Project description:RNAseq FASTq files of 181 bulk pre-treatment and 14 post-treatment tumors from GO30140 Ph1b group A and F and 177 bulk pre-treatment tumors of IMbrave150 PhIII

| EGAD00001008128 | EGA

Three-component mixed cell lines RNA-seq experiment

Project description:To generate this dataset in RNA-seq, we performed a mixing experiment, in which we mixed mRNAs from three cell lines: lung adenocarcinoma in humans (H1092), cancer-associated fibroblasts (CAFs) and tumor infiltrating lymphocytes (TIL), at different proportions to generate 32 samples, including 9 samples that correspond to three repeats of a pure cell line sample for three cell lines. The RNA amount of each tissue in the mixture samples was calculated on the basis of real RNA concentrations tested in the biologist’s lab. This dataset was generated in house and then used to generate the count table that counts the number of reads mapped to each exon for all the samples. This count data will be pre-processed by total count normalization and genes that contained zero counts are removed.

2018-10-12 | GSE121127 | GEO

3a3a56cf-bfd5-426e-8592-daccdca2a9f1 - samples

Project description:Raw count matrix of the 124 bulk tumor RNAseq samples from patients with hormone sensitive or castration resistant prostate cancer.

| EGAD00001008762 | EGA

Paired bulk and single-cell RNA-seq on high-grade serous ovarian cancer (HGSOC) samples

Project description:We have sequenced ovarian tumors in several different ways: 1) poly-A captured scRNA-seq, 2) poly-A captured pooled scRNA-seq in pools of 4 samples each 3) Bulk RNA-seq on ribo-depleted tumor chunks 4) Bulk RNA-seq on poly-A captured dissociated cells 5) Bulk RNA-seq on ribo-depleted dissociated cells

2022-11-13 | GSE217517 | GEO

6c38dd8d-d0b9-4118-946a-63e4f4fd6f9c - samples

Project description:RNAseq FASTq files from 728 bulk pre-treatment tumors from IMvigor010.

| EGAD00001007575 | EGA

Sammut2022 - Multi-omic machine learning model to predict pathological complete response for breast cancer neoadjuvant therapy

Project description:In this publication, researchers investigated the intricate relationship between breast cancers and their microenvironment, specifically focusing on predicting treatment responses using multi-omic machine learning model. They collected diverse data types including clinical, genomic, transcriptomic, and digital pathology profiles from pre-treatment biopsies of breast tumors. Leveraging this comprehensive multi-omic dataset, the team developed ensemble machine learning models using different algorithms (Logistic Regression, SVM and Random Forest). These predictive models identifies patients likely to achieve a pathological complete response (pCR) to therapy, showcasing their potential to enhance treatment selection. Please note that the authors also have an interactive dashboard to apply the fully-integrated NAT response model on new (or any desired) data. The user can find its link in their GitHub repository: https://github.com/micrisor/NAT-ML For more information and clarification, please refer to the ReadMe_NAT-ML document in the files section.

2023-08-28 | BIOMD0000001075 | BioModels

289a561c-a53e-41af-a150-43a78fd6ce74 - samples

Project description:TPM matrices of counts from RNAseq data for IMvigor010 from bulk pre-treatment tumors.

| EGAD00001007653 | EGA