Performance and clinical evaluation of the 92-gene real-time PCR assay for tumor classification

Highly Multiplexed Tissue Imaging in Precision Oncology and Translational Cancer Research

Precision oncology tailors treatment strategies to a patient's molecular and health data. Despite the essential clinical value of current diagnostic methods, hematoxylin and eosin morphology, immunohistochemistry, and gene panel sequencing offer an incomplete characterization. In contrast, highly multiplexed tissue imaging allows spatial analysis of dozens of markers at single-cell resolution enabling analysis of complex tumor ecosystems; thereby it has the potential to advance our understanding of cancer biology and supports drug development, biomarker discovery, and patient stratification. We describe available highly multiplexed imaging modalities, discuss their advantages and disadvantages for clinical use, and potential paths to implement these into clinical practice. Significance: This review provides guidance on how high-resolution, multiplexed tissue imaging of patient samples can be integrated into clinical workflows. It systematically compares existing and emerging technologies and outlines potential applications in the field of precision oncology, thereby bridging the ever-evolving landscape of cancer research with practical implementation possibilities of highly multiplexed tissue imaging into routine clinical practice.

Image-based deep learning model using DNA methylation data predicts the origin of cancer of unknown primary

Cancer of unknown primary (CUP) is a rare type of metastatic cancer in which the origin of the tumor is unknown. Since the treatment strategy for patients with metastatic tumors depends on knowing the primary site, accurate identification of the origin site is important. Here, we developed an image-based deep-learning model that utilizes a vision transformer algorithm for predicting the origin of CUP. Using DNA methylation dataset of 8,233 primary tumors from The Cancer Genome Atlas (TCGA), we categorized 29 cancer types into 18 organ classes and extracted 2,312 differentially methylated CpG sites (DMCs) from non-squamous cancer group and 420 DMCs from squamous cell cancer group. Using these DMCs, we created organ-specific DNA methylation images and used them for model training and testing. Model performance was evaluated using 394 metastatic cancer samples from TCGA (TCGA-meta) and 995 samples (693 primary and 302 metastatic cancers) obtained from 20 independent external studies. We identified that the DNA methylation image reveals a distinct pattern based on the origin of cancer. Our model achieved an overall accuracy of 96.95 % in the TCGA-meta dataset. In the external validation datasets, our classifier achieved overall accuracies of 96.39 % and 94.37 % in primary and metastatic tumors, respectively. Especially, the overall accuracies for both primary and metastatic samples of non-squamous cell cancer were exceptionally high, with 96.79 % and 96.85 %, respectively.

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

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

Novel MED15::ATF1 fusion in a pediatric melanoma with spitzoid features and aggressive presentation

Childhood melanoma is a rare and biologically heterogeneous pediatric malignancy. The differential diagnosis of pediatric melanoma is usually broad, including a wide variety of spindle cell or epithelioid neoplasms. Different molecular alterations affecting the MAPK and PI3K/AKT/mTOR pathways, tumor suppressor genes, and telomerase reactivation have been implicated in melanoma tumorigenesis and progression. Here, we report a novel MED15::ATF1 fusion in a pediatric melanoma with spitzoid features and an aggressive clinical course.

Advances in Cancer Research: Current and Future Diagnostic and Therapeutic Strategies

Cancers of unknown primary (CUP) exhibit significant cellular heterogeneity and malignancy, which poses significant challenges for diagnosis and treatment. Recent years have seen deeper insights into the imaging, pathology, and genetic characteristics of CUP, driven by interdisciplinary collaboration and the evolution of diagnostic and therapeutic strategies. However, due to their insidious onset, lack of evidence-based medicine, and limited clinical understanding, diagnosing and treating CUP remain a significant challenge. To inspire more creative and fantastic research, herein, we report and highlight recent advances in the diagnosis and therapeutic strategies of CUP. Specifically, we discuss advanced diagnostic technologies, including 12-deoxy-2-[fluorine-18]fluoro-D-glucose integrated with computed tomography (18F-FDG PET/CT) or 68Ga-FAPI (fibroblast activation protein inhibitor) PET/CT, liquid biopsy, molecular diagnostics, self-assembling nanotechnology, and artificial intelligence (AI). In particular, the discussion will extend to the effective treatment techniques currently available, such as targeted therapies, immunotherapies, and bio-nanotechnology-based therapeutics. Finally, a novel perspective on the challenges and directions for future CUP diagnostic and therapeutic strategies is discussed.

Deciphering the origin and therapeutic targets of cancer of unknown primary: a case report that illustrates the power of integrative whole-exome and transcriptome sequencing analysis

Cancer of unknown primary (CUP) represents a significant diagnostic and therapeutic challenge, being the third to fourth leading cause of cancer death, despite advances in diagnostic tools. This article presents a successful approach using a novel genomic analysis in the evaluation and treatment of a CUP patient, leveraging whole-exome sequencing (WES) and RNA sequencing (RNA-seq). The patient, with a history of multiple primary tumors including urothelial cancer, exhibited a history of rapid progression on empirical chemotherapy. The application of our approach identified a molecular target, characterized the tumor expression profile and the tumor microenvironment, and analyzed the origin of the tumor, leading to a tailored treatment. This resulted in a substantial radiological response across all metastatic sites and the predicted primary site of the tumor. We argue that a comprehensive genomic and molecular profiling approach, like the BostonGene© Tumor Portrait, can provide a more definitive, personalized treatment strategy, overcoming the limitations of current predictive assays. This approach offers a potential solution to an unmet clinical need for a standardized approach in identifying the tumor origin for the effective management of CUP.

Precision cancer medicine: Concepts, current practice, and future developments

Precision cancer medicine is a multidisciplinary team effort that requires involvement and commitment of many stakeholders including the society at large. Building on the success of significant advances in precision therapy for oncological patients over the last two decades, future developments will be significantly shaped by improvements in scalable molecular diagnostics in which increasingly complex multilayered datasets require transformation into clinically useful information guiding patient management at fast turnaround times. Adaptive profiling strategies involving tissue- and liquid-based testing that account for the immense plasticity of cancer during the patient's journey and also include early detection approaches are already finding their way into clinical routine and will become paramount. A second major driver is the development of smart clinical trials and trial concepts which, complemented by real-world evidence, rapidly broaden the spectrum of therapeutic options. Tight coordination with regulatory agencies and health technology assessment bodies is crucial in this context. Multicentric networks operating nationally and internationally are key in implementing precision oncology in clinical practice and support developing and improving the ecosystem and framework needed to turn invocation into benefits for patients. The review provides an overview of the diagnostic tools, innovative clinical studies, and collaborative efforts needed to realize precision cancer medicine.

A cross-cohort computational framework to trace tumor tissue-of-origin based on RNA sequencing

Carcinoma of unknown primary (CUP) is a type of metastatic cancer with tissue-of-origin (TOO) unidentifiable by traditional methods. CUP patients typically have poor prognosis but therapy targeting the original cancer tissue can significantly improve patients' prognosis. Thus, it's critical to develop accurate computational methods to infer cancer TOO. While qPCR or microarray-based methods are effective in inferring TOO for most cancer types, the overall prediction accuracy is yet to be improved. In this study, we propose a cross-cohort computational framework to trace TOO of 32 cancer types based on RNA sequencing (RNA-seq). Specifically, we employed logistic regression models to select 80 genes for each cancer type to create a combined 1356-gene set, based on transcriptomic data from 9911 tissue samples covering the 32 cancer types with known TOO from the Cancer Genome Atlas (TCGA). The selected genes are enriched in both tissue-specific and tissue-general functions. The cross-validation accuracy of our framework reaches 97.50% across all cancer types. Furthermore, we tested the performance of our model on the TCGA metastatic dataset and International Cancer Genome Consortium (ICGC) dataset, achieving an accuracy of 91.09% and 82.67%, respectively, despite the differences in experiment procedures and pipelines. In conclusion, we developed an accurate yet robust computational framework for identifying TOO, which holds promise for clinical applications. Our code is available at http://github.com/wangbo00129/classifybysklearn .

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).

[Molecular tumor diagnostics as the driving force behind precision oncology]

Molecular pathological diagnostics plays a central role in personalized oncology and requires multidisciplinary teamwork. It is just as relevant for the individual patient who is being treated with an approved therapy method or an individual treatment attempt as it is for prospective clinical studies that require the identification of specific therapeutic target structures or complex biomarkers for study inclusion. It is also of crucial importance for the generation of real-world data, which is becoming increasingly important for drug development. Future developments will be significantly shaped by improvements in scalable molecular diagnostics, in which increasingly complex and multi-layered data sets must be quickly converted into clinically useful information. One focus will be on the development of adaptive diagnostic strategies in order to be able to depict the enormous plasticity of a cancer disease over time.

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.

Cancer of unknown primary site in the mandibular region: A case report

The diagnosis and treatment of cancer of unknown primary site (CUP) present with difficulties and produce a poor prognosis. The current study presents the case of a patient with CUP in the mandibular region was treated with docetaxel and lobaplatin chemotherapy, and vascular embolization of the tumor. The tumor size was markedly reduced and the patient's quality of life improved following radiotherapy. The present case report is accompanied by a discussion of the literature to contextualize the treatment regimen for patients with CUP. These findings will support current treatment practices, inform oncologists and benefit patients with cancer.

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.

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.

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.

Machine Learning Models Predict the Primary Sites of Head and Neck Squamous Cell Carcinoma Metastases Based on DNA Methylation

In head and neck squamous cell cancers (HNSCs) that present as metastases with an unknown primary (HNSC-CUPs), the identification of a primary tumor improves therapy options and increases patient survival. However, the currently available diagnostic methods are laborious and do not offer a sufficient detection rate. Predictive machine learning models based on DNA methylation profiles have recently emerged as a promising technique for tumor classification. We applied this technique to HNSC to develop a tool that can improve the diagnostic workup for HNSC-CUPs. On a reference cohort of 405 primary HNSC samples, we developed four classifiers based on different machine learning models (random forest (RF), neural network (NN), elastic net penalized logistic regression (LOGREG), support vector machine (SVM)) that predict the primary site of HNSC tumors from their DNA methylation profile. The classifiers achieved high classification accuracies (RF=83%, NN=88%, LOGREG=SVM=89%) on an independent cohort of 64 HNSC metastases. Further, the NN, LOGREG, and SVM models significantly outperformed p16 status as a marker for an origin in the oropharynx. In conclusion, the DNA methylation profiles of HNSC metastases are characteristic for their primary sites and the classifiers developed in this study, which are made available to the scientific community, can provide valuable information to guide the diagnostic workup of HNSC-CUP. This article is protected by copyright. All rights reserved.

Real-world data analysis of patients with cancer of unknown primary

Cancer of unknown primary (CUP) is a heterogeneous malignancy in which the primary site of the tumor cannot be identified through standard work-up. The survival outcome of CUP is generally poor, and there is no consensus for treatment. Here, we comprehensively analyzed the real-world data of 218 patients with CUP (median age, 62 years [range, 19-91]; male, 62.3%). Next-generation sequencing was conducted in 22 (10%) patients, one of whom showed level 1 genetic alteration. Most (60.3%) patients were treated with empirical cytotoxic chemotherapy, and two patients received targeted therapy based on the NGS results. The median OS was 8.3 months (95% confidence interval [CI] 6.2-11.4), and the median progression-free survival of patients treated with chemotherapy was 4.4 months (95% CI 3.4-5.3). In multivariate Cox regression analysis, Eastern Cooperative Oncology Group performance status (ECOG PS) of 0 or 1 and localized disease were significantly associated with favorable survival outcomes. Collectively, we found that CUP patients had a poor prognosis after standard treatment, and those with localized disease who received local treatment and those with better PS treated with multiple lines of chemotherapy had better survival outcomes. Targeted therapies based on NGS results are expected to improve survival outcomes.

Pathological Features of Brain Metastases

Metastases are the most common intracranial tumors in adults. Lung cancer, melanoma, renal cell carcinoma, and breast cancer are the most common primary tumors that metastasize to the brain. Improved detection of small metastases by MRI, and improved systemic therapy for primary tumors, resulted in increased incidence of brain metastasis. Advances in neuroanesthesia and neurosurgery have significantly improved the safety of surgical resection of brain metastases. Surgical approach and active management have become applicable for many patients. Subsequently, brain metastases diagnosis no longer equals palliative treatment. Moreover, the demand for diagnosing brain masses has increased with its associated challenges.

Genomic and epigenomic biomarkers in colorectal cancer: From diagnosis to therapy

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths in the United States. Despite ongoing efforts aimed at increasing screening for CRC and early detection, and development of more effective therapeutic regimens, the overall morbidity and mortality from this malignancy remains a clinical challenge. Therefore, identifying and developing genomic and epigenomic biomarkers that can improve CRC diagnosis and help predict response to current therapies are of paramount importance for improving survival outcomes in CRC patients, sparing patients from toxicity associated with current regimens, and reducing the economic burden associated with these treatments. Although efforts to develop biomarkers over the past decades have achieved some success, the recent availability of high-throughput analytical tools, together with the use of machine learning algorithms, will likely hasten the development of more robust diagnostic biomarkers and improved guidance for clinical decision-making in the coming years. In this chapter, we provide a systematic and comprehensive overview on the current status of genomic and epigenomic biomarkers in CRC, and comment on their potential clinical significance in the management of patients with this fatal malignancy, including in the context of precision medicine.

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.

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

•

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

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

•

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

•

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

•

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

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

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.

Site-Specific and Targeted Therapy Based on Molecular Profiling by Next-Generation Sequencing for Cancer of Unknown Primary Site: A Nonrandomized Phase 2 Clinical Trial

Importance

Although profiling of gene expression and gene alterations by next-generation sequencing (NGS) to predict the primary tumor site and guide molecularly targeted therapy might be expected to improve clinical outcomes for cancer of unknown primary site (CUP), to our knowledge, no clinical trial has previously evaluated this approach.

Objective

To assess the clinical use of site-specific treatment, including molecularly targeted therapy based on NGS results, for patients with CUP.

Design, Setting, and Participants

This phase 2 clinical trial was conducted at 19 institutions in Japan and enrolled 111 previously untreated patients with the unfavorable subset of CUP between March 2015 and January 2018, with 97 patients being included in the efficacy analysis. Eligibility criteria included a diagnosis of unfavorable CUP after mandatory examinations, including pathological evaluation by immunohistochemistry, chest-abdomen-pelvis computed tomography scans, and a positron emission tomography scan.

Interventions

RNA and DNA sequencing for selected genes was performed simultaneously to evaluate gene expression and gene alterations, respectively. A newly established algorithm was applied to predict tumor origin based on these data. Patients received site-specific therapy, including molecularly targeted therapy, according to the predicted site and detected gene alterations.

Main Outcomes And Measures

The primary end point was 1-year survival probability. Secondary end points included progression-free survival (PFS), overall survival (OS), objective response rate, safety, efficacy according to predicted site, and frequency of gene alterations.

Results

Of 97 participants, 49 (50.5%) were women and the median (range) age was 64 (21-81) years. The cancer types most commonly predicted were lung (21 [21%]), liver (15 [15%]), kidney (15 [15%]), and colorectal (12 [12%]) cancer. The most frequent gene alterations were in TP53 (45 [46.4%]), KRAS (19 [19.6%]), and CDKN2A (18 [18.6%]). The 1-year survival probability, median OS, and median PFS were 53.1% (95% CI, 42.6%-62.5%), 13.7 months (95% CI, 9.3-19.7 months), and 5.2 months (95% CI, 3.3-7.1 months), respectively. Targetable EGFR mutations in tumor specimens were detected in 5 patients with predicted non-small-cell lung cancer (5.2%), 4 of whom were treated with afatinib; 2 of these patients achieved a durable PFS of longer than 6 months.

Conclusions and Relevance

This study's findings suggest that site-specific treatment, including molecularly targeted therapy based on profiling gene expression and gene alterations by NGS, can contribute to treating patients with the unfavorable subset of CUP.

Trial Registration

UMIN Identifier: UMIN000016794.

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.

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.

A machine learning framework to trace tumor tissue-of-origin of 13 types of cancer based on DNA somatic mutation

Carcinoma of unknown primary (CUP), defined as metastatic cancers with unknown cancer origin, occurs in 3-5 per 100 cancer patients in the United States. Heterogeneity and metastasis of cancer brings great difficulties to the follow-up diagnosis and treatment for CUP. To find the tissue-of-origin (TOO) of the CUP, multiple methods have been raised. However, the accuracies for computed tomography (CT) and positron emission tomography (PET) to identify TOO were 20%-27% and 24%-40% respectively, which were not enough for determining targeted therapies. In this study, we provide a machine learning framework to trace tumor tissue origin by using gene length-normalized somatic mutation sequencing data. Somatic mutation data was downloaded from the Data Portal (Release 28) of the International Cancer Genome Consortium (ICGC), and 4909 samples for 13 cancers was used to identify primary site of cancers. Optimal results were obtained based on a 600-gene set by using the random forest algorithm with 10-fold cross-validation, and the average accuracy and F1-score were 0.8822 and 0.8886 respectively across 13 types of cancer. In conclusion, we provide an effective computational framework to infer cancer tissue-of-origin by combining DNA sequencing and machine learning techniques, which is promising in assisting clinical diagnosis of cancers.

Predicting Cancer Tissue-of-Origin by a Machine Learning Method Using DNA Somatic Mutation Data

Patients with carcinoma of unknown primary (CUP) account for 3-5% of all cancer cases. A large number of metastatic cancers require further diagnosis to determine their tissue of origin. However, diagnosis of CUP and identification of its primary site are challenging. Previous studies have suggested that molecular profiling of tissue-specific genes could be useful in inferring the primary tissue of a tumor. The purpose of this study was to evaluate the performance somatic mutations detected in a tumor to identify the cancer tissue of origin. We downloaded the somatic mutation datasets from the International Cancer Genome Consortium project. The random forest algorithm was used to extract features, and a classifier was established based on the logistic regression. Specifically, the somatic mutations of 300 genes were extracted, which are significantly enriched in functions, such as cell-to-cell adhesion. In addition, the prediction accuracy on tissue-of-origin inference for 3,374 cancer samples across 13 cancer types reached 81% in a 10-fold cross-validation. Our method could be useful in the identification of cancer tissue of origin, as well as the diagnosis and treatment of cancers.

TOOme: A Novel Computational Framework to Infer Cancer Tissue-of-Origin by Integrating Both Gene Mutation and Expression

Metastatic cancers require further diagnosis to determine their primary tumor sites. However, the tissue-of-origin for around 5% tumors could not be identified by routine medical diagnosis according to a statistics in the United States. With the development of machine learning techniques and the accumulation of big cancer data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), it is now feasible to predict cancer tissue-of-origin by computational tools. Metastatic tumor inherits characteristics from its tissue-of-origin, and both gene expression profile and somatic mutation have tissue specificity. Thus, we developed a computational framework to infer tumor tissue-of-origin by integrating both gene mutation and expression (TOOme). Specifically, we first perform feature selection on both gene expressions and mutations by a random forest method. The selected features are then used to build up a multi-label classification model to infer cancer tissue-of-origin. We adopt a few popular multiple-label classification methods, which are compared by the 10-fold cross validation process. We applied TOOme to the TCGA data containing 7,008 non-metastatic samples across 20 solid tumors. Seventy four genes by gene expression profile and six genes by gene mutation are selected by the random forest process, which can be divided into two categories: (1) cancer type specific genes and (2) those expressed or mutated in several cancers with different levels of expression or mutation rates. Function analysis indicates that the selected genes are significantly enriched in gland development, urogenital system development, hormone metabolic process, thyroid hormone generation prostate hormone generation and so on. According to the multiple-label classification method, random forest performs the best with a 10-fold cross-validation prediction accuracy of 96%. We also use the 19 metastatic samples from TCGA and 256 cancer samples downloaded from GEO as independent testing data, for which TOOme achieves a prediction accuracy of 89%. The cross-validation validation accuracy is better than those using gene expression (i.e., 95%) and gene mutation (53%) alone. In conclusion, TOOme provides a quick yet accurate alternative to traditional medical methods in inferring cancer tissue-of-origin. In addition, the methods combining somatic mutation and gene expressions outperform those using gene expression or mutation alone.

Genomic profiling in oncology clinical practice

The development of high-throughput technologies such as next-generation sequencing for DNA sequencing together with the decrease in their cost has led to the progressive introduction of genomic profiling in our daily practice in oncology. Nowadays, genomic profiling is part of genetic counseling, cancer diagnosis, molecular characterization, and as a biomarker of prognosis and response to treatment. Furthermore, germline or somatic genomic characterization of the tumor may provide new treatment opportunities for patients with cancer. In this review, we will summarize the clinical applications and limitations of genomic profiling in oncology clinical practice, focusing on next-generation sequencing.

"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.

Multiclass cancer classification in fresh frozen and formalin-fixed paraffin-embedded tissue by DigiWest multiplex protein analysis

Histomorphology and immunohistochemistry are the most common ways of cancer classification in routine cancer diagnostics, but often reach their limits in determining the organ origin in metastasis. These cancers of unknown primary, which are mostly adenocarcinomas or squamous cell carcinomas, therefore require more sophisticated methodologies of classification. Here, we report a multiplex protein profiling-based approach for the classification of fresh frozen and formalin-fixed paraffin-embedded (FFPE) cancer tissue samples using the digital western blot technique DigiWest. A DigiWest-compatible FFPE extraction protocol was developed, and a total of 634 antibodies were tested in an initial set of 16 FFPE samples covering tumors from different origins. Of the 303 detected antibodies, 102 yielded significant correlation of signals in 25 pairs of fresh frozen and FFPE primary tumor samples, including head and neck squamous cell carcinomas (HNSC), lung squamous cell carcinomas (LUSC), lung adenocarcinomas (LUAD), colorectal adenocarcinomas (COAD), and pancreatic adenocarcinomas (PAAD). For this signature of 102 analytes (covering 88 total proteins and 14 phosphoproteins), a support vector machine (SVM) algorithm was developed. This allowed for the classification of the tissue of origin for all five tumor types studied here with high overall accuracies in both fresh frozen (90.4%) and FFPE (77.6%) samples. In addition, the SVM classifier reached an overall accuracy of 88% in an independent validation cohort of 25 FFPE tumor samples. Our results indicate that DigiWest-based protein profiling represents a valuable method for cancer classification, yielding conclusive and decisive data not only from fresh frozen specimens but also FFPE samples, thus making this approach attractive for routine clinical applications.

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.

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.

Randomized Phase II Trial Comparing Site-Specific Treatment Based on Gene Expression Profiling With Carboplatin and Paclitaxel for Patients With Cancer of Unknown Primary Site

PURPOSE

Although gene expression profiling is a promising diagnostic technique to determine the tissue of origin for patients with cancer of unknown primary site (CUP), no clinical trial has evaluated yet site-specific therapy directed by this approach compared with empirical chemotherapy. We therefore performed a randomized study to assess whether such site-specific therapy improves outcome compared with empirical chemotherapy in previously untreated patients with CUP.

PATIENTS AND METHODS

Comprehensive gene expression profiling was performed by microarray analysis, and an established algorithm was applied to predict tumor origin. Patients with CUP were randomly assigned (1:1) to receive standard site-specific therapy or empirical paclitaxel and carboplatin (PC). The primary end point was 1-year survival rate.

RESULTS

One hundred thirty patients were randomly assigned and had sufficient biopsy tissue for molecular analysis. Efficacy analysis was performed for 50 and 51 patients in the site-specific therapy and empirical PC arms, respectively. Cancer types most commonly predicted were pancreatic (21%), gastric (21%), and lymphoma (20%). The 1-year survival rate was 44.0% and 54.9% for site-specific treatment and empirical PC ( P = .264), respectively. Median overall and progression-free survival were 9.8 and 5.1 months, respectively, for site-specific treatment versus 12.5 and 4.8 months for empirical PC ( P = .896 and .550, respectively). Median overall survival (16.7 v 10.6 months; P = .116) and progression-free survival (5.5 v 3.9 months; P = .018) were better for predicted more-responsive than less-responsive tumor types.

CONCLUSION

Site-specific treatment that was based on microarray profiling did not result in a significant improvement in 1-year survival compared with empirical PC, although prediction of the original site seemed to be of prognostic value.

[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.

Immunohistochemistry for Diagnosis of Metastatic Carcinomas of Unknown Primary Site

Immunohistochemistry has become an essential ancillary examination for the identification and classification of carcinomas of unknown primary site (CUPs). Over the last decade, the diagnostic accuracy of organ- or tumour-specific immunomarkers and the clinical validation of effective immunohistochemical panels has improved significantly. When dealing with small sample sizes, diagnostic accuracy is crucial, particularly in the current era of targeted molecular and immune-based therapies. Effective systematic use of appropriate immunohistochemical panels enables accurate classification of most of the undifferentiated carcinomas as well as careful preservation of tissues for potential molecular or other ancillary tests. This review discusses the algorithmic approach to the diagnosis of CUPs using CK7 and CK20 staining patterns. It outlines the most frequently used tissue-specific antibodies, provides some pitfalls essential in avoiding potential diagnostic errors and discusses the complementary tools, such as molecular tumour profiling and mutation-specific antibodies, for the improvement of diagnosis and prediction of the treatment response.

Cancer of Unknown Primary: Opportunities and Challenges

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

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.