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WO 2012/033537 Al © (12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization International Bureau (10) International Publication Number (43) International Publication Date WO 2012/033537 Al 15 March 2012 (15.03.2012) PCT (51) International Patent Classification: (74) Agents: CLARK, David, L. et al; Wilson Sonsini C40B 30/06 (2006.01) Goodrich & Rosati, 650 Page Mill Road, Palo Alto, CA 94304-1050 (US). (21) International Application Number: PCT/US201 1/001565 (81) Designated States (unless otherwise indicated, for every kind of national protection available): AE, AG, AL, AM, (22) International Filing Date: AO, AT, AU, AZ, BA, BB, BG, BH, BR, BW, BY, BZ, 8 September 201 1 (08.09.201 1) CA, CH, CL, CN, CO, CR, CU, CZ, DE, DK, DM, DO, (25) Filing Language: English DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, IS, JP, KE, KG, KM, KN, KP, (26) Publication Language: English KR, KZ, LA, LC, LK, LR, LS, LT, LU, LY, MA, MD, (30) Priority Data: ME, MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, 61/381,067 8 September 2010 (08.09.2010) US NO, NZ, OM, PE, PG, PH, PL, PT, QA, RO, RS, RU, 61/440,523 8 February 201 1 (08.02.201 1) US RW, SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, 61/469,8 12 31 March 201 1 (3 1.03.201 1) us TM, TN, TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (71) Applicant (for all designated States except US): NODALITY, INC. [US/US]; 201 Gateway Blvd., South (84) Designated States (unless otherwise indicated, for every San Francisco, CA 94080 (US). kind of regional protection available): ARIPO (BW, GH, GM, KE, LR, LS, MW, MZ, NA, SD, SL, SZ, TZ, UG, (72) Inventors; and ZM, ZW), Eurasian (AM, AZ, BY, KG, KZ, MD, RU, TJ, (75) Inventors/ Applicants (for US only): LONGO, Diane TM), European (AL, AT, BE, BG, CH, CY, CZ, DE, DK, [US/US]; 1000 Foster City Blvd., Apt. 7404, Foster City, EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, CA 94404 (US). COVEY, Todd [US/US]; 3737 Brittan LV, MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, Avenue, San Carlos, CA 94070 (US). SOPER, David SM, TR), OAPI (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, [US/US]; 229 Mississippi St., San Francisco, CA 94107 GW, ML, MR, NE, SN, TD, TG). (US). NOLAN, Garry, P. [US/US]; 248 Upper Terrace, Published: San Francisco, CA 941 17 (US). CESANO, Alessandra [US/US]; 100 Baltic Circle #120, Redwood City, CA — with international search report (Art. 21(3)) 94065 (US). [Continued on next page] (54) Title: BENCHMARKS FOR NORMAL CELL IDENTIFICATION FIG. 2 © (57) Abstract: Provided herein are methods, compositions, and kits for determining cell signaling profiles in normal cells and o comparing the cell signaling profiles of normal cells to cell signaling profiles from a test sample. before the expiration of the time limit for amending the claims and to be republished in the event of receipt of amendments (Rule 48.2(h)) BENCHMARKS FOR NORMAL CELL IDENTIFICATION CROSS-REFERENCE [0001] This application claims the benefit of U.S. Patent Application Nos. 61/381,067 filed September 8, 2010; 61/440,523 filed February 8, 201 1; 61/469,812 filed March 31, 201 1; and 61/499,127 filed June 20, 201 1, which are incorporated by reference in their entireties. BACKGROUND OF THE INVENTION [0002] Personalized medicine seeks to provide prognoses, diagnoses and other actionable medical information for an individual based on their profile of one or more biomarkers. Many of these diagnostics use classifiers which are binary statistical models trained to identify biomarkers which differentiate diseased cells from non-diseased cells (i.e., normal cells). While these classifiers are beneficial, a major drawback of these methods is that they only aim to determine similarity between two states: disease and normal. Often, disease states are heterogeneous, which complicates the identification of biomarkers to distinguish disease states and the development of these classifiers. For example, a classifier may classify an individual as having a normal profile as compared to one or more disease states even though the individual biomarker profile is different from the biomarker profile observed in normal cells. This is referred to as a 'false negative' identification. In order to fully eliminate false negative identifications, the classifier can model data representing all possible disease states. Since the heterogeneity of disease makes it difficult to obtain and characterize samples of all disease states, false negatives are inevitable. [0003] Due to these limitations, in some instances it may be ideal to identify biomarkers to allow for the determination of similarity between cells from an individual and normal cells. Such a similarity comparison can benefit from the development of a statistical model that can characterize and distinguish normal cell data. SUMMARY OF THE INVENTION [0004] In general, in one aspect, a method is provided comprising: a) identifying an activation level of one or more activatable elements in a first cell-type from a test sample; b) identifying an activation level of the one or more activatable elements in a second cell-type from a test sample; and c) determining a similarity value based on steps a) and step b) and a statistical model, wherein the statistical model specifies a range of activation levels of one or more activatable elements in the first cell-type and the second cell-type in a plurality of normal samples, wherein the statistical model further specifies the variance of the activation levels of the one or more activatable elements associated with cells in the plurality of normal samples. In one embodiment, identifying the activation level of the one or more activatable comprises: d) identifying the activation level of the one or more activatable elements in single cells derived from the test sample; e) identifying one or more cell-type markers in single cells derived from the test sample; and f) gating discrete populations of single cells based on the one or more cell-type markers associated with the single cells. In another embodiment, the method further comprises generating the statistical model, wherein generating the statistical model comprises: d) identifying the activation level of the one or more activatable elements in single cells derived from the plurality of normal samples; e) identifying one or more cell-type markers in single cells derived from the plurality of normal samples; f gating cells in the plurality of normal samples based on the one or more cell-type markers associated with the single cells; and g) generating the statistical model that specifies the range of activation levels associated with cells in the normal samples. [0005] In another embodiment, the statistical model further specifies the variance of activation levels of the one or more activatable elements associated cells in the plurality of normal samples. In another embodiment, the one or more activatable elements are selected from the group consisting of: pStatl, pStat3, pStat4, pStat5, pStat6 and p-p38. In another embodiment, the method further comprises contacting the test sample and the plurality of normal samples with one or more modulators. In another embodiment, the one or more modulators is selected from the group consisting of: G-CSM, EPO, GM- CSF, H-27, IFNa and IL-6. [0006] In another embodiment, the test sample and the plurality of normal samples are derived from individuals with the same race, ethnicity, gender, or are in the same age-range. In another embodiment, the method further comprises normalizing the activation level of the one or more activatable elements in the first cell-type and the second cell-type based on a sample characteristic. In another embodiment, the sample characteristic comprises race, ethnicity, gender or age. In another embodiment, the identifying the activation level of the one or more activatable elements comprises flow cytometry. In another embodiment, the one or more activatable elements comprise one or more activatable elements from the plurality of normal samples that display variance of less than 50% of the activation level of the one or more activatable element in response to a modulator. In another embodiment, the similarity value is determined with a correlation metric or a fitting metric. [0007] In another embodiment, the method further comprises displaying the activation level of the one or more activatable elements from the test sample and the plurality of normal samples in a report. In another embodiment, the displaying comprises a scatterplot, a line graph with error bars, a histogram, a bar and whisker plot, a circle plot, a radar plot, a heat map, and/or a bar graph. [0008] In another embodiment, the method further comprises making a clinical decision based on the similarity value. In another embodiment, the clinical decision comprises a diagnosis, prognosis, or monitoring a subject from whom the test sample was derived. [0009] In another embodiment, the one or more activatable elements comprises one or more proteins. In another embodiment, the identifying the activation level of the one or more activatable elements comprises contacting the one or more activatable elements with one or more binding elements. In another embodiment, the one or more binding elements comprises one or more phospho-specific antibodies. In another embodiment, the determining comprises use of a computer. [0010] In another embodiment, the method further comprises administering a therapeutic agent to a subject from whom the test sample is derived based on the similarity value. In another embodiment, the method further comprises predicting a status of a second activatable element in a single cell from the test sample, wherein the second activatable element is different from the one or more activatable elements.
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