TY - JOUR
T1 - Multi-omics staging of locally advanced rectal cancer predicts treatment response
T2 - a pilot study
AU - Cicalini, Ilaria
AU - Chiarelli, Antonio Maria
AU - Chiacchiaretta, Piero
AU - Perpetuini, David
AU - Rosa, Consuelo
AU - Mastrodicasa, Domenico
AU - d'Annibale, Martina
AU - Trebeschi, Stefano
AU - Serafini, Francesco Lorenzo
AU - Cocco, Giulio
AU - Narciso, Marco
AU - Corvino, Antonio
AU - Cinalli, Sebastiano
AU - Genovesi, Domenico
AU - Lanuti, Paola
AU - Valentinuzzi, Silvia
AU - Pieragostino, Damiana
AU - Brocco, Davide
AU - Beets-Tan, Regina G H
AU - Tinari, Nicola
AU - Sensi, Stefano L
AU - Stuppia, Liborio
AU - Del Boccio, Piero
AU - Caulo, Massimo
AU - Delli Pizzi, Andrea
PY - 2024/5
Y1 - 2024/5
N2 - Treatment response assessment of rectal cancer patients is a critical component of personalized cancer care and it allows to identify suitable candidates for organ-preserving strategies. This pilot study employed a novel multi-omics approach combining MRI-based radiomic features and untargeted metabolomics to infer treatment response at staging. The metabolic signature highlighted how tumor cell viability is predictively down-regulated, while the response to oxidative stress was up-regulated in responder patients, showing significantly reduced oxoproline values at baseline compared to non-responder patients (p-value < 10 ). Tumors with a high degree of texture homogeneity, as assessed by radiomics, were more likely to achieve a major pathological response (p-value < 10 ). A machine learning classifier was implemented to summarize the multi-omics information and discriminate responders and non-responders. Combining all available radiomic and metabolomic features, the classifier delivered an AUC of 0.864 (± 0.083, p-value < 10 ) with a best-point sensitivity of 90.9% and a specificity of 81.8%. Our results suggest that a multi-omics approach, integrating radiomics and metabolomic data, can enhance the predictive value of standard MRI and could help to avoid unnecessary surgical treatments and their associated long-term complications.
AB - Treatment response assessment of rectal cancer patients is a critical component of personalized cancer care and it allows to identify suitable candidates for organ-preserving strategies. This pilot study employed a novel multi-omics approach combining MRI-based radiomic features and untargeted metabolomics to infer treatment response at staging. The metabolic signature highlighted how tumor cell viability is predictively down-regulated, while the response to oxidative stress was up-regulated in responder patients, showing significantly reduced oxoproline values at baseline compared to non-responder patients (p-value < 10 ). Tumors with a high degree of texture homogeneity, as assessed by radiomics, were more likely to achieve a major pathological response (p-value < 10 ). A machine learning classifier was implemented to summarize the multi-omics information and discriminate responders and non-responders. Combining all available radiomic and metabolomic features, the classifier delivered an AUC of 0.864 (± 0.083, p-value < 10 ) with a best-point sensitivity of 90.9% and a specificity of 81.8%. Our results suggest that a multi-omics approach, integrating radiomics and metabolomic data, can enhance the predictive value of standard MRI and could help to avoid unnecessary surgical treatments and their associated long-term complications.
KW - Magnetic resonance imaging
KW - Metabolomics
KW - Multi-omics
KW - Radiomics
KW - Rectal cancer
KW - Treatment response
U2 - 10.1007/s11547-024-01811-0
DO - 10.1007/s11547-024-01811-0
M3 - Article
SN - 0033-8362
VL - 129
SP - 712
EP - 726
JO - Radiologia medica
JF - Radiologia medica
IS - 5
ER -