40 results for "金蝶苍穹AI管理助手+Cosmic+应用案例+用户反馈"

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• Contribution of allelic imbalance to colorectal cancer

  Point mutations in cancer have been extensively studied but chromosomal gains and losses have been more challenging to interpret due to their unspecific nature. Here we examine high-resolution allelic imbalance (AI) landscape in 1699 colorectal cancers, 256 of which have been whole genome sequenced (WGSed). The imbalances pinpoint 38 genes as plausible AI targets based on previous knowledge, and unbiased CRISPR-Cas9 knockout and activation screens identified altogether 79 genes within AI peaks regulating cell growth. Genetic and functional data implicates loss of TP53 as a sufficient driver of AI. The WGS highlights an influence of copy number aberrations on the rate of detected somatic point mutations. Importantly, the data reveal several associations between AI target genes, suggesting a role for a network of lineage-determining transcription factors in colorectal tumorigenesis. Overall, the results unravel the contribution of AI in colorectal cancer and provide a plausible explanation why so few genes are commonly affected by point mutations in cancers.

  Study EGAS00001002966

• Development and validation of multivariate predictors of primary endocrine resistance to tamoxifen and aromatase inhibitors in luminal breast cancer reveal drug-specific differences

  Background: Endocrine therapy is highly effective in blocking the estrogen receptor pathway in HR+/HER2- early breast cancer (EBC). However, up to 40% of patients experience relapse during or after adjuvant endocrine therapy. Here, we investigate molecular mechanisms associated with primary resistance to endocrine therapy and develop predictive models. Results: TP53 mutations were prominently associated with primary resistance to both tamoxifen (TAM) and aromatase inhibitors (AI), with AI non-responders exhibiting resistance in up to 32% of cases. Additionally, we identified distinct DNA methylation patterns in TAM and AI non-responders, with TAM non-responders showing global DNA methylation loss, associated with KRAS signaling, apical junctions and epithelial-mesenchymal transition (EMT). Conversely, we observed methylation gain in AI non-responders affecting developmental transcription factors, hypoxia and estrogen signaling. TAM or AI resistance was associated with increased methylation-inferred proportions of immune cells and decreased proportions of endothelial cells. Based on these findings and patient age, we developed the Predictive Endocrine ResistanCe Index (PERCI). PERCI stratified NR and R cases in both treatment groups and cohorts with high accuracy (ROC AUC TAM discovery 93.9%, validation 83%; AI discovery 98.6%, validation 76.9%). A simplified PERCI efficiently predicted progression-free survival in the TCGA-BRCA sub-cohort (Kaplan-Meier log-rank p-value = 0.03 between low and high PERCI groups). Conclusions: We identified genomic and epigenomic features associated with primary resistance to TMA and AI. By combining information on genomic alterations, patient age, differential methylation and tumor microenvironment (TME) composition, we developed PERCI TAM and PERCI AI as novel predictors of primary resistance, with potential additional prognostic value. Applying PERCI in a clinical setting may allow patient-specific drug selection to overcome resistance.

  Study EGAS50000001102

• Estrogen Receptor Positive Breast Cancer: Aromatase Inhibitor Response Study

  Highly variable outcomes are observed in patients with estrogen receptor positive (ER+) breast cancer who undergo preoperative estrogen deprivation therapy with aromatase inhibitors (AI). In this study, 46 baseline tumor and normal genomes and 31 baseline tumor/normal exomes of participants selected from two clinical trials of neoadjuvant AI therapy on ER+ breast cancer were sequenced to identify somatic alterations that correlate with response to AI, to screen for therapeutic targets and to elucidate the genetic landscape of ER+ breast cancer. From the same set of patients we later performed deep genomic characterization of a subset of matched primary tumors after four months of AI therapy, generating comprehensive information about the range of changes that occur when ER+ breast cancers are subjected to estrogen deprivation. This data includes whole genome sequence and transcriptome data. To better understand tumor heterogeneity and the evolution of resistance to estrogen-deprivation therapy, a subset of these tumours, along with 38 additional cases were sequenced to greater depth using targeted capture with a gene panel.

  Study phs000472

• AI

  Allelic imbalance (AI) region calls: start and end positions and the measured mBAF and LRR mean of each region after the BAF segmentation algorithm. Software: Illumina GenomeStudio;PennCNV v. 1.0.4;BAF segmentation v1.2.0.

  Dataset EGAD00010001926

• A Genomics-Driven Artificial Intelligence-Based Model Classifies Breast Invasive Lobular Carcinoma and Discovers CDH1 Inactivating Mechanisms

  We developed an artificial intelligence (AI)-model applied to histological images using CDH1 biallelic mutations, pathognomonic for breast invasive lobular carcinoma (ILC), as ground truth. We evaluated the performance of the AI-model to predict the presence of CDH1 biallelic mutations and to diagnose ILC. Subsequently, we investigated the molecular underpinning cases of predicted by the model to harbor a CDH1 biallelic mutations but lacking these alterations according to targeted sequencing. Among this analyses, we subjected to whole genome sequencing (WGS) one ILC case lacking CDH1 biallelic mutations by targeted sequencing and lacking CDH1 promoter methylation to determine the molecular basis of its lobular phenotype.

  Study EGAS50000000485

• DAC for Breast Invasive Lobular Carcinoma CDH1 study

  We developed an artificial intelligence (AI)-model applied to histological images trained using CDH1 biallelic mutations, pathognomonic for breast invasive lobular carcinoma (ILC), as ground truth. We evaluated the performance of the AI-model to predict the presence of CDH1 biallelic mutations and to diagnose ILC. Subsequently, we investigated the molecular underpinning of cases predicted by the model to harbor a CDH1 biallelic mutations but lacking these alterations according to targeted sequencing. We then subjected one ILC lacking CDH1 biallelic mutations and lacking CDH1 promoter methylation to whole genome sequencing (WGS) to determine the molecular basis of its lobular phenotype.

  Dac EGAC50000000333

• Rapid Acceleration of Diagnostics - Digital Health Technologies (RADx-DHT): NIH Digital Health Solutions for COVID-19: Team SAE

  The goal of this project is to develop a smartphone-based platform to monitor and support individuals with COVID-19 symptoms (who may need testing) and those who have already tested positive. The app will integrate a Bluetooth-enabled thermometer and pulse oximeter into an approach uniquely designed for low-resource settings and underserved populations. This project will focus on the American Indian/Alaskan Native (AI/AN) underserved population and will determine the feasibility of the digital health solution to collect symptom survey data and vital data including temperature, pulse, and oxygen saturation level. The complete study will include 300 participants 100% medically underserved with at least 51% from the AI/AN community. A combination of hard and soft data will be collected consisting of vital data, medical history and questionnaire data, and participant COVID-19 viral and antibody testing results collected during the study.DOI: https://rapids.ll.mit.edu/10.57895/wv88-by98

  Study phs002534

• The Human Phenotype Project (HPP) is a large-scale deep-phenotype prospective longitudinal and ethnically diverse cohort

  The Human Phenotype Project (HPP) is a large-scale deep-phenotype prospective longitudinal and ethnically diverse cohort and biobank that we established. To date, approximately 28,000 participants have enrolled in the study, with over 13,000 having completed their initial visit. The project is aimed at identifying novel molecular signatures with diagnostic, prognostic and therapeutic value, and at developing AI-based predictive models for disease onset and progression. A unique feature of the HPP is its deep and longitudinal profiling that includes medical history, lifestyle and nutritional habits, vital signs, anthropometrics, blood tests, continuous glucose and sleep monitoring, imaging modalities and molecular profiling of the transcriptome, genetics, gut, vaginal and oral microbiome, metabolome and immune system. For several of these modalities, the HPP is the first or largest cohort of its kind. Our analyses of this data provide novel insights into the variation of phenotypes with age and ethnicity, highlighting the importance of having ethnically diverse cohorts and the need for establishing age-dependent norms. We demonstrate how the HPP can be used to unravel personalized molecular signatures of disease by comparison to patient-specific and disease-specific matched healthy controls. Leveraging the extensive dietary and lifestyle HPP data, we systematically identify associations between lifestyle factors and health outcomes. Finally, we present a multi-modal foundation AI model that is trained using self-supervised learning on diet and CGM data and outperforms existing methods in predicting future onset of disease. We extend this AI framework to integrate all data modalities of each subject as a continuous sequence of diverse medical events, creating a digital twin that can simulate interventions and predict health trajectories and outcomes.

  Study EGAS00001008040

• Identification of drug resistance genes in melanoma by Exome Sequencing

  The cell lines in this study are a combination of internally sequenced (cosmic) and externally sequenced cell lines known to be “double-wild-type” (lacking BRAF and NRAS somatic mutations). These sequences were realigned in this data set for consistency.

  Dataset EGAD00001003260

• Whole Exome Sequencing and RNA-Seq to Characterize the Somatic Breast Cancer Landscape Among Latinas in California

  Somatic mutational profiles are increasingly being used to guide treatment. However, there is a dearth of data from non-European populations. To address this gap, we conducted paired tumor/normal whole exome paired-end sequencing and tumor RNA-Seq on 146 breast tumors from 140 Hispanic/Latina (H/L) women with breast cancer. We characterized somatic mutations, copy number alterations, tumor subtypes [Prediction Analysis of Microarray 50 (PAM50)], Catalogue Of Somatic Mutations In Cancer (COSMIC) mutation signatures and expression profiles, and compared them to breast tumors from non-Hispanic White (White) women in The Cancer Genome Atlas (TCGA). Using MutSigCV, we found that the most commonly mutated genes, including TP53, RUNX1, PTEN, PIK3CA, MAP3K1, GATA3, CDH1 and CBFB, were similar to those in breast tumors from non-Hispanic white women. Novel findings were that outliers of BLTP2 (aka KIAA0100) over-expression and COSMIC mutational signature 16 were more common in tumors from H/L women than White women. We also identified that the known germline APOBEC3A/B deletion variant was significantly more common in H/L women and was associated with indigenous American ancestry. Data available through dbGaP will be whole-exome sequencing (WES) and RNA sequencing (RNA-Seq) data, as well as phenotypic data, including estrogen receptor, progesterone receptor, and HER2 receptor status, tumor histology, stage, and grade, and age at diagnosis.

  Study phs003218