Progress in refining the clinical management of cancer of unknown primary in the molecular era

Advancements in Diagnostics and Therapeutics for Cancer of Unknown Primary in the Era of Precision Medicine

Cancer of unknown primary (CUP), a set of histologically confirmed metastases that cannot be identified or traced back to its primary despite comprehensive investigations, accounts for 2-5% of all malignancies. CUP is the fourth leading cause of cancer-related deaths worldwide, with a median overall survival (OS) of 3-16 months. CUP has long been challenging to diagnose principally due to the occult properties of primary site. In the current era of molecular diagnostics, advancements in methodologies based on cytology, histology, gene expression profiling (GEP), and genomic and epigenomic analysis have greatly improved the diagnostic accuracy of CUP, surpassing 90%. Our center conducted the world's first phase III trial and demonstrated improved progression-free survival and favorable OS by GEP-guided site-specific treatment of CUP, setting the foundation of site-specific treatment in first-line management for CUP. In this review, we detailed the epidemiology, etiology, pathogenesis, as well as the histologic, genetic, and clinical characteristics of CUP. We also provided an overview of the advancements in the diagnostics and therapeutics of CUP over the past 50 years. Moving forward, we propose optimizing diagnostic modalities and exploring further-line treatment regimens as two focus areas for future studies on CUP.

Molecularly directed therapy in cancers of unknown primary: A systematic review and meta-analysis

BACKGROUND

Cancers of unknown primary (CUP) are associated with a high mortality rate, with limited therapeutic options available and platinum-based chemotherapy recommended as standard of care. Over the past decade, molecularly guided approaches aiming to adapt treatment strategies in patients with CUP based on predicted site of origin (site-specific approach) or genomic characteristics (tissue-agnostic approach) have been explored in clinical studies, with heterogenous findings identified.

METHODS

PubMed/MEDLINE, Scopus, Web of Science, Embase, the Cochrane Library, and conference abstracts of American Society of Clinical Oncology (ASCO) and European Society of Medical Oncology (ESMO) meetings were searched from inception until October 2024, for clinical studies that assessed molecularly directed therapies (MDT) in the management of patients with CUP, as compared to empiric treatment. A meta-analysis using a random-effects model and the inverse variance method was conducted, with a subgroup analysis by study design (randomized versus non-randomized). The primary endpoint was overall survival (OS), and the secondary endpoint was progression-free survival (PFS).

RESULTS

Six studies encompassing 1644 patients were included, of which 4 randomized controlled trials. A significant improvement of OS in patients with CUP treated with MDT versus empiric therapy was identified (HR: 0.75, 95 %CI: 0.62-0.91), with consistent results seen across randomized (HR: 0.86, 95 %CI: 0.73-1.01) and non-randomized (HR: 0.50, 95 %CI: 0.26-0.96). Similarly, PFS was significantly improved with MDT, as compared to empiric treatment (HR: 0.79, 95 %CI: 0.67-0.94).

CONCLUSION

The use of MDT is associated with improved survival outcomes among patients with CUP. These findings provide evidence that support the role of MDT as a potential novel standard of care in CUP treatment.

Skeletal Muscle Density as a Predictor of Prognosis and Physical Reserve in Patients with Cancer of Unknown Primary

Introduction: The Eastern Cooperative Oncology Group (ECOG) Performance Status (PS) is widely used to assess patient status but relies on subjective judgment and may not fully reflect their physical reserve. While studies have shown that skeletal muscle quality and quantity are associated with patient prognosis, their role in cancers of unknown primary (CUP) remains unclear. Therefore, this study aimed to investigate whether computed tomography (CT)-based skeletal muscle indicators reflect physical reserve and their prognostic value in patients with CUP. Methods: This study enrolled 184 patients with CUP, comprising both inpatients and outpatients, who were diagnosed at Seoul St. Mary's Hospital between 1 January 2008, and 30 June 2024. Overall survival (OS) was evaluated using the Kaplan-Meier method and analyzed using the log-rank test. Univariate and multivariate analyses were performed using Cox proportional hazard models. Statistical significance was defined as p < 0.05. Correlation analyses were conducted to evaluate the relationships between skeletal muscle density (SMD), skeletal muscle index (SMI), and other prognostic factors. Results: SMD was positively correlated with SMI and negatively correlated with age, neutrophil-to-lymphocyte ratio, Charlson Comorbidity Index (CCI), and ECOG-PS. Jonckheere's trend test revealed that SMD decreased significantly as CCI and ECOG-PS increased (p < 0.001), indicating that a higher comorbidity burden and poorer performance status were associated with lower SMD. Both ECOG-PS and SMD were identified as prognostic factors in the univariate analysis of survival; however, only SMD demonstrated statistical significance regarding prognostic value in the multivariate analysis (p = 0.004) Conclusions: SMD, as a measure of muscle quality, demonstrates superior prognostic value compared to the subjective ECOG-PS and may serve as a reliable objective tool for assessing physical reserve in patients with CUP.

Clinical characteristics and survival analysis of cancer of unknown primary

Cancer of unknown primary (CUP) is a diagnosis that the primary lesion cannot be confirmed by a series of imaging, endoscopic and pathological examinations. The present study aimed to assess the clinical characteristics and survival outcomes of patients with CUP. The present retrospective observational study included patients diagnosed with malignancies confirmed as CUP using histopathology at the Oncology Department of the Fourth Hospital of Hebei Medical University (Shijiazhuang, China) from January 2009 to January 2021. Clinical and pathological data, genetic testing results, treatment modalities and median overall survival (OS) were analyzed. A total of 107 patients were included, with a mean age of 56.59 years. The median follow-up period was 48.8 months. Adenocarcinoma was the most common pathological type (38.3%), followed by squamous cell carcinoma (31.8%) and neuroendocrine carcinoma (16.8%). The median OS was 28.4 months, with 1-, 2-, 3- and 4-year OS rates of 68.2, 54.1, 48.4 and 42.3%, respectively. Imaging revealed that 31 patients (29%) had visceral metastases, and these patients had a significantly shorter median OS compared with those without visceral metastases (8.9 vs. 69 months; P=0.001). Patients who received local treatment (n=31; 29%) had significantly longer survival times than those who did not (69 vs. 17.9 months; P=0.009). Of the 107 patients, 101 (94.4%) received systemic treatment. The median OS times for different treatment groups were as follows: Chemotherapy alone, 28.4 months; chemotherapy combined with immune checkpoint inhibitors, anti-angiogenic agents or targeted therapy, not reached; no chemotherapy, 8.0 months; and untreated, 9.4 months, with significant differences observed among the groups (P=0.008). The survival outcomes of patients with CUP varied based on the presence of visceral metastasis and the treatment modalities employed. Systemic treatments, particularly those incorporating targeted therapy, appear to have the potential to improve prognosis.

A machine learning-based method for feature reduction of methylation data for the classification of cancer tissue origin

BACKGROUND

Genome DNA methylation profiling is a promising yet costly method for cancer classification, involving substantial data. We developed an ensemble learning model to identify cancer types using methylation profiles from a limited number of CpG sites.

METHODS

Analyzing methylation data from 890 samples across 10 cancer types from the TCGA database, we utilized ANOVA and Gain Ratio to select the most significant CpG sites, then employed Gradient Boosting to reduce these to just 100 sites.

RESULTS

This approach maintained high accuracy across multiple machine learning models, with classification accuracy rates between 87.7% and 93.5% for methods including Extreme Gradient Boosting, CatBoost, and Random Forest. This method effectively minimizes the number of features needed without losing performance, helping to classify primary organs and uncover subgroups within specific cancers like breast and lung.

CONCLUSIONS

Using a gradient boosting feature selector shows potential for streamlining methylation-based cancer classification.

Diagnosis progress of carcinoma of unknown primary

Carcinoma of unknown primary (CUP) is a common and complex type of tumor in clinical practice, where the primary site cannot be determined through conventional diagnostic methods, posing significant challenges for clinical diagnosis and treatment. In recent years, advancements in gene expression profiling and genetic testing technologies have provided new perspectives for CUP research, driving progress in this field. By analyzing gene expression profiles, researchers can more effectively identify the tissue origin of tumors, thereby improving diagnostic accuracy. At the same time, the potential application of genetic testing is continuously being explored, offering new possibilities for personalized treatment. This article aims to discuss the latest advancements in the diagnosis of CUP, analyze the importance of gene expression profiling and genetic testing in tumor origin identification and their clinical applications, and summarize current research progress and future research directions, with the goal of providing a theoretical basis for the early diagnosis and treatment of CUP.

A deep learning method for predicting the origins of cervical lymph node metastatic cancer on digital pathological images

The metastatic cancer of cervical lymph nodes presents complex shapes and poses significant challenges for doctors in determining its origin. We established a deep learning framework to predict the status of lymph nodes in patients with cervical lymphadenopathy (CLA) by hematoxylin and eosin (H&E) stained slides. This retrospective study utilized 1,036 cervical lymph node biopsy specimens at the First Affiliated Hospital of Sun Yat-Sen University (FAHSYSU). A multiple-instance learning algorithm designed for key region identification was applied, and cross-validation experiments were conducted in the dataset. Additionally, the model distinguished between primary lymphoma and metastatic cancer with high prediction accuracy. We also validated our model and other models on an external dataset. Our model showed better generalization and achieved the best results on both internal and external datasets. This algorithm offers an approach for evaluating cervical lymph node status before surgery, significantly aiding physicians in preoperative diagnosis and treatment planning.

Image-based deep learning model using DNA methylation data predicts the origin of cancer of unknown primary

Cancer of unknown primary (CUP) is a rare type of metastatic cancer in which the origin of the tumor is unknown. Since the treatment strategy for patients with metastatic tumors depends on knowing the primary site, accurate identification of the origin site is important. Here, we developed an image-based deep-learning model that utilizes a vision transformer algorithm for predicting the origin of CUP. Using DNA methylation dataset of 8,233 primary tumors from The Cancer Genome Atlas (TCGA), we categorized 29 cancer types into 18 organ classes and extracted 2,312 differentially methylated CpG sites (DMCs) from non-squamous cancer group and 420 DMCs from squamous cell cancer group. Using these DMCs, we created organ-specific DNA methylation images and used them for model training and testing. Model performance was evaluated using 394 metastatic cancer samples from TCGA (TCGA-meta) and 995 samples (693 primary and 302 metastatic cancers) obtained from 20 independent external studies. We identified that the DNA methylation image reveals a distinct pattern based on the origin of cancer. Our model achieved an overall accuracy of 96.95 % in the TCGA-meta dataset. In the external validation datasets, our classifier achieved overall accuracies of 96.39 % and 94.37 % in primary and metastatic tumors, respectively. Especially, the overall accuracies for both primary and metastatic samples of non-squamous cell cancer were exceptionally high, with 96.79 % and 96.85 %, respectively.

Next-generation chemotherapy treatments based on black hole algorithms: From cancer remission to chronic disease management

PROBLEM

Therapeutic planning strategies have been developed to enhance the effectiveness of cancer drugs. Nevertheless, their performance is highly limited by the inefficient biological representativeness of predictive tumor growth models, which hinders their translation to clinical practice.

OBJECTIVE

This study proposes a disruptive approach to oncology based on nature-inspired control using realistic Black Hole physical laws, in which tumor masses are trapped to experience attraction dynamics on their path to complete remission or to become a chronic disease. This control method is designed to operate independently of individual patient idiosyncrasies, including high tumor heterogeneities and highly uncertain tumor dynamics, making it a promising avenue for advancing beyond the limitations of the traditional survival probabilistic paradigm.

DESIGN

Here, we provide a multifaceted study of chemotherapy therapeutic planning that includes: (1) the design of a pioneering controller algorithm based on physical laws found in the Black Holes; (2) investigation of the ability of this controller algorithm to ensure stable equilibrium treatments; and (3) simulation tests concerning tumor volume dynamics using drugs with significantly different pharmacokinetics (Cyclophosphamide and Atezolizumab), tumor volumes (200 mm3 and 12 732 mm3) and modeling characterizations (Gompertzian and Logistic tumor growth models).

RESULTS

Our results highlight the ability of this new astrophysical-inspired control algorithm to perform effective chemotherapy treatments for multiple tumor-treatment scenarios, including tumor resistance to chemotherapy, clinical scenarios modelled by time-dependent parameters, and highly uncertain tumor dynamics.

CONCLUSIONS

Our findings provide strong evidence that cancer therapy inspired by phenomena found in black holes can emerge as a disruptive paradigm. This opens new high-impacting research directions, exploring synergies between astrophysical-inspired control algorithms and Artificial Intelligence applied to advanced personalized cancer therapeutics.

Metastatic renal cell carcinoma with occult primary: a multicenter prospective cohort

Metastatic carcinoma of presumed renal origin (rCUP) has recently emerged as a new entity within the heterogeneous entity of Cancers of Unknown Primary (CUP) but their biological features and optimal therapeutic management remain unknown. We report the molecular characteristics and clinical outcome of a series of 25 rCUP prospectively identified within the French National Multidisciplinary Tumor Board for CUP. This cohort strongly suggests that rCUP share similarities with common RCC subtypes and benefit from renal-tailored systemic treatment. This study highlights the importance of integrating clinical and molecular data for optimal diagnosis and management of CUP.

Application of Transcriptome-Based Gene Set Featurization for Machine Learning Model to Predict the Origin of Metastatic Cancer

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.

Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning

Cancer of unknown primary (CUP) site poses diagnostic challenges due to its elusive nature. Many cases of CUP manifest as pleural and peritoneal serous effusions. Leveraging cytological images from 57,220 cases at four tertiary hospitals, we developed a deep-learning method for tumor origin differentiation using cytological histology (TORCH) that can identify malignancy and predict tumor origin in both hydrothorax and ascites. We examined its performance on three internal (n = 12,799) and two external (n = 14,538) testing sets. In both internal and external testing sets, TORCH achieved area under the receiver operating curve values ranging from 0.953 to 0.991 for cancer diagnosis and 0.953 to 0.979 for tumor origin localization. TORCH accurately predicted primary tumor origins, with a top-1 accuracy of 82.6% and top-3 accuracy of 98.9%. Compared with results derived from pathologists, TORCH showed better prediction efficacy (1.677 versus 1.265, P < 0.001), enhancing junior pathologists' diagnostic scores significantly (1.326 versus 1.101, P < 0.001). Patients with CUP whose initial treatment protocol was concordant with TORCH-predicted origins had better overall survival than those who were administrated discordant treatment (27 versus 17 months, P = 0.006). Our study underscores the potential of TORCH as a valuable ancillary tool in clinical practice, although further validation in randomized trials is warranted.

Retrospective analysis of clinical characteristics and outcomes of patients with carcinoma of unknown primary from three tertiary centers in Australia

BACKGROUND

Carcinoma of unknown primary (CUP) remains an important tumor entity and a disproportionate cause of cancer mortality. Little is known about the contemporary clinical characteristics, treatment patterns, and outcomes of CUP patients based on updated international classification guidelines. We evaluated a contemporary CUP cohort to provide insight into current clinical practice and the impact of tissue of origin assignment, site-specific and empirical therapy in a real-world setting.

METHODS

We conducted a retrospective cohort study of CUP patients, as defined by the updated European Society of Medical Oncology (ESMO) 2023 guidelines, across three tertiary referral centers in Australia between 2015 and 2022. We analyzed clinical characteristics, treatment patterns, and survival outcomes using the Kaplan-Meier method and Cox regression proportional hazard model between favorable and unfavorable risk groups.

RESULTS

We identified a total of 123 CUP patients (n = 86 unfavorable, n = 37 favorable risk as per the 2023 ESMO guidelines). Sixty-four patients (52%) were assigned a tissue of origin by the treating clinician. Median progression free survival (PFS) was 6.8 (95% confidence interval (CI) 5.1-12.1) months and overall survival (OS) 10.2 (95% CI 6.0-18.5) months. Unfavorable risk (hazard ratio [HR] 2.9, p = 0.006), poor performance status (HR 2.8, p < 0.001), and non-squamous histology (HR 2.5, p < 0.05) were associated with poor survival outcome. A total of 70 patients (57%) proceeded to systemic therapy. In patients with non-squamous histology and unfavorable risk, site-specific therapy compared to empirical chemotherapy did not improve outcome (median OS 8.2 vs. 11.8 months, p = 0.7).

CONCLUSIONS

In this real-world cohort, CUP presentations were heterogenous. Overall survival and rates of systemic treatment were poor. Poor performance status and unfavorable risk were associated with worse survival. For most patients, site-specific therapy did not improve survival outcome. Improved and timely access to diagnostic tests and therapeutics for this group of patients is urgently required.

Automatic origin prediction of liver metastases via hierarchical artificial-intelligence system trained on multiphasic CT data: a retrospective, multicentre study

Background

Currently, the diagnostic testing for the primary origin of liver metastases (LMs) can be laborious, complicating clinical decision-making. Directly classifying the primary origin of LMs at computed tomography (CT) images has proven to be challenging, despite its potential to streamline the entire diagnostic workflow.

Methods

We developed ALMSS, an artificial intelligence (AI)-based LMs screening system, to provide automated liver contrast-enhanced CT analysis for distinguishing LMs from hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC), as well as subtyping primary origin of LMs as six organ systems. We processed a CECT dataset between January 1, 2013 and June 30, 2022 (n = 3105: 840 HCC, 354 ICC, and 1911 LMs) for training and internally testing ALMSS, and two additional cohorts (n = 622) for external validation of its diagnostic performance. The performance of radiologists with and without the assistance of ALMSS in diagnosing and subtyping LMs was assessed.

Findings

ALMSS achieved average area under the curve (AUC) of 0.917 (95% confidence interval [CI]: 0.899-0.931) and 0.923 (95% [CI]: 0.905-0.937) for differentiating LMs, HCC and ICC on both the internal testing set and external testing set, outperformed that of two radiologists. Moreover, ALMSS yielded average AUC of 0.815 (95% [CI]: 0.794-0.836) and 0.818 (95% [CI]: 0.790-0.842) for predicting six primary origins on both two testing sets. Interestingly, ALMSS assigned origin diagnoses for LMs with pathological phenotypes beyond the training categories with average AUC of 0.761 (95% [CI]: 0.657-0.842), which verify the model's diagnostic expandability.

Interpretation

Our study established an AI-based diagnostic system that effectively identifies and characterizes LMs directly from multiphasic CT images.

Funding

National Natural Science Foundation of China, Guangdong Provincial Key Laboratory of Medical Image Processing.

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.

The practical utility of AI-assisted molecular profiling in the diagnosis and management of cancer of unknown primary: an updated review

Cancer of unknown primary (CUP) presents a complex diagnostic challenge, characterized by metastatic tumors of unknown tissue origin and a dismal prognosis. This review delves into the emerging significance of artificial intelligence (AI) and machine learning (ML) in transforming the landscape of CUP diagnosis, classification, and treatment. ML approaches, trained on extensive molecular profiling data, have shown promise in accurately predicting tissue of origin. Genomic profiling, encompassing driver mutations and copy number variations, plays a pivotal role in CUP diagnosis by providing insights into tumor type-specific oncogenic alterations. Mutational signatures (MS), reflecting somatic mutation patterns, offer further insights into CUP diagnosis. Known MS with established etiology, such as ultraviolet (UV) light-induced DNA damage and tobacco exposure, have been identified in cases of dedifferentiated/transdifferentiated melanoma and carcinoma. Deep learning models that integrate gene expression data and DNA methylation patterns offer insights into tissue lineage and tumor classification. In digital pathology, machine learning algorithms analyze whole-slide images to aid in CUP classification. Finally, precision oncology, guided by molecular profiling, offers targeted therapies independent of primary tissue identification. Clinical trials assigning CUP patients to molecularly guided therapies, including targetable alterations and tumor mutation burden as an immunotherapy biomarker, have resulted in improved overall survival in a subset of patients. In conclusion, AI- and ML-driven approaches are revolutionizing CUP management by enhancing diagnostic accuracy. Precision oncology utilizing enhanced molecular profiling facilitates the identification of targeted therapies that transcend the need to identify the tissue of origin, ultimately improving patient outcomes.

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.

Long-read transcriptome landscapes of primary and metastatic liver cancers at transcript resolution

BACKGROUND

The liver ranks as the sixth most prevalent site of primary cancer in humans, and it frequently experiences metastases from cancers originating in other organs. To facilitate the development of effective treatments and improve survival rates, it is crucial to comprehend the intricate and diverse transcriptome landscape of primary and metastatic liver cancers.

METHODS

We conducted long-read isoform sequencing and short-read RNA sequencing using a cohort of 95 patients with primary and secondary liver cancer who underwent hepatic resection. We compared the transcriptome landscapes of primary and metastatic liver cancers and systematically investigated hepatocellular carcinoma (HCC), paired primary tumours and liver metastases, and matched nontumour liver tissues.

RESULTS

We elucidated the full-length isoform-level transcriptome of primary and metastatic liver cancers in humans. Our analysis revealed isoform-level diversity in HCC and identified transcriptome variations associated with liver metastatis. Specific RNA transcripts and isoform switching events with clinical implications were profound in liver cancer. Moreover, we defined metastasis-specific transcripts that may serve as predictors of risk of metastasis. Additionally, we observed abnormalities in adjacent paracancerous liver tissues and characterized the immunological and metabolic alterations occurring in the liver.

CONCLUSIONS

Our findings underscore the power of full-length transcriptome profiling in providing novel biological insights into the molecular mechanisms underlying tumourigenesis. These insights will further contribute to improving treatment strategies for primary and metastatic liver cancers.

Identifying primary tumor site of origin for liver metastases via a combination of handcrafted and deep learning features

Liver is one of the most common sites for metastases, which can occur on account of primary tumors from multiple sites of origin. Identifying the primary site of origin (PSO) of a metastasis can help in guiding therapeutic options for liver metastases. In this pilot study, we hypothesized that computer extracted handcrafted (HC) histomorphometric features can be utilized to identify the PSO of liver metastases. Cellular features, including tumor nuclei morphological and graph features as well as cytoplasm texture features, were extracted by computer algorithms from 175 slides (114 patients). The study comprised three experiments: (1) comparing and (2) fusing a machine learning (ML) model trained with HC pathomic features and deep learning (DL)-based classifiers to predict site of origin; (3) identifying the section of the primary tumor from which metastases were derived. For experiment 1, we divided the cohort into training sets composed of primary and matched liver metastases [60 patients, 121 whole slide images (WSIs)], and a hold-out validation set (54 patients, 54 WSIs) composed solely of liver metastases of known site of origin. Using the extracted HC features of the training set, a combination of supervised machine classifiers and unsupervised clustering was applied to identify the PSO. A random forest classifier achieved areas under the curve (AUCs) of 0.83, 0.64, 0.82, and 0.64 in classifying the metastatic tumor from colon, esophagus, breast, and pancreas on the validation set. The top features related to nuclear and peri-nuclear shape and textural attributes. We also trained a DL network to serve as a direct comparison to our method. The DL model achieved AUCs for colon: 0.94, esophagus: 0.66, breast: 0.79, and pancreas: 0.67 in identifying PSO. A decision fusion-based strategy was deployed to fuse the trained ML and DL classifiers and achieved slightly better results than ML or DL classifier alone (colon: 0.93, esophagus: 0.68, breast: 0.81, and pancreas: 0.69). For the third experiment, WSI-level attention maps were also generated using a trained DL network to generate a composite feature similarity heat map between paired primaries and their associated metastases. Our experiments revealed that epithelium-rich and moderately differentiated tumor regions of primary tumors were quantitatively similar to paired metastatic tumors. Our findings suggest that a combination of HC and DL features could potentially help identify the PSO for liver metastases while at the same time also potentially identify the spatial sites of origin for the metastases within primary tumors.

Sequential PET/CT and pathological biomarker crosstalk predict response to PD-1 blockers alone or combined with sunitinib in propensity score-matched cohorts of cancer of unknown primary treatment

INTRODUCTION

The efficacy of immune checkpoint inhibitors (ICIs), including toripalimab and pembrolizumab, has not been confirmed in the treatment of cancer of unknown primary (CUP), which has a very poor prognosis. Combined with anti-angiogenic therapies, ICIs are hypothesized to be effective in prolonging overall survival. The study aims to give evidence on the treatment effects of sunitinib combined with ICIs, find pathological biomarkers associated with changes in volumetric 18F FDG PET/CT parameters, and investigate inner associations among these markers associated with response on PET/CT.

METHODS

The study recruited patients receiving combined treatment (ICIs + sunitinib), compared the effects of combined treatment with those of separate treatment and age-matched negative controls, and analyzed propensity score-matched (PSM) pairs. Markers associated with survival were identified, and their inner associations were tested using structural equation modeling.

RESULTS

A total of 292 patients were enrolled in the final analysis, with 53 patients receiving combined treatment. Survival analysis demonstrated significantly prolonged survival in either combined or separate treatment, with the combined arm showing better response when PSM-paired using pre-treatment whole-body PET/CT parameters. The angiogenic markers KDR and VEGF mediate the PD-1 blockade impact on volumetric value changes in positive and negative manners.

CONCLUSION

The anti-angiogenic agent sunitinib may potentiate PD-1 blockade by diminishing angiogenesis or its downstream effects. The combined separate treatment increased the survival of CUP patients, and the responses could be evaluated using volumetric PET/CT parameters.

Cancer metastasis under the magnifying glass of epigenetics and epitranscriptomics

Most of the cancer-associated mortality and morbidity can be attributed to metastasis. The role of epigenetic and epitranscriptomic alterations in cancer origin and progression has been extensively demonstrated during the last years. Both regulations share similar mechanisms driven by DNA or RNA modifiers, namely writers, readers, and erasers; enzymes responsible of respectively introducing, recognizing, or removing the epigenetic or epitranscriptomic modifications. Epigenetic regulation is achieved by DNA methylation, histone modifications, non-coding RNAs, chromatin accessibility, and enhancer reprogramming. In parallel, regulation at RNA level, named epitranscriptomic, is driven by a wide diversity of chemical modifications in mostly all RNA molecules. These two-layer regulatory mechanisms are finely controlled in normal tissue, and dysregulations are associated with every hallmark of human cancer. In this review, we provide an overview of the current state of knowledge regarding epigenetic and epitranscriptomic alterations governing tumor metastasis, and compare pathways regulated at DNA or RNA levels to shed light on a possible epi-crosstalk in cancer metastasis. A deeper understanding on these mechanisms could have important clinical implications for the prevention of advanced malignancies and the management of the disseminated diseases. Additionally, as these epi-alterations can potentially be reversed by small molecules or inhibitors against epi-modifiers, novel therapeutic alternatives could be envisioned.

Comprehensive analysis of cancer of unknown primary and recommendation of a histological and immunohistochemical diagnostic strategy from China

BACKGROUND

Previous studies on cancer of unknown primary (CUP) mainly focus on treatment and prognosis in western populations and lacked clinical evaluation of different IHC markers, so this study aimed to evaluate characteristics of CUP and recommend a diagnostic strategy from a single center in China.

METHODS AND RESULTS

Data of 625 patients with CUP were retrospectively collected and reviewed. The patients ranged in age from 20 to 91 years, with a female-to-male ratio of 1.3:1. The predominant histological type was poor or undifferentiated adenocarcinomas (308; 49.3%). The results of Canhelp-Origin molecular testing for the identification of the tissue of origin in 262 of 369 patients (71.0%) were considered predictable (similarity score > 45), with the most common predicted primary tumor site being the breast (57, 21.8%). Unpredictable molecular results correlated with more aggressive clinical parameters and poor survival. Thee positivity rates of several targeted antibodies (GATA3, GCDFP15, TTF1, Napsin A, and PAX8), based on the clinically predicted site, were lower than those reported for the corresponding primary tumors. Nonetheless, TRPS1 and INSM1 were reliable markers of predicted breast carcinoma (75.0%) and neuroendocrine tumors (83.3%), respectively. P16 expression, as well as HPV and EBER testing contributed significantly to the diagnosis of squamous cell carcinomas. Survival analysis revealed that older ages (> 57), ≥ 3 metastatic sites, non-squamous cell carcinomas, bone/liver/lung metastases, unpredictable molecular results, and palliative treatment correlated with poor overall survival.

CONCLUSIONS

We recommend a CUP diagnostic strategy involving the use of targeted antibody panels as per histological findings that is potentially applicable in clinical practice. The markers TRPS1, INSM1, and P16 expression, as well as HPV and EBER testing are particularly valuable in this aspect. Molecular testing is also predictive of survival rates.

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.

Artificial intelligence-based risk stratification, accurate diagnosis and treatment prediction in gynecologic oncology

As data-driven science, artificial intelligence (AI) has paved a promising path toward an evolving health system teeming with thrilling opportunities for precision oncology. Notwithstanding the tremendous success of oncological AI in such fields as lung carcinoma, breast tumor and brain malignancy, less attention has been devoted to investigating the influence of AI on gynecologic oncology. Hereby, this review sheds light on the ever-increasing contribution of state-of-the-art AI techniques to the refined risk stratification and whole-course management of patients with gynecologic tumors, in particular, cervical, ovarian and endometrial cancer, centering on information and features extracted from clinical data (electronic health records), cancer imaging including radiological imaging, colposcopic images, cytological and histopathological digital images, and molecular profiling (genomics, transcriptomics, metabolomics and so forth). However, there are still noteworthy challenges beyond performance validation. Thus, this work further describes the limitations and challenges faced in the real-word implementation of AI models, as well as potential solutions to address these issues.

Six-year experience of Australia's first dedicated cancer of unknown primary clinic

BACKGROUND

Diagnosis and management of cancers of unknown primary (CUP) remain challenging. This study examines the referral patterns, management and outcomes of patients referred to Australia's first dedicated CUP clinic.

METHODS

Retrospective medical record review was conducted for patients seen at the Peter MacCallum Cancer Centre CUP clinic between July 2014 and August 2020. Overall survival (OS) was analysed for patients with a CUP diagnosis where treatment information was available.

RESULTS

Of 361 patients referred, fewer than half had completed diagnostic work-up at the time of referral. A diagnosis of CUP was established in 137 (38%), malignancy other than CUP in 177 (49%) and benign pathology in 36 (10%) patients. Genomic testing was successfully completed in 62% of patients with initial provisional CUP and impacted management in 32% by identifying a tissue of origin or actionable genomic alteration. The use of site-specific, targeted therapy or immunotherapy was independently associated with longer OS compared to empirical chemotherapy.

CONCLUSION

Our specialised CUP clinic facilitated diagnostic work-up among patients with suspected malignancy and provided access to genomic testing and clinical trials for patients with a CUP diagnosis, all of which are important to improve outcomes in this patient population.

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.

erarchical tumor artificial intelligence classifier traces tissue of origin and tumor type in primary and metastasized tumors using DNA methylation

Human cancers are heterogenous by their cell composition and origination site. Cancer metastasis generates the conundrum of the unknown origin of migrated tumor cells. Tracing tissue of origin and tumor type in primary and metastasized cancer is vital for clinical significance. DNA methylation alterations play a crucial role in carcinogenesis and mark cell fate differentiation, thus can be used to trace tumor tissue of origin. In this study, we employed a novel tumor-type-specific hierarchical model using genome-scale DNA methylation data to develop a multilayer perceptron model, HiTAIC, to trace tissue of origin and tumor type in 27 cancers from 23 tissue sites in data from 7735 tumors with high resolution, accuracy, and specificity. In tracing primary cancer origin, HiTAIC accuracy was 99% in the test set and 93% in the external validation data set. Metastatic cancers were identified with a 96% accuracy in the external data set. HiTAIC is a user-friendly web-based application through https://sites.dartmouth.edu/salaslabhitaic/. In conclusion, we developed HiTAIC, a DNA methylation-based algorithm, to trace tumor tissue of origin in primary and metastasized cancers. The high accuracy and resolution of tumor tracing using HiTAIC holds promise for clinical assistance in identifying cancer of unknown origin.

From Biology to Diagnosis and Treatment: The Ariadne’s Thread in Cancer of Unknown Primary

Cancer of unknown primary (CUP) encloses a group of heterogeneous tumours, the primary sites for which cannot be identified at the time of diagnosis, despite extensive investigations. CUP has always posed major challenges both in its diagnosis and management, leading to the hypothesis that it is rather a distinct entity with specific genetic and phenotypic aberrations, considering the regression or dormancy of the primary tumour; the development of early, uncommon systemic metastases; and the resistance to therapy. Patients with CUP account for 1–3% of all human malignancies and can be categorised into two prognostic subsets according to their clinicopathologic characteristics at presentation. The diagnosis of CUP mainly depends on the standard evaluation comprising a thorough medical history; complete physical examination; histopathologic morphology and algorithmic immunohistochemistry assessment; and CT scan of the chest, abdomen, and pelvis. However, physicians and patients do not fare well with these criteria and often perform additional time-consuming evaluations to identify the primary tumour site to guide treatment decisions. The development of molecularly guided diagnostic strategies has emerged to complement traditional procedures but has been disappointing thus far. In this review, we present the latest data on CUP regarding the biology, molecular profiling, classification, diagnostic workup, and treatment.

Mutated axon guidance gene PLXNB2 sustains growth and invasiveness of stem cells isolated from cancers of unknown primary

The genetic changes sustaining the development of cancers of unknown primary (CUP) remain elusive. The whole-exome genomic profiling of 14 rigorously selected CUP samples did not reveal specific recurring mutation in known driver genes. However, by comparing the mutational landscape of CUPs with that of most other human tumor types, it emerged a consistent enrichment of changes in genes belonging to the axon guidance KEGG pathway. In particular, G842C mutation of PlexinB2 (PlxnB2) was predicted to be activating. Indeed, knocking down the mutated, but not the wild-type, PlxnB2 in CUP stem cells resulted in the impairment of self-renewal and proliferation in culture, as well as tumorigenic capacity in mice. Conversely, the genetic transfer of G842C-PlxnB2 was sufficient to promote CUP stem cell proliferation and tumorigenesis in mice. Notably, G842C-PlxnB2 expression in CUP cells was associated with basal EGFR phosphorylation, and EGFR blockade impaired the viability of CUP cells reliant on the mutated receptor. Moreover, the mutated PlxnB2 elicited CUP cell invasiveness, blocked by EGFR inhibitor treatment. In sum, we found that a novel activating mutation of the axon guidance gene PLXNB2 sustains proliferative autonomy and confers invasive properties to stem cells isolated from cancers of unknown primary, in EGFR-dependent manner.

The diagnosis and treatment for a patient with cancer of unknown primary: A case report

Background: Cancer of unknown primary (CUP) is a class of metastatic malignant tumors whose primary location cannot be determined. The diagnosis and treatment of CUP are a considerable challenge for clinicians. Herein, we report a CUP case whose corresponding primary tumor sites were successfully identified, and the patient received proper treatment. Case report: In February 2022, a 74-year-old woman was admitted to the Medical Oncology Department at Sir Run Run Shaw Hospital for new lung and intestinal tumors after more than 9 years of breast cancer surgery. After laparoscopically assisted right hemicolectomy, pathology revealed mucinous adenocarcinoma; the pathological stage was pT2N0M0. Results from needle biopsies of lung masses suggested poorly differentiated cancer, ER (-), PR (-), and HER2 (-), which combined with the clinical history, did not rule out metastatic breast cancer. A surgical pathology sample was needed to determine the origin of the tumor tissue, but the patient's chest structure showed no indications for surgery. Analysis of the tumor's traceable gene expression profile prompted breast cancer, and analysis of next-generation amplification sequencing (NGS) did not obtain a potential drug target. We developed a treatment plan based on comprehensive immunohistochemistry, a gene expression profile, and NGS analysis. The treatment plan was formulated using paclitaxel albumin and capecitabine in combination with radiotherapy. The efficacy evaluation was the partial response (PR) after four cycles of chemotherapy and two cycles combined with radiotherapy. Conclusion: This case highlighted the importance of identifying accurate primary tumor location for patients to benefit from treatment, which will provide a reference for the treatment decisions of CUP tumors in the future.

An accurate prediction of the origin for bone metastatic cancer using deep learning on digital pathological images

BACKGROUND

Determining the origin of bone metastatic cancer (OBMC) is of great significance to clinical therapeutics. It is challenging for pathologists to determine the OBMC with limited clinical information and bone biopsy.

METHODS

We designed a regional multiple-instance learning algorithm to predict the OBMC based on hematoxylin-eosin (H&E) staining slides alone. We collected 1041 cases from eight different hospitals and labeled 26,431 regions of interest to train the model. The performance of the model was assessed by ten-fold cross validation and external validation. Under the guidance of top3 predictions, we conducted an IHC test on 175 cases of unknown origins to compare the consistency of the results predicted by the model and indicated by the IHC markers. We also applied the model to identify whether there was tumor or not in a region, as well as distinguishing squamous cell carcinoma, adenocarcinoma, and neuroendocrine tumor.

FINDINGS

In the within-cohort, our model achieved a top1-accuracy of 91.35% and a top3-accuracy of 97.75%. In the external cohort, our model displayed a good generalizability with a top3-accuracy of 97.44%. The top1 consistency between the results of the model and the immunohistochemistry markers was 83.90% and the top3 consistency was 94.33%. The model obtained an accuracy of 98.98% to identify whether there was tumor or not and an accuracy of 93.85% to differentiate three types of cancers.

INTERPRETATION

Our model demonstrated good performance to predict the OBMC from routine histology and had great potential for assisting pathologists with determining the OBMC accurately.

FUNDING

National Science Foundation of China (61875102 and 61975089), Natural Science Foundation of Guangdong province (2021A15-15012379 and 2022A1515 012550), Science and Technology Research Program of Shenzhen City (JCYJ20200109110606054 and WDZC20200821141349001), and Tsinghua University Spring Breeze Fund (2020Z99CFZ023).

Epidemiology of Undifferentiated Carcinomas

Undifferentiated carcinomas are rare cancers that lack differentiation, such that they cannot be classified into any conventional histological subtype. These cancers are uniquely codified and are contrasted to carcinomas with an ascertained histology that are grade classified as poorly differentiated, undifferentiated, or anaplastic. Given their rarity, there are no standardized overviews of undifferentiated carcinomas in the literature, and it is unknown if their classification indicates a unique prognosis profile. In this study, we summarize the clinicodemographic and mortality outcomes of undifferentiated carcinomas in twelve primary sites and for unknown primaries, comprising 92.8% of all undifferentiated carcinomas diagnosed from 1975-2017 in the Surveillance, Epidemiology, and End Results Program (SEER). Incidence has decreased to 4 per 1 million cancer diagnoses since 1980. Relative to the most common undifferentiated cancers with a defined histology, undifferentiated carcinomas have overall worse prognosis, except in nasopharyngeal and salivary gland cancers (hazard ratio (HR) 0.7-1.3). After correction for age, sex, race, detection stage, and treatment (surgery, chemotherapy, and radiotherapy), the mortality HR averages 1.3-1.4 for these cancers relative to histologically ascertainable undifferentiated cancers. However, there is a wide variance depending on site, signifying that survival outcomes for undifferentiated carcinomas depend on factors related to site tumor biology.

Clinicopathological, molecular and prognostic characteristics of cancer of unknown primary in China: An analysis of 1420 cases

BACKGROUND

Cancer of unknown primary (CUP) is defined the presence of metastatic disease without an identified primary site. An unidentifiable primary site of cancer creates significant challenges for treatment selection. We aimed to describe the clinicopathological, molecular, and prognostic characteristics of Chinese CUP patients.

METHODS

Patients with oncologist-confirmed CUP were identified at Fudan University Shanghai Cancer Center from 2019 to 2020. Information on patient characteristics, tumor presentation, treatment, and outcome were retrospectively collected from the inpatient database and pathological consultation database for descriptive analysis. A multivariable logistic regression model was established to identify factors associated with patient prognosis.

RESULTS

A total of 1420 CUP patients were enrolled in this study. The baseline characteristics of the entire cohort included the following: median age (59 years old), female sex (45.8%), adenocarcinoma (47.7%), and poorly differentiated or undifferentiated tumors (92.1%). For the inpatient cohort, the most common sites where cancer spread included the lymph nodes (41.8%), bone (22.0%), liver (20.1%), and peritoneum/retroperitoneum (16.0%). A total of 77.4% and 58.2% of patients were treated with local therapy and systemic therapy, respectively. Four prognostic factors, including liver metastasis, peritoneal/retroperitoneal metastasis, number of metastatic sites (N ≥ 2), and systemic treatment, were independently associated with overall survival. Additionally, 24.8% (79/318) of patients received molecular testing, including PD-L1, human papillomavirus, genetic variation, and 90-gene expression tests for diagnosis or therapy selection.

CONCLUSION

Cancer of unknown primary remains a difficult cancer to diagnose and manage. Our findings improve our understanding of Chinese CUP patient characteristics, leading to improved care and outcomes for CUP patients.

Case Report: Successful Immunotherapy Improved the Prognosis of the Unfavorable Subset of Cancer of Unknown Primary

Background

Cancer of unknown primary (CUP) is heterogeneous and has a wide variety of clinical presentations and a poor prognosis in most patients, with a median overall survival of only 6 months. The development of molecular profiling contributes to precision therapy, and targeted drugs and immune checkpoint inhibitors (ICIs) greatly promote individualized treatment.

Case presentation

Here, we reported a case of an unfavorable subset of CUP who had a long time of survival after the immunotherapy-prominent comprehensive treatment. A 48-year-old man presented with back pain and a cough. A diagnostic work-up showed bone marrow, multiple bones, and lymph node metastasis. Lymph node pathology implies metastatic poorly differentiated cancer. Next-generation sequencing (NGS) showed no special targets, but the tumor proportion score (TPS) of programmed death-ligand 1 (PD-L1) was 80% and the tumor mutation burden (TMB) was 16.7 per million bases. After two cycles of pembrolizumab 200 mg D1 plus nanoparticle albumin-bound (nab)-paclitaxel 200 mg D1&8 (q3w), PET-CT and bone marrow aspiration cytology showed a complete response (CR). Subsequently, pembrolizumab alone was used for three months. The left inguinal lymph nodes showed new metastasis. After two cycles of the combination treatment of pembrolizumab and (nab)-paclitaxel, a partial response (PR) was achieved. After seven months, retroperitoneal lymph nodes showed new metastasis, and the sequential treatment with radiotherapy and pembrolizumab exhibited encouraging efficacy. To date, the patient has survived nearly 40 months with the combination therapy.

Conclusions

The ICI-prominent comprehensive treatment provided clinical benefit for the reported case of CUP. Thus, CUP patients with markers of benefiting from immunotherapy should be actively treated with immunotherapy to improve their prognosis.

A Machine Learning Method to Trace Cancer Primary Lesion Using Microarray-Based Gene Expression Data

Cancer of unknown primary site (CUP) is a heterogeneous group of cancers whose tissue of origin remains unknown after detailed investigation by conventional clinical methods. The number of CUP accounts for roughly 3%–5% of all human malignancies. CUP patients are usually treated with broad-spectrum chemotherapy, which often leads to a poor prognosis. Recent studies suggest that the treatment targeting the primary lesion of CUP will significantly improve the prognosis of the patient. Therefore, it is urgent to develop an efficient method to accurately detect tissue of origin of CUP in clinical cancer research. In this work, we developed a novel framework that uses Extreme Gradient Boosting (XGBoost) to trace the primary site of CUP based on microarray-based gene expression data. First, we downloaded the microarray-based gene expression profiles of 59,385 genes for 57,08 samples from The Cancer Genome Atlas (TCGA) and 6,364 genes for 3,101 samples from the Gene Expression Omnibus (GEO). Both data were divided into training and independent testing data with a ratio of 4:1. Then, we obtained in the training data 200 and 290 genes from TCGA and the GEO datasets, respectively, to train XGBoost models for the identification of the primary site of CUP. The overall 5-fold cross-validation accuracies of our methods were 96.9% and 95.3% on TCGA and GEO training datasets, respectively. Meanwhile, the macro-precision for the independent dataset reached 96.75% and 98.8% on, respectively, TCGA and GEO. Experimental results demonstrated that the XGBoost framework not only can reduce the cost of clinical cancer traceability but also has high efficiency, which might be useful in clinical usage.

Refinement of computational identification of somatic copy number alterations using DNA methylation microarrays illustrated in cancers of unknown primary

High-throughput genomic technologies are increasingly used in personalized cancer medicine. However, computational tools to maximize the use of scarce tissues combining distinct molecular layers are needed. Here we present a refined strategy, based on the R-package 'conumee', to better predict somatic copy number alterations (SCNA) from deoxyribonucleic acid (DNA) methylation arrays. Our approach, termed hereafter as 'conumee-KCN', improves SCNA prediction by incorporating tumor purity and dynamic thresholding. We trained our algorithm using paired DNA methylation and SNP Array 6.0 data from The Cancer Genome Atlas samples and confirmed its performance in cancer cell lines. Most importantly, the application of our approach in cancers of unknown primary identified amplified potentially actionable targets that were experimentally validated by Fluorescence in situ hybridization and immunostaining, reaching 100% specificity and 93.3% sensitivity.

Identification of tissue of origin in cancer of unknown primary using a targeted bisulfite sequencing panel

Aim: To construct a targeted bisulfite sequencing panel predicting origin of cancer of unknown primary. Methods: A bisulfite sequencing panel targeting 2793 tissue-specific markers was performed in 100 clinical samples. Results: The authors' prediction model showed 0.85 accuracy for the 'first-ranked' tissue type and 0.93 accuracy for the 'second-ranked' tissue type using 2793 tissue-specific markers and 0.84 accuracy for the 'first-ranked' tissue type and 0.92 accuracy for the 'second-ranked' tissue type when the number of tissue-specific markers was reduced to 514. Conclusion: Targeted bisulfite sequencing is a useful method for predicting the tissue of origin in patients with cancer of unknown primary.

SEOM-GECOD clinical guideline for unknown primary cancer (2021)

Cancer of unknown primary site (CUP) is defined as a heterogeneous group of tumors that appear as metastases, and of which standard diagnostic work-up fails to identify the origin. It is considered a separate entity with a specific biology, and nowadays molecular characteristics and the determination of actionable mutations may be important in a significant group of patients. In this guide, we summarize the diagnostic, therapeutic, and possible new developments in molecular medicine that may help us in the management of this unique disease entity.

An overview of artificial intelligence in oncology

Cancer is associated with significant morbimortality globally. Advances in screening, diagnosis, management and survivorship were substantial in the last decades, however, challenges in providing personalized and data-oriented care remain. Artificial intelligence (AI), a branch of computer science used for predictions and automation, has emerged as potential solution to improve the healthcare journey and to promote precision in healthcare. AI applications in oncology include, but are not limited to, optimization of cancer research, improvement of clinical practice (eg., prediction of the association of multiple parameters and outcomes - prognosis and response) and better understanding of tumor molecular biology. In this review, we examine the current state of AI in oncology, including fundamentals, current applications, limitations and future perspectives.

Site-specific therapy in cancers of unknown primary site: a systematic review and meta-analysis

Background

Cancer of unknown primary site (CUP) is a term applied to characterize pathologically confirmed metastatic cancer with unknown primary tumor origin. It remains uncertain whether patients with CUP benefit from site-specific therapy guided by molecular profiling.

Patients and methods

A systematic search in PubMed, Web of Science, Embase, Cochrane Library, and ClinicalTrials.gov, and of conference abstracts from January 1976 to January 2021 was performed to identify studies investigating the efficacy of site-specific therapy on patients with CUP. The quality of included studies was evaluated using the Cochrane risk of bias tool and Newcastle–Ottawa scale. Eligible studies were weighted and pooled for meta-analysis. Hazard ratios (HRs) for overall survival (OS) and progression-free survival (PFS) were assessed to compare the efficacy of site-specific therapy with empiric therapy in patients with CUP. In addition, subgroup analyses were conducted.

Results

Five studies comprising 1114 patients were identified, of which 454 patients received site-specific therapy, and 660 patients received empiric therapy. Our meta-analysis revealed that site-specific therapy was not significantly associated with improved PFS [HR 0.93, 95% confidence interval (CI) 0.74-1.17, P = 0.534] and OS (HR 0.75, 95% CI 0.55-1.03, P = 0.069), compared with empiric therapy. However, during subgroup analysis significantly improved OS was associated with site-specific therapy in the high-accuracy predictive assay subgroup (HR 0.46, 95% CI 0.26-0.81, P = 0.008) compared with the low accuracy predictive assay subgroup (HR 0.93, 95% CI 0.75-1.15, P = 0.509). Furthermore, compared with patients with less responsive tumor types, more survival benefit from site-specific therapy was found in patients with more responsive tumors (HR 0.67, 95% CI 0.46-0.97, P = 0.037).

Conclusions

Our results suggest that site-specific therapy is not significantly associated with improved survival outcomes; however, it might benefit patients with CUP with responsive tumor types.

•

Studies evaluating the role of site-specific therapy guided by molecular profiling in CUP provided contradictory results.

•

Site-specific therapy is not significantly associated with improved survival outcomes in the overall CUP population.

•

Molecularly defined site-specific therapy may improve OS only when high-accuracy assays assign CUP to responsive tumor types.

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.

Tumor mutation burden for predicting immune checkpoint blockade response: the more, the better

BACKGROUND

Recently, the US Food and Drug Administration (FDA) has approved immune checkpoint blockade (ICB) for treating cancer patients with tumor mutation burden (TMB) >10 mutations/megabase (mut/Mb). However, high TMB (TMB-H) defined by >10 mut/Mb fails to predict ICB response across different cancer types, which has raised serious concerns on the current FDA approval. Thus, to better implement TMB as a robust biomarker of ICB response, an optimal and generalizable TMB cut-off within and across cancer types must be addressed as soon as possible.

METHODS

Using Morris's and Kurzrock's cohorts (n=1662 and 102), we exhaustively tested all possible TMB cut-offs for predicting ICB treatment outcomes in 10 cancer types. The bootstrap method was applied to generate 10,000 randomly resampled cohorts using original cohorts to measure the reproducibility of TMB cut-off. ICB treatment outcomes were analyzed by overall survival, progression-free survival and objective response rate.

RESULTS

No universally valid TMB cut-off was available for all cancer types. Only in cancer types with higher TMB (category I), such as melanoma, colorectal cancer, bladder cancer, and non-small cell lung cancer, the associations between TMB-H and ICB treatment outcomes were less affected by TMB cut-off selection. Moreover, high TMB (category I) cancer types shared a wide range of TMB cut-offs and a universally optimal TMB cut-off of 13 mut/Mb for predicting favorable ICB outcomes. In contrast, low TMB (category II) cancer types, for which the prognostic associations were sensitive to TMB cut-off selection, showed markedly limited and distinct ranges of significantly favorable TMB cut-offs. Equivalent results were obtained in the analyses of pooled tumors.

CONCLUSIONS

Our finding-the correlation that TMB-H is more robustly associated with favorable ICB treatment outcomes in cancer types with higher TMBs-can be used to predict whether TMB could be a robust predictive biomarker in cancer types for which TMB data are available, but ICB treatment has not been investigated. This theory was tested in cancer of unknown primary successfully. Additionally, the universal TMB cut-off of 13 mut/Mb might reveal a general requirement to trigger the sequential cascade from somatic mutations to an effective antitumor immunity.

Meat consumption and cancer of unknown primary (CUP) risk: results from The Netherlands cohort study on diet and cancer

PURPOSE

Cancer of unknown primary (CUP) is a metastasised cancer for which no primary lesion could be identified during life. Research into CUP aetiology with respect to dietary factors is particularly scarce. This study investigates whether meat consumption is associated with CUP risk.

METHODS

Data was utilised from the prospective Netherlands cohort study that includes 1,20,852 participants aged 55-69 years. All participants completed a self-administered questionnaire on diet and other cancer risk factors at baseline. Cancer follow-up was established through record linkage to the Netherlands Cancer Registry and the Dutch Pathology Registry. A total of 899 CUP cases and 4111 subcohort members with complete and consistent dietary data were available for case-cohort analyses after 20.3 years of follow-up. Multivariable adjusted hazard ratios (HRs) were calculated using proportional hazards models.

RESULTS

We found a statistically significant positive association with beef and processed meat consumption and CUP risk in women (multivariable adjusted HR Q4 vs. Q1 1.47, 95% CI 1.04-2.07, Ptrend = 0.004 and Q4 vs. Q1 1.53, 95% CI 1.08-2.16, Ptrend = 0.001, respectively), and a non-significant positive association with processed meat consumption and CUP risk in men (multivariable adjusted HR Q4 vs. Q1 1.33, 95% CI 0.99-1.79, Ptrend = 0.15). No associations were observed between red meat (overall), poultry or fish consumption and CUP risk.

CONCLUSION

In this cohort, beef and processed meat consumption were positively associated with increased CUP risk in women, whereas a non-significant positive association was observed between processed meat consumption and CUP risk in men.

Real-world data analysis of patients with cancer of unknown primary

Cancer of unknown primary (CUP) is a heterogeneous malignancy in which the primary site of the tumor cannot be identified through standard work-up. The survival outcome of CUP is generally poor, and there is no consensus for treatment. Here, we comprehensively analyzed the real-world data of 218 patients with CUP (median age, 62 years [range, 19-91]; male, 62.3%). Next-generation sequencing was conducted in 22 (10%) patients, one of whom showed level 1 genetic alteration. Most (60.3%) patients were treated with empirical cytotoxic chemotherapy, and two patients received targeted therapy based on the NGS results. The median OS was 8.3 months (95% confidence interval [CI] 6.2-11.4), and the median progression-free survival of patients treated with chemotherapy was 4.4 months (95% CI 3.4-5.3). In multivariate Cox regression analysis, Eastern Cooperative Oncology Group performance status (ECOG PS) of 0 or 1 and localized disease were significantly associated with favorable survival outcomes. Collectively, we found that CUP patients had a poor prognosis after standard treatment, and those with localized disease who received local treatment and those with better PS treated with multiple lines of chemotherapy had better survival outcomes. Targeted therapies based on NGS results are expected to improve survival outcomes.

90-Gene Expression Profiling for Tissue Origin Diagnosis of Cancer of Unknown Primary

Cancer of unknown primary (CUP), in which metastatic diseases exist without an identifiable primary location, accounts for about 3-5% of all cancer diagnoses. Successful diagnosis and treatment of such patients are difficult. This study aimed to assess the expression characteristics of 90 genes as a method of identifying the primary site from CUP samples. We validated a 90-gene expression assay and explored its potential diagnostic utility in 44 patients at Jiangsu Cancer Hospital. For each specimen, the expression of 90 tumor-specific genes in malignant tumors was analyzed, and similarity scores were obtained. The types of malignant tumors predicted were compared with the reference diagnosis to calculate the accuracy. In addition, we verified the consistency of the expression profiles of the 90 genes in CUP secondary malignancies and metastatic malignancies in The Cancer Genome Atlas. We also reported a detailed description of the next-generation coding sequences for CUP patients. For each clinical medical specimen collected, the type of malignant tumor predicted and analyzed by the 90-gene expression assay was compared with its reference diagnosis, and the overall accuracy was 95.4%. In addition, the 90-gene expression profile generally accurately classified CUP into the cluster of its primary tumor. Sequencing of the exome transcriptome containing 556 high-frequency gene mutation oncogenes was not significantly related to the 90 genes analysis. Our results demonstrate that the expression characteristics of these 90 genes can be used as a powerful tool to accurately identify the primary sites of CUP. In the future, the inclusion of the 90-gene expression assay in pathological diagnosis will help oncologists use precise treatments, thereby improving the care and outcomes of CUP patients.

Gene Expression Profiling for Differential Diagnosis of Liver Metastases: A Multicenter, Retrospective Cohort Study

Background

Liver metastases (LM) are the most common tumors encountered in the liver and continue to be a significant cause of morbidity and mortality. Identification of the primary tumor of any LM is crucial for the implementation of effective and tailored treatment approaches, which still represents a difficult problem in clinical practice.

Methods

The resection or biopsy specimens and associated clinicopathologic data were archived from seven independent centers between January 2017 and December 2020. The primary tumor sites of liver tumors were verified through evaluation of available medical records, pathological and imaging information. The performance of a 90-gene expression assay for the determination of the site of tumor origin was assessed.

Result

A total of 130 LM covering 15 tumor types and 16 primary liver tumor specimens that met all quality control criteria were analyzed by the 90-gene expression assay. Among 130 LM cases, tumors were most frequently located in the colorectum, ovary and breast. Overall, the analysis of the 90-gene signature showed 93.1% and 100% agreement rates with the reference diagnosis in LM and primary liver tumor, respectively. For the common primary tumor types, the concordance rate was 100%, 95.7%, 100%, 93.8%, 87.5% for classifying the LM from the ovary, colorectum, breast, neuroendocrine, and pancreas, respectively.

Conclusion

The overall accuracy of 93.8% demonstrates encouraging performance of the 90-gene expression assay in identifying the primary sites of liver tumors. Future incorporation of the 90-gene expression assay in clinical diagnosis will aid oncologists in applying precise treatments, leading to improved care and outcomes for LM patients.

Keep your eyes peeled for long noncoding RNAs: Explaining their boundless role in cancer metastasis, drug resistance, and clinical application

Cancer metastasis and drug resistance are two major obstacles in the treatment of cancer and therefore, the leading cause of cancer-associated mortalities worldwide. Hence, an in-depth understanding of these processes and identification of the underlying key players could help design a better therapeutic regimen to treat cancer. Earlier thought to be merely transcriptional junk and having passive or secondary function, recent advances in the genomic research have unravelled that long noncoding RNAs (lncRNAs) play pivotal roles in diverse physiological as well as pathological processes including cancer metastasis and drug resistance. LncRNAs can regulate various steps of the complex metastatic cascade such as epithelial-mesenchymal transition (EMT), invasion, migration and metastatic colonization, and also affect the sensitivity of cancer cells to various chemotherapeutic drugs. A substantial body of literature for more than a decade of research evince that lncRNAs can regulate gene expression at different levels such as epigenetic, transcriptional, posttranscriptional, translational and posttranslational levels, depending on their subcellular localization and through their ability to interact with DNA, RNA and proteins. In this review, we mainly focus on how lncRNAs affect cancer metastasis by modulating expression of key metastasis-associated genes at various levels of gene regulation. We also discuss how lncRNAs confer cancer cells either sensitivity or resistance to various chemo-therapeutic drugs via different mechanisms. Finally, we highlight the immense potential of lncRNAs as prognostic and diagnostic biomarkers as well as therapeutic targets in cancer.