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Hwang J, Lee Y, Yoo SK, Kim JI. Image-based deep learning model using DNA methylation data predicts the origin of cancer of unknown primary. Neoplasia 2024; 55:101021. [PMID: 38943996 PMCID: PMC11261876 DOI: 10.1016/j.neo.2024.101021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 06/24/2024] [Indexed: 07/01/2024]
Abstract
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.
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Affiliation(s)
- Jinha Hwang
- Department of Laboratory Medicine, Korea University Anam Hospital, Seoul, the Republic of Korea
| | - Yeajina Lee
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, the Republic of Korea; Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, the Republic of Korea
| | - Seong-Keun Yoo
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Oncological Sciences, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
| | - Jong-Il Kim
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, the Republic of Korea; Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, the Republic of Korea.
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2
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Soares Dos Santos MP, Bernardo RMC, Vidal J, Moreira A, Torres DFM, Herdeiro CAR, Santos HA, Gonçalves G. Next-generation chemotherapy treatments based on black hole algorithms: From cancer remission to chronic disease management. Comput Biol Med 2024; 180:108961. [PMID: 39106673 DOI: 10.1016/j.compbiomed.2024.108961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 07/10/2024] [Accepted: 07/26/2024] [Indexed: 08/09/2024]
Abstract
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.
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Affiliation(s)
- Marco P Soares Dos Santos
- Center for Mechanical Technology & Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal; Intelligent Systems Associate Laboratory (LASI), Guimarães, Portugal.
| | - Rodrigo M C Bernardo
- Center for Mechanical Technology & Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal
| | - JoãoV Vidal
- Department of Physics and Aveiro Institute of Materials (CICECO), University of Aveiro, Aveiro, Portugal; Department of Physics and Institute for Nanostructures, Nanomodelling and Nanofabrication (I3N), University of Aveiro, Aveiro, Portugal
| | - Ana Moreira
- Department of Medical Sciences, Institute of Biomedicine (iBiMED), University of Aveiro, 3810-193, Aveiro, Portugal
| | - Delfim F M Torres
- Center for Research and Development in Mathematics and Applications (CIDMA), Department of Mathematics, University of Aveiro, Aveiro, Portugal
| | - Carlos A R Herdeiro
- Center for Research and Development in Mathematics and Applications (CIDMA), Department of Mathematics, University of Aveiro, Aveiro, Portugal
| | - Hélder A Santos
- Department of Biomaterials and Biomedical Technology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands; Drug Research Program, Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy, University of Helsinki, Helsinki, Finland
| | - Gil Gonçalves
- Center for Mechanical Technology & Automation (TEMA), Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal; Intelligent Systems Associate Laboratory (LASI), Guimarães, Portugal
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3
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Jacquin N, Flippot R, Masliah-Planchon J, Grisay G, Brillet R, Dupain C, Kamal M, Guillou I, Gruel N, Servant N, Gestraud P, Wong J, Cockenpot V, Goncalves A, Selves J, Blons H, Rouleau E, Delattre O, Gervais C, Le Tourneau C, Bièche I, Allory Y, Albigès L, Watson S. Metastatic renal cell carcinoma with occult primary: a multicenter prospective cohort. NPJ Precis Oncol 2024; 8:147. [PMID: 39025947 PMCID: PMC11258290 DOI: 10.1038/s41698-024-00648-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Accepted: 07/09/2024] [Indexed: 07/20/2024] Open
Abstract
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.
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Affiliation(s)
- Nicolas Jacquin
- INSERM U830, Diversity and Plasticity of Childhood Tumors Lab, PSL Research University, Institut Curie Research Center, Paris, France
- Department of Medical Oncology, Institut Godinot, Reims, France
| | - Ronan Flippot
- Department of Cancer Medicine, Institut Gustave Roussy, Université Paris-Saclay, Villejuif, France
| | | | - Guillaume Grisay
- Department of Cancer Medicine, Institut Gustave Roussy, Université Paris-Saclay, Villejuif, France
| | - Riwan Brillet
- Clinical Bioinformatic Unit, Department of Diagnostic and Theragnostic Medicine, Institut Curie Hospital, Paris, France
| | - Célia Dupain
- Department of Drug Development and Innovation (D3i), Institut Curie, Paris, France
| | - Maud Kamal
- Department of Drug Development and Innovation (D3i), Institut Curie, Paris, France
| | - Isabelle Guillou
- Department of Drug Development and Innovation (D3i), Institut Curie, Paris, France
| | - Nadège Gruel
- INSERM U830, Diversity and Plasticity of Childhood Tumors Lab, PSL Research University, Institut Curie Research Center, Paris, France
- Department of Translational Research, Institut Curie Hospital, Paris, France
| | - Nicolas Servant
- INSERM U900, CBIO-Centre for Computational Biology, Institut Curie Research Center, Mines ParisTech, Paris, France
| | - Pierre Gestraud
- INSERM U900, CBIO-Centre for Computational Biology, Institut Curie Research Center, Mines ParisTech, Paris, France
| | - Jennifer Wong
- Somatic Genetic Unit, Department of Genetics, Institut Curie Hospital, Paris, France
| | | | | | - Janick Selves
- Department of Pathology, University Hospital of Toulouse (IUCT), Toulouse, France
| | - Hélène Blons
- Department of Biochemistry, Pharmacogenetics and Molecular Oncology, Georges Pompidou European Hospital, APHP, Paris, France
| | - Etienne Rouleau
- PRISM Center for personalized medicine, Gustave Roussy Cancer Center, Villejuif, France
| | - Olivier Delattre
- INSERM U830, Diversity and Plasticity of Childhood Tumors Lab, PSL Research University, Institut Curie Research Center, Paris, France
- Somatic Genetic Unit, Department of Genetics, Institut Curie Hospital, Paris, France
| | - Claire Gervais
- Department of Medical Oncology, Georges Pompidou European Hospital, APHP, Paris, France
| | - Christophe Le Tourneau
- Department of Drug Development and Innovation (D3i), Institut Curie, Paris, France
- INSERM U900, Institut Curie, Saint-Cloud, France
- Paris-Saclay University, Paris, France
| | - Ivan Bièche
- Department of Genetics, Institut Curie Hospital, INSERM U1016, Université Paris Cité, Paris, France
| | - Yves Allory
- Department of Pathology, Institut Curie Hospital, Saint-Cloud, France.
- Université Versailles St-Quentin, Université Paris-Saclay, Montigny-le-Bretonneux, France.
| | - Laurence Albigès
- Department of Cancer Medicine, Institut Gustave Roussy, Université Paris-Saclay, Villejuif, France.
| | - Sarah Watson
- INSERM U830, Diversity and Plasticity of Childhood Tumors Lab, PSL Research University, Institut Curie Research Center, Paris, France.
- Department of Medical Oncology, Institut Curie Hospital, Paris, France.
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4
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Jeong Y, Chu J, Kang J, Baek S, Lee JH, Jung DS, Kim WW, Kim YR, Kang J, Do IG. Application of Transcriptome-Based Gene Set Featurization for Machine Learning Model to Predict the Origin of Metastatic Cancer. Curr Issues Mol Biol 2024; 46:7291-7302. [PMID: 39057073 PMCID: PMC11276602 DOI: 10.3390/cimb46070432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 07/03/2024] [Accepted: 07/03/2024] [Indexed: 07/28/2024] Open
Abstract
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.
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Affiliation(s)
- Yeonuk Jeong
- Oncocross Ltd., Seoul 04168, Republic of Korea (W.-W.K.); (Y.-R.K.)
| | - Jinah Chu
- Department of Pathology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea;
| | - Juwon Kang
- Oncocross Ltd., Seoul 04168, Republic of Korea (W.-W.K.); (Y.-R.K.)
- Yonsei Institute of Pharmaceutical Sciences, College of Pharmacy, Yonsei University, Incheon 21983, Republic of Korea
| | - Seungjun Baek
- Oncocross Ltd., Seoul 04168, Republic of Korea (W.-W.K.); (Y.-R.K.)
| | - Jae-Hak Lee
- Oncocross Ltd., Seoul 04168, Republic of Korea (W.-W.K.); (Y.-R.K.)
| | - Dong-Sub Jung
- Oncocross Ltd., Seoul 04168, Republic of Korea (W.-W.K.); (Y.-R.K.)
| | - Won-Woo Kim
- Oncocross Ltd., Seoul 04168, Republic of Korea (W.-W.K.); (Y.-R.K.)
| | - Yi-Rang Kim
- Oncocross Ltd., Seoul 04168, Republic of Korea (W.-W.K.); (Y.-R.K.)
| | - Jihoon Kang
- Oncocross Ltd., Seoul 04168, Republic of Korea (W.-W.K.); (Y.-R.K.)
| | - In-Gu Do
- Department of Pathology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea;
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5
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Rassy E, Pavlidis N. Predicting tumour origin with cytology-based deep learning: hype or hope? Nat Rev Clin Oncol 2024:10.1038/s41571-024-00906-x. [PMID: 38773339 DOI: 10.1038/s41571-024-00906-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/23/2024]
Affiliation(s)
- Elie Rassy
- Département de Médecine Oncologique, Gustave Roussy, Villejuif, France.
- Institut national de la santé et de la recherche médicale (INSERM) U1018, Université Paris-Saclay, Gustave Roussy, Villejuif, France.
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Tian F, Liu D, Wei N, Fu Q, Sun L, Liu W, Sui X, Tian K, Nemeth G, Feng J, Xu J, Xiao L, Han J, Fu J, Shi Y, Yang Y, Liu J, Hu C, Feng B, Sun Y, Wang Y, Yu G, Kong D, Wang M, Li W, Chen K, Li X. Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning. Nat Med 2024; 30:1309-1319. [PMID: 38627559 PMCID: PMC11108774 DOI: 10.1038/s41591-024-02915-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 03/07/2024] [Indexed: 04/26/2024]
Abstract
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.
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Affiliation(s)
- Fei Tian
- Department of Abdominal Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Dong Liu
- Department of Radiology, The First Affiliated Hospital of Suzhou University, Suzhou, China
| | - Na Wei
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Qianqian Fu
- Department of Pathology, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Lin Sun
- Department of Pathology, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Wei Liu
- Department of Pathology, The First Affiliated Hospital of Suzhou University, Suzhou, China
| | - Xiaolong Sui
- Department of Pathology, Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Kathryn Tian
- Harvard Dunster House, Harvard University, Cambridge, MA, USA
| | | | - Jingyu Feng
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Jingjing Xu
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Lin Xiao
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Junya Han
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingjie Fu
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yinhua Shi
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yichen Yang
- Tianjin Cancer Institute, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Jia Liu
- Department of Abdominal Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Chunhong Hu
- Department of Radiology, The First Affiliated Hospital of Suzhou University, Suzhou, China
| | - Bin Feng
- Department of Pathology, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Yan Sun
- Department of Pathology, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Yunjun Wang
- Department of Pathology, Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Guohua Yu
- Department of Pathology, Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Dalu Kong
- Department of Abdominal Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Meiyun Wang
- Department of Radiology, Henan Provincial People's Hospital, The People's Hospital of Zhengzhou University, Zhengzhou, China
| | - Wencai Li
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Kexin Chen
- Department of Epidemiology and Biostatistics, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China.
| | - Xiangchun Li
- Tianjin Cancer Institute, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China.
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7
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Boys EL, Gao B, Grimison P, Sutherland S, MacKenzie KL, Reddel RR, Liu J. Retrospective analysis of clinical characteristics and outcomes of patients with carcinoma of unknown primary from three tertiary centers in Australia. Cancer Med 2024; 13:e7052. [PMID: 38523552 PMCID: PMC10961596 DOI: 10.1002/cam4.7052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 02/08/2024] [Accepted: 02/16/2024] [Indexed: 03/26/2024] Open
Abstract
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.
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Affiliation(s)
- Emma L. Boys
- ProCan®, Children's Medical Research InstituteWestmeadNew South WalesAustralia
- Faculty of Medicine and HealthThe University of SydneySydneyNew South WalesAustralia
- Department of Medical OncologyCrown Princess Mary Cancer CentreWestmeadNew South WalesAustralia
- Blacktown Cancer and Haematology Centre, Blacktown HospitalBlacktownNew South WalesAustralia
| | - Bo Gao
- Faculty of Medicine and HealthThe University of SydneySydneyNew South WalesAustralia
- Department of Medical OncologyCrown Princess Mary Cancer CentreWestmeadNew South WalesAustralia
- Blacktown Cancer and Haematology Centre, Blacktown HospitalBlacktownNew South WalesAustralia
| | - Peter Grimison
- Faculty of Medicine and HealthThe University of SydneySydneyNew South WalesAustralia
- Chris O'Brien LifehouseSydneyNew South WalesAustralia
| | - Sarah Sutherland
- Faculty of Medicine and HealthThe University of SydneySydneyNew South WalesAustralia
- Chris O'Brien LifehouseSydneyNew South WalesAustralia
| | - Karen L. MacKenzie
- ProCan®, Children's Medical Research InstituteWestmeadNew South WalesAustralia
- School of Medical Science, Faculty of Medicine and HealthThe University of SydneySydneyNew South WalesAustralia
| | - Roger R. Reddel
- ProCan®, Children's Medical Research InstituteWestmeadNew South WalesAustralia
- Sydney Medical School, Faculty of Medicine and HealthThe University of SydneySydneyNew South WalesAustralia
| | - Jia Liu
- ProCan®, Children's Medical Research InstituteWestmeadNew South WalesAustralia
- The Kinghorn Cancer Centre, St Vincent's HospitalDarlinghurstNew South WalesAustralia
- School of Clinical Medicine, St Vincent's CampusUniversity of New South WalesSydneyNew South WalesAustralia
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8
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Xin H, Zhang Y, Lai Q, Liao N, Zhang J, Liu Y, Chen Z, He P, He J, Liu J, Zhou Y, Yang W, Zhou Y. Automatic origin prediction of liver metastases via hierarchical artificial-intelligence system trained on multiphasic CT data: a retrospective, multicentre study. EClinicalMedicine 2024; 69:102464. [PMID: 38333364 PMCID: PMC10847157 DOI: 10.1016/j.eclinm.2024.102464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 12/22/2023] [Accepted: 01/17/2024] [Indexed: 02/10/2024] Open
Abstract
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.
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Affiliation(s)
- Hongjie Xin
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yiwen Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Qianwei Lai
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Naying Liao
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Jing Zhang
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yanping Liu
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
- Department of Gastroenterology, The Second Affiliated Hospital, University of South China, Hengyang, China
| | - Zhihua Chen
- Department of Radiology, The Second Affiliated Hospital, University of South China, Hengyang, China
| | - Pengyuan He
- Department of Infectious Diseases, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China
| | - Jian He
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Junwei Liu
- Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yuchen Zhou
- Department of General Surgery, Cancer Center, Integrated Hospital of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China
| | - Wei Yang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Yuanping Zhou
- Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
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9
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Sun M, Xu B, Chen C, Zhu Y, Li X, Chen K. Tissue of origin prediction for cancer of unknown primary using a targeted methylation sequencing panel. Clin Epigenetics 2024; 16:25. [PMID: 38336771 PMCID: PMC10854167 DOI: 10.1186/s13148-024-01638-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 01/30/2024] [Indexed: 02/12/2024] Open
Abstract
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.
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Affiliation(s)
- Miaomiao Sun
- Department of Pathology, Henan Key Laboratory of Tumor Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Bo Xu
- Research and Development Division, Oriomics Biotech Inc, Hangzhou, China
| | - Chao Chen
- Department of Pathology, Henan Key Laboratory of Tumor Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Youjie Zhu
- Research and Development Division, Oriomics Biotech Inc, Hangzhou, China
| | - Xiaomo Li
- Research and Development Division, Oriomics Biotech Inc, Hangzhou, China.
| | - Kuisheng Chen
- Department of Pathology, Henan Key Laboratory of Tumor Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
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10
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Lorkowski SW, Dermawan JK, Rubin BP. The practical utility of AI-assisted molecular profiling in the diagnosis and management of cancer of unknown primary: an updated review. Virchows Arch 2024; 484:369-375. [PMID: 37999736 DOI: 10.1007/s00428-023-03708-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 11/07/2023] [Accepted: 11/14/2023] [Indexed: 11/25/2023]
Abstract
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.
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Affiliation(s)
- Shuhui Wang Lorkowski
- Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Josephine K Dermawan
- Robert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Brian P Rubin
- Robert J. Tomsich Pathology and Laboratory Medicine Institute, Cleveland Clinic, Cleveland, OH, 44195, USA.
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11
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Ma W, Wu H, Chen Y, Xu H, Jiang J, Du B, Wan M, Ma X, Chen X, Lin L, Su X, Bao X, Shen Y, Xu N, Ruan J, Jiang H, Ding Y. New techniques to identify the tissue of origin for cancer of unknown primary in the era of precision medicine: progress and challenges. Brief Bioinform 2024; 25:bbae028. [PMID: 38343328 PMCID: PMC10859692 DOI: 10.1093/bib/bbae028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 12/10/2023] [Accepted: 01/11/2024] [Indexed: 02/15/2024] Open
Abstract
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.
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Affiliation(s)
- Wenyuan Ma
- Department of Medical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Hui Wu
- Department of Medical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yiran Chen
- Department of Surgical Oncology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Hongxia Xu
- Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), Zhejiang University School of Medicine, Zhejiang University, Haining, China
| | - Junjie Jiang
- Department of Gastroenterology, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Bang Du
- Real Doctor AI Research Centre, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Mingyu Wan
- Department of Medical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaolu Ma
- Department of Medical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaoyu Chen
- Department of Medical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Lili Lin
- Department of Nuclear Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xinhui Su
- Department of Nuclear Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xuanwen Bao
- Department of Medical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yifei Shen
- Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Nong Xu
- Department of Medical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jian Ruan
- Department of Medical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Haiping Jiang
- Department of Medical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yongfeng Ding
- Department of Medical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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12
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Chen Z, Shi Q, Zhao Y, Xu M, Liu Y, Li X, Liu L, Sun M, Wu X, Shao Z, Xu Y, Wang L, He X. Long-read transcriptome landscapes of primary and metastatic liver cancers at transcript resolution. Biomark Res 2024; 12:4. [PMID: 38185659 PMCID: PMC10773130 DOI: 10.1186/s40364-023-00554-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 12/29/2023] [Indexed: 01/09/2024] Open
Abstract
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.
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Affiliation(s)
- Zhiao Chen
- Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University Shanghai Cancer Center, Fudan University, 302 Rm., 7# Bldg., 270 Dong An Road, 200032, Shanghai, China
- Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, Fudan University, 200032, Shanghai, China
- Shanghai Key Laboratory of Radiation Oncology, Fudan University Shanghai Cancer Center, Fudan University, 200032, Shanghai, China
| | - Qili Shi
- Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University Shanghai Cancer Center, Fudan University, 302 Rm., 7# Bldg., 270 Dong An Road, 200032, Shanghai, China
| | - Yiming Zhao
- Department of Hepatic Surgery, Fudan University Shanghai Cancer Center, Fudan University, 200032, Shanghai, China
| | - Midie Xu
- Department of Pathology, biobank, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Yizhe Liu
- Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University Shanghai Cancer Center, Fudan University, 302 Rm., 7# Bldg., 270 Dong An Road, 200032, Shanghai, China
| | - Xinrong Li
- Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University Shanghai Cancer Center, Fudan University, 302 Rm., 7# Bldg., 270 Dong An Road, 200032, Shanghai, China
| | - Li Liu
- Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University Shanghai Cancer Center, Fudan University, 302 Rm., 7# Bldg., 270 Dong An Road, 200032, Shanghai, China
| | - Menghong Sun
- Department of Pathology, biobank, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Xiaohua Wu
- Department of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Fudan University, 200032, Shanghai, China
| | - Zhimin Shao
- Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, Fudan University, 200032, Shanghai, China
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Fudan University, 200032, Shanghai, China
| | - Ye Xu
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, 200032, Shanghai, China.
| | - Lu Wang
- Department of Hepatic Surgery, Fudan University Shanghai Cancer Center, Fudan University, 200032, Shanghai, China.
| | - Xianghuo He
- Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University Shanghai Cancer Center, Fudan University, 302 Rm., 7# Bldg., 270 Dong An Road, 200032, Shanghai, China.
- Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, Fudan University, 200032, Shanghai, China.
- Shanghai Key Laboratory of Radiation Oncology, Fudan University Shanghai Cancer Center, Fudan University, 200032, Shanghai, China.
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Chen C, Lu C, Viswanathan V, Maveal B, Maheshwari B, Willis J, Madabhushi A. Identifying primary tumor site of origin for liver metastases via a combination of handcrafted and deep learning features. J Pathol Clin Res 2024; 10:e344. [PMID: 37822044 PMCID: PMC10766034 DOI: 10.1002/cjp2.344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 08/14/2023] [Accepted: 08/28/2023] [Indexed: 10/13/2023]
Abstract
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.
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Affiliation(s)
- Chuheng Chen
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOHUSA
| | - Cheng Lu
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOHUSA
| | - Vidya Viswanathan
- Wallace H. Coulter Department of Biomedical EngineeringGeorgia Institute of Technology and Emory UniversityAtlantaGAUSA
| | - Brandon Maveal
- Department of PathologyUniversity Hospitals Cleveland Medical Center and Case Western Reserve UniversityClevelandOHUSA
| | - Bhunesh Maheshwari
- Department of PathologyUniversity Hospitals Cleveland Medical Center and Case Western Reserve UniversityClevelandOHUSA
| | - Joseph Willis
- Department of Biomedical EngineeringCase Western Reserve UniversityClevelandOHUSA
- Department of PathologyUniversity Hospitals Cleveland Medical Center and Case Western Reserve UniversityClevelandOHUSA
| | - Anant Madabhushi
- Wallace H. Coulter Department of Biomedical EngineeringGeorgia Institute of Technology and Emory UniversityAtlantaGAUSA
- Radiology and Imaging Sciences, Biomedical Informatics (BMI) and PathologyGeorgia Institute of Technology and Emory UniversityAtlantaGAUSA
- Atlanta Veterans Administration Medical CenterAtlantaGAUSA
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14
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Wang Y, Huang Q, Zhong G, Lv J, Guo Q, Ma Y, Wang X, Zeng J. 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. Front Oncol 2023; 13:1191611. [PMID: 38205137 PMCID: PMC10777842 DOI: 10.3389/fonc.2023.1191611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 10/26/2023] [Indexed: 01/12/2024] Open
Abstract
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.
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Affiliation(s)
- Youlong Wang
- Hainan Hospital of PLA General Hospital, Department of General Surgery, Haitang District, Sanya, China
| | - Qi Huang
- Department of Clinical Laboratory, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Guanqing Zhong
- Department of Clinical Laboratory, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jun Lv
- Department of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Qinzhi Guo
- Pancreas Center of Guangdong Provincial People’s Hospital, Guangzhou, China
| | - Yifei Ma
- Department of Spine Surgery, The Second Affiliated Hospital, Shantou University Medical College, Shantou, China
| | - Xinjia Wang
- Department of Spine Surgery, The Second Affiliated Hospital, Shantou University Medical College, Shantou, China
| | - Jiling Zeng
- Department of Nuclear Medicine, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Center, Guangzhou, China
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15
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Janin M, Davalos V, Esteller M. Cancer metastasis under the magnifying glass of epigenetics and epitranscriptomics. Cancer Metastasis Rev 2023; 42:1071-1112. [PMID: 37369946 PMCID: PMC10713773 DOI: 10.1007/s10555-023-10120-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 06/15/2023] [Indexed: 06/29/2023]
Abstract
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.
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Affiliation(s)
- Maxime Janin
- Cancer Epigenetics Group, Josep Carreras Leukaemia Research Institute (IJC), IJC Building, Germans Trias I Pujol, Ctra de Can Ruti, Cami de Les Escoles S/N, 08916 Badalona, Barcelona, Spain
- Centro de Investigacion Biomedica en Red Cancer (CIBERONC), Madrid, Spain
| | - Veronica Davalos
- Cancer Epigenetics Group, Josep Carreras Leukaemia Research Institute (IJC), IJC Building, Germans Trias I Pujol, Ctra de Can Ruti, Cami de Les Escoles S/N, 08916 Badalona, Barcelona, Spain
| | - Manel Esteller
- Cancer Epigenetics Group, Josep Carreras Leukaemia Research Institute (IJC), IJC Building, Germans Trias I Pujol, Ctra de Can Ruti, Cami de Les Escoles S/N, 08916 Badalona, Barcelona, Spain.
- Centro de Investigacion Biomedica en Red Cancer (CIBERONC), Madrid, Spain.
- Institucio Catalana de Recerca I Estudis Avançats (ICREA), Barcelona, Catalonia, Spain.
- Physiological Sciences Department, School of Medicine and Health Sciences, University of Barcelona (UB), Barcelona, Catalonia, Spain.
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Ren M, Cai X, Jia L, Bai Q, Zhu X, Hu X, Wang Q, Luo Z, Zhou X. Comprehensive analysis of cancer of unknown primary and recommendation of a histological and immunohistochemical diagnostic strategy from China. BMC Cancer 2023; 23:1175. [PMID: 38041048 PMCID: PMC10691136 DOI: 10.1186/s12885-023-11563-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 10/24/2023] [Indexed: 12/03/2023] Open
Abstract
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.
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Affiliation(s)
- Min Ren
- Department of Pathology, Fudan University Shanghai Cancer Center, 270 Dong'an Road, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
- Institute of Pathology, Fudan University, Shanghai, 200032, China
| | - Xu Cai
- Department of Pathology, Fudan University Shanghai Cancer Center, 270 Dong'an Road, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
- Institute of Pathology, Fudan University, Shanghai, 200032, China
| | - Liqing Jia
- Department of Pathology, Fudan University Shanghai Cancer Center, 270 Dong'an Road, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
- Institute of Pathology, Fudan University, Shanghai, 200032, China
| | - Qianming Bai
- Department of Pathology, Fudan University Shanghai Cancer Center, 270 Dong'an Road, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
- Institute of Pathology, Fudan University, Shanghai, 200032, China
| | - Xiaoli Zhu
- Department of Pathology, Fudan University Shanghai Cancer Center, 270 Dong'an Road, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
- Institute of Pathology, Fudan University, Shanghai, 200032, China
| | - Xichun Hu
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
- Department of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
| | - Qifeng Wang
- Department of Pathology, Fudan University Shanghai Cancer Center, 270 Dong'an Road, Shanghai, 200032, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
- Institute of Pathology, Fudan University, Shanghai, 200032, China.
| | - Zhiguo Luo
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
- Department of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
| | - Xiaoyan Zhou
- Department of Pathology, Fudan University Shanghai Cancer Center, 270 Dong'an Road, Shanghai, 200032, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
- Institute of Pathology, Fudan University, Shanghai, 200032, China.
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17
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Štancl P, Karlić R. Machine learning for pan-cancer classification based on RNA sequencing data. Front Mol Biosci 2023; 10:1285795. [PMID: 38028533 PMCID: PMC10667476 DOI: 10.3389/fmolb.2023.1285795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 10/30/2023] [Indexed: 12/01/2023] Open
Abstract
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.
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Affiliation(s)
| | - Rosa Karlić
- Bioinformatics Group, Division of Molecular Biology, Department of Biology, Faculty of Science, University of Zagreb, Zagreb, Croatia
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18
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Jiang Y, Wang C, Zhou S. Artificial intelligence-based risk stratification, accurate diagnosis and treatment prediction in gynecologic oncology. Semin Cancer Biol 2023; 96:82-99. [PMID: 37783319 DOI: 10.1016/j.semcancer.2023.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 08/27/2023] [Accepted: 09/25/2023] [Indexed: 10/04/2023]
Abstract
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.
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Affiliation(s)
- Yuting Jiang
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE and State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan 610041, China; Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Chengdi Wang
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE and State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan 610041, China; Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Shengtao Zhou
- Department of Obstetrics and Gynecology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE and State Key Laboratory of Biotherapy, West China Second Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan 610041, China; Department of Pulmonary and Critical Care Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
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19
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van Mourik A, Tonkin-Hill G, O'Farrell J, Waller S, Tan L, Tothill RW, Bowtell D, Fox S, Fellowes A, Fedele C, Schofield P, Sivakumaran T, Wong HL, Mileshkin L. Six-year experience of Australia's first dedicated cancer of unknown primary clinic. Br J Cancer 2023; 129:301-308. [PMID: 37225894 PMCID: PMC10338450 DOI: 10.1038/s41416-023-02254-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 03/18/2023] [Accepted: 03/21/2023] [Indexed: 05/26/2023] Open
Abstract
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.
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Affiliation(s)
- Arielle van Mourik
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Gina Tonkin-Hill
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - John O'Farrell
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Shohei Waller
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Lavinia Tan
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, Australia
| | - Richard W Tothill
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, Australia
- Department of Clinical Pathology and Centre for Cancer Research, The University of Melbourne, Parkville, VIC, Australia
| | - David Bowtell
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, Australia
- Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Stephen Fox
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, Australia
- Department of Clinical Pathology and Centre for Cancer Research, The University of Melbourne, Parkville, VIC, Australia
- Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Andrew Fellowes
- Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Department of Pathology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | | | - Penelope Schofield
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, Australia
- Department of Psychology, and Iverson Health Innovation Research Institute Swinburne University, Melbourne, VIC, Australia
- Behavioural Sciences Unit, Health Services Research and Implementation Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Tharani Sivakumaran
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, Australia
| | - Hui-Li Wong
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, Australia.
| | - Linda Mileshkin
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, VIC, Australia
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20
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Michuda J, Breschi A, Kapilivsky J, Manghnani K, McCarter C, Hockenberry AJ, Mineo B, Igartua C, Dudley JT, Stumpe MC, Beaubier N, Shirazi M, Jones R, Morency E, Blackwell K, Guinney J, Beauchamp KA, Taxter T. Validation of a Transcriptome-Based Assay for Classifying Cancers of Unknown Primary Origin. Mol Diagn Ther 2023; 27:499-511. [PMID: 37099070 PMCID: PMC10300170 DOI: 10.1007/s40291-023-00650-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/02/2023] [Indexed: 04/27/2023]
Abstract
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.
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21
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Zhang Z, Lu Y, Vosoughi S, Levy J, Christensen B, Salas L. HiTAIC: hierarchical tumor artificial intelligence classifier traces tissue of origin and tumor type in primary and metastasized tumors using DNA methylation. NAR Cancer 2023; 5:zcad017. [PMID: 37089814 PMCID: PMC10113876 DOI: 10.1093/narcan/zcad017] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 04/04/2023] [Accepted: 04/13/2023] [Indexed: 04/25/2023] Open
Abstract
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.
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Affiliation(s)
- Ze Zhang
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
- Quantitative Biomedical Sciences Program, Guarini School of Graduate and Advanced Studies, Dartmouth College, Hanover, NH, USA
| | - Yunrui Lu
- Quantitative Biomedical Sciences Program, Guarini School of Graduate and Advanced Studies, Dartmouth College, Hanover, NH, USA
| | - Soroush Vosoughi
- Department of Computer Science, Dartmouth College, Hanover, NH, USA
| | - Joshua J Levy
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
- Quantitative Biomedical Sciences Program, Guarini School of Graduate and Advanced Studies, Dartmouth College, Hanover, NH, USA
- Department of Pathology and Dermatology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Brock C Christensen
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
- Quantitative Biomedical Sciences Program, Guarini School of Graduate and Advanced Studies, Dartmouth College, Hanover, NH, USA
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Lucas A Salas
- To whom correspondence should be addressed. Tel: +1 603 646 5420;
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22
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From Biology to Diagnosis and Treatment: The Ariadne’s Thread in Cancer of Unknown Primary. Int J Mol Sci 2023; 24:ijms24065588. [PMID: 36982662 PMCID: PMC10053301 DOI: 10.3390/ijms24065588] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/10/2023] [Accepted: 03/13/2023] [Indexed: 03/17/2023] Open
Abstract
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.
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23
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Brundu S, Napolitano V, Franzolin G, Lo Cascio E, Mastrantonio R, Sardo G, Cascardi E, Verginelli F, Sarnataro S, Gambardella G, Pisacane A, Arcovito A, Boccaccio C, Comoglio PM, Giraudo E, Tamagnone L. Mutated axon guidance gene PLXNB2 sustains growth and invasiveness of stem cells isolated from cancers of unknown primary. EMBO Mol Med 2023; 15:e16104. [PMID: 36722641 PMCID: PMC9994481 DOI: 10.15252/emmm.202216104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 12/28/2022] [Accepted: 01/11/2023] [Indexed: 02/02/2023] Open
Abstract
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.
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Affiliation(s)
| | - Virginia Napolitano
- Department of Life Sciences and Public HealthUniversità Cattolica del Sacro CuoreRomeItaly
| | | | - Ettore Lo Cascio
- Department of Biotechnological Sciences and Intensive CareUniversità Cattolica del Sacro CuoreRomeItaly
| | - Roberta Mastrantonio
- Department of Life Sciences and Public HealthUniversità Cattolica del Sacro CuoreRomeItaly
| | | | - Eliano Cascardi
- Candiolo Cancer InstituteFPO‐IRCCSTurinItaly
- Department of Medical SciencesUniversity of TurinTurinItaly
| | | | | | - Gennaro Gambardella
- Telethon Institute of Genetic and MedicinePozzuoliItaly
- Department of Electrical Engineering and Information TechnologyUniversity of Naples Federico IINaplesItaly
| | | | - Alessandro Arcovito
- Department of Biotechnological Sciences and Intensive CareUniversità Cattolica del Sacro CuoreRomeItaly
- Fondazione Policlinico Gemelli (FPG) – IRCCSRomeItaly
| | - Carla Boccaccio
- Candiolo Cancer InstituteFPO‐IRCCSTurinItaly
- Department of OncologyUniversity of TurinTurinItaly
| | | | - Enrico Giraudo
- Candiolo Cancer InstituteFPO‐IRCCSTurinItaly
- Department of Science and Drug TechnologyUniversity of TurinTurinItaly
| | - Luca Tamagnone
- Department of Life Sciences and Public HealthUniversità Cattolica del Sacro CuoreRomeItaly
- Fondazione Policlinico Gemelli (FPG) – IRCCSRomeItaly
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24
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Hu H, Pan Q, Shen J, Yao J, Fu G, Tian F, Yan N, Han W. The diagnosis and treatment for a patient with cancer of unknown primary: A case report. Front Genet 2023; 14:1085549. [PMID: 36741314 PMCID: PMC9894331 DOI: 10.3389/fgene.2023.1085549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 01/05/2023] [Indexed: 01/20/2023] Open
Abstract
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.
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Affiliation(s)
- Hong Hu
- Department of Medical Oncology, Qiantang Campus of Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Qin Pan
- Department of Medical Oncology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jiaying Shen
- Department of Medical Oncology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Junlin Yao
- Department of Medical Oncology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Guoxiang Fu
- Department of Pathology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Fengjuan Tian
- Department of Radiology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Na Yan
- Key Laboratory of Digital Technology in Medical Diagnostics of Zhejiang Province, Dian Diagnostics Group Co., Ltd., Hangzhou, Zhejiang, China
| | - Weidong Han
- Department of Medical Oncology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, China,*Correspondence: Weidong Han, hanwd@ zju.edu.cn
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25
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Zhu L, Shi H, Wei H, Wang C, Shi S, Zhang F, Yan R, Liu Y, He T, Wang L, Cheng J, Duan H, Du H, Meng F, Zhao W, Gu X, Guo L, Ni Y, He Y, Guan T, Han A. An accurate prediction of the origin for bone metastatic cancer using deep learning on digital pathological images. EBioMedicine 2022; 87:104426. [PMID: 36577348 PMCID: PMC9803701 DOI: 10.1016/j.ebiom.2022.104426] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 12/07/2022] [Accepted: 12/07/2022] [Indexed: 12/28/2022] Open
Abstract
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).
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Affiliation(s)
- Lianghui Zhu
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, China
| | - Huijuan Shi
- Department of Pathology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Huiting Wei
- Department of Pathology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Chengjiang Wang
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, China
| | - Shanshan Shi
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, China
| | - Fenfen Zhang
- Department of Pathology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Renao Yan
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, China
| | - Yiqing Liu
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, China
| | - Tingting He
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, China
| | - Liyuan Wang
- Department of Pathology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Junru Cheng
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, China
| | - Hufei Duan
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, China
| | - Hong Du
- Department of Pathology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Fengjiao Meng
- Department of Pathology, Zhongshan People's Hospital, Zhongshan, China
| | - Wenli Zhao
- Department of Pathology, The First People's Hospital of Huizhou, Huizhou, China
| | - Xia Gu
- Department of Pathology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Linlang Guo
- Department of Pathology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Yingpeng Ni
- Department of Pathology, Jieyang People's Hospital (Jieyang Affiliated Hospital, Sun Yat-Sen University), Jieyang, China
| | - Yonghong He
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, China,Corresponding author.
| | - Tian Guan
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Shenzhen, Guangdong, China,Corresponding author.
| | - Anjia Han
- Department of Pathology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China,Corresponding author.
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26
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Benesch MGK, O’Brien SBL. Epidemiology of Undifferentiated Carcinomas. Cancers (Basel) 2022; 14:cancers14235819. [PMID: 36497299 PMCID: PMC9740284 DOI: 10.3390/cancers14235819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 11/18/2022] [Accepted: 11/23/2022] [Indexed: 11/29/2022] Open
Abstract
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.
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27
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Qi P, Sun Y, Liu X, Wu S, Wo Y, Xu Q, Wang Q, Hu X, Zhou X. Clinicopathological, molecular and prognostic characteristics of cancer of unknown primary in China: An analysis of 1420 cases. Cancer Med 2022; 12:1177-1188. [PMID: 35822433 PMCID: PMC9883567 DOI: 10.1002/cam4.4973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 05/23/2022] [Accepted: 06/10/2022] [Indexed: 02/02/2023] Open
Abstract
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.
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Affiliation(s)
- Peng Qi
- Department of PathologyFudan University Shanghai Cancer CenterShanghaiChina,Department of Oncology, Shanghai Medical CollegeFudan UniversityShanghaiChina,Institute of PathologyFudan UniversityShanghaiChina,The Cancer of Unknown Primary Group of Pathology CommitteeChinese Research Hospital AssociationShanghaiChina
| | - Yifeng Sun
- The Canhelp Genomics Research CenterCanhelp Genomics Co., Ltd.HangzhouChina
| | - Xin Liu
- Department of Oncology, Shanghai Medical CollegeFudan UniversityShanghaiChina,Department of Head & Neck Tumors and Neuroendocrine TumorsFudan University Shanghai Cancer CenterShanghaiChina
| | - Sheng Wu
- The Canhelp Genomics Research CenterCanhelp Genomics Co., Ltd.HangzhouChina
| | - Yixin Wo
- The Canhelp Genomics Research CenterCanhelp Genomics Co., Ltd.HangzhouChina
| | - Qinghua Xu
- The Cancer of Unknown Primary Group of Pathology CommitteeChinese Research Hospital AssociationShanghaiChina,The Canhelp Genomics Research CenterCanhelp Genomics Co., Ltd.HangzhouChina,The Institute of Machine Learning and Systems Biology, College of Electronics and Information EngineeringTongji UniversityShanghaiChina,Xuzhou Engineering Research Center of Medical Genetics and Transformation, Department of GeneticsXuzhou Medical UniversityXuzhouChina
| | - Qifeng Wang
- Department of PathologyFudan University Shanghai Cancer CenterShanghaiChina,Department of Oncology, Shanghai Medical CollegeFudan UniversityShanghaiChina,Institute of PathologyFudan UniversityShanghaiChina,The Cancer of Unknown Primary Group of Pathology CommitteeChinese Research Hospital AssociationShanghaiChina
| | - Xichun Hu
- Department of Oncology, Shanghai Medical CollegeFudan UniversityShanghaiChina,Department of Medical OncologyFudan University Shanghai Cancer CenterShanghaiChina
| | - Xiaoyan Zhou
- Department of PathologyFudan University Shanghai Cancer CenterShanghaiChina,Department of Oncology, Shanghai Medical CollegeFudan UniversityShanghaiChina,Institute of PathologyFudan UniversityShanghaiChina,The Cancer of Unknown Primary Group of Pathology CommitteeChinese Research Hospital AssociationShanghaiChina
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Mei J, Wang H, Fan H, Ding J, Xu J. Case Report: Successful Immunotherapy Improved the Prognosis of the Unfavorable Subset of Cancer of Unknown Primary. Front Immunol 2022; 13:900119. [PMID: 35812375 PMCID: PMC9256999 DOI: 10.3389/fimmu.2022.900119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 05/23/2022] [Indexed: 12/15/2022] Open
Abstract
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.
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Affiliation(s)
| | | | | | - Junli Ding
- *Correspondence: Junying Xu, ; Junli Ding,
| | - Junying Xu
- *Correspondence: Junying Xu, ; Junli Ding,
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29
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Lu Q, Chen F, Li Q, Chen L, Tong L, Tian G, Zhou X. A Machine Learning Method to Trace Cancer Primary Lesion Using Microarray-Based Gene Expression Data. Front Oncol 2022; 12:832567. [PMID: 35530331 PMCID: PMC9071249 DOI: 10.3389/fonc.2022.832567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 03/21/2022] [Indexed: 11/17/2022] Open
Abstract
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.
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Affiliation(s)
- Qingfeng Lu
- Oncology Department, Daqing Oilfield General Hospital, Daqing, China
| | - Fengxia Chen
- Department of Thoracic Surgery, Hainan General Hospital, Haikou, China
| | - Qianyue Li
- Department of R&D, Geneis (Beijing) Co., Ltd., Beijing, China
| | - Lihong Chen
- Department of Emergency, Qingdao Eighth People's Hospital, Qingdao, China
| | - Ling Tong
- Department of Pathology, Chifeng Municipal Hospital, Chifeng Clinical Medical School of Inner Mongolia Medical University, Chifeng, China
| | - Geng Tian
- Department of R&D, Geneis (Beijing) Co., Ltd., Beijing, China
| | - Xiaohong Zhou
- Second Division of Cancer, Jiamusi Cancer Hospital, Jiamusi, China
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30
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Blecua P, Davalos V, de Villasante I, Merkel A, Musulen E, Coll-SanMartin L, Esteller M. Refinement of computational identification of somatic copy number alterations using DNA methylation microarrays illustrated in cancers of unknown primary. Brief Bioinform 2022; 23:6582004. [PMID: 35524475 PMCID: PMC9487591 DOI: 10.1093/bib/bbac161] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 03/30/2022] [Accepted: 04/10/2022] [Indexed: 11/14/2022] Open
Abstract
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.
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Affiliation(s)
- Pedro Blecua
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, Barcelona, Catalonia, Spain
| | - Veronica Davalos
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, Barcelona, Catalonia, Spain
| | - Izar de Villasante
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, Barcelona, Catalonia, Spain
| | - Angelika Merkel
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, Barcelona, Catalonia, Spain
| | - Eva Musulen
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, Barcelona, Catalonia, Spain.,Department of Pathology, Hospital Universitari General de Catalunya-Grupo Quirónsalud, Sant Cugat del Vallès, Barcelona, Catalonia, Spain
| | - Laia Coll-SanMartin
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, Barcelona, Catalonia, Spain
| | - Manel Esteller
- Josep Carreras Leukaemia Research Institute (IJC), Badalona, Barcelona, Catalonia, Spain.,Centro de Investigación Biomédica en Red de Cancer (CIBERONC), Madrid, Spain.,Institucio Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain.,Physiological Sciences Department, School of Medicine and Health Sciences, University of Barcelona (UB), Catalonia, Spain
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31
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Bae JM, Ahn JY, Lee H, Jang H, Han H, Jeong J, Cho NY, Kim K, Kang GH. Identification of tissue of origin in cancer of unknown primary using a targeted bisulfite sequencing panel. Epigenomics 2022; 14:615-628. [PMID: 35473295 DOI: 10.2217/epi-2021-0477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
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.
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Affiliation(s)
- Jeong Mo Bae
- Department of Pathology, Seoul National University College of Medicine, Seoul, Korea.,Laboratory of Epigenetics, Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Ji Young Ahn
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea
| | - Heonyi Lee
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea
| | | | | | | | - Nam-Yun Cho
- Laboratory of Epigenetics, Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Kwangsoo Kim
- Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Seoul, Korea
| | - Gyeong Hoon Kang
- Department of Pathology, Seoul National University College of Medicine, Seoul, Korea.,Laboratory of Epigenetics, Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
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32
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Losa F, Fernández I, Etxaniz O, Giménez A, Gomila P, Iglesias L, Longo F, Nogales E, Sánchez A, Soler G. SEOM-GECOD clinical guideline for unknown primary cancer (2021). Clin Transl Oncol 2022; 24:681-692. [PMID: 35320504 PMCID: PMC8986666 DOI: 10.1007/s12094-022-02806-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/09/2022] [Indexed: 11/16/2022]
Abstract
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.
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Affiliation(s)
- Ferrán Losa
- Hospital de Sant Joan Despí Moisés Broggi-ICO Hospitalet, Barcelona, Spain.
| | | | - Olatz Etxaniz
- Hospital Germans Trias I Pujol -ICO Badalona, Barcelona, Spain
| | | | - Paula Gomila
- Hospital Miguel Servet (Zaragoza)/H, de Barbastro, Spain
| | | | - Federico Longo
- Hospital Universitario Ramón y Cajal, IRYCIS, CIBERONC, Madrid, Spain
| | | | - Antonio Sánchez
- Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain
| | - Gemma Soler
- Hospital Durán i Reynals-ICO Hospitalet, Barcelona, Spain
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33
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Farina E, Nabhen JJ, Dacoregio MI, Batalini F, Moraes FY. An overview of artificial intelligence in oncology. Future Sci OA 2022; 8:FSO787. [PMID: 35369274 PMCID: PMC8965797 DOI: 10.2144/fsoa-2021-0074] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 01/19/2022] [Indexed: 11/23/2022] Open
Abstract
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.
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Affiliation(s)
- Eduardo Farina
- Department of Radiology, Federal University of São Paulo, SP, 04021-001, Brazil; Diagnósticos da America SA (Dasa), 05425-020, Brazil
| | - Jacqueline J Nabhen
- School of Medicine, Federal University of Paraná, Curitiba, PR, 80060-000, Brazil
| | - Maria Inez Dacoregio
- School of Medicine, State University of Centro-Oeste, Guarapuava, PR, 85040-167, Brazil
| | - Felipe Batalini
- Department of Medicine, Division of Medical Oncology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Fabio Y Moraes
- Department of Oncology, Division of Radiation Oncology, Queen's University, Kingston, ON, K7L 3N6, Canada
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Ding Y, Jiang J, Xu J, Chen Y, Zheng Y, Jiang W, Mao C, Jiang H, Bao X, Shen Y, Li X, Teng L, Xu N. Site-specific therapy in cancers of unknown primary site: a systematic review and meta-analysis. ESMO Open 2022; 7:100407. [PMID: 35248824 PMCID: PMC8897579 DOI: 10.1016/j.esmoop.2022.100407] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 01/22/2022] [Accepted: 01/25/2022] [Indexed: 12/01/2022] Open
Abstract
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.
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Affiliation(s)
- Y Ding
- Department of Medical Oncology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - J Jiang
- Department of Surgical Oncology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - J Xu
- Department of Thoracic Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Y Chen
- Department of Surgical Oncology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Y Zheng
- Department of Medical Oncology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - W Jiang
- Department of Colorectal Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou; China
| | - C Mao
- Department of Medical Oncology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - H Jiang
- Department of Medical Oncology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - X Bao
- Department of Medical Oncology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Y Shen
- Centre of Clinical Laboratory, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou; China; Key Laboratory of Clinical In Vitro Diagnostic Techniques of Zhejiang Province, Hangzhou; China; Institute of Laboratory Medicine, Zhejiang University, Hangzhou; China
| | - X Li
- Department of Surgery, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - L Teng
- Department of Surgical Oncology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
| | - N Xu
- Department of Medical Oncology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
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Sun W, Wu W, Wang Q, Yao Q, Feng Q, Wang Y, Sun Y, Liu Y, Lai Q, Zhang G, Qi P, Sun Y, Qian C, Ren W, Luo Z, Chen J, Wang H, Xu Q, Zhou X, Sun W, Lin D. Clinical validation of a 90-gene expression test for tumor tissue of origin diagnosis: a large-scale multicenter study of 1417 patients. J Transl Med 2022; 20:114. [PMID: 35255924 PMCID: PMC8900384 DOI: 10.1186/s12967-022-03318-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 02/23/2022] [Indexed: 12/22/2022] Open
Abstract
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. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-022-03318-6.
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Zheng M. Tumor mutation burden for predicting immune checkpoint blockade response: the more, the better. J Immunother Cancer 2022; 10:e003087. [PMID: 35101940 PMCID: PMC8804687 DOI: 10.1136/jitc-2021-003087] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/25/2021] [Indexed: 12/14/2022] Open
Abstract
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.
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Affiliation(s)
- Ming Zheng
- Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences, Beijing, China
- Beijing Institute of Basic Medical Sciences, Beijing, China
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37
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Real-world data analysis of patients with cancer of unknown primary. Sci Rep 2021; 11:23074. [PMID: 34845302 PMCID: PMC8630084 DOI: 10.1038/s41598-021-02543-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 10/15/2021] [Indexed: 01/08/2023] Open
Abstract
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.
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Zhang Y, Xia L, Ma D, Wu J, Xu X, Xu Y. 90-Gene Expression Profiling for Tissue Origin Diagnosis of Cancer of Unknown Primary. Front Oncol 2021; 11:722808. [PMID: 34692498 PMCID: PMC8529103 DOI: 10.3389/fonc.2021.722808] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 09/21/2021] [Indexed: 11/13/2022] Open
Abstract
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.
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Affiliation(s)
- Yi Zhang
- Department of Pathology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China
| | - Lei Xia
- Department of Pathology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China
| | - Dawei Ma
- Department of Pathology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China
| | - Jing Wu
- Department of Radiation Oncology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China
| | - Xinyu Xu
- Department of Pathology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China
| | - Youtao Xu
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China
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Wang Q, Li F, Jiang Q, Sun Y, Liao Q, An H, Li Y, Li Z, Fan L, Guo F, Xu Q, Wo Y, Ren W, Yue J, Meng B, Liu W, Zhou X. Gene Expression Profiling for Differential Diagnosis of Liver Metastases: A Multicenter, Retrospective Cohort Study. Front Oncol 2021; 11:725988. [PMID: 34631555 PMCID: PMC8493028 DOI: 10.3389/fonc.2021.725988] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 08/18/2021] [Indexed: 02/05/2023] Open
Abstract
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.
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Affiliation(s)
- Qifeng Wang
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Institute of Pathology, Fudan University, Shanghai, China.,The Cancer of Unknown Primary Group of Pathology Committee, Chinese Research Hospital Association, Shanghai, China
| | - Fen Li
- Department of Pathology, Chengdu Second People's Hospital, Chengdu, China
| | - Qingming Jiang
- Department of Pathology, Chongqing University Cancer Hospital, Chongqing, China
| | - Yifeng Sun
- The Canhelp Genomics Research Center, Canhelp Genomics Co., Ltd., Hangzhou, China
| | - Qiong Liao
- The Cancer of Unknown Primary Group of Pathology Committee, Chinese Research Hospital Association, Shanghai, China.,Department of Pathology, Sichuan Cancer Hospital, Chengdu, China
| | - Huimin An
- The Cancer of Unknown Primary Group of Pathology Committee, Chinese Research Hospital Association, Shanghai, China.,Department of Pathology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Yunzhu Li
- Department of Pathology, Sichuan Cancer Hospital, Chengdu, China
| | - Zhenyu Li
- Department of Pathology, Chongqing University Cancer Hospital, Chongqing, China
| | - Lifang Fan
- Department of Pathology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fang Guo
- Department of Pathology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qinghua Xu
- The Cancer of Unknown Primary Group of Pathology Committee, Chinese Research Hospital Association, Shanghai, China.,The Canhelp Genomics Research Center, Canhelp Genomics Co., Ltd., Hangzhou, China.,The Institute of Machine Learning and Systems Biology, College of Electronics and Information Engineering, Tongji University, Shanghai, China.,Xuzhou Engineering Research Center of Medical Genetics and Transformation, Department of Genetics, Xuzhou Medical University, Xuzhou, China
| | - Yixin Wo
- The Canhelp Genomics Research Center, Canhelp Genomics Co., Ltd., Hangzhou, China
| | - Wanli Ren
- The Canhelp Genomics Research Center, Canhelp Genomics Co., Ltd., Hangzhou, China
| | - Junqiu Yue
- The Cancer of Unknown Primary Group of Pathology Committee, Chinese Research Hospital Association, Shanghai, China.,Department of Pathology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Bin Meng
- The Cancer of Unknown Primary Group of Pathology Committee, Chinese Research Hospital Association, Shanghai, China.,Department of Pathology, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Weiping Liu
- Department of Pathology, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaoyan Zhou
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.,Institute of Pathology, Fudan University, Shanghai, China.,The Cancer of Unknown Primary Group of Pathology Committee, Chinese Research Hospital Association, Shanghai, China
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Barik GK, Sahay O, Behera A, Naik D, Kalita B. Keep your eyes peeled for long noncoding RNAs: Explaining their boundless role in cancer metastasis, drug resistance, and clinical application. Biochim Biophys Acta Rev Cancer 2021; 1876:188612. [PMID: 34391844 DOI: 10.1016/j.bbcan.2021.188612] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 08/07/2021] [Accepted: 08/08/2021] [Indexed: 12/12/2022]
Abstract
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.
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Affiliation(s)
- Ganesh Kumar Barik
- Cancer Biology Division, National Centre for Cell Science, Savitribai Phule Pune University, Ganeshkhind Road, Pune, Maharashtra 411007, India
| | - Osheen Sahay
- Proteomics Laboratory, National Centre for Cell Science, Savitribai Phule Pune University, Ganeshkhind Road, Pune, Maharashtra 411007, India
| | - Abhayananda Behera
- Department of Animal Biology, School of Life Sciences, University of Hyderabad, Hyderabad 500046, India
| | - Debasmita Naik
- Department of Animal Biology, School of Life Sciences, University of Hyderabad, Hyderabad 500046, India
| | - Bhargab Kalita
- Proteomics Laboratory, National Centre for Cell Science, Savitribai Phule Pune University, Ganeshkhind Road, Pune, Maharashtra 411007, India.
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Abraham J, Nabhan C, Oberley M, Korn WM, Spetzler D. Response to "The need for validation of MI GPSai in patients with CUP: Comment on: "Machine learning analysis using 77,044 genomic and transcriptomic profiles to accurately predict tumor type" by J Abraham et al.". Transl Oncol 2021; 14:101093. [PMID: 34167745 PMCID: PMC8236549 DOI: 10.1016/j.tranon.2021.101093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 04/02/2021] [Indexed: 11/12/2022] Open
Affiliation(s)
- Jim Abraham
- Caris Life Sciences, Phoenix, AZ; Arizona State University, Phoenix, AZ
| | - Chadi Nabhan
- Caris Life Sciences, Phoenix, AZ; University of South Carolina, Department of Clinical Pharmacy and Outcomes Sciences, Columbia, SC
| | | | - Wolfgang Michael Korn
- Caris Life Sciences, Phoenix, AZ; University of California in San Francisco, Division of Hematology and Oncology, San Francisco, CA
| | - David Spetzler
- Caris Life Sciences, Phoenix, AZ; Arizona State University, Phoenix, AZ.
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Vibert J, Pierron G, Benoist C, Gruel N, Guillemot D, Vincent-Salomon A, Le Tourneau C, Livartowski A, Mariani O, Baulande S, Bidard FC, Delattre O, Waterfall JJ, Watson S. Identification of Tissue of Origin and Guided Therapeutic Applications in Cancers of Unknown Primary Using Deep Learning and RNA Sequencing (TransCUPtomics). J Mol Diagn 2021; 23:1380-1392. [PMID: 34325056 DOI: 10.1016/j.jmoldx.2021.07.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 05/14/2021] [Accepted: 07/14/2021] [Indexed: 01/04/2023] Open
Abstract
Cancers of unknown primary (CUP) are metastatic cancers for which the primary tumor is not found despite thorough diagnostic investigations. Multiple molecular assays have been proposed to identify the tissue of origin (TOO) and inform clinical care; however, none has been able to combine accuracy, interpretability, and easy access for routine use. We developed a classifier tool based on the training of a variational autoencoder to predict tissue of origin based on RNA-sequencing data. We used as training data 20,918 samples corresponding to 94 different categories, including 39 cancer types and 55 normal tissues. The TransCUPtomics classifier was applied to a retrospective cohort of 37 CUP patients and 11 prospective patients. TransCUPtomics exhibited an overall accuracy of 96% on reference data for TOO prediction. The TOO could be identified in 38 (79%) of 48 CUP patients. Eight of 11 prospective CUP patients (73%) could receive first-line therapy guided by TransCUPtomics prediction, with responses observed in most patients. The variational autoencoder added further utility by enabling prediction interpretability, and diagnostic predictions could be matched to detection of gene fusions and expressed variants. TransCUPtomics confidently predicted TOO for CUP and enabled tailored treatments leading to significant clinical responses. The interpretability of our approach is a powerful addition to improve the management of CUP patients.
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Affiliation(s)
- Julien Vibert
- INSERM U830, Équipe Labellisée Ligue Nationale Contre le Cancer, Diversity and Plasticity of Childhood Tumors Lab, PSL Research University, Institut Curie Research Center, Paris, France
| | - Gaëlle Pierron
- Somatic Genetics Unit, Department of Genetics, Institut Curie Hospital, Paris, France
| | - Camille Benoist
- Clinical Bioinformatic Unit, Department of Diagnostic and Theranostic Medecine, Institut Curie Hospital, Paris, France
| | - Nadège Gruel
- INSERM U830, Équipe Labellisée Ligue Nationale Contre le Cancer, Diversity and Plasticity of Childhood Tumors Lab, PSL Research University, Institut Curie Research Center, Paris, France; Department of Translational Research, PSL Research University, Institut Curie Research Center, Paris, France
| | - Delphine Guillemot
- Somatic Genetics Unit, Department of Genetics, Institut Curie Hospital, Paris, France
| | - Anne Vincent-Salomon
- Department of Diagnostic and Theranostic Medecine, Institut Curie Hospital, Paris, France
| | - Christophe Le Tourneau
- Department of Drug Development and Innovation, INSERM U900, Paris-Saclay University, Institut Curie Hospital and Research Center, Paris and Saint-Cloud
| | - Alain Livartowski
- Department of Medical Oncology, Institut Curie Hospital, Paris, France
| | - Odette Mariani
- Department of Diagnostic and Theranostic Medecine, Institut Curie Hospital, Paris, France
| | - Sylvain Baulande
- Institut Curie Genomics of Excellence (ICGex) Platform, PSL Research University, Institut Curie Research Center, Paris, France
| | - François-Clément Bidard
- Department of Medical Oncology, Institut Curie Hospital, Paris, France; INSERM CIC-BT 1428, UVSQ, Paris-Saclay University, Saint-Cloud, France
| | - Olivier Delattre
- INSERM U830, Équipe Labellisée Ligue Nationale Contre le Cancer, Diversity and Plasticity of Childhood Tumors Lab, PSL Research University, Institut Curie Research Center, Paris, France; Somatic Genetics Unit, Department of Genetics, Institut Curie Hospital, Paris, France
| | - Joshua J Waterfall
- Department of Translational Research, PSL Research University, Institut Curie Research Center, Paris, France; INSERM U830, PSL Research University, Institut Curie Research Center, Paris, France
| | - Sarah Watson
- INSERM U830, Équipe Labellisée Ligue Nationale Contre le Cancer, Diversity and Plasticity of Childhood Tumors Lab, PSL Research University, Institut Curie Research Center, Paris, France; Department of Medical Oncology, Institut Curie Hospital, Paris, France.
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43
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Genomic alterations and possible druggable mutations in carcinoma of unknown primary (CUP). Sci Rep 2021; 11:15112. [PMID: 34302033 PMCID: PMC8302572 DOI: 10.1038/s41598-021-94678-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Accepted: 07/15/2021] [Indexed: 12/14/2022] Open
Abstract
Carcinoma of Unknown Primary (CUP) is a heterogeneous and metastatic disease where the primary site of origin is undetectable. Currently, chemotherapy is the only state-of-art treatment option for CUP patients. The molecular profiling of the tumour, particularly mutation detection, offers a new treatment approach for CUP in a personalized fashion using targeted agents. We analyzed the mutation and copy number alterations profile of 1709 CUP samples deposited in the AACR Project Genomics Evidence Neoplasia Information Exchange (GENIE) cohort and explored potentially druggable mutations. We identified 52 significant mutated genes (SMGs) among CUP samples, in which 13 (25%) of SMGs were potentially targetable with either drugs are approved for the know primary tumour or undergoing clinical trials. The most variants detected were TP53 (43%), KRAS (19.90%), KMT2D (12.60%), and CDKN2A (10.30%). Additionally, using pan-cancer analysis, we found similar variants of TERT promoter in CUP and NSCLC samples, suggesting that these mutations may serve as a diagnostic marker for identifying the primary tumour in CUP. Taken together, the mutation profiling analysis of the CUP tumours may open a new way of identifying druggable targets and consequently administrating appropriate treatment in a personalized manner.
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Bonatelli M, Fornari IF, Bernécule PN, Pinheiro LE, Costa RFA, Longatto-Filho A, Junior JNA, Silva ECA, Cárcano FM, Pinheiro C. Expression of Glycolysis-Related Proteins in Cancer of Unknown Primary Origin. Front Oncol 2021; 11:682665. [PMID: 34249728 PMCID: PMC8264765 DOI: 10.3389/fonc.2021.682665] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 06/07/2021] [Indexed: 12/27/2022] Open
Abstract
Introduction Cancer of unknown primary origin (CUP) is defined as metastatic cancer without identification of the primary site. Considering that only 15–20% of patients with CUP show a favorable outcome, identifying biomarkers may help improve the clinical management of patients who do not respond well to conventional therapies. In this context, the study of the metabolic profile of CUP may pave the way to establish new biomarkers and/or therapeutic targets; therefore, this study aimed to characterize the expression of metabolism-related proteins in CUP. Materials and Methods The expression of monocarboxylate transporters MCT1, MCT2 and MCT4, their chaperone CD147, the glucose transporter GLUT1 and the pH regulator CAIX was evaluated by immunohistochemistry in a series of 118 CUP patients, and the results were associated with the available clinicopathological information. Results The metabolism-related proteins MCT1, MCT4, CD147, GLUT1 and CAIX were expressed in a critical portion of the CUP (approximately 20 to 70%). MCT1 and CD147 were both more frequently expressed in cases with lymph nodes as metastasis dominant sites (p = 0.001) as well as in samples from lymph nodes (p <0.001 and p = 0.002, respectively), while MCT1 expression was more frequently expressed in squamous cell carcinomas (p = 0.045). A higher overall survival was observed in patients with tumors positive for GLUT1 and CAIX expression (p = 0.011 and p = 0.041, respectively), but none of the proteins was an independent prognostic factor for overall survival in multivariable analysis. Conclusion The results suggest that a portion of CUPs present a hyperglycolytic phenotype, which is associated with higher overall survival.
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Affiliation(s)
- Murilo Bonatelli
- Molecular Oncology Research Center, Barretos Cancer Hospital, Barretos, Brazil
| | - Isabella Fernandes Fornari
- Molecular Oncology Research Center, Barretos Cancer Hospital, Barretos, Brazil.,Barretos School of Health Sciences Dr. Paulo Prata-FACISB, Barretos, Brazil
| | - Priscila Neves Bernécule
- Molecular Oncology Research Center, Barretos Cancer Hospital, Barretos, Brazil.,Barretos School of Health Sciences Dr. Paulo Prata-FACISB, Barretos, Brazil
| | - Lara Esquiapatti Pinheiro
- Molecular Oncology Research Center, Barretos Cancer Hospital, Barretos, Brazil.,Barretos School of Health Sciences Dr. Paulo Prata-FACISB, Barretos, Brazil
| | - Ricardo Filipe Alves Costa
- Barretos School of Health Sciences Dr. Paulo Prata-FACISB, Barretos, Brazil.,Research and Teaching Institute, Barretos Cancer Hospital, Barretos, Brazil
| | - Adhemar Longatto-Filho
- Molecular Oncology Research Center, Barretos Cancer Hospital, Barretos, Brazil.,Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga, Portugal.,Laboratory of Medical Investigation (LIM-14), School of Medicine, University of São Paulo, São Paulo, Brazil
| | | | | | - Flávio Mavignier Cárcano
- Barretos School of Health Sciences Dr. Paulo Prata-FACISB, Barretos, Brazil.,Medical Oncology Department, Barretos Cancer Hospital, Barretos, Brazil
| | - Céline Pinheiro
- Molecular Oncology Research Center, Barretos Cancer Hospital, Barretos, Brazil.,Barretos School of Health Sciences Dr. Paulo Prata-FACISB, Barretos, Brazil
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Hermans KEPE, van den Brandt PA, Loef C, Jansen RLH, Schouten LJ. Meat consumption and cancer of unknown primary (CUP) risk: results from The Netherlands cohort study on diet and cancer. Eur J Nutr 2021; 60:4579-4593. [PMID: 34155531 PMCID: PMC8572219 DOI: 10.1007/s00394-021-02600-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 05/26/2021] [Indexed: 01/12/2023]
Abstract
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.
Supplementary Information The online version contains supplementary material available at 10.1007/s00394-021-02600-5.
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Affiliation(s)
- Karlijn E P E Hermans
- Department of Epidemiology, GROW School for Oncology and Developmental Biology, Maastricht University, PO Box 616, 6200, Maastricht, The Netherlands.
| | - Piet A van den Brandt
- Department of Epidemiology, GROW School for Oncology and Developmental Biology, Maastricht University, PO Box 616, 6200, Maastricht, The Netherlands
| | - Caroline Loef
- Department of Research, Comprehensive Cancer Organisation the Netherlands, Utrecht, The Netherlands
| | - Rob L H Jansen
- Department of Internal Medicine, Medical Oncology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Leo J Schouten
- Department of Epidemiology, GROW School for Oncology and Developmental Biology, Maastricht University, PO Box 616, 6200, Maastricht, The Netherlands
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Costantini M, Montella RA, Fadda MP, Garganese G, Di Leone A, Sanchez AM, Franceschini G, Rinaldi PM. Axillary Nodal Metastases from Carcinoma of Unknown Primary (CUPAx): Role of Contrast-Enhanced Spectral Mammography (CESM) in Detecting Occult Breast Cancer. J Pers Med 2021; 11:465. [PMID: 34070278 PMCID: PMC8225076 DOI: 10.3390/jpm11060465] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 05/21/2021] [Accepted: 05/21/2021] [Indexed: 12/14/2022] Open
Abstract
Axillary lymph node metastases of occult breast cancer (CUPAx) is an unusual condition that represents both a diagnostic and therapeutic challenge. The first steps in the diagnostic work-up of patients with CUPAx are the histological analysis of the lymph node metastasis and the execution of basic breast diagnostic imaging (mammography and ultrasound). In the case of occult breast cancer, breast Magnetic Resonance (MR) must be performed. Breast MR identifies a suspicious lesion in many patients and second-look ultrasound detects a corresponding ultrasound alteration in about half of cases, allowing the performance of a US-guided biopsy. In the case of an MR-only lesion, MR-guided biopsy is mandatory. We present a case of CUPAx in which contrast-enhanced spectral mammography (CESM) is used to help the detection of occult breast cancer and to guide stereotactic vacuum breast biopsy (VABB). CESM is a new breast imaging technique that is proving to have good performance in breast cancer detection and that is showing potential in the identification of occult breast cancer in a CUPAx setting. The use of an innovative and personalized breast imaging approach in breast cancer patients improves diagnostic possibilities and promises to become the focus in decision strategies.
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Affiliation(s)
- Melania Costantini
- Radiology Unit, Mater Olbia Hospital (Qatar Foundation Endowment and Policlinico Universitario Agostino Gemelli IRCCS Foundation), 07026 Olbia, Italy; (M.C.); (M.P.F.); (P.M.R.)
- Area Diagnostica per Immagini, Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Roma, Italy
| | - Rino Aldo Montella
- Radiology Unit, Mater Olbia Hospital (Qatar Foundation Endowment and Policlinico Universitario Agostino Gemelli IRCCS Foundation), 07026 Olbia, Italy; (M.C.); (M.P.F.); (P.M.R.)
| | - Maria Paola Fadda
- Radiology Unit, Mater Olbia Hospital (Qatar Foundation Endowment and Policlinico Universitario Agostino Gemelli IRCCS Foundation), 07026 Olbia, Italy; (M.C.); (M.P.F.); (P.M.R.)
| | - Giorgia Garganese
- Gynecology and Breast Care Center, Mater Olbia Hospital (Qatar Foundation Endowment and Policlinico Universitario Agostino Gemelli IRCCS Foundation), 07026 Olbia, Italy;
- Dipartimento Scienze della Vita e Sanità Pubblica, Sezione Ginecologia e Ostetricia, Università Cattolica del Sacro Cuore, 00168 Roma, Italy
| | - Alba Di Leone
- Multidisciplinary Breast Center, Dipartimento Scienze della Salute della Donna e del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Roma, Italy; (A.D.L.); (A.M.S.); (G.F.)
| | - Alejandro Martin Sanchez
- Multidisciplinary Breast Center, Dipartimento Scienze della Salute della Donna e del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Roma, Italy; (A.D.L.); (A.M.S.); (G.F.)
| | - Gianluca Franceschini
- Multidisciplinary Breast Center, Dipartimento Scienze della Salute della Donna e del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Roma, Italy; (A.D.L.); (A.M.S.); (G.F.)
- Istituto di Semeiotica Chirurgica, Università Cattolica del Sacro Cuore, 00168 Roma, Italy
| | - Pierluigi Maria Rinaldi
- Radiology Unit, Mater Olbia Hospital (Qatar Foundation Endowment and Policlinico Universitario Agostino Gemelli IRCCS Foundation), 07026 Olbia, Italy; (M.C.); (M.P.F.); (P.M.R.)
- Area Diagnostica per Immagini, Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Roma, Italy
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AI-based pathology predicts origins for cancers of unknown primary. Nature 2021; 594:106-110. [PMID: 33953404 DOI: 10.1038/s41586-021-03512-4] [Citation(s) in RCA: 231] [Impact Index Per Article: 77.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Accepted: 04/01/2021] [Indexed: 12/16/2022]
Abstract
Cancer of unknown primary (CUP) origin is an enigmatic group of diagnoses in which the primary anatomical site of tumour origin cannot be determined1,2. This poses a considerable challenge, as modern therapeutics are predominantly specific to the primary tumour3. Recent research has focused on using genomics and transcriptomics to identify the origin of a tumour4-9. However, genomic testing is not always performed and lacks clinical penetration in low-resource settings. Here, to overcome these challenges, we present a deep-learning-based algorithm-Tumour Origin Assessment via Deep Learning (TOAD)-that can provide a differential diagnosis for the origin of the primary tumour using routinely acquired histology slides. We used whole-slide images of tumours with known primary origins to train a model that simultaneously identifies the tumour as primary or metastatic and predicts its site of origin. On our held-out test set of tumours with known primary origins, the model achieved a top-1 accuracy of 0.83 and a top-3 accuracy of 0.96, whereas on our external test set it achieved top-1 and top-3 accuracies of 0.80 and 0.93, respectively. We further curated a dataset of 317 cases of CUP for which a differential diagnosis was assigned. Our model predictions resulted in concordance for 61% of cases and a top-3 agreement of 82%. TOAD can be used as an assistive tool to assign a differential diagnosis to complicated cases of metastatic tumours and CUPs and could be used in conjunction with or in lieu of ancillary tests and extensive diagnostic work-ups to reduce the occurrence of CUP.
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Cancer of unknown primary stem-like cells model multi-organ metastasis and unveil liability to MEK inhibition. Nat Commun 2021; 12:2498. [PMID: 33941777 PMCID: PMC8093243 DOI: 10.1038/s41467-021-22643-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Accepted: 03/16/2021] [Indexed: 12/13/2022] Open
Abstract
Cancers of unknown primary (CUPs), featuring metastatic dissemination in the absence of a primary tumor, are a biological enigma and a fatal disease. We propose that CUPs are a distinct, yet unrecognized, pathological entity originating from stem-like cells endowed with peculiar and shared properties. These cells can be isolated in vitro (agnospheres) and propagated in vivo by serial transplantation, displaying high tumorigenicity. After subcutaneous engraftment, agnospheres recapitulate the CUP phenotype, by spontaneously and quickly disseminating, and forming widespread established metastases. Regardless of different genetic backgrounds, agnospheres invariably display cell-autonomous proliferation and self-renewal, mostly relying on unrestrained activation of the MAP kinase/MYC axis, which confers sensitivity to MEK inhibitors in vitro and in vivo. Such sensitivity is associated with a transcriptomic signature predicting that more than 70% of CUP patients could be eligible to MEK inhibition. These data shed light on CUP biology and unveil an opportunity for therapeutic intervention. Cancer of unknown primary (CUP) is a mysterious malignancy featuring metastatic dissemination in the absence of a recognizable primary tumor. By characterizing CUP cancer stem cells we show that self-sustained long-term propagation and sensitivity to MEK inhibition are CUP common features.
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Rassy E, Boussios S, Chebly A, Farra C, Kattan J, Pavlidis N. Comparative genomic characterization of melanoma of known and unknown primary. Clin Transl Oncol 2021; 23:2302-2308. [PMID: 33934271 DOI: 10.1007/s12094-021-02629-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 04/22/2021] [Indexed: 12/16/2022]
Abstract
BACKGROUND This study aims to genomically characterize melanoma of unknown primary (MUP) in comparison to melanomas of cutaneous primary (MCP). METHODS Eligible cases were collected from the MSK-IMPACT™ Clinical Sequencing Cohort published in the cBioPortal database. Genomic analysis was performed using a hybridization-capture-based next-generation sequencing assay designed to detect mutations, small insertions and deletions, copy number alterations, and genomic rearrangements. RESULTS Among 462 patients of whom 18.4% had MUP, brain metastasis was more common among patients with MUP (23% vs 7.1%). The differences in genomic profiling between MCP and MUP did not reach statistical significance. The 187 MCP and 44 MUP patients treated with immune checkpoint inhibitors had a median overall survival of 49 and 44 months, respectively (p = 0.705). CONCLUSIONS The differences in somatic mutation patterns and survival outcomes were not statistically significant. These findings may allude to similar carcinogenic processes but should be considered exploratory and interpreted with caution.
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Affiliation(s)
- E Rassy
- Gustave Roussy, Département de médecine oncologique, 94805, Villejuif, France. .,Department of Oncology, Faculty of Medicine, Saint Joseph University, Beirut, Lebanon.
| | - S Boussios
- Faculty of Life Sciences and Medicine, School of Cancer and Pharmaceutical Sciences, King's College London, London, SE1 9RT, UK.,Medway NHS Foundation Trust, Windmill Road, Gillingham, Kent, ME7 5NY, UK.,AELIA Organization, 9th Km Thessaloniki, Thermi, 57001, Thessaloniki, Greece
| | - A Chebly
- Medical Genetics Unit, Faculty of Medicine, Saint Joseph University, Beirut, Lebanon
| | - C Farra
- Medical Genetics Unit, Faculty of Medicine, Saint Joseph University, Beirut, Lebanon.,Department of Genetics, Hotel Dieu de France Medical Center, Beirut, Lebanon
| | - J Kattan
- Department of Oncology, Faculty of Medicine, Saint Joseph University, Beirut, Lebanon
| | - N Pavlidis
- University of Ioannina, 45110, Ioannina, Greece
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Pavlidis N, Todorovic V, Rassy E, Khaled H, Peccatori F, Eniu A, Aapro M, Gligorov J. The management of patients with cancer of unknown primary in middle-income countries: an ESO-AROME survey. Future Oncol 2020; 17:151-157. [PMID: 33305604 DOI: 10.2217/fon-2020-0677] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Aim: To report on the management strategies in patients with cancer of unknown primary (CUP) in middle-income countries. Methods: We conceived a survey of 20 items concerning the management of patients with CUP in daily clinical practice. Only participants from lower- and higher-middle-income countries, as per the World Bank Classification, were eligible for this study. Results: The indications for the first-line treatment did not differ between the two economic regions, whereas those for second-line treatment were more prevalent in higher-middle-income countries. The use of targeted therapy based on immunohistochemistry alone was higher in lower-middle-income countries, although the access to CUP classifiers was similar between the two regions. Conclusions: Proper recommendations must ensure that the economic burden is minimized and that other benefits outweigh the limited survival benefit achieved in patients with CUP.
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Affiliation(s)
- Nicholas Pavlidis
- University of Ioannina, Niacrhos Avenue, Ioannina, 45 000, Greece.,European School of Oncology College, 20121, Milan, Italy
| | - Vladimir Todorovic
- Oncology & Radiotherapy Clinic, Clinical Center of Montenegro, University of Montenegro, 81000, Podgorica, Montenegro
| | - Elie Rassy
- Department of Medical Oncology, Institut Gustave Roussy, 94800, Villejuif, France.,Faculty of Medicine, Department of Oncology, Saint Joseph University, 11-5076, Beirut, Lebanon
| | - Hussein Khaled
- Medical Oncology Department, National Cancer Institute, Cairo University, 11796, Giza, Egypt
| | - Fedro Peccatori
- Fertility & Procreation Unit, Gynecologic Oncology Program, European Institute of Oncology IRCCS, 20141, Milan, Italy.,European School of Oncology, 20121, Milan, Italy
| | - Alex Eniu
- European School of Oncology College, 20121, Milan, Italy.,Hospital Riviera Chablais, 1847, Rennaz, Switzerland
| | - Matti Aapro
- European School of Oncology, 20121, Milan, Italy.,Breast Center, Genolier Cancer Centre, 1272, Genolier, Switzerland
| | - Joseph Gligorov
- Institut Universitaire de Cancerologie APHP-Sorbonne Université, 75006, Paris, France
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