Multisite validation study to determine performance characteristics of a 92-gene molecular cancer classifier

Gene expression accurately distinguishes liver metastases of small bowel and pancreas neuroendocrine tumors

Small bowel (SBNETs) and pancreatic neuroendocrine tumors (PNETs) often present with liver metastases. Although liver biopsy establishes a neuroendocrine diagnosis, the primary tumor site is frequently unknown without exploratory surgery. Gene expression differences in metastases may distinguish primary SBNETs and PNETs. This study sought to determine expression differences of four genes in neuroendocrine metastases and to create a gene expression algorithm to distinguish the primary site. Nodal and liver metastases from SBNETs and PNETs (n = 136) were collected at surgery under an Institutional Review Board-approved protocol. Quantitative PCR measured expression of bombesin-like receptor-3, opioid receptor kappa-1, oxytocin receptor, and secretin receptor in metastases. Logistic regression models defined an algorithm predicting the primary tumor site. Models were developed on a training set of 21 nodal metastases and performance was validated on an independent set of nodal and liver metastases. Expression of all four genes was significantly different in SBNET compared to PNET metastases. The optimal model employed expression of bombesin-like receptor-3 and opioid receptor kappa-1. When these genes did not amplify, the algorithm used oxytocin receptor and secretin receptor expression, which allowed classification of all 136 metastases with 94.1 % accuracy. In the independent liver metastasis validation set, 52/56 (92.9 %) were correctly classified. Positive predictive values were 92.5 % for SBNETs and 93.8 % for PNETs. This validated algorithm accurately distinguishes SBNET and PNET metastases based on their expression of four genes. High accuracy in liver metastases demonstrates applicability to the clinical setting. Studies assessing this algorithm's utility in prospective clinical decision-making are warranted.

Development and validation of a microRNA based diagnostic assay for primary tumor site classification of liver core biopsies

Identification of the primary tumor site in patients with metastatic cancer is clinically important, but remains a challenge. Hence, efforts have been made towards establishing new diagnostic tools. Molecular profiling is a promising diagnostic approach, but tissue heterogeneity and inadequacy may negatively affect the accuracy and usability of molecular classifiers. We have developed and validated a microRNA-based classifier, which predicts the primary tumor site of liver biopsies, containing a limited number of tumor cells. Concurrently we explored the influence of surrounding normal tissue on classification. MicroRNA profiling was performed using quantitative Real-Time PCR on formalin-fixed paraffin-embedded samples. 278 primary tumors and liver metastases, representing nine primary tumor classes, as well as normal liver samples were used as a training set. A statistical model was applied to adjust for normal liver tissue contamination. Performance was estimated by cross-validation, followed by independent validation on 55 liver core biopsies with a tumor content as low as 10%. A microRNA classifier developed, using the statistical contamination model, showed an overall classification accuracy of 74.5% upon independent validation. Two-thirds of the samples were classified with high-confidence, with an accuracy of 92% on high-confidence predictions. A classifier trained without adjusting for liver tissue contamination, showed a classification accuracy of 38.2%. Our results indicate that surrounding normal tissue from the biopsy site may critically influence molecular classification. A significant improvement in classification accuracy was obtained when the influence of normal tissue was limited by application of a statistical contamination model.

Cost effectiveness of a 92-gene assay for the diagnosis of metastatic cancer

OBJECTIVES

To estimate the clinical and economic trade-offs involved in using a molecular assay (92-gene assay, CancerTYPE ID) to aid in identifying the primary site of difficult-to-diagnose metastatic cancers and to explore whether the 92-gene assay can be used to standardize the diagnostic process and costs for clinicians, patients, and payers.

METHODS

Four decision-analytic models were developed to project the lifetime clinical and economic impact of incorporating the 92-gene assay compared with standard care alone. For each model, total and incremental costs, life-years, quality-adjusted life-years (QALYs), incremental cost-effectiveness ratios (ICERs), and the proportion of patients treated correctly versus incorrectly were projected from the payer perspective. Model inputs were based on published literature, analyses of SEER (Surveillance Epidemiology and End RESULTS) data, publicly available data, and interviews with clinical experts.

RESULTS

In all four models, the 92-gene assay increased the proportion of patients treated correctly, decreased the proportion of patients treated with empiric therapy, and increased quality-adjusted survival. In the primary model, the ICER was $50,273/QALY; thus, the 92-gene assay is therefore cost effective when considering a societal willingness-to-pay threshold of $100,000/QALY. These findings were robust across sensitivity analyses.

CONCLUSIONS

Use of the 92-gene assay for diagnosing metastatic tumors of uncertain origin is associated with reduced misdiagnoses, increased survival, and improved quality of life. Incorporating the assay into current practice is a cost-effective approach to standardizing diagnostic methods while improving patient care. Limitations of this analysis are the lack of data availability and resulting modeling simplifications, although sensitivity analyses showed these to not be key drivers of results.

Metastatic Tumors of Unknown Origin: Ancillary Testing in Cytologic Specimens

The application of ancillary studies, such as immunostains, to cytopathology has improved the ability to make accurate diagnoses with precise subclassification. Even with these techniques, there are still aspiration and exfoliative cytology cases for which it remains difficult to definitively determine the source and/or subtype. This article focuses on the well-established and novel ancillary studies used in the modern era of cancer diagnoses in cytopathology, particularly in the diagnostic work-up of metastatic tumors without a known primary.

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.

Molecular profiling diagnosis in unknown primary cancer: accuracy and ability to complement standard pathology

BACKGROUND

Molecular tumor profiling (MTP) is a potentially powerful diagnostic tool for identifying the tissue of origin in patients with cancer of unknown primary (CUP). However, validation of the accuracy and clinical value of MTP has been difficult because the anatomic primary site in most patients is never identified.

METHODS

From March 2008 through January 2010, clinicopathologic data from 171 CUP patients who had MTP (CancerTYPE ID; bioTheranostics, Inc, San Diego, CA) performed on archived material were evaluated. The accuracy of MTP diagnoses was evaluated by comparison with (1) latent primary tumor sites found months/years later; (2) initial single diagnoses by immunohistochemistry (IHC); and (3) additional directed IHC and/or clinicopathologic findings evaluated after MTP diagnoses.

RESULTS

A single MTP diagnosis was made in 144 of 149 patients with adequate tumor specimens. Eighteen of 24 patients with latent primaries discovered months to years later had correct diagnoses by MTP (75%), and these diagnoses compared favorably with IHC. Single IHC diagnoses matched MTP diagnoses in 40 of 52 patients (77%). IHC predictions of 2 or more possible primaries compared poorly with MTP diagnoses. However, additional targeted IHC and clinical/histologic evaluation supported the MTP diagnosis in 26 of 35 patients (74%). Clinical features were usually consistent with MTP diagnoses (70%).

CONCLUSIONS

The diagnostic accuracy of this MTP assay was supported by a high level of agreement with identified latent primaries (75%), single IHC diagnoses (77%), and additional directed IHC and/or clinical/histologic findings (74%) prompted by the MTP diagnoses. MTP complements standard pathologic evaluation in determining the tissue of origin in patients with CUP, particularly when IHC is inconclusive.

Diagnostic work-up of carcinoma of unknown primary: from immunohistochemistry to molecular profiling

Carcinoma of unknown primary (CUP) remains a common and challenging clinical problem. The aim of diagnostic work-up in CUP is to classify as specifically as possible the cancer affecting the patient, according to the broad tumour type, subtype and, where possible, site of origin. This classification currently best predicts patient outcome and guides optimal treatment. a stepwise approach to diagnostic work-up is described. although pathology is based on morphology, the assessment of tissue-specific genes through immunohistochemistry (IHC) substantially helps tumour classification at each diagnostic step. For IHC in CUP, recent improvements include more standardised approaches and marker panels plus new markers. Tissue-specific genes are also being used in CUP work-up through molecular profiling. Large-scale profiles of hundreds of tumours of different types have been generated, compared and used to generate diagnostic algorithms. Commercial tests for CUP classification have been developed at the mRNa and microRNA and (miRNA) levels and validated in metastatic tumours and CUPs. While currently optimal pathology and IHC remain the 'gold standard' for CUP diagnostic work-up, and full clinical correlation is vital, the molecular tests appear to perform well: in the main diagnostic challenge of undifferentiated or poorly differentiated tumours, molecular profiling performs as well as or better than IHC.

Cancer of unknown primary site: improved patient management with molecular and immunohistochemical diagnosis

Cancer of unknown primary site (CUP) is a common heterogeneous clinicopathologic syndrome, but investigations and publications regarding these patients are rare. For the last 20 years, empiric "broad-spectrum" chemotherapy has been the standard therapy for the majority of these patients. More recently, improved immunocytochemistry and advent of gene-expression profiling have provided the diagnostic tools necessary to accurately define the tissue of origin in most patients. Molecular profiling assays complement standard pathologic diagnosis, and a recently reported large prospective study demonstrated an improvement in outcome for patients treated with site-specific therapy directed by the molecular assay diagnoses compared with empiric chemotherapy. Survival in molecularly diagnosed patients was as expected for those particular tumor types. The evaluation of patients has become more standardized. The empiric-chemotherapy era is ending and customized therapies based on accurate tissue of origin diagnoses have arrived. Eventually the recognition of the molecular aberrations responsible for the growth and metastasis of solid tumors, regardless of the tissue of origin, will lead to more precise and effective therapy for patients with advanced cancers.

Blinded comparator study of immunohistochemical analysis versus a 92-gene cancer classifier in the diagnosis of the primary site in metastatic tumors

Accurate tumor classification is fundamental to inform predictive biomarker testing and optimize therapy. Gene expression-based tests are proposed as diagnostic aids in cases with uncertain diagnoses. This study directly compared the diagnostic accuracy of IHC analysis versus molecular classification using a 92-gene RT-PCR assay for determination of the primary tumor site. This prospectively defined blinded study of diagnostically challenging cases included 131 high-grade, primarily metastatic tumors. Cases were reviewed and reference diagnoses established through clinical correlation. Blinded FFPE sections were evaluated by either IHC/morphology analysis or the 92-gene assay. The final analysis included 122 cases. The 92-gene assay demonstrated overall accuracy of 79% (95% CI, 71% to 85%) for tumor classification versus 69% (95% CI, 60% to 76%) for IHC/morphology analysis (P = 0.019). Mean IHC use was 7.9 stains per case (median, 8; range, 2 to 15). IHC/morphology analysis accuracy was 79%, 80%, and 46% when 1 to 6 (n = 42), 7 to 9 (n = 41), and >9 (n = 39) IHC stains were used, respectively, versus 81%, 85%, and 69%, respectively, with the 92-gene assay. Results from this blinded series of high-grade metastatic cases demonstrate superior accuracy with the 92-gene assay versus standard-of-care IHC analysis and strongly support the diagnostic utility of molecular classification in difficult-to-diagnose metastatic cancer.