To address the ever-growing cancer incidence and mortality rates, effective treatment methods are indispensable (Bray et al., 2018). They rely on detection of the disease at the earliest possible stage to allow antitumour interventions and thus improve survival rates (Bannister and Broggio, 2016; Schiffman et al., 2015). Early detection is therefore a crucial factor in the global fight against cancer.

However, the clinical benefits versus the potential harms and costs of several cancer detection approaches remain controversial (Schiffman et al., 2015). Due to the limited sensitivity and specificity of current medical diagnostics, cancer can either be overlooked (false negatives) or falsely detected (false positives), leading to either delayed interventions or unnecessary, potentially harmful investigations or psychological stress (Srivastava et al., 2019). Hence, there is a high need to complement current medical diagnostics with time- and cost-efficient, non-invasive or minimally invasive methods that could possibly lead to new screening and detection approaches, prior to tissue-biopsy-based molecular profiling or prognosis (Wan et al., 2017).

Molecular analyses of human serum and plasma provide systemic molecular information and enabling novel routes of diagnostics (Amelio et al., 2020; Wan et al., 2017). So far, most liquid biopsies predominantly rely on the analysis of a few pre-selected analytes and biomarkers. Although the emergence of highly sensitive and molecule-specific methods in the fields of proteomics (Geyer et al., 2019; Geyer et al., 2017; Uzozie and Aebersold, 2018), metabolomics (Roig et al., 2017; Xia et al., 2013), and genomics (Abbosh et al., 2017; Han et al., 2017; Otandault et al., 2019) has led to the discovery of thousands of different biomarker candidates, only a few of them have been validated and transferred to the clinic so far (Poste, 2011).

Technological developments of the last decade brought a paradigm change regarding liquid biopsies. Instead of relying on a single molecular marker, recent approaches focus on combining information across a broad range of molecules to investigate changes in molecular patterns and identify disease-specific physiologies. However, the combination of various omics techniques (i.e. multi-omics) still requires complex and target-specific sample preparation as well as elaborate ways of merging different datasets (Hasin et al., 2017; Karczewski and Snyder, 2018; Malone et al., 2020; Yoo et al., 2018). Moreover, increasing the number of analytical methods involved often leads to unfeasibly high costs for broad clinical use.

This is where infrared molecular spectroscopy prevails – it captures signals from all types of molecules in a sample in a single time- and cost-effective measurement in a label-free fashion. When applied to blood serum or plasma samples, infrared spectroscopy provides a so-called infrared molecular fingerprint (IMF) reflecting the chemical composition of a sample, that is, the person’s molecular blood phenotype (Huber et al., 2021). Even though the IMF of a highly complex biofluid such as blood serum and plasma can only partially be traced back to its molecular origin, it may deliver a plethora of information sensitive and specific to the health state of the individual. In a recent longitudinal study, we have shown that defined workflows to collect, store, process, and measure human liquid biopsies lead to reproducible IMFs in healthy, non-symptomatic individuals that are stable over clinically relevant time scales (Huber et al., 2021).

Numerous studies have shown the potential of blood-based IMFs for the detection of cancer, notably brain (Butler et al., 2019; Hands, 2014; Sala et al., 2020b), breast (Backhaus et al., 2010; Elmi et al., 2017; Ghimire et al., 2020; Zelig et al., 2015), bladder (Ollesch et al., 2014), lung (Ollesch et al., 2016), prostate (Medipally et al., 2020), and other cancer entities (Anderson et al., 2020; Ollesch et al., 2014; Sala et al., 2020a), with some of the studies reporting specificities and sensitivities higher than 90% (Anderson et al., 2020; Backhaus et al., 2010; Butler et al., 2019; Ghimire et al., 2020; Medipally et al., 2020; Ollesch et al., 2014). Despite these promising initial results, only a few studies involved more than 75 individuals per group (Anderson et al., 2020). Additionally, the majority of these studies had a high risk of bias due to patient selection applied (Anderson et al., 2020). In fact, it was shown that IMFs are susceptible to external confounding factors, such as those related to sample handling and data collection, as well as to inherent biological variations (e.g. age and gender) unrelated to cancer (Diem, 2018). Furthermore, the differences observed in IMFs may be due to the innate immune response and other concomitant factors (Diem, 2018; Fabian et al., 2005). Thus, the specificity of IMF to a certain cancer must be evaluated by investigation of appropriate, carefully selected reference groups.

Altogether, there is a need for studies that address the issues listed above by (i) systematically investigating the pre-analytical factors (Cameron et al., 2020; Huber et al., 2021), (ii) studying the molecular origin of the infrared fingerprints (Voronina et al., 2021), and (iii) adequately applying machine learning tools with involvement of a sufficient number of participants. To date, the latter requirement has only been met in studies investigating the applicability of infrared fingerprinting to bladder (Ollesch et al., 2014), breast (Backhaus et al., 2010), and brain cancer detection (Butler et al., 2019). In addition to the capacity to detect cancer, whether different cancer entities have sufficiently different infrared spectral signatures to be distinguished from each other has so far not been evaluated.

Our present multi-institutional, multi-disease study addresses the above issues to rigorously assess the feasibility of IMFs for high-throughput detection of four common cancer entities as phenotypes, thus referred to as ‘onco-IR-phenotyping.’ Using Fourier-transform infrared (FTIR) transmission spectroscopy of liquid samples, we measured blood serum and plasma samples from 1927 individuals, among these 161 breast cancer, 118 bladder cancer, 278 prostate cancer, and 214 lung cancer patients, prior to any cancer-related therapy, along with non-symptomatic reference individuals and study participants with diseases and/or benign pathologies of the same organ (i.e. organ-specific symptomatic references). By applying support vector machine (SVM) to train models for binary classification, we obtained detection efficiencies in the range of 0.78–0.89 (area under the receiver operating characteristic [ROC] curve [AUC]), with the detection efficiency strongly correlating with the severity of the disease. The results of this prospectively conducted study suggest that infrared fingerprinting of liquid plasma and serum may offer a means of robust and reliable detection of different types of cancer. Furthermore, we reveal that the spectral signatures attributable to different cancer types differ significantly from each other, which facilitates classification between different states and thus carries a translational potential not previously reported.

We demonstrated the feasibility of blood-based IMF to detect lung, breast, bladder, and prostate cancer with good efficiency. Although previous smaller studies have yielded fairly high classification efficiencies (Backhaus et al., 2010; Elmi et al., 2017; Ghimire et al., 2020; Medipally et al., 2020; Ollesch et al., 2016; Ollesch et al., 2014; Zelig et al., 2015), they were either based on low number of participants or might have been affected by confounding factors. Here we provided a rigorous multi-institutional study setup with more than 100 individuals in each case and reference group, 1927 individuals in total, with all case and reference cohorts matched for major confounding factors (n = 1639 individuals upon matching). In addition, we observed that visible infrared spectral signatures correlate with tumour stage, suggesting that the IMFs are significantly affected not only by the presence of tumours but also by the progression of the disease. Furthermore, similar cancer detection efficiencies were achieved with IMFs obtained from blood serum and plasma. This not only confirms the robustness of the results, but also reveals that the method is applicable to both biofluids.

This study provides strong indications that blood-based IMF patterns can be utilized to identify various cancer entities, and therefore provides a foundation for a possible future in vitro diagnostic method. However, IMF-based testing is still at the evaluation stage of essay development, and further steps have to be undertaken to evaluate the clinical utility, reliability, and robustness of the IMF approach (Ignatiadis et al., 2021).

First, the machine learning models built within this work will have to be tested with fully independent sample sets. Although the study was designed to account for and minimize the effect of confounding factors, we are aware that these cannot be fully excluded, especially considering that machine learning algorithms are susceptible to them (Zhao et al., 2020). To this end, we freeze the current machine learning models, each trained on the data of the entire cohorts of the current study (Figure 2—source data 2), and will apply them to a consecutively prospective sample collection to better rule out potential confounders.

Second, it needs to be studied in more detail whether IMFs pick up molecular patterns that are specific to a primary disease process or, more generally, to any secondary inflammatory response. At the current stage, both options seem possible as altered immune responses are also known as primary disease drivers in the context of cancer and may affect genome instability, cancer cell proliferation, anti-apoptotic signalling, angiogenesis, and, last but not least, cancer cell dissemination (Hanahan and Weinberg, 2011). This link to systemic effects makes it still difficult to distinguish cancer-specific IMFs (onco-IR-phenotypes) from comorbidities with a strong immune signature (like COPD) in humans in vivo. Nevertheless, we did obtain distinct spectral patterns for all four common cancer entities (Figure 3b) indicating different, potentially disease-specific molecular alterations of the IMFs. These changes are likely to be linked to cancer-induced changes since classification accuracy is higher with more advanced cancer stages, which is also reflected in more pronounced differential fingerprints with larger tumour size.

To gain deeper understanding of the specificity of observed spectral changes to the disease patterns studied, it is helpful to investigate their molecular origin. In this context, we do not consider the approach of assigning spectral positions/features to characteristic vibrational modes of functional molecular entities most appropriate. Although widely used in the IR community, due to the very many molecular assignments possible for each spectral position, unambiguous statements of molecular changes herein are not feasible. Instead, a much deeper analysis is required, as recently revealed by Sangster et al., 2006; Voronina et al., 2021. The latter work involves combination of infrared spectroscopy and quantitative mass spectrometry on the part of the lung cancer sample set used in the current study as well, identifying the molecular origin of the differential infrared fingerprints. These can be partially explained by a characteristic change of proteins that are known to also change due to systemic inflammatory signals. Thereby we highlight the need for further biochemical investigations into the molecular origin of the observed spectral signatures, generally required in the field to address this question conclusively.

In-depth information about the molecular origin of the observed spectral disease patterns will help identify the clinical setting(s) where infrared fingerprinting can make largest contributions to cancer care (e.g. screening, diagnosis, prognosis, treatment monitoring, or surveillance). The specificity of spectral signatures to cancer, along with the obtained sensitivity and specificity in the binary classification (Table 1), will influence whether the approach may best complement primary diagnostics, be possibly suited for screening, or even be used for molecular profiling and prognostication. When further validated, blood-based IMFs could aid residing medical challenges: More specifically, it may complement radiological and clinical chemistry examinations prior to invasive tissue biopsies. Given less than 60 μl of sample are required, sample preparation time and effort are negligible, and measurement time is within minutes, the approach may be well suited for high-throughput screening or provide additional information for clinical decision process. Thus, minimally invasive IMF cancer detection could integratively help raise the rate of pre-metastatic cancer detection in clinical testing. However, further detailed research (e.g. as performed for an FTIR-based blood serum test for brain tumour; Gray et al., 2018) is needed to identify an appropriate clinical setting in which the proposed test can be used with the greatest benefit (in terms of cost-effectiveness and clinical utility).

Moreover, given the recent evidence of high within-person stability of IMFs over time (Huber et al., 2021), serial longitudinal liquid biopsies and infrared fingerprinting of respective samples could eliminate between-person variability by self-referencing and thereby facilitate even more efficient and possibly earlier cancer detection. Once (i) a precise clinical setting is defined and (ii) large-scale, stratified clinical studies controlled for comorbidities can be realized, a systematic, direct comparison to established diagnostics will become feasible and the full potential of infrared fingerprinting can be quantitatively assessed.

For further improvements in the accuracy of the envisioned medical assay, the IR fingerprinting methodology needs to be improved in parallel. Molecular specificity is inherently limited in IR spectroscopy due to the spectral overlap of absorption bands of individual molecules. This might be tackled by chemical pre-fractionation (Hughes et al., 2014; Petrich et al., 2009; Voronina et al., 2021) or by combining IR spectroscopy to methods like liquid chromatography. However, such a pre-fractioning, but also IR fingerprinting itself, would benefit even more from increased spectroscopic sensitivity. Sensitivity of the current commercially available FTIR spectrometer is however limited to detection of highly abundant molecules. Recent developments in infrared spectroscopy demonstrate the possibility to increase the detectable molecular dynamic range to five orders of magnitude (Pupeza et al., 2020) and therefore have the potential to improve the efficiency of infrared fingerprinting.

In summary, infrared fingerprinting reveals the potential for effective detection and distinction of various common cancer types already at its current stage and implementation. Future developments, in terms of instrumentation as well as methodology, have the potential to further improve the detection efficiency. This study presents a general high-throughput and cost-effective framework, and along this, highlights the possibility for extending infrared fingerprinting to other disease entities.

The datasets analysed within the scope of the study cannot be published publicly due to privacy regulations under the General Data Protection Regulation (EU) 2016/679. The raw data includes clinical data from patients, including textual clinical notes and contain information that could potentially compromise subjects' privacy or consent, and therefore cannot be shared. However, the trained machine learning models for the binary classification of bladder, breast, prostate, and lung cancer are provided within Figure 2—source data 4, along with description and code for importing them in a python script. The custom code used for the production of the results presented in this manuscript is stored in a persistent repository at the Leibniz Supercomputing Center of the Bavarian Academy of Sciences and Humanities (LRZ), located in Garching, Germany. The entire code can only be shared upon reasonable request, as its correct use depends heavily on the settings of the experimental setup and the measuring device and should therefore be clarified with the authors.

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Author details

1. Marinus Huber

   1. Ludwig Maximilians University Munich (LMU), Department of Laser Physics, Garching, Germany
   2. Max Planck Institute of Quantum Optics (MPQ), Laboratory for Attosecond Physics, Garching, Germany

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review and editing

   Contributed equally with

   Kosmas V Kepesidis

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5309-4475

2. Kosmas V Kepesidis

   1. Ludwig Maximilians University Munich (LMU), Department of Laser Physics, Garching, Germany
   2. Max Planck Institute of Quantum Optics (MPQ), Laboratory for Attosecond Physics, Garching, Germany

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review and editing

   Contributed equally with

   Marinus Huber

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6391-7743

3. Liudmila Voronina

   Ludwig Maximilians University Munich (LMU), Department of Laser Physics, Garching, Germany

   Contribution

   Data curation, Formal analysis, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

4. Frank Fleischmann

   Ludwig Maximilians University Munich (LMU), Department of Laser Physics, Garching, Germany

   Contribution

   Data curation, Investigation, Methodology, Supervision

   Competing interests

   No competing interests declared

5. Ernst Fill

   Max Planck Institute of Quantum Optics (MPQ), Laboratory for Attosecond Physics, Garching, Germany

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

6. Jacqueline Hermann

   Ludwig Maximilians University Munich (LMU), Department of Laser Physics, Garching, Germany

   Contribution

   Methodology, Project administration, Supervision, Validation

   Competing interests

   No competing interests declared

7. Ina Koch

   Asklepios Biobank for Lung Diseases, Department of Thoracic Surgery, Member of the German Center for Lung Research, DZL, Asklepios Fachkliniken München-Gauting, Munich, Germany

   Contribution

   Data curation, Investigation, Resources, Validation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8766-017X

8. Katrin Milger-Kneidinger

   University Hospital of the Ludwig Maximilians University Munich (LMU), Department of Internal Medicine V, Munich, Germany

   Contribution

   Resources

   Competing interests

   No competing interests declared

9. Thomas Kolben

   University Hospital of the Ludwig Maximilians University Munich (LMU), Department of Obstetrics and Gynecology, Breast Center and Comprehensive Cancer Center (CCLMU), Munich, Germany

   Contribution

   Resources, Supervision

   Competing interests

   No competing interests declared

10. Gerald B Schulz

    University Hospital of the Ludwig Maximilians University Munich (LMU), Department of Urology, Munich, Germany

    Contribution

    Investigation

    Competing interests

    No competing interests declared

11. Friedrich Jokisch

    University Hospital of the Ludwig Maximilians University Munich (LMU), Department of Urology, Munich, Germany

    Contribution

    Investigation

    Competing interests

    No competing interests declared

12. Jürgen Behr

    University Hospital of the Ludwig Maximilians University Munich (LMU), Department of Internal Medicine V, Munich, Germany

    Contribution

    Conceptualization, Resources, Supervision, Writing – review and editing

    Competing interests

    No competing interests declared

13. Nadia Harbeck

    University Hospital of the Ludwig Maximilians University Munich (LMU), Department of Obstetrics and Gynecology, Breast Center and Comprehensive Cancer Center (CCLMU), Munich, Germany

    Contribution

    Conceptualization, Resources, Supervision, Writing – review and editing

    Competing interests

    No competing interests declared

14. Maximilian Reiser

    University Hospital of the Ludwig Maximilians University Munich (LMU), Department of Clinical Radiology, Munich, Germany

    Contribution

    Conceptualization, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

15. Christian Stief

    University Hospital of the Ludwig Maximilians University Munich (LMU), Department of Urology, Munich, Germany

    Contribution

    Conceptualization, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

16. Ferenc Krausz

    1. Ludwig Maximilians University Munich (LMU), Department of Laser Physics, Garching, Germany
    2. Max Planck Institute of Quantum Optics (MPQ), Laboratory for Attosecond Physics, Garching, Germany

    Contribution

    Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Visualization, Writing – review and editing

    Competing interests

    No competing interests declared

17. Mihaela Zigman

    1. Ludwig Maximilians University Munich (LMU), Department of Laser Physics, Garching, Germany
    2. Max Planck Institute of Quantum Optics (MPQ), Laboratory for Attosecond Physics, Garching, Germany

    Contribution

    Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Visualization, Writing – original draft, Writing – review and editing

    For correspondence

    mihaela.zigman@mpq.mpg.de

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-8306-1922

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