Nat Commun: Radiogenomics features reveal infigenesic traits associated with breast cancer survival.

Cellular and molecular heterogeneity is common in solid tumors.
human cancer contains different proportions of different genomic subclonals, each with unique gene expression patterns and biological functions.
heterogeneity promotes adverse clinical outcomes, including resistance to treatment and reduced total survival (OS).
, the heterogeneity of tumors and complex tissues is a major challenge.
anti-regeneron method applied to gene expression spectrum can estimate basic genomic subclonals.
, for example, the data curly accumulation applied to the tumor/substation gene expression spectrum identified immuno-subclonals associated with decreased survival rates in cancer patients.
decomposing genomes are often associated with cancer treatment, such as some immuno-checkpoint blocking therapies.
, however, there is an urgent need for significant improvements in tools and workflows to identify clinical and biological genomic subclonals from heterogeneic tumors, better predict prognostics and guide treatment decisions.
current method of gene expression anti-converse uses reference information from tissue mixtures, where patterns of gene expression associated with different disease states are explored in the underlying matrix.
, however, these methods do not identify unknown biomarkers.
addition, although recent studies of tumor heterogeneity have mostly focused on molecular features of tissue samples, these techniques require the collection of invasive biopsy samples.
radiology can be used to mine high-volume quantitative and non-invasive image features to improve cancer diagnosis and treatment.
Recently, researchers published a paper in the journal Nature Communications, hypothesically, that unsealed de-regeneration of gene expression spectrum could reveal genomic subclonals that affect cancer-related biological function and patient survival, and that radiogenomic features at the imaging level could capture significant potential tumor heterogeneity at the molecular level.
in multi-scale modeling of biological systems, the term multiscale refers to the use of data from more than one scale.
researchers used two scales of data, particularly transcriptional and imaging data.
tumor subclonal heterogeneity is the size of the genome (transcription group), a completely unserended anti-regeneration method is applied to gene expression spectrum recognition.
in-tumor heterogeneity at the imaging scale is characterized by a set of radiogeographic characteristic analyses.
researchers further identified the non-invasive radiogenomics characteristics associated with these two scales, and divided patients into groups with different subclonal compositions.
researchers studied the biological and clinical relevance of simulating heterogeneity in multiscale tumors by conducting radiogenomic analysis of 1,310 samples of breast cancer patients in five data sets in three data queues.
analysis is carried out in three stages.
first (phase 1), using completely unsealed anti-converse analysis of gene expression spectrum to identify genomic subclonals.
the biological function of subclonals through gene avotation analysis (GSEA), identify prognostal genomic markers using genomic development datasets, and test them on genomic test data sets.
The second stage (phase 2), the radiology marker is established by mapping the radiological features to the composition of the prognostic subclonal in a separate dataset containing matching imaging and gene expression data for each tumor.
Phase III (Phase III), the prognostic value of the identified radiogenomics characteristics is further tested on two other separate data sets containing imaging and survival results data.
the study provides a non-invasive and repeatable method for identifying tumor genome subclonals and their potential biological functions, which are valuable for clinical applications.
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