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Recent omics analyses of human biofluids provide opportunities to probe selected species of biomolecules for disease diagnostics. Fourier-transform infrared (FTIR) spectroscopy investigates the full repertoire of molecular species within a sample at once. Here, we present a multi-institutional study in which we analysed infrared fingerprints of plasma and serum samples from 1639 individuals with different solid tumours and carefully matched symptomatic and non-symptomatic reference individuals. Focusing on breast, bladder, prostate, and lung cancer, we find that infrared molecular fingerprinting is capable of detecting cancer: training a support vector machine algorithm allowed us to obtain binary classification performance in the range of 0.78–0.89 (area under the receiver operating characteristic curve [AUC]), with a clear correlation between AUC and tumour load. Intriguingly, we find that the spectral signatures differ between different cancer types. This study lays the foundation for high-throughput onco-IR-phenotyping of four common cancers, providing a cost-effective, complementary analytical tool for disease recognition.

Background Breast cancer screening is currently predominantly based on mammography, tainted with the occurrence of both false positivity and false negativity, urging for innovative strategies, as effective detection of early-stage breast cancer bears the potential to reduce mortality. Here we report the results of a prospective pilot study on breast cancer detection using blood plasma analyzed by Fourier-transform infrared (FTIR) spectroscopy – a rapid, cost-effective technique with minimal sample volume requirements and potential to aid biomedical diagnostics. FTIR has the capacity to probe health phenotypes via the investigation of the full repertoire of molecular species within a sample at once, within a single measurement in a high-throughput manner. In this study, we take advantage of cross-molecular fingerprinting to probe for breast cancer detection. Methods We compare two groups: 26 patients diagnosed with breast cancer to a same-sized group of age-matched healthy, asymptomatic female participants. Training with support-vector machines (SVM), we derive classification models that we test in a repeated 10-fold cross-validation over 10 times. In addition, we investigate spectral information responsible for BC identification using statistical significance testing. Results Our models to detect breast cancer achieve an average overall performance of 0.79 in terms of area under the curve (AUC) of the receiver operating characteristic (ROC). In addition, we uncover a relationship between the effect size of the measured infrared fingerprints and the tumor progression. Conclusion This pilot study provides the foundation for further extending and evaluating blood-based infrared probing approach as a possible cross-molecular fingerprinting modality to tackle breast cancer detection and thus possibly contribute to the future of cancer screening.

Health state transitions are reflected in characteristic changes in the molecular composition of biofluids. Detecting these changes in parallel, across a broad spectrum of molecular species, could contribute to the detection of abnormal physiologies. Fingerprinting of biofluids by infrared vibrational spectroscopy offers that capacity. Whether its potential for health monitoring can indeed be exploited critically depends on how stable infrared molecular fingerprints (IMFs) of individuals prove to be over time. Here we report a proof-of-concept study that addresses this question. Using Fourier-transform infrared spectroscopy, we have fingerprinted blood serum and plasma samples from 31 healthy, non-symptomatic individuals, who were sampled up to 13 times over a period of 7 weeks and again after 6 months. The measurements were performed directly on liquid serum and plasma samples, yielding a time- and cost-effective workflow and a high degree of reproducibility. The resulting IMFs were found to be highly stable over clinically relevant time scales. Single measurements yielded a multiplicity of person-specific spectral markers, allowing individual molecular phenotypes to be detected and followed over time. This previously unknown temporal stability of individual biochemical fingerprints forms the basis for future applications of blood-based infrared spectral fingerprinting as a multiomics-based mode of health monitoring. Health status transitions are reflected as characteristic changes in molecular composition of biofluids. Here, the authors apply infrared molecular fingerprinting and reveal that blood-based phenotypes are sufficiently stable over time, providing the basis for time- and cost-effective health monitoring.

The proper functioning of living systems and physiological phenotypes depends on molecular composition. Yet simultaneous quantitative detection of a wide variety of molecules remains a challenge 1 – 8 . Here we show how broadband optical coherence opens up opportunities for fingerprinting complex molecular ensembles in their natural environment. Vibrationally excited molecules emit a coherent electric field following few-cycle infrared laser excitation 9 – 12 , and this field is specific to the sample’s molecular composition. Employing electro-optic sampling 10 , 12 – 15 , we directly measure this global molecular fingerprint down to field strengths 10 7 times weaker than that of the excitation. This enables transillumination of intact living systems with thicknesses of the order of 0.1 millimetres, permitting broadband infrared spectroscopic probing of human cells and plant leaves. In a proof-of-concept analysis of human blood serum, temporal isolation of the infrared electric-field fingerprint from its excitation along with its sampling with attosecond timing precision results in detection sensitivity of submicrograms per millilitre of blood serum and a detectable dynamic range of molecular concentration exceeding 10 5 . This technique promises improved molecular sensitivity and molecular coverage for probing complex, real-world biological and medical settings. A vibrational spectroscopy technique that measures the electric field emitted from organic molecules following infrared illumination allows their molecular fingerprints to be separated from the excitation background, even in complex biological samples.

Background Infrared molecular fingerprinting has been identified as a new minimally invasive technological tool for disease diagnosis. While the utility of cross-molecular infrared fingerprints of serum and plasma for in vitro cancer diagnostics has been recently demonstrated, their potential for stratifying and predicting the prognosis of lung cancer remained unexplored. This study investigates the capability of this approach to predict survival and stratify lung cancer patients. Methods Molecular fingerprinting through vibrational spectroscopy is employed to probe lung cancer. Fourier-transform infrared (FTIR) spectroscopy is applied to blood sera from 160 therapy-naive lung cancer patients, who were followed for up to 4 years. Machine learning is then utilized to evaluate the prognostic utility of this new approach. Additionally, a case-control study involving 501 individuals is analyzed to investigate the relationship between FTIR spectra and disease progression. Results Overall, we establish a strong correlation between the infrared fingerprints and disease progression, specifically in terms of tumor stage. Furthermore, we demonstrate that infrared fingerprinting provides insights into patient survival at performance levels comparable to those of tumor stage and relevant blood-based biomarkers. Conclusions Identifying the combined capacity of infrared fingerprinting to complement primary lung cancer diagnostics and to assist in the assessment of lung cancer survival represents the first proof-of-concept study underscoring the potential of this profiling platform. This may provide new avenues for the development of tailored, personalized treatment decision-making.

Human biofluids serve as indicators of various physiological states, and recent advances in molecular profiling technologies hold great potential for enhancing clinical diagnostics. Leveraging recent developments in laser-based electric-field molecular fingerprinting, we assess its potential for in vitro diagnostics. In a proof-of-concept clinical study involving 2533 participants, we conducted randomized measurement campaigns to spectroscopically profile bulk venous blood plasma across lung, prostate, breast, and bladder cancer. Employing machine learning, we detected infrared signatures specific to therapy-naı̈ve cancer states, distinguishing them from matched control individuals with a cross-validation ROC AUC of 0.88 for lung cancer and values ranging from 0.68 to 0.69 for the other three cancer entities. In an independent held-out test data set, designed to reflect different experimental conditions from those used during model training, we achieved a lung cancer detection ROC AUC of 0.81. Our study demonstrates that electric-field molecular fingerprinting is a robust technological framework broadly applicable to disease phenotyping under real-world conditions.

Abstract Molecular analytics increasingly utilize machine learning (ML) for predictive modeling based on data acquired through molecular profiling technologies. However, developing robust models that accurately capture physiological phenotypes is challenged by the dynamics inherent to biological systems, variability stemming from analytical procedures, and the resource-intensive nature of obtaining sufficiently representative datasets. Here, we propose and evaluate a new method: Contextual Out-of-Distribution Integration (CODI). Based on experimental observations, CODI generates synthetic data that integrate unrepresented sources of variation encountered in real-world applications into a given molecular fingerprint dataset. By augmenting a dataset with out-of-distribution variance, CODI enables an ML model to better generalize to samples beyond the seed training data, reducing the need for extensive experimental data collection. Using three independent longitudinal clinical studies and a case–control study, we demonstrate CODI’s application to several classification tasks involving vibrational spectroscopy of human blood. We showcase our approach’s ability to enable personalized fingerprinting for multiyear longitudinal molecular monitoring and enhance the robustness of trained ML models for improved disease detection. Our comparative analyses reveal that incorporating CODI into the classification workflow consistently leads to increased robustness against data variability and improved predictive accuracy.

Background Wnt proteins are a family of secreted signaling molecules that regulate key developmental processes in metazoans. The molecular basis of Wnt binding to Frizzled and LRP5/6 co-receptors has long been unknown due to the lack of structural data on Wnt ligands. Only recently, the crystal structure of the Wnt8-Frizzled8-cysteine-rich-domain (CRD) complex was solved, but the significance of interaction sites that influence Wnt signaling has not been assessed. Results Here, we present an extensive structure-function analysis of mouse Wnt3a in vitro and in vivo . We provide evidence for the essential role of serine 209, glycine 210 (site 1) and tryptophan 333 (site 2) in Fz binding. Importantly, we discovered that valine 337 in the site 2 binding loop is critical for signaling without contributing to binding. Mutations in the presumptive second CRD binding site (site 3) partly abolished Wnt binding. Intriguingly, most site 3 mutations increased Wnt signaling, probably by inhibiting Wnt-CRD oligomerization. In accordance, increasing amounts of soluble Frizzled8-CRD protein modulated Wnt3a signaling in a biphasic manner. Conclusions We propose a concentration-dependent switch in Wnt-CRD complex formation from an inactive aggregation state to an activated high mobility state as a possible modulatory mechanism in Wnt signaling gradients.

Doc number: 23 Abstract: During the EMBO course 'Imaging of Neural Development in Zebrafish', held on September 9-15th 2013, researchers from different backgrounds shared their latest results, ideas and practical expertise on zebrafish as a model to address open questions regarding nervous system development.