Cancer Biology Research
- (The University of Chicago - Alvin Wei-Cheng Wong)
- Overview
"To understand how cancer develops and progresses, researchers first need to investigate the biological differences between normal cells and cancer cells. This work focuses on the mechanisms that underlie fundamental processes such as cell growth, the transformation of normal cells to cancer cells, and the spread, or metastasis, of cancer cells.
Knowledge gained from such studies deepens our understanding of cancer and produces insights that could lead to the development of new clinical interventions. For example, studies of cell signaling pathways in normal cells and cancer cells have contributed greatly to our knowledge about the disease, revealing molecular alterations that are shared among different types of cancer and pointing to possible strategies for treatment.
The last few decades of basic research in cancer biology have created a broad base of knowledge that has been critical to progress against the disease. In fact, many advances in the prevention, diagnosis, and treatment of cancer would not have occurred without the knowledge that has come from investigating basic questions about the biology of cancer." -- [NIH: National Cancer Institute]
- Key Research Areas
Cancer biology research investigates the fundamental differences and changes in normal cells that lead to uncontrolled growth and the development of cancer, focusing on areas like cell transformation, metastasis, genetic mutations, cell signaling, and metabolic reprogramming.
This research aims to discover new diagnostic and therapeutic strategies, including targeted therapies, immunotherapies, and ways to overcome drug resistance, by understanding the complex processes that drive cancer from initiation to progression.
- Cell Growth & Transformation: Researchers study the molecular mechanisms by which normal cells become cancerous and lose their normal growth controls.
- Metastasis: A key focus is understanding how cancer cells spread from their original site to other parts of the body, which is a major cause of cancer-related mortality.
- Cell Signaling Networks: Research involves mapping the intricate signaling pathways within cells and how they are altered in cancer, providing targets for new drugs.
- Cancer Cell Metabolism: Scientists investigate how cancer cells alter their metabolic processes to support their rapid growth and survival.
- Genetics & Genomics: Understanding the specific genes and proteins that are altered in cancer cells is crucial for developing targeted treatments.
- Immune Response: Another area is the study of the immune system's role in recognizing and eliminating cancer cells, and how this can be harnessed to create cancer vaccines and other immunotherapies.
- Drug Resistance: A significant effort goes into understanding how cancer cells become resistant to therapies and developing ways to overcome this resistance.
- Research Goals and Ongoing Advancements in Cancer Biology Research
Recent and ongoing advancements in cancer biology research across diagnostics, treatments, personalized medicine, and prevention:
1. Improved diagnostics:
- Biomarkers: Scientists are developing sophisticated biomarker technologies to detect cancer non-invasively at very early stages. Biomarkers, which include proteins, DNA fragments, and other molecules shed by tumors, can be found in a patient's blood or urine. The use of these signatures offers a less invasive alternative to traditional tissue biopsies.
- Liquid biopsies: This technology analyzes circulating tumor DNA (ctDNA) from a simple blood sample. It is being refined for earlier detection, monitoring tumor progression, and assessing treatment effectiveness, providing a more complete picture of the cancer's status.
- Artificial intelligence (AI): AI and machine learning are increasingly used to analyze large datasets from imaging and pathology to enhance diagnostic accuracy and aid in early screening. AI models are being trained to predict a person's risk of developing cancer, such as lung cancer, years in advance.
- Advanced imaging: New imaging techniques use radioactive particles that bind to molecules on cancer cells, making them more visible on PET scans. This can help identify patients who may respond to specific targeted drugs.
- Cancer vaccines: Personalized cancer vaccines, often using mRNA technology, are being developed to train the immune system to recognize and attack cancer cells, with the goal of reducing recurrence. Clinical trials are underway to test their effectiveness.
- Immunotherapy: Beyond checkpoint inhibitors, new approaches are emerging to help the immune system fight cancer. These include engineering fat cells to "starve" tumors, manipulating specific immune cells, and developing novel compounds to boost immune responses.
- CAR T-cell therapy: This treatment involves genetically altering a patient's immune T-cells to attack cancer cells. Research is focused on expanding its effectiveness to more cancer types, particularly solid tumors, and better managing its side effects.
- Targeted therapies and combinations: Researchers are identifying new drug combinations and new targets to overcome treatment resistance. For example, some therapies target specific proteins inside cells, while other research focuses on finding doses that are effective with fewer side effects.
- Genomic profiling: Comprehensive profiling of tumor cells, including DNA mutations and other abnormalities, is used to create a personalized "tumor profile." This information helps doctors select precisely targeted treatments that are more likely to be effective.
- Clinical trials based on mutations: Initiatives like the National Cancer Institute's (NCI) Molecular Analysis for Therapy Choice (MATCH) trial match patients to therapies based on the genetic characteristics of their tumors rather than the cancer's location.
- Biomarker-driven strategies: The growing understanding of how individual cancers respond to treatment is leading to biomarker-driven therapeutic strategies. By analyzing the tumor's biology, doctors can predict which patients are most likely to benefit from a particular treatment.
- Data analytics and precision oncology: The integration of AI and machine learning with vast amounts of patient data is enabling more precise and predictive oncology. This allows for evidence-based treatment options and more informed decision-making.
- Risk factor identification: Ongoing research aims to identify the molecular and social factors that influence cancer risk and progression, such as lifestyle choices and genetic predispositions.
- Cancer vaccines for prevention: Beyond treating existing cancers, researchers are exploring prophylactic vaccines to prevent certain cancers, particularly those linked to viruses like HPV.
- Targeted interventions: By understanding a person's genetic makeup and environmental exposures, researchers hope to one day be able to offer personalized measures to screen for and prevent cancer.
- Gene-based strategies and risk reduction: Scientists are developing gene-based strategies and other advanced tools to aid in earlier detection and prevention. Studies are also assessing the impact of cancer risk-reduction measures for people with hereditary cancer risks, such as those with BRCA mutations.
- Interdisciplinary Approaches in Cancer Biology Research
Interdisciplinary approaches are essential in cancer biology to tackle the complexity of the disease, which involves multiple scales, from genetics to clinical presentation. Fields such as systems biology and engineering offer new ways to understand and combat cancer by integrating diverse fields and data.
A. Cancer systems biology:
Cancer systems biology takes a holistic approach to understanding cancer as a complex biological ecosystem, rather than focusing on single mutations. By combining experimental data with computational and mathematical modeling, this field helps researchers:
- Embrace complexity: This approach tackles the inherent heterogeneity of tumors within and across patients, a major obstacle for diagnosis and treatment.
- Integrate data: It allows for the comprehensive analysis of multi-omics data (including genomics, transcriptomics, and proteomics) to build predictive models of cancer.
- Provide a "bird's-eye view": This perspective helps biologists and oncologists understand how a single alteration can affect an entire tumor system.
- Generate hypotheses: Modeling of molecular networks helps identify key signaling pathways and guides research into how cancer starts, spreads, and responds to therapy.
B. Key collaborative initiatives:
Interdisciplinary collaborations, often involving scientists from multiple institutions and diverse backgrounds, are crucial for advancing cancer research.
1. Cancer Systems Biology Consortium (CSBC):
- Purpose: A National Cancer Institute (NCI) initiative, the CSBC explicitly integrates experimental biology with computational and mathematical methods to create predictive models of cancer behavior.
- Collaborators: It brings together a wide range of experts, including cancer biologists, engineers, mathematicians, and oncologists, to address pressing cancer issues.
- Examples of research: CSBC projects focus on understanding the mechanisms of drug resistance, metastasis, and the role of the immune system and tumor evolution in cancer progression.
2. Cancer engineering:
Purpose: Cancer engineering uses innovative approaches from physical and engineering sciences to accelerate discoveries in cancer biology.
Key fields: This interdisciplinary approach integrates a wide range of fields, including:
- Biofabrication and materials science
- Microsystems and fluid mechanics
- Robotics, imaging, and spectroscopy
- Mathematics, modeling, and system dynamics
3. Broad Institute's Cancer Program:
Purpose: A large-scale collaborative project that leverages innovation and data on a large scale. The program focuses on creating resources for the research community.
Key projects:
- The Cancer Genome Atlas (TCGA): A project funded by the NCI and NHGRI to catalogue the genomic abnormalities in many tumor types.
- Human Tumor Atlas Network (HTAN): Constructs 3D maps of the cellular and molecular features of human tumors and their microenvironments over time.
- Cancer Dependency Map: A large-scale project to identify genetic vulnerabilities of cancer cells to guide drug discovery.
- Personalized and precision medicine: By integrating multi-omics data and patient-specific computational models, systems biology can help tailor treatments to an individual patient's unique cancer profile.
- Overcoming drug resistance: Data integration helps identify the mechanisms that cause tumors to develop resistance to therapies. This allows for the design of more effective combination therapies.
- Identifying biomarkers: Multi-omics integration can uncover new biomarkers for early detection, prognosis, and monitoring a patient's response to treatment.
- Clinical translation: Collaborative, team-based initiatives are crucial for bridging the gap between basic research and clinical applications, ensuring new findings ultimately benefit patients.
[More to come ...]
