Determining Tissue of Origin for Metastatic Cancers: Meta-analysis and Literature Review of Immunohistochemistry Performance

A Fast, Affordable, and Minimally Invasive Diagnostic Test for Cancer of Unknown Primary Using DNA Methylation Profiling

Currently, we cannot provide a conclusive diagnosis for 3% to 5% of people who are confronted with cancer. These patients have cancer of unknown primary (CUP), ie, a metastasized cancer for which the tissue of origin cannot be determined. Studies have shown that the DNA methylation profile is a unique "fingerprint" that can be used to classify tumors. Here we used cell-free reduced representation bisulfite sequencing (cfRRBS), a technique that allows us to identify the methylation profile starting from minimal amounts of highly fragmented DNA, for CUP diagnosis on formalin-fixed paraffin-embedded (FFPE) tissue and liquid biopsies. We collected 80 primary tumor FFPE samples covering 16 tumor entities together with 15 healthy plasma samples to use as a custom cfRRBS reference data set. Entity-specific methylation regions are defined for each entity to build a classifier based on nonnegative least squares deconvolution. This classification framework was tested on 30 FFPE, 19 plasma, and 40 pleural and peritoneal effusion samples of both known metastatic tumors and clinical CUPs for which pathological investigation finally resulted in a cancer diagnosis. Using this framework, 27 of 30 FFPE (all CUPs) and 16 of 19 plasma samples (10/13 CUPs) obtained an accurate diagnosis, with a minimal DNA input of 400 pg. Diagnosis of the 40 pleural and peritoneal effusion samples is possible in 9 of 27 samples with negative/inconclusive cytology (6/13 CUPs), showing that cell-free DNA (cfDNA) methylation profiling could complement routine cytologic analysis. However, a low "cfDNA - high-molecular weight DNA ratio" has a considerable impact on the prediction accuracy. Moreover, the accuracy improves significantly if the predicted tumor percentage is >7%. This proof-of-concept study shows the feasibility of using DNA methylation profiling on FFPE and liquid biopsy samples such as blood, ascites, and pleural effusions in a fast and affordable way. Our novel RRBS-based technique requires minimal DNA input, can be performed in <1 week, and is highly adaptable to specific diagnostic problems as we only use 5 FFPE references per tumor entity. We believe that cfRRBS methylation profiling could be a valuable addition to the pathologist's toolbox in the diagnosis of CUPs.

Using Copy Number Variation Data and Neural Networks to Predict Cancer Metastasis Origin Achieves High Area under the Curve Value with a Trade-Off in Precision

The accurate identification of the primary tumor origin in metastatic cancer cases is crucial for guiding treatment decisions and improving patient outcomes. Copy number alterations (CNAs) and copy number variation (CNV) have emerged as valuable genomic markers for predicting the origin of metastases. However, current models that predict cancer type based on CNV or CNA suffer from low AUC values. To address this challenge, we employed a cutting-edge neural network approach utilizing a dataset comprising CNA profiles from twenty different cancer types. We developed two workflows: the first evaluated the performance of two deep neural networks-one ReLU-based and the other a 2D convolutional network. In the second workflow, we stratified cancer types based on anatomical and physiological classifications, constructing shallow neural networks to differentiate between cancer types within the same cluster. Both approaches demonstrated high AUC values, with deep neural networks achieving a precision of 60%, suggesting a mathematical relationship between CNV type, location, and cancer type. Our findings highlight the potential of using CNA/CNV to aid pathologists in accurately identifying cancer origins with accessible clinical tests.

Comprehensive analysis of cancer of unknown primary and recommendation of a histological and immunohistochemical diagnostic strategy from China

BACKGROUND

Previous studies on cancer of unknown primary (CUP) mainly focus on treatment and prognosis in western populations and lacked clinical evaluation of different IHC markers, so this study aimed to evaluate characteristics of CUP and recommend a diagnostic strategy from a single center in China.

METHODS AND RESULTS

Data of 625 patients with CUP were retrospectively collected and reviewed. The patients ranged in age from 20 to 91 years, with a female-to-male ratio of 1.3:1. The predominant histological type was poor or undifferentiated adenocarcinomas (308; 49.3%). The results of Canhelp-Origin molecular testing for the identification of the tissue of origin in 262 of 369 patients (71.0%) were considered predictable (similarity score > 45), with the most common predicted primary tumor site being the breast (57, 21.8%). Unpredictable molecular results correlated with more aggressive clinical parameters and poor survival. Thee positivity rates of several targeted antibodies (GATA3, GCDFP15, TTF1, Napsin A, and PAX8), based on the clinically predicted site, were lower than those reported for the corresponding primary tumors. Nonetheless, TRPS1 and INSM1 were reliable markers of predicted breast carcinoma (75.0%) and neuroendocrine tumors (83.3%), respectively. P16 expression, as well as HPV and EBER testing contributed significantly to the diagnosis of squamous cell carcinomas. Survival analysis revealed that older ages (> 57), ≥ 3 metastatic sites, non-squamous cell carcinomas, bone/liver/lung metastases, unpredictable molecular results, and palliative treatment correlated with poor overall survival.

CONCLUSIONS

We recommend a CUP diagnostic strategy involving the use of targeted antibody panels as per histological findings that is potentially applicable in clinical practice. The markers TRPS1, INSM1, and P16 expression, as well as HPV and EBER testing are particularly valuable in this aspect. Molecular testing is also predictive of survival rates.

Brain Metastases from Breast Cancer Histologically Exhibit Solid Growth Pattern with at Least Focal Comedonecrosis: A Histopathologic Study on a Monocentric Series of 30 Cases

Since there are no morphological clues capable of making a pathologist suspect a possible mammary origin of a metastatic lesion without adequate clinical information, the histologic diagnosis of brain metastasis from BC is still based on the immunohistochemical expression of mammary gland markers such as GATA-3, ERs, PgRs and HER-2. The present retrospective study aimed to select purely morphological features capable of suggesting the mammary origin of a metastatic carcinoma in the brain. The following histological features were collected from a series of 30 cases of brain metastases from breast cancer: (i) a solid growth pattern; (ii) the presence of comedonecrosis; and (iii) glandular differentiation. Our results showed that most cases histologically exhibited a solid growth pattern with at least focal comedonecrosis, producing an overall morphology closely reminiscent of mammary high-grade ductal carcinoma in situ. Although the above-mentioned morphological parameters are not strictly specific to a mammary origin, they may have an important diagnostic utility for leading pathologists to suspect a possible breast primary tumor and to include GATA-3, ERs, PgRs and HER-2 in the immunohistochemical panel.

DNA methylation profiling to determine the primary sites of metastatic cancers using formalin-fixed paraffin-embedded tissues

Identifying the primary site of metastatic cancer is critical to guiding the subsequent treatment. Approximately 3-9% of metastatic patients are diagnosed with cancer of unknown primary sites (CUP) even after a comprehensive diagnostic workup. However, a widely accepted molecular test is still not available. Here, we report a method that applies formalin-fixed, paraffin-embedded tissues to construct reduced representation bisulfite sequencing libraries (FFPE-RRBS). We then generate and systematically evaluate 28 molecular classifiers, built on four DNA methylation scoring methods and seven machine learning approaches, using the RRBS library dataset of 498 fresh-frozen tumor tissues from primary cancer patients. Among these classifiers, the beta value-based linear support vector (BELIVE) performs the best, achieving overall accuracies of 81-93% for identifying the primary sites in 215 metastatic patients using top-k predictions (k = 1, 2, 3). Coincidentally, BELIVE also successfully predicts the tissue of origin in 81-93% of CUP patients (n = 68).

Machine learning for genetics-based classification and treatment response prediction in cancer of unknown primary

Cancer of unknown primary (CUP) is a type of cancer that cannot be traced back to its primary site and accounts for 3-5% of all cancers. Established targeted therapies are lacking for CUP, leading to generally poor outcomes. We developed OncoNPC, a machine-learning classifier trained on targeted next-generation sequencing (NGS) data from 36,445 tumors across 22 cancer types from three institutions. Oncology NGS-based primary cancer-type classifier (OncoNPC) achieved a weighted F1 score of 0.942 for high confidence predictions ([Formula: see text]) on held-out tumor samples, which made up 65.2% of all the held-out samples. When applied to 971 CUP tumors collected at the Dana-Farber Cancer Institute, OncoNPC predicted primary cancer types with high confidence in 41.2% of the tumors. OncoNPC also identified CUP subgroups with significantly higher polygenic germline risk for the predicted cancer types and with significantly different survival outcomes. Notably, patients with CUP who received first palliative intent treatments concordant with their OncoNPC-predicted cancers had significantly better outcomes (hazard ratio (HR) = 0.348; 95% confidence interval (CI) = 0.210-0.570; P = [Formula: see text]). Furthermore, OncoNPC enabled a 2.2-fold increase in patients with CUP who could have received genomically guided therapies. OncoNPC thus provides evidence of distinct CUP subgroups and offers the potential for clinical decision support for managing patients with CUP.

An Appraisal of Immunohistochemical Stain Use in Hepatic Metastasis Highlights the Effectiveness of the Individualized, Case-Based Approach: Analysis of Data From a Tertiary Care Medical Center

CONTEXT.—

Liver biopsy plays an important role in the clinical management of metastases and often requires workup using immunohistochemical (IHC) markers, but the approach varies among institutions.

OBJECTIVE.—

To evaluate the utility of a morphologic pattern-based, individualized approach in the workup of hepatic metastases.

DESIGN.—

All liver biopsies with metastasis between 2015 and 2018 were identified from our institutional database and were reviewed. The morphologic pattern of the metastasis and IHC markers used in each case were recorded. The final identification of primary site of the tumor was assessed based on all the available clinicopathologic data. The academic ranking and practice pattern of the pathologist signing out the case were also recorded.

RESULTS.—

A total of 406 liver biopsies with metastasis were identified, and the cases were classified as adenocarcinoma (253 of 406; 62%), carcinoma not otherwise specified (12 of 406; 3%), neuroendocrine neoplasm (54 of 406; 13%), poorly differentiated carcinoma (43 of 406; 11%), nonepithelial tumor (24 of 406; 6%), and squamous cell carcinoma (20 of 406; 5%). The primary site was unknown in 39% (158 of 406) at the time of liver biopsy. A primary site was determined in 97% (395 of 406) of all cases, and only 3% (11 of 406) remained true carcinoma of unknown primary. The average number of IHC markers/case in patients with known primary was 2.6, compared with 5.9 with an initial unknown primary and 9.5 in cases of true carcinoma of unknown primary.

CONCLUSIONS.—

An individualized, case-based approach seems to be highly cost-effective and uses fewer IHC markers compared with preset panels that often comprise 10 or more IHC markers.

Utility of RNA Expression to Determine the Tissue of Origin of Malignancies with an Inconclusive Histopathology

We present 2 cases of cancer of unknown origin in which RNA-based cancer classification testing provided vital insight and directed treatment management. The tissue of origin could not be determined in both of these patients utilizing morphology and immunohistochemical analysis of the tissue samples. Next-generation sequencing and tumor-of-origin testing using an RNA-based molecular cancer classifier were performed to elucidate the possible tissue of origin. A 61-year-old male with a history of localized basal cell carcinoma presented with a 4.4-cm axillary lymph node in addition to upper extremity edema and supraclavicular lymphadenopathy. RNA-based tumor origin testing revealed skin basal or squamous cell carcinoma as the likely tissue of origin, with a probability of 97%. He received vismodegib, a hedgehog inhibitor, after progression on cemiplimab and experienced a partial response by RECIST criteria, which is currently ongoing for over a year. A 74-year-old female patient with a remote history of ovarian cancer for which she underwent resection and adjuvant chemotherapy presented 15 years later with abdominal pain. The diagnostic workup revealed a 2-cm pancreatic mass and enlarged peritoneal lymph nodes. RNA sequencing revealed a 99% likelihood of the tissue of origin being serous ovarian carcinoma. Subsequently, she underwent surgery and adjuvant chemotherapy and is currently in remission with letrozole maintenance. Genomic data already plays a crucial role in therapeutic decision-making for individuals with cancer. These cases highlight the complementary role of genomic data in the diagnostic workup of cancer, leading to favorable patient outcomes.

Next-generation sequencing in breast pathology: real impact on routine practice over a decade since its introduction

The diagnosis, histomolecular classes of breast cancers (luminal A, luminal B, HER2-enriched, and basal-like), and accurate prediction of prognosis are commonly determined using morphological and phenotypical analyses in clinical practice worldwide. Therapeutic strategies are mostly based on the disease stage and molecular subclasses of breast cancer. Targeted therapies, such as anti-HER2s, poly-ADP ribose polymerase inhibitors or, to a lesser extent, phosphatidylinositol 3 kinase inhibitors, have substantially improved breast cancer patient prognosis over the past decades. Human epidermal growth factor receptor 2 (HER2) overexpression is widely determined based on immunohistochemistry, while next-generation sequencing (NGS) is currently employed to assess the presence of molecular alterations, including breast cancer gene 1 (BRCA1) and 2 or phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) mutations, which are targets of these new approved therapies. In addition, next-generation sequencing (NGS) can aid the pathologist in challenging situations, such as a diagnostic workup for a metastatic carcinoma in lymph nodes of unknown origin, differential diagnosis of spindle cell tumourtumor in the breast between metaplastic carcinoma, malignant PT and sarcoma, o, as well as determining relatedness between primary breast cancers and recurrences. NGS offers a powerful tool that enables the pathologist to combine morphological analyses together with molecular alterations in challenging diagnostic situations.

Machine learning-based tissue of origin classification for cancer of unknown primary diagnostics using genome-wide mutation features

Cancers of unknown primary (CUP) origin account for ∼3% of all cancer diagnoses, whereby the tumor tissue of origin (TOO) cannot be determined. Using a uniformly processed dataset encompassing 6756 whole-genome sequenced primary and metastatic tumors, we develop Cancer of Unknown Primary Location Resolver (CUPLR), a random forest TOO classifier that employs 511 features based on simple and complex somatic driver and passenger mutations. CUPLR distinguishes 35 cancer (sub)types with ∼90% recall and ∼90% precision based on cross-validation and test set predictions. We find that structural variant derived features increase the performance and utility for classifying specific cancer types. With CUPLR, we could determine the TOO for 82/141 (58%) of CUP patients. Although CUPLR is based on machine learning, it provides a human interpretable graphical report with detailed feature explanations. The comprehensive output of CUPLR complements existing histopathological procedures and can enable improved diagnostics for CUP patients.

Identification of tissue of origin in cancer of unknown primary using a targeted bisulfite sequencing panel

Aim: To construct a targeted bisulfite sequencing panel predicting origin of cancer of unknown primary. Methods: A bisulfite sequencing panel targeting 2793 tissue-specific markers was performed in 100 clinical samples. Results: The authors' prediction model showed 0.85 accuracy for the 'first-ranked' tissue type and 0.93 accuracy for the 'second-ranked' tissue type using 2793 tissue-specific markers and 0.84 accuracy for the 'first-ranked' tissue type and 0.92 accuracy for the 'second-ranked' tissue type when the number of tissue-specific markers was reduced to 514. Conclusion: Targeted bisulfite sequencing is a useful method for predicting the tissue of origin in patients with cancer of unknown primary.

90-Gene Expression Profiling for Tissue Origin Diagnosis of Cancer of Unknown Primary

Cancer of unknown primary (CUP), in which metastatic diseases exist without an identifiable primary location, accounts for about 3-5% of all cancer diagnoses. Successful diagnosis and treatment of such patients are difficult. This study aimed to assess the expression characteristics of 90 genes as a method of identifying the primary site from CUP samples. We validated a 90-gene expression assay and explored its potential diagnostic utility in 44 patients at Jiangsu Cancer Hospital. For each specimen, the expression of 90 tumor-specific genes in malignant tumors was analyzed, and similarity scores were obtained. The types of malignant tumors predicted were compared with the reference diagnosis to calculate the accuracy. In addition, we verified the consistency of the expression profiles of the 90 genes in CUP secondary malignancies and metastatic malignancies in The Cancer Genome Atlas. We also reported a detailed description of the next-generation coding sequences for CUP patients. For each clinical medical specimen collected, the type of malignant tumor predicted and analyzed by the 90-gene expression assay was compared with its reference diagnosis, and the overall accuracy was 95.4%. In addition, the 90-gene expression profile generally accurately classified CUP into the cluster of its primary tumor. Sequencing of the exome transcriptome containing 556 high-frequency gene mutation oncogenes was not significantly related to the 90 genes analysis. Our results demonstrate that the expression characteristics of these 90 genes can be used as a powerful tool to accurately identify the primary sites of CUP. In the future, the inclusion of the 90-gene expression assay in pathological diagnosis will help oncologists use precise treatments, thereby improving the care and outcomes of CUP patients.

Self-organization in cancer: Implications for histopathology, cancer cell biology, and metastasis

Pathologists use histological features to classify tumors and assign site of origin for metastasis. How and why tumors organize the way they do and recreate their histological organization during metastasis is unknown. Here, I discuss the concept of "histostasis" conferring tumors a histological memory and hypothesize its implications for metastasis.

Machine learning analysis using 77,044 genomic and transcriptomic profiles to accurately predict tumor type

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CUP occurs in as many as 3–5% of patients when standard diagnostic tests are not able to determine the origin of cancer.

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MI GPSai (Genomic Prevalence Score) is an AI that uses genomic and transcriptomic data to elucidate tumor origin.

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The algorithm was trained on molecular data from 57,489 cases and validated on 19,555 cases.

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MI GPSai predicted the tumor type out of 21 options in the labeled data set with an accuracy of over 94% on 93% of cases.

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When also considering the second highest prediction, the accuracy increases to 97%.

Cancer of Unknown Primary (CUP) occurs in 3–5% of patients when standard histological diagnostic tests are unable to determine the origin of metastatic cancer. Typically, a CUP diagnosis is treated empirically and has very poor outcomes, with median overall survival less than one year. Gene expression profiling alone has been used to identify the tissue of origin but struggles with low neoplastic percentage in metastatic sites which is where identification is often most needed. MI GPSai, a Genomic Prevalence Score, uses DNA sequencing and whole transcriptome data coupled with machine learning to aid in the diagnosis of cancer. The algorithm trained on genomic data from 34,352 cases and genomic and transcriptomic data from 23,137 cases and was validated on 19,555 cases. MI GPSai predicted the tumor type in the labeled data set with an accuracy of over 94% on 93% of cases while deliberating amongst 21 possible categories of cancer. When also considering the second highest prediction, the accuracy increases to 97%. Additionally, MI GPSai rendered a prediction for 71.7% of CUP cases. Pathologist evaluation of discrepancies between submitted diagnosis and MI GPSai predictions resulted in change of diagnosis in 41.3% of the time. MI GPSai provides clinically meaningful information in a large proportion of CUP cases and inclusion of MI GPSai in clinical routine could improve diagnostic fidelity. Moreover, all genomic markers essential for therapy selection are assessed in this assay, maximizing the clinical utility for patients within a single test.

Gene expression profiling for the diagnosis of multiple primary malignant tumors

BACKGROUND

The incidence of multiple primary malignant tumors (MPMTs) is rising due to the development of screening technologies, significant treatment advances and increased aging of the population. For patients with a prior cancer history, identifying the tumor origin of the second malignant lesion has important prognostic and therapeutic implications and still represents a difficult problem in clinical practice.

METHODS

In this study, we evaluated the performance of a 90-gene expression assay and explored its potential diagnostic utility for MPMTs across a broad spectrum of tumor types. Thirty-five MPMT patients from Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University and Fudan University Shanghai Cancer Center were enrolled; 73 MPMT specimens met all quality control criteria and were analyzed by the 90-gene expression assay.

RESULTS

For each clinical specimen, the tumor type predicted by the 90-gene expression assay was compared with its pathological diagnosis, with an overall accuracy of 93.2% (68 of 73, 95% confidence interval 0.84-0.97). For histopathological subgroup analysis, the 90-gene expression assay achieved an overall accuracy of 95.0% (38 of 40; 95% CI 0.82-0.99) for well-moderately differentiated tumors and 92.0% (23 of 25; 95% CI 0.82-0.99) for poorly or undifferentiated tumors, with no statistically significant difference (p-value > 0.5). For squamous cell carcinoma specimens, the overall accuracy of gene expression assay also reached 87.5% (7 of 8; 95% CI 0.47-0.99) for identifying the tumor origins.

CONCLUSIONS

The 90-gene expression assay provides flexibility and accuracy in identifying the tumor origin of MPMTs. Future incorporation of the 90-gene expression assay in pathological diagnosis will assist oncologists in applying precise treatments, leading to improved care and outcomes for MPMT patients.

A Machine Learning Approach for Tracing Tumor Original Sites With Gene Expression Profiles

Some carcinomas show that one or more metastatic sites appear with unknown origins. The identification of primary or metastatic tumor tissues is crucial for physicians to develop precise treatment plans for patients. With unknown primary origin sites, it is challenging to design specific plans for patients. Usually, those patients receive broad-spectrum chemotherapy, while still having poor prognosis though. Machine learning has been widely used and already achieved significant advantages in clinical practices. In this study, we classify and predict a large number of tumor samples with uncertain origins by applying the random forest and Naive Bayesian algorithms. We use the precision, recall, and other measurements to evaluate the performance of our approach. The results have showed that the prediction accuracy of this method was 90.4 for 7,713 samples. The accuracy was 80% for 20 metastatic tumors samples. In addition, the 10-fold cross-validation is used to evaluate the accuracy of classification, which reaches 91%.

Identification of the origin of brain metastases based on the relative methylation orderings of CpG sites

Accurate diagnosis of the origin of brain metastases (BMs) is crucial for tailoring an effective therapy to improve patients' prognosis. BMs of unknown origin account for approximately 2-14% of patients with BMs. Hence, the aim of this study was to identify the original cancer type of BMs based on their DNA methylation profiles. The DNA methylation profiles of glioma (GM), BM, and seven other types of primary cancers were collected. In comparison with GM, the reversal CpG site pairs were identified for each of the seven other types of primary cancers based on the within-sample relative methylation orderings (RMOs) of the CpG sites. Then, using the reversal CpG site pairs, GMs were distinguished from BMs and the seven other types of primary cancers. All 61 of the GM samples were correctly identified as GM. The cancer type was also identified for the non-GM samples. For the seven other types of primary cancers, greater than 93% of samples of each cancer type were correctly identified as their corresponding cancer type, except for breast cancer, which had an 88% accuracy. For 133 BM samples, 132 BM samples were identified as non-GM, and 95% of the 133 BM samples were correctly classified into their corresponding original cancer types. The RMO-based method can accurately identify the origin of BMs, which is important for precision treatment.

Development and Clinical Validation of a 90-Gene Expression Assay for Identifying Tumor Tissue Origin

The accurate identification of tissue origin in patients with metastatic cancer is critical for effective treatment selection but remains a challenge. The aim of this study is to develop a gene expression assay for tumor molecular classification and integrate it with clinicopathologic evaluations to identify the tissue origin for cancer of uncertain primary (CUP). A 90-gene expression signature, covering 21 tumor types, was identified and validated with an overall accuracy of 89.8% (95% CI, 0.87-0.92) in 609 tumor samples. More specifically, the classification accuracy reached 90.4% (95% CI, 0.87-0.93) for 323 primary tumors and 89.2% (95% CI, 0.85-0.92) for 286 metastatic tumors, with no statistically significant difference (P = 0.71). Furthermore, in a real-life cohort of 141 CUP patients, predictions by the 90-gene expression signature were consistent or compatible with the clinicopathologic features in 71.6% of patients (101/141). Findings suggest that this novel gene expression assay could efficiently predict the primary origin for a broad spectrum of tumor types and support its diagnostic utility of molecular classification in difficult-to-diagnose metastatic cancer. Additional studies are ongoing to further evaluate the clinical utility of this novel gene expression assay in predicting primary site and directing therapy for CUP patients.

Current Practices and Novel Techniques in the Diagnosis and Management of Neuroendocrine Tumors of Unknown Primary

Neuroendocrine tumors (NETs) comprise a heterogeneous group of neoplasms in which tumor staging/prognosis and response to treatments depend heavily on accurate and timely identification of the anatomic primary site or NET subtype. Despite recent technological advancements and use of multiple diagnostic modalities, 10% to 14% of newly diagnosed NETs are not fully characterized based on subtype or anatomic primary site. Inability to fully characterize NETs of unknown primary may cause delays in surgical intervention and limit potential treatment options. To address this unmet need, clinical validity and utility are being demonstrated for novel approaches that improve NET subtype or anatomic primary site identification. Functional imaging using Ga-radiolabeled DOTATATE positron emission tomography/computed tomography has been shown to overcome some false-positive and resolution issues associated with octreotide scanning and computed tomography/magnetic resonance imaging. Using a genomic approach, molecular tumor classification based on differential gene expression has demonstrated high diagnostic accuracy in blinded validation studies of different NET types and subtypes. Given the widespread availability of these technologies, we propose an algorithm for the workup of NETs of unknown primary that integrates these approaches. Including these technologies in the standard workup will lead to better NET subtype identification and improved treatment optimization for patients.

Lung-Seeking Metastases

Metastases from different cancer types most often affect the lung parenchyma. Moreover, the lungs are among the most frequent sites of growth of metastatic masses of uncertain/unknown lineage of origin. Thus, with regards to pulmonary neoplastic parenchymal nodules, the critical issue is to determine if they are IN the lung or OF the lung. In this review, we highlight the clinical, instrumental and molecular features which characterize lung metastases, mainly focusing on recently advancing and emerging concepts regarding the metastatic niche, inflammation, angiogenesis, immune modulation and gene expression. A novel issue is related to the analysis of biomechanical forces which cooperate in the expansion of tumor masses in the lungs. We here aim to analyze the biological, genetic and pathological features of metastatic lesions to the lungs, here referred to as site of metastatic growth. This point should be a crucial part of the algorithm for a proper diagnostic and therapeutic approach in the era of personalized medicine.

Colorectal metastasis to the gallbladder mimicking a primary gallbladder malignancy: histopathological and molecular characteristics

AIMS

Outcomes of colorectal cancer (CRC) treatment and survival have steadily improved during the past decades, accompanied by an increased risk of developing second primary tumours and metastatic tumours at unusual sites. Metastatic CRC can show mucosal colonisation, thereby mimicking a second primary tumour. This potential confusion could lead to incorrect diagnosis and consequently inadequate treatment of the patient. The aim of this study was to differentiate between metastatic CRC and a second primary (gallbladder cancer, GBC) using a combination of standard histopathology and molecular techniques.

METHODS AND RESULTS

Ten consecutive patients with both CRC and GBC were identified in our region using the Dutch National Pathology Archive (PALGA). Two patients served as negative controls. Histology of GBC was reviewed by nine pathologists. A combination of immunohistochemistry, microsatellite analysis, genomewide DNA copy number analysis and targeted somatic mutation analysis was used to aid in differential diagnosis. In two patients, CRC and GBC were clonally related, as confirmed by somatic mutation analysis. For one case, this was confirmed by genomewide DNA copy number analysis. However, in both cases, pathologists initially considered the GBC as a second primary tumour.

CONCLUSIONS

Metastatic CRC displaying mucosal colonisation is often misinterpreted as a second primary tumour. A combination of traditional histopathology and molecular techniques improves this interpretation, and lowers the risk of inadequate treatment.

Molecular characterisation and liquid biomarkers in Carcinoma of Unknown Primary (CUP): taking the 'U' out of 'CUP'

Cancers of Unknown Primary (CUP) comprise a heterogeneous clinical entity of confirmed metastatic cancer where the primary site of origin is undetectable. It has a poor prognosis with limited treatment options. CUP is historically under-researched; however, understanding its biology has the potential to not only improve treatment and survival by implementation of biomarkers for patient management, but also to greatly contribute to our understanding of carcinogenesis and metastasis across all cancer types. Here we review the current advances in CUP research and explore the debated hypotheses underlying its biology. The evolution of molecular profiling and tissue-of-origin classifiers have the potential to transform the diagnosis, classification and therapeutic management of patients with CUP but robust evidence to support widespread use is lacking. Precision medicine has transformed treatment strategy in known tumour types; in CUP, however, there remains a clinical need for a better understanding of molecular characteristics to establish the potential role of novel or existing therapeutics. The emergence of liquid biopsies as a source of predictive and prognostic biomarkers within known tumour types is gaining rapid ground and this review explores the potential utility of liquid biopsies in CUP.

Evaluation of Carcinoma of Unknown Primary on Cytologic Specimens

Carcinoma of unknown primary is defined as metastatic carcinoma without a clinically obvious primary tumor. Determining the tissue of origin in carcinoma of unknown primary is important for site-directed therapy. Immunohistochemistry is the most widely used tool for the work-up of metastases, but molecular profiling assays are also available. This review provides an overview of immunohistochemical stains in the work-up of metastatic carcinoma, with a focus on newer site-specific markers, and discusses the role of gene expression profiling assays for determining tissue of origin. The utility of cytopathology specimens in the evaluation of carcinoma of unknown primary also is highlighted.

Cancer of Unknown Primary: Opportunities and Challenges

Cancer of unknown primary (CUP) is defined as histologically proven metastatic tumors whose primary site cannot be identified during pretreatment evaluation. Among all malignancies, 3%–5% remained as CUP even after the extensive radiological and pathological workup. Immunohistochemistry and molecular gene expression tumor profiling are being utilized to predict the tissue of origin. Unfortunately, the survival of these patients remains poor (6–9 months) except in 20% of patients who belong to a favorable subset (12–36 months). There is a need to understand the basic biology and to identify the molecular pathways which can be targeted with small molecules. This article reviews our current approach as well as treatment evolution occurred in the past three decades.

An integrated tool for determining the primary origin site of metastatic tumours

Aims

Cancers of unknown primary sites account for 3%–5% of all malignant neoplasms. Current diagnostic workflows based on immunohistochemistry and imaging tests have low accuracy and are highly subjective. We aim to develop and validate a gene-expression classifier to identify potential primary sites for metastatic cancers more accurately.

Methods

We built the largest Reference Database (RefDB) reported to date, composed of microarray data from 4429 known tumour samples obtained from 100 different sources and divided into 25 cancer superclasses formed by 58 cancer subclass. Based on specific profiles generated by 95 genes, we developed a gene-expression classifier which was first trained and tested by a cross-validation. Then, we performed a double-blinded retrospective validation study using a real-time PCR-based assay on a set of 105 metastatic formalin-fixed, paraffin-embedded (FFPE) samples. A histopathological review performed by two independent pathologists served as a reference diagnosis.

Results

The gene-expression classifier correctly identified, by a cross-validation, 86.6% of the expected cancer superclasses of 4429 samples from the RefDB, with a specificity of 99.43%. Next, the performance of the algorithm for classifying the validation set of metastatic FFPE samples was 83.81%, with 99.04% specificity. The overall reproducibility of our gene-expression-classifier system was 97.22% of precision, with a coefficient of variation for inter-assays and intra-assays and intra-lots <4.1%.

Conclusion

We developed a complete integrated workflow for the classification of metastatic tumour samples which may help on tumour primary site definition.

Precision medicine based on epigenomics: the paradigm of carcinoma of unknown primary

Epigenetic alterations are a common hallmark of human cancer. Single epigenetic markers are starting to be incorporated into clinical practice; however, the translational use of these biomarkers has not been validated at the 'omics' level. The identification of the tissue of origin in patients with cancer of unknown primary (CUP) is an example of how epigenomics can be incorporated in clinical settings, addressing an unmet need in the diagnostic and clinical management of these patients. Despite the great diagnostic advances made in the past decade, the use of traditional diagnostic procedures only enables the tissue of origin to be determined in ∼30% of patients with CUP. Thus, development of molecularly guided diagnostic strategies has emerged to complement traditional procedures, thereby improving the clinical management of patients with CUP. In this Review, we present the latest data on strategies using epigenetics and other molecular biomarkers to guide therapeutic decisions involving patients with CUP, and we highlight areas warranting further research to engage the medical community in this unmet need.

Single Jejunum Metastasis from Breast Cancer Arising Twelve Years after the Initial Treatment

Metastatic involvement of gastrointestinal tract from breast cancer is a rare event. We report the case of a 61-year-old woman presenting with bowel obstruction, related to metastasis of a primary breast cancer she had 12 years earlier (a triple-negative invasive ductal carcinoma treated with surgery and chemotherapy). Bowel obstruction was caused by a 20-centimeter tumor in the jejunum, involving also the transverse colon. The patient underwent en bloc resection of tumor with jejunum and transverse bowel segment and received adjuvant chemotherapy with carboplatin and paclitaxel. Twenty months later, she was alive without disease recurrence.

Performance and prognostic utility of the 92-gene assay in the molecular subclassification of ampullary adenocarcinoma

Background

Ampullary adenocarcinoma is a rare gastrointestinal cancer associated with diverse outcomes due to clinical and pathological heterogeneity. Standardized methods to better prognosticate and inform therapeutic selection for ampullary adenocarcinoma are needed. This study explored the novel use and potential prognostic utility of a 92-gene cancer classifier in ampullary adenocarcinomas.

Methods

In this prospectively-defined, blinded study of ampullary adenocarcinoma [N =54; stage T3 or higher (57 %); Grade III (44 %); Node positive (55 %)], the performance of a 92-gene classifier was examined to predict the ampullary subtype that was derived from histomorphological examination of resected ampullary samples. Outcome data for relapse-free survival (RFS) and overall survival (OS) were plotted to compare the prognostic utility of histological subtyping, histomolecular phenotyping, and the 92-gene classifier. Multivariate analysis was used to determine clinicopathological variables that were independently associated with overall survival.

Results

The 92-gene classifier demonstrated sensitivities and specificities of 85 % [95 % CI, 66–94] and 68 % [95 % CI, 48–84] and 64 % [95 % CI, 46–79] and 88 % [95 % CI, 70–98] for the pancreaticobiliary and intestinal histological subtypes, respectively. For the 92-gene classifier, improved outcomes were observed for the intestine versus the pancreaticobiliary prediction (median OS 108.1 v 36.4 months; HR, 2.17; 95 % CI, 0.98 to 4.79; P = 0.05). Similar results were seen for ampullary adenocarcinoma stratification by histological subtype (P = 0.04) and histomolecular phenotype (P = 0.02). Within poorly differentiated ampullary adenocarcinomas only the 92-gene classifier demonstrated statistically significant differences in RFS and OS (P < 0.05).

Conclusions

Prognostic stratification of ampullary adenocarcinoma was similar for the 92-gene classifier, histological subtype, and histomolecular phenotype. The 92-gene classifier provides an unbiased standardized molecular-based approach to stratify ampullary tumors.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-016-2677-3) contains supplementary material, which is available to authorized users.

Cancer of unknown primary ultimately diagnosed as male breast cancer: A rare case report

Cancers of unknown primary (CUP) constitute a significant diagnostic and therapeutic challenge for clinicians and a frequent cause of cancer-related mortality in Western countries. Immunohistochemistry assays are commonly used to identify the primary cancer, but fail in approximately one-third of cases. The identification of the possible origin of CUP is crucial, as it may help select the appropriate treatment options. We herein present the case of a 54-year-old male patient, who presented with lower back pain in June, 2013. Following a thorough investigation, the clinical and pathological findings could not identify the primary cancer, leading towards a misdiagnosis. Ultimately, microRNA testing of the resected spine lesion was able to identify the primary tumor as male breast cancer and allow for optimal treatment of the patient.

Development and validation of a gene expression tumour classifier for cancer of unknown primary

Accurate identification of the primary tumour in cancer of unknown primary (CUP) is required for effective treatment selection and improved patient outcomes. The aim of this study was to develop and validate a gene expression tumour classifier and integrate it with histopathology to identify the likely site of origin in CUP.RNA was extracted from 450 formalin fixed, paraffin embedded samples of known origin comprising 18 tumour groups. Whole genome expression analysis was performed using a bead-based array. Classification of the tumours made use of a binary support vector machine, together with recursive feature elimination. A hierarchical tumour classifier was developed and incorporated with conventional histopathology to identify the origins of metastatic tumours.The classifier demonstrated an accuracy of 88% for correctly predicting the tumour type on a validation set of known tumours (n = 94). For CUP samples (n = 49) having a final clinical diagnosis, the classifier improved the accuracy of histology alone for both single and multiple predictions. Furthermore, where histology alone could not suggest any specific diagnosis, the classifier was able to correctly predict the primary site of origin.We demonstrate the integration of gene expression profiling with conventional histopathology to aid the investigation of CUP.

Application of immunohistochemistry in gastrointestinal and liver neoplasms: new markers and evolving practice

CONTEXT

Diagnosis of primary gastrointestinal and liver neoplasms is usually straightforward. Immunohistochemistry is most helpful to differentiate metastatic carcinomas with morphologic similarity and to resolve tumors of unknown origin. Recently, several new markers highly sensitive and specific for primary liver and gastrointestinal tumors have been discovered. Their potential diagnostic application has not been widely appreciated by general practicing pathologists. In addition, a new trend in immunohistochemistry application has started, focusing on assessing predictive markers (such as human epidermal growth factor receptor 2) and mutation-specific markers (v-raf murine sarcoma viral oncogene homolog B V600E) to directly guide clinical management. Practicing pathologists need to be aware of and prepared for this evolving trend.

OBJECTIVES

To summarize the usefulness of several recently discovered immunohistochemical markers in the study of gastrointestinal and liver tumors; to suggest the most current and effective immunohistochemical panels addressing common diagnostic challenges for these tumors; to share practical experience and useful tips for human epidermal growth factor receptor 2 testing in gastric and gastroesophageal junction adenocarcinoma and v-raf murine sarcoma viral oncogene homolog B V600E immunohistochemistry in colorectal carcinoma.

DATA SOURCES

Sources include literature review, and authors' research data and practice experience. The cases illustrated are selected from the pathology archives of the Geisinger Medical Center (Danville, Pennsylvania).

CONCLUSIONS

Application of immunohistochemistry in gastrointestinal and liver tumors continues to evolve. New tumor-specific markers constantly emerge and help pathologists to further improve diagnostic accuracy. Assessment of predictive and prognostic markers by immunohistochemistry in routine pathologic diagnosis is a new trend and will greatly facilitate the advancement of personalized cancer therapy.

Immunohistochemistry in undifferentiated neoplasm/tumor of uncertain origin

CONTEXT

Immunohistochemistry has become an indispensable ancillary study in the identification and classification of undifferentiated neoplasms/tumors of uncertain origin. The diagnostic accuracy has significantly improved because of the continuous discoveries of tissue-specific biomarkers and the development of effective immunohistochemical panels.

OBJECTIVES

To identify and classify undifferentiated neoplasms/tumors of uncertain origin by immunohistochemistry.

DATA SOURCES

Literature review and authors' research data and personal practice experience were used.

CONCLUSIONS

To better guide therapeutic decisions and predict prognostic outcomes, it is crucial to differentiate the specific lineage of an undifferentiated neoplasm. Application of appropriate immunohistochemical panels enables the accurate classification of most undifferentiated neoplasms. Knowing the utilities and pitfalls of each tissue-specific biomarker is essential for avoiding potential diagnostic errors because an absolutely tissue-specific biomarker is exceptionally rare. We review frequently used tissue-specific biomarkers, provide effective panels, and recommend diagnostic algorithms as a standard approach to undifferentiated neoplasms.

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.

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.

Efficient identification of miRNAs for classification of tumor origin

Carcinomas of unknown primary origin constitute 3% to 5% of all newly diagnosed metastatic cancers, with the primary source difficult to classify with current histological methods. Effective cancer treatment depends on early and accurate identification of the tumor; patients with metastases of unknown origin have poor prognosis and short survival. Because miRNA expression is highly tissue specific, the miRNA profile of a metastasis may be used to identify its origin. We therefore evaluated the potential of miRNA profiling to identify the primary tumor of known metastases. Two hundred eight formalin-fixed, paraffin-embedded samples, representing 15 different histologies, were profiled on a locked nucleic acid-enhanced microarray platform, which allows for highly sensitive and specific detection of miRNA. On the basis of these data, we developed and cross-validated a novel classification algorithm, least absolute shrinkage and selection operator, which had an overall accuracy of 85% (CI, 79%-89%). When the classifier was applied on an independent test set of 48 metastases, the primary site was correctly identified in 42 cases (88% accuracy; CI, 75%-94%). Our findings suggest that miRNA expression profiling on paraffin tissue can efficiently predict the primary origin of a tumor and may provide pathologists with a molecular diagnostic tool that can improve their capability to correctly identify the origin of hitherto unidentifiable metastatic tumors and, eventually, enable tailored therapy.

A multicenter study directly comparing the diagnostic accuracy of gene expression profiling and immunohistochemistry for primary site identification in metastatic tumors

Metastatic tumors with an uncertain primary site can be a difficult clinical problem. In tens of thousands of patients every year, no confident diagnosis is ever issued, making standard-of-care treatment impossible. Gene expression profiling (GEP) tests currently available to analyze these difficult-to-diagnose tumors have never been directly compared with the diagnostic standard of care, immunochemistry (IHC). This prospectively conducted, blinded, multicenter study compares the diagnostic accuracy of GEP with IHC in identifying the primary site of 157 formalin-fixed paraffin-embedded specimens from metastatic tumors with known primaries, representing the 15 tissues on the GEP test panel. Four pathologists rendered diagnoses by selecting from 84 stains in 2 rounds. GEP was performed using the Pathwork Tissue of Origin Test. Overall, GEP accurately identified 89% of specimens, compared with 83% accuracy using IHC (P=0.013). In the subset of 33 poorly differentiated and undifferentiated carcinomas, GEP accuracy exceeded that of IHC (91% to 71%, P=0.023). In specimens for which pathologists rendered their final diagnosis with a single round of stains, both IHC and GEP exceeded 90% accuracy. However, when the diagnosis required a second round, IHC significantly underperformed GEP (67% to 83%, P<0.001). GEP has been validated as accurate in diagnosing the primary site in metastatic tumors. The Pathwork Tissue of Origin Test used in this study was significantly more accurate than IHC when used to identify the primary site, with the most pronounced superiority observed in specimens that required a second round of stains and in poorly differentiated and undifferentiated metastatic carcinomas.

Novel microRNA-based assay demonstrates 92% agreement with diagnosis based on clinicopathologic and management data in a cohort of patients with carcinoma of unknown primary

Background

Cancer of unknown or uncertain primary is a major diagnostic and clinical challenge, since identifying the tissue-of-origin of metastases is crucial for selecting optimal treatment. MicroRNAs are a family of non-coding, regulatory RNA molecules that are tissue-specific, with a great potential to be excellent biomarkers.

Methods

In this study we tested the performance of a microRNA-based assay in formalin-fixed paraffin-embedded samples from 84 CUP patients.

Results

The microRNA based assay agreed with the clinical diagnosis at presentation in 70% of patients; it agreed with the clinical diagnosis obtained after patient management, taking into account response and outcome data, in 89% of patients; it agreed with the final clinical diagnosis reached with supplemental immunohistochemical stains in 92% of patients, indicating a 22% improvement in agreement from diagnosis at presentation to the final clinical diagnosis. In 18 patients the assay disagreed with the presentation diagnosis and was in agreement with the final clinical diagnosis, which may have resulted in the administration of more effective chemotherapy. In three out of four discordant cases in which supplemental IHC was performed, the IHC results validated the assay’s molecular diagnosis.

Conclusions

This novel microRNA-based assay shows high accuracy in identifying the final clinical diagnosis in a real life CUP patient cohort and could be a useful tool to facilitate administration of optimal therapy.

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.

Clinical utility of gene-expression profiling for tumor-site origin in patients with metastatic or poorly differentiated cancer: impact on diagnosis, treatment, and survival

PURPOSE

The primary tissue-site origin in over 4% of cancers remains uncertain despite thorough clinicopathological evaluation. This study assessed the effect of a Food and Drug Administration-cleared 2,000-gene–expression-profiling (GEP) test on primary tissue-site working diagnoses and management for metastatic and poorly differentiated cancers.

METHODS

Clinical information was collected from physicians ordering the GEP test for patients with difficult to diagnose cancers. Endpoints included diagnostic procedures, physicians’ working diagnoses and treatment recommendations before and after GEP result availability, and physician reports of the test's usefulness for clinical decision making. Patient date of death was obtained, with a minimum of one year follow-up from date of biopsy.

RESULTS

Sixty-five physicians participated in the study (n=107 patients). Before GEP, patients underwent 3.2 investigations on average (e.g., radiology, endoscopy). Ten immunohistochemistry tests were used per biopsy (SD 5.2). After GEP testing, physicians changed the primary working diagnosis for 50% of patients (95% CI: 43%,58%) and management for 65% of patients (95% CI: 58%,73%). With GEP results, the recommendation for guideline-consistent chemotherapy increased from 42% to 65% of patients, and the recommendation for non-guideline-consistent regimens declined from 28% to 13%. At last follow-up, 69 patients had died, and median survival was 14.0 months (95% CI: 10.2,18.6). Thirty-three percent of patients were alive at 2 years.

CONCLUSION

In patients with difficult-to-diagnose cancers, GEP changed the working diagnosis and management for the majority of patients. Patients for whom the GEP test was ordered had longer median survival than that historically reported for patients enrolled in treatment trials for cancer of unknown primary.

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.

Comparison of histopathology to gene expression profiling for the diagnosis of metastatic cancer

Background

Determining the primary site of metastatic cancer with confidence can be challenging. Pathologists commonly use a battery of immunohistochemical (IHC) stains to determine the primary site. Gene expression profiling (GEP) has found increasing use, particularly in the most difficult cases. In this pilot study, a direct comparison between GEP and IHC-guided methods was performed.

Methods

Ten archived formalin-fixed paraffin embedded metastatic tumor samples for which the primary site had been clinically determined were selected. Five pathologists who were blinded to the diagnosis were asked to determine the primary site using IHC and other stains selected from a panel of 84 stains. Each pathologist was provided patient sex, biopsy site and gross sample description only. Slides were digitized using ScanScope®XT at 0.25 μm/pixel. Each evaluating pathologist was allowed to provide a diagnosis in three stages: initial (after reviewing the H&E image), intermediate (after reviewing images from the first batch of stains) and final diagnosis (after the second batch of stains if requested). GEP was performed using the only FDA-cleared test for this intended use, the Pathwork Tissue of Origin Test. No sample information was provided for GEP testing except for patient sex. Results were reported as the tumor tissue type with the highest similarity score.

Results

In this feasibility study, GEP determined the correct primary site in 9 of the 10 cases (90%), compared to the IHC-guided method which determined the correct primary site for 32 of 50 case evaluations (average 64%, range 50% to 80%). The five pathologists directing the IHC-guided method ordered an average of 8.8 stains per case (range 1 to 18). GEP required an average of 3 slides per case (range 1 to 4).

Conclusions

Results of the pilot study suggest that GEP provides correct primary site identification in a higher percentage of metastatic cases than IHC-guided methods, and uses less tissue. A larger comparative effectiveness study using this study design is needed to confirm the results.

Virtual slides

The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1749854104745508

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.