📅 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
