Non-invasive tumor genotyping using radiogenomic biomarkers, a systematic review and oncology-wide pathway analysis

Abstract

With targeted treatments playing an increasing role in oncology, the need arises for fast non-invasive genotyping in clinical practice. Radiogenomics is a rapidly evolving field of research aimed at identifying imaging biomarkers useful for non-invasive genotyping. Radiogenomic genotyping has the advantage that it can capture tumor heterogeneity, can be performed repeatedly for treatment monitoring, and can be performed in malignancies for which biopsy is not available. In this systematic review of 187 included articles, we compiled a database of radiogenomic associations and unraveled networks of imaging groups and gene pathways oncology-wide. Results indicated that ill-defined tumor margins and tumor heterogeneity can potentially be used as imaging biomarkers for 1p/19q codeletion in glioma, relevant for prognosis and disease profiling. In non-small cell lung cancer, FDG-PET uptake and CT-ground-glass-opacity features were associated with treatment-informing traits including EGFR-mutations and ALK-rearrangements. Oncology-wide gene pathway analysis revealed an association between contrast enhancement (imaging) and the targetable VEGF-signalling pathway. Although the need of independent validation remains a concern, radiogenomic biomarkers showed potential for prognosis prediction and targeted treatment selection. Quantitative imaging enhanced the potential of multiparametric radiogenomic models. A wealth of data has been compiled for guiding future research towards robust non-invasive genomic profiling.

Integrative Radiogenomics Approach for Risk Assessment of Post-Operative Metastasis in Pathological T1 Renal Cell Carcinoma: A Pilot Retrospective Cohort Study

“…Among 10 checkpoint modulators, programmed death-ligand 1/2 (PD-L1/PD-L2) and T-cell immunoglobulin and mucin domain-3 (TIM-3) were significantly decreased in high ES cases. As imaging biomarkers may have clinical implications in aiding patient selection for targeted therapies [5], we examined the association of anticancer drugs and their target genes using RCC cell lines from the Cancer Cell Line Encyclopedia (CCLE) dataset [22]. A number of drug-target relationships (i.e., sensitive or resistant) between gene expression and tumor response were identified (Figure 6f).…”

“…Recently, radiomics has emerged as a powerful tool to comprehensively extract qualitative or quantitative (computer-extracted) phenotypic imaging features from conventional imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) [5][6][7]. Radiomics has several advantages, including noninvasiveness, ease of use for serial monitoring, clinical implementation using standard-of-care imaging, and data acquisition from the entire heterogeneous tumor [5,8,9].…”

“…Recently, radiomics has emerged as a powerful tool to comprehensively extract qualitative or quantitative (computer-extracted) phenotypic imaging features from conventional imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) [5][6][7]. Radiomics has several advantages, including noninvasiveness, ease of use for serial monitoring, clinical implementation using standard-of-care imaging, and data acquisition from the entire heterogeneous tumor [5,8,9]. Radiogenomics can integrate multiscale data at the fine-grained genome level to more macro-multiparametric imaging data through high-throughput computing to develop new tools to provide insight into relationships between imaging and cellular and subcellular data, reflecting underlying multifaceted and heterogeneous phenotypes [5][6][7]10,11].…”

What can artificial intelligence teach us about the molecular mechanisms underlying disease?

“…Genetic mutations, including EGFR mutations and anaplastic lymphoma kinase (ALK) gene rearrangements, that are associated with improved response to certain TKIs, are associated with image features derived from FDG PET in NSCLC [46]. With EGFR, findings are conflicting, with some studies predominantly showing high FDG uptake in EGFRmutated tumours, reflecting increased glycolysis through AKT signalling [47], and others showing lower uptake [48].…”

Relationship Between Tumor Mutational Burden and Maximum Standardized Uptake Value in PET (Positron Emission Tomography) Scan in Cancer Patients

“…Creating a link between imaging ndings and genomic data in patients with cancer is crucial in the evolving world of genomics. Radiologic markers have shown promise for non-invasive identi cation of molecular properties [1]. Imaging markers can provide surrogate genomic markers from imaging data for diagnosis, prognosis and strati cation of cancer patients in the emerging eld of personalized medicine.…”