10.1 Cancer genomics and precision oncology

Cancer genomics revolutionizes our understanding of tumor biology and treatment. By identifying genetic alterations driving cancer growth, we can develop targeted therapies and personalized treatment plans. This field combines cutting-edge sequencing technologies with data analysis to improve patient outcomes.

Precision oncology applies genomic insights to clinical practice. It involves genomic profiling of tumors, interpreting results, and matching patients with targeted therapies. This approach aims to maximize treatment efficacy while minimizing side effects, ushering in a new era of personalized cancer care.

Genomics in Cancer Biology

Genetic Basis of Cancer

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• Cancer is a genetic disease caused by mutations in oncogenes, tumor suppressor genes, and DNA repair genes that lead to uncontrolled cell growth and division
  • Oncogenes are genes that promote cell growth and division when mutated or overexpressed (RAS, MYC)
  • Tumor suppressor genes are genes that inhibit cell growth and division when functioning properly, but can lead to cancer when inactivated by mutations or deletions (TP53, RB1)
  • DNA repair genes maintain genomic stability by repairing DNA damage, and their inactivation can lead to the accumulation of mutations and cancer development (BRCA1, MLH1)

Genomic Profiling Techniques

• Genomic profiling techniques such as next-generation sequencing (NGS) and microarrays are used to identify genetic alterations in cancer cells
  • NGS technologies include whole-genome sequencing (WGS), whole-exome sequencing (WES), and targeted sequencing panels that focus on specific genes or regions of interest
  • Microarrays include DNA microarrays that measure gene expression levels and copy number variations (CNVs), and protein microarrays that measure protein levels and post-translational modifications
• Genomic alterations identified by profiling techniques include:
  • Single nucleotide variants (SNVs): point mutations that change a single base pair in the DNA sequence
  • Copy number variations (CNVs): deletions or amplifications of large segments of DNA
  • Structural variants (SVs): chromosomal rearrangements such as translocations, inversions, and fusions

Cancer Heterogeneity and Evolution

• Genomic alterations in cancer cells can be classified as:
  • Driver mutations: mutations that directly contribute to cancer development and progression by conferring a selective advantage to cancer cells
  • Passenger mutations: mutations that do not confer a selective advantage to cancer cells and are not directly involved in cancer development
• Intratumor heterogeneity refers to the genetic diversity within a single tumor, while intertumor heterogeneity refers to the genetic differences between tumors from different patients or different sites within the same patient
  • Intratumor heterogeneity can lead to the emergence of drug-resistant subclones and treatment failure
  • Intertumor heterogeneity can explain the variable clinical outcomes and therapeutic responses observed in patients with the same cancer type
• Clonal evolution is the process by which cancer cells acquire genetic alterations over time, leading to the emergence of distinct subclonal populations with different phenotypic properties and therapeutic vulnerabilities
  • Linear evolution occurs when a single clone acquires sequential mutations and expands over time
  • Branched evolution occurs when multiple clones evolve independently and coexist within the same tumor
• Genomic profiling can identify molecular subtypes of cancer with distinct clinical outcomes and therapeutic responses
  • Breast cancer intrinsic subtypes: luminal A, luminal B, HER2-enriched, and basal-like
  • Colorectal cancer consensus molecular subtypes (CMS): CMS1 (MSI immune), CMS2 (canonical), CMS3 (metabolic), and CMS4 (mesenchymal)

Genomic Profiling for Cancer Care

Diagnostic and Prognostic Biomarkers

• Genomic profiling can identify diagnostic biomarkers that distinguish cancer from normal tissue or different cancer types
  • BCR-ABL fusion in chronic myeloid leukemia (CML)
  • EWSR1-FLI1 fusion in Ewing's sarcoma
• Prognostic biomarkers are genomic alterations that predict clinical outcomes independent of treatment
  • BRCA1/2 mutations in ovarian cancer are associated with improved survival and response to platinum-based chemotherapy
  • TP53 mutations are associated with poor prognosis in multiple cancer types, including breast, colon, and lung cancer

Predictive Biomarkers and Companion Diagnostics

• Predictive biomarkers are genomic alterations that predict response or resistance to specific therapies
  • EGFR mutations in non-small cell lung cancer (NSCLC) predict response to EGFR tyrosine kinase inhibitors (TKIs) such as erlotinib and gefitinib
  • KRAS mutations in colorectal cancer predict resistance to anti-EGFR antibodies such as cetuximab and panitumumab
• Companion diagnostics are FDA-approved tests that are required for the safe and effective use of a corresponding drug
  • The cobas EGFR Mutation Test is a companion diagnostic for selecting NSCLC patients for treatment with erlotinib
  • The THxID BRAF Kit is a companion diagnostic for selecting melanoma patients for treatment with the BRAF inhibitor vemurafenib

Emerging Biomarkers and Liquid Biopsy

• Tumor mutation burden (TMB) is an emerging biomarker that measures the total number of mutations in a tumor and may predict response to immune checkpoint inhibitors (ICIs) across multiple cancer types
  • High TMB (≥10 mutations/megabase) is associated with improved response to ICIs in melanoma, NSCLC, and other solid tumors
  • TMB can be measured by whole-exome sequencing (WES) or targeted sequencing panels such as the FoundationOne CDx assay
• Liquid biopsy techniques such as circulating tumor DNA (ctDNA) sequencing can non-invasively monitor cancer progression, detect minimal residual disease (MRD), and identify mechanisms of acquired resistance to targeted therapies
  • ctDNA is DNA released from tumor cells into the bloodstream that can be detected by ultra-sensitive sequencing methods such as digital PCR or next-generation sequencing (NGS)
  • ctDNA can detect mutations present in a small fraction of tumor cells and monitor the emergence of resistant clones during treatment with targeted therapies such as EGFR TKIs in NSCLC

Precision Oncology in Practice

Challenges in Implementing Precision Oncology

• Genomic profiling requires adequate tumor tissue sampling and quality control measures to ensure accurate and reproducible results
  • Small biopsies or fine-needle aspirates may not yield enough DNA for comprehensive genomic profiling
  • Formalin fixation and paraffin embedding (FFPE) can degrade DNA quality and introduce artifacts
• Interpreting the clinical significance of genomic alterations requires a multidisciplinary team approach involving pathologists, medical oncologists, molecular biologists, and bioinformaticians
  • Genomic alterations may have different functional consequences depending on the cancer type and clinical context
  • Variants of unknown significance (VUS) require further functional validation or clinical correlation to determine their actionability
• Off-label use of targeted therapies based on genomic profiling results requires careful consideration of potential risks and benefits, as well as navigating reimbursement and regulatory hurdles
  • Off-label use may be justified in patients with advanced cancer who have exhausted standard treatment options, but requires informed consent and close monitoring for toxicity
  • Reimbursement for off-label use varies by insurance provider and may require prior authorization or appeal

Opportunities and Ethical Considerations

• Implementing precision oncology requires ongoing education and training of healthcare providers to keep up with the rapidly evolving landscape of genomic technologies and targeted therapies
  • Medical schools and residency programs are incorporating genomics education into their curricula
  • Continuing medical education (CME) courses and online resources are available for practicing oncologists to stay up-to-date on the latest advances in precision oncology
• Precision oncology offers the opportunity to improve patient outcomes by matching the right drug to the right patient at the right time
  • Basket trials and umbrella trials are testing the efficacy of targeted therapies across multiple cancer types and molecular subtypes
  • Combination therapies that target multiple oncogenic pathways or combine targeted therapies with immunotherapies are being explored to overcome resistance and improve patient outcomes
• Precision oncology also raises ethical and social issues around access to genomic testing and targeted therapies, as well as data privacy and sharing
  • Genomic testing and targeted therapies can be expensive and may not be covered by all insurance plans, leading to disparities in access and outcomes
  • Sharing of genomic data across institutions and countries is necessary for advancing research and identifying rare mutations, but requires robust data security and privacy protections

Cancer Genomics in Drug Development

Targeted Therapy Development

• Cancer genomics has led to the development of targeted therapies that selectively inhibit oncogenic drivers
  • Imatinib (Gleevec) for BCR-ABL-positive chronic myeloid leukemia (CML) and gastrointestinal stromal tumors (GIST)
  • Trastuzumab (Herceptin) for HER2-positive breast cancer and gastric cancer
  • Vemurafenib (Zelboraf) for BRAF V600E-mutant melanoma and non-small cell lung cancer (NSCLC)
• Targeted therapies can be small molecule inhibitors that block the activity of oncogenic kinases or other enzymes, or monoclonal antibodies that bind to and inhibit oncogenic receptors or ligands
  • Small molecule inhibitors include tyrosine kinase inhibitors (TKIs) such as imatinib, erlotinib, and crizotinib, and serine/threonine kinase inhibitors such as vemurafenib and trametinib
  • Monoclonal antibodies include trastuzumab, cetuximab, and bevacizumab

Innovative Clinical Trial Designs

• Basket trials are clinical trials that enroll patients with different cancer types based on a common genomic alteration, and test the efficacy of a targeted therapy across multiple histologies
  • The BRAF V600E mutation is found in multiple cancer types, including melanoma, NSCLC, colorectal cancer, and thyroid cancer, and has been targeted by basket trials of BRAF inhibitors such as vemurafenib and dabrafenib
  • The NCI-MATCH trial is a large basket trial that assigns patients to targeted therapy arms based on their tumor's molecular profile, regardless of cancer type
• Umbrella trials are clinical trials that enroll patients with a single cancer type and assign them to different treatment arms based on their genomic profile
  • The BATTLE trial in NSCLC assigned patients to erlotinib, vandetanib, bexarotene, or sorafenib based on the expression of EGFR, KRAS, BRAF, and VEGF
  • The I-SPY 2 trial in breast cancer assigns patients to neoadjuvant therapy arms based on their HER2, hormone receptor, and MammaPrint risk status
• Adaptive trial designs allow for the modification of trial parameters based on interim analyses of genomic biomarkers and clinical outcomes
  • The FOCUS4 trial in colorectal cancer stratifies patients into molecular subtypes and allows for the addition or discontinuation of treatment arms based on futility or efficacy analyses
  • The GBM AGILE trial in glioblastoma uses Bayesian adaptive randomization to assign patients to the most promising therapies based on their molecular profile and ongoing results

Immunotherapy and Combination Therapy

• Cancer genomics has also led to the development of immunotherapies that target the immune system rather than the tumor itself
  • Immune checkpoint inhibitors (ICIs) block the PD-1/PD-L1 or CTLA-4 pathways that normally suppress T cell activation, allowing for enhanced anti-tumor immune responses
  • Pembrolizumab (Keytruda) and nivolumab (Opdivo) are anti-PD-1 antibodies approved for multiple cancer types, including melanoma, NSCLC, and colorectal cancer with high microsatellite instability (MSI-H)
• Combination therapies that target multiple oncogenic pathways or combine targeted therapies with immunotherapies are being explored to overcome resistance and improve patient outcomes
  • The combination of BRAF and MEK inhibitors (dabrafenib + trametinib) improves survival in BRAF V600E-mutant melanoma compared to BRAF inhibitor monotherapy
  • The combination of the EGFR TKI osimertinib with the anti-PD-L1 antibody durvalumab is being tested in EGFR-mutant NSCLC to overcome resistance to EGFR TKIs and enhance anti-tumor immunity
• Combination therapies pose challenges in terms of toxicity and cost
  • Combining targeted therapies with overlapping toxicity profiles (e.g. EGFR and MEK inhibitors) can lead to dose-limiting side effects and require careful management
  • The high cost of targeted therapies and immunotherapies can limit access and strain healthcare budgets, requiring value-based pricing and reimbursement models