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Background: Many markers for a “beyond-RAS” selection of CRC patients receiving cetuximab have been suggested, but none entered clinical practice mainly because prospective ...validation was lacking. Aim of our study was to evaluate if a molecular profile prospectively analysed was able to predict patients’ clinical outcome more accurately than RAS status alone. Methods: K-RAS (exons 2, 3, 4) wild-type CRC patients, candidates to second/third-line cetuximab with chemotherapy were prospectively allocated, after informed consent, into 2 groups on the basis of their molecular profile: favourable (BRAF and PIK3CA exon 20 wild type, EGFR GCN >2.6, HER-3 Rajkumar score <8, IGF-1 immunostaining <2) and unfavourable (any of the previous markers altered or mutated). After the introduction of N-RAS status (exons 2, 3, 4) only RAS wild type patients were considered eligible for the study. Primary aim was response rate (RR). To detect a difference in terms of RR among patients with an unfavourable profile (estimated around 25%) and patients with a favourable profile (estimated around 60%) required sample size was 46 patients. Secondary endpoints were PFS and OS. Results: 46 patients were enrolled. 17 patients (37%) were allocated to the favourable and 29 patients (63%) to the unfavourable profile. Patients resulted comparable for clinical characteristics (age, sex, ECOG PS, previous treatments, number of metastatic sites). Patients with the unfavourable profile showed 2 BRAF mutations, 3 PIK3CA exon 20 mutations, 18 cases of EGFR GCN <2.6, 20 cases of HER-3 and 16 cases of IGF-1 overexpression respectively. RAS analysis of patients enrolled before the introduction of all RAS status (35 patients) revealed 1 N-RAS mutation (3%) in a patient already allocated to the unfavourable group for HER-3 overexpression. RR in the favourable and unfavourable group was 11/17 (65%) and 2/29 (7%) (p= 0.007) respectively. The favourable group also showed an improved median PFS (8 months vs. 3 months, p <0.0001) and OS (15 months vs. 6 months, p <0.0001). Conclusions: A beyond RAS selection may further improve clinical outcome. This approach may also allow an early switch to alternative treatment for unfavourable profile patients.
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Background: Translational research identified numerous putative markers for a “beyond-k-RAS” selection of colorectal cancer patients receiving cetuximab, but none of these entered ...clinical practice mainly because prospective validation is lacking. The aim of our study was to evaluate whether a panel of biomarkers, prospectively analysed may be able to predict patients’ clinical outcome more accurately than k-RAS status alone. Methods: Metastatic, K-RAS wild type colorectal cancer patients, candidate to receive second/third-line cetuximab with chemotherapy have been prospectively allocated, after informed consent, into 2 groups on the basis of their genetic profile: favourable (BRAF and PIK3CA exon 20 wild type, EGFR GCN ≥ 2.6, HER-3 Rajkumar score ≤ 8, IGF-1 immunostaining < 2) and unfavourable (any of the previous markers altered or mutated). All patients received cetuximab treatment as planned by treating physician who was unaware of biomarkers results. To detect a difference in terms of response rate (RR) among patients with an unfavourable profile (estimated around 25%) and patients with a favourable profile (estimated around 60%), assuming a probability alpha of 0.05 and beta of 0.05, required sample size will be 46 patients. Results: 31 patients have been enrolled, most patients (27, 86%) received cetuximab as third-line. Eleven patients (35%) were allocated to the favourable profile and 20 patients (75%) to the unfavourable profile. Patients with the unfavourable profile showed 1 BRAF mutation, 2 PIK3CA exon 20 mutations, 12 cases of EGFR GCN < 2.6, 13 cases of HER-3 and 11 cases of IGF-1 overexpression respectively. RR in the favourable and unfavourable group was 7/11 (64%) and 1/20 (5%) (p= 0.008) respectively. The favourable group also showed an improved median TTP (8 months vs. 2.6 months, p = 0.0007) and OS (16 months vs. 6 months, p = 0.0002). Conclusions: Our results suggest that prospective selection of candidates for cetuximab may be able to improve clinical outcome in patients with a favourable profile. This approach, if confirmed, may also allow an early switch to alternative treatment in patients with an unfavourable profile.
Introduction With the increasing prevalence of type 2 diabetes mellitus (T2DM), there is an urgent need to discover effective therapeutic targets for this complex condition. Coding and non-coding ...RNAs, with traditional biochemical parameters, have shown promise as viable targets for therapy. Machine learning (ML) techniques have emerged as powerful tools for predicting drug responses. Method In this study, we developed an ML-based model to identify the most influential features for drug response in the treatment of type 2 diabetes using three medicinal plant-based drugs (Rosavin, Caffeic acid, and Isorhamnetin), and a probiotics drug (Z-biotic), at different doses. A hundred rats were randomly assigned to ten groups, including a normal group, a streptozotocin-induced diabetic group, and eight treated groups. Serum samples were collected for biochemical analysis, while liver tissues (L) and adipose tissues (A) underwent histopathological examination and molecular biomarker extraction using quantitative PCR. Utilizing five machine learning algorithms, we integrated 32 molecular features and 12 biochemical features to select the most predictive targets for each model and the combined model. Results and discussion Our results indicated that high doses of the selected drugs effectively mitigated liver inflammation, reduced insulin resistance, and improved lipid profiles and renal function biomarkers. The machine learning model identified 13 molecular features, 10 biochemical features, and 20 combined features with an accuracy of 80% and AUC (0.894, 0.93, and 0.896), respectively. This study presents an ML model that accurately identifies effective therapeutic targets implicated in the molecular pathways associated with T2DM pathogenesis.
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