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

Diagnostic Utility of a 90-Gene Expression Assay (Canhelp-Origin) for Patients with Metastatic Cancer with an Unclear or Unknown Diagnosis

BACKGROUND

Metastatic cancers with unclear or unknown origins pose significant challenges in diagnosis and management, frequently leading to suboptimal outcomes. Studies have demonstrated that a 90-gene expression assay is effective in predicting the primary origin and guiding the site-specific therapy to improve prognosis. This study aimed to evaluate the clinical effectiveness of a 90-gene expression assay in patients with unclear or unknown diagnoses.

METHODS

The study encompassed patients for whom a 90-gene expression assay was requested as part of standard care. Data on patient demographics, tumor characteristics, and clinical history were collected. The assay's performance was evaluated by comparing its predicted tumor type with the final histopathological diagnosis.

RESULTS

Among 303 cases analyzed, a 90-gene expression assay successfully identified a molecular-based tumor type for 295 (97.4%) patients. Comparison with histopathological diagnosis revealed an overall agreement of 88.5% (170/192). In patients with a single suspected primary site (n = 140), the assay confirmed the suspected diagnosis in 90.7% of cases. For those with a differential diagnosis (n = 52), the assay narrowed down the possibilities in 82.7% of cases. Moreover, in cases where the histopathology report indicated cancer of unknown primary (n = 103), the assay offered a molecular tumor type prediction with potential clinical significance.

CONCLUSIONS

This study demonstrates the significant impact of a 90-gene expression assay on diagnosis and potential treatment selection for difficult-to-diagnose patients, highlighting its clinical value as a standardized molecular approach to streamline further diagnostic testing for patients with metastatic cancer of unclear or unknown origin. Further prospective study is required to assess whether employing molecular diagnostic classifiers enhances clinical outcomes in these patients.

Spectrum of Findings Seen in Patients With IDH1/2-Mutant Cholangiocarcinoma

BACKGROUND

Cholangiocarcinoma-with a growing incidence rate and poor prognosis-is not an aggressive tumor that is not uncommon. Molecular profiling can reveal actionable aberrations in at least a third of the tumors. This is especially so in the case of intrahepatic cholangiocarcinoma (ICC), where mutations in the isocitrate dehydrogenase 1 and 2 genes (IDH1/2) make up 15%-20% of these tumors. IDH1/2 mutant ICC is a rare disease that has not been adequately reported. To expand the spectrum of findings seen in these patients, we present a single institution case series.

METHODS AND RESULTS

We descriptively characterize the clinical, radiological, and histopathological findings of 12 such patients. IDH1/2 mutant ICC was found in elderly women, with two-thirds of patients having additional co-mutations. Anecdotally, patients who did receive systemic and/or locoregional therapies had long-term durable outcomes.

CONCLUSION

Our findings indicate an increasing need to personalize an approach for these patients with specific molecular alterations. With the advent of the IDH1 inhibitor ivosidenib and other inhibitors in this space, IDH1/2 mutation has both prognostic and predictive value. Our series builds upon the patterns and findings seen in these patients.

Personalized Therapy Selection by Integration of Molecular Cancer Classification by the 92-Gene Assay and Tumor Profiling in Patients With Cancer of Unknown Primary

PURPOSE

Cancer of unknown primary (CUP) is a syndrome comprising metastatic cancers without a clinically identified primary site. Although patients with CUP have an unfavorable prognosis, treatment with site-specific therapies guided by clinical features, standard pathology, and molecular assays can improve overall survival. The 92-gene assay (CancerTYPE ID) is a gene expression-based classifier that helps identify the tissue of origin for metastatic cancers with unknown or uncertain diagnoses. This study reports the frequency of selected molecular aberrations of oncogenes, including KRAS, IDH1/2, BRCA1/2, and BRAF, in patients with CUP in the MOSAIC database to highlight potential treatment options.

METHODS

MOSAIC is a database of patients with CUP submitted for CancerTYPE ID testing and NeoTYPE biomarker testing. Tumor biopsy samples were analyzed by CancerTYPE ID for tumor type identification and further tested for molecular aberrations of oncogenes, including KRAS, IDH1/2, BRCA1/2, and BRAF.

RESULTS

CancerTYPE ID identified a specific tumor type in 92.5% (2,929 of 3,168) of CUP cases in the MOSAIC database. The most commonly identified histological type was adenocarcinoma (75.4%), with pancreaticobiliary being the most common molecularly diagnosed cancer (24.9%). Aberrations in KRAS, IDH1/2, BRCA, and BRAF genes were identified in 18.8% (n = 597) of biopsies. A cancer-specific US Food and Drug Administration (FDA)-approved or investigational targeted therapy was potentially available for 24.6% (n = 147) of these patients.

CONCLUSION

This retrospective analysis supports incorporating CancerTYPE ID into the evaluation for patients with CUP to help determine the tissue of origin and identify actionable genetic alterations. This approach may allow more patients with CUP to benefit from site-specific FDA-approved targeted therapies or enrollment into clinical trials.

Advancing head and neck cancer management: Unveiling the diagnostic and therapeutic potentials of molecular profiling

BACKGROUND

Head and neck cancers (HNC) present diagnostic challenges due to multifocal disease manifestations, posing difficulties in distinguishing between metastatic disease and second primary malignancies (SPM). This complexity underscores the need for advanced diagnostic approaches. Emerging technologies, such as next-generation sequencing (NGS) and molecular classifier assays, show promise in providing precise insights into the diverse etiologies of HNC.

METHOD

In this article, we employed NGS and molecular classifier assays to delve into three distinct clinical cases. The objective was to showcase the instrumental role of these technologies in facilitating accurate diagnoses and differentiating between metastatic disease and SPM in HNC cases.

RESULTS

The results of this series highlight the effectiveness of NGS and molecular classifier assays in enhancing diagnostic accuracy for HNC and contributing to the precise differentiation of disease etiologies. The utilization of these advanced technologies proved instrumental in avoiding unnecessary interventions and paved the way for more targeted and effective treatment strategies.

CONCLUSION

Our findings underscore the necessity of incorporating advanced molecular testing technologies into the diagnostic and therapeutic approaches for HNC, thereby championing a more nuanced and effective approach to managing these complex cases.

A platform-independent AI tumor lineage and site (ATLAS) classifier

Histopathologic diagnosis and classification of cancer plays a critical role in guiding treatment. Advances in next-generation sequencing have ushered in new complementary molecular frameworks. However, existing approaches do not independently assess both site-of-origin (e.g. prostate) and lineage (e.g. adenocarcinoma) and have minimal validation in metastatic disease, where classification is more difficult. Utilizing gradient-boosted machine learning, we developed ATLAS, a pair of separate AI Tumor Lineage and Site-of-origin models from RNA expression data on 8249 tumor samples. We assessed performance independently in 10,376 total tumor samples, including 1490 metastatic samples, achieving an accuracy of 91.4% for cancer site-of-origin and 97.1% for cancer lineage. High confidence predictions (encompassing the majority of cases) were accurate 98-99% of the time in both localized and remarkably even in metastatic samples. We also identified emergent properties of our lineage scores for tumor types on which the model was never trained (zero-shot learning). Adenocarcinoma/sarcoma lineage scores differentiated epithelioid from biphasic/sarcomatoid mesothelioma. Also, predicted lineage de-differentiation identified neuroendocrine/small cell tumors and was associated with poor outcomes across tumor types. Our platform-independent single-sample approach can be easily translated to existing RNA-seq platforms. ATLAS can complement and guide traditional histopathologic assessment in challenging situations and tumors of unknown primary.

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.

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).

Validation of a Transcriptome-Based Assay for Classifying Cancers of Unknown Primary Origin

INTRODUCTION

Cancers assume a variety of distinct histologies, and may originate from a myriad of sites including solid organs, hematopoietic cells, and connective tissue. Clinical decision-making based on consensus guidelines such as the National Comprehensive Cancer Network (NCCN) is often predicated on a specific histologic and anatomic diagnosis, supported by clinical features and pathologist interpretation of morphology and immunohistochemical (IHC) staining patterns. However, in patients with nonspecific morphologic and IHC findings-in addition to ambiguous clinical presentations such as recurrence versus new primary-a definitive diagnosis may not be possible, resulting in the patient being categorized as having a cancer of unknown primary (CUP). Therapeutic options and clinical outcomes are poor for patients with CUP, with a median survival of 8-11 months.

METHODS

Here, we describe and validate the Tempus Tumor Origin (Tempus TO) assay, an RNA-sequencing-based machine learning classifier capable of discriminating between 68 clinically relevant cancer subtypes. Model accuracy was assessed using primary and/or metastatic samples with known subtype.

RESULTS

We show that the Tempus TO model is 91% accurate when assessed on both a retrospectively held out cohort and a set of samples sequenced after model freeze that collectively contained 9210 total samples with known diagnoses. When evaluated on a cohort of CUPs, the model recapitulated established associations between genomic alterations and cancer subtype.

DISCUSSION

Combining diagnostic prediction tests (e.g., Tempus TO) with sequencing-based variant reporting (e.g., Tempus xT) may expand therapeutic options for patients with cancers of unknown primary or uncertain histology.

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.

A comparison of DNA sequencing and gene expression profiling to assist tissue of origin diagnosis in cancer of unknown primary

Cancer of unknown primary (CUP) is a syndrome defined by clinical absence of a primary cancer after standardised investigations. Gene expression profiling (GEP) and DNA sequencing have been used to predict primary tissue of origin (TOO) in CUP and find molecularly guided treatments; however, a detailed comparison of the diagnostic yield from these two tests has not been described. Here, we compared the diagnostic utility of RNA and DNA tests in 215 CUP patients (82% received both tests) in a prospective Australian study. Based on retrospective assessment of clinicopathological data, 77% (166/215) of CUPs had insufficient evidence to support TOO diagnosis (clinicopathology unresolved). The remainder had either a latent primary diagnosis (10%) or clinicopathological evidence to support a likely TOO diagnosis (13%) (clinicopathology resolved). We applied a microarray (CUPGuide) or custom NanoString 18-class GEP test to 191 CUPs with an accuracy of 91.5% in known metastatic cancers for high-medium confidence predictions. Classification performance was similar in clinicopathology-resolved CUPs - 80% had high-medium predictions and 94% were concordant with pathology. Notably, only 56% of the clinicopathology-unresolved CUPs had high-medium confidence GEP predictions. Diagnostic DNA features were interrogated in 201 CUP tumours guided by the cancer type specificity of mutations observed across 22 cancer types from the AACR Project GENIE database (77,058 tumours) as well as mutational signatures (e.g. smoking). Among the clinicopathology-unresolved CUPs, mutations and mutational signatures provided additional diagnostic evidence in 31% of cases. GEP classification was useful in only 13% of cases and oncoviral detection in 4%. Among CUPs where genomics informed TOO, lung and biliary cancers were the most frequently identified types, while kidney tumours were another identifiable subset. In conclusion, DNA and RNA profiling supported an unconfirmed TOO diagnosis in one-third of CUPs otherwise unresolved by clinicopathology assessment alone. DNA mutation profiling was the more diagnostically informative assay. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Clinical features of cancer with unknown primary site (clinical features, treatment, prognosis of cancer with unknown primary site)

Cancer of unknown primary site(CUPs) is a metastatic syndrome with an unidentifiable primary tumor, even after extensive workup to seek the primary site. CUPs accounts for about 3%-5% of the total number of all cancer diagnoses worldwide. The current precision medicine era has reclassified patients with CUPs into the favorable and unfavorable prognostic subset. In this study clinical characteristics and treatment of patients of CUPs were retropactively analysed. Thirty-two patients treated from July 2016 to October 2021 were included in the Affiliated Tumor Hospital of Tianjin Medical University(Tianjin, China).Common symptoms were anemia, fever, enlarged lymph nodes, abdominal pain, edema/multiple serous cavity effusion. Patients with good prognostic factors achieved good outcomes with treatment, conversely, patients with poor prognosis were generally treated empirically and had poorer outcomes. After anti-tumor treatment, the total effective rate was 41 percent(41% was the percentage of patients who achievedtumour respons). To the end of follow-up, after anti-tumor treatment, the median Overall Survival(OS) of patients was 5.4 months.

Developmental Deconvolution for Classification of Cancer Origin

Cancer is partly a developmental disease, with malignancies named based on cell or tissue of origin. However, a systematic atlas of tumor origins is lacking. Here we map the single-cell organogenesis of 56 developmental trajectories to the transcriptomes of over 10,000 tumors across 33 cancer types. We deconvolute tumor transcriptomes into signals for individual developmental trajectories. Using these signals as inputs, we construct a developmental multilayer perceptron (D-MLP) classifier that outputs cancer origin. D-MLP (ROC-AUC: 0.974 for top prediction) outperforms benchmark classifiers. We analyze tumors from patients with cancer of unknown primary (CUP), selecting the most difficult cases in which extensive multimodal workup yielded no definitive tumor type. Interestingly, CUPs form groups distinguished by developmental trajectories, and classification reveals diagnosis for patient tumors. Our results provide an atlas of tumor developmental origins, provide a tool for diagnostic pathology, and suggest developmental classification may be a useful approach for patient tumors.

SIGNIFICANCE

Here we map the developmental trajectories of tumors. We deconvolute tumor transcriptomes into signals for mammalian developmental programs and use this information to construct a deep learning classifier that outputs tumor type. We apply the classifier to CUP and reveal the developmental origins of patient tumors. See related commentary by Wang, p. 2498. This article is highlighted in the In This Issue feature, p. 2483.

Tumor Genomic Profiling to Determine Tissue Origin of Cancers of Unknown Primary: A Single Institute Experience With its Utility and Impact on Patient Management

Tumor genomic profiling represents a promising tool in diagnosis and management of cancer of unknown primary. We report our experience on the impact of genomic profiling in elucidating primary tumor site, correlation with pathologic findings and patient management. Tissue or cytology specimens from 22 cancers of unknown primary were referred for genomic profiling. Reports were available to review in 18 cases; 3 samples were inadequate for analysis. Of the remaining 15 cases, primary tumor site was suggested in 12 cases (80%), whereas it remained indeterminate in 3 (20%). Of the 12 cases, molecular profiling was concordant with light microscopy findings in 3 patients, whereas in 2 cases molecular testing identified a sarcoma, contradicting light microscopy and immunohistochemistry findings. The suggested primary was confirmed by additional immunohistochemistry in 1 case and by endoscopic biopsy in another. In 5 cases, follow-up biopsy or additional testing were not considered necessary for patient management. Three patients received palliative care and 12 received various chemotherapy regimens. Five patients died within a year, whereas 9 were alive more than a year after diagnosis, 3 of who were alive >3 years after diagnosis. In conclusion, genomic profiling helped confirm the original diagnosis and suggested primary sites in two third of our cases. Although many patients may be at a disease stage too advanced to withstand further investigations or underg aggressive therapy, molecular testing improves diagnostic accuracy and may thus assist in selection of the most appropriate therapy.

Comprehensive genomic and epigenomic analysis in cancer of unknown primary guides molecularly-informed therapies despite heterogeneity

The benefit of molecularly-informed therapies in cancer of unknown primary (CUP) is unclear. Here, we use comprehensive molecular characterization by whole genome/exome, transcriptome and methylome analysis in 70 CUP patients to reveal substantial mutational heterogeneity with TP53, MUC16, KRAS, LRP1B and CSMD3 being the most frequently mutated known cancer-related genes. The most common fusion partner is FGFR2, the most common focal homozygous deletion affects CDKN2A. 56/70 (80%) patients receive genomics-based treatment recommendations which are applied in 20/56 (36%) cases. Transcriptome and methylome data provide evidence for the underlying entity in 62/70 (89%) cases. Germline analysis reveals five (likely) pathogenic mutations in five patients. Recommended off-label therapies translate into a mean PFS ratio of 3.6 with a median PFS1 of 2.9 months (17 patients) and a median PFS2 of 7.8 months (20 patients). Our data emphasize the clinical value of molecular analysis and underline the need for innovative, mechanism-based clinical trials.

The identification of molecular biomarkers in cancer of unknown primary site (CUP) cases may enable the improvement of prognosis in these patients. Here, the authors integrate whole genome/exome, transcriptome and methylome data in 70 CUP patients, recommend therapies based on their analysis and report clinical outcome data.

Clinicopathological, molecular and prognostic characteristics of cancer of unknown primary in China: An analysis of 1420 cases

BACKGROUND

Cancer of unknown primary (CUP) is defined the presence of metastatic disease without an identified primary site. An unidentifiable primary site of cancer creates significant challenges for treatment selection. We aimed to describe the clinicopathological, molecular, and prognostic characteristics of Chinese CUP patients.

METHODS

Patients with oncologist-confirmed CUP were identified at Fudan University Shanghai Cancer Center from 2019 to 2020. Information on patient characteristics, tumor presentation, treatment, and outcome were retrospectively collected from the inpatient database and pathological consultation database for descriptive analysis. A multivariable logistic regression model was established to identify factors associated with patient prognosis.

RESULTS

A total of 1420 CUP patients were enrolled in this study. The baseline characteristics of the entire cohort included the following: median age (59 years old), female sex (45.8%), adenocarcinoma (47.7%), and poorly differentiated or undifferentiated tumors (92.1%). For the inpatient cohort, the most common sites where cancer spread included the lymph nodes (41.8%), bone (22.0%), liver (20.1%), and peritoneum/retroperitoneum (16.0%). A total of 77.4% and 58.2% of patients were treated with local therapy and systemic therapy, respectively. Four prognostic factors, including liver metastasis, peritoneal/retroperitoneal metastasis, number of metastatic sites (N ≥ 2), and systemic treatment, were independently associated with overall survival. Additionally, 24.8% (79/318) of patients received molecular testing, including PD-L1, human papillomavirus, genetic variation, and 90-gene expression tests for diagnosis or therapy selection.

CONCLUSION

Cancer of unknown primary remains a difficult cancer to diagnose and manage. Our findings improve our understanding of Chinese CUP patient characteristics, leading to improved care and outcomes 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.

SEOM-GECOD clinical guideline for unknown primary cancer (2021)

Cancer of unknown primary site (CUP) is defined as a heterogeneous group of tumors that appear as metastases, and of which standard diagnostic work-up fails to identify the origin. It is considered a separate entity with a specific biology, and nowadays molecular characteristics and the determination of actionable mutations may be important in a significant group of patients. In this guide, we summarize the diagnostic, therapeutic, and possible new developments in molecular medicine that may help us in the management of this unique disease entity.

SEOM-GETTHI clinical guideline for the practical management of molecular platforms (2021)

The improvement of molecular alterations in cancer as well as the development of technology has allowed us to bring closer to clinical practice the determination of molecular alterations in the diagnosis and treatment of cancer. The use of multidetermination platforms is spreading in most Spanish hospitals. The objective of these clinical practice guides is to review their usefulness, and establish usage guidelines that guide their incorporation into clinical practice.

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.

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.

Gene Expression Profiling for Differential Diagnosis of Liver Metastases: A Multicenter, Retrospective Cohort Study

Background

Liver metastases (LM) are the most common tumors encountered in the liver and continue to be a significant cause of morbidity and mortality. Identification of the primary tumor of any LM is crucial for the implementation of effective and tailored treatment approaches, which still represents a difficult problem in clinical practice.

Methods

The resection or biopsy specimens and associated clinicopathologic data were archived from seven independent centers between January 2017 and December 2020. The primary tumor sites of liver tumors were verified through evaluation of available medical records, pathological and imaging information. The performance of a 90-gene expression assay for the determination of the site of tumor origin was assessed.

Result

A total of 130 LM covering 15 tumor types and 16 primary liver tumor specimens that met all quality control criteria were analyzed by the 90-gene expression assay. Among 130 LM cases, tumors were most frequently located in the colorectum, ovary and breast. Overall, the analysis of the 90-gene signature showed 93.1% and 100% agreement rates with the reference diagnosis in LM and primary liver tumor, respectively. For the common primary tumor types, the concordance rate was 100%, 95.7%, 100%, 93.8%, 87.5% for classifying the LM from the ovary, colorectum, breast, neuroendocrine, and pancreas, respectively.

Conclusion

The overall accuracy of 93.8% demonstrates encouraging performance of the 90-gene expression assay in identifying the primary sites of liver tumors. Future incorporation of the 90-gene expression assay in clinical diagnosis will aid oncologists in applying precise treatments, leading to improved care and outcomes for LM patients.

AI-based pathology predicts origins for cancers of unknown primary

Cancer of unknown primary (CUP) origin is an enigmatic group of diagnoses in which the primary anatomical site of tumour origin cannot be determined^1,2. This poses a considerable challenge, as modern therapeutics are predominantly specific to the primary tumour³. Recent research has focused on using genomics and transcriptomics to identify the origin of a tumour^4-9. However, genomic testing is not always performed and lacks clinical penetration in low-resource settings. Here, to overcome these challenges, we present a deep-learning-based algorithm-Tumour Origin Assessment via Deep Learning (TOAD)-that can provide a differential diagnosis for the origin of the primary tumour using routinely acquired histology slides. We used whole-slide images of tumours with known primary origins to train a model that simultaneously identifies the tumour as primary or metastatic and predicts its site of origin. On our held-out test set of tumours with known primary origins, the model achieved a top-1 accuracy of 0.83 and a top-3 accuracy of 0.96, whereas on our external test set it achieved top-1 and top-3 accuracies of 0.80 and 0.93, respectively. We further curated a dataset of 317 cases of CUP for which a differential diagnosis was assigned. Our model predictions resulted in concordance for 61% of cases and a top-3 agreement of 82%. TOAD can be used as an assistive tool to assign a differential diagnosis to complicated cases of metastatic tumours and CUPs and could be used in conjunction with or in lieu of ancillary tests and extensive diagnostic work-ups to reduce the occurrence of CUP.

A Novel XGBoost Method to Infer the Primary Lesion of 20 Solid Tumor Types From Gene Expression Data

Purpose

Establish a suitable machine learning model to identify its primary lesions for primary metastatic tumors in an integrated learning approach, making it more accurate to improve primary lesions’ diagnostic efficiency.

Methods

After deleting the features whose expression level is lower than the threshold, we use two methods to perform feature selection and use XGBoost for classification. After the optimal model is selected through 10-fold cross-validation, it is verified on an independent test set.

Results

Selecting features with around 800 genes for training, the R²-score of a 10-fold CV of training data can reach 96.38%, and the R²-score of test data can reach 83.3%.

Conclusion

These findings suggest that by combining tumor data with machine learning methods, each cancer has its corresponding classification accuracy, which can be used to predict primary metastatic tumors’ location. The machine-learning-based method can be used as an orthogonal diagnostic method to judge the machine learning model processing and clinical actual pathological conditions.

Cancer of Unknown Primary: Challenges and Progress in Clinical Management

Simple Summary

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

Abstract

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

Cancer of unknown primary

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

The role of molecular profiling in the diagnosis and management of metastatic undifferentiated cancer of unknown primary✰: Molecular profiling of metastatic cancer of unknown primary

Cancer of unknown primary (CUP) refers to metastatic tumors for which the primary tumor of origin cannot be determined at the time of diagnosis, despite extensive clinicopathologic investigations. Molecular profiling is increasingly able to predict a probable primary tumor type for CUP when clinicopathologic workup is inconclusive. Numerous studies have explored the use of various molecular profiling techniques for identification of site/tissue of origin of CUP. These techniques include gene expression profiling utilizing microarray, reverse transcriptase polymerase chain reaction, RNA-sequencing, somatic gene mutation profiling with next-generation DNA sequencing, and epigenomics including DNA methylation profiling. Despite the generally poor prognosis of CUP, a minority of patients can expect to benefit from targeted therapy despite being agnostic to the tissue of origin. Studies have explored the use of various molecular profiling techniques to predict prognostic and therapeutic biomarkers, with the goal of improving outcome for patients with CUP. However, discordant results between non-randomized and randomized clinical trials in evaluating tumor-type specific therapies raise uncertainties of the benefits of molecularly-predicted tissue of origin-based treatment in routine clinical use. Nevertheless, the current overall trend is in favor of using molecular tools to refine the diagnosis and clinical management of patients with CUP. More large-cohort, randomized prospective studies are needed to assess and validate the utility and feasibility of molecular profiling to uncover potentially targetable genetic alterations. These efforts will also yield further biological insights into the biology and pathogenesis of CUP (Graphical Abstract).

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.

A Neural Network Framework for Predicting the Tissue-of-Origin of 15 Common Cancer Types Based on RNA-Seq Data

Sequencing-based identification of tumor tissue-of-origin (TOO) is critical for patients with cancer of unknown primary lesions. Even if the TOO of a tumor can be diagnosed by clinicopathological observation, reevaluations by computational methods can help avoid misdiagnosis. In this study, we developed a neural network (NN) framework using the expression of a 150-gene panel to infer the tumor TOO for 15 common solid tumor cancer types, including lung, breast, liver, colorectal, gastroesophageal, ovarian, cervical, endometrial, pancreatic, bladder, head and neck, thyroid, prostate, kidney, and brain cancers. To begin with, we downloaded the RNA-Seq data of 7,460 primary tumor samples across the above mentioned 15 cancer types, with each type of cancer having between 142 and 1,052 samples, from the cancer genome atlas. Then, we performed feature selection by the Pearson correlation method and performed a 150-gene panel analysis; the genes were significantly enriched in the GO:2001242 Regulation of intrinsic apoptotic signaling pathway and the GO:0009755 Hormone-mediated signaling pathway and other similar functions. Next, we developed a novel NN model using the 150 genes to predict tumor TOO for the 15 cancer types. The average prediction sensitivity and precision of the framework are 93.36 and 94.07%, respectively, for the 7,460 tumor samples based on the 10-fold cross-validation; however, the prediction sensitivity and precision for a few specific cancers, like prostate cancer, reached 100%. We also tested the trained model on a 20-sample independent dataset with metastatic tumor, and achieved an 80% accuracy. In summary, we present here a highly accurate method to infer tumor TOO, which has potential clinical implementation.

Liver metastases from pituitary carcinomas mimicking visceral well-differentiated neuroendocrine tumors: a series of four cases

Background

Pathologists frequently encounter neuroendocrine tumors (NETs) presenting as multiple liver masses in routine practice. Most often, these are well-differentiated tumors with characteristic histologic features. In contrast, pituitary carcinoma is very rare, and there is limited data on its natural history and pathologic characterization.

Methods

The aim of this study was to describe clinical characteristics, histomorphology, immunophenotype and follow-up of pituitary carcinoma involving the liver and mimicking well-differentiated NETs of visceral origin. We selected a group of well-differentiated NETs of the pancreas to use as immunophenotypic controls. We identified 4 patients (age range, 51 to 73) with pituitary corticotroph carcinoma with liver metastases. Three patients presented with Cushing syndrome.

Results

All cases histologically resembled well-differentiated NETs of visceral origin with Ki-67 proliferation indices of 5–42% and expression of T-PIT; metastatic tumors were not immunoreactive with CDX2, Islet 1 or TTF-1.

Conclusions

Frequently, these cases display adrenocorticotropic hormone (ACTH) secretion and pituitary-specific transcription factor immunohistochemistry may be used as a reliable marker to distinguish metastatic pituitary carcinoma from NETs of visceral origin in addition to delineating a corticotroph carcinoma from somatotroph, lactotroph, thyrotroph, and gonadotroph lineage. Although rare, the differential diagnosis of pituitary carcinoma should be considered in metastatic well-differentiated NETs in which the site of origin is uncertain. In summary, pituitary corticotroph carcinoma can metastasize to the liver and mimic well-differentiated NET.

"Metastatic Cancer of Unknown Primary" or "Primary Metastatic Cancer"?

Cancer of unknown primary (CUP) is an umbrella term used to classify a heterogeneous group of metastatic cancers based on the absence of an identifiable primary tumor. Clinically, CUPs are characterized by a set of distinct features comprising early metastatic dissemination in an atypical pattern, an aggressive clinical course, poor response to empiric chemotherapy and, consequently, a short life expectancy. Two opposing strategies to change the dismal prognosis for the better are pursued. On the one hand, following the traditional tissue-gnostic approach, more and more sophisticated tissue-of-origin (TOO) classifier assays are employed to push identification of the putative primary to its limits with the clear intent of allowing tumor-site specific treatment. However, robust evidence supporting its routine clinical use is still lacking, notably with two recent randomized clinical trials failing to show a patient benefit of TOO-prediction based site-specific treatment over empiric chemotherapy in CUP. On the other hand, with regards to a tissue-agnostic strategy, precision medicine approaches targeting actionable genomic alterations have already transformed the treatment for many known tumor types. Yet, an unmet need remains for well-designed clinical trials to scrutinize its potential role in CUP beyond anecdotal case reports. In the absence of practice changing results, we believe that the emphasis on finding the presumed unknown primary tumor at all costs, implicit in the term CUP, has biased recent research in the field. Focusing on the distinct clinical features shared by all CUPs, we advocate adopting the term primary metastatic cancer (PMC) to denominate a distinct cancer entity instead. In our view, PMC should be considered the archetype of metastatic disease and as such, despite accounting for a mere 2–3% of malignancies, unraveling the mechanisms at play goes beyond improving the prognosis of patients with PMC and promises to greatly enhance our understanding of the metastatic process and carcinogenesis across all cancer types.

A Review on Cancer of Unknown Primary Origin: The Role of Molecular Biomarkers in the Identification of Unknown Primary Origin

The primary site cannot be found after clinical and pathological evaluation, which are called cancers of unknown primary origin (CUP). CUPs may resemble a specific primary tumor site which shares common clinicopathological characteristics and prognosis. However, it may be present as a distinct disease entity with undifferentiated pathological features. More than 4% of patients are diagnosed as CUP. These patients were diagnosed as malignant tumors by cytology or pathology. And they were usually treated with empirical chemotherapy and associated with a poor prognosis. How to accurately diagnose and treat a cancer of unknown primary origin is a major clinical concern. To address this question, a complex assessment is carried out which includes a complete medical history of the patient, physical examination, complete blood count, urinalysis, serum chemistries, histologic evaluation, chest radiograph, computed tomography, magnetic resonance imaging, and immunohistochemistry (IHC) studies. Molecular diagnostic information reflects that CUP's molecular characteristics are similar to primary tumors with the development of genomics and the expansion of gene sequencing technology. Gene expression profiling is the most commonly used molecular diagnostic method for CUP. In this chapter, we summarize the current diagnostic methods and challenges of CUP, and the clinical value of the molecular-level tumor diagnostic technique.

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.

Gene Expression Profiling for Diagnosis of Triple-Negative Breast Cancer: A Multicenter, Retrospective Cohort Study

Background: Triple-negative breast cancer (TNBC) accounts for 12–20% of all breast cancers. Diagnosis of TNBC is sometimes quite difficult based on morphological assessment and immunohistochemistry alone, particularly in the metastatic setting with no prior history of breast cancer.

Methods: Molecular profiling is a promising diagnostic approach that has the potential to provide an objective classification of metastatic tumors with unknown primary. In this study, performance of a novel 90-gene expression signature for determination of the site of tumor origin was evaluated in 115 TNBC samples. For each specimen, expression profiles of the 90 tumor-specific genes were analyzed, and similarity scores were obtained for each of the 21 tumor types on the test panel. Predicted tumor type was compared to the reference diagnosis to calculate accuracy. Furthermore, rank product analysis was performed to identify genes that were differentially expressed between TNBC and other tumor types.

Results: Analysis of the 90-gene expression signature resulted in an overall 97.4% (112/115, 95% CI: 0.92–0.99) agreement with the reference diagnosis. Among all specimens, the signature correctly classified 97.6% of TNBC from the primary site (41/42) and lymph node metastasis (41/42) and 96.8% of distant metastatic tumors (30/31). Furthermore, a list of genes, including AZGP1, KRT19, and PIGR, was identified as differentially expressed between TNBC and other tumor types, suggesting their potential use as discriminatory markers.

Conclusion: Our results demonstrate excellent performance of a 90-gene expression signature for identification of tumor origin in a cohort of both primary and metastatic TNBC samples. These findings show promise for use of this novel molecular assay to aid in differential diagnosis of TNBC, particularly in the metastatic setting.

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.

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.

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.

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

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

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.

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

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

SEOM clinical guideline on unknown primary cancer (2017)

Cancer of unknown primary site is a histologically confirmed cancer that manifests in advanced stage, with no identifiable primary site following standard diagnostic procedures. Patients are initially categorized based on the findings of the initial biopsy: adenocarcinoma, squamous-cell carcinoma, neuroendocrine carcinoma, and poorly differentiated carcinoma. Appropriate patient management requires understanding several clinical and pathological features that aid in identifying several subsets of patients with more responsive tumors.

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.

Occult Breast Carcinoma Presenting as Scalp Metastasis

Breast cancer is the most common tumor among women, and approximately 6% of the patients have de novo metastatic breast cancer. Occult breast cancer accounts for only 0.1–0.8% of the cases and most commonly presents with axillary lymphadenopathy. Scalp metastases are rare and have been described as a sign of progression or widespread metastatic disease. Here, we describe a rare case of de novo metastatic breast cancer to the scalp as the single site of spread and without an identifiable primary breast tumor.

The pathogenesis, diagnosis, and management of metastatic tumors to the ovary: a comprehensive review

Secondary tumors of the ovary account for 10-25% of all ovarian malignancies. The most common tumors that give rise to ovarian metastases include breast, colorectal, endometrial, stomach, and appendix cancer. The correct diagnosis of secondary ovarian tumors may be challenging as they are not infrequently misdiagnosed as primary ovarian cancer, particularly in the case of mucinous adenocarcinomas. The distinction from the latter is essential, as it requires different treatment. Immunohistochemistry plays an important role in distinguishing primary ovarian tumors from extra-ovarian metastases and, furthermore, may suggest the primary tumor site. Despite extensive study, some cases remain equivocal even after assessing a broad spectrum of antigens. Therefore, gene expression profiling represents an approach able to further discriminate equivocal findings, and one that has been proven effective in determining the origin of cancer of unknown primary site. The available data concerning secondary ovarian tumors is rather limited owing to the relative heterogeneity of this group and the practical absence of any prospective trials. However, several intriguing questions are encountered in daily practice, including rational diagnostic workup, the role of cytoreductive surgery, and consequent adjuvant chemotherapy. This review seeks to address these issues comprehensively and summarize current knowledge on the epidemiology, pathogenesis, and management of secondary ovarian tumors, including further discussion on the different pathways of metastatisation, metastatic organotropism, and their possible molecular mechanisms.

Genomic Adequacy from Solid Tumor Core Needle Biopsies of ex Vivo Tissue and in Vivo Lung Masses: Prospective Study

Purpose To identify the variables and factors that affect the quantity and quality of nucleic acid yields from imaging-guided core needle biopsy. Materials and Methods This study was approved by the institutional review board and compliant with HIPAA. The authors prospectively obtained 232 biopsy specimens from 74 patients (177 ex vivo biopsy samples from surgically resected masses were obtained from 49 patients and 55 in vivo lung biopsy samples from computed tomographic [CT]-guided lung biopsies were obtained from 25 patients) and quantitatively measured DNA and RNA yields with respect to needle gauge, number of needle passes, and percentage of the needle core. RNA quality was also assessed. Significance of correlations among variables was assessed with analysis of variance followed by linear regression. Conditional probabilities were calculated for projected sample yields. Results The total nucleic acid yield increased with an increase in the number of needle passes or a decrease in needle gauge (two-way analysis of variance, P < .0001 for both). However, contrary to calculated differences in volume yields, the effect of needle gauge was markedly greater than the number of passes. For example, the use of an 18-gauge versus a 20-gauge biopsy needle resulted in a 4.8-5.7 times greater yield, whereas a double versus a single pass resulted in a 2.4-2.8 times greater yield for 18- versus 20-gauge needles, respectively. Ninety-eight of 184 samples (53%) had an RNA integrity number of at least 7 (out of a possible score of 10). Conclusion With regard to optimizing nucleic acid yields in CT-guided lung core needle biopsies used for genomic analysis, there should be a preference for using lower gauge needles over higher gauge needles with more passes. ^©RSNA, 2016 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on October 21, 2016.

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.

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.