Why Proteomics Has Not Succeeded In The Past Cancer As An Example

The inability of these protein biomarkers to detect all cancers (false negatives) reflects both the progressive nature of cancer and its heterogeneity. Cancer is not a single disease but rather an accumulation of several events, genetic and epigenetic, arising in a single cell over a long period of time. Proteins overexpressed in late stage cancers may not be overexpressed in earlier stages and, therefore, are not useful for early cancer detection. For example, the CA125 antigen is not highly expressed in many Stage I ovarian cancers. Also, because tumors are heterogeneous, the same sets of proteins are not necessarily overexpressed in each individual tumor. For example, while most patients with high-grade prostate cancers have increased levels of PSA, approximately 15% of these patients do not have an elevated PSA level. The reciprocal problem of biomarkers indicating the presence of cancer when none is present (false positives) results because these proteins are not uniquely produced by tumors. For example, PSA is produced by prostatitis (inflammation of the prostate) and benign prostatic hyperplasia (BPH), and elevated CA125 levels are caused by endometriosis and pelvic inflammation.

The performance of any biomarker can be described in terms of its specificity and sensitivity. In the context of cancer biomarkers, sensitivity refers to the proportion of case subjects (individuals with confirmed disease) who test positive for the biom-arker, and specificity refers to the proportion of control subjects (individuals without disease) who test negative for the biomarker. An ideal biomarker test would have 100% sensitivity and specificity; i.e., everyone with cancer would have a positive test, and everyone without cancer would have a negative test. None of the currently available protein biomarkers achieve 100% sensitivity and specificity. For example, as described above, PSA tests achieve 70 to 90% sensitivity and only about 25% specificity, which results in many men having biopsies when they do not have detectable prostrate cancer. The serum protein biomarker for breast cancer CA15.3 has only 23% sensitivity and 69% specificity. Other frequently used terms are positive predictive value (PPV), the chance that a person with a positive test has cancer, and negative predictive value (NPV), the chance that a person with a negative test does not have cancer. PPV is affected by the prevalence of disease in the screened population. For a given sensitivity and specificity, the higher the prevalence, the higher the PPV. Even when a biomarker provides high specificity and sensitivity, it may not be useful for screening the general population if the cancer has low prevalence. For example, a biomarker with 100% sensitivity and 95% specificity has a PPV of only 17% for a cancer with 1% prevalence (only 17 out of 100 people with a positive test for the biomarker actually have cancer) and 2% for a cancer with 0.1% prevalence. The prevalence of ovarian cancer in the general population is about 0.04%. Thus, a biomarker used to screen the general population must have significantly higher specificity and sensitivity than a biomarker used to monitor an at-risk population.

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