The first step in treating cancer is diagnosis. This step is challenging because cancer cells can look very much like non-cancer cells, and because so many forms exist even within one type of cancer. The conventional means of diagnosing cancer by imaging and by viewing cells under a microscope often do not give enough information for diagnosis; we need greater detail — measurements of specific molecules that indicate whether cells are cancerous and what type of cancer cell they are.
The Haab Laboratory analyzes blood and tissue samples to find such molecular indicators, or biomarkers, for the diagnosis and prognosis of pancreatic cancer. Their research has led to the discovery of particular types of carbohydrates — also called glycans — that are produced only by certain types of pancreatic cancer cells and that are secreted at high levels into the blood. The lab’s current goals are to define the glycans and proteins that give the most accurate information about pancreatic cancer; and to establish their use as blood tests and cellular assays for examining patients and guiding treatment. These goals are accelerated by the lab’s novel methods of analyzing glycans and proteins in blood, tissue and cell culture samples.
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- 171 studies published from Nov. 1, 2020 to Oct. 1, 2021
- 68 studies in high-impact journals from Nov. 1, 2020-Oct. 1, 2021
- 41 clinical trials launched
Brian Haab, Ph.D.
Professor, Department of Cell Biology; Assistant Dean, Van Andel Institute Graduate School
Areas of Expertise
Pancreatic cancer, glycobiology, biomarker development, technology development, microarray methods
Dr. Haab obtained his Ph.D. in chemistry from the University of California at Berkeley in 1998. He then served as a postdoctoral fellow in the laboratory of Patrick Brown in the Department of Biochemistry at Stanford University. Dr. Haab joined VAI as a Special Program Investigator in May 2000, became a Scientific Investigator in 2004, and was promoted to Senior Scientific Investigator in 2007. He also serves as an assistant dean of Van Andel Institute Graduate School and as an adjunct professor in the Department of Biochemistry and Molecular Biology and the Genetics Program at Michigan State University.
Tests to Detect and Diagnose Pancreatic Cancer
The successful treatment of pancreatic cancer critically depends on achieving an accurate and early diagnosis, but this can be frustratingly difficult. Conventional methods of evaluating patients—assessing scans, visual inspection of cells from a biopsy, and weighing behavioral, health, and demographic data—do not have the detail necessary to distinguish between benign and malignant disease or between cancers with vastly different behaviors. Sometimes a physician can see a mass or other unusual feature in the pancreas but is unsure what it is. Is it benign or cancerous? And if it is cancer, what is the best course of treatment?
Our research builds on the concept that molecular-level information will provide details about a condition that are not observable by conventional methods. Molecular biomarkers could provide such information and enable physicians to make accurate diagnoses and develop optimal treatment plans. We are making progress toward this goal for pancreatic cancer. For example, in recent publications in Molecular and Cellular Proteomics and the Journal of Proteome Research, we disclosed carbohydrate-based biomarkers in the blood serum that improve upon the widely used blood test called CA19-9. By using a panel of three or more independent biomarkers, we detected a greater percentage of cancers than we could with any individual biomarker. We are seeking to substantiate those findings and to evaluate their clinical value using serum samples from several clinical sites.
Other research is aimed at further improving the biomarker tests. The results so far suggest that each individual biomarker arises from a distinct subpopulation of cancer patients and from a characteristic cell type. This finding is important because the biomarkers may reveal differences between subgroups of tumors—a possibility we are exploring in the research described below. For the purpose of improving our blood tests, determining the characteristics of the cells that produce each biomarker, as well as of the cells that do not produce any of our biomarkers, will help to optimize a blood test to accurately identify cancers across the entire spectrum of patients.
The ultimate goal is to get the new tests established in clinical laboratories in order to benefit patients. To that end, we are working with industry partners to transfer our biomarker assays to the clinical laboratory setting and to begin analyzing patient samples received consecutively from clinical sites. If we have good results, we hope to initiate clinical trials for the diagnosis of pancreatic cancer and, eventually, for evaluations of surveillance among people at elevated risk for pancreatic cancer.
Better treatment through subtyping
Pancreatic cancer characteristics, such as the cell types within the tumor, the amount of metastasis, the responses to treatments, and overall outcomes, vary greatly among patients. So far, identifying the underlying causes of such differences and predicting the behavior of individual tumors have not been possible. If we could determine what drives the differences between the tumors or identify molecules that help predict the behavior of each tumor, we could establish better treatment plans for each patient or determine the drugs that work best against each subtype.
Our research is revealing major groupings of tumors based on the carbohydrates on the surface of, and in the secretions from, cancer cells. The carbohydrates are related to the CA19-9 antigen and have distinct biological functions. In current research we want to determine the molecular nature of the subgroups of cells and whether the subgroups have different levels of aggressiveness or different responses to particular drugs. We are using new approaches for measuring carbohydrates and proteins in tumor tissue, and we are employing powerful new software—introduced in our recent publication in Analytical Chemistry—to examine the cell types that produce each carbohydrate-based biomarker. We are using that information to evaluate whether certain types of cells predict clinical behavior. As advances and new options in treatments become available, this type of research is increasingly important for guiding clinical decisions. We are working closely with our physician collaborators to evaluate on a case-by-case basis the value of the molecular information and to guide our research toward improving the tests. Ultimately, physicians could use the molecular tests on material from biopsies, surgical resections, or blood samples.
CarboGrove is a database of the binding specificities of glycan-binding proteins. The results are derived from MotifFinder analysis of glycan array data from a wide variety of glycan array sources. In addition to presenting detailed summaries of lectin and antibody binding, the database offers programmatic access to the data through an API as well as a web server for running MotifFinder.Visit CarboGrove
MotifFinder enables the automated analysis of glycan array data. The program provides information about the binding determinants of lectins and glycosidases that were incubated on the arrays. Full descriptions and instructions are provided in the download material.
MotifFinder Request Form
Please fill out the form below to request the MotifFinder software.
MotifFinder Request Form
"*" indicates required fields
SignalFinder-Microarray (SignalFinderMA) enables the automated analysis of microarray images. SignalFinderMA automatically detects individual spots within a microarray and outputs statistics for each spot and for replicate spots. The program can accommodate multiple arrays within a single image. Full descriptions and instructions are provided in the download material.
SignalFinder-Microarray Request Form
Please fill out the form below to request the SignalFinder-Microarray software.
"*" indicates required fields
SignalFinderIF is the core of the SignalFinder Suite. SignalFInderIF enables the automated analysis of pathology tissues stained with immunofluorescence and imaged with a microscope or digital pathology platform. SignalFinderIF implements the Segment Fit Thresholding (SFT) algorithm to determine and quantify signal objectively from immunofluorescence images. The software provides the ability to select as many channels as are available from the image file as well as multiple thresholds selected as multipliers of the base threshold with user selection of channels, thresholds and multiplier. SignalFinder also allows selection of multiple tissues and provides automated tissue detection for tissue selection from tissue microarrays (TMAs). The software supports multiple image formats and can be quickly adapted to most image formats, upon request.
SignalFinderIF Request Form
Please fill out the form below to request the SignalFinderIF software.
"*" indicates required fields
For a full list of Dr. Haab’s publications, please visit PubMed.
Klamer ZL, Harris CM, Beirne JM, Kelly JE, Zhang J, Haab BB. 2022. CarboGrove: a resource of glycan-binding specificities through analyzed glycan-array datasets from all platforms. Glycobio.
Gao C, Wisniewski L, Liu Y, Staal B, Beddows I, Plenker D, Aldakkak M, Hall J, Barnett D, Gouda MK, Allen P, Drake R, Zureikat A, Huang Y, Evans D, Singhi A, Brand RE, Tuveson DA, Tsai S, Haab BB. 2021. Detection of chemotherapy-resistant pancreatic cancer using a glycan biomarker, sTRA. Clin Cancer Res 27(1):226-236.
Klamer Z, Haab BB. 2021. Combined analysis of multiple glycan-array datasets: New explorations of protein-glycan interactions. Analyt Chem.
McDowell CT, Klamer Z, Hall J, West CA, Wisniewski L, Powers TW, Angel P, Mehta AS, Lewin DN, Haab BB, Drake R. 2020. Imaging mass spectrometry and lectin analysis of N-linked glycans in carbohydrate antigen defined pancreatic cancer tissues. Mol Cell Proteomics 20:100012.
*Highlighted by ASBMBToday
Gao CF, Wisniewski LB, Liu Y, Staal B, Beddows I, Plenker D, Aldakkak M, Hall J, Barnett D, Kheir Gouda M, Allen PJ, Drake RR, Zureikat AM, Huang Y, Evans DB, Singhi AD, Brand RE, Tuveson DA, Tsai S, Haab BB. 2020. Detection of chemotherapy-resistant pancreatic cancer using a glycan biomarker, sTRA. Clin Cancer Res.
Staal B*, Liu Y*, Barnett D*, Hsueh P, Zonglin H, Gao C, Partyka K, Hurd MW, Singhi AD, Drake RR, Huang Y, Maitra A, Brand RE, Haab BB. 2019. The sTRA plasma biomarker: Blinded validation of improved accuracy over CA19-9 in pancreatic cancer diagnosis. Clin Cancer Res.
Barnett D, Haab BB. 2019. Automated identification and quantification of signals in multichannel immunofluorescence images: The SignalFinder platform. Am J Pathol.
Klamer Z, Hsueh P, Ayala-Talavera D, Haab B. 2019. Deciphering protein glycosylation by computational integration of on-chip profiling, glycan-array data, and mass spectrometry. Mol Cell Proteomics 18(1):28-40.
Klamer Z, Staal B, Prudden AR, Liu L, Smith DF, Boons GJ, Haab BB. 2017. Mining high-complexity motifs in glycans: A new language to uncover the fine specificities of lectins and glycosidases.
Barnett D, Liu Y, Partyka K, Huang Y, Tang H, Hostetter G, Brand RE, Singhi AD, Drake RR, Haab BB. 2017. The CA19-9 and Sialyl-TRA antigens define separate subpopulations of pancreatic cancer cells. Sci Rep 7:4020.
Reatini BS, Ensink E, Liau B, Sinha JY, Powers TW, Partyka K, Bern M, Brand RE, Rudd PM, Kletter D, Drake RR, Haab BB. 2016. Characterizing protein glycosylation through on-chip glycan medication and probing. Anal Chem.
Tang H, Partyka K, Hsueh P, Sinha JY, Kletter D, Zeh H, Huang Y, Brand RE, Haab BB. 2016. Glycans related to the CA19-9 antigen are elevated in distinct subsets of pancreatic cancers and improve diagnostic accuracy over CA19-9. Cell Mol Gastroenterol Hepatol 2(2):201–215.
Haab BB, Huang Y, Balasenthil S, Partyka K, Tang H, Anderson M, Allen P, Sasson A, Zeh H, Kaul K, Kletter D, Ge S, Bern M, Kwon R, Blasutig I, Srivastava S, Frazier ML, Sen S, Hollingsworth MA, Rinaudo JA, Killary AM, Brand RE. 2015. Definitive characterization of CA 19-9 in resectable pancreatic cancer using a reference set of serum and plasma specimens. PLoS One. 10(10):e0139049.
Ensink E, Sinha J, Sinha A, Tang H, Calderone HM, Hostetter G, Winter J, Cherba D, Brand RE, Allen PJ, Sempere LF, Haab BB. 2015. Segment and fit thresholding: A new method for image analysis applied to microarray and immunofluorescence data. Anal Chem 87(19):9715–9721.
Tang H, Hsueh P, Kletter D, Bern M, Haab BB. 2015. The detection and discovery of glycan motifs in biological samples using lectins and antibodies: new methods and opportunities. Adv Cancer Res 126:167–202.
Kletter D, Curnutte B, Maupin K, Bern M, Haab BB. 2015. Exploring the specificities of glycan-binding proteins using glycan array data and the GlycoSearch software. Methods Mol Biol 1273:203–214.
Tang H, Singh S, Partyka K, Kletter D, Hsueh P, Yadav J, Ensink E, Bern M, Hostetter G, Hartman D, Huang Y, Brand RE, Haab BB. 2015. Glycan motif profiling reveals plasma sialyl-Lewis X elevations in pancreatic cancers that are negative for CA 19-9. Mol Cell Proteomics 14(5):1323–1333.
Singh S, Pal K, Yadav J, Tang H, Partyka K, Kletter D, Hsueh P, Ensink E, Birenda KC, Hostetter G, Xu H, Bern M, Smith D, Mehta A, Brand R, Melcher K, Haab BB. 2015. Upregulation of glycans containing 31 fucose in a subset of pancreatic cancers uncovered using fusion-tagged lectins. J Proteome Res 14(6):2594–605.
Powers TW, Neely BA, Shao Y, Tang H, Troyer DA, Mehta AS, Haab BB, Drake RR. MALDI imaging mass spectrometry profiling of N-glycans in formalin-fixed paraffin embedded clinical tissue blocks and tissue microarrays. PLoS One 9(9):e106255.
Kletter D, Bern M, Haab BB. 2014. Mining and using glycan array data with the GlycoSearch analysis program and GlycanBinder database. In: Naoyuki Taniguchi, T. E., Gerald Hart, Peter Seeberger, Chi-Huey Wong, ed. Glycoscience: Biology and Medicine, Springer, Japan.
Sinha A, Cherba D, Bartlam H, Lenkiewicz E, Evers L, Barrett MT, Haab BB. 2014. Mesenchymal-like pancreatic cancer cells harbor specific genomic alterations more frequently than their epithelial counterparts. Mol Oncol 8(7):1253–1265.
Partyka K, Wang S, Zhao P, Haab BB. 2014. Array-based immunoassays with rolling-circle amplification detection. Methods Mol Biol 1105: 3–15.
Gbormittah FO, Haab BB, Partyka K, Garcia-Ott C, Hincapie M, Hancock WS. 2014. Characterization of glycoproteins in pancreatic cyst fluid using a high performance multiple lectin affinity chromatography platform. J Proteome Res 13(1): 289–299.
Cao Z, Maupin K, Curnutte B, Fallon B, Feasley CL, Brouhard E, Kwon R, West CM, Cunningham J, Brand R, Castelli P, Crippa S, Feng Z, Allen P, Simeone DM, Haab BB. 2013. Specific glycoforms of MUC5AC and endorepellin accurately distinguish mucinous from non-mucinous pancreatic cysts. Mol Cell Proteomics 12(10): 2724–2734.
McCarter C, Kletter D, Tang H, Partyka K, Ma Y, Singh S, Yadav J, Bern M, Haab BB. 2013. Prediction of glycan motifs using quantitative analysis of multi-lectin binding: motifs on MUC1 produced by cultured pancreatic cancer cells. Proteomics Clin Appl 7(9–10): 632–641.
Kletter D, Cao Z, Bern M, Haab BB. 2013. Determining lectin specificity from glycan array data using motif segregation and the GlycoSearch software. Curr Protoc Chem Biol 5(2): 157–169.
Haab BB, Partyka K, Cao Z. 2013. Using antibody arrays to measure protein abundance and glycosylation: considerations for optimal performance. Curr Protoc Protein Sci 73: Unit 27.6.
Fallon BP, Curnutte B, Maupin KA, Partyka K, Choi S, Brand RE, Langmead CJ, Tembe W, Haab BB. 2013. The Marker State Space (MSS) method for classifying clinical samples. PLoS One 8(6):e65905.
Kletter D, Singh S, Bern M, Haab BB. 2013. Global comparisons of lectin-glycan interactions using a database of analyzed glycan array data. Molecular & Cellular Proteomics 12(4):1026–1035.
Cao Z, Partyka K, McDonald M, Brouhard E, Hincapie M, Brand RE, Hancock WS, Haab BB. 2013. Modulation of glycan detection on specific glycoproteins by lectin multimerization. Anal Chem 85(3):1689–1698.
Haab BB. 2012. Using lectins in biomarker research: addressing the limitations of sensitivity and availability. Proteomics Clin Appl 6(7-8):346–350.
Maupin KA, Liden D, Haab BB. 2012. The fine-specificity of mannose-binding and galactose-binding lectins revealed using outlier-motif analysis of glycan array data. Glycobiology 22(1):160–169.
Partyka K, Maupin KA, Brand RE, Haab BB. 2012. Diverse monoclonal antibodies against the CA 19-9 antigen show variation in binding specificity with consequences for clinical interpretation. Proteomics 12(13): 2212–2220.
Partyka K, McDonald M, Maupin KA, Brand R, Kwon R, Simeone DM, Allen P, Haab BB. 2012. Comparison of surgical and endoscopic sample collection for pancreatic cyst fluid biomarker identification. J Proteome Res 11(5): 2904–2911.
Yue T, Maupin K, Fallon B, Li L, Partyka K, Anderson M, Brenner DE, Kaul K, Zeh H, Moser AJ, Simeone DM, Feng Z, Brand RE, Haab BB. 2011. Enhanced discrimination of malignant from benign pancreatic disease by measuring the CA 19-9 antigen on specific protein carriers. PLoS One 6(12): e29180.
Yue T, Partyka K, Maupin K, Hurley M, Andrews P, Kaul K, Moser JA, Zeh H, Brand RE, Haab BB. 2011. Identification of blood-protein carriers of the CA 19-9 antigen and characterization of prevalence in pancreatic diseases. Proteomics 11(18):3665–3674.
Bergsma D, Buchweitz J, Chen S, Gerszten R, Haab BB. 2010. Antibody-array interaction mapping, a new method to detect protein complexes applied to the discovery and study of serum amyloid P interactions with kininogen in human plasma. Mol Cell Proteomics 9(3): 446–456.
Haab BB. 2010. Antibody-lectin sandwich arrays for biomarker and glycobiology studies. Expert Rev Proteomics 7(1): 9–11.
Haab BB, Porter A, Yue T, Li L, Scheiman J, Anderson MA, Barnes D, Schmidt CM, Feng Z, Simeone DM. 2010. Glycosylation variants of mucins and CEACAMs as candidate biomarkers for the diagnosis of pancreatic cystic neoplasms. Ann Surg 251(5): 937–945.
Maupin KA, Sinha A, Eugster E, Miller J, Ross J, Paulino V, Keshamouni VG, Tran N, Berens M, Webb C, Haab BB. 2010. Glycogene expression alterations associated with pancreatic cancer epithelial-mesenchymal transition in complementary model systems. PLoS One 5(9): e13002.
Porter A, Yue T, Heeringa L, Day S, Suh E, Haab BB. 2010. A motif-based analysis of glycan array data to determine the specificities of glycan-binding proteins. Glycobiology 20(3): 369–380.
Zeng Z, Hincapie M, Haab BB, Hanash S, Pitteri SJ, Kluck S, Hogan JM, Kennedy J, Hancock WS. 2010. The development of an integrated platform to identify breast cancer glycoproteome changes in human serum. J Chromatogr A 1217(19): 3307–3315.
Hung KE, Faca V, Song K, Sarracino DA, Richard LG, Krastins B, Forrester S, Porter A, Kunin A, Mahmood U, Haab BB, Hanash SM, Kucherlapati R. 2009. Comprehensive proteome analysis of an Apc mouse model uncovers proteins associated with intestinal tumorigenesis. Cancer Prev Res (Phila) 2(3): 224–233.
Shao C, Chen S, Chen L, Cobos E, Wang JS, Haab BB, Gao W. 2009. Antibody microarray analysis of serum glycans in esophageal squamous cell carcinoma cases and controls. Proteomics Clin Appl 3(8): 923–931.
Wu YM, Nowack DD, Omenn GS, Haab BB. 2009. Mucin glycosylation is altered by pro-inflammatory signaling in pancreatic cancer cells. J Proteome Res 8(4):1876–1886.
Yue T, Goldstein IJ, Hollingsworth MA, Kaul K, Brand RE, Haab BB. 2009. The prevalence and nature of glycan alterations on specific proteins in pancreatic cancer patients revealed using antibody-lectin sandwich arrays. Mol Cell Proteomics 8(7): 1697–1707.
Chen S, LaRoche T, Hamelinck D, Bergsma D, Brenner D, Simeone D, Brand RE, Haab BB. 2007. Multiplexed analysis of glycan variation on native proteins captured by antibody microarrays. Nat Methods 4(5): 437–444.
Forrester S, Hung KE, Kuick R, Kucherlapati R, Haab BB. 2007. Low-volume, high-throughput sandwich immunoassays for profiling plasma proteins in mice: identification of early-stage systemic inflammation in a mouse model of intestinal cancer. Mol Oncol 1(2): 216–225.
Forrester S, Qiu J, Mangold L, Partin A, Misek D, Phinney B, Whitten D, Andrews P, Diamandis D, Omenn GS, Hanash S, Haab BB. 2007. An experimental strategy for quantitative analysis of the humoral immune response to prostate cancer antigens using natural protein microarrays. Proteomics Clin Appl 1(5): 494–505.
Assistant Research Technician
ChongFeng Gao, Ph.D.
Senior Research Scientist
Research Data Technician
Bioinformatics Analyst II
Alfredo Reyes Oliveras
Van Andel Institute Graduate School Student
Thesis: Linking glycosylation changes in pancreatic cancer with metabolic dependencies
Senior Administrative Assistant II