Integrating ToF-SIMS and machine learning reveals a mammary tumor-associated multi-ion signature.

Abstract

Cancer biomarkers should be specific, reproducible, and accessible from practical biological samples. Although omics-based approaches have substantially advanced biomarker discovery, many strategies still rely on individual molecules or limited molecular panels, which may not fully capture the complexity of tumor biology. Whether tumor-associated chemical signatures can improve cancer detection remains an open question. Here, we investigated whether a breast tumor-associated multi-ion signature could be detected in mammary gland tissue from the MMTV-PyMT breast cancer mouse model, as well as in liver, serum, and fur collected from the same animals. Low-mass ions (m/z 50-250) were analyzed using time-of-flight secondary ion mass spectrometry (ToF-SIMS) combined with multivariate analysis and machine learning. A robust mammary tumor multi-ion signature reliably distinguished tumor-bearing from healthy mice, with both partial least squares-discriminant analysis (PLS-DA) and random forest achieving an area under the receiver operating characteristic curve (AUC) of 0.997 under leave-one-animal-out cross-validation. Animal-label permutation testing confirmed that classification performance was significantly greater than expected by chance, and 98 of 313 ions differed significantly at the animal level. While liver and serum samples showed little evidence of tumor-associated discrimination, fur showed the highest classification performance and the greatest overlap in discriminative ions with mammary tissue. These findings demonstrate that low-mass ToF-SIMS enables robust identification of mammary tumor-associated multi-ion signatures, but that their detectability varies across biological matrices. Although the evidence in fur remained exploratory, it suggests that some tumor-associated chemical information may extend beyond the primary tumor and warrants further investigation of hair and related minimally invasive samples for cancer detection.

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