Genomewide copy number alteration screening of circulating plasma DNA: potential for the detection of incipient tumors.

Background: Early cancer diagnosis might improve survival rates. As circulating tumor DNA (ctDNA) carries cancer-specific modifications, it has great potential as a noninvasive biomarker for detection of incipient tumors. Patients and methods: We collected cell-free DNA (cfDNA) samples of 1002 elderly without a prior malignancy, carried out whole-genome massive parallel sequencing and scrutinized the mapped sequences for the presence of (sub)chromosomal copy number alterations (CNAs) predictive for a malignancy. When imbalances were detected, 6-monthly clinical follow-up was carried out. Results: In 3% of participants chromosomal imbalances were detected. Follow-up analyses, including whole-body MRI screening, confirmed the presence of five hematologic malignancies: one Hodgkin lymphoma (HL), stage II; three non-HL (type chronic lymphocytic leukemia, Rai I-Binet A; type SLL, stage III; type mucosa-associated lymphoid tissue, stage I) and one myelodysplastic syndrome with excess blasts, stage II. The CNAs detected in cfDNA were tumor-specific. Furthermore, one case was identified with monoclonal B-cell lymphocytosis, a potential precursor of B-cell malignancy. In 24 additional individuals, CNAs were identified but no cancer diagnosis was made. For 9 of them, the aberrant cfDNA profile originated from peripheral blood cells. For 15 others the origin of aberrations in cfDNA remains undetermined. Conclusion(s): Genomewide profiling of cfDNA in apparently healthy individuals enables the detection of incipient hematologic malignancies as well as clonal mosaicism with unknown clinical significance. CNA screening of cellular DNA of peripheral blood in elderly has established that clonal mosaicism for these chromosomal anomalies predicts a 5- to 10-fold enhanced risk of a subsequent cancer. We demonstrate that cfDNA screening detects CNAs, which are not only derived from peripheral blood, but even more from other tissues. Since the clinical relevance of clonal mosaics in other tissues remains unknown, long-term follow-up is warranted. Taken together, this study demonstrates that genomewide cfDNA analysis has potential as an unbiased screening approach for hematological malignancies and premalignant conditions.

A four-gene methylation marker panel as triage test in high-risk human papillomavirus positive patients.

Cervical neoplasia-specific biomarkers, e.g. DNA methylation markers, with high sensitivity and specificity are urgently needed to improve current population-based screening on (pre)malignant cervical neoplasia. We aimed to identify new cervical neoplasia-specific DNA methylation markers and to design and validate a methylation marker panel for triage of high-risk human papillomavirus (hr-HPV) positive patients. First, high-throughput quantitative methylation-specific PCRs (QMSP) on a novel OpenArray™ platform, representing 424 primers of 213 cancer specific methylated genes, were performed on frozen tissue samples from 84 cervical cancer patients and 106 normal cervices. Second, the top 20 discriminating methylation markers were validated by LightCycler® MSP on frozen tissue from 27 cervical cancer patients and 20 normal cervices and ROCs and test characteristics were assessed. Three new methylation markers were identified (JAM3, EPB41L3 and TERT), which were subsequently combined with C13ORF18 in our four-gene methylation panel. In a third step, our methylation panel detected in cervical scrapings 94% (70/74) of cervical cancers, while in a fourth step 82% (32/39) cervical intraepithelial neoplasia grade 3 or higher (CIN3+) and 65% (44/68) CIN2+ were detected, with 21% positive cases for ≤CIN1 (16/75). Finally, hypothetical scenario analysis showed that primary hr-HPV testing combined with our four-gene methylation panel as a triage test resulted in a higher identification of CIN3 and cervical cancers and a higher percentage of correct referrals compared to hr-HPV testing in combination with conventional cytology. In conclusion, our four-gene methylation panel might provide an alternative triage test after primary hr-HPV testing.

Accurate prediction of protein functional class from sequence in the Mycobacterium tuberculosis and Escherichia coli genomes using data mining.

The analysis of genomics data needs to become as automated as its generation. Here we present a novel data-mining approach to predicting protein functional class from sequence. This method is based on a combination of inductive logic programming clustering and rule learning. We demonstrate the effectiveness of this approach on the M. tuberculosis and E. coli genomes, and identify biologically interpretable rules which predict protein functional class from information only available from the sequence. These rules predict 65% of the ORFs with no assigned function in M. tuberculosis and 24% of those in E. coli, with an estimated accuracy of 60-80% (depending on the level of functional assignment). The rules are founded on a combination of detection of remote homology, convergent evolution and horizontal gene transfer. We identify rules that predict protein functional class even in the absence of detectable sequence or structural homology. These rules give insight into the evolutionary history of M. tuberculosis and E. coli.

Warmr: a data mining tool for chemical data.

Data mining techniques are becoming increasingly important in chemistry as databases become too large to examine manually. Data mining methods from the field of Inductive Logic Programming (ILP) have potential advantages for structural chemical data. In this paper we present Warmr, the first ILP data mining algorithm to be applied to chemoinformatic data. We illustrate the value of Warmr by applying it to a well studied database of chemical compounds tested for carcinogenicity in rodents. Data mining was used to find all frequent substructures in the database, and knowledge of these frequent substructures is shown to add value to the database. One use of the frequent substructures was to convert them into probabilistic prediction rules relating compound description to carcinogenesis. These rules were found to be accurate on test data, and to give some insight into the relationship between structure and activity in carcinogenesis. The substructures were also used to prove that there existed no accurate rule, based purely on atom-bond substructure with less than seven conditions, that could predict carcinogenicity. This results put a lower bound on the complexity of the relationship between chemical structure and carcinogenicity. Only by using a data mining algorithm, and by doing a complete search, is it possible to prove such a result. Finally the frequent substructures were shown to add value by increasing the accuracy of statistical and machine learning programs that were trained to predict chemical carcinogenicity. We conclude that Warmr, and ILP data mining methods generally, are an important new tool for analysing chemical databases.

The utility of different representations of protein sequence for predicting functional class.

MOTIVATION: Data Mining Prediction (DMP) is a novel approach to predicting protein functional class from sequence. DMP works even in the absence of a homologous protein of known function. We investigate the utility of different ways of representing protein sequence in DMP (residue frequencies, phylogeny, predicted structure) using the Escherichia coli genome as a model. RESULTS: Using the different representations DMP learnt prediction rules that were more accurate than default at every level of function using every type of representation. The most effective way to represent sequence was using phylogeny (75% accuracy and 13% coverage of unassigned ORFs at the most general level of function: 69% accuracy and 7% coverage at the most detailed). We tested different methods for combining predictions from the different types of representation. These improved both the accuracy and coverage of predictions, e.g. 40% of all unassigned ORFs could be predicted at an estimated accuracy of 60% and 5% of unassigned ORFs could be predicted at an estimated accuracy of 86%.