New techniques to identify the tissue of origin for cancer of unknown primary in the era of precision medicine: progress and challenges.

Despite a standardized diagnostic examination, cancer of unknown primary (CUP) is a rare metastatic malignancy with an unidentified tissue of origin (TOO). Patients diagnosed with CUP are typically treated with empiric chemotherapy, although their prognosis is worse than those with metastatic cancer of a known origin. TOO identification of CUP has been employed in precision medicine, and subsequent site-specific therapy is clinically helpful. For example, molecular profiling, including genomic profiling, gene expression profiling, epigenetics and proteins, has facilitated TOO identification. Moreover, machine learning has improved identification accuracy, and non-invasive methods, such as liquid biopsy and image omics, are gaining momentum. However, the heterogeneity in prediction accuracy, sample requirements and technical fundamentals among the various techniques is noteworthy. Accordingly, we systematically reviewed the development and limitations of novel TOO identification methods, compared their pros and cons and assessed their potential clinical usefulness. Our study may help patients shift from empirical to customized care and improve their prognoses.

Tissue of origin detection for cancer tumor using low-depth cfDNA samples through combination of tumor-specific methylation atlas and genome-wide methylation density in graph convolutional neural networks.

BACKGROUND: Cell free DNA (cfDNA)-based assays hold great potential in detecting early cancer signals yet determining the tissue-of-origin (TOO) for cancer signals remains a challenging task. Here, we investigated the contribution of a methylation atlas to TOO detection in low depth cfDNA samples. METHODS: We constructed a tumor-specific methylation atlas (TSMA) using whole-genome bisulfite sequencing (WGBS) data from five types of tumor tissues (breast, colorectal, gastric, liver and lung cancer) and paired white blood cells (WBC). TSMA was used with a non-negative least square matrix factorization (NNLS) deconvolution algorithm to identify the abundance of tumor tissue types in a WGBS sample. We showed that TSMA worked well with tumor tissue but struggled with cfDNA samples due to the overwhelming amount of WBC-derived DNA. To construct a model for TOO, we adopted the multi-modal strategy and used as inputs the combination of deconvolution scores from TSMA with other features of cfDNA. RESULTS: Our final model comprised of a graph convolutional neural network using deconvolution scores and genome-wide methylation density features, which achieved an accuracy of 69% in a held-out validation dataset of 239 low-depth cfDNA samples. CONCLUSIONS: In conclusion, we have demonstrated that our TSMA in combination with other cfDNA features can improve TOO detection in low-depth cfDNA samples.

A comparison of DNA sequencing and gene expression profiling to assist tissue of origin diagnosis in cancer of unknown primary.

Cancer of unknown primary (CUP) is a syndrome defined by clinical absence of a primary cancer after standardised investigations. Gene expression profiling (GEP) and DNA sequencing have been used to predict primary tissue of origin (TOO) in CUP and find molecularly guided treatments; however, a detailed comparison of the diagnostic yield from these two tests has not been described. Here, we compared the diagnostic utility of RNA and DNA tests in 215 CUP patients (82% received both tests) in a prospective Australian study. Based on retrospective assessment of clinicopathological data, 77% (166/215) of CUPs had insufficient evidence to support TOO diagnosis (clinicopathology unresolved). The remainder had either a latent primary diagnosis (10%) or clinicopathological evidence to support a likely TOO diagnosis (13%) (clinicopathology resolved). We applied a microarray (CUPGuide) or custom NanoString 18-class GEP test to 191 CUPs with an accuracy of 91.5% in known metastatic cancers for high-medium confidence predictions. Classification performance was similar in clinicopathology-resolved CUPs - 80% had high-medium predictions and 94% were concordant with pathology. Notably, only 56% of the clinicopathology-unresolved CUPs had high-medium confidence GEP predictions. Diagnostic DNA features were interrogated in 201 CUP tumours guided by the cancer type specificity of mutations observed across 22 cancer types from the AACR Project GENIE database (77,058 tumours) as well as mutational signatures (e.g. smoking). Among the clinicopathology-unresolved CUPs, mutations and mutational signatures provided additional diagnostic evidence in 31% of cases. GEP classification was useful in only 13% of cases and oncoviral detection in 4%. Among CUPs where genomics informed TOO, lung and biliary cancers were the most frequently identified types, while kidney tumours were another identifiable subset. In conclusion, DNA and RNA profiling supported an unconfirmed TOO diagnosis in one-third of CUPs otherwise unresolved by clinicopathology assessment alone. DNA mutation profiling was the more diagnostically informative assay. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Plasma cell-free DNA promise monitoring and tissue injury assessment of COVID-19.

Coronavirus 2019 (COVID-19) is a complex disease that affects billions of people worldwide. Currently, effective etiological treatment of COVID-19 is still lacking; COVID-19 also causes damages to various organs that affects therapeutics and mortality of the patients. Surveillance of the treatment responses and organ injury assessment of COVID-19 patients are of high clinical value. In this study, we investigated the characteristic fragmentation patterns and explored the potential in tissue injury assessment of plasma cell-free DNA in COVID-19 patients. Through recruitment of 37 COVID-19 patients, 32 controls and analysis of 208 blood samples upon diagnosis and during treatment, we report gross abnormalities in cfDNA of COVID-19 patients, including elevated GC content, altered molecule size and end motif patterns. More importantly, such cfDNA fragmentation characteristics reflect patient-specific physiological changes during treatment. Further analysis on cfDNA tissue-of-origin tracing reveals frequent tissue injuries in COVID-19 patients, which is supported by clinical diagnoses. Hence, our work demonstrates and extends the translational merit of cfDNA fragmentation pattern as valuable analyte for effective treatment monitoring, as well as tissue injury assessment in COVID-19.

Cell-Free DNA Fragmentomics: The Novel Promising Biomarker.

Cell-free DNA molecules are released into the plasma via apoptotic or necrotic events and active release mechanisms, which carry the genetic and epigenetic information of its origin tissues. However, cfDNA is the mixture of various cell fragments, and the efficient enrichment of cfDNA fragments with diagnostic value remains a great challenge for application in the clinical setting. Evidence from recent years shows that cfDNA fragmentomics' characteristics differ in normal and diseased individuals without the need to distinguish the source of the cfDNA fragments, which makes it a promising novel biomarker. Moreover, cfDNA fragmentomics can identify tissue origins by inferring epigenetic information. Thus, further insights into the fragmentomics of plasma cfDNA shed light on the origin and fragmentation mechanisms of cfDNA during physiological and pathological processes in diseases and enhance our ability to take the advantage of plasma cfDNA as a molecular diagnostic tool. In this review, we focus on the cfDNA fragment characteristics and its potential application, such as fragment length, end motifs, jagged ends, preferred end coordinates, as well as nucleosome footprints, open chromatin region, and gene expression inferred by the cfDNA fragmentation pattern across the genome. Furthermore, we summarize the methods for deducing the tissue of origin by cfDNA fragmentomics.

Circulating microRNA Panels for Detection of Liver Cancers and Liver-Metastasizing Primary Cancers.

Malignant liver tumors, including primary malignant liver tumors and liver metastases, are among the most frequent malignancies worldwide. The disease carries a poor prognosis and poor overall survival, particularly in cases involving liver metastases. Consequently, the early detection and precise differentiation of malignant liver tumors are of paramount importance for making informed decisions regarding patient treatment. Significant research efforts are currently directed towards the development of diagnostic tools for different types of cancer using minimally invasive techniques. A prominent area of focus within this research is the evaluation of circulating microRNA, for which dysregulated expression is well documented in different cancers. Combining microRNAs in panels using serum or plasma samples derived from blood holds great promise for better sensitivity and specificity for detection of certain types of cancer.

Clinical validation of a 90-gene expression test for tumor tissue of origin diagnosis: a large-scale multicenter study of 1417 patients.

BACKGROUND: Once malignancy tumors were diagnosed, the determination of tissue origin and tumor type is critical for clinical management. Although the significant advance in imaging techniques and histopathological approaches, the diagnosis remains challenging in patients with metastatic and poorly differentiated or undifferentiated tumors. Gene expression profiling has been demonstrated the ability to classify multiple tumor types. The present study aims to assess the performance of a 90-gene expression test for tumor classification (i.e. the determination of tumor tissue of origin) in real clinical settings. METHODS: Formalin-fixed paraffin-embedded samples and associated clinicopathologic information were collected from three cancer centers between January 2016 and January 2021. A total of 1417 specimens that met quality control criteria (RNA quality, tumor cell content ≥ 60% and so on) were analyzed by the 90-gene expression test to identify the tumor tissue of origin. The performance was evaluated by comparing the test results with histopathological diagnosis. RESULTS: The 1417 samples represent 21 main tumor types classified by common tissue origins and anatomic sites. Overall, the 90-gene expression test reached an accuracy of 94.4% (1338/1417, 95% CI: 0.93 to 0.96). Among different tumor types, sensitivities were ranged from 74.2% (head&neck tumor) to 100% (adrenal carcinoma, mesothelioma, and prostate cancer). Sensitivities for the most prevalent cancers of lung, breast, colorectum, and gastroesophagus are 95.0%, 98.4%, 93.9%, and 90.6%, respectively. Moreover, specificities for all 21 tumor types are greater than 99%. CONCLUSIONS: These findings showed robust performance of the 90-gene expression test for identifying the tumor tissue of origin and support the use of molecular testing as an adjunct to tumor classification, especially to those poorly differentiated or undifferentiated tumors in clinical practice.

Gene expression profiling for the diagnosis of multiple primary malignant tumors.

BACKGROUND: The incidence of multiple primary malignant tumors (MPMTs) is rising due to the development of screening technologies, significant treatment advances and increased aging of the population. For patients with a prior cancer history, identifying the tumor origin of the second malignant lesion has important prognostic and therapeutic implications and still represents a difficult problem in clinical practice. METHODS: In this study, we evaluated the performance of a 90-gene expression assay and explored its potential diagnostic utility for MPMTs across a broad spectrum of tumor types. Thirty-five MPMT patients from Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University and Fudan University Shanghai Cancer Center were enrolled; 73 MPMT specimens met all quality control criteria and were analyzed by the 90-gene expression assay. RESULTS: For each clinical specimen, the tumor type predicted by the 90-gene expression assay was compared with its pathological diagnosis, with an overall accuracy of 93.2% (68 of 73, 95% confidence interval 0.84-0.97). For histopathological subgroup analysis, the 90-gene expression assay achieved an overall accuracy of 95.0% (38 of 40; 95% CI 0.82-0.99) for well-moderately differentiated tumors and 92.0% (23 of 25; 95% CI 0.82-0.99) for poorly or undifferentiated tumors, with no statistically significant difference (p-value > 0.5). For squamous cell carcinoma specimens, the overall accuracy of gene expression assay also reached 87.5% (7 of 8; 95% CI 0.47-0.99) for identifying the tumor origins. CONCLUSIONS: The 90-gene expression assay provides flexibility and accuracy in identifying the tumor origin of MPMTs. Future incorporation of the 90-gene expression assay in pathological diagnosis will assist oncologists in applying precise treatments, leading to improved care and outcomes for MPMT patients.

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.

ENOX2-based early detection (ONCOblot) of asbestos-induced malignant mesothelioma 4-10 years in advance of clinical symptoms.

BACKGROUND: Malignant mesothelioma is an aggressive, almost uniformly fatal tumor, caused primarily by exposure to asbestos. In this study, serum presence of mesothelioma-specific protein transcript variants of ecto-nicotinamide adenine dinucleotide oxidase disulfide-thiol exchanger 2 (ENOX2), a recently identified marker of malignancy, were investigated using the ONCOblot tissue of origin cancer detection test. METHODS: Sequential serum samples collected from asbestos-exposed individuals prior to the development of frank mesothelioma were assayed for ENOX2 presence by 2-D gel immunoblot analysis to determine how long in advance of clinical symptoms mesothelioma-specific ENOX2 transcript variants could be detected. RESULTS: Two mesothelioma-specific ENOX2 protein transcript variants were detected in the serum of asbestos-exposed individuals 4-10 years prior to clinical diagnosis of malignant mesothelioma (average 6.2 years). Either one or both ENOX2 protein transcript variants indicative of malignant mesothelioma were absent in 14 of 15 subjects diagnosed with benign pleural plaques either with or without accompanying asbestosis. CONCLUSIONS: In a population of asbestos-exposed subjects who eventually developed malignant mesothelioma, ENOX2 protein transcript variants characteristic of malignant mesothelioma were present in serum 4-10 years in advance of clinical symptoms. As with all biomarker studies, these observations require validation in a larger, independent cohort of patients and should include prospective as well as retrospective sampling.

DNA methylation profiles of ovarian cysts resemble ovarian tissues but not endometrial tissues.

INTRODUCTION: Endometriosis is a heritable, complex chronic inflammatory disease, for which much of the causal pathogenic mechanism remain unknown.Despite the high prevalence of ovarian chocolate cyst, its origin is still under debate. METHODS: Prevailing retrograde menstruation model predicts that ectopic endometrial cells migrate and develop into ovarian chocolate cyst. However, other models were also proposed. Genome-wide association studies (GWASs) have proved successful in identifying common genetic variants of moderate effects for various complex diseases. RESULTS: A growing body of evidence shows that the remodeling of retrograde endometrial tissues to the ectopic endometriotic lesions involves multiple epigenetic alterations, such as DNA methylation, histone modification, and microRNA expression.Because DNA methylation states exhibit a tissue specific pattern, we profiled the DNA methylation for ovarian cysts and paired eutopic endometrial and ovarian tissues from four patients. Surprisingly, DNA methylation profiles showed the ovarian cysts were closely grouped with normal ovarian but not endometrial tissues. CONCLUSIONS: These results suggested alterative origin of ovarian cysts or strong epigenetic reprogramming of infiltrating endometrial cells after seeding the ovarian tissue. The data provide contributing to the pathogenesis and pathophysiology of endometriosis.

Mesenchymal stromal/stem cell tissue source and in vitro expansion impact extracellular vesicle protein and miRNA compositions as well as angiogenic and immunomodulatory capacities.

Recently, therapies utilizing extracellular vesicles (EVs) derived from mesenchymal stromal/stem cells (MSCs) have begun to show promise in clinical trials. However, EV therapeutic potential varies with MSC tissue source and in vitro expansion through passaging. To find the optimal MSC source for clinically translatable EV-derived therapies, this study aims to compare the angiogenic and immunomodulatory potentials and the protein and miRNA cargo compositions of EVs isolated from the two most common clinical sources of adult MSCs, bone marrow and adipose tissue, across different passage numbers. Primary bone marrow-derived MSCs (BMSCs) and adipose-derived MSCs (ASCs) were isolated from adult female Lewis rats and expanded in vitro to the indicated passage numbers (P2, P4, and P8). EVs were isolated from the culture medium of P2, P4, and P8 BMSCs and ASCs and characterized for EV size, number, surface markers, protein content, and morphology. EVs isolated from different tissue sources showed different EV yields per cell, EV sizes, and protein yield per EV. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of proteomics data and miRNA seq data identified key proteins and pathways associated with differences between BMSC-EVs and ASC-EVs, as well as differences due to passage number. In vitro tube formation assays employing human umbilical vein endothelial cells suggested that both tissue source and passage number had significant effects on the angiogenic capacity of EVs. With or without lipopolysaccharide (LPS) stimulation, EVs more significantly impacted expression of M2-macrophage genes (IL-10, Arg1, TGFß) than M1-macrophage genes (IL-6, NOS2, TNFα). By correlating the proteomics analyses with the miRNA seq analysis and differences observed in our in vitro immunomodulatory, angiogenic, and proliferation assays, this study highlights the trade-offs that may be necessary in selecting the optimal MSC source for development of clinical EV therapies.

Cell-free DNA methylation reveals cell-specific tissue injury and correlates with disease severity and patient outcomes in COVID-19.

BACKGROUND: The recently identified methylation patterns specific to cell type allows the tracing of cell death dynamics at the cellular level in health and diseases. This study used COVID-19 as a disease model to investigate the efficacy of cell-specific cell-free DNA (cfDNA) methylation markers in reflecting or predicting disease severity or outcome. METHODS: Whole genome methylation sequencing of cfDNA was performed for 20 healthy individuals, 20 cases with non-hospitalized COVID-19 and 12 cases with severe COVID-19 admitted to intensive care unit (ICU). Differentially methylated regions (DMRs) and gene ontology pathway enrichment analyses were performed to explore the locus-specific methylation difference between cohorts. The proportion of cfDNA derived from lung and immune cells to a given sample (i.e. tissue fraction) at cell-type resolution was estimated using a novel algorithm, which reflects lung injuries and immune response in COVID-19 patients and was further used to evaluate clinical severity and patient outcome. RESULTS: COVID­19 patients had globally reduced cfDNA methylation level compared with healthy controls. Compared with non-hospitalized COVID-19 patients, the cfDNA methylation pattern was significantly altered in severe patients with the identification of 11,156 DMRs, which were mainly enriched in pathways related to immune response. Markedly elevated levels of cfDNA derived from lung and more specifically alveolar epithelial cells, bronchial epithelial cells, and lung endothelial cells were observed in COVID-19 patients compared with healthy controls. Compared with non-hospitalized patients or healthy controls, severe COVID-19 had significantly higher cfDNA derived from B cells, T cells and granulocytes and lower cfDNA from natural killer cells. Moreover, cfDNA derived from alveolar epithelial cells had the optimal performance to differentiate COVID-19 with different severities, lung injury levels, SOFA scores and in-hospital deaths, with the area under the receiver operating characteristic curve of 0.958, 0.941, 0.919 and 0.955, respectively. CONCLUSION: Severe COVID-19 has a distinct cfDNA methylation signature compared with non-hospitalized COVID-19 and healthy controls. Cell type-specific cfDNA methylation signature enables the tracing of COVID-19 related cell deaths in lung and immune cells at cell-type resolution, which is correlated with clinical severities and outcomes, and has extensive application prospects to evaluate tissue injuries in diseases with multi-organ dysfunction.

Circulating cell-free DNA-based multi-cancer early detection.

Patients benefit considerably from early detection of cancer. Existing single-cancer tests have various limitations, which could be effectively addressed by circulating cell-free DNA (cfDNA)-based multi-cancer early detection (MCED). With sensitive detection and accurate localization of multiple cancer types at a very low and fixed false-positive rate (FPR), MCED has great potential to revolutionize early cancer detection. Herein, we review state-of-the-art approaches for cfDNA-based MCED and their limitations and discuss both technical and clinical challenges in the development and application of MCED tests. Given the constant improvements in technology and understanding of cancer biology, we propose that a cfDNA-based targeted sequencing assay that integrates multimodal features should be optimized for MCED.

Tissue of origin prediction for cancer of unknown primary using a targeted methylation sequencing panel.

RATIONALE: Cancer of unknown primary (CUP) is a group of rare malignancies with poor prognosis and unidentifiable tissue-of-origin. Distinct DNA methylation patterns in different tissues and cancer types enable the identification of the tissue of origin in CUP patients, which could help risk assessment and guide site-directed therapy. METHODS: Using genome-wide DNA methylation profile datasets from The Cancer Genome Atlas (TCGA) and machine learning methods, we developed a 200-CpG methylation feature classifier for CUP tissue of origin prediction (MFCUP). MFCUP was further validated with public-available methylation array data of 2977 specimens and targeted methylation sequencing of 78 Formalin-fixed paraffin-embedded (FFPE) samples from a single center. RESULTS: MFCUP achieved an accuracy of 97.2% in a validation cohort (n = 5923) representing 25 cancer types. When applied to an Infinium 450 K array dataset (n = 1052) and an Infinium EPIC (850 K) array dataset (n = 1925), MFCUP achieved an overall accuracy of 93.4% and 84.8%, respectively. Based on MFCUP, we established a targeted bisulfite sequencing panel and validated it with FFPE sections from 78 patients of 20 cancer types. This methylation sequencing panel correctly identified tissue of origin in 88.5% (69/78) of samples. We also found that the methylation levels of specific CpGs can distinguish one cancer type from others, indicating their potential as biomarkers for cancer diagnosis and screening. CONCLUSION: Our methylation-based cancer classifier and targeted methylation sequencing panel can predict tissue of origin in diverse cancer types with high accuracy.

Characterizing the origin of blood plasma proteins from organ tissues in rainbow trout (Oncorhynchus mykiss) using a comparative non-targeted proteomics approach.

Protein expression patterns adapt to various cues to meet the needs of an organism. The dynamicity of an organism's proteome can therefore reveal information about an organism's health. Proteome databases contain limited information regarding organisms outside of medicinal biology. The UniProt human and mouse proteomes are extensively reviewed and ∼50 % of both proteomes include tissue specificity, while >99 % of the rainbow trout proteome lacks tissue specificity. This study aimed to expand knowledge on the rainbow trout proteome with a focus on understanding the origin of blood plasma proteins. Blood, brain, heart, liver, kidney, and gills were collected from adult rainbow trout, plasma and tissue proteins were analyzed using liquid chromatography tandem mass spectrometry. Over 10,000 proteins were identified across all groups. Our data indicated that the majority of the plasma proteome is shared amongst multiple tissue types, though 4-7 % of the plasma proteome is uniquely originated from each tissue (gill > heart > liver > kidney > brain).

Utility of RNA Expression to Determine the Tissue of Origin of Malignancies with an Inconclusive Histopathology.

We present 2 cases of cancer of unknown origin in which RNA-based cancer classification testing provided vital insight and directed treatment management. The tissue of origin could not be determined in both of these patients utilizing morphology and immunohistochemical analysis of the tissue samples. Next-generation sequencing and tumor-of-origin testing using an RNA-based molecular cancer classifier were performed to elucidate the possible tissue of origin. A 61-year-old male with a history of localized basal cell carcinoma presented with a 4.4-cm axillary lymph node in addition to upper extremity edema and supraclavicular lymphadenopathy. RNA-based tumor origin testing revealed skin basal or squamous cell carcinoma as the likely tissue of origin, with a probability of 97%. He received vismodegib, a hedgehog inhibitor, after progression on cemiplimab and experienced a partial response by RECIST criteria, which is currently ongoing for over a year. A 74-year-old female patient with a remote history of ovarian cancer for which she underwent resection and adjuvant chemotherapy presented 15 years later with abdominal pain. The diagnostic workup revealed a 2-cm pancreatic mass and enlarged peritoneal lymph nodes. RNA sequencing revealed a 99% likelihood of the tissue of origin being serous ovarian carcinoma. Subsequently, she underwent surgery and adjuvant chemotherapy and is currently in remission with letrozole maintenance. Genomic data already plays a crucial role in therapeutic decision-making for individuals with cancer. These cases highlight the complementary role of genomic data in the diagnostic workup of cancer, leading to favorable patient outcomes.

Investigation of Different Library Preparation and Tissue of Origin Deconvolution Methods for Urine and Plasma cfDNA Methylome Analysis.

Methylation sequencing is a promising approach to infer the tissue of origin of cell-free DNA (cfDNA). In this study, a single- and a double-stranded library preparation approach were evaluated with respect to their technical biases when applied on cfDNA from plasma and urine. Additionally, tissue of origin (TOO) proportions were evaluated using two deconvolution methods. Sequencing cfDNA from urine using the double-stranded method resulted in a substantial within-read methylation bias and a lower global methylation (56.0% vs. 75.8%, p ≤ 0.0001) compared to plasma cfDNA, both of which were not observed with the single-stranded approach. Individual CpG site-based TOO deconvolution resulted in a significantly increased proportion of undetermined TOO with the double-stranded method (urine: 32.3% vs. 1.9%; plasma: 5.9% vs. 0.04%; p ≤ 0.0001), but no major differences in proportions of individual cell types. In contrast, fragment-level deconvolution led to multiple cell types, with significantly different TOO proportions between the two methods. This study thus outlines potential limitations of double-stranded library preparation for methylation analysis of cfDNA especially for urinary cfDNA. While the double-stranded method allows jagged end analysis in addition to TOO analysis, it leads to significant methylation bias in urinary cfDNA, which single-stranded methods can overcome.

Real-world histopathological approach to malignancy of undefined primary origin (MUO) to diagnose cancers of unknown primary (CUPs).

The aim of this study is to envisage a streamlined pathological workup to rule out CUPs in patients presenting with MUOs. Sixty-four MUOs were classified using standard histopathology. Clinical data, immunocytochemical markers, and results of molecular analysis were recorded. MUOs were histologically subdivided in clear-cut carcinomas (40 adenocarcinomas, 11 squamous, and 3 neuroendocrine carcinomas) and unclear-carcinoma features (5 undifferentiated and 5 sarcomatoid tumors). Cytohistology of 7/40 adenocarcinomas suggested an early metastatic cancer per se. In 33/40 adenocarcinomas, CK7/CK20 expression pattern, gender, and metastasis sites influenced tissue-specific marker selection. In 23/40 adenocarcinomas, a "putative-immunophenotype" of tissue of origin addressed clinical-diagnostic examinations, identifying 9 early metastatic cancers. Cell lineage markers were used to confirm squamous and neuroendocrine differentiation. Pan-cytokeratins were used to confirm the epithelial nature of poorly differentiated tumors, followed by tissue and cell lineage markers, which identified one melanoma. In total, 47/64 MUOs (73.4%) were confirmed CUP. Molecular analysis, feasible in 37/47 CUPs (78.7%), had no diagnostic impact. Twenty CUP patients, mainly with squamous carcinomas and adenocarcinomas with putative-gynecologic-immunophenotypes, presented with only lymph node metastases and had longer median time to progression and overall survival (< 0.001), compared with patients with other metastatic patterns. We propose a simplified histology-driven workup which could efficiently rule out CUPs and identify early metastatic cancer.

Machine learning for pan-cancer classification based on RNA sequencing data.

Despite recent improvements in cancer diagnostics, 2%-5% of all malignancies are still cancers of unknown primary (CUP), for which the tissue-of-origin (TOO) cannot be determined at the time of presentation. Since the primary site of cancer leads to the choice of optimal treatment, CUP patients pose a significant clinical challenge with limited treatment options. Data produced by large-scale cancer genomics initiatives, which aim to determine the genomic, epigenomic, and transcriptomic characteristics of a large number of individual patients of multiple cancer types, have led to the introduction of various methods that use machine learning to predict the TOO of cancer patients. In this review, we assess the reproducibility, interpretability, and robustness of results obtained by 20 recent studies that utilize different machine learning methods for TOO prediction based on RNA sequencing data, including their reported performance on independent data sets and identification of important features. Our review investigates the strengths and weaknesses of different methods, checks the correspondence of their results, and identifies potential issues with datasets used for model training and testing, assessing their potential usefulness in a clinical setting and suggesting future improvements.