Diagnostic marker

Abstract

The present disclosure relates to methods of diagnosing and treating breast cancer in a subject comprising determining the level of one or more lipid biomarkers in a biological sample obtained from the subject. Systems and kits for use in these methods are also provided.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to Australian Provisional Patent Application No. 2022901245 filed on May 10, 2022 and Australian Provisional Patent Application No. 2022903097 filed on October 20, 2022, the contents of which are incorporated herein by reference in their entirety. Technical Field

[0003] The present disclosure relates to methods of diagnosing and treating breast cancer. Background Art

[0004] In 2020, 2.3 million women were diagnosed with breast cancer and 685,000 died worldwide. At the end of 2020, 7.8 million women diagnosed with breast cancer in the past 5 years were alive, making it the most common cancer in the world (World Health Organisation). Although breast cancer is primarily a female disease, 1 in 1,100 men may also develop the disease (Society, 2016).

[0005] The key to surviving breast cancer is early detection and treatment. The current gold standard for detection is through mammograms, however, it is known to be less effective in younger populations. Therefore, there is still a need for more accurate breast cancer screening tests for women of all ages, such as detecting cancer at the cellular level and before it metastasizes (Mistry and French, 2016). Summary of the invention

[0006] The present disclosure is based on the unexpected discovery of a variety of lipid biomarkers that can be easily detected to determine breast cancer in women. By extension, these lipid biomarkers (or lipidomic markers) have been demonstrated to be promising in identifying patients who need treatment for breast cancer.

[0007] In a first aspect, the present disclosure provides a method for diagnosing breast cancer in a subject, the method comprising the step of measuring the level of one or more lipid biomarkers in a biological sample from the subject, wherein the one or more lipid biomarkers are selected from the group consisting of: Cer (d36: 1), Cer (d42: 0), Cer (d42: 1), DG (34: 2), Hex2Cer (d34: 1), Hex2Cer (d42: 2), LPC (14: 0), LPC (16:0e), LPC(17:1), LPC(18:3), LPE(22:6), LPI(20:4), PC(32:1), PC(34:1), PC(35:4), PC(36:2), PC(36:3 ), PC(37:4), PC(38:5), PE(34:1), PE(34:2p), PE(36:4), PE(38:3p), PE(38:4), PE(O-38:5), PE(O-40:6), PI (32:1), PI(34:1), PI(36:1), PI(38:6), PS(38:4), PS(40:6), PS(40:7), SM(d32:2), SM(d33:1), SM(d35:1) , SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM(d42:1), SM(d42:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof; and wherein the level of one or more lipid biomarkers is diagnostic or indicative of the subject having the breast cancer.

[0008] In some examples of the present method, Cer(d36:1), Cer(d42:0), DG(34:2), Hex2Cer(d34:1), Hex2Cer(d42:2), LPE(22:6), LPI(20:4), PC(32:1), PC(34:1), PC(35:4), PC(36:2), PC(36:3), PC(37:4), PC(38:5), PE(34:1), PI(32:1), PI(34:1), PS(38:4), PS(40:6), SM(d32:2), SM(d33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM(d42:1), SM(d42:4) and / or SM(d44:4), or fragments, variants or derivatives thereof; and / or Cer( 42:1), LPC(14:0), LPC(16:0e), LPC(17:1), LPC(18:3), PE(34:1), PE(34:2p), PE(36:4), PE(38:3p), P E(38:4), PE(O-38:5), PE(O-40:6), PI(32:1), PI(34:1), PI(36:1), PI(38:6), PS(38:4), PS(40:6), PS( Decreased levels of TG(54:4), TG(54:7), TG(55:1), TG(55:2), TG(55:3), TG(55:4), TG(55:5), TG(55:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and / or TG(62:2), or fragments, variants or derivatives thereof, are diagnostic or indicative of the subject suffering from the breast cancer.

[0009] Suitably, the method further comprises the step of administering to said subject a treatment for said breast cancer.

[0010] In a second aspect, the present disclosure provides a method for measuring the level of one or more lipid biomarkers in a biological sample from a subject, the method comprising the following steps:

[0011] (a) providing the biological sample; and

[0012] (b) measuring the level of the one or more lipid biomarkers in the biological sample, wherein the one or more lipid biomarkers are selected from the group consisting of: Cer (d36:1), Cer (d42:0), Cer (d42:1), DG (34:2), Hex2Cer (d34:1), Hex2Cer (d42:2), LPC (14:0), LPC (16:0e), LPC (17:1), LPC (18:3), LPE (22:6), LPI (20:4), PC (32:1), PC (34:1), PC (35:4), PC (36:2), PC (36:3), PC (37:4), PC (38:5), PE (34:1), PE (34:2p), PE (36:4), PE (38:3p), PE (38:4), PE (O-38:5), PE (O-40:6) ,PI(32:1),PI(34:1),PI(36:1),PI(38:6),PS(38:4),PS(40:6),PS(40:7),SM(d32:2),SM(d33 :1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM(d42: 1), SM(d42:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof.

[0013] Suitably, the subject is suspected of having breast cancer, or has been previously diagnosed with breast cancer.

[0014] In some examples, the measuring step comprises determining the presence or absence of the following in the biological sample of the subject: (i) Cer (d36:1), Cer (d42:0), DG (34:2), Hex2Cer (d34:1), Hex2Cer (d42:2), LPE (22:6), LPI (20:4), PC (32:1), PC (34:1), PC (35:4), PC (36:2), PC (36:3), PC (37:4) , PC(38:5), PE(34:1), PI(32:1), PI(34:1), PS(38:4), PS(40:6), SM(d32:2), SM(d33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM(d42:1), SM(d42:4) and / or SM(d44:4), or a fragment, variant or derivative thereof; and / or (ii) Cer(42:1), LPC(14:0), LPC(16:0e), LPC(17:1), LPC(18:3), PE(34:1), PE(34:2p), PE(36:4), PE(38:3p), PE(38:4), PE(O-38:5), PE(O-40:6), PI(32:1), PI(34:1), PI(36:1), PI(38:4), :6), PS(38:4), PS(40:6), PS(40:7), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and / or TG(62:2), or fragments, variants or derivatives thereof.

[0015] In a third aspect, the present disclosure provides a method for treating breast cancer in a subject, the method comprising the step of treating the subject, wherein the level of one or more lipid biomarkers has been measured in a biological sample from the subject, which is diagnostic or indicative of the subject having the breast cancer, wherein the one or more lipid biomarkers are selected from the group consisting of: Cer (d36:1), Cer (d42:0), Cer (d42:1), DG (34:2), Hex2 Cer(d34:1), Hex2Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(17:1), LPC(18:3), LPE(22:6), LPI(20:4), PC( 32:1), PC(34:1), PC(35:4), PC(36:2), PC(36:3), PC(37:4), PC(38:5), PE(34:1), PE(34:2p), PE(36:4), PE (38:3p), PE(38:4), PE(O-38:5), PE(O-40:6), PI(32:1), PI(34:1), PI(36:1), PI(38:6), PS(38:4), PS(40: 6), PS(40:7), SM(d32:2), SM(d33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2 ), SM(d41:3), SM(d42:1), SM(d42:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof.

[0016] Suitably, said treatment comprises administering to said subject a therapeutically effective amount of an anti-cancer therapy.

[0017] In some embodiments of the present method, Cer(d36:1), Cer(d42:0), DG(34:2), Hex2Cer(d34:1), Hex2Cer(d42:2), LPE(22:6), LPI(20:4), PC(32:1), PC(34:1), PC(35:4), PC(36:2), PC(36:3), PC(37:4), PC(38:5), PE(34:1), PI(32:1) , PI(34:1), PS(38:4), PS(40:6), SM(d32:2), SM(d33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM(d42:1), SM(d42:4) and / or SM(d44:4), or fragments, variants or derivatives thereof; and / or Ce r(42:1), LPC(14:0), LPC(16:0e), LPC(17:1), LPC(18:3), PE(34:1), PE(34:2p), PE(36:4), PE(38:3p) , PE(38:4), PE(O-38:5), PE(O-40:6), PI(32:1), PI(34:1), PI(36:1), PI(38:6), PS(38:4), PS(40:6), Reduced levels of PS(40:7), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and / or TG(62:2), or fragments, variants or derivatives thereof, are measured from the biological sample of the subject.

[0018] Referring to the methods of the above aspects, the one or more lipid biomarkers suitably include LPC(14:0) or a fragment, variant or derivative thereof, and one or more other lipid biomarkers selected from the group consisting of Cer(d36:1), Cer(d42:0), Cer(d42:1), DG(34:2), Hex2Cer(d34:1), Hex2Cer(d42:2), LPC(16:0e), LPC(17:1), LPC(18:3), LPE(22:6), LPI(20:4), PC(32:1), PC(34:1), PC(35:4), PC(36:2), PC(36:3), PC(37:4), PC(38:5), PE(34:1), PE(34:2p), PE(36:4), PE(38:3p), PE(38:4), PE(38:5), PE(38:6), PE(38:7), PE(38:8), PE(38:9), PE(38:10), PE(38:11), PE(38:12), PE(38:13), PE(38:14), PE(38:15), PE(38:17), PE(38:18), PE(38:19). 40:6), PI(32:1), PI(34:1), PI(36:1), PI(38:6), PS(38:4), PS(40:6), PS(40:7), SM(d32:2), SM( d33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM(d4 2:1), SM(d42:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof.

[0019] For the above aspects, the one or more lipid biomarkers suitably include PI(38:6) or a fragment, variant or derivative thereof, and one or more other lipid biomarkers selected from the group consisting of Cer(d36:1), Cer(d42:0), Cer(d42:1), DG(34:2), Hex2Cer(d34:1), Hex2Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(17:1), LPC(18:3), LPE(22:6), LPI(20:4), PC(32:1), PC(34:1), PC(35:4), PC(36:2), PC(36:3), PC(37:4), PC(38:5), PE(34:1), PE(34:2p), PE(36:4), PE(38:3p), PE(38:4), PE(0-38:5 ), PE(O-40:6), PI(32:1), PI(34:1), PI(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d32:2), SM(d 33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM(d42 :1), SM(d42:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof.

[0020] In some examples of the methods of the above aspects, the one or more lipid biomarkers include LPC(14:0) and PI(38:6), or fragments, variants or derivatives thereof, and optionally one or more other lipid biomarkers selected from the group consisting of Cer(d36:1), Cer(d42:0), Cer(d42:1), DG(34:2), Hex2Cer(d34:1), Hex2Cer(d42:2), LPC(16:0e), LPC(17:1), LPC(18:3), LPE(22:6), LPI(20:4), PC(32:1), PC(34:1), PC(35:4), PC(36:2), PC(36:3), PC(37:4), PC(38:5), PE(34:1), PE(34:2p), PE(36:4), PE(38:3p), PE(38:4), PE(38:5), and PE(34:1). 8:5), PE(O-40:6), PI(32:1), PI(34:1), PI(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d32:2), SM (d33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM(d 42:1), SM(d42:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof.

[0021] In certain examples of the methods of the above aspects, the one or more lipid biomarkers are selected from the group consisting of LPC (14:0), PC (32:1), PC (38:5), PE (34:1), PS (38:4), SM (d36:2), SM (d38:4), TG (54:4), TG (56:1) and TG (58:2), or fragments, variants or derivatives thereof.

[0022] In other examples of the methods of the above aspects, the one or more lipid biomarkers are selected from the group consisting of LPC (14:0), PC (32:1), PC (36:2), PC (38:5), PE (34:1), PI (34:1), PS (38:4), SM (d36:2), SM (d38:4), SM (d44:4), TG (52:3e), TG (53:4), TG (54:4), TG (54:5), TG (54:6), TG (56:1), TG (58:2) and TG (58:3), or fragments, variants or derivatives thereof.

[0023] In various examples of the methods of the above aspects, the one or more lipid biomarkers are selected from the group consisting of: LPC (14:0), PE (34:1), PS (38:4), SM (d36:2), TG (52:3e) and TG (58:3), or fragments, variants or derivatives thereof.

[0024] In certain examples of the methods of the above aspects, the one or more lipid biomarkers are selected from the group consisting of: LPC (14:0), LPC (16:0e), LPE (22:6), PE (34:2p), PE (38:3p), PI (36:1), PI (38:6), PS (38:4), PS (40:6), SM (d33:1), SM (d35:1) and TG (57:1), or fragments, variants or derivatives thereof. To this end, the one or more lipid biomarkers may include:

[0025] (a) LPC (14:0), PS (38:4) and TG (57:1), or fragments, variants or derivatives thereof;

[0026] (b) LPC (16:0e), LPE (22:6) and PI (38:6), or fragments, variants or derivatives thereof;

[0027] (c) PE(34:2p), PE(38:3p), PI(36:1), PI(38:6) and PS(40:6), or fragments, variants or derivatives thereof;

[0028] (d) LPC (14:0), PI (38:6) and SM (d33:1), or fragments, variants or derivatives thereof; or

[0029] (e) LPC (14:0), PI (38:6) and SM (d35:1), or fragments, variants or derivatives thereof;

[0030] and optionally one or more other lipid biomarkers selected from the group consisting of LPC (14:0), LPC (16:0e), LPE (22:6), PE (34:2p), PE (38:3p), PI (36:1), PI (38:6), PS (38:4), PS (40:6), SM (d33:1), SM (d35:1) and TG (57:1), or fragments, variants or derivatives thereof.

[0031] In various examples of the methods of the above aspects, one or more lipid biomarkers described herein are selected from the group consisting of: LPC (14:0), LPE (22:6), PI (38:6), PE (34:2p), SM (d35:1), PS (38:4) and PS (38:4), or fragments, variants or derivatives thereof.

[0032] Suitably, for the above aspects, the level of said one or more lipid biomarkers is or has been measured at least in part by mass spectrometry.

[0033] Suitably, the predictive accuracy of the method of the above aspects as determined by the ROC AUC value is at least about 0.65, at least about 0.70, at least about 0.75 or at least about 0.80.

[0034] In a fourth aspect, the present disclosure provides a system for determining whether a subject has breast cancer, the system comprising:

[0035] A mass spectrometer unit configured to determine the level of one or more lipid biomarkers in a biological sample obtained from the subject, wherein the one or more lipid biomarkers are selected from the group consisting of: Cer(d36:1), Cer(d42:0), Cer(d42:1), DG(34:2), Hex2Cer(d34:1), Hex2Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(17:1), LPC(18:3), LPE(22:6), LPI(20:4), PC(32:1), PC(34:1), PC(35:4), PC(36:2), PC(36:3), PC(37:4), PC(38:5), PE(34:1), PE(34:2p), PE(36:4), PE(38:3p), PE(38:4), PE(38:5), PE(38:6), PE(38:7), PE(38:8), PE(38:9), PE(38:10), PE(38:11), PE(38:12), PE(38:13), PE(38:14), PE(38:15), PE(38:17), PE(38:18), PE(38:19). -40:6), PI(32:1), PI(34:1), PI(36:1), PI(38:6), PS(38:4), PS(40:6), PS(40:7), SM(d32:2), SM (d33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM(d4 2:1), SM(d42:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof; and

[0036] A processing unit is configured to use or analyze the level of the one or more lipid biomarkers to determine the presence or absence of the breast cancer in the subject.

[0037] In a fifth aspect, the present disclosure provides a kit for determining whether a subject has breast cancer, the kit comprising one or more reagents for determining the level of one or more lipid biomarkers in a biological sample obtained from the subject, wherein the one or more lipid biomarkers are selected from the group consisting of: Cer (d36:1), Cer (d42:0), Cer (d42:1), DG (34:2), Hex2Cer (d34:1), Hex2Cer (d42:2), LPC (14:0), LPC (16:0e), LPC (17:1), LPC (18:3), LPE (22:6), LPI (20:4), PC (32:1), PC (34:1), PC (35:4), PC (36:2), PC (36:3), PC (37:4), PC (38:5), PE (34:1), PE (34:2p), PE (36:4), PE (38:3p), PE(38:4), PE(O-38:5), PE(O-40:6), PI(32:1), PI(34:1), PI(36:1), PI(38:6), PS(38:4), PS(40:6), PS( 40:7), SM(d32:2), SM(d33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM (d41:3), SM(d42:1), SM(d42:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof.

[0038] Suitably, the one or more reagents comprise one or more probes, each probe being specific or selective for one of the one or more lipid biomarkers.

[0039] Referring to the above aspects, the one or more lipid biomarkers suitably include two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of: Cer (d36: 1), Cer (d42: 0), Cer (d42: 1), DG (34: 2), Hex2Cer (d34: 1), Hex2Cer (d42: 2), LPC (1 4:0), LPC(16:0e), LPC(17:1), LPC(18:3), LPE(22:6), LPI(20:4), PC(32:1), PC(34:1), PC(35:4), PC (36:2), PC(36:3), PC(37:4), PC(38:5), PE(34:1), PE(34:2p), PE(36:4), PE(38:3p), PE(38:4), PE(O -38:5), PE(O-40:6), PI(32:1), PI(34:1), PI(36:1), PI(38:6), PS(38:4), PS(40:6), PS(40:7), SM(d 32:2), SM(d33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3) , SM(d42:1), SM(d42:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof.

[0040] In some examples of the above aspects, the one or more lipid biomarkers include two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers, or five or more lipid biomarkers selected from the group consisting of LPC (14:0), PC (32:1), PC (38:5), PE (34:1), PS (38:4), SM (d36:2), SM (d38:4), TG (54:4), TG (56:1) and TG (58:2), or fragments, variants or derivatives thereof.

[0041] In other examples of the above aspects, the one or more lipid biomarkers include two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers, or five or more lipid biomarkers selected from the group consisting of LPC (14:0), PC (32:1), PC (36:2), PC (38:5), PE (34:1), PI (34:1), PS (38:4), SM (d36:2), SM (d38:4), SM (d44:4), TG (52:3e), TG (53:4), TG (54:4), TG (54:5), TG (54:6), TG (56:1), TG (58:2) and TG (58:3), or fragments, variants or derivatives thereof.

[0042] In specific examples of the above aspects, the one or more lipid biomarkers include two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers, or five or more lipid biomarkers selected from the group consisting of: LPC (14:0), PE (34:1), PS (38:4), SM (d36:2), TG (52:3e) and TG (58:3), or fragments, variants or derivatives thereof.

[0043] In various examples of the above aspects, the one or more lipid biomarkers include two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers, or five or more lipid biomarkers selected from the group consisting of LPC (14:0), LPC (16:0e), LPE (22:6), PE (34:2p), PE (38:3p), PI (36:1), PI (38:6), PS (38:4), PS (40:6), SM (d33:1), SM (d35:1) and TG (57:1), or fragments, variants or derivatives thereof.

[0044] With regard to the above aspects, the biological sample suitably is or comprises a blood sample, a plasma sample and / or a serum sample.

[0045] Suitably, the system of the fourth aspect or the kit of the fifth aspect is for use in the method of the first, second or third aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The following drawings form a part of this specification and are included to further demonstrate certain aspects of the present disclosure. The present disclosure may be better understood by referring to one or more of these drawings in conjunction with the detailed description of the specific embodiments presented herein. It will be appreciated by those skilled in the art that many changes and / or modifications may be made to the above embodiments without departing from the broad general scope of the present disclosure. Therefore, the present embodiments should be considered to be illustrative and non-restrictive in all respects.

[0047] Figure 1 . Violin plots of the following lipids with feature importance > 0.05: (a) LPC (14:0); (b) TG (16:0_18:1_23:0); (c) LPC (18:3); and (d) PS (18:0_20:4).

[0048] Figure 2 . Violin plots of the following lipids with feature importance > 0.05: (a) PI (18:2_20:4); (b) LPE (22:6); (c) LPC (16:0e); (d) Cer (d18:1_24:0); and (e) PE (16:0_20:4).

[0049] Figure 3 . Violin plots of the following lipids with feature importance > 0.05: (a) PI (18:2_20:4); (b) PE (16:0p_18:2); (c) PI (18:0_18:1); (d) PS (18:0_22:6); and (e) PE (20:1p_18:2).

[0050] Figure 4 . Violin plots of the following lipids with feature importance > 0.05: (a) LPC (14:0); (b) SM (d18:1_15:0); (c) LPC (17:1); (d) PI (18:2_20:4); and (e) SM (d41:2).

[0051] Figure 5 . Violin plots of the following lipids with feature importance > 0.05: (a) PI (18:2_20:4); (b) LPC (14:0); and (c) SM (d35:1).

[0052] Figure 6 Overlapping ROC of candidate markers generated from dataset 1

[0053] Figure 7 Overlapping ROC of candidate markers generated from dataset 2

[0054] Figure 8Overlapping ROC of candidate markers generated from dataset 3

[0055] Fig. 9 Overlapping ROC of candidate markers generated from dataset 4

[0056] Fig.10 Overlapping ROC of candidate markers generated from dataset 234 (validation)

[0057] Fig.11 . Volcano plot showing fold change and log p-value - more significant changes are at the top. The initial marker set was selected based on significance values ​​(p-value < 0.05) after initial filtering; lipid ions are shown as full identifiers were not available at an earlier stage.

[0058] Fig.12 Filtering based on fold change and p-value was used to select an additional set of 18 markers from cohort 3, and stepwise regression and significance were then used in a logistic model to refine the combined set.

[0059] Fig.13 In this approach, Cohort 4 was used primarily for validation, as described in Example 1. Repeating the process of identifying differentially expressed lipids for Cohort 4 showed that four biomarkers identified in Example 1 were present in the top 10 selections; these markers included a small set of 6 markers "Common 6-ML_GBM" that were common to both approaches.

[0060] Fig.14 An initial 18-member panel was established using Cohorts 1 and 2—performance was estimated based on the next available cohort (Cohort 3) by leave-one-out cross-validation.

[0061] Fig.15 . A second 18-member panel was built using Cohorts 1 to 3—performance was estimated based on the next available cohort (Cohort 4) by leave-one-out cross-validation.

[0062] Fig.16 . Six-member panel - Lipid 1 box plot (order of each dataset from left to right: control, breast cancer)

[0063] Fig.17 . Six-member panel - Lipid 2 box plot (order of each dataset pair from left to right: control, breast cancer)

[0064] Fig.18 . Six-member panel - Lipid 2 box plot, same m / z (order from left to right for each dataset pair: control, breast cancer)

[0065] Fig.19. Six-member panel - lipid 3 box plot (the order from left to right for each dataset pair is: control, breast cancer)

[0066] Fig. 20 . Six-member panel - Lipid 3 box plot; same m / z (order from left to right for each dataset pair: control, breast cancer)

[0067] Fig.21 . Six-member panel - lipid 4 box plot (the order from left to right for each data set is: control, breast cancer)

[0068] Fig. 22 . Six-member panel - lipid 5 box plot (the order from left to right for each dataset pair is: control, breast cancer)

[0069] Fig.23 .Six-member panel - lipid 6 box plot (the order from left to right for each dataset pair is: control, breast cancer)

[0070] Fig.24 .ROC curve performance of the 18-marker panel estimated based on cohort 2 by leave-one-out cross-validation.

[0071] Fig.25 . ROC curve performance of the 18-marker panel estimated based on cohort 3 by leave-one-out cross-validation.

[0072] Fig.26 . ROC curve performance of the 400Reoptimised panel estimated based on cohort 4 by leave-one-out cross-validation.

[0073] Fig. 27 . ROC curve performance of the 400Reoptimised panel estimated based on cohorts 3 and 4 by leave-one-out cross-validation.

[0074] Fig.28 . ROC curve performance of the restricted 9 lipid panel estimated based on cohort 2 by leave-one-out cross-validation.

[0075] Fig.29 ROC curve performance of the top 10optimised panels estimated from cohorts 2 and 3 by leave-one-out cross-validation.

[0076] Fig.30 . ROC curve performance of the 6FourMarkers panel estimated based on cohorts 2 to 4 by leave-one-out cross-validation.

[0077] Fig.31 . ROC curve performance of the 6FourMarkers panel estimated based on cohorts 3 and 4 by leave-one-out cross-validation.

[0078] Fig.32 . ROC curve performance of the Common 6ML-GBM panel estimated based on cohorts 3 and 4 by leave-one-out cross-validation.

[0079] Fig.33 . ROC curve performance of the Cohort2Optimised group based on cohort 2 estimation by leave-one-out cross-validation.

[0080] Fig.34 Lipids from the three BCAL groups are superimposed on the volcano plot (fold change is fasting / fed). Some markers are differentially expressed between the fed and fasted states. ML-GBM group lipids (red) are not differentially expressed (i.e., present at significantly different concentrations) between fasting / fed.

[0081] Fig.35 Correlation of ML-GBM signature scores obtained based on various combinations of sample types.

[0082] Fig.36 .Boxplot of ML-GBM scores showing median and interquartile range for all sample categories; NEV = non-fasting EV, FEV = fasting EV, FP = fasting plasma, NV = non-fasting plasma. Low scores indicate correct “control” classification. DETAILED DESCRIPTION

[0083] General techniques and definitions

[0084] Unless specifically defined otherwise, all technical and scientific terms used herein should be construed to have the same meaning as commonly understood by one of ordinary skill in the art (e.g., genomics, immunology, molecular biology, immunohistochemistry, biochemistry, oncology, and pharmacology).

[0085] The present disclosure is performed using, unless otherwise indicated, conventional techniques of molecular biology, microbiology, recombinant DNA techniques, and immunology. These procedures are described, for example, in Sambrook, Fritsch and Maniatis, Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratories, New York, 4th edition (2012), Volumes I, II and III in their entirety; DNA Cloning: A Practical Approach, Volumes I and II (DN Glover, 2nd edition, 1995), IRL Press, Oxford, in its entirety; Oligonucleotide Synthesis: A Practical Approach (MJ Gait, ed. 1984) IRL Press, Oxford, in its entirety; and in particular the paper by Gait, ppl-22; Atkinson et al., pp. 35-81; Sproat et al., pp. 83-115; and Wu et al., pp. 135-151; 4. Nucleic Acid Hybridization: A Practical Approach (BD Hames and SJ Higgins, eds. 1985) IRL Press, Oxford, in its entirety; Immobilized Cells and Enzymes: A Practical Approach (1986) IRL Press, Oxford, entire text; Perbal, B., A Practical Guide to Molecular Cloning (1984) and Methods In Enzymology (S. Colowick and N. Kaplan, eds. Academic Press, Inc.), entire series.

[0086] Those skilled in the art will appreciate that the present disclosure is susceptible to variations and modifications other than those specifically described. It should be understood that the present disclosure encompasses all such variations and modifications. The present disclosure also encompasses all steps, features, compositions and compounds individually or collectively mentioned or described in this specification, and any and all combinations of any two or more of the steps or features.

[0087] The present disclosure is not to be limited in scope by the specific embodiments described herein, which are intended for illustrative purposes only. As described herein, functionally equivalent products, compositions, and methods are clearly within the scope of the present disclosure.

[0088] Each feature of any particular aspect or embodiment of the disclosure may be applied mutatis mutandis to any other aspect or embodiment of the disclosure.

[0089] Throughout this specification, references to individual steps, compositions of matter, groups of steps, or groups of compositions of matter shall be considered to cover both one and more (i.e., one and more) of such steps, compositions of matter, groups of steps, or groups of compositions of matter, unless specifically stated otherwise or the context requires otherwise.

[0090] As used herein, the singular forms "a," "an," "and," and "the" include plural forms of these terms unless the context clearly dictates otherwise.

[0091] The term "and / or", such as "X and / or Y" should be understood as "X and Y" or "X or Y", and should be considered to provide clear support for both or either meanings.

[0092] Throughout the present specification, the word "comprise" or variations such as "comprises" or "comprising" should be understood to imply the inclusion of stated elements, integers or steps or groups of elements, groups of integers or groups of steps, but not the exclusion of any other elements, integers or steps or groups of elements, groups of integers or groups of steps.

[0093] Those skilled in the art will appreciate that many changes and / or modifications may be made to the above embodiments without departing from the broad general scope of the present disclosure. Therefore, the present embodiments should be considered to be illustrative and not restrictive in all aspects.

[0094] Diagnostic Methods

[0095] The inventors of this case surprisingly showed that the concentration levels of specific lipids from lipidomic markers in biological samples (such as blood samples) can be used to diagnose subjects with breast cancer. Advantageously, this method can allow doctors to make appropriate, informed and timely follow-up and treatment decisions based on this information.

[0096] Therefore, the inventors of the present invention have developed a method for diagnosing breast cancer.

[0097] Thus, in one broad form, the present disclosure provides a method for measuring the level of one or more lipid biomarkers in a biological sample from a subject, the method comprising the steps of:

[0098] (a) providing the biological sample; and

[0099] (b) measuring the level of one or more lipid biomarkers in a biological sample, wherein the one or more lipid biomarkers are selected from the group consisting of Cer(d36:1), Cer(d42:0), Cer(d42:1), DG(34:2), Hex2Cer(d34:1), Hex2Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(17:1), LPC(18:3), LPE(22:6), LPI(20:4), PC(32:1), PC(34:1), PC(35:4), PC(36:2), PC(36:3), PC(37:4), PC(38:5), PE(34:1), PE(34:2p), PE(36:4), PE(38:3p), PE(38:4), PE(O-38:5), PE(O-40:6), P I(32:1), PI(34:1), PI(36:1), PI(38:6), PS(38:4), PS(40:6), PS(40:7), SM(d32:2), SM(d33:1 ), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM(d42:1 ), SM(d42:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof.

[0100] In a related broad form, the present disclosure provides a method for diagnosing breast cancer in a subject, the method comprising the step of measuring the level of one or more lipid biomarkers in a biological sample from the subject, wherein the one or more lipid biomarkers are selected from the group consisting of: Cer (d36:1), Cer (d42:0), Cer (d42:1), DG (34:2), Hex2Cer (d34:1), Hex2Cer (d42:2), LPC (14:0), LP C(16:0e), LPC(17:1), LPC(18:3), LPE(22:6), LPI(20:4), PC(32:1), PC(34:1), PC(35:4), PC(36:2), PC(36 :3), PC(37:4), PC(38:5), PE(34:1), PE(34:2p), PE(36:4), PE(38:3p), PE(38:4), PE(O-38:5), PE(O-40:6) ,PI(32:1),PI(34:1),PI(36:1),PI(38:6),PS(38:4),PS(40:6),PS(40:7),SM(d32:2),SM(d33:1),SM(d35 :1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM(d42:1), SM(d42:4), SM(d44 :4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof, and wherein the level of one or more lipid biomarkers is diagnostic or indicative for the subject having breast cancer.

[0101] With respect to the aspects described herein, the term "subject" includes, but is not limited to, mammals, including humans, performance animals (e.g., horses, camels, greyhounds), livestock (e.g., cows, sheep, horses), and pets (e.g., cats and dogs). In one example, the subject is a human. In some examples, the subject is a woman. In other examples, the subject is a man.

[0102] As generally used herein, the terms "cancer," "tumor," "malignant," and "malignancy" refer to a disease or condition, or to cells or tissues associated with such a disease or condition, characterized by abnormal cell proliferation, differentiation, and / or migration, often accompanied by an abnormal molecular phenotype comprising one or more genetic mutations or other genetic changes associated with neoplasia, tumor marker expression, loss of tumor suppressor expression or activity, and / or abnormal cell surface marker expression.

[0103] The term "breast cancer" refers to a condition characterized by the abnormal rapid growth of abnormal cells in one or both breasts of a subject. Breast cancer may include, but is not limited to, ductal carcinoma in situ (DCIS), invasive breast cancer (e.g., invasive carcinoma), inflammatory breast cancer, breast angiosarcoma, phyllodes tumor of the breast, and / or Paget's disease of the nipple. As used herein, "invasive carcinoma" or "invasive breast cancer" refers to a type of cancer that may include, but is not limited to, invasive ductal carcinoma (IDC), infiltrating ductal carcinoma, invasive lobular carcinoma (ILC), adenoid cystic (or adenocystic) carcinoma, low-grade adenosquamous carcinoma, medullary carcinoma, mucinous (or colloid) carcinoma, papillary carcinoma, tubular carcinoma, metaplastic carcinoma, micropapillary carcinoma, and / or a mixed carcinoma with the characteristics of both invasive ductal carcinoma and lobular carcinoma. Suitably, the breast cancer to be diagnosed in a subject is selected from IDC, DCIS, and ILC. In a specific example, the breast cancer to be diagnosed in the subject is IDC. In certain examples, the breast cancer to be diagnosed in the subject is DCIS. In other examples, the breast cancer to be diagnosed in the subject is ILC.

[0104] The skilled person will further understand that breast cancer may include any aggressive breast cancer and cancer subtypes known in the art, such as triple-negative breast cancer, lymph node-positive (LN+) breast cancer, HER2-positive (HER2+) breast cancer, PR-negative (PR - )Breast cancer, PR positive (PR + )Breast cancer, ER negative (ER - ) breast cancer and ER-positive (ER+) breast cancer.

[0105] Breast cancer can also be any stage or grade (such as I, II, III or IV stage), therefore may comprise metastatic breast cancer.Suitably, breast cancer to be diagnosed is early stage cancer (such as 1st stage or 2nd stage breast cancer).In a particular instance, breast cancer to be diagnosed is 1st stage breast cancer.In other instances, breast cancer to be diagnosed is 2nd stage breast cancer.Or, breast cancer to be diagnosed may be late stage cancer (such as 3rd stage or 4th stage breast cancer).In a particular instance, breast cancer to be diagnosed is 3rd stage breast cancer.In other instances, breast cancer to be diagnosed is 4th stage breast cancer.

[0106] As used herein, the terms "diagnosis" and "diagnosing" refer to methods by which one of ordinary skill in the art can assess and / or determine whether a patient or subject suffers from a specified disease or condition, such as determining whether breast cancer exists. Those skilled in the art typically diagnose based on one or more diagnostic indicators or markers, the presence, absence or amount of which indicates whether a disease, disorder or condition exists. It should also be understood that these terms do not indicate the ability to determine with 100% accuracy whether a particular disease exists, nor do they indicate that a specified process or result is more likely to occur. On the contrary, one of ordinary skill in the art will understand that the terms "diagnostic" and "diagnosing" refer to an increased probability that a subject will suffer from a certain disease, disorder or condition (such as breast cancer).

[0107] Suitably, the methods described herein are combined with (e.g., before and / or after) one or more other diagnostic tests known in the art (e.g., breast examinations; breast imaging, such as mammograms, ultrasound, and MRI; biopsy). In this regard, the present method can be used as a preliminary screening test to identify subjects who may benefit from further diagnostic tests. Additionally or alternatively, the present method can be used to confirm the presence or absence of breast cancer as indicated by a previous diagnostic test. Therefore, in some instances, the present method may include initial or early steps and / or subsequent steps of performing one or more further diagnostic tests on the subject in question. In an alternative instance, the method described herein is performed without any further diagnostic tests as a primary diagnostic test for breast cancer.

[0108] Suitably, if the level (such as concentration level or expression level) of one or more lipid biomarkers in the biological sample from the subject is changed or adjusted, then the breast cancer of the subject can be diagnosed. In one example, the expression of the first subset of one or more lipid biomarkers or the increase of the concentration level and / or the expression of the second subset of one or more lipid biomarkers or the reduction of the concentration level are diagnostic or indicative for the subject to suffer from breast cancer. As shown in the examples herein, some lipid biomarkers (such as PE (34: 1), PI (32: 1), PI (34: 1), PS (38: 4) and PS (40: 6)) can show the expression or concentration level of the first subject group diagnostic for breast cancer and improve, while showing the expression or concentration level of the second subject group diagnostic for breast cancer and reducing. Therefore, the level of these lipid biomarkers is reduced or increased (such as relative to a threshold value or a reference level) for the breast cancer of the subject can be indicative or diagnostic.

[0109] In a specific example, Cer(d36:1), Cer(d42:0), DG(34:2), Hex2Cer(d34:1), Hex2Cer(d42:2), LPE(22:6), LPI(20:4), PC(32:1), PC(34:1), PC(35:4), PC(36:2), PC(36:3), PC(37:4), PC(38:5), PE(34:1), PI(32:1), PI (d34:1), PS(38:4), PS(40:6), SM(d32:2), SM(d33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM(d42:1), SM(d42:4) and / or SM(d44:4), or fragments, variants or derivatives thereof; and / or Cer( 42:1), LPC(14:0), LPC(16:0e), LPC(17:1), LPC(18:3), PE(34:1), PE(34:2p), PE(36:4), PE(38:3p), PE(38:4), PE(O-38:5), PE(O-40:6), PI(32:1), PI(34:1), PI(36:1), PI(38:6), PS(38:4), PS(40:6), P Decreased levels of S(40:7), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and / or TG(62:2), or fragments, variants or derivatives thereof, are diagnostic or indicative of breast cancer in the subject.

[0110] In some embodiments, the measuring step comprises determining the presence or absence of the following in a biological sample of the subject: (i) Cer(d36:1), Cer(d42:0), DG(34:2), Hex2Cer(d34:1), Hex2Cer(d42:2), LPE(22:6), LPI(20:4), PC(32:1), PC(34:1), PC(35:4), PC(36:2), PC(36:3), PC(37:4), PC(3 8:5), PE(34:1), PI(32:1), PI(34:1), PS(38:4), PS(40:6), SM(d32:2), SM(d33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM(d42:1), SM(d42:4) and / or SM(d44:4), or a piece thereof and / or (ii) Cer(42:1), LPC(14:0), LPC(16:0e), LPC(17:1), LPC(18:3), PE(34:1), PE(34:2p), PE(36:4), PE(38:3p), PE(38:4), PE(O-38:5), PE(O-40:6), PI(32:1), PI(34:1), PI(36:1), PI(38:6 ), PS(38:4), PS(40:6), PS(40:7), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and / or TG(62:2), or fragments, variants or derivatives thereof.

[0111] According to a specific example, the measuring step comprises determining whether there is a reduced level of LPC(14:0) and / or PI(38:6) or a fragment, variant or derivative thereof in the biological sample of the subject. More specifically, the measuring step may comprise determining whether there is a reduced level of LPC(14:0) or a fragment, variant or derivative thereof in the biological sample of the subject. Similarly, the measuring step may comprise determining whether there is a reduced level of PI(38:6) or a fragment, variant or derivative thereof in the biological sample of the subject. More specifically, the measuring step may comprise determining whether there is a reduced level of LPC(14:0) and PI(38:6) or a fragment, variant or derivative thereof in the biological sample of the subject.

[0112] Suitably, the method of the present invention further comprises a step of determining or calculating a risk or diagnostic score based on the level of one or more lipid biomarkers, for example, by the methods described herein. In some instances, the diagnostic score of the subject is determined to be 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99% or higher.

[0113] Suitably, the diagnostic methods described herein may comprise the step of administering a treatment to the subject. For example, this may comprise administering a therapeutically effective amount of a treatment, such as those anti-cancer treatments described herein, to the subject when the level of one or more lipid biomarkers (and / or the risk or diagnostic score derived therefrom) is diagnostic or indicative of breast cancer in the subject.

[0114] Treatment

[0115] Among other things, the methods described herein can improve patient outcomes by diagnosing subjects with breast cancer who could potentially benefit from its treatment.

[0116] Accordingly, the present inventors have developed methods of treating breast cancer in a subject.

[0117] In a broad form, the present disclosure provides a method of treating breast cancer in a subject, the method comprising the steps of treating (e.g., performing surgery) and / or administering a therapeutically effective amount of an anti-cancer therapy to the subject, wherein the level of one or more lipid biomarkers has been measured in a biological sample from the subject, the level being diagnostic or indicative of breast cancer in the subject, wherein the one or more lipid biomarkers are selected from the group consisting of: Cer(d36:1), Cer(d42:0), Cer(d42:1), D G(34:2), Hex2Cer(d34:1), Hex2Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(17:1), LPC(18:3), LPE(22:6), LPI (20:4), PC(32:1), PC(34:1), PC(35:4), PC(36:2), PC(36:3), PC(37:4), PC(38:5), PE(34:1), PE(34:2p), PE( 36:4), PE(38:3p), PE(38:4), PE(O-38:5), PE(O-40:6), PI(32:1), PI(34:1), PI(36:1), PI(38:6), PS(38:4), PS(40:6), PS(40:7), SM(d32:2), SM(d33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d :4), SM(d41:2), SM(d41:3), SM(d42:1), SM(d42:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof.

[0118] Suitably, the methods of the invention comprise an initial step of measuring the level of one or more lipid biomarkers in a biological sample from a subject.

[0119] Suitably, the present method has determined the risk or diagnostic score using the level (such as concentration level or expression level) of one or more lipid biomarkers. Risk or diagnostic score can be diagnostic or indicative for subjects suffering from breast cancer. In some instances, the diagnostic score is generated at least in part by a logistic model. For this reason, the diagnostic score can be in the form of the probability that the subject suffers from breast cancer, so that in the absence of additional information, 50% or higher scores provide that the probability that the subject suffers from breast cancer is higher than the probability of not suffering from breast cancer. In some instances, it is determined that the diagnostic score of the subject is 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99% or higher.

[0120] As used herein, the term "therapeutically effective amount" describes an amount of a given agent (e.g., an anticancer agent) or treatment (e.g., chemotherapy, radiotherapy, molecular targeted therapy, and immunotherapy) sufficient to achieve a desired effect in a subject treated with the agent. For example, this can be the amount of a composition comprising one or more agents necessary to alleviate, relieve, and / or prevent cancer or a cancer-related disease, disorder, or condition. In some instances, a "therapeutically effective amount" is sufficient to reduce or eliminate cancer symptoms. In other instances, a "therapeutically effective amount" is an amount sufficient to achieve a desired biological effect, such as an amount that effectively reduces or prevents cancer growth and / or metastasis.

[0121] Ideally, a therapeutically effective amount of an agent is an amount sufficient to induce a desired result in a subject without causing substantial cytotoxic effects. The effective amount of an agent that can be used to mitigate, alleviate and / or prevent breast cancer will depend on the subject being treated, the type and severity of any associated disease, disorder and / or condition (e.g., the number and location of any phase transitions), and the manner in which the therapeutic composition is administered.

[0122] Suitably, the various agents, anticancer agents or cancer treatments described herein are administered to a subject as a pharmaceutical composition comprising a pharmaceutically acceptable carrier, diluent or excipient. In this regard, any dosage form and route of administration, such as those provided herein, can be employed to provide the compositions of the present disclosure to a subject.

[0123] "Pharmaceutically acceptable carrier, diluent or excipient" means a solid or liquid filler, diluent or encapsulating material that can be safely used for systemic administration. Depending on the specific route of administration, a variety of carriers well known in the art can be used. These carriers can be selected from the group comprising sugars, starches, cellulose and its derivatives, malt, gelatin, talc, calcium sulfate, liposomes and other lipid-based carriers, vegetable oils, synthetic oils, polyols, alginic acid, phosphate buffered solutions, emulsifiers, isotonic saline and salts (such as mineral acid salts, including hydrochlorides, bromides and sulfates; organic acid salts, such as acetates, propionates and malonates; and pyrogen-free water).

[0124] An available reference describing pharmaceutically acceptable carriers, diluents and excipients is Remington's Pharmaceutical Sciences (Mack Publishing Co. NJ USA, 1991), which is incorporated herein by reference.

[0125] The compositions of the present disclosure can be provided to patients by any safe administration route, for example, oral, rectal, parenteral, sublingual, buccal, intravenous, intraarticular, intramuscular, intradermal, subcutaneous, inhalation, intraocular, intraperitoneal, intracerebroventricular, transdermal, etc.

[0126] Dosage forms include tablets, dispersants, suspensions, injections, solutions, syrups, dragees, capsules, suppositories, aerosols, transdermal patches, etc. These dosage forms can also include injections designed specifically for this purpose or implants of controlled release devices or other forms of implants modified and acting in this manner in addition. The controlled release of therapeutic agents can be achieved by, for example, coating therapeutic agents with hydrophobic polymers, which include acrylic resins, waxes, higher aliphatic alcohols, polylactic acid and polyglycolic acid and some cellulose derivatives, such as hydroxypropyl methylcellulose. In addition, controlled release can be achieved by using other polymer matrices, liposomes and / or microspheres.

[0127] Compositions of the present disclosure suitable for oral or parenteral administration may be presented in the form of discrete units (such as capsules, sachets or tablets), powders or granules, or solutions or suspensions in aqueous liquids, non-aqueous liquids, oil-in-water emulsions or water-in-oil liquid emulsions, each containing a predetermined amount of one or more therapeutic agents of the present disclosure. These compositions may be prepared by any pharmaceutical method, which may include the step of combining one or more of the above-described agents with a carrier constituting one or more essential ingredients. Typically, the composition is prepared by uniformly and intimately blending the agent of the present disclosure with a liquid carrier or a finely powdered solid carrier or both, and then, if necessary, shaping the product into the desired presentation form.

[0128] The above compositions can be administered in a manner compatible with the dosage formulation and in a pharmaceutically effective amount. In the context of the present disclosure, the dose administered to the patient should be sufficient to produce a beneficial response in the patient within an appropriate time period. The amount of drug to be administered can depend on the subject to be treated, including its age, sex, weight and general health, and these factors will depend on the judgment of the practitioner.

[0129] It is contemplated that the various agents, anticancer agents, or cancer treatments described herein can be formulated as discrete doses, such as in the form of a kit. Such a kit may also include a drug instruction sheet, including printed instructions for using the agent simultaneously, in parallel, in sequence, continuously, alternately, or alone in the treatment, improvement, and / or prevention of a cancer described herein in a patient in need. Therefore, the above kit is suitable for use in a method for treating, improving, and / or preventing breast cancer (including one or more symptoms, consequences, sequelae, or complications thereof as described herein).

[0130] Alternatively, the various therapeutic agents described herein may be formulated together in a composition, which optionally comprises a pharmaceutically acceptable carrier, excipient, or diluent.

[0131] Methods for treating breast cancer can be prophylactic, preventive or therapeutic and are suitable for treating breast cancer in mammals (especially humans). As used herein, "treating", "treat" or "treatment" refers to a therapeutic intervention, action policy or scheme that at least improves cancer symptoms after cancer and / or its symptoms at least begin to develop. As used herein, "preventing", "prevent" or "prevention" refers to a therapeutic intervention, action policy or scheme that begins before the onset of cancer and / or cancer symptoms to prevent, inhibit or delay the development or progression of cancer or symptoms.

[0132] Cancer treatment

[0133] The skilled artisan will appreciate that the cancer treatments used in the methods described herein may include, but are not limited to, drug therapy, chemotherapy antibodies, nucleic acids and other biomolecule therapy, radiation therapy, surgery, nutritional therapy, relaxation or meditation therapy, and other natural or holistic therapies. Typically, drugs, biomolecules (e.g., antibodies, inhibitory nucleic acids, such as siRNA), or chemotherapeutic agents are referred to herein as "anti-cancer therapeutic agents" or "anti-cancer agents."

[0134] Suitably, treatment is or includes one or more of surgery (eg lumpectomy or mastectomy), chemotherapy, radiation therapy, molecular targeted therapy and immunotherapy.

[0135] As generally used herein, the term "chemotherapy" or "chemotherapeutic agent" refers broadly to a treatment or agent that uses a cytostatic or cytotoxic agent (i.e., a compound) to reduce or eliminate the growth or proliferation of undesirable cells (e.g., cancer cells). Thus, the term may refer to a cytotoxic or cytostatic agent used to treat a proliferative disorder (e.g., cancer). The cytotoxic effect of the agent may be, but need not be, the result of one or more of nucleic acid intercalation or binding, DNA or RNA alkylation, RNA or DNA synthesis inhibition, inhibition of another nucleic acid-related activity (e.g., protein synthesis), or any other cytotoxic effect.

[0136] Exemplary chemotherapeutic agents include, but are not limited to, alkylating agents (e.g., nitrogen mustards such as chlorambucil, cyclophosphamide, isofamide, mechlorethamine hydrochloride, melphalan, and uracil mustard; aziridines such as thiotepa; mesylate esters such as busulfan; nitrosoureas such as carmustine, lomustine, and streptozocin; platinum complexes such as cisplatin and carboplatin, oxaliplatin, nedaplatin, triplatin tetranitrate, phenanthriplatin, picoplatin, satraplatin, and lipoplatin; bioreductive alkylating agents such as thiotepa; alkylators, such as mitomycin, procarbazine, dacarbazine, and altretamine; DNA strand breakers (e.g., bleomycin); topoisomerase II inhibitors (e.g., amsacrine, actinomycin D, daunorubicin, idarubicin, mitoxantrone, doxorubicin, etoposide, and teniposide); DNA minor groove binders (e.g., plicamydin); antimetabolites (e.g., folate antagonists, such as methotrexate and trimethoprim-sulfamethoxazole); trimetrexate; pyrimidine antagonists such as fluorouracil, fluorodeoxyuridine, CB3717, azacitidine, cytarabine, and floxuridine; purine antagonists such as mercaptopurine, 6-thioguanine, fludarabine, pentostatin; asparginase; and ribonucleotide reductase inhibitors such as hydroxyurea); tubulin-interacting agents (e.g., vincristine, vinblastine, and Taxol); hormonal agents (e.g., estrogens; conjugated estrogens; ethinyl estradiol;diethylstilbesterol; chlortrianisen; idenestrol; progestins such as hydroxyprogesterone caproate, medroxyprogesterone, and megestrol acetate; and androgens such as testosterone, testosterone propionate, fluoxymesterone, and methyltestosterone); adrenocortical steroids (e.g., prednisone, dexamethasone, methylprednisolone, and prednisolone); luteinizing hormone-releasing agents or gonadotropin-releasing hormone agonists (e.g., leuprolide acetate and goserelin acetate); acetate); and antihormonal agents (e.g., tamoxifen; antiandrogens such as flutamide; aromatase inhibitors such as anastrozole, exemestane, and letrozole; and antiadrenal agents such as mitotane and aminoglutethimide). ;

[0137] As used herein, the term "radiation therapy" or "radiotherapy" refers to the medical use of ionizing radiation, usually as part of cancer treatment to control or destroy malignant cells. It can also be used as part of adjuvant therapy to prevent tumor recurrence after surgery to remove the primary malignancy.

[0138] Radiotherapy can be delivered by a device placed outside the patient (external radiotherapy) or a source placed inside the patient (internal radiotherapy or brachytherapy) or intravenously or orally. It can also be delivered by a radioisotope delivered systemically. Radiotherapy can be planned and administered in conjunction with imaging-based techniques such as computed tomography (CT) or magnetic resonance imaging (MRI) to accurately determine the radiation dose and position to be administered. In various embodiments, radiotherapy includes systemic radiotherapy, conventional external beam radiotherapy, stereotactic radiosurgery, stereotactic radiotherapy, three-dimensional conformal radiotherapy, intensity modulated radiotherapy (IMRT), image-guided radiotherapy, tomotherapy and / or brachytherapy. In some instances, radiotherapy includes stereotactic radiotherapy or intensity modulated radiotherapy (IMRT).

[0139] As used herein, "molecular targeted therapy" or "molecular targeted therapeutic agent" refers to a therapy that targets a specific class of proteins involved in cancer growth or signal transduction. In some instances, other anticancer agents described herein are or include tyrosine kinase inhibitors. The term "tyrosine kinase" refers to an enzyme that is capable of transferring a phosphate group from ATP to a tyrosine residue in a protein. Protein phosphorylation by tyrosine kinases is an important mechanism in signal transduction for regulating enzyme activity and cellular events such as cell survival or proliferation. In a specific example, the molecular targeted therapy includes one or more of the following: human epidermal growth factor receptor 2 (HER2; also known as ErbB-2, NEU, HER-2 and CD340) inhibitors (e.g., trastuzumab, pertuzumab, neratinib, tucatinib), PARP inhibitors (e.g., olaparib, talazoparib), CDK4 / 6 inhibitors (e.g., abemaciclib), PI3K inhibitors (e.g., alpelisib), dual HER2 / EGFR inhibitors (e.g., lapatinib), and neurotrophic T receptor kinase (NTRK) inhibitors (e.g., entrectinib, larotrectinib).

[0140] In the case of cancer, immunotherapy or immunotherapeutic agents use or modify the subject's immune mechanism to promote or facilitate cancer treatment. In this regard, immunotherapy or immunotherapeutic agents for the treatment of cancer include cell-based therapy, antibody therapy (e.g., anti-PD1, anti-PDL1 or anti-CTLA4 antibodies) and cytokine therapy. These therapies all take advantage of the following phenomenon: cancer cells usually have slightly different molecules called cancer antigens on their surface, which can be detected by the immune system of cancer subjects. Therefore, immunotherapy is used to stimulate the immune system of cancer patients to attack cancer cells by using these cancer antigens as targets.

[0141] Non-limiting examples of immunotherapies or immunotherapeutic agents include adalimumab, alemtuzumab, basiliximab, belimumab, bevacizumab, BMS-936559, brentuximab, certolizumab, cituximab, daclizumab, eculizumab, ibritumomab, infliximab, ipilimumab, lambrolkizumab, mepolizumab, izumab, MPDL3280A, muromonab, natalizumab, nivolumab, ofatumumab, omalizumab, pembrolizumab, pexelizumab, pidilizumab, rituximab, tocilizumab, tositumomab, trastuzumab, ustekinumab, abatacept, alefacept, and denileukin diftitox. In certain examples, the immunotherapeutic agent is an immune checkpoint inhibitor, such as an anti-PD1 antibody (e.g., pidilizumab, nivolumab, lamblokuzumab, pembrolizumab), an anti-PDL1 antibody (e.g., BMS-936559, MPDL3280A), and / or an anti-CTLA4 antibody (e.g., ipilimumab).

[0142] Lipid biomarkers

[0143] As described herein, the inventors of the present invention have discovered that the concentration levels of specific lipid biomarkers in a blood or plasma sample from a subject can be diagnostic for breast cancer. Without being bound by any theory, it is believed that the lipid biomarkers are derived from extracellular vesicles, such as exosomes, present in the biological sample. Other sources of lipid biomarkers co-isolated with extracellular vesicles may also contribute, such as apolipoproteins or lipid droplets.

[0144] As used herein, the term "lipid" refers to a group of organic compounds with lipophilic or amphiphilic properties, including but not limited to bis(monoacylglycerol)phosphate (BMP), cholesterol ester (CE), ceramide (Cer), diacylglycerol (DG or DAG), dihydroleukotriene B4 (DH-LTB4), fatty acids (FA), ganglioside A2 (GA2), ganglioside M3 (GM3), hexosylceramide (HexCer), dihexosylceramide (Hex2Cer), hexosyldihydroceramide (HexDHCer), lactosylceramide (LacCer), lysophosphatidic acid (LysoPA or LPA), lysophosphatidylcholine (LysoPC or LPC), lysophosphatidylcholine-acetal phospholipid (Lys oPC-pmg), lysophosphatidylethanolamine (LysoPE or LPE), lysophosphatidylethanolamine-plasmalogen (LysoPE-pmg), lysophosphatidylserine (LysoPS or LPS), lysophosphatidylinositol (LPI), monoacylglycerol (MAG), phosphatidylcholine (PC), phosphatidylcholine-plasmalogen (PC-pmg), phosphatidylethanolamine (PE), phosphatidylethanolamine-plasmalogen (PE-pmg), prostaglandin A1 (PGA1), prostaglandin B1 (PGB1), phosphatidylinositol (PI), phosphatidylserine (PS), sphingomyelin (SM), sphingosine, triacylglycerol (TG or TAG), and tetrahydro-12-keto-leukotriene B4 (TH-12-keto-LTB4).

[0145] As used herein, the term "biomarker" refers to a lipid molecule whose level is indicative or diagnostic for breast cancer in a subject. It should be understood that the term "biomarker" is intended to encompass all classes, forms (e.g., phosphorylated or oxidized forms), fragments (e.g., lipid head groups, fatty acyl chains) and variants of lipid biomarkers known in the art, such as those provided herein. It is also contemplated that, unless otherwise indicated, the ether-connected lipids described herein (e.g., PC, PE, PS, etc.) encompass their alkyl ether and alkenyl ether forms (e.g., PE (40: 7e) alkyl ethers and alkenyl ethers include PE (O-40: 7) and PE (P-40: 6) respectively). For this reason, the 'O' prefix is ​​used to represent the presence of an alkyl ether substituent, and the "P-" prefix or "p" suffix is ​​used for alkenyl ether substituents.

[0146] For the methods, systems and kits described herein, one or more lipid biomarkers described herein can be selected from one or more (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, etc.) lipid classes, such as Cer, DG, Hex2Cer, LPC, LPE, LPI, PC, PE, PI, PS, SM and TG.

[0147] In some instances, one or more lipid biomarkers include LPC (14: 0) and / or PI (38: 6). More specifically, one or more lipid biomarkers may include LPC (14: 0) and / or PI (18: 2-20: 4). In other instances, one or more lipid biomarkers include LPC (14: 0) and PI (38: 6). More specifically, one or more lipid biomarkers may include LPC (14: 0) and PI (18: 2-20: 4).

[0148] In some examples, the one or more lipid biomarkers include LPC(14:0) or a fragment, variant, or derivative thereof. In these examples, the one or more lipid biomarkers can optionally further comprise one or more other lipid biomarkers selected from the group consisting of Cer (d36:1), Cer (d42:0), Cer (d42:1), DG (34:2), Hex2Cer (d34:1), Hex2Cer (d42:2), LPC (16:0e), LPC (17:1), LPC (18:3), LPE (22:6), LPI (20:4), PC (32:1), PC (34:1), PC (35:4), PC (36:2), PC (36:3), PC (37:4), PC (38:5), PE (34:1), PE (34:2p), PE (36:4), PE (38:3p), PE (38:4), PE (O-38:5), PE (O-40:6), PI (32:1), ), PI(34:1), PI(36:1), PI(38:6), PS(38:4), PS(40:6), PS(40:7), SM(d32:2), SM(d33:1), SM (d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM(d42:1), S M(d42:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof.

[0149] In other examples, the one or more lipid biomarkers include PI(38:6) or a fragment, variant, or derivative thereof. For these examples, the one or more lipid biomarkers can optionally further comprise one or more other lipid biomarkers selected from the group consisting of Cer (d36: 1), Cer (d42:0), Cer (d42: 1), DG (34:2), Hex2Cer (d34: 1), Hex2Cer (d42:2), LPC (14:0), LPC (16:0e), LPC (17: 1), LPC (18:3), LPE (22:6), LPI (20:4), PC (32: 1), PC (34: 1), PC (35:4), PC (36:2), PC (36:3), PC (37:4), PC (38:5), PE (34: 1), PE (34:2p), PE (36:4), PE (38:3p), PE (38:4), PE (0-38:5), PE (0-40: 6), PI(32:1), PI(34:1), PI(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d32:2), SM(d33:1), SM (d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM(d42:1), S M(d42:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof.

[0150] In certain examples, the one or more lipid biomarkers include LPC (14:0) and PI (38:6), or fragments, variants, or derivatives thereof. In these examples, the one or more lipid biomarkers can optionally further comprise one or more other lipid biomarkers selected from the group consisting of Cer (d36:1), Cer (d42:0), Cer (d42:1), DG (34:2), Hex2Cer (d34:1), Hex2Cer (d42:2), LPC (16:0e), LPC (17:1), LPC (18:3), LPE (22:6), LPI (20:4), PC (32:1), PC (34:1), PC (35:4), PC (36:2), PC (36:3), PC (37:4), PC (38:5), PE (34:1), PE (34:2p), PE (36:4), PE (38:3p), PE (38:4), PE (O-38:5), PE (O-40:6), PI (O-40:4), :4), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof.

[0151] Suitably, the one or more lipid biomarkers are selected from the group consisting of LPC (14:0), PC (32:1), PC (38:5), PE (34:1), PS (38:4), SM (d36:2), SM (d38:4), TG (54:4), TG (56:1) and TG (58:2), or fragments, variants or derivatives thereof. In various examples, the one or more lipid biomarkers comprise or consist of LPC (14:0), PC (32:1), PC (38:5), PE (34:1), PS (38:4), SM (d36:2), SM (d38:4), TG (54:4), TG (56:1) and TG (58:2), or fragments, variants or derivatives thereof.

[0152] Suitably, one or more lipid biomarkers described herein are selected from the group consisting of LPC(14:0), PC(32:1), PC(36:2), PC(38:5), PE(34:1), PI(34:1), PS(38:4), SM(d36:2), SM(d38:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(58:2) and TG(58:3), or fragments, variants or derivatives thereof. In certain instances, the one or more lipid biomarkers include or consist of LPC (14:0), PC (32:1), PC (36:2), PC (38:5), PE (34:1), PI (34:1), PS (38:4), SM (d36:2), SM (d38:4), SM (d44:4), TG (52:3e), TG (53:4), TG (54:4), TG (54:5), TG (54:6), TG (56:1), TG (58:2) and TG (58:3), or fragments, variants or derivatives thereof. More specifically, the one or more lipid biomarkers may be selected from the group consisting of LPC (14:0), PE (34:1), PS (38:4), SM (d36:2), TG (52:3e) and TG (58:3), or fragments, variants or derivatives thereof. In some examples, the one or more lipid biomarkers include or consist of LPC (14:0), PE (34:1), PS (38:4), SM (d36:2), TG (52:3e) and TG (58:3), or fragments, variants or derivatives thereof.

[0153] Suitably, the one or more lipid biomarkers described herein are selected from the group consisting of LPC (14: 0), LPE (22: 6), PI (38: 6), PE (34: 2p), SM (d35: 1), PS (38: 4) and PS (38: 4), or fragments, variants or derivatives thereof. In certain instances, the one or more lipid biomarkers comprise or consist of LPC (14: 0), LPE (22: 6), PI (38: 6), PE (34: 2p), SM (d35: 1), PS (38: 4) and PS (38: 4), or fragments, variants or derivatives thereof.

[0154] Suitably, the one or more lipid biomarkers are selected from the group consisting of Cer (d42:1), LPC (14:0), LPC (16:0e), LPC (17:1), LPE (22:6), PE (34:2p), PE (36:4), PE (38:3p), PI (36:1), PI (38:6), PS (38:4), PS (40:6), SM (d33:1), SM (d35:1), SM (d41:2) and TG (57:1), or fragments, variants or derivatives thereof. In a particular example, the one or more lipid biomarkers include or consist of Cer (d42:1), LPC (14:0), LPC (16:0e), LPC (17:1), LPE (22:6), PE (34:2p), PE (36:4), PE (38:3p), PI (36:1), PI (38:6), PS (38:4), PS (40:6), SM (d33:1), SM (d35:1), SM (d41:2) and TG (57:1), or fragments, variants or derivatives thereof. In some instances, the one or more lipid biomarkers are selected from the group consisting of LPC (14: 0), LPC (16: 0e), LPE (22: 6), PE (34: 2p), PE (38: 3p), PI (36: 1), PI (38: 6), PS (38: 4), PS (40: 6), SM (d33: 1), SM (d35: 1) and TG (57: 1), or fragments, variants or derivatives thereof. In certain instances, the one or more lipid biomarkers include or consist of LPC (14: 0), LPC (16: 0e), LPE (22: 6), PE (34: 2p), PE (38: 3p), PI (36: 1), PI (38: 6), PS (38: 4), PS (40: 6), SM (d33: 1), SM (d35: 1) and TG (57: 1), or fragments, variants or derivatives thereof.

[0155] In some examples, the one or more lipid biomarkers include or consist of LPC (14: 0), PS (38: 4) and TG (57: 1), or fragments, variants or derivatives thereof. In other examples, the one or more lipid biomarkers include or consist of LPC (16: 0e), LPE (22: 6) and PI (38: 6), or fragments, variants or derivatives thereof. In a specific example, the one or more lipid biomarkers include or consist of PE (34: 2p), PE (38: 3p), PI (36: 1), PI (38: 6) and PS (40: 6), or fragments, variants or derivatives thereof. In various examples, the one or more lipid biomarkers include or consist of LPC (14: 0), PI (38: 6) and SM (d33: 1), or fragments, variants or derivatives thereof. In certain instances, the one or more lipid biomarkers include or consist of LPC (14: 0), PI (38: 6) and SM (d35: 1), or fragments, variants or derivatives thereof. For the above examples, the one or more lipid biomarkers may optionally further comprise one or more other lipid biomarkers provided herein, such as those selected from the group consisting of LPC (14: 0), LPC (16: 0e), LPE (22: 6), PE (34: 2p), PE (38: 3p), PI (36: 1), PI (38: 6), PS (38: 4), PS (40: 6), SM (d33: 1), SM (d35: 1) and TG (57: 1), or fragments, variants or derivatives thereof.

[0156] Also provided are lipid "variants", such as naturally occurring variants, isobars and isomers (including stereoisomers) of lipid biomarkers provided herein. For this reason, it is also envisioned that the lipid biomarkers described herein can encompass a set of one or more isomers thereof. For example, PC (32: 1) is a lipid or lipid biomarker, which is a set of one or more phosphatidylcholine isomers having 32 carbons in the acyl chain and a double bond in any one of the two acyl chains. Exemplary isomers of lipid biomarkers described herein are provided in the following table. Suitably, each lipid biomarker isomer has the same molecular weight. Although lipid biomarkers can encompass the total number of their isomers, biological samples from subjects may contain only one isomer, two isomers, three isomers, four isomers, five isomers, etc., or any number of isomers less than the total number of all possible isomers of the lipid biomarkers. Therefore, lipid biomarkers can refer to one or more isomers constituting the entire set of possible isomers.

[0157] Isomer table

[0158]

[0159]

[0160]

[0161]

[0162]

[0163]

[0164]

[0165]

[0166]

[0167]

[0168]

[0169] *As measured by mass spectrometry as described herein.

[0170] In a specific example, PE(O-40:6) includes PE(40:5p) and PE(40:6e). For example, since PE(O-40:6) encompasses PE(40:5p) and PE(40:6e), one or more lipid biomarkers described herein can be selected from the group consisting of: Cer(d36:1), Cer(d42:0), Cer(d42:1), DG(34:2), Hex2Cer(d34:1), Hex2Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(17:1), LPC(18:3), LPE(22:6), LPI(20:4), PC(32:1), PC(34:1), PC(35:4), PC(36:2), PC(36:3), PC(37:4), PC(38:5), PE(34:1), PE(34:2p), PE(36:4), PE(38:3p), PE(38:4), PE(O-38:5) (e.g. PE(38:4p) and PE(38:5e)), PE(40:5p), PE(40:6e), PI(32:1), PI(34:1), PI(36:1), PI(38:6), PS(38:4), PS(40:6), PS(40:7), SM(d32:2), SM(d33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM(d42:1), SM(d42:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof.

[0171] In a specific example, PE(O-38:5) includes PE(38:4p) and PE(38:5e). For example, since PE(O-38:5) encompasses PE(38:4p) and PE(38:5e), one or more lipid biomarkers described herein can be selected from the group consisting of: Cer(d36:1), Cer(d42:0), Cer(d42:1), DG(34:2), Hex2Cer(d34:1), Hex2Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(17:1), LPC(18:3), LPE(22:6), LPI(20:4), PC(32:1), PC(34:1), PC(35:4), PC(36:2), PC(36:3), PC(37:4), PC(38:5), PE(34:1), PE(34:2p), PE(36:4), PE(38:3p), PE(38:4), PE(38:4p), PE(38:5e) PE(O-40:6) (such as PE(40:5p) and PE(40:6e)), PI(32:1), PI(34:1), PI(36:1), PI(38:6), PS(38:4), PS(40:6), PS(40:7), SM(d32:2)、 SM(d33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM(d42:1), SM(d42:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof.

[0172] In other examples, PE(34:2p) includes the isomer PE(34:3e) and can be used interchangeably herein.

[0173] It is also contemplated that the lipid biomarkers described herein may encompass or be interchangeable with one or more isobars thereof. The term "isobar" generally refers to different lipids having almost or substantially the same mass (e.g., m / z ratio) and that may not be distinguished from each other on the analytical platform used in its detection (e.g., for mass spectrometry, different lipids in an isobar may elute at almost the same time and have similar or identical quantitative ions and therefore cannot be distinguished).

[0174] Lipid can be defined according to the following formula: XXX (YY: ZZ), wherein XXX is the abbreviation of lipid class or group (indicating lipid head group in many cases), YY is the number of carbons in acyl chain, and ZZ is the number in acyl chain. Similar symbols (such as XXX (YY1: ZZ1_YY2: ZZ2) or XXX (YY1: ZZ1_YY2: ZZ2_YY3_ZZ3) can be used to define lipid isomers, wherein the number refers to the specific acyl chain of lipid. However, it is contemplated that the lipid defined in this article can be identified by different naming annotations or nomenclature known in the art (see, for example, Liebisch et al., J Lipid Res, June 2013; 54 (6): 1523-1530; Lipidomics Standards Initiative Consortium, Nat Metab, August 2019; 1 (8): 745-747).

[0175] It is also envisioned that the lipid biomarker may additionally encompass one or more other lipid biomarkers that are similarly or equivalently expressed (e.g., exhibiting similar concentration distributions) to the lipid biomarker. For this reason, in terms of, for example, diagnosticity or indicativeness of breast cancer to a subject, a lipid biomarker may exhibit substantial collinearity with one or more other lipid biomarkers. Collinearity refers to a strong correlation or linear relationship between a pair of predictors (e.g., a pair of lipid biomarkers), and the collinearity between multiple predictors is referred to as multicollinearity. Therefore, in some instances, one or more lipid biomarkers include one or more other lipid biomarkers, such as those summarized in Examples 1 to 3 below, which exhibit collinearity with one or more of the one or more lipid biomarkers described in the examples provided herein. In other instances, lipid biomarkers rarely or do not exhibit collinearity with one or more other lipid biomarkers.

[0176] Also provided are fragments of lipid biomarkers, comprising a lipid head group and an acyl chain or a fragment thereof, which accounts for less than 100% of the complete lipid biomarker molecule. In this regard, the skilled artisan will appreciate that MRM analysis of lipid biomarkers by mass spectrometry can include fragmenting the lipid into its component parts (e.g., a lipid head group and one or more acyl chains) to aid in identifying and quantifying the lipid biomarkers, as described in more detail below.

[0177] High-resolution accurate mass MS (HRMS) can also be used to perform reliable and sensitive quantitative analysis of lipid biomarkers, similar to the quantitative analysis of MRM (for review, see Rochat, Trends in Analytical Chemistry, 2016). Parallel reaction monitoring (PRM) is an ion monitoring technique based on high-resolution and high-precision mass spectrometry. PRM can be based on, for example, the Q Exactive Orbitrap TM (Thermo Scientific TM ) system or Sciex 7500 (Sciex TM ) system as a representative quadrupole-high-resolution mass spectrometry platform. Unlike MRM, which monitors a specific transition at one time, the high resolution and mass accuracy of PRM full scan (MS1) and tandem mass spectrometry (MS / MS) scanning can generate sufficient selectivity by monitoring all MS / MS fragment ions of each target precursor lipid.

[0178] Suitably, determine the level (e.g., concentration or expression level) of two or more lipid biomarkers (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54 or 55 lipid biomarkers provided herein for use in methods described herein. In some instances, the methods described herein include the steps of determining the level or concentration of three or more lipid biomarkers described herein. In other instances, the methods described herein include the steps of determining the level or concentration of four or more lipid biomarkers described herein. In some instances, the methods described herein include steps for determining the levels or concentrations of five or more lipid biomarkers described herein. In some instances, the methods described herein include steps for determining the levels or concentrations of six or more lipid biomarkers described herein. In various instances, the methods described herein include steps for determining the levels or concentrations of seven or more lipid biomarkers described herein. In a specific instance, the methods described herein include steps for determining the levels or concentrations of eight or more lipid biomarkers described herein. In some instances, the methods described herein include steps for determining the levels or concentrations of nine or more lipid biomarkers described herein. In other instances, the methods described herein include steps for determining the levels or concentrations of ten or more lipid biomarkers described herein. In some instances, the methods described herein include steps for determining the levels or concentrations of eleven or more lipid biomarkers described herein. In some instances, the methods described herein include steps for determining the levels or concentrations of twelve or more lipid biomarkers described herein. In various instances, the methods described herein include steps for determining the levels or concentrations of thirteen or more lipid biomarkers described herein. In a specific example, the method described herein includes the step of determining the level or concentration of fourteen or more lipid biomarkers described herein. In various examples, the method described herein includes the step of determining the level or concentration of fifteen or more lipid biomarkers described herein. In a specific example, the method described herein includes the step of determining the level or concentration of sixteen or more lipid biomarkers described herein. In some examples, the method described herein includes the step of determining the level or concentration of seventeen or more lipid biomarkers described herein. In some examples, the method described herein includes the step of determining the level or concentration of eighteen or more lipid biomarkers described herein.In certain examples, the methods described herein include the step of determining the levels or concentrations of nineteen or more lipid biomarkers described herein. In various examples, the methods described herein include the step of determining the levels or concentrations of twenty or more lipid biomarkers described herein.

[0179] In one example, the methods of the present disclosure comprise determining Cer(d36:1) and at least one other lipid biomarker described herein (e.g., Cer(d42:0), Cer(d42:1), DG(34:2), Hex2Cer(d34:1), Hex2Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(17:1), LPC(18:3), LPE(22:6), LPI(20:4), PC(32:1), PC(34:1), PC(35:4), PC(36:2), PC(36:3), PC(37:4), PC(38:5), PE(34:1), PE(34:2p), PE(36:4), PE(38:3p), PE(38:4), PE(0-38:5), PE(0-40:6), PI(0-40:4), PI(0-40:4), PI(0-40:4). 34:1), PI(36:1), PI(38:6), PS(38:4), PS(40:6), PS(40:7), SM(d32:2), SM(d33:1), SM(d35:1 ), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM(d42:1), SM(d42: 4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2) and fragments, variants or derivatives thereof).

[0180] In one example, the methods of the present disclosure comprise the step of determining the level of Cer(d42:0) and at least one other lipid biomarker described herein.

[0181] In one example, the methods of the present disclosure comprise the step of determining the level of Cer(d42:1) and at least one other lipid biomarker described herein.

[0182] In one example, the methods of the present disclosure comprise the step of determining the level of DG(34:2) and at least one other lipid biomarker described herein.

[0183] In one example, the methods of the present disclosure comprise the step of determining the level of Hex2Cer (d34:1) and at least one other lipid biomarker described herein.

[0184] In one example, the methods of the present disclosure comprise the step of determining the level of Hex2Cer (d42:2) and at least one other lipid biomarker described herein.

[0185] In one example, the methods of the present disclosure comprise the step of determining the level of LPC(14:0) and at least one other lipid biomarker described herein.

[0186] In one example, the methods of the present disclosure comprise the step of determining the level of LPC(16:0e) and at least one other lipid biomarker described herein.

[0187] In one example, the methods of the present disclosure comprise the step of determining the level of LPC(17:1) and at least one other lipid biomarker described herein.

[0188] In one example, the methods of the present disclosure comprise the step of determining the level of LPC(18:3) and at least one other lipid biomarker described herein.

[0189] In one example, the methods of the present disclosure comprise the step of determining the level of LPE(22:6) and at least one other lipid biomarker described herein.

[0190] In one example, the methods of the present disclosure comprise the step of determining the level of LPI(20:4) and at least one other lipid biomarker described herein.

[0191] In one example, the methods of the present disclosure comprise the step of determining the level of PC(32:1) and at least one other lipid biomarker described herein.

[0192] In one example, the methods of the present disclosure comprise the step of determining the level of PC(34:1) and at least one other lipid biomarker described herein.

[0193] In one example, the methods of the present disclosure comprise the step of determining the level of PC(35:4) and at least one other lipid biomarker described herein.

[0194] In one example, the methods of the present disclosure comprise the step of determining the level of PC(36:2) and at least one other lipid biomarker described herein.

[0195] In one example, the methods of the present disclosure comprise the step of determining the level of PC(36:3) and at least one other lipid biomarker described herein.

[0196] In one example, the methods of the present disclosure comprise the step of determining the level of PC(37:4) and at least one other lipid biomarker described herein.

[0197] In one example, the methods of the present disclosure comprise the step of determining the level of PC(38:5) and at least one other lipid biomarker described herein.

[0198] In one example, the methods of the present disclosure comprise the step of determining the level of PE(34:1) and at least one other lipid biomarker described herein.

[0199] In one example, the methods of the present disclosure comprise the step of determining the level of PE(34:2p) and at least one other lipid biomarker described herein.

[0200] In one example, the methods of the present disclosure comprise the step of determining the level of PE(36:4) and at least one other lipid biomarker described herein.

[0201] In one example, the methods of the present disclosure comprise the step of determining the level of PE (38:3p) and at least one other lipid biomarker described herein.

[0202] In one example, the methods of the present disclosure comprise the step of determining the level of PE(38:4) and at least one other lipid biomarker described herein.

[0203] In one example, the methods of the present disclosure include the step of determining the level of PE(0-38:5) (e.g., PE(38:4p) and / or PE(38:5e)) and at least one other lipid biomarker described herein. More specifically, the methods of the present disclosure may include the step of determining the level of PE(38:4p) and at least one other lipid biomarker described herein. More specifically, the methods of the present disclosure may include the step of determining the level of PE(38:5e) and at least one other lipid biomarker described herein.

[0204] In one example, the methods of the present disclosure include the step of determining the level of PE(0-40:6) (e.g., PE(40:5p) and / or PE(40:6e)) and at least one other lipid biomarker described herein. More specifically, the methods of the present disclosure may include the step of determining the level of PE(40:5p) and at least one other lipid biomarker described herein. More specifically, the methods of the present disclosure may include the step of determining the level of PE(40:6e) and at least one other lipid biomarker described herein.

[0205] In one example, the methods of the present disclosure comprise the step of determining the level of PI(32:1) and at least one other lipid biomarker described herein.

[0206] In one example, the methods of the present disclosure comprise the step of determining the level of PI(34:1) and at least one other lipid biomarker described herein.

[0207] In one example, the methods of the present disclosure comprise the step of determining the level of PI(36:1) and at least one other lipid biomarker described herein.

[0208] In one example, the methods of the present disclosure comprise the step of determining the level of PI(38:6) and at least one other lipid biomarker described herein.

[0209] In one example, the methods of the present disclosure comprise the step of determining the level of PS(38:4) and at least one other lipid biomarker described herein.

[0210] In one example, the methods of the present disclosure comprise the step of determining the level of PS(40:6) and at least one other lipid biomarker described herein.

[0211] In one example, the methods of the present disclosure comprise the step of determining the level of PS(40:7) and at least one other lipid biomarker described herein.

[0212] In one example, the methods of the present disclosure comprise the step of determining the level of SM(d32:2) and at least one other lipid biomarker described herein.

[0213] In one example, the methods of the present disclosure comprise the step of determining the level of SM(d33:1) and at least one other lipid biomarker described herein.

[0214] In one example, the methods of the present disclosure comprise the step of determining the level of SM(d35:1) and at least one other lipid biomarker described herein.

[0215] In one example, the methods of the present disclosure comprise the step of determining the level of SM (d36:1) and at least one other lipid biomarker described herein.

[0216] In one example, the methods of the present disclosure comprise the step of determining the level of SM(d36:2) and at least one other lipid biomarker described herein.

[0217] In one example, the methods of the present disclosure comprise the step of determining the level of SM(d38:4) and at least one other lipid biomarker described herein.

[0218] In one example, the methods of the present disclosure comprise the step of determining the level of SM (d40:3) and at least one other lipid biomarker described herein.

[0219] In one example, the methods of the present disclosure comprise the step of determining the level of SM(d41:2) and at least one other lipid biomarker described herein.

[0220] In one example, the methods of the present disclosure comprise the step of determining the level of SM(d41:3) and at least one other lipid biomarker described herein.

[0221] In one example, the methods of the present disclosure comprise the step of determining the level of SM(d42:1) and at least one other lipid biomarker described herein.

[0222] In one example, the methods of the present disclosure comprise the step of determining the level of SM(d42:4) and at least one other lipid biomarker described herein.

[0223] In one example, the methods of the present disclosure comprise the step of determining the level of SM(d44:4) and at least one other lipid biomarker described herein.

[0224] In one example, the methods of the present disclosure comprise the step of determining the level of TG(52:3e) and at least one other lipid biomarker described herein.

[0225] In one example, the methods of the present disclosure comprise the step of determining the level of TG(53:4) and at least one other lipid biomarker described herein.

[0226] In one example, the methods of the present disclosure comprise the step of determining the level of TG(54:3) and at least one other lipid biomarker described herein.

[0227] In one example, the methods of the present disclosure comprise the step of determining the level of TG(54:4) and at least one other lipid biomarker described herein.

[0228] In one example, the methods of the present disclosure comprise the step of determining the level of TG(54:5) and at least one other lipid biomarker described herein.

[0229] In one example, the methods of the present disclosure comprise the step of determining the level of TG(54:6) and at least one other lipid biomarker described herein.

[0230] In one example, the methods of the present disclosure comprise the step of determining the level of TG(56:1) and at least one other lipid biomarker described herein.

[0231] In one example, the methods of the present disclosure comprise the step of determining the level of TG(57:1) and at least one other lipid biomarker described herein.

[0232] In one example, the methods of the present disclosure comprise the step of determining the level of TG(58:1) and at least one other lipid biomarker described herein.

[0233] In one example, the methods of the present disclosure comprise the step of determining the level of TG(58:2) and at least one other lipid biomarker described herein.

[0234] In one example, the methods of the present disclosure comprise the step of determining the level of TG(58:3) and at least one other lipid biomarker described herein.

[0235] In one example, the methods of the present disclosure comprise the step of determining the level of TG(59:2) and at least one other lipid biomarker described herein.

[0236] In one example, the methods of the present disclosure comprise the step of determining the level of TG(60:1) and at least one other lipid biomarker described herein.

[0237] In one example, the methods of the present disclosure comprise the step of determining the level of TG(62:2) and at least one other lipid biomarker described herein.

[0238] Any method disclosed herein may not comprise measuring any other biomarker. Thus, the methods disclosed herein may comprise excluding any other biomarker from the analysis.

[0239] In certain examples, the methods disclosed herein do not comprise measuring the levels of PC(32:1), PC(38:5) and / or TG(54:4). To this end, the one or more lipid biomarkers may not comprise PC(32:1), PC(38:5) and / or TG(54:4), or fragments, variants or derivatives thereof.

[0240] In some examples, the one or more lipid biomarkers are selected from the group consisting of Cer (d36:1), Cer (d42:0), Cer (d42:1), DG (34:2), Hex2Cer (d34:1), Hex2Cer (d42:2), LPC (14:0), LPC (16:0e), LPC (17:1), LPC (18:3), LPE (22:6), LPI (20:4), PC (34:1), PC (35:4), PC (36:2), PC (36:3), PC (37:4), PE (34:1), PE (34:2p), PE (36:4), PE (38:3p), PE (38:4), PE (O-38:5), PE (O-40:6), PI (32:1), PI (34:1 ), PI(36:1), PI(38:6), PS(38:4), PS(40:6), PS(40:7), SM(d32:2), SM(d33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM(d42:1), SM(d42:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof.

[0241] In some examples, the one or more lipid biomarkers may not include Cer(d36:1), LPC(16:0e), PC(32:1), PC(34:1), PC(36:2), PC(36:3), PC(38:5), PE(38:3p), PE(38:4), PE(O-38:5), SM(d36:1), SM(d42:1), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5) and / or TG(54:6), or fragments, variants or derivatives thereof.

[0242] In some examples, the one or more lipid biomarkers are selected from the group consisting of: Cer (d42:0), Cer (d42:1), DG (34:2), Hex2Cer (d34:1), Hex2Cer (d42:2), LPC (14:0), LPC (17:1), LPC (18:3), LPE (22:6), LPI (20:4), PC (35:4), PC (37:4), PE (34:1), PE (34:2p), PE (36:4), PE (40:5p), PE (40:6e), PI (32:1), PI (34:1), P I(36:1), PI(38:6), PS(38:4), PS(40:6), PS(40:7), SM(d32:2), SM(d33:1), SM(d35:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM(d42:4), SM(d44:4), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2)), or fragments, variants or derivatives thereof.

[0243] Determine levels of lipid biomarkers

[0244] It will be appreciated by those skilled in the art that the expression level, abundance or concentration of one or more lipid biomarkers can be determined by any means known in the art. The terms "determining", "measuring", "evaluating", "assessing", "quantifying", "calculating" and "assaying" are used interchangeably herein and may include any form of measurement known in the art, such as those described below. Such determination may include detecting the presence or absence of one or more lipid biomarkers in a biological sample obtained from a subject and / or determining its concentration level.

[0245] Suitable means for determining the concentration or expression level of one or more lipid biomarkers include, but are not limited to, nuclear magnetic resonance (NMR) spectroscopy, surface plasmon resonance (SPR), chromatographic techniques, mass spectrometry, biosensors, and any combination of these techniques.

[0246] In certain instances, the concentration or expression level of one or more lipid biomarkers is measured by mass spectrometry. Mass spectrometry (MS) is an analytical technique that measures the mass-to-charge ratio (m / z) of charged particles. It is primarily used to determine the elemental composition of a sample or molecule, and to elucidate the chemical structure of molecules such as peptides, lipids, and other compounds. MS works by ionizing compounds to produce charged molecules or molecular fragments and measuring their mass-to-charge ratio. MS instruments typically consist of three modules: (1) an ion source that is capable of converting gas phase sample molecules into ions (or, in the case of electrospray ionization, causing ions in solution to enter the gas phase); (2) a mass analyzer that sorts ions according to their mass by applying an electromagnetic field; and (3) a detector that measures the value of an indicator quantity and thus provides data for calculating the abundance of each ion present.

[0247] Suitable mass spectrometry methods for use in the present disclosure include, but are not limited to, one or more of the following: electrospray ionization mass spectrometry (ESI-MS), ESI-MS / MS, ESI-MS / (MS)n, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption / ionization time-of-flight mass spectrometry (SELDI-TOF-MS), tandem liquid chromatography-mass spectrometry (LC-MS / MS) mass spectrometry, desorption / ionization on silicon (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS / MS, APCI-(MS)n, atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS / MS and APPI-(MS)n, quadrupole mass spectrometry, Fourier transform mass spectrometry (FTMS), and ion trap mass spectrometry, wherein n is an integer greater than zero. In certain examples, the concentration or expression level of one or more lipid biomarkers is determined at least in part by using liquid chromatography-mass spectrometry (LC-MS).

[0248] As noted above, MS ionizes lipids and sorts ions based on their mass-to-charge ratio. It has been widely used to characterize lipids, especially with soft ionization techniques, such as the development of electrospray ionization (ESI) and matrix-assisted laser desorption ionization (MALDI). Lipid extraction is typically the first step in lipid analysis, and lipid components (organic phase) are separated from other components (such as proteins and nucleic acids) (aqueous phase). However, some examples utilize single-phase lipid extraction.

[0249] The extraction method usually involves the application of a mixture of methanol, chloroform and water for phase separation. However, shotgun lipidomics methods have also been developed that omit the chromatographic separation and sample processing described above and analyze all lipid classes together, and instead use ionization additives to help distinguish specific lipids. The MS method in this case may involve lipid digestion, fragmentation or denaturation, followed by LC-MS or LC-MS / MS (tandem MS) to obtain the mass-to-charge ratio of the specific lipid head groups and / or acyl chains that constitute the lipid biomarkers described herein.

[0250] Suitably, the one or more lipid biomarkers or one or more fragments thereof are then subjected to quantitative mass spectrometry analysis, including but not limited to selected reaction monitoring mass spectrometry (SRM), high resolution data independent analysis (SWATH), multiple reaction monitoring (MRM) and / or MSI-based quantification.

[0251] In certain examples, MRM assays are used, which employ specific lipids and their fragments (transitions) as identifiers of individual lipid biomarkers.

[0252] In a specific example, the MS method of the present invention is performed in a positive ion mode and / or an anion mode. Generally, when in a positive ion mode, lipids can form small cationic adducts due to the ionization process. The formation of cationic adducts of lipid molecular species caused by the affinity of cations with dipoles present in lipid species depends on the availability of small cations. For example, these adducts can include H + NH4 + , Li + 、Na + , K + and (-H20+H) + In the anionic mode, lipid species are displayed in deprotonated form or with anionic adducts, depending on whether the lipid molecular species carries a net ionic bond. For example, PE, PI, PS, PA, and PG are all acidic lipid classes (i.e., ionic bonds are present) and can therefore be detected as deprotonated ions. Some lipids belong to the polar lipid class without ionizable bonds, or PC and SM are strong zwitterionic lipid classes, all of which can react with small anions (e.g., Cl - 、CH3COO - and HCOO - ) form anionic adducts, depending on the concentration present and its affinity to these lipid species.

[0253] In some examples, one or more lipid biomarkers described herein comprise the ion patterns listed in Tables 8 to 26. Suitably, the ion patterns are specific to the MS methods described in the corresponding examples.

[0254] In other examples, one or more lipid biomarkers described herein have a retention time or elution time as listed in Tables 8 to 26 or about the retention time or elution time. Suitably, the retention time is determined according to the MS method described in the corresponding example.

[0255] In certain examples, one or more lipid biomarkers described herein have an accurate mass, neutral mass, or mass-to-charge ratio (m / z) as listed above in the Isomer Table or Tables 8 to 23 or about as described. Suitably, the neutral mass is determined according to the MS method described in the corresponding example.

[0256] In some applications, various ionization techniques can be coupled with a mass spectrometer as provided herein to generate the desired information. Non-limiting exemplary ionization techniques that can be used with the present disclosure include, but are not limited to, matrix-assisted laser desorption ionization (MALDI), desorption electrospray ionization (DESI), direct assistance in real time (DART), surface-assisted laser desorption ionization (SALDI), or electrospray ionization (ESI).

[0257] In some applications, HPLC and UHPLC can be coupled with mass spectrometers so that many other lipid separation techniques can be performed before mass spectrometry. Some exemplary separation techniques that can be used to separate desired analytes (such as lipids) from matrix background include, but are not limited to, reversed phase liquid chromatography (RP-LC) of lipids, off-line liquid chromatography (LC) before MALDI, 1-dimensional gel separation, 2-dimensional gel separation, strong cation exchange (SCX) chromatography, strong anion exchange (SAX) chromatography, weak cation exchange (WCX) and weak anion exchange (WAX). One or more of the above techniques can be used before mass spectrometry.

[0258] In some instances, the expression or concentration of a lipid biomarker in a subject will be higher compared to a reference value determined from a control, whereas for certain lipid biomarkers, the expression or concentration of the biomarker is decreased relative to the reference value from a control.

[0259] Suitably, the expression or concentration level of the first subset of one or more lipid biomarkers increases, indicating that the subject suffers from breast cancer or is associated with breast cancer in the subject; and / or the expression or concentration level of the second subset of one or more lipid biomarkers (e.g., not present in the first subset of one or more lipid biomarkers) decreases, indicating that the subject suffers from breast cancer or is associated with breast cancer in the subject. In other examples, the concentration or expression level of the first subset of one or more lipid biomarkers decreases, indicating that the subject does not suffer from breast cancer or is associated with breast cancer in the subject; and / or the expression or concentration level of the second subset of one or more lipid biomarkers (e.g., not present in the first subset of one or more lipid biomarkers) increases, indicating that the subject does not suffer from breast cancer or is associated with breast cancer in the subject.

[0260] As will be appreciated by those skilled in the art, the level or expression level of any lipid biomarker described herein can be relatively (i) higher, improved or increased; or (ii) lower, reduced or decreased compared to the expression level in a control or reference sample or compared to a threshold expression level. In various instances, if the expression level exceeds the average and / or median expression level of a reference population, it can be classified as higher, improved or increased. In some instances, if the expression level is lower than the average and / or median expression level of a reference population, it can be classified as lower, reduced or reduced. In this regard, the reference population can be a group of subjects suffering from breast cancer. Alternatively, the reference population can be a group of subjects known to have no cancer.

[0261] As used herein, terms such as "higher", "increase" and "increase" refer to an increase in the amount or level of a lipid biomarker, such as in a biological sample, compared to a control or reference level or amount. The concentration or expression level of a lipid biomarker can be relative or absolute (i.e., relatively or absolutely higher, increased or increased). In some instances, if the concentration or expression level of a lipid biomarker is higher than the concentration or expression level of a control or reference level or amount of a lipid biomarker by more than about 0.5%, 1%, 2%, 3%, 4%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 100%, 150%, 200%, 300%, 400% or at least about 500%, then the level of the lipid biomarker is higher, increased or increased.

[0262] As used herein, the terms "lower", "reduced" and "reduced" refer to a lower amount or level of a lipid biomarker, such as in a biological sample, compared to a control or reference level or amount. The concentration or expression level of a lipid biomarker can be relative or absolute (i.e., relatively or absolutely lower, reduced or reduced). In some instances, if the concentration or expression level of a lipid biomarker is less than about 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20% or 10% of the concentration or expression level or amount of a lipid biomarker at a control or reference level or amount, or even less than about 5%, 4%, 3%, 2%, 1%, 0.5%, 0.1%, 0.01%, 0.001% or 0.0001%, then the concentration or expression of the lipid biomarker is lower, reduced or reduced.

[0263] The term "control sample" generally refers to a biological sample from a (healthy) non-diseased individual who does not suffer from cancer or more specifically does not suffer from breast cancer. In some instances, the control sample can be from a subject known to be free of cancer, or more specifically, free of breast cancer. Alternatively, the control sample can be from a subject in which the cancer is in remission. The control sample can be a combined, averaged or separate sample. An internal control is a marker from the same biological sample being tested.

[0264] In some instances, reference level or amount is determined according to the measurement of the biomarker in the corresponding biomarker panel from a healthy individual population. As used herein, the term "healthy individual" refers to a person or population known not to suffer from breast cancer. In some instances, control or reference level is determined according to the measurement of the corresponding biomarker in a "typical population". Preferably, a "typical population" will show a breast cancer spectrum at different stages of disease progression. Particularly preferably, a "typical population" shows the expression characteristics of a subject group (such as a female subject) as described herein.

[0265] In another example, the reference level or amount can be derived from an established data set comprising one or more of:

[0266] 1. A dataset comprising measurements of lipid biomarkers in a subject or population of subjects known to have breast cancer;

[0267] 2. A dataset comprising measurements of lipid biomarkers of the subject being examined, wherein the measurements have been previously performed, e.g., when the subject was known to be healthy (i.e., without breast cancer); and / or

[0268] 3. A dataset comprising measurements of lipid biomarkers of a healthy individual or a population of healthy individuals.

[0269] In certain examples, a dataset comprising measurements of lipid biomarkers can be obtained from a population of subjects known to have breast cancer, healthy individuals, or a population of healthy individuals in a fasting state, a non-fasting state, or a combination thereof.

[0270] As used herein, concentration or expression level can be the absolute amount or relative amount of lipid expressed. Thus, in some instances, the concentration or expression level of any one of the one or more lipid biomarkers is compared to a control concentration or expression level (e.g., the lipid concentration or expression level of one or more "housekeeping" lipids or molecules in a biological sample of a subject).

[0271] In other examples, the concentration or expression level of any one of the one or more lipid biomarkers is compared to a threshold concentration or expression level (e.g., the concentration or expression level of lipid biomarkers in a biological sample from a control subject who does not suffer from breast cancer and / or the average or median concentration or expression level of lipid biomarkers in a biological sample from a breast cancer patient population). The threshold concentration or expression level is typically a quantitative concentration or expression level of a lipid biomarker. Typically, the concentration level or expression level of a lipid biomarker in a sample that exceeds or is below the threshold concentration or expression level is predictive for a particular disease state or outcome (e.g., whether breast cancer is present). The nature and value (if any) of the threshold concentration or expression level will typically vary based on the method selected for determining the concentration or expression of one or more lipid biomarkers used in determining, for example, a breast cancer diagnosis of a subject.

[0272] Those skilled in the art will be able to use any method known in the art for measuring lipid biomarker concentration, abundance or expression (such as those described herein) to determine the threshold level of any one of one or more lipid biomarkers in the sample that can be used to determine, for example, to determine whether the relevant subject has breast cancer. In each example, the threshold level is the average and / or median concentration or expression level (median or absolute value) of lipid biomarkers in a reference population such as those suffering from or not suffering from breast cancer. In addition, the concept of threshold concentration or expression level should not be limited to a single value or result. In this regard, threshold concentration or expression level can encompass multiple threshold concentrations or expression levels, which can represent, for example, a high, medium or low probability that a subject suffers from breast cancer.

[0273] In view of the foregoing, any of the methods disclosed herein may include a step of establishing a reference level or threshold level for the concentration or expression of one or more lipid biomarkers.

[0274] Suitably, the predictive accuracy of the methods described herein as determined by ROC AUC value is at least about 0.65 (e.g., at least about 0.65, 0.66, 0.67, 0.68, 0.69, 0.70, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.80, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, or any range therein). More specifically, the predictive accuracy of the described methods is suitably at least about 0.70. Even more specifically, the prediction accuracy of the described methods is suitably at least about 0.75. Even more specifically, the prediction accuracy of the described methods is suitably at least about 0.80. Even more specifically, the prediction accuracy of the described methods is suitably at least about 0.85. Even more specifically, the prediction accuracy of the described methods is suitably at least about 0.90.

[0275] Calculate risk or diagnostic scores

[0276] For the methods described herein, determining whether a subject has breast cancer can include the step of calculating a risk score or a diagnostic score.

[0277] The term "risk score" or "disease risk score" refers to a value calculated using one or more characteristic values ​​or scores that indicates an undesirable physiological state of a patient, such as the presence of cancer. In certain instances, the term "risk score" refers to a numerical representation of the current degree of risk or probability of a patient developing a particular disease or condition.

[0278] Risk scores can be calculated using the concentration or expression level or expression marker of one or more lipid biomarkers (such as in a diagnostic lipid biomarker panel (e.g., 2, 3, 4, 5, etc. or more)), including those described above. To this end, the methods described herein include the steps of obtaining the risk score of the lipid biomarker combination described above or listed in any one of Examples 1 and 2 (e.g., Tables 8 to 23). The concentration or expression marker of lipids can be determined using the normalized concentration or expression level of lipid biomarkers in the sample and the independent diagnostic value of lipid biomarkers based on the correlation between the concentration or expression of lipid biomarkers and the presence or absence of the disease. Any method for determining the concentration or expression marker of lipid biomarkers known in the art can be used. After determining the concentration or expression level or expression marker of individual lipid biomarkers (such as in a panel of two or more lipid biomarkers described herein), the risk score can be calculated by combining the concentration or expression level and / or expression marker of each lipid biomarker in its panel. The method for calculating risk scores can be as described in the examples.

[0279] In certain examples, the risk score is calculated at least in part by logistic regression. For example, a linear combination of the concentration or expression level of one or more lipid biomarkers and various coefficients determined by previous training can be generated and then used to estimate the log odds of cancer. The log odds can then be converted to the probability that the subject has breast cancer by logistic regression. In other examples, the risk score is calculated at least in part by partial least squares discriminant analysis.

[0280] Therefore, the patient's risk score can be calculated as follows:

[0281] Cancer probability = e I+∑ci*Li / (1+e l+∑ci *Li )

[0282] Where the intercept I and the coefficient c i are the specific logistic regression model parameters calculated in advance based on the training data, and the value L i Represents the normalized lipid abundance of various lipids in the panel measured for the corresponding patient samples.

[0283] The calculated risk score of the present disclosure can be used to determine the likelihood of a subject having breast cancer. Typically, the calculated risk score can be compared to a reference risk score. In some instances, (i) if the risk score is equal to or higher than the reference risk score, the subject has breast cancer, and (ii) if the risk score is lower than the reference risk score, the subject does not have breast cancer. It is envisioned that the subject's diagnosis and / or risk score can be used to determine whether the subject should be treated with an anticancer agent. Therefore, in other instances, (i) if the risk score is equal to or higher than the reference risk score, the subject will be given an anticancer treatment, and (ii) if the risk score is lower than the reference risk score, the subject will not be given an anticancer treatment.

[0284] In some instances, the risk score is compared to a threshold risk score (such as a median or average risk score) to determine a diagnosis of breast cancer in a subject. If the risk score is equal to or higher than the threshold risk score, the subject is as expected to have breast cancer. Alternatively, if the risk score is lower than the threshold risk score, the subject is as expected not to have breast cancer. The threshold risk score can be the corresponding median or average of the risk score calculated for each subject in a population of subjects with breast cancer and / or the corresponding median or average of the risk score calculated for each subject in a population of subjects without breast cancer (such as those described in Example 1).

[0285] Reagent test kit

[0286] The present disclosure also contemplates kits for detecting lipid biomarkers, which may be suitable for use in the methods described herein.

[0287] In a broad form, the present disclosure provides a kit for determining the presence or absence of breast cancer in a subject, the kit comprising one or more reagents for determining the level of one or more lipid biomarkers in a biological sample obtained from the subject, wherein the one or more lipid biomarkers are selected from the group consisting of: Cer(d36:1), Cer(d42:0), Cer(d42:1), DG(34:2), Hex2Cer(d34:1), Hex2Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(17:1), LPC(18:3), LPE(22:6), LPI(20:4), PC(32:1), PC(34:1), PC(35:4), PC(36:2), PC(36:3), PC(37:4), PC(38:5), PE(34:1), PE(34:2p), PE(36:4), PE(38:3p), PE(38:4), PE(O-38:5), PE(O-40:6), PI(32:1), PI(34:1), PI(36:1), PI(38:6), PS(38:4), PS(40:6), PS( 40:7), SM(d32:2), SM(d33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM (d41:3), SM(d42:1), SM(d42:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof.

[0288] Any agent or probe that can specifically bind to a biomarker gene product will be applicable, such as a primer set, a labeled nucleic acid probe, an aptamer, an antibody, and / or an antibody fragment. Other components of the kit will typically include a label, a secondary antibody, an inhibitor, a cofactor, and a control lipid product formulation to allow the user to quantify and / or assess whether the concentration or expression level is correctly measured. Biosensors, including optical biosensors (e.g., SPR-based sensors, interference measurement-based sensors, waveguide-based sensors), electrochemical biosensors, and mechanical biosensors are particularly suitable assays that can be easily performed by a technician using kit components.

[0289] In some examples, a kit can include a substrate, such as a microtiter plate, onto which are immobilized capture probes or antibodies corresponding to the lipid biomarkers being measured.

[0290] In some examples, the kit includes beads having immobilized thereon capture probes or antibodies corresponding to the lipid biomarkers being measured.

[0291] Optionally, the kit also includes components for detecting the binding of the probe (such as an antibody) to the lipid biomarker. These components include reporter molecules, such as enzymes (such as horseradish peroxidase or alkaline phosphatase), dyes, radionucleotides, luminescent groups, chemiluminescent groups, fluorescent groups, biotin or colloidal particles, such as colloidal gold or selenium. Suitably, this reporter molecule is directly attached to the antibody.

[0292] In one example, the kit may additionally include a reference sample. Suitably, the reference sample includes a reference lipid, which is detected by an antibody and / or can be labeled or modified to be distinguished from a natural lipid. Preferably, the reference lipid has a known concentration. This reference lipid is particularly used as a standard. Therefore, the various known concentrations of this reference lipid can be detected using diagnostic assays described herein.

[0293] The instructions provided in the kits of the present disclosure are typically written instructions on a label or drug insert (e.g., paper included in the kit), but machine-readable instructions (e.g., instructions contained on a magnetic or optical storage disk) are also acceptable. The instructions associated with the use of the reagents described herein typically contain information about determining the concentration or expression level of one or more lipid biomarkers and guidance about the dosage, dosing regimen, and route of administration for a given treatment. The kit may also include a description of selecting an individual who has breast cancer and is therefore suitable for treatment.

[0294] In a specific example, the reference data is on a computer-readable medium (e.g., software embodying any one or more of the methods or functions described herein or utilized by the methods or functions). The computer-readable medium may be contained on a storage device such as a computer memory (e.g., a hard drive or solid-state drive), and may include computer-readable code components that, when selectively executed by a processor, implement one or more aspects of the present disclosure.

[0295] system

[0296] The present disclosure also contemplates systems for detecting lipid biomarkers that may be suitable for use with the methods described herein.

[0297] In one broad form, the present disclosure provides a system for determining whether a subject has breast cancer, the system comprising:

[0298] A mass spectrometer unit configured to determine the level of one or more lipid biomarkers in a biological sample obtained from a subject, wherein the one or more lipid biomarkers are selected from the group consisting of: Cer(d36:1), Cer(d42:0), Cer(d42:1), DG(34:2), Hex2Cer(d34:1), Hex2Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(17:1), LPC(18:3), LPE(22:6), LPI(20:4), PC(32:1), PC(34:1), PC(35:4), PC(36:2), PC(36:3), PC(37:4), PC(38:5), PE(34:1), PE(34:2p), PE(36:4), PE(38:3p), PE(38:4), PE(38:5), PE(38:6), PE(38:7), PE(38:8), PE(38:9), PE(38:10), PE(38:11), PE(38:12), PE(38:13), PE(38:14), PE(38:15), PE(38:17), PE(38:18), PE(38:19). 40:6), PI(32:1), PI(34:1), PI(36:1), PI(38:6), PS(38:4), PS(40:6), PS(40:7), SM(d32:2), SM( d33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM(d4 2:1), SM(d42:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof; and

[0299] A processing unit is configured to use or analyze the level of one or more lipid biomarkers to determine the presence or absence of breast cancer in a subject.

[0300] It should be noted that the step of determining the concentration level or expression level can be performed by a mass spectrometer unit and / or can be performed at least in part by a pre-processing unit. The pre-processing unit can be the same or different from the processing unit that performs the step of analyzing the concentration or expression level. For example, the pre-processing unit can receive data from the mass spectrometer unit, the data indicating the number of fragments (e.g., lipid head groups and / or acyl chains) of lipid biomarkers of corresponding mass values. The data can also represent the residence time of a specific fragment. The pre-processing unit can then process the data to determine the lipid biomarker as a combination of fragments, thereby calculating the corresponding concentration or expression level.

[0301] Suitably, the mass spectrometer unit and the processing unit are units as described herein.

[0302] Computer implemented method

[0303] It is contemplated that one or more steps of the methods described herein can be automated or implemented by a computer, in which sense the disclosed methods are implemented as software codes stored on a non-volatile data storage medium. The computer executes the software code, which causes the computer to perform the methods disclosed herein.

[0304] For example, the concentration level or expression level of one or more lipid biomarkers can be compared with, for example, a reference or threshold level or value, and can be performed by a computer that performs a software code that describes a comparison step. Therefore, the comparison can be performed by a computer or a computing device, such as by a processing unit. The value and reference amount of the determined amount or detected amount of one or more lipid biomarkers in the sample from the experimenter can be, for example, compared with each other, and the comparison can be automatically performed by a computer program that performs an algorithm for comparison. In addition, the calculation of risk or diagnostic score and / or its comparison with reference to risk or diagnostic score can be automatically performed by a computer program that performs an algorithm for comparison. Suitably, these algorithms can be trained on one or more cases and / or control samples. In some instances, a processor can utilize concentration or expression level data and / or risk or diagnostic score to calculate the possibility that the experimenter in question suffers from breast cancer.

[0305] The computer program performing the evaluation will suitably provide the required evaluation in a suitable output format. For computer-assisted comparison, the value of the determined quantity may be compared with a value corresponding to a suitable reference value stored in a database by the computer program. The computer program may further evaluate the results of the comparison, i.e. automatically provide the required evaluation in a suitable output format.

[0306] In some examples, the methods of the present disclosure include one or more of the following broad steps:

[0307] (i) optionally performing measurement of the concentration or expression level of one or more lipid biomarkers described herein;

[0308] (ii) inputting or receiving the value from (i) into a processing system configured to determine whether breast cancer is present in the subject;

[0309] (iii) optionally calculating, by a processing system, a risk or diagnostic score based on the level or expression level of one or more lipid biomarkers;

[0310] (iv) comparing, by a processing system, the concentration or expression level and / or risk or diagnostic score obtained in step (iii) with a threshold or reference value; and

[0311] (v) Determining whether the subject has breast cancer.

[0312] The methods of the present disclosure appropriately allow integration into existing or newly developed pathology architectures or platform systems. For example, the present disclosure contemplates a method that allows a user to determine a subject's status (e.g., whether breast cancer exists), the method comprising the following steps:

[0313] (a) receiving data in the form of concentration or expression level of one or more lipid biomarkers for a test sample, wherein the one or more lipid biomarkers are selected from the group consisting of: Cer(d36:1), Cer(d42:0), Cer(d42:1), DG(34:2), Hex2Cer(d34:1), Hex2Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(17:1), LPC(18:3), LPE(22:6), LPI(20:4), PC(32:1), PC(34:1), PC(35:4), PC(36:2), PC(36:3), PC(37:4), PC(38:5), PE(34:1), PE(34:2p), PE(36:4), PE(38:3p), PE(38:4), PE(O-38:5), PE(O-40:4), :6), PI(32:1), PI(34:1), PI(36:1), PI(38:6), PS(38:4), PS(40:6), PS(40:7), SM(d32:2), SM(d 33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM(d42 :1), SM(d42:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof;

[0314] (b) optionally, such as with a processing unit or system, processing the subject data by univariate and / or multivariate analysis and / or machine learning algorithms (e.g., LASSO penalized multivariate Cox regression, logistic regression, partial least squares discriminant analysis, random forests, decision trees, gradient boosting) to provide a risk or diagnostic score;

[0315] (c) determining the condition of the subject based on the result of comparing the concentration or expression level and / or risk or diagnostic score with a predetermined or reference concentration or expression level and / or risk or diagnostic score value, such as using a processing unit or system; and

[0316] (d) communicating an indication of the subject's status to a user.

[0317] In some examples, the above methods further comprise the step of producing or generating concentration level or expression level data by determining the concentration level or expression level of one or more lipid biomarkers in a biological sample obtained from the subject, for example by using one or more of those methods described herein, wherein the one or more lipid biomarkers are selected from the group consisting of: Cer (d36:1), Cer (d42:0), Cer (d42:1), DG (34:2), Hex2Cer (d34 :1), Hex2Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(17:1), LPC(18:3), LPE(22:6), LPI(20:4), PC(32:1), PC(34:1), PC(35:4), PC(36:2), PC(36:3), PC(37:4), PC(38:5), PE(34:1), PE(34:2p), PE(36:4), PE(38:3 p), PE(38:4), PE(O-38:5), PE(O-40:6), PI(32:1), PI(34:1), PI(36:1), PI(38:6), PS(38:4), PS(40:6), P S(40:7), SM(d32:2), SM(d33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), S M(d41:3), SM(d42:1), SM(d42:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof.

[0318] In one example, the method further comprises:

[0319] (a) enabling a user to determine data using a remote terminal station; and

[0320] (b) Transmitting data from the terminal station to the base station through the communication network.

[0321] The base station may comprise first and second processing systems, in which case the method may comprise:

[0322] (a) transmitting data to a first processing system;

[0323] (b) causing the first processing system to perform a univariate or multivariate analysis function to generate a risk or diagnostic score.

[0324] The method may further comprise:

[0325] (a) transmitting the results of the univariate or multivariate analysis function and / or the determined concentration or expression level of the lipid biomarker to a second processing system; and

[0326] (b) causing the second processing system to determine the status of the subject.

[0327] The second processing system may be coupled to a database adapted to store predetermined data and / or univariate or multivariate analysis functions, such that the computer-implemented method may include:

[0328] (a) querying a database to obtain at least selected predetermined data from the database or to access a univariate or multivariate analysis function; and

[0329] (b) comparing the selected predetermined data with the subject data or generating a predicted probability index.

[0330] The second processing system may be coupled to a database, the method comprising storing the data in the database, eg by means of a memory unit.

[0331] The reference concentration or expression level data comprises a level or concentration or expression level determined for one or more lipid biomarkers within a biological sample selected from the group consisting of:

[0332] (i) a biological sample from a normal or healthy subject (e.g., a normal or healthy subject without breast cancer);

[0333] (ii) a biological sample from a subject previously diagnosed or confirmed to have breast cancer;

[0334] (iii) an extract of any one of (i) to (ii);

[0335] (iv) a dataset comprising the concentration or expression level of a lipid biomarker in a normal or healthy individual or a population of normal or healthy individuals;

[0336] (vi) a dataset comprising the concentration or expression levels of lipid biomarkers in an individual or a population of individuals suffering from breast cancer; and

[0337] (vii) a dataset comprising the concentration or expression level of a lipid biomarker in the tested subject, wherein the concentration or expression level is determined for a sample obtained at an earlier time point when the subject is known not to have breast cancer.

[0338] Obtaining samples from subjects

[0339] The methods disclosed herein may also include an initial or early step of providing or collecting a biological sample from a subject that appropriately contains lipid microvesicles or extracellular vesicles. Such a sample can be obtained by freshly collecting the sample, or can be obtained from a previously collected and stored sample. For example, the sample can be obtained from a previously collected and stored (e.g., refrigerated or frozen) blood or serum sample. Suitably, the sample is obtained by freshly collecting the sample from the subject. Alternatively, the sample can be obtained from a sample previously collected and stored from the subject.

[0340] In a particular instance, the subject has fasted before collection or is in a fasting state when collecting a biological sample for further testing by the methods provided herein. As used herein, the term "fasting" refers to a state in which no food or drink is consumed within a time period (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11 or 12 hours and any range therein) before providing a biological sample for testing.

[0341] In alternative examples, the subject is not fasting before collection or is in a non-fasting (or "eating") state when collecting a biological sample for further testing by the methods provided herein. The term "non-fasting" as generally used herein refers to a state of consuming food and / or beverages within at least about 3 hours to about 12 hours (e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11 or 12 hours and any range therein) of providing a biological sample for testing.

[0342] The method of the present disclosure can be performed on various biological samples. As used herein, the term "biological sample" is appropriately a sample obtained from a subject. For example, the biological sample can be a body fluid of a subject. Suitable biological samples generally include a certain concentration of lipid microvesicles or extracellular vesicles. Suitable biological samples can also include circulating lipid microvesicles or extracellular vesicles. Circulating lipid microvesicles or extracellular vesicles can be found in body fluids (e.g., blood, plasma, serum, urine, vomitus, tears, sputum, etc.) or other excreta (e.g., feces). In some instances, the biological sample is or includes blood, plasma, or serum. In a specific instance, the biological sample is blood. In other instances, the biological sample is plasma. In various instances, the biological sample is serum.

[0343] Suitably, the biological sample has been enriched for extracellular vesicles and / or its lipid content. In a specific instance, the biological sample is or includes an extracellular vesicle or exosome population. For some instances, the biological sample is or includes an extracellular vesicle or exosome population obtained from a patient diagnosed with or suspected of having breast cancer. According to a specific instance, the biological sample is purified or partially purified according to the present disclosure to separate extracellular vesicles therefrom, including cancer-derived extracellular vesicles. For example, a serum sample can be purified to remove cells. In certain instances, prior to performing the method of the present disclosure, other components (e.g., debris, albumin) originally in the biological sample are removed or partially removed from the biological sample. In one example, the biological sample is substantially free of cells.

[0344] The sample comprises an extract, derivative, fraction or suspension of an original biological sample obtained from a subject as disclosed herein.

[0345] Suitably, the biological samples described herein include a certain concentration of extracellular vesicles, such as cancer-derived extracellular vesicles or exosomes. Suitable biological samples may also include circulating extracellular vesicles. Circulating extracellular vesicles can be found in body fluids (e.g., blood, plasma, serum, urine, vomitus, tears, sputum, etc.) or other excreta (e.g., feces). In a specific example, the biological sample is or includes an extracellular vesicle sample. In other examples, the biological sample is or includes an exosome sample (exosome sample or exosomal sample). To this end, aspects of the present disclosure appropriately include determining the level of EV or exosome expression or concentration of the lipid biomarkers described above in the biological sample obtained from the subject. In other words, the present method may include determining or measuring the expression levels of two or more EV-related or exosome-related biomarkers provided herein, excluding protein biomarkers that are not related to EV or exosomes (e.g., biomarkers related to free protein or free RNA in a biological sample).

[0346] Therefore, determining the EV expression level of a biomarker includes determining the expression level of the biomarker associated with an EV or EV population. Similarly, determining the exosome expression level of a biomarker includes determining the expression level of the biomarker associated with an exosome or an exosome population. The terms "EV-associated", "exosome-associated", etc. refer to biomolecules, such as lipid biomarkers, located within / inside or on the surface of EVs or exosomes, respectively. For this purpose, EVs and exosomes can carry or contain cellular biomolecules within their lipid membranes. Since EVs and exosomes are typically formed by cell membranes, they can also carry molecules on their surfaces. In addition, since some biomolecules bind to those molecules on the surface of EVs and exosomes, these biomolecules can also be indirectly regarded as "EV-associated" or "exosome-associated". In addition, the present disclosure contemplates the detection of EV- or exosome-associated biomarkers, even after such biomarkers have been separated from the EVs or exosomes to which they may have been initially associated.

[0347] Prior to performing the measurement of one or more lipid biomarker levels, the biological sample may be subjected to any suitable pretreatment steps to improve the accuracy and / or efficiency of the measurement. These pretreatment steps may include extraction, centrifugation (e.g., ultracentrifugation), lyophilization, fractionation, separation (e.g., using column or gel chromatography), concentration, or evaporation. In some cases, such treatment may include one or more extractions with a solution comprising any suitable solvent or solvent combination, such as, but not limited to, acetonitrile, water, chloroform, methanol, butylated hydroxytoluene, trichloroacetic acid, toluene, hexane, benzene, or a combination thereof. In some instances, the biological sample may undergo one or more treatment steps to separate, concentrate, or enrich for extracellular vesicles and / or their lipid content. Suitably, the biological sample is an extracellular vesicle sample or a sample that has been enriched for extracellular vesicles.

[0348] As used herein, the term "extracellular vesicle" or "EV" refers to a cell-derived vesicle including a membrane that encapsulates the internal space. Extracellular vesicles include all membrane-bound vesicles, and their diameters are generally smaller than the diameter of their source cells. More specifically, extracellular vesicles can include small extracellular vesicles (50-200nm), microvesicles (0.2-1μm), exosomes and exosomes (30-150nm) and cancer bodies (oncosomes) (1μm to 10μm). According to various examples, extracellular vesicles are or include exosomes. In some instances, the average diameter of the extracellular vesicles is between about 10 nm and about 250 nm (e.g., about 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250 nm, or any range therein), more specifically between about 50 nm and about 200 nm, even more specifically between about 30 nm and about 150 nm).

[0349] In view of the above, biological samples (e.g., blood, plasma, and / or serum) may have been enriched for extracellular vesicles, such as cancer-derived extracellular vesicles or exosomes. In some instances, the biological sample is a purified or partially purified extracellular vesicle or exosome population. In other instances, the biological sample is a purified exosome population. In other instances, the biological sample is a partially purified exosome population. Therefore, in some instances, the methods described herein include an initial or early step of at least partially enriching, concentrating, isolating, or purifying a population or portion of extracellular vesicles or exosomes from a biological sample of the patient in question, for example, by those methods described herein.

[0350] A population or fraction of extracellular vesicles or exosomes can be enriched, concentrated, isolated or purified from a biological fluid such as those provided above to facilitate removal of contaminating proteins, lipoproteins, etc. To this end, the extracellular vesicles or exosomes can be isolated by any means known in the art, such as, but not limited to, polyethylene glycol precipitation, high MW centrifugal filtration, ultracentrifugation, size exclusion chromatography, exosome precipitation (e.g., ExoQuick from System Biosciences), tangential flow filtration, affinity-based exosome capture (e.g., affinity purification with binding molecules or antibodies to CD63, CD81, CD82, CD9, Alix, annexin, EpCAM, and Rab5), immunomagnetic capture (e.g., EXO-PAGE from INOVIQ), or immunomagnetic capture (e.g., EXO-PAGE from INOVIQ). System) and any combination thereof. In various examples, the extracellular vesicles or exosomes are at least partially isolated from the biological sample by ultracentrifugation. In certain examples, the extracellular vesicles or exosomes are at least partially isolated from the biological sample by size exclusion chromatography. In other examples, the extracellular vesicles or exosomes are at least partially isolated from the biological sample by affinity-based methods, more specifically immunomagnetic capture.

[0351] As used herein, the terms "isolate," "isolating," or "isolation" refer to a material, such as an extracellular vesicle or exosome, that has been removed from its natural state or otherwise artificially manipulated. An isolated material may be substantially or essentially free of components that normally accompany it in its natural state, or may be manipulated so as to be in an artificial state with components that normally accompany it in its natural state. These terms encompass the gross physical separation of a material (including an extracellular vesicle) from its natural environment (e.g., removal / purification from a biological sample obtained from a subject suspected of having breast cancer).

[0352] Thus, any method disclosed herein may include the step of obtaining a biological sample from a subject and determining the expression, concentration, or abundance level of one or more lipid biomarkers in the sample. Alternatively, any method disclosed herein may not include the step of obtaining a biological sample from a subject and determining the expression, concentration, or abundance level of one or more lipid biomarkers in the sample. Instead, the expression, concentration, or abundance level of one or more lipid biomarkers in the sample may have been previously determined.

[0353] In order that the preferred embodiments of the present disclosure may be more fully understood and put into practical practice, reference is made to the following non-limiting examples.

[0354] Examples

[0355] Example 1. Exploratory evaluation of lipidomic biomarkers in four breast cancer cohorts

[0356] The purpose of this example is to establish a predictive model for breast cancer based on lipidomics data.

[0357] Abbreviations

[0358] AUC Area under the curve BMI body mass index DCIS Ductal Carcinoma in Situ GB Gradient Boosting HBV Hepatitis B virus HCV Hepatitis C virus HDL High-density lipoprotein HIV Human immunodeficiency virus IDC Invasive ductal carcinoma ILC Invasive lobular carcinoma IQR Interquartile range LC-MS Liquid chromatography-mass spectrometry LDL Low-density lipoprotein ML Machine Learning NPV Negative predictive value PCA Principal component analysis PPV Positive predictive value ROC Receiver Operating Characteristics SD Standard Deviation

[0359] Study Design

[0360] Four plasma sample pools obtained from adult female patients with informed consent during 2018, 2019, 2020, and 2021 were used for lipidomics analysis. The disease characteristics of the individuals from whom the samples were derived covered various stages of invasive ductal carcinoma (IDC), ductal carcinoma in situ (DCIS), invasive lobular carcinoma (ILC), and normal controls, as summarized in Table 1. All individuals fasted before blood draw. It is unknown whether any individual is included in more than one member of the dataset.

[0361] Table 1: Disease classification counts for the four datasets

[0362]

[0363]

[0364] Case

[0365] In all four data sets, breast cancer type was confirmed clinically and morphologically; breast cancer stage was confirmed by biopsy.

[0366] Patients with breast cancer were excluded according to the following criteria:

[0367] ●Pregnant or breastfeeding

[0368] History of HBV, HCV, or HIV

[0369] Prior breast cancer treatment

[0370] ●Previous breast cancer surgery

[0371] In Datasets 2 to 4, cases met the additional criteria of absence of any of the following:

[0372] Other chronic diseases

[0373] History of cardiovascular disease or diabetes

[0374] Hyperlipidemia or dyslipidemia

[0375] Radiation therapy or chemotherapy

[0376] Comparison

[0377] In all four datasets, normal controls were individuals without a diagnosis of breast cancer who were class-matched to cases by age and BMI.

[0378] In Data Sets 2 to 4, normal controls met the additional criteria of absence of:

[0379] Chronic diseases

[0380] History of cardiovascular disease or diabetes

[0381] Hyperlipidemia or dyslipidemia

[0382] Any history of cancer

[0383] Radiation therapy or chemotherapy

[0384] Sample size considerations

[0385] The current analysis was a post hoc exploratory analysis of existing data. The four datasets included in the current analysis were pilot datasets that were derived without power calculations for the current analysis.

[0386] Analyze groups

[0387] The following analysis groups were considered for exploration purposes:

[0388] 1. Dataset 1

[0389] 2. Dataset 2

[0390] 3. Dataset 3

[0391] 4. Dataset 4

[0392] 5. Dataset 2 + Dataset 3 + Dataset 4

[0393] The test performance calculations involved multiple populations as shown in Table 3.

[0394] Table 3: Analysis groups

[0395]

[0396] General considerations for data analysis

[0397] Multiple batches

[0398] The more relaxed inclusion criteria of Dataset 1 meant that it could include individuals diagnosed with or receiving treatment for a trait that affects lipid profile (such as dyslipidemia).

[0399] Although datasets 2 and 3 are similar in design, all four datasets were acquired at different time points and performed on separate laboratory runs. There were six laboratory batches in total, as datasets 2 and 3 were each analyzed in two parts.

[0400] Typically, in these cases, a range of breast cancer types and stages are considered. For these reasons, in the first case, the search for predictors was performed one at a time in each dataset.

[0401] Multiple comparisons and multiplicity

[0402] For statistical power, no adjustment was made for multiple testing.

[0403] Study Group

[0404] Clinical characteristics

[0405] Tabular summaries of clinical characteristics were performed one dataset at a time according to data availability (Tables 4 to 7). All tables were stratified by case / control status and included items for age, BMI, HDL, and LDL. In addition, total cholesterol and triglycerides were tabulated for datasets 2 to 4. All participants self-reported white, non-Hispanic ethnicity.

[0406] Categorical variables were summarized as n (%); continuous symmetrically distributed variables were summarized as mean (SD); and continuously distributed variables with skewed distributions were summarized as median (IQR).

[0407] Nonparametric tests were used to compare the distribution of these variables for cases and controls. Pearson's Chi-Squared test was applied to categorical variables; Mann-Witney U Test was applied to continuously distributed variables. Results are expressed to three decimal places or as p<0.001. Missing values ​​for clinical data were not estimated. It should be noted that for Dataset 2, batches were confused with case-control status (p<0.001). This means that after adjusting the batches, the statistical power of the data set will be reduced.

[0408] Differences in lipid levels (cholesterol, HDL, and LDL) were observed between cases and controls in Cohort 3 and / or Cohort 4. Due to the complex interrelationship between lipids and disease state, it was decided not to apply statistical adjustment to any of these measurements.

[0409] Table 4: Demographic characteristics of dataset 1

[0410] statistics Case Comparison P count N 45 45 NA Stage I disease n(％) 15(33.3) 0(0) NA Stage II disease n(％) 15(33.3) 0(0) NA Stage III disease n(％) 11(24.4) 0(0) NA Stage IV disease n(％) 4(8.9) 0(0) NA Tumor grade G1 n(％) 1(2.2) 0(0) NA Tumor grade G2 n(％) 38(84.4) 0(0) NA Tumor grade G3 n(％) 5(11.1) 0(0) NA age Mean (SD) 58.69(12.62) 59.67(11.25) 0.728 BMI Mean (SD) 29.26(6.81) 28.33(5.55) 0.561 HDL Mean (SD) 1.16(0.4) 0.97(0.27) 0.346 LDL Mean (SD) 2.66(1.01) 2.43(0.92) 0.907

[0411] Table 5: Demographic characteristics of dataset 2

[0412]

[0413]

[0414] Table 6: Demographic characteristics of dataset 3

[0415] statistics Case Comparison P count N 100 101 NA batch n(％) 47(47) 46(45.5) 0.948 Stage I disease n(％) 50(50) 0(0) NA Stage II disease n(％) 50(50) 0(0) NA Tumor grade G1 n(％) 5(5.1) 0(0) NA Tumor grade G2 n(％) 76(77.6) 0(0) NA Tumor grade G3 n(％) 15(15.3) 0(0) NA age Mean (SD) 54.56(11.39) 52.77(10.24) 0.224 BMI Mean (SD) 27.04(4.79) 26.76(4.25) 0.698 cholesterol Mean (SD) 4.95(0.76) 5.19(0.68) 0.027 HDL Mean (SD) 1.34(0.36) 1.39(0.37) 0.424 LDL Mean (SD) 3.34(0.81) 3.51(0.75) 0.134 TGL Median (IQR) 1.23(0.84,1.53) 1.28(0.96,1.69) 0.282

[0416] Table 7: Demographic characteristics of Dataset 4

[0417]

[0418]

[0419] Biomarker data analysis

[0420] introduction

[0421] Generate lipidomics data using liquid chromatography-mass spectrometry (LC-MS). TM Lipids were identified and quantified using the software Skyline. The process involved peak detection and database matching, as well as generating quality scores for filtering purposes. Data to be statistically analyzed included normalized lipid concentrations.

[0422] Exploratory Data Analysis

[0423] The analyses in this section were performed one dataset at a time, combining cases and controls. In addition, a merged dataset consisting of datasets 2 to 4 was considered. The latter is labeled '234'.

[0424] There were no missing values ​​in the data, but zeros were found. In the combined case-control set, lipids with a coefficient of variation less than 0.20 were excluded from further consideration. The distribution of each lipid was plotted as a histogram, and a global natural logarithm transformation (ln(1+x)) was applied to improve symmetry.

[0425] Principal component analysis (PCA) was performed and the results were plotted in two-dimensional and three-dimensional plots, colored according to case-control status. If extreme outliers were detected, they were excluded and replotted. For analysis populations involving multiple laboratory batches, batch adjustment was applied while adapting to case-control status by using the empirical Bayesian method (Johnson and Rabinovic, 2007). PCA plots were replotted.

[0426] After completing the above steps, five analysis-ready datasets are available, including 394 lipids, 386 lipids, 382 lipids, 380 lipids, and 373 lipids for datasets 1, 2, 3, 4, and 234, respectively.

[0427] Training and validation sets of dataset 234

[0428] Dataset '234' was split into training and validation sets in a 2:1 ratio, balancing cohort, case / control status, and age. The training set was used for marker discovery; the validation set was used to test performance calculations. The following marker discovery steps were performed on each dataset individually and on the 234 training set; there were five rounds in total.

[0429] Initial coarse filter

[0430] Each lipid was tested one at a time in a logistic model for association with case / control status, adjusting for any covariates established in the previous step. Any lipid with a p-value ≥ 0.05 was excluded from further consideration.

[0431] Determination of lipid clusters

[0432] For the purpose of deriving knowledge-based markers for breast cancer, the remaining lipids were grouped according to their numerical correlation. A similarity matrix was created for the 30 strongest associations generated for each data set. Hierarchical clustering analysis was applied and a dendrogram was drawn.

[0433] Machine Learning

[0434] Machine learning via gradient boosting (Friedman 2001; Friedman 2002; Chen et al., 2021). Gradient boosting is a method for determining a regression function that minimizes the expected value of a loss function. It is an iterative method in which the negative gradient of the loss function is calculated, a regression model is fitted, the gradient descent step size is chosen, and the regression function is updated. The gradient is roughly estimated using a regression tree, and at each iteration, the gradient determines the direction the function needs to move to improve the fit to the data.

[0435] The loss function for the current binary endpoint is specified as Bernoulli. A learning rate is introduced to dampen the proposed moves and prevent overfitting. The minimum number of observations per terminal node is 10. Two-way interactions are allowed. Random subsampling of half of the observations is applied (without replacement) to achieve variance reduction in gradient estimates. There are 100 rounds of gradient boosting for each model.

[0436] The result for each lipid is an estimate of its relative impact or feature importance. To prevent overfitting, variable selection was achieved by selecting the five lipids with the largest estimated relative impact. One or more of them were excluded if their estimated relative impact was <5%. The selected variables (lipids) and their estimated relative impact / feature importance were tabulated (Tables 8 to 12). Violin plots show the distribution of lipid concentrations according to case / control status ( Figures 1 to 5 ).

[0437] Machine learning results for dataset 1

[0438] Table 8: Five lipids with the highest estimated relative impact / feature importance

[0439] feature Feature Importance LPC(14:0)+H_1.973_467.3012 0.200695 TG(16:0_18:1_23:0)+NH4_17.622_930.8615 0.118482 LPC(18:3)+H_1.907_517.3168 0.083962 PS(18:0_20:4)-H_8.806_811.5363 0.050535 PC(18:1e_20:4)+H_10.456_793.5985 0.045696

[0440] Machine learning results for dataset 2

[0441] Table 9: Five lipids with the highest estimated relative impact / feature importance

[0442] feature Feature Importance PI(18:2_20:4)-H_6.223_882.5258 0.234965 LPE(22:6)-H_2.243_525.2855 0.087382 LPC(16:0e)+H_3.3_481.3532 0.065528 Cer(d18:1_24:0)+H_13.775_649.6373 0.055938 PE(16:0_20:4)+H_8.346_739.5152 0.051667

[0443] Machine learning results for dataset 3

[0444] Table 10: Five lipids with the highest estimated relative impact / feature importance

[0445] feature Feature Importance PI(18:2_20:4)-H_6.223_882.5258 0.206972816 PE(16:0p_18:2)+H_9.427_699.5203 0.111700063 PI(18:0_18:1)-H_10.026_864.5728 0.078094035 PS(18:0_22:6)-H_8.422_835.5363 0.073597258 PE(20:1p_18:2)+H_11.17_753.5672 0.057497532

[0446] Machine learning results for dataset 4

[0447] Table 11: Five lipids with the highest estimated relative impact / feature importance

[0448] feature Feature Importance LPC(14:0)+H_1.973_467.3012 0.094207 SM(d18:1_15:0)+H_7.004_688.5519 0.087243 LPC(17:1)+H_2.536_507.3325 0.076116 PI(18:2_20:4)-H_6.223_882.5258 0.075970 SM(d41:2)+H_11.798_798.6615 0.055660

[0449] Machine learning results of dataset 234 (training set)

[0450] Table 12: Five lipids with the highest estimated relative impact / feature importance

[0451] feature Feature Importance PI(18:2_20:4)-H_6.223_882.5258 0.177488 LPC(14:0)+H_1.973_467.3012 0.072399 SM(d35:1)+H_8.528_716.5832 0.072387 LPC(18:1e)+H_3.411_507.3689 0.034876 PS(18:0_20:4)-H_8.806_811.5363 0.034358

[0452] Candidate logo

[0453] For each lipid set, a logistic model was fitted by regressing the case / control status of the selected lipid. If goodness of fit was not lost, such as when two highly correlated lipids were included, some models were simplified. The resulting models are given in Table 13 and 17.

[0454] Table 13: Candidate landmarks generated from dataset 1

[0455] Estimated value Standard error Z-value Pr(>|z|) (intercept) 2.20 0.91 2.42 0.015619 LPC(14:0)+H_1.973_467.3012 -369.35 147.62 -2.50 0.012351 PS(18:0_20:4)-H_8.806_811.5363 61.31 21.38 2.87 0.004136 TG(16:0_18:1_23:0)+NH4_17.622_930.8615 -760.03 249.47 -3.05 0.002315

[0456] Table 14: Candidate landmarks generated from dataset 2

[0457] Estimated value Standard error z-value Pr(>|z|) (intercept) 1.05 0.59 1.78 0.075118 LPC(16:0e)+H_3.3_481.3532 -744.89 170.74 -4.36 1.28E-05 LPE(22:6)-H_2.243_525.2855 331.13 70.52 4.70 2.66E-06 PI(18:2_20:4)-H_6.223_882.5258 -3696.53 814.09 -4.54 5.61E-06

[0458] Table 15: Candidate landmarks generated from dataset 3

[0459] Estimated value Standard error z-value Pr(>|z|) (intercept) 7.11 1.07 6.66 2.69E-11 PE(16:0p_18:2)+H_9.427_699.5203 -77.85 28.80 -2.70 0.006876 PE(20:1p_18:2)+H_11.17_753.5672 -165.86 66.80 -2.48 0.013034 PI(18:0_18:1)-H_10.026_864.5728 -111.39 47.94 -2.32 0.020153 PI(18:2_20:4)-H_6.223_882.5258 -10369.53 2411.57 -4.30 1.71E-05 PS(18:0_22:6)-H_8.422_835.5363 -186.23 62.53 -2.98 0.002898

[0460] Table 16: Candidate landmarks generated from dataset 4

[0461] Estimated value Standard error z-value Pr(>|z|) (intercept) 0.47 0.53 0.89 0.375192 LPC(14:0)+H_1.973_467.3012 -34.17 5.61 -6.10 1.09E-09 PI(18:2_20:4)-H_6.223_882.5258 -4651.38 1335.37 -3.48 0.000495 SM(d18:1_15:0)+H_7.004_688.5519 27.99 4.62 6.05 1.42E-09

[0462] Table 17: Candidate landmarks generated from dataset 234 (training)

[0463]

[0464]

[0465] Test performance calculation

[0466] The test performance of each candidate marker generated from a single dataset was determined by applying it to the remaining three individual datasets (as independent patient sets).

[0467] The test performance of the candidate markers derived from data set 234 (training set) is determined by applying to data set 1 and data set 234 (validation set). The test performance calculation takes the following form. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the prediction probability threshold range are calculated. A table is generated to summarize the performance within the classification threshold range to allow greater or lesser weights to be applied to true positive and false positive findings. A receiver operating characteristic (ROC) curve is generated to plot the sensitivity as a function of (1-specificity). In addition, the area under the curve (AUC) is estimated.

[0468] Performance of candidate marker 1

[0469] This signature was supported by data from cohorts 3 and 4 (AUCs 0.76 and 0.74, respectively). Figure 6 Show overlaid ROC curves.

[0470] Performance of Candidate Mark 2

[0471] This signature is supported by the data in cohort 3 (AUC = 0.79). Performance in cohorts 1 and 4 is weaker (AUC 0.69 and 0.68, respectively), but still supported. Figure 7 Overlaid ROC curves are shown.

[0472] Performance of Candidate Mark 3

[0473] This signature is supported by the data in cohorts 1 and 4 (AUC 0.73 and 0.72, respectively). Performance in cohort 2 is slightly weaker (AUC = 0.68), but still supported. Figure 8 Overlaid ROC curves are shown.

[0474] Performance of Candidate Mark 4

[0475] This signature was supported by data from cohorts 2 and 3 (AUCs 0.78 and 0.85, respectively). Fig. 9 Overlaid ROC curves are shown.

[0476] Performance of Candidate Mark 234

[0477] This marker is very close in composition to candidate marker 4. It is supported by data in dataset 234 validation (AUC = 0.83). Performance in group 1 is weaker (AUC = 0.69), but still supported. Fig.10 Overlaid ROC curves are shown.

[0478] Example 2

[0479] In this example, the inventors of this case processed and analyzed 4 groups containing plasma over time, where the first group was obtained in 2018 and group 4 was obtained in 2021 (as in the above group of Example 1). During this time, various statistical analyses were performed with the goal of finding lipid panels that distinguished cancer from control samples in the provided groups.

[0480] Study Design

[0481] The study design, number and description of cases in each cohort, treatment batches (described as Pools 1 to 6), and clinical characteristics are described in Example 1 above.

[0482] General considerations for data analysis

[0483] At all stages of data analysis, the focus was on diagnosis of early-stage cancer (stages I and II); specific differences were also considered at earlier stages of the analysis.

[0484] Improvements in the datasets and data structures used over time

[0485] After groups 1 to 3 became available, a merged manually curated dataset of common lipids was compiled in 2020; this improved version of the data contains approximately 450 lipid identifiers that can be mechanically matched in all subsequent groups, and to which group 4 was subsequently added. This is known as the BCAL lipid database.

[0486] Data merging and lipid identifiers

[0487] Prior to manually curating the BCAL lipid database, data merging was performed at the bioinformatics level by matching any exact lipid ions as much as possible and additionally using a combination of m / z value matches with retention time ranking combinations. This approach was used for the original three groups and was superseded by using the lipid database once it became available.

[0488] Data processing

[0489] For all analyses, the following steps were taken:

[0490] ● Remove the -5 Low average lipid

[0491] ● Discard lipids with missing values ​​(this only affects earlier analyses, as the lipid database does not contain missing values)

[0492] ● Normalize the remaining data to adjust for possible differences in total sample size

[0493] ● Apply natural logarithm transformation

[0494] ● Visually inspect the distribution of the obtained data using box plots and density plots.

[0495] ●Examine overall clustering by generating a principal component plot

[0496] ● Outliers not removed

[0497] Batch Normalization

[0498] Whenever different datasets are combined, batch processing differences are evident, so batch normalization was evaluated. Starting from cohort 2, set 3 batch sample replicates became available in addition to technical injection replicates, and were used to evaluate the utility of batch normalization using replicate correlations and coefficients of variation. An adapted version of internal reference scale (IRS) normalization was found to provide good performance (Plubell, 2017) and was subsequently used for batch normalization.

[0499] Variable selection

[0500] At each stage of the analysis, lipid biomarkers were selected based on their ability to discriminate between controls and cancer in the currently available datasets, and the selections were validated in subsequent sets.

[0501] Selection of the initial 18 lipid biomarkers was performed using Set 1 and Set 2, bioinformatically combined as described above (see Table 18 below). A mixed effects model with random batch effects was run to find differentially expressed markers for the available sets using the nlme R package implementation (Pinheiro, 2000).

[0502] Table 18 - Selection of the initial 18 species Step 1 - Set 1 and Set 2 groups

[0503]

[0504]

[0505] An initial process filter was run to retain all markers showing signs of differential expression between control-early or control-stage 1-stage 2 using either ANOVA or a mixed effects model with batch effects.

[0506] For either of these models, any lipid with a Benjamini and Hochberg adjusted p-value < 0.05 (Benjamini, 1995) was selected, resulting in a subset of the original 110 markers of interest.

[0507] The filtered marker set was generated to run differential expression between control and early cancer samples, selecting only the highest differentially expressed markers based on p-value < 0.05 and fold change criteria (initial 18 member panel - "Initial18Step1Set 1and Set 2"). The volcano plot below shows lipid fold changes and p-values ​​(Goedhart, 2020), where lipid ion identifiers were available at the time.

[0508] When Set 3 becomes available, repeat the above steps using the entire Group 2.

[0509] Following this process, the initial 18-member panel was augmented with an additional 9 markers using Set 2 and Set 3 data, resulting in the "Initial 18+9more Cohort2 NewVS" panel, which represents an intermediate panel of interest (Table 19).

[0510] Table 19—Initial 18+9more Cohort2 NewVS

[0511]

[0512]

[0513] After group 3 (sets 4 and 5) was completed, differential expression using ANOVA was used for the merged and IRS batch normalized data, and the top few up-regulated and down-regulated lipids were added to the pre-existing group (18 member 400Reoptimised lipid group - "Cohort 2-3final"; see Table 21 below). Similarly, the distribution of fold changes and p-values ​​is visualized on the volcano map below. The middle addition of the 18 most differentially expressed markers of group 3 (based on p-value <0.05, the top 9 most up-regulated and the top 9 most down-regulated in the fold change of cancer / control in differentially expressed lipids) was retained as "Cohort 3additions Cohort 3Plsda add 9up 9down" (Table 20).

[0514] Table 20—Cohort 3additions Cohort 3Plsda add 9up 9down

[0515]

[0516] Table 21 - BCAL Current 18 Members 400 Reoptimised Groups (Cohort 2-3final)

[0517]

[0518]

[0519] The subsequent filtering of the smaller groups was done by stepwise regression or further discarding the combination of lipids that did not contribute significantly to the logistic model distinguishing between the control and the early stage using the core stats R package (Dobson, 1990) available embodiments. This resulted in the selection of smaller lipid subsets (the initial 18-member 9 lipid group—"18-9lipids Restrict9Lipids", the 10-member subset from the 18-member 400Optimised group, "Top10optimised for patent Top10Coeff5050train"; see Tables 22 and 23 below, and the subset "Cohort 2optimised" obtained by selecting "Initial 18+9more Cohort2 NewVS" from group 2 lipids in the same manner; see Table 24 below).

[0520] Table 22 - Initial 18-member 9 lipid panel - "18-9lipids Restrict9Lipids"

[0521]

[0522] Table 23 - 10 member subset "Top 10optimised forpatent Top10Coeff5050train" from 18 member 400Optimised panel

[0523]

[0524]

[0525] Table 24 - Cohort 2 optimized

[0526]

[0527] Cohort 4 was not used directly for variable selection, but the consistency of fold-change control / cancer across all cohorts was used to select a smaller 6 lipid biomarker panel from the 400 Reoptimised panel that showed consistent performance across all batches ("6FourMarkers Panel"; see Table 25).

[0528] Table 25-6 FourMarkers Group

[0529]

[0530]

[0531] Additionally, similar to the previous cohorts, the top 10 differentially abundant lipids for Cohort 4 were also identified by a combination of fold change and p-value cutoffs and used to select a small set of 6 lipids that were common to those 12 lipid biomarkers identified in Example 1 ("Common 6-ML_GBM"; see Table 26).

[0532] Table 26-Common 6-ML_GBM

[0533] Lipid biomarkers m / z LPC(14:0)+H_1.973_467.3012 467.3012 LPE(22:6)-H_2.243_525.2855 525.2855 PI(38:6)-H_6.759_882.5258 882.5258 PE(34:2p)+H_9.427_699.5203 699.5203 SM(d35:1)+H_8.528_716.5832 716.5832 PS(38:4)-H_10.319_811.5363 811.5363 PS(38:4)-H_8.806_811.5363 811.5363

[0534] Classification and machine learning methods

[0535] In the context of Sets 1 and 2, initial analyses performed on Sets 1 and 2 evaluated random forests (Breiman, 2001), PLSDA (Kuhn, 2020) implemented using the caret R package, logistic regression, and support vector machines (implemented in the R package e1071); logistic regression was chosen as giving the best and more easily interpretable results using a smaller lipid panel on the initial dataset.

[0536] Wherever a lipid panel was evaluated, its classification potential was assessed using leave-one-out cross-validation, where the model was trained on all samples except one and model performance was assessed on the remaining samples. The process was repeated for all samples in turn, ensuring that the data used for testing was not used for training at any time. This approach is particularly appropriate in the early stages when only a few data sets are available.

[0537] For each subgroup obtained by variable selection or set selection on one group, its classification success is evaluated on the next available group.

[0538] As noted above, in addition to the smaller subgroups that were refined later, variable selection was performed using only Cohorts 1, 2, and 3, which contained only invasive ductal carcinoma (IDC) samples in terms of cancer subclasses, which ensured that the results obtained on Cohort 4 showed that these groups still had classification potential on a dataset containing different early-stage cancer subclasses (DCIS and ILC).

[0539] This sequential analysis pathway combined with different choices of variable selection methods may at least partially explain the differences in lipid biomarkers identified by the statistical data analysis of Example 1 and the method described in Example 2. Nevertheless, many lipid biomarkers (see 6 Biomarker Overlap Panels below) overlap between the diagnostic panels of Examples 1 and 2, demonstrating a high degree of consistency in the identification of related lipid biomarkers by the two different methods.

[0540] Group Details

[0541] Limited to 6 marker groups - "6FourMarkers Group"

[0542] This panel contains two markers common to Example 1. As an extended example, Figures 16 to 23 The boxplots in detail show the conservation patterns of expression or concentration in several available sets.

[0543] 6-marker panel shared with validation report "Common 6-ML_GBM"

[0544] This panel is an important overlapping panel that contains all lipid markers identified as diagnostically relevant by Examples 1 and 2 and includes all lipid markers in the previous 6-member panel as well as from Figure 3 Two of the m / z values ​​for all lipids (Volcano plot of Group 4).

[0545] ROC Curve

[0546] The ROC curves for each group from Example 2 are Fig.14 , 15 33. These curves demonstrate the diagnostic potential of each panel in patients with breast cancer (including early breast cancer). All curves were generated using leave-one-out cross validation.

[0547] Example 3

[0548] Using cohorts 3 and 4, the inventors randomly drew m / z values ​​(6-member or 18-member panels) to compare with the diagnostic values ​​of the diagnostic panels identified in Examples 1 and 2. For the random 6-member panel, 1000 simulations were run. For the random 18-member panel, 100 simulations were run. We then tabulated the random panels that achieved high random precision (defined as >75%) and compared their composition with the previously identified panels (see Tables 27 to 29 below).

[0549] result

[0550] The 6 lipid biomarker panel was one of the best performers, with ML_GBM from Table 23 being the best performer in its analysis (see Table 24), and the 6FourMarkers panel from Table 22 ranking third in its analysis (see Table 25). A high proportion of the best performing panels (random or otherwise) also contained the LPC (14:0) marker. Some random panels achieved similar diagnostic performance.

[0551] When summarizing the most frequently occurring lipids from the well-performing random panels, three from the ML_GBM panel were in the top 5 (see Table 27).

[0552] Table 27-6 Analysis of lipid biomarker panel ML_GBM

[0553]

[0554] Table 28—Analysis of the 6 lipid biomarker panel “6FourMarkers Panel”

[0555]

[0556]

[0557]

[0558] Table 30 - Frequency of lipid biomarkers in random runs

[0559]

[0560]

[0561]

[0562] Example 4

[0563] In this example, the inventors conducted experiments to examine whether fed extracellular vesicle (EV) samples can be used together with fasted EV samples to detect breast cancer, and whether fasted or fed plasma samples can be used directly to detect breast cancer. The inventors also aimed to determine whether any of their existing lipid markers determined using EV fasting samples from various past cohorts could be used to evaluate fed EV samples or fasted plasma samples.

[0564] Study Design

[0565] Lipids in fasting and non-fasting EV and plasma samples were extracted from 60 control patients. Matched samples were analyzed in two separate laboratories, internally at BCAL (n=26 in a total of 60 samples) and externally at Baker Heart and Diabetes Institute (n=60 samples). The BCAL laboratory samples contained a complete complement of lipids previously measured in past cohorts and therefore in previous lipid markers of interest. The Baker laboratory samples did not identify a complete complement of BCAL lipids, but a large number of patient samples were evaluated.

[0566] The resulting lipid quantifications were tested against existing BCAL markers whenever possible, and correlations of classification scores obtained between matched fasting and fed samples are reported.

[0567] Data analysis

[0568] Identification of lipids that change with fasting / fed states

[0569] Matched control samples were used to discover which lipids changed in concentration in EV samples between fasting and fed states and in plasma alone and similarly between plasma and EV samples.

[0570] In terms of data processing, lipids with missing values ​​were removed. The data were shifted by a small value of eps = 0.001 to allow for logarithmic transformation of zero values. A two-tailed paired t-test was applied to all lipids separately, and any lipid was considered significantly different if the FDR-adjusted (Benjamini and Hochberg) p-value was less than 0.05.

[0571] Identification of lipid markers showing robust results in fasted or fed samples

[0572] The lipid markers of the invention were examined to see if the concentration of lipids varied depending on fasting / fed state; it was speculated that lipid markers that did not change when fed might also potentially be used to classify fed samples. The lipid panels studied were the 18-member 400Reoptimised lipid panel, the 6FourMarkers panel, and the Common 6-ML_GBM panel.

[0573] For each of the lipid signatures considered (typically trained using cohorts 2 to 3), the model prediction script was run to normalize the new data, apply the signature and generate a cancer probability for each sample.

[0574] Fed / fasted samples were matched side by side and the resulting scores were compared to generate fitted lines and statistics (fit line coefficient, R-squared, correlation, significance of correlation).

[0575] result

[0576] Differential abundance in the fasting state

[0577] No statistically significant differences were observed in lipid concentrations in the BCAL Common 6-ML_GBM subgroup between the fed and fasted states in either EV or plasma samples ( Fig.34 The other two groups contained several lipids that showed significant differences in their concentrations between the fed and fasted states. Table 31 details all the group m / z values ​​of lipids whose concentrations changed with the fasted / fed state.

[0578] Table 31. Marker lipid abundance changes with fed / fed state; sham means no significant changes were observed. None of the lipids present in the ML_GBM panel changed with fasting state in either EVs or plasma.

[0579]

[0580] Correlation of lipid marker scores in the BCAL dataset (n=26)

[0581] Table 32 summarizes the score alignment fits for the markers of interest. Common 6-ML_GBM produced the best score for correlation between fasting and fed matched EV samples, second only to the baseline instance consisting of frozen replicates previously analyzed by BCAL. Post-fasting EV and plasma samples were also highly correlated.

[0582] Table 32. Correlation of the obtained classification scores with various lipid markers for the samples analyzed (n=26 control samples); the groups containing fewer markers that varied with the fasting state yielded better score correlations

[0583]

[0584] Similarity of lipid marker scores in external laboratory datasets (n=60)

[0585] Although not all lipids from the BCAL signature were included in this analysis, all six lipids from Common6-ML_GBM could be matched between the two datasets; Fig.35 The following Table 33 summarizes the score alignments obtained with the markers on the new dataset (n=60 controls). The matching scores obtained are significantly correlated. Since all samples are controls, samples with low cancer probability scores represent the correct classification ( Fig.36 ). For both post-fasting and non-fasting samples, the median score was less than 0.5, while for both post-fasting and non-fasting EV samples, more than 75% of the samples had a model probability < 0.6.

[0586] Table 33. Correlation of scores obtained with the ML_GBM panel for samples analyzed on the new dataset (n=60 control samples)

[0587]

[0588] in conclusion

[0589] Experiments performed showed that the lipid biomarker panels described herein (e.g., Common6-ML_GBM) may not contain any lipids whose concentrations change significantly between the fed and fasted states. Therefore, the Common6-ML_GBM lipid signature can potentially be used to evaluate non-fasting patient EV samples as well as post-fasting EV samples. The classification algorithm scores generated based on the new test data were significantly correlated between patient-matched post-fasting and non-fasting EV samples and between fasting plasma and post-fasting EV samples, both in data generated internally by BCAL and on external laboratory data.

[0590] References

[0591] Chen T, He T, Benesty M et al (2021).xgboost: Extreme Gradient Boosting.Rpackage version 1.4.1.1.https: / / CRAN.R-project.org / package=xgboost

[0592] Friedman JH(2001). Greedy function approximation: a gradient boosting machine. Ann Statist 29(5):1189-1232.

[0593] Friedman JH (2002). Stochastic gradient boosting. Comput Stat Data Anal38(4):367-378.

[0594] Johnson WE, Li C, Rabinovic A(2007). Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8(1):118-27.

[0595] Mistry DAH,French PW(2016).Circulating phospholipids as biomarkers ofbreast cancer:a review.Breast Cancer:Basic and Clinical Research 10:191-196。

[0596] Society AC.Cancer Facts and Figures 2016.Atlanta:American CancerSociety,Inc.Available at:http: / / www.cancer.org / cancer / breastcancerinmen / detailedguide / breast-cancer-in-men-key-statistics

Claims

1. A method for diagnosing breast cancer in a subject, the method comprising the step of measuring the level of one or more lipid biomarkers in a biological sample from the subject, wherein the one or more lipid biomarkers are selected from the group consisting of: Cer (d36: 1), Cer (d42:0), Cer (d42: 1), DG (34:2), Hex2Cer (d34: 1), Hex2Cer (d42:2), LPC (14:0), LPC (16:0e), LPC(17:1), LPC(18:3), LPE(22:6), LPI(20:4), PC(32:1), PC(34:1), PC(35:4), PC(36:2), PC(36:3), PC(3 7:4), PC(38:5), PE(34:1), PE(34:2p), PE(36:4), PE(38:3p), PE(38:4), PE(O-38:5), PE(O-40:6), PI(32:1 ), PI(34:1), PI(36:1), PI(38:6), PS(38:4), PS(40:6), PS(40:7), SM(d32:2), SM(d33:1), SM(d35:1), SM( d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM(d42:1), SM(d42:4), SM(d44:4), TG( :2), or fragments, variants or derivatives thereof; and wherein the level of one or more lipid biomarkers is diagnostic or indicative of the subject having the breast cancer.

2. The method according to claim 1, wherein Cer (d36:1), Cer (d42:0), DG (34:2), Hex2Cer (d34:1), Hex2Cer (d42:2), LPE (22:6), LPI (20:4), PC (32:1), PC (34:1), PC (35:4), PC (36:2), PC (36:3), PC (37:4), PC (38:5), PE (34:1), PI (3 2:1), PI(34:1), PS(38:4), PS(40:6), SM(d32:2), SM(d33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM(d42:1), SM(d42:4) and / or SM(d44:4), or fragments, variants or derivatives thereof; and / or C er(42:1),LPC(14:0),LPC(16:0e),LPC(17:1),LPC(18:3),PE(34:1),PE(34:2p),PE(36:4),PE(38:3p) , PE(38:4), PE(O-38:5), PE(O-40:6), PI(32:1), PI(34:1), PI(36:1), PI(38:6), PS(38:4), PS(40:6), PS Decreased levels of TG(40:7), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and / or TG(62:2), or fragments, variants or derivatives thereof, are diagnostic or indicative of the subject suffering from the breast cancer.

3. The method according to claim 1 or claim 2, further comprising the step of administering to the subject a treatment for the breast cancer.

4. A method for measuring the level of one or more lipid biomarkers in a biological sample from a subject, the method comprising the steps of: (a) providing the biological sample; and (b) measuring the level of the one or more lipid biomarkers in the biological sample, wherein the one or more lipid biomarkers are selected from the group consisting of: Cer (d36:1), Cer (d42:0), Cer (d42:1), DG (34:2), Hex2Cer (d34:1), Hex2Cer (d42:2), LPC (14:0), LPC (16:0e), LPC (17:1), LPC(18:3), LPE(22:6), LPI(20:4), PC(32:1), PC(34:1), PC(35:4), PC(36:2), PC(36:3), PC(37:4), PC(38:5), PE(34:1), PE(34:2p), PE(36:4), PE(38:3p), PE(38:4), PE(O-38:5), PE(O-40:6), PI(32:1), PI(34:1), PI(36:1), PI(38:6), PS(38:4), PS(40:6), PS(40:7), SM(d32:2), SM(d33: 1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM(d42:1), SM(d42:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof.

5. The method of claim 4, wherein the subject is suspected of having breast cancer, or has been previously diagnosed with breast cancer.

6. The method of claim 4 or claim 5, wherein the measuring step comprises determining the presence or absence of the following in the biological sample of the subject: (i) Cer (d36:1), Cer (d42:0), DG (34:2), Hex2Cer (d34:1), Hex2Cer (d42:2), LPE (22:6), LPI (20:4), PC (32:1), PC (34:1), PC (35:4), PC (36:2), PC (36:4), :3), PC(37:4), PC(38:5), PE(34:1), PI(32:1), PI(34:1), PS(38:4), PS(40:6), SM(d32:2), SM(d33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM(d42:1), SM(d42:4), and / or SM (d44:4), or a fragment, variant or derivative thereof; and / or (ii) Cer(42:1), LPC(14:0), LPC(16:0e), LPC(17:1), LPC(18:3), PE(34:1), PE(34:2p), PE(36:4), PE(38:3p), PE(38:4), PE(O-38:5), PE(O-40:6), PI(32:1), PI(34:1), PI(36:1), P I(38:6), PS(38:4), PS(40:6), PS(40:7), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and / or TG(62:2), or fragments, variants or derivatives thereof.

7. A method for treating breast cancer in a subject, the method comprising the step of treating the subject, wherein the level of one or more lipid biomarkers has been measured in a biological sample from the subject, which is diagnostic or indicative of the subject having the breast cancer, wherein the one or more lipid biomarkers are selected from the group consisting of: Cer (d36:1), Cer (d42:0), Cer (d42:1), DG (34:2), Hex2Cer (d34 :1), Hex2Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(17:1), LPC(18:3), LPE(22:6), LPI(20:4), PC(32:1), PC(34:1), PC(35:4), PC(36:2), PC(36:3), PC(37:4), PC(38:5), PE(34:1), PE(34:2p), PE(36:4), PE(38:3 p), PE(38:4), PE(O-38:5), PE(O-40:6), PI(32:1), PI(34:1), PI(36:1), PI(38:6), PS(38:4), PS(40:6), P S(40:7), SM(d32:2), SM(d33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), S M(d41:3), SM(d42:1), SM(d42:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof.

8. The method of claim 7, wherein the treating comprises administering to the subject a therapeutically effective amount of an anti-cancer therapy.

9. The method according to claim 7 or claim 8, wherein Cer (d36: 1), Cer (d42: 0), DG (34: 2), Hex2Cer (d34: 1), Hex2Cer (d42: 2), LPE (22: 6), LPI (20: 4), PC (32: 1), PC (34: 1), PC (35: 4), PC (36: 2), PC (36: 3), PC (37: 4), PC (38: 5), PE (34: 1) , PI(32:1), PI(34:1), PS(38:4), PS(40:6), SM(d32:2), SM(d33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM(d42:1), SM(d42:4) and / or SM(d44:4), or fragments, variants or derivatives thereof ;and / or Cer(42:1), LPC(14:0), LPC(16:0e), LPC(17:1), LPC(18:3), PE(34:1), PE(34:2p), PE(36:4), PE(3 8:3p), PE(38:4), PE(O-38:5), PE(O-40:6), PI(32:1), PI(34:1), PI(36:1), PI(38:6), PS(38:4), PS(40: 6), PS(40:7), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and / or TG(62:2), or fragments, variants or derivatives thereof, are measured from the biological sample of the subject.

10. The method of any one of the preceding claims, wherein the one or more lipid biomarkers comprise LPC(14:0) or a fragment, variant or derivative thereof, and one or more other lipid biomarkers selected from the group consisting of Cer(d36:1), Cer(d42:0), Cer(d42:1), DG(34:2), Hex2Cer(d34:1), Hex2Cer(d42:2), LPC(16:0e), LPC(17:1), LPC(18:3), LPE(22:6), LPI(20:4), PC(32:1), PC(34:1), PC(35:4), PC(36:2), PC(36:3), PC(37:4), PC(38:5), PE(34:1), PE(34:2p), PE(36:4), PE(38:3p), PE(38:4), PE(O-38:5) , PE(O-40:6), PI(32:1), PI(34:1), PI(36:1), PI(38:6), PS(38:4), PS(40:6), PS(40:7), SM(d32:2 ), SM(d33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM (d42:1), SM(d42:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof.

11. The method of any of the preceding claims, wherein the one or more lipid biomarkers comprises PI(38:6) or a fragment, variant or derivative thereof, and one or more other lipid biomarkers selected from the group consisting of Cer(d36:1), Cer(d42:0), Cer(d42:1), DG(34:2), Hex2Cer(d34:1), Hex2Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(17:1), LPC(18:3), LPE(22:6), LPI(20:4), PC(32:1), PC(34:1), PC(35:4), PC(36:2), PC(36:3), PC(37:4), PC(38:5), PE(34:1), PE(34:2p), PE(36:4), PE(38:3p), PE(38:4), P E(O-38:5), PE(O-40:6), PI(32:1), PI(34:1), PI(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d32:2 ), SM(d33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM (d42:1), SM(d42:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof.

12. The method of any one of the preceding claims, wherein the one or more lipid biomarkers comprise LPC(14:0) and PI(38:6) or fragments, variants or derivatives thereof, and optionally one or more other lipid biomarkers selected from the group consisting of Cer(d36:1), Cer(d42:0), Cer(d42:1), DG(34:2), Hex2Cer(d34:1), Hex2Cer(d42:2), LPC(16:0e), LPC(17:1), LPC(18:3), LPE(22:6), LPI(20:4), PC(32:1), PC(34:1), PC(35:4), PC(36:2), PC(36:3), PC(37:4), PC(38:5), PE(34:1), PE(34:2p), PE(36:4), PE(38:3p), PE(38:4) , PE(O-38:5), PE(O-40:6), PI(32:1), PI(34:1), PI(36:1), PS(38:4), PS(40:6), PS(40:7), SM(d32: 2), SM(d33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), S M(d42:1), SM(d42:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof.

13. The method of any one of claims 1 to 10, wherein the one or more lipid biomarkers are selected from the group consisting of LPC (14:0), PC (32:1), PC (38:5), PE (34:1), PS (38:4), SM (d36:2), SM (d38:4), TG (54:4), TG (56:1) and TG (58:2), or fragments, variants or derivatives thereof.

14. The method of any one of claims 1 to 10, wherein the one or more lipid biomarkers are selected from the group consisting of LPC (14:0), PC (32:1), PC (36:2), PC (38:5), PE (34:1), PI (34:1), PS (38:4), SM (d36:2), SM (d38:4), SM (d44:4), TG (52:3e), TG (53:4), TG (54:4), TG (54:5), TG (54:6), TG (56:1), TG (58:2) and TG (58:3), or fragments, variants or derivatives thereof.

15. The method of claim 14, wherein the one or more lipid biomarkers are selected from the group consisting of LPC (14:0), PE (34:1), PS (38:4), SM (d36:2), TG (52:3e) and TG (58:3), or fragments, variants or derivatives thereof.

16. The method of any one of claims 1 to 11, wherein the one or more lipid biomarkers are selected from the group consisting of LPC (14:0), LPC (16:0e), LPE (22:6), PE (34:2p), PE (38:3p), PI (36:1), PI (38:6), PS (38:4), PS (40:6), SM (d33:1), SM (d35:1) and TG (57:1), or fragments, variants or derivatives thereof.

17. The method of claim 16, wherein the one or more lipid biomarkers include: (a) LPC (14:0), PS (38:4) and TG (57:1), or fragments, variants or derivatives thereof; (b) LPC (16:0e), LPE (22:6) and PI (38:6), or fragments, variants or derivatives thereof; (c) PE(34:2p), PE(38:3p), PI(36:1), PI(38:6) and PS(40:6), or fragments, variants or derivatives thereof; (d) LPC (14:0), PI (38:6) and SM (d33:1), or fragments, variants or derivatives thereof; or (e) LPC (14:0), PI (38:6) and SM (d35:1), or fragments, variants or derivatives thereof; and optionally one or more other lipid biomarkers selected from the group consisting of: LPC(14:0), LPC(16:0e), LPE(22:6), PE(34:2p), PE(38:3p), PI(36:1), PI(38:6), PS(38:4), PS(40:6), SM(d33:1), SM(d35:1) and TG(57:1), or fragments, variants or derivatives thereof.

18. The method of any one of claims 1 to 12, wherein the one or more lipid biomarkers described herein are selected from the group consisting of LPC (14:0), LPE (22:6), PI (38:6), PE (34:2p), SM (d35:1), PS (38:4) and PS (38:4, or fragments, variants or derivatives thereof.

19. The method according to any one of the preceding claims, wherein the level of one or more lipid biomarkers is or has been measured at least in part by mass spectrometry.

20. The method of any one of the preceding claims, wherein the predictive accuracy of the method as determined by the ROC AUC value is at least about 0.65, at least about 0.70, at least about 0.75, or at least about 0.

80.

21. A system for determining whether a subject has breast cancer, the system comprising: A mass spectrometer unit configured to determine the level of one or more lipid biomarkers in a biological sample obtained from the subject, wherein the one or more lipid biomarkers are selected from the group consisting of: Cer(d36:1), Cer(d42:0), Cer(d42:1), DG(34:2), Hex2Cer(d34:1), Hex2Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(17:1), LPC(18:3), LPE(22:6), LPI(20:4), PC(32:1), PC(34:1), PC(35:4), PC(36:2), PC(36:3), PC(37:4), PC(38:5), PE(34:1), PE(34:2p), PE(36:4), PE(38:3p), PE(38:4), PE(O-38:5) , PE(O-40:6), PI(32:1), PI(34:1), PI(36:1), PI(38:6), PS(38:4), PS(40:6), PS(40:7), SM(d32:2), SM(d33:1), SM(d35:1), SM(d36: 1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d41:3), SM(d42:1), SM(d42:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof; and A processing unit is configured to use or analyze the level of the one or more lipid biomarkers to determine the presence or absence of the breast cancer in the subject.

22. A kit for determining the presence or absence of breast cancer in a subject, the kit comprising one or more reagents for determining the level of one or more lipid biomarkers in a biological sample obtained from the subject, wherein the one or more lipid biomarkers are selected from the group consisting of: Cer(d36:1), Cer(d42:0), Cer(d42:1), DG(34:2), Hex2Cer(d34:1), Hex2Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(17:1), LPC(18:3), LPE(22:6), LPI(20:4), PC(32:1), PC(34:1), PC(35:4), PC(36:2), PC(36:3), PC(37:4), PC(38:5), PE(34:1), PE(34:2p), PE(36:4), PE(38:3p), PE(3 8:4), PE(O-38:5), PE(O-40:6), PI(32:1), PI(34:1), PI(36:1), PI(38:6), PS(38:4), PS(40:6), PS(40: 7), SM(d32:2), SM(d33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM(d 41:3), SM(d42:1), SM(d42:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof.

23. The kit of claim 22, wherein the one or more reagents comprise one or more probes, each probe being specific or selective for one of the one or more lipid biomarkers.

24. The method, system or kit of any of the preceding claims, wherein the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of Cer (d36: 1), Cer (d42: 0), Cer (d42: 1), DG (34: 2), Hex2Cer (d34: 1), He x2Cer(d42:2), LPC(14:0), LPC(16:0e), LPC(17:1), LPC(18:3), LPE(22:6), LPI(20:4), PC(32:1), PC(34 :1), PC(35:4), PC(36:2), PC(36:3), PC(37:4), PC(38:5), PE(34:1), PE(34:2p), PE(36:4), PE(38:3p), P E(38:4), PE(O-38:5), PE(O-40:6), PI(32:1), PI(34:1), PI(36:1), PI(38:6), PS(38:4), PS(40:6), PS(4 0:7), SM(d32:2), SM(d33:1), SM(d35:1), SM(d36:1), SM(d36:2), SM(d38:4), SM(d40:3), SM(d41:2), SM( d41:3), SM(d42:1), SM(d42:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:3), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(57:1), TG(58:1), TG(58:2), TG(58:3), TG(59:2), TG(60:1) and TG(62:2), or fragments, variants or derivatives thereof.

25. The method, system or kit of claim 24, wherein the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of LPC (14:0), PC (32:1), PC (38:5), PE (34:1), PS (38:4), SM (d36:2), SM (d38:4), TG (54:4), TG (56:1) and TG (58:2), or fragments, variants or derivatives thereof.

26. The method, system or kit of claim 24, wherein the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of LPC(14:0), PC(32:1), PC(36:2), PC(38:5), PE(34:1), PI(34:1), PS(38:4), SM(d36:2), SM(d38:4), SM(d44:4), TG(52:3e), TG(53:4), TG(54:4), TG(54:5), TG(54:6), TG(56:1), TG(58:2) and TG(58:3), or fragments, variants or derivatives thereof.

27. The method, system or kit of claim 26, wherein the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of: LPC (14:0), PE (34:1), PS (38:4), SM (d36:2), TG (52:3e) and TG (58:3), or fragments, variants or derivatives thereof.

28. The method, system or kit of claim 24, wherein the one or more lipid biomarkers comprise two or more lipid biomarkers, three or more lipid biomarkers, four or more lipid biomarkers or five or more lipid biomarkers selected from the group consisting of LPC (14:0), LPC (16:0e), LPE (22:6), PE (34:2p), PE (38:3p), PI (36:1), PI (38:6), PS (38:4), PS (40:6), SM (d33:1), SM (d35:1) and TG (57:1), or fragments, variants or derivatives thereof.

29. The method, system or kit according to any one of the preceding claims, wherein the biological sample is or comprises a blood sample, a plasma sample and / or a serum sample.

30. A system or kit according to any one of claims 21 to 29 for use in a method according to any one of claims 1 to 20.

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