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

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.

Metastatic Breast Cancer: Cytology Diagnosis with Implications for Treatment

Breast cancer is among the most frequent malignancies in women worldwide. While early detection and effective treatment provide many women with a cure and prevent their cancer from spreading, metastases to distant sites still occur in around 20% of women suffering from breast cancer. These relapses occur in many forms and locations and are as varied as the primary breast tumors. Metastatic spread makes a cancer incurable and potentially lethal, but new, targeted treatments can offer control of the cancer cells if the features of new targets are unlocked by advanced diagnostic testing. The article offers an overview of the pathomechanisms of metastatic progression and describes the types of metastases, such as hormone-receptor-positive and -negative breast cancers, and HER2-overexpressing or triple-negative types. Once distant metastatic spread occurs, cytology allows a precise diagnosis to confirm the breast origin. Other molecular targets include ESR1 and PIK3CA mutations, MSI, NTRK fusion, PD-L1 expression and others, which can be obtained also from cytology material and used to determine eligibility for emerging targeted therapeutic options. We outline the diagnostic features of metastatic breast cancer in cytology samples, together with validated and emergent biomarkers that may provide new, targeted treatment options.

Evaluating DNA Methylation, Gene Expression, Somatic Mutation, and Their Combinations in Inferring Tumor Tissue-of-Origin

Carcinoma of unknown primary (CUP) is a type of metastatic cancer, the primary tumor site of which cannot be identified. CUP occupies approximately 5% of cancer incidences in the United States with usually unfavorable prognosis, making it a big threat to public health. Traditional methods to identify the tissue-of-origin (TOO) of CUP like immunohistochemistry can only deal with around 20% CUP patients. In recent years, more and more studies suggest that it is promising to solve the problem by integrating machine learning techniques with big biomedical data involving multiple types of biomarkers including epigenetic, genetic, and gene expression profiles, such as DNA methylation. Different biomarkers play different roles in cancer research; for example, genomic mutations in a patient’s tumor could lead to specific anticancer drugs for treatment; DNA methylation and copy number variation could reveal tumor tissue of origin and molecular classification. However, there is no systematic comparison on which biomarker is better at identifying the cancer type and site of origin. In addition, it might also be possible to further improve the inference accuracy by integrating multiple types of biomarkers. In this study, we used primary tumor data rather than metastatic tumor data. Although the use of primary tumors may lead to some biases in our classification model, their tumor-of-origins are known. In addition, previous studies have suggested that the CUP prediction model built from primary tumors could efficiently predict TOO of metastatic cancers (Lal et al., 2013; Brachtel et al., 2016). We systematically compared the performances of three types of biomarkers including DNA methylation, gene expression profile, and somatic mutation as well as their combinations in inferring the TOO of CUP patients. First, we downloaded the gene expression profile, somatic mutation and DNA methylation data of 7,224 tumor samples across 21 common cancer types from the cancer genome atlas (TCGA) and generated seven different feature matrices through various combinations. Second, we performed feature selection by the Pearson correlation method. The selected features for each matrix were used to build up an XGBoost multi-label classification model to infer cancer TOO, an algorithm proven to be effective in a few previous studies. The performance of each biomarker and combination was compared by the 10-fold cross-validation process. Our results showed that the TOO tracing accuracy using gene expression profile was the highest, followed by DNA methylation, while somatic mutation performed the worst. Meanwhile, we found that simply combining multiple biomarkers does not have much effect in improving prediction accuracy.

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.

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.

Prognostic Importance of Cell Cycle Regulators Cyclin D1 (CCND1) and Cyclin-Dependent Kinase Inhibitor 1B (CDKN1B/p27) in Sporadic Gastric Cancers

Background. Gastric cancer is known for a notable variety in the course of the disease. Clinical factors, such as tumor stage, grade, and localization, are key in patient survival. It is expected that molecular factors such as somatic mutations and gene amplifications are also underlying tumor biological behavior and may serve as factors for prognosis estimation. Aim. The purpose of this study was to examine gene amplifications from a panel of genes to uncover potential prognostic marker candidates. Methods. A panel of gene amplifications including 71 genes was tested by multiplex ligation-dependent probe amplification (MLPA) technique in 76 gastric cancer samples from a Caucasian population. The correlation of gene amplification status with patient survival was determined by the Kaplan-Meier method. Results. The amplification of two cell cycle regulators, CCND1 and CDKN1B, was identified to have a negative prognostic role. The medial survival of patients with gastric cancer displaying amplification compared to patients without amplification was 192 versus 725 days for CCND1 (P = 0.0012) and 165 versus 611 days for CDKN1B (P = 0.0098). Conclusion. Gene amplifications of CCND1 and CDKN1B are potential candidates to serve as prognostic markers for the stratification of patients based on the estimate of survival in the management of gastric cancer patients.