Personalised treatment for cancer: role of biomarkers

Multivariate Risk Analysis of RAS, BRAF and EGFR Mutations Allelic Frequency and Coexistence as Colorectal Cancer Predictive Biomarkers

Simple Summary

The colorectal cancer (CRC) stage and evolution should be described by biomarker profiles. In 60 CRC cases, KRAS, NRAS, BRAF, and EGFR mutations were analyzed by droplet digital PCR (ddPCR). KRAS G12/G13 mutation was present in all patients with variable allelic frequencies. KRAS Q61 mutation was correlated with tumor invasion beyond the subserosa and poor differentiation, both associated with worst prognosis. Tumors with NRAS and BRAF mutations were prevalently localized on the right segment colon. The discovery of the KRAS Q61 role in tumor phenotypes provides the foundation for new therapeutic strategies and perspectives on molecular subtypes classification of CRC.

Abstract

Background: Biomarker profiles should represent a coherent description of the colorectal cancer (CRC) stage and its predicted evolution. Methods: Using droplet digital PCR, we detected the allelic frequencies (AF) of KRAS, NRAS, BRAF, and EGFR mutations from 60 tumors. We employed a pair-wise association approach to estimate the risk involving AF mutations as outcome variables for clinical data and as predicting variables for tumor-staging. We evaluated correlations between mutations of AFs and also between the mutations and histopathology features (tumor staging, inflammation, differentiation, and invasiveness). Results: KRAS G12/G13 mutations were present in all patients. KRAS Q61 was significantly associated with poor differentiation, high desmoplastic reaction, invasiveness (ypT4), and metastasis (ypM1). NRAS and BRAF were associated with the right-side localization of tumors. Diabetic patients had a higher risk to exhibit NRAS G12/G13 mutations. BRAF and NRAS G12/G13 mutations co-existed in tumors with invasiveness limited to the submucosa. Conclusions: The associations we found and the mutational AF we reported may help to understand disease processes and may be considered as potential CCR biomarker candidates. In addition, we propose representative mutation panels associated with specific clinical and histopathological features of CRC, as a unique opportunity to refine the degree of personalization of CRC treatment.

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

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.