Personalization of medical treatments in oncology: time for rethinking the disease concept to improve individual outcomes

Abstract

The agenda of pharmacology discovery in the field of personalized oncology was dictated by the search of molecular targets assumed to deterministically drive tumor development. In this perspective, genes play a fundamental “causal” role while cells simply act as causal proxies, i.e., an intermediate between the molecular input and the organismal output. However, the ceaseless genomic change occurring across time within the same primary and metastatic tumor has broken the hope of a personalized treatment based only upon genomic fingerprint. Indeed, current models are unable in capturing the unfathomable complexity behind the outbreak of a disease, as they discard the contribution of non-genetic factors, environment constraints, and the interplay among different tiers of organization. Herein, we posit that a comprehensive personalized model should view at the disease as a “historical” process, in which different spatially and timely distributed factors interact with each other across multiple levels of organization, which collectively interact with a dynamic gene-expression pattern. Given that a disease is a dynamic, non-linear process — and not a static-stable condition — treatments should be tailored according to the “timing-frame” of each condition. This approach can help in detecting those critical transitions through which the system can access different attractors leading ultimately to diverse outcomes — from a pre-disease state to an overt illness or, alternatively, to recovery. Identification of such tipping points can substantiate the predictive and the preventive ambition of the Predictive, Preventive and Personalized Medicine (PPPM/3PM). However, an unusual effort is required to conjugate multi-omics approaches, data collection, and network analysis reconstruction (eventually involving innovative Artificial Intelligent tools) to recognize the critical phases and the relevant targets, which could help in patient stratification and therapy personalization.

Microsatellite instability assessment is instrumental for Predictive, Preventive and Personalised Medicine: status quo and outlook

“…That is the way how the diagnosis and management of individuals with LS or their at-risk relatives should move from reactive testing to proactive Predictive, Preventive and Personalised Medicine (3PM). This shift is fundamental to improve patient outcomes and deliver socio-economic benefit [ 17 , 18 ].…”

Applied Molecular-Based Quality Control of Biobanked Samples for Multi-Omics Approach

“…To personalize oncology, we must go beyond genetics and understand the complexity of cancer through multi-omics analysis. Protein posttranslational modifications give information about epigenetic inheritance and chromatin relaxation or compaction [2,3]. Metabolomics is also a crucial part of this puzzle since the provided results are the most dependent on the environment and lifestyle.…”

An Analysis of Transcriptomic Burden Identifies Biological Progression Roadmaps for Hematological Malignancies and Solid Tumors

“…Currently, most precision medicine strategies focus on targeting select mutational abnormalities with notable successes such as EGFR, c-met [ 1 , 2 ]. The effectiveness of precision medicine can be limited due to a lack of actionable targets, high mutation rates and the dynamically evolving signaling circuitry associated with oncogenic disease progression [ 3 ]. These lessons were reported from several genomics guided precision medicine based clinical trials, which includes, SHIVA, Molecular Screening for Cancer Treatment Optimization (MOSCATO-01), Copenhagen Prospective Personalized Oncology (CoPPO), MAST, PERMED 01, and PREDICT [ 4 , 5 , 6 , 7 , 8 , 9 ].…”

“…A challenge in advancing precision oncology is the lack of knowledge about the chronological order of biological progression paths taken by tumors [ 3 ]. An imperative aspect of translational science is the generation of knowledge for precision medicine applications through the pooling and analysis of databases on the omics level.…”