Biomarkers in Chemical Pathology

• Analytical & Clinical Aspects of biomarkers in Chemical Pathology

Definition of Biomarkers

• Biomarkers are biological indicators of normal biological processes, pathogenic processes, or responses to therapeutic interventions, according to the FDA and NIH.
• They are measurable cellular or molecular entities like DNA, RNA, proteins, or metabolites from tissue or liquid biopsies (blood, urine, saliva).
• Biomarkers are measurable quantitatively or qualitatively.
• Biomarkers are used to diagnose diseases or pathogenic processes, monitor patients during treatment, and allow for prognosis.

Ideal Biomarker Features:

• Reliable
• Modifiable with treatment
• Cost-efficient for follow-up
• Safe and easy to measure
• Sensitive and specific

Biomarker Development Process

• Biomarker development involves iterative steps that begin with the discovery of the biomarker in healthy and diseased samples.
• Rational, evidence-based biomarker creation maintains clinical and scientific necessity.
• Development requires a collaborative approach with regulatory science involving many different fields.
• The field is changing quickly due to growth in computation, analysis, and measurement.
• The development process includes:
  • Pre-analytical and analytical validation
  • Clinical validation
  • Regulatory approval
  • Demonstration of clinical utility
• The pre-analytical phase standardizes indicators and analyzes quality indicators like process, storage, and sample collection.
• The analytical validation phase analyzes the biomarker's performance parameters to ensure the test is repeatable, dependable, and has the right level of specificity and sensitivity.
• A biomarker is connected to clinical and biological endpoints through a graded evidential qualification process.

Discovery and Qualification Phases:

• Discovery phase involves identifying candidate biomarkers
• Qualification phase includes:
  • Confirming differential expression of candidate biomarkers
  • Assessing specificity and sensitivity of candidate biomarkers
  • Developing and optimizing assays.

Biomarker Classification

• Biomarkers are classified by:
  • Genetic and molecular biology, characteristics, and clinical applications
• Types of biomarkers include:
  • Cellular, imaging, molecular, chemical, genetic, and protein biomarkers
• Clinical application biomarkers:
  • Diagnostic, therapeutic, and prognostic biomarkers

Diagnostic Biomarkers

• Diagnostic biomarkers are used to detect or confirm the presence of a disease or medical condition and can provide information about the characteristics of a disease.
• Prostate-Specific Antigen (PSA):
  • Used to diagnose and monitor prostate cancer
  • High PSA levels indicate prostate cancer
  • Changes in PSA levels over time are useful to monitor disease progression or response to treatment.
• C-Reactive Protein (CRP)
  • Used to assess inflammation in the body
  • Elevated levels are associated with inflammatory diseases like rheumatoid arthritis, lupus, and cardiovascular diseases.

Monitoring Biomarkers

• They are measured repeatedly to assess the status of a disease or medical condition, or to quantify exposure to a medical product or environmental agent.
• They play a role in disease management and treatment.
• Hemoglobin A1c (HbA1c)
  • Used to diagnose and monitor diabetes
  • HbA1c levels reflect average blood glucose levels over the past three months
  • HbA1c levels are used to monitor disease progression or the effectiveness of diabetes treatments.
• Brain Natriuretic Peptide (BNP)
  • Used to monitor heart failure
  • BNP is released by the heart in response to increased pressure and volume.
  • BNP levels are used to assess the severity of heart failure and guide treatment decisions.

Safety (Critical) Biomarkers

• Indicate the likelihood, presence, or extent of toxicity as an adverse effect of exposure to a medical product or environmental agent.
• Liver function tests (LFTs):
  • Measure levels of enzymes and proteins produced by the liver
  • Used to monitor liver function and detect drug-induced liver injury (DILI).
• Creatinine clearance:
  • Measures kidney function
  • Used to monitor the potential nephrotoxicity of medications like antibiotics or chemotherapy drugs.

Applications of Biomarkers:

• Cancer
• Heart Failure (e.g., NT-ProBNP)
• Neurological diseases (e.g., Cystatin-C for motor neuron disease diagnosis or progression)
• Lung diseases
• Kidney diseases
• Liver diseases
• Gastrointestinal diseases
• Skeletal muscle and bone diseases

Advantages of Biomarkers:

• Precision of measurement
• Economical
• Less bias than questionnaires
• Rapid warning signal
• Reliable; validity can be established
• Homogeneity of risk or disease
• Disease mechanisms often studied
• Objective assessment

Disadvantages of Biomarkers:

• Timing is critical
• Expensive (cost for analyses)
• Storage (longevity of sample)
• Normal range difficult to establish
• Ethical responsibility
• Laboratory Errors

Challenges in Developing Biomarkers

• Scientific basis for some biomarkers cannot always be verified, creating difficulties in the qualification and validation
• Important to avoid incorrectly interpreting biomarker measurements and connecting a biomarker to a disease.
• The cost of developing a biomarker may increase due to longer clinical trials or more testing requirements
• It often takes a lot of time and resources to develop and qualify biomarkers. Favorable benefit-risk analysis is typically needed for drug regulatory approval.

Analytical and Clinical Aspects of Biomarkers

• Concept of use of biomarkers
  • Nature and properties of markers e.g. insulin (protein), hCG (glycoprotein)
  • Origin – System of synthesis, metabolism, excretion etc.
• Detection, analysis, and interpretation of biomarkers
  • Usage(Diagnosis, monitoring treatment, prognosis therapeutic)
  • Analysis : Preanalytics/Analytics/Postanalytics e.g. sample/patient preparation
    • Timing of blood taking e.g. TDM drugs
    • Circadian/diurnal variation e.g cortisol,
    • Analyte interferences e.g. Bilirubin/hemoglobin on creatinine
  • Interpretation on biomarkers result(Artefact or actual change? Extent of change?)

Clinicopathological Correlations (Diagnosis) of Biomarkers

• Relates observable signs and symptoms to laboratory examination results, forming a clinicopathologic study
• Clinical picture/diagnosis of diseases with biomarkers correlations are marker specific:
  • Markers specific for the diseases ?
  • Staging or prognosis of the disease?
  • One or more pathology? etc.

Tasks with Biomarkers in Chemical Pathology: Technical Aspects

• Awareness of elements affecting lab result outcomes
• Preanalytical phase: sample and patient preparation
• Analytical phase: methods & interference
• Postanalytical phase
  • Lab result data interpretation:
    • QC analysis with Westgard rules, reference range check, cumulative result (delta check), clinical & drug history, clinicopathological correlation
    • Awareness of disease/disorder & its impact
  • Lab result reporting:
    • Urgency of report at critical change (phone call)
    • Attention/follow-up
    • Format (numeric/text)

Tasks with Biomarkers in Chemical Pathology: Clinical Aspects

• Tasks done by Pathologist/Scientific Officer/Senior Medical Technologist
• To confirm disease/disorder, order tests, validate results-
• Steps of test results/case data interpretation include
  • Most striking abnormalities with the biomarkers
  • Differential diagnosis (underlying cause)
  • Pathophysiology
  • Further & most important confirmatory tests

Hyperkalaemia (high blood K+) Clinical Example

• A 72-year-old male is found collapsed at home, incontinent of urine and feces, and confused upon arrival at the ED
• Clinical history of congestive cardiac failure (CCF), hypertension, type 2 diabetes (diet-controlled), and osteoarthritis.
• Taking enalapril for hypertension, spironolactone & metoprolol for CCF, and celebrex for osteoarthritis.

Hyperkalaemia: Most Striking Abnormalities

• Confusion.
• Initial observations: BP 78/60, Pulse 74, Respiratory rate 32, SPO2 91%.
• Arterial Blood Gas (ABG): Potassium of 9.0 (RR:3.6-6.2), pH of 7.23 (RR:7.5-7.45), and Blood Glucose Level of 32mmol/l (random: <7).
• Overall impression: hyperventilated, metabolic acidosis, hyperkalemia, and hyperglycemia.

Hyperkalaemia: Differential Diagnosis (Underlying Cause)

• Classic Causes of Hyperkalaemia
  • Excessive exogenous potassium load (increased intake)
    • Potassium supplements (IV or Oral)
    • Excess in diet
    • Salt substitutes (e.g. potassium salts of penicillin)
  • Excessive endogenous potassium load (increased production)
    • Hemolysis
    • Rhabdomyolysis
    • Extensive burns
    • Tumor Lysis Syndrome
    • Intense physical activity
    • Trauma (especially crush injuries and ischaemia)
  • Redistribution (shift from intracellular to extracellular fluid)
    • Acidosis (metabolic or respiratory)
    • Insulin deficiency
    • Drugs (Succinylcholine, Beta-blockers, Digoxin (acute intoxication or overdose))
    • Hyperkalaemic familial periodic paralysis
  • Diminished potassium excretion (decreased excretion)
    • Decreased glomerular filtration rate (eg, acute or end-stage chronic renal failure)
    • Decreased mineralocorticoid activity
    • Defect in tubular secretion (eg, renal tubular acidosis IV)
    • Drugs (eg, NSAIDs, cyclosporine, potassium-sparing diuretics, ACE Inhibitors)
  • Pseudohyperkalemia (Factitious):
    • Hemolysis (in laboratory tube) most common
    • Thrombocytosis
    • Leukocytosis
    • Venepuncture technique (e.g. prolonged tourniquet application)

Hyperkalaemia: Pathophysiology

• Interplay/association/cause-effect of:
  • Insulin deficiency/resistance
  • Electrolyte imbalance
  • Acid-base imbalance
  • Cardio-respiratory disturbance
• Diabetic ketoacidosis with coma (DKA) is associated with hyperglycemia and ketoacidosis.
• Hyperosmolar hyperglycemia coma (HHC) mainly has severe hyperglycemia and hyperosmolarity.
• Renal failure in diabetic patient (two pathologies)

Hyperkalaemia: Further Confirmatory Tests

• Plasma & urine osmolality.
• Anion gap.
• Osmolal gap.
• Ketone analysis in blood/urine etc.

Factors Affecting Test Result Interpretation

• Biochemical results are quantitative (concentration or activity).
• Results vary due to analytical or biological variation.
• Normality is assessed considering variations in health and disease .

Result Interpretation Variables

• Previous results
• Medical details
• Biological variation (age, gender, ethnicity, time, pregnancy) Diet. Drugs
• Exercise/stress

Reference Interval

• Clinical laboratory tests are interpreted by comparing results with a reference interval.
• Reference ranges are calculated from a healthy population.
• Mean ±2SD includes 95% of values, minimizing overlap.
• 5% (1 in 20) healthy results don't fall in the reference interval

Additional Factors Affecting Reference Intervals:

• The further the result is from the reference interval-more likely to indicate pathology.
• Reference ranges may need to be age or sex related.
• What is "normal" testing parameters may be different between different populations
• Each laboratory establishes and uses their own reference intervals to accommodate geographical biological and analytical variations .
• Target values (e.g. cholesterol) have replaced the reference interval for some tests.

Methods Normal Distribution Assessment:

• Skewness
• Kutosis

Transforming Data to Normal Distribution :

• Good inclusion & exclusion criteria
• Exclude outlier e.g. data truncation
• Use algorithm etc.

Criteria for Normal Distribution

• Evaluate the modality, skewness, and kurtosis of your data

IFCC Definition of Terms:

• Reference Individual: Selected according to defined criteria for comparison.
• Reference Population: Consists of measurable cellular or molecular entities like DNA, RNA, proteins, or metabolites from tissue or liquid biopsies (blood, urine, and saliva).
• Reference Sample Group: An adequate number of reference individuals randomly drawn from the reference population.
• Reference Distribution: Reference values statistical distribution
• Reference Limit: Derived from reference distribution for descriptive purposes
• Reference Interval: Interval including two reference limits.

Terms to Measure Test Accuracy:

• Sensitivity: refers to the ability of a test to correctly identify individuals who have a particular disease or condition
• Specificity: refers to the ability of a test to correctly identify individuals who do not have the disease or condition
• Positive Predictive Value (PPV): refers to the likelihood that a person with a positive test result actually has the disease or condition
• Negative Predictive Value (NPV): refers to the likelihood that a person with a negative test result actually does not have the disease or condition
• Need to have QAP (IQC/QEA) already to monitor/ensure test accuracy Sensitivity = TP/TP + FN Specificity = TN/FP + TN

Positive and Negative Predictive Value (PPV and NPV)

• PPV depends on prevalence. It measures the probability that a positive test result correlates to a correct one.
• NPV also depends on prevalence. It measures the probability that a negative test result correlates to a correct one.
• Sensitivity and specificity indicate the concordance of a test , while PPV and NPV indicate how effectively the test measures the intended target.

ROC Curve

• ROC curve: graph showing the performance of a classification model at all classification cutoffs (Receiver operating characteristic curve)

Points ROC to note:

• Different cut-offs impact sensitivity and specificity
• Increase sensitivity, then specificity decreases
• A lower cut-off is helpful for screening , While a higher cut-off is helpful for confirmatory test.
• The AUC (Area Under Curve) that has the highest area, is the ideal ROC curve
• Used to ensure test accurancy
• Can be used to see compare between the two kits

Test Accuracy

• Diagnostic cut off set too high- results will have higher specificity, which results in low False positives
• Diagnostic cut off at the low end- Results will have higher sensitivity, which results in higher number of False positives

Summary Considerations for Biomarkers:

• Factors affect the result interpretation
• BeAware of Healthy & Disease population overlapping;This will have an affect on how the sampling data is built up and how to reference interval.
• Remember your tests: TP,FP,TN,FN, sensitivity, specificity, PPV, NPV
• Utility of tests e.g. screening, confirmatory
• Choose cut-off in consideration of disease prevalence, diagnostic requirement and treatment outcome
• Evidence: Base studies for example multi-centers analysis on test ability and results

Analytical Aspects: Supplementary information regarding analysis

• Basic lab math and stats Describes simple stats
• Sample statistics, such as bias , how the samples vary, Levey Jennings/ Internal QC data performance
• What the tests show -normal vs borderline vs abnormal

Descriptive statistics

• Describes the properties of sample data variation

Inferential statistics

• Use properties of population, and drawing concclusions Common stats that are of importnce-
• • Students T test
• -- Wilcoxon/Mann-Whitney test
• --- ANOVA Important for Lab usage-
• Show relationship or correlation amongst the following ---
• ----Pearson/ Speakman
• ---- Deming
• ---- Passing-Bablok
• ---- Bland - Altman plot show

Final Reminders for your Chemical Pathology study

• Analyte (Disorder cases) versus Disease cases
• Main Themes in Biomakers regarding -Clinical relevance : consider what and how, including Why study the marker
  • Lab tests and steps involving diagnosis
  • Diagnostic/strategeic alorithm
  • Analyte measurement principle/ Methods
  • Cases based on test interpretation