Cancer Core Europe: Leveraging Institutional Synergies to Advance Oncology Research and Care Globally.

Cancer Core Europe brings together the expertise, resources, and interests of seven leading cancer institutes committed to leveraging collective innovation and collaboration in precision oncology. Through targeted efforts addressing key medical challenges in cancer and partnerships with multiple stakeholders, the consortium seeks to advance cancer research and enhance equitable patient care.

A guide to artificial intelligence for cancer researchers.

Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to a readily accessible tool for cancer researchers. AI-based tools can boost research productivity in daily workflows, but can also extract hidden information from existing data, thereby enabling new scientific discoveries.

Tumor Size Is Not Everything: Advancing Radiomics as a Precision Medicine Biomarker in Oncology Drug Development and Clinical Care. A Report of a Multidisciplinary Workshop Coordinated by the RECIST Working Group.

Radiomics, the science of extracting quantifiable data from routine medical images, is a powerful tool that has many potential applications in oncology. The Response Evaluation Criteria in Solid Tumors Working Group (RWG) held a workshop in May 2022, which brought together various stakeholders to discuss the potential role of radiomics in oncology drug development and clinical trials, particularly with respect to response assessment. This article summarizes the results of that workshop, reviewing radiomics for the practicing oncologist and highlighting the work that needs to be done to move forward the incorporation of radiomics into clinical trials.

Case report: Exceptional and durable response to Radium-223 and suspension of androgen deprivation therapy in a metastatic castration-resistant prostate cancer patient.

Despite the development of new therapies in the last few years, metastatic prostate cancer (PCa) is still a lethal disease. Radium-223 (Ra-223) is approved for patients with advanced castration-resistant prostate cancer (CRPC) with bone metastases and no visceral disease. However, patients' outcomes are heterogenous, and there is lack of validated predictive biomarkers of response, while biomarkers for early identification of patients who benefit from treatment are limited.

An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI.

Noninvasive differential diagnosis of brain tumors is currently based on the assessment of magnetic resonance imaging (MRI) coupled with dynamic susceptibility contrast (DSC). However, a definitive diagnosis often requires neurosurgical interventions that compromise patients' quality of life. We apply deep learning on DSC images from histology-confirmed patients with glioblastoma, metastasis, or lymphoma.

MYC targeting by OMO-103 in solid tumors: a phase 1 trial.

Among the 'most wanted' targets in cancer therapy is the oncogene MYC, which coordinates key transcriptional programs in tumor development and maintenance. It has, however, long been considered undruggable. OMO-103 is a MYC inhibitor consisting of a 91-amino acid miniprotein.

Differentiating IDH-mutant astrocytomas and 1p19q-codeleted oligodendrogliomas using DSC-PWI: high performance through cerebral blood volume and percentage of signal recovery percentiles.

Objective: Presurgical differentiation between astrocytomas and oligodendrogliomas remains an unresolved challenge in neuro-oncology. This research aims to provide a comprehensive understanding of each tumor's DSC-PWI signatures, evaluate the discriminative capacity of cerebral blood volume (CBV) and percentage of signal recovery (PSR) percentile values, and explore the synergy of CBV and PSR combination for pre-surgical differentiation.

Methods: Patients diagnosed with grade 2 and 3 IDH-mutant astrocytomas and IDH-mutant 1p19q-codeleted oligodendrogliomas were retrospectively retrieved (2010-2022).

Advanced Diffusion-Weighted MRI for Cancer Microstructure Assessment in Body Imaging, and Its Relationship With Histology.

Diffusion-weighted magnetic resonance imaging (DW-MRI) aims to disentangle multiple biological signal sources in each imaging voxel, enabling the computation of innovative maps of tissue microstructure. DW-MRI model development has been dominated by brain applications. More recently, advanced methods with high fidelity to histology are gaining momentum in other contexts, for example, in oncological applications of body imaging, where new biomarkers are urgently needed.

PET-based radiomics signature can predict durable responses to CAR T-cell therapy in patients with large B-cell lymphoma.

Chimeric antigen receptor (CAR) T-cell therapy is a promising treatment option for relapsed or refractory (R/R) large B-cell lymphoma (LBCL). However, only a subset of patients will present long-term benefit. In this study, we explored the potential of PET-based radiomics to predict treatment outcomes with the aim of improving patient selection for CAR T-cell therapy.

Radiomics and outcome prediction to antiangiogenic treatment in advanced gastroenteropancreatic neuroendocrine tumours: findings from the phase II TALENT trial.

Background: More accurate predictive biomarkers in patients with gastroenteropancreatic neuroendocrine tumours (GEP-NETs) are needed. This study aims to investigate radiomics-based tumour phenotypes as a surrogate biomarker of the tumour vasculature and response prediction to antiangiogenic targeted agents in patients with GEP-NETs.

Methods: In this retrospective study, a radiomics signature was developed in patients with GEP-NETs and liver metastases receiving lenvatinib.

RNF43 mutations predict response to anti-BRAF/EGFR combinatory therapies in BRAF metastatic colorectal cancer.

Anti-BRAF/EGFR therapy was recently approved for the treatment of metastatic BRAF colorectal cancer (mCRC). However, a large fraction of patients do not respond, underscoring the need to identify molecular determinants of treatment response. Using whole-exome sequencing in a discovery cohort of patients with mCRC treated with anti-BRAF/EGFR therapy, we found that inactivating mutations in RNF43, a negative regulator of WNT, predict improved response rates and survival outcomes in patients with microsatellite-stable (MSS) tumors.

Facts and Hopes on the Use of Artificial Intelligence for Predictive Immunotherapy Biomarkers in Cancer.

Immunotherapy by immune checkpoint inhibitors has become a standard treatment strategy for many types of solid tumors. However, the majority of patients with cancer will not respond, and predicting response to this therapy is still a challenge. Artificial intelligence (AI) methods can extract meaningful information from complex data, such as image data.

Radiomic Signatures Associated with CD8 Tumour-Infiltrating Lymphocytes: A Systematic Review and Quality Assessment Study.

The tumour immune microenvironment influences the efficacy of immune checkpoint inhibitors. Within this microenvironment are CD8-expressing tumour-infiltrating lymphocytes (CD8 TILs), which are an important mediator and marker of anti-tumour response. In practice, the assessment of CD8 TILs via tissue sampling involves logistical challenges.