A5062850341- Cancer Genomics and Diagnostics
- PARP inhibition in cancer therapy
- Pancreatic and Hepatic Oncology Research
- AI in cancer detection
- Cancer Immunotherapy and Biomarkers
- Radiomics and Machine Learning in Medical Imaging
- Lung Cancer Treatments and Mutations
- Bioinformatics and Genomic Networks
- Ferroptosis and cancer prognosis
- 3D Printing in Biomedical Research
- Computational Drug Discovery Methods
- CRISPR and Genetic Engineering
- Cancer-related molecular mechanisms research
- Hepatocellular Carcinoma Treatment and Prognosis
- Head and Neck Cancer Studies
- vaccines and immunoinformatics approaches
- Lung Cancer Diagnosis and Treatment
- Cell Image Analysis Techniques
- Cardiac, Anesthesia and Surgical Outcomes
- Statistical Methods in Clinical Trials
- Immune Cell Function and Interaction
- Health and Medical Research Impacts
- Cholangiocarcinoma and Gallbladder Cancer Studies
- Colorectal Cancer Treatments and Studies
- Advanced Causal Inference Techniques
Tel Aviv University
2018-2019
Germline BRCA-associated pancreatic ductal adenocarcinoma (glBRCA PDAC) tumors are susceptible to platinum and PARP inhibition. The clinical outcomes of 125 patients with glBRCA PDAC were stratified based on the spectrum response platinum/PARP inhibition: (i) refractory [overall survival (OS) <6 months], (ii) durable followed by acquired resistance (OS <36 months), (iii) long-term responders >36 months). Patient-derived xenografts (PDX) generated from 25 at different time points. Response...
Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers.We present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions uses them to predict patient's variety of therapies multiple cancer types without training on previous treatment data. We study ENLIGHT two...
Evolution of cancer is driven by few somatic mutations that disrupt cellular processes, causing abnormal proliferation and tumor development, whereas most have no impact on progression. Distinguishing those mutated genes drive tumorigenesis in a patient primary goal therapy: Knowledge these the pathways which they operate can illuminate disease mechanisms indicate potential therapies drug targets. Current research focuses mainly cohort-level driver gene identification but patient-specific...
Fibrolamellar carcinoma (FLC) is a rare cancer of the liver that most commonly affects children and young adults. There no clear standard care for disease, whose response to treatment seems be very different from hepatocellular carcinoma. We present case FLC in patient her mid 30s recurred persisted despite resection multiple lines treatment. Following transcriptomic analysis, combination ipilimumab (anti-CTLA4) nivolumab (anti-PD-1) led complete remission, although common biomarkers immune...
Introduction Immune checkpoint inhibitors (ICI) have improved outcomes in non-small cell lung cancer (NSCLC). Nevertheless, the clinical benefit of ICI as monotherapy or combination with chemotherapy remains widely varied and existing biomarkers limited predictive value. We present an analysis ENLIGHT-DP, a novel transcriptome-based biomarker directly from histopathology slides, patients adenocarcinoma (LUAD) treated platinum-based chemotherapy. Methods retrospectively scanned...
Many drugs show promising results in laboratory research but eventually fail to clinical benefit. One main reason for this translational gap is that the cancer models used are inadequate. Most lack complex tumor-stromal-immune cell interactions with their tumor microenvironment (TME) required progression. Conventional 2D models, where cells grow on rigid plastic plates mainly as mono-cultures, recapitulate these interactions. Hence, we developed two platforms of 3D better mimic TME: (i) a...
Abstract Background: A major aim in precision oncology is establishing predictive biomarkers to enable improved treatment matching for patients. mRNA expression one of the most available and explored modalities generation such models. Several works have demonstrated that supervised learning using transcriptomic data can generate biomarkers. However, scarcity datasets couple with drug response severely limit this approach. In contrast, unsupervised models offer a potential broadly applicable...
Abstract Background: Transcriptomics-based predictors show great promise in identifying responders to immune checkpoint inhibitors (ICI) who are rejected by standard biomarkers like PD-L1. However, the reliance on gene-expression data makes these less applicable clinic. On other hand, H&E-stained slides commonly available, so predicting response from slide scans could greatly improve accessibility. We have previously described DeepPT - a deep learning model infer gene expression H&E...
2630 Background: Metastatic or locally advanced cutaneous squamous cell carcinoma (CSCC) not amenable to local therapy is treated with programmed death (PD)-1 inhibitors, namely Cemiplimab . While response rates are relatively high at approximately 45%, no predictive biomarkers for PD-1 inhibition have been validated in CSCC subjecting some patients, especially older, unnecessary immune-related adverse events (irAE). We present a retrospective analysis of ENLIGHT-DP, novel biomarker CSCC,...
Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin (H&E)-stained tumor slides precision oncology. We present ENLIGHT-DeepPT, an approach predicting response to multiple targeted immunotherapies from H&E-slides. In difference existing approaches that aim predict treatment directly slides, ENLIGHT-DeepPT is indirect two-step consisting of (1) DeepPT, a new deep-learning framework predicts genome-wide mRNA expression (2) ENLIGHT, which based on DeepPT...
ABSTRACT Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin (H&E)-stained tumor slides precision oncology. We present ENLIGHT-DeepPT, an approach predicting response to multiple targeted immunotherapies from H&E-slides. In difference existing approaches that aim predict treatment directly slides, ENLIGHT-DeepPT is indirect two-step consisting of (1) DeepPT, a new deep-learning framework predicts genome-wide mRNA expression (2) ENLIGHT, which...
1551 Background: In recent years, the use of tumour molecular profiling within clinic has allowed for more accurate cancer diagnostics, as well delivery precision oncology. Rapid advances in digital histopathology have extraction clinically relevant information embedded tumor slides by applying machine learning methods, capitalizing on advancements image analysis via deep learning. However, many supervised approaches, predicting response to therapy using whole slide images (WSI) tissue...
Abstract Background Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers. Methods We present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions uses them to predict patient’s variety of therapies multiple cancer types, without training on previous...
The 2017 guidelines of the American College Cardiology and Heart Association propose substantial changes to hypertension management. lower blood pressure threshold defining promote more aggressive treatments. Thus, individuals are now classified as hypertensive a result, medication usage may become extensive. An inevitable byproduct greater use is higher incidence adverse effects. Here, we examined these issues by developing models that predict both cardiovascular events other based on...
Abstract Background Evolution of cancer is driven by few somatic mutations that disrupt cellular processes, causing abnormal proliferation and tumor development, while most have no impact on progression. Distinguishing those mutated genes drive tumorigenesis in a patient primary goal therapy: Knowledge these the pathways which they operate can illuminate disease mechanisms indicate potential therapies drug targets. Current research focuses mainly cohort-level driver gene identification, but...
<h3>Background</h3> Immune checkpoint blockers (ICB) are revolutionizing cancer treatment, approved for increasingly more types. The most common biomarkers currently used routinely to select patients ICB TMB, microsatellite stability and PDL1 presentation. However, some that do not meet the criteria of these markers still respond ICBs. This calls complementary could better identify responders ICBs importantly, discover where standard fail so. Here, we focus on predicting response anti-PD1 in...
2665 Background: Immune checkpoint blockers (ICB), and primarily PD-1/PD-L1 inhibitors, are in the forefront of contemporary clinical oncology have become an integral part treatment many malignancies, including non-small cell lung cancer (NSCLC). Nevertheless, tumor response to ICB varies widely. Predictive markers commonly used distinguish patients likely respond ICB, such as PD-L1 expression mutational burden (TMB) limited predictive value, which calls for development practical more...
e15170 Background: A key goal in precision oncology is to develop predictive biomarkers that can identify patients likely benefit from a given treatment. The main hurdle for generating such the availability of datasets match features each patient (clinical, omics etc.) with treatment and individual outcome data. Such are hard obtain approved drugs, nonexistent drugs development. In contrast, there ample data matching survival data, but methods use validate drug-specific lacking. Here, we...
2666 Background: Immune checkpoint blockers (ICB) are revolutionizing cancer treatment, and being approved for an increasingly wide range of types. The most common biomarkers currently in use to select patients ICB PD-L1 expression on tumor cells, as measured by IHC, mutational burden (TMB) microsatellite instability (MSI), both NGS. However, some that negative these markers still respond ICB. This calls complementary better identify responders ICB, especially current biomarkers. Here, we...
<h3>Background</h3> Immune checkpoint inhibitors (ICI) are in the forefront of contemporary clinical oncology and have become an integral part treatment many malignancies, including advanced or recurrent head-and-neck squamous cell carcinoma (HNSCC). Positive CPS score stratifies survival with programmed death (PD)-1 but has limited predictive value, which calls for development practical more accurate biomarkers.<sup>1</sup> We previously presented results a novel marker response to PD-1...
1553 Background: Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers. Methods: We present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions uses them to predict patient’s variety of therapies multiple cancer types, importantly, without training on...