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Discovery of cancer risk variants in the sequence of the germline genome can shed light on carcinogenesis. Here we describe gene burden association analyses, aggregating rare missense and loss of function variants, at 22 cancer sites, including 130,991 cancer cases and 733,486 controls from Iceland, Norway and the United Kingdom. We identified four genes associated with increased cancer risk; the pro-apoptotic BIK for prostate cancer, the autophagy involved ATG12 for colorectal cancer, TG for thyroid cancer and CMTR2 for both lung cancer and cutaneous melanoma. Further, we found genes with rare variants that associate with decreased risk of cancer; AURKB for any cancer, irrespective of site, and PPP1R15A for breast cancer, suggesting that inhibition of PPP1R15A may be a preventive strategy for breast cancer. Our findings pinpoint several new cancer risk genes and emphasize autophagy, apoptosis and cell stress response as a focus point for developing new therapeutics., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

In the last decades, the development of high-throughput molecular assays has revolutionised cancer diagnostics, paving the way for the concept of personalised cancer medicine. This progress has been driven by the introduction of such technologies through biomarker-driven oncology trials. In this review, strengths and limitations of various state-of-the-art sequencing technologies, including gene panel sequencing (DNA and RNA), whole-exome/whole-genome sequencing and whole-transcriptome sequencing, are explored, focusing on their ability to identify clinically relevant biomarkers with diagnostic, prognostic and/or predictive impact. This includes the need to assess complex biomarkers, for example microsatellite instability, tumour mutation burden and homologous recombination deficiency, to identify patients suitable for specific therapies, including immunotherapy. Furthermore, the crucial role of biomarker analysis and multidisciplinary molecular tumour boards in selecting patients for trial inclusion is discussed in relation to various trial concepts, including drug repurposing. Recognising that today's exploratory techniques will evolve into tomorrow's routine diagnostics and clinical study inclusion assays, the importance of emerging technologies for multimodal diagnostics, such as proteomics and in vivo drug sensitivity testing, is also discussed. In addition, key regulatory aspects and the importance of patient engagement in all phases of a clinical trial are described. Finally, we propose a set of recommendations for consideration when planning a new precision cancer medicine trial., (© 2024 The Authors. Journal of Internal Medicine published by John Wiley & Sons Ltd on behalf of Association for Publication of The Journal of Internal Medicine.)

The Cancer Drug Development Forum (CDDF)'s 'Histology independent drug development - is this the future for cancer drugs?' workshop was set up to explore the current landscape of histology independent drug development, review the current regulatory landscape and propose recommendations for improving the conduct of future trials. The first session considered lessons learnt from previous trials, including innovative solutions for reimbursement. The session explored why overall survival represents the most valuable endpoint, and the importance of duration of response, which can be captured with swimmer and spider plots. The second session on biomarker development and treatment optimisation considered current regulations for companion diagnostics, FDA guidance on histology independent drug development in oncology, and the need to establish cut-offs for the biomarker of tumour mutational burden to identify the patients most likely to benefit from PDL1 treatment. The third session reviewed novel trial designs, including basket, umbrella and platform trials, and statistical approaches of hierarchical modelling where homogeneity between study cohorts enables information to be borrowed between cohorts. The discussion highlighted the need to agree 'common assessment standards' to facilitate pooling of data across studies. In the fourth session, the sharing of data sets was recognised as a key step for improving equity of access to precision medicines across Europe. The session considered how the European Health Data Space (EHDS) could streamline access to medical records, emphasizing the importance of introducing greater accountability into the digital space. In conclusion the workshop proposed 11 recommendations to facilitate histology agnostic drug development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Genome-scale screening experiments in cancer produce long lists of candidate genes that require extensive interpretation for biological insight and prioritization for follow-up studies. Interrogation of gene lists frequently represents a significant and time-consuming undertaking, in which experimental biologists typically combine results from a variety of bioinformatics resources in an attempt to portray and understand cancer relevance. As a means to simplify and strengthen the support for this endeavor, we have developed oncoEnrichR, a flexible bioinformatics tool that allows cancer researchers to comprehensively interrogate a given gene list along multiple facets of cancer relevance. oncoEnrichR differs from general gene set analysis frameworks through the integration of an extensive set of prior knowledge specifically relevant for cancer, including ranked gene-tumor type associations, literature-supported proto-oncogene and tumor suppressor gene annotations, target druggability data, regulatory interactions, synthetic lethality predictions, as well as prognostic associations, gene aberrations and co-expression patterns across tumor types. The software produces a structured and user-friendly analysis report as its main output, where versions of all underlying data resources are explicitly logged, the latter being a critical component for reproducible science. We demonstrate the usefulness of oncoEnrichR through interrogation of two candidate lists from proteomic and CRISPR screens. oncoEnrichR is freely available as a web-based service hosted by the Galaxy platform (https://oncotools.elixir.no), and can also be accessed as a stand-alone R package (https://github.com/sigven/oncoEnrichR)., (© 2023 The Authors. International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.)

Functional precision medicine is a new, emerging area that can guide cancer treatment by capturing information from direct perturbations of tumor-derived, living cells, such as by drug sensitivity screening. Precision cancer medicine as currently implemented in clinical practice has been driven by genomics, and current molecular tumor boards rely extensively on genomic characterization to advise on therapeutic interventions. However, genomic biomarkers can only guide treatment decisions for a fraction of the patients. In this review we provide an overview of the current state of functional precision medicine, highlight advances for drug-sensitivity screening enabled by cell culture models, and discuss how artificial intelligence (AI) can be coupled to functional precision medicine to guide patient stratification., Competing Interests: Declaration of interests S.S.S. has received honoraria from AbbVie and AstraZeneca, and research support from BeiGene and TG Therapeutics. Å.F. has received honoraria from Bayer, Novartis, Pierre Fabre, Amgen, and Pfizer. The other authors declare no conflicts of interest., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background: Matching treatment based on tumour molecular characteristics has revolutionized the treatment of some cancers and has given hope to many patients. Although personalized cancer care is an old concept, renewed attention has arisen due to recent advancements in cancer diagnostics including access to high-throughput sequencing of tumour tissue. Targeted therapies interfering with cancer specific pathways have been developed and approved for subgroups of patients. These drugs might just as well be efficient in other diagnostic subgroups, not investigated in pharma-led clinical studies, but their potential use on new indications is never explored due to limited number of patients., Methods: In this national, investigator-initiated, prospective, open-label, non-randomized combined basket- and umbrella-trial, patients are enrolled in multiple parallel cohorts. Each cohort is defined by the patient's tumour type, molecular profile of the tumour, and study drug. Treatment outcome in each cohort is monitored by using a Simon two-stage-like 'admissible' monitoring plan to identify evidence of clinical activity. All drugs available in IMPRESS-Norway have regulatory approval and are funded by pharmaceutical companies. Molecular diagnostics are funded by the public health care system., Discussion: Precision oncology means to stratify treatment based on specific patient characteristics and the molecular profile of the tumor. Use of targeted drugs is currently restricted to specific biomarker-defined subgroups of patients according to their market authorization. However, other cancer patients might also benefit of treatment with these drugs if the same biomarker is present. The emerging technologies in molecular diagnostics are now being implemented in Norway and it is publicly reimbursed, thus more cancer patients will have a more comprehensive genomic profiling of their tumour. Patients with actionable genomic alterations in their tumour may have the possibility to try precision cancer drugs through IMPRESS-Norway, if standard treatment is no longer an option, and the drugs are available in the study. This might benefit some patients. In addition, it is a good example of a public-private collaboration to establish a national infrastructure for precision oncology. Trial registrations EudraCT: 2020-004414-35, registered 02/19/2021; ClinicalTrial.gov: NCT04817956, registered 03/26/2021., (© 2022. The Author(s).)

The value of high-throughput germline genetic testing is increasingly recognized in clinical cancer care. Disease-associated germline variants in cancer patients are important for risk management and surveillance, surgical decisions and can also have major implications for treatment strategies since many are in DNA repair genes. With the increasing availability of high-throughput DNA sequencing in cancer clinics and research, there is thus a need to provide clinically oriented sequencing reports for germline variants and their potential therapeutic relevance on a per-patient basis. To meet this need, we have developed the Cancer Predisposition Sequencing Reporter (CPSR), an open-source computational workflow that generates a structured report of germline variants identified in known cancer predisposition genes, highlighting markers of therapeutic, prognostic and diagnostic relevance. A fully automated variant classification procedure based on more than 30 refined American College of Medical Genetics and Genomics (ACMG) criteria represents an integral part of the workflow. Importantly, the set of cancer predisposition genes profiled in the report can be flexibly chosen from more than 40 virtual gene panels established by scientific experts, enabling customization of the report for different screening purposes and clinical contexts. The report can be configured to also list actionable secondary variant findings, as recommended by ACMG. CPSR demonstrates comparable sensitivity and specificity for the detection of pathogenic variants when compared to other algorithms in the field. Technically, the tool is implemented in Python/R, and is freely available through Docker technology. Source code, documentation, example reports and installation instructions are accessible via the project GitHub page: https://github.com/sigven/cpsr., (© 2021 The Authors. International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC.)

Telomerase-based therapeutic cancer vaccines (TCVs) have been under clinical investigation for the past two decades. Despite past failures, TCVs have gained renewed enthusiasm for their potential to improve the efficacy of checkpoint inhibition. Telomerase stands as an attractive target for TCVs due to its almost universal presence in cancer and its essential function promoting tumor growth. Herein, we review tumor telomerase biology that may affect the efficacy of therapeutic vaccination and provide insights on optimal vaccine design and treatment combinations. Tumor types possessing mechanisms of increased telomerase expression combined with an immune permissive tumor microenvironment are expected to increase the therapeutic potential of telomerase-targeting cancer vaccines. Regardless, rational treatment combinations, such as checkpoint inhibitors, are likely necessary to bring out the true clinical potential of TCVs., Competing Interests: EBE and GG are employees of Ultimovacs ASA. SM and GG are shareholders in Ultimovacs ASA. SM is a founder, shareholder, and board member of Immuneed AB, Vivologica AB, and Strike pharma AB and an employee of Ultimovacs AB. GG is an inventor of a UV1 vaccine patent. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Ellingsen, Mangsbo, Hovig and Gaudernack.)

Background: In precision cancer medicine, the challenge is to prioritize DNA driver events, account for resistance markers, and procure sufficient information for treatment that maintains patient safety. The MetAction project, exploring how tumor molecular vulnerabilities predict therapy response, first established the required workflow for DNA sequencing and data interpretation (2014-2015). Here, we employed it to identify molecularly matched therapy and recorded outcome in end-stage cancer (2016-2019). Material and methods: Metastatic tissue from 26 patients (16 colorectal cancer cases) was sequenced by the Oncomine assay. The study tumor boards interpreted called variants with respect to sensitivity or resistance to matched therapy and recommended single-agent or combination treatment if considered tolerable. The primary endpoint was the rate of progression-free survival 1.3-fold longer than for the most recent systemic therapy. The objective response rate and overall survival were secondary endpoints. Results: Both common and rare actionable alterations were identified. Thirteen patients were found eligible for therapy following review of tumor sensitivity and resistance variants and patient tolerability. The interventions were inhibitors of ALK/ROS1-, BRAF-, EGFR-, FGFR-, mTOR-, PARP-, or PD-1-mediated signaling for 2-3 cases each. Among 10 patients who received treatment until radiologic evaluation, 6 (46% of the eligible cases) met the primary endpoint. Four colorectal cancer patients (15% of the total study cohort) had objective response. The only serious adverse event was a transient colitis, which appeared in 1 of the 2 patients given PD-1 inhibitor with complete response. Apart from those two, overall survival was similar for patients who did and did not receive study treatment. Conclusions: The systematic MetAction approach may point forward to a refined framework for how to interpret the complexity of sensitivity versus resistance and patient safety that resides in tumor sequence data, for the possibly improved outcome of precision cancer medicine in future studies. ClinicalTrials.gov, identifier: NCT02142036.

Like physics in the 19th century, biology and molecular biology in particular, has been fertilized and enhanced like few other scientific fields, by the incorporation of mathematical methods. In the last decades, a whole new scientific field, bioinformatics, has developed with an output of over 30,000 papers a year (Pubmed search using the keyword "bioinformatics"). Huge databases of mass throughput data have been established, with ArrayExpress alone containing more than 2.7 million assays (October 2019). Computational methods have become indispensable tools in molecular biology, particularly in one of the most challenging areas of cancer research, multidrug resistance (MDR). However, confronted with a plethora of different algorithms, approaches, and methods, the average researcher faces key questions: Which methods do exist? Which methods can be used to tackle the aims of a given study? Or, more generally, how do I use computational biology/bioinformatics to bolster my research? The current review is aimed at providing guidance to existing methods with relevance to MDR research. In particular, we provide an overview on: a) the identification of potential biomarkers using expression data; b) the prediction of treatment response by machine learning methods; c) the employment of network approaches to identify gene/protein regulatory networks and potential key players; d) the identification of drug-target interactions; e) the use of bipartite networks to identify multidrug targets; f) the identification of cellular subpopulations with the MDR phenotype; and, finally, g) the use of molecular modeling methods to guide and enhance drug discovery. This review shall serve as a guide through some of the basic concepts useful in MDR research. It shall give the reader some ideas about the possibilities in MDR research by using computational tools, and, finally, it shall provide a short overview of relevant literature., (Copyright © 2019. Published by Elsevier Ltd.)

Summary: Individual tumor genomes pose a major challenge for clinical interpretation due to their unique sets of acquired mutations. There is a general scarcity of tools that can (i) systematically interrogate cancer genomes in the context of diagnostic, prognostic, and therapeutic biomarkers, (ii) prioritize and highlight the most important findings and (iii) present the results in a format accessible to clinical experts. We have developed a stand-alone, open-source software package for somatic variant annotation that integrates a comprehensive set of knowledge resources related to tumor biology and therapeutic biomarkers, both at the gene and variant level. Our application generates a tiered report that will aid the interpretation of individual cancer genomes in a clinical setting., Availability and Implementation: The software is implemented in Python/R, and is freely available through Docker technology. Documentation, example reports, and installation instructions are accessible via the project GitHub page: https://github.com/sigven/pcgr., Contact: sigven@ifi.uio.no., Supplementary Information: Supplementary data are available at Bioinformatics online.

In the microenvironment of a malignancy, tumor cells do not exist in isolation, but rather in a diverse ecosystem consisting not only of heterogeneous tumor-cell clones, but also normal cell types such as fibroblasts, vasculature, and an extensive pool of immune cells at numerous possible stages of activation and differentiation. This results in a complex interplay of diverse cellular signaling systems, where the immune cell component is now established to influence cancer progression and therapeutic response. It is experimentally difficult and laborious to comprehensively and systematically profile these distinct cell types from heterogeneous tumor samples in order to capitalize on potential therapeutic and biomarker discoveries. One emerging solution to address this challenge is to computationally extract cell-type specific information directly from bulk tumors. Such in silico approaches are advantageous because they can capture both the cell-type specific profiles and the tissue systems level of cell-cell interactions. Accurately and comprehensively predicting these patterns in tumors is an important challenge to overcome, not least given the success of immunotherapeutic drug treatment of several human cancers. This is especially challenging for subsets of closely related immune cell phenotypes with relatively small gene expression differences, which have critical functional distinctions. Here, we outline the existing and emerging novel bioinformatics strategies that can be used to profile the tumor immune landscape., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.)

Background: The immune contribution to cancer progression is complex and difficult to characterize. For example in tumors, immune gene expression is detected from the combination of normal, tumor and immune cells in the tumor microenvironment. Profiling the immune component of tumors may facilitate the characterization of the poorly understood roles immunity plays in cancer progression. However, the current approaches to analyze the immune component of a tumor rely on incomplete identification of immune factors., Methods: To facilitate a more comprehensive approach, we created a ranked immunological relevance score for all human genes, developed using a novel strategy that combines text mining and information theory. We used this score to assign an immunological grade to gene expression profiles, and thereby quantify the immunological component of tumors. This immunological relevance score was benchmarked against existing manually curated immune resources as well as high-throughput studies. To further characterize immunological relevance for genes, the relevance score was charted against both the human interactome and cancer information, forming an expanded interactome landscape of tumor immunity. We applied this approach to expression profiles in melanomas, thus identifying and grading their immunological components, followed by identification of their associated protein interactions., Results: The power of this strategy was demonstrated by the observation of early activation of the adaptive immune response and the diversity of the immune component during melanoma progression. Furthermore, the genome-wide immunological relevance score classified melanoma patient groups, whose immunological grade correlated with clinical features, such as immune phenotypes and survival., Conclusions: The assignment of a ranked immunological relevance score to all human genes extends the content of existing immune gene resources and enriches our understanding of immune involvement in complex biological networks. The application of this approach to tumor immunity represents an automated systems strategy that quantifies the immunological component in complex disease. In so doing, it stratifies patients according to their immune profiles, which may lead to effective computational prognostic and clinical guides.

Successful adaptation of microarray technology for high-throughput screening of proteins requires a large number of purified recombinant proteins, e.g., antibodies for use as capture molecules. Phage surface display technology has been used for the surface expression of proteins, peptides or cDNA repertoires expressed by tumor cells. It does not require protein purification, as recombinant phages can be spotted on glass slides and used in a high-throughput screening format. Biopanning of phage libraries on patient serum antibodies is expected to enrich for antibody-binding phages for the fabrication of diagnostic and/or prognostic B-cell epitope microarrays. In contrast to other immunological techniques, microarrays can measure the antibody levels against different epitopes in a single test. This chapter highlights the recent advances in phage-based microarray technology to profile humoral immune responses in cancer patients.

The metastasis-promoting protein S100A4 stimulates metastatic progression through both intracellular and extracellular functions. Extracellular activities of S100A4 include stimulation of angiogenesis, regulation of cell death and increased cell motility and invasion, but the exact molecular mechanisms by which extracellular S100A4 exerts these effects are incompletely elucidated. The aim of the present study was to characterize S100A4-induced signal transduction mechanisms and to identify S100A4 target genes. We demonstrate that extracellular S100A4 activates the transcription factor NF-kappaB in a subset of human cancer cell lines through induction of phosphorylation and subsequent degradation of the NF-kappaB inhibitor IkappaBalpha. Concomitantly, S100A4 induced a sustained activation of the MAP kinase JNK, whereas no increased activity of the MAP kinases p38 or ERK was observed. Microarray analyses identified 136 genes as being significantly regulated by S100A4 treatment, and potentially interesting S100A4-induced gene products include IkappaBalpha, p53, ephrin-A1 and optineurin. Increased expression of ephrin-A1 and optineurin was validated using RT-PCR, Western blotting and functional assays. Furthermore, S100A4-stimulated transcription of these target genes was dependent on activation of the NF-kappaB pathway. In conclusion, these findings contribute to the understanding of the complex molecular mechanisms responsible for the diverse biological functions of extracellular S100A4, and provide further evidence of how S100A4 may stimulate metastatic progression., (Copyright 2008 Wiley-Liss, Inc.)

Functional proteomics is a promising technique for the rational identification of novel therapeutic targets and biological markers. The studies of protein-protein interactions have been gained from the development of high-throughput technologies such as the yeast two-hybrid system, protein arrays, phage display, and systematic analysis of interaction maps for the prediction of protein functions. Because antibodies are used extensively as diagnostic and clinical tools, the characterization of their antigen specificity is of prime importance. Indeed, screening protein arrays with sera from patients with either cancer or autoimmune diseases would facilitate the identification of autoantibody signatures that can be used for diagnosis and/or prognosis of patients. The usefulness of multiplexed measurements lies not only in the ability to screen many individual marker candidates but also in evaluating the use of multiple markers in combination. Here, we review the advantage of protein and serum screening of peptides and cDNA repertoires displayed on phages as well as the fabrication of protein microarrays for probing immune responses in patients.

Background: Using DNA microarrays, we have developed two novel models for tumor classification and target gene prediction. First, gene expression profiles are summarized by optimally selected Self-Organizing Maps (SOMs), followed by tumor sample classification by Fuzzy C-means clustering. Then, the prediction of marker genes is accomplished by either manual feature selection (visualizing the weighted/mean SOM component plane) or automatic feature selection (by pair-wise Fisher's linear discriminant)., Results: The proposed models were tested on four published datasets: (1) Leukemia (2) Colon cancer (3) Brain tumors and (4) NCI cancer cell lines. The models gave class prediction with markedly reduced error rates compared to other class prediction approaches, and the importance of feature selection on microarray data analysis was also emphasized., Conclusions: Our models identify marker genes with predictive potential, often better than other available methods in the literature. The models are potentially useful for medical diagnostics and may reveal some insights into cancer classification. Additionally, we illustrated two limitations in tumor classification from microarray data related to the biology underlying the data, in terms of (1) the class size of data, and (2) the internal structure of classes. These limitations are not specific for the classification models used.

Background: There has been a substantial increase in our understanding of the pathogenesis of cancer over the past decades, and new treatment modalities are being developed on the basis of this knowledge., Material and Methods: The literature is reviewed, and the status of gene therapy protocols approved in Norway is presented., Results: About 70% of the more than 400 clinical gene therapy studies started are targeted at cancer. Several different principles are used, including gene therapy targeted at tumour suppressor genes, oncogenes, or central signaling molecules, as well as "suicide gene" therapy. In addition, various gene therapy protocols aim at strengthening immune responses. Most studies have been early clinical trials primarily designed to study safety, applicability and toxicity. Several of these phase I and II studies have, however, shown partial remission of tumours and, in rare cases, complete remission, although curation has not yet been shown. In some trials, including TP53 gene therapy trials, effects on tumour size have been observed in up to 50% of the patients. Up until now, only two phase III and one phase II/III studies have been initiated, but results from these studies have not yet been published. The two first gene therapy protocols approved in Norway are also targeted at cancer. So far, six patients in Norway have undergone gene therapy., Interpretation: As of today, the results should be seen as promising for some of the principles which are being tried out; their clinical importance must, however, be documented in larger controlled clinical trials.

In recent years, there has been an exponential increase in the number of p53 mutations identified in human cancers. The p53 mutation database consists of a list of point mutations in thep53 gene of human tumors and cell lines, compiled from the published literature and made available through electronic media. The database is now maintained at the International Agency for Research on Cancer (IARC) and is updated twice a year. The current version contains records on 5091 published mutations and is expected to surpass the 6000 mark in the January 1997 release. The database is available in various formats through the European Bioinformatics Institute (EBI) ftp server at: ftp://ftp.ebi.ac.uk/pub/databases/p53/ or by request from IARC (p53database@iarc.fr) and will be searchable through the SRS system in the near future. This report provides a description of the criteria for inclusion of data and of the current formats, a summary of the relevance ofp53 mutation analysis to clinical and biological questions, and a brief discussion of the prospects for future developments.

In 1994 we described a list of approximately 2500 point mutations in the p53 gene of human tumors and cell lines which we had compiled from the published literature and made available electronically through the file server at the EMBL Data Library. This database, updated twice a year, now contains records on 4496 published mutations (July 1995 release) and can be obtained from the EMBL Outstation-the European Bioinformatics Institute (EBI) through the network or on CD-ROM. This report describes the criteria for inclusion of data in this database, a description of the current format and a brief discussion of the current relevance of p53 mutation analysis to clinical and biological questions.

The spectrum of somatic TP53 single basepair substitutions detected in 955 cancers was compared with that of 2,224 different germline mutations in 279 different human genes (other than TP53), reported as the cause of inherited disease. This comparison reveals that, disregarding a relatively small subset (12%) of TP53 mutations that probably result from the action of exogenous mutagens, both the relative rates and the nearest-neighbor spectra of single basepair substitutions are similar in the two datasets. This spectral resemblance suggests that a substantial proportion of cancer-associated somatic TP53 mutations result from endogenous cellular mechanisms. The likelihood of clinical observation of a particular mutation type differs, however, between tumors and genetic diseases, when the chemical properties of the resulting amino acid substitutions are considered. Together with a sixfold higher observation likelihood for mutations at evolutionarily conserved residues, this finding argues that selection is a critical factor in determining which TP53 mutations are found to be associated with human cancer.

A data base is described in which over 2,500 mutations in the p53 gene of human tumors and tumor cell lines are compiled from a systematic search of reports published before 1 January 1994. Data from 1994 are being added intermittently, with a systematic search and update scheduled for December, 1994. The compilation has been deposited with the EMBL Data Library and is available in electronic form free of charge. This report contains a rationale for the compilation, a brief summary of the major findings and a description of the data base.

Detection of DNA variation in cancer is central to the identification of relevant genes and mutations involved in the tumourigenic process. Diverse methods exist for such detection. One category of methods is for the detection of frequent sites for larger DNA alterations in cancer. Such areas may provide clues to the positioning of relevant genes, such as loss of heterozygosity (LOH) as in the case of tumour suppressor genes. Another category of methods is for the detection of single base mutations within specific genes. Frequently, such mutations may obliterate normal protein function. Among the most well-known are DGGE, SSCP, the HOT-method and direct sequencing. The methods for detection of DNA variation of these different levels are discussed. Two methods are presented in more detail. At the large-scale level, two-dimensional DNA fingerprinting has the potential of revealing the extent and location of altered DNA regions. This method is demonstrated using a panel of breast cancer patients. As an example of methods for the small-scale level, a recent development from DGGE, constant denaturant gel electrophoresis (CDGE) is demonstrated. This method has successfully been applied for the detection of mutations in a number of genes. Results with this method in studies of the RB1 gene are given, and its applicability as a screening tool for base mutations is discussed.