Molecular Oncology Training

Understanding Molecular Biology Techniques

1. Analyzing Single-Cell Genomics
   1. Isolating single cells using microfluidics.
   2. Performing whole-genome or whole-transcriptome amplification to increase the amount of DNA/RNA from single cells.
   3. Sequencing amplified products to explore cellular heterogeneity within tumor populations.
   4. Analyzing data to identify unique genetic and expression profiles that contribute to tumor progression and drug resistance.
2. Conducting Epigenetic Analysis
   1. Extracting DNA and performing bisulfite treatment to convert unmethylated cytosines to uracil.
   2. Amplifying converted DNA and sequencing to identify methylation patterns across the genome.
   3. Interpreting data to understand epigenetic changes like DNA methylation and histone modifications in cancer cells.

Cell Culture and Handling

1. Generating Organoids from Cancer Tissues
   1. Isolating stem cells or tumor cells from patient tissues.
   2. Culturing cells in a 3D matrix to form organoids that mimic the microarchitecture of original tumors.
   3. Using organoids for drug testing and personalized medicine approaches.
2. Conducting Hypoxia Studies in Cell Culture
   1. Utilizing specialized hypoxia chambers to culture cancer cells under low oxygen conditions.
   2. Examining changes in gene expression and metabolic pathways in response to hypoxia.
   3. Assessing cell survival, proliferation, and adaptation mechanisms under stress conditions that mimic the tumor microenvironment.

Protein Workflows

1. Conducting Multiplex Protein Assays
   1. Utilizing multiplex immunoassays to quantify multiple protein biomarkers simultaneously from a single sample.
   2. Applying technologies like Luminex xMAP or similar platforms to analyze protein expressions in cancer samples.
   3. Interpreting data to evaluate pathways involved in cancer progression and response to therapies.

Gene Expression Analysis

1. Performing Chromatin Immunoprecipitation followed by Sequencing (ChIP-seq)
   1. Cross-linking DNA and proteins in cancer cells to preserve protein-DNA interactions.
   2. Shearing cross-linked DNA and immunoprecipitating with antibodies against specific protein markers of interest.
   3. Sequencing the co-precipitated DNA to identify binding sites of DNA-associated proteins.
   4. Analyzing data to understand regulatory mechanisms and identify potential therapeutic targets.
2. Conducting CRISPR Screens in Cancer Cells
   1. Designing sgRNAs targeting genes suspected to be involved in cancer progression.
   2. Transfecting cancer cells with CRISPR-Cas9 and sgRNAs.
   3. Selecting successfully edited cells and assessing changes in their proliferation, survival, and metastatic potential.
   4. Analyzing outcomes to identify key genes that can be potential targets for cancer therapy.

Module 1 Benefits: Understanding the Impact

1. Skill Development: Hands-on practice with fundamental techniques builds a solid skill set necessary for advanced research and clinical applications in oncology.
2. Enhanced Understanding: Direct involvement in experiments enhances understanding of molecular mechanisms behind cancer, improving the ability to develop targeted treatments.
3. Career Advancement: Proficiency in these techniques can lead to better job opportunities in research, diagnostics, and therapeutic development.

Duration: 6 Months

Fee: Rs 2,60,000/-

Advanced DNA Sequencing Techniques

1. Next-Generation Sequencing (NGS) (Software/Tool: Illumina Sequencing Platforms)
   1. Preparing libraries by fragmenting DNA, adding adapters, and amplifying via PCR.
   2. Loading DNA libraries into an Illumina sequencer and performing cluster generation and sequencing.
   3. Using Illuminas software for real-time data analysis, including base calling, read alignment, and quality control checks.
2. Single-cell Sequencing (Software/Tool: 10x Genomics)
   1. Isolating single cells using microfluidic devices from 10x Genomics to encapsulate cells in droplets with barcoded beads.
   2. Performing lysis, reverse transcription, and cDNA amplification while maintaining single-cell resolution.
   3. Preparing sequencing libraries from barcoded cDNA and sequencing on an NGS platform.
   4. Using 10x Genomics software to demultiplex reads and perform data analysis, identifying cell types and states.
3. Exome Sequencing for Mutation Analysis (Software/Tool: Illumina Exome Sequencing)
   1. Extracting DNA from cancer cells or tissue and enriching coding regions (exons) using capture kits.
   2. Sequencing the enriched exomes to identify mutations that may drive cancer development.
   3. Analyzing sequence data to detect novel and known mutations impacting gene function.

Genetic Manipulation

1. CRISPR-Cas9 Genome Editing (Software/Tool: Benchling for guide RNA design)
   1. Designing guide RNAs using Benchling, based on target DNA sequence specificity and off-target predictions.
   2. Delivering CRISPR-Cas9 components into cells via plasmid, viral vectors, or RNP complexes.
   3. Selecting and expanding edited cells, verifying edits through sequencing or PCR.
2. RNAi and shRNA Knockdown (Software/Tool: Thermo Fisher siRNA Design Tool)
   1. Designing siRNAs or shRNAs using the Thermo Fisher siRNA Design Tool to target specific mRNA sequences for knockdown.
   2. Transfecting siRNAs or shRNAs into cells using lipid nanoparticles or viral vectors for delivery.
   3. Assessing knockdown efficiency through qPCR or Western blotting to measure target gene expression or protein levels.
3. TAL Effector Nucleases (TALENs) Genome Editing (Software/Tool: TALE-NT)
   1. Designing TALEN pairs targeting specific genomic sequences using the TALE-NT design tool.
   2. Assembling TALEN constructs and delivering them into cancer cells via electroporation.
   3. Screening for successful genomic edits and analyzing off-target effects.

Cancer Genomics

1. Whole Genome Cancer Profiling (Software/Tool: Broad Institute’s Genome Analysis Toolkit)
   1. Extracting genomic DNA from cancer tissues and preparing libraries for whole genome sequencing.
   2. Sequencing the DNA using a high-throughput platform to cover the entire genome extensively.
   3. Using the Genome Analysis Toolkit (GATK) to process sequencing data, identify variants, and analyze structural rearrangements.
2. Targeted Gene Panel Analysis (Software/Tool: QIAGEN Digital Insights)
   1. Designing and utilizing targeted gene panels specific to cancer-associated genes for deep sequencing.
   2. Performing high-throughput sequencing to achieve high coverage of target regions, ensuring detection of low-frequency variants.
   3. Analyzing sequencing data using QIAGEN Digital Insights software to interpret genetic mutations and their potential impacts on cancer.
3. Liquid Biopsy and Circulating Tumor DNA Analysis (Software/Tool: Guardant Health)
   1. Collecting blood samples from patients and isolating circulating tumor DNA (ctDNA).
   2. Utilizing advanced sequencing technologies to analyze ctDNA for tumor-derived genetic alterations.
   3. Assessing ctDNA data to monitor tumor evolution, response to treatment, and detect resistance mechanisms.

Duration: 6 Months

Fees: Rs 3,50,000/-

Module 2 Benefits: Enhancing Research Capabilities

1. Comprehensive Skills: Mastery of advanced genetic analysis techniques opens new research avenues and deeper insights into the genetic basis of diseases.
2. Innovative Research: Enables researchers to conduct cutting-edge research, using state-of-the-art tools to explore genetic mutations and their implications in cancer.
3. Improved Diagnostics: Skills in advanced genetic profiling can lead to the development of novel diagnostic tools, enhancing early detection and personalized treatment plans.

Immunological Techniques

1. Tumor Infiltrating Lymphocyte (TIL) Analysis (Software/Tool: ImageStreamX)
   1. Isolating lymphocytes from tumor tissues using density gradient centrifugation.
   2. Staining cells with antibodies targeting various immune cell markers.
   3. Analyzing cells using ImageStreamX to combine flow cytometry with detailed imaging, identifying and characterizing TILs.
2. Checkpoint Blockade Assay (Software/Tool: FACSDiva)
   1. Incubating cancer cell lines with immune cells in the presence of checkpoint inhibitors.
   2. Measuring immune cell activation and cancer cell killing via flow cytometry and cytotoxicity assays.
   3. Analyzing data to assess the efficacy of checkpoint inhibitors in modulating immune responses against cancer cells.
3. Antigen Processing and Presentation Assay (Software/Tool: N/A)
   1. Culturing cancer cells with labeled antigens and monitoring antigen processing.
   2. Staining for MHC molecules and antigen peptides to assess the presentation profile on cancer cells.
   3. Using microscopy to evaluate antigen presentation and its implications for immune recognition.

In Vivo Techniques

1. Patient-Derived Orthotopic Xenografts (PDOX) (Software/Tool: N/A)
   1. Implanting patient-derived tumors into the corresponding organ in immunodeficient mice.
   2. Monitoring tumor growth and metastasis closely to mimic patient-specific tumor environment and response.
   3. Using these models to test personalized therapeutic regimens and study cancer progression and metastasis.
2. Metabolic Imaging in Live Animals (Software/Tool: Hyperion Imaging System)
   1. Injecting animals with metabolic tracers that are detectable via imaging modalities.
   2. Using the Hyperion Imaging System to visualize metabolic changes in tumors in response to treatment.
   3. Correlating imaging data with metabolic pathways to identify therapeutic targets.

High-Throughput Screening

1. Combination Drug Screening (Software/Tool: Synthace)
   1. Using robotic systems to dispense multiple drugs in varying combinations and concentrations to cancer cells.
   2. Measuring cell viability and synergistic effects of drug combinations using automated microscopy and viability assays.
   3. Using Synthace software for automated setup and analysis of complex experimental designs.
2. Genome-Scale CRISPR-Cas9 Knockout Screens (Software/Tool: CRISPResso)
   1. Designing and delivering a library of sgRNAs targeting a broad range of genomic loci into cancer cells.
   2. Selecting cells that survive under specific conditions to identify critical genes for cancer cell survival and drug resistance.
   3. Analyzing editing outcomes using CRISPResso to assess knockout efficiency and identify gene targets.

Bioinformatics Tools for Oncology

1. Single-Cell RNA-seq Data Analysis (Software/Tool: Seurat)
   1. Processing and analyzing single-cell RNA-seq data to identify distinct cell populations within tumors.
   2. Using Seurat for data normalization, dimensionality reduction, and clustering to discover cellular heterogeneity.
   3. Integrating clinical data to link cellular profiles with patient outcomes and therapeutic responses.
2. Multi-Omics Data Integration (Software/Tool: MultiOmix)
   1. Aggregating genomic, transcriptomic, and proteomic data from cancer studies.
   2. Using MultiOmix to perform comprehensive integration and analysis, uncovering molecular signatures and predictive biomarkers.
   3. Applying statistical models to correlate omics data with clinical parameters and treatment outcomes.

Duration: 6 Months

Fees: Rs 4,00,000

Module 3 Benefits: Advancing Personalized Medicine

1. Personalized Treatment Strategies: Proficiency in these specialized techniques enables the development of personalized treatment plans based on individual genetic and proteomic profiles.
2. Enhanced Drug Development: Skills in high-throughput screening contribute to faster and more effective drug discovery processes, potentially lowering the time and cost associated with bringing new drugs to market.
3. Clinical Research Applications: Applying these techniques in clinical research settings enhances the ability to conduct rigorous, impactful studies that can lead to better patient outcomes.

Advanced Imaging Technologies

1. Multi-photon Microscopy (Software/Tool: ImageJ with plugins for advanced imaging)
   1. Preparing tissue samples with fluorescent markers specific to cancer-related proteins or structures.
   2. Using multi-photon microscopy to penetrate deeper into the tissue, reducing photobleaching and photodamage.
   3. Acquiring images and using ImageJ plugins to analyze cell morphology, interactions, and microenvironment in 3D.
2. Super-resolution Imaging (Software/Tool: Nikon NIS-Elements)
   1. Staining cancer cells with fluorescent probes that highlight specific molecular components of interest.
   2. Applying techniques like STED, PALM, or STORM to surpass the diffraction limit of conventional microscopy.
   3. Using Nikon NIS-Elements to process and visualize data for detailed subcellular localization and molecular dynamics.
3. Time-lapse Confocal Microscopy (Software/Tool: Zeiss Zen Software)
   1. Culturing live cancer cells on specialized imaging dishes and treating with drugs or signaling molecules.
   2. Recording changes over time using confocal microscopy to observe drug responses and cell behavior in real-time.
   3. Analyzing videos and images with Zeiss Zen software to quantify cellular dynamics and treatment effects.

Artificial Intelligence and Machine Learning

1. AI in Cancer Diagnosis (Software/Tool: Google AI for medical imaging analysis)
   1. Training AI models using large datasets of medical images (e.g., MRIs, CT scans) annotated with diagnostic information.
   2. Applying trained models to new cancer imaging datasets to predict malignancy, tumor staging, and other clinical parameters.
   3. Evaluating model accuracy and reliability using statistical analysis tools integrated within the Google AI platform.
2. Machine Learning for Genomic Data Analysis (Software/Tool: Python with scikit-learn library)
   1. Collecting and preparing genomic data from cancer patients, including mutation profiles, gene expression levels, and epigenetic markers.
   2. Developing machine learning models in Python using scikit-learn to identify patterns and predictive biomarkers for cancer prognosis and therapy selection.
   3. Testing and validating models using cross-validation techniques to ensure generalizability and robustness of predictions.
3. Deep Learning for Pathology Image Analysis (Software/Tool: TensorFlow)
   1. Using deep neural networks to analyze pathology slides for features like tumor grading and lymph node involvement.
   2. Training models on a vast array of annotated histological images to learn distinguishing features of various cancer types.
   3. Integrating these models into clinical workflows to assist pathologists in diagnosing and understanding cancer progression.

Bioprinting and Tissue Engineering

1. 3D Bioprinting of Tumor Models (Software/Tool: Organovo 3D Bioprinter)
   1. Designing 3D models of tumors using CAD software to mimic the tumor architecture and microenvironment.
   2. Using Organovo 3D Bioprinter to print layers of cancer and stromal cells along with supportive biomaterials.
   3. Culturing bioprinted models and using them for drug testing and understanding tumor-stroma interactions.
2. Tissue Engineering for Cancer Research (Software/Tool: Autodesk Fusion 360 for model design)
   1. Designing scaffolds using Autodesk Fusion 360 that mimic the physical characteristics of the tumor microenvironment.
   2. Fabricating scaffolds using biocompatible materials and seeding them with cancer cells.
   3. Studying cancer cell behavior in engineered tissues, including invasion and angiogenesis.
3. Synthetic Tissue Constructs for Metastasis Study (Software/Tool: Simplify3D)
   1. Creating complex structures that model the tissue barriers and vascular systems encountered by metastasizing cancer cells.
   2. Embedding cancer cells in these constructs to study migration patterns and test metastasis-inhibiting drugs.
   3. Analyzing cell migration and drug effects using microscopy and molecular assays to evaluate therapeutic strategies.

Duration: 8 Months

Fees: Rs 5,50,000

Module 4 Benefits: Driving Innovation in Oncology

1. Technological Advancement: Utilization of cutting-edge technologies pushes the boundaries of what is possible in cancer research and therapy.
2. Enhanced Precision: Advanced imaging and AI provide unprecedented precision in tumor detection and treatment planning, significantly improving outcomes.
3. Novel Research Tools: Techniques like bioprinting offer innovative ways to create more accurate and controllable in vitro cancer models, enhancing research capabilities.

Data Acquisition and Management

1. Techniques for High-throughput Data Collection (Software/Tool: Next Generation Sequencing platforms)
   1. Setting up sequencing experiments using Next Generation Sequencing platforms to capture genomic, transcriptomic, or epigenomic data from cancer samples.
   2. Optimizing sample preparation and sequencing parameters to maximize data quality and throughput.
   3. Monitoring sequencing runs in real-time to ensure data integrity and address any issues promptly.
2. Best Practices for Data Storage and Management (Software/Tool: Cloud storage solutions like AWS S3)
   1. Establishing protocols for secure data transfer from sequencing platforms to cloud storage solutions such as AWS S3.
   2. Implementing data management plans that comply with both local and international data protection regulations (e.g., GDPR, HIPAA).
   3. Using tools for data encryption, backup, and disaster recovery to ensure data availability and integrity.
3. High-throughput Proteomics Data Collection (Software/Tool: Mass Spectrometry platforms)
   1. Preparing cancer tissue and cell samples for proteomic analysis using mass spectrometry.
   2. Using automated sample handling systems to enhance throughput and reduce sample variability.
   3. Analyzing proteomic data to uncover protein expression patterns and post-translational modifications unique to cancer cells.

Analytics and Data Interpretation

1. Utilizing Bioinformatics for Genetic Analysis (Software/Tool: Bioinformatics software like BLAST)
   1. Preparing sequencing data for analysis by aligning reads to reference genomes using bioinformatics software such as BLAST.
   2. Analyzing genetic variations and mutations relevant to cancer using variant calling tools.
   3. Interpreting the results to identify potential drug targets and diagnostic markers.
2. Data Mining Techniques for Identifying Biomarkers (Software/Tool: R and Python for statistical analysis)
   1. Applying advanced statistical and machine learning techniques in R and Python to analyze large datasets for patterns that predict disease outcome or drug response.
   2. Validating identified biomarkers through cross-referencing with existing databases and literature.
   3. Visualizing data insights using graphical representations to facilitate understanding and reporting.
3. Integrative Omics Analysis (Software/Tool: Integrative Genomics Viewer)
   1. Combining genomic, transcriptomic, and proteomic data to provide a comprehensive view of cancer biology.
   2. Using software tools like the Integrative Genomics Viewer to visualize and interpret complex datasets.
   3. Identifying key pathways and networks disrupted in cancer, facilitating new therapeutic targets.

Integrating Informatics into Clinical Practice

1. Development of Decision Support Systems (Software/Tool: Clinical decision support software)
   1. Designing and implementing decision support systems that integrate patient data with the latest research to provide real-time guidance on cancer treatment options.
   2. Ensuring systems are interoperable with existing electronic health records and can pull relevant patient data to aid in decision making.
   3. Training clinical staff on how to effectively use decision support tools to enhance patient outcomes.
2. Personalized Medicine Applications (Software/Tool: Personalized medicine platforms)
   1. Integrating genomic and clinical data to create personalized patient profiles that inform tailored treatment plans.
   2. Utilizing platforms that can dynamically update and revise treatment strategies based on ongoing patient data and emerging research.
   3. Collaborating with multidisciplinary teams to implement and monitor personalized treatment plans, ensuring they are effective and adjusted as necessary.
3. Artificial Intelligence in Therapeutic Development (Software/Tool: TensorFlow for Medical Imaging)
   1. Developing AI models to simulate drug interactions and predict therapeutic outcomes in virtual patient models.
   2. Integrating AI with high-throughput screening data to accelerate the discovery and optimization of oncology drugs.
   3. Using AI-driven platforms to personalize chemotherapy and radiation therapy plans based on predicted tumor responses.

Duration: 8 Months

Fees: Rs 5,50,000

Module 5 Benefits: Enhancing Precision in Cancer Research and Treatment

1. Improved Diagnostic Accuracy: Big data analytics enable more accurate and timely diagnosis by identifying patterns that are not obvious through traditional methods.
2. Enhanced Treatment Personalization: Informatics tools help tailor treatments to individual genetic profiles, improving outcomes and reducing side effects.
3. Accelerated Research: Rapid analysis of large datasets speeds up research, leading to quicker discoveries and innovations in cancer treatment.

AI in Cancer Diagnosis and Prognosis

1. Deep Learning for Histopathological Image Analysis (Software/Tool: TensorFlow)
   1. Training deep learning models to analyze histopathological slides and identify cancerous tissues.
   2. Using convolutional neural networks (CNNs) to detect subtle morphological features indicative of specific cancer types.
   3. Evaluating model performance using a validation set of annotated slides.
2. AI for Radiomics in Cancer Detection (Software/Tool: PyTorch)
   1. Extracting quantitative features from radiological images using AI-driven image processing tools.
   2. Training machine learning models to correlate radiomic features with cancer presence and stage.
   3. Assessing the predictive power of radiomic features for personalized treatment planning.
3. Neural Networks for Predicting Cancer Progression (Software/Tool: TensorFlow)
   1. Developing neural networks to predict patient-specific cancer progression based on clinical data inputs.
   2. Integrating time-series analysis to monitor changes in tumor markers over time.
   3. Using predictive models to suggest interventions and monitor treatment effectiveness.

AI-Driven Genetic Analysis

1. AI for Genomic Variant Interpretation (Software/Tool: IBM Watson Genomics)
   1. Using IBM Watson to analyze genetic sequencing data and identify pathogenic variants associated with cancer.
   2. Integrating AI insights with clinical data to provide a comprehensive genomic profile for each patient.
   3. Developing a report that suggests targeted therapies based on identified genetic mutations.
2. Machine Learning for Epigenetic Pattern Recognition (Software/Tool: Python)
   1. Applying machine learning techniques to detect and interpret epigenetic modifications that influence cancer.
   2. Training models to identify correlations between epigenetic changes and cancer phenotypes.
   3. Utilizing predictive analytics to suggest potential epigenetic therapies.

Automating Laboratory Processes

1. Automated Cell Culturing and Monitoring (Software/Tool: Automated cell culture systems)
   1. Implementing robotic systems for the seeding, feeding, and harvesting of cell cultures in high-throughput experiments.
   2. Integrating real-time monitoring systems to track cell growth and morphology.
   3. Using data collected from automated systems to optimize culture conditions and experimental protocols.
2. AI-Enhanced High-Throughput Screening for Drug Efficacy (Software/Tool: Drug discovery AI platforms)
   1. Using AI to design and execute high-throughput screening assays to evaluate thousands of compounds for anticancer activity.
   2. Applying advanced algorithms to analyze screening data and identify promising drug candidates.
   3. Automating the process of lead optimization to improve the efficacy and selectivity of identified compounds.
3. Integration of AI with CRISPR-Cas9 for Enhanced Genome Editing (Software/Tool: CRISPR AI tools)
   1. Leveraging AI to predict the outcomes of CRISPR-Cas9 genome edits, enhancing precision and reducing off-target effects.
   2. Automating the design and testing of guide RNAs for targeted mutations in cancer genes.
   3. Using machine learning models to analyze post-editing results and refine editing strategies.

Additional AI-Driven Techniques

1. AI for Personalized Chemotherapy Regimens (Software/Tool: Clinical AI systems)
   1. Developing AI models to analyze patient data and predict responses to various chemotherapy agents.
   2. Integrating pharmacogenomic data to tailor chemotherapy treatments to individual genetic profiles.
   3. Using clinical decision support systems to recommend personalized chemotherapy protocols.
2. Virtual Tumor Boards Powered by AI (Software/Tool: AI-based decision support platforms)
   1. Utilizing AI to aggregate and analyze patient data from diverse sources for multidisciplinary treatment planning.
   2. Enhancing tumor board discussions with AI-driven insights, highlighting key patient data and suggesting evidence-based treatments.
   3. Facilitating remote collaboration among oncologists, radiologists, and pathologists through AI-supported platforms.
3. AI-Driven Molecular Diagnostic Development (Software/Tool: Diagnostic AI analytics)
   1. Applying AI to develop and validate new molecular diagnostic tests that predict cancer susceptibility and drug response.
   2. Using AI to analyze large datasets from genomic, transcriptomic, and proteomic studies to identify diagnostic markers.
   3. Integrating diagnostic AI with clinical practice to provide early detection and personalized treatment planning.

Duration: 8 Months

Fees: Rs 6,50,000

Module 6 Benefits: Transforming Oncology with AI

1. Enhanced Diagnostic Accuracy: AI improves the accuracy and speed of cancer diagnostics, especially in imaging and genetic analysis.
2. Personalized Treatment Plans: AI-driven tools facilitate the development of tailored therapies based on individual patient data, optimizing treatment efficacy.
3. Efficiency in Research and Development: AI accelerates the pace of research, from drug discovery to clinical trials, enhancing productivity and innovation.