US 12,228,574 B2
Markers for the early detection of colon cell proliferative disorders
Hayley Warsinske, Daly City, CA (US); Adam Drake, Burlingame, CA (US); Krishnan Kanna Palaniappan, San Francisco, CA (US); Brian D. O'Donovan, San Francisco, CA (US); and John Hawkins, San Francisco, CA (US)
Assigned to Freenome Holdings, Inc., San Francisco, CA (US)
Filed by Freenome Holdings, Inc., South San Francisco, CA (US)
Filed on Feb. 1, 2023, as Appl. No. 18/163,169.
Application 18/163,169 is a continuation of application No. PCT/US2021/063337, filed on Dec. 14, 2021.
Claims priority of provisional application 63/128,545, filed on Dec. 21, 2020.
Prior Publication US 2023/0176058 A1, Jun. 8, 2023
Int. Cl. G01N 33/48 (2006.01); G01N 33/50 (2006.01); G01N 33/574 (2006.01)
CPC G01N 33/57419 (2013.01) [G01N 2333/52 (2013.01); G01N 2333/82 (2013.01)] 20 Claims
 
1. A method of detecting a cancer in a subject using a computer specifically programmed to detect the cancer, wherein the cancer comprises a colorectal cancer, wherein the computer is programmed with instructions to perform at least:
(a) obtaining a protein profile of the subject comprising a measured amount of a protein from a pre-determined protein panel comprising at least six proteins selected from the group consisting of: epidermal growth factor (EGF), fibroblast growth factor 2 (FGF-2), FMS-like tyrosine kinase 3 ligand (FLT3L), Fractalkine, Interleukin-1 alpha (IL-1a), IL-2, IL-6, IL-8, GRO-alpha-MGSA (GROa), Macrophage inflammatory protein-3 alpha (MIP-3a), Complement component C2, Complement component C9, Factor D, Factor I, Mannose-binding lectin (MBL), matrix metalloproteinase-2 (MMP-2), Growth/Differentiation Factor-15 (GDF-15), Osteonectin, Periostin, Angiopoietin-like 4 (ANGPTL4), FGF-21, FGF-23, hepatocyte growth factor (HGF), Angiopoietin-2, Bone Morphogenetic Protein-9 (BMP-9), IL-1RII, hepatocyte growth factor receptor (HGFR), IL-6ra, Osteopontin (OPN), Tenascin-C, Thrombospondin-2, urokinase plasminogen activator surface receptor (uPAR), CD44, Kallikrein-6, Mesothelin, epithelial cellular adhesion molecule (EpCAM), Apo A1, alpha-1-acid glycoprotein (AGP), Alpha-2-macroglobulin (A2MB), Fetuin A, haptoglobin (HP), L-Selectin, Complement component Clq, Complement component C3, Complement component C3b, Factor B, Factor H, Properdin, Agouti-Related Protein (AGRP), MMP-12, Cytokeratin-19 fragment 21-1 (CYFRA21-1), Human epididymis protein 4 (HE4), total prostate-specific antigen (PSA), Macrophage migration inhibitory factor (MIF), alpha fetoprotein (AFP), cancer antigen 125 (CA125), CA19-9, CA15-3 (MUC-1), and carcinoembryonic antigen (CEA), in a biological sample obtained or derived from the subject;
(b) detecting an increased risk of the colorectal cancer in the subject, wherein the detecting comprises processing the protein profile using a trained machine learning model, wherein the trained machine learning model is trained with training data comprising: (i) a first set of biological samples obtained or derived from subjects with advanced adenoma, (ii) a second set of biological samples obtained or derived from control subjects without advanced adenoma, (iii) a third set of biological samples obtained or derived from subjects with benign polyp, and (iv) a fourth set of biological samples obtained or derived from control subjects without benign polyp, to provide an output value indicative of the increased risk of the colorectal cancer, wherein the trained machine learning model is trained to differentiate between a presence or an absence of advanced adenoma and a presence or an absence of benign polyp; and
responsive to detecting the increased risk of the colorectal cancer, treating the subject with a surgery, a chemotherapy, an immunotherapy, or a radiotherapy for the increased risk of the colorectal cancer.