Validation and reproducibility of a microarray-based gene expression test for tumor identification in formalin-fixed, paraffin-embedded specimens

Carcinoma of unknown primary (CUP): an update for histopathologists

Carcinoma of unknown primary (CUP) is a heterogeneous group of metastatic cancers in which the site of origin is not identifiable. These carcinomas have a poor outcome due to their late presentation with metastatic disease, difficulty in identifying the origin and delay in treatment. The aim of the pathologist is to broadly classify and subtype the cancer and, where possible, to confirm the likely primary site as this information best predicts patient outcome and guides treatment. In this review, we provide histopathologists with diagnostic practice points which contribute to identifying the primary origin in such cases. We present the current clinical evaluation and management from the point of view of the oncologist. We discuss the role of the pathologist in the diagnostic pathway including the control of pre-analytical conditions, assessment of sample adequacy, diagnosis of cancer including diagnostic pitfalls, and evaluation of prognostic and predictive markers. An integrated diagnostic report is ideal in cases of CUP, with results discussed at a forum such as a molecular tumour board and matched with targeted treatment. This highly specialized evolving area ultimately leads to personalized oncology and potentially improved outcomes for patients.

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.

Histopathological Evaluation and Molecular Diagnostic Tests for Peritoneal Metastases with Unknown Primary Site-a Review

Cancer of unknown primary (CUP) is a well-studied entity with guidelines available for the management of patients with CUP. The peritoneum represents one of the metastatic sites in CUP, and peritoneal metastases (PM) could present as CUP. PM of unknown origin remains a poorly studied clinical entity. There is only one series of 15 cases, one population-based study, and few other case reports on this subject. Studies on CUP, in general, cover some common tumour histological types like adenocarcinomas and squamous carcinomas. Some of these tumours may have a good prognosis though majority have high-grade disease with a poor long-term outcome. Some of the histological tumour types commonly seen in the clinical scenario of PM like mucinous carcinoma have not been studied. In this review, we divide PM into five histological types-adenocarcinomas, serous carcinomas, mucinous carcinomas, sarcomas and other rare varieties. We provide algorithms to identify the primary tumour site using immunohistochemistry when imaging, and endoscopy fails to establish the primary tumour site. The role of molecular diagnostic tests for PM or unknown origin is also discussed. Current literature on site-specific systemic therapy based on gene expression profiling does not show a clear benefit of this approach over empirical systemic therapies.

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.

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.

Therapeutic Actionability of Circulating Cell-Free DNA Alterations in Carcinoma of Unknown Primary

Cancer of unknown primary (CUP) is a metastatic disease with unidentifiable primary tumor. Somatic alterations can be assessed noninvasively via liquid biopsies interrogating cell-free DNA (cfDNA).

METHODS

We evaluated 1,931 patients with CUP with a cfDNA next-generation sequencing panel (73-74 genes).

RESULTS

Overall, 1,739 patients (90%) had ≥ 1 cfDNA alteration. We then explored alteration actionability (per the levels of evidence from the OncoKB database); 825 patients (47.4% of 1,739) had level 1, level 2, or resistance/R1 alterations. Among 40 clinically annotated patients with CUP who had cfDNA evaluated, higher degrees of matching treatment to alterations (Matching Score > 50% v ≤ 50%) was the only variable predicting improved outcome: longer median progression-free survival (10.4 v 2.5 months; P = .002), overall survival (13.4 v 5.7 months; P = .07, trend), and higher clinical benefit rate (stable disease ≥ 6 months/partial response/complete response; 83% v 25%; P = .003).

CONCLUSION

In summary, cfDNA frequently reveals strong level-of-evidence actionable alterations in CUP, and high degrees of matching to therapy correlates with better 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.

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.

Cancer of Unknown Primary in the Molecular Era

Cancer of unknown primary (CUP) is a rare malignancy that presents with metastatic disease and no identifiable site of origin. Most patients have unfavorable features and attempts to treat based on tissue-of-origin identification have not yielded a survival advantage compared with empiric chemotherapy. Next-generation sequencing has revealed genomic alterations that can be targeted in selected cases, suggesting that CUP represents a unique malignancy in which the genomic aberrations may be integral to the diagnosis. Recent trials focusing on tailored combination therapy matched to the genomic alterations in each cancer are providing new avenues of clinical investigation. Here, we discuss recent findings on molecular aberrations in CUP and how the genomic and immune landscape can be leveraged to optimize therapy.

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.

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

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

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.

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.

Large scale, robust, and accurate whole transcriptome profiling from clinical formalin-fixed paraffin-embedded samples

Transcriptome profiling can provide information of great value in clinical decision-making, yet RNA from readily available formalin-fixed paraffin-embedded (FFPE) tissue is often too degraded for quality sequencing. To assess the clinical utility of FFPE-derived RNA, we performed ribo-deplete RNA extractions on > 3200 FFPE slide samples; 25 of these had direct FFPE vs. fresh frozen (FF) replicates, 57 were sequenced in 2 different labs, 87 underwent multiple library analyses, and 16 had direct microdissected vs. macrodissected replicates. Poly-A versus ribo-depletion RNA extraction methods were compared using transcriptomes of TCGA cohort and 3116 FFPE samples. Compared to FF, FFPE transcripts coding for nuclear/cytoplasmic proteins involved in DNA packaging, replication, and protein synthesis were detected at lower rates and zinc finger family transcripts were of poorer quality. The greatest difference in extraction methods was in histone transcripts which typically lack poly-A tails. Encouragingly, the overall sequencing success rate was 81%. Exome coverage was highly concordant in direct FFPE and FF replicates, with 98% agreement in coding exon coverage and a median correlation of whole transcriptome profiles of 0.95. We provide strong rationale for clinical use of FFPE-derived RNA based on the robustness, reproducibility, and consistency of whole transcriptome profiling.

Predicting cancer origins with a DNA methylation-based deep neural network model

Cancer origin determination combined with site-specific treatment of metastatic cancer patients is critical to improve patient outcomes. Existing pathology and gene expression-based techniques often have limited performance. In this study, we developed a deep neural network (DNN)-based classifier for cancer origin prediction using DNA methylation data of 7,339 patients of 18 different cancer origins from The Cancer Genome Atlas (TCGA). This DNN model was evaluated using four strategies: (1) when evaluated by 10-fold cross-validation, it achieved an overall specificity of 99.72% (95% CI 99.69%-99.75%) and sensitivity of 92.59% (95% CI 91.87%-93.30%); (2) when tested on hold-out testing data of 1,468 patients, the model had an overall specificity of 99.83% and sensitivity of 95.95%; (3) when tested on 143 metastasized cancer patients (12 cancer origins), the model achieved an overall specificity of 99.47% and sensitivity of 95.95%; and (4) when tested on an independent dataset of 581 samples (10 cancer origins), the model achieved overall specificity of 99.91% and sensitivity of 93.43%. Compared to existing pathology and gene expression-based techniques, the DNA methylation-based DNN classifier showed higher performance and had the unique advantage of easy implementation in clinical settings. In summary, our study shows that DNA methylation-based DNN models has potential in both diagnosis of cancer of unknown primary and identification of cancer cell types of circulating tumor cells.

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.

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.

Carcinoma of Unknown Primary with EML4-ALK Fusion Response to ALK Inhibitors

With the advent of next-generation sequencing (NGS) and precision medicine, investigators have determined that tumors from different tissue sources that have the same types of genetic mutations will have a positive response to the same targeted therapy. This finding has prompted us to seek potential therapeutic targets for patients with carcinoma of unknown primary (CUP) using NGS technology. Here, we reported a case of a woman with CUP resistance to chemotherapy. We detected 450 cancer-related gene alterations using three metastatic tumor specimens and found the presence of EML4 exon13 and ALK exon20 fusion. The tumor did respond to crizotinib, a first-generation ALK inhibitor. When her tumor progressed, circulating tumor DNA detection revealed ALK L1196 M and G1269A mutation resistance to crizotinib, but she had a response to brigatinib. This case revealed that NGS technology used to detect the genetic alterations in patients with CUP might be a reliable method to find potential therapeutic targets, although the primary lesion could not always be confirmed. KEY POINTS: This case exemplifies responsiveness to ALK inhibitor in carcinoma of unknown primary (CUP) with EML4-ALK fusion.Next-generation sequencing is an important diagnostic tool to find potential therapeutic targets in CUP.Liquid biopsy may be useful to provide critical information about resistance mechanisms in CUP to guide sequential treatment decision with targeted therapy.

Long noncoding RNA CAR10 promotes lung adenocarcinoma metastasis via miR-203/30/SNAI axis

Long noncoding RNAs (lncRNAs) play an important role in lung adenocarcinoma (LUAD) metastasis. Here, we found that lncRNA chromatin-associated RNA 10 (CAR10) was upregulated in the tumor tissue of patients with LUAD and enhanced tumor metastasis in vitro and in vivo. Mechanistically, CAR10 induced epithelial-to-mesenchymal transition (EMT) by directly binding with miR-30 and miR-203 and then regulating the expression of SNAI1 and SNAI2. CAR10 overexpression was positively correlated with a poor prognosis in LUAD patients, whereas overexpression of both CAR10 and SNAI was correlated with even worse clinical outcomes. In conclusion, the CAR10/miR-30/203/SNAI axis is a novel and potential therapeutic target for LUAD.

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.

The Potential Clinical and Economic Value of Primary Tumour Identification in Metastatic Cancer of Unknown Primary Tumour: A Population-Based Retrospective Matched Cohort Study

PURPOSE

Several genomic tests have recently been developed to identify the primary tumour in cancer of unknown primary tumour (CUP). However, the value of identifying the primary tumour in clinical practice for CUP patients remains questionable and difficult to prove in randomized trials.

OBJECTIVE

We aimed to assess the clinical and economic value of primary tumour identification in CUP using a retrospective matched cohort study.

METHODS

We used the Manitoba Cancer Registry to identify all patients initially diagnosed with metastatic cancer between 2002 and 2011. We defined patients as having CUP if their primary tumour was found 6 months or more after initial diagnosis or never found during the course of disease. Otherwise, we considered patients to have metastatic cancer from a known primary tumour (CKP). We linked all patients with Manitoba Health databases to estimate their direct healthcare costs using a phase-of-care approach. We used the propensity score matching technique to match each CUP patient with a CKP patient on clinicopathologic characteristics. We compared treatment patterns, overall survival (OS) and phase-specific healthcare costs between the two patient groups and assessed association with OS using Cox regression adjustment.

RESULTS

Of 5839 patients diagnosed with metastatic cancer, 395 had CUP (6.8%); 1:1 matching created a matched group of 395 CKP patients. CUP patients were less likely to receive surgery, radiation, hormonal and targeted therapy and more likely to receive cytotoxic empiric chemotherapeutic agents. Having CUP was associated with reduced OS (hazard ratio [HR] 1.31; 95% confidence interval 1.1-1.58), but this lost statistical significance with adjustment for treatment differences. CUP patients had a significant increase in the mean net cost of initial diagnostic workup before diagnosis and a significant reduction in the mean net cost of continuing cancer care.

CONCLUSION

Identifying the primary tumour in CUP patients might enable the use of more effective therapies, improve OS and allow more efficient allocation of healthcare resources.

Robust transcriptional tumor signatures applicable to both formalin-fixed paraffin-embedded and fresh-frozen samples

Formalin-fixed paraffin-embedded (FFPE) samples represent a valuable resource for clinical researches. However, FFPE samples are usually considered an unreliable source for gene expression analysis due to the partial RNA degradation. In this study, through comparing gene expression profiles between FFPE samples and paired fresh-frozen (FF) samples for three cancer types, we firstly showed that expression measurements of thousands of genes had at least two-fold change in FFPE samples compared with paired FF samples. Therefore, for a transcriptional signature based on risk scores summarized from the expression levels of the signature genes, the risk score thresholds trained from FFPE (or FF) samples could not be applied to FF (or FFPE) samples. On the other hand, we found that more than 90% of the relative expression orderings (REOs) of gene pairs in the FF samples were maintained in their paired FFPE samples and largely unaffected by the storage time. The result suggested that the REOs of gene pairs were highly robust against partial RNA degradation in FFPE samples. Finally, as a case study, we developed a REOs-based signature to distinguish liver cirrhosis from hepatocellular carcinoma (HCC) using FFPE samples. The signature was validated in four datasets of FFPE samples and eight datasets of FF samples. In conclusion, the valuable FFPE samples can be fully exploited to identify REOs-based diagnostic and prognostic signatures which could be robustly applicable to both FF samples and FFPE samples with degraded RNA.

Metastatic diseases of the central nervous system - neuropathologic aspects

A large percentage of patients with cancer will develop brain metastases, and many of them will die within a few months following diagnosis of intracranial metastasis. Although the majority of the central nervous system metastases are derived from a well-known primary neoplasm, about 5-10% of brain metastases are from an unknown source, making the tissue diagnosis a first step in the search for a primary malignancy. The pathologist utilizes several immunohistochemical and molecular diagnostic tools for such investigation, helping the clinical oncologist to narrow down the clinical and radiologic exploration. Recently, analysis of actionable biomarkers for target therapy in brain metastasis has become significant due to reports of discrepancy of potential biomarkers between primary tumors and metastatic brain deposits.

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.

Intragraft Molecular Pathways Associated with Tolerance Induction in Renal Transplantation

The modern immunosuppression regimen has greatly improved short-term allograft outcomes but not long-term allograft survival. Complications associated with immunosuppression, specifically nephrotoxicity and infection risk, significantly affect graft and patient survival. Inducing and understanding pathways underlying clinical tolerance after transplantation are, therefore, necessary. We previously showed full donor chimerism and immunosuppression withdrawal in highly mismatched allograft recipients using a bioengineered stem cell product (FCRx). Here, we evaluated the gene expression and microRNA expression profiles in renal biopsy samples from tolerance-induced FCRx recipients, paired donor organs before implant, and subjects under standard immunosuppression (SIS) without rejection and with acute rejection. Unlike allograft samples showing acute rejection, samples from FCRx recipients did not show upregulation of T cell- and B cell-mediated rejection pathways. Gene expression pathways differed slightly between FCRx samples and the paired preimplantation donor organ samples, but most of the functional gene networks overlapped. Notably, compared with SIS samples, FCRx samples showed upregulation of genes involved in pathways, like B cell receptor signaling. Additionally, prediction analysis showed inhibition of proinflammatory regulators and activation of anti-inflammatory pathways in FCRx samples. Furthermore, integrative analyses (microRNA and gene expression profiling from the same biopsy sample) identified the induction of regulators with demonstrated roles in the downregulation of inflammatory pathways and maintenance of tissue homeostasis in tolerance-induced FCRx samples compared with SIS samples. This pilot study highlights the utility of molecular intragraft evaluation of pathways related to FCRx-induced tolerance and the use of integrative analyses for identifying upstream regulators of the affected downstream molecular pathways.

The clinical significance of occult gynecologic primary tumours in metastatic cancer

OBJECTIVE

We estimated the frequency of occult gynecologic primary tumours (gpts) in patients with metastatic cancer from an uncertain primary and evaluated the effect on disease management and overall survival (os).

METHODS

We used Manitoba administrative health databases to identify all patients initially diagnosed with metastatic cancer during 2002-2011. We defined patients as having an "occult" primary tumour if the primary was classified at least 6 months after the initial diagnosis. Otherwise, we considered patients to have "obvious" primaries. We then compared clinicopathologic and treatment characteristics and 2-year os for women with occult and with obvious gpts. We used Cox regression adjustment and propensity score methods to assess the effect on os of having an occult gpt.

RESULTS

Among the 5953 patients diagnosed with metastatic cancer, occult primary tumours were more common in women (n = 285 of 2552, 11.2%) than in men (n = 244 of 3401, 7.2%). In women, gpts were the most frequent occult primary tumours (n = 55 of 285, 19.3%). Compared with their counterparts having obvious gpts, women with occult gpts (n = 55) presented with similar histologic and metastatic patterns but received fewer gynecologic diagnostic examinations during diagnostic work-up. Women with occult gpts were less likely to undergo surgery, waited longer for radiotherapy, and received a lesser variety of chemotherapeutic agents. Having an occult compared with an obvious gpt was associated with decreased os (hazard ratio: 1.62; 95% confidence interval: 1.2 to 2.35). Similar results were observed in adjusted analyses.

CONCLUSIONS

In women with metastatic cancer from an uncertain primary, gpts constitute the largest clinical entity. Accurate diagnosis of occult gpts early in the course of metastatic cancer might lead to more effective treatment decisions and improved survival outcomes.

Genomic variants link to hepatitis C racial disparities

Chronic liver diseases are one of the major public health issues in United States, and there are substantial racial disparities in liver cancer-related mortality. We previously identified racially distinct alterations in the expression of transcripts and proteins of hepatitis C (HCV)-induced hepatocellular carcinoma (HCC) between Caucasian (CA) and African American (AA) subgroups. Here, we performed a comparative genome-wide analysis of normal vs. HCV+ (cirrhotic state), and normal adjacent tissues (HCCN) vs. HCV+HCC (tumor state) of CA at the gene and alternative splicing levels using Affymetrix Human Transcriptome Array (HTA2.0). Many genes and splice variants were abnormally expressed in HCV+ more than in HCV+HCC state compared with normal tissues. Known biological pathways related to cell cycle regulations were altered in HCV+HCC, whereas acute phase reactants were deregulated in HCV+ state. We confirmed by quantitative RT-PCR that SAA1, PCNA-AS1, DAB2, and IFI30 are differentially deregulated, especially in AA compared with CA samples. Likewise, IHC staining analysis revealed altered expression patterns of SAA1 and HNF4α isoforms in HCV+ liver samples of AA compared with CA. These results demonstrate that several splice variants are primarily deregulated in normal vs. HCV+ stage, which is certainly in line with the recent observations showing that the pre-mRNA splicing machinery may be profoundly remodeled during disease progression, and may, therefore, play a major role in HCV racial disparity. The confirmation that certain genes are deregulated in AA compared to CA tissues also suggests that there is a biological basis for the observed racial disparities.

Utility of Genomic Analysis In Circulating Tumor DNA from Patients with Carcinoma of Unknown Primary

Carcinoma of unknown primary (CUP) is a rare and difficult-to-treat malignancy, the management of which might be improved by the identification of actionable driver mutations. We interrogated 54 to 70 genes in 442 patients with CUP using targeted clinical-grade, next-generation sequencing of circulating tumor DNA (ctDNA). Overall, 80% of patients exhibited ctDNA alterations; 66% (290/442) ≥1 characterized alteration(s), excluding variants of unknown significance. TP53-associated genes were most commonly altered [37.8% (167/442)], followed by genes involved in the MAPK pathway [31.2% (138/442)], PI3K signaling [18.1% (80/442)], and the cell-cycle machinery [10.4% (46/442)]. Among 290 patients harboring characterized alterations, distinct genomic profiles were observed in 87.9% (255/290) of CUP cases, with 99.7% (289/290) exhibiting potentially targetable alterations. An illustrative patient with dynamic changes in ctDNA content during therapy and a responder given a checkpoint inhibitor-based regimen because of a mismatch repair gene anomaly are presented. Our results demonstrate that ctDNA evaluation is feasible in CUP and that most patients harbor a unique somatic profile with pharmacologically actionable alterations, justifying the inclusion of noninvasive liquid biopsies in next-generation clinical trials. Cancer Res; 77(16); 4238-46. ©2017 AACR.

The Clinical Significance of Occult Gastrointestinal Primary Tumours in Metastatic Cancer: A Population Retrospective Cohort Study

Purpose

The purpose of this study was to estimate the incidence of occult gastrointestinal (GI) primary tumours in patients with metastatic cancer of uncertain primary origin and evaluate their influence on treatments and overall survival (OS).

Materials and Methods

We used population heath data from Manitoba, Canada to identify all patients initially diagnosed with metastatic cancer between 2002 and 2011. We defined patients to have “occult” primary tumour if the primary was found at least 6 months after initial diagnosis. Otherwise, we considered primary tumours as “obvious.” We used propensity-score methods to match each patient with occult GI tumour to four patients with obvious GI tumour on all known clinicopathologic features. We compared treatments and 2-year survival data between the two patient groups and assessed treatment effect on OS using Cox regression adjustment.

Results

Eighty-three patients had occult GI primary tumours, accounting for 17.6% of men and 14% of women with metastatic cancer of uncertain primary. A 1:4 matching created a matched group of 332 patients with obvious GI primary tumour. Occult cases compared to the matched group were less likely to receive surgical interventions and targeted biological therapy, and more likely to receive cytotoxic empiric chemotherapeutic agents. Having an occult GI tumour was associated with reduced OS and appeared to be a nonsignificant independent predictor of OS when adjusting for treatment differences.

Conclusion

GI tumours are the most common occult primary tumours in men and the second most common in women. Patients with occult GI primary tumours are potentially being undertreated with available GI site-specific and targeted therapies.

Comprehensive genetic testing identifies targetable genomic alterations in most patients with non-small cell lung cancer, specifically adenocarcinoma, single institute investigation

This study reviews extensive genetic analysis in advanced non-small cell lung cancer (NSCLC) patients in order to: describe how targetable mutation genes interrelate with the genes identified as variants of unknown significance; assess the percentage of patients with a potentially targetable genetic alterations; evaluate the percentage of patients who had concurrent alterations, previously considered to be mutually exclusive; and characterize the molecular subset of KRAS.

Thoracic Oncology Research Program Databases at the University of Chicago provided patient demographics, pathology, and results of genetic testing. 364 patients including 289 adenocarcinoma underwent genotype testing by various platforms such as FoundationOne, Caris Molecular Intelligence, and Response Genetics Inc. For the entire adenocarcinoma cohort, 25% of patients were African Americans; 90% of KRAS mutations were detected in smokers, including current and former smokers; 46% of EGFR and 61% of ALK alterations were detected in never smokers. 99.4% of patients, whose samples were analyzed by next-generation sequencing (NGS), had genetic alterations identified with an average of 10.8 alterations/tumor throughout different tumor subtypes. However, mutations were not mutually exclusive.

NGS in this study identified potentially targetable genetic alterations in the majority of patients tested, detected concurrent alterations and provided information on variants of unknown significance at this time but potentially targetable in the future.

[Standard of care of carcinomas on cancer of unknown primary site in 2016]

Patients with Cancer of unknown primary (cup) represent 2-10%, and have disseminated cancers for which we cannot find the primary site despite the clinical, pathological and radiological exams at our disposal. Diagnosis is based on a thorough clinical and histopathologic examination as well as new imaging techniques. Several clinicopathologic entities requiring specific treatment can be identified. Genome sequencing and liquid biopsy (circulating tumor cells and tumor free DNA) could allow further advances in the diagnosis. Therapeutically, in addition to surgery, radiotherapy and chemotherapy, precision medicine provides new therapeutic approaches.

Cost-effectiveness of using a gene expression profiling test to aid in identifying the primary tumour in patients with cancer of unknown primary

We aimed to investigate the cost-effectiveness of a 2000-gene-expression profiling (GEP) test to help identify the primary tumor site when clinicopathological diagnostic evaluation was inconclusive in patients with cancer of unknown primary (CUP). We built a decision-analytic-model to project the lifetime clinical and economic consequences of different clinical management strategies for CUP. The model was parameterized using follow-up data from the Manitoba Cancer Registry, cost data from Manitoba Health administrative databases and secondary sources. The 2000-GEP-based strategy compared to current clinical practice resulted in an incremental cost-effectiveness ratio (ICER) of $44,151 per quality-adjusted life years (QALY) gained. The total annual-budget impact was $36.2 million per year. A value-of-information analysis revealed that the expected value of perfect information about the test's clinical impact was $4.2 million per year. The 2000-GEP test should be considered for adoption in CUP. Field evaluations of the test are associated with a large societal benefit.

Application of a Clinical Whole-Transcriptome Assay for Staging and Prognosis of Prostate Cancer Diagnosed in Needle Core Biopsy Specimens

Molecular and genomic analysis of microscopic quantities of tumor from formalin-fixed, paraffin-embedded biopsy specimens has many unique challenges. Herein, we evaluated the feasibility of obtaining transcriptome-wide RNA expression to measure prognostic classifiers in diagnostic prostate needle core biopsy specimens. One-hundred fifty-eight samples from diagnostic needle core biopsy specimens (BX) and radical prostatectomies (RPs) were collected from 33 patients at three hospitals; each patient provided up to six tumor and benign samples. Genome-wide transcriptomic profiles were generated using Affymetrix Human Exon arrays for comparison of gene expression alterations and prognostic signatures between the BX and RP samples. A sufficient amount of RNA (>100 ng) was obtained from all RP specimens (n = 77) and from 72 of 81 of BX specimens. Of transcriptomic features detected in RP, 95% were detectable in BX tissues and demonstrated a high correlation (r = 0.96). Likewise, an expression signature pattern validated on RPs (Decipher prognostic test) showed correlation between BX and RP (r = 0.70). Of matched BX and RP pairs, 25% showed discordant molecular subtypes. Genome-wide exon arrays yielded data of comparable quality from biopsy and RP tissues. The high concordance of tumor-associated gene expression changes between BX and RP samples provides evidence for the adequate performance of the assay platform with samples from prostate needle biopsy specimens with limited tumor volume.

Gene expression profiling identifies responsive patients with cancer of unknown primary treated with carboplatin, paclitaxel, and everolimus: NCCTG N0871 (alliance)

BACKGROUND

Carboplatin (C) and paclitaxel (P) are standard treatments for carcinoma of unknown primary (CUP). Everolimus, an mTOR inhibitor, exhibits activity in diverse cancer types. We did a phase II trial combining everolimus with CP for CUP. We also evaluated whether a gene expression profiling (GEP) test that predicts tissue of origin (TOO) could identify responsive patients.

PATIENTS AND METHODS

A tumor biopsy was required for central confirmation of CUP and GEP. Patients with metastatic, untreated CUP received everolimus (30 mg weekly) with P (200 mg/m(2)) and C (area under the curve 6) every 3 weeks. The primary end point was response rate (RR), with 22% needed for success. The GEP test categorized patients into two groups: those having a TOO where CP is versus is not considered standard therapy.

RESULTS

Of 45 assessable patients, the RR was 36% (95% confidence interval 22% to 51%), which met criteria for success. Grade ≥3 toxicities were predominantly hematologic (80%). Adequate tissue for GEP was available in 38 patients and predicted 10 different TOOs. Patients with a TOO where platinum/taxane is a standard (n = 19) tended to have higher RR (53% versus 26%) and significantly longer PFS (6.4 versus 3.5 months) and OS (17.8 versus 8.3 months, P = 0.005), compared with patients (n = 19) with a TOO where platinum/taxane is not standard.

CONCLUSIONS

Everolimus combined with CP demonstrated promising antitumor activity and an acceptable side-effect profile. A tumor biomarker identifying TOO may be useful to select CUP patients for specific antitumor regimens.

CLINICALTRIALSGOV

NCT00936702.

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

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

Using prior knowledge from cellular pathways and molecular networks for diagnostic specimen classification

For many complex diseases, an earlier and more reliable diagnosis is considered a key prerequisite for developing more effective therapies to prevent or delay disease progression. Classical statistical learning approaches for specimen classification using omics data, however, often cannot provide diagnostic models with sufficient accuracy and robustness for heterogeneous diseases like cancers or neurodegenerative disorders. In recent years, new approaches for building multivariate biomarker models on omics data have been proposed, which exploit prior biological knowledge from molecular networks and cellular pathways to address these limitations. This survey provides an overview of these recent developments and compares pathway- and network-based specimen classification approaches in terms of their utility for improving model robustness, accuracy and biological interpretability. Different routes to translate omics-based multifactorial biomarker models into clinical diagnostic tests are discussed, and a previous study is presented as example.

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

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

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

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