DOI: 10.5061/dryad.zw3r228jc
title: Synthetic bulk RNA-Seq transcriptomic profiles representing 10 Cancer hallmarks

description: Evidence before this study   We conducted an extensive literature
 search using Google Scholar without language restrictions, employing
 search terms such as “(Predicting OR Classifying OR Annotating) and
 (cancer hallmarks) AND (Deep OR Machine Learning) OR (Artificial
 Intelligence OR AI).” Despite notable advances in molecular oncology and
 computational methodologies, a critical gap remains: no existing machine
 learning or deep learning framework comprehensively predicts cancer
 hallmarks from tumor biopsy samples. Current research primarily targets
 specific molecular pathways associated with individual hallmarks, leaving
 clinicians without an integrated model to interpret hallmark activity at
 the level of an individual tumor. Moreover, the absence of wet-lab
 techniques capable of annotating all cancer hallmarks in biopsy samples
 has further impeded progress, limiting the clinical utility of
 hallmark-related insights for precision oncology.   Added value
 of this study   This study introduces OncoMark, a novel neural
 multi-task learning (N-MTL) framework designed to predict cancer hallmark
 activity from transcriptomic data obtained from biopsy samples. OncoMark
 addresses the lack of hallmark-specific data by generating synthetic
 biopsy datasets annotated with hallmark activity, meticulously modeled to
 reflect real-world tumor biology while maintaining clinical relevance. The
 framework employs a multi-task learning approach to capture
 interdependencies among hallmarks, advancing beyond isolated predictions
 to offer a holistic view of tumor biology. Validation on six independent
 datasets comprising 159 patient samples demonstrated its generalizability
 and reproducibility. Further external validation using eight datasets,
 encompassing over 11,679 cancer and 8348 normal patient samples,
 reinforced its robustness. To promote clinical integration, a
 user-friendly web-based tool was developed, enabling seamless access for
 oncologists and researchers.   Implications of all the available
 evidence   The OncoMark framework represents a transformative
 advancement in cancer diagnostics and treatment planning. By enabling
 accurate and reproducible prediction of hallmark activity from biopsy
 samples, this model paves the way for precision oncology at scale. Its
 ability to systematically capture hallmark interdependencies provides
 deeper insights into tumor behavior, guiding the development of
 individualized, targeted therapies. The incorporation of a web-based
 interface ensures the accessibility of this innovation to clinicians
 worldwide, bridging the gap between computational oncology and clinical
 practice. Following further validation and integration into healthcare
 workflows, OncoMark has the potential to improve cancer outcomes by
 delivering timely, cost-effective, and precise tumor analyses,
 facilitating informed therapeutic decision-making with unparalleled
 precision.
description: <strong>Dataset Collection and
 Processing</strong>  
 We utilized a large-scale dataset comprising 2.7 million
 single-cell transcriptomes derived from 14 tumor types, collected from 922
 patients across 51 independent studies conducted globally. This dataset
 was sourced from the Weizmann Institute's 3CA repository.
 Quality Control  
 Before generating synthetic datasets for model training, the
 raw single-cell transcriptomic data underwent a rigorous quality control
 (QC) process. Cells with over 15% mitochondrial transcript content, fewer
 than 200, or more than 6,000 expressed mRNA transcripts were excluded to
 ensure data reliability.  
 <strong>Gene Set Curation</strong>
   Gene sets representing cancer
 hallmarks were compiled from multiple databases, retaining only genes
 identified in at least two independent sources. This selection was refined
 through manual literature reviews to exclude genes without direct or
 indirect roles in hallmark-related pathways.
   <strong>Digital
 Scoring</strong>  
 Using the curated gene sets, Digital Scores were calculated for
 each of the 10 cancer hallmarks across all cells using the Mann-Whitney U
 test. To ensure robust binary classification, hallmark presence or absence
 was determined through Otsu’s thresholding method. Tissue-specific digital
 score thresholds were calculated to account for variations in hallmark
 expression across different tumor tissues.
   <strong>Synthetic Data
 Generation</strong>  
 To simulate clinical biopsy conditions while preserving
 biological fidelity, synthetic biopsy datasets were created by aggregating
 200 hallmark-specific cells from each patient sample. Cells were grouped
 by hallmark status (positive or negative) to generate distinct
 hallmark-specific synthetic samples, ensuring no overlap across samples
 and minimizing cross-sample contamination. Synthetic datasets with
 positive and negative ground truths were created separately for each
 hallmark, facilitating robust model training and mimicking the
 heterogeneous composition of real-world clinical samples.
  
 <strong>Validation</strong>
   For validation purposes, six external
 studies were processed using the same synthetic data creation methods
 applied to the training data. In these datasets, all hallmark-positive
 cells for each patient were pooled to generate synthetic datasets
 resembling bulk RNA sequencing data. This approach ensured consistency in
 data processing while allowing the model to generalize effectively to
 clinically relevant bulk transcriptomic datasets.
description: # Synthetic bulk RNA-Seq transcriptomic profiles representing 10 Cancer
 hallmarks
 [https://doi.org/10.5061/dryad.zw3r228jc](https://doi.org/10.5061/dryad.zw3r228jc) ## Description of the data and file structure ### Data Description: Experimental Efforts This dataset comprises single-cell transcriptomic data from the Weizmann 3CA repository, encompassing 2.7 million single-cell transcriptomes from 14 tumor types, collected from 922 patients across 51 global studies. The primary objective of the experimental efforts was to generate synthetic datasets for training and validating computational models to identify and analyze cancer hallmarks at the single-cell resolution. Single-cell RNA sequencing (scRNA-seq) data underwent a rigorous quality control process to ensure reliability and biological relevance. This included exclusion criteria based on mitochondrial transcript content (&gt;15%) and mRNA transcript counts (&lt;200 or &gt;6,000 transcripts). Gene sets corresponding to 10 established cancer hallmarks were curated from multiple databases and literature, focusing on genes with direct or indirect involvement in hallmark-related pathways. Digital Scores for each hallmark were calculated using the Mann-Whitney U test, and hallmark classification was determined via tissue-specific Otsu’s thresholding. To simulate clinical biopsy conditions, hallmark-positive and hallmark-negative cells were aggregated into synthetic biopsy datasets comprising 200 cells per sample, stratified by hallmark status. These datasets mimic the heterogeneity and composition of real-world tumor biopsies while ensuring no overlap between synthetic samples. The synthetic datasets were developed to facilitate robust model training and validation, enabling generalization to external datasets and bulk transcriptomic data. Validation was conducted on six external studies using pooled hallmark-positive cells to emulate bulk RNA sequencing conditions, ensuring consistency and clinical relevance. These datasets serve as a critical resource for advancing computational approaches in cancer hallmark identification and characterization. ### Files and variables ### File: `meta_hallmark_present.csv` **Description:** This file contains metadata for all the samples present in the corresponding `data_hallmark_present.csv` file. **Variables:** * **Sample Name:** Unique identifier for each sample. * **Cancer:** Type of cancer associated with the sample. * **Hallmark:** Specific hallmark represented in the sample. --- ### File: `meta_hallmark_absent.csv` **Description:** This file contains metadata for all the samples present in the corresponding `data_hallmark_absent.csv` file. **Variables:** * **Sample Name:** Unique identifier for each sample. * **Cancer:** Type of cancer associated with the sample. * **Hallmark:** Specific hallmark absent in the sample. --- ### File: `data_hallmark_present.csv` **Description:** This file contains synthetic bulk transcriptomics data where the specified hallmark is present. **Variables:** * **Gene Names:** Features representing gene names. * **Values:** Corresponding raw counts for each gene. --- ### File: `data_hallmark_absent.csv` **Description:** This file contains synthetic bulk transcriptomics data where the specified hallmark is absent. **Variables:** * **Gene Names:** Features representing gene names. * **Values:** Corresponding raw counts for each gene. --- ### File: `hallmark_curated_genes.csv` **Description:** This file contains manually curated gene sets for each of the 10 cancer hallmarks. **Variables:** * **Hallmark Names:** Each column representing each hallmarks. * **Values:** Each column values represent curated genes for that hallmarks. --- ## Code/software ### Viewing and Analyzing the Data: Required Software and Workflow #### **Required Software** To view and analyze the dataset, free and open-source software such as **Python** is recommended. Specifically, the analysis utilizes the **pandas** library to read, manipulate, and explore the data. * **Python Version:** any (tested with Python 3.11) * **Required Packages:** * `pandas` (version 1.3.0 or later) #### **Workflow and File Relationships** The provided dataset files (`meta_hallmark_present.csv`, `meta_hallmark_absent.csv`, `data_hallmark_present.csv`, and `data_hallmark_absent.csv`) can be analyzed using the following workflow. The relationships between the metadata and data files are as follows: * **Metadata files (`meta_hallmark_present.csv` and `meta_hallmark_absent.csv`)**: Contain descriptive information about the samples in the corresponding data files. * **Data files (`data_hallmark_present.csv` and `data_hallmark_absent.csv`)**: Contain synthetic bulk transcriptomics data, with raw gene counts. #### **Code to View and Process Data** The dataset can be loaded and processed using the following Python code: ``` import pandas as pd # Load the data files data_pos = pd.read_csv('data_hallmark_present.csv', index_col=0) # Data where hallmark is present data_neg = pd.read_csv('data_hallmark_absent.csv', index_col=0) # Data where hallmark is absent # Load the metadata files meta_pos = pd.read_csv('meta_hallmark_present.csv', index_col=0) meta_neg = pd.read_csv('meta_hallmark_absent.csv', index_col=0) # Extract patient IDs from sample names in the metadata meta_pos['sample'] = meta_pos.index.str.split('&').str[0] # Extract patient ID for samples with hallmark present meta_neg['sample'] = meta_neg.index.str.split('&').str[0] # Extract patient ID for samples with hallmark absent # Display loaded data print("Data with hallmark present:") print(data_pos.head()) print("\nMetadata for hallmark present:") print(meta_pos.head()) print("\nData with hallmark absent:") print(data_neg.head()) print("\nMetadata for hallmark absent:") print(meta_neg.head()) ``` #### **Loaded Files and Processed Variables** 1. **`data_hallmark_present.csv` and `data_hallmark_absent.csv`**:\ These files are loaded into `data_pos` and `data_neg` respectively using the `pandas.read_csv()` function. The `index_col=0` argument ensures the first column is used as the row index. 2. **`meta_hallmark_present.csv` and `meta_hallmark_absent.csv`**:\ These files are loaded into `meta_pos` and `meta_neg` respectively. The `index_col=0` argument is similarly applied. A new column, `sample`, is created by extracting the patient ID from the sample name (assuming the ID is before the '&' character). #### **Software and Workflow Overview** * **Software:** Python and `pandas` * **Files Relationship:** * `meta_hallmark_present.csv` ↔ `data_hallmark_present.csv` * `meta_hallmark_absent.csv` ↔ `data_hallmark_absent.csv` * **Steps:** 1. Load the data and metadata files using `pandas.read_csv`. 2. Process the metadata to extract patient IDs. 3. Display or analyze the data using Python tools and libraries. This workflow allows seamless viewing and processing of the dataset files. For detailed analysis, additional tools such as visualization libraries (`matplotlib` or `seaborn`) can also be used. ## Access information Other publicly accessible locations of the data: * This is the only place where data can be found. Data was derived from the following sources: * [Weizmann| Curated Cancer Cell Atlas](https://www.weizmann.ac.il/sites/3CA/) ### Version changes 22-October-2025: Added file `hallmark_curated_genes.csv`.