Aristotle: A single blood test for pan-cancer screening.

Abstract

e15037 Background: Blood participates in physiological and pathological changes throughout the body which are reflected as changes in cellular gene expression. The involvement of the immune system in cancer suppression and promotion through immunoediting exemplifies this. These characteristics make blood a promising non-invasive surrogate tissue for cancer diagnosis and monitoring. Methods: A multi-class cancer classification model, Aristotle, was developed with logistic regression using Affymetrix gene expression profiles generated from 2,845 unique human whole peripheral blood samples. The model was evaluated through 100 iterations of 2-fold cross-validation. Results: The 1,013 cancer patient samples (age 61±13, 40.6% ♀), include cancer of the bladder (n = 87, age 65±11, 23.3% ♀), breast (n = 94, age 54±10, 98.4% ♀), colon (n = 205, age 64±13, 39.5% ♀)/colon polyps (n = 226, age 61±11, 36.0% ♀), cervix (n = 36, age 55±13), endometrium (n = 25, age 49±12), liver (n = 16, age 57±11, 12.5% ♀), nasopharynx (n = 78, age 51±13, 26.9% ♀), ovaries (n = 64, age 55±12), prostate (n = 160, age 68±9) or stomach (n = 22, age 59±11, 28.6% ♀); and 1,832 control samples (age 53±16, 38.9% ♀), including healthy subjects (n = 1,042, age 55±16, 37.0 ♀) and subjects with ankylosing spondylitis (n = 59, age 39±12, 18.6% ♀), heart failure (n = 193, age 67±12, 40.0% ♀), inflammatory bowel disease (n = 51, age 37±9, 40.0% ♀), osteoarthritis (n = 248, age 53±15, 45.6% ♀), rheumatoid arthritis (n = 40, age 57±17, 84.6% ♀), schizophrenia (n = 110, age 36±13, 43.6% ♀) or other (n = 89, age 50±14, 41.1% ♀). Aristotle classified cancer profiles with sensitivities from 55.6-100% and positive predictive values from 5.6-77.7% at a specificity of 99.0% (Table). The mean false positive rate for the 11 cancer classes ranged from 0.3- 6.8%. Conclusions: Our study demonstrates the clinical viability of using whole peripheral blood for multi-class cancer classification. Aristotle was capable of classifying patients with different types of cancer with clinically relevant accuracy. We are confident that Aristotle holds great promise to help address the need for practical and non-invasive early-stage cancer diagnostics. [Table: see text]