RNA Sequencing-Based Single Sample Predictors of Molecular Subtype and Risk of Recurrence for Clinical Assessment of Early-Stage Breast Cancer

ABSTRACTBackgroundMultigene expression assays for molecular subtypes and biomarkers can aid clinical management of early breast cancer. Based on RNA-sequencing we aimed to develop robust single-sample predictor (SSP) models for conventional clinical markers as well as molecular intrinsic subtype and risk of recurrence (ROR) that provide clinically relevant prognostic stratification.MethodsA uniformly accrued breast cancer cohort of 7743 patients with RNA-sequencing data from fresh tissue was divided into a training set (n=5250) and a reserved test set (n=2412). We trained SSPs for PAM50 molecular subtypes and ROR assigned by nearest-centroid (NC) methods and SSPs for conventional clinical markers from histopathology data. Additionally, SSP classifications were compared with Prosigna in two external clinical series (ABiM, n=100 and OSLO2-EMIT0, n=103 cases).ResultsIn the test set, agreement between SSP and NC classifications for PAM50 (five subtypes) and Subtype (four subtypes) was high (85%, Kappa=0.78) and very high (90%, Kappa=0.84) respectively. Accuracy for ROR risk category was high (84%, Kappa=0.75, weighted Kappa=0.90). The prognostic value for SSP and NC classification was assessed as equivalent and added clinically relevant prognostic information. Agreement for SSP and histopathology was very high or high for receptor status, while moderate and poor for Ki67 status and Nottingham histological grade (NHG), respectively. SSP concordance with Prosigna was high for subtype (OSLO=83% and ABiM=80%, Kappa=0.73 and 0.72, respectively) and moderate and high for ROR risk category (68% and 84%, Kappa=0.50 and 0.70, weighted Kappa=0.70 and 0.78). In pooled analysis, concordance between SSP and Prosigna for emulated treatment recommendation dichotomized for chemotherapy (yes vs. no) was high (85%, Kappa=0.66). In postmenopausal ER+/ HER2-/N0 patients SSP application suggested changed treatment recommendations for up to 17% of patients, with nearly balanced escalation and de-escalation of therapy.ConclusionsRobust SSP models, mimicking histopathological variables, PAM50, and ROR classifications can be derived from RNA-sequencing that closely matches clinical tests. Agreement and outcome analyses suggest that NC and SSP models are interchangeable on a group-level and nearly so on a patient level. Retrospective evaluation in ER+/HER2-/ N0 early breast cancer suggested that molecular testing could lead to a changed therapy recommendation for about one-fifth of patients.

Oxford Nanopore MinION Direct RNA-Seq for Systems Biology

Long-read direct RNA sequencing developed by Oxford Nanopore Technologies (ONT) is quickly gaining popularity for transcriptome studies, while fast turnaround time and low cost make it an attractive instrument for clinical applications. There is a growing interest to utilize transcriptome data to unravel activated biological processes responsible for disease progression and response to therapies. This trend is of particular interest for precision medicine which aims at single-patient analysis. Here we evaluated whether gene abundances measured by MinION direct RNA sequencing are suited to produce robust estimates of pathway activation for single sample scoring methods. We performed multiple RNA-seq analyses for a single sample that originated from the HepG2 cell line, namely five ONT replicates, and three replicates using Illumina NovaSeq. Two pathway scoring methods were employed—ssGSEA and singscore. We estimated the ONT performance in terms of detected protein-coding genes and average pairwise correlation between pathway activation scores using an exhaustive computational scheme for all combinations of replicates. In brief, we found that at least two ONT replicates are required to obtain reproducible pathway scores for both algorithms. We hope that our findings may be of interest to researchers planning their ONT direct RNA-seq experiments.

Applicability of single sample method to test strength parameters of sandstone

The single sample method allows the mechanical parameters of rocks to be obtained with very few rock samples; however, the method has not been widely used. This is mainly because the yield point of the single sample method is more difficult to control than the conventional triaxial compressive test and the effect of the different control methods on the measured data is not well understood. The single sample method obtains the strength parameters of the rock by loading a single rock sample with multiple stages of confining pressure. Multistage loading tests are divided into peak strength control and long-term strength control according to yield point control. In this study, multistage loading tests of sandstone were carried out to obtain strength parameters using long-term strength control. The results show that sandstones undergo seriously brittle damage in conventional triaxial compressive tests. Although the sandstones have been rigorously selected, they still vary considerably, and long-term strength points are more difficult to control. The error of strength parameters of sandstone obtained using the single sample method may exceed 20% compared to those obtained by conventional triaxial compressive tests. So this method must be used with caution for sandstones.

Tailoring the sampling time of single-sample GFR measurement according to expected renal function: a multisite audit

BackgroundThe 2018 BNMS Glomerular Filtration Rate (GFR) guidelines recommend a single-sample technique with the sampling time dictated by the expected renal function, but this is not known with any accuracy before the test. We aimed to assess whether the sampling regime suggested in the guidelines is optimal, and determine the expected error in GFR result if the sample time is chosen incorrectly. We can then infer the degree of flexibility in the sampling regime.Methods Data from 8946 patients referred for GFR assessment at 6 different hospitals for a variety of indications were reviewed. The difference between the single-sample (Fleming) GFR result at each sample time and the slope-intercept GFR result at each hospital was calculated. A second dataset of 775 studies from one hospital with nine samples collected from 5 minutes to 8 hours post injection was analysed to provide a reference GFR to which the single sample results were compared.Results Recommended single-sample times have been revised: for estimated GFR above 80 ml/min/1.73m2 a 2 hour sample is recommended, giving mean difference from slope-intercept GFR of -2.08 ml/min/1.73m2 (1333 GFR tests included). Between 30 and 80 ml/min/1.73m2 a 4 hour sample is recommended, giving a 1.95 ml/min/1.73m2 mean difference (2057 GFR tests included). The standard deviation of the differences is 3.50 ml/min/1.73m2 at 2 hours and 2.56 ml/min/1.73m2 at 4 hours for GFR results in the recommended range. It is 5.81 ml/min/1.73m2 at 2 hours and 5.70 ml/min/1.73m2 at 4 hours for GFR results outside the recommended range. ConclusionThe results of this multisite study demonstrate a reassuringly wide range of sample times for an acceptably accurate single-sample GFR result. Modified recommended single-sample times have been proposed in line with the results, and the reported errors for both sample times can be used for error analysis of a mistimed sample.

m6A-Mediated Tumor Invasion and Methylation Modification in Breast Cancer Microenvironment

Background. We analyzed the n6-methyladenosine (m6A) modification patterns of immune cells infiltrating the tumor microenvironment of breast cancer (BC) to provide a new perspective for the early diagnosis and treatment of BC. Methods. Based on 23 m6A regulatory factors, we identified m6A-related gene characteristics and m6A modification patterns in BC through unsupervised cluster analysis. To examine the differences in biological processes among various m6A modification modes, we performed genomic variation analysis. We then quantified the relative infiltration levels of different immune cell subpopulations in the tumor microenvironment of BC using the CIBERSORT algorithm and single-sample gene set enrichment analysis. Univariate Cox analysis was used to screen for m6A characteristic genes related to prognosis. Finally, we evaluated the m6A modification pattern of patients with a single BC by constructing the m6Ascore based on principal component analysis. Results. We identified three different m6A modification patterns in 2128 BC samples. A higher abundance of the immune infiltration of the m6Acluster C was indicated by the results of CIBERSORT and the single-sample gene set enrichment analysis. Based on the m6A characteristic genes obtained through screening, the m6Ascore was determined. The BC patients were segregated into m6Ascore groups of low and high categories, which revealed significant survival benefits among patients with low m6Ascores. Additionally, the high-m6Ascore group had a higher mutation frequency and was associated with low PD-L1 expression, and the m6Ascore and tumor mutation burden showed a positive correlation. In addition, treatment effects were better in patients in the high-m6Ascore group. Conclusions. In case of a single patient with BC, the immune cell infiltration characteristics of the tumor microenvironment and the m6A methylation modification pattern could be evaluated using the m6Ascore. Our results provide a foundation for improving personalized immunotherapy of BC.