A5010487226- Gene expression and cancer classification
- Bioinformatics and Genomic Networks
- Cancer Genomics and Diagnostics
- Radiomics and Machine Learning in Medical Imaging
- Computational Drug Discovery Methods
- AI in cancer detection
- Breast Cancer Treatment Studies
- Cancer Immunotherapy and Biomarkers
- Cancer Cells and Metastasis
- Advanced Breast Cancer Therapies
- Molecular Biology Techniques and Applications
- Sarcoma Diagnosis and Treatment
- RNA modifications and cancer
- Artificial Intelligence in Healthcare and Education
- CAR-T cell therapy research
- HER2/EGFR in Cancer Research
- Genetics, Bioinformatics, and Biomedical Research
- Pharmacogenetics and Drug Metabolism
- Single-cell and spatial transcriptomics
- Cancer-related molecular mechanisms research
- Medical Imaging Techniques and Applications
- PARP inhibition in cancer therapy
- Head and Neck Cancer Studies
- Genetic factors in colorectal cancer
- Epigenetics and DNA Methylation
University of Toronto
2016-2025
Princess Margaret Cancer Centre
2016-2025
University Health Network
2016-2025
Ontario Institute for Cancer Research
2016-2025
Vector Institute
2016-2025
Public Health Ontario
2021-2025
Artificial Intelligence in Medicine (Canada)
2019-2025
Kingston Health Sciences Centre
2023-2025
Queens University
2023-2025
Queen's University
2023-2025
Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics refers to the comprehensive quantification of tumour phenotypes applying a large number quantitative image features. Here we present radiomic analysis 440 features quantifying intensity, shape and texture, which are extracted from computed tomography data 1,019 patients with lung or head-and-neck cancer. We find have prognostic power in independent sets cancer patients, many...
Background: Histologic grade in breast cancer provides clinically important prognostic information. However, 30%–60% of tumors are classified as histologic 2. This is associated with an intermediate risk recurrence and thus not informative for clinical decision making. We examined whether was gene expression profiles cancers such could be used to improve grading. Methods: analyzed microarray data from 189 invasive carcinomas three published datasets carcinomas. identified differentially...
Abstract Purpose: Recently, a 76-gene prognostic signature able to predict distant metastases in lymph node–negative (N−) breast cancer patients was reported. The aims of this study conducted by TRANSBIG were independently validate these results and compare the outcome with clinical risk assessment. Experimental Design: Gene expression profiling frozen samples from 198 N− systemically untreated done at Bordet Institute, blinded data independent Veridex. Genomic defined Veridex, data....
CD4⁺ T cells are critical regulators of immune responses, but their functional role in human breast cancer is relatively unknown. The goal this study was to produce an image infiltrating tumors using limited ex vivo manipulation better understand the differences associated with patient prognosis. We performed comprehensive molecular profiling isolated from untreated invasive primary and found that cell subpopulations included follicular helper (Tfh) cells, which have not previously been...
A number of microarray studies have reported distinct molecular profiles breast cancers (BC), such as basal-like, ErbB2-like, and two to three luminal-like subtypes. These were associated with different clinical outcomes. However, although the basal ErbB2 subtypes are repeatedly recognized, identification estrogen receptor (ER) -positive has been inconsistent. Therefore, refinement their definition is needed.We previously a gene expression grade index (GGI), which defines histologic based on...
Due to advances in the acquisition and analysis of medical imaging, it is currently possible quantify tumor phenotype. The emerging field Radiomics addresses this issue by converting images into minable data extracting a large number quantitative imaging features. One main challenges segmentation. Where manual delineation time consuming prone inter-observer variability, has been shown that semi-automated approaches are fast reduce variability. In study, semiautomatic region growing...
Using gene-expression data from over 6,000 breast cancer patients, we report herein that high CD73 expression is associated with a poor prognosis in triple-negative cancers (TNBC). Because anthracycline-based chemotherapy regimens are standard treatment for TNBC, investigated the relationship between and anthracycline efficacy. In TNBC patients treated anthracycline-only preoperative chemotherapy, gene was significantly lower rate of pathological complete response or disappearance invasive...
Abstract Summary: The survcomp package provides functions to assess and statistically compare the performance of survival/risk prediction models. It implements state-of-the-art statistics (i) measure risk models; (ii) combine these statistical estimates from multiple datasets using a meta-analytical framework; (iii) competitive Availability: R/Bioconductor is provided open source under Artistic-2.0 License with user manual containing installation, operating instructions use case scenarios on...
Abstract Radiomics provides a comprehensive quantification of tumor phenotypes by extracting and mining large number quantitative image features. To reduce the redundancy compare prognostic characteristics radiomic features across cancer types, we investigated cancer-specific feature clusters in four independent Lung Head & Neck (H&N) cohorts (in total 878 patients). Radiomic were extracted from pre-treatment computed tomography (CT) images. Consensus clustering resulted eleven...
Abstract Background Estrogen receptor positive (ER+) breast cancers (BC) are heterogeneous with regard to their clinical behavior and response therapies. The ER is currently the best predictor of anti-estrogen agent tamoxifen, yet up 30–40% ER+BC will relapse despite tamoxifen treatment. New prognostic biomarkers further biological understanding resistance required. We used gene expression profiling develop an outcome-based using a training set 255 ER+ BC samples from women treated adjuvant...
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor patient survival. Toward understanding the underlying molecular alterations that drive PDAC oncogenesis, we conducted comprehensive proteogenomic analysis of 140 pancreatic cancers, 67 normal adjacent tissues, and 9 tissues. Proteomic, phosphoproteomic, glycoproteomic analyses were used to characterize proteins their modifications. In addition, whole-genome sequencing, whole-exome methylation, RNA sequencing...
Breast cancer in young women is associated with poor prognosis. We aimed to define the role of gene expression signatures predicting prognosis and understand biological differences according age.Patients were assigned molecular subtypes [estrogen receptor (ER)(+)/HER2(-); HER2(+), ER(-)/HER2(-))] using a three-gene classifier. evaluated whether previously published proliferation, stroma, immune-related added prognostic information Adjuvant! online tested their interaction age Cox model for...
Understanding the tumor immune microenvironment (TIME) promises to be key for optimal cancer therapy, especially in triple-negative breast (TNBC). Integrating spatial resolution of cells with laser capture microdissection gene expression profiles, we defined distinct TIME stratification TNBC, implications current therapies including checkpoint blockade. TNBCs an immunoreactive exhibited tumoral infiltration granzyme B+CD8+ T (GzmB+CD8+ cells), a type 1 IFN signature, and elevated multiple...
PIK3CA mutations are reported to be present in approximately 25% of breast cancer (BC), particularly the estrogen receptor–positive (ER+) and HER2-overexpressing (HER2+) subtypes, making them one most common genetic aberrations BC. In experimental models, these have been shown activate AKT induce oncogenic transformation, hence lesions hypothesized render tumors highly sensitive therapeutic PI3K/mTOR inhibition. By analyzing gene expression protein data from nearly 1,800 human BCs, we report...
Abstract Summary: Breast cancer is one of the most frequent cancers among women. Extensive studies into molecular heterogeneity breast have produced a plethora subtype classification and prognosis prediction algorithms, as well numerous gene expression signatures. However, reimplementation these algorithms tedious but important task to enable comparison existing signatures models between each other with new models. Here, we present genefu R/Bioconductor package, multi-tiered compendium...
Single sample predictors (SSPs) and Subtype classification models (SCMs) are gene expression–based classifiers used to identify the four primary molecular subtypes of breast cancer (basal-like, HER2-enriched, luminal A, B). SSPs use hierarchical clustering, followed by nearest centroid classification, based on large sets tumor-intrinsic genes. SCMs a mixture Gaussian distributions genes with expression specifically correlated three key (estrogen receptor [ER], HER2, aurora kinase A [AURKA])....