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Background In high-throughput studies, hundreds to millions of hypotheses are typically tested. Statistical methods that control the false discovery rate (FDR) have emerged as popular and powerful tools for error rate control. While classic FDR methods use only p values as input, more modern FDR methods have been shown to increase power by incorporating complementary information as informative covariates to prioritize, weight, and group hypotheses. However, there is currently no consensus on how the modern methods compare to one another. We investigate the accuracy, applicability, and ease of use of two classic and six modern FDR-controlling methods by performing a systematic benchmark comparison using simulation studies as well as six case studies in computational biology. Results Methods that incorporate informative covariates are modestly more powerful than classic approaches, and do not underperform classic approaches, even when the covariate is completely uninformative. The majority of methods are successful at controlling the FDR, with the exception of two modern methods under certain settings. Furthermore, we find that the improvement of the modern FDR methods over the classic methods increases with the informativeness of the covariate, total number of hypothesis tests, and proportion of truly non-null hypotheses. Conclusions Modern FDR methods that use an informative covariate provide advantages over classic FDR-controlling procedures, with the relative gain dependent on the application and informativeness of available covariates. We present our findings as a practical guide and provide recommendations to aid researchers in their choice of methods to correct for false discoveries.

ABSTRACT Despite high initial efficacy, targeted therapies eventually fail in advanced cancers, as tumors develop resistance and relapse. In contrast to the substantial body of research on the molecular mechanisms of resistance, understanding of how resistance evolves remains limited. Using an experimental model of ALK positive NSCLC, we explored the evolution of resistance to different clinical ALK inhibitors. We found that resistance can originate from heterogeneous, weakly resistant subpopulations with variable sensitivity to different ALK inhibitors. Instead of the commonly assumed stochastic single hit (epi) mutational transition, or drug-induced reprogramming, we found evidence for a hybrid scenario involving the gradual, multifactorial adaptation to the inhibitors through acquisition of multiple cooperating genetic and epigenetic adaptive changes. Additionally, we found that during this adaptation tumor cells might present unique, temporally restricted collateral sensitivities, absent in therapy naïve or fully resistant cells, suggesting the potential for new therapeutic interventions, directed against evolving resistance. Acquired resistance to cancer therapies reflects the ability of cancers to adapt to therapy-imposed selective pressures. Here, the authors elucidate the dynamics of developing resistance to ALK inhibitors in an ALK+ lung cancer cell line showing that resistance originates from drug-specific tolerant cancer cells and it develops as a gradual adaptation.

Purpose Estrogen-receptor (ER) and progesterone-receptor (PR) expression levels in breast cancer, which have been principally compared via binomial descriptors, can vary widely across tumors. We sought to characterize ER and PR expression levels using semi-quantitative analyses of receptor staining in germline pathogenic variant (PV) carriers of cancer predisposition genes. Methods We conducted a retrospective chart review of patients who underwent germline genetic testing for cancer predisposition genes at a tertiary cancer center genetics clinic. We performed comparisons of semi-quantitative ER and PR percentage staining levels across carriers and non-carriers of cancer predisposition genes. Results Breast cancers from BRCA1 PV carriers expressed significantly lower ER (15.2% vs 78.2%, p  < 0.001) and lower PR (6.8% vs 41.1%, p  < 0.001) staining compared to non-PV carriers. Similarly, breast cancers of BRCA2 (66.7% vs 78.2%, p  = 0.005) and TP53 (50.6% vs 78.2%, p  = 0.015) PV tumors also displayed moderate decreases in ER staining. Conversely, CHEK2 tumors displayed higher ER (93.1% vs 78.2%, p  = 0.005) and PR (72% vs 48.8%, p  = 0.001) staining when compared to non-PV carriers. We observed a wide range of dispersion across the ER and PR staining levels of the carriers and noncarriers. ER and PR ranges of dispersion of CHEK2 tumors were uniquely narrower than all other groups. Conclusion The findings of our study suggest that precise expression levels of ER and PR in breast cancers can vary widely. These differences are further augmented when comparing expression staining across PV and non-PV carriers, suggesting potentially unique tumorigenesis and progression pathways influenced by germline cancer predisposition genes.

Primary effusion lymphoma (PEL) has a very poor prognosis. To evaluate the contributions of enhancers/promoters interactions to PEL cell growth and survival, here we produce H3K27ac HiChIP datasets in PEL cells. This allows us to generate the PEL enhancer connectome, which links enhancers and promoters in PEL genome-wide. We identify more than 8000 genomic interactions in each PEL cell line. By incorporating HiChIP data with H3K27ac ChIP-seq data, we identify interactions between enhancers/enhancers, enhancers/promoters, and promoters/promoters. HiChIP further links PEL super-enhancers to PEL dependency factors MYC, IRF4, MCL1, CCND2, MDM2, and CFLAR. CRISPR knock out of MEF2C and IRF4 significantly reduces MYC and IRF4 super-enhancer H3K27ac signal. Knock out also reduces MYC and IRF4 expression. CRISPRi perturbation of these super-enhancers by tethering transcription repressors to enhancers significantly reduces target gene expression and reduces PEL cell growth. These data provide insights into PEL molecular pathogenesis. Primary effusion lymphoma (PEL) has a very poor prognosis. Here, the authors perform H3K27ac HiChIP in PEL cells and generate the PEL enhancer connectome, linking enhancers and promoters in PEL, as well as super-enhancers to dependency factors.

Epstein-Barr virus (EBV) uses latency programs to colonize the memory B-cell reservoir, and each program is associated with human malignancies. However, knowledge remains incomplete of epigenetic mechanisms that maintain the highly restricted latency I program, present in memory and Burkitt lymphoma cells, in which EBNA1 is the only EBV-encoded protein expressed. Given increasing appreciation that higher order chromatin architecture is an important determinant of viral and host gene expression, we investigated roles of Wings Apart-Like Protein Homolog (WAPL), a host factor that unloads cohesin to control DNA loop size and that was discovered as an EBNA2-associated protein. WAPL knockout (KO) in Burkitt cells de-repressed LMP1 and LMP2A expression, but not other EBV oncogenes, to yield a viral program reminiscent of EBV latency II, which is rarely observed in B-cells. WAPL KO also increased LMP1/2A levels in latency III lymphoblastoid cells. WAPL KO altered EBV genome architecture, triggering formation of DNA loops between the LMP promoter region and the EBV origins of lytic replication ( oriLyt ). Hi-C analysis further demonstrated that WAPL KO reprogrammed EBV genomic DNA looping. LMP1 and LMP2A de-repression correlated with decreased histone repressive marks at their promoters. We propose that EBV coopts WAPL to negatively regulate latent membrane protein expression to maintain Burkitt latency I.

Background Somatic copy number alternations (SCNAs) can be utilized to infer tumor subclonal populations in whole genome seuqncing studies, where usually their read count ratios between tumor-normal paired samples serve as the inferring proxy. Existing SCNA based subclonal population inferring tools consider the GC bias of tumor and normal sample is of the same fature, and could be fully offset by read count ratio. However, we found that, the read count ratio on SCNA segments presents a Log linear biased pattern, which influence existing read count ratios based subclonal inferring tools performance. Currently no correction tools take into account the read ratio bias. Results We present Pre-SCNAClonal, a tool that improving tumor subclonal population inferring by correcting GC-bias at SCNAs level. Pre-SCNAClonal first corrects GC bias using Markov chain Monte Carlo probability model, then accurately locates baseline DNA segments (not containing any SCNAs) with a hierarchy clustering model. We show Pre-SCNAClonal’s superiority to exsiting GC-bias correction methods at any level of subclonal population. Conclusions Pre-SCNAClonal could be run independently as well as serving as pre-processing/gc-correction step in conjuntion with exsiting SCNA-based subclonal inferring tools.

Epstein-Barr virus (EBV) immortalization of resting B lymphocytes (RBLs) to lymphoblastoid cell lines (LCLs) models human DNA tumor virus oncogenesis. RBL and LCL chromatin interaction maps are compared to identify the spatial and temporal genome architectural changes during EBV B cell transformation. EBV induces global genome reorganization where contact domains frequently merge or subdivide during transformation. Repressed B compartments in RBLs frequently switch to active A compartments in LCLs. LCLs gain 40% new contact domain boundaries. Newly gained LCL boundaries have strong CTCF binding at their borders while in RBLs, the same sites have much less CTCF binding. Some LCL CTCF sites also have EBV nuclear antigen (EBNA) leader protein EBNALP binding. LCLs have more local interactions than RBLs at LCL dependency factors and super-enhancer targets. RNA Pol II HiChIP and FISH of RBL and LCL further validate the Hi-C results. EBNA3A inactivation globally alters LCL genome interactions. EBNA3A inactivation reduces CTCF and RAD21 DNA binding. EBNA3C inactivation rewires the looping at the CDKN2A/B and AICDA loci. Disruption of a CTCF site at AICDA locus increases AICDA expression. These data suggest that EBV controls lymphocyte growth by globally reorganizing host genome architecture to facilitate the expression of key oncogenes. The dynamic and temporal changes of host genome architecture during Epstein-Barr virus (EBV) transformation are not well known. Here the authors transform human primary B lymphocyte into lymphoblastoid cell lines (LCLs) with EBV and show that the host 3D genome is rewired to facilitate expression of key oncogenes.

Super enhancers (SE), large genomic elements that activate transcription and drive cell identity, have been found with cancer-specific gene regulation in human cancers. Recent studies reported the importance of understanding the cooperation and function of SE internal components, i.e., the constituent enhancers (CE). However, there are no pan-cancer studies to identify cancer-specific SE signatures at the constituent level. Here, by revisiting pan-cancer SE activities with H3K27Ac ChIP-seq datasets, we report fingerprint SE signatures for 28 cancer types in the NCI-60 cell panel. We implement a mixture model to discriminate active CEs from inactive CEs by taking into consideration ChIP-seq variabilities between cancer samples and across CEs. We demonstrate that the model-based estimation of CE states provides improved functional interpretation of SE-associated regulation. We identify cancer-specific CEs by balancing their active prevalence with their capability of encoding cancer type identities. We further demonstrate that cancer-specific CEs have the strongest per-base enhancer activities in independent enhancer sequencing assays, suggesting their importance in understanding critical SE signatures. We summarize fingerprint SEs based on the cancer-specific statuses of their component CEs and build an easy-to-use R package to facilitate the query, exploration, and visualization of fingerprint SEs across cancers.

Epstein-Barr virus (EBV) was discovered as the first human tumor virus in endemic Burkitt lymphoma, the most common childhood cancer in sub-Saharan Africa. In Burkitt lymphoma and in 200,000 EBV-associated cancers per year, epigenetic mechanisms maintain viral latency, during which lytic cycle factors are silenced. This property complicated EBV’s discovery and facilitates tumor immunoevasion. DNA methylation and chromatin-based mechanisms contribute to lytic gene silencing. Here, we identified histone chaperones CAF1 and HIRA, which have key roles in host DNA replication-dependent and replication-independent pathways, respectively, as important for EBV latency. EBV strongly upregulates CAF1 in newly infected B-cells, where viral genomes acquire histone 3.1 and 3.3 variants prior to the first mitosis. Since histone chaperones ATRX and DAXX also function in maintenance of EBV latency, our results suggest that EBV coopts multiple histone pathways to reprogram viral genomes and highlight targets for lytic induction therapeutic strategies. Epstein-Barr virus (EBV) infects 95% of adults worldwide and causes infectious mononucleosis. EBV is associated with endemic Burkitt lymphoma, Hodgkin lymphoma, posttransplant lymphomas, nasopharyngeal and gastric carcinomas. In these cancers and in most infected B-cells, EBV maintains a state of latency, where nearly 80 lytic cycle antigens are epigenetically suppressed. To gain insights into host epigenetic factors necessary for EBV latency, we recently performed a human genome-wide CRISPR screen that identified the chromatin assembly factor CAF1 as a putative Burkitt latency maintenance factor. CAF1 loads histones H3 and H4 onto newly synthesized host DNA, though its roles in EBV genome chromatin assembly are uncharacterized. Here, we found that CAF1 depletion triggered lytic reactivation and virion secretion from Burkitt cells, despite also strongly inducing interferon-stimulated genes. CAF1 perturbation diminished occupancy of histones 3.1 and 3.3 and of repressive histone 3 lysine 9 and 27 trimethyl (H3K9me3 and H3K27me3) marks at multiple viral genome lytic cycle regulatory elements. Suggestive of an early role in establishment of latency, EBV strongly upregulated CAF1 expression in newly infected primary human B-cells prior to the first mitosis, and histone 3.1 and 3.3 were loaded on the EBV genome by this time point. Knockout of CAF1 subunit CHAF1B impaired establishment of latency in newly EBV-infected Burkitt cells. A nonredundant latency maintenance role was also identified for the DNA synthesis-independent histone 3.3 loader histone regulatory homologue A (HIRA). Since EBV latency also requires histone chaperones alpha thalassemia/mental retardation syndrome X-linked chromatin remodeler (ATRX) and death domain-associated protein (DAXX), EBV coopts multiple host histone pathways to maintain latency, and these are potential targets for lytic induction therapeutic approaches. IMPORTANCE Epstein-Barr virus (EBV) was discovered as the first human tumor virus in endemic Burkitt lymphoma, the most common childhood cancer in sub-Saharan Africa. In Burkitt lymphoma and in 200,000 EBV-associated cancers per year, epigenetic mechanisms maintain viral latency, during which lytic cycle factors are silenced. This property complicated EBV’s discovery and facilitates tumor immunoevasion. DNA methylation and chromatin-based mechanisms contribute to lytic gene silencing. Here, we identified histone chaperones CAF1 and HIRA, which have key roles in host DNA replication-dependent and replication-independent pathways, respectively, as important for EBV latency. EBV strongly upregulates CAF1 in newly infected B-cells, where viral genomes acquire histone 3.1 and 3.3 variants prior to the first mitosis. Since histone chaperones ATRX and DAXX also function in maintenance of EBV latency, our results suggest that EBV coopts multiple histone pathways to reprogram viral genomes and highlight targets for lytic induction therapeutic strategies.

Pathway-level survival analysis offers the opportunity to examine molecular pathways and immune signatures that influence patient outcomes. However, available survival analysis algorithms are limited in pathway-level function and lack a streamlined analytical process. Here we present a comprehensive pathway-level survival analysis suite, PATH-SURVEYOR, which includes a Shiny user interface with extensive features for systematic exploration of pathways and covariates in a Cox proportional-hazard model. Moreover, our framework offers an integrative strategy for performing Hazard Ratio ranked Gene Set Enrichment Analysis and pathway clustering. As an example, we applied our tool in a combined cohort of melanoma patients treated with checkpoint inhibition (ICI) and identified several immune populations and biomarkers predictive of ICI efficacy. We also analyzed gene expression data of pediatric acute myeloid leukemia (AML) and performed an inverse association of drug targets with the patient’s clinical endpoint. Our analysis derived several drug targets in high-risk KMT2A-fusion-positive patients, which were then validated in AML cell lines in the Genomics of Drug Sensitivity database. Altogether, the tool offers a comprehensive suite for pathway-level survival analysis and a user interface for exploring drug targets, molecular features, and immune populations at different resolutions.