Case Study: Discovering a Potential Biomarker for Cancer Treatment Using Single-Cell RNA Sequencing

Our client aimed to discover biomarkers for a novel cancer drug to enhance patient stratification and treatment efficacy. They required a robust technological solution to handle complex biological data and uncover treatment-responsive biomarkers from the heterogeneous tumor microenvironment.


The complexity and variability of tumor cells at the single-cell level demanded a high-throughput, precise method to capture and analyze intricate gene expression profiles, making single-cell RNA sequencing (scRNA-seq) an essential tool.

AWS Infrastructure Setup & Execution

To address these needs, we built and customized a comprehensive AWS infrastructure that supported the entire data lifecycle:

1. Data Collection and Import

AWS S3 Buckets

Configured to store raw scRNA-seq data securely, handling inputs from various pre-clinical studies involving multiple cancer cell types and conditions.

Data Uploads

Automated data uploads triggered AWS Lambda functions for initial preprocessing, ensuring efficient handling of incoming datasets.

2. Data Preprocessing

AWS Lambda

Implemented to conduct initial quality checks and normalization of scRNA-seq data, preparing it for detailed analysis.

Preprocessing Workflow

Leveraged community-driven, best-practice pipelines from nf-core (Nextflow), tailored for our specific cancer research needs, enhancing reproducibility and accuracy.

3. Data Processing and Analysis

AWS Batch

Managed batch computing jobs to process data at scale. It dynamically allocated resources based on the computational demands of the analysis.

EC2 Spot Instances

Utilized for executing intensive data analysis workflows, including advanced machine learning models and statistical analyses to identify unique transcriptional signatures and cell population roles in cancer.

4. Insight Generation and Reporting

Data Aggregation

Leveraged AWS Redshift to aggregate analysis results, providing a robust platform for deeper insights and data transformation.

Visualization and Reporting

Developed comprehensive reports and visualizations to illustrate findings, such as potential therapeutic targets and biomarkers, using the processed data stored in Redshift.

5. Collaboration and Iteration

Version Control

Utilized GitHub for hosting and version-controlling analysis code, including Python scripts and R notebooks, facilitating collaboration and continuous integration of improvements.

Continuous Feedback

Enabled a collaborative environment where researchers could access shared scripts and results, discuss methodologies, and validate findings through further experiments.

6. Security and Monitoring

AWS CloudWatch

Monitored the entire infrastructure, providing logs and alerts to ensure operational health and performance optimization.

Data Security

Implemented stringent access controls and data encryption managed by AWS Key Management Service (KMS) to protect sensitive data.


This AWS-powered pipeline enabled the client’s research team to efficiently handle large datasets typical of single-cell studies, significantly reducing the time to insight and enhancing the accuracy of cancer cell profiling. By leveraging our scalable and robust AWS infrastructure, the team focused on the biological implications of their data, accelerating the pace of discovery and development of targeted cancer therapies.


The project facilitated the identification of several key biomarkers correlated with the cancer type targeted by the client’s drug, empowering the client to refine their drug development strategy towards personalized medicine, thus enhancing drug efficacy and patient outcomes.


This case study exemplifies our expertise in integrating cutting-edge data science, biological research, and cloud infrastructure to advance cancer treatment. Our solution not only streamlined the biomarker discovery process but also ensured high levels of data security and operational efficiency, showcasing our commitment to driving innovation in pharmaceutical research.

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