## 2 Results
### Integration of Omics (Microbiomics and Metabolomics) Data and Patient Metadata  
Next, we aimed to find multivariate associations among GM profiles, metabolomic data, and clinical (i.e., NLR) and body composition datasets in relation to therapeutic response. To this end, we implemented an N-integration framework using multiblock sPLS-DA (see Materials and Methods for further details). By confirming the above results, we found that all datasets had discriminative features for classifying samples into non-responder and responder groups (Supplementary Figure S2). The magnitude of the associations between these parameters and therapeutic responses was then investigated using DIABLO, which largely confirmed and partially extended the previous results (Figure 3A). In particular, body composition parameters, such as BMI, TAT, the visceral-to-subcutaneous adipose tissue (VATSAT) ratio, skeletal muscle (SM), skeletal muscle index (SMI), and fat-free mass (FFM), were strongly associated with the responder group. On the other hand, non-responders showed strong associations with neutrophil count and NLR. In terms of microbial taxa, the therapeutic response was associated with _Oscillospira_ and _Phascalorctobacterium_, while non-response was confirmed to be associated with _Fusobacterium_, _Veillonella_, _Streptococcus_, _Dorea_, _Clostridium_, and _Actinomyces_. From a metabolomic standpoint, higher levels of btytric acid and derivatives (i.e., butyl and propyl esters), along with higher levels of cyclohexanecarboxylic acid ethyl ester and 2-heptanone 6-methyl-, were strongly associated with non-responders. Finally, the therapeutic response was confirmed to be associated with 1-hexanol 2-ethyl-, 2H-indol-2-one 1,3-dihydro-, 2-hexanone, 5,9-undecadien-2-one 6,10-dimethyl-, and anethole. When reconstructing the network of associations derived via the integration analysis (Figure 3B), we identified the following distinct modules: (i) a response-associated module that linked the above-mentioned body composition parameters to _Phuscalorctobacterium_ and _Oscillospira_, as well as to the metabolites 2-hexanone, anethole, 2H-indol-2-one 1,3-dihydro-, and 1-hexanol 2-ethyl-; and (ii) a non-response-associated module that linked _Streptococcus_, _Actinomyces_, _Veillonella_, _Dorea_, and _Fusobacterium_ to btytric acid and _Actinomyces_. Figure 2: Fecal metabolome diversity at baseline in advanced melanoma patients in relation to therapeutic response. (**A**) PCoA based on the Euclidean distances between the fecal metabolomic profiles of responders and non-responders. Ellipses include 95% confidence areas based on the standard error of the weighted average of the sample coordinates. Significant separation between groups was found (\(p\) = 0.05, Adonis). (**B**) Boxplots showing the within-group variance in Euclidean distances. Wilcoxon test, *** for \(p\) < 0.001.  
acid (and derivatives) and cyclohexancarboxylic acid ethyl ester, as well as higher levels of neutrophils (and NLR). Interestingly, higher basal fecal levels of butyric acid were also associated with decreased survival (Cox proportional hazards model, \(p\leq 0.05\)) (Figure 4). Figure 3: Integration of omics (microbiomics and metabolomics) data and host metadata in responder and non-responder patients with advanced melanoma. (**A**) Heatmap that shows associations between therapeutic response and bacterial genera, metabolites, body composition, and complete blood count parameters in advanced melanoma patients at baseline (i.e., before initiation of therapy). The plotted association values, which result from DIABLO, were scaled and trimmed to three standard deviation ranges. The contributions of all components generated via sPLS-DA were taken into account. (**B**) Association network between bacterial genera, metabolites, and host metadata. In brief, a pairwise similarity matrix was obtained from the latent components of sPLS by calculating the sum of the correlations between the original variables and each of the latent components of the model.