Neoplastic Diseases and Other Acquired Disorders
Core concepts
Understand the molecular hallmarks of cancer: oncogene activation (KRAS, EGFR, BRAF, MYC), tumor suppressor loss (TP53, RB1, APC, PTEN), and DNA repair defects (mismatch repair, homologous recombination, BRCA1/2). Know the difference between driver and passenger mutations, clonal evolution, and the role of tumor heterogeneity.
Be able to connect molecular findings to diagnosis, prognosis, and therapy: BCR-ABL1 in CML, JAK2 V617F in myeloproliferative neoplasms, FLT3 and NPM1 in AML, EGFR and ALK in lung cancer, BRAF V600E in melanoma, MSI-high and tumor mutational burden as immunotherapy biomarkers, and homologous recombination deficiency in ovarian and breast cancer. Understand minimal residual disease monitoring and liquid biopsy.
Traps
Questions test the distinction between germline and somatic variants (variant allele fraction near 50% in a tumor may suggest germline; confirm with normal tissue). Another trap is confusing a prognostic marker with a predictive marker. Remember that MSI testing by immunohistochemistry and by PCR are complementary, and that a BRAF V600E result can help distinguish sporadic from Lynch-associated colorectal cancer.
Rule of thumb
When interpreting a tumor variant, ask three questions: Is it a known driver? Is it germline or somatic? Is it actionable (therapy, prognosis, or eligibility for a trial)?
Worked example
A colorectal tumor shows loss of MLH1 and PMS2 on immunohistochemistry; BRAF V600E is present, favoring a sporadic MLH1-methylated tumor rather than Lynch syndrome, and germline testing may be deferred with appropriate counseling.
Infectious Disease Molecular Testing
Core concepts
Understand nucleic acid amplification testing (NAAT) for detection and quantitation: PCR, transcription-mediated amplification, and isothermal methods. Know the difference between qualitative detection (presence/absence) and quantitative viral load monitoring (e.g., HIV, HCV, CMV, EBV). Understand genotyping for resistance (HIV, HCV, TB), and the use of sequencing for outbreak tracking and strain typing.
Be able to explain the importance of assay design: target selection, inclusivity and exclusivity, limit of detection, and the effect of mutations in primer/probe regions causing false negatives. Know the role of internal controls and the distinction between extraction and amplification controls.
Traps
Questions test the difference between a positive result and active infection (contamination, colonization, latent virus), and the interpretation of viral load changes (log10 changes). Another trap is assuming a negative NAAT excludes infection when the specimen was inadequate or collected too early. Watch for primer/probe escape mutants.
Rule of thumb
For quantitative assays, a change of less than 0.5 log10 is generally within assay variability; a 1 log10 (10-fold) change is clinically meaningful. Always confirm specimen adequacy and internal control performance before interpreting a negative result.
Worked example
An HIV viral load rises from 1,000 to 5,000 copies/mL (about 0.7 log10). This may represent a real increase or assay variability; repeat testing and assess adherence and resistance before changing therapy.
Identity Testing, Histocompatibility, Immunologic Diseases, and Forensic Testing
Core concepts
Know short tandem repeat (STR) profiling for identity and forensic testing: allele frequencies, match probability, random match probability, and the product rule. Understand kinship analysis, chimerism monitoring after transplant, and the use of Y-STR and mitochondrial DNA in forensic cases.
For histocompatibility, understand HLA class I (A, B, C) and class II (DR, DQ, DP) genes, HLA typing methods (SSP, SSO, Sanger sequencing, NGS), and the importance of high-resolution typing for transplant matching. Know the concepts of HLA antibody detection (PRA, virtual crossmatch, single-antigen beads) and the role of donor-specific antibodies in rejection.
Traps
Questions test the product rule (multiplying allele frequencies across independent loci), the difference between a match probability and a likelihood ratio, and the fact that HLA matching is more critical for hematopoietic stem cell transplant than for solid organ transplant. Another trap is confusing HLA class I (expressed on all nucleated cells) with class II (antigen-presenting cells, activated endothelium).
Rule of thumb
For identity testing, the more independent loci and the rarer the alleles, the stronger the discrimination. For transplant, high-resolution HLA typing and donor-specific antibody assessment drive risk.
Worked example
A forensic profile matches at 13 CODIS STR loci. The random match probability is calculated by multiplying the genotype frequencies across loci; a very small probability supports the match but does not by itself establish identity without context and statistics.
Pharmacogenomic Testing
Core concepts
Understand how genetic variation affects drug metabolism, transport, and targets. Know the major pharmacogenes: CYP2D6, CYP2C19, CYP2C9, CYP3A4/5, TPMT, NUDT15, DPYD, UGT1A1, HLA-B57:01, HLA-B15:02, and G6PD. Be familiar with star allele nomenclature and the diplotype-to-phenotype translation (poor, intermediate, normal, rapid, ultrarapid metabolizer).
Understand the clinical actionability framework: CPIC guidelines and FDA labeling. Know examples: CYP2C19 poor metabolizers and clopidogrel, CYP2D6 ultrarapid metabolizers and codeine, HLA-B57:01 and abacavir hypersensitivity, HLA-B15:02 and carbamazepine in Asian populations, TPMT/NUDT15 and thiopurines, DPYD and fluoropyrimidines, G6PD and rasburicase.
Traps
Questions test the difference between genotype and phenotype (a genotype may not predict phenotype because of drug interactions or rare variants), and the fact that star allele definitions vary by reference database. Another trap is assuming a single variant captures all function; many genes have copy-number variation (CYP2D6) that must be assessed. Remember that HLA associations are ancestry-dependent.
Rule of thumb
Translate diplotype to phenotype using the current CPIC or DPWG table, then apply the guideline's recommendation. For CYP2D6, always consider copy number and inhibitors.
Worked example
A patient is CYP2C19 *2/*2 (poor metabolizer) and is prescribed clopidogrel after stent placement. CPIC recommends an alternative antiplatelet agent (e.g., prasugrel or ticagrelor) because clopidogrel activation is impaired.
Informatics in Molecular Genetic Pathology
Core concepts
Understand the informatics pipeline: sample tracking, sequence alignment (reference genome, BAM/CRAM), variant calling (VCF), annotation (transcript selection, population databases, in silico predictors), and classification (ACMG/AMP criteria). Know the difference between a variant call and a variant interpretation, and the role of databases (ClinVar, gnomAD, COSMIC, OMIM, HGMD).
Be familiar with laboratory information systems, electronic health records, and the importance of data security, privacy (HIPAA), and interoperability standards (HL7, FHIR). Understand the concept of a bioinformatics pipeline validation, reproducibility, and version control.
Traps
Questions test the difference between analytical pipeline performance and clinical interpretation, and the risk of incidental findings from broad sequencing. Another trap is assuming a variant absent from a database is benign; absence of evidence is not evidence of absence. Watch for transcript selection errors that change HGVS nomenclature.
Rule of thumb
For variant classification, apply ACMG/AMP criteria systematically: population frequency, computational prediction, functional data, segregation, and de novo status. Document the evidence and the transcript used.
Worked example
A novel missense variant in a disease gene is absent from gnomAD, predicted damaging by multiple in silico tools, and segregates with disease in three affected relatives. Applying ACMG criteria (PM2, PP3, PP1), it may be classified as likely pathogenic, pending functional confirmation.