Immune-checkpoint blockade has revolutionized cancer therapy. In particular, inhibition of programmed cell death protein 1 (PD-1) has been found to be effective for the treatment of metastatic melanoma and other cancers. Despite a dramatic increase in progression-free survival, a large proportion of patients do not show durable responses. Therefore, predictive biomarkers of a clinical response are urgently needed. Here we used high-dimensional single-cell mass cytometry and a bioinformatics pipeline for the in-depth characterization of the immune cell subsets in the peripheral blood of patients with stage IV melanoma before and after 12 weeks of anti-PD-1 immunotherapy. During therapy, we observed a clear response to immunotherapy in the T cell compartment. However, before commencing therapy, a strong predictor of progression-free and overall survival in response to anti-PD-1 immunotherapy was the frequency of CD14CD16HLA-DR monocytes. We confirmed this by conventional flow cytometry in an independent, blinded validation cohort, and we propose that the frequency of monocytes in PBMCs may serve in clinical decision support.

Introduction: At the prior data cutoff (February 9, 2017) the ALEX trial showed superior investigator-assessed progression-free survival (PFS) for alectinib versus crizotinib in untreated, anaplastic lymphoma kinase (ALK)-positive, advanced NSCLC (hazard ratio ¼ 0.47, 95% confidence interval: 0.34-0.65, p < 0.001). The median PFS in the alectinib arm was not reached versus 11.1 months with crizotinib. Retrospective analyses suggest that the echinoderm microtubule-associated proteinlike 4 gene-ALK variant (EML4-ALK) may influence ALK-inhibitor treatment benefit. We present updated analyses, including exploratory subgroup analysis by EML4-ALK variant, after an additional 10 months' followup (cutoff December 1, 2017). Methods: Patients were randomized to receive twice-daily alectinib, 600 mg, or crizotinib, 250 mg, until disease progression, toxicity, death, or withdrawal. PFS was determined by the investigators. Baseline plasma and tissue biomarker samples were analyzed by using hybridcapture, next-generation sequencing to determine EML4-ALK variant. Results: Baseline characteristics were balanced. Investigatorassessed PFS was prolonged with alectinib (stratified hazard ratio ¼ 0.43, 95% confidence interval: 0.32-0.58). The median PFS times were 34.8 months with alectinib and 10.9 months with crizotinib. EML4-ALK fusions were detectable in 129 patient plasma samples and 124 tissue samples; variants 1, 2, and 3/ab did not affect PFS, objective response rate, or duration of response. Investigator-assessed PFS was longer for alectinib than for crizotinib across EML4-ALK variants 1, 2, and 3a/b in plasma and tissue. Despite longer treatment duration (27.0 months in the case of alectinib versus 10.8 months in the case of crizotinib), the safety of alectinib compared favorably with that of crizotinib. Conclusion: Alectinib continues to demonstrate superior investigator-assessed PFS versus crizotinib in untreated ALK-positive NSCLC, irrespective of EML4-ALK variant.

High dimensional mass and flow cytometry (HDCyto) experiments have become a method of choice for high throughput interrogation and characterization of cell populations.Here, we present an R-based pipeline for differential analyses of HDCyto data, largely based on Bioconductor packages. We computationally define cell populations using FlowSOM clustering, and facilitate an optional but reproducible strategy for manual merging of algorithm-generated clusters. Our workflow offers different analysis paths, including association of cell type abundance with a phenotype or changes in signaling markers within specific subpopulations, or differential analyses of aggregated signals. Importantly, the differential analyses we show are based on regression frameworks where the HDCyto data is the response; thus, we are able to model arbitrary experimental designs, such as those with batch effects, paired designs and so on. In particular, we apply generalized linear mixed models to analyses of cell population abundance or cell-population-specific analyses of signaling markers, allowing overdispersion in cell count or aggregated signals across samples to be appropriately modeled. To support the formal statistical analyses, we encourage exploratory data analysis at every step, including quality control (e.g. multi-dimensional scaling plots), reporting of clustering results (dimensionality reduction, heatmaps with dendrograms) and differential analyses (e.g. plots of aggregated signals).

BackgroundRNA-seq has been a boon to the quantitative analysis of transcriptomes. A notable application is the detection of changes in transcript usage between experimental conditions. For example, discovery of pathological alternative splicing may allow the development of new treatments or better management of patients. From an analysis perspective, there are several ways to approach RNA-seq data to unravel differential transcript usage, such as annotation-based exon-level counting, differential analysis of the percentage spliced in, or quantitative analysis of assembled transcripts. The goal of this research is to compare and contrast current state-of-the-art methods, and to suggest improvements to commonly used work flows.ResultsWe assess the performance of representative work flows using synthetic data and explore the effect of using non-standard counting bin definitions as input to DEXSeq, a state-of-the-art inference engine. Although the canonical counting provided the best results overall, several non-canonical approaches were as good or better in specific aspects and most counting approaches outperformed the evaluated event- and assembly-based methods. We show that an incomplete annotation catalog can have a detrimental effect on the ability to detect differential transcript usage in transcriptomes with few isoforms per gene and that isoform-level prefiltering can considerably improve false discovery rate control.ConclusionCount-based methods generally perform well in the detection of differential transcript usage. Controlling the false discovery rate at the imposed threshold is difficult, particularly in complex organisms, but can be improved by prefiltering the annotation catalog.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0862-3) contains supplementary material, which is available to authorized users.

High-dimensional mass and flow cytometry (HDCyto) experiments have become a method of choice for high-throughput interrogation and characterization of cell populations. Here, we present an updated R-based pipeline for differential analyses of HDCyto data, largely based on Bioconductor packages. We computationally define cell populations using FlowSOM clustering, and facilitate an optional but reproducible strategy for manual merging of algorithm-generated clusters. Our workflow offers different analysis paths, including association of cell type abundance with a phenotype or changes in signalling markers within specific subpopulations, or differential analyses of aggregated signals. Importantly, the differential analyses we show are based on regression frameworks where the HDCyto data is the response; thus, we are able to model arbitrary experimental designs, such as those with batch effects, paired designs and so on. In particular, we apply generalized linear mixed models or linear mixed models to analyses of cell population abundance or cell-population-specific analyses of signaling markers, allowing overdispersion in cell count or aggregated signals across samples to be appropriately modeled. To support the formal statistical analyses, we encourage exploratory data analysis at every step, including quality control (e.g., multi-dimensional scaling plots), reporting of clustering results (dimensionality reduction, heatmaps with dendrograms) and differential analyses (e.g., plots of aggregated signals).

It is widely accepted that complex interactions between cancer cells and their surrounding microenvironment contribute to disease development, chemo-resistance and disease relapse. In light of this observed interdependency, novel therapeutic interventions that target specific cancer stroma cell lineages and their interactions are being sought. To this end, we studied a mouse model of human T cell acute lymphoblastic leukaemia (T-ALL) and used intravital microscopy to monitor the progression of disease within the bone marrow at both the tissue-wide and single cell level over time, from bone marrow seeding to development/selection of chemo-resistance. We observed highly dynamic cellular interactions and promiscuous distribution of leukaemia cells that migrated across the bone marrow, without showing any preferential association with bone marrow sub-compartments. Unexpectedly, this behaviour was maintained throughout disease development, from the earliest bone marrow seeding to response and resistance to chemotherapy. Our results reveal that T-ALL cells do not depend on specific bone marrow microenvironments for propagation of disease, nor for the selection of chemo-resistant clones, suggesting a stochastic mechanism underlies these processes. Yet, while T-ALL infiltration and progression are independent of the stroma, accumulated disease burden leads to rapid, selective remodelling of the endosteal space, resulting in a complete loss of mature osteoblastic cells whilst perivascular cells are maintained. This outcome leads to a shift in the balance of endogenous bone marrow stroma, towards a composition associated with less efficient haematopoietic stem cell function1. This novel, dynamic analysis of T-ALL interactions with the bone marrow microenvironment in vivo, supported by evidence from human T-ALL samples, highlights that future therapeutic interventions should target the migration and promiscuous interactions of cancer cells with the surrounding microenvironment, rather than specific bone marrow stroma, in order to combat the invasion by and survival of chemo-resistant T-ALL cells.

High-dimensional flow and mass cytometry allow cell types and states to be characterized in great detail by measuring expression levels of more than 40 targeted protein markers per cell at the single-cell level. However, data analysis can be difficult, due to the large size and dimensionality of datasets as well as limitations of existing computational methods. Here, we present diffcyt , a new computational framework for differential discovery analyses in high-dimensional cytometry data, based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics. Our approach provides improved statistical performance, including for rare cell populations, along with flexible experimental designs and fast runtimes in an open-source framework.

There are many instances in genomics data analyses where measurements are made on a multivariate response. For example, alternative splicing can lead to multiple expressed isoforms from the same primary transcript. There are situations where differences (e.g. between normal and disease state) in the relative ratio of expressed isoforms may have significant phenotypic consequences or lead to prognostic capabilities. Similarly, knowledge of single nucleotide polymorphisms (SNPs) that affect splicing, so-called splicing quantitative trait loci (sQTL) will help to characterize the effects of genetic variation on gene expression. RNA sequencing (RNA-seq) has provided an attractive toolbox to carefully unravel alternative splicing outcomes and recently, fast and accurate methods for transcript quantification have become available. We propose a statistical framework based on the Dirichlet-multinomial distribution that can discover changes in isoform usage between conditions and SNPs that affect relative expression of transcripts using these quantifications. The Dirichlet-multinomial model naturally accounts for the differential gene expression without losing information about overall gene abundance and by joint modeling of isoform expression, it has the capability to account for their correlated nature. The main challenge in this approach is to get robust estimates of model parameters with limited numbers of replicates. We approach this by sharing information and show that our method improves on existing approaches in terms of standard statistical performance metrics. The framework is applicable to other multivariate scenarios, such as Poly-A-seq or where beta-binomial models have been applied (e.g., differential DNA methylation). Our method is available as a Bioconductor R package called DRIMSeq.