SUMMARY: Cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) has emerged as a promising liquid biopsy technology to detect cancers and monitor treatments. While several bioinformatics tools for DNA methylation analysis have been adapted for cfMeDIP-seq data, an end-to-end pipeline and quality control framework specifically for this data type is still lacking. Here, we present the MEDIPIPE, which provides a one-stop solution for cfMeDIP-seq data quality control, methylation quantification, and sample aggregation. The major advantages of MEDIPIPE are: (i) ease of implementation and reproducibility with Snakemake containerized execution environments that will be automatically deployed via Conda; (ii) flexibility to handle different experimental settings with a single configuration file; and (iii) computationally efficiency for large-scale cfMeDIP-seq profiling data analysis and aggregation. AVAILABILITY AND IMPLEMENTATION: This pipeline is an open-source software under the MIT license and it is freely available at https://github.com/pughlab/MEDIPIPE.

Background: Identification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated computational steps like data normalization, dimensionality reduction and cell clustering. However, assigning cell type labels to cell clusters is still conducted manually by most researchers, resulting in limited documentation, low reproducibility and uncontrolled vocabularies. Two bottlenecks to automating this task are the scarcity of reference cell type gene expression signatures and the fact that some dedicated methods are available only as web servers with limited cell type gene expression signatures. Methods: In this study, we benchmarked four methods (CIBERSORT, GSEA, GSVA, and ORA) for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used scRNA-seq datasets from liver, peripheral blood mononuclear cells and retinal neurons for which reference cell type gene expression signatures were available. Results: Our results show that, in general, all four methods show a high performance in the task as evaluated by receiver operating characteristic curve analysis (average area under the curve (AUC) = 0.94, sd = 0.036), whereas precision-recall curve analyses show a wide variation depending on the method and dataset (average AUC = 0.53, sd = 0.24). Conclusions: CIBERSORT and GSVA were the top two performers. Additionally, GSVA was the fastest of the four methods and was more robust in cell type gene expression signature subsampling simulations. We provide an extensible framework to evaluate other methods and datasets at https://github.com/jdime/scRNAseq_cell_cluster_labeling.

Background: Identification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated steps from normalization to cell clustering. However, assigning cell type labels to cell clusters is often conducted manually, resulting in limited documentation, low reproducibility and uncontrolled vocabularies. This is partially due to the scarcity of reference cell type signatures and because some methods support limited cell type signatures. Methods: In this study, we benchmarked five methods representing first-generation enrichment analysis (ORA), second-generation approaches (GSEA and GSVA), machine learning tools (CIBERSORT) and network-based neighbor voting (METANEIGHBOR), for the task of assigning cell type labels to cell clusters from scRNA-seq data. We used five scRNA-seq datasets: human liver, 11 Tabula Muris mouse tissues, two human peripheral blood mononuclear cell datasets, and mouse retinal neurons, for which reference cell type signatures were available. The datasets span Drop-seq, 10X Chromium and Seq-Well technologies and range in size from ~3,700 to ~68,000 cells. Results: Our results show that, in general, all five methods perform well in the task as evaluated by receiver operating characteristic curve analysis (average area under the curve (AUC) = 0.91, sd = 0.06), whereas precision-recall analyses show a wide variation depending on the method and dataset (average AUC = 0.53, sd = 0.24). We observed an influence of the number of genes in cell type signatures on performance, with smaller signatures leading more frequently to incorrect results. Conclusions: GSVA was the overall top performer and was more robust in cell type signature subsampling simulations, although different methods performed well using different datasets. METANEIGHBOR and GSVA were the fastest methods. CIBERSORT and METANEIGHBOR were more influenced than the other methods by analyses including only expected cell types. We provide an extensible framework that can be used to evaluate other methods and datasets at https://github.com/jdime/scRNAseq_cell_cluster_labeling.

Background

CFI-400945 is a first-in-class oral inhibitor of polo-like kinase 4 (PLK4) that regulates centriole duplication. Primary objectives of this first-in-human phase 1 trial were to establish the safety and tolerability of CFI-400945 in patients with advanced solid tumours. Secondary objectives included pharmacokinetics, pharmacodynamics, efficacy, and recommended phase 2 dose (RP2D).

Methods

Continuous daily oral dosing of CFI-400945 was evaluated using a 3+3 design guided by incidence of dose-limiting toxicities (DLTs) in the first 28-day cycle. Safety was assessed by CTCAE v4.0. ORR and CBR were evaluated using RECIST v1.1.

Results

Forty-three patients were treated in dose escalation from 3 to 96 mg/day, and 9 were treated in 64 mg dose expansion. After DLT occurred at 96 and 72 mg, 64 mg was established as the RP2D. Neutropenia was a common high-grade (19%) treatment-related adverse event at ≥ 64 mg. Half-life of CFI-400945 was 9 h, with C_max achieved 2-4 h following dosing. One PR (45 cycles, ongoing) and two SD ≥ 6 months were observed (ORR = 2%; CBR = 6%).

Conclusions

CFI-400945 is well tolerated at 64 mg with dose-dependent neutropenia. Favourable pharmacokinetic profiles were achieved with daily dosing. Response rates were low without biomarker pre-selection. Disease-specific and combination studies are ongoing.

Trial registration

Clinical Trials Registration Number - NCT01954316 (Oct 1st, 2013).

Purpose

The genetic differences between human papilloma virus (HPV)-positive and -negative head and neck squamous cell carcinomas (HNSCC) remain largely unknown. To identify differential biology and novel therapeutic targets for both entities, we determined mutations and copy-number aberrations in a large cohort of locoregionally advanced HNSCC.

Experimental design

We performed massively parallel sequencing of 617 cancer-associated genes in 120 matched tumor/normal samples (42.5% HPV-positive). Mutations and copy-number aberrations were determined and results validated with a secondary method.

Results

The overall mutational burden in HPV-negative and HPV-positive HNSCC was similar with an average of 15.2 versus 14.4 somatic exonic mutations in the targeted cancer-associated genes. HPV-negative tumors showed a mutational spectrum concordant with published lung squamous cell carcinoma analyses with enrichment for mutations in TP53, CDKN2A, MLL2, CUL3, NSD1, PIK3CA, and NOTCH genes. HPV-positive tumors showed unique mutations in DDX3X, FGFR2/3 and aberrations in PIK3CA, KRAS, MLL2/3, and NOTCH1 were enriched in HPV-positive tumors. Currently targetable genomic alterations were identified in FGFR1, DDR2, EGFR, FGFR2/3, EPHA2, and PIK3CA. EGFR, CCND1, and FGFR1 amplifications occurred in HPV-negative tumors, whereas 17.6% of HPV-positive tumors harbored mutations in fibroblast growth factor receptor genes (FGFR2/3), including six recurrent FGFR3 S249C mutations. HPV-positive tumors showed a 5.8% incidence of KRAS mutations, and DNA-repair gene aberrations, including 7.8% BRCA1/2 mutations, were identified.

Conclusions

The mutational makeup of HPV-positive and HPV-negative HNSCC differs significantly, including targetable genes. HNSCC harbors multiple therapeutically important genetic aberrations, including frequent aberrations in the FGFR and PI3K pathway genes. See related commentary by Krigsfeld and Chung, p. 495.

Antitumor T cells are subject to multiple mechanisms of negative regulation. Recent findings that innate lymphoid cells (ILCs) regulate adaptive T cell responses led us to examine the regulatory potential of ILCs in the context of cancer. We identified a unique ILC population that inhibits tumor-infiltrating lymphocytes (TILs) from high-grade serous tumors, defined their suppressive capacity in vitro, and performed a comprehensive analysis of their phenotype. Notably, the presence of this CD56⁺CD3^- population in TIL cultures was associated with reduced T cell numbers, and further functional studies demonstrated that this population suppressed TIL expansion and altered TIL cytokine production. Transcriptome analysis and phenotypic characterization determined that regulatory CD56⁺CD3^- cells exhibit low cytotoxic activity, produce IL-22, and have an expression profile that overlaps with those of natural killer (NK) cells and other ILCs. NKp46 was highly expressed by these cells, and addition of anti-NKp46 antibodies to TIL cultures abrogated the ability of these regulatory ILCs to suppress T cell expansion. Notably, the presence of these regulatory ILCs in TIL cultures corresponded with a striking reduction in the time to disease recurrence. These studies demonstrate that a previously uncharacterized ILC population regulates the activity and expansion of tumor-associated T cells.

BACKGROUND: Neuroblastoma is an embryonal cancer of the developing sympathetic nervous system. The genetic contribution of rare pathogenic or likely pathogenic germline variants in patients without a family history remains unclear. METHODS: Germline DNA sequencing was performed on 786 neuroblastoma patients. The frequency of rare cancer predisposition gene pathogenic or likely pathogenic variants in patients was compared with 2 cancer-free control cohorts. Matched tumor DNA sequencing was evaluated for second hits, and germline DNA array data from 5585 neuroblastoma patients and 23 505 cancer-free control children were analyzed to identify rare germline copy number variants. Patients with germline pathogenic or likely pathogenic variants were compared with those without to test for association with clinical characteristics, tumor features, and survival. RESULTS: We observed 116 pathogenic or likely pathogenic variants involving 13.9% (109 of 786) of neuroblastoma patients, representing a statistically significant excess burden compared with cancer-free participants (odds ratio [OR] = 1.60, 95% confidence interval [CI] = 1.27 to 2.00). BARD1 harbored the most statistically significant enrichment of pathogenic or likely pathogenic variants (OR = 32.30, 95% CI = 6.44 to 310.35). Rare germline copy number variants disrupting BARD1 were identified in patients but absent in cancer-free participants (OR = 29.47, 95% CI = 1.52 to 570.70). Patients harboring a germline pathogenic or likely pathogenic variant had a worse overall survival compared with those without (P = 8.6 x 10-3). CONCLUSIONS: BARD1 is an important neuroblastoma predisposition gene harboring both common and rare germline pathogenic or likely pathogenic variations. The presence of any germline pathogenic or likely pathogenic variant in a cancer predisposition gene was independently predictive of worse overall survival. As centers move toward paired tumor-normal sequencing at diagnosis, efforts should be made to centralize data and provide an infrastructure to support cooperative longitudinal prospective studies of germline pathogenic variation.

Metastatic prostate cancer remains a major clinical challenge and metastatic lesions are highly heterogeneous and difficult to biopsy. Liquid biopsy provides opportunities to gain insights into the underlying biology. Here, using the highly sensitive enrichment-based sequencing technology, we provide analysis of 60 and 175 plasma DNA methylomes from patients with localized and metastatic prostate cancer, respectively. We show that the cell-free DNA methylome can capture variations beyond the tumor. A global hypermethylation in metastatic samples is observed, coupled with hypomethylation in the pericentromeric regions. Hypermethylation at the promoter of a glucocorticoid receptor gene NR3C1 is associated with a decreased immune signature. The cell-free DNA methylome is reflective of clinical outcomes and can distinguish different disease types with 0.989 prediction accuracy. Finally, we show the ability of predicting copy number alterations from the data, providing opportunities for joint genetic and epigenetic analysis on limited biological samples.

Prostate cancer is the second most commonly diagnosed malignancy among men worldwide. Recurrently mutated in primary and metastatic prostate tumors, FOXA1 encodes a pioneer transcription factor involved in disease onset and progression through both androgen receptor-dependent and androgen receptor-independent mechanisms. Despite its oncogenic properties however, the regulation of FOXA1 expression remains unknown. Here, we identify a set of six cis-regulatory elements in the FOXA1 regulatory plexus harboring somatic single-nucleotide variants in primary prostate tumors. We find that deletion and repression of these cis-regulatory elements significantly decreases FOXA1 expression and prostate cancer cell growth. Six of the ten single-nucleotide variants mapping to FOXA1 regulatory plexus significantly alter the transactivation potential of cis-regulatory elements by modulating the binding of transcription factors. Collectively, our results identify cis-regulatory elements within the FOXA1 plexus mutated in primary prostate tumors as potential targets for therapeutic intervention.

Medulloblastomas are the most common malignant brain tumours in children. Identifying and understanding the genetic events that drive these tumours is critical for the development of more effective diagnostic, prognostic and therapeutic strategies. Recently, our group and others described distinct molecular subtypes of medulloblastoma on the basis of transcriptional and copy number profiles. Here we use whole-exome hybrid capture and deep sequencing to identify somatic mutations across the coding regions of 92 primary medulloblastoma/normal pairs. Overall, medulloblastomas have low mutation rates consistent with other paediatric tumours, with a median of 0.35 non-silent mutations per megabase. We identified twelve genes mutated at statistically significant frequencies, including previously known mutated genes in medulloblastoma such as CTNNB1, PTCH1, MLL2, SMARCA4 and TP53. Recurrent somatic mutations were newly identified in an RNA helicase gene, DDX3X, often concurrent with CTNNB1 mutations, and in the nuclear co-repressor (N-CoR) complex genes GPS2, BCOR and LDB1. We show that mutant DDX3X potentiates transactivation of a TCF promoter and enhances cell viability in combination with mutant, but not wild-type, β-catenin. Together, our study reveals the alteration of WNT, hedgehog, histone methyltransferase and now N-CoR pathways across medulloblastomas and within specific subtypes of this disease, and nominates the RNA helicase DDX3X as a component of pathogenic β-catenin signalling in medulloblastoma.