Article
|
Open Access
Featured
-
-
Article
| Open AccessNivolumab and ipilimumab in recurrent or refractory cancer of unknown primary: a phase II trial
Standard of care for unfavorable-risk cancer of unknown primary (CUP) comprises platinum-based chemotherapy as first-line treatment, however therapeutic options remain limited. Here the authors report the results of a phase II trial of combined nivolumab (anti-PD1) and ipilimumab (anti-CTLA4) in patients with unfavorable CUP.
- Maria Pouyiourou
- , Bianca N. Kraft
- & Alwin Krämer
-
Article
| Open AccessDNA methylation profiling to determine the primary sites of metastatic cancers using formalin-fixed paraffin-embedded tissues
Molecular tests that can determine the tissue of origin of cancers of unknown primary (CUP) are still needed. Here, the authors develop a DNA methylation profiling assay and a machine learning classifier to predict the origin of metastatic tumours in CUP patients using formalin-fixed, paraffin embedded samples.
- Shirong Zhang
- , Shutao He
- & Hongcang Gu
-
Article
| Open AccessDeep transfer learning enables lesion tracing of circulating tumor cells
Liquid biopsy offers great promise for noninvasive cancer diagnostics, while the lack of adequate target characterization and analysis hinders its wide application. Here, the authors design a transfer learning-based algorithm to transfer lesion labels from the primary cancer cell atlas to circulating tumor cells.
- Xiaoxu Guo
- , Fanghe Lin
- & Jia Song
-
Article
| Open AccessComprehensive genomic and epigenomic analysis in cancer of unknown primary guides molecularly-informed therapies despite heterogeneity
The identification of molecular biomarkers in cancer of unknown primary site (CUP) cases may enable the improvement of prognosis in these patients. Here, the authors integrate whole genome/exome, transcriptome and methylome data in 70 CUP patients, recommend therapies based on their analysis and report clinical outcome data.
- Lino Möhrmann
- , Maximilian Werner
- & Hanno Glimm
-
Article
| Open AccessMachine learning-based tissue of origin classification for cancer of unknown primary diagnostics using genome-wide mutation features
The original tumor location can be unclear for metastatic tumors. Here, the authors show that DNA sequencing of whole genomes can be used to classify metastatic tumors using a machine learning model, Cancer of Unknown Primary Location Resolver, in order to improve diagnosis and inform treatment decisions.
- Luan Nguyen
- , Arne Van Hoeck
- & Edwin Cuppen
-
Article
| Open AccessCancer of unknown primary stem-like cells model multi-organ metastasis and unveil liability to MEK inhibition
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.
- Federica Verginelli
- , Alberto Pisacane
- & Carla Boccaccio
-
Article
| Open AccessA deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns
Some cancer patients first present with metastases where the location of the primary is unidentified; these are difficult to treat. In this study, using machine learning, the authors develop a method to determine the tissue of origin of a cancer based on whole sequencing data.
- Wei Jiao
- , Gurnit Atwal
- & Christian von Mering