An Optimized Ensemble Deep Learning Model for Predicting Plant miRNA-IncRNA Based on Artificial Gorilla Troops Algorithm.

MicroRNAs (miRNA) are small, non-coding regulatory molecules whose effective alteration might result in abnormal gene manifestation in the downstream pathway of their target. miRNA gene variants can impact miRNA transcription, maturation, or target selectivity, impairing their usefulness in plant growth and stress responses. Simple Sequence Repeat (SSR) based on miRNA is a newly introduced functional marker that has recently been used in plant breeding. MicroRNA and long non-coding RNA (lncRNA) are two examples of non-coding RNA (ncRNA) that play a vital role in controlling the biological processes of animals and plants. According to recent studies, the major objective for decoding their functional activities is predicting the relationship between lncRNA and miRNA. Traditional feature-based classification systems' prediction accuracy and reliability are frequently harmed because of the small data size, human factors' limits, and huge quantity of noise. This paper proposes an optimized deep learning model built with Independently Recurrent Neural Networks (IndRNNs) and Convolutional Neural Networks (CNNs) to predict the interaction in plants between lncRNA and miRNA. The deep learning ensemble model automatically investigates the function characteristics of genetic sequences. The proposed model's main advantage is the enhanced accuracy in plant miRNA-IncRNA prediction due to optimal hyperparameter tuning, which is performed by the artificial Gorilla Troops Algorithm and the proposed intelligent preying algorithm. IndRNN is adapted to derive the representation of learned sequence dependencies and sequence features by overcoming the inaccuracies of natural factors in traditional feature architecture. Working with large-scale data, the suggested model outperforms the current deep learning model and shallow machine learning, notably for extended sequences, according to the findings of the experiments, where we obtained an accuracy of 97.7% in the proposed method.

MYCN drives chemoresistance in small cell lung cancer while USP7 inhibition can restore chemosensitivity.

Small cell lung cancer (SCLC) is an aggressive neuroendocrine cancer characterized by initial chemosensitivity followed by emergence of chemoresistant disease. To study roles for MYCN amplification in SCLC progression and chemoresistance, we developed a genetically engineered mouse model of MYCN-overexpressing SCLC. In treatment-naïve mice, MYCN overexpression promoted cell cycle progression, suppressed infiltration of cytotoxic T cells, and accelerated SCLC. MYCN overexpression also suppressed response to cisplatin-etoposide chemotherapy, with similar findings made upon MYCL overexpression. We extended these data to genetically perturb chemosensitive patient-derived xenograft (PDX) models of SCLC. In chemosensitive PDX models, overexpression of either MYCN or MYCL also conferred a switch to chemoresistance. To identify therapeutic strategies for MYCN-overexpressing SCLC, we performed a genome-scale CRISPR-Cas9 sgRNA screen. We identified the deubiquitinase USP7 as a MYCN-associated synthetic vulnerability. Pharmacological inhibition of USP7 resensitized chemoresistant MYCN-overexpressing PDX models to chemotherapy in vivo. Our findings show that MYCN overexpression drives SCLC chemoresistance and provide a therapeutic strategy to restore chemosensitivity.

MAX Functions as a Tumor Suppressor and Rewires Metabolism in Small Cell Lung Cancer.

Small cell lung cancer (SCLC) is a highly aggressive and lethal neoplasm. To identify candidate tumor suppressors we applied CRISPR/Cas9 gene inactivation screens to a cellular model of early-stage SCLC. Among the top hits was MAX, the obligate heterodimerization partner for MYC family proteins that is mutated in human SCLC. Max deletion increases growth and transformation in cells and dramatically accelerates SCLC progression in an Rb1/Trp53-deleted mouse model. In contrast, deletion of Max abrogates tumorigenesis in MYCL-overexpressing SCLC. Max deletion in SCLC resulted in derepression of metabolic genes involved in serine and one-carbon metabolism. By increasing serine biosynthesis, Max-deleted cells exhibit resistance to serine depletion. Thus, Max loss results in metabolic rewiring and context-specific tumor suppression.

Targeting NOTCH activation in small cell lung cancer through LSD1 inhibition.

Small cell lung cancer (SCLC) is a recalcitrant, aggressive neuroendocrine-type cancer for which little change to first-line standard-of-care treatment has occurred within the last few decades. Unlike nonsmall cell lung cancer (NSCLC), SCLC harbors few actionable mutations for therapeutic intervention. Lysine-specific histone demethylase 1A (LSD1 also known as KDM1A) inhibitors were previously shown to have selective activity in SCLC models, but the underlying mechanism was elusive. Here, we found that exposure to the selective LSD1 inhibitor ORY-1001 activated the NOTCH pathway, resulting in the suppression of the transcription factor ASCL1 and the repression of SCLC tumorigenesis. Our analyses revealed that LSD1 bound to the NOTCH1 locus, thereby suppressing NOTCH1 expression and downstream signaling. Reactivation of NOTCH signaling with the LSD1 inhibitor reduced the expression of ASCL1 and neuroendocrine cell lineage genes. Knockdown studies confirmed the pharmacological inhibitor-based results. In vivo, sensitivity to LSD1 inhibition in SCLC patient-derived xenograft (PDX) models correlated with the extent of consequential NOTCH pathway activation and repression of a neuroendocrine phenotype. Complete and durable tumor regression occurred with ORY-1001-induced NOTCH activation in a chemoresistant PDX model. Our findings reveal how LSD1 inhibitors function in this tumor and support their potential as a new and targeted therapy for SCLC.

Crebbp Loss Drives Small Cell Lung Cancer and Increases Sensitivity to HDAC Inhibition.

CREBBP, encoding an acetyltransferase, is among the most frequently mutated genes in small cell lung cancer (SCLC), a deadly neuroendocrine tumor type. We report acceleration of SCLC upon Crebbp inactivation in an autochthonous mouse model. Extending these observations beyond the lung, broad Crebbp deletion in mouse neuroendocrine cells cooperated with Rb1/Trp53 loss to promote neuroendocrine thyroid and pituitary carcinomas. Gene expression analyses showed that Crebbp loss results in reduced expression of tight junction and cell adhesion genes, including Cdh1, across neuroendocrine tumor types, whereas suppression of Cdh1 promoted transformation in SCLC. CDH1 and other adhesion genes exhibited reduced histone acetylation with Crebbp inactivation. Treatment with the histone deacetylase (HDAC) inhibitor Pracinostat increased histone acetylation and restored CDH1 expression. In addition, a subset of Rb1/Trp53/Crebbp-deficient SCLC exhibited exceptional responses to Pracinostat in vivo Thus, CREBBP acts as a potent tumor suppressor in SCLC, and inactivation of CREBBP enhances responses to a targeted therapy.Significance: Our findings demonstrate that CREBBP loss in SCLC reduces histone acetylation and transcription of cellular adhesion genes, while driving tumorigenesis. These effects can be partially restored by HDAC inhibition, which exhibited enhanced effectiveness in Crebbp-deleted tumors. These data provide a rationale for selectively treating CREBBP-mutant SCLC with HDAC inhibitors. Cancer Discov; 8(11); 1422-37. ©2018 AACR. This article is highlighted in the In This Issue feature, p. 1333.

NFIB overexpression cooperates with Rb/p53 deletion to promote small cell lung cancer.

Small cell lung cancer (SCLC) is a highly aggressive neuroendocrine tumor type that is typically metastatic upon diagnosis. We have a poor understanding of the factors that control SCLC progression and metastasis. TheNFIB transcription factor is frequently amplified in mouse models of SCLC, but clear evidence that NFIB promotes SCLC in vivo is lacking. We report that in mouse models, Nfib amplifications are far more frequent in liver metastases over primary SCLC, suggesting roles in tumor progression/metastasis. Overexpression of Nfib in a sensitized mouse model led to acceleration of SCLC, indicating that Nfib functions as a bona fide oncogene. Suppression of Nfib expression in cell lines derived from the doxycycline-inducible Rb/p53/TET-Nfib model led to increased apoptosis and suppression of proliferation. Transcriptional analysis revealed that Nfib regulates the expression of genes related to axon guidance, focal adhesion and extracellular matrix-receptor interactions. These data indicate that Nfib is a potent oncogene in SCLC, and the enrichment of Nfib amplifications in liver metastases over primary SCLC points to Nfib as a candidate driver of SCLC metastasis.