EVOKE (Evolutionary Vector Of Key Events) is a comprehensive bioinformatics analysis platform designed to explore the key molecular mechanisms and dynamic evolutionary features in the inflammation-to-cancer transformation process. It aims to systematically characterize molecular events at single-cell resolution during the progression of various cancers.
The EVOKE platform integrates 7,147,991 single-cell data points from 12 cancer types in humans and mice, covering 8 evolutionary vectors (spanning healthy tissue, inflammation, primary tumor, and metastasis stages). Through multidimensional data mining and visualization functions, EVOKE is dedicated to revealing:
EVOKE not only provides standardized bioinformatics analysis workflows but also features an interactive visualization interface, enabling researchers to more conveniently gain in-depth insights into the molecular landscape and cellular ecosystem of the inflammation-to-cancer transformation process.
EVOKE is designed with the following six core modules:
The EVOKE data platform comprehensively integrates 7,147,991 single-cell data points, covering 12 cancer types from humans and mice across 8 evolutionary vectors (spanning healthy tissue, inflammation, primary tumor, and metastasis stages).
The homepage offers a convenient quick-search feature, enabling users to efficiently query and explore target data.
Additionally, the homepage features an interactive word cloud highlighting key information across the four stages of inflammation-to-cancer transformation. Clicking on a keyword in the word cloud directs users to the corresponding search page, which displays the relevant search results.
The Browse page is a key module in the EVOKE platform, designed for systematic data exploration by Cancer Type or Progression Type. Users can quickly select specific cancers or disease progression stages via intuitive dropdown menus to delve into associated cellular ecosystems, trajectory patterns, and key gene regulatory information.
The Browse page adopts a dual-column layout:
When users first enter the main page, they get an overall view of the data used in the EVOKE platform and the results from our analysis.
On the left, a scrolling bar chart shows the main cell types at four different stages: healthy, inflammation, primary tumor, and metastasis. The animation helps users clearly see how the tumor microenvironment changes as cancer progresses.
In the center, a word cloud highlights the most frequently mentioned terms from our analysis, giving users a quick sense of the key information.
At the bottom, another bar chart focuses on the top four dominant cell types and shows how their proportions change across the four stages. This helps explain why it's important to study how the microenvironment evolves during cancer development.
Selecting Cancer Type in the left menu reveals a list of specific cancers, such as Breast Invasive Carcinoma, Clear Cell Renal Cell Carcinoma, and Colorectal Adenocarcinoma. Each cancer type is further subdivided into disease progression stages: Inflammation, Tumorigenesis, Malignant Transformation, and Metastasis. Clicking a cancer name directs users to its dedicated browse page, displaying data and charts for all stages.
The page is organized into three main sections:
| Section | Subsection | Description |
|---|---|---|
| Cell Section | Tumor Microenvironment | Visualizes the composition and changes in the tumor microenvironment across stages. |
| Cell Proportion | Displays the proportion of cell types and their changes across progression stages. | |
| Impact of Progression | Analyzes the influence of disease progression on cellular ecosystems. | |
| Trajectory Section | Trajectory of Progression | Shows developmental trajectories of cell states across stages. |
| Trajectory Information | Details the foundational data and methods used for trajectory construction. | |
| T-Impact of Progression | Analyzes the impact of different stages on trajectory direction and speed. | |
| Trajectory Correlation | Compares trajectory similarities across cancer types or stages. | |
| Gene Section | Key Genes of Progression | Identifies genes critical to the evolutionary process. |
| Gene Network | Analyzes regulatory networks and modules among genes. | |
| Prognostic Impact | Evaluates the association of key genes with patient prognosis. | |
| Function Regulation | Displays functional enrichment and pathway regulation patterns. |
Clicking any submodule in the left menu automatically scrolls the right display to the corresponding charts and explanations, enabling users to focus on their areas of interest.
Selecting Progression Type in the left menu allows users to view data across multiple cancer types by stage, with two perspectives:
The Search page is a core module enabling multidimensional queries to quickly locate data related to specific genes, cell types, cancer types, or disease stages. It is divided into two sections: Quick Search and Advanced Search, catering to exploratory and precise queries, respectively.
The platform provides key file downloads, divided into two sections:
Clicking a category directs users to a detailed download page, where they can select individual cancer or pan-cancer data tables for download.
The Tumor Microenvironment chart displays the activity profiles of various cell types within the selected cancer. Hovering over a data point in the chart reveals detailed information specific to that point, providing insights into the cellular dynamics and interactions within the tumor microenvironment.
The Cell Proportion chart illustrates the dynamic changes in the infiltration of various cell types across different sample stages of the target cancer. Additionally, we performed further clustering of these cell types, as shown in the rightmost panel labeled Progression Cluster. This allows users to assess whether certain cell types exhibit similar infiltration patterns during cancer progression.
Users may also choose to display individual cell types within the target cancer, allowing for a focused examination of their infiltration dynamics across various stages of progression.
This section provides a detailed illustration of how various cell types exhibit shifting preferences across different stages of disease progression in the selected cancer. It helps users gain deeper insights into how the cellular ecosystem drives cancer evolution and uncovers potential underlying biological mechanisms.
This section illustrates the dynamic perturbations of individual cell types throughout cancer progression, analyzed at the level of each patient. By hovering over different data points, users can view detailed information about a specific cell type from a specific patient. As shown in the example below for CRC (colorectal cancer), compared to the Tumorigenesis stage, patients are more broadly distributed during the Inflammation and Cancer Metastasis stages. This indicates greater heterogeneity and more pronounced perturbations in these stages.
This section presents all progression trajectories identified in the selected cancer type. The table displays the starting and ending states of each trajectory, along with their associated driver genes and the top five most significantly expressed key genes during the transition.
By clicking on any Trajectory ID in the table, users can view more detailed information below, including the dynamic changes in cell states along the selected trajectory and the specific patients associated with it.
When hovering over specific positions in the trajectory plot, the interactive chart will automatically display detailed information about the indicated cell, including its cell type, pseudotime point, and corresponding sample stage.
As shown in the figure below, for the CRC_TR001 trajectory, the progression starts from ENS glia cells and ends with macrophages.
In addition, we provide an interactive plot that dynamically illustrates the proportional changes of different sample stages along the pseudotime trajectory. This allows users to intuitively observe how cell states shift throughout the course of cancer progression.
In this section, we visualize the similarity relationships among progression trajectories within the selected cancer type. By clicking on any pathway in the network graph, the panel on the right will display other trajectories related to the selected one. This helps users better understand the dynamic changes of trajectories across different sample stages and progression phases.
The "Marker Genes in Progression" table displays cell type-specific marker genes expressed in different sample types within the selected cancer. In contrast, the "Key Genes of Progression" table highlights the driver genes associated with various progression stages during cancer development.
When clicking on a marker gene in the table, the panel below will automatically display its interaction network as well as its dynamic expression pattern along the trajectory.
As shown in the figure below, the plot illustrates how the expression levels of the driver genes HLA-DPA1, HLA-DQB1, and HLA-DRA change throughout cancer progression.
This section presents key genes associated with prognosis in the selected cancer type. Interactive visualizations, including forest plots and survival curves, are provided to illustrate the prognostic relevance of these genes.
When a gene is selected from the table, the corresponding dynamic plots on the right and below will update automatically to reflect its prognostic impact.
This section visualizes the dominant biological functions across different stages of cancer progression, helping users intuitively understand the dynamic shifts in functional activity that drive tumor evolution.
Yunpeng Zhang: zhangyp@hrbmu.edu.cn
Xia Li: lixia@hrbmu.edu.cn
Jing Bai: baijing@hrbmu.edu.cn
College of Bioinformatics Science and Technology
Harbin Medical University 194 Xuefu Road, Harbin 150081, China
Phone: 86-451-86615922
Fax: 86-451-86615922
Email: zhangyp@hrbmu.edu.cn