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The implementation of cancer molecular characterization in clinical practice has improved prognostic re-definition, extending the eligibility to a continuously increasing number of targeted treatments. Broad molecular profiling technologies better than organ-based approaches are believed to serve such dynamic purposes. We here present the workflow our institution adopted to run a comprehensive cancer genome profiling in clinical practice. This article describes the workflow designed to make a comprehensive cancer genome profiling program feasible and sustainable in a large-volume referral hospital. Keywords: bioinformatics; cancer; comprehensive cancer genome profiling; genomics; target therapy.

Traditional healthcare paradigms rely on the disease-centered approach aiming at reducing human nature by discovering specific drivers and biomarkers that cause the advent and progression of diseases. This reductive approach is not always suitable to understand and manage complex conditions, such as multimorbidity and cancer. Multimorbidity requires considering heterogeneous data to tailor preventing and targeting interventions. Personalized Medicine represents an innovative approach to address the care needs of multimorbid patients considering relevant patient characteristics, such as lifestyle and individual preferences, in opposition to the more traditional “one-size-fits-all” strategy focused on interventions designed at the population level. Integration of omic (e.g., genomics) and non-strictly medical (e.g., lifestyle, the exposome) data is necessary to understand patients’ complexity. Artificial Intelligence can help integrate and manage heterogeneous data through advanced machine learning and bioinformatics algorithms to define the best treatment for each patient with multimorbidity and cancer. The experience of an Italian research hospital, leader in the field of oncology, may help to understand the multifaceted issue of managing multimorbidity and cancer in the framework of Personalized Medicine.

Breast cancer represents the first cause of cancer worldwide and the leading cause of cancer mortality for women. Therefore, new therapies are needed to improve the prognosis of women diagnosed with this disease. In this review, we summarize the new drug indications for the treatment of breast cancer approved by European Medicines Agency between January 2015 and June 2021. In particular, we analyzed the clinical trials results leading to approvals and their update (when available), according to setting (localized and locally advanced or metastatic) and clinical features (hormone receptor positive, HER2 positive, triple negative, BRCA 1/2 mutation). The aim of this paper is to describe the clinical benefit obtained with the new indications.

Clinical trials in cancer treatment are imperative in enhancing patients’ survival and quality of life outcomes. The lack of communication among professionals may produce a non-optimization of patients’ accrual in clinical trials. We developed a specific platform, called “Digital Research Assistant” (DRA), to report real-time every available clinical trial and support clinician. Healthcare professionals involved in breast cancer working group agreed nine minimal fields of interest to preliminarily classify the characteristics of patients’ records (including omic data, such as genomic mutations). A progressive web app (PWA) was developed to implement a cross-platform software that was scalable on several electronic devices to share the patients’ records and clinical trials. A specialist is able to use and populate the platform. An AI algorithm helps in the matchmaking between patient’s data and clinical trial’s inclusion criteria to personalize patient enrollment. At the same time, an easy configuration allows the application of the DRA in different oncology working groups (from breast cancer to lung cancer). The DRA might represent a valid research tool supporting clinicians and scientists, in order to optimize the enrollment of patients in clinical trials. User Experience and Technology The acceptance of participants using the DRA is topic of a future analysis.

Introduction: Ovarian cancer (OC) represents the leading cause of death among gynecological cancers. Despite novel compound classes like vascular endothelial growth factor (VEGF) inhibitors or poly-ADP ribose polymerase (PARP) inhibitors are available, which improve significantly efficacy of platinum-based chemotherapy, OC prognosis remains poor and innovative strategies are needed. The induction of tumor specific immune response with a therapeutic intent is a very challenging approach. Oregovomab is a murine monoclonal antibody direct to the tumor-associated antigen CA125 that stimulate a host cytotoxic immune response against tumor cells expressing CA125. Areas covered: This paper reviews the preclinical and clinical published data underlying the use of oregovomab in advanced OC. A literature search was performed in PubMed for oregovomab, ovarian cancer, anti-CA125, and on ClinicalTrials.gov for currently ongoing trials. Expert opinion: Oregovomab demonstrated a significant improvement in progression-free and overall survival in advanced OC treatment when administered simultaneously with first-line chemotherapy. This promising schedule is currently investigated in a phase III trial. Since oral treatments as PARP-inhibitors have recently been approved in the OC first-line setting, the possible role of oregovomab needs still to be defined, also considering the intravenous route of administration. The easy to manage toxicity profile makes oregovomab an ideal candidate for association strategies.

Clinical trials in cancer treatment are imperative in enhancing patients’ survival and quality of life outcomes. The lack of communication among professionals may produce a non-optimization of patients’ accrual in clinical trials. We developed a specific platform, called “Digital Research Assistant” (DRA), to report real-time every available clinical trial and support clinician. Healthcare professionals involved in breast cancer working group agreed nine minimal fields of interest to preliminarily classify the characteristics of patients’ records (including omic data, such as genomic mutations). A progressive web app (PWA) was developed to implement a cross-platform software that was scalable on several electronic devices to share the patients’ records and clinical trials. A specialist is able to use and populate the platform. An AI algorithm helps in the matchmaking between patient’s data and clinical trial’s inclusion criteria to personalize patient enrollment. At the same time, an easy configuration allows the application of the DRA in different oncology working groups (from breast cancer to lung cancer). The DRA might represent a valid research tool supporting clinicians and scientists, in order to optimize the enrollment of patients in clinical trials. User Experience and Technology The acceptance of participants using the DRA is topic of a future analysis.

Background: High grade serous ovarian cancer (HGSOC) is among the deadliest human cancers and its prognosis remains extremely poor. Tumor heterogeneity and rapid acquisition of resistance to conventional chemotherapeutic approaches strongly contribute to poor outcome of patients. The clinical landscape of HGSOC has been radically transformed since the advent of targeted therapies in the last decade. Nevertheless, the lack of predictive biomarkers informing on the differential clinical benefit in select subgroups, and allowing patient-centric approaches, currently limits the efficacy of these novel therapies. Thus, rational selection of the best possible treatment for each patient represents a clinical priority in order to improve outcome, while limiting undesirable effects. Main body: In this review, we describe the state of the art and the unmet needs in HGSOC management, illustrate the treatment options that are available and the biomarkers that are currently employed to orient clinical decisions. We also describe the ongoing clinical trials that are testing new therapeutic approaches for HGSOC. Next, we introduce the organoid technology as a promising, expanding strategy to study cancer and to develop personalized therapeutic approaches. In particular, we discuss recent studies that have characterized the translational potential of Patient's Derived Organoids (PDOs) to inform on drug sensitivity of HGSOC patients. Conclusions: PDOs can predict the response of patients to treatments and may therefore guide therapeutic decisions. Although preliminary results appear encouraging, organoids still need to be generated and expanded efficiently to enable drug screening in a clinically meaningful time window. A new generation of clinical trials based on the organoid technology should guarantee tailored approaches to ovarian cancer management, as it is now clear that the one-size-fits-all approach cannot lead to efficient and meaningful therapeutic advancements.