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Purpose: Despite promising preclinical studies, toxicities have precluded combinations of chemotherapy and DNA damage response (DDR) inhibitors. We hypothesized that tumor-targeted chemotherapy delivery might enable clinical translation of such combinations. Patients and Methods: In a phase I trial, we combined sacituzumab govitecan, antibody–drug conjugate (ADC) that delivers topoisomerase-1 inhibitor SN-38 to tumors expressing Trop-2, with ataxia telangiectasia and Rad3-related (ATR) inhibitor berzosertib. Twelve patients were enrolled across three dose levels. Results: Treatment was well tolerated, with improved safety over conventional chemotherapy-based combinations, allowing escalation to the highest dose. No dose-limiting toxicities or clinically relevant ≥grade 4 adverse events occurred. Tumor regressions were observed in 2 patients with neuroendocrine prostate cancer, and a patient with small cell lung cancer transformed from EGFR-mutant non–small cell lung cancer. Conclusions: ADC-based delivery of cytotoxic payloads represents a new paradigm to increase efficacy of DDR inhibitors.

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Pharmacokinetic assessment

Trial protocol dose limiting toxicity criteria

Purpose:Despite promising preclinical studies, toxicities have precluded combinations of chemotherapy and DNA damage response (DDR) inhibitors. We hypothesized that tumor-targeted chemotherapy delivery might enable clinical translation of such combinations.Patients and Methods:In a phase I trial, we combined sacituzumab govitecan, antibody–drug conjugate (ADC) that delivers topoisomerase-1 inhibitor SN-38 to tumors expressing Trop-2, with ataxia telangiectasia and Rad3-related (ATR) inhibitor berzosertib. Twelve patients were enrolled across three dose levels.Results:Treatment was well tolerated, with improved safety over conventional chemotherapy-based combinations, allowing escalation to the highest dose. No dose-limiting toxicities or clinically relevant ≥grade 4 adverse events occurred. Tumor regressions were observed in 2 patients with neuroendocrine prostate cancer, and a patient with small cell lung cancer transformed from EGFR-mutant non–small cell lung cancer.Conclusions:ADC-based delivery of cytotoxic payloads represents a new paradigm to increase efficacy of DDR inhibitors.

Complex age-associated phenotypes are caused, in part, by an interaction between an individual’s genotype and age. The mechanisms governing such interactions are however not entirely understood. Here, we provide a novel transcriptional mechanism-based framework–SNiPage, to investigate such interactions, whereby a transcription factor (TF) whose expression changes with age (age-associated TF), binds to a polymorphic regulatory element in an allele-dependent fashion, rendering the target gene’s expression dependent on both, the age and the genotype. Applying SNiPage to GTEx, we detected ~637 significant TF-SNP-Gene triplets on average across 25 tissues, where the TF binds to a regulatory SNP in the gene’s promoter or putative enhancer and potentially regulates its expression in an age- and allele-dependent fashion. The detected SNPs are enriched for epigenomic marks indicative of regulatory activity, exhibit allele-specific chromatin accessibility, and spatial proximity to their putative gene targets. Furthermore, the TF-SNP interaction-dependent target genes have established links to aging and to age-associated diseases. In six hypertension-implicated tissues, detected interactions significantly inform hypertension state of an individual. Lastly, the age-interacting SNPs exhibit a greater proximity to the reported phenotype/diseases-associated SNPs than eSNPs identified in an interaction-independent fashion. Overall, we present a novel mechanism-based model, and a novel framework SNiPage, to identify functionally relevant SNP-age interactions in transcriptional control and illustrate their potential utility in understanding complex age-associated phenotypes., Author summary Numerous traits, such as cardiovascular diseases and cancer, are associated with age. However, these associations vary across races and ethnicities, suggesting an interplay between age and the genetic background in determining the trait. Although previously studies have attempted to detect Age-Genotype interactions based on statistical models, they are mostly devoid of mechanism, thus limiting their efficacy and scope in informing therapeutic strategies. Here, we propose a novel framework to investigate such interactions, by incorporating a specific transcription-based mechanism in the model. More specifically, our model is based on the mechanistic scenario that an age-associated transcription factor (TF) binds to a regulatory polymorphism (SNP) in an allele-specific manner to regulate the transcription of the downstream gene in an Age- and Genotype-specific fashion. By analyzing 25 tissues in the GTEx consortium, we detected tissue specific SNP-TF-Gene interaction triplets and functionally validated the detected SNP based on epigenomic and functional data. What’s more, multiple lines of evidence link detected interactions to aging and to age-associated diseases. We expect our new methodological framework and the detected functionally relevant interactions will enhance understanding of the underlying mechanism of SNP-Age interaction and its contribution to age-associated diseases.

Interactions between thymic epithelial cells (TEC) and developing thymocytes are essential for T cell development, but molecular insights on TEC and thymus homeostasis are still lacking. Here we identify distinct transcriptional programs of TEC that account for their age-specific properties, including proliferation rates, engraftability and function. Further analyses identify Myc as a regulator of fetal thymus development to support the rapid increase of thymus size during fetal life. Enforced Myc expression in TEC induces the prolonged maintenance of a fetal-specific transcriptional program, which in turn extends the growth phase of the thymus and enhances thymic output; meanwhile, inducible expression of Myc in adult TEC similarly promotes thymic growth. Mechanistically, this Myc function is associated with enhanced ribosomal biogenesis in TEC. Our study thus identifies age-specific transcriptional programs in TEC, and establishes that Myc controls thymus size., Thymic epithelial cells (TEC) are essential for the maturation of functional T cells, while thymus size is proportional to the overall output efficiency. Here the authors show, using transcriptome analyses, that mouse fetal TEC maintain a Myc-dependent genetic program to ensure a rapid increase in thymus size, and thereby expedited T cell generation.

Alternative splicing contributes to phenotypic diversity at multiple biological scales, and its dysregulation is implicated in both ageing and age-associated diseases in human. Cross-tissue variability in splicing further complicates its links to age-associated phenotypes and elucidating these links requires a comprehensive map of age-associated splicing changes across multiple tissues. Here, we generate such a map by analyzing ~8500 RNA-seq samples across 48 tissues in 544 individuals. Employing a stringent model controlling for multiple confounders, we identify 49,869 tissue-specific age-associated splicing events of 7 distinct types. We find that genome-wide splicing profile is a better predictor of biological age than the gene and transcript expression profiles, and furthermore, age-associated splicing provides additional independent contribution to age-associated complex diseases. We show that the age-associated splicing changes may be explained, in part, by concomitant age-associated changes of the upstream splicing factors. Finally, we show that our splicing-based model of age can successfully predict the relative ages of cells in 8 of the 10 paired longitudinal data as well as in 2 sets of cell passage data. Our study presents the first systematic investigation of age-associated splicing changes across tissues, and further strengthening the links between age-associated splicing and age-associated diseases.

Mammalian gene expression is often regulated by distal enhancers. However, little is known about higher order functional organization of enhancers. Using ∼100 K P300-bound regions as candidate enhancers, we investigated their correlated activity across 72 cell types based on DNAse hypersensitivity. We found widespread correlated activity between enhancers, which decreases with increasing inter-enhancer genomic distance. We found that correlated enhancers tend to share common transcription factor (TF) binding motifs, and several chromatin modification enzymes preferentially interact with these TFs. Presence of shared motifs in enhancer pairs can predict correlated activity with 73% accuracy. Also, genes near correlated enhancers exhibit correlated expression and share common function. Correlated enhancers tend to be spatially proximal. Interestingly, weak enhancers tend to correlate with significantly greater numbers of other enhancers relative to strong enhancers. Furthermore, strong/weak enhancers preferentially correlate with strong/weak enhancers, respectively. We constructed enhancer networks based on shared motif and correlated activity and show significant functional enrichment in their putative target gene clusters. Overall, our analyses show extensive correlated activity among enhancers and reveal clusters of enhancers whose activities are coordinately regulated by multiple potential mechanisms involving shared TF binding, chromatin modifying enzymes and 3D chromatin structure, which ultimately co-regulate functionally linked genes.

To predict in vivo occupancy of a transcription factor (TF), current models consider only the immediate genomic context of a putative binding site (BS) – impact of the site’s spatial chromatin context is not known. Using clusters of spatially proximal enhancers, or archipelagos, and DNase footprints to quantify TF occupancy, we report for the first time an emergent group-level effect on occupancy, whereby BS within an archipelago experience greater in vivo occupancy than comparable BS outside archipelagos, i.e. BS not in spatial proximity with other homotypic BS. This occupancy boost is tissue-specific and scales robustly with the total number of BS, or enhancers, for the TF in the archipelago. Interestingly, enhancers within an archipelago are non-uniformly impacted by the occupancy boost; specifically, archipelago enhancers that are enriched for BS corresponding to degenerate motifs exhibit the greatest occupancy boost, as well as the highest overall accessibility, evolutionary selection, and expression at neighboring gene loci. Strikingly, archipelago-wide activity scales with expression of TFs with degenerate, but not specific, motifs. We explain these results through biophysical modelling, which suggests that spatially proximal homotypic BS facilitate TF diffusion, and induce boosts in local TF concentration and occupancy. Together, we demonstrate for the first time cooperativity among genomically distal homotypic BS that is contingent upon their spatial proximity, consistent with a TF diffusion model. Through leveraging of three-dimensional chromatin structure and TF availability, weak archipelago binding sites crowdsource their occupancy as well as context specificity, with coordinated switch-like effect on overall archipelago activity.

Materials and methods Using P300-bound regions as putative enhancers [1] and using cell-type-specific DNAse hypersensitivity (HS) at these enhancers as an operational definition of enhancer activity, here we perform a detailed investigation of enhancer activity correlation across 15 cell types, followed by analysis of mechanistic underpinnings and functional consequences of correlated enhancer activity. We initially identify pairs of highly correlated enhancers from the same (cis) and from different chromosomes, after accounting for HS autocorrelation affecting cis-pairs. Using nonparametric tests and controlling for dependencies, highly correlated pairs are compared with background pairs for: enrichment of co-occurring binding motifs; for correlated gene expression across the 15 cell samples sourced for HS data; for shared gene function; for evidence of interactions between shared enhancer-binding transcription factors (TFs) and chromatin-modifying enzymes; and for Hi-C evidence of pair co-localization. The relationship between correlated enhancers now established, we conclude by scaling this pairs perspective to the building and validating of an enhancer network.

Background Understanding the evolution of biological networks can provide insight into how their modular structure arises and how they are affected by environmental changes. One approach to studying the evolution of these networks is to reconstruct plausible common ancestors of present-day networks, allowing us to analyze how the topological properties change over time and to posit mechanisms that drive the networks’ evolution. Further, putative ancestral networks can be used to help solve other difficult problems in computational biology, such as network alignment. Results We introduce a combinatorial framework for encoding network histories, and we give a fast procedure that, given a set of gene duplication histories, in practice finds network histories with close to the minimum number of interaction gain or loss events to explain the observed present-day networks. In contrast to previous studies, our method does not require knowing the relative ordering of unrelated duplication events. Results on simulated histories and real biological networks both suggest that common ancestral networks can be accurately reconstructed using this parsimony approach. A software package implementing our method is available under the Apache 2.0 license at http://cbcb.umd.edu/kingsford-group/parana. Conclusions Our parsimony-based approach to ancestral network reconstruction is both efficient and accurate. We show that considering a larger set of potential ancestral interactions by not assuming a relative ordering of unrelated duplication events can lead to improved ancestral network inference.