Enhlink infers distal and context-specific enhancer-promoter linkages.

Enhancers play a crucial role in regulating gene expression and their functional status can be queried with cell type precision using using single-cell (sc)ATAC-seq. To facilitate analysis of such data, we developed Enhlink, a novel computational approach that leverages single-cell signals to infer linkages between regulatory DNA sequences, such as enhancers and promoters. Enhlink uses an ensemble strategy that integrates cell-level technical covariates to control for batch effects and biological covariates to infer robust condition-specific links and their associated p-values. It can integrate simultaneous gene expression and chromatin accessibility measurements of individual cells profiled by multi-omic experiments for increased specificity. We evaluated Enhlink using simulated and real scATAC-seq data, including those paired with physical enhancer-promoter links enumerated by promoter capture Hi-C and with multi-omic scATAC-/RNA-seq data we generated from the mouse striatum. These examples demonstrated that our method outperforms popular alternative strategies. In conjunction with eQTL analysis, Enhlink revealed a putative super-enhancer regulating key cell type-specific markers of striatal neurons. Taken together, our analyses demonstrate that Enhlink is accurate, powerful, and provides features that can lead to novel biological insights.

Molecular response in newly diagnosed chronic-phase chronic myeloid leukemia: prediction modeling and pathway analysis.

Tyrosine kinase inhibitor therapy revolutionized chronic myeloid leukemia treatment and showed how targeted therapy and molecular monitoring could be used to substantially improve survival outcomes. We used chronic myeloid leukemia as a model to understand a critical question: why do some patients have an excellent response to therapy, while others have a poor response? We studied gene expression in whole blood samples from 112 patients from a large phase III randomized trial (clinicaltrials gov. Identifier: NCT00471497), dichotomizing cases into good responders (BCR::ABL1 ≤10% on the International Scale by 3 and 6 months and ≤0.1% by 12 months) and poor responders (failure to meet these criteria). Predictive models based on gene expression demonstrated the best performance (area under the curve =0.76, standard deviation =0.07). All of the top 20 pathways overexpressed in good responders involved immune regulation, a finding validated in an independent data set. This study emphasizes the importance of pretreatment adaptive immune response in treatment efficacy and suggests biological pathways that can be targeted to improve response.

Deep learning image analysis quantifies tumor heterogeneity and identifies microsatellite instability in colon cancer.

BACKGROUND AND OBJECTIVES: Deep learning utilizing convolutional neural networks (CNNs) applied to hematoxylin & eosin (H&E)-stained slides numerically encodes histomorphological tumor features. Tumor heterogeneity is an emerging biomarker in colon cancer that is, captured by these features, whereas microsatellite instability (MSI) is an established biomarker traditionally assessed by immunohistochemistry or polymerase chain reaction. METHODS: H&E-stained slides from The Cancer Genome Atlas (TCGA) colon cohort are passed through the CNN. Resulting imaging features are used to cluster morphologically similar slide regions. Tile-level pairwise similarities are calculated and used to generate a tumor heterogeneity score (THS). Patient-level THS is then correlated with TCGA-reported biomarkers, including MSI-status. RESULTS: H&E-stained images from 313 patients generated 534 771 tiles. Deep learning automatically identified and annotated cells by type and clustered morphologically similar slide regions. MSI-high tumors demonstrated significantly higher THS than MSS/MSI-low (p < 0.001). THS was higher in MLH1-silent versus non-silent tumors (p < 0.001). The sequencing derived MSIsensor score also correlated with THS (r = 0.51, p < 0.0001). CONCLUSIONS: Deep learning provides spatially resolved visualization of imaging-derived biomarkers and automated quantification of tumor heterogeneity. Our novel THS correlates with MSI-status, indicating that with expanded training sets, translational tools could be developed that predict MSI-status using H&E-stained images alone.

Deep learning features encode interpretable morphologies within histological images.

Convolutional neural networks (CNNs) are revolutionizing digital pathology by enabling machine learning-based classification of a variety of phenotypes from hematoxylin and eosin (H&E) whole slide images (WSIs), but the interpretation of CNNs remains difficult. Most studies have considered interpretability in a post hoc fashion, e.g. by presenting example regions with strongly predicted class labels. However, such an approach does not explain the biological features that contribute to correct predictions. To address this problem, here we investigate the interpretability of H&E-derived CNN features (the feature weights in the final layer of a transfer-learning-based architecture). While many studies have incorporated CNN features into predictive models, there has been little empirical study of their properties. We show such features can be construed as abstract morphological genes ("mones") with strong independent associations to biological phenotypes. Many mones are specific to individual cancer types, while others are found in multiple cancers especially from related tissue types. We also observe that mone-mone correlations are strong and robustly preserved across related cancers. Importantly, linear mone-based classifiers can very accurately separate 38 distinct classes (19 tumor types and their adjacent normals, AUC = [Formula: see text] for each class prediction), and linear classifiers are also highly effective for universal tumor detection (AUC = [Formula: see text]). This linearity provides evidence that individual mones or correlated mone clusters may be associated with interpretable histopathological features or other patient characteristics. In particular, the statistical similarity of mones to gene expression values allows integrative mone analysis via expression-based bioinformatics approaches. We observe strong correlations between individual mones and individual gene expression values, notably mones associated with collagen gene expression in ovarian cancer. Mone-expression comparisons also indicate that immunoglobulin expression can be identified using mones in colon adenocarcinoma and that immune activity can be identified across multiple cancer types, and we verify these findings by expert histopathological review. Our work demonstrates that mones provide a morphological H&E decomposition that can be effectively associated with diverse phenotypes, analogous to the interpretability of transcription via gene expression values. Our work also demonstrates mones can be interpreted without using a classifier as a proxy.

A community-based approach to image analysis of cells, tissues and tumors.

Emerging multiplexed imaging platforms provide an unprecedented view of an increasing number of molecular markers at subcellular resolution and the dynamic evolution of tumor cellular composition. As such, they are capable of elucidating cell-to-cell interactions within the tumor microenvironment that impact clinical outcome and therapeutic response. However, the rapid development of these platforms has far outpaced the computational methods for processing and analyzing the data they generate. While being technologically disparate, all imaging assays share many computational requirements for post-collection data processing. As such, our Image Analysis Working Group (IAWG), composed of researchers in the Cancer Systems Biology Consortium (CSBC) and the Physical Sciences - Oncology Network (PS-ON), convened a workshop on "Computational Challenges Shared by Diverse Imaging Platforms" to characterize these common issues and a follow-up hackathon to implement solutions for a selected subset of them. Here, we delineate these areas that reflect major axes of research within the field, including image registration, segmentation of cells and subcellular structures, and identification of cell types from their morphology. We further describe the logistical organization of these events, believing our lessons learned can aid others in uniting the imaging community around self-identified topics of mutual interest, in designing and implementing operational procedures to address those topics and in mitigating issues inherent in image analysis (e.g., sharing exemplar images of large datasets and disseminating baseline solutions to hackathon challenges through open-source code repositories).

Mutant U2AF1-induced alternative splicing of H2afy (macroH2A1) regulates B-lymphopoiesis in mice.

Somatic mutations in spliceosome genes are found in ∼50% of patients with myelodysplastic syndromes (MDS), a myeloid malignancy associated with low blood counts. Expression of the mutant splicing factor U2AF1(S34F) alters hematopoiesis and mRNA splicing in mice. Our understanding of the functionally relevant alternatively spliced target genes that cause hematopoietic phenotypes in vivo remains incomplete. Here, we demonstrate that reduced expression of H2afy1.1, an alternatively spliced isoform of the histone H2A variant gene H2afy, is responsible for reduced B cells in U2AF1(S34F) mice. Deletion of H2afy or expression of U2AF1(S34F) reduces expression of Ebf1 (early B cell factor 1), a key transcription factor for B cell development, and mechanistically, H2AFY is enriched at the EBF1 promoter. Induced expression of H2AFY1.1 in U2AF1(S34F) cells rescues reduced EBF1 expression and B cells numbers in vivo. Collectively, our data implicate alternative splicing of H2AFY as a contributor to lymphopenia induced by U2AF1(S34F) in mice and MDS.

Bayesian multi-source regression and monocyte-associated gene expression predict BCL-2 inhibitor resistance in acute myeloid leukemia.

The FDA recently approved eight targeted therapies for acute myeloid leukemia (AML), including the BCL-2 inhibitor venetoclax. Maximizing efficacy of these treatments requires refining patient selection. To this end, we analyzed two recent AML studies profiling the gene expression and ex vivo drug response of primary patient samples. We find that ex vivo samples often exhibit a general sensitivity to (any) drug exposure, independent of drug target. We observe that this "general response across drugs" (GRD) is associated with FLT3-ITD mutations, clinical response to standard induction chemotherapy, and overall survival. Further, incorporating GRD into expression-based regression models trained on one of the studies improved their performance in predicting ex vivo response in the second study, thus signifying its relevance to precision oncology efforts. We find that venetoclax response is independent of GRD but instead show that it is linked to expression of monocyte-associated genes by developing and applying a multi-source Bayesian regression approach. The method shares information across studies to robustly identify biomarkers of drug response and is broadly applicable in integrative analyses.

Open Problems in Extracellular RNA Data Analysis: Insights From an ERCC Online Workshop.

We now know RNA can survive the harsh environment of biofluids when encapsulated in vesicles or by associating with lipoproteins or RNA binding proteins. These extracellular RNA (exRNA) play a role in intercellular signaling, serve as biomarkers of disease, and form the basis of new strategies for disease treatment. The Extracellular RNA Communication Consortium (ERCC) hosted a two-day online workshop (April 19-20, 2021) on the unique challenges of exRNA data analysis. The goal was to foster an open dialog about best practices and discuss open problems in the field, focusing initially on small exRNA sequencing data. Video recordings of workshop presentations and discussions are available (https://exRNA.org/exRNAdata2021-videos/). There were three target audiences: experimentalists who generate exRNA sequencing data, computational and data scientists who work with those groups to analyze their data, and experimental and data scientists new to the field. Here we summarize issues explored during the workshop, including progress on an effort to develop an exRNA data analysis challenge to engage the community in solving some of these open problems.

The clonal evolution of metastatic colorectal cancer.

Tumor heterogeneity and evolution drive treatment resistance in metastatic colorectal cancer (mCRC). Patient-derived xenografts (PDXs) can model mCRC biology; however, their ability to accurately mimic human tumor heterogeneity is unclear. Current genomic studies in mCRC have limited scope and lack matched PDXs. Therefore, the landscape of tumor heterogeneity and its impact on the evolution of metastasis and PDXs remain undefined. We performed whole-genome, deep exome, and targeted validation sequencing of multiple primary regions, matched distant metastases, and PDXs from 11 patients with mCRC. We observed intricate clonal heterogeneity and evolution affecting metastasis dissemination and PDX clonal selection. Metastasis formation followed both monoclonal and polyclonal seeding models. In four cases, metastasis-seeding clones were not identified in any primary region, consistent with a metastasis-seeding-metastasis model. PDXs underrepresented the subclonal heterogeneity of parental tumors. These suggest that single sample tumor sequencing and current PDX models may be insufficient to guide precision medicine.

Multiple Myeloma DREAM Challenge reveals epigenetic regulator PHF19 as marker of aggressive disease.

While the past decade has seen meaningful improvements in clinical outcomes for multiple myeloma patients, a subset of patients does not benefit from current therapeutics for unclear reasons. Many gene expression-based models of risk have been developed, but each model uses a different combination of genes and often involves assaying many genes making them difficult to implement. We organized the Multiple Myeloma DREAM Challenge, a crowdsourced effort to develop models of rapid progression in newly diagnosed myeloma patients and to benchmark these against previously published models. This effort lead to more robust predictors and found that incorporating specific demographic and clinical features improved gene expression-based models of high risk. Furthermore, post-challenge analysis identified a novel expression-based risk marker, PHF19, which has recently been found to have an important biological role in multiple myeloma. Lastly, we show that a simple four feature predictor composed of age, ISS, and expression of PHF19 and MMSET performs similarly to more complex models with many more gene expression features included.

A multiple myeloma-specific capture sequencing platform discovers novel translocations and frequent, risk-associated point mutations in IGLL5.

Multiple myeloma (MM) is a disease of copy number variants (CNVs), chromosomal translocations, and single-nucleotide variants (SNVs). To enable integrative studies across these diverse mutation types, we developed a capture-based sequencing platform to detect their occurrence in 465 genes altered in MM and used it to sequence 95 primary tumor-normal pairs to a mean depth of 104×. We detected cases of hyperdiploidy (23%), deletions of 1p (8%), 6q (21%), 8p (17%), 14q (16%), 16q (22%), and 17p (4%), and amplification of 1q (19%). We also detected IGH and MYC translocations near expected frequencies and non-silent SNVs in NRAS (24%), KRAS (21%), FAM46C (17%), TP53 (9%), DIS3 (9%), and BRAF (3%). We discovered frequent mutations in IGLL5 (18%) that were mutually exclusive of RAS mutations and associated with increased risk of disease progression (p = 0.03), suggesting that IGLL5 may be a stratifying biomarker. We identified novel IGLL5/IGH translocations in two samples. We subjected 15 of the pairs to ultra-deep sequencing (1259×) and found that although depth correlated with number of mutations detected (p = 0.001), depth past ~300× added little. The platform provides cost-effective genomic analysis for research and may be useful in individualizing treatment decisions in clinical settings.

Expression profiling of snoRNAs in normal hematopoiesis and AML.

Small nucleolar RNAs (snoRNAs) are noncoding RNAs that contribute to ribosome biogenesis and RNA splicing by modifying ribosomal RNA and spliceosome RNAs, respectively. We optimized a next-generation sequencing approach and a custom analysis pipeline to identify and quantify expression of snoRNAs in acute myeloid leukemia (AML) and normal hematopoietic cell populations. We show that snoRNAs are expressed in a lineage- and development-specific fashion during hematopoiesis. The most striking examples involve snoRNAs located in 2 imprinted loci, which are highly expressed in hematopoietic progenitors and downregulated during myeloid differentiation. Although most snoRNAs are expressed at similar levels in AML cells compared with CD34+, a subset of snoRNAs showed consistent differential expression, with the great majority of these being decreased in the AML samples. Analysis of host gene expression, splicing patterns, and whole-genome sequence data for mutational events did not identify transcriptional patterns or genetic alterations that account for these expression differences. These data provide a comprehensive analysis of the snoRNA transcriptome in normal and leukemic cells and should be helpful in the design of studies to define the contribution of snoRNAs to normal and malignant hematopoiesis.

KRAS Mutation and Consensus Molecular Subtypes 2 and 3 Are Independently Associated with Reduced Immune Infiltration and Reactivity in Colorectal Cancer.

Purpose:KRAS mutation is a common canonical mutation in colorectal cancer, found at differing frequencies in all consensus molecular subtypes (CMS). The independent immunobiological impacts of RAS mutation and CMS are unknown. Thus, we explored the immunobiological effects of KRAS mutation across the CMS spectrum.Experimental Design: Expression analysis of immune genes/signatures was performed using The Cancer Genome Atlas (TCGA) RNA-seq and the KFSYSCC microarray datasets. Multivariate analysis included KRAS status, CMS, tumor location, MSI status, and neoantigen load. Protein expression of STAT1, HLA-class II, and CXCL10 was analyzed by digital IHC.Results: The Th1-centric co-ordinate immune response cluster (CIRC) was significantly, albeit modestly, reduced in KRAS-mutant colorectal cancer in both datasets. Cytotoxic T cells, neutrophils, and the IFNγ pathway were suppressed in KRAS-mutant samples. The expressions of STAT1 and CXCL10 were reduced at the mRNA and protein levels. In multivariate analysis, KRAS mutation, CMS2, and CMS3 were independently predictive of reduced CIRC expression. Immune response was heterogeneous across KRAS-mutant colorectal cancer: KRAS-mutant CMS2 samples have the lowest CIRC expression, reduced expression of the IFNγ pathway, STAT1 and CXCL10, and reduced infiltration of cytotoxic cells and neutrophils relative to CMS1 and CMS4 and to KRAS wild-type CMS2 samples in the TCGA. These trends held in the KFSYSCC dataset.Conclusions:KRAS mutation is associated with suppressed Th1/cytotoxic immunity in colorectal cancer, the extent of the effect being modulated by CMS subtype. These results add a novel immunobiological dimension to the biological heterogeneity of colorectal cancer. Clin Cancer Res; 24(1); 224-33. ©2017 AACR.

Recurrent somatic mutations affecting B-cell receptor signaling pathway genes in follicular lymphoma.

Follicular lymphoma (FL) is the most common form of indolent non-Hodgkin lymphoma, yet it remains only partially characterized at the genomic level. To improve our understanding of the genetic underpinnings of this incurable and clinically heterogeneous disease, whole-exome sequencing was performed on tumor/normal pairs from a discovery cohort of 24 patients with FL. Using these data and mutations identified in other B-cell malignancies, 1716 genes were sequenced in 113 FL tumor samples from 105 primarily treatment-naive individuals. We identified 39 genes that were mutated significantly above background mutation rates. CREBBP mutations were associated with inferior PFS. In contrast, mutations in previously unreported HVCN1, a voltage-gated proton channel-encoding gene and B-cell receptor signaling modulator, were associated with improved PFS. In total, 47 (44.8%) patients harbor mutations in the interconnected B-cell receptor (BCR) and CXCR4 signaling pathways. Histone gene mutations were more frequent than previously reported (identified in 43.8% of patients) and often co-occurred (17.1% of patients). A novel, recurrent hotspot was identified at a posttranslationally modified residue in the histone H2B family. This study expands the number of mutated genes described in several known signaling pathways and complexes involved in lymphoma pathogenesis (BCR, Notch, SWitch/sucrose nonfermentable (SWI/SNF), vacuolar ATPases) and identified novel recurrent mutations (EGR1/2, POU2AF1, BTK, ZNF608, HVCN1) that require further investigation in the context of FL biology, prognosis, and treatment.

Mutant U2AF1-expressing cells are sensitive to pharmacological modulation of the spliceosome.

Somatic mutations in spliceosome genes are detectable in ∼50% of patients with myelodysplastic syndromes (MDS). We hypothesize that cells harbouring spliceosome gene mutations have increased sensitivity to pharmacological perturbation of the spliceosome. We focus on mutant U2AF1 and utilize sudemycin compounds that modulate pre-mRNA splicing. We find that haematopoietic cells expressing mutant U2AF1(S34F), including primary patient cells, have an increased sensitivity to in vitro sudemycin treatment relative to controls. In vivo sudemycin treatment of U2AF1(S34F) transgenic mice alters splicing and reverts haematopoietic progenitor cell expansion induced by mutant U2AF1 expression. The splicing effects of sudemycin and U2AF1(S34F) can be cumulative in cells exposed to both perturbations-drug and mutation-compared with cells exposed to either alone. These cumulative effects may result in downstream phenotypic consequences in sudemycin-treated mutant cells. Taken together, these data suggest a potential for treating haematological cancers harbouring U2AF1 mutations with pre-mRNA splicing modulators like sudemycins.

Defining the cellular lineage hierarchy in the interfollicular epidermis of adult skin.

The interfollicular epidermis regenerates from heterogeneous basal skin cell populations that divide at different rates. It has previously been presumed that infrequently dividing basal cells known as label-retaining cells (LRCs) are stem cells, whereas non-LRCs are short-lived progenitors. Here we employ the H2B-GFP pulse-chase system in adult mouse skin and find that epidermal LRCs and non-LRCs are molecularly distinct and can be differentiated by Dlx1(CreER) and Slc1a3(CreER) genetic marking, respectively. Long-term lineage tracing and mathematical modelling of H2B-GFP dilution data show that LRCs and non-LRCs constitute two distinct stem cell populations with different patterns of proliferation, differentiation and upward cellular transport. During homeostasis, these populations are enriched in spatially distinct skin territories and can preferentially produce unique differentiated lineages. On wounding or selective killing, they can temporarily replenish each other's territory. These two discrete interfollicular stem cell populations are functionally interchangeable and intrinsically well adapted to thrive in distinct skin environments.

Mutant U2AF1 Expression Alters Hematopoiesis and Pre-mRNA Splicing In Vivo.

Heterozygous somatic mutations in the spliceosome gene U2AF1 occur in ∼ 11% of patients with myelodysplastic syndromes (MDS), the most common adult myeloid malignancy. It is unclear how these mutations contribute to disease. We examined in vivo hematopoietic consequences of the most common U2AF1 mutation using a doxycycline-inducible transgenic mouse model. Mice expressing mutant U2AF1(S34F) display altered hematopoiesis and changes in pre-mRNA splicing in hematopoietic progenitor cells by whole transcriptome analysis (RNA-seq). Integration with human RNA-seq datasets determined that common mutant U2AF1-induced splicing alterations are enriched in RNA processing genes, ribosomal genes, and recurrently mutated MDS and acute myeloid leukemia-associated genes. These findings support the hypothesis that mutant U2AF1 alters downstream gene isoform expression, thereby contributing to abnormal hematopoiesis in patients with MDS.

[(18)F]FHBG PET/CT Imaging of CD34-TK75 Transduced Donor T Cells in Relapsed Allogeneic Stem Cell Transplant Patients: Safety and Feasibility.

Described herein is a first-in-man attempt to both genetically modify T cells with an imagable suicide gene and track these transduced donor T cells in allogeneic stem cell transplantation recipients using noninvasive positron emission tomography/computerized tomography (PET/CT) imaging. A suicide gene encoding a human CD34-Herpes Simplex Virus-1-thymidine kinase (CD34-TK75) fusion enabled enrichment of retrovirally transduced T cells (TdT), control of graft-versus-host disease and imaging of TdT migration and expansion in vivo in mice and man. Analysis confirmed that CD34-TK75-enriched TdT contained no replication competent γ-retrovirus, were sensitive to ganciclovir, and displayed characteristic retroviral insertion sites (by targeted sequencing). Affinity-purified CD34-TK75(+)-selected donor T cells (1.0-13 × 10(5))/kg were infused into eight patients who relapsed after allogeneic stem cell transplantation. Six patients also were administered 9-[4-((18)F)fluoro-3-hydroxymethyl-butyl]guanine ([(18)F]FHBG) to specifically track the genetically modified donor T cells by PET/CT at several time points after infusion. All patients were assessed for graft-versus-host disease, response to ganciclovir, circulating TdT cells (using both quantitative polymerase chain reaction and [(18)F]FHBG PET/CT imaging), TdT cell clonal expansion, and immune response to the TdT. This phase 1 trial demonstrated that genetically modified T cells and [(18)F]FHBG can be safely infused in patients with relapsed hematologic malignancies after allogeneic stem cell transplantation.

SciClone: inferring clonal architecture and tracking the spatial and temporal patterns of tumor evolution.

The sensitivity of massively-parallel sequencing has confirmed that most cancers are oligoclonal, with subpopulations of neoplastic cells harboring distinct mutations. A fine resolution view of this clonal architecture provides insight into tumor heterogeneity, evolution, and treatment response, all of which may have clinical implications. Single tumor analysis already contributes to understanding these phenomena. However, cryptic subclones are frequently revealed by additional patient samples (e.g., collected at relapse or following treatment), indicating that accurately characterizing a tumor requires analyzing multiple samples from the same patient. To address this need, we present SciClone, a computational method that identifies the number and genetic composition of subclones by analyzing the variant allele frequencies of somatic mutations. We use it to detect subclones in acute myeloid leukemia and breast cancer samples that, though present at disease onset, are not evident from a single primary tumor sample. By doing so, we can track tumor evolution and identify the spatial origins of cells resisting therapy.

Clonal architecture of secondary acute myeloid leukemia defined by single-cell sequencing.

Next-generation sequencing has been used to infer the clonality of heterogeneous tumor samples. These analyses yield specific predictions-the population frequency of individual clones, their genetic composition, and their evolutionary relationships-which we set out to test by sequencing individual cells from three subjects diagnosed with secondary acute myeloid leukemia, each of whom had been previously characterized by whole genome sequencing of unfractionated tumor samples. Single-cell mutation profiling strongly supported the clonal architecture implied by the analysis of bulk material. In addition, it resolved the clonal assignment of single nucleotide variants that had been initially ambiguous and identified areas of previously unappreciated complexity. Accordingly, we find that many of the key assumptions underlying the analysis of tumor clonality by deep sequencing of unfractionated material are valid. Furthermore, we illustrate a single-cell sequencing strategy for interrogating the clonal relationships among known variants that is cost-effective, scalable, and adaptable to the analysis of both hematopoietic and solid tumors, or any heterogeneous population of cells.