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Why Havenâ€™t We Cured Cancer? Seventy years since the first reported use of cancer chemotherapy, malignancies are the second most common cause of death among children and adults. So why are the headlines rarely punctuated with cancer success stories? One explanation is that, although classifying cancers is relatively straightforward, understanding the basis of cancer heterogeneity is complex. Over the years, we have become quite proficient at cataloging cancer according to patterns of epidemiology and pathology. Each cancer is recognized to occur at a particular age, to occur more frequently in one sex than another, and to have a particular morphologyâ€”usually resembling the originating tissue. Advances in imaging and histology have enabled us to further segregate cancer diagnoses into distinct stages and grades that predict different treatment responses and prognoses. Despite this exhaustive work, our attempts to understand the processes that generate the different forms of cancer have proved far less fruitful, hamstringing efforts to advance therapy in the clinic. Failure to determine the biological basis of histologically similar but clinically and molecularly distinct cancers (intertumoral heterogeneity) has proved especially limiting, preventing the development of preclinical models of the full spectrum of human cancers and fostering a clinical trials culture that accepts â€˜â€˜all comersâ€™â€™ with only the broadest categories of histological criteria to filter eligibility. Our failure to define the origins of cancer subtypes is not for want of trying. However, our relatively crude understanding of what drives cancers, coupled with uncertainty about initiating cell types, has prevented investigators from making the jump from correlative observation to functional understanding. Recently, a string of publications suggest that the genomic revolution may provide a route through this impasse. Microarray technologies have transformed the depth with which we can interrogate cancers like leukemias (Ross et al., 2004), breast cancers (Sotiriou et al., 2003), and brain tumors (Gibson et al., 2010; Johnson et al., 2010; Northcott et al., 2010; Cancer Genome Atlas Research Network, 2008), partitioning these diseases into robust subgroups according to genome-wide patterns of gene expression, copy number alteration, and mutation. These genomic profiles correlate with long-recognized epidemiological, pathological, and clinical characteristics; provide fundamental biological insights; and detect molecular echoes of tumor origins. Lessons from Leukemia Different types of chromosomal translocationsâ€”the principal oncogenic mutations in the bloodâ€”have long been associated with specific subtypes of leukemia. Genomic, stem cell, and cancer assays have taught us important lessons about the basis of this â€˜â€˜matching.â€™â€™ First, the different forms of leukemia appear to arise from distinct points in the hematopoietic lineage that are susceptible to specific translocations. For example, the BCR-ABL translocation seen in human chronic myeloid leukemia (CML) only initiates CML in uncommitted hematopoietic stem cells (HSCs) (Huntly et al., 2004), whereas translocations involving the mixed-lineage leukemia (MLL) gene can initiate acute leukemias in both HSCs and committed progenitor cells (BarabeÂ´ et al., 2007; Chen et al., 2008; Krivtsov et al., 2006). What is the biology behind this translocation-lineage stage matching? Comparative gene expression profiling suggests that the answer might lie in the capacity of translocations to activate key leukemogenic programs. Extensive self-renewal is considered a requisite feature of leukemic stem cells. When committed, nonself-renewing granulocyte macrophage progenitors (GMP) are transduced with MLL-AF9, they generate AML. The leukemic stem cells in this model retain a GMP-like gene expression profile, but they also acquire an aberrant self-renewal signature and self-renewal capacity, normally seen only in HSCs (Krivtsov et al., 2006). Because BCR-ABL does not appear to activate self-renewal, but rather enhanced cell proliferation and survival, its leukemogenic potential might be restricted to HSCs that already possess the capacity to self-renew (Huntly et al., 2004; Schemionek et al., 2010). Further probing of gene expression profile differences between normal and transformed hematopoietic cells has also highlighted new therapeutic opportunities. The transcriptome of MLL-AF9 transformed GMPs encodes a reactivated b-catenin (Ctnnb1) signal that drives leukemogenic self-renewal and that might be blocked for therapeutic gain (Wang et al., 2010). Although it is intuitive that cancers arise from specific combinations of mutations and susceptible cell types, these landmark studies of leukemia demonstrate the power of genomic technologies to decipher this process. Importantly, these data demonstrate that mutations can activate oncogenic signals without globally reprogramming the initiating cell. As we shall see, the legacy of the initiating cell transcriptome within cancer cells Cell 145, April 1, 2011 Âª2011 Elsevier Inc. 25 can provide crucial clues to tumor origins as well as unmask novel therapeutic targets. Charting Cancer Origins in Solid Tissues The availability of assays for each stage in the hematopoietic lineage, as well as the liquidity of blood, has accelerated understanding of leukemogenesis beyond that of solid tumorigenesis. But studies of solid cancers are catching up. The rigid anatomical organization of solid tissues has allowed investigators to map cells that express transcriptomes similar to those seen in cancers, and improved techniques to isolate and culture cells in solid tissue hierarchies have advanced the study of these populations. These studies have identified cells in solid tissues that share the transcriptomes of solid tumors and might therefore initiate cancer (Figure 1). Like most solid tissues, those of the central nervous system (CNS) give rise to a variety of cancers classified according to patterns of histology. Intracranial ependymomas are the third most common brain tumor of children and carry a much worse prognosis than spinal forms of the disease that predominate in adults. Ependymomas contain transcriptomes and DNA Figure 1. Cross-Species Genomics Matches Cells in Developing and Adult Mouse Tissues with Human Tumor Subgroups Matching of developing mouse tissues with childhood cancers (left). Different cell stages in an embryonic mouse tissue hierarchy are colored	[ "Why", "Havenâ€™t", "We", "Cured", "Cancer", "?", "Seventy", "years", "since", "the", "first", "reported", "use", "of", "cancer", "chemotherapy", ",", "malignancies", "are", "the", "second", "most", "common", "cause", "of", "death", "among", "children", "and", "adults", ".", "So", "why", "are", "the", "headlines", "rarely", "punctuated", "with", "cancer", "success", "stories", "?", "One", "explanation", "is", "that", ",", "although", "classifying", "cancers", "is", "relatively", "straightforward", ",", "understanding", "the", "basis", "of", "cancer", "heterogeneity", "is", "complex", ".", "Over", "the", "years", ",", "we", "have", "become", "quite", "proficient", "at", "cataloging", "cancer", "according", "to", "patterns", "of", "epidemiology", "and", "pathology", ".", "Each", "cancer", "is", "recognized", "to", "occur", "at", "a", "particular", "age", ",", "to", "occur", "more", "frequently", "in", "one", 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Why Havenâ€™t We Cured Cancer? Seventy years since the first reported use of cancer chemotherapy, malignancies are the second most common cause of death among children and adults. So why are the headlines rarely punctuated with cancer success stories? One explanation is that, although classifying cancers is relatively straightforward, understanding the basis of cancer heterogeneity is complex. Over the years, we have become quite proficient at cataloging cancer according to patterns of epidemiology and pathology. Each cancer is recognized to occur at a particular age, to occur more frequently in one sex than another, and to have a particular morphologyâ€”usually resembling the originating tissue. Advances in imaging and histology have enabled us to further segregate cancer diagnoses into distinct stages and grades that predict different treatment responses and prognoses. Despite this exhaustive work, our attempts to understand the processes that generate the different forms of cancer have proved far less fruitful, hamstringing efforts to advance therapy in the clinic. Failure to determine the biological basis of histologically similar but clinically and molecularly distinct cancers (intertumoral heterogeneity) has proved especially limiting, preventing the development of preclinical models of the full spectrum of human cancers and fostering a clinical trials culture that accepts â€˜â€˜all comersâ€™â€™ with only the broadest categories of histological criteria to filter eligibility. Our failure to define the origins of cancer subtypes is not for want of trying. However, our relatively crude understanding of what drives cancers, coupled with uncertainty about initiating cell types, has prevented investigators from making the jump from correlative observation to functional understanding. Recently, a string of publications suggest that the genomic revolution may provide a route through this impasse. Microarray technologies have transformed the depth with which we can interrogate cancers like leukemias (Ross et al., 2004), breast cancers (Sotiriou et al., 2003), and brain tumors (Gibson et al., 2010; Johnson et al., 2010; Northcott et al., 2010; Cancer Genome Atlas Research Network, 2008), partitioning these diseases into robust subgroups according to genome-wide patterns of gene expression, copy number alteration, and mutation. These genomic profiles correlate with long-recognized epidemiological, pathological, and clinical characteristics; provide fundamental biological insights; and detect molecular echoes of tumor origins. Lessons from Leukemia Different types of chromosomal translocationsâ€”the principal oncogenic mutations in the bloodâ€”have long been associated with specific subtypes of leukemia. Genomic, stem cell, and cancer assays have taught us important lessons about the basis of this â€˜â€˜matching.â€™â€™ First, the different forms of leukemia appear to arise from distinct points in the hematopoietic lineage that are susceptible to specific translocations. For example, the BCR-ABL translocation seen in human chronic myeloid leukemia (CML) only initiates CML in uncommitted hematopoietic stem cells (HSCs) (Huntly et al., 2004), whereas translocations involving the mixed-lineage leukemia (MLL) gene can initiate acute leukemias in both HSCs and committed progenitor cells (BarabeÂ´ et al., 2007; Chen et al., 2008; Krivtsov et al., 2006). What is the biology behind this translocation-lineage stage matching? Comparative gene expression profiling suggests that the answer might lie in the capacity of translocations to activate key leukemogenic programs. Extensive self-renewal is considered a requisite feature of leukemic stem cells. When committed, nonself-renewing granulocyte macrophage progenitors (GMP) are transduced with MLL-AF9, they generate AML. The leukemic stem cells in this model retain a GMP-like gene expression profile, but they also acquire an aberrant self-renewal signature and self-renewal capacity, normally seen only in HSCs (Krivtsov et al., 2006). Because BCR-ABL does not appear to activate self-renewal, but rather enhanced cell proliferation and survival, its leukemogenic potential might be restricted to HSCs that already possess the capacity to self-renew (Huntly et al., 2004; Schemionek et al., 2010). Further probing of gene expression profile differences between normal and transformed hematopoietic cells has also highlighted new therapeutic opportunities. The transcriptome of MLL-AF9 transformed GMPs encodes a reactivated b-catenin (Ctnnb1) signal that drives leukemogenic self-renewal and that might be blocked for therapeutic gain (Wang et al., 2010). Although it is intuitive that cancers arise from specific combinations of mutations and susceptible cell types, these landmark studies of leukemia demonstrate the power of genomic technologies to decipher this process. Importantly, these data demonstrate that mutations can activate oncogenic signals without globally reprogramming the initiating cell. As we shall see, the legacy of the initiating cell transcriptome within cancer cells Cell 145, April 1, 2011 Âª2011 Elsevier Inc. 25 can provide crucial clues to tumor origins as well as unmask novel therapeutic targets. Charting Cancer Origins in Solid Tissues The availability of assays for each stage in the hematopoietic lineage, as well as the liquidity of blood, has accelerated understanding of leukemogenesis beyond that of solid tumorigenesis. But studies of solid cancers are catching up. The rigid anatomical organization of solid tissues has allowed investigators to map cells that express transcriptomes similar to those seen in cancers, and improved techniques to isolate and culture cells in solid tissue hierarchies have advanced the study of these populations. These studies have identified cells in solid tissues that share the transcriptomes of solid tumors and might therefore initiate cancer (Figure 1). Like most solid tissues, those of the central nervous system (CNS) give rise to a variety of cancers classified according to patterns of histology. Intracranial ependymomas are the third most common brain tumor of children and carry a much worse prognosis than spinal forms of the disease that predominate in adults. Ependymomas contain transcriptomes and DNA Figure 1. Cross-Species Genomics Matches Cells in Developing and Adult Mouse Tissues with Human Tumor Subgroups Matching of developing mouse tissues with childhood cancers (left). Different cell stages in an embryonic mouse tissue hierarchy are colored

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"cancers", "like", "leukemias", "(", "Ross", "et", "al.", ",", "2004", ")", ",", "breast", "cancers", "(", "Sotiriou", "et", "al.", ",", "2003", ")", ",", "and", "brain", "tumors", "(", "Gibson", "et", "al.", ",", "2010", ";", "Johnson", "et", "al.", ",", "2010", ";", "Northcott", "et", "al.", ",", "2010", ";", "Cancer", "Genome", "Atlas", "Research", "Network", ",", "2008", ")", ",", "partitioning", "these", "diseases", "into", "robust", "subgroups", "according", "to", "genome-wide", "patterns", "of", "gene", "expression", ",", "copy", "number", "alteration", ",", "and", "mutation", ".", "These", "genomic", "profiles", "correlate", "with", "long-recognized", "epidemiological", ",", "pathological", ",", "and", "clinical", "characteristics", ";", "provide", "fundamental", "biological", "insights", ";", "and", "detect", "molecular", "echoes", "of", "tumor", "origins", ".", "Lessons", "from", "Leukemia", "Different", "types", "of", "chromosomal", "translocationsâ€", "”", "the", "principal", "oncogenic", "mutations", "in", "the", "bloodâ€", "”", "have", "long", "been", "associated", "with", "specific", "subtypes", "of", "leukemia", ".", "Genomic", ",", "stem", "cell", ",", "and", "cancer", "assays", "have", "taught", "us", "important", "lessons", "about", "the", "basis", "of", "this", "â€˜â€˜matching.â€™â€™", "First", ",", "the", "different", "forms", "of", "leukemia", "appear", "to", "arise", "from", "distinct", "points", "in", "the", "hematopoietic", "lineage", "that", "are", "susceptible", "to", "specific", "translocations", ".", "For", "example", ",", "the", "BCR-ABL", "translocation", "seen", "in", "human", "chronic", "myeloid", "leukemia", "(", "CML", ")", "only", "initiates", "CML", "in", "uncommitted", "hematopoietic", "stem", "cells", "(", "HSCs", ")", "(", "Huntly", "et", "al.", ",", "2004", ")", ",", "whereas", "translocations", "involving", "the", "mixed-lineage", "leukemia", "(", "MLL", ")", "gene", "can", "initiate", "acute", "leukemias", "in", "both", "HSCs", "and", "committed", "progenitor", "cells", "(", "BarabeÂ´", "et", "al.", ",", "2007", ";", "Chen", "et", "al.", ",", "2008", ";", "Krivtsov", "et", "al.", ",", "2006", ")", ".", "What", "is", "the", "biology", "behind", "this", "translocation-lineage", "stage", "matching", "?", "Comparative", "gene", "expression", "profiling", "suggests", "that", "the", "answer", "might", "lie", "in", "the", "capacity", "of", "translocations", "to", "activate", "key", "leukemogenic", "programs", ".", "Extensive", "self-renewal", "is", "considered", "a", "requisite", "feature", "of", "leukemic", "stem", "cells", ".", "When", "committed", ",", "nonself-renewing", "granulocyte", "macrophage", "progenitors", "(", "GMP", ")", "are", "transduced", "with", "MLL-AF9", ",", "they", "generate", "AML", ".", "The", "leukemic", "stem", "cells", "in", "this", "model", "retain", "a", "GMP-like", "gene", "expression", "profile", ",", "but", "they", "also", "acquire", "an", "aberrant", "self-renewal", "signature", "and", "self-renewal", "capacity", ",", "normally", "seen", "only", "in", "HSCs", "(", "Krivtsov", "et", "al.", ",", "2006", ")", ".", "Because", "BCR-ABL", "does", "not", "appear", "to", "activate", "self-renewal", ",", "but", "rather", "enhanced", "cell", "proliferation", "and", "survival", ",", "its", "leukemogenic", "potential", "might", "be", "restricted", "to", "HSCs", "that", "already", "possess", "the", "capacity", "to", "self-renew", "(", "Huntly", "et", "al.", ",", "2004", ";", "Schemionek", "et", "al.", ",", "2010", ")", ".", "Further", "probing", "of", "gene", "expression", "profile", "differences", "between", "normal", "and", "transformed", "hematopoietic", "cells", "has", "also", "highlighted", "new", "therapeutic", "opportunities", ".", "The", "transcriptome", "of", "MLL-AF9", "transformed", "GMPs", "encodes", "a", "reactivated", "b-catenin", "(", "Ctnnb1", ")", "signal", "that", "drives", "leukemogenic", "self-renewal", "and", "that", "might", "be", "blocked", "for", "therapeutic", "gain", "(", "Wang", "et", "al.", ",", "2010", ")", ".", "Although", "it", "is", "intuitive", "that", "cancers", "arise", "from", "specific", "combinations", "of", "mutations", "and", "susceptible", "cell", "types", ",", "these", "landmark", "studies", "of", "leukemia", "demonstrate", "the", "power", "of", "genomic", "technologies", "to", "decipher", "this", "process", ".", "Importantly", ",", "these", "data", "demonstrate", "that", "mutations", "can", "activate", "oncogenic", "signals", "without", "globally", "reprogramming", "the", "initiating", "cell", ".", "As", "we", "shall", "see", ",", "the", "legacy", "of", "the", "initiating", "cell", "transcriptome", "within", "cancer", "cells", "Cell", "145", ",", "April", "1", ",", "2011", "Âª2011", "Elsevier", "Inc.", "25", "can", "provide", "crucial", "clues", "to", "tumor", "origins", "as", "well", "as", "unmask", "novel", "therapeutic", "targets", ".", "Charting", "Cancer", "Origins", "in", "Solid", "Tissues", "The", "availability", "of", "assays", "for", "each", "stage", "in", "the", "hematopoietic", "lineage", ",", "as", "well", "as", "the", "liquidity", "of", "blood", ",", "has", "accelerated", "understanding", "of", "leukemogenesis", "beyond", "that", "of", "solid", "tumorigenesis", ".", "But", "studies", "of", "solid", "cancers", "are", "catching", "up", ".", "The", "rigid", "anatomical", "organization", "of", "solid", "tissues", "has", "allowed", "investigators", "to", "map", "cells", "that", "express", "transcriptomes", "similar", "to", "those", "seen", "in", "cancers", ",", "and", "improved", "techniques", "to", "isolate", "and", "culture", "cells", "in", "solid", "tissue", "hierarchies", "have", "advanced", "the", "study", "of", "these", "populations", ".", "These", "studies", "have", "identified", "cells", "in", "solid", "tissues", "that", "share", "the", "transcriptomes", "of", "solid", "tumors", "and", "might", "therefore", "initiate", "cancer", "(", "Figure", "1", ")", ".", "Like", "most", "solid", "tissues", ",", "those", "of", "the", "central", "nervous", "system", "(", "CNS", ")", "give", "rise", "to", "a", "variety", "of", "cancers", "classified", "according", "to", "patterns", "of", "histology", ".", "Intracranial", "ependymomas", "are", "the", "third", "most", "common", "brain", "tumor", "of", "children", "and", "carry", "a", "much", "worse", "prognosis", "than", "spinal", "forms", "of", "the", "disease", "that", "predominate", "in", "adults", ".", "Ependymomas", "contain", "transcriptomes", "and", "DNA", "Figure", "1", ".", "Cross-Species", "Genomics", "Matches", "Cells", "in", "Developing", "and", "Adult", "Mouse", "Tissues", "with", "Human", "Tumor", "Subgroups", "Matching", "of", "developing", "mouse", "tissues", "with", "childhood", "cancers", "(", "left", ")", ".", "Different", "cell", "stages", "in", "an", "embryonic", "mouse", "tissue", "hierarchy", "are", "colored" ]

Datasets:

SHS
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cancer_test_data3

Dataset Card for "cancer_test_data3"