Use of high density antibody arrays to validate and discover cancer serum biomarkers

Perhaps the greatest barrier to translation of serum biomarker discoveries is the inability to evaluate putative biomarkers in high throughput validation studies. Here we report on the development, production, and implementation of a high-density antibody microarray used to evaluate large numbers of candidate ovarian cancer serum biomarkers. The platform was shown to be useful for evaluation of individual antibodies for comparative analysis, such as with disease classification, and biomarker validation and discovery. We demonstrate its performance by showing that known tumor markers behave as expected. We also identify several promising biomarkers from a candidate list and generate hypotheses to support new discovery studies.     

A framework for evaluating biomarkers for early detection: validation of biomarker panels for ovarian cancer

A panel of biomarkers may improve predictive performance over individual markers. Although many biomarker panels have been described for ovarian cancer, few studies used prediagnostic samples to assess the potential of the panels for early detection. We conducted a multisite systematic evaluation of biomarker panels using prediagnostic serum samples from the Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) screening trial. Using a nested case-control design, levels of 28 biomarkers were measured laboratory-blinded in 118 serum samples obtained before cancer diagnosis and 951 serum samples from matched controls. Five predictive models, each containing 6 to 8 biomarkers, were evaluated according to a predetermined analysis plan. Three sequential analyses were conducted: blinded validation of previously established models (step 1); simultaneous split-sample discovery and validation of models (step 2); and exploratory discovery of new models (step 3). Sensitivity, specificity, sensitivity at 98% specificity, and AUC were computed for the models and CA125 alone among 67 cases diagnosed within one year of blood draw and 476 matched controls. In step 1, one model showed comparable performance to CA125, with sensitivity, specificity, and AUC at 69.2%, 96.6%, and 0.892, respectively. Remaining models had poorer performance than CA125 alone. In step 2, we observed a similar pattern. In step 3, a model derived from all 28 markers failed to show improvement over CA125. Thus, biomarker panels discovered in diagnostic samples may not validate in prediagnostic samples; utilizing prediagnostic samples for discovery may be helpful in developing validated early detection panels.     

Proteomics of gliomas: initial biomarker discovery and evolution of technology

Gliomas are a group of aggressive brain tumors that diffusely infiltrate adjacent brain tissues, rendering them largely incurable, even with multiple treatment modalities and agents. Mostly asymptomatic at early stages, they present in several subtypes with astrocytic or oligodendrocytic features and invariably progress to malignant forms. Gliomas are difficult to classify precisely because of interobserver variability during histopathologic grading. Identifying biological signatures of each glioma subtype through protein biomarker profiling of tumor or tumor-proximal fluids is therefore of high priority. Such profiling not only may provide clues regarding tumor classification but may identify clinical biomarkers and pathologic targets for the development of personalized treatments. In the past decade, differential proteomic profiling techniques have utilized tumor, cerebrospinal fluid, and plasma from glioma patients to identify the first candidate diagnostic, prognostic, predictive, and therapeutic response markers, highlighting the potential for glioma biomarker discovery. The number of markers identified, however, has been limited, their reproducibility between studies is unclear, and none have been validated for clinical use. Recent technological advancements in methodologies for high-throughput profiling, which provide easy access, rapid screening, low sample consumption, and accurate protein identification, are anticipated to accelerate brain tumor biomarker discovery. Reliable tools for biomarker verification forecast translation of the biomarkers into clinical diagnostics in the foreseeable future. Herein we update the reader on the recent trends and directions in glioma proteomics, including key findings and established and emerging technologies for analysis, together with challenges we are still facing in identifying and verifying potential glioma biomarkers.     

Prognostic and Predictive Biomarkers in Resected Colon Cancer: Current Status and Future Perspectives for Integrating Genomics into Biomarker Discovery

The number of agents that are potentially effective in the adjuvant treatment of locally advanced resectable colon cancer is increasing. Consequently, it is important to ascertain which subgroups of patients will benefit from a specific treatment. Despite more than two decades of research into the molecular genetics of colon cancer, there is a lack of prognostic and predictive molecular biomarkers with proven utility in this setting. A secondary objective of the Pan European Trials in Adjuvant Colon Cancer‐3 trial, which compared irinotecan in combination with 5‐fluorouracil and leucovorin in the postoperative treatment of stage III and stage II colon cancer patients, was to undertake a translational research study to assess a panel of putative prognostic and predictive markers in a large colon cancer patient cohort. The Cancer and Leukemia Group B 89803 trial, in a similar design, also investigated the use of prognostic and predictive biomarkers in this setting. In this article, the authors, who are coinvestigators from these trials and performed similar investigations of biomarker discovery in the adjuvant treatment of colon cancer, review the current status of biomarker research in this field, drawing on their experiences and considering future strategies for biomarker discovery in the postgenomic era.     

Ovarian cancer biomarker performance in prostate, lung, colorectal, and ovarian cancer screening trial specimens

Establishing a cancer screening biomarker's intended performance requires "phase III" specimens obtained in asymptomatic individuals before clinical diagnosis rather than "phase II" specimens obtained from symptomatic individuals at diagnosis. We used specimens from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial to evaluate ovarian cancer biomarkers previously assessed in phase II sets. Phase II specimens from 180 ovarian cancer cases and 660 benign disease or general population controls were assembled from four Early Detection Research Network or Ovarian Cancer Specialized Program of Research Excellence sites and used to rank 49 biomarkers. Thirty-five markers, including 6 additional markers from a fifth site, were then evaluated in PLCO proximate specimens from 118 women with ovarian cancer and 474 matched controls. Top markers in phase II specimens included CA125, HE4, transthyretin, CA15.3, and CA72.4 with sensitivity at 95% specificity ranging from 0.73 to 0.40. Except for transthyretin, these markers had similar or better sensitivity when moving to phase III specimens that had been drawn within 6 months of the clinical diagnosis. Performance of all markers declined in phase III specimens more remote than 6 months from diagnosis. Despite many promising new markers for ovarian cancer, CA125 remains the single-best biomarker in the phase II and phase III specimens tested in this study.     

Role of biologic markers in patient selection and application to disease prevention

Aromatase inhibitors (AIs) are now under investigation for the treatment of early stage breast cancer and for disease prevention as alternatives to standard treatment with tamoxifen. Currently identified genetic risk factors of breast cancer include BRCA-1/BRCA-2 mutations, ATM mutations, and history of high estrogen levels, as evidenced by plasma analyses and/or dense bones. To date, estrogen receptor (ER) and progesterone receptor (PgR) status has predictive value for determining response to therapy in patients with hormone receptor-positive breast cancer (ER+ and/or PgR+ tumors). Recent studies have shown AIs to be safer and more effective than tamoxifen in postmenopausal women with advanced disease. Some data suggest that letrozole may be a more effective treatment than tamoxifen for patients with ER+ and/or PgR+ early breast cancers expressing ErbB-1 and/or ErbB-2. Changes in cell proliferation markers (e.g., S-phase fraction and Ki67 antigen), plasma lipid levels, and the bone resorption marker C-terminal peptide are biomarkers that have been evaluated for preventive and prognostic value in breast cancer patients and normal volunteers. Results from biomarker screens can be used to define inclusion criteria for clinical trials and eventually to individualize treatment. Gene expression profiling (microarray analysis), i.e., genomic and proteomic studies, will probably advance the discovery of new biomarkers for breast cancer prevention and treatment.     

Blood biomarkers for prostate cancer detection and prognosis

Prostate cancer is the most non-cutaneous malignancy diagnosed in men in the USA. The discovery of prostate-specific antigen (PSA) revolutionized prostate cancer diagnosis and management in the 1990s. Despite its remarkable performance as a marker for prostate cancer, PSA is not prostate cancer specific. PSA can be released by normal as well as hyperplastic prostate cells, which undermines the specificity of PSA for prostate cancer diagnosis. Hence, there is a need for new biomarkers that can detect prostate cancer and, in addition, distinguish indolent from biologically aggressive cancers. Moreover, the emergence of new therapeutic approaches for prostate cancer cannot flourish without a more reliable set of markers to serve as prognosticators, targets and surrogate end points of disease progression and response to treatment. As the most useful clinical biomarkers are likely to be those assayed from blood, there is an increasing interest in profiling blood proteins. With recent advances in biotechnology such as high-throughput molecular analyses, many potential blood biomarkers have been identified and are currently under investigation.     

Cancer biomarkers: closer to delivering on their promise

Taguchi et?al. describe, in this issue of Cancer Cell, a quantitative comparative biomarker discovery approach that integrates animal lung cancer models with validation in well-controlled human clinical study sets. This approach overcomes many of the major barriers that have held back the field of cancer biomarkers in the past.     

Strategies for discovering novel cancer biomarkers through utilization of emerging technologies

The introduction of technologies such as mass spectrometry and protein and DNA arrays, combined with our understanding of the human genome, has enabled simultaneous examination of thousands of proteins and genes in single experiments, which has led to renewed interest in discovering novel biomarkers for cancer. The modern technologies are capable of performing parallel analyses as opposed to the serial analyses conducted with older methods, and they therefore provide opportunities to identify distinguishing patterns (signatures or portraits) for cancer diagnosis and classification as well as to predict response to therapies. Furthermore, these technologies provide the means by which new, single tumor markers could be discovered through use of reasonable hypotheses and novel analytical strategies. Despite the current optimism, a number of important limitations to the discovery of novel single tumor markers have been identified, including study design bias, and artefacts related to the collection and storage of samples. Despite the fact that new technologies and strategies often fail to identify well-established cancer biomarkers and show a bias toward the identification of high-abundance molecules, these technological advances have the capacity to revolutionize biomarker discovery. It is now necessary to focus on careful validation studies in order to identify the strategies and biomarkers that work and bring them to the clinic as early as possible.     

Predictive biomarkers for personalised anti-cancer drug use: Discovery to clinical implementation

A priority translational research objective in cancer medicine is the discovery of novel therapeutic targets for solid tumours. Ideally, co-discovery of predictive biomarkers occurs in parallel to facilitate clinical development of agents and ultimately personalise clinical use. However, the identification of clinically useful predictive biomarkers for solid tumours has proven challenging with many initially promising biomarkers failing to translate into clinically useful applications. In particular, the ‘failure’ of a predictive biomarker has often only become apparent at a relatively late stage in investigation. Recently, the field has recognised the need to develop a robust clinical biomarker development methodology to facilitate the process. This review discusses the recent progress in this area focusing on the key stages in the biomarker development process: discovery, validation, qualification and implementation. Concentrating on predictive biomarkers for selecting systemic therapies for individual patients in the clinic, the advances and progress in each of these stages in biomarker development are outlined and the key remaining challenges are discussed. Specific examples are discussed to illustrate the challenges identified and how they have been addressed. Overall, we find that significant progress has been made towards a formalised biomarker developmental process. This holds considerable promise for facilitating the translation of predictive biomarkers from discovery to clinical implementation. Further enhancements could eventually be found through alignment with regulatory processes.     

Cancer survivorship--genetic susceptibility and second primary cancers: research strategies and recommendations

Cancer survivors constitute 3.5% of the United States population, but second primary malignancies among this high-risk group now account for 16% of all cancer incidence. Although few data currently exist regarding the molecular mechanisms for second primary cancers and other late outcomes after cancer treatment, the careful measurement and documentation of potentially carcinogenic treatments (chemotherapy and radiotherapy) provide a unique platform for in vivo research on gene-environment interactions in human carcinogenesis. We review research priorities identified during a National Cancer Institute (NCI)-sponsored workshop entitled "Cancer Survivorship--Genetic Susceptibility and Second Primary Cancers." These priorities include 1) development of a national research infrastructure for studies of cancer survivorship; 2) creation of a coordinated system for biospecimen collection; 3) development of new technology, bioinformatics, and biomarkers; 4) design of new epidemiologic methods; and 5) development of evidence-based clinical practice guidelines. Many of the infrastructure resources and design strategies that would facilitate research in this area also provide a foundation for the study of other important nonneoplastic late effects of treatment and psychosocial concerns among cancer survivors. These research areas warrant high priority to promote NCI's goal of eliminating pain and suffering related to cancer.     

Biomarkers in Lung Cancer Screening: Achievements,Promises, and Challenges

The present review is an update of the research and development efforts regarding the use of molecular biomarkers in the lung cancer screening setting. The two main unmet clinical needs, namely, the refinement of risk to improve the selection of individuals undergoing screening and the characterization of undetermined nodules found during the computed tomography–based screening process are the object of the biomarkers described in the present review. We first propose some principles to optimize lung cancer biomarker discovery projects. Then, we summarize the discovery and developmental status of currently promising molecular candidates, such as autoantibodies, complement fragments, microRNAs, circulating tumor DNA, DNA methylation, blood protein profiling, or RNA airway or nasal signatures. We also mention other emerging biomarkers or new technologies to follow, such as exhaled breath biomarkers, metabolomics, sputum cell imaging, genetic predisposition studies, and the integration of next-generation sequencing into study of circulating DNA. We also underline the importance of integrating different molecular technologies together with imaging, radiomics, and artificial intelligence. We list a number of completed, ongoing, or planned trials to show the clinical utility of molecular biomarkers. Finally, we comment on future research challenges in the field of biomarkers in the context of lung cancer screening and propose a design of a trial to test the clinical utility of one or several candidate biomarkers.     

Development of molecular biomarkers in individualized treatment of colorectal cancer

Colorectal cancer is a leading cause of cancer mortality despite recent expansion of treatment options in metastatic colorectal cancer (mCRC). Our knowledge about key signaling pathways in colorectal tumors has contributed to the identification of specific molecular markers of response to targeted agents. In this review we discuss well-established and potential predictive biomarkers of benefit with epidermal growth factor receptor (EGFR) inhibitors. Data derived from multiple phase III trials have indicated that KRAS mutations can be considered a highly specific negative biomarker of response to anti-EGFR monoclonal antibodies. Other molecular aberrations in pathways downstream of EGFR such as BRAF, NRAS, and PIK3CA mutations, and PTEN loss are also reviewed. Moreover biomarkers of efficacy to classic chemotherapeutic agents as well as recent advances regarding high-throughput technologies and circulating tumor cells are also considered. Personalized cancer medicine in the mCRC scenario seems to be near reality, but validation of many biomarkers in prospective clinical trials is urgently warranted.     

Assessment of 54 biomarkers for biopsy-detectable prostate cancer.

OBJECTIVE: We analyzed the association of 54 biomarkers from seven classes including adipokines, immune response metalloproteinases, adhesion molecules, and growth factors with prostate cancer risk adjusting for the Prostate Cancer Prevention Trial (PCPT) risk score. METHODS: A total of 123 incident prostate cancer cases and 127 age-matched controls were selected from subjects in the San Antonio Center for Biomarkers of Risk of Prostate Cancer cohort study. Prediagnostic serum concentrations were measured in the sample collected at baseline using LabMAP technology. The odds ratios (OR) of prostate cancer risk associated with serum concentrations of 54 markers were estimated using univariate conditional logistic regression before and after adjustment for the PCPT risk score. Two-way hierarchical unsupervised clustering techniques were used to evaluate whether the 54-marker panel distinguished cases from controls. RESULTS: Vascular endothelial growth factor, resistin, interleukin 1Ra (IL-1Ra), granulocyte colony-stimulating factor, matrix metalloproteinase-3, plasminogen activator inhibitor, and kallikrein-8 were statistically significantly (P < 0.05) underexpressed in prostate cancer cases, and alpha-fetoprotein was statistically significantly overexpressed in prostate cancer cases, but all had area underneath the receiver-operating characteristic curve <60%; none were statistically significant adjusting for multiple comparisons (P < 0.0008) or after adjustment for the PCPT risk score. Statistical clustering of patients by the marker panel did not distinguish a separate group of cases from controls. CONCLUSIONS: This age-matched case-control study did not support findings of increased diagnostic potential from a 54-marker panel when compared with the conventional risk factors incorporated in the PCPT risk calculator. Future discovery of new biomarkers should always be tested and compared against conventional risk factors before applying them in clinical practice.     

Biomarker Clinical Trials in Lung Cancer: Design,Logistics, Challenges,and Practical Considerations

Treatment for lung cancer has evolved in the past 3 decades starting with platinum-based chemotherapy as the standard of care, regardless of histology, in the early 1990s to the current age of biomarker-driven therapy. Consequently, clinical trials in lung cancer have evolved in response to this new shift of paradigm, leading to novel approaches that simultaneously shorten the development process and allow evaluation of multiple patient cohorts. Herein, we provide an overview of the landscape of lung cancer clinical trials in the era of targeted therapies, precision medicine, and biomarkers. Specific trials are given as examples to illustrate the design paradigms. The paper is organized by drug development phases starting with early-phase biomarker discovery to proof-of-concept trials to definitive trials. We also present some thoughts on future directions.     

Developing imaging strategies for castration resistant prostate cancer

Recent advances in the understanding of castrate-resistant prostate cancer (CRPC) have lead to a growing number of experimental therapies, many of which are directed against the androgen-receptor (AR) signaling axis. These advances generate the need for reliable molecular imaging biomarkers to non-invasively determine efficacy, and to better guide treatment selection of these promising AR-targeted drugs. Methods. We draw on our own experience, supplemented by review of the current literature, to discuss the systematic development of imaging biomarkers for use in the context of CRPC, with a focus on bone scintigraphy, F-18 fluorodeoxyglucose (FDG)-positron emission tomography (PET) and PET imaging of the AR signaling axis. Results. The roadmap to biomarker development mandates rigorous standardization and analytic validation of an assay before it can be qualified successfully for use in an appropriate clinical context. The Prostate Cancer Working Group 2 (PCWG2) criteria for "radiographic" progression by bone scintigraphy serve as a paradigm of this process. Implemented by the Prostate Cancer Clinical Trials Consortium (PCCTC), these consensus criteria may ultimately enable the co-development of more potent and versatile molecular imaging biomarkers. Purported to be superior to single-photon bone scanning, the added value of Na(18)F-PET for imaging of bone metastases is still uncertain. FDG-PET already plays an integral role in the management of many diseases, but requires further evaluation before being qualified in the context of CRPC. PET tracers that probe the AR signaling axis, such as (18)F-FDHT and (89)Zr-591, are now under development as pharmacodynamic markers, and as markers of efficacy, in tandem with FDG-PET. Semi-automated analysis programs for facilitating PET interpretation may serve as a valuable tool to help navigate the biomarker roadmap. Conclusions. Molecular imaging strategies, particularly those that probe the AR signaling axis, have the potential to accelerate drug development in CRPC. The development and use of analytically valid imaging biomarkers will increase the likelihood of clinical qualification, and ultimately lead to improved patient outcomes.     

SELDI-TOF MS serum protein profiles in breast cancer: assessment of robustness and validity

There is an urgent need for new serum markers that can be applied in e.g. the early detection of breast cancer. Following detection of new, potential biomarkers, such as those reported by Vlahou et al. (Clin Breast Cancer 2003;4:230-239) and Laronga et al. (Dis Markers 2003;19:229-238), assessment of both their robustness and validity is essential to confirm their clinical applicability. We therefore aimed to determine robustness and validity of biomarkers reported by the authors mentioned, by analysis of an independent sample set (breast cancer: n=47, normal women: n=45) in our laboratory, according to the methods described by both authors. Although all markers for the differentiation between breast cancer patients and normal women, discovered in the study of Vlahou et al., were recovered in our validation data set, none had sufficient performance to be applied as a classifier. The markers discovered by Laronga et al. in the differentiation between lymph node positive and -negative breast cancer patients were in part recovered from our validation data set, but were also not applicable as a classifier. In conclusion, although (part of) the proteins discovered and designated as markers by either author could be detected, their validity as biomarkers could not be confirmed by the current study. This finding stresses that, when reporting on a potential biomarker, confirmation of both robustness and validity is essential in obtaining its true clinical applicability.