
PD-1 Checkpoint Modulation and Genetic Engineering in Cancer Immunotherapy
Explore immune checkpoint biology, PD-1/PD-L1 signaling, and genetic engineering strategies shaping the future of cancer immunotherapy.
Skills you will gain:
About Program:
This 3-day workshop provides a focused introduction to immune modulation in oncology, with special emphasis on the PD-1/PD-L1 checkpoint pathway, tumor immune escape, biomarker analysis, and directed genetic engineering approaches. Participants will learn how immune checkpoint signaling influences cancer progression and how tools such as CRISPR, CAR-T concepts, and computational analysis can support next-generation cancer immunotherapy research.
The workshop also includes basic hands-on activities using free tools such as Google Colab, Python, NetworkX, BioPython, Pandas, and visualization libraries.
Aim: The aim of this workshop is to provide researchers, academicians, and industry professionals with conceptual and practical knowledge of PD-1 checkpoint modulation, tumor immune response, and genetic engineering strategies used in modern cancer immunotherapy.
Program Objectives:
- To introduce the fundamentals of cancer immunology and immune modulation.
- To explain the role of PD-1/PD-L1 signaling in tumor immune escape.
- To understand immune checkpoint blockade and its clinical importance in oncology.
- To explore biomarkers involved in immunotherapy response and resistance.
- To provide basic computational exposure to immune checkpoint pathway mapping and biomarker visualization.
- To introduce CRISPR-based and immune-cell engineering approaches in cancer therapy.
- To discuss CAR-T, TCR-T, and engineered T-cell strategies in next-generation immunotherapy.
- To highlight current challenges, safety concerns, and future directions in cancer immunotherapy research.
What you will learn?
📅 Day 1: Fundamentals of Immune Modulation & PD-1/PD-L1 Signaling in Oncology
- Focus: Understanding cancer immunology, immune surveillance, tumor immune escape, and the biological importance of PD-1/PD-L1 signaling in oncology.
- Introduction to cancer immunology and the role of the immune system in identifying and eliminating tumor cells.
- Understanding tumor immune evasion, immune suppression mechanisms, and immune checkpoint pathways in cancer.
- Exploring PD-1 and PD-L1 signaling pathways and their importance in T-cell regulation and cancer progression.
- Learning about T-cell activation, T-cell exhaustion, immune escape, and clinical checkpoint blockade therapy.
- Overview of immune checkpoint inhibitors and limitations such as resistance, relapse, and non-response.
🛠️ Hands-on:
- Create a simple immune checkpoint interaction network using Google Colab.
- Map important cancer immunology markers including PDCD1, CD274, CTLA4, LAG3, TIGIT, CD8A, IFNG, and FOXP3.
🧰 Tools Covered: Google Colab, Python, NetworkX, Matplotlib, Pandas
📅 Day 2: Tumor Microenvironment, Biomarkers & Immunotherapy Response Analysis
- Focus: Analyzing the tumor immune microenvironment, immune-related biomarkers, and computational approaches for immunotherapy response prediction.
- Understanding the tumor immune microenvironment and the role of immune cells in cancer progression and therapy response.
- Exploring T-cell infiltration, regulatory T cells, macrophages, and immune suppression in cancer.
- Learning key biomarkers used in cancer immunotherapy, including PD-L1 expression, tumor mutational burden, and microsatellite instability.
- Understanding immune-related gene expression signatures and biomarker-based patient stratification in oncology.
- Introduction to computational immuno-oncology and challenges in predicting response to immune checkpoint inhibitors.
🛠️ Hands-on:
- Work with a sample gene-expression dataset and perform basic analysis of immune checkpoint markers.
- Load gene-expression data, identify immune-related genes, visualize checkpoint marker expression, and create bar plots and heatmaps.
- Interpret basic biomarker patterns for immunotherapy response analysis.
🧰 Tools Covered: Google Colab, Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn
📅 Day 3: Directed Genetic Engineering for Next-Generation Cancer Immunotherapy
- Focus: Understanding genetic engineering strategies used to improve immune-cell function and develop next-generation cancer immunotherapies.
- Introduction to genetic engineering in cancer immunotherapy and CRISPR/Cas9 applications in immune-cell engineering.
- Understanding PD-1 knockout strategies in T cells and engineering immune cells for improved anti-tumor response.
- Exploring CAR-T cell therapy, TCR-T cell therapy, and tumor-infiltrating lymphocyte-based immunotherapy concepts.
- Learning synthetic biology approaches for cancer treatment and strategies to overcome resistance to PD-1/PD-L1 checkpoint therapy.
- Discussing safety challenges including off-target effects, immune toxicity, and ethical concerns in genetic engineering.
- Future trends in AI-assisted immunotherapy, personalized cancer vaccines, and engineered immune-cell platforms.
🛠️ Hands-on:
- Perform an educational CRISPR guide RNA screening simulation using Google Colab and a sample DNA sequence.
- Understand the logic of CRISPR target identification, find possible guide RNA regions, and apply basic ranking criteria.
- Interpret guide RNA selection conceptually and discuss safety and ethical considerations in genetic engineering.
- This activity is designed for educational and computational learning only, not for wet-lab protocol design.
🧰 Tools Covered: Google Colab, Python, BioPython, Pandas, NumPy, Matplotlib
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!

Intended For :
- Researchers working in cancer biology, immunology, biotechnology, and molecular biology
- Academicians and faculty members from life sciences, biomedical sciences, and biotechnology
- PhD scholars and postgraduate students interested in oncology and immunotherapy
- Industry professionals working in biotech, pharma, diagnostics, and biomedical research
- Medical and clinical research professionals interested in cancer immunotherapy
- Bioinformatics and computational biology learners exploring immuno-oncology data analysis
- Students and professionals interested in CRISPR, CAR-T, and genetic engineering applications in cancer therapy
,
- Researchers working in cancer biology, immunology, biotechnology, and molecular biology
- Academicians and faculty members from life sciences, biomedical sciences, and biotechnology
- PhD scholars and postgraduate students interested in oncology and immunotherapy
- Industry professionals working in biotech, pharma, diagnostics, and biomedical research
- Medical and clinical research professionals interested in cancer immunotherapy
- Bioinformatics and computational biology learners exploring immuno-oncology data analysis
- Students and professionals interested in CRISPR, CAR-T, and genetic engineering applications in cancer therapy
Career Supporting Skills
Program Outcomes
- Understand the biological role of PD-1/PD-L1 signaling in cancer immunotherapy.
- Explain how tumors escape immune surveillance through checkpoint pathways.
- Describe the mechanism and importance of immune checkpoint inhibitors.
- Identify key biomarkers associated with immunotherapy response and resistance.
- Perform basic immune checkpoint pathway mapping using free computational tools.
- Analyze and visualize sample immune-related gene-expression data.
- Understand the role of CRISPR and genetic engineering in immune-cell therapy.
- Explain the concept of PD-1 knockout, CAR-T cells, TCR-T cells, and engineered T-cell strategies.
- Discuss translational challenges, safety limitations, and future opportunities in cancer immunotherapy.
- Apply foundational knowledge of computational immuno-oncology for research and academic purposes.
