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Enrichment of Immune signaling in the integrated pan-cancer cluster. Different level of evidence support the association of the integrated pan-cancer cluster with an immune phenotype. (A) Enrichment of T- and B-cell signaling shown on the integrated map, yellow gradient (see Supplemental Methods). (B) Enrichment of high ESTIMATE scores in the pan-cancer samples, waterfall plot with pan-cancer cluster samples in red. (C) Enrichment of the immune-related pathways identified by differential expression analysis. (D) Unsupervised analysis of master regulator activities inferred by the MARINa method. Gene clusters enriched for T- and B-cell signaling, interferon signaling, and TNFA via NFKB signaling, red font. (E) The top enriched pathways based on the output of master regulator scores derived with MARINa are immune-related. (F) Copy number events in the integrated pan-cancer cluster compared to other samples in the full cohort. The pan-cancer cluster shows a lower number of copy number events in both arm-level events and focal events. Arm-level events (pan-cancer group on the right, background cohort on the left). (G) Same as F but focal events (pan-cancer group on the right, background cohort on the left). (H) Purity estimates in the integrated pan-cancer cluster compared to all other samples in the cohort. The pan-cancer cluster (green box) shows lower purity when compared to the whole cohort as a background (yellow box). This finding is consistent with other analyses indicating high immune signaling in the pan-cancer cluster.

Supplemental Figure 1: RSS transformed correlations correct for platform-specific inflation biases. Maps produced from different molecular data types. Samples in the maps are colored by tissue of origin. (A) Similarity score distributions for every pair of samples for each individual data platform. Spearman Rho was computed for mRNA, miRNA, RPPA, SCNV, and methylation platforms. HOCUS score was used for mutation data. (B) RSS-standardized similarity scores for every pair of samples for each individual data platform. The density curves demonstrate that RSS transformation corrects for platform-specific inflation biases. (C) (i)-(vi) Maps produced from each of the six molecular data types using OpenOrd layout. (vii) mRNA expression map produced using Principal Component Analysis method. Principal components 1 and 2 are used to represent the mRNA expression space. (viii) mRNA expression map produced using tSNE method. Dimensions 1 and 2 are used to represent the mRNA expression space. (D) Maps produced from inferred gene activities using the PARADIGM and SPIA methods. (E) Maps integrating more than one molecular data type.

Molecular subtype visualization. Known biology recapitulated by the mRNA expression map. (A) BRCA molecular subtypes and their layout in the map. The map shows that tumors of the same molecular subtype tend to cluster together, with very little mixing of those subtypes, indicating that those subtypes are indeed molecularly different. (B) Estrogen signaling (yellow) and basal signaling (blue) are the top differentiating programs between the basal and non-basal BRCA tumors. Very few tumors exhibit signals of both programs (green). The group of samples labeled as Basal subtype in part A predominantly exhibits the basal signaling program and the group of samples consisting of LumA (for luminal A) and LumB (for luminal B) in part A predominantly exhibits the estrogen signaling program. (C) Association of TP53 mutations (blue) with basal breast tumors and PIK3CA mutations (yellow) with luminal tumors demonstrates at-a-glance view of genomic event association with molecular subtypes in the map. Samples that exhibit both mutations are shown in green. (D) COAD and READ tumors cluster together (left) and the map separates genomically stable and unstable tumors (right). (E) BLCA tumors separate into three previously discovered molecular subtypes. These subtypes are BLCA-core, BLCA-lung-like, and BLCA-squamous-like. (F) KIRC tumors are deficient in MSH2 (activity level indicated by the intensity of yellow), a component of the DNA mismatch repair pathway. This is a known characterization of KIRC tumors. (G) Co-occurrence of VHL mutations (green) and high HIF1A activity (indicated by the intensity of yellow) in KIRC tumors. Samples colored in yellow indicate an absence of VHL mutation but high HIF1A activity. Samples colored in green indicate both the presence of VHL mutation and high HIF1A activity. No samples show a presence of VHL mutation and low HIF1A activity (such samples would be colored in blue). (H) Spatial correlation analysis (SCA). Mutual exclusivity of RB1 mutations and CDKN2A deletions. These two events do not co-occur in the same patients even when they occur in patients clustering in the same regions of the map. (I) SCA - positive spatial correlation of RB1 mutations and PTEN mutations. In regions of the map where both of these events occur, they tend to occur in the same patients. (J) The overlap in gene signatures distinctive of the Tumor Map's relative positioning of BRCA molecular subtypes. HER2+ tumors share more differentially expressed genes in common between luminal than basal tumors. The subtype groupings are defined by BRCA sample annotations. Differential expression analysis resulted in 150-gene signatures for each subtype. Venn-diagram of these gene signatures shows the overlap between gene sets for each of the subtype.

More detail into the methodology behind the resource are provided as well as proof-of-concept benchmarks.

Vast amounts of molecular data are being collected on tumor samples, which provide unique opportunities for discovering trends within and between cancer subtypes. Such cross-cancer analyses require computational methods that enable intuitive and interactive browsing of thousands of samples based on their molecular similarity. We created a portal called TumorMap to assist in exploration and statistical interrogation of high-dimensional complex “omics” data in an interactive and easily interpretable way. In the TumorMap, samples are arranged on a hexagonal grid based on their similarity to one another in the original genomic space and are rendered with Google's Map technology. While the important feature of this public portal is the ability for the users to build maps from their own data, we pre-built genomic maps from several previously published projects. We demonstrate the utility of this portal by presenting results obtained from The Cancer Genome Atlas project data. Cancer Res; 77(21); e111–4. ©2017 AACR.

Video showing basic functionality of the TumorMap for the first-time user.

Supplemental Table 1A: Differential expression scores used in the pan-cancer cluster GSEA analysis. These scores represent integrated t-statistic scores from the differential analysis within each tissue (see Supplemental Methods). Supplemental Table 1B: Full results of the GSEA analysis of the pan-cancer cluster, based on differential expression scores. Supplemental Table 1C: Per-sample ESTIMATE scores used in analysis of the pan-cancer cluster. Supplemental Table 1D: Per-sample surrogate purity scores used in analysis of the pan-cancer cluster. Supplemental Table 1E: List of genes located on sex chromosomes that were excluded from the methylation dataset in order to analyze only autosomal gene features. Supplemental Table 1F: Attributes available in the TumorMap that annotate metadata of the samples, along with descriptions of those attributes. Supplemental Table 1G: Statistical tests computed by different attribute enrichment analysis (AEA) tools available in the TumorMap. Supplemental Table 1H: List of 82 samples in the pan-cancer cluster in the integrated map as well as tissue composition, along with the number of samples, in the integrated pan-cancer cluster. Supplemental Table 1I: Input data for the LAML survival analysis.

Supplemental Figure 5: (A) Overview of the method to compute differential expression for samples in the integrated pan-cancer group vs. other samples in the TCGA cohort. Tissue composition imbalance was corrected for by performing t-tests within each tissue. For each gene, t-statistics were computed within each tumor type separately and then summarized per-gene t-statistics were calculated as an arithmetic mean, weighted by the inverse variance of the all the tissue-specific t-statistics values. (B) The distribution of log-transformed TPM values estimated from the mRNA-Seq data showing a normal-like distribution. (C) Comparing the log-transformed TPM values of the mRNA-Seq data to a normal distribution shows a reasonable agreement with the tumor data having slightly heavier tails. (D) Venn diagram representing the innate and adaptive immune systems and different levels of evidence supporting higher activity of each of the components of those systems in the pan-cancer cluster when compared to the rest of the TCGA cohort. We found evidence of both innate and adaptive immune system signaling with a number of different analyses. (E) Detailed version of the immune-related pathway characterizing the integrated pan-cancer cluster. This version of the pathway shows additional genes and their connections that are not shown in the summarized version of the immune signaling pathway of Figure 1D of the main text.

Tissue and molecular subtype distribution among the samples in the entire cohort (A-B), and in the pan-cancer cluster (C-D). The pie charts represent the number of samples from each tissue of origin in the entire cohort (A) and the integrated pan-cancer cluster (C). Black and white matrices illustrate the presence of molecular features of each platform (x-axis) across samples (y-axis), in the entire cohort (B) or in the integrated pan-cancer cluster (D). Data available for this sample for a given platform is marked black, otherwise the entry is white.

PDF file, 260K.

PDF file, 634K, BamBam output of peaks and rearrangements for TCGA-GBM-0145, TCGA-GBM-0152 and TCGA-GBM-0648 (Whole Genome Sequence analysis).

PDF file, 215K, Reconstructing circular amplicons (S1); TCGA-GBM-06-0152 harbors 2 circular amplicons (S2); Cumulative number of peaks called in 264 GBM exomes as a function of the tumor vs. matched normal relative coverage threshold used to define a peak (S3).

PDF file, 1782K, BamBam output of peaks and rearrangements (TCGA-GBM Exome analysis).

PDF file, 142K, Data-based validation of exome analysis (Whole Genome Sequencing analysis).

The Internet of Things (IoT) provides a simple framework to control online devices easily. IoT is now a commonplace tool used by technology companies but is rarely used in biology experiments. IoT can benefit cloud biology research through alarm notifications, automation, and the real-time monitoring of experiments. We developed an IoT architecture to control biological devices and implemented it in lab experiments. Lab devices for electrophysiology, microscopy, and microfluidics were created from the ground up to be part of a unified IoT architecture. The system allows each device to be monitored and controlled from an online web tool. We present our IoT architecture so other labs can replicate it for their own experiments.

The divergence of chimpanzee and bonobo provides one of the few examples of recent hominid speciation1,2. Here we describe a fully annotated, high-quality bonobo genome assembly, which was constructed without guidance from reference genomes by applying a multiplatform genomics approach. We generate a bonobo genome assembly in which more than 98% of genes are completely annotated and 99% of the gaps are closed, including the resolution of about half of the segmental duplications and almost all of the full-length mobile elements. We compare the bonobo genome to those of other great apes1,3–5 and identify more than 5,569 fixed structural variants that specifically distinguish the bonobo and chimpanzee lineages. We focus on genes that have been lost, changed in structure or expanded in the last few million years of bonobo evolution. We produce a high-resolution map of incomplete lineage sorting and estimate that around 5.1% of the human genome is genetically closer to chimpanzee or bonobo and that more than 36.5% of the genome shows incomplete lineage sorting if we consider a deeper phylogeny including gorilla and orangutan. We also show that 26% of the segments of incomplete lineage sorting between human and chimpanzee or human and bonobo are non-randomly distributed and that genes within these clustered segments show significant excess of amino acid replacement compared to the rest of the genome., A high-quality bonobo genome assembly provides insights into incomplete lineage sorting in hominids and its relevance to gene evolution and the genetic relationship among living hominids.

Co-option of transposable elements (TEs) to become part of existing or new enhancers is an important mechanism for evolution of gene regulation. However, contributions of lineage-specific TE insertions to recent regulatory adaptations remain poorly understood. Gibbons present a suitable model to study these contributions as they have evolved a lineage-specific TE called LAVA (LINE-AluSz-VNTR-AluLIKE), which is still active in the gibbon genome. The LAVA retrotransposon is thought to have played a role in the emergence of the highly rearranged structure of the gibbon genome by disrupting transcription of cell cycle genes. In this study, we investigated whether LAVA may have also contributed to the evolution of gene regulation by adopting enhancer function. We characterized fixed and polymorphic LAVA insertions across multiple gibbons and found 96 LAVA elements overlapping enhancer chromatin states. Moreover, LAVA was enriched in multiple transcription factor binding motifs, was bound by an important transcription factor (PU.1), and was associated with higher levels of gene expression in cis. We found gibbon-specific signatures of purifying/positive selection at 27 LAVA insertions. Two of these insertions were fixed in the gibbon lineage and overlapped with enhancer chromatin states, representing putative co-opted LAVA enhancers. These putative enhancers were located within genes encoding SETD2 and RAD9A, two proteins that facilitate accurate repair of DNA double-strand breaks and prevent chromosomal rearrangement mutations. Co-option of LAVA in these genes may have influenced regulation of processes that preserve genome integrity. Our findings highlight the importance of considering lineage-specific TEs in studying evolution of gene regulatory elements.

De novo assembly of a human genome using nanopore long-read sequences has been reported but it used more than 150,000 CPU hours and weeks of wall-clock time. To enable rapid human genome assembly we present Shasta, a de novo long read assembler, and polishing algorithms named MarginPolish and HELEN. Using a single PromethION nanopore sequencer and our toolkit, we assembled eleven highly contiguous human genomes de novo in nine days. We achieved ~63x coverage, 42 Kb read N50, and 6.5x coverage in 100 Kb+ reads using three flow cells per sample. Shasta produced a complete haploid human genome assembly in under six hours on a single commercial compute node. MarginPolish and HELEN polished haploid assemblies to more than 99.9% identity (QV30) with nanopore reads alone. Addition of proximity ligation (Hi-C) sequencing enabled near chromosome-level scaffolds for all eleven genomes. We compare our assembly performance to existing methods for diploid, haploid, and trio-binned human samples and report superior accuracy and speed., Editors summary High contiguity human genomes can be assembled de novo in 6 hours using nanopore long-read sequences and the Shasta toolkit.

Background Nearly half the human genome consists of repeat elements, most of which are retrotransposons, and many of which play important biological roles. However repeat elements pose several unique challenges to current bioinformatic analyses and visualization tools, as short repeat sequences can map to multiple genomic loci resulting in their misclassification and misinterpretation. In fact, sequence data mapping to repeat elements are often discarded from analysis pipelines. Therefore, there is a continued need for standardized tools and techniques to interpret genomic data of repeats. Results We present the UCSC Repeat Browser, which consists of a complete set of human repeat reference sequences derived from annotations made by the commonly used program RepeatMasker. The UCSC Repeat Browser also provides an alignment from the human genome to these references, uses it to map the standard human genome annotation tracks, and presents all of them as a comprehensive interface to facilitate work with repetitive elements. It also provides processed tracks of multiple publicly available datasets of particular interest to the repeat community, including ChIP-seq datasets for KRAB Zinc Finger Proteins (KZNFs) – a family of proteins known to bind and repress certain classes of repeats. We used the UCSC Repeat Browser in combination with these datasets, as well as RepeatMasker annotations in several non-human primates, to trace the independent trajectories of species-specific evolutionary battles between LINE 1 retroelements and their repressors. Furthermore, we document at https://repeatbrowser.ucsc.edu how researchers can map their own human genome annotations to these reference repeat sequences. Conclusions The UCSC Repeat Browser allows easy and intuitive visualization of genomic data on consensus repeat elements, circumventing the problem of multi-mapping, in which sequencing reads of repeat elements map to multiple locations on the human genome. By developing a reference consensus, multiple datasets and annotation tracks can easily be overlaid to reveal complex evolutionary histories of repeats in a single interactive window. Specifically, we use this approach to retrace the history of several primate specific LINE-1 families across apes, and discover several species-specific routes of evolution that correlate with the emergence and binding of KZNFs.

Draft genome assemblies for multiple mammalian species combined with new technologies to map transcripts from diverse RNA samples to these genomes developed in the early 2000s revealed that the mammalian transcriptome was vastly larger and more complex than previously anticipated. Efforts to comprehensively catalog the identity and features of transcripts present in a variety of species, tissues and cell lines revealed that a large fraction of the mammalian genome is transcribed in at least some settings. A large number of these transcripts encode long non-coding RNAs (lncRNAs). Many lncRNAs overlap or are anti-sense to protein coding genes and others overlap small RNAs. However, a large number are independent of any previously known mRNA or small RNA. While the functions of a majority of these lncRNAs are unknown, many appear to play roles in gene regulation. Many lncRNAs have species-specific and cell type specific expression patterns and their evolutionary origins are varied. While technological challenges have hindered getting a full picture of the diversity and transcript structure of all of the transcripts arising from lncRNA loci, new technologies including single molecule nanopore sequencing and single cell RNA sequencing promise to generate a comprehensive picture of the mammalian transcriptome.

Simultaneous longitudinal imaging across multiple conditions and replicates has been crucial for scientific studies aiming to understand biological processes and disease. Yet, imaging systems capable of accomplishing these tasks are economically unattainable for most academic and teaching laboratories around the world. Here, we propose the Picroscope, which is the first low-cost system for simultaneous longitudinal biological imaging made primarily using off-the-shelf and 3D-printed materials. The Picroscope is compatible with standard 24-well cell culture plates and captures 3D z-stack image data. The Picroscope can be controlled remotely, allowing for automatic imaging with minimal intervention from the investigator. Here, we use this system in a range of applications. We gathered longitudinal whole organism image data for frogs, zebrafish, and planaria worms. We also gathered image data inside an incubator to observe 2D monolayers and 3D mammalian tissue culture models. Using this tool, we can measure the behavior of entire organisms or individual cells over long-time periods., Ly et al. report a multi-well imaging system (the Picroscope) that can perform longitudinal bright-field z-stack imaging for 24 wells simultaneously. They demonstrate its capability by imaging zebrafish embryos, planaria worms, development of Xenopus tropicalis over a 28-h period and embryonic stem cell monolayer and organoids inside an incubator.

New cell culture techniques have led to complex tissue models in biological experiments. For example, 3-D cerebral organoids provide a more realistic model of the human cortical tissue. However, these cell culture experiments are restricted by high costs and limited labor. A massively scalable and cost efficient platform for tissue experiments would benefit genomics, neuroscience, and translational medicine by enabling advanced high throughput tissue screens. Cloud computing and the Internet of Things (IoT) provide new tools for managing multiple experiments in parallel that are remotely controlled through automation. We introduce a cloud-based IoT architecture that takes advantage of these tools to offer an environment where researchers can run thousands of cell culture experiments at once. This technology allows studies with cell cultures to be performed at scales far beyond a single lab setting, democratizing access to advanced tissue models and enabling new avenues of research. IoT Cloud LaboratoryExperiments are automated through cloud connected devices to allow scalability, reproducibility, and online monitoring. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=177 SRC="FIGDIR/small/453595v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@14421e7org.highwire.dtl.DTLVardef@dce92org.highwire.dtl.DTLVardef@75037borg.highwire.dtl.DTLVardef@37afa9_HPS_FORMAT_FIGEXP M_FIG C_FIG

INTRODUCTION: To faithfully distribute genetic material to daughter cells during cell division, spindle fibers must couple to DNA by means of a structure called the kinetochore, which assembles at each chromosome’s centromere. Human centromeres are located within large arrays of tandemly repeated DNA sequences known as alpha satellite (αSat), which often span millions of base pairs on each chromosome. Arrays of αSat are frequently surrounded by other types of tandem satellite repeats, which have poorly understood functions, along with nonrepetitive sequences, including transcribed genes. Previous genome sequencing efforts have been unable to generate complete assemblies of satellite-rich regions because of their scale and repetitive nature, limiting the ability to study their organization, variation, and function. RATIONALE: Pericentromeric and centromeric (peri/centromeric) satellite DNA sequences have remained almost entirely missing from the assembled human reference genome for the past 20 years. Using a complete, telomere-to-telomere (T2T) assembly of a human genome, we developed and deployed tailored computational approaches to reveal the organization and evolutionary patterns of these satellite arrays at both large and small length scales. We also performed experiments to map precisely which αSat repeats interact with kinetochore proteins. Last, we compared peri/centromeric regions among multiple individuals to understand how these sequences vary across diverse genetic backgrounds. RESULTS: Satellite repeats constitute 6.2% of the T2T-CHM13 genome assembly, with αSat representing the single largest component (2.8% of the genome). By studying the sequence relationships of αSat repeats in detail across each centromere, we found genome-wide evidence that human centromeres evolve through “layered expansions.” Specifically, distinct repetitive variants arise within each centromeric region and expand through mechanisms that resemble successive tandem duplications, whereas older flanking sequences shrink and diverge over time. We also revealed that the most recently expanded repeats within each αSat array are more likely to interact with the inner kinetochore protein Centromere Protein A (CENP-A), which coincides with regions of reduced CpG methylation. This suggests a strong relationship between local satellite repeat expansion, kinetochore positioning, and DNA hypomethylation. Furthermore, we uncovered large and unexpected structural rearrangements that affect multiple satellite repeat types, including active centromeric αSat arrays. Last, by comparing sequence information from nearly 1600 individuals’ Xchromosomes, we observed that individuals with recent African ancestry possess the greatest genetic diversity in the region surrounding the centromere, which sometimes contains a predominantly African αSat sequence variant. CONCLUSION: The genetic and epigenetic properties of centromeres are closely interwoven through evolution. These findings raise important questions about the specific molecular mechanisms responsible for the relationship between inner kinetochore proteins, DNA hypomethylation, and layered αSat expansions. Even more questions remain about the function and evolution of non-αSat repeats. To begin answering these questions, we have produced a comprehensive encyclopedia of peri/centromeric sequences in a human genome, and we demonstrated how these regions can be studied with modern genomic tools. Our work also illuminates the rich genetic variation hidden within these formerly missing regions of the genome, which may contribute to health and disease. This unexplored variation underlines the need for more T2T human genome assemblies from genetically diverse individuals.

ObjectiveNeural activity represents a functional readout of neurons that is increasingly important to monitor in a wide range of experiments. Extracellular recordings have emerged as a powerful technique for measuring neural activity because these methods do not lead to the destruction or degradation of the cells being measured. Current approaches to electrophysiology have a low throughput of experiments due to manual supervision and expensive equipment. This bottleneck limits broader inferences that can be achieved with numerous long-term recorded samples.ApproachWe developed Piphys, an inexpensive open source neurophysiological recording platform that consists of both hardware and software. It is easily accessed and controlled via a standard web interface through Internet of Things (IoT) protocols.Main ResultsWe used a Raspberry Pi as the primary processing device and Intan bioamplifier. We designed a hardware expansion circuit board and software to enable voltage sampling and user interaction. This standalone system was validated with primary human neurons, showing reliability in collecting real-time neural activity.SignificanceThe hardware modules and cloud software allow for remote control of neural recording experiments as well as horizontal scalability, enabling long-term observations of development, organization, and neural activity at scale.

Background The reproducibility of gene expression measured by RNA sequencing (RNA-Seq) is dependent on the sequencing depth. While unmapped or non-exonic reads do not contribute to gene expression quantification, duplicate reads contribute to the quantification but are not informative for reproducibility. We show that mapped, exonic, non-duplicate (MEND) reads are a useful measure of reproducibility of RNA-Seq datasets used for gene expression analysis. Findings In bulk RNA-Seq datasets from 2,179 tumors in 48 cohorts, the fraction of reads that contribute to the reproducibility of gene expression analysis varies greatly. Unmapped reads constitute 1–77% of all reads (median [IQR], 3% [3–6%]); duplicate reads constitute 3–100% of mapped reads (median [IQR], 27% [13–43%]); and non-exonic reads constitute 4–97% of mapped, non-duplicate reads (median [IQR], 25% [16–37%]). MEND reads constitute 0–79% of total reads (median [IQR], 50% [30–61%]). Conclusions Because not all reads in an RNA-Seq dataset are informative for reproducibility of gene expression measurements and the fraction of reads that are informative varies, we propose reporting a dataset's sequencing depth in MEND reads, which definitively inform the reproducibility of gene expression, rather than total, mapped, or exonic reads. We provide a Docker image containing (i) the existing required tools (RSeQC, sambamba, and samblaster) and (ii) a custom script to calculate MEND reads from RNA-Seq data files. We recommend that all RNA-Seq gene expression experiments, sensitivity studies, and depth recommendations use MEND units for sequencing depth.

A high-quality rhesus macaque genome Genome technology has improved substantially since the first full organismal genomes were generated. Applying new technology, Warren et al. refined the genome of the rhesus macaque, a model nonhuman primate. Long-read technology and other recent advances in sequencing technology were applied to generate a genome with far fewer gaps and helped to refine the locations and numbers of repetitive elements. Furthermore, the authors performed resequencing among populations to identify the genetic variability of the rhesus macaque. Thus, a previously incomplete and inaccurate set of sequence information is now fully resolved, improving gene mapping for biomedical and comparative genetic studies. Science , this issue p. eabc6617

A low cost remote imaging platform for biological applications was developed. The "Picroscope" is a device that allows the user to perform longitudinal imaging studies on multi-well cell culture plates. Here we present the network architecture and software used to facilitate communication between modules within the device as well as external cloud services. A web based console was created to control the device and view experiment results. Post processing tools were developed to analyze captured data in the cloud. The result is a platform for controlling biological experiments from outside the lab., The authors Pierre V Baudin and Victoria T Ly contributed equally to this work. 21 pages, 12 figures

Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community.

Co-option of transposable elements (TEs) to become part of existing or new enhancers is an important mechanism for evolution of gene regulation. However, contributions of lineage-specific TE insertions to recent regulatory adaptations remain poorly understood. Gibbons present a suitable model to study these contributions as they have evolved a lineage-specific TE called LAVA (LINE-AluSz-VNTR-Alu(LIKE)), which is still active in the gibbon genome. The LAVA retrotransposon is thought to have played a role in the emergence of the highly rearranged structure of the gibbon genome by disrupting transcription of cell cycle genes. In this study, we investigated whether LAVA may have also contributed to the evolution of gene regulation by adopting enhancer function. We characterized fixed and polymorphic LAVA insertions across multiple gibbons and found 96 LAVA elements overlapping enhancer chromatin states. Moreover, LAVA was enriched in multiple transcription factor binding motifs, was bound by an important transcription factor (PU.1), and was associated with higher levels of gene expression in cis. We found gibbon-specific signatures of purifying/positive selection at 27 LAVA insertions. Two of these insertions were fixed in the gibbon lineage and overlapped with enhancer chromatin states, representing putative co-opted LAVA enhancers. These putative enhancers were located within genes encoding SETD2 and RAD9A, two proteins that facilitate accurate repair of DNA double-strand breaks and prevent chromosomal rearrangement mutations. Co-option of LAVA in these genes may have influenced regulation of processes that preserve genome integrity. Our findings highlight the importance of considering lineage-specific TEs in studying evolution of gene regulatory elements.

Precision oncology has primarily relied on coding mutations as biomarkers of response to therapies. While transcriptome analysis can provide valuable information, incorporation into workflows has been difficult. For example, the relative rather than absolute gene expression level needs to be considered, requiring differential expression analysis across samples. However, expression programs related to the cell-of-origin and tumor microenvironment effects confound the search for cancer-specific expression changes. To address these challenges, we developed an unsupervised clustering approach for discovering differential pathway expression within cancer cohorts using gene expression measurements. The hydra approach uses a Dirichlet process mixture model to automatically detect multimodally distributed genes and expression signatures without the need for matched normal tissue. We demonstrate that the hydra approach is more sensitive than widely-used gene set enrichment approaches for detecting multimodal expression signatures. Application of the hydra analysis framework to small blue round cell tumors (including rhabdomyosarcoma, synovial sarcoma, neuroblastoma, Ewing sarcoma, and osteosarcoma) identified expression signatures associated with changes in the tumor microenvironment. The hydra approach also identified an association between ATRX deletions and elevated immune marker expression in high-risk neuroblastoma. Notably, hydra analysis of all small blue round cell tumors revealed similar subtypes, characterized by changes to infiltrating immune and stromal expression signatures., Author summary Pediatric cancers generally have few somatic mutations. To increase the number of actionable treatment leads, precision pediatric oncology initiatives also analyze tumor gene expression patterns. However, currently available approaches for gene expression data analysis in the clinical setting often use arbitrary thresholds for assessing overexpression and assume gene expression is normally distributed. These methods also rely on reference distributions of related cancer types or normal samples for assessing expression distributions. Often adequate normal samples are not available, and comparing matched cancer cohorts without accounting for subtype expression overestimates the uncertainty in the analysis. We developed a computational framework to automatically detect multimodal expression distributions within well-defined disease populations. Our analysis of small blue round cell tumors (including rhabdomyosarcoma, synovial sarcoma, neuroblastoma, Ewing sarcoma and osteosarcoma) discovered a significant number of multimodally expressed genes. Multimodally expressed genes were associated with proliferative signaling, extracellular matrix organization, and immune signaling pathways across cancer types. Expression signatures correlated with differences in patient outcomes for MYCN non-amplified neuroblastoma, osteosarcoma, and synovial sarcoma. The low mutation rate in pediatric cancers has led some to suggest that pediatric cancers are less immunogenic. However, our analysis suggests that immune infiltration can be identified across small blue round cell tumors. Thus, further research into modulating immune cells for patient benefit may be warranted.

Genetic changes causing brain size expansion in human evolution have remained elusive. Notch signaling is essential for radial glia stem cell proliferation and is a determinant of neuronal number in the mammalian cortex. We find that three paralogs of human-specific NOTCH2NL are highly expressed in radial glia. Functional analysis reveals that different alleles of NOTCH2NL have varying potencies to enhance Notch signaling by interacting directly with NOTCH receptors. Consistent with a role in Notch signaling, NOTCH2NL ectopic expression delays differentiation of neuronal progenitors, while deletion accelerates differentiation into cortical neurons. Furthermore, NOTCH2NL genes provide the breakpoints in 1q21.1 distal deletion/duplication syndrome, where duplications are associated with macrocephaly and autism and deletions with microcephaly and schizophrenia. Thus, the emergence of human-specific NOTCH2NL genes may have contributed to the rapid evolution of the larger human neocortex, accompanied by loss of genomic stability at the 1q21.1 locus and resulting recurrent neurodevelopmental disorders.

Li Fraumeni syndrome (LFS) is a hereditary cancer predisposition syndrome caused by germline mutations in TP53. TP53 is the most common mutated gene in human cancer, occurring in 30–50% of glioblastomas (GBM). Here, we highlight a precision medicine platform to identify potential targets for a GBM patient with LFS. We used a comparative transcriptomics approach to identify genes that are uniquely overexpressed in the LFS GBM patient relative to a cancer compendium of 12,747 tumor RNA sequencing data sets, including 200 GBMs. STAT1 and STAT2 were identified as being significantly overexpressed in the LFS patient, indicating ruxolitinib, a Janus kinase 1 and 2 inhibitors, as a potential therapy. The LFS patient had the highest level of STAT1 and STAT2 expression in an institutional high-grade glioma cohort of 45 patients, further supporting the cancer compendium results. To empirically validate the comparative transcriptomics pipeline, we used a combination of adherent and organoid cell culture techniques, including ex vivo patient-derived organoids (PDOs) from four patient-derived cell lines, including the LFS patient. STAT1 and STAT2 expression levels in the four patient-derived cells correlated with levels identified in the respective parent tumors. In both adherent and organoid cultures, cells from the LFS patient were among the most sensitive to ruxolitinib compared to patient-derived cells with lower STAT1 and STAT2 expression levels. A spheroid-based drug screening assay (3D-PREDICT) was performed and used to identify further therapeutic targets. Two targeted therapies were selected for the patient of interest and resulted in radiographic disease stability. This manuscript supports the use of comparative transcriptomics to identify personalized therapeutic targets in a functional precision medicine platform for malignant brain tumors.

Objective. Neural activity represents a functional readout of neurons that is increasingly important to monitor in a wide range of experiments. Extracellular recordings have emerged as a powerful technique for measuring neural activity because these methods do not lead to the destruction or degradation of the cells being measured. Current approaches to electrophysiology have a low throughput of experiments due to manual supervision and expensive equipment. This bottleneck limits broader inferences that can be achieved with numerous long-term recorded samples. Approach. We developed Piphys, an inexpensive open source neurophysiological recording platform that consists of both hardware and software. It is easily accessed and controlled via a standard web interface through Internet of Things (IoT) protocols. Main results. We used a Raspberry Pi as the primary processing device along with an Intan bioamplifier. We designed a hardware expansion circuit board and software to enable voltage sampling and user interaction. This standalone system was validated with primary human neurons, showing reliability in collecting neural activity in near real-time. Significance. The hardware modules and cloud software allow for remote control of neural recording experiments as well as horizontal scalability, enabling long-term observations of development, organization, and neural activity at scale.

Synovial sarcoma (SS) is an aggressive soft-tissue malignancy, accounting for 10% of all soft-tissue sarcomas. These tumors can occur at any age but most often affect young adults and adolescents, developing deep in the distal extremities. The prognosis of SS tumors is poor, with a 5-year survival rate of 36-76%, a high rate of metastasis and few treatments. The purpose of this study was to identify novel overexpressed oncogenes that could serve as druggable targets for treating synovial sarcoma patients. We compared the RNA-Seq expression profiles of a cohort of 36 synovial sarcomas to our compendium of RNA-Seq expression data from 12,236 tumor samples (treehousegenomics.ucsc.edu) from pediatric and adult cancer patients. In comparing gene expression in the synovial sarcoma cohort samples against the compendium samples, gene expression outliers were defined as having expression above the gene-specific outlier threshold as defined by the Tukey's outlier method. Among the overexpression outliers, pathway enrichment analysis was used to identify common and druggable pathways, with implications for potential therapeutics for patients with SS. Our analysis identified the overexpression of members of the Sonic Hedgehog pathway in the majority of synovial sarcoma samples. For example, GLI1 expression exceeded the outlier threshold in 35 out of 36 samples. This pathway can be targeted by available small molecule inhibitors. Ongoing work focuses on evaluating the role of Sonic Hedgehog signaling in the pathogenesis of SS using pharmacological inhibition, CRISPRi studies in cell line models of the disease and nanopore sequencing. We currently have 4 patient-derived synovial sarcoma cell lines (HSSY-II, SYO-1, YAMATO, and ASKA) that we can grow in both adherent conditions and in 3D cell culture as sarcospheres. We detected the expression of the SYT-SSX fusion transcript in each of the cell lines by RT-PCR to confirm the cell lines maintained expression of the pathogenic fusion. This work has implications for using comparative tumor RNA-seq derived gene expression data for nominating novel druggable targets specific to synovial sarcoma tumors. Citation Format: Yvonne A. Vasquez, Jacob Pfeil, Letitia Mueller, Holly Beale, Alfred G. Lyle, Lauren Sanders, Katrina Learned, Ellen Kephart, Anouk van den Bout, Allison Cheney, Sahar Hosseinzadeh, Isabel Bjork, Sofie R. Salama, Olena Vaske. Identifying potential druggable targets for synovial sarcoma using comparative RNA-seq analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3035.

Vast amounts of molecular data are being collected on tumor samples, which provide unique opportunities for discovering trends within and between cancer subtypes. Such cross-cancer analyses require computational methods that enable intuitive and interactive browsing of thousands of samples based on their molecular similarity. We created a portal called TumorMap to assist in exploration and statistical interrogation of high-dimensional complex “omics” data in an interactive and easily interpretable way. In the TumorMap, samples are arranged on a hexagonal grid based on their similarity to one another in the original genomic space and are rendered with Google's Map technology. While the important feature of this public portal is the ability for the users to build maps from their own data, we pre-built genomic maps from several previously published projects. We demonstrate the utility of this portal by presenting results obtained from The Cancer Genome Atlas project data. Cancer Res; 77(21); e111–4. ©2017 AACR.

This cohort study evaluates the feasibility and utility of incorporating comparative gene expression information into the precision medicine framework for difficult-to-treat pediatric and young adult patients with cancer., Key Points Question Is it feasible and useful to compare the tumor RNA sequencing data of a child or young adult with the tumor RNA sequencing data of thousands of other patients (of all ages) in a research setting? Findings Among 144 tumor samples from children and young adults, comparative RNA sequencing analysis, conducted across 4 precision medicine studies in the United States and Canada, was feasible and potentially useful for 99 of 144 pediatric and young adult cancer samples. In contrast, DNA mutation information was potentially useful for only 34 of 74 samples. Meaning This study’s findings suggest that open sharing and combined analysis of tumor RNA sequencing data from pediatric and young adult patients treated on different clinical trials may represent a feasible approach and may produce useful clinical and biological information for individual patients., Importance Pediatric cancers are epigenetic diseases; therefore, considering tumor gene expression information is necessary for a complete understanding of the tumorigenic processes. Objective To evaluate the feasibility and utility of incorporating comparative gene expression information into the precision medicine framework for difficult-to-treat pediatric and young adult patients with cancer. Design, Setting, and Participants This cohort study was conducted as a consortium between the University of California, Santa Cruz (UCSC) Treehouse Childhood Cancer Initiative and clinical genomic trials. RNA sequencing (RNA-Seq) data were obtained from the following 4 clinical sites and analyzed at UCSC: British Columbia Children’s Hospital (n = 31), Lucile Packard Children’s Hospital at Stanford University (n = 80), CHOC Children’s Hospital and Hyundai Cancer Institute (n = 46), and the Pacific Pediatric Neuro-Oncology Consortium (n = 24). The study dates were January 1, 2016, to March 22, 2017. Exposures Participants underwent tumor RNA-Seq profiling as part of 4 separate clinical trials at partner hospitals. The UCSC either downloaded RNA-Seq data from a partner institution for analysis in the cloud or provided a Docker pipeline that performed the same analysis at a partner institution. The UCSC then compared each participant’s tumor RNA-Seq profile with more than 11 000 uniformly analyzed tumor profiles from pediatric and young adult patients with cancer, downloaded from public data repositories. These comparisons were used to identify genes and pathways that are significantly overexpressed in each patient’s tumor. Results of the UCSC analysis were presented to clinical partners. Main Outcomes and Measures Feasibility of a third-party institution (UCSC Treehouse Childhood Cancer Initiative) to obtain tumor RNA-Seq data from patients, conduct comparative analysis, and present analysis results to clinicians; and proportion of patients for whom comparative tumor gene expression analysis provided useful clinical and biological information. Results Among 144 samples from children and young adults (median age at diagnosis, 9 years; range, 0-26 years; 72 of 118 [61.0%] male [26 patients sex unknown]) with a relapsed, refractory, or rare cancer treated on precision medicine protocols, RNA-Seq–derived gene expression was potentially useful for 99 of 144 samples (68.8%) compared with DNA mutation information that was potentially useful for only 34 of 74 samples (45.9%). Conclusions and Relevance This study’s findings suggest that tumor RNA-Seq comparisons may be feasible and highlight the potential clinical utility of incorporating such comparisons into the clinical genomic interpretation framework for difficult-to-treat pediatric and young adult patients with cancer. The study also highlights for the first time to date the potential clinical utility of harmonized publicly available genomic data sets.

BackgroundThe accuracy of gene expression as measured by RNA sequencing (RNA-Seq) is dependent on the amount of sequencing performed. However, some types of reads are not informative for determining this accuracy. Unmapped and non-exonic reads do not contribute to gene expression quantification. Duplicate reads can be the product of high gene expression or technical errors.FindingsWe surveyed bulk RNA-Seq datasets from 2179 tumors in 48 cohorts to determine the fractions of uninformative reads. Total sequence depth was 0.2-668 million reads (median (med.) 61 million; interquartile range (IQR) 53 million). Unmapped reads constitute 1-77% of all reads (med. 3%; IQR 3%); duplicate reads constitute 3-100% of mapped reads (med. 27%; IQR 30%); and non-exonic reads constitute 4-97% of mapped, non-duplicate reads (med. 25%; IQR 21%). Informative reads--Mapped, Exonic, Non-duplicate (MEND) reads--constitute 0-79% of total reads (med. 50%; IQR 31%). Further, we find that MEND read counts have a 0.22 Pearson correlation to the number of genes expressed above 1 Transcript Per Million, while total reads have a correlation of −0.05.ConclusionsSince the fraction of uninformative reads vary, we propose using only definitively informative reads, MEND reads, for the purposes of asserting the accuracy of gene expression measured in a bulk RNA-Seq experiment. We provide a Docker image containing 1) the existing required tools (RSeQC, sambamba and samblaster) and 2) a custom script. We recommend that all results, sensitivity studies and depth recommendations use MEND units.