Patent Application
20210325395
The present invention relates to methods for detecting pancreatic cancer, and biomarkers and arrays for use in the same.
Pancreatic ductal adenocarcinoma (PDAC) is the 4th most common cancer-related cause of death (Siegel et al, 2012). Multiple factors account for its poor prognosis and early diagnosis provides today the only possibility for cure. PDAC is often detected at late stages with 80% of patients not eligible for surgery due to either locally advanced or metastatic disease (Hidalgo, 2010; Porta et al, 2005; Siegel et al, 2012).
The biological diversity of tumours due to its localization in pancreatic cancer has been previously demonstrated. Tumours in the body/tail of pancreas are rarer than tumour in the head of pancreas (77% of PDAC). Because of differences in e.g., blood supply, and lymphatic and venous backflow, there are also differences in the disease presentation with body/tail tumours causing less jaundice, more pain, higher albumin and CEA levels and lower CA19-9 levels.
Body/tail tumours are more often detected at a later stage than head tumours and have a higher rate of metastasis. As the biological differences can result in different treatment efficiency, biomarkers that can discriminate between tumour localization would be of clinical relevance and could pave the way for personalized treatment strategies. However, few differences have been found on a genetic level, with no significant variation in the overall number of mutations, deletions and amplifications, or in K-ras point mutations.
Accordingly, there is a continuing need to provide methods for determining biomarkers that can determine the locality and/or presence of pancreatic cancer tumours.
A major problem with tumours of the body/tail in comparison with pancreatic head cancer is distant metastasis, especially in the liver, and resection of the tumour does not increase postoperative survival in metastatic disease. On the other hand, patients with local-stage body/tail tumours had higher survival rates compared with local-stage pancreatic head cancer.
Several antibodies identified markers that showed on differential protein expression levels between head and body/tail tumours. A condensed signatures differentiating the groups could be defined. Consequently, these results are encouraging for a future development of a blood protein biomarker signature discriminating body/tail and head tumours at an early disease stage.
Taken together, we provide information that serum protein markers associated with different tumour locations in the pancreas could be identified. Serum protein markers associated with tumour localization were identified.
A first aspect of the invention provides a method for determining the locality of and/or diagnosing pancreatic cancer in an individual comprising or consisting of the steps of:
wherein the expression in the test sample of the one or more biomarker selected from the group defined in Table A (i), (ii) or (iii) is indicative of the locality and/or presence of pancreatic cancer in the individual.
By “sample to be tested”, “test sample” or “control sample” we include a tissue or fluid sample taken or derived from an individual. Preferably the sample to be tested is provided from a mammal. The mammal may be any domestic or farm animal. Preferably, the mammal is a rat, mouse, guinea pig, cat, dog, horse or a primate. Most preferably, the mammal is human. Preferably the sample is a cell or tissue sample (or derivative thereof) comprising or consisting of plasma, plasma cells, serum, tissue cells or equally preferred, protein or nucleic acid derived from a cell or tissue sample. Preferably test and control samples are derived from the same species.
In an alternative or additional embodiment the tissue sample is pancreatic tissue. In an alternative or additional embodiment, the cell sample is a sample of pancreatic cells.
By “expression” we mean the level or amount (relative and/or absolute) of a gene product such as ctDNA (circulating DNA), mRNA or protein. Expression may be used to define clusters associated with disease states of interest. Alternatively or additionally, “expression” excludes the measurement of ctDNA.
Methods of detecting and/or measuring the concentration of protein and/or nucleic acid are well known to those skilled in the art, see for example Sambrook and Russell, 2001, Cold Spring Harbor Laboratory Press.
By “biomarker” we mean a naturally-occurring biological molecule, or component or fragment thereof, the measurement of which can provide information useful in determining the locality and/or presence of pancreatic cancer. For example, the biomarker may be a naturally-occurring nucleic acid, protein or carbohydrate moiety, or an antigenic component or fragment thereof.
By ‘determining the locality of pancreatic cancer,’ ‘indicative of the pancreatic cancer locality’ and the like we include determining (or providing indication of) whether the pancreatic cancer is located in and/or originated from (a) the head of the pancreas; or (b) the body and/or tail of the pancreas.
The terms ‘pancreas head, ‘pancreas neck,’ ‘pancreas body’ and ‘pancreas tail’ are well-known and understood by the skilled person. Hence, by ‘the head of the pancreas,’ ‘the neck of the pancreas,’ the body of the pancreas' and ‘the tail of the pancreas’ we include the conventional understanding of the terms by the skilled person.
Alternatively or additionally, by ‘the head of the pancreas’ we mean or include foundational model of anatomy identification number (FMAID) 10468 (for more information on the FMA and FMAIDs, see Rosse & Cornelius, 2003, ‘A reference ontology for biomedical informatics: the Foundational Model of Anatomy,’ J. Biomed. Informatics, 36(6): 478-500 and the FMA browser, accessible at http://xiphoid.biostr.washington.edu/fma/index.html). Synonyms for ‘the head of the pancreas’ include ‘right extremity of pancreas,’ ‘pancreatic head’ and ‘caput pancreatis’.
Alternatively or additionally, by ‘the neck of the pancreas’ we mean or include FMAID 14517. Synonyms for ‘the neck of the pancreas’ include ‘pancreatic neck’ and ‘collum pancreatis’.
Alternatively or additionally, by ‘the body of the pancreas’ we mean or include FMAID 14518. Synonyms for ‘the body of the pancreas’ include ‘pancreatic body’ and ‘corpus pancreatis’.
Alternatively or additionally, FMAID numbers comprise or consist of the FMA definitions current on Sep. 21, 2015.
Alternatively or additionally, by ‘the head of the pancreas’ we include the head and/or neck of the pancreas. Hence, alternatively or additionally, by ‘determining the locality of pancreatic cancer,’ ‘indicative of the pancreatic cancer locality’ and the like we include determining (or providing indication of) whether the pancreatic cancer is located in and/or originated from (a) the head and/or neck of the pancreas; or (b) the body and/or tail of the pancreas.
Alternatively or additionally, by ‘the body/tail of the pancreas’ we include the neck, body and/or tail of the pancreas. Hence, alternatively or additionally, by ‘determining the locality of pancreatic cancer,’ ‘indicative of the pancreatic cancer locality’ and the like we include determining (or providing indication of) whether the pancreatic cancer is located in and/or originated from (a) the head of the pancreas; or (b) the neck, body and/or tail of the pancreas.
By located in the head and/or neck of the pancreas' we include that at least greater than 50% of the tumour is located in the head and/or neck of the pancreas, for example, ≥51%, ≥52%, ≥53%, ≥54%, ≥55%, ≥56%, ≥57%, ≥58%, ≥59%, ≥60%, ≥61%, ≥62%, ≥63%, ≥64%, ≥65%, ≥66%, ≥67%, ≥68%, ≥69%, ≥70%, ≥71%, ≥72%, ≥73%, ≥74%, ≥75%, ≥76%, ≥77%, ≥78%, ≥79%, ≥80%, ≥81%, ≥82%, ≥83%, ≥84%, ≥85%, ≥86%, ≥87%, ≥88%, ≥89%, ≥90%, ≥91%, ≥92%, ≥93%, ≥94%, ≥95%, ≥96%, ≥97%, ≥98%, ≥99% or 100% of the tumour is located in the head and/or neck of the pancreas.
By ‘originated from the head and/or neck of the pancreas’ we include that the pancreatic cancer comprises or consists of pancreatic cancer that is located outside of the head and/or neck of the pancreas but originated from a primary tumour located in head and/or neck of the pancreas. Thus, metastases of pancreatic cancer from a primary tumour located in the head of the pancreas may be included.
By ‘located in the neck, body and/or tail of the pancreas’ we include that at least greater than 50% of the tumour is located in the neck, body and/or tail of the pancreas, for example, ≥51, ≥52%, ≥53%, ≥54%, ≥55%, ≥56%, ≥57%, ≥58%, ≥59%, ≥60%, ≥61%, ≥62%, ≥63%, ≥64%, ≥65%, ≥66%, ≥67%, ≥68%, ≥69%, ≥70%, ≥71%, ≥72%, ≥73%, ≥74%, ≥75%, ≥76%, ≥77%, ≥78%, ≥79%, ≥80%, ≥81%, ≥82%, ≥83%, ≥84%, ≥85%, ≥86%, ≥87%, ≥88%, ≥89%, ≥90%, ≥91%, ≥92%, ≥93%, ≥94%, ≥95%, ≥96%, ≥97%, ≥98%, ≥99% or ≥100% of the tumour is located in the neck, body and/or tail of the pancreas.
By ‘originated from the neck, body and/or tail of the pancreas’ we include that the pancreatic cancer comprises or consists of pancreatic cancer that is located outside of the neck, body and/or tail of the pancreas but originated from a primary tumour located in the neck, body and/or tail of the pancreas. Thus, metastases of pancreatic cancer from a primary tumour located in the neck, body and/or tail of the pancreas may be included.
Alternatively or additionally the individual is determined to be afflicted with pancreatic cancer. The individual afflicted with pancreatic cancer may diagnosed as having pancreatic cancer prior to step (a), during step (a) and/or following step (a).
The pancreatic cancer may be diagnosed using one or more biomarkers of the present invention (i.e., concurrent diagnosis and locality determination using the same or different biomarkers of the invention for each).
Alternatively or additionally the pancreatic cancer may be diagnosed using conventional clinical methods known in the art. For example, those methods described in Ducreux et al., 2015, ‘Cancer of the pancreas: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up’ Annals of Oncology, 26 (Supplement 5): v56-v68 and/or Freelove & Walling, 2006, ‘Pancreatic Cancer: Diagnosis and Management’ American Family Physician, 73(3):485-492 which are incorporated herein by reference
Accordingly, the pancreatic cancer may be diagnosed using one or more method selected from the group consisting of:
Alternatively and/or additionally, the pancreatic cancer may be diagnosed using detection of biomarkers for the diagnosis of pancreatic cancer. For example, the pancreatic cancer may be diagnosed with one or more biomarker or diagnostic method described in the group consisting of:
Alternatively or additionally the method further comprises or consists of the steps of:
wherein the locality and/or presence of pancreatic cancer is identified in the event that the expression in the test sample of the one or more biomarkers measured in step (b) is different from the expression in the control sample of the one or more biomarkers measured in step (d).
By “is different to the presence and/or amount in a control sample” we mean the presence and/or amount of the one or more biomarker in the test sample differs from that of the one or more control sample (or to predefined reference values representing the same). Preferably the presence and/or amount is no more than 40% of that of the one or more negative control sample, for example, no more than 39%, 38%, 37%, 36%, 35%, 34%, 33%, 32%, 31%, 30%, 29%, 28%, 27%, 26%, 25%, 24%, 23%, 22%, 21%, 20%, 19%, 18%, 17%, 16%, 15%, 14%, 13%, 12%, 11%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1% or 0%.
In an alternative or additional embodiment the presence and/or amount in the test sample of the one or more biomarker measured in step (b) is significantly different (i.e., statistically significantly different) from the presence and/or amount of the one or more biomarker measured in step (d) or the predetermined reference values. For example, as discussed in the accompanying Examples, significant difference between the presence and/or amount of a particular biomarker in the test and control samples may be classified as those where p<0.05 (for example, where p<0.04, p<0.03, p<0.02 or where p<0.01).
The one or more control sample may be from a healthy individual (i.e., an individual unaffiliated by any disease or condition), an individual afflicted with a non-pancreatic disease or condition or an individual afflicted with a benign pancreatic disease or condition (for example, acute or chronic pancreatitis).
Alternatively or additionally the method further comprises or consists of the steps of:
wherein the locality and/or presence of pancreatic cancer is identified in the event that the expression in the test sample of the one or more biomarkers measured in step (b) corresponds to the expression in the control sample of the one or more biomarkers measured in step (f).
By “corresponds to the expression in the control sample” we include that the expression of the one or more biomarkers in the sample to be tested is the same as or similar to the expression of the one or more biomarkers of the positive control sample. Preferably the expression of the one or more biomarkers in the sample to be tested is identical to the expression of the one or more biomarkers of the positive control sample.
Differential expression (up-regulation or down regulation) of biomarkers, or lack thereof, can be determined by any suitable means known to a skilled person. Differential expression is determined to a p-value of a least less than 0.05 (p=<0.05), for example, at least <0.04, <0.03, <0.02, <0.01, <0.009, <0.005, <0.001, <0.0001, <0.00001 or at least <0.000001. Preferably, differential expression is determined using a support vector machine (SVM). Preferably, the SVM is an SVM as described below.
It will be appreciated by persons skilled in the art that differential expression may relate to a single biomarker or to multiple biomarkers considered in combination (i.e., as a biomarker signature). Thus, a p-value may be associated with a single biomarker or with a group of biomarkers. Indeed, proteins having a differential expression p-value of greater than 0.05 when considered individually may nevertheless still be useful as biomarkers in accordance with the invention when their expression levels are considered in combination with one or more other biomarkers.
As exemplified in the accompanying examples, the expression of certain proteins in a tissue, blood, serum or plasma test sample may be indicative of pancreatic cancer disease state in an individual (e.g., locality and/or presence). For example, the relative expression of certain serum proteins in a single test sample may be indicative of the locality and/or presence of pancreatic cancer in an individual.
When referring to a “normal” disease state we include individuals not afflicted with chronic pancreatitis (ChP) or acute inflammatory pancreatitis (AIP). Preferably the individuals are not afflicted with any pancreatic disease or disorder. Most preferably, the individuals are healthy individuals, i.e., they are not afflicted with any disease or disorder.
Alternatively or additionally the method further comprises or consists of the steps of:
wherein the location of pancreatic cancer in the test sample is identified as being located in and/or originating from head (and/or neck) of the pancreas in the event that the expression in the test sample of the one or more biomarkers measured in step (b) corresponds to the expression in the control sample of the one or more biomarkers measured in step (h); and wherein the location of pancreatic cancer in the test sample is identified as being located in and/or originating from the body and/or tail of the pancreas in the event that the expression in the test sample of the one or more biomarkers measured in step (b) is different from the expression in the control sample of the one or more biomarkers measured in step (h).
Alternatively or additionally the method further comprises or consists of the steps of:
wherein the location of pancreatic cancer in the test sample is identified as being located in and/or originating from the (neck), body and/or tail of the pancreas in the event that the expression in the test sample of the one or more biomarkers measured in step (b) corresponds to the expression in the control sample of the one or more biomarkers measured in step (j); and wherein the location of pancreatic cancer in the test sample is identified as being located in and/or originating from the head of the pancreas in the event that the expression in the test sample of the one or more biomarkers measured in step (b) is different from the expression in the control sample of the one or more biomarkers measured in step (j).
Alternatively or additionally step (b) comprises or consists of measuring the expression of one or more of the biomarkers listed in Table A, for example, at least 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, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123 or 124 of the biomarkers listed in Table A.
In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of PRD14. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of HsHec1. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of hSpindly. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of GNAI3. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of GRIP-2. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of HsMAD2. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of TBC1D9. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of MAPKK6. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of MAPK9. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of MAPK8. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of ORP-3. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of MUC1. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of PTK6. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of PTPN1. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of R-PTP-eta. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of R-PTP-O. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of PGAM5. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of STAT1. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of EGFR. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of Surface Ag X. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CIMS (1). In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CIMS (11). In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CIMS (12). In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CIMS (13). In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CIMS (14). In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CIMS (15). In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CIMS (16). In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CIMS (17). In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CIMS (18). In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CIMS (2). In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CIMS (20). In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CIMS (21). In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CIMS (22). In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CIMS (23). In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CIMS (24). In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CIMS (25). In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CIMS (26). In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CIMS (27). In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CIMS (28). In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CIMS (29). In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CIMS (3). In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CIMS (30). In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CIMS (31). In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CIMS (4). In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CIMS (5). In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CIMS (6). In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CIMS (7). In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CIMS (9). In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of Apo-A1. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of Apo-A4. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of ATP-5B. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of B-galactosidase. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of BTK. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of C1 inh. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of C1s. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of C3. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of C4. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of C5. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CD40. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CDK-2. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of Cystatin C. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of Eotaxin. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of Factor B. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of FASN. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of GAK. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of GLP-1 R. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of GM-CSF. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of Her2/ErbB2. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of ICAM-1. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of IFN-γ. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of IL-10. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of IL-13. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of IL-1β. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of IL-5. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of IL-8. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of Integrin a-10. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of Integrin a-11. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of JAK3. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of KSYK. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of LDL. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of Leptin. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of MAPK1. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of MCP-3. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of MCP-4. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of MYOM2. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of ORP-3. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of Osteopontin. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of P85A. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of Procathepsin W. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of Properdin. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of PSA. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of RPS6KA2. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of Sialyl Lewis X. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of STAP2. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of TM peptide. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of TNF-α. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of UCHL5. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of UPF3B. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of Angiomotin. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CD40 ligand. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of CHX10. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of GLP-1. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of HADH2. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of HLA-DR/DP. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of IgM. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of IL-11. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of IL-12. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of IL-16. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of IL-18. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of IL-1a. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of IL-1ra. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of IL-3. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of IL-4. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of IL-6. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of IL-7. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of IL-9. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of Lewis X. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of MCP-1. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of RANTES. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of sox11a. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of TGF-β1. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of TNF-b. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of TNFRSF3. In an alternative or additional embodiment, step (b) comprises or consists of or excludes measuring the expression of VEGF.
By “transmembrane peptide” or “TM peptide” we mean a peptide derived from a 10TM protein, to which the scFv antibody construct of SEQ ID NO: 1 below has specificity (wherein the CDR sequences are indicated by bold, italicised text):
KGLEWV
FTISRDNSKNTLYLQMNSLRAEDTAVYYCAR
GTWFDPWGQGTLVTVSSGGGGSGGGGSGGGGSQSVLTQPPSASGTPGQRV
WYQQLPGTAPKLLIY
GVPDRFSGSKS
FGGGTKLTVLG
Hence, this scFv may be used or any antibody, or antigen binding fragment thereof, that competes with this scFv for binding to the 10TM protein. For example, the antibody, or antigen binding fragment thereof, may comprise the same CDRs as present in SEQ ID NO:1.
It will be appreciated by persons skilled in the art that such an antibody may be produced with an affinity tag (e.g. at the C-terminus) for purification purposes. For example, an affinity tag of SEQ ID NO: 2 below may be utilised:
Alternatively or additionally step (b) comprises or consists of measuring the expression of one or more of the biomarkers listed in Table A(i), for example, at least 2 of the biomarkers listed in Table A(i).
Alternatively or additionally step (b) comprises or consists of measuring the expression of PRD14 and/or HsHec1, for example, measuring the expression of PRD14, measuring the expression of HsHec1, or measuring the expression of PRD14 and HsHec1.
Alternatively or additionally step (b) comprises or consists of measuring the expression of all of the biomarkers listed in Table A(i).
Alternatively or additionally step (b) comprises or consists of measuring the expression of 1 or more of the biomarkers listed in Table (A)(ii), for example at least 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 or 46 of the biomarkers listed in Table A(ii).
Alternatively or additionally step (b) comprises or consists of measuring the expression of all of the biomarkers listed in Table A(ii).
Alternatively or additionally step (b) comprises or consists of measuring the expression of 1 or more biomarkers from the biomarkers listed in Table A(iii), for example at least 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 or 50 of the biomarkers listed in Table A(iii).
Alternatively or additionally step (b) comprises or consists of measuring the expression of all of the biomarkers listed in Table A(iii).
Alternatively or additionally step (b) comprises or consists of measuring the expression of 1 or more biomarkers from the biomarkers listed in Table A(iv), for example at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25 or 26 of the biomarkers listed in Table A(iv).
Alternatively or additionally step (b) comprises or consists of measuring the expression of all of the biomarkers listed in Table A(iv).
Alternatively or additionally step (b) comprises or consists of measuring the expression in the test sample of all of the biomarkers defined in Table A.
In an alternative or additional embodiment, step (b) comprises or consists of measuring the expression of 1 or more biomarkers from the biomarkers listed in Table 1, for example at least 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 or 37 of the biomarkers listed in Table 1.
In an alternative or additional embodiment, step (b) comprises or consists of measuring the expression of 1 or more biomarkers from the biomarkers listed in Table 2, for example at least 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 or 35 of the biomarkers listed in Table 2.
In an alternative or additional embodiment, step (b) comprises or consists of measuring the expression of 1 or more biomarkers from the biomarkers listed in Table 3.
In an alternative or additional embodiment, step (b) comprises or consists of measuring the expression of 1 or more biomarkers from the biomarkers listed in Table 4.
Alternatively or additionally the pancreatic cancer is selected from the group consisting of adenocarcinoma, adenosquamous carcinoma, signet ring cell carcinoma, hepatoid carcinoma, colloid carcinoma, undifferentiated carcinoma, undifferentiated carcinomas with osteoclast-like giant cells, malignant serous cystadenoma, pancreatic sarcoma, and tubular papillary pancreatic adenocarcinoma.
Alternatively or additionally the pancreatic cancer is an adenocarcinoma, for example, pancreatic ductal adenocarcinoma.
Generally, the diagnosis/determination is made with an ROC AUC of at least 0.51, for example with an ROC AUC of at least, 0.52, 0.53, 0.54, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95, 0.96, 0.97, 0.98, 0.99 or with an ROC AUC of 1.00. Preferably, diagnosis is made with an ROC AUC of at least 0.85, and most preferably with an ROC AUC of 1.
Typically, diagnosis is performed using a support vector machine (SVM), such as those available from http://cran.r-project.org/web/packages/e1071/index.html (e.g. e1071 1.5-24). However, any other suitable means may also be used.
Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other. Intuitively, an SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on.
More formally, a support vector machine constructs a hyperplane or set of hyperplanes in a high or infinite dimensional space, which can be used for classification, regression or other tasks. Intuitively, a good separation is achieved by the hyperplane that has the largest distance to the nearest training datapoints of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier. For more information on SVMs, see for example, Burges, 1998, Data Mining and Knowledge Discovery, 2:121-167.
In an alternative or additional embodiment of the invention, the SVM is ‘trained’ prior to performing the methods of the invention using biomarker profiles from individuals with known disease status (for example, individuals known to have pancreatic cancer, individuals known to have acute inflammatory pancreatitis, individuals known to have chronic pancreatitis or individuals known to be healthy). By running such training samples, the SVM is able to learn what biomarker profiles are associated with pancreatic cancer. Once the training process is complete, the SVM is then able whether or not the biomarker sample tested is from an individual with pancreatic cancer.
However, this training procedure can be by-passed by pre-programming the SVM with the necessary training parameters. For example, diagnoses can be performed according to the known SVM parameters using an SVM algorithm based on the measurement of any or all of the biomarkers listed in Table A.
It will be appreciated by skilled persons that suitable SVM parameters can be determined for any combination of the biomarkers listed in Table A by training an SVM machine with the appropriate selection of data (i.e. biomarker measurements from individuals with known pancreatic cancer status). Alternatively, the Table 1-5 data may be used to determine a particular pancreatic cancer-associated disease state according to any other suitable statistical method known in the art.
In an alternative or additional embodiment the presence and/or amount in the test sample of the one or more biomarker measured in step (b) is significantly different (i.e., statistically significantly different) from the presence and/or amount of the one or more biomarker measured in step (d) or the predetermined reference values. For example, as discussed in the accompanying Examples, significant difference between the presence and/or amount of a particular biomarker in the test and control samples may be classified as those where p<0.05 (for example, where p<0.04, p<0.03, p<0.02 or where p<0.01).
Alternatively, the data provided in the present figures and tables may be used to determine a particular pancreatic cancer-associated disease state according to any other suitable statistical method known in the art, such as Principal Component Analysis (RCA) and other multivariate statistical analyses (e.g., backward stepwise logistic regression model). For a review of multivariate statistical analysis see, for example, Schervish, Mark J. (November 1987). “A Review of Multivariate Analysis”. Statistical Science 2 (4): 396-413 which is incorporated herein by reference.
Preferably, the method of the invention has an accuracy of at least 60%, for example 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% accuracy.
Preferably, the method of the invention has a sensitivity of at least 60%, for example 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sensitivity.
Preferably, the method of the invention has a specificity of at least 60%, for example 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% specificity.
By “accuracy” we mean the proportion of correct outcomes of a method, by “sensitivity” we mean the proportion of all PaC positive sample that are correctly classified as positives, and by “specificity” we mean the proportion of all PaC negative samples that are correctly classified as negatives.
In an alternative or additional embodiment, the individual not afflicted with pancreatic cancer is not afflicted with pancreatic cancer (PaC), chronic pancreatitis (ChP) or acute inflammatory pancreatitis (AIP). More preferably, the individual not afflicted with pancreatic cancer is a healthy individual not afflicted with any pancreatic disease or condition. Even more preferably, the individual not afflicted with pancreatic cancer is not afflicted with any disease or condition. Most preferably, the individual not afflicted with pancreatic cancer is a healthy individual. Alternatively or additionally, by a “healthy individual” we include individuals considered by a skilled person to be physically vigorous and free from physical disease.
However, in another embodiment the individual not afflicted with pancreatic cancer is afflicted with chronic pancreatitis. In still another embodiment, the individual not afflicted with pancreatic cancer is afflicted with acute inflammatory pancreatitis.
Alternatively or additionally step (b), (d), (f), (h) and/or step (j) is performed using a first binding agent capable of binding to the one or more biomarkers.
It will be appreciated by persons skilled in the art that the first binding agent may comprise or consist of a single species with specificity for one of the protein biomarkers or a plurality of different species, each with specificity for a different protein biomarker.
Suitable binding agents (also referred to as binding molecules) can be selected from a library, based on their ability to bind a given motif, as discussed below.
At least one type of the binding agents, and more typically all of the types, may comprise or consist of an antibody or antigen-binding fragment of the same, or a variant thereof.
Methods for the production and use of antibodies are well known in the art, for example see Antibodies: A Laboratory Manual, 1988, Harlow & Lane, Cold Spring Harbor Press, ISBN-13: 978-0879693145, Using Antibodies: A Laboratory Manual, 1998, Harlow & Lane, Cold Spring Harbor Press, ISBN-13: 978-0879695446 and Making and Using Antibodies: A Practical Handbook, 2006, Howard & Kaser, CRC Press, ISBN-13: 978-0849335280 (the disclosures of which are incorporated herein by reference).
Thus, a fragment may contain one or more of the variable heavy (VH) or variable light (VL) domains. For example, the term antibody fragment includes Fab-like molecules (Better et al (1988) Science 240, 1041); Fv molecules (Skerra et al (1988) Science 240, 1038); single-chain Fv (ScFv) molecules where the VH and VL partner domains are linked via a flexible oligopeptide (Bird et al (1988) Science 242, 423; Huston et al (1988) Proc. Natl. Acad. Sci. USA 85, 5879) and single domain antibodies (dAbs) comprising isolated V domains (Ward et al (1989) Nature 341, 544).
The term “antibody variant” includes any synthetic antibodies, recombinant antibodies or antibody hybrids, such as but not limited to, a single-chain antibody molecule produced by phage-display of immunoglobulin light and/or heavy chain variable and/or constant regions, or other immuno-interactive molecule capable of binding to an antigen in an immunoassay format that is known to those skilled in the art.
A general review of the techniques involved in the synthesis of antibody fragments which retain their specific binding sites is to be found in Winter & Milstein (1991) Nature 349, 293-299.
Molecular libraries such as antibody libraries (Clackson et al, 1991, Nature 352, 624-628; Marks et al, 1991, J Mol Biol 222(3): 581-97), peptide libraries (Smith, 1985, Science 228(4705): 1315-7), expressed cDNA libraries (Santi et al (2000) J Mol Biol 296(2): 497-508), libraries on other scaffolds than the antibody framework such as affibodies (Gunneriusson et al, 1999, Appl Environ Microbiol 65(9): 4134-40) or libraries based on aptamers (Kenan et al, 1999, Methods Mol Biol 118, 217-31) may be used as a source from which binding molecules that are specific for a given motif are selected for use in the methods of the invention.
The molecular libraries may be expressed in vivo in prokaryotic (Clackson et al, 1991, op. cit.; Marks et al, 1991, op. cit.) or eukaryotic cells (Kieke et al, 1999, Proc Natl Acad Sci USA, 96(10):5651-6) or may be expressed in vitro without involvement of cells (Hanes & Pluckthun, 1997, Proc Natl Acad Sci USA 94(10):4937-42; He & Taussig, 1997, Nucleic Acids Res 25(24):5132-4; Nemoto et al, 1997, FEBS Lett, 414(2):405-8).
In cases when protein based libraries are used often the genes encoding the libraries of potential binding molecules are packaged in viruses and the potential binding molecule is displayed at the surface of the virus (Clackson et al, 1991, op. cit.; Marks et al, 1991, op. cit; Smith, 1985, op. cit.).
The most commonly used such system today is filamentous bacteriophage displaying antibody fragments at their surfaces, the antibody fragments being expressed as a fusion to the minor coat protein of the bacteriophage (Clackson et al, 1991, op. cit.; Marks et al, 1991, op. cit). However, also other systems for display using other viruses (EP 39578), bacteria (Gunneriusson et al, 1999, op. cit.; Daugherty et al, 1998, Protein Eng 11(9):825-32; Daugherty et al, 1999, Protein Eng 12(7):613-21), and yeast (Shusta et al, 1999, J Mol Biol 292(5):949-56) have been used.
In addition, display systems have been developed utilising linkage of the polypeptide product to its encoding mRNA in so called ribosome display systems (Hanes & Pluckthun, 1997, op. cit.; He & Taussig, 1997, op. cit.; Nemoto et al, 1997, op. cit.), or alternatively linkage of the polypeptide product to the encoding DNA (see U.S. Pat. No. 5,856,090 and WO 98/37186).
When potential binding molecules are selected from libraries one or a few selector peptides having defined motifs are usually employed. Amino acid residues that provide structure, decreasing flexibility in the peptide or charged, polar or hydrophobic side chains allowing interaction with the binding molecule may be used in the design of motifs for selector peptides.
For example:
Typically, selection of binding agents may involve the use of array technologies and systems to analyse binding to spots corresponding to types of binding molecules.
In an alternative or additional embodiment, the first binding agent(s) is/are immobilised on a surface (e.g. on a multiwell plate or array).
The variable heavy (VH) and variable light (VL) domains of the antibody are involved in antigen recognition, a fact first recognised by early protease digestion experiments. Further confirmation was found by “humanisation” of rodent antibodies. Variable domains of rodent origin may be fused to constant domains of human origin such that the resultant antibody retains the antigenic specificity of the rodent parented antibody (Morrison et al (1984) Proc. Natl. Acad. Sci. USA 81, 6851-6855).
That antigenic specificity is conferred by variable domains and is independent of the constant domains is known from experiments involving the bacterial expression of antibody fragments, all containing one or more variable domains. These molecules include Fab-like molecules (Better et al (1988) Science 240, 1041); Fv molecules (Skerra et al (1988) Science 240, 1038); single-chain Fv (ScFv) molecules where the VH and VL partner domains are linked via a flexible oligopeptide (Bird et al (1988) Science 242, 423; Huston et al (1988) Proc. Natl. Acad. Sci. USA 85, 5879) and single domain antibodies (dAbs) comprising isolated V domains (Ward et al (1989) Nature 341, 544). A general review of the techniques involved in the synthesis of antibody fragments which retain their specific binding sites is to be found in Winter & Milstein (1991) Nature 349, 293-299.
By “ScFv molecules” we mean molecules wherein the VH and VL partner domains are linked via a flexible oligopeptide.
The advantages of using antibody fragments, rather than whole antibodies, are several-fold. The smaller size of the fragments may lead to improved pharmacological properties, such as better penetration of solid tissue. Effector functions of whole antibodies, such as complement binding, are removed. Fab, Fv, ScFv and dAb antibody fragments can all be expressed in and secreted from E. coli, thus allowing the facile production of large amounts of the said fragments.
Whole antibodies, and F(ab′)2 fragments are “bivalent”. By “bivalent” we mean that the said antibodies and F(ab′)2 fragments have two antigen combining sites. In contrast, Fab, Fv, ScFv and dAb fragments are monovalent, having only one antigen combining sites.
The antibodies may be monoclonal or polyclonal. Suitable monoclonal antibodies may be prepared by known techniques, for example those disclosed in “Monoclonal Antibodies: A manual of techniques”, H Zola (CRC Press, 1988) and in “Monoclonal Hybridoma Antibodies: Techniques and applications”, J G R Hurrell (CRC Press, 1982), both of which are incorporated herein by reference.
In an alternative or additional embodiment, the first binding agent immobilised on a surface (e.g. on a multiwell plate or array).
The advantages of using antibody fragments, rather than whole antibodies, are several-fold. The smaller size of the fragments may lead to improved pharmacological properties, such as better penetration of solid tissue. Effector functions of whole antibodies, such as complement binding, are removed. Fab, Fv, ScFv and dAb antibody fragments can all be expressed in and secreted from E. coli, thus allowing the facile production of large amounts of the said fragments.
Whole antibodies, and F(ab′)2 fragments are “bivalent”. By “bivalent” we mean that the said antibodies and F(ab′)2 fragments have two antigen combining sites. In contrast, Fab, Fv, ScFv and dAb fragments are monovalent, having only one antigen combining sites.
The antibodies may be monoclonal or polyclonal. Suitable monoclonal antibodies may be prepared by known techniques, for example those disclosed in “Monoclonal Antibodies: A manual of techniques”, H Zola (CRC Press, 1988) and in “Monoclonal Hybridoma Antibodies: Techniques and applications”, J G R Hurrell (CRC Press, 1982), both of which are incorporated herein by reference.
Hence, the first binding agent may comprise or consist of an antibody or an antigen-binding fragment thereof. Preferably, the antibody or antigen-binding fragment thereof is a recombinant antibody or antigen-binding fragment thereof. The antibody or antigen-binding fragment thereof may be selected from the group consisting of: scFv, Fab, and a binding domain of an immunoglobulin molecule.
The first binding agent may be immobilised on a surface.
Alternatively or additionally the first binding agent comprises or consists of an antibody or an antigen-binding fragment thereof, e.g., a recombinant antibody or antigen-binding fragment thereof. The antibody or antigen-binding fragment thereof may be selected from the group consisting of: scFv; Fab; a binding domain of an immunoglobulin molecule.
Alternatively or additionally the one or more biomarkers in the test sample are labelled with a detectable moiety.
By a “detectable moiety” we include the meaning that the moiety is one which may be detected and the relative amount and/or location of the moiety (for example, the location on an array) determined.
Suitable detectable moieties are well known in the art.
Thus, the detectable moiety may be a fluorescent and/or luminescent and/or chemiluminescent moiety which, when exposed to specific conditions, may be detected. For example, a fluorescent moiety may need to be exposed to radiation (i.e. light) at a specific wavelength and intensity to cause excitation of the fluorescent moiety, thereby enabling it to emit detectable fluorescence at a specific wavelength that may be detected.
Alternatively, the detectable moiety may be an enzyme which is capable of converting a (preferably undetectable) substrate into a detectable product that can be visualised and/or detected. Examples of suitable enzymes are discussed in more detail below in relation to, for example, ELISA assays.
Alternatively, the detectable moiety may be a radioactive atom which is useful in imaging. Suitable radioactive atoms include 99mTc and 123I for scintigraphic studies. Other readily detectable moieties include, for example, spin labels for magnetic resonance imaging (MRI) such as 123I again, 131I, 111In, 19F, 13C, 15N, 17O, gadolinium, manganese or iron. Clearly, the agent to be detected (such as, for example, the one or more biomarkers in the test sample and/or control sample described herein and/or an antibody molecule for use in detecting a selected protein) must have sufficient of the appropriate atomic isotopes in order for the detectable moiety to be readily detectable.
The radio- or other labels may be incorporated into the agents of the invention (i.e. the proteins present in the samples of the methods of the invention and/or the binding agents of the invention) in known ways. For example, if the binding moiety is a polypeptide it may be biosynthesised or may be synthesised by chemical amino acid synthesis using suitable amino acid precursors involving, for example, fluorine-19 in place of hydrogen. Labels such as 99mTc, 123I, 186Rh, 188Rh and 111In can, for example, be attached via cysteine residues in the binding moiety. Yttrium-90 can be attached via a lysine residue. The IODOGEN method (Fraker et al (1978) Biochem. Biophys. Res. Comm. 80, 49-57) can be used to incorporate 123I. Reference (“Monoclonal Antibodies in Immunoscintigraphy”, J-F Chatal, CRC Press, 1989) describes other methods in detail. Methods for conjugating other detectable moieties (such as enzymatic, fluorescent, luminescent, chemiluminescent or radioactive moieties) to proteins are well known in the art.
Preferably, the one or more biomarkers in the control sample(s) are labelled with a detectable moiety. The detectable moiety may be selected from the group consisting of: a fluorescent moiety; a luminescent moiety; a chemiluminescent moiety; a radioactive moiety; an enzymatic moiety. However, it is preferred that the detectable moiety is biotin.
Alternatively or additionally the one or more biomarkers in the control sample(s) are labelled with a detectable moiety. The detectable moiety may be selected from, for example, the group consisting of: a fluorescent moiety; a luminescent moiety; a chemiluminescent moiety; a radioactive moiety; an enzymatic moiety. Alternatively or additionally the detectable moiety is biotin.
Alternatively or additionally step (b), (d), (f), (h) and/or step (j) is performed using an assay comprising a second binding agent capable of binding to the one or more biomarkers, the second binding agent comprising a detectable moiety.
Alternatively or additionally the second binding agent comprises or consists of an antibody or an antigen-binding fragment thereof, e.g., a recombinant antibody or antigen-binding fragment thereof. The antibody or antigen-binding fragment thereof may be selected from the group consisting of: scFv; Fab; a binding domain of an immunoglobulin molecule.
Alternatively or additionally the detectable moiety is selected from the group consisting of: a fluorescent moiety; a luminescent moiety; a chemiluminescent moiety; a radioactive moiety; an enzymatic moiety, e.g., a fluorescent moiety (for example an Alexa Fluor dye, e.g. Alexa647).
Alternatively or additionally the method comprises or consists of an ELISA (Enzyme Linked Immunosorbent Assay).
Preferred assays for detecting serum or plasma proteins include enzyme linked immunosorbent assays (ELISA), radioimmunoassay (RIA), immunoradiometric assays (IRMA) and immunoenzymatic assays (IEMA), including sandwich assays using monoclonal and/or polyclonal antibodies. Exemplary sandwich assays are described by David et al in U.S. Pat. Nos. 4,376,110 and 4,486,530, hereby incorporated by reference. Antibody staining of cells on slides may be used in methods well known in cytology laboratory diagnostic tests, as well known to those skilled in the art.
Typically, the assay is an ELISA (Enzyme Linked Immunosorbent Assay) which typically involves the use of enzymes giving a coloured reaction product, usually in solid phase assays. Enzymes such as horseradish peroxidase and phosphatase have been widely employed. A way of amplifying the phosphatase reaction is to use NADP as a substrate to generate NAD which now acts as a coenzyme for a second enzyme system. Pyrophosphatase from Escherichia coli provides a good conjugate because the enzyme is not present in tissues, is stable and gives a good reaction colour. Chemi-luminescent systems based on enzymes such as luciferase can also be used.
ELISA methods are well known in the art, for example see The ELISA Guidebook (Methods in Molecular Biology), 2000, Crowther, Humana Press, ISBN-13: 978-0896037281 (the disclosures of which are incorporated by reference).
Conjugation with the vitamin biotin is frequently used since this can readily be detected by its reaction with enzyme-linked avidin or streptavidin to which it binds with great specificity and affinity.
However, step (b), (d), (f), (h) and/or step (j) is alternatively performed using an array. Arrays per se are well known in the art. Typically they are formed of a linear or two-dimensional structure having spaced apart (i.e. discrete) regions (“spots”), each having a finite area, formed on the surface of a solid support. An array can also be a bead structure where each bead can be identified by a molecular code or colour code or identified in a continuous flow. Analysis can also be performed sequentially where the sample is passed over a series of spots each adsorbing the class of molecules from the solution. The solid support is typically glass or a polymer, the most commonly used polymers being cellulose, polyacrylamide, nylon, polystyrene, polyvinyl chloride or polypropylene. The solid supports may be in the form of tubes, beads, discs, silicon chips, microplates, polyvinylidene difluoride (PVDF) membrane, nitrocellulose membrane, nylon membrane, other porous membrane, non-porous membrane (e.g. plastic, polymer, perspex, silicon, amongst others), a plurality of polymeric pins, or a plurality of microtitre wells, or any other surface suitable for immobilising proteins, polynucleotides and other suitable molecules and/or conducting an immunoassay. The binding processes are well known in the art and generally consist of cross-linking covalently binding or physically adsorbing a protein molecule, polynucleotide or the like to the solid support. By using well-known techniques, such as contact or non-contact printing, masking or photolithography, the location of each spot can be defined. For reviews see Jenkins, R. E., Pennington, S. R. (2001, Proteomics, 2,13-29) and Lal et al (2002, Drug Discov Today 15; 7(18 Suppl):S143-9).
Typically the array is a microarray. By “microarray” we include the meaning of an array of regions having a density of discrete regions of at least about 100/cm2, and preferably at least about 1000/cm2. The regions in a microarray have typical dimensions, e.g., diameters, in the range of between about 10-250 μm, and are separated from other regions in the array by about the same distance. The array may also be a macroarray or a nanoarray.
Once suitable binding molecules (discussed above) have been identified and isolated, the skilled person can manufacture an array using methods well known in the art of molecular biology.
Alternatively or additionally the array is a bead-based array. Alternatively or additionally the array is a surface-based array. Alternatively or additionally the array is selected from the group consisting of: macroarray; microarray; nanoarray.
Alternatively or additionally the method comprises:
Alternatively or additionally step (b), (d), (f), (h) and/or (j) comprises measuring the expression of a nucleic acid molecule encoding the one or more biomarkers.
Alternatively or additionally the nucleic acid molecule is a ctDNA molecule, a cDNA molecule or an mRNA molecule. Alternatively or additionally the nucleic acid molecule is not a ctDNA molecule.
Alternatively or additionally the nucleic acid molecule is a cDNA molecule.
Alternatively or additionally measuring the expression of the one or more biomarker(s) in step (b), (d) and/or (f) is performed using a method selected from the group consisting of Southern hybridisation, Northern hybridisation, polymerase chain reaction (PCR), reverse transcriptase PCR (RT-PCR), quantitative real-time PCR (qRT-PCR), nanoarray, microarray, macroarray, autoradiography and in situ hybridisation.
Alternatively or additionally measuring the expression of the one or more biomarker(s) in step (b) is determined using a DNA microarray.
Alternatively or additionally measuring the expression of the one or more biomarker(s) in step (b), (d), (f), (h) and/or (j) is performed using one or more binding moieties, each individually capable of binding selectively to a nucleic acid molecule encoding one of the biomarkers identified in Table A.
Alternatively or additionally the one or more binding moieties each comprise or consist of a nucleic acid molecule. Alternatively or additionally the one or more binding moieties each comprise or consist of DNA, RNA, PNA, LNA, GNA, TNA or PMO. Alternatively or additionally the one or more binding moieties each comprise or consist of DNA. Alternatively or additionally the one or more binding moieties are 5 to 100 nucleotides in length.
Alternatively or additionally the one or more nucleic acid molecules are 15 to 35 nucleotides in length. Alternatively or additionally the binding moiety comprises a detectable moiety. The detectable moiety may be selected from the group consisting of: a fluorescent moiety; a luminescent moiety; a chemiluminescent moiety; a radioactive moiety (for example, a radioactive atom); or an enzymatic moiety. The detectable moiety may comprise or consist of a radioactive atom. The radioactive atom may be selected from the group consisting of technetium-99m, iodine-123, iodine-125, iodine-131, indium-111, fluorine-19, carbon-13, nitrogen-15, oxygen-17, phosphorus-32, sulphur-35, deuterium, tritium, rhenium-186, rhenium-188 and yttrium-90. Alternatively or additionally the detectable moiety of the binding moiety may be a fluorescent moiety.
Alternatively or additionally the sample provided in step (b), (d), (f), (h) and/or (j) is selected from the group consisting of unfractionated blood, plasma, serum, tissue fluid, pancreatic tissue, pancreatic juice, bile and urine.
Alternatively or additionally the sample provided in step (b), (d), (f), (h) and/or (j) is selected from the group consisting of unfractionated blood, plasma and serum. Alternatively or additionally the sample provided in step (b), (d), (f), (h) and/or (j) is plasma.
Alternatively or additionally the method comprises the step of:
wherein, in the event that the pancreatic cancer is determined to be located in and/or originated from the head of the pancreas, the pancreatic cancer therapy is conventional; in the event that pancreatic cancer is determined to be located in and/or originated from the body or tail of the pancreas, the pancreatic cancer therapy is treated more aggressively than dictated by convention; and
wherein, in the event that pancreatic cancer is not found to be present, the individual is not provided pancreatic cancer therapy.
Alternatively or additionally, in the event that the pancreatic cancer is determined to be located in and/or originated from the body/tail of the pancreas, the pancreatic cancer therapy is conventional; in the event that pancreatic cancer is determined to be located in and/or originated from the head of the pancreas, the pancreatic cancer therapy is treated more aggressively than dictated by convention.
In the event that the individual is not diagnosed with pancreatic cancer, they may be subjected to further monitoring for pancreatic cancer (for example, using the methods described in the present specification).
By ‘conventional’ pancreatic cancer therapy we include those methods known to the skilled person including those described in Ducreux et al., 2015, ‘Cancer of the pancreas: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up’ Annals of Oncology, 26 (Supplement 5): v56-v68 and/or Freelove & Walling, 2006, ‘Pancreatic Cancer: Diagnosis and Management’ American Family Physician, 73(3):485-492. See also, the treatment strategy shown in
By ‘treated more aggressively than dictated by convention’ we include that the treatment regime provided to the individual is consistent with the treatment of a high pancreatic cancer grade, for example, one, two or three cancer stages higher. For example, in the treatment strategy shown in
Stage 1 is the earliest stage. The cancer is contained inside the pancreas, although it may be quite large. There is no cancer in the lymph nodes close to the pancreas and no sign that it has spread anywhere else in the body. Stage 1 is also referred to as resectable pancreatic cancer. In Stage 2 the cancer has started to grow outside the pancreas into nearby tissues and/or there is cancer in lymph nodes near the pancreas. Stage 2 is also referred to as borderline resectable pancreatic cancer.
In Stage 3 the cancer has spread into large blood vessels near the pancreas but hasn't spread to distant sites of the body such as the liver or lungs. Stage 3 is also referred to as locally advanced pancreatic cancer.
In Stage 4 the cancer has spread to distant sites such as the liver or lungs. Stage 4 is also referred to as metastatic pancreatic cancer.
Alternatively or additionally the pancreatic cancer therapy is selected from the group consisting of surgery, chemotherapy, immunotherapy, chemoimmunotherapy and thermochemotherapy.
In an alternative or additional embodiment the breast cancer therapy is selected from the group consisting of surgery, chemotherapy, immunotherapy, chemoimmunotherapy and thermochemotherapy (e.g., AC chemotherapy; Capecitabine and docetaxel chemotherapy (Taxotere®); CMF chemotherapy; Cyclophosphamide; EC chemotherapy; ECF chemotherapy; E-CMF chemotherapy (Epi-CMF); Eribulin (Halaven®); FEC chemotherapy; FEC-T chemotherapy; Fluorouracil (5FU); GemCarbo chemotherapy; Gemcitabine (Gemzar®); Gemcitabine and cisplatin chemotherapy (GemCis or GemCisplat); GemTaxol chemotherapy; Idarubicin (Zavedos®); Liposomal doxorubicin (DaunoXome®); Mitomycin (Mitomycin C Kyowa®); Mitoxantrone; MM chemotherapy; MMM chemotherapy; Paclitaxel (Taxol®); TAC chemotherapy; Taxotere and cyclophosphamide (TC) chemotherapy; Vinblastine (Velbe®); Vincristine (Oncovin®); Vindesine (Eldisine®); and Vinorelbine (Navelbine®)).
Accordingly, the present invention comprises an antineoplastic agent for use in treating pancreatic cancer wherein the dosage regime is determined based on the results of the method of the first aspect of the invention.
The present invention comprises the use of an antineoplastic agent in treating pancreatic cancer wherein the dosage regime is determined based on the results of the method of the first aspect of the invention.
The present invention comprises the use of an antineoplastic agent in the manufacture of a medicament for treating pancreatic cancer wherein the dosage regime is determined based on the results of the method of the first aspect of the invention.
The present invention comprises a method of treating pancreatic cancer comprising providing a sufficient amount of an antineoplastic agent wherein the amount of antineoplastic agent sufficient to treat the pancreatic cancer is determined based on the results of the method of the first aspect of the invention.
In one embodiment, the antineoplastic agent comprises or consists of an alkylating agent (ATC code L01a), an antimetabolite (ATC code L01b), a plant alkaloid or other natural product (ATC code L01c), a cytotoxic antibiotic or a related substance (ATC code L01d), or another antineoplastic agents (ATC code L01x).
Hence, in one embodiment the antineoplastic agent comprises or consists of an alkylating agent selected from the group consisting of a nitrogen mustard analogue (for example cyclophosphamide, chlorambucil, melphalan, chlormethine, ifosfamide, trofosfamide, prednimustine or bendamustine) an alkyl sulfonate (for example busulfan, treosulfan, or mannosulfan) an ethylene imine (for example thiotepa, triaziquone or carboquone) a nitrosourea (for example carmustine, lomustine, semustine, streptozocin, fotemustine, nimustine or ranimustine) an epoxides (for example etoglucid) or another alkylating agent (ATC code L01ax, for example mitobronitol, pipobroman, temozolomide or dacarbazine).
In a another embodiment the antineoplastic agent comprises or consists of an antimetabolite selected from the group consisting of a folic acid analogue (for example methotrexate, raltitrexed, pemetrexed or pralatrexate), a purine analogue (for example mercaptopurine, tioguanine, cladribine, fludarabine, clofarabine or nelarabine) or a pyrimidine analogue (for example cytarabine, fluorouracil (5-FU), tegafur, carmofur, gemcitabine, capecitabine, azacitidine or decitabine).
In a still further embodiment the antineoplastic agent comprises or consists of a plant alkaloid or other natural product selected from the group consisting of a vinca alkaloid or a vinca alkaloid analogue (for example vinblastine, vincristine, vindesine, vinorelbine or vinflunine), a podophyllotoxin derivative (for example etoposide or teniposide) a colchicine derivative (for example demecolcine), a taxane (for example paclitaxel, docetaxel or paclitaxel poliglumex) or another plant alkaloids or natural product (ATC code L01cx, for example trabectedin).
In one embodiment the antineoplastic agent comprises or consists of a cytotoxic antibiotic or related substance selected from the group consisting of an actinomycine (for example dactinomycin), an anthracycline or related substance (for example doxorubicin, daunorubicin, epirubicin, aclarubicin, zorubicin, idarubicin, mitoxantrone, pirarubicin, valrubicin, amrubicin or pixantrone) or another (ATC code L01dc, for example bleomycin, plicamycin, mitomycin or ixabepilone).
In a further embodiment the antineoplastic agent comprises or consists of another antineoplastic agent selected from the group consisting of a platinum compound (for example cisplatin, carboplatin, oxaliplatin, satraplatin or polyplatillen) a methylhydrazine (for example procarbazine) a monoclonal antibody (for example edrecolomab, rituximab, trastuzumab, alemtuzumab, gemtuzumab, cetuximab, bevacizumab, panitumumab, catumaxomab or ofatumumab) a sensitizer used in photodynamic/radiation therapy (for example porfimer sodium, methyl aminolevulinate, aminolevulinic acid, temoporfin or efaproxiral) or a protein kinase inhibitor (for example imatinib, gefitinib, erlotinib, sunitinib, sorafenib, dasatinib, lapatinib, nilotinib, temsirolimus, everolimus, pazopanib, vandetanib, afatinib, masitinib or toceranib).
In a still further embodiment the antineoplastic agent comprises or consists of another neoplastic agent selected from the group consisting of amsacrine, asparaginase, altretamine, hydroxycarbamide, lonidamine, pentostatin, miltefosine, masoprocol, estramustine, tretinoin, mitoguazone, topotecan, tiazof urine, irinotecan (camptosar), alitretinoin, mitotane, pegaspargase, bexarotene, arsenic trioxide, denileukin diftitox, bortezomib, celecoxib, anagrelide, oblimersen, sitimagene ceradenovec, vorinostat, romidepsin, omacetaxine mepesuccinate, eribulin or folinic acid.
In one embodiment the antineoplastic agent comprises or consists of a combination of one or more antineoplastic agent, for example, one or more antineoplastic agent defined herein. One example of a combination therapy used in the treatment of pancreatic cancer is FOLFIRINOX which is made up of the following four drugs:
A second aspect of the invention provides an array for determining the locality and/or presence of pancreatic cancer in an individual, the array binding agents comprising or consisting of one or more binding agent as defined in the first aspect of the invention.
Alternatively or additionally the one or more binding agent is capable of binding to all of the biomarkers/proteins defined in Table A (i.e., at least one binding agent is provided for each of the biomarkers listed in Table A).
In an alternative or additional embodiment, the array does not comprise binding moiety for one or more expressed human gene product absent from those biomarkers defined in step (b); for example, ≥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, ≥55, ≥56, ≥57, ≥58, ≥59, ≥60, ≥61, ≥62, ≥63, ≥64, ≥65, ≥66, ≥67, ≥68, ≥69, ≥70, ≥71, ≥72, ≥73, ≥74, ≥75, ≥76, ≥77, ≥78, ≥79, ≥80, ≥81, 82, ≥83, ≥84, ≥85, ≥86, ≥87, ≥88, ≥89, ≥90, ≥91, ≥92, ≥93, ≥94, ≥95, ≥96, ≥97, ≥98, ≥99 or ≥100 expressed human gene products absent from those biomarkers defined in step (b).
In an alternative or additional embodiment, the array does not comprise binding moiety for any expressed human gene product except for those biomarkers defined in step (b).
In an alternative or additional embodiment, in addition to the binding moieties for biomarkers defined in step (b), the arrays and methods of the invention include binding moieties for one or more control gene expression product (e.g., 1, 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, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100 control gene expression products). For example, arrays consisting of binding moieties for only a defined number of Table A biomarkers may (or may not) additional comprise binding moiety for one or more control gene expression product.
By ‘gene expression products’ we include the same molecule types detected by the binding agents for the biomarkers of the invention.
A third aspect of the invention provides the use of one or more biomarkers selected from the group defined in Table A as a biomarker for determining the locality and/or presence of pancreatic cancer in an individual.
Alternatively or additionally all of the proteins defined in Table A are used as a marker for determining the locality and/or presence of pancreatic cancer in an individual. Alternatively or additionally the use is in vitro.
A fourth aspect of the invention provides a kit for determining the locality of pancreatic cancer comprising:
Alternatively or additionally the kit comprises a second binding agent as defined in the first aspect of the invention.
A second aspect of the present invention provides an array for determining the locality and/or presence of pancreatic cancer in an individual comprising one or more binding agent as defined in the first aspect of the present invention.
Preferred, non-limiting examples which embody certain aspects of the invention will now be described, with reference to the following tables and figures:
The condensed signature is defined as the remaining antibodies (biomarkers) when the samellest error is obtained. The most important antibodies are retained the longest. The top 3 most important markers are 11-12, STAT1, and PGAM5. The elimination order of 37 longest retained biomarkers are shown in Table 1.
ChT, chemotherapy; RT, radiotherapy; 5-FU, 5-fluorouracil; LV, leucovorin; PS, performance status; ULN, upper limit of normal.
We have defined plasma biomarker capable of differentiating pancreatic cancer tumours based on localization in the pancreas (body/tail vs head).
Material and Methods Plasma Samples: This study was approved by the Ethics Committee of Tianjin Medical University Cancer Institute and Hospital (TMUCIH). After informed consent, blood was collected at TMUCIH, plasma was isolated and stored at −80° C. A total of 213 plasma samples were used for this study (Table I). The enrolled PDAC patients (n=118) were all Chinese Han ethnicity and treated at TMUCIH. None of the patients had received chemotherapy or radiotherapy at the time the samples were taken. All PDAC samples were confirmed by cytology. Patients were diagnosed with pancreatic ductal adenocarcinoma (PDAC) with the following exceptions: Malignant serous cystadenoma (n=1), pancreatic sarcoma (n=2), tubular papillary pancreatic adenocarcinoma (n=1). Five patients were diagnosed with PDAC with liver metastasis. Data on tumour stage and size at diagnosis (Table I), and tumour location within the pancreas were based on clinical pathology. Normal control (NC) samples (n=95) were collected from healthy inhabitants of Tianjin at their routine physical examination at TMUCIH, and were genetically unrelated to the PDAC patients.
The entire set of samples was labelled at one single occasion, using a previously optimized protocol (14). Briefly, 5 μL of crude samples were diluted 1:45 in PBS-EDTA (4 mM), resulting in an approximate protein concentration of 2 mg/mL, and labelled with a 15:1 molar excess of biotin to protein, using 0.6 mM EZ-Link Sulfo-NHS-LC-Biotin (Thermo Fisher Scientific, Rockford, Ill., USA). Unbound biotin was removed by dialysis against PBS-EDTA for 72 hours, using Slide-A-Lyzer MINI dialysis device with 10K MWCO (Thermo Fisher Scientific). Labelled samples were aliquoted and stored at −20° C. until used for microarray experiments.
Generation of antibody microarrays: The antibody microarrays contained 350 human recombinant scFv antibodies, selected and generated from in-house designed phage display antibody libraries (Table II). Most of the antibodies have previously been used in array applications (18-20), and a majority has been validated, using e.g. ELISA, mass spectrometry, spiking and/or blocking experiments (Table II). Eighty-six antibodies raised against cancer related biomarker proteins as part of the EU funded AFFINOMICS project (21) were novel to this study, but the high on-chip functionality of the scFv framework used has been demonstrated in an independent study (Säll et al, manuscript in preparation). The antibodies were produced in E. coli and purified from the periplasm, using a MagneHis Protein Purification system (Promega, Madison, Wis., USA). The elution buffer was exchanged for PBS, using Zeba 96-well desalt spin plates (Pierce). The protein concentration was measured, using a NanoDrop spectrophotometer and the purity was checked using 10% SDS-PAGE. The entire set of 350 antibodies were produced in less than three weeks, and used for microarray printing within two weeks upon completion of production. The optimal printing concentration, defined as the highest concentration not resulting in a saturated signal was determined for each antibody by titrations in an arbitrarily selected biotinylated plasma and serum samples.
Antibody microarrays were produced on black MaxiSorp slides (NUNC, Roskilde, Denmark), using a non-contact printer (SciFlexarrayer S11, Scienion, Berlin, Germany). Fourteen identical subarrays (16,600 data points) were printed on each slide, each array consisting of 35×34 spots with a spot diameter of 130 μm and a spot-to-spot center distance of 200 μm. Each subarray consisted of three segments, separated by rows of Alexa Fluor647-labelled BSA. Antibodies were diluted to their optimal printing concentration (50-300 μg/mL) in a black polypropylene 384-well plate (NUNC). Alexa Fluor555-Cadeverine (0.1 μg/mL, ThermoFisher Scientific, Waltham, Mass., USA) was added to each well to assist the spot localization and signal quantification. Each antibody was printed in three replicates, one in each array segment. The entire set of slides used for this study was printed at a single occasion. Slides were stored in plastic boxes, contained in laminated foil pouches (Corning, Corning, N.Y., USA), with silica gel. The pouches were heat sealed to protect from light and humidity. The slides were shipped to TMUCIH, Tianjin, China, and used for analysis within four weeks after printing.
Antibody microarray analysis: Ten slides (140 individual subarrays) were run per day. The slides were mounted in hybridization gaskets (Schott, Jena, Germany) and blocked with 150 μL PBSMT (1% (w/v) milk, 1% (v/v) Tween-20 in PBS) per array for 1.5 h. All incubation steps were performed at RT in Biomixer II hybridization stations (CaptialBio, Beijing, China) on slow rotation (6 rpm). Meantime, aliquots of labelled serum samples were thawed on ice, diluted 1:10 in PBSMT in 96-well dilution plates. The arrays were washed four times with PBST (0.05% (v/v) Tween-20 in PBS), before transferring 120 μL of each sample from the dilution plates, and incubated for 2 h. Next, slides were washed four times with PBST, before applying 1 μg/mL Alexa Fluor647-Streptavidin (ThermoFisher Scientific, Waltham, Mass., USA), in PBSMT and incubated for 1 h. Again, slides were washed four times with PBST before being dismounted from the hybridization chambers, quickly immersed in dH2O, and dried under a stream of N2. The slides were immediately scanned in a LuxScan 10K Microarray scanner (CapitalBio) at 10 μm resolution using the 635 nm excitation laser for visualizing bound proteins, and the 532 nm excitation laser for visualizing printed antibodies.
Data acquisition, quality control and pre-processing: Signal intensities were quantified using the ScanArray Express software version 4.0 (Perkin Elmer Life and Analytical Sciences) with the fixed circle option. For each microarray, a grid was positioned using the Alexa Fluor555 signals from microarray printing. The same grid was then used to quantify the Alexa Fluor647 signal corresponding to the relative level of bound protein. Eleven samples (10 PDAC and 1 NC) were not quantified due to poor quality images resulting from of high background and/or low overall signals. For quantified arrays, the spot saturation, mean intensity and signal-to-noise ratio of each spot were evaluated. Fourteen antibodies were excluded because (i) the median signal intensity was below the cut-off limit, defined as the background (average PBS signal)+2 standard deviations (n=8), (ii) saturated signal in the lowest scanner intensity setting in more than 50% of samples (n=1), and (iii) inadequate antibody printing (n=5). Based on the remaining 202 samples and 336 antibodies, a dataset was assembled using the mean spot intensity after local background subtraction. Each data point represented an average of the three replicate spots, unless any replicate CV exceeded 15% from the mean value, in which case it was dismissed and the average of the two remaining replicates was used instead. The average CV of replicates was 7.9% (±4.1%). Applying a cut-off CV of 15%, 79% of data values were calculated from all three replicates and the remaining 21% from two replicates.
The logged data was normalized, using the empirical Bayes algorithm ComBat (22) for adjusting technical variation, followed by a linear scaling of data from each array to adjust for variations in sample background level. The scaling factor was based on the 20% of antibodies with the lowest standard deviation across all samples and was calculated by dividing the intensity sum of these antibodies on each array with the average sum across all arrays (13, 23).
Data analysis: The sample and variable distribution was analyzed and visualized, using a principal component analysis based program (Qlucore, Lund, Sweden). ANOVA was applied for an initial filtering of data. The performance of individual markers was evaluated, using Student's t-test, Benjamini Hochberg procedure for false discovery rate control (q-values), and fold changes. Separation of different subgroups within the data was also assessed, using the support vector machine (SVM) function in R, applying a linear kernel with the cost of constraints set to 1. Models for discriminating two groups were created, using a leave-one-out cross validation procedure. When defining a condensed biomarker signature (body/tail vs head), the antibodies were filtered, using a SVM-based Backward Elimination algorithm which excludes one antibody at the time and iteratively eliminates the antibody that was excluded when the smallest Kullback-Leibler divergence was obtained in the classification analysis (body/tail vs head), as previously described (24). Using the R-package, the performance of the SVM models were assessed, using receiver operating characteristics (ROC) curves and reported as area under the curve (AUC) values.
Results
Markers Associated with Tumour Location:
The samples were grouped by the primary tumour location in the pancreas. Backward elimination was used to define the best condensed signature capable of differentiating tumours based on localization, body/tail vs. head. The condensed signature, composed of 37 antibodies, including a core of three antibodies directed against IL-12, STAT1 and PGAM5, is shown in Table 1. The ROC AUC values describing the differentiation is shown for the core signature, and then for adding the biomarkers one by one, is also shown in Table 2. The AUC for the core signature was found to be 0.73, and was 1.0 for the full condensed signature.
Next, additionally important analytes for differentiating tumours located in body/tail vs head was identified by defining differentially expressed biomarkers. To this end, the samples were grouped by the primary tumour location in the pancreas. The AUC for Head (n=63) vs. Body/Tail (n=39) localized tumours was 0.64 (p=5.4e-3). Applying a cut-off of p<0.05, 37 antibodies showed significantly different intensity levels in Head vs. Body/Tail (Table 2).
Discussion
The biological diversity of tumours due to its localization in pancreatic cancer has been previously demonstrated (42). Tumours in the body/tail of pancreas are rarer than tumour in the head of pancreas (77% of PDAC) (43). Because of differences in e.g. blood supply and lymphatic and venous backflow, there are also differences in the disease presentation with body/tail tumours causing less jaundice, more pain, higher albumin and CEA levels and lower CA19-9 levels (44, 45). Body/tail tumours are more often detected at a later stage than head tumours and have a higher rate of metastasis. As the biological differences can result in different treatment efficiency (46), biomarkers that can discriminate between tumour localization would be of clinical relevance and could pave the way for personalized treatment strategies. However, few differences have been found on a genetic level, with no significant variation in the overall number of mutations, deletions and amplifications, or in K-ras point mutations (42). In the current study, several antibodies identified markers that showed differential protein expression levels between head and body/tail tumours. A condensed signature, based on 37 antibodies, differentiating the groups was defined.
indicates data missing or illegible when filed
L-12-54
-STAT1-2
-PGAM5-3
-UCHL5-1
xy_2
L-7-31
TP5B_3
HX10_3
IA_G4
-MD2L1-2
LA-DR/DP
-PRD14-2
-SPDLY-1
-MD2L1-3
indicates data missing or illegible when filed
Abstract
Background: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with rapid tumour progression and poor prognosis.
Methods: To mimic a real life test situation, a multicenter trial comprising a serum sample cohort, including 338 patients with either PDAC, other pancreatic diseases (OPD) or controls with non-pancreatic conditions (NPC), were analyzed on 293-plex recombinant antibody microarrays targeting immunoregulatory and cancer-associated antigens.
Results: We have identified protein profiles associated with the location of the primary tumour in the pancreas.
Introduction
Pancreatic ductal adenocarcinoma (PDAC) is the 4th most common cancer-related cause of death (Siegel et al, 2012). Multiple factors account for its poor prognosis and early diagnosis provides today the only possibility for cure. PDAC is often detected at late stages with 80% of patients not eligible for surgery due to either locally advanced or metastatic disease (Hidalgo, 2010; Porta et al, 2005; Siegel et al, 2012).
Material and Methods
Samples
This retrospective study analyzed 338 serum samples from patients with PDAC (n=156), other pancreatic disease (OPD) (n=152), and controls (NPC) (n=30) that were collected after local ethical approval and informed consent at five different hospitals in Spain (Hospital del Mar, Barcelona; Hospital Vall Hebron, Barcelona; Hospital Mútua de Terrassa, Terrassa; Hospital Son Dureta, Palma de Mallorca; Hospital General Universitario de Elche, Elche), as part of the PANKRAS II study (Parker et al, 2011; Porta et al, 1999) from 1992-1995 (Table 1). The study included patients with a suspicion of PDAC managed in the participating hospitals, and one sample drawn from each patient, using standardized protocols. A panel of experts validated by consensus the final diagnosis of all patients through a careful revision of clinical and pathological records and follow-up information (Porta et al, 2000). NPC control patients were mainly attended in the services of general surgery & digestive and traumatology of the participant hospitals, mostly including orthopedic fractures and hernias (Table 1, footnote). Samples were collected before any treatment was given, separated within 3 h and stored as 1 mL aliquots at −80° C. The entire set of samples was labelled at a single occasion, using a previously optimized protocol (Carlsson et al, 2010; Wingren et al, 2007). Briefly, crude samples were diluted 1:45 in PBS, resulting in an approximate protein concentration of 2 mg/mL, and labelled with a 15:1 molar excess of biotin to protein, using 0.6 mM EZ-Link Sulfo-NHS-LC-Biotin (Pierce, Rockford, Ill., USA). Unbound biotin was removed by dialysis against PBS for 72 hours. Labelled samples were aliquoted and stored at −20° C.
Antibodies
The antibody microarrays contained 293 human recombinant scFv antibodies directed against 98 known antigens and 31 peptides motifs (Olsson et al, 2012). Most antibodies were selected against immunoregulatory proteins and have previously demonstrated robust on-chip functionality (Steinhauer et al, 2002; Wingren & Borrebaeck, 2008; Wingren et al, 2005). Several binders have also been validated, using ELISA, mass spectrometry, spiking and/or blocking experiments (Supplementary Table I). In addition, 76 scFvs targeting 28 additionally antigens were selected from the Hell-11 phage display library (Säll et al, manuscript in preparation) against predominantly cancer-associated targets, including kinases and other enzymes, transcriptional regulators, cytokines, and receptors. Although these binders have not previously been used in microarray applications, their on-chip functionality has been demonstrated in an independent study (Säll et al, manuscript in preparation). The antibodies were produced in E. coli and purified from the periplasm, using a MagneHis Protein Purification system (Promega, Madison, Wis., USA). The elution buffer was exchanged for PBS, using Zeba 96-well desalt spin plates (Pierce). The protein yield was measured using NanoDrop (Thermo Scientific, Wilmington, Del., USA) and the purity was checked using 10% SDS-PAGE (Invitrogen, Carlsbad, Calif., USA).
Antibody Microarrays
Antibody microarrays were produced on black MaxiSorp slides (NUNC, Roskilde, Denmark), using a non-contact printer (SciFlexarrayer S11, Scienion, Berlin, Germany). Thirteen identical subarrays were printed on each slide, each array consisting of 33×31 spots (130 μm spot diameter) with 200 μm spot-to-spot center distance. Each subarray consisted of 3 segments, separated by rows of labelled BSA (Supplementary
Each slide was mounted in a hybridization gasket (Schott, Jena, Germany) and blocked with PBSMT (1% (w/v) milk, 1% (v/v) Tween-20 in PBS) for 1 h. Meantime, aliquots of labelled serum samples were thawed on ice and diluted 1:10 in PBSMT. The slides were washed 4 times with PBST (0.05% (v/v) Tween-20 in PBS) before 120 μL of the samples were added. Samples were incubated for 2 h on a rocking table, slides washed 4 times with PBST, incubated with 1 μg/mL Streptavidin-Alexa in PBSMT for 1 h on a rocking table, and again washed 4 times with PBST. Finally, the slides were dismounted from the hybridization chambers, quickly immersed in dH2O, and dried under a stream of N2. The slides were immediately analyzed, using a confocal microarray scanner (PerkinElmer Life and Analytical Sciences, Wellesley, Mass., USA) at 10 μm resolution, using 60% PMT gain and 90% laser power. Signal intensities were quantified, using the ScanArray Express software version 4.0 (PerkinElmer Life and Analytical Sciences) with the fixed circle option. After local background subtraction, intensity values were used for data analysis. Data acquisition was performed by a trained member of the research team and blinded to the sample classification and clinical data.
Data Pre-Processing
An average of the 3 replicate spots was used, unless any replicate CV exceeded 15% from the mean value, in which case it was dismissed and the average of the 2 remaining replicates was used instead. The average CV of replicates was 8.3% (±5.5%). Applying a cut-off CV of 15%, 70% of data values were calculated from all 3 replicates and the remaining 30% from 2 replicates.
For evaluation of normalization strategies and data distribution, the data was visualized using 3D principal component analysis (PCA) with ANOVA filtering (Qlucore A B, Lund, Sweden). Two samples (OPD) were excluded as barely any signals were obtained from them for reasons that were not further explored. Of note, PCA on log 10 raw data showed no significant (p<0.01) differences between: i) sample subarray positioning on slide, ii) patient gender, iii) patient age, and iv) participating clinical centre. Minor systematic differences were observed between days of analysis (rounds 1-5, likely due to small differences in humidity during array printing, in particular for day 1), which could be neutralized by normalization. The data was normalized in two steps. First, differences between rounds (days) of analysis was eliminated, using a subtract group mean strategy (Wu & Wooldridge, 2005). The average intensity from each antibody was calculated within each round of analysis and subtracted from the single values, thus zero-centering the data. The global mean signal from each antibody was added to each respective data point to avoid negative values. Second, array-to-array differences (e.g. inherent sample background fluorescence differences) were handled by calculating a scaling factor for each subarray, based on the 20% of antibodies with the lowest CV, as has been previously described (Carlsson et al, 2008; Ingvarsson et al, 2008). Normalization of data was visualized in PCA plots).
Data Analysis
Two-group comparisons were performed using PCA, Student's t-test, Benjamini Hochberg procedure for false discovery rate control (q-values), and fold changes. A group ANOVA was also performed (Qlucore). SVM analysis was performed in R, using a linear kernel with the cost of constraints set to 1
Results
Tumour Site Location
The serum samples could be discriminated depending on the location of the primary tumour in the pancreas. PCA indicated that patients with tumours located in the body or the tail of the pancreas clustered closer to NPC subjects compared to patients with tumours in the head of the pancreas (
Discussion
A new finding was the observation that serum protein markers associated with tumour localization were identified. A major problem with tumours of the body/tail in comparison with pancreatic head cancer is distant metastasis, especially in the liver, and resection of the tumour does not increase postoperative survival in metastatic disease (Wu et al, 2007). On the other hand, patients with local-stage body/tail tumours had higher survival rates compared with local-stage pancreatic head cancer (Lau et al, 2010). Our data indicated that markers in samples from patients with body/tail tumours clustering closer to the NPC controls, as compared to samples from patients with pancreatic head tumours. This may be explained by a more profound systemic impact of the head tumours, as these are prone to invade the surrounding mesenteric blood vessels connecting the pancreas to the duodenum (Hidalgo, 2010), or by changes secondary to biliary obstruction. As the biological differences can result in different treatment efficiency (Wu T C et al 2007), biomarkers that can discriminate between tumour localization are of clinical relevance.
indicates data missing or illegible when filed
| Number | Date | Country | Kind |
|---|---|---|---|
| 1516801.6 | Sep 2015 | GB | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2016/072617 | 9/22/2016 | WO | 00 |
