30+ Cancer Research Topics for High School Students
- Stephen Turban
- Jul 8
- 8 min read
Cancer research is a broad and rapidly evolving field that brings together biology, medicine, data science, public health, psychology, and technology. If you're interested in exploring a research topic in this area, studying cancer can help you understand how diseases develop, how treatments are designed, and how scientific discoveries translate into improvements in patient care. It also offers opportunities to engage with real datasets, the scientific literature, and emerging technologies shaping modern healthcare.
Why should I research cancer in high school?
Cancer research allows you to investigate complex scientific and societal questions while developing skills in critical thinking, data analysis, scientific writing, and evidence-based reasoning. Depending on your interests, you might explore topics related to genetics, immunology, epidemiology, artificial intelligence, drug development, public health, or the psychological impact of disease. These projects can help you gain exposure to interdisciplinary research methods, deepen your understanding of biomedical science, and identify areas of medicine or research you may want to pursue in the future.
Without further ado, here are 30 cancer research topics for high school students to get you started!
If you’re looking for online summer research programs, check out our blog here.
Cancer Genetics and Genomics
Cancer starts when the genetic instructions controlling cell growth and division break down. Research here focuses on how mutations form, which genes are affected, and how those changes drive disease. With genomic sequencing, researchers can now map mutations across entire tumor cells and compare them to healthy tissue to find patterns. As a high school student, you can dig into this through a mix of molecular biology and data analysis. Try reviewing studies that link specific mutations to certain cancer types, or explore publicly available genomic datasets to see how mutations connect to patient outcomes.
Investigate how tumor suppressor genes such as BRCA1, BRCA2, and TP53 regulate cell division and maintain DNA integrity. You can also examine how mutations in these genes disrupt normal cellular checkpoints, leading to uncontrolled growth and increased cancer risk.
Select a specific cancer type and use publicly available datasets like The Cancer Genome Atlas (TCGA) to examine its mutation profile. Focus on identifying frequently altered genes, patterns that may indicate distinct subtypes, and differences across patient groups.
Examine the role of oncogenes in cancer development by comparing how specific genes contribute to uncontrolled cell growth across different cancer types. Focus on genes such as RAS or HER2 to understand how mutations or overexpression alter normal signaling pathways.
Explore the use of liquid biopsies to detect and monitor cancer via circulating tumor DNA (ctDNA) in blood samples. Observe how ctDNA is identified and how it compares to traditional methods such as imaging or tissue biopsy. You can review clinical studies to evaluate how accurately this approach detects cancer, tracks treatment response, or identifies relapse.
Study how epigenetic changes, such as DNA methylation and histone modification, influence cancer development without altering the DNA sequence. Focus on how these changes affect gene expression, including the silencing of tumor suppressor genes or activation of oncogenes.
Cancer Immunology
Think of your immune system as a highly trained security force. Cancer, however, is a master of disguise. Cancer immunology explores how these "security" cells find and fight tumors, and how tumors try to hide or shut them down. As a student, you can investigate how we are currently "re-training" the body to win this battle. Here are five areas to explore:
Examine how cancer cells evade immune detection by altering surface markers such as MHC-I molecules. Focus on how reduced or absent MHC-I expression limits immune cells' ability to recognize and target abnormal cells. Another direction is to explore research on therapies designed to restore immune recognition in these tumors.
Investigate the mechanism of immune checkpoint inhibitors (such as PD-1/PD-L1 blockers) and analyze published clinical trial data comparing their effectiveness across cancer types. Checkpoint proteins like PD-1 and CTLA-4 normally prevent immune cells from attacking healthy tissue, but tumors exploit these pathways to suppress anti-tumor immunity. Drugs that block these checkpoints have shown striking results in cancers like melanoma and non-small cell lung cancer.
Explore clinical data on CAR-T cell therapy and study how T cells are engineered to recognize and destroy specific tumor antigens, and what the current limitations of this approach are. CAR-T therapy involves extracting a patient's own T cells, genetically engineering them to express a receptor targeting a specific protein on cancer cells, and reinfusing them. This approach has produced remarkable remissions in certain blood cancers but has shown limited success in solid tumors.
Study the tumor microenvironment, which includes the surrounding immune cells, blood vessels, and signaling molecules that interact with a tumor. Focus on how these components influence immune responses, including ways in which tumors modify nearby cells to reduce immune activity.
Analyze how therapeutic cancer vaccines work, using published data from trials on vaccines targeting melanoma or prostate cancer. Unlike preventive vaccines, therapeutic cancer vaccines aim to train the immune system to recognize and attack existing tumor cells by presenting tumor-associated antigens to immune effectors.
Cancer Epidemiology and Public Health
Cancer epidemiology is about looking at the big picture. Instead of peering through a microscope, you’re looking at entire populations to see who gets sick and why. By using massive datasets from organizations like the WHO or the CDC, you can track how geography, money, and lifestyle habits change the face of the disease. As a student, you can use these public "gold mines" of data to spot trends that others might miss. Here are five ways to dive into the data:
Analyze publicly available cancer incidence and mortality data (from the CDC, WHO, or the NIH's SEER database) to identify demographic or geographic disparities in a specific cancer type.
Investigate the relationship between socioeconomic status and cancer screening rates, using published epidemiological studies to understand how income and access to care affect outcomes.
Research the epidemiology of HPV-related cancers (cervical, oropharyngeal) and examine trends in incidence before and after widespread HPV vaccine adoption.
Explore the role of environmental exposures such as air pollution, pesticide use, or industrial chemicals in cancer incidence in specific populations or regions.
Investigate how tobacco control policies have affected lung cancer incidence over time across different countries, using publicly available longitudinal datasets.
Cancer and Data Science
Data science is the new frontier of cancer research. Instead of just looking at one sample, scientists now use "Big Data", everything from genetic codes to thousands of X-rays, to find patterns that the human eye might miss. By combining computer science with medicine, researchers can predict how a tumor might grow or which treatment will work best.
As a student, you can use basic coding or statistical tools to explore these massive public datasets. Here are five ways to bridge the gap between tech and oncology:
Explore how machine learning models are used to classify medical images as benign or malignant. You can work with publicly available histopathology image datasets and study how models are trained and evaluated. Focus on understanding how accuracy is measured and what challenges arise in image-based classification.
Use published gene expression datasets to examine how molecular patterns are linked to cancer outcomes. You can explore how researchers group patients based on gene activity and study how these groups relate to survival or disease progression.
Investigate how natural language processing (NLP) is being used to extract structured data from unstructured clinical notes and pathology reports to support cancer research.
Research the use of AI in radiology for early cancer detection, comparing the performance of published AI models to radiologist benchmarks in detecting lung nodules or breast masses.
Analyze patterns in large publicly available cancer clinical trial datasets to identify which patient subgroups tend to be underrepresented in trials and what this means for the generalizability of results.
Cancer Treatment and Drug Development
Developing new cancer treatments is an ongoing challenge. Scientists are constantly working on smarter therapies while cancer cells continue evolving to survive them. Researching this field means understanding how treatments work at a biological level and why they sometimes stop working. As a student, you can explore the science behind drug development through any of these five angles:
Study how cancer cells become resistant to treatments such as chemotherapy over time. Focus on the biological changes that allow tumor cells to survive despite drug exposure. You can examine research on how resistance develops and review strategies scientists are testing to address this problem.
Examine how certain treatments are designed to act on specific genetic changes in cancer cells. You can study examples such as imatinib in leukemia to understand how these treatments differ from traditional chemotherapy. Focus on how targeting specific mutations influences effectiveness and side effects.
Explore how researchers use the concept of synthetic lethality to target cancer cells with specific genetic weaknesses. You can study cases such as PARP inhibitors used in BRCA-related cancers to understand how this method selectively affects tumor cells.
Investigate how gut bacteria may influence how patients respond to cancer treatments, especially immunotherapy. You can review studies that examine the relationship between microbiome composition and treatment outcomes.
Research how cancer care differs when the focus shifts from curing the disease to managing symptoms and improving quality of life. You can examine how pain management, supportive care, and patient well-being are addressed in clinical settings.
Cancer Psychology and Social Science
Cancer does not affect just the body. It changes daily life for patients, families, friends, and even whole communities. This area of research focuses on the mental, emotional, and social impact of the disease. If you are interested in psychology or sociology, this gives you a way to study how people, relationships, and social systems affect health. Here are five ways you can explore the human side of cancer:
Investigate how a cancer diagnosis affects the mental health of adolescents and young adults, focusing on outcomes such as anxiety, identity changes, and social disruption. You can review both qualitative and quantitative studies to identify common challenges and gaps in mental health support for this age group.
Examine how health literacy influences cancer screening and treatment decisions in low-income communities. You can study how differences in understanding medical information affect screening rates, early diagnosis, and participation in treatment.
Analyze how cancer is portrayed in popular media and whether these representations shape public understanding of risk, treatment options, or survivorship in accurate or misleading ways.
Investigate how clinical trial participation varies across racial and ethnic groups in oncology. You can examine barriers such as access to care, eligibility criteria, and trust in medical systems, along with proposed solutions. This topic also explores how underrepresentation affects treatment equity and research outcomes.
Study how caregiving for cancer patients affects family members, focusing on psychological stress, emotional strain, and social challenges. You can examine factors that contribute to caregiver burden and review research on support systems or interventions designed to reduce distress.
Artificial Intelligence in Cancer Research
Artificial intelligence is increasingly used in cancer research to analyze complex medical data, including imaging, genomic information, and clinical records. This area focuses on how computational models are applied to tasks such as detection, prediction, and treatment planning. In your research, you can explore how AI systems process large datasets and how their use is being evaluated in medical contexts.
Examine how deep learning models are used to identify cancer in medical images such as mammograms, CT scans, or pathology slides. You can review studies that compare the performance of AI systems with trained clinicians and analyze how accuracy is measured.
Investigate how artificial intelligence is used to identify potential drug candidates and predict how cancer cells respond to treatment. Study how computational models screen molecular compounds and analyze published case studies of AI-assisted drug development.
Explore how the use of AI in cancer diagnosis and treatment raises questions about bias, transparency, and access. Review research data on how model performance varies across different populations and what this means for healthcare equity.
One other option—the Lumiere Research Scholar Program
If you’re interested in pursuing independent research, consider applying to one of the Lumiere Research Scholar Programs, selective online high school programs for students founded with researchers at Harvard and Oxford. Last year, we had over 4,000 students apply for 500 spots in the program! You can find the application form here.
Also check out the Lumiere Research Inclusion Foundation, a non-profit research program for talented, low-income students. Last year, we had 150 students on full need-based financial aid!
Stephen is one of the founders of Lumiere and a Harvard College graduate. He founded Lumiere as a PhD student at Harvard Business School. Lumiere is a selective research program where students work 1-1 with a research mentor to develop an independent research paper.