• UGI USER
  • Jun 07, 2024
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WILL UNDERSTANDING CANCER BECOME A DATA PROBLEM?

Relevance: GS 2 – Health, Government Policies and Intervention

Why in the News?

  • The World Health Organization (WHO) reports approximately 33,000 new cases of brain cancer annually in India.
  • The Global Cancer Observatory 2020 ranks brain cancer as the 19th most common type of cancer.
  • Behind these alarming statistics, countless stories of pain and unrest unfold.

About the diagnosis

  • Families endure the pain and uncertainty of caring for loved ones who may never recover.
  • There have been significant breakthroughs in oncology research worldwide.
  • Current Diagnostic Challenges:
    • The current standard of care for diagnosing cancer often requires invasive and risky procedures, such as surgeries to extract tissue samples for analysis.
    • Risks from these procedures range from short-term paralysis to death.
  • Hope for Easier Diagnosis:
    • The use of data analytical tools can ease the process of diagnosis. This advancement is likely to reduce risks, discomfort, and pain for patients and their families.
    • Innovations and solutions at the intersection of healthcare and technology will benefit both patients and medical professionals.
    • Can we make diagnosis easier for patients and their families? The answer is a resounding yes. The solution lies in our genes.

Basic Science of Genes:

  • We all learned about genes, DNA, and RNA in high school science.
  • These are the fundamental building blocks of life, shaping our traits and health.
  • Today’s science links people’s genes to their susceptibility to diseases like cancer.

Understanding Mutations:

  • Imagine you have a recipe for a sweet dish. If you accidentally write “salt” instead of “sugar,” the dish will turn out very differently.
  • Similarly, DNA is like the recipe for making and maintaining our bodies.
  • A mistake in the DNA code, called a mutation, can change how our cells behave.
  • Just as the wrong ingredient alters a recipe, a DNA mutation can change cell growth and function, sometimes leading to cancer.
  • Importance of Identifying Mutations:
    • It is crucial to understand the mutations in genes that cause cancer.
    • Research indicates there are close to 3,000 cancer-causing genes.
    • Each gene contains thousands of DNA codes, each potentially holding vital information about cancer development.
  • Challenge of Data Analysis: The sheer volume of data analysis for identifying these mutations can be overwhelming and seemingly impossible for humans.

Next-Generation Sequencing (NGS):

  • Enter Next-Generation Sequencing (NGS), a cutting-edge technology transforming our ability to decipher the genetic code with speed and precision.
  • To provide context, the Human Genome Project began in 1990 and was completed in 2003, taking about 13 years and costing about $3 billion.
    • Today’s technology can accomplish the same process in possibly less than a week, costing under $1,000.
  • Cancer Diagnostics and NGS:
    • Advancements in Next-Generation Sequencing (NGS) have led to the development of liquid biopsy, a less invasive alternative to surgery.

Concept of Liquid Biopsy:

  • Think of a detective investigating a case, collecting various types of evidence.
  • Similarly, clinicians collect a small blood sample from the patient instead of performing invasive surgery.
  • This blood sample is analyzed for genetic patterns indicating the presence of cancer cells.

Role of Genetic Biomarkers:

  • Genetic biomarkers in the blood sample are like fingerprints and footprints at a crime scene.
  • They provide crucial clues about the patient’s health, answering questions like: Is there a malignancy? If so, what type of malignancy?
  • Challenges and Data Analysis:
    • Producing real-time results with precision requires rigorous data analysis.
    • Genetic data from numerous tumor and blood samples must be processed using artificial intelligence and machine learning algorithms.
    • Big data analytics platforms are essential to detect patterns that might be missed by the human eye.
  • Investments and Technology:
    • Chip makers and sequencing technology firms are investing heavily in the NGS domain.
    • These tools enable researchers to process large amounts of information quickly and accurately.

Future Promise in Oncology:

  • The future of oncology holds immense promise with the combination of data and innovative technologies like NGS.
  • Solving the complexities of cancer may be as much a data problem as a biological one.
  • As the saying goes, “data has become the new oil.” Standing at the forefront of a new era in cancer research, this analogy rings particularly true.

Concerns with this technology

  • Data Privacy: Protecting patient genetic information from unauthorized access and misuse.
  • Cost: Ensuring the affordability of NGS and related technologies for widespread use.
  • Data Management: Handling and storing the massive volumes of data generated by NGS.
  • Accuracy: Maintaining the precision and reliability of genetic analyses to avoid false positives/negatives.
  • Accessibility: Making advanced diagnostic tools available in low-resource settings.
  • Ethical Issues: Addressing potential ethical dilemmas related to genetic information and testing.
  • Integration: Incorporating new technologies into existing healthcare systems effectively.
  • Regulation: Developing robust regulatory frameworks to oversee the use of NGS and AI in diagnostics.

Way forward to these concerns

  • Data Encryption: Implementing robust encryption methods to protect patient genetic information.
  • Funding and Subsidies: Increasing government and private funding to subsidize the cost of NGS technologies.
  • Efficient Data Systems: Developing advanced data management systems to handle and store large datasets efficiently.
  • Quality Control: Establishing stringent quality control measures to ensure accuracy in genetic analyses.
  • Wider Access: Expanding infrastructure and training to make NGS tools available in low-resource settings.
  • Ethical Guidelines: Creating comprehensive ethical guidelines for the use of genetic information and testing.
  • Healthcare Integration: Designing seamless integration strategies for new technologies within healthcare systems.
  • Regulatory Standards: Formulating clear and robust regulatory standards to govern the use of NGS and AI in diagnostics.

Associate articles

Source: The Hindu

Mains question

Discuss the role of Next-Generation Sequencing (NGS) in transforming cancer diagnostics. What are the challenges in adopting such diagnostics techniques? Propose solutions to address these challenges in the Indian context. (250 words)