Copy number signatures predict chromothripsis and clinical outcomes in newly diagnosed multiple myeloma

Chromothripsis is detectable in 20–30% of newly diagnosed multiple myeloma (NDMM) patients and is emerging as a new independent adverse prognostic factor. In this study we interrogate 752 NDMM patients using whole genome sequencing (WGS) to investigate the relationship of copy number (CN) signatures to chromothripsis and show they are highly associated. CN signatures are highly predictive of the presence of chromothripsis (AUC = 0.90) and can be used identify its adverse prognostic impact. The ability of CN signatures to predict the presence of chromothripsis is confirmed in a validation series of WGS comprised of 235 hematological cancers (AUC = 0.97) and an independent series of 34 NDMM (AUC = 0.87). We show that CN signatures can also be derived from whole exome data (WES) and using 677 cases from the same series of NDMM, we are able to predict both the presence of chromothripsis (AUC = 0.82) and its adverse prognostic impact. CN signatures constitute a flexible tool to identify the presence of chromothripsis and is applicable to WES and WGS data.

Copy number signatures predict chromothripsis and associate with poor clinical outcomes in patients with newly diagnosed multiple myeloma

Chromothripsis is detectable in 20-30% of newly diagnosed multiple myeloma (NDMM) patients and is emerging as a new independent adverse prognostic factor. In this study, we interrogate 752 NDMM patients using whole genome sequencing (WGS) to study the relationship of copy number (CN) signatures to chromothripsis and show they are highly associated. CN signatures are highly predictive of the presence of chromothripsis (AUC=0.90) and can be used to identify its adverse prognostic impact. The ability of CN signatures to predict the presence of chromothripsis was confirmed in a validation series of WGS comprised of 235 hematological cancers (AUC=0.97) and an independent series of 34 NDMM (AUC=0.87). We show that CN signatures can also be derived from whole exome data (WES) and using 677 cases from the same series of NDMM, we were able to predict both the presence of chromothripsis (AUC=0.82) and its adverse prognostic impact. CN signatures constitute a flexible tool to identify the presence of chromothripsis and is applicable to WES and WGS data.

TP53 abnormalities correlate with immune infiltration and associate with response to flotetuzumab immunotherapy in AML

Somatic TP53 mutations and 17p deletions with genomic loss of TP53 occur in 37% to 46% of acute myeloid leukemia (AML) with adverse-risk cytogenetics and correlate with primary induction failure, high risk of relapse, and dismal prognosis. Herein, we aimed to characterize the immune landscape of TP53-mutated AML and determine whether TP53 abnormalities identify a patient subgroup that may benefit from immunotherapy with flotetuzumab, an investigational CD123 × CD3 bispecific dual-affinity retargeting antibody (DART) molecule. The NanoString PanCancer IO360 assay was used to profile 64 diagnostic bone marrow (BM) samples from patients with TP53-mutated (n = 42) and TP53-wild-type (TP53-WT) AML (n = 22) and 45 BM samples from patients who received flotetuzumab for relapsed/refractory (R/R) AML (15 cases with TP53 mutations and/or 17p deletion). The comparison between TP53-mutated and TP53-WT primary BM samples showed higher expression of IFNG, FOXP3, immune checkpoints, markers of immune senescence, and phosphatidylinositol 3-kinase-Akt and NF-κB signaling intermediates in the former cohort and allowed the discovery of a 34-gene immune classifier prognostic for survival in independent validation series. Finally, 7 out of 15 patients (47%) with R/R AML and TP53 abnormalities showed complete responses to flotetuzumab (<5% BM blasts) on the CP-MGD006-01 clinical trial (NCT #02152956) and had significantly higher tumor inflammation signature, FOXP3, CD8, inflammatory chemokine, and PD1 gene expression scores at baseline compared with nonresponders. Patients with TP53 abnormalities who achieved a complete response experienced prolonged survival (median, 10.3 months; range, 3.3-21.3 months). These results encourage further study of flotetuzumab immunotherapy in patients with TP53-mutated AML.

Blood platelet RNA enables the detection of multiple sclerosis

Background In multiple sclerosis (MS), clinical assessment, MRI and cerebrospinal fluid are important in the diagnostic process. However, no blood biomarker has been confirmed as a useful tool in the diagnostic work-up. Objectives Blood platelets contain a rich spliced mRNA repertoire that can alter during megakaryocyte development but also during platelet formation and platelet circulation. In this proof of concept study, we evaluate the diagnostic potential of spliced blood platelet RNA for the detection of MS. Methods We isolated and sequenced platelet RNA of blood samples obtained from 57 MS patients and 66 age- and gender-matched healthy controls (HCs). 60% was used to develop a particle swarm-optimized (PSO) support vector machine classification algorithm. The remaining 40% served as an independent validation series. Results In total, 1249 RNAs with differential spliced junction expression levels were identified between platelets of MS patients as compared to HCs, including EPSTI1, IFI6, and RPS6KA3, in line with reported inflammatory signatures in the blood of MS patients. The RNAs were subsequently used as input for a MS classifier, capable of detecting MS with 80% accuracy in the independent validation series. Conclusions Spliced platelet RNA may enable the blood-based diagnosis of MS, warranting large-scale validation.

Prognostic and predictive value of an autophagy-related signature for early relapse in stages I–III colon cancer

We postulated that expression differences of autophagy-related genes are instrumental in stratifying the risk of early relapse after surgery and evaluating the prognosis of patients with stages I–III colon cancer. Therefore, propensity score matching analysis was performed between patients in early relapse group and long-term survival group from GSE39582 test series and internal validation series. Using Cox regression model, a nine-autophagy-related signature (CAPN2, ATG16L2, TP63, SIRT1, RPS6KB1, PEX3, ATG5, UVRAG, NAF1) was established to classify patients into those at high risk of early relapse (high-risk group), and those at low risk of early relapse (low-risk group). Relapse-free survival (RFS) was significantly different between the two groups in test [hazard ratio (HR): 2.019, 95% confidence interval (CI): 1.362–2.992, P < 0.001], internal validation (HR: 2.464, 95% CI: 1.196–5.079, P < 0.001) and another two external validation series (GSE14333—HR: 2.250, 95% CI: 1.227–4.126, P = 0.007; GSE33113—HR: 5.552, 95% CI: 2.098–14.693, P < 0.001). Then, based on RFS, we developed a nomogram, integrating the nine-autophagy-related classifier and four clinicopathological risk factors to evaluate prognosis of stages I–III colon cancer patients. Time-dependent receiver operating curve at 2 years showed that the integrated signature (area under curve = 0.758) had better prognostic accuracy than American Joint Committee on Cancer TNM stage (area under curve = 0.620). In conclusion, we identified and built a nine-autophagy-related signature, a credible approach to early relapse prediction in stages I–III colon cancer patients, which can assist physicians in devising more efficient therapeutic strategies.

Discovery of a Novel Five Immune-Associated Fingerprints for Classification and Survival Prediction in Chronic Lymphocytic Leukemia

Introduction Current staging methods do not accurately predict clinical outcome of patients with chronic lymphocytic leukemia (CLL) especially in the new era of immunotherapy. Recent studies suggested that immune-related gene signature predicted survival in glioblastoma, breast cancer and colorectal cancer. However, none related studies have been elucidated in CLL. Here, we hypothesized that risk-prediction model integrated with the immune signature could serve as an effective prognostic indicator in CLL. This study aimed to identify reliable and distinct immune-associated fingerprints for robust classification and survival prediction in patients with CLL. Methods A total of 720 de novo CLL patients from multiple cohorts were enrolled with informed consents in the present study. LASSO Cox regression model was utilized to calculate immune-associated risk score (I-score) in R software. Principal Component Analysis (PCA) were performed to present the distribution of risk score. Moreover, the prognostic capability of the five immune-related fingerprints was demonstrated by PCR and ROC curve analysis with leave-one-out cross validation in the training set. Functional enrichment analyses of GO and KEGG in gene expression datasets were performed. Association between I-score and hallmark gene sets from the Molecular Signatures Database (MSigDB) were analyzed using GSEA software. Furthermore, preclinical experiments were conduct to examine the pathological mechanism of constitutive genes of I-score in CLL cell lines (MEC1, EHEB) and primary cells. Additionally, genomic regulatory network was displayed in Cytoscape software. Results In the present study, we performed a comprehensive analysis to dissect the immune-associated fingerprints in CLL. A total of 305 clinical annotated CLL patients and 56 healthy donors with gene expression data were obtained from three independent cohorts. Two gene sets (immune system process, M13664 and immune response, M19817) were extracted from the MSigDB and combined to integrate the immune-related gene set containing 322 genes. Illustrated in the volcano plots, differentiated expressed genes of CLL cells comparing with normal B cells were calculated by limma test (|Log2Fold Change|>1, p<0.01; Figure 1A-C). Venn diagram was delineated to generate the specific CLL immune-associated gene expression panel, with 8 genes were identified (Figure 1D). PCA showed a different distribution pattern, confirming the enhanced immune phenotype in CLL. Then, we further investigate the association of the immuno-signature with clinical outcomes of CLL patients. By Lasso Cox regression analysis, the prognostic immune-related fingerprints were identified in the training set (Figure 2A). The risk score method was established: I-score = (-0.538)*CD3D expression+(-0.077)*CD83 expression+0.364*LAX1 expression+0.191*IL2RA expression+0.362*AIM2 expression, consisting of protective genes (CD3D, CD83) and risky genes (LAX1, IL2RA and AIM2; Figure 2B). Based on the median level of I-score as cut-off value, stratified high-risk patients were observed with significantly shorter overall survival compared with the low-risk group (Hazard Ratio, HR=5.493, p<0.001; Figure 3A). Univariate and multivariate cox regression analyses confirmed high I-score as an independent prognosis biomarker in CLL patients. To reduce disparity of diverse populations, we evaluated I-score in two independent centers of US and Germany with the same formula. The prognostic value of the immune fingerprints were corroborated in the internal validation series (HR=2.200, p=0.023; Figure 3B) and validation series (HR=1.769, p=0.004; Figure 3C). Intriguingly, we also observed the significant correlation between high I-score and 17p13 deletion (p=0.0316; Figure 3D), in accordance with patients' inferior outcome. Moreover, functional enrichment analyses of differentiated expressed genes stratified by I-score indicated that immune related BCR signaling pathway contributed to pathogenesis of CLL (Figure 4). Conclusion To date, our study provides evidence for the first time that distinct immuno-related fingerprints predict survival in CLL. I-score is demonstrated as an efficient classification tool and robust method for prognosis evaluation, greatly facilitating risk stratification and individualized management of CLL patients. Disclosures No relevant conflicts of interest to declare.

ICR142 Benchmarker: evaluating, optimising and benchmarking variant calling performance using the ICR142 NGS validation series

Evaluating, optimising and benchmarking of next generation sequencing (NGS) variant calling performance are essential requirements for clinical, commercial and academic NGS pipelines. Such assessments should be performed in a consistent, transparent and reproducible fashion, using independently, orthogonally generated data. Here we present ICR142 Benchmarker, a tool to generate outputs for assessing germline base substitution and indel calling performance using the ICR142 NGS validation series, a dataset of Illumina platform-based exome sequence data from 142 samples together with Sanger sequence data at 704 sites. ICR142 Benchmarker provides summary and detailed information on the sensitivity, specificity and false detection rates of variant callers. ICR142 Benchmarker also automatically generates a single page report highlighting key performance metrics and how performance compares to widely-used open-source tools. We used ICR142 Benchmarker with VCF files outputted by GATK, OpEx and DeepVariant to create a benchmark for variant calling performance. This evaluation revealed pipeline-specific differences and shared challenges in variant calling, for example in detecting indels in short repeating sequence motifs. We next used ICR142 Benchmarker to perform regression testing with DeepVariant versions 0.5.2 and 0.6.1. This showed that v0.6.1 improves variant calling performance, but there was evidence of minor changes in indel calling behaviour that may benefit from attention. The data also allowed us to evaluate filters to optimise DeepVariant calling, and we recommend using 30 as the QUAL threshold for base substitution calls when using DeepVariant v0.6.1. Finally, we used ICR142 Benchmarker with VCF files from two commercial variant calling providers to facilitate optimisation of their in-house pipelines and to provide transparent benchmarking of their performance. ICR142 Benchmarker consistently and transparently analyses variant calling performance based on the ICR142 NGS validation series, using the standard VCF input and outputting informative metrics to enable user understanding of pipeline performance. ICR142 Benchmarker is freely available at https://github.com/RahmanTeamDevelopment/ICR142_Benchmarker/releases.

The ICR142 NGS validation series: a resource for orthogonal assessment of NGS analysis

To provide a useful community resource for orthogonal assessment of NGS analysis software, we present the ICR142 NGS validation series. The dataset includes high-quality exome sequence data from 142 samples together with Sanger sequence data at 704 sites; 416 sites with variants and 288 sites at which variants were called by an NGS analysis tool, but no variant is present in the corresponding Sanger sequence. The dataset includes 293 indel variants and 247 negative indel sites, and thus the ICR142 validation dataset is of particular utility in evaluating indel calling performance. The FASTQ files and Sanger sequence results can be accessed in the European Genome-phenome Archive under the accession number EGAS00001001332.

ICR142 Benchmarker: evaluating, optimising and benchmarking variant calling using the ICR142 NGS validation series

Evaluating, optimising and benchmarking of next generation sequencing (NGS) variant calling performance are essential requirements for clinical, commercial and academic NGS pipelines. Such assessments should be performed in a consistent, transparent and reproducible fashion, using independently, orthogonally generated data. Here we present ICR142 Benchmarker, a tool to generate outputs for assessing variant calling performance using the ICR142 NGS validation series, a dataset of exome sequence data from 142 samples together with Sanger sequence data at 704 sites. ICR142 Benchmarker provides summary and detailed information on the sensitivity, specificity and false detection rates of variant callers. ICR142 Benchmarker also automatically generates a single page report highlighting key performance metrics and how performance compares to widely-used open-source tools. We used ICR142 Benchmarker with VCF files outputted by GATK, OpEx and DeepVariant to create a benchmark for variant calling performance. This evaluation revealed pipeline-specific differences and shared challenges in variant calling, for example in detecting indels in short repeating sequence motifs. We next used ICR142 Benchmarker to perform regression testing with versions 0.5.2 and 0.6.1 of DeepVariant. This showed that v0.6.1 improves variant calling performance, but there was evidence of some minor changes in indel calling behaviour that may benefit from attention in future updates. The data also allowed us to evaluate filters to optimise DeepVariant calling, and we recommend using 30 as the QUAL threshold for base substitution calls when using DeepVariant v0.6.1. Finally, we used ICR142 Benchmarker with VCF files from two commercial variant calling providers to facilitate optimisation of their in-house pipelines and to provide transparent benchmarking of their performance. ICR142 Benchmarker consistently and transparently analyses variant calling performance based on the ICR142 NGS validation series, using the standard VCF input and outputting informative metrics to enable user understanding of pipeline performance. ICR142 Benchmarker is freely available at https://github.com/RahmanTeamDevelopment/ICR142_Benchmarker/releases.

The ICR639 CPG NGS validation series: A resource to assess analytical sensitivity of cancer predisposition gene testing

The analytical sensitivity of a next generation sequencing (NGS) test reflects the ability of the test to detect real sequence variation. The evaluation of analytical sensitivity relies on the availability of gold-standard, validated, benchmarking datasets. For NGS analysis the availability of suitable datasets has been limited. Most laboratories undertake small scale evaluations using in-house data, and/or rely on in silico generated datasets to evaluate the performance of NGS variant detection pipelines. Cancer predisposition genes (CPGs), such as BRCA1 and BRCA2, are amongst the most widely tested genes in clinical practice today. Hundreds of providers across the world are now offering CPG testing using NGS methods. Validating and comparing the analytical sensitivity of CPG tests has proved difficult, due to the absence of comprehensive, orthogonally validated, benchmarking datasets of CPG pathogenic variants. To address this we present the ICR639 CPG NGS validation series. This dataset comprises data from 639 individuals. Each individual has sequencing data generated using the TruSight Cancer Panel (TSCP), a targeted NGS assay for the analysis of CPGs, together with orthogonally generated data showing the presence of at least one CPG pathogenic variant per individual. The set consists of 645 pathogenic variants in total. There is strong representation of the most challenging types of variants to detect, with 339 indels, including 16 complex indels and 24 with length greater than five base pairs and 74 exon copy number variations (CNVs) including 23 single exon CNVs. The series includes pathogenic variants in 31 CPGs, including 502 pathogenic variants in BRCA1 or BRCA2, making this an important comprehensive validation dataset for providers of BRCA1 and BRCA2 NGS testing. We have deposited the TSCP FASTQ files of the ICR639 series in the European Genome-phenome Archive (EGA) under accession number EGAD00001004134.