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.

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.

Development and validation of a gene expression tumour classifier for cancer of unknown primary

Accurate identification of the primary tumour in cancer of unknown primary (CUP) is required for effective treatment selection and improved patient outcomes. The aim of this study was to develop and validate a gene expression tumour classifier and integrate it with histopathology to identify the likely site of origin in CUP.RNA was extracted from 450 formalin fixed, paraffin embedded samples of known origin comprising 18 tumour groups. Whole genome expression analysis was performed using a bead-based array. Classification of the tumours made use of a binary support vector machine, together with recursive feature elimination. A hierarchical tumour classifier was developed and incorporated with conventional histopathology to identify the origins of metastatic tumours.The classifier demonstrated an accuracy of 88% for correctly predicting the tumour type on a validation set of known tumours (n = 94). For CUP samples (n = 49) having a final clinical diagnosis, the classifier improved the accuracy of histology alone for both single and multiple predictions. Furthermore, where histology alone could not suggest any specific diagnosis, the classifier was able to correctly predict the primary site of origin.We demonstrate the integration of gene expression profiling with conventional histopathology to aid the investigation of CUP.

Molecular profiling diagnosis in unknown primary cancer: accuracy and ability to complement standard pathology

BACKGROUND

Molecular tumor profiling (MTP) is a potentially powerful diagnostic tool for identifying the tissue of origin in patients with cancer of unknown primary (CUP). However, validation of the accuracy and clinical value of MTP has been difficult because the anatomic primary site in most patients is never identified.

METHODS

From March 2008 through January 2010, clinicopathologic data from 171 CUP patients who had MTP (CancerTYPE ID; bioTheranostics, Inc, San Diego, CA) performed on archived material were evaluated. The accuracy of MTP diagnoses was evaluated by comparison with (1) latent primary tumor sites found months/years later; (2) initial single diagnoses by immunohistochemistry (IHC); and (3) additional directed IHC and/or clinicopathologic findings evaluated after MTP diagnoses.

RESULTS

A single MTP diagnosis was made in 144 of 149 patients with adequate tumor specimens. Eighteen of 24 patients with latent primaries discovered months to years later had correct diagnoses by MTP (75%), and these diagnoses compared favorably with IHC. Single IHC diagnoses matched MTP diagnoses in 40 of 52 patients (77%). IHC predictions of 2 or more possible primaries compared poorly with MTP diagnoses. However, additional targeted IHC and clinical/histologic evaluation supported the MTP diagnosis in 26 of 35 patients (74%). Clinical features were usually consistent with MTP diagnoses (70%).

CONCLUSIONS

The diagnostic accuracy of this MTP assay was supported by a high level of agreement with identified latent primaries (75%), single IHC diagnoses (77%), and additional directed IHC and/or clinical/histologic findings (74%) prompted by the MTP diagnoses. MTP complements standard pathologic evaluation in determining the tissue of origin in patients with CUP, particularly when IHC is inconclusive.