Search

To enable computational analysis of regulatory networks within the cancer cell in its natural tumor microenvironment, we develop a two-stage histoepigenetic analysis method. The first stage involves iterative computational deconvolution to estimate sample-specific cancer-cell intrinsic expression of a gene of interest. The second stage places the gene within a network module. We validate the method in simulation experiments, show improved performance relative to differential expression analysis from bulk samples, and apply it to illuminate the role of the mesothelin (MSLN) network in pancreatic ductal adenocarcinoma (PDAC). The network analysis and subsequent experimental validation in a panel of PDAC cell lines suggests AKT activation by MSLN through two known activators, retinoic acid receptor gamma (RARG) and tyrosine kinase non receptor 2 (TNK2). Taken together, these results demonstrate the potential of histoepigenetic analysis to reveal cancer-cell specific molecular interactions directly from patient tumor profiles.

Triple negative breast cancer (TNBC) is a disease of poor prognosis, with the majority classified as the basal-like subtype associated with epithelial-mesenchymal transition and metastasis. Because basal breast cancers originate from proliferative luminal progenitor-like cells upon dysregulation of proper luminal differentiation, genes regulating luminal-basal transition are critical to elucidate novel therapeutic targets to improve TNBC outcomes. Herein we demonstrate that the tumor suppressor DEAR1/TRIM62 is a critical regulator of luminal cell fate. DEAR1 loss in human mammary epithelial cells results in significantly enhanced mammosphere formation that is accelerated in the presence of TGF-β/SMAD3 signaling. Mammospheres formed following DEAR1 loss are enriched for ALDH1A1 and CK5 expression, EpCAM−/CD49f+ and CD44high/24low basal-like epithelial cells, indicating that DEAR1 regulates stem/progenitor cell properties and luminal-basal progenitor transition. We show that DEAR1 maintains luminal differentiation as a novel ubiquitin ligase for SNAI2/SLUG, a master regulator driving stemness and generation of basal-like progenitor populations. We also identify a significant inverse correlation between DEAR1 and SNAI2 expression in a 103 TNBC case cohort and show that low DEAR1 expression significantly correlates with young age of onset and shorter time to metastasis, suggesting DEAR1 could serve as a biomarker to stratify early onset TNBCs for targeted stem cell therapies.

Background Cell-type heterogeneity of tumors is a key factor in tumor progression and response to chemotherapy. Tumor cell-type heterogeneity, defined as the proportion of the various cell-types in a tumor, can be inferred from DNA methylation of surgical specimens. However, confounding factors known to associate with methylation values, such as age and sex, complicate accurate inference of cell-type proportions. While reference-free algorithms have been developed to infer cell-type proportions from DNA methylation, a comparative evaluation of the performance of these methods is still lacking. Results Here we use simulations to evaluate several computational pipelines based on the software packages MeDeCom, EDec, and RefFreeEWAS. We identify that accounting for confounders, feature selection, and the choice of the number of estimated cell types are critical steps for inferring cell-type proportions. We find that removal of methylation probes which are correlated with confounder variables reduces the error of inference by 30–35%, and that selection of cell-type informative probes has similar effect. We show that Cattell’s rule based on the scree plot is a powerful tool to determine the number of cell-types. Once the pre-processing steps are achieved, the three deconvolution methods provide comparable results. We observe that all the algorithms’ performance improves when inter-sample variation of cell-type proportions is large or when the number of available samples is large. We find that under specific circumstances the methods are sensitive to the initialization method, suggesting that averaging different solutions or optimizing initialization is an avenue for future research. Conclusion Based on the lessons learned, to facilitate pipeline validation and catalyze further pipeline improvement by the community, we develop a benchmark pipeline for inference of cell-type proportions and implement it in the R package medepir.

Background There is an unmet need for noninvasive imaging markers that can help identify the aggressive subtype(s) of pancreatic ductal adenocarcinoma (PDAC) at diagnosis and at an earlier time point, and evaluate the efficacy of therapy prior to tumor reduction. In the past few years, there have been two major developments with potential for a significant impact in establishing imaging biomarkers for PDAC and pancreatic cancer premalignancy: (1) hyperpolarized metabolic (HP)-magnetic resonance (MR), which increases the sensitivity of conventional MR by over 10,000-fold, enabling real-time metabolic measurements; and (2) applications of artificial intelligence (AI). Objective Our objective of this review was to discuss these two exciting but independent developments (HP-MR and AI) in the realm of PDAC imaging and detection from the available literature to date. Methods A systematic review following the PRISMA extension for Scoping Reviews (PRISMA-ScR) guidelines was performed. Studies addressing the utilization of HP-MR and/or AI for early detection, assessment of aggressiveness, and interrogating the early efficacy of therapy in patients with PDAC cited in recent clinical guidelines were extracted from the PubMed and Google Scholar databases. The studies were reviewed following predefined exclusion and inclusion criteria, and grouped based on the utilization of HP-MR and/or AI in PDAC diagnosis. Results Part of the goal of this review was to highlight the knowledge gap of early detection in pancreatic cancer by any imaging modality, and to emphasize how AI and HP-MR can address this critical gap. We reviewed every paper published on HP-MR applications in PDAC, including six preclinical studies and one clinical trial. We also reviewed several HP-MR–related articles describing new probes with many functional applications in PDAC. On the AI side, we reviewed all existing papers that met our inclusion criteria on AI applications for evaluating computed tomography (CT) and MR images in PDAC. With the emergence of AI and its unique capability to learn across multimodal data, along with sensitive metabolic imaging using HP-MR, this knowledge gap in PDAC can be adequately addressed. CT is an accessible and widespread imaging modality worldwide as it is affordable; because of this reason alone, most of the data discussed are based on CT imaging datasets. Although there were relatively few MR-related papers included in this review, we believe that with rapid adoption of MR imaging and HP-MR, more clinical data on pancreatic cancer imaging will be available in the near future. Conclusions Integration of AI, HP-MR, and multimodal imaging information in pancreatic cancer may lead to the development of real-time biomarkers of early detection, assessing aggressiveness, and interrogating early efficacy of therapy in PDAC.

Synthetic lethality occurs when a single gene alteration is compatible with cell viability, but an additional co-occurring genetic alteration leads to cell death. In the context of cancer therapy, synthetic lethality can occur through the inhibition of a target that is selectively essential to tumors harboring a specific genetic alteration. Gene paralog pairs represent one promising class of synthetic lethal cancer targets, wherein the function of one paralog is lost in tumor cells, rendering them dependent on the remaining paralog to carry out an essential cellular process. To identify essential gene paralog pairs as starting points for drug discovery programs, we mined publicly available CRISPR genetic loss-of-function data and associated molecular datasets collected across a diverse panel of cancer cell lines. We first identified pairs of gene paralogs where one paralog was essential in a subset of cell lines, and then filtered these genes based on function, known literature, enrichment in specific lineages and integration of external datasets. These efforts identified VPS4A as a synthetic lethal target in cancers harboring copy number loss of VPS4B. VPS4A and VPS4B are highly homologous AAA ATPases that carry out multiple essential cellular processes including nuclear membrane remodeling and endosomal membrane biogenesis. VPS4B loss occurs as a passenger deletion during loss of the tumor suppressors SMAD2 and SMAD4. Loss of VPS4B creates a genetic dependency on VPS4A to drive essential VPS4-dependent processes. VPS4B deletion occurs at a frequency of up to 3% in multiple solid tumor types including esophageal, head and neck, pancreatic and colorectal cancers. To further explore the potential of VPS4A as a therapeutic target in VPS4B-deleted tumors, we first validated the synthetic lethal relationship between VPS4A/B using isogenic cell line pairs. HCT116 cells with an engineered homozygous loss of VPS4B, but not wild-type HCT116 cells, showed profound cell kill in response to genetic silencing of VPS4A. Moreover, simultaneous siRNA-mediated knockdown of VPS4A and VPS4B resulted in cell death across a panel of cancer cell lines (e.g. H1975, Panc0403), while knockdown of either gene alone was compatible with cell viability. Encouraged by these results, we profiled several previously reported small-molecule inhibitors of VPS4A (e.g. DBeQ and MSC1094308) in a suite of biochemical assays. Notably, these molecules were inactive against VPS4A. We have discovered a novel series of VPS4A inhibitors and are advancing this inhibitor series through lead optimization. Potent, selective, and pharmacologically active VPS4A inhibitors are expected to be well tolerated and have strong single-agent activity in tumors bearing VPS4B homozygous deletions. Citation Format: Meredith Kuo, Jason Chen, Sacha Holland, Eugene Lurie, An-Angela Ngoc Van, Francesco Parlati, Tayna Santos, Eric Sjogren, Natalija Sotirovska, Susanne Steggerda, Andrew MacKinnon. Identification of novel VPS4A inhibitors for the treatment of VPS4B-deleted cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1816.

BACKGROUND There is an unmet need for noninvasive imaging markers that can help identify the aggressive subtype(s) of pancreatic ductal adenocarcinoma (PDAC) at diagnosis and at an earlier time point, and evaluate the efficacy of therapy prior to tumor reduction. In the past few years, there have been two major developments with potential for a significant impact in establishing imaging biomarkers for PDAC and pancreatic cancer premalignancy: (1) hyperpolarized metabolic (HP)-magnetic resonance (MR), which increases the sensitivity of conventional MR by over 10,000-fold, enabling real-time metabolic measurements; and (2) applications of artificial intelligence (AI). OBJECTIVE Our objective of this review was to discuss these two exciting but independent developments (HP-MR and AI) in the realm of PDAC imaging and detection from the available literature to date. METHODS A systematic review following the PRISMA extension for Scoping Reviews (PRISMA-ScR) guidelines was performed. Studies addressing the utilization of HP-MR and/or AI for early detection, assessment of aggressiveness, and interrogating the early efficacy of therapy in patients with PDAC cited in recent clinical guidelines were extracted from the PubMed and Google Scholar databases. The studies were reviewed following predefined exclusion and inclusion criteria, and grouped based on the utilization of HP-MR and/or AI in PDAC diagnosis. RESULTS Part of the goal of this review was to highlight the knowledge gap of early detection in pancreatic cancer by any imaging modality, and to emphasize how AI and HP-MR can address this critical gap. We reviewed every paper published on HP-MR applications in PDAC, including six preclinical studies and one clinical trial. We also reviewed several HP-MR–related articles describing new probes with many functional applications in PDAC. On the AI side, we reviewed all existing papers that met our inclusion criteria on AI applications for evaluating computed tomography (CT) and MR images in PDAC. With the emergence of AI and its unique capability to learn across multimodal data, along with sensitive metabolic imaging using HP-MR, this knowledge gap in PDAC can be adequately addressed. CT is an accessible and widespread imaging modality worldwide as it is affordable; because of this reason alone, most of the data discussed are based on CT imaging datasets. Although there were relatively few MR-related papers included in this review, we believe that with rapid adoption of MR imaging and HP-MR, more clinical data on pancreatic cancer imaging will be available in the near future. CONCLUSIONS Integration of AI, HP-MR, and multimodal imaging information in pancreatic cancer may lead to the development of real-time biomarkers of early detection, assessing aggressiveness, and interrogating early efficacy of therapy in PDAC.

Bacterial communities associated with roots impact the health and nutrition of the host plant. The dynamics of these microbial assemblies over the plant life cycle are, however, not well understood. Here, we use dense temporal sampling of 1,510 samples from root spatial compartments to characterize the bacterial and archaeal components of the root-associated microbiota of field grown rice (Oryza sativa) over the course of 3 consecutive growing seasons, as well as 2 sites in diverse geographic regions. The root microbiota was found to be highly dynamic during the vegetative phase of plant growth and then stabilized compositionally for the remainder of the life cycle. Bacterial and archaeal taxa conserved between field sites were defined as predictive features of rice plant age by modeling using a random forest approach. The age-prediction models revealed that drought-stressed plants have developmentally immature microbiota compared to unstressed plants. Further, by using genotypes with varying developmental rates, we show that shifts in the microbiome are correlated with rates of developmental transitions rather than age alone, such that different microbiota compositions reflect juvenile and adult life stages. These results suggest a model for successional dynamics of the root-associated microbiota over the plant life cycle., Author summary Plant roots are colonized by complex communities of bacterial and archaeal microbiota from the soil, with the potential to affect plant nutrition and fitness. Although root-associated microbes are known to have the potential to be utilized to promote crop productivity, their exploitation has been hindered by a lack of understanding of the compositional dynamics of these communities. Here we investigate temporal changes in the root-associated bacterial and archaeal communities throughout the plant life cycle in field-grown rice over multiple seasons and locations. Our results indicate that root microbiota composition varies with both chronological age and the developmental stage of the plants. We find that a major compositional shift correlates with the transition to reproductive growth, suggestive of distinct root microbiota associations for the juvenile and adult plant phases. The results from this study highlight dynamic relationships between plant growth and associated microbiota that should be considered in strategies for the successful manipulation of microbial communities to enhance crop performance.

INTRODUCTION A majority of imbalances in DNA methylation between homologous chromosomes in humans are sequence-dependent; the DNA sequence differences between the two chromosomes cause differences in the methylation state of neighboring cytosines on the same chromosome. The analyses of this sequence-dependent allele-specific methylation (SD-ASM) traditionally involved measurement of average methylation levels across many cells. Detailed understanding of SD-ASM at the single-cell and single-chromosome levels is lacking. This gap in understanding may hide the connection between SD-ASM, ubiquitous stochastic cell-to-cell and chromosome-to-chromosome variation in DNA methylation, and the puzzling and evolutionarily conserved patterns of intermediate methylation at gene regulatory loci. RATIONALE Whole-genome bisulfite sequencing (WGBS) provides the ultimate single-chromosome level of resolution and comprehensive whole-genome coverage required to explore SD-ASM. However, the exploration of the link between SD-ASM, stochastic variation in DNA methylation, and gene regulation requires deep coverage by WGBS across tissues and individuals and the context of other epigenomic marks and gene transcription. RESULTS We constructed maps of allelic imbalances in DNA methylation, histone marks, and gene transcription in 71 epigenomes from 36 distinct cell and tissue types from 13 donors. Deep (1691-fold) combined WGBS read coverage across 49 methylomes revealed CpG methylation imbalances exceeding 30% differences at 5% of the loci, which is more conservative than previous estimates in the 8 to 10% range; a similar value (8%) is observed in our dataset when we lowered our threshold for detecting allelic imbalance to 20% methylation difference between the two alleles. Extensive sequence-dependent CpG methylation imbalances were observed at thousands of heterozygous regulatory loci. Stochastic switching, defined as random transitions between fully methylated and unmethylated states of DNA, occurred at thousands of regulatory loci bound by transcription factors (TFs). Our results explain the conservation of intermediate methylation states at regulatory loci by showing that the intermediate methylation reflects the relative frequencies of fully methylated and fully unmethylated epialleles. SD-ASM is explainable by different relative frequencies of methylated and unmethylated epialleles for the two alleles. The differences in epiallele frequency spectra of the alleles at thousands of TF-bound regulatory loci correlated with the differences in alleles’ affinities for TF binding, which suggests a mechanistic explanation for SD-ASM. We observed an excess of rare variants among those showing SD-ASM, which suggests that an average human genome harbors at least ~200 detrimental rare variants that also show SD-ASM. The methylome’s sensitivity to genetic variation is unevenly distributed across the genome, which is consistent with buffering of housekeeping genes against the effects of random mutations. By contrast, less essential genes with tissue-specific expression patterns show sensitivity, thus providing opportunity for evolutionary innovation through changes in gene regulation. CONCLUSION Analysis of allelic epigenome maps provides a unifying model that links sequence-dependent allelic imbalances of the epigenome, stochastic switching at gene regulatory loci, selective buffering of the regulatory circuitry against the effects of random mutations, and disease-associated genetic variation.

Bacterial communities associated with roots impact the health and nutrition of the host plant. While a multitude of static factors are known to influence the composition of the root-associated microbiota, the dynamics of these microbial assemblies over the plant life cycle are poorly understood. Here, we use dense temporal sampling of spatial compartments to characterize the root-associated microbiota of field grown rice (Oryza sativa)over the course of three consecutive growing seasons and two sites in diverse geographic regions. The root microbiota was found to be highly dynamic during the vegetative phase of plant growth, then stabilizes compositionally for the remainder of the life cycle. Bacterial taxa conserved between field sites can be used as predictive features of rice plant age by modeling using a random forests approach. The age-prediction models were used to reveal that drought stressed plants have developmentally delayed microbiota compared to unstressed plants. Further, by using genotypes with varying developmental rates, we show that shifts in the microbiome are correlated with rates of developmental transitions rather than age alone, such that different microbiota compositions reflect juvenile and adult life stages. These results suggest a model for successional dynamics of the root-associated microbiota over the plant life cycle.

Plants depend upon beneficial interactions between roots and microbes for nutrient availability, growth promotion, and disease suppression. High-throughput sequencing approaches have provided recent insights into root microbiomes, but our current understanding is still limited relative to animal microbiomes. Here we present a detailed characterization of the root-associated microbiomes of the crop plant rice by deep sequencing, using plants grown under controlled conditions as well as field cultivation at multiple sites. The spatial resolution of the study distinguished three root-associated compartments, the endosphere (root interior), rhizoplane (root surface), and rhizosphere (soil close to the root surface), each of which was found to harbor a distinct microbiome. Under controlled greenhouse conditions, microbiome composition varied with soil source and genotype. In field conditions, geographical location and cultivation practice, namely organic vs. conventional, were factors contributing to microbiome variation. Rice cultivation is a major source of global methane emissions, and methanogenic archaea could be detected in all spatial compartments of field-grown rice. The depth and scale of this study were used to build coabundance networks that revealed potential microbial consortia, some of which were involved in methane cycling. Dynamic changes observed during microbiome acquisition, as well as steady-state compositions of spatial compartments, support a multistep model for root microbiome assembly from soil wherein the rhizoplane plays a selective gating role. Similarities in the distribution of phyla in the root microbiomes of rice and other plants suggest that conclusions derived from this study might be generally applicable to land plants.