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Engineering and computational advances have opened many new avenues in cancer research, particularly when being exploited in interdisciplinary approaches. For example, the combination of microfluidics, novel sequencing technologies, and computational analyses has been crucial to enable single-cell assays, giving a detailed picture of tumor heterogeneity for the very first time. In a similar way, these 'tech' disciplines have been elementary for generating large data sets in multidimensional cancer 'omics' approaches, cell–cell interaction screens, 3D tumor models, and tissue level analyses. In this review we summarize the most important technology and computational developments that have been or will be instrumental for transitioning classical cancer research to a large data-driven, high-throughput, high-content discipline across all biological scales. Advanced sequencing and barcoding technologies allow the analysis of tumor heterogeneity and evolution by combining multi-omics data sets and including spatial information. Miniaturized microfluidic assay formats can be exploited to obtain detectable concentrations of analytes from single cells and to perform large-scale drug screens on very limited patient material. Microfluidics allows the analysis of the tumor microenvironment (TME) by pairing cells for interaction studies and by generating highly complex and geometrically defined tumor models in vitro. Advances in computational analysis provide essential tools to integrate different data modalities, allowing a holistic description of the tumor. Machine learning facilitates the identification of relevant patterns in high-throughput data, thus enabling their use for cancer diagnosis and prognosis. [ABSTRACT FROM AUTHOR]

Owing to recent advances in genomic technologies, personalized oncology is poised to fundamentally alter cancer therapy. In this paradigm, the mutational and transcriptional profiles of tumors are assessed, and personalized treatments are designed based on the specific molecular abnormalities relevant to each patient's cancer. To date, such approaches have yielded impressive clinical responses in some patients. However, a major limitation of this strategy has also been revealed: the vast majority of tumor mutations are not targetable by current pharmacological approaches. Immunotherapy offers a promising alternative to exploit tumor mutations as targets for clinical intervention. Mutated proteins can give rise to novel antigens (called neoantigens) that are recognized with high specificity by patient T cells. Indeed, neoantigen-specific T cells have been shown to underlie clinical responses to many standard treatments and immunotherapeutic interventions. Moreover, studies in mouse models targeting neoantigens, and early results from clinical trials, have established proof of concept for personalized immunotherapies targeting next-generation sequencing identified neoantigens. Here, we review basic immunological principles related to T-cell recognition of neoantigens, and we examine recent studies that use genomic data to design personalized immunotherapies. We discuss the opportunities and challenges that lie ahead on the road to improving patient outcomes by incorporating immunotherapy into the paradigm of personalized oncology. [ABSTRACT FROM AUTHOR]

Abstract: The identification of predictive factors of response is critical for the development and appropriate use of anti-cancer agents. The evaluation of biomarkers is usually performed by analyzing the primary tumor tissues but this approach does not take into account potential discrepancies between primary tumor and secondary lesions. This review proposes to describe currently available data regarding differential expression of established biomarkers between primary tumor and matched metastasis. In light of recent data, the need of iterative biopsies in metastatic setting has been suggested but technical and methodological limits in such analyses should not be ignored and this strategy cannot be definitively validated. Complementary studies are still needed since the question of spatial and temporal variability of biomarkers in solid tumors is clearly a key issue in an era where personalized therapy is strongly advocated by clinicians, researchers and patients. [Copyright &y& Elsevier]

Radiotherapy is a primary therapeutic modality widely utilized with curative intent. Traditionally tumor response was hypothesized to be due to high levels of cell death induced by irreparable DNA damage. However, the immunomodulatory aspect of radiation is now widely accepted. As such, interest into the combination of radiotherapy and immunotherapy is increasing, the synergy of which has the potential to improve tumor regression beyond that observed after either treatment alone. However, questions regarding the timing (sequential vs concurrent) and dose fractionation (hyper-, standard-, or hypo-fractionation) that result in improved anti-tumor immune responses, and thus potentially enhanced tumor inhibition, remain. Here we discuss the biological response to radiotherapy and its immunomodulatory properties before giving an overview of pre-clinical data and clinical trials concerned with answering these questions. Finally, we review published mathematical models of the impact of radiotherapy on tumor-immune interactions. Ranging from considering the impact of properties of the tumor microenvironment on the induction of anti-tumor responses, to the impact of choice of radiation site in the setting of metastatic disease, these models all have an underlying feature in common: the push towards personalized therapy. [ABSTRACT FROM AUTHOR]

The development towards targeted treatments in oncology has been accompanied by significant improvements in molecular imaging. Yet, broad application of novel imaging techniques has partly been slowed down due to economical considerations. Building on the broad positive evidence of its diagnostic accuracy, modelling of effects on long-term costs and effectiveness may help to foster a broader application and acceptance of comprehensive molecular imaging techniques, such as PET/MRI. In this article, common economic evaluation techniques and cost-effectiveness analysis (CEA) evaluation methods will be introduced including Markov models and incremental cost-effectiveness ratios (ICER). This is complemented with a review of literature on recently published cost-effectiveness of molecular imaging. Additionally, the strategic relevance of CEAs for the molecular imaging community is discussed and combined with a global outlook. [ABSTRACT FROM AUTHOR]

• Comprehensive genomic profiling increases the efficiency of tissue for processing. • It is probably more sensitive to detect actionable targets than previous techniques. • Companion diagnostics using this process improve relapse-free and overall survival. Given the lack of a gold standard, the clinical usefulness of Comprehensive Genomic Profiling (CGP) has not been established. This systematic review aimed to evaluate evidence about the clinical benefit of CGP for patients with Non-small cell lung carcinoma (NSCLC). All controlled studies that evaluated the ability of CGP to detect actionable targets (ATs) reported increases in the number of samples with ATs. The frequency of ATs detected in uncontrolled case series ranged from 0.7 % for RET mutations to 45 % for EGFR mutations. The studies that evaluated therapies targeted to EGFR, ALK, ROS-1, MET, and RET mutations documented significant improvement in clinical outcomes. This review suggests that CGP tests may be clinically helpful for treating patients with NSCLC. Although current evidence is associated with a high risk of bias, the significant impact of NSCLC on individuals and society may justify the routine use of CGP testing for this disease. [ABSTRACT FROM AUTHOR]

Purpose: Using artificial intelligence techniques, we compute optimal personalized protocols for temozolomide administration in a population of patients with variability.Methods: Our optimizations are based on a Pharmacokinetics/Pharmacodynamics (PK/PD) model with population variability for temozolomide, inspired by Faivre et al. [10] and Panetta et al. [25,26]. The patient pharmacokinetic parameters can only be partially observed at admission and are progressively learned by Bayesian inference during treatment. For every patient, we seek to minimize tumor size while avoiding severe toxicity, i.e. maintaining an acceptable toxicity level. The optimization algorithm we rely on borrows from the field of artificial intelligence.Results: Optimal personalized protocols (OPP) achieve a sizable decrease in tumor size at the population level but also patient-wise. The tumor size is on average 67.2 g lighter than with the standard maximum-tolerated dose protocol (MTD) after 336 days (12 MTD cycles). The corresponding 90% confidence interval for average tumor size reduction amounts to 58.6-82.7 g. When treated with OPP, less patients experience severe toxicity in comparison to MTD.Major Findings: We quantify in-silico the benefits offered by personalized oncology in the case of temozolomide administration. To do so, we compute optimal personalized protocols for a population of heterogeneous patients using artificial intelligence techniques. At each treatment day, the protocol is updated by taking into account the feedback obtained from patient's reaction to the drug administration. Personalized protocols greatly differ from each other, and from the standard MTD protocol. Benefits of personalization are very sizable: tumor sizes are much smaller on average and also patient-wise, while severe toxicity is made less frequent. [ABSTRACT FROM AUTHOR]

Heterogeneity has long been recognized as a feature of some primary breast cancers manifesting as mixed histopathological subtypes or variable expression of the therapeutic targets ER, PgR and HER2. The recent emergence of next generation sequencing (NGS) technologies has revolutionized our understanding of the extent and nature of subclonal diversification. Careful examination of primary breast cancers often reveals multiple genomically distinct subclones that may contain driver alterations that follow spatial patterns of segregation. Subclonality is of clinical relevance as it forms the substrate of selection and can give rise to aggressive clinical features such as invasiveness, metastasis and treatment resistance. However, spatial and temporal intra-tumoral heterogeneity pose fundamental challenges to representative sampling and consequently the feasibility of a personalized medicine approach. Fundamental clinical and biological questions are starting to be addressed by applying NGS to the study of intra-tumoral heterogeneity and the insights that it provides should be used to better inform the prospective design of clinico-genomics trials. [ABSTRACT FROM AUTHOR]

Personalized (or precision) medicine aims to individualize therapeutic interventions, based on OMICS data such as genomics, proteomics, metabolomics etc', profiling together with histopathological insights to the type, stage, and the grade of the disease, as well as on the potential response of a particular patient to a particular treatment regimen. With next generation sequencing technologies, it is now possible to identify all germline variants of an individual in an affordable cost and thus paving the way for clinicians to provide healthcare from an individual perspective. In this special issue of Cancer Letters termed "Trends in Personalized Cancer Research" we bring together physicians and scientists summarizing the state-of-the-art in precision medicine from hematological malignancies and solid tumors with the current gold standard diagnostic and therapy to basic and translational research utilizing nanotechnology and RNA interference strategies for future personalized theranostics in oncology. [ABSTRACT FROM AUTHOR]