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Here, we show Raman spectroscopy (RS), a highly sensitive and label\u2010free analytical tool, is effective in metabolic profiling across diverse cancer types in patient sera from both pancreatic ductal adenocarcinoma (PDAC) and locally advanced rectal cancer (LARC). We also combine metabolic data with proteomic signatures to predict treatment response. Our data show RS peaks successfully differentiate PDAC patients from healthy controls. Peaks associated with sugars, tyrosine, and DNA\/RNA distinguish PDAC patients from chronic pancreatitis, an inflammatory condition that is notoriously difficult to discern from PDAC via current clinical approaches. Furthermore, our study is expanded to investigate response to chemoradiation therapy in LARC patient sera where at pre\u2010treatment multiple metabolites including glycine, carotenoids, and sugars are jointly correlated to the neoadjuvant rectal (NAR) score indicative of poor prognosis. Via classical univariate AUC\u2013ROC analysis, several RS peaks were found to have an AUC&gt;0.7, highlighting the potential of RS in identifying key metabolites for differentiating complete and poor responders of treatment. Gene set enrichment analysis revealed enrichment of metabolic, immune, and DDR\u2010related pathways associated with CRT response. Notably, RS\u2010derived metabolites were significantly correlated with multiple immune signaling proteins and DDR markers, suggesting these distinct analytes converge to reflect systemic changes within the tumor microenvironment. By integrating metabolic, proteomic, and DDR data, we identified pre\u2010treatment activation of galactose and glycerolipid metabolism, and post\u2010treatment engagement of cell cycle and p53 signaling pathways. 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