BackgroundThe study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent study screened for the response of a thousand human cancer cell lines to a wide collection of anti-cancer drugs and illuminated the link between cellular genotypes and vulnerability. However, due to essential differences between cell lines and tumors, to date the translation into predicting drug response in tumors remains challenging. Recently, advances in deep learning have revolutionized bioinformatics and introduced new techniques to the integration of genomic data. Its application on pharmacogenomics may fill the gap between genomics and drug response and improve the prediction of drug response in tumors.ResultsWe proposed a deep learning model to predict drug response (DeepDR) based on mutation and expression profiles of a cancer cell or a tumor. The model contains three deep neural networks (DNNs), i) a mutation encoder pre-trained using a large pan-cancer dataset (The Cancer Genome Atlas; TCGA) to abstract core representations of high-dimension mutation data, ii) a pre-trained expression encoder, and iii) a drug response predictor network integrating the first two subnetworks. Given a pair of mutation and expression profiles, the model predicts IC50 values of 265 drugs. We trained and tested the model on a dataset of 622 cancer cell lines and achieved an overall prediction performance of mean squared error at 1.96 (log-scale IC50 values). The performance was superior in prediction error or stability than two classical methods (linear regression and support vector machine) and four analog DNN models of DeepDR, including DNNs built without TCGA pre-training, partly replaced by principal components, and built on individual types of input data. We then applied the model to predict drug response of 9059 tumors of 33 cancer types. Using per-cancer and pan-cancer settings, the model predicted both known, including EGFR inhibitors in non-small cell lung cancer and tamoxifen in ER+ breast cancer, and novel drug targets, such as vinorelbine for TTN-mutated tumors. The comprehensive analysis further revealed the molecular mechanisms underlying the resistance to a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer potential of a novel agent, CX-5461, in treating gliomas and hematopoietic malignancies.ConclusionsHere we present, as far as we know, the first DNN model to translate pharmacogenomics features identified from in vitro drug screening to predict the response of tumors. The results covered both well-studied and novel mechanisms of drug resistance and drug targets. Our model and findings improve the prediction of drug response and the identification of novel therapeutic options.

Thyroid transcription factor-1 (TTF-1) is a transcription factor that plays a role in the development and physiology of the thyroid and lungs. Expression of TTF-1 is used as a marker of lung and thyroid clinically. Commercially available clones of TTF-1 monoclonal antibodies, 8G7G3/1 and SPT24, have been reported to have different sensitivities for the detection of neoplasms of different origins. Although they are used extensively in daily practice, a comprehensive comparative study of these antibodies in a wide variety of neoplasms is lacking. We examined TTF-1 expression in primary tumors of the lung, prostrate, pancreas, stomach, salivary glands, breast, bladder, colon and squamous cell carcinoma of the head and neck and compared the results obtained with both TTF-1 clones. The SPT24 clone detected more primary lung tumors of all histologic subtypes. Importantly, the SPT24 clone detected a significantly higher number of squamous cell carcinomas and carcinoid tumors of the lung. Among non-pulmonary primary tumors, a significant number of invasive urothelial carcinoma of the bladder (5.1%) was TTF-1 positive. Additionally, a small proportion of prostate (1.2%), stomach (0.9%), salivary gland (1.8%), and colon (2.5%) carcinomas were positive with both clones. Notably, both clones stained the same non-pulmonary tumors with similar intensity and distribution. Carcinomas of the pancreas, breast and squamous cell carcinomas of the head and neck were negative with both clones. In summary, the SPT24 clone detected a higher number of pulmonary non-small cell tumors of all histologic subtypes while both clones stained a similar proportion of non-pulmonary tumors.

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Genome-wide loss-of-function screens have revealed genes essential for cancer cell proliferation, called cancer dependencies. It remains challenging to link cancer dependencies to the molecular compositions of cancer cells or to unscreened cell lines and further to tumors. Here, we present DeepDEP, a deep learning model that predicts cancer dependencies using integrative genomic profiles. It uses a unique unsupervised pretraining that captures unlabeled tumor genomic representations to improve the learning of cancer dependencies. We demonstrated DeepDEP's improvement over conventional machine learning methods and validated the performance with three independent datasets. By systematic model interpretations, we extended the current dependency maps with functional characterizations of dependencies and a proof-of-concept in silico assay of synthetic essentiality. We applied DeepDEP to pan-cancer tumor genomics and built the first pan-cancer synthetic dependency map of 8000 tumors with clinical relevance. In summary, DeepDEP is a novel tool for investigating cancer dependency with rapidly growing genomic resources.

When the 3 imaging patterns are shown on IVP and CT, tubercular cultures or biopsies are suggested to make the definite diagnosis of urinary tuberculosis. Thus, treatment can be initiated as early as possible.

Lung cancer is the leading cause of cancer death worldwide, and brain metastasis is a major cause of morbidity and mortality in lung cancer. CDH2 (N-cadherin, a mesenchymal marker of the epithelial-mesenchymal transition) and ADAM9 (a type I transmembrane protein) are related to lung cancer brain metastasis; however, it is unclear how they interact to mediate this metastasis. Because microRNAs regulate many biological functions and disease processes (e.g., cancer) by down-regulating their target genes, microRNA microarrays were used to identify ADAM9-regulated miRNAs that target CDH2 in aggressive lung cancer cells. Luciferase assays and western blot analysis showed that CDH2 is a target gene of miR-218. MiR-218 was generated from pri-mir-218-1, which is located in SLIT2, in non-invasive lung adenocarcinoma cells, whereas its expression was inhibited in aggressive lung adenocarcinoma. The down-regulation of ADAM9 up-regulated SLIT2 and miR-218, thus down-regulating CDH2 expression. This study revealed that ADAM9 activates CDH2 through the release of miR-218 inhibition on CDH2 in lung adenocarcinoma.

Lung cancer has a very high prevalence of brain metastasis, which results in a poor clinical outcome. Up-regulation of a disintegrin and metalloproteinase 9 (ADAM9) in lung cancer cells is correlated with metastasis to the brain. However, the molecular mechanism underlying this correlation remains to be elucidated. Since angiogenesis is an essential step for brain metastasis, microarray experiments were used to explore ADAM9-regulated genes that function in vascular remodeling. The results showed that the expression levels of vascular endothelial growth factor A (VEGFA), angiopoietin-2 (ANGPT2), and tissue plasminogen activator (PLAT) were suppressed in ADAM9-silenced cells, which in turn leads to decreases in angiogenesis, vascular remodeling, and tumor growth in vivo. Furthermore, simultaneous high expression of ADAM9 and VEGFA or of ADAM9 and ANGPT2 was correlated with poor prognosis in a clinical dataset. These findings suggest that ADAM9 promotes tumorigenesis through vascular remodeling, particularly by increasing the function of VEGFA, ANGPT2, and PLAT.