OBJECTIVE: In this systematic review and individual patient data (IPD) meta-analysis, we analysed the diagnostic performance of [18F]FDG PET/CT in detecting primary tumours in patients with CUP and evaluated whether the location of the predominant metastatic site influences the diagnostic performance.
METHODS: A systematic literature search from January 2005 to February 2024 was performed to identify articles describing the diagnostic performance of [18F]FDG PET/CT for primary tumour detection in CUP. Individual patient data retrieved from original articles or obtained from corresponding authors were grouped by the predominant metastatic site. The diagnostic performance of [18F]FDG PET/CT in detecting the underlying primary tumour was compared between predominant metastatic sites.
RESULTS: A total of 1865 patients from 32 studies were included. The largest subgroup included patients with predominant bone metastases (n = 622), followed by liver (n = 369), lymph node (n = 358), brain (n = 316), peritoneal (n = 70), lung (n = 67), and soft tissue (n = 23) metastases, leaving a small group of other/undefined metastases (n = 40). [18F]FDG PET/CT resulted in pooled detection rates to identify the primary tumour of 0.74 (for patients with predominant brain metastases), 0.54 (liver-predominant), 0.49 (bone-predominant), 0.46 (lung-predominant), 0.38 (peritoneal-predominant), 0.37 (lymph node-predominant), and 0.35 (soft-tissue-predominant).
CONCLUSIONS: This individual patient data meta-analysis suggests that the ability of [18F]FDG PET/CT to identify the primary tumour in CUP depends on the distribution of metastatic sites. This finding emphasises the need for more tailored diagnostic approaches in different patient populations. In addition, alternative diagnostic tools, such as new PET tracers or whole-body (PET/)MRI, should be investigated.

A 56-year-old man presented to our hospital with dyspnea on exertion for two months. Bilateral pleural effusions were found, and a close examination revealed a chylothorax, including adenocarcinoma. The primary tumor could not be identified by systemic examination. Therefore, the patient was diagnosed with cancer of unknown primary origin (CUP) presenting with chylothorax. Chemotherapy was administered for CUP, and thoracentesis, pleurodesis, ascites puncture, and nutritional therapy were performed for chylothorax and chylous ascites. Although drainage frequency and tumor marker levels (CA19-9, DUPAN-2, and Span-1) temporarily decreased, disease control deteriorated, and the patient died 12 months after the initial diagnosis.

Identifying the primary site of origin of metastatic cancer is vital for guiding treatment decisions, especially for patients with cancer of unknown primary (CUP). Despite advanced diagnostic techniques, CUP remains difficult to pinpoint and is responsible for a considerable number of cancer-related fatalities. Understanding its origin is crucial for effective management and potentially improving patient outcomes. This study introduces a machine learning framework, ONCOfind-AI, that leverages transcriptome-based gene set features to enhance the accuracy of predicting the origin of metastatic cancers. We demonstrate its potential to facilitate the integration of RNA sequencing and microarray data by using gene set scores for characterization of transcriptome profiles generated from different platforms. Integrating data from different platforms resulted in improved accuracy of machine learning models for predicting cancer origins. We validated our method using external data from clinical samples collected through the Kangbuk Samsung Medical Center and Gene Expression Omnibus. The external validation results demonstrate a top-1 accuracy ranging from 0.80 to 0.86, with a top-2 accuracy of 0.90. This study highlights that incorporating biological knowledge through curated gene sets can help to merge gene expression data from different platforms, thereby enhancing the compatibility needed to develop more effective machine learning prediction models.

Patients with cancer of unknown primary (CUP) carry the double burden of an aggressive disease and reduced access to therapies. Experimental models are pivotal for CUP biology investigation and drug testing. We derived two CUP cell lines (CUP#55 and #96) and corresponding patient-derived xenografts (PDXs), from ascites tumor cells. CUP cell lines and PDXs underwent histological, immune-phenotypical, molecular, and genomic characterization confirming the features of the original tumor. The tissue-of-origin prediction was obtained from the tumor microRNA expression profile and confirmed by single-cell transcriptomics. Genomic testing and fluorescence in situ hybridization analysis identified FGFR2 gene amplification in both models, in the form of homogeneously staining region (HSR) in CUP#55 and double minutes in CUP#96. FGFR2 was recognized as the main oncogenic driver and therapeutic target. FGFR2-targeting drug BGJ398 (infigratinib) in combination with the MEK inhibitor trametinib proved to be synergic and exceptionally active, both in vitro and in vivo. The effects of the combined treatment by single-cell gene expression analysis revealed a remarkable plasticity of tumor cells and the greater sensitivity of cells with epithelial phenotype. This study brings personalized therapy closer to CUP patients and provides the rationale for FGFR2 and MEK targeting in metastatic tumors with FGFR2 pathway activation.

UNASSIGNED: Cancers of unknown primary are aggressive and rare malignancies with a complex diagnosis and management. Here we present a case in which imaging, pathology, and molecular biology did not match for a specific tumor site and the importance of a multidisciplinary team for these complicated cases.
UNASSIGNED: A man in his 70s with strong smoking history under workup for suspicion of metastatic lung cancer underwent lung mass biopsy. Immunohistochemical stains corresponded to hepatocellular/cholangiocarcinoma or germ cell tumor; however, dedicated liver and testicular studies including imaging and iscochrome 12p FISH were negative. Additionally, somatic variant profiling was not specific for any malignancy nor targetable variants. Given the pattern of disease, risk factors, and patient history, the patient received treatment for lung adenocarcinoma (carboplatin, pemetrexed, and pembrolizumab). The patient had a drastic improvement in dyspnea, weight gain, and was able to return to work.
UNASSIGNED: This report describes a case in which immunohistochemistry and molecular profiling did not identify the tissue of origin and highlights the importance of a multidisciplinary team to reach a diagnosis and guide treatment without delaying patient care in patients with these diagnoses.

A subset of head and neck squamous cell carcinomas present solely as metastatic disease in the neck and are of unknown primary origin (SCCUP). Most primary tumors will ultimately be identified, usually in the oropharynx. In a minority of cases, the primary site remains elusive. Here, we examine the role of ancillary testing, including mutational signature analysis (MSA), to help identify likely primary sites in such cases. Twenty-two cases of SCCUP in the neck, collected over a 10-year period, were classified by morphology and viral status; including human papillomavirus (HPV) testing by p16 immunohistochemistry (IHC) and RT-qPCR, as well as Epstein-Barr virus (EBV) testing by EBER-ISH. CD5 and c-KIT (CD117) IHC was done to evaluate for possible thymic origin in all virus-negative cases. Whole exome sequencing, followed by MSA, was used to identify UV signature mutations indicative of cutaneous origin. HPV was identified in 12 of 22 tumors (54.5%), favoring an oropharyngeal origin, and closely associated with nonkeratinizing tumor morphology (Fisher\'s exact test; p = 0.0002). One tumor with indeterminant morphology had discordant HPV and p16 status (p16+/HPV-). All tumors were EBV-negative. Diffuse expression of CD5 and c-KIT was identified in 1 of 10 virus-negative SCCUPs (10%), suggesting a possible ectopic thymic origin rather than a metastasis. A UV mutational signature, indicating cutaneous origin, was identified in 1 of 10 (10%) virus-negative SCCUPs. A cutaneous auricular primary emerged 3 months after treatment in this patient. Primary tumors became clinically apparent in 2 others (1 hypopharynx, 1 hypopharynx/larynx). Thus, after follow-up, 6 tumors remained unclassifiable as to the possible site of origin (27%). Most SCCUPs of the neck in our series were HPV-associated and thus likely of oropharyngeal origin. UV signature mutation analysis and additional IHC for CD5 and c-KIT for possible thymic origin may aid in further classifying virus-negative unknown primaries. Close clinical inspection of hypopharyngeal mucosa may also be helpful, as a subset of primary tumors later emerged at this site.

UNASSIGNED: Tumor tissue origin detection is of great importance in determining the appropriate course of treatment for cancer patients. Classifiers based on gene expression and DNA methylation profiles have been confirmed to be feasible and reliable to predict the tumor primary. However, few works have been performed to compare the performance of these classifiers based on different profiles.
UNASSIGNED: Using gene expression and DNA methylation profiles from The Cancer Genome Atlas (TCGA) project, eight machine learning methods were employed for the tumor tissue origin detection. We then evaluated the predictive performance using DNA methylation, mRNA, microRNA (miRNA) and long non-coding RNA (lncRNA) expression profiles in a comparative manner. A statistical method was introduced to select the most informative CpG sites.
UNASSIGNED: We found that LASSO is the most predictive models based on various profiles. Further analyses indicated that the results derived from DNA methylation (overall accuracy: 97.77%) are better than those derived from mRNA expression (overall accuracy: 88.01%), microRNA expression (overall accuracy: 91.03%) and lncRNA expression (overall accuracy: 95.7%). It has been suggested that we can achieve an overall accuracy >90% using only 1,000 methylated CpG sites for prediction.
UNASSIGNED: In this work, we comprehensively evaluated the performance of classifiers based on different profiles for the tumor origin detection. Our findings demonstrated the effectiveness of DNA methylation as biomarker for tracing tumor tissue origin using LASSO and neural network.

Cancer of unknown primary (CUP) represents metastatic cancer where the primary site remains unidentified despite standard diagnostic procedures. To determine the tumor origin in such cases, we developed BPformer, a deep learning method integrating the transformer model with prior knowledge of biological pathways. Trained on transcriptomes from 10,410 primary tumors across 32 cancer types, BPformer achieved remarkable accuracy rates of 94%, 92%, and 89% in primary tumors and primary and metastatic sites of metastatic tumors, respectively, surpassing existing methods. Additionally, BPformer was validated in a retrospective study, demonstrating consistency with tumor sites diagnosed through immunohistochemistry and histopathology. Furthermore, BPformer was able to rank pathways based on their contribution to tumor origin identification, which helped to classify oncogenic signaling pathways into those that are highly conservative among different cancers versus those that are highly variable depending on their origins.

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

UNASSIGNED: Cancer of unknown primary (CUP) is a difficult clinical entity to manage. The aim of the study was to investigate the sociodemographic and pathological characteristics, treatment options, and factors affecting overall survival (OS) in CUP patients whose primary tumor was not detected during follow-up.
UNASSIGNED: A total of 243 CUP patients whose primary tumors could not be detected during follow-up were included in the study. Their demographic characteristics, survival outcomes, and prognostic factors were investigated.
UNASSIGNED: Of the 243 patients included in this study, 61.7% were male and 38.3% were female, and the median age was 61 (range: 19-90) years. The most common histological type was adenocarcinoma (79%). The median follow-up time of the patients was 30.3 months (95% CI: 11.4-49.3), the median OS time was 9.1 months (95% CI: 7.2-11.0), and 72.4% of the patients received at least 1 line of chemotherapy (CT). The difference in survival between the patients who did and did not receive CT was statistically significant (median OS: 10.1 vs. 4.2 months, p = 0.003). According to the multivariate analysis, the presence of cholestasis (HR: 0.48, 95% CI: 0.29-0.79, p = 0.004), lung metastasis (HR: 0.69, 95% CI: 0.51-0.95, p = 0.001), second-line chemotherapy (HR: 1.69, 95% CI: 1.14-2.49, p < 0.001), and Eastern Cooperative Oncology Group (ECOG) performance status (HR: 0.20, 95% CI: 0.10-0.40, p < 0.001) were independent prognostic factors influencing OS.
UNASSIGNED: CUP patients who receive multiple lines of chemotherapy tend to have longer survival. This is the first study to report cholestasis as a prognostic factor in CUP patients. In addition, the presence of lung metastases, not receiving second-line chemotherapy, and ECOG performance status (≥2) were found to be independent poor prognostic factors.