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

Tracing unknown tumor origins with a biological-pathway-based transformer model

Cancer of unknown primary (CUP) represents metastatic cancer where the primary site remains unidentified despite standard diagnostic procedures. To determine the tumor origin in such cases, we developed BPformer, a deep learning method integrating the transformer model with prior knowledge of biological pathways. Trained on transcriptomes from 10,410 primary tumors across 32 cancer types, BPformer achieved remarkable accuracy rates of 94%, 92%, and 89% in primary tumors and primary and metastatic sites of metastatic tumors, respectively, surpassing existing methods. Additionally, BPformer was validated in a retrospective study, demonstrating consistency with tumor sites diagnosed through immunohistochemistry and histopathology. Furthermore, BPformer was able to rank pathways based on their contribution to tumor origin identification, which helped to classify oncogenic signaling pathways into those that are highly conservative among different cancers versus those that are highly variable depending on their origins.

Clinical outcomes of patients diagnosed with cancer of unknown primary or malignancy of undefined primary origin who were referred to a regional cancer center

BACKGROUND

A regional cancer hospital has been identified to be crucial in the management of malignancies of undefined primary origin (MUO) and cancer of unknown primary (CUP). This hospital primarily consists of oncologists with expertise in CUP, pathologists, and interventional radiologists. Early consultation or referral of MUO and CUP to a cancer hospital is deemed important.

METHODS

This study retrospectively collected and analyzed the clinical, pathological, and outcome data of all patients (n = 407) referred to the Aichi Cancer Center Hospital (ACCH) in Japan over an 8-year period.

RESULTS

In total, 30% of patients were referred for a second opinion. Among 285 patients, 13% had non-neoplastic disease or confirmed primary site and 76% had confirmed CUP (cCUP), with 29% of cCUP being identified as favorable risk. In 155 patients with unfavorable-risk CUP, 73% had primary sites predicted by immunohistochemistry (IHC) and distribution of metastatic sites, whereas 66% of them received site-specific therapies based on the predicted primary sites. The median overall survival (OS) was found to be poor in patients with MUO (1 month) and provisional CUP (6 months). In addition, the median OS of 206 patients with cCUP treated at the ACCH was 16 months (favorable risk, 27 months; unfavorable risk, 12 months). No significant difference was noted in OS between patients with non-predictable and predictable primary-sites (13 vs 12 months, p = 0.411).

CONCLUSION

The outcome of patients with unfavorable-risk CUP remains to be poor. Site-specific therapy based on IHC is not recommended for all patients with unfavorable-risk CUP.

Carcinoma of Unknown Primary Origin: Application of Immunohistochemistry With Emphasis to Different Cytokeratin 7 and 20 Staining Patterns

BACKGROUND

Although the primary origin of some carcinomas may be obscure to clinicians, its identification is crucial as it affects prognosis and treatment (especially novel targeted therapies). Immunohistochemistry (IHC) may be helpful in identifying the primary origin of carcinomas. This retrospective survey aimed to evaluate the frequency and accuracy of each IHC marker used to determine the origin of carcinomas.

METHODS

The review of pathology department archives revealed 307 cases of cancer of unknown primary origin (CUP) between 2015 and 2020, which were accessible in the department archives. Demographic information, site of biopsy, clinical and pathologic diagnoses, and IHC results of the patients were collected.

RESULTS

The patients included 157 (51.15%) men and 150 (48.85%) women. The age of the patients ranged from 14 to 92 years, including 106 (34.5%) expired cases. In 27% of cases, the primary origin of carcinoma remained unknown. The agreement between pathologic and clinical diagnoses was 59%. The most common pattern of cytokeratin (CK) expression in CUP was CK7+/CK20- (55.3%), followed by CK7-/CK20- (19%), CK7+/CK20+ (15%), and CK7-/CK20+ (10.7%), respectively.

CONCLUSION

The IHC analysis may improve the diagnosis of CUPs. However, the origin of some cases remains unknown despite an IHC analysis, thereby necessitating the use of more diagnostic procedures or gene expression studies for reaching a definitive diagnosis.

Predicting Mismatch-Repair Status in Rectal Cancer Using Multiparametric MRI-Based Radiomics Models: A Preliminary Study

Detecting mismatch-repair (MMR) status is crucial for personalized treatment strategies and prognosis in rectal cancer (RC). A preoperative, noninvasive, and cost-efficient predictive tool for MMR is critically needed. Therefore, this study developed and validated machine learning radiomics models for predicting MMR status in patients directly on preoperative MRI scans. Pathologically confirmed RC cases administered surgical resection in two distinct hospitals were examined in this retrospective trial. Totally, 78 and 33 cases were included in the training and test sets, respectively. Then, 65 cases were enrolled as an external validation set. Radiomics features were obtained from preoperative rectal MR images comprising T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), contrast-enhanced T1-weighted imaging (T1WI), and combined multisequences. Four optimal features related to MMR status were selected by the least absolute shrinkage and selection operator (LASSO) method. Support vector machine (SVM) learning was adopted to establish four predictive models, i.e., Model_T2WI, Model_DWI, Model_CE-T1WI, and Model_combination, whose diagnostic performances were determined and compared by receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Model_combination had better diagnostic performance compared with the other models in all datasets (all p < 0.05). The usefulness of the proposed model was confirmed by DCA. Therefore, the present pilot study showed the radiomics model combining multiple sequences derived from preoperative MRI is effective in predicting MMR status in RC cases.

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.

Clinical validation of a 90-gene expression test for tumor tissue of origin diagnosis: a large-scale multicenter study of 1417 patients

BACKGROUND

Once malignancy tumors were diagnosed, the determination of tissue origin and tumor type is critical for clinical management. Although the significant advance in imaging techniques and histopathological approaches, the diagnosis remains challenging in patients with metastatic and poorly differentiated or undifferentiated tumors. Gene expression profiling has been demonstrated the ability to classify multiple tumor types. The present study aims to assess the performance of a 90-gene expression test for tumor classification (i.e. the determination of tumor tissue of origin) in real clinical settings.

METHODS

Formalin-fixed paraffin-embedded samples and associated clinicopathologic information were collected from three cancer centers between January 2016 and January 2021. A total of 1417 specimens that met quality control criteria (RNA quality, tumor cell content ≥ 60% and so on) were analyzed by the 90-gene expression test to identify the tumor tissue of origin. The performance was evaluated by comparing the test results with histopathological diagnosis.

RESULTS

The 1417 samples represent 21 main tumor types classified by common tissue origins and anatomic sites. Overall, the 90-gene expression test reached an accuracy of 94.4% (1338/1417, 95% CI: 0.93 to 0.96). Among different tumor types, sensitivities were ranged from 74.2% (head&neck tumor) to 100% (adrenal carcinoma, mesothelioma, and prostate cancer). Sensitivities for the most prevalent cancers of lung, breast, colorectum, and gastroesophagus are 95.0%, 98.4%, 93.9%, and 90.6%, respectively. Moreover, specificities for all 21 tumor types are greater than 99%.

CONCLUSIONS

These findings showed robust performance of the 90-gene expression test for identifying the tumor tissue of origin and support the use of molecular testing as an adjunct to tumor classification, especially to those poorly differentiated or undifferentiated tumors in clinical practice.

Radiomics and Radiogenomics in Evaluation of Colorectal Cancer Liver Metastasis

Colorectal cancer is one common digestive malignancy, and the most common approach of blood metastasis of colorectal cancer is through the portal vein system to the liver. Early detection and treatment of liver metastasis is the key to improving the prognosis of the patients. Radiomics and radiogenomics use non-invasive methods to evaluate the biological properties of tumors by deeply mining the texture features of images and quantifying the heterogeneity of metastatic tumors. Radiomics and radiogenomics have been applied widely in the detection, treatment, and prognostic evaluation of colorectal cancer liver metastases. Based on the imaging features of the liver, this paper reviews the current application of radiomics and radiogenomics in the diagnosis, treatment, monitor of disease progression, and prognosis of patients with colorectal cancer liver metastases.

Biomarker discovery studies for patient stratification using machine learning analysis of omics data: a scoping review

OBJECTIVE

To review biomarker discovery studies using omics data for patient stratification which led to clinically validated FDA-cleared tests or laboratory developed tests, in order to identify common characteristics and derive recommendations for future biomarker projects.

DESIGN

Scoping review.

METHODS

We searched PubMed, EMBASE and Web of Science to obtain a comprehensive list of articles from the biomedical literature published between January 2000 and July 2021, describing clinically validated biomarker signatures for patient stratification, derived using statistical learning approaches. All documents were screened to retain only peer-reviewed research articles, review articles or opinion articles, covering supervised and unsupervised machine learning applications for omics-based patient stratification. Two reviewers independently confirmed the eligibility. Disagreements were solved by consensus. We focused the final analysis on omics-based biomarkers which achieved the highest level of validation, that is, clinical approval of the developed molecular signature as a laboratory developed test or FDA approved tests.

RESULTS

Overall, 352 articles fulfilled the eligibility criteria. The analysis of validated biomarker signatures identified multiple common methodological and practical features that may explain the successful test development and guide future biomarker projects. These include study design choices to ensure sufficient statistical power for model building and external testing, suitable combinations of non-targeted and targeted measurement technologies, the integration of prior biological knowledge, strict filtering and inclusion/exclusion criteria, and the adequacy of statistical and machine learning methods for discovery and validation.

CONCLUSIONS

While most clinically validated biomarker models derived from omics data have been developed for personalised oncology, first applications for non-cancer diseases show the potential of multivariate omics biomarker design for other complex disorders. Distinctive characteristics of prior success stories, such as early filtering and robust discovery approaches, continuous improvements in assay design and experimental measurement technology, and rigorous multicohort validation approaches, enable the derivation of specific recommendations for future studies.

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.

MicroRNA expression profiling with a droplet digital PCR assay enables molecular diagnosis and prognosis of cancers of unknown primary

Metastasis is responsible for the majority of cancer‐related deaths. Particularly, challenging is the management of metastatic cancer of unknown primary site (CUP), whose tissue of origin (TOO) remains undetermined even after extensive investigations and whose therapy is rather unspecific and poorly effective. Molecular approaches to identify the most probable TOO of CUPs can overcome some of these issues. In this study, we applied a predetermined set of 89 microRNAs (miRNAs) to infer the TOO of 53 metastatic cancers of unknown or uncertain origin. The miRNA expression was assessed with droplet digital PCR in 159 samples, including primary tumors from 17 tumor classes (reference set) and metastases of known and unknown origin (test set). We combined two different statistical models for class prediction to obtain the most probable TOOs: the nearest shrunken centroids approach of Prediction Analysis of Microarrays (PAMR) and the least absolute shrinkage and selection operator (LASSO) models. The molecular test was successful for all formalin‐fixed paraffin‐embedded samples and provided a TOO identification within 1 week from the biopsy procedure. The most frequently predicted origins were gastrointestinal, pancreas, breast, lung, and bile duct. The assay was applied also to multiple metastases from the same CUP, collected from different metastatic sites: The predictions showed a strong agreement, intrinsically validating our assay. The final CUPs' TOO prediction was compared with the clinicopathological hypothesis of primary site. Moreover, a panel of 13 miRNAs proved to have prognostic value and be associated with overall survival in CUP patients. Our study demonstrated that miRNA expression profiling in CUP samples could be employed as diagnostic and prognostic test. Our molecular analysis can be performed on request, concomitantly with standard diagnostic workup and in association with genetic profiling, to offer valuable indications about the possible primary site, thereby supporting treatment decisions.

Ultrasound Image-Based Radiomics: An Innovative Method to Identify Primary Tumorous Sources of Liver Metastases

OBJECTIVES

To develop radiomic models of B-mode ultrasound (US) signatures for determining the origin of primary tumors in metastatic liver disease.

METHODS

A total of 254 patients with a diagnosis of metastatic liver disease were included in this retrospective study. The patients were divided into 3 groups depending on the origin of the primary tumor: group 1 (digestive tract versus non-digestive tract tumors), group 2 (breast cancer versus non-breast cancer), and group 3 (lung cancer versus other malignancies). The patients in each group were allocated to a training or testing set (a ratio of 8:2). The region of interest of liver metastasis was determined through manual differentiation of the tumors, and radiomic signatures were acquired from B-mode US images. Optimal features were selected to develop 3 radiomic models using multiple-dimensionality reduction and classifier screening. The area under the curve (AUC) of the receiver operating characteristic curve was applied to assess each model's performance.

RESULTS

A total of 5936 features were extracted, and 40, 6, and 14 optimal features were sequentially identified for the development of radiomic models for groups 1, 2, and 3, respectively, with training set AUC values of 0.938, 0.974, and 0.768 and testing set AUC values of 0.767, 0.768, and 0.750. The differences in age, sex, and number of liver metastatic lesions varied greatly between the 4 primary tumors (P < .050).

CONCLUSIONS

B-mode US radiomic models could be effective supplemental means to identify the origin of hepatic metastatic lesions (ie, unknown primary sites).

Identification of Tumor Tissue of Origin with RNA-Seq Data and Using Gradient Boosting Strategy

BACKGROUND

Cancer of unknown primary (CUP) is a type of malignant tumor, which is histologically diagnosed as a metastatic carcinoma while the tissue-of-origin cannot be identified. CUP accounts for roughly 5% of all cancers. Traditional treatment for CUP is primarily broad-spectrum chemotherapy; however, the prognosis is relatively poor. Thus, it is of clinical importance to accurately infer the tissue-of-origin of CUP.

METHODS

We developed a gradient boosting framework to trace tissue-of-origin of 20 types of solid tumors. Specifically, we downloaded the expression profiles of 20,501 genes for 7713 samples from The Cancer Genome Atlas (TCGA), which were used as the training data set. The RNA-seq data of 79 tumor samples from 6 cancer types with known origins were also downloaded from the Gene Expression Omnibus (GEO) for an independent data set.

RESULTS

400 genes were selected to train a gradient boosting model for identification of the primary site of the tumor. The overall 10-fold cross-validation accuracy of our method was 96.1% across 20 types of cancer, while the accuracy for the independent data set reached 83.5%.

CONCLUSION

Our gradient boosting framework was proven to be accurate in identifying tumor tissue-of-origin on both training data and independent testing data, which might be of practical usage.

Cell-blocks and immunohistochemistry

The interpretation of results on immunostained cell-block sections has to be compared with the cumulative published data derived predominantly from formalin-fixed paraffin-embedded (FFPE) tissue sections. Because of this, it is important to recognize that the fixation and processing protocol should not be different from the routinely processed FFPE surgical pathology tissue. Exposure to non-formalin fixatives or reagents may interfere with the diagnostic immunoreactivity pattern. The immunoprofile observed on such cell-blocks, which are not processed in a manner similar to the surgical pathology specimens, may not be representative resulting in aberrant results. The field of immunohistochemistry (IHC) is advancing continuously with the standardization of many immunomarkers. A variety of technical advances such as multiplex IHC with refined methodologies and automation is increasing its role in clinical applications. The recent addition of rabbit monoclonal antibodies has further improved sensitivity. As compared to the mouse monoclonal antibodies, the rabbit monoclonal antibodies have 10 to 100 fold higher antigen affinity. Most of the scenarios involve the evaluation of coordinate immunostaining patterns in cell-blocks with relatively scant diagnostic material without proper orientation which is usually retained in most of the surgical pathology specimens. These challenges are addressed if cell-blocks are prepared with some dedicated methodologies such as NextGen CelBloking™ (NGCB) kits. Cell-blocks prepared by NGCB kits also facilitate the easy application of the SCIP (subtractive coordinate immunoreactivity pattern) approach for proper evaluation of coordinate immunoreactivity. Various cell-block and IHC-related issues are discussed in detail.

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.

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.

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.

Brain Metastasis from Unknown Primary Tumour: Moving from Old Retrospective Studies to Clinical Trials on Targeted Agents

Simple Summary

Brain metastases (BMs) are the most common intracranial tumours in adults and occur up to 3–10 times more frequently than primary brain tumours. In up to 15% of patients with BM, the primary tumour cannot be identified. These cases are known as BM of cancer of unknown primary (CUP) (BM-CUP). The understanding of BM-CUP, despite its relative frequency and unfavourable outcome, is still incomplete and clear indications on management are missing. The aim of this review is to summarize current evidence on the diagnosis and treatment of BM-CUP.

Abstract

Brain metastases (BMs) are the most common intracranial tumours in adults and occur up to 3–10 times more frequently than primary brain tumours. BMs may be the cause of the neurological presenting symptoms in patients with otherwise previously undiagnosed cancer. In up to 15% of patients with BMs, the primary tumour cannot be identified. These cases are known as BM of cancer of unknown primary (CUP) (BM-CUP). CUP has an early and aggressive metastatic spread, poor response to chemotherapy, and poor prognosis. The pathogenesis of CUP seems to be characterized by a specific underlying pro-metastatic signature. The understanding of BM-CUP, despite its relative frequency and unfavourable outcome, is still incomplete and clear indications on management are missing. Advances in diagnostic tools, molecular characterization, and target therapy have shifted the paradigm in the approach to metastasis from CUP: while earlier studies stressed the importance of finding the primary tumour and deciding on treatment based on the primary diagnosis, most recent studies focus on the importance of identifying targetable molecular markers in the metastasis itself. The aim of this review is to summarize current evidence on BM-CUP, from the diagnosis and pathogenesis to the treatment, with a focus on available studies and ongoing clinical trials.

CUP-AI-Dx: A tool for inferring cancer tissue of origin and molecular subtype using RNA gene-expression data and artificial intelligence

BACKGROUND

Cancer of unknown primary (CUP), representing approximately 3-5% of all malignancies, is defined as metastatic cancer where a primary site of origin cannot be found despite a standard diagnostic workup. Because knowledge of a patient's primary cancer remains fundamental to their treatment, CUP patients are significantly disadvantaged and most have a poor survival outcome. Developing robust and accessible diagnostic methods for resolving cancer tissue of origin, therefore, has significant value for CUP patients.

METHODS

We developed an RNA-based classifier called CUP-AI-Dx that utilizes a 1D Inception convolutional neural network (1D-Inception) model to infer a tumor's primary tissue of origin. CUP-AI-Dx was trained using the transcriptional profiles of 18,217 primary tumours representing 32 cancer types from The Cancer Genome Atlas project (TCGA) and International Cancer Genome Consortium (ICGC). Gene expression data was ordered by gene chromosomal coordinates as input to the 1D-CNN model, and the model utilizes multiple convolutional kernels with different configurations simultaneously to improve generality. The model was optimized through extensive hyperparameter tuning, including different max-pooling layers and dropout settings. For 11 tumour types, we also developed a random forest model that can classify the tumour's molecular subtype according to prior TCGA studies. The optimised CUP-AI-Dx tissue of origin classifier was tested on 394 metastatic samples from 11 tumour types from TCGA and 92 formalin-fixed paraffin-embedded (FFPE) samples representing 18 cancer types from two clinical laboratories. The CUP-AI-Dx molecular subtype was also independently tested on independent ovarian and breast cancer microarray datasets FINDINGS: CUP-AI-Dx identifies the primary site with an overall top-1-accuracy of 98.54% in cross-validation and 96.70% on a test dataset. When applied to two independent clinical-grade RNA-seq datasets generated from two different institutes from the US and Australia, our model predicted the primary site with a top-1-accuracy of 86.96% and 72.46% respectively.

INTERPRETATION

The CUP-AI-Dx predicts tumour primary site and molecular subtype with high accuracy and therefore can be used to assist the diagnostic work-up of cancers of unknown primary or uncertain origin using a common and accessible genomics platform.

FUNDING

NIH R35 GM133562, NCI P30 CA034196, Victorian Cancer Agency Australia.

Defining a Distinct Immunotherapy Eligible Subset of Patients with Cancer of Unknown Primary Using Gene Expression Profiling with the 92-Gene Assay

Background

Although recent advances in immunotherapy have transformed the treatment landscape for many anatomically defined cancers, these therapies are currently not approved for patients diagnosed with cancer of unknown primary (CUP). Molecular cancer classification using gene expression profiling (GEP) assays has the potential to identify tumor type and putative primary cancers and thereby may allow consideration of immune checkpoint inhibitor (ICI) therapy options for a subset of patients with CUP. Herein, we evaluated and characterized the ability of a 92‐gene assay (CancerTYPE ID) to provide a molecular diagnosis and identify putative tumor types that are known to be sensitive to ICI therapies in patients with CUP or uncertain diagnosis.

Findings

A total of 24,426 cases from a large‐scale research database of 92‐gene assay clinical cases were classified, of which 9,350 (38%) were predicted to have an ICI‐eligible tumor type. All ICIs with approved indications as of March 2020 were included in the analysis. Non‐small cell lung cancer (NSCLC) was the most frequent molecular diagnosis and accounted for 33% of the ICI‐eligible tumor types identified and 13% of the overall reportable results. In addition to NSCLC, the assay also frequently identified urothelial carcinomas, gastric cancer, and head and neck squamous cell carcinoma. The distributions of identified tumor types with indications for ICI therapy were similar across age and gender.

Conclusions

Results suggest that molecular profiling with the 92‐gene assay identifies a subset of ICI‐eligible putative primary cancers in patients with CUP. We propose a treatment strategy based on available tests, including clinicopathologic features, GEP, and ICI biomarkers of response.

Regulatory approval of immune checkpoint inhibitors (ICI) is restricted to anatomically defined cancers with a known primary. This article reports cases submitted for 92‐gene assay testing with an unknown or uncertain diagnosis for which the subsequent post‐test report included a tumor type linked to an FDA‐approved ICI therapy, with the goal of identifying characteristics of cancers of unknown primary tumors that might benefit from immunotherapy.

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.

90-gene signature assay for tissue origin diagnosis of brain metastases

Background

Brain metastases (BM) are the most common intracranial tumors. 2–14% of BM patients present with unknown primary site despite intensive evaluations. This study aims to evaluate the performance of a 90-gene expression signature in determining the primary sites for BM samples.

Methods

The sequence-based gene expression profiles of 708 primary brain tumors (PBT) collected from The Cancer Genome Atlas (TCGA) database were analyzed by the 90-gene expression signature, with a similarity score for each of 21 common tumor types. We then used Optimal Binning algorithm to generate a threshold for separating PBT from BM. Eighteen PBT samples were analyzed to substantiate the reliability of the threshold. In addition, the performance of the 90-gene expression signature for molecular classification of metastatic brain tumors was validated in a cohort of 48 BM samples with the known origin. For each BM sample, the tumor type with the highest similarity score was considered tissue of origin. When a sample was diagnosed as PBT, but the similarity score below the threshold, the second prediction was considered as the primary site.

Results

A threshold of the similarity score, 70, was identified to discriminate PBT from BM (PBT: > 70, BM: ≤ 70) with an accuracy of 99% (703/708, 95% CI 98–100%). The 90-gene expression signature was further validated with 18 PBT and 44 BM samples. The results of 18 PBT samples matched reference diagnosis with a concordance rate of 100%, and all similarity scores were above the threshold. Of 44 BM samples, the 90-gene expression signature accurately predicted primary sites in 89% (39/44, 95% CI 75–96%) of the cases.

Conclusions

Our findings demonstrated the potential that the 90-gene expression signature could serve as a powerful tool for accurately identifying the primary sites of metastatic brain tumors.

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.

Targeted sequencing identifies the mutational signature of double primary and metastatic malignancies: a case report

BACKGROUND

The accurate identification of the tissue of origin is critical for optimal management of cancer patients particularly those who develop multiple malignancies; however, conventional diagnostic methods at times may fail to provide conclusive diagnosis of the origin of the malignancy. Herein, we describe the use of targeted sequencing in distinguishing the primary and metastatic tumors in a patient with metachronous malignancies in the lung, colon and kidney.

CASE PRESENTATION

In December 2016, a 55-year-old Chinese male was diagnosed with stage IB lung adenosquamous carcinoma and treated with left lower lobectomy and 4 cycles of platinum-based chemotherapy. After being disease-free for 3.5 months, three colonic polyps were discovered and were diagnosed as invasive adenocarcinoma after polypectomy. Within 5.4 months from the polypectomy, squamous cell renal carcinoma was identified and was managed by radical nephrectomy. Immunohistochemistry results were inconclusive on the origin of the kidney tumor. Hence, the three archived surgical tissue samples were sequenced using a targeted panel with 520 cancer-related genes. Analysis revealed similar mutational signature between the lung and kidney tumors and a distinct mutational profile for the colon tumor, suggesting that the lung and colon malignancies were primary tumors, while the kidney tumor originated from the lung, revealing a diagnosis of metastatic double primary cancer - lung carcinoma with renal cell metastasis and second primary colon carcinoma.

CONCLUSION

Mutational profiling using targeted sequencing is valuable not only for the detection of actionable mutations, but also in the identification of the origin of tumors. This diagnostic approach should be considered in similar scenarios.

Ancillary Techniques in Cytologic Specimens Obtained from Solid Lesions of the Pancreas: A Review

Advanced methods of molecular characterization have elucidated the genetic, epigenetic, and proteomic alterations associated with the broad spectrum of pancreatic disease, particularly neoplasia. Next-generation sequencing, in particular, has revealed the genomic diversity among pancreatic ductal adenocarcinoma, neuroendocrine and acinar tumors, solid pseudopapillary neoplasm, and other pancreatico-biliary neoplasms. Differentiating these entities from one another by morphologic analysis alone may be challenging, especially when examining the small quantities of diagnostic material inherent to cytologic specimens. In order to enhance the sensitivity and specificity of pancreatic cytomorphology, multiple diagnostic, prognostic, and predictive ancillary tests have been and continue to be developed. Although a great number of such tests have been developed for evaluation of specimens collected from cystic lesions and strictures, ancillary techniques also play a significant role in the evaluation of cytologic specimens obtained from solid lesions of the pancreas. Furthermore, while some tests have been developed to differentiate diagnostic entities from one another, others have been developed to simply identify dysplasia and malignancy. Ancillary studies are particularly important in the subset of cases for which cytomorphologic analysis provides a result that is equivocal or insufficient to guide clinical management. Selection of appropriate ancillary testing modalities requires familiarity with both their methodology and the molecular basis of the pancreatic diseases for which testing is being performed.

Passenger mutations accurately classify human tumors

Determining the cancer type and molecular subtype has important clinical implications. The primary site is however unknown for some malignancies discovered in the metastatic stage. Moreover liquid biopsies may be used to screen for tumoral DNA, which upon detection needs to be assigned to a site-of-origin. Classifiers based on genomic features are a promising approach to prioritize the tumor anatomical site, type and subtype. We examined the predictive ability of causal (driver) somatic mutations in this task, comparing it against global patterns of non-selected (passenger) mutations, including features based on regional mutation density (RMD). In the task of distinguishing 18 cancer types, the driver mutations-mutated oncogenes or tumor suppressors, pathways and hotspots-classified 36% of the patients to the correct cancer type. In contrast, the features based on passenger mutations did so at 92% accuracy, with similar contribution from the RMD and the trinucleotide mutation spectra. The RMD and the spectra covered distinct sets of patients with predictions. In particular, introducing the RMD features into a combined classification model increased the fraction of diagnosed patients by 50 percentage points (at 20% FDR). Furthermore, RMD was able to discriminate molecular subtypes and/or anatomical site of six major cancers. The advantage of passenger mutations was upheld under high rates of false negative mutation calls and with exome sequencing, even though overall accuracy decreased. We suggest whole genome sequencing is valuable for classifying tumors because it captures global patterns emanating from mutational processes, which are informative of the underlying tumor biology.

Integrating a 92-Gene Expression Analysis for the Management of Neuroendocrine Tumors of Unknown Primary

Background: Neuroendocrine tumors (NETs) are rare tumors that can originate from any part of the body. Often, imaging or exploratory surgery can assist in the identification of the tumor primary site, which is critical to the management of the disease. Neuroendocrine tumors (NETs) of unknown primary constitute approximately 10-15% of all NETs. Determining the original site of the tumor is critical to providing appropriate and effective treatment. Methods: We performed a retrospective review of neuroendocrine tumors at our institution between 2012 and 2016 using a 92-gene cancer ID analysis. Results: 56 patients with NETs of unknown primary were identified. Samples for 38 of the 56 underwent the 92-gene cancer ID analysis. The primary site of the tumor was identified with >95% certainty in 35 of the 38 patients. Conclusion: The 92-gene cancer ID analysis identified a primary site in 92% of our NETs study cohort that previously had been unknown. The results have direct implications on management of patients with regard to FDA-approved treatment options.

Multi-Institutional, Prospective Clinical Utility Study Evaluating the Impact of the 92-Gene Assay (CancerTYPE ID) on Final Diagnosis and Treatment Planning in Patients With Metastatic Cancer With an Unknown or Unclear Diagnosis

Purpose

Metastatic cancers of unknown primary or with unclear diagnoses pose diagnostic and management challenges, often leading to poor outcomes. Studies of the 92-gene assay have demonstrated improved diagnostic accuracy compared with standard pathology techniques and improved survival in patients treated on the basis of assay results. The current study assessed the clinical impact of the 92-gene assay on diagnostic and treatment decisions for patients with unknown or uncertain diagnoses.

Methods

Patients in this prospective, multi-institutional, decision-impact study included those for whom the 92-gene assay was ordered as part of routine care. Participating physicians completed electronic case report forms that contained standardized, specialty-specific questionnaires. Data collection included patient and tumor characteristics and clinical history. The key study objective of clinical impact was calculated on the basis of changes in final diagnosis and treatment after testing.

Results

Data collection included 444 patients, 107 physicians (73 oncologists and 34 pathologists), and 28 sites. Molecular diagnoses from 22 different tumor types and subtypes across all cases were provided in 95.5% of patients with a reportable result (n = 397). Physicians reported that the 92-gene assay was used broadly for diagnostic dilemmas that ranged from single suspected tumor type (29%) to a differential diagnosis of two or more suspected tumor types (30%) or cancers of unknown primary (41%). Integration of 92-gene assay results led to a change in the recommended treatment in 47% of patients.

Conclusion

Findings from this clinical utility study demonstrate that the 92-gene assay led to a change in treatment decisions in every other patient case. These data additionally define the role of this assay in clinical practice and strongly support the consideration of molecular tumor typing in the diagnosis and treatment planning of patients with metastatic cancer with unknown or uncertain diagnosis.

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.

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.

Molecular classification of cancer with the 92-gene assay in cytology and limited tissue samples

BACKGROUND

Detailed molecular evaluation of cytology and limited tissue samples is increasingly becoming the standard for cancer care. Reproducible and accurate diagnostic approaches with reduced demands on cellularity are an ongoing unmet need. This study evaluated the performance of a 92-gene assay for molecular diagnosis of tumor type/subtype in cytology and limited tissue samples.

METHODS

Clinical validation of accuracy for the 92-gene assay in limited tissue samples such as cytology cell blocks, core biopsies and small excisions was conducted in a blinded multi-institutional study (N = 109, 48% metastatic, 53% grade II and III). Analytical success rate and diagnostic utility were evaluated in a consecutive series of 644 cytology cases submitted for clinical testing.

RESULTS

The 92-gene assay demonstrated 91% sensitivity (95% CI [0.84, 0.95]) for tumor classification, with high accuracy maintained irrespective of specimen type (100%, 92%, and 86% in FNA/cytology cell blocks, core biopsies, and small excisions, respectively; p = 0.26). The assay performed equally well for metastatic versus primary tumors (90% vs 93%, p = 0.73), and across histologic grades (100%, 90%, 89%, in grades I, II, and III, respectively; p = 0.75). In the clinical case series, a molecular diagnosis was reported in 87% of the 644 samples, identifying 23 different tumor types and allowing for additional mutational analysis in selected cases.

CONCLUSIONS

These findings demonstrate high accuracy and analytical success rate of the 92-gene assay, supporting its utility in the molecular diagnosis of cancer for specimens with limited tissue.

CT Image-based Decision Support System for Categorization of Liver Metastases Into Primary Cancer Sites: Initial Results

RATIONALE AND OBJECTIVES

This study aimed to provide decision support for the human expert, to categorize liver metastases into their primary cancer sites. Currently, once a liver metastasis is detected, the process of finding the primary site is challenging, time-consuming, and requires multiple examinations. The proposed system can support the human expert in localizing the search for the cancer source by prioritizing the examinations to probable cancer sites.

MATERIALS AND METHODS

The suggested method is a learning-based approach, using computed tomography (CT) data as the input source. Each metastasis is circumscribed by a radiologist in portal phase and in non-contrast CT images. Visual features are computed from these images, combined into feature vectors, and classified using support vector machine classification. A variety of different features were explored and tested. A leave-one-out cross-validation technique was conducted for classification evaluation. The methods were developed on a set of 50 lesion cases taken from 29 patients.

RESULTS

Experiments were conducted on a separate set of 142 lesion cases taken from 71 patients with four different primary sites. Multiclass categorization results (four classes) achieved low accuracy results. However, the proposed system was found to provide promising results of 83% and 99% for top-2 and top-3 classification tasks, respectively. Moreover, when compared to the experts' ability to distinguish the different metastases, the system shows improved results.

CONCLUSIONS

Automated systems, such as the one proposed, show promising new results and demonstrate new capabilities that, in the future, will be able to provide decision and treatment support for radiologists and oncologists, toward more efficient detection and treatment of cancer.

Diagnosis and management of metastatic neoplasms with unknown primary

In cancer of unknown primary (CUP), metastases are clinically and histologically confirmed, but the primary tumor site remains elusive after extensive work-up. CUPs make up for 2-3% of all epithelial malignancies. The two prevailing histologies are adenocarcinomas and undifferentiated carcinomas, whereas squamous cell carcinomas, neuroendocrine carcinomas and rare histologies account for the remaining 10%. The diagnostic work-up in CUP relies strongly on a detailed immunohistological (IHC) analysis in order to characterize the tumor type, nowadays aided by molecular techniques. Diagnostics also include a thorough clinical examination, a basic lab draw with the most relevant tumor markers, and cross sectional imaging. Additional PET-CT is recommended in cervical lymph nodes suggestive of head and neck cancer and in limited metastases potentially treatable in curative intent. As for treatment, it is paramount to identify patients who fall into one of the six well defined "favorable" subset categories, namely extragonadal germ cell tumors, adenocarcinoma with isolated unilateral axillary lymph nodes in female patients, squamous cell carcinoma with neck lymph nodes, squamous cell carcinoma with inguinal lymph nodes, serous papillary peritoneal carcinomatosis in females and blastic bone metastasis in males with elevated PSA. These subsets are distinct both regarding the required treatment and the comparably favorable prognosis. Within the remaining "unfavorable" group, patients of colon and renal cancer type should be identified based on IHC and clinical picture, since the prognosis of these patients seems to improve with the use of therapy tailored to the presumed primary as well. For the few patients with limited metastases it should be assessed whether they are candidates for surgery, radiotherapy or surgery followed by irradiation in curative intent. The remaining majority of patients are treated with empiric palliative chemotherapy, typically a platinum - paclitaxel combination, though the level of evidence for this therapy recommendation is low. Gemcitabine alone or in combination can be used as an alternative. Decoding of the molecular profiles in CUP offers the prospect of targeted therapy with novel agents. However, there appears to be no uniform molecular pattern for CUP, and the observed molecular diversity thus poses a challenge to respective clinical trials.

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.

The clinical utility of molecular genetic cancer profiling

INTRODUCTION

Next-Generation-Sequencing (NGS) has enabled gene mutation profiling - cataloguing sequence variants and modifications in clinical assays encompassing tens to thousands of genes in tumors and in germlines. The clinical benefit of applying multi-gene NGS to diverse applications in various malignancies remains to be demonstrated.

AREAS COVERED

Applications of gene mutation profiling in oncology include screening cancer-prone families, classification of malignancies, treatment selection, and monitoring the response to treatment of solid tumors (the 'liquid biopsy'). Google Scholar was used to search PubMed for the period 2011-2016 using combinations of the following search terms: 'clinical utility', NGS, 'molecular diagnostics'. Expert commentary: Clinical studies are in progress pairing mutation profiling with streamlined new trial designs to speed identification of promising drug-target combinations and to see if genotype-informed treatment selection will improve outcome across a spectrum of histologies. The analytical advantages and falling cost of NGS make focused gene panels likely to become the dominant modality in molecular diagnostic testing even if trials eventually discourage use of large panels to test all malignancies.

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.

Poorly differentiated neoplasms of unknown primary site: diagnostic usefulness of a molecular cancer classifier assay

PURPOSE

Definition of the lineage of poorly differentiated neoplasms (PDNs) presenting as cancer of unknown primary site (CUP) is important since many of these tumors are treatment-sensitive. Gene expression profiling and a molecular cancer classifier assay (MCCA) may provide a new method of diagnosis when standard pathologic evaluation and immunohistochemical (IHC) staining is unsuccessful.

PATIENTS AND METHODS

Thirty of 751 CUP patients (4%) seen from 2000-2012 had PDNs without a definitive lineage diagnosed by histology or IHC (median 18 stains, range 9-46). Biopsies from these 30 patients had MCCA (92-gene reverse transcriptase-polymerase chain reaction mRNA) performed. Additional IHC, gene sequencing, fluorescent in situ hybridization for specific genetic alterations, and repeat biopsies were performed to support MCCA diagnoses, and clinical features correlated. Seven patients had MCCA performed initially and received site-specific therapy.

RESULTS

Lineage diagnoses were made by MCCA in 25 of 30 (83 %) patients, including ten carcinomas (three germ cell, two neuroendocrine, five others), eight sarcomas [three peritoneal mesotheliomas, one primitive neuroectodermal tumor (PNET), four others], five melanomas, and two lymphomas. Additional IHC and genetic testing [BRAF, i(12)p] supported the MCCA diagnoses in 11 of 16 tumors. All seven patients (two germ cell, two neuroendocrine, two mesothelioma, one lymphoma) responded to site-specific therapy based on the MCCA diagnosis, and remain alive (five progression-free) from 25+ to 72+ months.

CONCLUSION

The MCCA provided a specific lineage diagnosis and tissue of origin in most patients with PDNs unclassifiable by standard pathologic evaluation. Earlier use of MCCA will expedite diagnosis and direct appropriate first-line therapy, which is potentially curative for several of these tumor types.

Practical Applications in Immunohistochemistry: Carcinomas of Unknown Primary Site

CONTEXT

-Identification of the site of origin of carcinoma of unknown primary using immunohistochemistry is a frequent requirement of anatomic pathologists. Diagnostic accuracy is crucial, particularly in the current era of targeted therapies and smaller sample sizes.

OBJECTIVES

-To provide practical guidance and suggestions for classifying carcinoma of unknown primary using both proven and new antibodies, as well as targeting panels based on integration of morphologic and clinical features.

DATA SOURCES

-Literature review, the authors' practice experience, and authors' research.

CONCLUSIONS

-With well-performed and interpreted immunohistochemistry panels, anatomic pathologists can successfully identify the site of origin of carcinoma of unknown primary. It is crucial to understand not only the diagnostic uses of the many available antibodies but also the potential limits and pitfalls.

A 2015 update on predictive molecular pathology and its role in targeted cancer therapy: a review focussing on clinical relevance

In April 2013 our group published a review on predictive molecular pathology in this journal. Although only 2 years have passed many new facts and stimulating developments have happened in diagnostic molecular pathology rendering it worthwhile to present an up-date on this topic. A major technical improvement is certainly given by the introduction of next-generation sequencing (NGS; amplicon, whole exome, whole genome) and its application to formalin-fixed paraffin-embedded (FFPE) tissue in routine diagnostics. Based on this 'revolution' the analyses of numerous genetic alterations in parallel has become a routine approach opening the chance to characterize patients' malignant tumors much more deeply without increasing turn-around time and costs. In the near future this will open new strategies to apply 'off-label' targeted therapies, e.g. for rare tumors, otherwise resistant tumors etc. The clinically relevant genetic aberrations described in this review include mutation analyses of RAS (KRAS and NRAS), BRAF and PI3K in colorectal cancer, KIT or PDGFR alpha as well as BRAF, NRAS and KIT in malignant melanoma. Moreover, we present several recent advances in the molecular characterization of malignant lymphoma. Beside the well-known mutations in NSCLC (EGFR, ALK) a number of chromosomal aberrations (KRAS, ROS1, MET) have become relevant. Only very recently has the clinical need for analysis of BRCA1/2 come up and proven as a true challenge for routine diagnostics because of the genes' special structure and hot-spot-free mutational distribution. The genetic alterations are discussed in connection with their increasingly important role in companion diagnostics to apply targeted drugs as efficient as possible. As another aspect of the increasing number of druggable mutations, we discuss the challenges personalized therapies pose for the design of clinical studies to prove optimal efficacy particularly with respect to combination therapies of multiple targeted drugs and conventional chemotherapy. Such combinations would lead to an extremely high complexity that would hardly be manageable by applying conventional study designs for approval, e.g. by the FDA or EMA. Up-coming challenges such as the application of methylation assays and proteomic analyses on FFPE tissue will also be discussed briefly to open the door towards the ultimate goal of reading a patients' tissue as 'deeply' as possible. Although it is yet to be shown, which levels of biological information are most informative for predictive pathology, an integrated molecular characterization of tumors will likely offer the most comprehensive view for individualized therapy approaches. To optimize cancer treatment we need to understand tumor biology in much more detail on morphological, genetic, proteomic as well as epigenetic grounds. Finally, the complex challenges on the level of drug design, molecular diagnostics, and clinical trials make necessary a close collaboration among academic institutions, regulatory authorities and pharmaceutical companies.

Cancer of Unknown Primary origin in the genomic era: Elucidating the dark box of cancer

Cancer of Unknown Primary (CUP) comprises a heterogeneous disease group with diagnosis of metastatic malignancy in the absence of an identifiable primary site after diagnostic work up. CUP may either resemble a specific primary tumor site sharing common clinicopathological characteristics and prognosis, or present as a distinct disease entity with undifferentiated pathological features, usually bearing dismal prognosis. Diagnosis and management have traditionally been based on clinicopathological characteristics and therapeutic strategies have been mainly empirical. In the last decade, the advent of massive gene sequencing and the advances in genomic technologies have shed light on the genomic landscape of CUP. Several gene panel tests are currently commercially available and are used in an effort to correlate the genomic characteristics of a specific CUP tumor to those of a known primary tumor, guiding thus therapeutic management. Nevertheless, these efforts are hampered by the rarity of CUP and the inability to validate the results of such tests due to the paucity of randomized clinical trials. In the current work, we provide an overview of CUP with emphasis on the impact of the genome sequencing technologies on diagnosis and management of these tumors. We also discuss potential implications of genomics for the future treatment of CUP and address the challenges of the implementation of these therapeutic strategies in routine clinical practice.

Metastatic Adenocarcinoma of Unknown Origin Presenting as Small Bowel Perforation

Metastatic malignant tumors that originate from occult primaries are defined as “cancers of unknown origin.” We herein present the case of a 59-year-old man who presented with small bowel perforation secondary to metastatic adenocarcinoma of an unknown primary site. Imaging exhibited two pulmonary nodules, neither of which was dominant, along with mediastinal and retroperitoneal lymphadenopathy. Immunohistochemical profiling of the small bowel biopsy specimens revealed the tumor was most likely pulmonary in origin.

A practical method to determine the site of unknown primary in metastatic neuroendocrine tumors

INTRODUCTION

The site of a primary neuroendocrine tumor (NET) tumor is unknown before treatment in approximately 20% of small bowel (SBNET) and pancreatic (PNET) cases despite extensive workup. It can be difficult to discern a PNET from an SBNET on hematoxylin and eosin stains, and thus, more focused diagnostic tests are required. Immunohistochemistry (IHC) and gene expression profiling are two methods used to identify the tissue of origin from biopsied metastases.

METHODS

Tissue microarrays were created from operative specimens and stained with up to seven antibodies used in the NET-specific IHC algorithm. Expression of four genes for differentiating between PNETs and SBNETs was determined by quantitative polymerase chain reaction and then used in a previously validated gene expression classifier (GEC) algorithm designed to determine the primary site from gastrointestinal NET metastases.

RESULTS

The accuracy of the IHC algorithm in identifying the primary tumor site from a set of 37 metastases was 89%, with only one incorrect call. Three other samples were indeterminate as the result of pan-negative staining. The GEC's accuracy in a set of 136 metastases was 94%. The algorithm identified the primary tumor site in all cases in which IHC failed.

CONCLUSION

Performing IHC, followed by GEC for indeterminate cases, identifies accurately the primary site in SBNET and PNET metastases in virtually all patients.

[The role of pathology in the diagnostics of CUP syndrome]

Cancers of unknown primary (CUP) origin account for 2-3 % of all malignancies in Germany and represent a heterogeneous, often aggressive and clinically challenging group of tumors with early metastatic dissemination for which a standardized diagnostic work-up initially fails to identify the primary site of origin at the time of diagnosis. This article reviews the options and challenges of tissue-based conventional as well as molecular diagnostic procedures to categorize this heterogeneous group of neoplasms. The role of pathology in the diagnostics of CUP syndrome is described as part of a multidisciplinary effort involving oncologists, surgeons and radiologists with the ultimate goal of assisting clinical reasoning and decision-making.

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.