A Novel “Reactomics” Approach for Cancer Diagnostics

Non-invasive detection and monitoring of lethal diseases, such as cancer, are considered as effective factors in treatment and survival. We describe a new disease diagnostic approach, denoted “reactomics”, based upon reactions between blood sera and an array of vesicles comprising different lipids and polydiacetylene (PDA), a chromatic polymer. We show that reactions between sera and such a lipid/PDA vesicle array produce chromatic patterns which depend both upon the sera composition as well as the specific lipid constituents within the vesicles. The chromatic patterns were processed through machine-learning algorithms, and the bioinformatics analysis could distinguish both between cancer-bearing and healthy patients, respectively, as well between two types of cancers. Size-separation and enzymatic digestion experiments indicate that lipoproteins are the primary components in sera which react with the chromatic biomimetic vesicles. This colorimetric reactomics concept is highly generic, robust, and does not require a priori knowledge upon specific disease markers in sera. Therefore, it could be employed as complementary or alternative approach for disease diagnostics.

[1]  T. Tomonaga,et al.  The isolation and identification of apolipoprotein C-I in hormone-refractory prostate cancer using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. , 2009, Asian journal of andrology.

[2]  H. Ringsdorf,et al.  Molecular Architecture and Function of Polymeric Oriented Systems: Models for the Study of Organization, Surface Recognition, and Dynamics of Biomembranes , 1988 .

[3]  E. Diamandis,et al.  Cancer biomarkers: can we turn recent failures into success? , 2010, Journal of the National Cancer Institute.

[4]  R. Jelinek,et al.  Peptide-membrane interactions studied by a new phospholipid/polydiacetylene colorimetric vesicle assay. , 2000, Biochemistry.

[5]  E. Wachtel,et al.  Biomimetic lipid/polymer colorimetric membranes: molecular and cooperative properties. , 2003, Journal of lipid research.

[6]  Daniel W Chan,et al.  Cancer Proteomics: Serum Diagnostics for Tumor Marker Discovery , 2004, Annals of the New York Academy of Sciences.

[7]  D. Häussinger,et al.  New multi protein patterns differentiate liver fibrosis stages and hepatocellular carcinoma in chronic hepatitis C serum samples. , 2006, World journal of gastroenterology.

[8]  Michelle A. Anderson,et al.  Characterization of apolipoprotein and apolipoprotein precursors in pancreatic cancer serum samples via two-dimensional liquid chromatography and mass spectrometry. , 2007, Journal of chromatography. A.

[9]  D. Harats,et al.  Serum Apolipoproteins C-I and C-III Are Reduced in Stomach Cancer Patients: Results from MALDI-Based Peptidome and Immuno-Based Clinical Assays , 2011, PloS one.

[10]  David H Perlman,et al.  Evaluation of an on-target sample preparation system for matrix-assisted laser desorption/ionization time-of-flight mass spectrometry in conjunction with normal-flow peptide high-performance liquid chromatography for peptide mass fingerprint analyses. , 2007, Rapid communications in mass spectrometry : RCM.

[11]  Jos H Beijnen,et al.  Identification of serum proteins discriminating colorectal cancer patients and healthy controls using surface-enhanced laser desorption ionisation-time of flight mass spectrometry. , 2006, World journal of gastroenterology.

[12]  A. Porgador,et al.  Lipoprotein interactions with chromatic membranes as a novel marker for oxidative stress-related diseases. , 2009, Biochimica et biophysica acta.

[13]  David F Ransohoff,et al.  Promises and limitations of biomarkers. , 2009, Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer.

[14]  Robert B. Gennis,et al.  Biomembranes: Molecular Structure and Function , 1988 .

[15]  Ethem Alpaydin,et al.  Introduction to machine learning , 2004, Adaptive computation and machine learning.

[16]  A. Baranova,et al.  Tumor markers: the potential of "omics" approach. , 2010, Current molecular medicine.

[17]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.