CalcAMP: A New Machine Learning Model for the Accurate Prediction of Antimicrobial Activity of Peptides.

To combat infection by microorganisms host organisms possess a primary arsenal via the innate immune system. Among them are defense peptides with the ability to target a wide range of pathogenic organisms, including bacteria, viruses, parasites, and fungi. Here, we present the development of a novel machine learning model capable of predicting the activity of antimicrobial peptides (AMPs), CalcAMP. AMPs, in particular short ones (<35 amino acids), can become an effective solution to face the multi-drug resistance issue arising worldwide. Whereas finding potent AMPs through classical wet-lab techniques is still a long and expensive process, a machine learning model can be useful to help researchers to rapidly identify whether peptides present potential or not. Our prediction model is based on a new data set constructed from the available public data on AMPs and experimental antimicrobial activities. CalcAMP can predict activity against both Gram-positive and Gram-negative bacteria. Different features either concerning general physicochemical properties or sequence composition have been assessed to retrieve higher prediction accuracy. CalcAMP can be used as an promising prediction asset to identify short AMPs among given peptide sequences.

Analytical Validation of SKY92 for the Identification of High-Risk Multiple Myeloma.

Multiple myeloma (MM) is an incurable plasma cell cancer with a large variability in survival. Patients with MM classified as high risk by the SKY92 gene expression classifier are at high risk of relapse and short survival. Analytical validation of the SKY92 assay was performed with primary bone marrow specimens from 12 patients with MM and 7 reference cell line specimens. The SKY92 results were 100% concordant with the reference and/or their expected result for sensitivity, specificity, microarray stability, and RLT buffer stability. The SKY92 results were 90% concordant for primary specimen stability, 96.4% concordant for intermediate precision, and 80% to 100% concordant for RNA stability. For the cell-line reproducibility, the concordance was at least 92.9%, except for one near-cut point specimen. For the clinical specimen reproducibility, the concordance was 100%, except for two near-cut point specimens. Three independent laboratories were concordant in ≥77.8% and ≥92.9% of experiments for patient specimens and cell lines, respectively. Statistical acceptance thresholds were developed as Δ ≤1.48 (change in SKY92 score) and SD ≤0.45 (SD across SKY92 scores). Using the Clinical and Laboratory Standards Institute method of choice (EP05-A2/A3), restricted maximum likelihood, the observed Δ values (0 to 1.14) and SDs (0.22 to 0.31) passed acceptance criteria. Thus, we successfully present analytical validation for the SKY92 assay as a prognostic molecular test for individual patients with MM.

Prognostic Validation of SKY92 and Its Combination With ISS in an Independent Cohort of Patients With Multiple Myeloma.

BACKGROUND: High risk and low risk multiple myeloma patients follow a very different clinical course as reflected in their PFS and OS. To be clinically useful, methodologies used to identify high and low risk disease must be validated in representative independent clinical data and available so that patients can be managed appropriately. A recent analysis has indicated that SKY92 combined with the International Staging System (ISS) identifies patients with different risk disease with high sensitivity. PATIENTS AND METHODS: Here we computed the performance of eight gene expression based classifiers SKY92, UAMS70, UAMS80, IFM15, Proliferation Index, Centrosome Index, Cancer Testis Antigen and HM19 as well as the combination of SKY92/ISS in an independent cohort of 91 newly diagnosed MM patients. RESULTS: The classifiers identified between 9%-21% of patients as high risk, with hazard ratios (HRs) between 1.9 and 8.2. CONCLUSION: Among the eight signatures, SKY92 identified the largest proportion of patients (21%) also with the highest HR (8.2). Our analysis also validated the combination SKY92/ISS for identification of three classes; low risk (42%), intermediate risk (37%) and high risk (21%). Between low risk and high risk classes the HR is >10.

A standardized microarray assay for the independent gene expression markers in AML: EVI1 and BAALC.

High levels of BAALC, ERG, EVI1 and MN1 expression have been associated with shorter overall survival in AML but standardized and clinically validated assays are lacking. We have therefore developed and optimized an assay for standardized detection of these prognostic genes for patients with intermediate cytogenetic risk AML. In a training set of 147 intermediate cytogenetic risk cases we performed cross validations at 5 percentile steps of expression level and observed a bimodal significance profile for BAALC expression level and unimodal significance profiles for ERG and MN1 levels with no statistically significant cutoff points near the median expression level of BAALC, ERG or MN1. Of the possible cutoff points for expression levels of BAALC, ERG and MN1, just the 30th and 75th percentile of BAALC expression level and the 30th percentile of MN1 expression level cutoff points showed clinical significance. Of these only the 30th percentile of BAALC expression level reproduced in an independent verification (extended training) data set of 242 cytogenetically normal AML cases and successfully validated in an external cohort of 215 intermediate cytogenetic risk AML cases. Finally, we show independent prognostic value for high EVI1 and low BAALC in multivariate analysis with other clinically relevant molecular AML markers. We have developed a highly standardized molecular assay for the independent gene expression markers EVI1 and BAALC.

Detection of CEBPA double mutants in acute myeloid leukemia using a custom gene expression array.

Double (bi-allelic) mutations in the gene encoding the CCAAT/enhancer-binding protein-alpha (CEBPA) transcription factor have a favorable prognostic impact in acute myeloid leukemia (AML). Double mutations in CEBPA can be detected using various techniques, but it is a notoriously difficult gene to sequence due to its high GC-content. Here we developed a two-step gene expression classifier for accurate and standardized detection of CEBPA double mutations. The key feature of the two-step classifier is that it explicitly removes cases with low CEBPA expression, thereby excluding CEBPA hypermethylated cases that have similar gene expression profiles as a CEBPA double mutant, which would result in false-positive predictions. In the second step, we have developed a 55 gene signature to identity the true CEBPA double-mutation cases. This two-step classifier was tested on a cohort of 505 unselected AML cases, including 26 CEBPA double mutants, 12 CEBPA single mutants, and seven CEBPA promoter hypermethylated cases, on which its performance was estimated by a double-loop cross-validation protocol. The two-step classifier achieves a sensitivity of 96.2% (95% confidence interval [CI] 81.1 to 99.3) and specificity of 100.0% (95% CI 99.2 to 100.0). There are no false-positive detections. This two-step CEBPA double-mutation classifier has been incorporated on a microarray platform that can simultaneously detect other relevant molecular biomarkers, which allows for a standardized comprehensive diagnostic assay. In conclusion, gene expression profiling provides a reliable method for CEBPA double-mutation detection in patients with AML for clinical use.