balance between minimalism and accuracy. The ranked lists can also be combined in various other combinations, depending on the specific differential being considered; Table 1 shows examples of various minimalist classifiers in this study, all developed based on highly-ranked probes from TCGA analyses. b Example of a highly informative CpG marker (cg24727122, OSM, chr22: 30662972) from the X vs. all analysis that accurately separates acute myeloid leukemias (n = 135, red circle) from the other 27 TCGA cancer types (n = 5827; AUC = 1.00). c Heat map for the AUCs of the top-ranked CpG biomarker identified in the 378 pairwise differential analyses; the top AUCs were above 0.98 for most pairwise differentials in TCGA training cases (see Supplementary Table 8 for the numerical values). ACC adrenocortical carcinoma, BLCA bladder carcinoma, BRCA breast invasive carcinoma, CESC cervical and endocervical cancers, CHOL cholangiocarcinoma, CORE colorectal adenocarcinoma, DLBC diffuse large B-cell lymphoma, ESCC esophageal squamous cell carcinoma, GBMLGG glioma (glioblastoma and low-grade glioma), GEAD gastric and esophageal carcinoma, HNSC head and neck squamous cell carcinoma, KIPAN pan-kidney cohort (clear cell, chromophobe, and papillary renal cell carcinoma), LAML acute myeloid leukemia, LIHC liver hepatocellular carcinoma, LUAD lung adenocarcinoma, LUSC lung squamous cell carcinoma, MESO mesothelioma, PAAD pancreatic adenocarcinoma, PCPG pheochromocytoma and paraganglioma, PRAD prostate adenocarcinoma, SARC sarcoma, SKCM skin cutaneous melanoma, TGCT testicular germ cell tumor, THCA thyroid carcinoma, THYM thymoma, UCEC uterine corpus endometrial carcinoma, UCS uterine carcinosarcoma, UVM uveal melanoma.

    Article Snippet: Gathering of publicly-available DNA methylation array data TCGA Human Methylation 450K array data from fresh primary cases were downloaded from the Broad Institute GDAC website ( https://gdac.broadinstitute.org ).

    Techniques: Marker, Biomarker Discovery

    a t-distributed stochastic neighbor embedding (t-SNE) plot for the smallest hybrid model based on information from 53 unique CpG sites shows excellent separation of cancer classes. b Heat map for the confusion matrix for the smallest hybrid model; see Supplementary Table 12 for the numbers of cases in each cell. c The relationship between classifier confidence and accuracy: the numbers of cases/percentages of validation set/accuracies for the high, moderate, and low confidence groups are 994 cases/39% of validation cases/100% accuracy, 1147/45% of cases/98% accuracy, and 434/17% of cases/73% accuracy, respectively. d Correctly classified cases have statistically higher tumor purities compared with incorrectly classified cases (Wilcoxon test p value = 2.8 × 10−4), although the difference in the distributions is modest. e Density scatter plot showing a direct correlation between purity and prediction confidence (Spearman rho = 0.19); many TCGA cases have fairly high purities (>50%) and many have high confidence predictions. Conceivably, these 53 probes could be quantitatively evaluated via next-generation sequencing. ACC adrenocortical carcinoma, BLCA bladder carcinoma, BRCA breast invasive carcinoma, CESC cervical and endocervical cancers, CHOL cholangiocarcinoma, CORE colorectal adenocarcinoma, DLBC diffuse large B-cell lymphoma, ESCC esophageal squamous cell carcinoma, GBMLGG glioma (glioblastoma and low-grade glioma), GEAD gastric and esophageal carcinoma, HNSC head and neck squamous cell carcinoma, KIPAN pan-kidney cohort (clear cell, chromophobe, and papillary renal cell carcinoma), LAML acute myeloid leukemia, LIHC liver hepatocellular carcinoma, LUAD lung adenocarcinoma, LUSC lung squamous cell carcinoma, MESO mesothelioma, PAAD pancreatic adenocarcinoma, PCPG pheochromocytoma and paraganglioma, PRAD prostate adenocarcinoma, SARC sarcoma, SKCM skin cutaneous melanoma, TGCT testicular germ cell tumor, THCA thyroid carcinoma, THYM thymoma, UCEC uterine corpus endometrial carcinoma, UCS uterine carcinosarcoma, UVM uveal melanoma.

    Journal: Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc

    Article Title: Minimalist approaches to cancer tissue-of-origin classification by DNA methylation

    doi: 10.1038/s41379-020-0547-7

    Figure Lengend Snippet: a t-distributed stochastic neighbor embedding (t-SNE) plot for the smallest hybrid model based on information from 53 unique CpG sites shows excellent separation of cancer classes. b Heat map for the confusion matrix for the smallest hybrid model; see Supplementary Table 12 for the numbers of cases in each cell. c The relationship between classifier confidence and accuracy: the numbers of cases/percentages of validation set/accuracies for the high, moderate, and low confidence groups are 994 cases/39% of validation cases/100% accuracy, 1147/45% of cases/98% accuracy, and 434/17% of cases/73% accuracy, respectively. d Correctly classified cases have statistically higher tumor purities compared with incorrectly classified cases (Wilcoxon test p value = 2.8 × 10−4), although the difference in the distributions is modest. e Density scatter plot showing a direct correlation between purity and prediction confidence (Spearman rho = 0.19); many TCGA cases have fairly high purities (>50%) and many have high confidence predictions. Conceivably, these 53 probes could be quantitatively evaluated via next-generation sequencing. ACC adrenocortical carcinoma, BLCA bladder carcinoma, BRCA breast invasive carcinoma, CESC cervical and endocervical cancers, CHOL cholangiocarcinoma, CORE colorectal adenocarcinoma, DLBC diffuse large B-cell lymphoma, ESCC esophageal squamous cell carcinoma, GBMLGG glioma (glioblastoma and low-grade glioma), GEAD gastric and esophageal carcinoma, HNSC head and neck squamous cell carcinoma, KIPAN pan-kidney cohort (clear cell, chromophobe, and papillary renal cell carcinoma), LAML acute myeloid leukemia, LIHC liver hepatocellular carcinoma, LUAD lung adenocarcinoma, LUSC lung squamous cell carcinoma, MESO mesothelioma, PAAD pancreatic adenocarcinoma, PCPG pheochromocytoma and paraganglioma, PRAD prostate adenocarcinoma, SARC sarcoma, SKCM skin cutaneous melanoma, TGCT testicular germ cell tumor, THCA thyroid carcinoma, THYM thymoma, UCEC uterine corpus endometrial carcinoma, UCS uterine carcinosarcoma, UVM uveal melanoma.

    Article Snippet: Gathering of publicly-available DNA methylation array data TCGA Human Methylation 450K array data from fresh primary cases were downloaded from the Broad Institute GDAC website ( https://gdac.broadinstitute.org ).

    Techniques: Biomarker Discovery, Next-Generation Sequencing

    For FFPE primary cases (a, b; n=339, 12 cancer types), the model trained on TCGA fresh primary data (a) was far less accurate (74.6%vs. 97.6%, left panels) and less confident (right panels) on independent validation of FFPE primary cancers than the model trained on combined TCGA and randomly selected FFPE primary cases (b). Since the two models used the same 55 probes, a, b suggest that probes identified via TCGA analyses are robust—although classifiers may need to be retrained on new data. For FFPE brain metastasis (c, d; n=45, including three CUPs, four cancer types): c) t-SNE plot shows that FFPE brain metastasis cases (Ys, n=42) are well-separated, and cluster with their fresh primary counterparts (dots); CUPs are indicated by black open circles. d The heat map/confusion matrix for the classifier trained on FFPE brain metastases, validated on the 39 FFPE brain metastases of known origins (six unique probes, 95.2% accuracy); CUPs are not shown here. e A single probe (cg22280705) accurately separated all nine lymph node metastases and 98.5% of 32 melanoma and 235 breast carcinoma primaries on validation. BLCA bladder carcinoma, BRCA breast invasive carcinoma, CORE colorectal adenocarcinoma, DLBC diffuse large B-cell lymphoma, GBMLGG glioma (glioblastoma and low-grade glioma), GCT germ cell tumor (intracranial), HNSC head and neck squamous cell carcinoma, KIPAN pan-kidney cohort (clear cell, chromophobe, and papillary renal cell carcinoma), LUAD lung adenocarcinoma, LUSC lung squamous cell carcinoma, PRAD prostate adenocarcinoma, SKCM skin cutaneous melanoma, UCEC uterine corpus endometrial carcinoma.

    Journal: Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc

    Article Title: Minimalist approaches to cancer tissue-of-origin classification by DNA methylation

    doi: 10.1038/s41379-020-0547-7

    Figure Lengend Snippet: For FFPE primary cases (a, b; n=339, 12 cancer types), the model trained on TCGA fresh primary data (a) was far less accurate (74.6%vs. 97.6%, left panels) and less confident (right panels) on independent validation of FFPE primary cancers than the model trained on combined TCGA and randomly selected FFPE primary cases (b). Since the two models used the same 55 probes, a, b suggest that probes identified via TCGA analyses are robust—although classifiers may need to be retrained on new data. For FFPE brain metastasis (c, d; n=45, including three CUPs, four cancer types): c) t-SNE plot shows that FFPE brain metastasis cases (Ys, n=42) are well-separated, and cluster with their fresh primary counterparts (dots); CUPs are indicated by black open circles. d The heat map/confusion matrix for the classifier trained on FFPE brain metastases, validated on the 39 FFPE brain metastases of known origins (six unique probes, 95.2% accuracy); CUPs are not shown here. e A single probe (cg22280705) accurately separated all nine lymph node metastases and 98.5% of 32 melanoma and 235 breast carcinoma primaries on validation. BLCA bladder carcinoma, BRCA breast invasive carcinoma, CORE colorectal adenocarcinoma, DLBC diffuse large B-cell lymphoma, GBMLGG glioma (glioblastoma and low-grade glioma), GCT germ cell tumor (intracranial), HNSC head and neck squamous cell carcinoma, KIPAN pan-kidney cohort (clear cell, chromophobe, and papillary renal cell carcinoma), LUAD lung adenocarcinoma, LUSC lung squamous cell carcinoma, PRAD prostate adenocarcinoma, SKCM skin cutaneous melanoma, UCEC uterine corpus endometrial carcinoma.

    Article Snippet: Gathering of publicly-available DNA methylation array data TCGA Human Methylation 450K array data from fresh primary cases were downloaded from the Broad Institute GDAC website ( https://gdac.broadinstitute.org ).

    Techniques: Biomarker Discovery

    The distributions of methylation values for pancreatic and gastric cancers are similar in ICGC/GEO and TCGA validation datasets. Conceivably, analysis of single CpG sites can be carried out via quantitative PCR. ICGC International Cancer Genome Consortium, GEAD gastric/esophageal adenocarcinoma, GEO Gene Expression Omnibus, PAAD pancreatic adenocarcinoma, STAD gastric adenocarcinoma.

    Journal: Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc

    Article Title: Minimalist approaches to cancer tissue-of-origin classification by DNA methylation

    doi: 10.1038/s41379-020-0547-7

    Figure Lengend Snippet: The distributions of methylation values for pancreatic and gastric cancers are similar in ICGC/GEO and TCGA validation datasets. Conceivably, analysis of single CpG sites can be carried out via quantitative PCR. ICGC International Cancer Genome Consortium, GEAD gastric/esophageal adenocarcinoma, GEO Gene Expression Omnibus, PAAD pancreatic adenocarcinoma, STAD gastric adenocarcinoma.

    Article Snippet: Gathering of publicly-available DNA methylation array data TCGA Human Methylation 450K array data from fresh primary cases were downloaded from the Broad Institute GDAC website ( https://gdac.broadinstitute.org ).

    Techniques: Methylation, Biomarker Discovery, Real-time Polymerase Chain Reaction, Gene Expression

    We trained a classifier using data from TCGA and FFPE methylation array cases. This classifier was tested on 15 institutional FFPE primary cases, for which both methyl-seq (triangles) and EPIC array (circles) data were available (top panel). The reference cancer type is color coded in the sample name labels on the X-axis and the predictions of the classifier are color coded (by cancer type) and plotted with respect to their levels of confidence. The classifier correctly predicted 11 out of 15 diagnoses (73.3%; of which, 10 of 15 had medium or high levels of confidence) based on methyl-seq data, and 14 out of 15 diagnoses (93.3%) based on array data. Below each sample, the correlations between methylation levels across the 24 probes for the two platforms are shown (bottom panel). BLCA bladder carcinoma, CESC cervical and endocervical cancers, CORE colorectal adenocarcinoma, LUAD lung adenocarcinoma, SKCM skin cutaneous melanoma, UCEC uterine corpus endometrial carcinoma.

    Journal: Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc

    Article Title: Minimalist approaches to cancer tissue-of-origin classification by DNA methylation

    doi: 10.1038/s41379-020-0547-7

    Figure Lengend Snippet: We trained a classifier using data from TCGA and FFPE methylation array cases. This classifier was tested on 15 institutional FFPE primary cases, for which both methyl-seq (triangles) and EPIC array (circles) data were available (top panel). The reference cancer type is color coded in the sample name labels on the X-axis and the predictions of the classifier are color coded (by cancer type) and plotted with respect to their levels of confidence. The classifier correctly predicted 11 out of 15 diagnoses (73.3%; of which, 10 of 15 had medium or high levels of confidence) based on methyl-seq data, and 14 out of 15 diagnoses (93.3%) based on array data. Below each sample, the correlations between methylation levels across the 24 probes for the two platforms are shown (bottom panel). BLCA bladder carcinoma, CESC cervical and endocervical cancers, CORE colorectal adenocarcinoma, LUAD lung adenocarcinoma, SKCM skin cutaneous melanoma, UCEC uterine corpus endometrial carcinoma.

    Article Snippet: Gathering of publicly-available DNA methylation array data TCGA Human Methylation 450K array data from fresh primary cases were downloaded from the Broad Institute GDAC website ( https://gdac.broadinstitute.org ).

    Techniques: Methylation

    Comparison of methylation platforms for research and clinical practice.

    Journal: Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc

    Article Title: Minimalist approaches to cancer tissue-of-origin classification by DNA methylation

    doi: 10.1038/s41379-020-0547-7

    Figure Lengend Snippet: Comparison of methylation platforms for research and clinical practice.

    Article Snippet: Gathering of publicly-available DNA methylation array data TCGA Human Methylation 450K array data from fresh primary cases were downloaded from the Broad Institute GDAC website ( https://gdac.broadinstitute.org ).

    Techniques: Comparison, Methylation, Mutagenesis, DNA Methylation Assay, Sequencing, Genome Wide, Biomarker Discovery

    TSKU expression levels in different cancer types. ( A ) Elevated or decreased TSKU expression in data sets of different cancers compared with normal tissues in the Oncomine database. ( B ) TSKU mRNA levels in multiple tumor types from the TCGA database were analyzed by TIMER. (* P < 0.05, ** P <0.01, *** P < 0.001).

    Journal: Aging (Albany NY)

    Article Title: Tsukushi is a novel prognostic biomarker and correlates with tumor-infiltrating B cells in non-small cell lung cancer

    doi: 10.18632/aging.202403

    Figure Lengend Snippet: TSKU expression levels in different cancer types. ( A ) Elevated or decreased TSKU expression in data sets of different cancers compared with normal tissues in the Oncomine database. ( B ) TSKU mRNA levels in multiple tumor types from the TCGA database were analyzed by TIMER. (* P < 0.05, ** P <0.01, *** P < 0.001).

    Article Snippet: We assessed the proportion of six tumor-infiltrating cells in the tumor and normal tissues of lung cancer patients using the EpiDISH algorithm via the TCGA Infinium Human Methylation 450K arrays.

    Techniques: Expressing

    Correlations between differential TSKU methylation and expression in LUAD and LUSC. TCGA Infinium 450K methylation probes in the promoter region, including the cg20708175 and cg20886049 probes; ( A – C ) Scatterplots of correlations between differential TSKU methylation and expression level of all CpG sites (probes) in the promoter ( A ), cg20708175 ( B ), and cg20886049 ( C ) in LUAD (N = 471). ( D – F ) Scatterplots of correlations between differential TSKU methylation and expression level of all CpG sites (probes) in the promoter ( D ), cg20708175 ( E ), and cg20886049 ( F ) in LUSC (N = 406). Cor(r): the value determined by calculating the Pearson correlation coefficient.

    Journal: Aging (Albany NY)

    Article Title: Tsukushi is a novel prognostic biomarker and correlates with tumor-infiltrating B cells in non-small cell lung cancer

    doi: 10.18632/aging.202403

    Figure Lengend Snippet: Correlations between differential TSKU methylation and expression in LUAD and LUSC. TCGA Infinium 450K methylation probes in the promoter region, including the cg20708175 and cg20886049 probes; ( A – C ) Scatterplots of correlations between differential TSKU methylation and expression level of all CpG sites (probes) in the promoter ( A ), cg20708175 ( B ), and cg20886049 ( C ) in LUAD (N = 471). ( D – F ) Scatterplots of correlations between differential TSKU methylation and expression level of all CpG sites (probes) in the promoter ( D ), cg20708175 ( E ), and cg20886049 ( F ) in LUSC (N = 406). Cor(r): the value determined by calculating the Pearson correlation coefficient.

    Article Snippet: We assessed the proportion of six tumor-infiltrating cells in the tumor and normal tissues of lung cancer patients using the EpiDISH algorithm via the TCGA Infinium Human Methylation 450K arrays.

    Techniques: Methylation, Expressing

    Correlations between TSKU methylation and the proportions of infiltrating immune cells in LUAD and LUSC. ( A ) The proportions of tumor-infiltrating immune cells (TIICs) in every sample using the TCGA Infinium 450K methylation data in LUAD (N = 460). ( B ) The proportions of TIICs in every sample using the TCGA Infinium 450K methylation data in LUSC (N = 372). ( C ) Comparing the proportions of TIICs in tumor tissues (N = 460) and normal tissues (N = 32) in LUAD datasets. ( D ) Comparing the proportions of TIICs in tumor tissues (N = 372) and normal tissue (N = 43) in LUSC datasets. ( E ) Comparing the proportions of different TIICs between groups with high TSKU methylation levels (N = 230) and low TSKU methylation levels (N = 230) in LUAD samples. ( F ) Comparing the proportions of different TIICs between groups with high TSKU methylation levels (N = 185) and low TSKU methylation levels (N = 185) in LUSC samples (LM, low methylation; HM, high methylation).

    Journal: Aging (Albany NY)

    Article Title: Tsukushi is a novel prognostic biomarker and correlates with tumor-infiltrating B cells in non-small cell lung cancer

    doi: 10.18632/aging.202403

    Figure Lengend Snippet: Correlations between TSKU methylation and the proportions of infiltrating immune cells in LUAD and LUSC. ( A ) The proportions of tumor-infiltrating immune cells (TIICs) in every sample using the TCGA Infinium 450K methylation data in LUAD (N = 460). ( B ) The proportions of TIICs in every sample using the TCGA Infinium 450K methylation data in LUSC (N = 372). ( C ) Comparing the proportions of TIICs in tumor tissues (N = 460) and normal tissues (N = 32) in LUAD datasets. ( D ) Comparing the proportions of TIICs in tumor tissues (N = 372) and normal tissue (N = 43) in LUSC datasets. ( E ) Comparing the proportions of different TIICs between groups with high TSKU methylation levels (N = 230) and low TSKU methylation levels (N = 230) in LUAD samples. ( F ) Comparing the proportions of different TIICs between groups with high TSKU methylation levels (N = 185) and low TSKU methylation levels (N = 185) in LUSC samples (LM, low methylation; HM, high methylation).

    Article Snippet: We assessed the proportion of six tumor-infiltrating cells in the tumor and normal tissues of lung cancer patients using the EpiDISH algorithm via the TCGA Infinium Human Methylation 450K arrays.

    Techniques: Methylation