Microbiome confounders and quantitative profiling challenge predicted microbial targets in colorectal cancer development.

Despite substantial progress in cancer microbiome research, recognized confounders and advances in absolute microbiome quantification remain underused; this raises concerns regarding potential spurious associations. Here we study the fecal microbiota of 589 patients at different colorectal cancer (CRC) stages and compare observations with up to 15 published studies (4,439 patients and controls total). Using quantitative microbiome profiling based on 16S ribosomal RNA amplicon sequencing, combined with rigorous confounder control, we identified transit time, fecal calprotectin (intestinal inflammation) and body mass index as primary microbial covariates, superseding variance explained by CRC diagnostic groups. Well-established microbiome CRC targets, such as Fusobacterium nucleatum, did not significantly associate with CRC diagnostic groups (healthy, adenoma and carcinoma) when controlling for these covariates. In contrast, the associations of Anaerococcus vaginalis, Dialister pneumosintes, Parvimonas micra, Peptostreptococcus anaerobius, Porphyromonas asaccharolytica and Prevotella intermedia remained robust, highlighting their future target potential. Finally, control individuals (age 22-80 years, mean 57.7 years, standard deviation 11.3) meeting criteria for colonoscopy (for example, through a positive fecal immunochemical test) but without colonic lesions are enriched for the dysbiotic Bacteroides2 enterotype, emphasizing uncertainties in defining healthy controls in cancer microbiome research. Together, these results indicate the importance of quantitative microbiome profiling and covariate control for biomarker identification in CRC microbiome studies.

Integration of polygenic and gut metagenomic risk prediction for common diseases.

Multiomics has shown promise in noninvasive risk profiling and early detection of various common diseases. In the present study, in a prospective population-based cohort with ~18 years of e-health record follow-up, we investigated the incremental and combined value of genomic and gut metagenomic risk assessment compared with conventional risk factors for predicting incident coronary artery disease (CAD), type 2 diabetes (T2D), Alzheimer disease and prostate cancer. We found that polygenic risk scores (PRSs) improved prediction over conventional risk factors for all diseases. Gut microbiome scores improved predictive capacity over baseline age for CAD, T2D and prostate cancer. Integrated risk models of PRSs, gut microbiome scores and conventional risk factors achieved the highest predictive performance for all diseases studied compared with models based on conventional risk factors alone. The present study demonstrates that integrated PRSs and gut metagenomic risk models improve the predictive value over conventional risk factors for common chronic diseases.

Gut microbiome-derived bacterial extracellular vesicles in patients with solid tumours.

INTRODUCTION: Gut microbiome-derived nanoparticles, known as bacterial extracellular vesicles (bEVs), have garnered interest as promising tools for studying the link between the gut microbiome and human health. The diverse composition of bEVs, including their proteins, mRNAs, metabolites, and lipids, makes them useful for investigating diseases such as cancer. However, conventional approaches for studying gut microbiome composition alone may not be accurate in deciphering host-gut microbiome communication. In clinical microbiome research, there is a gap in the knowledge on the role of bEVs in solid tumor patients. OBJECTIVES: Analyzing the functionality of bEVs using (meta)genomics and proteomics could highlight the unique aspects of host-gut microbiome interactions in solid tumor patients. Therefore, we performed a comparative analysis of the proteome and microbiota composition of gut microbiome-derived bEVs isolated from patients with solid tumors and healthy controls. METHODS: After isolating bEVs from the feces of solid tumor patients and healthy controls, we performed spectrometry analysis of their proteomes and next-generation sequencing (NGS) of the 16S gene. We also investigated the gut microbiomes of feces from patients and controls using 16S sequencing and used machine learning to classify the samples into patients and controls based on their bEVs and fecal microbiomes. RESULTS: Solid tumor patients showed decreased microbiota richness and diversity in both the bEVs and feces. However, the bEV proteomes were more diverse in patients than in the controls and were enriched with proteins associated with the metabolism of amino acids and carbohydrates, nucleotide binding, and oxidoreductase activity. Metadata classification of samples was more accurate using fecal bEVs (100%) compared with fecal samples (93%). CONCLUSION: Our findings suggest that bEVs are unique functional entities. There is a need to explore bEVs together with conventional gut microbiome analysis in functional cancer research to decipher the potential of bEVs as cancer diagnostic or therapeutic biomarkers.

Bone marrow metabolism is affected by body weight and response to exercise training varies according to anatomical location.

AIM: High body weight is a protective factor against osteoporosis, but obesity also suppresses bone metabolism and whole-body insulin sensitivity. However, the impact of body weight and regular training on bone marrow (BM) glucose metabolism is unclear. We studied the effects of regular exercise training on bone and BM metabolism in monozygotic twin pairs discordant for body weight. METHODS: We recruited 12 monozygotic twin pairs (mean ± SD age 40.4 ± 4.5 years; body mass index 32.9 ± 7.6, mean difference between co-twins 7.6 kg/m2 ; eight female pairs). Ten pairs completed the 6-month long training intervention. We measured lumbar vertebral and femoral BM insulin-stimulated glucose uptake (GU) using 18 F-FDG positron emission tomography, lumbar spine bone mineral density and bone turnover markers. RESULTS: At baseline, heavier co-twins had higher lumbar vertebral BM GU (p < .001) and lower bone turnover markers (all p < .01) compared with leaner co-twins but there was no significant difference in femoral BM GU, or bone mineral density. Training improved whole-body insulin sensitivity, aerobic capacity (both p < .05) and femoral BM GU (p = .008). The training response in lumbar vertebral BM GU was different between the groups (time × group, p = .02), as GU tended to decrease in heavier co-twins (p = .06) while there was no change in leaner co-twins. CONCLUSIONS: In this study, regular exercise training increases femoral BM GU regardless of weight and genetics. Interestingly, lumbar vertebral BM GU is higher in participants with higher body weight, and training counteracts this effect in heavier co-twins even without reduction in weight. These data suggest that BM metabolism is altered by physical activity.

Machine learning approaches in microbiome research: challenges and best practices.

Microbiome data predictive analysis within a machine learning (ML) workflow presents numerous domain-specific challenges involving preprocessing, feature selection, predictive modeling, performance estimation, model interpretation, and the extraction of biological information from the results. To assist decision-making, we offer a set of recommendations on algorithm selection, pipeline creation and evaluation, stemming from the COST Action ML4Microbiome. We compared the suggested approaches on a multi-cohort shotgun metagenomics dataset of colorectal cancer patients, focusing on their performance in disease diagnosis and biomarker discovery. It is demonstrated that the use of compositional transformations and filtering methods as part of data preprocessing does not always improve the predictive performance of a model. In contrast, the multivariate feature selection, such as the Statistically Equivalent Signatures algorithm, was effective in reducing the classification error. When validated on a separate test dataset, this algorithm in combination with random forest modeling, provided the most accurate performance estimates. Lastly, we showed how linear modeling by logistic regression coupled with visualization techniques such as Individual Conditional Expectation (ICE) plots can yield interpretable results and offer biological insights. These findings are significant for clinicians and non-experts alike in translational applications.

Dealing with dimensionality: the application of machine learning to multi-omics data.

MOTIVATION: Machine learning (ML) methods are motivated by the need to automate information extraction from large datasets in order to support human users in data-driven tasks. This is an attractive approach for integrative joint analysis of vast amounts of omics data produced in next generation sequencing and other -omics assays. A systematic assessment of the current literature can help to identify key trends and potential gaps in methodology and applications. We surveyed the literature on ML multi-omic data integration and quantitatively explored the goals, techniques and data involved in this field. We were particularly interested in examining how researchers use ML to deal with the volume and complexity of these datasets. RESULTS: Our main finding is that the methods used are those that address the challenges of datasets with few samples and many features. Dimensionality reduction methods are used to reduce the feature count alongside models that can also appropriately handle relatively few samples. Popular techniques include autoencoders, random forests and support vector machines. We also found that the field is heavily influenced by the use of The Cancer Genome Atlas dataset, which is accessible and contains many diverse experiments. AVAILABILITY AND IMPLEMENTATION: All data and processing scripts are available at this GitLab repository: https://gitlab.com/polavieja_lab/ml_multi-omics_review/ or in Zenodo: https://doi.org/10.5281/zenodo.7361807. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The gut microbiome is a significant risk factor for future chronic lung disease.

BACKGROUND: The gut-lung axis is generally recognized, but there are few large studies of the gut microbiome and incident respiratory disease in adults. OBJECTIVE: We sought to investigate the association and predictive capacity of the gut microbiome for incident asthma and chronic obstructive pulmonary disease (COPD). METHODS: Shallow metagenomic sequencing was performed for stool samples from a prospective, population-based cohort (FINRISK02; N = 7115 adults) with linked national administrative health register-derived classifications for incident asthma and COPD up to 15 years after baseline. Generalized linear models and Cox regressions were used to assess associations of microbial taxa and diversity with disease occurrence. Predictive models were constructed using machine learning with extreme gradient boosting. Models considered taxa abundances individually and in combination with other risk factors, including sex, age, body mass index, and smoking status. RESULTS: A total of 695 and 392 statistically significant associations were found between baseline taxonomic groups and incident asthma and COPD, respectively. Gradient boosting decision trees of baseline gut microbiome abundance predicted incident asthma and COPD in the validation data sets with mean area under the curves of 0.608 and 0.780, respectively. Cox analysis showed that the baseline gut microbiome achieved higher predictive performance than individual conventional risk factors, with C-indices of 0.623 for asthma and 0.817 for COPD. The integration of the gut microbiome and conventional risk factors further improved prediction capacities. CONCLUSIONS: The gut microbiome is a significant risk factor for incident asthma and incident COPD and is largely independent of conventional risk factors.

Potential pathobionts in vaginal microbiota are affected by fish oil and/or probiotics intervention in overweight and obese pregnant women.

New means to stabilize the microbial balance during pregnancy could benefit maternal health. Our objectives were to investigate in overweight/obese pregnant women 1) the impact of long-chain polyunsaturated fatty acids (fish oil) and/or probiotics on the vaginal microbiota, 2) its relation to gestational diabetes mellitus (GDM) and 3) its interaction with vaginal active matrix metalloproteinase-8 (aMMP-8) and serum high sensitivity C-reactive protein (hsCRP) and phosphorylated insulin-like growth factor-binding protein-1 (phIGFBP-1), IGFBP-1 and aMMP-8. The women were allocated to fish oil + placebo, probiotics + placebo, fish oil + probiotics and placebo + placebo-groups, from early pregnancy onwards (fish oil: 1.9 g docosahexaenoic acid and 0.22 g eicosapentaenoic acid; probiotics: Lacticaseibacillus rhamnosus HN001 (formerly Lactobacillus rhamnosus HN001) and Bifidobacterium animalis ssp. lactis 420, 1010 colony-forming units each). Vaginal and serum samples (early pregnancy, n = 112; late pregnancy, n = 116), were analyzed for vaginal microbiota using 16S rRNA gene amplicon sequencing and vaginal aMMP-8 and serum hsCRP, aMMP-8, phIGFBP-1 and IGFBP-1 by immunoassays. GDM was diagnosed from a 2-h 75 g OGTT. ClinicalTrials.gov, NCT01922791. The intervention exerted effects on many low-abundant bacteria. Compared to the placebo-group, there was a lower abundance of potential pathobionts, namely Ureaplasma urealyticum in the fish oil-group, Ureaplasma, U. urealyticum and Prevotella disiens in the probiotics-group, Dialister invisus and Prevotella timonensis in the fish oil + probiotics-group. Moreover, probiotics decreased the abundance of a few potential pathobionts during pregnancy. Many bacteria were related to GDM. The vaginal aMMP-8 level correlated significantly with α-diversity and inversely with two Lactobacillus species. Dietary interventions, especially probiotics, may have beneficial effects on the vaginal microbiota during pregnancy.

Combined effects of host genetics and diet on human gut microbiota and incident disease in a single population cohort.

Human genetic variation affects the gut microbiota through a complex combination of environmental and host factors. Here we characterize genetic variations associated with microbial abundances in a single large-scale population-based cohort of 5,959 genotyped individuals with matched gut microbial metagenomes, and dietary and health records (prevalent and follow-up). We identified 567 independent SNP-taxon associations. Variants at the LCT locus associated with Bifidobacterium and other taxa, but they differed according to dairy intake. Furthermore, levels of Faecalicatena lactaris associated with ABO, and suggested preferential utilization of secreted blood antigens as energy source in the gut. Enterococcus faecalis levels associated with variants in the MED13L locus, which has been linked to colorectal cancer. Mendelian randomization analysis indicated a potential causal effect of Morganella on major depressive disorder, consistent with observational incident disease analysis. Overall, we identify and characterize the intricate nature of host-microbiota interactions and their association with disease.

Impact of combined consumption of fish oil and probiotics on the serum metabolome in pregnant women with overweight or obesity.

BACKGROUND: If a pregnant woman is overweight, this can evoke metabolic alterations that may have health consequences for both mother and child. METHODS: Pregnant women with overweight/obesity (n = 358) received fish oil+placebo, probiotics+placebo, fish oil+probiotics or placebo+placebo from early pregnancy onwards. The serum metabolome was analysed from fasting samples with a targeted NMR-approach in early and late pregnancy. GDM was diagnosed by OGTT. FINDINGS: The intervention changed the metabolic profile of the women, but the effect was influenced by their GDM status. In women without GDM, the changes in nine lipids (FDR<0.05) in the fish oil+placebo-group differed when compared to the placebo+placebo-group. The combination of fish oil and probiotics induced changes in more metabolites, 46 of the lipid metabolites differed in women without GDM when compared to placebo+placebo-group; these included reduced increases in the concentrations and lipid constituents of VLDL-particles and less pronounced alterations in the ratios of various lipids in several lipoproteins. In women with GDM, no differences were detected in the changes of any metabolites due to any of the interventions when compared to the placebo+placebo-group (FDR<0.05). INTERPRETATION: Fish oil and particularly the combination of fish oil and probiotics modified serum lipids in pregnant women with overweight or obesity, while no such effects were seen with probiotics alone. The effects were most evident in the lipid contents of VLDL and LDL only in women without GDM. FUNDING: State Research Funding for university-level health research in the Turku University Hospital Expert Responsibility Area, Academy of Finland, the Diabetes Research Foundation, the Juho Vainio Foundation, Janssen Research & Development, LLC.

Gut microbiota of patients with different subtypes of gastric cancer and gastrointestinal stromal tumors.

BACKGROUND: Gastric adenocarcinoma is associated with H. pylori infection and inflammation that can result in the dysbiosis of gastric microbiota. The association of intestinal microbiota with gastric adenocarcinoma subtypes or with gastric gastrointestinal stromal tumors (GIST) is however not well known. Therefore, we performed 16S rRNA gene sequencing on DNA isolated from stool samples of Finnish patients and controls to study differences in microbiota among different histological subtypes of gastric adenocarcinoma, gastric GIST and healthy controls. RESULTS: We found that gut microbiota alpha diversity was lowest in diffuse adenocarcinoma patients, followed by intestinal type and GIST patients, although the differences were not significant compared to controls. Beta-diversity analysis however showed significant differences in microbiota composition for all subtypes compared to controls. Significantly higher abundance of Enterobacteriaceae was observed in both adenocarcinoma subtypes, whereas lower abundance of Bifidobacteriaceae was seen only in diffuse adenocarcinoma and of Oscillibacter in intestinal adenocarcinoma. Both GIST and adenocarcinoma patients had higher abundance of Enterobacteriaceae and lower abundance of Lactobacillaceae and Oscillibacter while lower abundance of Lachnoclostridium, Bifidobacterium, Parabacteroides and Barnesiella was seen only in the adenocarcinoma patients. CONCLUSIONS: Our analysis shows association of higher Enterobacteriaceae abundance with all types of gastric tumors. Therefore it could be potentially useful as a marker of gastric malignancies. Lower gut microbiota diversity might be indicative of poorly differentiated, invasive, advanced or aggressive tumors and could possibly be a prognostic marker for gastric tumors.

Partial restoration of normal intestinal microbiota in morbidly obese women six months after bariatric surgery.

We studied the impact of bariatric surgery on the intestinal microbiota of morbidly obese study subjects. A total of 13 morbidly obese women (five of which had type 2 diabetes) and 14 healthy age- and gender-matched controls were recruited and the microbiota composition of fecal samples were determined by using a phylogenetic microarray. Sampling of the patients took place just one month before and 6 months after the operation. Within six months after bariatric surgery, the obese subjects had lost on average a quarter of their weight whereas four of the five of the diabetic subjects were in remission. Bariatric surgery was associated with an increased microbial community richness and Bacteroidetes/Firmicutes ratio. In addition, we observed an increased relative abundance of facultative anaerobes, such as Streptococcus spp., and a reduction in specific butyrate-producing Firmicutes. The observed postoperative alterations in intestinal microbiota reflect adaptation to the changing conditions in the gastrointestinal tract, such as energy restriction and the inability to process fiber-rich foods after bariatric surgery.

Gut Microbiota and Host Gene Mutations in Colorectal Cancer Patients and Controls of Iranian and Finnish Origin.

BACKGROUND/AIM: Gut microbiota plays an important role in colorectal cancer (CRC) and its composition in CRC patients can be influenced by ethnicity and tumour genomics. Herein, the aim was to study the possible associations of ethnicity and gene mutations with the gut microbiota in CRC patients. MATERIALS AND METHODS: Bacterial composition in stool samples of 83 CRC patients and 60 controls from Iran and Finland was studied by 16S rRNA gene sequencing. The association of gut microbiota composition with CRC, host mutations in KRAS, NRAS and TP53, and ethnicity analysed. RESULTS: Beta diversity analysis indicated significant differences between the Iranian and Finnish gut microbiota composition, in both controls and patients' groups. The Iranian controls had higher abundance of Prevotella and lower abundance of Bacteroides compared to the Finnish controls, while the Finnish patients had higher abundance of Clostridium compared to Iranian patients. Abundance of Ruminococcus was higher in patients compared to the controls. Higher abundances of Herbaspirillum, Catenibacterium and lower abundances of Barnesiella were associated with mutations in NRAS, TP53, and RAS respectively. CONCLUSION: A possible link of host gene mutations with gut bacterial composition is suggested.

Stool Microbiota Composition Differs in Patients with Stomach, Colon, and Rectal Neoplasms.

BACKGROUND: Microbial ecosystems that inhabit the human gut form central component of our physiology and metabolism, regulating and modulating both health and disease. Changes or disturbances in the composition and activity of this gut microbiota can result in altered immunity, inflammation, and even cancer. AIM: To compare the composition and diversity of gut microbiota in stool samples from patient groups based on the site of neoplasm in the gastrointestinal tract (GIT) and to assess the possible contribution of the bacterial composition to tumorigenesis. METHODS: We studied gut microbiota by16S RNA gene sequencing from stool DNA of 83 patients, who were diagnosed with different GIT neoplasms, and 13 healthy individuals. RESULTS: As compared to healthy individuals, stools of patients with stomach neoplasms had elevated levels of Enterobacteriaceae, and those with rectal neoplasms had lower levels of Bifidobacteriaceae. Lower abundance of Lactobacillaceae was seen in patients with colon neoplasms. Abundance of Lactobacillaceae was higher in stools of GIT patients sampled after cancer treatment compared to samples collected before start of any treatment. In addition to site-specific differences, higher abundances of Ruminococcus, Subdoligranulum and lower abundances of Lachnoclostridium and Oscillibacter were observed in overall GIT neoplasms as compared to healthy controls CONCLUSION: Our study demonstrates that the alterations in gut microbiota vary according to the site of GIT neoplasm. The observed lower abundance of two common families, Lactobacillaceae and Bifidobacteriaceae, and the increased abundance of Enterobacteriaceae could provide indicators of compromised gut health and potentially facilitate GIT disease monitoring.

Stability estimation of autoregulated genes under Michaelis-Menten-type kinetics.

Feedback loops are typical motifs appearing in gene regulatory networks. In some well-studied model organisms, including Escherichia coli, autoregulated genes, i.e., genes that activate or repress themselves through their protein products, are the only feedback interactions. For these types of interactions, the Michaelis-Menten (MM) formulation is a suitable and widely used approach, which always leads to stable steady-state solutions representative of homeostatic regulation. However, in many other biological phenomena, such as cell differentiation, cancer progression, and catastrophes in ecosystems, one might expect to observe bistable switchlike dynamics in the case of strong positive autoregulation. To capture this complex behavior we use the generalized family of MM kinetic models. We give a full analysis regarding the stability of autoregulated genes. We show that the autoregulation mechanism has the capability to exhibit diverse cellular dynamics including hysteresis, a typical characteristic of bistable systems, as well as irreversible transitions between bistable states. We also introduce a statistical framework to estimate the kinetics parameters and probability of different stability regimes given observational data. Empirical data for the autoregulated gene SCO3217 in the SOS system in Streptomyces coelicolor are analyzed. The coupling of a statistical framework and the mathematical model can give further insight into understanding the evolutionary mechanisms toward different cell fates in various systems.

Fat, fibre and cancer risk in African Americans and rural Africans.

Rates of colon cancer are much higher in African Americans (65:100,000) than in rural South Africans (<5:100,000). The higher rates are associated with higher animal protein and fat, and lower fibre consumption, higher colonic secondary bile acids, lower colonic short-chain fatty acid quantities and higher mucosal proliferative biomarkers of cancer risk in otherwise healthy middle-aged volunteers. Here we investigate further the role of fat and fibre in this association. We performed 2-week food exchanges in subjects from the same populations, where African Americans were fed a high-fibre, low-fat African-style diet and rural Africans a high-fat, low-fibre western-style diet, under close supervision. In comparison with their usual diets, the food changes resulted in remarkable reciprocal changes in mucosal biomarkers of cancer risk and in aspects of the microbiota and metabolome known to affect cancer risk, best illustrated by increased saccharolytic fermentation and butyrogenesis, and suppressed secondary bile acid synthesis in the African Americans.

Copy number alterations and neoplasia-specific mutations in MELK, PDCD1LG2, TLN1, and PAX5 at 9p in different neoplasias.

Genetic alterations affecting 9p are commonly present in many cancer types and many cancer-related genes are located in this chromosomal region. We sequenced all of the genes located in a 32Mb region of 9p by targeted next generation sequencing (NGS) in 96 patients with different cancer types, including acute lymphoblastic leukemia, bone malignant fibrous histiocytoma/undifferentiated pleomorphic sarcoma, fibrosarcoma, Ewing's sarcoma, and lung carcinoma. Copy number alterations (CNA), and mutations were studied from the NGS data. We detected a deletion at the CDKN2A locus as being the most frequent genetic alteration in all cancer types. In addition to this locus, NGS also identified other small regions of copy number loss and gain. However, different cancer types did not reveal any statistically significant differences with regard to CNA frequency or type. Of the 191 genes within the target region, two novel recurrent mutations were found in the MELK and PDCD1LG2 genes. The most commonly mutated gene in sarcomas was TLN1 (8%) and PAX5 in ALL (9%). Mutations in PAX5, and RUSC2, were seen exclusively in ALL patients and those in KIAA1432, CA9, TLN1, and MELK only in sarcomas (MFH, FS, EFT). Thus using targeted NGS of the 9p region, in addition to commonly deleted CDKN2A locus, we were able to identify a number of small deletions and gains, as well as novel recurrent mutations in different cancer types. © 2014 Wiley Periodicals, Inc.

Targeted resequencing of 9p in acute lymphoblastic leukemia yields concordant results with array CGH and reveals novel genomic alterations.

Genetic alterations of the short arm of chromosome 9 are frequent in acute lymphoblastic leukemia. We performed targeted sequencing of 9p region in 35 adolescent and adult acute lymphoblastic leukemia patients and sought to investigate the sensitivity of detecting copy number alterations in comparison with array comparative genomic hybridization (aCGH), and besides, to detect novel genetic anomalies. We found a high concordance of copy number variations (CNVs) as detected by next generation sequencing (NGS) and aCGH. By both methodologies, the recurrent deletion at CDKN2A/B locus was identified, whereas NGS revealed additional, small regions of CNVs, seen more frequently in adult patients, while aCGH was better at detecting larger CNVs. Also, by NGS, we detected novel structural variations, novel SNVs and small insertion/deletion variants. Our results show that NGS, in addition to detecting mutations and other genetic aberrations, can be used to study CNVs.

Cancer gene prioritization by integrative analysis of mRNA expression and DNA copy number data: a comparative review.

A variety of genome-wide profiling techniques are available to investigate complementary aspects of genome structure and function. Integrative analysis of heterogeneous data sources can reveal higher level interactions that cannot be detected based on individual observations. A standard integration task in cancer studies is to identify altered genomic regions that induce changes in the expression of the associated genes based on joint analysis of genome-wide gene expression and copy number profiling measurements. In this review, we highlight common approaches to genomic data integration and provide a transparent benchmarking procedure to quantitatively compare method performances in cancer gene prioritization. Algorithms, data sets and benchmarking results are available at http://intcomp.r-forge.r-project.org.

Homozygous deletions of cadherin genes in chondrosarcoma-an array comparative genomic hybridization study.

Chondrosarcoma is a malignant bone tumor that is often resistant to chemotherapy and radiotherapy. We applied high resolution oligonucleotide array comparative genomic hybridization to 46 tumor specimens from 44 patients with chondrosarcoma and identified several genes with potential importance for the development of chondrosarcoma. Several homozygous deletions were detected. The tumor suppressor genes CDKN2A and MTAP were each homozygously deleted in four of the cases, and the RB1 gene was homozygously deleted in one. Two homozygous deletions of MTAP did not affect CDKN2A. Deletions were also found to affect genes of the cadherin family, including CDH4 and CDH7, each of which had a targeted homozygous loss in one case, and CDH19, which had a targeted homozygous loss in two cases. Loss of the EXT1 and EXT2 genes was uncommon; EXT1 was homozygously deleted in none and EXT2 in two of the cases, and large heterozygous losses including EXT1 and/or EXT2 were seen in three cases. Targeted gains and amplifications affected the MYC, E2F3, CDK6, PDGFRA, KIT, and PDGFD genes in one case each. The data indicate that chondrosarcomas develop through a combination of genomic imbalances that often affect the RB1 signaling pathway. The inactivation of cadherin genes may also be critical in the pathogenesis of the tumor.