A prognostic framework for predicting lung signet ring cell carcinoma via a machine learning based cox proportional hazard model.

PURPOSE: Signet ring cell carcinoma (SRCC) is a rare type of lung cancer. The conventional survival nomogram used to predict lung cancer performs poorly for SRCC. Therefore, a novel nomogram specifically for studying SRCC is highly required. METHODS: Baseline characteristics of lung signet ring cell carcinoma were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression and random forest analysis were performed on the training group data, respectively. Subsequently, we compared results from these two types of analyses. A nomogram model was developed to predict 1-year, 3-year, and 5-year overall survival (OS) for patients, and receiver operating characteristic (ROC) curves and calibration curves were used to assess the prediction accuracy. Decision curve analysis (DCA) was used to assess the clinical applicability of the proposed model. For treatment modalities, Kaplan-Meier curves were adopted to analyze condition-specific effects. RESULTS: We obtained 731 patients diagnosed with lung signet ring cell carcinoma (LSRCC) in the SEER database and randomized the patients into a training group (551) and a validation group (220) with a ratio of 7:3. Eight factors including age, primary site, T, N, and M.Stage, surgery, chemotherapy, and radiation were included in the nomogram analysis. Results suggested that treatment methods (like surgery, chemotherapy, and radiation) and T-Stage factors had significant prognostic effects. The results of ROC curves, calibration curves, and DCA in the training and validation groups demonstrated that the nomogram we constructed could precisely predict survival and prognosis in LSRCC patients. Through deep verification, we found the constructed model had a high C-index, indicating that the model had a strong predictive power. Further, we found that all surgical interventions had good effects on OS and cancer-specific survival (CSS). The survival curves showed a relatively favorable prognosis for T0 patients overall, regardless of the treatment modality. CONCLUSIONS: Our nomogram is demonstrated to be clinically beneficial for the prognosis of LSRCC patients. The surgical intervention was successful regardless of the tumor stage, and the Cox proportional hazard (CPH) model had better performance than the machine learning model in terms of effectiveness.

The context-dependent epigenetic and organogenesis programs determine 3D vs. 2D cellular fitness of MYC-driven cancer.

3D cellular-specific epigenetic and transcriptomic reprogramming is critical to organogenesis and tumorigenesis. Here we dissect the distinct cell fitness in 2D (normoxia vs. chronic hypoxia) vs 3D (normoxia) culture conditions. We identify over 600 shared essential genes and additional context-specific fitness genes and pathways. Knockout of the VHL-HIF1 pathway results in incompatible fitness defects under normoxia vs. 1% oxygen or 3D culture conditions. Moreover, deletion of each of the mitochondrial respiratory electron transport chain complex has distinct fitness outcomes. Notably, multicellular organogenesis signaling pathways including TGFß-SMAD specifically constrict the uncontrolled cell proliferation in 3D while inactivation of epigenetic modifiers (Bcor, Kmt2d, Mettl3 and Mettl14) has opposite outcomes in 2D vs. 3D. We further identify a 3D-dependent synthetic lethality with partial loss of Prmt5 due to a reduction of Mtap expression resulting from 3D-specific epigenetic reprogramming. Our study highlights unique epigenetic, metabolic and organogenesis signaling dependencies under different cellular settings.

A Review for Artificial Intelligence Based Protein Subcellular Localization.

Proteins need to be located in appropriate spatiotemporal contexts to carry out their diverse biological functions. Mislocalized proteins may lead to a broad range of diseases, such as cancer and Alzheimer's disease. Knowing where a target protein resides within a cell will give insights into tailored drug design for a disease. As the gold validation standard, the conventional wet lab uses fluorescent microscopy imaging, immunoelectron microscopy, and fluorescent biomarker tags for protein subcellular location identification. However, the booming era of proteomics and high-throughput sequencing generates tons of newly discovered proteins, making protein subcellular localization by wet-lab experiments a mission impossible. To tackle this concern, in the past decades, artificial intelligence (AI) and machine learning (ML), especially deep learning methods, have made significant progress in this research area. In this article, we review the latest advances in AI-based method development in three typical types of approaches, including sequence-based, knowledge-based, and image-based methods. We also elaborately discuss existing challenges and future directions in AI-based method development in this research field.

USP1 Expression Driven by EWS::FLI1 Transcription Factor Stabilizes Survivin and Mitigates Replication Stress in Ewing Sarcoma.

In this study, we identify USP1 as a transcriptional target of EWS::FLI1 and demonstrate the requisite function of USP1 in Ewing sarcoma (EWS) cell survival in response to endogenous replication stress. EWS::FLI1 oncogenic transcription factor drives most EWS, a pediatric bone cancer. EWS cells display elevated levels of R-loops and replication stress. The mechanism by which EWS cells override activation of apoptosis or cellular senescence in response to increased replication stress is not known. We show that USP1 is overexpressed in EWS and EWS::FLI1 regulates USP1 transcript levels. USP1 knockdown or inhibition arrests EWS cell growth and induces cell death by apoptosis. Mechanistically, USP1 regulates Survivin (BIRC5/API4) protein stability and the activation of caspase-9 and caspase-3/7 in response to endogenous replication stress. Notably, USP1 inhibition sensitizes cells to doxorubicin and etoposide treatment. Together, our study demonstrates that USP1 is regulated by EWS::FLI1, the USP1-Survivin axis promotes EWS cell survival, and USP1 inhibition sensitizes cells to standard of care chemotherapy. IMPLICATIONS: High USP1 and replication stress levels driven by EWS::FLI1 transcription factor in EWS are vulnerabilities that can be exploited to improve existing treatment avenues and overcome drug resistance.

Embedded Bioprinting of Breast Tumor Cells and Organoids Using Low-Concentration Collagen-Based Bioinks.

Bioinks for 3D bioprinting of tumor models should not only meet printability requirements but also accurately maintain and support phenotypes of tumor surrounding cells to recapitulate key tumor hallmarks. Collagen is a major extracellular matrix protein for solid tumors, but low viscosity of collagen solution has made 3D bioprinted cancer models challenging. This work produces embedded, bioprinted breast cancer cells and tumor organoid models using low-concentration collagen I based bioinks. The biocompatible and physically crosslinked silk fibroin hydrogel is used to generate the support bath for the embedded 3D printing. The composition of the collagen I based bioink is optimized with a thermoresponsive hyaluronic acid-based polymer to maintain the phenotypes of both the noninvasive epithelial and invasive breast cancer cells, as well as cancer-associated fibroblasts. Mouse breast tumor organoids are bioprinted using optimized collagen bioink to mimic in vivo tumor morphology. A vascularized tumor model is also created using a similar strategy, with significantly enhanced vasculature formation under hypoxia. This study shows the great potential of embedded bioprinted breast tumor models utilizing a low-concentration collagen-based bioink for advancing the understanding of tumor cell biology and facilitating drug discovery research.

Etiology of oncogenic fusions in 5,190 childhood cancers and its clinical and therapeutic implication.

Oncogenic fusions formed through chromosomal rearrangements are hallmarks of childhood cancer that define cancer subtype, predict outcome, persist through treatment, and can be ideal therapeutic targets. However, mechanistic understanding of the etiology of oncogenic fusions remains elusive. Here we report a comprehensive detection of 272 oncogenic fusion gene pairs by using tumor transcriptome sequencing data from 5190 childhood cancer patients. We identify diverse factors, including translation frame, protein domain, splicing, and gene length, that shape the formation of oncogenic fusions. Our mathematical modeling reveals a strong link between differential selection pressure and clinical outcome in CBFB-MYH11. We discover 4 oncogenic fusions, including RUNX1-RUNX1T1, TCF3-PBX1, CBFA2T3-GLIS2, and KMT2A-AFDN, with promoter-hijacking-like features that may offer alternative strategies for therapeutic targeting. We uncover extensive alternative splicing in oncogenic fusions including KMT2A-MLLT3, KMT2A-MLLT10, C11orf95-RELA, NUP98-NSD1, KMT2A-AFDN and ETV6-RUNX1. We discover neo splice sites in 18 oncogenic fusion gene pairs and demonstrate that such splice sites confer therapeutic vulnerability for etiology-based genome editing. Our study reveals general principles on the etiology of oncogenic fusions in childhood cancer and suggests profound clinical implications including etiology-based risk stratification and genome-editing-based therapeutics.

A Sequence Obfuscation Method for Protecting Personal Genomic Privacy.

With the technological advances in recent decades, determining whole genome sequencing of a person has become feasible and affordable. As a result, large-scale individual genomic sequences are produced and collected for genetic medical diagnoses and cancer drug discovery, which, however, simultaneously poses serious challenges to the protection of personal genomic privacy. It is highly urgent to develop methods which make the personal genomic data both utilizable and confidential. Existing genomic privacy-protection methods are either time-consuming for encryption or with low accuracy of data recovery. To tackle these problems, this paper proposes a sequence similarity-based obfuscation method, namely IterMegaBLAST, for fast and reliable protection of personal genomic privacy. Specifically, given a randomly selected sequence from a dataset of genomic sequences, we first use MegaBLAST to find its most similar sequence from the dataset. These two aligned sequences form a cluster, for which an obfuscated sequence was generated via a DNA generalization lattice scheme. These procedures are iteratively performed until all of the sequences in the dataset are clustered and their obfuscated sequences are generated. Experimental results on benchmark datasets demonstrate that under the same degree of anonymity, IterMegaBLAST significantly outperforms existing state-of-the-art approaches in terms of both utility accuracy and time complexity.

The nuclear receptor ERR cooperates with the cardiogenic factor GATA4 to orchestrate cardiomyocyte maturation.

Estrogen-related receptors (ERR) α and γ were shown recently to serve as regulators of cardiac maturation, yet the underlying mechanisms have not been delineated. Herein, we find that ERR signaling is necessary for induction of genes involved in mitochondrial and cardiac-specific contractile processes during human induced pluripotent stem cell-derived cardiomyocyte (hiPSC-CM) differentiation. Genomic interrogation studies demonstrate that ERRγ occupies many cardiomyocyte enhancers/super-enhancers, often co-localizing with the cardiogenic factor GATA4. ERRγ interacts with GATA4 to cooperatively activate transcription of targets involved in cardiomyocyte-specific processes such as contractile function, whereas ERRγ-mediated control of metabolic genes occurs independent of GATA4. Both mechanisms require the transcriptional coregulator PGC-1α. A disease-causing GATA4 mutation is shown to diminish PGC-1α/ERR/GATA4 cooperativity and expression of ERR target genes are downregulated in human heart failure samples suggesting that dysregulation of this circuitry may contribute to congenital and acquired forms of heart failure.

Special Issue on Bioinformatics and Machine Learning for Cancer Biology.

Cancer is a leading cause of death worldwide, claiming millions of lives each year [...].

Genomic profiling identifies genes and pathways dysregulated by HEY1-NCOA2 fusion and shines a light on mesenchymal chondrosarcoma tumorigenesis.

Mesenchymal chondrosarcoma is a rare, high-grade, primitive mesenchymal tumor. It accounts for around 2-10% of all chondrosarcomas and mainly affects adolescents and young adults. We previously described the HEY1-NCOA2 as a recurrent gene fusion in mesenchymal chondrosarcoma, an important breakthrough for characterizing this disease; however, little study had been done to characterize the fusion protein functionally, in large part due to a lack of suitable models for evaluating the impact of HEY1-NCOA2 expression in the appropriate cellular context. We used iPSC-derived mesenchymal stem cells (iPSC-MSCs), which can differentiate into chondrocytes, and generated stable transduced iPSC-MSCs with inducible expression of HEY1-NCOA2 fusion protein, wildtype HEY1 or wildtype NCOA2. We next comprehensively analyzed both the DNA binding properties and transcriptional impact of HEY1-NCOA2 expression by integrating genome-wide chromatin immunoprecipitation sequencing (ChIP-seq) and expression profiling (RNA-seq). We demonstrated that HEY1-NCOA2 fusion protein preferentially binds to promoter regions of canonical HEY1 targets, resulting in transactivation of HEY1 targets, and significantly enhances cell proliferation. Intriguingly, we identified that both PDGFB and PDGFRA were directly targeted and upregulated by HEY1-NCOA2; and the fusion protein, but not wildtype HEY1 or NCOA2, dramatically increased the level of phospho-AKT (Ser473). Our findings provide a rationale for exploring PDGF/PI3K/AKT inhibition in treating mesenchymal chondrosarcoma. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Improving bulk RNA-seq classification by transferring gene signature from single cells in acute myeloid leukemia.

The advances in single-cell RNA sequencing (scRNA-seq) technologies enable the characterization of transcriptomic profiles at the cellular level and demonstrate great promise in bulk sample analysis thereby offering opportunities to transfer gene signature from scRNA-seq to bulk data. However, the gene expression signatures identified from single cells are typically inapplicable to bulk RNA-seq data due to the profiling differences of distinct sequencing technologies. Here, we propose single-cell pair-wise gene expression (scPAGE), a novel method to develop single-cell gene pair signatures (scGPSs) that were beneficial to bulk RNA-seq classification to transfer knowledge across platforms. PAGE was adopted to tackle the challenge of profiling differences. We applied the method to acute myeloid leukemia (AML) and identified the scGPS from mouse scRNA-seq that allowed discriminating between AML and control cells. The scGPS was validated in bulk RNA-seq datasets and demonstrated better performance (average area under the curve [AUC] = 0.96) than the conventional gene expression strategies (average AUC$\le$ 0.88) suggesting its potential in disclosing the molecular mechanism of AML. The scGPS also outperformed its bulk counterpart, which highlighted the benefit of gene signature transfer. Furthermore, we confirmed the utility of scPAGE in sepsis as an example of other disease scenarios. scPAGE leveraged the advantages of single-cell profiles to enhance the analysis of bulk samples revealing great potential of transferring knowledge from single-cell to bulk transcriptome studies.

Alzheimer's disease-associated U1 snRNP splicing dysfunction causes neuronal hyperexcitability and cognitive impairment.

Recent proteome and transcriptome profiling of Alzheimer's disease (AD) brains reveals RNA splicing dysfunction and U1 small nuclear ribonucleoprotein (snRNP) pathology containing U1-70K and its N-terminal 40-KDa fragment (N40K). Here we present a causative role of U1 snRNP dysfunction to neurodegeneration in primary neurons and transgenic mice (N40K-Tg), in which N40K expression exerts a dominant-negative effect to downregulate full-length U1-70K. N40K-Tg recapitulates N40K insolubility, erroneous splicing events, neuronal degeneration and cognitive impairment. Specifically, N40K-Tg shows the reduction of GABAergic synapse components (e.g., the GABA receptor subunit of GABRA2), and concomitant postsynaptic hyperexcitability that is rescued by a GABA receptor agonist. Crossing of N40K-Tg and the 5xFAD amyloidosis model indicates that the RNA splicing defect synergizes with the amyloid cascade to remodel the brain transcriptome and proteome, deregulate synaptic proteins, and accelerate cognitive decline. Thus, our results support the contribution of U1 snRNP-mediated splicing dysfunction to AD pathogenesis.

Targeting the spliceosome through RBM39 degradation results in exceptional responses in high-risk neuroblastoma models.

Aberrant alternative pre-mRNA splicing plays a critical role in MYC-driven cancers and therefore may represent a therapeutic vulnerability. Here, we show that neuroblastoma, a MYC-driven cancer characterized by splicing dysregulation and spliceosomal dependency, requires the splicing factor RBM39 for survival. Indisulam, a "molecular glue" that selectively recruits RBM39 to the CRL4-DCAF15 E3 ubiquitin ligase for proteasomal degradation, is highly efficacious against neuroblastoma, leading to significant responses in multiple high-risk disease models, without overt toxicity. Genetic depletion or indisulam-mediated degradation of RBM39 induces significant genome-wide splicing anomalies and cell death. Mechanistically, the dependency on RBM39 and high-level expression of DCAF15 determine the exquisite sensitivity of neuroblastoma to indisulam. Our data indicate that targeting the dysregulated spliceosome by precisely inhibiting RBM39, a vulnerability in neuroblastoma, is a valid therapeutic strategy.

A Critical Role for Estrogen-Related Receptor Signaling in Cardiac Maturation.

RATIONALE: The heart undergoes dramatic developmental changes during the prenatal to postnatal transition, including maturation of cardiac myocyte energy metabolic and contractile machinery. Delineation of the mechanisms involved in cardiac postnatal development could provide new insight into the fetal shifts that occur in the diseased heart and unveil strategies for driving maturation of stem cell-derived cardiac myocytes. OBJECTIVE: To delineate transcriptional drivers of cardiac maturation. METHODS AND RESULTS: We hypothesized that ERR (estrogen-related receptor) α and γ, known transcriptional regulators of postnatal mitochondrial biogenesis and function, serve a role in the broader cardiac maturation program. We devised a strategy to knockdown the expression of ERRα and γ in heart after birth (pn-csERRα/γ [postnatal cardiac-specific ERRα/γ]) in mice. With high levels of knockdown, pn-csERRα/γ knockdown mice exhibited cardiomyopathy with an arrest in mitochondrial maturation. RNA sequence analysis of pn-csERRα/γ knockdown hearts at 5 weeks of age combined with chromatin immunoprecipitation with deep sequencing and functional characterization conducted in human induced pluripotent stem cell-derived cardiac myocytes (hiPSC-CM) demonstrated that ERRγ activates transcription of genes involved in virtually all aspects of postnatal developmental maturation, including mitochondrial energy transduction, contractile function, and ion transport. In addition, ERRγ was found to suppress genes involved in fibroblast activation in hearts of pn-csERRα/γ knockdown mice. Disruption of Esrra and Esrrg in mice during fetal development resulted in perinatal lethality associated with structural and genomic evidence of an arrest in cardiac maturation, including persistent expression of early developmental and noncardiac lineage gene markers including cardiac fibroblast signatures. Lastly, targeted deletion of ESRRA and ESRRG in hiPSC-CM derepressed expression of early (transcription factor 21 or TCF21) and mature (periostin, collagen type III) fibroblast gene signatures. CONCLUSIONS: ERRα and γ are critical regulators of cardiac myocyte maturation, serving as transcriptional activators of adult cardiac metabolic and structural genes, an.d suppressors of noncardiac lineages including fibroblast determination.