Predictive  Prognostic Tests in Surgical Pathology: A Critical Assessment

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STATEMENT OF THE PROBLEM. -- Through the 1980's, surgical pathology was focused almost exclusively on diagnosis, and any prognostic" information provided by that discipline took the form of determining accurate staging and grading information-- In the 1990's, an increasing amount of attention

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Predictive Prognostic Tests in Surgical Pathology: A Critical Assessment

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1. Predictive & Prognostic Tests in Surgical Pathology: A Critical Assessment Mark R. Wick, M.D.

2. STATEMENT OF THE PROBLEM

3. To better understand the course of the neoplasm in question To attempt to predict the outcome for an individual patient To select appropriate treatment modalities To explain variation in treatment outcomes To address patient anxiety INTENDED USES OF TISSUE-BASED “PROGNOSTIC” TESTS IN CLINICAL PRACTICE

4. Each year, ~185,000 new cases are seen of invasive breast carcinoma in the U.S. 70% of invasive breast carcinomas are ductal carcinomas, not further specified (129,500 tumors/year) 60% of IDCs (77,000 tumors/year) are stage I lesions, 30% (23,100) of which will eventually metastasize and threaten the patient’s life THE PROTOTYPICAL MALIGNANT NEOPLASM SUBJECTED TO PROGNOSTICATION: INVASIVE DUCTAL BREAST CARCINOMA

5. Availability of test methods that are reproducible and applicable to routinely-processed tissue specimens Finances (private vs. insurance-based vs. public) Politics (lobby groups; influence of governmental agencies [FDA/U.S. Congress]) FACTORS AFFECTING THE DEVELOPMENT & IMPLEMENTATION OF TISSUE-BASED PROGNOSTIC TESTS FOR BREAST CANCER

6. Constituents of TNM stage (macroscopic & histologic) Other histologic parameters (grade, histotype, inflammatory host response, angiolymphatic invasion, etc.) Measures of cell replication (mitotic indices; flow cytometric/immunohistologic indicators of S-phase fraction (e.g., Ki-67; anti-PCNA) Quantitative DNA-based (flow cytometric; cytometric) Stromal vascularity-related (intratumoral blood vessel density; expression of angiogenesis factors by tumor cells) Genes/gene products affecting cell growth & differentiation (measured by quantitative nucleic acid blotting techniques, in-situ hybridization methods, quantitative PCR, and semi-quantitative or quantitative immunohistologic procedures) GENERAL CATEGORIES OF TISSUE-BASED “PROGNOSTIC” MARKERS

10. Ki67 and PCNA Proliferating cell nuclear antigen is a 36kD non-histone nuclear peptide that modulates the function of DNA polymerase delta. It is most abundant during S phase, but may also be detected in G1 and G2/M Ki-67 is a mouse monoclonal antibody that specifically binds a non-histone nuclear matrix peptide distinct from PCNA that is present in greatest amounts during G2/M

12. Cell cycle analysis and/or staining for PCNA and Ki67 has been said to provide independent prognostic information in multivariate analyses of invasive breast carcinomas in histologic Elston grade III ductal carcinomas (the largest subgroup)

14. Hormonal receptor proteins are most often seen in breast carcinomas in postmenopausal patients The role of these proteins is to mediate the trophic effects of cell growth of estrogen & progesterone The impact of ERP/PRP status on prognosis is much less important in stage I tumors than in stage II-IV lesions; hence, in low stage disease, this information is predictive (of clinical response to hormone antagonists) rather than prognostic

15. nm23 protein Mutant p53 protein c-myc protein Ha/Ki/N-ras proteins PS2 protein c-erbB-2/HER-2/neu protein EGFR protein “Heat shock” proteins int-2/hst/bcl-1 proteins Mutant RB-1 protein PUTATIVE “GENE-BASED” PROGNOSTIC TISSUE MARKERS FOR INVASIVE DUCTAL CARCINOMA OF THE BREAST

17. p53 Protein p53 gene is present on the short arm of chromosome 17 Codes for a 393 amino acid protein that effects regulation of the cell cyle by arresting it in the G1 phase, to allow for DNA repair, or alternatively, apoptosis and “programmed cell death” of irreparably-damaged cells Therefore, “wild-type” (normal) p53 protein serves an anti-transformational (“tumor suppressor”) role Mutated p53 protein binds to wild-type p53 and inactivates it, “allowing” for malignant transformation to occur Mutant p53 protein can be detected immunohistochemically

20. HER-2 gene is located on long arm of chromosome 17 Belongs to the group of genes that code for tyrosine kinases This growth factor receptor has an important role in regulation of cell metabolism and growth Amplification of the HER-2 gene (i.e., multiple gene copies in the same cell) has been observed in approximately 30% of invasive breast carcinomas, by FISH analysis or Southern blots Immunohistological corollary of this finding is strong cell membrane-based immunoreactivity for HER-2 protein

21. CURRENT AREAS OF DIFFICULTY IN THE CLINICAL APPLICATION OF PUTATIVE PROGNOSTIC MARKERS FOR MALIGNANCIES 1. Vague definition of data types 2. Methodologic reproducibility and cross-validation 3. Elimination of clinical bias related to case selection, heterogeneity of tumor characteristics, & treatments 4. Relative lack of stringent biostatistical analysis of results 5. Natural human tendency to embrace “new” tests in difficult clinical decision-making

23. DATA CATEGORIES 1. Categorical-- e.g., morphologic diagnostic subsets. Should be uniform in analyses of putative prognostic factors 2. Binary-- positive/negative. Ideal characteristic for prognostic marker, but one which is virtually unattainable biologically. 3. Semiquantitative/Quantitative-- self-explanatory. This is the data category that is most dependent on methodologic accuracy & reproducibility, and that which fits best with most proven prognosticators

24. How many is enough??-- a common question in the structuring of clinical studies designed to assess independent significance of a given variable There is no universal answer to this query, BUT one can be certain that improper application of statistical methods (e.g., Paired t-tests instead of Wilcoxon’s signed rank test for continuous dependent variables with non-parametric distributions) can produce erroneous but seemingly valid results Study sizes that seem “large” to any given individual observer may in fact be woefully inadequate STATISTICAL ANALYSIS: Methods & Clinical Sample Sizes

25. DANGERS OF BINARY DATA SETS Results are often employed in a situationally contingent fashion; i.e., positive results incite a therapeutic intervention (or lack thereof), whereas negative results do not Clinical impact of false negatives and false positives is amplified de facto in this setting Need for duplicate testing and proof of interobserver agreement (reproducibility; kappa statistics) is therefore greater than that applying to other data categories

26. BIOSTATISTICAL ANALYSIS: PRECEPTS & RECOMMENDATIONS Binary data can be satisfactorily evaluated by Bayesian parameters of specificity & sensitivity, providing that corrections are made for disease prevalence in the general population Semiquantitative or Quantitative data require more sophisticated biostatistical paradigms, such as receiver-operating characteristic (ROC) curves; likelihood ratios (LRs); and various tests of multivariate statistical independence (e.g., Wilcoxon analysis; ANOVA analysis; Kruskal-Wallis testing)

27. Interpretative and decision-making application of binary (categorical) data can be facilitated by construction of partially-redundant algorithms based on constellations of test results. This paradigm may have analogies in the realm of “prognostic” testing. HOWEVER, it cannot compensate for poor methodology and resulting spurious findings. COMPENSATION FOR BIOLOGICAL VARIATION IN BINARY DATA SETS: Example: Algorithms in Immunohistology

29. ANALYSIS OF NON-BINARY NON-CATEGORIAL DATA: Likelihood Ratios Likelihood ratios express the odds that a predetermined level of a diagnostic or prognostic test result would be expected in a patient with the target condition under evaluation The LR of a prognostic category is the ratio of the proportion of patients with disease who have the particular prognosis in question, as compared with a demographically-matched control population

30. RECEIVER-OPERATING CHARACTERISTIC (ROC) CURVE ANALYSES Allows for assessment of overall prognostic accuracy of a semiquantitative or quantitative scheme ROC curves are graphs of the paris of true-positive (sensitivity) and false-positive (100%-specificity) rates for a given observer as the criteria for a given prognostic category are varied ROC plot for an observer who is making random assessments is a line with slope of 45O Area under the curve of the ROC plot is a measure of overall prognostic accuracy

32. METHODOLOGICAL REPRODUCIBILITY & CROSS-VALIDATION 1. REPRODUCIBILITY: The use of “biologically-proven” analyte-positive cases as run-related controls (e.g., breast carcinoma tissue from a patient who objectively responded to tamoxifen treatment, in ERP assays) 2. CROSS VALIDATION: Interanalytical agreement of results. Examples: Immunohistologic ERP vs. DCC results Mutant p53 oncoprotein: Immunohistologic “positivity” vs. Western blot results c-erbB2 gene amplification: Immunohistologic cell membrane staining pattern vs. Southern blot results vs. Quantitative PCR results vs. FISH data

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