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Patient-derived cells hold great promise for precision medicine approaches in human health. Human dermal fibroblasts have been a major source of cells for reprogramming and differentiating into specific cell types for disease modeling. Postmortem human dura mater has been suggested as a primary source of fibroblasts for in vitro modeling of neurodegenerative diseases. Although fibroblast-like cells from human and mouse dura mater have been previously described, their utility for reprogramming and direct differentiation protocols has not been fully established. In this study, cells derived from postmortem dura mater are directly compared to those from dermal biopsies of living subjects. In two instances, we have isolated and compared dermal and dural cell lines from the same subject. Notably, striking differences were observed between cells of dermal and dural origin. Compared to dermal fibroblasts, postmortem dura mater-derived cells demonstrated different morphology, slower growth rates, and a higher rate of karyotype abnormality. Dura mater-derived cells also failed to express fibroblast protein markers. When dermal fibroblasts and dura mater-derived cells from the same subject were compared, they exhibited highly divergent gene expression profiles that suggest dura mater cells originated from a mixed mural lineage. Given their postmortem origin, somatic mutation signatures of dura mater-derived cells were assessed and suggest defective DNA damage repair. This study argues for rigorous karyotyping of postmortem derived cell lines and highlights limitations of postmortem human dura mater-derived cells for modeling normal biology or disease-associated pathobiology. Graphical abstract

Cancer cells often co-opt post-transcriptional regulatory mechanisms to achieve pathologic expression of gene networks that drive metastasis. Translational control is a major regulatory hub in oncogenesis; however, its effects on cancer progression remain poorly understood. Here, to address this, we used ribosome profiling to compare genome-wide translation efficiencies of poorly and highly metastatic breast cancer cells and patient-derived xenografts. We developed dedicated regression-based methods to analyse ribosome profiling and alternative polyadenylation data, and identified heterogeneous nuclear ribonucleoprotein C (HNRNPC) as a translational controller of a specific mRNA regulon. We found that HNRNPC is downregulated in highly metastatic cells, which causes HNRNPC-bound mRNAs to undergo 3′ untranslated region lengthening and, subsequently, translational repression. We showed that modulating HNRNPC expression impacts the metastatic capacity of breast cancer cells in xenograft mouse models. In addition, the reduced expression of HNRNPC and its regulon is associated with the worse prognosis in breast cancer patient cohorts.

Antisense RNAs are ubiquitous in human cells, yet their role is largely unexplored. Here we profiled antisense RNAs in the MDA-MB-231 breast cancer cell line and its highly lung metastatic derivative. We identified one antisense RNA that drives cancer progression by upregulating the redox enzyme NADPH quinone dehydrogenase 1 (NQO1), and named it NQO1-AS. Knockdown of either NQO1 or NQO1-AS reduced lung colonization in a mouse model, and investigation into the role of NQO1 indicated that it is broadly protective against oxidative damage and ferroptosis. Breast cancer cells in the lung are dependent on this pathway, and this dependence can be exploited therapeutically by inducing ferroptosis while inhibiting NQO1. Together, our findings establish a role for NQO1-AS in the progression of breast cancer by regulating its sense mRNA post-transcriptionally. Because breast cancer predominantly affects females, the disease models used in this study are of female origin and the results are primarily applicable to females.

Large-scale sequencing efforts of thousands of tumor samples have been undertaken to understand the mutational landscape of the coding genome. However, the vast majority of germline and somatic variants occur within non-coding portions of the genome. These genomic regions do not directly encode for specific proteins, but can play key roles in cancer progression, for example by driving aberrant gene expression control. Here, we designed computational models to identify recurrently mutated non-coding regulatory regions that drive tumor progression. Application of this approach to whole-genome sequencing (WGS) data from a large cohort of metastatic castration-resistant prostate cancer (mCRPC) revealed a large set of recurrently mutated regions. We used (i)in silicaprioritization of functional non-coding mutations, (ii) massively parallel reporter assays, and (iii)in vivaCRISPR-interference (CRISPRi) screens in xenografted mice to systematically identify and validate driver regulatory regions that drive mCRPC. We discovered that one of these enhancer regions, GH22I030351, acts on a bidirectional promoter to simultaneously modulate expression of U2-associated splicing factor SF3A1 and chromosomal protein CCDC157. We found that both SF3A1 and CCDC157 are promoters of tumor growth in xenograft models of prostate cancer. We also nominated a number of transcription factors, including SOX6, to be responsible for higher expression of SF3A1 and CCDC157. Taken together, we have described and validated an integrative computational and experimental framework that enables systematic identification of non-coding regulatory regions that drive human cancers.Statement of SignificanceWe developed an integrated computational and experimental platform to identify and characterize non-coding driver regulatory regions in metastatic prostate cancer patient data. One found enhancer region, GH22I030351, was shown to act on a bidirectional promoter that simultaneously regulates previously uncharacterized genes SF3A1 and CCDC157 in a tumor-promoting manner.

Supplementary Figure 6

RBMS1 putative regulons, RBMS1 target list, RBMS1 80-gene signature list

Supplementary Methods

The description of benchmarking PRADA

The schematics of the data flow used for analysis

Identifying master regulators that drive pathologic gene expression is a key challenge in precision oncology. Here, we have developed an analytic framework, named PRADA, that identifies oncogenic RNA-binding proteins through the systematic detection of coordinated changes in their target regulons. Application of this approach to data collected from clinical samples, patient-derived xenografts, and cell line models of colon cancer metastasis revealed the RNA-binding protein RBMS1 as a suppressor of colon cancer progression. We observed that silencing RBMS1 results in increased metastatic capacity in xenograft mouse models, and that restoring its expression blunts metastatic liver colonization. We have found that RBMS1 functions as a posttranscriptional regulator of RNA stability by directly binding its target mRNAs. Together, our findings establish a role for RBMS1 as a previously unknown regulator of RNA stability and as a suppressor of colon cancer metastasis with clinical utility for risk stratification of patients.Significance:By applying a new analytic approach to transcriptomic data from clinical samples and models of colon cancer progression, we have identified RBMS1 as a suppressor of metastasis and as a post-transcriptional regulator of RNA stability. Notably, RBMS1 silencing and downregulation of its targets are negatively associated with patient survival.See related commentary by Carter, p. 1261.This article is highlighted in the In This Issue feature, p. 1241

GI toxicity is a common dose-limiting adverse effect of platin chemotherapy treatment. Up to 50% of cancer survivors continue to experience symptoms of chronic constipation or diarrhea induced by their chemotherapy for many years after their treatment. This drug toxicity is largely attributed to damage to enteric neurons that innervate the GI tract and control GI motility. The mechanisms responsible for platin-induced enteric neurotoxicity and potential preventative strategies have remained unknown. Here, we use human pluripotent stem cell derived enteric neurons to establish a new model system capable of uncovering the mechanism of platin-induced enteric neuropathy. Utilizing this scalable system, we performed a high throughput screen and identified drug candidates and pathways involved in the disease. Our analyses revealed that excitotoxicity through muscarinic cholinergic signaling is a key driver of platin-induced enteric neuropathy. Using single nuclei transcriptomics and functional assays, we discovered that this disease mechanism leads to increased susceptibility of specific neuronal subtypes, including inhibitory nitrergic neurons, to platins. Histological assessment of the enteric nervous system in platin-treated patients confirmed the selective loss of nitrergic neurons. Finally, we demonstrated that pharmacological and genetic inhibition of muscarinic cholinergic signaling is sufficient to rescue enteric neurons from platin excitotoxicityin vitroand can prevent platin-induced constipation and degeneration of nitrergic neurons in mice. These studies define the mechanisms of platin-induced enteric neuropathy and serve as a framework for uncovering cell type-specific manifestations of cellular stress underlying numerous intractable peripheral neuropathies.

Summary/AbstractThe neural crest (NC) is highly multipotent and generates diverse lineages in the developing embryo. However, spatiotemporally distinct NC populations display differences in fate potential, such as increased gliogenic and parasympathetic potential from later migrating, nerve-associated Schwann cell precursors (SCPs). Interestingly, while melanogenic potential is shared by both early migrating NC and SCPs, differences in melanocyte identity resulting from differentiation through these temporally distinct progenitors have not been determined. Here, we leverage a human pluripotent stem cell (hPSC) model of NC temporal patterning to comprehensively characterize human NC heterogeneity, fate bias, and lineage development. We captured the transition of NC differentiation between temporally and transcriptionally distinct melanogenic progenitors and identified modules of candidate transcription factor and signaling activity associated with this transition. For the first time, we established a protocol for the directed differentiation of melanocytes from hPSCs through a SCP intermediate, termed trajectory 2 (T2) melanocytes. Leveraging an existing protocol for differentiating early NC-derived melanocytes, termed trajectory 1 (T1), we performed the first comprehensive comparison of transcriptional and functional differences between these distinct melanocyte populations, revealing differences in pigmentation and unique expression of transcription factors, ligands, receptors and surface markers. We found a significant link between the T2 melanocyte transcriptional signature and decreased survival in melanoma patients in the cancer genome atlas (TCGA). We performed anin vivoCRISPRi screen of T1 and T2 melanocyte signature genes in a human melanoma cell line and discovered several T2-specific markers that promote lung metastasis in mice. We further demonstrated that one of these factors, SNRPB, regulates the splicing of transcripts involved in metastasis relevant functions such as migration, cell adhesion and proliferation. Overall, this study identifies distinct developmental trajectories as a source of diversity in melanocytes and implicates the unique molecular signature of SCP-derived melanocytes in metastatic melanoma.

MicroRNAs are recognized as key drivers in many cancers, but targeting them with small molecules remains a challenge. We present RiboStrike, a deep learning framework that identifies small molecules against specific microRNAs. To demonstrate its capabilities, we applied it to microRNA-21 (miR-21), a known driver of breast cancer. To ensure the selected molecules only targeted miR-21 and not other microRNAs, we also performed a counter-screen against DICER, an enzyme involved in microRNA biogenesis. Additionally, we used auxiliary models to evaluate toxicity and select the best candidates. Using datasets from various sources, we screened a pool of nine million molecules and identified eight, three of which showed anti-miR-21 activity in both reporter assays and RNA sequencing experiments. One of these was also tested in mouse models of breast cancer, resulting in a significant reduction of lung metastases. These results demonstrate RiboStrike’s ability to effectively screen for microRNA-targeting compounds in cancer.

Data required to run the reproducible R notebooks and make the manuscript's figures.

The search for new approaches in cancer therapy requires a mechanistic understanding of cancer vulnerabilities and anti-cancer drug mechanisms of action. Problematically, some effective therapeutics target cancer vulnerabilities that we do not understand and have poorly defined mechanisms of anti-cancer activity. One such drug is decitabine, which is a frontline therapeutic approved for the treatment of high-risk acute myeloid leukemia (AML). Decitabine is thought to kill cancer cells selectively via inhibition of DNA methyltransferase enzymes, but the genes and mechanisms involved remain unclear. Here, we apply an integrated multiomics and CRISPR functional genomics approach to identify genes and processes associated with response to decitabine in AML cells. Our integrated multiomics approach reveals RNA dynamics are key regulators of DNA hypomethylation induced cell death. Specifically, regulation of RNA decapping, splicing and RNA methylation emerge as critical regulators of decitabine killing. Our results provide insights into the mechanisms of decitabine anti-cancer activity in treatment of AML and identify combination therapies which could potentiate decitabine anti-cancer activity.

Antisense RNAs are ubiquitous in human cells, yet the role that they play in healthy and diseased states remains largely unexplored. Here, we developed a computational framework to catalog and profile antisense RNAs and applied it to poorly and highly metastatic breast cancer cell lines. We identified one antisense RNA that plays a functional role in driving breast cancer progression by upregulating the redox enzyme NQO1, and hence named NQO1-antisense RNA or NQO1-AS. This upregulation occurs via a stabilizing interaction between NQO1-AS and its complementary region in the 3’UTR of NQO1 mRNA. By increasing expression of NQO1 protein, breast cancer cells are able to tolerate higher levels of oxidative stress, enabling them to colonize the lung. During this process the cancer cells become dependent on NQO1 to protect them from ferroptosis. We have shown that this dependence can be exploited therapeutically in xenograft models of metastasis. Together, our findings establish a previously unknown role for NQO1-AS in the progression of breast cancer by serving as a post-transcriptional regulator of RNA processing and decay for its sense mRNA.

Cancer cells often co-opt post-transcriptional regulatory mechanisms to achieve pathologic expression of gene networks that drive metastasis. Translational control is a major regulatory hub in oncogenesis, however its effects on cancer progression remain poorly understood. To address this, we used ribosome profiling to compare genome-wide translation efficiencies of poorly and highly metastatic breast cancer cells and patient-derived xenografts. We developed novel regression-based methods to analyze ribosome profiling and alternative polyadenylation data, and identified HNRNPC as a translational controller of a specific mRNA regulon. Mechanistically, HNRNPC, in concert with PABPC4, binds near to poly(A) signals, thereby governing the alternative polyadenylation of a set of mRNAs. We found that HNRNPC and PABPC4 are downregulated in highly metastatic cells, which causes HNRNPC-bound mRNAs to undergo 3’ UTR lengthening and subsequently, translational repression. We showed that modulating HNRNPC expression impacts the metastatic capacity of breast cancer cells in xenograft mouse models. We also found that a small molecule, previously shown to induce a distal-to-proximal poly(A) site switching, counteracts the HNRNPC-PABPC4 driven deregulation of alternative polyadenylation and decreases the metastatic lung colonization by breast cancer cells in vivo.

Patient-derived cells hold great promise for precision medicine approaches in human health. Fibroblast cells have been a major source of human cells for reprogramming and differentiating into specific cell types for disease modeling. Such cells can be isolated at various stages during life (presymptomatic, symptomatic, and postmortem) and thus can potentially be used to model different phases of disease progression. In certain circumstances, however, tissues are not collected during life and only postmortem tissues are the only available source of fibroblasts. Fibroblasts cultured from postmortem human dura mater of individuals with neurodegenerative diseases have been suggested as a primary source of cells for in vitro modeling of neurodegenerative diseases. Although fibroblast-like cells from human and mouse dura mater have been previously described, their utility for reprogramming and direct differentiation protocols requires further characterization. In this study, cells derived from dermal biopsies performed in living subjects were compared to cells derived from postmortem dura mater. In two instances, we have isolated and compared dermal and dural cell lines from the same subject. Notably, striking differences between the dermis and dura mater-derived cell lines were found. Compared to dermal fibroblasts, postmortem dura mater-derived cells demonstrated different morphology, exhibited slower growth rates, failed to express fibroblast protein markers, and exhibited significant differences in gene expression profiles. In addition, dura mater-derived cells were found to exhibit a high rate of chromosomal abnormalities, particularly in the loss of the Y chromosome. Our study highlights potential limitations of postmortem human dura mater-derived cells for disease modeling, argues for rigorous karyotyping prior to reprograming, and brings into question the identity of dura mater-derived cells as belonging to a fibroblast lineage.

Characterizing a cancer spliceosome Cells undergo many genomic changes as they progress toward metastatic cancer. One aspect of this change is to RNA expression and splicing isoforms, but how these differences affect tumor progression is not well characterized. Fish et al. developed a computational framework called pyTEISER that identifies structural cis-regulatory elements that control diverse types of RNA regulation. Applying pyTEISER to models of breast cancer metastasis, they discovered an RNA short-stem-loop element that forms a “structural splicing enhancer” that acts in cis to regulate alternative splicing of RNA transcripts. One of these interactions encompasses the RNA-binding protein SNRPA1 and results in alternative exon inclusion that affects metastatic capacity in xenograft models. Thus, RNA element binding may play a role in splicing regulation and is potentially an important component of the cis-splicing code. Science , this issue p. eabc7531

Background: Liquid biopsies have emerged as effective diagnostic tools in disease monitoring and minimal residual disease detection. Circulating tumor DNA (ctDNA) was recently shown to be a predictor of poor response and recurrence in breast cancer. However, ctDNA shedding from breast tumors can rapidly decrease during treatment, resulting in reduced sensitivity in measuring early changes in tumor response or residual cancer burden (RCB) after neoadjuvant chemotherapy (NAC). We recently reported the discovery of orphan non-coding RNAs (oncRNAs), a class of small RNAs that are not present in healthy cells, but emerge from cancer cells. Similar to ctDNA, tumor-released oncRNAs can be used to detect the presence of an underlying tumor; however, since they are actively released by cancer cells, their abundance in the cell-free compartment is substantially higher than ctDNA. Therefore, we hypothesized that monitoring circulating oncRNAs in blood permits a more sensitive approach to measuring treatment response (i.e., pathologic complete response, or pCR) and estimating RCB. Patients and Methods: Cell-free RNA (cfRNA) was extracted from ~1 ml sera of 72 breast cancer patients treated in the neoadjuvant I-SPY 2 TRIAL with NAC alone or combined with MK-2206 (AKT inhibitor) treatment. For each patient, treatment-naïve samples (T0) were compared with samples from post-treatment and prior to surgery (T3) time-point. RNA samples were subjected to small RNA sequencing (SMARTer), and the presence and abundance of cell-free oncRNA species were then determined by identifying and counting the reads that map to oncRNA loci across samples. Notably, oncRNAs species were pre-annotated from the Cancer Genome Atlas (TCGA), and our approach does not require bespoke personalized assays. We used a machine-learning model to compare abundance of cfRNA species before and after treatment (i.e., T3-T0) to predict pCR and RCB. For this, we split our cohort into a training and a testing set (48 and 24) and trained a model to simultaneously learn the presence of residual disease (pCR vs. no pCR) and its extent (RCB). We then measured the performance of our model on the held-out test data and the entire dataset. To confirm the robustness of our model, we also employed a leave-one-out strategy, whereby pCR and RCBIndex of each patient was predicted using a model that was trained on the other patients in the cohort. Finally, to assess the ability of our oncRNA-based model to risk-stratify patients who fail to achieve pCR (without having been explicitly trained on relapse data), we used the model’s oncRNA score to predict patients at the highest risk of distant recurrence (n=8 out of 36) and performed a multivariate Cox analysis, controlling for HR/Her2 status (median follow-up time was 4.8 years). Results: The model’s accuracy for predicting pCR—based on changes in circulating oncRNA species between T3 and T0—was 85% for the training data and 79% for the held-out test data (positive predictive value of 75% and negative predictive value of 83%) with combined accuracy of 83%; precision 86% and recall 83%; Pearson R=0.5 for RCB. A leave-one-out strategy showed similar performance (area under ROC of 0.77 versus 0.81 in train-test split). Finally, among the patients who failed to achieve pCR, we observed a significantly higher risk of distant recurrence in those with the highest scores (DRFS: hazard-ratio = 8.4, ANOVA P Citation Format: Hani Goodarzi, Albertas Navickas, Jefferey Wang, Kristle Garcia, Mark J Magbanua, Lisa Fish, Lamorna Brown Swigart, Gillian Hirst, Denise Wolf, Christina Yau, Jo Chien, Carol Simmons, Amy Delson, Laura Esserman, Laura van 't Veer. Tumor-released circulating orphan non-coding RNAs reflect treatment response and survival in breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD9-04.

Background: Metabolic reprogramming is a hallmark of breast cancer progression. However, the underlying regulatory pathways that initiate and maintain this process remain largely unexplored. Recently, we have identified a novel antisense RNA that helps protect breast cancer cells against oxidative stress by reprogramming their metabolic redox state. Using cell line and patient-derived xenograft models, as well as direct measurements in clinical samples, we have demonstrated the unique regulatory functions of this antisense RNA in increasing the metastatic capacity of breast cancer cells. Results: Non-coding RNAs have emerged as major drivers of metastatic progression. We have recently demonstrated that specific classes of non-coding RNAs, such as tRNAs (Goodarzi et al, Cell, 2016) and tRNA fragments (Goodarzi et al, Cell, 2015), play major roles in breast cancer metastasis as post-transcriptional regulators of gene expression. However, these types of regulatory RNAs constitute only a fraction of the non-coding RNAs that are aberrantly expressed in highly metastatic cells. For example, antisense RNAs are a large but often ignored class of RNAs with poorly understood cellular functions. We recently developed a new computational algorithm to systematically annotate antisense RNAs and identify those that are associated with metastatic progression based on data from cell line and patient-derived xenograft models, as well as matched primary and metastatic tumors from triple-negative breast cancer patients. We identified a previously unknown antisense RNA, which is transcribed from a locus in the 3’ UTR of the gene NQO1 (and is hence named NQO1-AS). Both NQO1-AS and NQO1 are significantly upregulated in highly metastatic breast cancer cells, and we have shown that the NQO1 sense mRNA is stabilized by the expression of NQO1-AS. Our results indicate that NQO1-AS forms a stable duplex with the 3’ UTR of NQO1 and induces the expression of a longer and more stable isoform of NQO1 mRNA. Metabolomic measurements in NQO1 knockdown and control cells revealed that increased NQO1 activity enables cancer cells to better tolerate the oxidative stress experienced during metastasis. We demonstrated this by performing lung metastasis assays in xenograft models. To confirm the clinical relevance of these findings, we performed comprehensive clinical association studies, and also used quantitative PCR and immunohistochemistry to measure NQO1 levels across all disease stages. We observed a highly significant association between higher NQO1 and NQO1-AS expression and metastatic relapse. Methods: We developed a method named iRAs to annotate and quantify antisense RNAs. We used global run-on assays (GRO-seq) and RNA sequencing in poorly and highly metastatic breast cancer cells to identify NQO1-AS as a novel pro-metastatic antisense RNA. We used both Gapmers and CRISPR-i to knock down NQO1-AS and to measure its impact on NQO1 mRNA stability and expression. We used CRISPRi to silence NQO1 in both MDA-231 and HCC-1806 breast cancer lines, and measured the metabolic consequences of NQO1 knockdown by measuring NADPH flux as well as performing general metabolomic profiling. We also used in vivo lung colonization assays (n=5 mice in each arm) to measure the metastatic capacity of NQO1 knockdown cells. Log-rank test (univariate) and Cox Proportional Hazard Models (multivariate) were used to perform survival analyses in METABRIC and the kmplot aggregate dataset. Mann-Whitney or ANOVA was used to compare expression of NQO1 and NQO1-AS across samples stratified based on sub-type and tumor grade/stage in public datasets as well as our own measurements in clinical samples (n=96; 5 healthy, 23 stage I, 30 stage II, 29 stage III, and 9 stage IV). IHC was performed on tissue microarrays from CHTN (Breast Progression), and blinded scoring was used to assess NQO1 levels. Citation Format: Hani Goodarzi, Bruce Culbertson, Kristle Garcia, Lisa Fish. A sense-antisense RNA interaction drives metabolic reprogramming in metastatic breast cancer [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS19-01.