Premed · Premed · Genetics
Lecture 27: Personalized Medicine and Genomic Medicine
Genetics
Learning Objectives
By the end of this lecture, students will be able to:
- Define personalized (precision) medicine and explain how genomic information is transforming clinical practice
- Describe the role of whole exome and whole genome sequencing in diagnosing rare genetic diseases
- Explain how tumor genomic profiling guides cancer treatment selection
- Discuss the integration of pharmacogenomics, polygenic risk scores, and multi-omics data in clinical decision-making
- Identify the barriers to implementing genomic medicine in routine clinical practice
- Describe current and emerging applications of gene therapy and RNA-based therapeutics
Lecture Content
I. From Genetics to Genomic Medicine
Personalized (precision) medicine: tailoring medical treatment to the individual characteristics of each patient, particularly their genomic profile. Key milestones: Human Genome Project completed (2003): first reference human genome sequence ($2.7 billion, 13 years) Cost of whole genome sequencing has plummeted: ~$1000 in 2020, approaching $200 in current era. Large biobank initiatives: UK Biobank (500,000 participants), All of Us (NIH, 1 million+), Genomics England (100,000 Genomes Project) Increasing integration of genomic data into electronic health records. Three pillars of genomic medicine: Diagnostic genomics: identifying the genetic cause of disease. Predictive genomics: assessing risk for future disease. Therapeutic genomics: guiding treatment selection based on genetic information. Genomic medicine applies across the entire medical spectrum — rare diseases, cancer, common diseases, pharmacotherapy, and reproductive health.
II. Diagnostic Genomics: Solving Rare Diseases
~7,000 known rare diseases, ~80% have a genetic basis. Many patients endure a "diagnostic odyssey" — years of inconclusive tests before receiving a diagnosis. Whole exome sequencing (WES) and whole genome sequencing (WGS) have transformed rare disease diagnosis: WES diagnostic yield: ~25-40% for undiagnosed patients with suspected genetic conditions. WGS provides additional yield by detecting non-coding variants, structural variants, and repeat expansions. Trio sequencing (proband + both parents): increases diagnostic power by identifying de novo mutations and enabling segregation analysis. The diagnostic pipeline: Clinical phenotyping (detailed description of the patient's features using HPO terms) Sequencing (WES or WGS) Variant filtering: remove common variants (MAF > 1% in population databases like gnomAD) Variant prioritization: predicted functional impact, inheritance model, gene-phenotype match. Variant classification (ACMG criteria) Clinical correlation and reporting. Functional validation if needed (cell-based assays, animal models) Impact of diagnosis: even when no specific treatment exists, a diagnosis can: End the diagnostic odyssey and provide closure. Enable accurate recurrence risk counseling. Connect families with condition-specific support groups. Identify targeted surveillance for associated complications. In some cases, reveal a treatable condition or clinical trial opportunity.
<image>Panel A: The diagnostic odyssey illustrated — a timeline showing a patient's journey from symptom onset through multiple specialist visits, inconclusive tests, and years of uncertainty, then the turning point of genomic sequencing leading to a definitive molecular diagnosis; average time to diagnosis with and without genomic sequencing is compared. Panel B: The WES/WGS diagnostic pipeline — a funnel diagram showing the steps from raw sequencing data (millions of variants) through filtering (common variant removal, quality control), prioritization (functional prediction, inheritance model matching), ACMG classification, and final clinical report with the causative variant identified. Panel C: Bar chart showing diagnostic yield of WES across different clinical categories — neurodevelopmental disorders, skeletal dysplasias, metabolic diseases, undiagnosed diseases programs — with yields ranging from 25-50%, and the additional yield from WGS shown as an incremental bar.</image>
III. Precision Oncology
Cancer is the area where genomic medicine has had the greatest clinical impact to date. Tumor genomic profiling is now standard of care for many cancer types: Identifies actionable driver mutations to guide targeted therapy selection. Determines MSI status and tumor mutational burden (TMB) to predict immunotherapy response. Detects resistance mutations to inform treatment changes. Key examples of genomic-guided therapy: EGFR mutations in non-small cell lung cancer → erlotinib, osimertinib. ALK fusions in lung cancer → crizotinib, alectinib. HER2 amplification in breast cancer → trastuzumab, pertuzumab. BRAF V600E in melanoma → vemurafenib + cobimetinib. BCR-ABL in CML → imatinib. BRCA1/2 mutations → PARP inhibitors. MSI-high / dMMR tumors (any type) → pembrolizumab (tissue-agnostic approval) NTRK fusions (any tumor type) → larotrectinib, entrectinib (tissue-agnostic approval) Liquid biopsy: circulating tumor DNA (ctDNA) analysis from a blood draw. Non-invasive monitoring of tumor mutations over time. Early detection of resistance mutations. Minimal residual disease (MRD) detection after treatment. Potential for cancer screening (multi-cancer early detection tests under development) Challenges in precision oncology: Tumor heterogeneity and clonal evolution → resistance to targeted therapy. Only ~15-30% of cancer patients currently have an actionable mutation with an approved targeted therapy. Need for combination approaches and functional genomics to expand actionable targets.
IV. Polygenic Risk Scores and Common Disease Prediction
Polygenic risk scores (PRS): aggregate the effects of many common genetic variants (from GWAS) into a single score predicting disease risk. PRS have been developed for many common diseases: Coronary artery disease, type 2 diabetes, breast cancer, prostate cancer, atrial fibrillation, Alzheimer disease, inflammatory bowel disease. Potential clinical applications: Risk stratification: identify individuals at high genetic risk for earlier or more intensive screening. Example: high PRS for coronary artery disease → earlier statin initiation, more aggressive lifestyle modification. Example: high PRS for breast cancer → enhanced screening (MRI in addition to mammography) starting at an earlier age. Current limitations: PRS explain a modest proportion of disease risk (typically 5-15% of variance) Most PRS were developed in European-descent populations → reduced accuracy in other populations (transferability problem) Interaction with clinical risk factors and family history needs better integration. Risk of exacerbating health disparities if implemented unevenly. Currently not recommended for population-wide screening by most clinical guidelines but being evaluated in clinical trials.
<image>Panel A: Precision oncology workflow — a tumor biopsy undergoes genomic profiling (NGS panel or WES), the results are reviewed by a molecular tumor board, actionable mutations are identified, and matched targeted therapies are selected; a parallel pathway shows liquid biopsy for monitoring; the workflow is illustrated as a cyclical process with treatment, monitoring, and adaptation. Panel B: Polygenic risk score concept — a human figure with many small genetic variant effects (shown as plus and minus signs scattered across chromosomes) being summed into a single PRS value; a population distribution of PRS is shown with the tails highlighted, and a comparison of disease risk (e.g., coronary artery disease) between the lowest and highest PRS quintiles is displayed as a bar chart. Panel C: The transferability problem — PRS performance (measured as area under the curve, AUC) plotted for different ancestry groups (European, East Asian, South Asian, African, Hispanic/Latino), showing declining performance as genetic distance from the discovery population increases, emphasizing the need for diverse GWAS cohorts.</image>
V. Gene Therapy and RNA-Based Therapeutics
Gene therapy: introduction of genetic material into cells to treat or prevent disease. In vivo gene therapy (deliver the gene directly to the patient): AAV vectors: deliver functional gene copies to target tissues. Luxturna (voretigene neparvovec): AAV2-RPE65 for inherited retinal dystrophy (biallelic RPE65 mutations) — subretinal injection restores vision. Zolgensma (onasemnogene abeparvovec): AAV9-SMN1 for spinal muscular atrophy type 1 — one-time IV infusion. Hemgenix (etranacogene dezaparvovec): AAV5-FIX for hemophilia B. Ex vivo gene therapy (modify patient cells outside the body, then return them): CASGEVY: CRISPR-edited autologous HSCs for sickle cell disease and transfusion-dependent beta-thalassemia. Lentiviral-modified HSCs: betibeglogene autotemcel (Zynteglo) for beta-thalassemia. CAR-T cell therapy: genetically engineered T cells for B-cell malignancies. RNA-based therapeutics: Antisense oligonucleotides (ASOs): nusinersen (Spinraza) for SMA; modifies SMN2 pre-mRNA splicing to increase functional SMN protein. siRNA (small interfering RNA): patisiran (Onpattro) — lipid nanoparticle-delivered siRNA targeting TTR mRNA for hereditary transthyretin amyloidosis; givosiran for acute hepatic porphyria. mRNA therapeutics: COVID-19 vaccines (Pfizer-BioNTech, Moderna) demonstrated the platform; now being explored for genetic diseases and cancer vaccines. Challenges: Cost: gene therapies range from $500,000 to $3.5 million per treatment. Durability: how long do the effects last? Some require re-dosing. Immunogenicity: pre-existing antibodies to AAV vectors may limit eligibility. Scalability and equitable access globally.
VI. Multi-Omics and the Future of Precision Medicine
Multi-omics integration: combining genomics, transcriptomics, proteomics, metabolomics, and epigenomics for a comprehensive molecular picture. Genomics: DNA sequence variants. Transcriptomics: gene expression profiles (RNA-seq) Proteomics: protein abundance and modifications. Metabolomics: small molecule metabolites. Epigenomics: DNA methylation, histone modifications. Single-cell omics: analyzing individual cells rather than bulk tissue — reveals cellular heterogeneity. Artificial intelligence and machine learning: increasingly used to interpret complex genomic data, predict variant pathogenicity, identify drug targets, and integrate multi-omics data. Digital health integration: wearable devices, continuous monitoring combined with genomic risk profiles for personalized prevention strategies. Newborn genomic sequencing: pilot programs underway (BabySeq, Newborn Genomes Programme) evaluating the feasibility and utility of WGS for all newborns.
VII. Barriers to Implementation
Clinical education: most physicians have limited formal training in genomics. Data interpretation: the volume and complexity of genomic data exceed current clinical infrastructure. Health equity: genomic databases are disproportionately European-descent; under-served populations may not benefit equally. Reimbursement and cost: insurance coverage for genomic testing and gene therapies is inconsistent. Ethical, legal, and social implications (ELSI): ongoing need for updated policies on data privacy, discrimination, and consent. Infrastructure: clinical decision support systems, variant databases, and interdisciplinary teams (genetic counselors, clinical geneticists, bioinformaticians) must be expanded.
