Chromosomal translocations encode oncogenic fusion proteins that have been proven to be causally involved in tumorigenesis. Our understanding of whether such genomic alterations also affect ...non-coding RNAs is limited, and their impact on circular RNAs (circRNAs) has not been explored. Here, we show that well-established cancer-associated chromosomal translocations give rise to fusion circRNAs (f-circRNA) that are produced from transcribed exons of distinct genes affected by the translocations. F-circRNAs contribute to cellular transformation, promote cell viability and resistance upon therapy, and have tumor-promoting properties in in vivo models. Our work expands the current knowledge regarding molecular mechanisms involved in cancer onset and progression, with potential diagnostic and therapeutic implications.
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•F-circRNAs are generated from cancer-associated chromosomal translocations•F-circRNAs promote transformation and cell survival upon treatment•F-circRNAs confer resistance to treatment in tumor cells
Chromosomal translocations associated with cancer give rise to fusion circRNAs which contribute to cellular transformation, affect cell viability, and have tumor-promoting properties in in vivo models.
Stromal cells within the tumor microenvironment are essential for tumor progression and metastasis. Surprisingly little is known about the factors that drive the transcriptional reprogramming of ...stromal cells within tumors. We report that the transcriptional regulator heat shock factor 1 (HSF1) is frequently activated in cancer-associated fibroblasts (CAFs), where it is a potent enabler of malignancy. HSF1 drives a transcriptional program in CAFs that complements, yet is completely different from, the program it drives in adjacent cancer cells. This CAF program is uniquely structured to support malignancy in a non-cell-autonomous way. Two central stromal signaling molecules—TGF-β and SDF1—play a critical role. In early-stage breast and lung cancer, high stromal HSF1 activation is strongly associated with poor patient outcome. Thus, tumors co-opt the ancient survival functions of HSF1 to orchestrate malignancy in both cell-autonomous and non-cell-autonomous ways, with far-reaching therapeutic implications.
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•Reprogramming of cancer-associated fibroblasts by HSF1 enables malignant progression•Distinct transcriptional programs are driven by HSF1 in stromal versus malignant cells•HSF1 activation in tumor stroma is strongly associated with poor patient outcome•Dual roles in both tumor cells and stroma make HSF1 an attractive anticancer target
HSF1 drives a transcriptional program in stromal cells that potentiates tumor cell malignancy and is associated with poor patient outcomes.
The ERG gene is fused to TMPRSS2 in approximately 50% of prostate cancers (PrCa), resulting in its overexpression. However, whether this is the sole mechanism underlying ERG elevation in PrCa is ...currently unclear. Here we report that ERG ubiquitination and degradation are governed by the Cullin 3-based ubiquitin ligase SPOP and that deficiency in this pathway leads to aberrant elevation of the ERG oncoprotein. Specifically, we find that truncated ERG (ΔERG), encoded by the ERG fusion gene, is stabilized by evading SPOP-mediated destruction, whereas prostate cancer-associated SPOP mutants are also deficient in promoting ERG ubiquitination. Furthermore, we show that the SPOP/ERG interaction is modulated by CKI-mediated phosphorylation. Importantly, we demonstrate that DNA damage drugs, topoisomerase inhibitors, can trigger CKI activation to restore the SPOP/ΔERG interaction and its consequent degradation. Therefore, SPOP functions as a tumor suppressor to negatively regulate the stability of the ERG oncoprotein in prostate cancer.
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•The E3 ubiquitin ligase SPOP promotes ERG degradation in a CKI-dependent manner•Prostate cancer-associated SPOP mutants fail to promote ERG destruction•ERG fusion proteins evade SPOP-mediated degradation and could be restored by CKI•Etoposide-induced ERG fusion protein degradation depends on SPOP and CKI
Gan et al. report that the Cullin 3SPOP E3 ubiquitin ligase plays a critical tumor-suppressive role in prostate cancer by negatively controlling ERG stability. Therefore, either SPOP mutation or ERG fusion can lead to elevated ERG protein levels by evading the SPOP-mediated degradation pathway to promote prostate cancer progression.
Background and Aims
Advanced fibrosis attributable to NASH is a leading cause of end‐stage liver disease.
Approach and Results
In this phase 2b trial, 392 patients with bridging fibrosis or ...compensated cirrhosis (F3‐F4) were randomized to receive placebo, selonsertib 18 mg, cilofexor 30 mg, or firsocostat 20 mg, alone or in two‐drug combinations, once‐daily for 48 weeks. The primary endpoint was a ≥1‐stage improvement in fibrosis without worsening of NASH between baseline and 48 weeks based on central pathologist review. Exploratory endpoints included changes in NAFLD Activity Score (NAS), liver histology assessed using a machine learning (ML) approach, liver biochemistry, and noninvasive markers. The majority had cirrhosis (56%) and NAS ≥5 (83%). The primary endpoint was achieved in 11% of placebo‐treated patients versus cilofexor/firsocostat (21%; P = 0.17), cilofexor/selonsertib (19%; P = 0.26), firsocostat/selonsertib (15%; P = 0.62), firsocostat (12%; P = 0.94), and cilofexor (12%; P = 0.96). Changes in hepatic collagen by morphometry were not significant, but cilofexor/firsocostat led to a significant decrease in ML NASH CRN fibrosis score (P = 0.040) and a shift in biopsy area from F3‐F4 to ≤F2 fibrosis patterns. Compared to placebo, significantly higher proportions of cilofexor/firsocostat patients had a ≥2‐point NAS reduction; reductions in steatosis, lobular inflammation, and ballooning; and significant improvements in alanine aminotransferase (ALT), aspartate aminotransferase (AST), bilirubin, bile acids, cytokeratin‐18, insulin, estimated glomerular filtration rate, ELF score, and liver stiffness by transient elastography (all P ≤ 0.05). Pruritus occurred in 20%‐29% of cilofexor versus 15% of placebo‐treated patients.
Conclusions
In patients with bridging fibrosis and cirrhosis, 48 weeks of cilofexor/firsocostat was well tolerated, led to improvements in NASH activity, and may have an antifibrotic effect. This combination offers potential for fibrosis regression with longer‐term therapy in patients with advanced fibrosis attributable to NASH.
The term ‘field effect' (also known as field defect, field cancerization, or field carcinogenesis) has been used to describe a field of cellular and molecular alteration, which predisposes to the ...development of neoplasms within that territory. We explore an expanded, integrative concept, ‘etiologic field effect', which asserts that various etiologic factors (the exposome including dietary, lifestyle, environmental, microbial, hormonal, and genetic factors) and their interactions (the interactome) contribute to a tissue microenvironmental milieu that constitutes a ‘field of susceptibility' to neoplasia initiation, evolution, and progression. Importantly, etiological fields predate the acquisition of molecular aberrations commonly considered to indicate presence of filed effect. Inspired by molecular pathological epidemiology (MPE) research, which examines the influence of etiologic factors on cellular and molecular alterations during disease course, an etiologically focused approach to field effect can: (1) broaden the horizons of our inquiry into cancer susceptibility and progression at molecular, cellular, and environmental levels, during all stages of tumor evolution; (2) embrace host–environment–tumor interactions (including gene-environment interactions) occurring in the tumor microenvironment; and, (3) help explain intriguing observations, such as shared molecular features between bilateral primary breast carcinomas, and between synchronous colorectal cancers, where similar molecular changes are absent from intervening normal colon. MPE research has identified a number of endogenous and environmental exposures which can influence not only molecular signatures in the genome, epigenome, transcriptome, proteome, metabolome and interactome, but also host immunity and tumor behavior. We anticipate that future technological advances will allow the development of in vivo biosensors capable of detecting and quantifying ‘etiologic field effect' as abnormal network pathology patterns of cellular and microenvironmental responses to endogenous and exogenous exposures. Through an ‘etiologic field effect' paradigm, and holistic systems pathology (systems biology) approaches to cancer biology, we can improve personalized prevention and treatment strategies for precision medicine.
Computational methods have made substantial progress in improving the accuracy and throughput of pathology workflows for diagnostic, prognostic, and genomic prediction. Still, lack of ...interpretability remains a significant barrier to clinical integration. We present an approach for predicting clinically-relevant molecular phenotypes from whole-slide histopathology images using human-interpretable image features (HIFs). Our method leverages >1.6 million annotations from board-certified pathologists across >5700 samples to train deep learning models for cell and tissue classification that can exhaustively map whole-slide images at two and four micron-resolution. Cell- and tissue-type model outputs are combined into 607 HIFs that quantify specific and biologically-relevant characteristics across five cancer types. We demonstrate that these HIFs correlate with well-known markers of the tumor microenvironment and can predict diverse molecular signatures (AUROC 0.601-0.864), including expression of four immune checkpoint proteins and homologous recombination deficiency, with performance comparable to 'black-box' methods. Our HIF-based approach provides a comprehensive, quantitative, and interpretable window into the composition and spatial architecture of the tumor microenvironment.
Two large-scale pharmacogenomic studies were published recently in this journal. Genomic data are well correlated between studies; however, the measured drug response data are highly discordant. ...Although the source of inconsistencies remains uncertain, it has potential implications for using these outcome measures to assess gene-drug associations or select potential anticancer drugs on the basis of their reported results.
Background and Aims
Manual histological assessment is currently the accepted standard for diagnosing and monitoring disease progression in NASH, but is limited by variability in interpretation and ...insensitivity to change. Thus, there is a critical need for improved tools to assess liver pathology in order to risk stratify NASH patients and monitor treatment response.
Approach and Results
Here, we describe a machine learning (ML)‐based approach to liver histology assessment, which accurately characterizes disease severity and heterogeneity, and sensitively quantifies treatment response in NASH. We use samples from three randomized controlled trials to build and then validate deep convolutional neural networks to measure key histological features in NASH, including steatosis, inflammation, hepatocellular ballooning, and fibrosis. The ML‐based predictions showed strong correlations with expert pathologists and were prognostic of progression to cirrhosis and liver‐related clinical events. We developed a heterogeneity‐sensitive metric of fibrosis response, the Deep Learning Treatment Assessment Liver Fibrosis score, which measured antifibrotic treatment effects that went undetected by manual pathological staging and was concordant with histological disease progression.
Conclusions
Our ML method has shown reproducibility and sensitivity and was prognostic for disease progression, demonstrating the power of ML to advance our understanding of disease heterogeneity in NASH, risk stratify affected patients, and facilitate the development of therapies.
Genes encoding components of the PI3K-AKT-mTOR signaling axis are frequently mutated in cancer, but few mutations have been characterized in MTOR, the gene encoding the mTOR kinase. Using publicly ...available tumor genome sequencing data, we generated a comprehensive catalog of mTOR pathway mutations in cancer, identifying 33 MTOR mutations that confer pathway hyperactivation. The mutations cluster in six distinct regions in the C-terminal half of mTOR and occur in multiple cancer types, with one cluster particularly prominent in kidney cancer. The activating mutations do not affect mTOR complex assembly, but a subset reduces binding to the mTOR inhibitor DEPTOR. mTOR complex 1 (mTORC1) signaling in cells expressing various activating mutations remains sensitive to pharmacologic mTOR inhibition, but is partially resistant to nutrient deprivation. Finally, cancer cell lines with hyperactivating MTOR mutations display heightened sensitivity to rapamycin both in culture and in vivo xenografts, suggesting that such mutations confer mTOR pathway dependency.
The categorization of intraductal proliferative lesions of the breast based on routine light microscopic examination of histopathologic sections is in many cases challenging, even for experienced ...pathologists. The development of computational tools to aid pathologists in the characterization of these lesions would have great diagnostic and clinical value. As a first step to address this issue, we evaluated the ability of computational image analysis to accurately classify DCIS and UDH and to stratify nuclear grade within DCIS. Using 116 breast biopsies diagnosed as DCIS or UDH from the Massachusetts General Hospital (MGH), we developed a computational method to extract 392 features corresponding to the mean and standard deviation in nuclear size and shape, intensity, and texture across 8 color channels. We used L1-regularized logistic regression to build classification models to discriminate DCIS from UDH. The top-performing model contained 22 active features and achieved an AUC of 0.95 in cross-validation on the MGH data-set. We applied this model to an external validation set of 51 breast biopsies diagnosed as DCIS or UDH from the Beth Israel Deaconess Medical Center, and the model achieved an AUC of 0.86. The top-performing model contained active features from all color-spaces and from the three classes of features (morphology, intensity, and texture), suggesting the value of each for prediction. We built models to stratify grade within DCIS and obtained strong performance for stratifying low nuclear grade vs. high nuclear grade DCIS (AUC = 0.98 in cross-validation) with only moderate performance for discriminating low nuclear grade vs. intermediate nuclear grade and intermediate nuclear grade vs. high nuclear grade DCIS (AUC = 0.83 and 0.69, respectively). These data show that computational pathology models can robustly discriminate benign from malignant intraductal proliferative lesions of the breast and may aid pathologists in the diagnosis and classification of these lesions.
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