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Homologous recombination deficiency (HRD) is a prevalent in approximately 17% of tumors and is associated with enhanced sensitivity to anticancer therapies inducing double-strand DNA breaks. Accurate detection of HRD would therefore allow improved patient selection and outcome of conventional and targeted anticancer therapies. However, current clinical assessment of HRD mainly relies on determining germline BRCA1/2 mutational status and is insufficient for adequate patient stratification as mechanisms of HRD occurrence extend beyond functional BRCA1/2 loss. HRD, regardless of BRCA1/2 status, is associated with specific forms of genomic and mutational signatures termed HRD scar. Detection of this HRD scar might therefore be a more reliable biomarker for HRD. This review discusses and compares different methods of assessing HRD and HRD scar, their advances into the clinic, and their potential implications for precision oncology.

Lung cancer is the leading cause of cancer related deaths worldwide. The development of orthotopic mouse models of lung cancer, which recapitulates the disease more realistically compared to the widely used subcutaneous tumor models, is expected to critically aid the development of novel therapies to battle lung cancer or related comorbidities such as cachexia. However, follow-up of tumor take, tumor growth and detection of therapeutic effects is difficult, time consuming and requires a vast number of animals in orthotopic models. Here, we describe a solution for the fully automatic segmentation and quantification of orthotopic lung tumor volume and mass in whole-body mouse computed tomography (CT) scans. The goal is to drastically enhance the efficiency of the research process by replacing time-consuming manual procedures with fast, automated ones. A deep learning algorithm was trained on 60 unique manually delineated lung tumors and evaluated by four-fold cross validation. Quantitative performance metrics demonstrated high accuracy and robustness of the deep learning algorithm for automated tumor volume analyses (mean dice similarity coefficient of 0.80), and superior processing time (69 times faster) compared to manual segmentation. Moreover, manual delineations of the tumor volume by three independent annotators was sensitive to bias in human interpretation while the algorithm was less vulnerable to bias. In addition, we showed that besides longitudinal quantification of tumor development, the deep learning algorithm can also be used in parallel with the previously published method for muscle mass quantification and to optimize the experimental design reducing the number of animals needed in preclinical studies. In conclusion, we implemented a method for fast and highly accurate tumor quantification with minimal operator involvement in data analysis. This deep learning algorithm provides a helpful tool for the noninvasive detection and analysis of tumor take, tumor growth and therapeutic effects in mouse orthotopic lung cancer models.

Glioblastoma multiforme (GBM) is a common and aggressive malignant brain cancer with a mean survival time of approximately 15 months after initial diagnosis. Currently, the standard-of-care (SOC) treatment for this disease consists of radiotherapy (RT) with concomitant and adjuvant temozolomide (TMZ). We sought to develop an orthotopic preclinical model of GBM and to optimize a protocol for non-invasive monitoring of tumor growth, allowing for determination of the efficacy of SOC therapy using a targeted RT strategy combined with TMZ. A strong correlation (r = 0.80) was observed between contrast-enhanced (CE)-CT-based volume quantification and bioluminescent (BLI)-integrated image intensity when monitoring tumor growth, allowing for BLI imaging as a substitute for CE-CT. An optimized parallel-opposed single-angle RT beam plan delivered on average 96% of the expected RT dose (20, 30 or 60 Gy) to the tumor. Normal tissue on the ipsilateral and contralateral sides of the brain were spared 84% and 99% of the expected dose, respectively. An increase in median survival time was demonstrated for all SOC regimens compared to untreated controls (average 5.2 days, p < 0.05), but treatment was not curative, suggesting the need for novel treatment options to increase therapeutic efficacy.

Hypoxia—a common feature of the majority of solid tumors—is a negative prognostic factor, as it is associated with invasion, metastasis and therapy resistance. To date, a variety of methods are available for the assessment of tumor hypoxia, including the use of positron emission tomography (PET). A plethora of hypoxia PET tracers, each with its own strengths and limitations, has been developed and successfully validated, thereby providing useful prognostic or predictive information. The current review focusses on [18F]-HX4, a promising next-generation hypoxia PET tracer. After a brief history of its development, we discuss and compare its characteristics with other hypoxia PET tracers and provide an update on its progression into the clinic. Lastly, we address the potential applications of assessing tumor hypoxia using [18F]-HX4, with a focus on improving patient-tailored therapies.

Most solid cancers contain regions of necrotic tissue. The extent of necrosis is associated with poor survival, most likely because it reflects aggressive tumour outgrowth and inflammation. Intravenously injected spores of anaerobic bacteria from the genus Clostridium infiltrate and selectively germinate in these necrotic regions, providing cancer-specific colonisation. The specificity of this system was first demonstrated over 60 years ago and evidence of colonisation has been confirmed in multiple tumour models. The use of “armed” clostridia, such as in Clostridium Directed Enzyme Prodrug Therapy (CDEPT), may help to overcome some of the described deficiencies of using wild-type clostridia for treatment of cancer, such as tumour regrowth from a well-vascularised outer rim of viable cells. Successful preclinical evaluation of a transferable gene that metabolises both clinical stage positron emission tomography (PET) imaging agents (for whole body vector visualisation) as well as chemotherapy prodrugs (for conditional enhancement of efficacy) would be a valuable early step towards the prospect of “armed” clostridia entering clinical evaluation. The ability to target the immunosuppressive hypoxic tumour microenvironment using CDEPT may offer potential for synergy with recently developed immunotherapy strategies. Ultimately, clostridia may be most efficacious when combined with conventional therapies, such as radiotherapy, that sterilise viable aerobic tumour cells.