Open Access

Genomic and proteomic approaches for studying human cancer: Prospects for true patient-tailored therapy

  • Kristen M. Carr1Email author,
  • Kevin Rosenblatt1,
  • Emanuel F. Petricoin1 and
  • Lance A. Liotta1
Human Genomics20041:134

DOI: 10.1186/1479-7364-1-2-134

Received: 16 October 2003

Accepted: 16 October 2003

Published: 1 January 2004

Abstract

Global gene expression analysis is beginning to move from the laboratories of basic investigators to large-scale clinical trials. The potential of this technology to improve diagnosis and tailored treatment of human disease may soon be realised, now that several comprehensive studies have demonstrated the utility of gene expression profiles for the classification of tumours into distinct, clinically relevant subtypes and the prediction of clinical outcomes. In addition, new data from the emerging proteomics platforms add another layer of molecular information to the study of human disease, as scientists attempt to catalogue a complete inventory of the proteins encoded by the genome and to establish a 'biosignature' profile of human health and disease. As a result, it is anticipated that, together, these technologies will facilitate the comprehensive study of genes, gene products and signalling pathways so that the objective of personalised molecular medicine can be achieved. This paper will review the studies that best demonstrate how genomics and proteomics technologies can be used to improve cancer diagnosis and treatment it will specifically highlight the important work being incorporated into clinical trials.

Keywords

molecular medicine gene expression analysis proteomics clinical trials

Introduction

Cancer researchers have made significant progress in identifying a new 'molecular taxonomy' of cancer through the use of genomics technologies. Specifically, the use of DNA microarrays has created robust molecular phenotypes for many tumours, including brain [1, 2], breast [310], colon [11, 12], gastric [13], kidney [14], leukaemia [1517], lymphoma [1820], lung [2123], mela-noma [24], ovary [2528], prostate [2932] and small, round blue-cell tumours of childhood [33]. In a subset of these studies, the gene expression profiles strongly suggest that this information would improve diagnosis and predict clinical outcome when compared with the standardised prognostic criteria, such as tumour grade, tumour size, patient age and patient performance status [6, 7, 19, 20]. These recent breakthroughs in the laboratory have been qualified successes. The lack of a standardised method for data collection, data analysis and validation, however, has made it difficult to rigorously compare studies from different laboratories, and has thus hampered the introduction of this type of data into clinical medicine. Fortunately, the microarray field has proposed universal standardisation guidelines to help scientists and clinicians accurately compare the results from different laboratories, and thereby has potentially paved the way for the use of gene expression analysis in clinical medicine [34, 35]. Based on the published work of van't Veer et al [6]. and van de Vijver et al [7]. the Netherlands Cancer Institute in Amsterdam announced in January 2003 that it would become the first institution in the world to use DNA microarray analysis to make treatment decisions regarding women with breast cancer [36]. As of today, four additional institutions are incorporating gene expression patterns into clinical trials of breast cancer (Table 1) [37], and the field awaits the results with great interest.
Table 1

Clinical trials using gene expression analysis

Sponsor

Principal investigator, centre

Disease, application

European Organization for Research and Treatment of Cancer

The Netherlands Cancer Institute, Amsterdam, The Netherlands

Breast cancer, comparing van't Veer and van de Vijver prognostic signature to St Gallen criteria

Avon Foundation

Daniel Haber, Massachusetts General Hospital, Boston, MA, USA

Breast cancer, validating van't Veer and van de Vijver prognostic signature

Breast Cancer Research Foundation, Millennium Pharmaceuticals

Lajos Pusztai, Anderson Cancer Center, Houston, TX, USA

Breast cancer, validating own signature for predicting drug response

Millennium Pharmaceuticals

Kenneth Anderson, Dana-Farber Cancer Center, Boston, MA, USA

Multiple myeloma, validating own signature for response to Velcade

National Cancer Institute

Jenny Chang, Baylor College of Medicine, Houston, TX, USA

Breast cancer, validating own 90-gene prognostic signature

Table adapted from Branca, M. (2003), Science Vol. 300, p. 238

The encouraging early results from the DNA microarray technology has inspired the development of a variety of array-based platforms [3842] and contributed to the development of a system-wide study of proteins -- the field of modern pro-teomics. Traditionally, two-dimensional gel electrophoresis (2-D PAGE) with mass spectrometry has been the basis of proteomic technology [43]. Although this technique has provided rich information about individual proteins, its clinical usefulness for the study of the proteome is limited. The limitations of this technique for clinical diagnostics are primarily due to low sample throughput compared with other techniques. Additionally, there are technical challenges relating to the reproducibility of the 2-D gels, the relatively low numbers of proteins that can be resolved on each gel and the limited sensitivity (ie low abundant proteins are not easily identified). Modern proteomics technology, as discussed in several recent reviews, is developing quickly and is focused on mass spec-trometry-based serum pattern profiling and protein micro-arrays in continuing efforts to discover new molecular markers and therapeutic targets in human cancer [4446]. The use of serum proteomic pattern analysis has created diagnostic signatures for ovarian [47, 48], breast [49], prostate [50] and liver cancers [51]. In the Laboratory of Pathology at the National Cancer Institute (NCI), in collaboration with the Food and Drug Administration (FDA), this proteome-based strategy is being used for early detection of ovarian cancer in high-risk women. Preliminary data is so encouraging that the technology is on track to enter clinical trials in the very near future [47]. For women living with the spectre of an aggressive disease, such as ovarian cancer, a reliable screening test would represent a major step forward in the diagnostic capabilities of the physicians who treat them.

Clinical applications of genomics

The integration of 'global gene profiling' into clinical medicine is exemplified in 2003 by the fact that in the early weeks of the severe acute respiratory syndrome (SARS) pandemic in 2003, the Centers for Disease Control sent tissue samples and viral cultures from SARS patients to Dr Joseph DeRisi's research team at the University of California, San Francisco. They explored the origin of the novel coronavirus using their custom oligonucleotide DNA microarray which represents 1,000 viruses [52, 53]. Of the 12,000 oligonucleotides spotted on the array, the patient samples hybridised only to a group of eight oligonucleotides representing two virus families: Coro-naviridae and Astroviridae [54]. Although the identification of the novel coronavirus associated with SARS was made using a broad range of laboratory testing, this example demonstrates the power of microarray technology as a molecular diagnostic tool to test the spectrum of viruses in a single assay and to narrow, to a finite number, the potential pathogens in an unknown disease with a nonspecific clinical presentation. Admittedly, the current review is focused on genomic applications to cancer, but this example deserves special mention, given the worldwide attention on SARS. Moreover, as illustrated in this example, the integration of genomic analysis with traditional clinical and histological assessments is finding its way into clinical management.

Breast cancer -- a genomic approach

The van't Veer et al [6]. and van de Vijver et al [7]. expression data on breast cancer have been discussed extensively since the announcement that, collectively, the two groups were beginning a large clinical trial for women with stage I-II disease at the Netherlands Cancer Institute [6, 7, 55, 56]. The upcoming trial has the potential to significantly improve the current breast cancer classification system, and influence treatment, if the original results can be reproduced in a much larger population of women. The first study by van't Veer et al [6]. identified a molecular profile of 70 genes that could be used to predict which patients, in a group of young women diagnosed with stage I-II breast cancer who did not have axillary lymph node metastases, will develop distant metastases within five years. The study generated great interest because of its implications for the use of adjuvant treatments in patients with early stage breast cancer. Current statistics show that the majority of patients with stage I-II breast cancer who have histologically tumour-free axillary lymph nodes will be disease-free after five years when treated with breast-conserving surgery plus radiation therapy or mastectomy -- the current recommended therapy for localised breast cancer [57]. Unfortunately, nearly 20 per cent of women treated appropriately for their primary breast cancer will develop metastatic disease and, thus, are the best candidates for adjuvant therapy. Identifying these candidates using the current clinical criteria, however, is incredibly difficult. The recommendations of the National Institutes of Health (NIH) Consensus Panel and the International Consensus Panel from the St Gallen Conference, using their respective prognostic and predictive criteria (hereafter collectively referred to as the 'conventional consensus criteria') to determine eligibility for adjuvant therapy, advise treatment for up to 90 per cent of patients with lymph node-negative breast cancer [58, 59]. When van't Veer and colleagues [6] applied the 70-gene expression profile to the patient cohort in their study, the application allowed them to accurately differentiate between low-risk and high-risk women, thereby reducing in number the patients who would ordinarily be advised to receive adjuvant treatment from between 70-90 per cent -- based on the conventional consensus criteria -- to 40 per cent.

To validate the prognostic value of the 70-gene expression profile as an accurate predictor of the risk of distant metastases, van de Vijver et al. studied 295 new breast cancer tumours, which included specimens from women with histologically positive axillary lymph nodes [7]. Currently, nearly all women with positive lymph nodes are treated with some type of adjuvant therapy. The expression analysis of the validation study again proved to be a more accurate predictor of distant metastases within five years than the conventional consensus criteria and, thus, improved the likelihood of identifying the patients who will benefit most from adjuvant therapy. Specifically, the probability of remaining metastasis-free after ten years was 85.2 per cent in the patients classified as having a 'good-prognosis signature' and 50.6 per cent in the patients classified as having a 'poor-prognosis signature'. In addition, the gene expression profile was predictive of survival. The overall ten-year survival rate was 94.5 per cent in patients with a 'good-prognosis signature' and 54.6 per cent in patients with a 'poor-prognosis signature'. Three of the important clinical characteristics of the conventional consensus criteria were associated with the gene expression profile -- age of the patient, histological grade of the tumour and oestrogen receptor (OR) status. Of note, one of the most widely accepted prognostic factors -- nodal status -- was not associated with the gene expression profile, as those with node-positive disease and node-negative disease were nearly equally distributed in the two groups. The Dutch team is now ready to use this information in a trial of 5,000 women with stage I-II breast cancer, to decide who will receive adjuvant therapy.

The preliminary data provide an exciting start for the integration of genomics approaches into the clinic, and they demonstrate the potential of the technology to improve diagnostics and treatment. It will be interesting to learn whether or not the gene expression profile will be validated in a larger group of women, given that the expression profile was generated from young women (< 53 years of age), while the median age for the diagnosis of breast cancer is between 60-65 years of age. In addition, as anticipated when making treatment decisions for stage I-II patients with breast cancer, a small percentage of women will be misclassified as having a 'good-prognosis signature' -- ie a low risk for developing distant metastases, will not receive adjuvant systemic therapy and will develop metastases within five years. Although this misclassification error rate is not significantly higher than the misclassification rate that occurs today, using the best clinical criteria available to the physician, further work should be done so that this technology can be introduced into the clinic without denying women available treatments. An upcoming study to be directed by Daniel Haber at the Massachusetts General Hospital underscores this point, as he will not use the gene expression profiles from his study to decide on treatment options, but will rather validate the Dutch prognostic profile. Lastly, it is interesting that 97 per cent of the tumours in the 'good-prognosis signature' were OR positive. Other gene expression profiling studies have observed that the OR status of a breast tumour has a strong influence on the subsequent classifications [60]. As 63 per cent of the tumours in the 'poor-prognosis signature' identified by van de Vijver et al [7]. were OR positive, however, further studies are required to detect the additional pathways -- along with the oestrogen signalling pathway -- that determine the classification. The addition of future proteomic data to the breast cancer gene expression analysis may help to identify those additional signalling pathways that are important for a good prognosis.

Ovarian cancer -- a proteomics approach

Ovarian cancer is diagnosed at an advanced clinical stage -- when the ovarian cancer cells have metastasised from the ovary to the pelvis, peritoneal cavity or other distant sites -- in more than two-thirds of women [61]. The five-year survival rate for these late-stage patients is 35-40 per cent, despite the best possible surgical and chemotherapeutic treatment. If, however, ovarian cancer is detected while it is still confined to the ovary (stage I) and treated appropriately, the five-year survival rate is optimistic (95 per cent). Unfortunately, early-stage ovarian cancer is difficult to detect because patients are frequently asymptomatic and few reliable tumour markers exist. Thus, the development of dependable serum markers for the early detection of ovarian cancer would improve the survival rate of women facing this disease. At the NCI-FDA Clinical Proteomics Program, a proteomics approach is being used for the early detection of a variety of cancers, as well as non-neoplastic diseases such as infectious diseases, autoimmune diseases, vascular diseases, prenatal diagnosis and transplantation rejection [44, 62]. Several clinical trials are being planned, including the first of its kind for ovarian cancer. The identification of specific and sensitive molecular markers for epithelial ovarian cancer is a priority of the NCI-FDA Clinical Proteomics Program (Table 2), and this proposed trial has the potential to develop the first reliable screening test for ovarian cancer, if the protein signature that was identified in a preliminary study of women with a high risk of developing the disease can be applied to screening women in the general population.
Table 2

Clinical trials using proteomic analysis

Sponsor

Principal investigator, centre

Disease, application

NCI

Elise Kohn, Warren Grant Magnuson Clinical Center, NIH, Bethesda, MD, USA

Ovarian cancer, developing serum proteomic profile associated with first clinical response

NCI

Elise Kohn, Warren Grant Magnuson Clinical Center, NIH, Bethesda, MD, USA

Breast and ovarian cancer, protein microarray to assess response to EGFR inhibitors and Gleevac

NCI

Susan Bates, Warren Grant Magnuson Clinical Center, NIH, Bethesda, MD, USA

Breast and ovarian cancer, protein microarray to assess response to Herceptin/Taxol combination therapy

NCI

Sam Hwang, Warren Grant Magnuson Clinical Center, NIH, Bethesda, MD, USA

Inflammatory and neoplastic skin diseases, developing serum proteomic profile for diagnosis

NCI

Michael Solomon, Warren Grant Magnuson Clinical Center, NIH, Bethesda, MD, USA

Cardiac disease, developing serum proteomic profile for diagnosis of acute cardiac allograft rejection

NCI

Mahrukh Hussain, Warren Grant Magnuson Clinical Center, NIH, Bethesda, MD, USA

Ovarian cancer, developing serum proteomic profile associated with relapse

Abbreviations: EGFR, epidermal growth factor receptor

Petricoin et al., using mass spectroscopy coupled with an artificial intelligence computer algorithm, developed a system of low molecular weight serum protein profiling and identified a specific protein signature that was associated with ovarian cancer from asymptomatic women with a high risk of developing the disease [47]. In the study, serum samples from 50 healthy women and 50 women with ovarian cancer were used as a 'training set' to identify a serum protein signature that was specific for ovarian cancer. Validation of an additional 116 unknown serum samples resulted in a highly accurate classification of the patients without ovarian cancer (95 per cent), as well as those with benign disease (100 per cent) and these with ovarian cancer (100 per cent) -- including all stage I cancers. Overall, this result yielded 100 per cent sensitivity and 95 per cent specificity for the identification of ovarian cancer. The positive predictive value for the sample set was 94 per cent, compared with 35 per cent for the CA-125 serum marker for the same samples.

The significant implication of the NCI-FDA study is that the serum protein signature accurately identified all of the cases of early-stage ovarian cancer, making it a potential diagnostic tool that has so far been lacking in the clinical laboratory. In addition, this study promises to create some very realistic opportunities for the clinical management of ovarian cancer, as the generation of the diagnostic mass spectra requires only a small serum sample, and the results can be obtained within 30 minutes in a cost-effective manner. The most pressing goal for now, however, is to validate the original results in a large, multi-institutional clinical trial so that this new diagnostic tool will soon be available for patients and physicians. The results of the trial will be important for a better understanding of epithelial ovarian tumours, as the specific molecular and protein pathways involved in the development of this disease remain unknown. It is hoped that adding a serum proteomics approach to the current screening methods for ovarian cancer will translate into improved diagnostics and treatments for what is currently an unpredictable and aggressive cancer.

Another goal of the NCI-FDA Clinical Proteomics Program is to use protein microarrays to profile the functional state of the signalling pathways that comprise the protein networks within human tissue cells (Figure 1). It is anticipated that the data generated from such studies will identify specific signalling pathways that are deregulated in a variety of human diseases. Moreover, it is the authors' hope that understanding these signalling pathways will facilitate the development of new drugs that target specific 'nodes' in the protein circuitry, and thereby the design of a treatment regimen that will have greater efficacy with less toxicity. For example, one ongoing clinical application at the NCI involves a phase II clinical trial studying the type III receptor tyrosine kinase inhibitor, imatinib mesylate, for the treatment of women with epithelial ovarian cancer. In this trial, biopsies are taken from the primary tumour prior to the initiation of therapy; four weeks into the treatment regimen, a protein microarray is used to determine whether the receptor tyrosine kinase pathways are active and to correlate the clinical efficacy and toxicity of the therapy. This concept is the premise for molecular-targeted therapy, and similar studies involving other types of cancer are currently underway at the NCI.
https://static-content.springer.com/image/art%3A10.1186%2F1479-7364-1-2-134/MediaObjects/40246_2003_Article_117_Fig1_HTML.jpg
Figure 1

Profiling the disease signature for application to individualisation of therapy. From: Liotta, LA. et al. (2001), JAMA Vol. 286, p. 18. Used with permission.

Conclusion

The application of serum proteomics and gene expression analysis to early diagnosis and treatment decisions is a daunting challenge. It will require parties practising in previously disparate disciplines to work together and understand the data, which may be in the form of clinical information, mathematical algorithms and mass spectrometry. The challenge of 'clinical proteomics' and DNA microarrays is no longer just the development of new technologies, but rather the best use and integration of these technologies for the diagnosis and treatment of disease. The process of this integration represents a new, evolving field of 'translational medicine'.

Authors’ Affiliations

(1)
Laboratory of Pathology, National Cancer Institute/National Institutes of Health

References

  1. MacDonald TJ, Brown KM, LaFleur B, et al: 'Expression profiling of medulloblastoma: PDGFRA and the RAS/MAPK pathway as therapeutic targets for metastatic disease'. Nat Genet. 2001, 29: 143-152. 10.1038/ng731.View ArticlePubMedGoogle Scholar
  2. Pomeroy SL, Tamayo P, Gaasenbeek M, et al: 'Prediction of central nervous system embryonal tumour outcome based on gene expression'. Nature. 2002, 415: 436-442. 10.1038/415436a.View ArticlePubMedGoogle Scholar
  3. Perou CM, Sorlie T, Eisen MB, et al: 'Molecular portraits of human breast tumours'. Nature. 2000, 406: 747-752. 10.1038/35021093.View ArticlePubMedGoogle Scholar
  4. Sorlie T, Perou CM, Tibshirani R, et al: 'Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications'. Proc Natl Acad Sci USA. 2001, 98: 10869-10874. 10.1073/pnas.191367098.PubMed CentralView ArticlePubMedGoogle Scholar
  5. Sorlie T, Tibshirani R, Parker J, et al: 'Repeated observation of breast tumor subtypes in independent gene expression data sets'. Proc Natl Acad Sci USA. 2003, 100: 8418-8423. 10.1073/pnas.0932692100.PubMed CentralView ArticlePubMedGoogle Scholar
  6. van't Veer LJ, Dai H, van de Vijver MJ, et al: 'Gene expression profiling predicts clinical outcome of breast cancer'. Nature. 2002, 415: 530-536. 10.1038/415530a.View ArticleGoogle Scholar
  7. van de Vijver MJ, He YD, van't Veer LJ, et al: 'A gene-expression signature as a predictor of survival in breast cancer'. N Engl J Med. 2002, 347: 1999-2009. 10.1056/NEJMoa021967.View ArticlePubMedGoogle Scholar
  8. West M, Blanchette C, Dressman H, et al: 'Predicting the clinical status of human breast cancer by using gene expression profiles'. Proc Natl Acad Sci USA. 2001, 98: 11462-11467. 10.1073/pnas.201162998.PubMed CentralView ArticlePubMedGoogle Scholar
  9. Hedenfalk I, Duggan D, Chen Y, et al: 'Gene-expression profiles in hereditary breast cancer'. N Engl J Med. 2001, 344: 539-548. 10.1056/NEJM200102223440801.View ArticlePubMedGoogle Scholar
  10. Gruvberger S, Ringner M, Chen Y, et al: 'Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns'. Cancer Res. 2001, 61: 5979-5984.PubMedGoogle Scholar
  11. Alon U, Barkai N, Notterman DA, et al: 'Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays'. Proc Natl Acad Sci USA. 1999, 96: 6745-6750. 10.1073/pnas.96.12.6745.PubMed CentralView ArticlePubMedGoogle Scholar
  12. Zou TT, Selaru FM, Xu Y, et al: 'Application of cDNA microarrays to generate a molecular taxonomy capable of distinguishing between colon cancer and normal colon'. Oncogene. 2002, 21: 4855-4862. 10.1038/sj.onc.1205613.View ArticlePubMedGoogle Scholar
  13. Hippo Y, Taniguchi H, Tsutsumi S, et al: 'Global gene expression analysis of gastric cancer by oligonucleotide microarrays'. Cancer Res. 2002, 62: 233-240.PubMedGoogle Scholar
  14. Takahashi M, Rhodes DR, Furge KA, et al: 'Gene expression profiling of clear cell renal cell carcinoma: Gene identification and prognostic classification'. Proc Natl Acad Sci USA. 2001, 98: 9754-9759. 10.1073/pnas.171209998.PubMed CentralView ArticlePubMedGoogle Scholar
  15. Yeoh EJ, Ross ME, Shurtleff SA, et al: 'Classification, subtype discovery, and prediction of outcome in pediatric acute lympho- blastic leukemia by gene expression profiling'. Cancer Cell. 2002, 1: 133-143. 10.1016/S1535-6108(02)00032-6.View ArticlePubMedGoogle Scholar
  16. Hofmann WK, de Vos S, Elashoff D, et al: 'Relation between resistance of Philadelphia-chromosome-positive acute lymphoblastic leukaemia to the tyrosine kinase inhibitor STI571 and gene-expression profiles: A gene-expression study'. Lancet. 2002, 359: 481-486. 10.1016/S0140-6736(02)07678-X.View ArticlePubMedGoogle Scholar
  17. Ferrando AA, Neuberg DS, Staunton J, et al: 'Gene expression signatures define novel oncogenic pathways in T cell acute lymphoblastic leukemia'. Cancer Cell. 2002, 1: 75-87. 10.1016/S1535-6108(02)00018-1.View ArticlePubMedGoogle Scholar
  18. Alizadeh AA, Eisen MB, Davis RE, et al: 'Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling'. Nature. 2000, 403: 503-511. 10.1038/35000501.View ArticlePubMedGoogle Scholar
  19. Shipp MA, Ross KN, Tamayo P, et al: 'Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning'. Nat Med. 2002, 8: 68-74. 10.1038/nm0102-68.View ArticlePubMedGoogle Scholar
  20. Rosenwald A, Wright G, Chan WC, et al: 'The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma'. N Engl J Med. 2002, 346: 1937-1947. 10.1056/NEJMoa012914.View ArticlePubMedGoogle Scholar
  21. Beer DG, Kardia SL, Huang CC, et al: 'Gene-expression profiles predict survival of patients with lung adenocarcinoma'. Nat Med. 2002, 8: 816-824.PubMedGoogle Scholar
  22. Garber ME, Troyanskaya OG, Schluens K, et al: 'Diversity of gene expression in adenocarcinoma of the lung'. Proc Natl Acad Sci USA. 2001, 98: 13784-13789. 10.1073/pnas.241500798.PubMed CentralView ArticlePubMedGoogle Scholar
  23. Bhattacharjee A, Richards WG, Staunton J, et al: 'Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses'. Proc Natl Acad Sci USA. 2001, 98: 13790-13795. 10.1073/pnas.191502998.PubMed CentralView ArticlePubMedGoogle Scholar
  24. Bittner M, Meltzer P, Chen Y, et al: 'Molecular classification of cutaneous malignant melanoma by gene expression profiling'. Nature. 2000, 406: 536-540. 10.1038/35020115.View ArticlePubMedGoogle Scholar
  25. Wang K, Gan L, Jeffery E, et al: 'Monitoring gene expression profile changes in ovarian carcinomas using cDNA microarray'. Gene. 1999, 229: 101-108. 10.1016/S0378-1119(99)00035-9.View ArticlePubMedGoogle Scholar
  26. Welsh JB, Zarrinkar PP, Sapinoso LM, et al: 'Analysis of gene expression profiles in normal and neoplastic ovarian tissue samples identifies candidate molecular markers of epithelial ovarian cancer'. Proc Natl Acad Sci USA. 2001, 98: 1176-1181. 10.1073/pnas.98.3.1176.PubMed CentralView ArticlePubMedGoogle Scholar
  27. Jazaeri AA, Yee CJ, Sotiriou C, et al: 'Gene expression profiles of BRCA1-linked, BRCA2-linked, and sporadic ovarian cancers'. J Natl Cancer Inst. 2002, 94: 990-1000. 10.1093/jnci/94.13.990.View ArticlePubMedGoogle Scholar
  28. Sawiris GP, Sherman-Baust CA, Becker KG, et al: 'Development of a highly specialized cDNA array for the study and diagnosis of epithelial ovarian cancer'. Cancer Res. 2002, 62: 2923-2928.PubMedGoogle Scholar
  29. Singh D, Febbo PG, Ross K, et al: 'Gene expression correlates of clinical prostate cancer behavior'. Cancer Cell. 2002, 1: 203-209. 10.1016/S1535-6108(02)00030-2.View ArticlePubMedGoogle Scholar
  30. LaTulippe E, Satagopan J, Smith A, et al: 'Comprehensive gene expression analysis of prostate cancer reveals distinct transcriptional programs associated with metastatic disease'. Cancer Res. 2002, 62: 4499-4506.PubMedGoogle Scholar
  31. Welsh JB, Sapinoso LM, Su AI, et al: 'Analysis of gene expression identifies candidate markers and pharmacological targets in prostate cancer'. Cancer Res. 2001, 61: 5974-5978.PubMedGoogle Scholar
  32. Dhanasekaran SM, Barrette TR, Ghosh D, et al: 'Delineation of prognostic biomarkers in prostate cancer'. Nature. 2001, 412: 822-826. 10.1038/35090585.View ArticlePubMedGoogle Scholar
  33. Khan J, Wei JS, Ringner M, et al: 'Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks'. Nat Med. 2001, 7: 673-679. 10.1038/89044.PubMed CentralView ArticlePubMedGoogle Scholar
  34. Brazma A, Hingamp P, Quackenbush J, et al: 'Minimum information about a microarray experiment (MIAME) - toward standards for microarray data'. Nat Genet. 2001, 29: 365-371. 10.1038/ng1201-365.View ArticlePubMedGoogle Scholar
  35. Knight J: 'Minimum standards set out for gene-expression data'. Nature. 2002, 415: 946-View ArticlePubMedGoogle Scholar
  36. Schubert CM: 'Microarray to be used as routine clinical screen'. Nat Med. 2003, 9: 9-View ArticlePubMedGoogle Scholar
  37. Branca M: 'Genetics and medicine. Putting gene arrays to the test'. Science. 2003, 300: 238-10.1126/science.300.5617.238.View ArticlePubMedGoogle Scholar
  38. Pollack JR, Perou CM, Alizadeh AA, et al: 'Genome-wide analysis of DNA copy-number changes using cDNA microarrays'. Nat Genet. 1999, 23: 41-46.View ArticlePubMedGoogle Scholar
  39. Heiskanen MA, Bittner ML, Chen Y, et al: 'Detection of gene amplification by genomic hybridization to cDNA microarrays'. Cancer Res. 2000, 60: 799-802.PubMedGoogle Scholar
  40. Ziauddin J, Sabatini DM: 'Microarrays of cells expressing defined cDNAs'. Nature. 2001, 411: 107-110. 10.1038/35075114.View ArticlePubMedGoogle Scholar
  41. Diehn M, Eisen MB, Botstein D, et al: 'Large-scale identification of secreted and membrane-associated gene products using DNA microarrays'. Nat Genet. 2000, 25: 58-62. 10.1038/75603.View ArticlePubMedGoogle Scholar
  42. Robinson WH, DiGennaro C, Hueber W, et al: 'Autoantigen microarrays for multiplex characterization of autoantibody responses'. Nat Med. 2002, 8: 295-301. 10.1038/nm0302-295.View ArticlePubMedGoogle Scholar
  43. Aebersold R, Mann M: 'Mass spectrometry-based proteo-mics'. Nature. 2003, 422: 198-207. 10.1038/nature01511.View ArticlePubMedGoogle Scholar
  44. Liotta LA, Kohn EC, Petricoin EF: 'Clinical proteomics: Personalized molecular medicine'. JAMA. 2001, 286: 2211-2214. 10.1001/jama.286.18.2211.View ArticlePubMedGoogle Scholar
  45. MacBeath G: 'Protein microarrays and proteomics'. Nat Genet. 2002, 32 (Suppl): 526-532. Chipping Forcast 11View ArticlePubMedGoogle Scholar
  46. Petricoin EF, Liotta LA: 'Mass spectrometry-based diagnostics: The upcoming revolution in disease detection'. Clin Chem. 2003, 49: 533-534. 10.1373/49.4.533.View ArticlePubMedGoogle Scholar
  47. Petricoin EF, Ardekani AM, Hitt BA, et al: 'Use of proteomic patterns in serum to identify ovarian cancer'. Lancet. 2002, 359: 572-577. 10.1016/S0140-6736(02)07746-2.View ArticlePubMedGoogle Scholar
  48. Ardekani AM, Liotta LA, Petricoin EF: 'Clinical potential of proteomics in the diagnosis of ovarian cancer'. Expert Rev Mol Diagn. 2002, 2: 312-320. 10.1586/14737159.2.4.312.View ArticlePubMedGoogle Scholar
  49. Li J, Zhang Z, Rosenzweig J, et al: 'Proteomics and bioinfor-matics approaches for identification of serum biomarkers to detect breast cancer'. Clin Chem. 2002, 48: 1296-1304.PubMedGoogle Scholar
  50. Adam BL, Qu Y, Davis JW, et al: 'Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men'. Cancer Res. 2002, 62: 3609-3614.PubMedGoogle Scholar
  51. Poon TC, Yip TT, Chan AT, et al: 'Comprehensive proteomic profiling identifies serum proteomic signatures for detection of hepatocellular carcinoma and its subtypes'. Clin Chem. 2003, 49: 752-760. 10.1373/49.5.752.View ArticlePubMedGoogle Scholar
  52. Wang D, Coscoy L, Zylberberg M, et al: 'Microarray-based detection and genotyping of viral pathogens'. Proc Natl Acad Sci USA. 2002, 99: 15687-15692. 10.1073/pnas.242579699.PubMed CentralView ArticlePubMedGoogle Scholar
  53. Knight J: 'Researchers get to grips with cause of pneumonia epidemic'. Nature. 2003, 422: 547-548. 10.1038/422547a.View ArticlePubMedGoogle Scholar
  54. Ksiazek TG, Erdman D, Goldsmith CS, et al: 'A novel coronavirus associated with severe acute respiratory syndrome'. N Engl J Med. 2003, 348: 1953-1966. 10.1056/NEJMoa030781.View ArticlePubMedGoogle Scholar
  55. Kallioniemi A: 'Molecular signatures of breast cancer - predicting the future'. N Engl J Med. 2002, 347: 2067-2068. 10.1056/NEJMe020152.View ArticlePubMedGoogle Scholar
  56. Borg A, Ferno M, Peterson C: 'Predicting the future of breast cancer'. Nat Med. 2003, 9: 16-18. 10.1038/nm0103-16.View ArticlePubMedGoogle Scholar
  57. Caldas C, Aparicio SA: 'The molecular outlook'. Nature. 2002, 415: 484-485. 10.1038/415484a.View ArticlePubMedGoogle Scholar
  58. Eifel P, Axelson JA, Costa J, et al: 'National Institutes of Health Consensus Development Conference Statement: Adjuvant therapy for breast cancer November 1-3, 2000'. J Natl Cancer Inst. 2001, 93: 979-989.View ArticlePubMedGoogle Scholar
  59. Goldhirsch A, Glick JH, Gelber RD, et al: 'Meeting highlights: International Consensus Panel on the Treatment of Primary Breast Cancer'. J Natl Cancer Inst. 1998, 90: 1601-1608. 10.1093/jnci/90.21.1601.View ArticlePubMedGoogle Scholar
  60. Gruvberger SK, Ringner M, Eden P, et al: 'Expression profiling to predict outcome in breast cancer: The influence of sample selection'. Breast Cancer Res. 2002, 5: 23-26. 10.1186/bcr548.PubMed CentralView ArticlePubMedGoogle Scholar
  61. Ozols R, Rubin S, Thomas G, et al: Epithelial Ovarian Cancer. 2000, Lippincott Williams and Wilkins, Philadelphia, PAGoogle Scholar
  62. Service RF: 'Genetics and medicine. Recruiting genes, proteins for a revolution in diagnostics'. Science. 2003, 300: 236-239. 10.1126/science.300.5617.236.View ArticlePubMedGoogle Scholar

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© Henry Stewart Publications 2004