Interlaboratory performance of a microarray-based gene expression test to determine tissue of origin in poorly differentiated and undifferentiated cancers

From patterns to patients: Advances in clinical machine learning for cancer diagnosis, prognosis, and treatment

Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict patient outcomes, and inform treatment planning. Here, we review recent applications of ML across the clinical oncology workflow. We review how these techniques are applied to medical imaging and to molecular data obtained from liquid and solid tumor biopsies for cancer diagnosis, prognosis, and treatment design. We discuss key considerations in developing ML for the distinct challenges posed by imaging and molecular data. Finally, we examine ML models approved for cancer-related patient usage by regulatory agencies and discuss approaches to improve the clinical usefulness of ML.

Cancer of Unknown Primary: Challenges and Progress in Clinical Management

Simple Summary

Patients with cancer of unknown primary site suffer the burden of an uncertain disease, which is characterized by the impossibility to identify the tissue where the tumor has originated. The identification of the primary site of a tumor is of great importance for the patient to have access to site-specific treatments and be enrolled in clinical trials. Therefore, patients with cancer of unknown primary have reduced therapeutic opportunities and poor prognosis. Advancements have been made in the molecular characterization of this tumor, which could be used to infer the tumor site-of-origin and thus broaden the diagnostic outcome. Moreover, we describe here the novel therapeutic opportunities that are based on the genetic and immunophenotypic characterization of the tumor, and thus independent from the tumor type, which could provide most benefit to patients with cancer of unknown primary.

Abstract

Distant metastases are the main cause of cancer-related deaths in patients with advanced tumors. A standard diagnostic workup usually contains the identification of the tissue-of-origin of metastatic tumors, although under certain circumstances, it remains elusive. This disease setting is defined as cancer of unknown primary (CUP). Accounting for approximately 3–5% of all cancer diagnoses, CUPs are characterized by an aggressive clinical behavior and represent a real therapeutic challenge. The lack of determination of a tissue of origin precludes CUP patients from specific evidence-based therapeutic options or access to clinical trial, which significantly impacts their life expectancy. In the era of precision medicine, it is essential to characterize CUP molecular features, including the expression profile of non-coding RNAs, to improve our understanding of CUP biology and identify novel therapeutic strategies. This review article sheds light on this enigmatic disease by summarizing the current knowledge on CUPs focusing on recent discoveries and emerging diagnostic strategies.

Cancer of unknown primary

Cancers of unknown primary (CUPs) are histologically confirmed, metastatic malignancies with a primary tumor site that is unidentifiable on the basis of standard evaluation and imaging studies. CUP comprises 2-5% of all diagnosed cancers worldwide and is characterized by early and aggressive metastasis. Current standard evaluation of CUP requires histopathologic evaluation and identification of favorable risk subtypes that can be more definitively treated or have superior outcomes. Current standard treatment of the unfavorable risk subtype requires assessment of prognosis and consideration of empiric chemotherapy. The use of molecular tissue of origin tests to identify the likely primary tumor site has been extensively studied, and here we review the rationale and the evidence for and against the use of such tests in the assessment of CUPs. The expanding use of next generation sequencing in advanced cancers offers the potential to identify a subgroup of patients who have actionable genomic aberrations and may allow for further personalization of therapy.

The Journal of Molecular Diagnostics: 20 Years Defining Professional Practice

This editorial highlights 20 years of JMD defining professional practice.

[2018 Consensus statement by the Spanish Society of Pathology and the Spanish Society of Medical Oncology on the diagnosis and treatment of cancer of unknown primary]

Cancer of unknown primary is defined as a heterogeneous group of tumours that present with metastasis, and in which attempts to identify the original site have failed. They differ from other primary tumours in their biological features and how they spread, which means they can be considered a separate entity. There are several hypotheses regarding their origin, but the most plausible explanation for their aggressiveness and chemoresistance seems to involve chromosomal instability. Depending on the type of study done, cancer of unknown primary can account for 2-9% of all cancer patients, mostly 60-75 years old. This article reviews the main clinical, pathological and molecular studies conducted to analyse and determine the origin of cancer of unknown primary. The main strategies for patient management and treatment, by both clinicians and pathologists, are also addressed.

The Potential Clinical and Economic Value of Primary Tumour Identification in Metastatic Cancer of Unknown Primary Tumour: A Population-Based Retrospective Matched Cohort Study

PURPOSE

Several genomic tests have recently been developed to identify the primary tumour in cancer of unknown primary tumour (CUP). However, the value of identifying the primary tumour in clinical practice for CUP patients remains questionable and difficult to prove in randomized trials.

OBJECTIVE

We aimed to assess the clinical and economic value of primary tumour identification in CUP using a retrospective matched cohort study.

METHODS

We used the Manitoba Cancer Registry to identify all patients initially diagnosed with metastatic cancer between 2002 and 2011. We defined patients as having CUP if their primary tumour was found 6 months or more after initial diagnosis or never found during the course of disease. Otherwise, we considered patients to have metastatic cancer from a known primary tumour (CKP). We linked all patients with Manitoba Health databases to estimate their direct healthcare costs using a phase-of-care approach. We used the propensity score matching technique to match each CUP patient with a CKP patient on clinicopathologic characteristics. We compared treatment patterns, overall survival (OS) and phase-specific healthcare costs between the two patient groups and assessed association with OS using Cox regression adjustment.

RESULTS

Of 5839 patients diagnosed with metastatic cancer, 395 had CUP (6.8%); 1:1 matching created a matched group of 395 CKP patients. CUP patients were less likely to receive surgery, radiation, hormonal and targeted therapy and more likely to receive cytotoxic empiric chemotherapeutic agents. Having CUP was associated with reduced OS (hazard ratio [HR] 1.31; 95% confidence interval 1.1-1.58), but this lost statistical significance with adjustment for treatment differences. CUP patients had a significant increase in the mean net cost of initial diagnostic workup before diagnosis and a significant reduction in the mean net cost of continuing cancer care.

CONCLUSION

Identifying the primary tumour in CUP patients might enable the use of more effective therapies, improve OS and allow more efficient allocation of healthcare resources.

2018 consensus statement by the Spanish Society of Pathology and the Spanish Society of Medical Oncology on the diagnosis and treatment of cancer of unknown primary

Cancer of unknown primary (CUP) is defined as a heterogeneous group of tumours that present with metastasis, and in which attempts to identify the original site have failed. They differ from other primary tumours in their biological features and how they spread, which means that they can be considered a separate entity. There are several hypotheses regarding their origin, but the most plausible explanation for their aggressiveness and chemoresistance seems to involve chromosomal instability. Depending on the type of study done, CUP can account for 2–9% of all cancer patients, mostly 60–75 years old. This article reviews the main clinical, pathological, and molecular studies conducted to analyse and determine the origin of CUP. The main strategies for patient management and treatment, by both clinicians and pathologists, are also addressed.

Pan-cancer transcriptome analysis reveals a gene expression signature for the identification of tumor tissue origin

Carcinoma of unknown primary, wherein metastatic disease is present without an identifiable primary site, accounts for ~3-5% of all cancer diagnoses. Despite the development of multiple diagnostic workups, the success rate of primary site identification remains low. Determining the origin of tumor tissue is, thus, an important clinical application of molecular diagnostics. Previous studies have paved the way for gene expression-based tumor type classification. In this study, we have established a comprehensive database integrating microarray- and sequencing-based gene expression profiles of 16 674 tumor samples covering 22 common human tumor types. From this pan-cancer transcriptome database, we identified a 154-gene expression signature that discriminated the origin of tumor tissue with an overall leave-one-out cross-validation accuracy of 96.5%. The 154-gene expression signature was first validated on an independent test set consisting of 9626 primary tumors, of which 97.1% of cases were correctly classified. Furthermore, we tested the signature on a spectrum of diagnostically challenging tumors. An overall accuracy of 92% was achieved on the 1248 tumor specimens that were poorly differentiated, undifferentiated or from metastatic tumors. Thus, we have identified a 154-gene expression signature that can accurately classify a broad spectrum of tumor types. This gene panel may hold a promise to be a useful additional tool for the determination of the tumor origin.

Cost-effectiveness of using a gene expression profiling test to aid in identifying the primary tumour in patients with cancer of unknown primary

We aimed to investigate the cost-effectiveness of a 2000-gene-expression profiling (GEP) test to help identify the primary tumor site when clinicopathological diagnostic evaluation was inconclusive in patients with cancer of unknown primary (CUP). We built a decision-analytic-model to project the lifetime clinical and economic consequences of different clinical management strategies for CUP. The model was parameterized using follow-up data from the Manitoba Cancer Registry, cost data from Manitoba Health administrative databases and secondary sources. The 2000-GEP-based strategy compared to current clinical practice resulted in an incremental cost-effectiveness ratio (ICER) of $44,151 per quality-adjusted life years (QALY) gained. The total annual-budget impact was $36.2 million per year. A value-of-information analysis revealed that the expected value of perfect information about the test's clinical impact was $4.2 million per year. The 2000-GEP test should be considered for adoption in CUP. Field evaluations of the test are associated with a large societal benefit.

Gene expression profiling identifies responsive patients with cancer of unknown primary treated with carboplatin, paclitaxel, and everolimus: NCCTG N0871 (alliance)

BACKGROUND

Carboplatin (C) and paclitaxel (P) are standard treatments for carcinoma of unknown primary (CUP). Everolimus, an mTOR inhibitor, exhibits activity in diverse cancer types. We did a phase II trial combining everolimus with CP for CUP. We also evaluated whether a gene expression profiling (GEP) test that predicts tissue of origin (TOO) could identify responsive patients.

PATIENTS AND METHODS

A tumor biopsy was required for central confirmation of CUP and GEP. Patients with metastatic, untreated CUP received everolimus (30 mg weekly) with P (200 mg/m(2)) and C (area under the curve 6) every 3 weeks. The primary end point was response rate (RR), with 22% needed for success. The GEP test categorized patients into two groups: those having a TOO where CP is versus is not considered standard therapy.

RESULTS

Of 45 assessable patients, the RR was 36% (95% confidence interval 22% to 51%), which met criteria for success. Grade ≥3 toxicities were predominantly hematologic (80%). Adequate tissue for GEP was available in 38 patients and predicted 10 different TOOs. Patients with a TOO where platinum/taxane is a standard (n = 19) tended to have higher RR (53% versus 26%) and significantly longer PFS (6.4 versus 3.5 months) and OS (17.8 versus 8.3 months, P = 0.005), compared with patients (n = 19) with a TOO where platinum/taxane is not standard.

CONCLUSIONS

Everolimus combined with CP demonstrated promising antitumor activity and an acceptable side-effect profile. A tumor biomarker identifying TOO may be useful to select CUP patients for specific antitumor regimens.

CLINICALTRIALSGOV

NCT00936702.

Gene expression profiling in patients with carcinoma of unknown primary site: from translational research to standard of care

Carcinoma of unknown primary site (CUP) is diagnosed in approximately 3 % of patients with advanced cancer, and most patients have traditionally been treated with empiric chemotherapy. As treatments improve and become more specific for individual solid tumor types, therapy with a single empiric combination chemotherapy regimen becomes increasingly inadequate. Gene expression profiling (GEP) is a new diagnostic method that allows prediction of the site of tumor origin based on gene expression patterns retained from the normal tissues of origin. In blinded studies in tumors of known origin, GEP assays correctly identified the site of origin in 85 % of cases and compares favorably with immunohistochemical (IHC) staining. In patients with CUP, GEP is able to predict a site of origin in >95 % of patients versus 35-55 % for IHC staining. Although confirmation of the accuracy of these predictions is difficult, the diagnoses made by IHC staining and GEP are identical in 77 % of cases when IHC staining predicts a single primary site. GEP diagnoses appear to be most useful when IHC staining is inconclusive. Site-specific treatment of CUP patients based on GEP and/or IHC predictions appears to improve overall outcomes; patients predicted to have treatment-sensitive tumor types derived the most benefit. GEP adds to the diagnostic evaluation of patients with CUP and should be included when IHC staining is unable to predict a single site of origin. Site-specific treatment, based on tissue of origin diagnosis, should replace empiric chemotherapy in patients with CUP.

Proteomic Workflows for Biomarker Identification Using Mass Spectrometry - Technical and Statistical Considerations during Initial Discovery

Identification of biomarkers capable of differentiating between pathophysiological states of an individual is a laudable goal in the field of proteomics. Protein biomarker discovery generally employs high throughput sample characterization by mass spectrometry (MS), being capable of identifying and quantifying thousands of proteins per sample. While MS-based technologies have rapidly matured, the identification of truly informative biomarkers remains elusive, with only a handful of clinically applicable tests stemming from proteomic workflows. This underlying lack of progress is attributed in large part to erroneous experimental design, biased sample handling, as well as improper statistical analysis of the resulting data. This review will discuss in detail the importance of experimental design and provide some insight into the overall workflow required for biomarker identification experiments. Proper balance between the degree of biological vs. technical replication is required for confident biomarker identification.

Re-overcoming barriers in translating biomarkers to clinical practice

IMPORTANCE OF THE FIELD

Recently, there has been growing evidence for the concept of personalized medicine as the implementation of genomic and molecular information in the delivery of healthcare. In parallel, the identification of biomarkers has become of enormous significance as a prerequisite for individualized intervention regimens.

AREAS COVERED IN THIS REVIEW

Biomarkers are developed to improve prevention, diagnosis or therapeutic outcome of a given disease. Although each application reveals distinct developmental strategies, evidence-based approval of new biomarkers is important for the success of new drugs, diagnostic tests or recommendations in preventive medicine. Current hurdles to bringing biomarkers into clinical practice are reviewed, thereby focusing on adequate approaches to overcome these limitations in the future.

WHAT THE READER WILL GAIN

The reader will get an introduction to strategies resolving actual barriers in clinical biomarker development.

TAKE HOME MESSAGE

The identification of evidence-based biomarkers is crucial for the success of individualized therapeutic approaches. Developmental strategies have to be adapted to clinical need, thereby focusing on biomarker validation in clinical settings as well as on the establishment of standardized biomarker test systems for routine application. Consortia have been established bringing together representatives of government, academia and industry to improve future biomarker development.

Discovery and validation of gene classifiers for endocrine-disrupting chemicals in zebrafish (danio rerio)

BACKGROUND

Development and application of transcriptomics-based gene classifiers for ecotoxicological applications lag far behind those of biomedical sciences. Many such classifiers discovered thus far lack vigorous statistical and experimental validations. A combination of genetic algorithm/support vector machines and genetic algorithm/K nearest neighbors was used in this study to search for classifiers of endocrine-disrupting chemicals (EDCs) in zebrafish. Searches were conducted on both tissue-specific and tissue-combined datasets, either across the entire transcriptome or within individual transcription factor (TF) networks previously linked to EDC effects. Candidate classifiers were evaluated by gene set enrichment analysis (GSEA) on both the original training data and a dedicated validation dataset.

RESULTS

Multi-tissue dataset yielded no classifiers. Among the 19 chemical-tissue conditions evaluated, the transcriptome-wide searches yielded classifiers for six of them, each having approximately 20 to 30 gene features unique to a condition. Searches within individual TF networks produced classifiers for 15 chemical-tissue conditions, each containing 100 or fewer top-ranked gene features pooled from those of multiple TF networks and also unique to each condition. For the training dataset, 10 out of 11 classifiers successfully identified the gene expression profiles (GEPs) of their targeted chemical-tissue conditions by GSEA. For the validation dataset, classifiers for prochloraz-ovary and flutamide-ovary also correctly identified the GEPs of corresponding conditions while no classifier could predict the GEP from prochloraz-brain.

CONCLUSIONS

The discrepancies in the performance of these classifiers were attributed in part to varying data complexity among the conditions, as measured to some degree by Fisher's discriminant ratio statistic. This variation in data complexity could likely be compensated by adjusting sample size for individual chemical-tissue conditions, thus suggesting a need for a preliminary survey of transcriptomic responses before launching a full scale classifier discovery effort. Classifier discovery based on individual TF networks could yield more mechanistically-oriented biomarkers. GSEA proved to be a flexible and effective tool for application of gene classifiers but a similar and more refined algorithm, connectivity mapping, should also be explored. The distribution characteristics of classifiers across tissues, chemicals, and TF networks suggested a differential biological impact among the EDCs on zebrafish transcriptome involving some basic cellular functions.

Gene expression-based diagnostics for molecular cancer classification of difficult to diagnose tumors

INTRODUCTION

Standardized methods for accurate tumor classification are of critical importance for cancer diagnosis and treatment, particularly in diagnostically-challenging cases where site-directed therapies are an option. Molecular diagnostics for tumor classification, subclassification and site of origin determination based on advances in gene expression profiling have translated into clinical practice as complementary approaches to clinicopathological evaluations.

AREAS COVERED

In this review, the foundational science of gene expression-based cancer classification, technical and clinical considerations for clinical translation, and an overview of molecular signatures of tumor classification that are available for clinical use will be discussed. Proposed approaches will also be described for further integration of molecular tests for cancer classification into the diagnostic paradigm using a tissue-based strategy as a key component to direct evaluation.

EXPERT OPINION

Increasing evidence of improved patient outcomes with the application of site and molecularly-targeted cancer therapy through use of molecular tools highlights the growing potential for these gene expression-based diagnostics to positively impact patient management. Looking forward, the availability of adequate tissue will be a significant issue and limiting factor as cancer diagnosis progresses; when the tumor specimen is limited, use of molecular classification may be a reasonable early step in the evaluation, particularly if the tumor is poorly-differentiated and has atypical features.

92-Gene molecular profiling in identification of cancer origin: a retrospective study in Chinese population and performance within different subgroups

Background

After cancer diagnosis, therapy for the patient is largely dependent on the tumor origin, especially when a metastatic tumor is being treated. However, cases such as untypical metastasis, poorly differentiated tumors or even a limited number of tumor cells may lead to challenges in identifying the origin. Moreover, approximately 3% to 5% of total solid tumor patients will not have to have their tumor origin identified in their lifetime. The THEROS CancerTYPE ID® is designed for identifying the tumor origin with an objective, rapid and standardized procedure.

Methodology and Principal Findings

This is a blinded retrospective study to evaluate performance of the THEROS CancerTYPE ID® in a Chinese population. In total, 184 formalin-fixed paraffin-embedded (FFPE) samples of 23 tumor origins were collected from the tissue bank of Fudan University Shanghai Cancer Center (FDUSCC). A standard tumor cell enrichment process was used, and the prediction results were compared with reference diagnosis, which was confirmed by two experienced pathologists at FDUSCC. All of the 184 samples were successfully analyzed, and no tumor specimens were excluded because of sample quality issues. In total, 151 samples were correctly predicted. The agreement rate was 82.1%. A Pearson Chi-square test shows that there is no difference between this study and the previous evaluation test performed by bioTheranostics Inc. No statistically significant decrease was observed in either the metastasis group or tumors with high grades.

Conclusions

A comparable result with previous work was obtained. Specifically, specimens with a high probability score (>0.85) have a high chance (agreement rate = 95%) of being correctly predicted. No performance difference was observed between primary and metastatic specimens, and no difference was observed among three tumor grades. The use of laser capture micro-dissection (LCM) makes the THEROS CancerTYPE ID® accessible to almost all of the cancer patients with different tumor statuses.

Comprehensive review of the diagnosis and treatment of biliary tract cancer 2012. Part I: diagnosis-clinical staging and pathology

Biliary tract cancers (gallbladder cancer, intra- and extra-hepatic cholangiocarcinoma, and selected periampullary cancers) accounted for 12,760 new cases of cancer in the USA in 2010. These tumors have a dismal prognosis with most patients presenting with advanced disease. Early, accurate diagnosis is essential, both for potential cure where possible and for optimal palliative therapy in all others. This review examines the currently available and emerging technologies for diagnosis and treatment of this group of diseases.

Occult breast cancer presenting as metastatic adenocarcinoma of unknown primary: clinical presentation, immunohistochemistry, and molecular analysis

We report a rare presentation of a 66-year-old female with diffuse metastatic adenocarcinoma of unknown primary involving liver, lymphatic system and bone metastases. The neoplastic cells were positive for CK7 and OC125, while negative for CK20, thyroid transcription factor 1, CDX2, BRST-2, chromogranin, synaptophysin, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2/neu). Fluorescence in situ hybridization showed no amplification of the HER2/neu gene. Molecular profiling reported a breast cancer origin with a very high confidence score of 98%. The absence of immunohistochemistry staining for ER, PR, and HER2/neu further classified her cancer as triple-negative breast cancer. Additional studies revealed high expression levels of topoisomerase (Topo) I, androgen receptor, and ribonucleoside-diphosphate reductase large subunit; the results were negative for thymidylate synthase, Topo II-a and O6-methylguanine-DNA methyltransferase. The patient was initially treated with a combination regimen of cisplatin and etoposide, and she experienced a rapid resolution of cancer-related symptoms. Unfortunately, her therapy was complicated by a cerebrovascular accident (CVA), which was thought to be related to cisplatin and high serum mucin. After recovery from the CVA, the patient was successfully treated with second-line chemotherapy based on her tumor expression profile. We highlight the role of molecular profiling in the diagnosis and management of this patient and the implication of personalized chemotherapy in this challenging disease.

Invasive markers identified by gene expression profiling in pancreatic cancer

BACKGROUND

Molecular profiling has proven utility as a diagnostic and predictive tool in clinical oncology. However, a clinically relevant gene expression profile in pancreatic cancer remains elusive.

METHODS

Primary and metastatic pancreatic cancer cell lines (BxPC-3 and AsPC-1), were stimulated with phorbol-12-myristate 13-acetate (PMA), a known inducer of cell invasion. Affymetrix gene expression microarray analysis was performed, comparing gene expression to unstimulated controls. Differential expression was identified using ArrayAssist, and confirmed using quantitative real-time PCR. Bioinformatic analysis was performed using Pathway Studio and GOstat. The derived gene expression was further validated in fresh frozen pancreatic tumour samples. The ability of the derived 3 gene expression markersto differentiate between pancreatic adenocarcinoma (PDAC) and other neoplasms, and its association with clinicopathological variables was examined.

RESULTS

PMA-induced significant changes in cell line gene expression, from which distinctive 3 potential invasive markers were derived. Expression of these genes, uPA, MMP-1 and IL1-R1 was confirmed in human pancreatic tumours, and was found to differentiate PDAC from other pancreatic neoplasms. The expression of IL1-R1 in PDAC is a novel finding. We found that the expression of MMP-1 was associated with high-grade PDAC (p = 0.035, Wilcoxon rank sum).

CONCLUSION

We have identified three potential invasive markers, uPA, MMP-1 and IL1-R1, whose gene expression may differentiate PDAC from other pancreatic neoplasms, and potentially reflect a more invasive phenotype.

Clinical verification of the performance of the pathwork tissue of origin test: utility and limitations

Gene expression-based assays have been introduced into the clinical arena to assist in the diagnosis of poorly differentiated or undifferentiated tumors. The US Food and Drug Administration has cleared the microarray-based Pathwork Tissue of Origin (TOO) Test (Pathwork Diagnostics, Sunnyvale, CA) for the molecular characterization of such challenging specimens. We aimed at verifying the analytic and clinical performance of this test on 43 poorly differentiated and undifferentiated tumor samples, including 6 off-panel cases and 7 cancers of unknown primary (CUP). Our results showed 97% (95% confidence interval, 80.4%-99.8%) agreement between the Pathwork TOO Test result and the complete diagnosis, which included clinical correlations and immunohistochemical staining, after the original diagnosis. We concluded that for off-panel and CUP samples, the tissue type and the cell type may be confounded by the Pathwork TOO Test and that careful clinicopathologic assessment is needed when interpreting results from this helpful ancillary tool for pathologists.

Classification of unknown primary tumors with a data-driven method based on a large microarray reference database

We present a new method to analyze cancer of unknown primary origin (CUP) samples. Our method achieves good results with classification accuracy (88% leave-one-out cross validation for primary tumors from 56 categories, 78% for CUP samples), and can also be used to study CUP samples on a gene-by-gene basis. It is not tied to any a priori defined gene set as many previous methods, and is adaptable to emerging new information.

Human asthma phenotypes: from the clinic, to cytokines, and back again

A large body of experimental evidence supports the hypothesis that T-helper 2 (Th2) cytokines orchestrate allergic airway inflammation in animal models. However, human asthma is heterogeneous with respect to clinical features, cellular sources of inflammation, and response to common therapies. This disease heterogeneity has been investigated using sputum cytology as well as unbiased clustering approaches using cellular and clinical data. Important differences in cytokine-driven inflammation may underlie this heterogeneity, and studies in human subjects with asthma have begun to elucidate these molecular differences. This molecular heterogeneity may be assessed by existing biomarkers (induced sputum evaluation or exhaled nitric oxide testing) or may require novel biomarkers. Effective testing and application of emerging therapies that target Th2 cytokines will depend on accurate and easily obtained biomarkers of this molecular heterogeneity in asthma. Furthermore, whether other non-Th2 cytokine pathways underlie airway inflammation in specific subsets of patients with asthma is an unresolved question and an important goal of future research using both mouse models and human studies.

Evaluation of a gene expression microarray-based assay to determine tissue type of origin on a diverse set of 49 malignancies

The Tissue of Origin Frozen (TOO-FRZ) assay from Pathwork Diagnostics has been cleared by the Food and Drug Administration as a diagnostic study for malignancies of unknown primary. The goal of this study was to evaluate the performance of TOO-FRZ on a diverse collection of malignancies. We collected a diverse set of 49 malignancies. We classified each case into 1 of 4 groups: common morphology from a tissue type included in the TOO-FRZ assay (n=29), uncommon morphology from a tissue type included in the TOO-FRZ assay (n=10), tumor from a tissue type not included in the TOO-FRZ assay (n=3), and malignancies of unknown primary (n=7). We found strong diagnostic performance for common morphologies from tissue types on the TOO-FRZ [overall accuracy=26 of 29 (90%, 95% CI, 73% to 97%)], with perfect performance in all tissue types except gastric (0 of 2) and pancreatic (1 of 2) tissues. There was a significant decline in performance for uncommon morphologies from tissue types included in the TOO-FRZ assay [6 of 10 (60%) cases with an indeterminate result, 1 of 10 (10%) cases with an incorrect prediction, and 3 of 10 (30%) with a correct prediction] and for tumors from tissue types not included in the assay (incorrect prediction in 2 of 3 cases). For the 7 malignancies of unknown primary in our study set, the TOO-FRZ provided a likely clinically useful result in only 2 of 7 cases. These results provide an insight into the strengths and limitations of this molecular assay for the surgical pathologist, and our findings suggest future directions for research in this area.

Clinical utility of microarrays: current status, existing challenges and future outlook

Microarray-based clinical tests have become powerful tools in the diagnosis and treatment of diseases. In contrast to traditional DNA-based tests that largely focus on single genes associated with rare conditions, microarray-based tests are ideal for the study of diseases with underlying complex genetic causes. Several microarray based tests have been translated into clinical practice such as MammaPrint and AmpliChip CYP450. Additional cancer-related microarray-based tests are either in the process of FDA review or under active development, including Tissue of Tumor Origin and AmpliChip p53. All diagnostic microarray testing is ordered by physicians and tested by a Clinical Laboratories Improvement Amendment-certified (CLIA) reference laboratory. Recently, companies offering consumer based microarray testing have emerged. Individuals can order tests online and service providers deliver the results directly to the clients via a password-protected secure website. Navigenics, 23andMe and deCODE Genetics represent pioneering companies in this field. Although the progress of these microarray-based tests is extremely encouraging with the potential to revolutionize the recognition and treatment of common diseases, these tests are still in their infancy and face technical, clinical and marketing challenges. In this article, we review microarray-based tests which are currently approved or under review by the FDA, as well as the consumer-based testing. We also provide a summary of the challenges and strategic solutions in the development and clinical use of the microarray-based tests. Finally, we present a brief outlook for the future of microarray-based clinical applications.

Localization of sporadic neuroendocrine tumors by gene expression analysis of their metastases

A characteristic of human gastroenteropancreatic neuroendocrine tumors (GEP-NET) is a minute unobtrusive primary tumor which often cannot be detected by common physical examinations. It therefore remains unidentified until the tumor has spread and space-occupying metastases cause clinical symptoms leading to diagnosis. Cases in which the primary cannot be located are referred to as NET with CUP-syndrome (cancer of unknown primary syndrome). With the help of array-CGH (comparative genomic hybridization, Agilent 105K) and gene expression analysis (Agilent 44K), microdissected primaries and their metastases were compared to identify up- and down-regulated genes which can be used as a marker for tumor progression. In a next analysis step, a hierarchical clustering of 41.078 genes revealed three genes [C-type lectin domain family 13 member A (CD302), peptidylprolyl isomerase containing WD40 repeat (PPWD1) and abhydrolase domain containing 14B (ABHD14B)] which expression levels can categorize the metastases into three groups depending on the localization of their primary. Because cancer therapy is dependent on the localization of the primary, the gene expression level of these three genes are promising markers to unravel the CUP syndrome in NET.

Unique patterns of metastases in common and rare types of malignancy

This review on the unique patterns of metastases by common and rare types of cancer addresses regional lymphatic metastases but also demonstrates general principles by consideration of vital organ metastases. These general features of successfully treated metastases are relationships to basic biological behavior as illustrated by disease-free interval, organ-specific behavior, oligo-metastatic presentation, genetic control of the metastatic pattern, careful selection of patients for surgical resection, and the necessity of complete resection of the few patients eligible for long-term survival after resection of vital organ metastasis. Lymph node metastases, while illustrating these general features, are not related to overall survival because lymph node metastases themselves do not destroy a vital organ function, and therefore have no causal relationship to overall survival. When a cancer cell spreads to a regional lymph node, does it also simultaneously spread to the systemic site or sites? Alternatively, does the cancer spread to the regional lymph node first and then it subsequently spreads to the distant site(s) after an incubation period of growth in the lymph node? Of course, if the cancer is in its incubation stage in the lymph node, then removal of the lymph node in the majority of cases with cancer cells may be curative. The data from the sentinel lymph node era, particularly in melanoma and breast cancer, is consistent with the spectrum theory of cancer progression to the sentinel lymph node in the majority of cases prior to distant metastasis. Perhaps, different subsets of cancer may be better defined with relevant biomarkers so that mechanisms of metastasis can be more accurately defined on a molecular and genomic level.

MicroRNA profiling for the identification of cancers with unknown primary tissue-of-origin

Cancer of unknown primary (CUP) represents a common and important clinical problem. There is evidence that most CUPs are metastases of carcinomas whose primary site cannot be recognized. Driven by the hypothesis that the knowledge of primary cancer could improve patient's prognosis, we investigated microRNA expression profiling as a tool for identifying the tissue of origin of metastases. We assessed microRNA expression from 101 formalin-fixed, paraffin-embedded (FFPE) samples from primary cancers and metastasis samples by using a microarray platform. Forty samples representing ten different cancer types were used for defining a cancer-type-specific microRNA signature, which was used for predicting primary sites of metastatic cancers. A 47-miRNA signature was identified and used to estimate tissue-of-origin probabilities for each sample. Overall, accuracy reached 100% for primary cancers and 78% for metastases in our cohort of samples. When the signature was applied to an independent published dataset of 170 samples, accuracy remained high: correct prediction was found within the first two options in 86% of the metastasis cases (first prediction was correct in 68% of cases). This signature was also applied to predict 16 CUPs. In this group, first predictions exhibited probabilities higher than 90% in most of the cases. These results establish that FFPE samples can be used to reveal the tissue of origin of metastatic cancers by using microRNA expression profiling and suggest that the approach, if applied, could provide strong indications for CUPs, whose correct diagnosis is presently undefined.

Unknown primary tumors

An unknown primary tumor (UPT) is defined by the presence of a metastatic cancer without a known primary site of origin despite a standardized diagnostic workup. Clinically, UPTs show rapid progression and early dissemination, with signs and symptoms related to the metastatic site. The molecular bases of their biology remain largely unknown, with no evidence as to whether they represent a distinct biological entity. Immunohistochemistry remain the best diagnostic tool in term of cost-effectiveness, but the time-consuming "algorithmic process" it relies on has led to the application of new molecular techniques for the identification of the primary site of UPTs. For example, several microarray or miRNA classifications of UPTs have been used, with an accuracy in the prediction of the primary site as high as 90%. It should be noted that validating a prediction of tissue origin is challenging in these patients, since most of them will never have a primary site identified. Moreover, prospective studies to determine whether selection of treatment options based on such profiling methods actually improves patient outcome are still missing. In the last few years functional imaging (i.e. FDG-PET/CT) has gained a main role in the detection of the site of origin of UPTs and is currently recommended by the European Association of Nuclear Medicine. However, despite recent refinements in the diagnostic workup, the site of origin of UPT often remains elusive. As a consequence, treatment of patients with UPT is still empirical and inadequate.

Is there a niche for DNA microarrays in molecular diagnostics?

DNA microarrays, 15 years after their appearance, have achieved presence in a number of medical settings. Several tests have been introduced and have obtained regulatory approval, mostly in the fields of bacterial identification, mutation detection and the global assessment of genome alterations, a particularly successful case being the whole-genome assay of copy-number variations. Gene-expression applications have been less successful because of technical issues (e.g., reproducibility, platform-to-platform consistency and statistical issues in data analysis) and difficulties in demonstrating the clinical utility of expression signatures. In their different applications, DNA arrays have faced competition from PCR-based assays for low and intermediate multiplicity. Now they have a new competitor, new-generation sequencing, that can provide a wealth of direct sequence information, or digital gene-expression data, at a constantly decreasing cost. In this article we evaluate the strengths and weaknesses of the DNA microarray approach to diagnostics, and highlight the fields in which it is most likely to achieve a durable presence.

Validation and reproducibility of a microarray-based gene expression test for tumor identification in formalin-fixed, paraffin-embedded specimens

Tumors whose primary site is challenging to diagnose represent a considerable proportion of new cancer cases. We present validation study results for a gene expression-based diagnostic test (the Pathwork Tissue of Origin Test) that aids in determining the tissue of origin using formalin-fixed, paraffin-embedded (FFPE) specimens. Microarray data files were generated for 462 metastatic, poorly differentiated, or undifferentiated FFPE tumor specimens, all of which had a reference diagnosis. The reference diagnoses were masked, and the microarray data files were analyzed using a 2000-gene classification model. The algorithm quantifies the similarity between RNA expression patterns of the study specimens and the 15 tissues on the test panel. Among the 462 specimens, overall agreement with the reference diagnosis was 89% (95% CI, 85% to 91%). In addition to the positive test results (ie, rule-ins), an average of 12 tissues for each specimen could be ruled out with >99% probability. The large size of this study increases confidence in the test results. A multisite reproducibility study showed 89.3% concordance between laboratories. The Tissue of Origin Test makes the benefits of microarray-based gene expression tests for tumor diagnosis available for use with the most common type of histology specimen (ie, FFPE).

Consistency of predictive signature genes and classifiers generated using different microarray platforms

Microarray-based classifiers and associated signature genes generated from various platforms are abundantly reported in the literature; however, the utility of the classifiers and signature genes in cross-platform prediction applications remains largely uncertain. As part of the MicroArray Quality Control Phase II (MAQC-II) project, we show in this study 80-90% cross-platform prediction consistency using a large toxicogenomics data set by illustrating that: (1) the signature genes of a classifier generated from one platform can be directly applied to another platform to develop a predictive classifier; (2) a classifier developed using data generated from one platform can accurately predict samples that were profiled using a different platform. The results suggest the potential utility of using published signature genes in cross-platform applications and the possible adoption of the published classifiers for a variety of applications. The study reveals an opportunity for possible translation of biomarkers identified using microarrays to clinically validated non-array gene expression assays.

Clinical presentation of carcinoma of unknown primary: 14 years of experience

A carcinoma of unknown primary (CUP) is a histologically confirmed metastatic cancer without a definitive primary site after performing a detailed medical examination. The purpose of the study was to classify unfavorable CUPs into more reliable disease entities, which reflect the clinical course. We reviewed the medical records of patients diagnosed with a CUP between January 1995 and March 2008. Patients were classified into a conventional favorable-risk group and a newly proposed unfavorable-risk group according to the clinicopathologic features. Five hundred eighty-six patients were diagnosed with CUPs. Fifty-six (9.6%) patients were classified in the conventional favorable-risk group, and 486 (82.9%) patients were classified in the unfavorable-risk group. We further classified the 486 patients into six subgroups with an unfavorable risk, while excluding 29 patients (5.0%) who were not classifiable. The overall survival of the conventional favorable-risk group was 47.0 months (95% CI, 11.1~82.9 months), which was significantly longer than that of any subgroup of the newly proposed unfavorable-risk group (P < 0.001). Patients with squamous cell carcinoma in the abdominopelvic cavity showed similar overall survival with unfavorable-risk group (P = 0.484). Women with non-papillary malignant ascites had a survival in between the favorable and unfavorable groups (P <  0.001). The newly proposed unfavorable-risk group may assist in classifying CUP patients with an unfavorable risk in a clinically more meaningful way. Squamous cell carcinoma in the abdominopelvic cavity should be considered in the unfavorable-risk group and women with non-papillary malignant ascites in an intermediate-risk group. Further studies with molecular profiling would help in classifying and treating patients with CUPs and an unfavorable risk.

The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Quantitative expression profiling in formalin-fixed paraffin-embedded samples by affymetrix microarrays

To date, few studies have systematically characterized microarray gene expression signal performance with degraded RNA from fixed (FFPE) in comparison with intact RNA from unfixed fresh-frozen (FF) specimens. RNA was extracted and isolated from paired tumor and normal samples from both FFPE and FF kidney, lung, and colon tissue specimens and microarray signal dynamics on both the raw probe and probeset level were evaluated. A contrast metric was developed to directly compare microarray signal derived from RNA extracted from matched FFPE and FF specimens. Gene-level summaries were then compared to determine the degree of overlap in expression profiles. RNA extracted from FFPE material was more degraded and fragmented than FF, resulting in a reduced dynamic range of expression signal. In addition, probe performance was not affected uniformly and declined sharply toward 5' end of genes. The most significant differences in FFPE versus FF signal were consistent across three tissue types and enriched with ribosomal genes. Our results show that archived FFPE samples can be used to profile for expression signatures and assess differential expression similar to unfixed tissue sources. This study provides guidelines for application of these methods in the discovery, validation, and clinical application of microarray expression profiling with FFPE material.

Predicting the site of origin of tumors by a gene expression signature derived from normal tissues

Multiple expression signatures for the prediction of the site of origin of metastatic cancer of unknown primary origin (CUP) have been developed. Owing to their limited coverage of tumor types and suboptimal prediction accuracy on distinct tumors, there is still room for alternative CUP gene expression signatures. Whereas in past studies, CUP classifiers were trained solely on data from tumor samples, we now use expression patterns from normal tissues for classifier training. This approach potentially avoids pitfalls related to the representation of genetically heterogeneous tumor subtypes during classifier training. Two expression data sets of normal human tissues have been reanalyzed to derive an expression signature for liver, prostate, kidney, ovarian and lung tissues. In reciprocal validation, classifiers trained on either data set achieved overall accuracies greater than 97%. Classifiers trained on combined expression data from both normal tissue data sets were able to predict the site of origin in a cohort of 652 primary tumors with approximately 90% accuracy. Prediction accuracies of primary cancer-based classifiers were in the same range, as determined by cross-validation on this cohort. For individual tumor types, normal tissue-based classifiers achieved sensitivities in the range of 64-99% and specificities in the range of 92-100%. Primary origins for 12 of 20 metastases were predicted correctly, with false predictions highlighting the need for accurate sample preparation to avoid contaminations by metastases-surrounding tissue. We conclude that gene expression patterns of normal tissues harbor phenotypic information that is retained in tumors and can be sufficient to recover the type of primary tumor from expression patterns alone.

Gene expression profiles help identify the tissue of origin for metastatic brain cancers

BACKGROUND

Metastatic brain cancers are the most common intracranial tumor and occur in about 15% of all cancer patients. In up to 10% of these patients, the primary tumor tissue remains unknown, even after a time consuming and costly workup. The Pathwork Tissue of Origin Test (Pathwork Diagnostics, Redwood City, CA, USA) is a gene expression test to aid in the diagnosis of metastatic, poorly differentiated and undifferentiated tumors. It measures the expression pattern of 1,550 genes in these tumors and compares it to the expression pattern of a panel of 15 known tumor types. The purpose of this study was to evaluate the performance of the Tissue of Origin Test in the diagnosis of primary sites for metastatic brain cancer patients.

METHODS

Fifteen fresh-frozen metastatic brain tumor specimens of known origins met specimen requirements. These specimens were entered into the study and processed using the Tissue of Origin Test. Results were compared to the known primary site and the agreement between the two results was assessed.

RESULTS

Fourteen of the fifteen specimens produced microarray data files that passed all quality metrics. One originated from a tissue type that was off-panel. Among the remaining 13 cases, the Tissue of Origin Test accurately predicted the available diagnosis in 12/13 (92.3%) cases.

DISCUSSION

This study demonstrates the accuracy of the Tissue of Origin Test when applied to predict the tissue of origin of metastatic brain tumors. This test could be a very useful tool for pathologists as they classify metastatic brain cancers.

Diagnosis of metastatic neoplasms: molecular approaches for identification of tissue of origin

CONTEXT

Tumors of uncertain or unknown origin are estimated to constitute 3% to 5% of all metastatic cancer cases. Patients with these types of tumors show worse outcomes when compared to patients in which a primary tumor is identified. New molecular tests that identify molecular signatures of a tissue of origin have become available.

OBJECTIVE

To review the literature on existing molecular approaches to the diagnosis of metastatic tumors of uncertain origin and discuss the current status and future developments in this area.

DATA SOURCES

Published peer-reviewed literature, available information from medical organizations (National Comprehensive Cancer Network), and other publicly available information from tissue-of-origin test providers and/or manufacturers.

CONCLUSIONS

Molecular tests for tissue-of-origin determination in metastatic tumors are available and have the potential to significantly impact patient management. However, available validation data indicate that not all tests have shown adequate performance characteristics for clinical use. Pathologists and oncologists should carefully evaluate claims for accuracy and clinical utility for tissue-of-origin tests before using test results in patient management. The personalized medicine revolution includes the use of molecular tools for identification/confirmation of the site of origin for metastatic tumors, and in the future, this strategy might also be used to determine specific therapeutic approaches.