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PubMed1999

The Cancer Genome Anatomy Project: EST sequencing and the genetics of cancer progression.

As the process of tumor progression proceeds from the normal cellular state to a preneoplastic condition and finally to the fully invasive form, the molecular characteristics of the cell change as well. These characteristics can be considered a molecular fingerprint of the cell at each stage of progression and, analogous to fingerprinting a criminal, can be used as markers of the progression process. Based on this premise, the Cancer Genome Anatomy Project was initiated with the broad goal of determining the comprehensive molecular characterization of normal, premalignant, and malignant tumor cells, thus making a reality the identification of all major cellular mechanisms leading to tumor initiation and progression ([Strausberg, R.L., Dahl, C.A., and Klausner, R.D. (1997). "New opportunities for uncovering the molecular basis of cancer." Nat. Genet., 16: 415-516.], www.ncbi.nlm.nih.gov/ncicgap/). The expectation of determining the genetic fingerprints of cancer progression will allow for 1) correlation of disease progression with therapeutic outcome; 2) improved evaluation of disease treatment; 3) stimulation of novel approaches to prevention, detection, and therapy; and 4) enhanced diagnostic tools for clinical applications. Whereas acquiring the comprehensive molecular analysis of cancer progression may take years, results from initial, short-term goals are currently being realized and are proving very fruitful.

Databases, Factual↗
PubMed2001

The Cancer Genome Anatomy Project: new resources for reading the molecular signatures of cancer.

The Cancer Genome Anatomy Project (CGAP) has built informational, technological, and physical resources to interface genomics with basic and clinical cancer research. The CGAP web site (http://cgap.nci.nih.gov) provides informatics tools for in silico analysis of the CGAP datasets as well as information for accessing each of the CGAP resources. Published in 2001 by John Wiley & Sons, Ltd.

Animals↗
PubMedDate not supplied

A new cancer genome anatomy project web resource for the community.

The National Cancer Institute's Cancer Genome Anatomy Project (CGAP) is developing publicly accessible information, technology, and material resources that provide a platform for the interface of cancer research and genomics. CGAP's efforts have focused toward (1) building and annotating catalogues of genes expressed during cancer development, (2) identifying polymorphisms in those genes, and (3) developing resources for the molecular characterization of cancer-related chromosomal aberrations. To date, CGAP has produced more than 1,000,000 expressed sequence tags, approximately 3,300,000 serial analysis of gene expression tags, and identified more than 10,000 human gene-based single-nucleotide polymorphisms. To enhance access to these datasets by the research community, a new Cancer Genome Project web site (http://cgap.nci.nih.gov/) is being introduced. The web site includes genomic data for humans and mice, including transcript sequence, gene expression patterns, single-nucleotide polymorphisms, clone resources, and cytogenetic information. Descriptions of the methods and reagents used in deriving the CGAP datasets are also provided. An extensive suite of informatics tools facilitates queries and analysis of the CGAP data by the community. One of the newest features of the CGAP web site is an electronic version of the Mitelman Database of Chromosome Aberrations in Cancer.

Chromosome Aberrations↗
PubMed2005

Integrating microarray gene expression object model and clinical document architecture for cancer genomics research.

Systematic integration of genomic-scale expression profiles with clinical information may facilitate cancer genomics research. MAGE-OM (Microarray Gene Expression Object Model) defines standard objects for genomic but not for clinical data. HL7 CDA (Clinical Document Architecture) is a document model for clinical information, describing syntax (generic structure) but not semantics. We designed a document template in XML Schema with additional constraints for CDA to define content semantics, enabling data model-level integration of MAGE-OM and CDA for cancer genomics research.

Gene Expression Profiling↗
PubMed2026

Development of a Computational Histology Artificial Intelligence-Powered Prognostic Biomarker in Colorectal Cancer in The Cancer Genome Atlas.

BACKGROUND: Risk stratification in colorectal cancer (CRC) plays an important role in treatment decision-making. As such, prognostic biomarkers that can augment risk stratification have clinical value. Quantitative histologic features from routine hematoxylin and eosin (H&E)-stained whole slide images (WSIs) provide a novel avenue for biomarker discovery. In this study, we explored the potential for a computational histology artificial intelligence (CHAI) platform to develop and validate a prognostic biomarker in CRC. METHODS: The Cancer Genome Atlas Colorectal Adenocarcinoma project was utilized for this study, with inclusion of all subjects (stage I-IV) with available digitized H&E specimens. The cohort was split into development and validation cohorts by a stratified random split. The previously developed CHAI platform was applied in the development cohort to construct a continuous risk score from histologic features associated with progression-free interval (PFI) that was dichotomized based on an optimized cutpoint for distinguishing PFI into a high risk CHAI (+) and lower risk CHAI (-). PFI was compared between CHAI (+) and CHAI (-) patients in the validation cohort in multivariable Cox proportional hazards models. Time-dependent area under the curve (tdAUC) and C-indices were also calculated for PFI. RESULTS: A total of 583 participants were included in the study, with 409 assigned to the validation cohort. The CHAI biomarker classified 229 participants (56%) as CHAI (+) and 180 (44%) as CHAI (-) in the validation set. CHAI (+) participants had worse PFI in a multivariable analysis adjusting for available clinicopathologic variables (hazard ratio (HR) = 2.65; 95% confidence interval (CI), 1.63-4.30). TdAUC for the CHAI biomarker was 0.60 (95% CI, 0.53-0.67) at 12 months, 0.62 (0.55-0.69) at 36 months, and 0.67 (0.55-0.79) at 60 months; the C-index was 0.62 (95% CI, 0.58-0.67). CONCLUSIONS: The CHAI platform was used to develop a prognostic digital pathology biomarker in CRC. This demonstrates the feasibility and potential to apply this artificial intelligence-based digital pathology biomarker platform for risk stratification in CRC and supports its further study.

Artificial intelligence↗
PubMed2024

Neotelomeres and telomere-spanning chromosomal arm fusions in cancer genomes revealed by long-read sequencing.

Alterations in the structure and location of telomeres are pivotal in cancer genome evolution. Here, we applied both long-read and short-read genome sequencing to assess telomere repeat-containing structures in cancers and cancer cell lines. Using long-read genome sequences that span telomeric repeats, we defined four types of telomere repeat variations in cancer cells: neotelomeres where telomere addition heals chromosome breaks, chromosomal arm fusions spanning telomere repeats, fusions of neotelomeres, and peri-centromeric fusions with adjoined telomere and centromere repeats. These results provide a framework for the systematic study of telomeric repeats in cancer genomes, which could serve as a model for understanding the somatic evolution of other repetitive genomic elements.

Humans↗
PubMed2026

Interpreting Mutation Co-Occurrence in Cancer Genomics Under Biological Context.

Somatic mutation patterns observed in cancer genomes are widely used to generate hypotheses about functional relationships among cancer genes and signaling pathways. However, mutation co-occurrence and mutual exclusivity are assessed at multiple levels, including cohorts, bulk specimens, lesions, regions, clones, and individual cells, although each observational level supports a different scope of inference. In this structured narrative review, we clarify these inferential boundaries and distinguish marginal from conditional association, as well as negative association from complete mutual exclusivity. A hypothetical numerical example of Simpson's reversal illustrates how marginal and conditional associations can differ and why negative association with non-zero overlap should be distinguished from complete mutual exclusivity. We then synthesize evidence from bulk, multi-region, phylogenetic, and single-cell analyses to examine spatial and clonal localization, interclonal cooperation, single-cell error and detection power, and genetic versus non-genetic resistance. We also provide a decision guide for method selection and a staged framework for functional validation. Overall, statistical association, physical localization, and functional interaction are related but distinct inferential targets that require different data, assumptions, and forms of validation.

clonal evolution↗
PubMed2006

Methods for detection of subtle mutations in cancer genomes.

With the realization that cancer is a genetic disease, detection of mutations in genomic DNA has become an important discipline in many areas of cancer research. Although the publication of the human genome sequence and the immense technological advancements have facilitated the analysis of cancer genomes, detection of mutations in tumor specimens may still be challenging and fraught with technical problems. In this review, we describe current technologies for detection of small DNA mutations, including mutation scanning techniques to search for unknown mutations, and diagnostic techniques to detect known cancer mutations. We outline the principles of the different techniques and discuss their advantages and limitations. We also discuss critical issues that must be considered before choosing methodology, including sensitivity, specificity, limit of detection, throughput and cost, quantity and quality of template DNA, available equipment, and personnel expertise.

DNA Fingerprinting↗
PubMed2026

LCR-modules: a collection of workflows for cancer genome analysis.

MOTIVATION: The surge of genomic data from advanced sequencing technologies is outpacing current analytical pipelines. We introduce LCR-modules, an open-source suite of bioinformatics tools designed for flexible and automated cancer genome data analysis. LCR-modules enables reproducible analysis of diverse cancer genomics data at scale. The suite comprises 49 Snakemake-based workflows organized into three levels, facilitating tasks from low-level quality control to complex cohort-level analyses. LCR-modules supports various sequencing types and integrates pipelines such as mutation calling, expression quantification, and cohort-level aggregation, ensuring flexibility and reproducibility. LCR-modules represents a significant advancement in genomic data analysis, reducing barriers in reproducibility and scalability and has already been applied to a combination of exomes and genomes from over 10 800 samples. AVAILABILITY: No new data were generated in support of this research. The source code for the LCR-modules is openly available at https://github.com/LCR-BCCRC/lcr-modules.

Software↗
PubMed2023

Neotelomeres and Telomere-Spanning Chromosomal Arm Fusions in Cancer Genomes Revealed by Long-Read Sequencing.

Alterations in the structure and location of telomeres are key events in cancer genome evolution. However, previous genomic approaches, unable to span long telomeric repeat arrays, could not characterize the nature of these alterations. Here, we applied both long-read and short-read genome sequencing to assess telomere repeat-containing structures in cancers and cancer cell lines. Using long-read genome sequences that span telomeric repeat arrays, we defined four types of telomere repeat variations in cancer cells: neotelomeres where telomere addition heals chromosome breaks, chromosomal arm fusions spanning telomere repeats, fusions of neotelomeres, and peri-centromeric fusions with adjoined telomere and centromere repeats. Analysis of lung adenocarcinoma genome sequences identified somatic neotelomere and telomere-spanning fusion alterations. These results provide a framework for systematic study of telomeric repeat arrays in cancer genomes, that could serve as a model for understanding the somatic evolution of other repetitive genomic elements.

Telomere↗
PubMed2005

An overview of lung cancer genomics and proteomics.

Lung cancer is the cause of nearly 170,000 cancer deaths in the United States each year, accounting for nearly 25% of all deaths from cancer. The 5-yr survival rate for lung cancer is < 15% from the time of diagnosis. This is largely due to the late stage of diagnosis and the lack of effective treatments, reflecting the need for a better understanding of the mechanisms that underlie lung carcinogenesis. Unlike the study of a single gene, protein, or pathway, genomic and proteomic technologies enable a systematic overview that provides the potential to improve our understanding of this disease. Ultimately, this could improve the diagnosis, prognosis, and clinical management of patients with lung cancer. Here, we review studies that generated profiles of gene and protein expression in lung cancer specimens and relevant model systems, and make recommendations to facilitate the clinical application of these technologies.

Animals↗
PubMed2026

Mutual Information-based Prognostic Biomarker Discovery in Cancer Genomics: Conceptual Framework and Representative Applications of MI-POG.

Mutual information (MI)-based approaches have increasingly been applied to cancer genomics; however, their use for genome-wide prognostic biomarker discovery remains relatively underexplored. The present article summarizes the conceptual workflow of Mutual Information-based Prognostic Omics Gene (MI-POG) based on previously published applications in breast cancer, lower-grade glioma, and other cancer datasets. The framework consists of clinical endpoint discretization, genome-wide MI-based screening, candidate ranking, and downstream validation using conventional survival-analysis approaches. Previous MI-POG applications identified solute carrier family 20 member 1 (SLC20A1) as a prognostic biomarker in hormone receptor-positive breast cancer. Elevated SLC20A1 expression was associated with unfavorable survival outcomes and was independently validated in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort. Methodological analyses demonstrated how survival endpoints can be integrated into an information-theoretic framework through fixed-time outcome discretization, enabling model-independent assessment of molecular-clinical dependencies. Applications across multiple cancer datasets suggested the potential applicability of the framework across biologically distinct tumor types, although further validation will be required to establish its robustness and generalizability. In conclusion, MI-POG can be formalized as an information-theoretic framework for genome-wide identification of prognostic biomarkers by quantifying molecular-clinical dependencies using mutual information. Representative applications from previously published studies suggest that MI-POG may complement conventional survival-analysis approaches and provide a useful strategy for biomarker discovery, although additional benchmarking and prospective validation will be required.

Humans↗
PubMed2001

New drug targets for genomic cancer therapy: successes, limitations, opportunities and future challenges.

Cancer drug therapy is undergoing a major transition from the previous pregenomic cytotoxic era to the new postgenomic era. Future mechanism-based therapeutic agents will increasingly be designed to act on molecular targets that are causally involved in the malignant progression of human cancers. Such agents are predicted to show greater therapeutic selectivity for cancer versus normal cells. New cancer drug targets are identified and validated in various ways. The determination of the normal human genome sequence, followed by that of multiple cancer genomes, is accelerating target discovery. Other new technologies, particularly high throughput screening, combinatorial chemistry and gene expression microarrays, are increasing the speed and efficiency of drug development. Examples of new molecular therapeutics showing promising activity in the clinic include Herceptin, Glivec and Iressa. However, many challenges remain as we test the vision of individualised combinatorial genome-based therapy, using drugs targeted to every significant molecular abnormality in cancer.

Angiogenesis Inhibitors↗
PubMed2000

[Cancer genome or the development of molecular portraits of tumors].

The rapid development of cancer genomics is due to important progresses in oncogenesis, human genome sequencing and emergence of new technologies in genome and transcriptome analysis. In this context, the aim of the French program 'Cartes d'Identites des Tumeurs--Molecular Portraits of Tumors' is to build a public data base containing a pan genome assessment of genome and transcriptome alterations in the major types of tumors as well as in relevant normal cells and experimental models. Data mining is done in the context of genome annotations and clinical and biological informations attached to the enrolled samples. The goal of the program is to define new tests useful for diagnostic procedures in clinical laboratories and new targets for biological treatments of tumors.

France↗
PubMed2025

Severus detects somatic structural variation and complex rearrangements in cancer genomes using long-read sequencing.

For the detection of somatic structural variation (SV) in cancer genomes, long-read sequencing is advantageous over short-read sequencing with respect to mappability and variant phasing. However, most current long-read SV detection methods are not developed for the analysis of tumor genomes characterized by complex rearrangements and heterogeneity. Here, we present Severus, a breakpoint graph-based algorithm for somatic SV calling from long-read cancer sequencing. Severus works with matching normal samples, supports unbalanced cancer karyotypes, can characterize complex multibreak SV patterns and produces haplotype-specific calls. On a comprehensive multitechnology cell line panel, Severus consistently outperforms other long-read and short-read methods in terms of SV detection F1 score (harmonic mean of the precision and recall). We also illustrate that compared to long-read methods, short-read sequencing systematically misses certain classes of somatic SVs, such as insertions or clustered rearrangements. We apply Severus to several clinical cases of pediatric leukemia/lymphoma, revealing clinically relevant cryptic rearrangements missed by standard genomic panels.

Humans↗
PubMed2005

A survey of homozygous deletions in human cancer genomes.

Homozygous deletions of recessive cancer genes and fragile sites are known to occur in human cancers. We identified 281 homozygous deletions in 636 cancer cell lines. Of these deletions, 86 were homozygous deletions of known recessive cancer genes, 17 were of sequenced common fragile sites, and 178 were in genomic regions that do not overlap known recessive oncogenes or fragile sites ("unexplained" homozygous deletions). Some cancer cell lines have multiple homozygous deletions whereas others have none, suggesting intrinsic variation in the tendency to develop this type of genetic abnormality (P < 0.001). The 178 unexplained homozygous deletions clustered into 131 genomic regions, 27 of which exhibit homozygous deletions in more than one cancer cell line. This degree of clustering indicates that the genomic positions of the unexplained homozygous deletions are not randomly determined (P < 0.001). Many homozygous deletions, including those that are in multiple clusters, do not overlap known genes and appear to be in intergenic DNA. Therefore, to elucidate further the pathogenesis of homozygous deletions in cancer, we investigated the genome landscape within unexplained homozygous deletions. The gene count within homozygous deletions is low compared with the rest of the genome. There are also fewer short interspersed nuclear elements (SINEs), long interspersed nuclear elements (LINEs), and low-copy-number repeats (LCRs). However, DNA within homozygous deletions has higher flexibility. These features may signal the presence of currently unrecognized zones of susceptibility to DNA rearrangement. They may also reflect a tendency to reduce the adverse effects of homozygous deletions by minimizing the number of genes removed.

Cell Line, Tumor↗
PubMed2006

Analysis of gene expression profile in colon cancer using the Cancer Genome Anatomy Project and RNA interference.

OBJECTIVE: To investigate the changes in the gene expression profile in colon cancer to further identify gene markers that may be useful in the management of this disease. METHODS: Data from serial analysis of gene expression (SAGE) collected by the Cancer Genome Anatomy Project (CGAP) were used to detect the difference in gene expression between normal tissue and colon cancer, and were further confirmed in a sample of 20 patients using RT-PCR. To identify the functions of differential genes in regulating the cell growth of colon cancer, RNA interference (RNAi) was used to block one of these genes in the colon cancer cell line HCT-116. RESULTS: Expression changes of greater than twofold in two SAGE libraries of colon cancer compared to two of normal tissue were observed for 216 tags of a total of 195,160 transcript tags (54 up-regulated genes and 136 down-regulated genes). Subsequent analysis of 17 genes by RT-PCR confirmed the reliability of this analysis. RNAi-mediated blockage of one of these genes, transforming growth factor (TGF)beta1, significantly reduced the growth of a colon cancer cell line. CONCLUSIONS: The combination of CGAP analysis and RNAi provides an excellent system to rapidly define the specific genes that are up-regulated in cancer to impact the growth of cancer cells. Further study on these differential overexpressed genes may provide gene markers for the detection and treatment of colon cancer.

Biomarkers, Tumor↗