A spectroscopic liquid biopsy for the earlier detection of multiple cancer types.

BACKGROUND: A rapid, low-cost blood test that can be applied to reliably detect multiple different cancer types would be transformational. METHODS: In this large-scale discovery study (n = 2092 patients) we applied the Dxcover® Cancer Liquid Biopsy to examine eight different cancers. The test uses Fourier transform infrared (FTIR) spectroscopy and machine-learning algorithms to detect cancer. RESULTS: Area under the receiver operating characteristic curve (ROC) values were calculated for eight cancer types versus symptomatic non-cancer controls: brain (0.90), breast (0.76), colorectal (0.91), kidney (0.91), lung (0.91), ovarian (0.86), pancreatic (0.84) and prostate (0.86). We assessed the test performance when all eight cancer types were pooled to classify 'any cancer' against non-cancer patients. The cancer versus asymptomatic non-cancer classification detected 64% of Stage I cancers when specificity was 99% (overall sensitivity 57%). When tuned for higher sensitivity, this model identified 99% of Stage I cancers (with specificity 59%). CONCLUSIONS: This spectroscopic blood test can effectively detect early-stage disease and can be fine-tuned to maximise either sensitivity or specificity depending on the requirements from different healthcare systems and cancer diagnostic pathways. This low-cost strategy could facilitate the requisite earlier diagnosis, when cancer treatment can be more effective, or less toxic. STATEMENT OF TRANSLATIONAL RELEVANCE: The earlier diagnosis of cancer is of paramount importance to improve patient survival. Current liquid biopsies are mainly focused on single tumour-derived biomarkers, which limits test sensitivity, especially for early-stage cancers that do not shed enough genetic material. This pan-omic liquid biopsy analyses the full complement of tumour and immune-derived markers present within blood derivatives and could facilitate the earlier detection of multiple cancer types. There is a low barrier to integrating this blood test into existing diagnostic pathways since the technology is rapid, simple to use, only minute sample volumes are required, and sample preparation is minimal. In addition, the spectroscopic liquid biopsy described in this study has the potential to be combined with other orthogonal tests, such as cell-free DNA, which could provide an efficient route to diagnosis. Cancer treatment can be more effective when given earlier, and this low-cost strategy has the potential to improve patient prognosis.

Optical Screening and Classification of Drug Binding to Proteins in Human Blood Serum.

Protein-drug interactions in the human bloodstream are important factors in applications ranging from drug design, where protein binding influences efficacy and dose delivery, to biomedical diagnostics, where rapid, quantitative measurements could guide optimized treatment regimes. Current measurement approaches use multistep assays, which probe the protein-bound drug fraction indirectly and do not provide fundamental structural or dynamic information about the in vivo protein-drug interaction. We demonstrate that ultrafast 2D-IR spectroscopy can overcome these issues by providing a direct, label-free optical measurement of protein-drug binding in blood serum samples. Four commonly prescribed drugs, known to bind to human serum albumin (HSA), were added to pooled human serum at physiologically relevant concentrations. In each case, spectral changes to the amide I band of the serum sample were observed, consistent with binding to HSA, but were distinct for each of the four drugs. A machine-learning-based classification of the serum samples achieved a total cross-validation prediction accuracy of 92% when differentiating serum-only samples from those with a drug present. Identification on a per-drug basis achieved correct drug identification in 75% of cases. These unique spectroscopic signatures of the drug-protein interaction thus enable the detection and differentiation of drug containing samples and give structural insight into the binding process as well as quantitative information on protein-drug binding. Using currently available instrumentation, the 2D-IR data acquisition required just 1 min and 10 µL of serum per sample, and so these results pave the way to fast, specific, and quantitative measurements of protein-drug binding in vivo with potentially invaluable applications for the development of novel therapies and personalized medicine.

Liquid biopsies: the future of cancer early detection.

Cancer is a worldwide pandemic. The burden it imposes grows steadily on a global scale causing emotional, physical, and financial strains on individuals, families, and health care systems. Despite being the second leading cause of death worldwide, many cancers do not have screening programs and many people with a high risk of developing cancer fail to follow the advised medical screening regime due to the nature of the available screening tests and other challenges with compliance. Moreover, many liquid biopsy strategies being developed for early detection of cancer lack the sensitivity required to detect early-stage cancers. Early detection is key for improved quality of life, survival, and to reduce the financial burden of cancer treatments which are greater at later stage detection. This review examines the current liquid biopsy market, focusing in particular on the strengths and drawbacks of techniques in achieving early cancer detection. We explore the clinical utility of liquid biopsy technologies for the earlier detection of solid cancers, with a focus on how a combination of various spectroscopic and -omic methodologies may pave the way for more efficient cancer diagnostics.

Augmentation of FTIR spectral datasets using Wasserstein generative adversarial networks for cancer liquid biopsies.

Over recent years, deep learning (DL) has become more widely used within the field of cancer diagnostics. However, DL often requires large training datasets to prevent overfitting, which can be difficult and expensive to acquire. Data augmentation is a method that can be used to generate new data points to train DL models. In this study, we use attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectra of patient dried serum samples and compare non-generative data augmentation methods to Wasserstein generative adversarial networks (WGANs) in their ability to improve the performance of a convolutional neural network (CNN) to differentiate between pancreatic cancer and non-cancer samples in a total cohort of 625 patients. The results show that WGAN augmented spectra improve CNN performance more than non-generative augmented spectra. When compared with a model that utilised no augmented spectra, adding WGAN augmented spectra to a CNN with the same architecture and same parameters, increased the area under the receiver operating characteristic curve (AUC) from 0.661 to 0.757, presenting a 15% increase in diagnostic performance. In a separate test on a colorectal cancer dataset, data augmentation using a WGAN led to an increase in AUC from 0.905 to 0.955. This demonstrates the impact data augmentation can have on DL performance for cancer diagnosis when the amount of real data available for model training is limited.

Recurrent neural networks for time domain modelling of FTIR spectra: application to brain tumour detection.

Attenuated total reflectance (ATR)-Fourier transform infrared (FTIR) spectroscopy alongside machine learning (ML) techniques is an emerging approach for the early detection of brain cancer in clinical practice. A crucial step in the acquisition of an IR spectrum is the transformation of the time domain signal from the biological sample to a frequency domain spectrum via a discrete Fourier transform. Further pre-processing of the spectrum is typically applied to reduce non-biological sample variance, and thus to improve subsequent analysis. However, the Fourier transformation is often assumed to be essential even though modelling of time domain data is common in other fields. We apply an inverse Fourier transform to frequency domain data to map these to the time domain. We use the transformed data to develop deep learning models utilising Recurrent Neural Networks (RNNs) to differentiate between brain cancer and control in a cohort of 1438 patients. The best performing model achieves a mean (cross-validated score) area under the receiver operating characteristic (ROC) curve (AUC) of 0.97 with sensitivity of 0.91 and specificity of 0.91. This is better than the optimal model trained on frequency domain data which achieves an AUC of 0.93 with sensitivity of 0.85 and specificity of 0.85. A dataset comprising 385 patient samples which were prospectively collected in the clinic is used to test a model defined with the best performing configuration and fit to the time domain. Its classification accuracy is found to be comparable to the gold-standard for this dataset demonstrating that RNNs can accurately classify disease states using spectroscopic data represented in the time domain.

2D-IR spectroscopy of proteins in H2O-A Perspective.

The form of the amide I infrared absorption band provides a sensitive probe of the secondary structure and dynamics of proteins in the solution phase. However, the frequency coincidence of the amide I band with the bending vibrational mode of H2O has necessitated the widespread use of deuterated solvents. Recently, it has been demonstrated that ultrafast 2D-IR spectroscopy allows the detection of the protein amide I band in H2O-based fluids, meaning that IR methods can now be applied to study proteins in physiologically relevant solvents. In this perspective, we describe the basis of the 2D-IR method for observing the protein amide I band in H2O and show how this development has the potential to impact areas ranging from our fundamental appreciation of protein structural dynamics to new applications for 2D-IR spectroscopy in the analytical and biomedical sciences. In addition, we discuss how the spectral response of water, rather than being a hindrance, now provides a basis for new approaches to data pre-processing, standardization of 2D-IR data collection, and signal quantification. Ultimately, we visualize a direction of travel toward the creation of 2D-IR spectral libraries that can be linked to advanced computational methods for use in high-throughput protein screening and disease diagnosis.

Datura quids at Pinwheel Cave, California, provide unambiguous confirmation of the ingestion of hallucinogens at a rock art site.

While debates have raged over the relationship between trance and rock art, unambiguous evidence of the consumption of hallucinogens has not been reported from any rock art site in the world. A painting possibly representing the flowers of Datura on the ceiling of a Californian rock art site called Pinwheel Cave was discovered alongside fibrous quids in the same ceiling. Even though Native Californians are historically documented to have used Datura to enter trance states, little evidence exists to associate it with rock art. A multianalytical approach to the rock art, the quids, and the archaeological context of this site was undertaken. Liquid chromatography-mass spectrometry (LC-MS) results found hallucinogenic alkaloids scopolamine and atropine in the quids, while scanning electron microscope analysis confirms most to be Datura wrightii Three-dimensional (3D) analyses of the quids indicate the quids were likely masticated and thus consumed in the cave under the paintings. Archaeological evidence and chronological dating shows the site was well utilized as a temporary residence for a range of activities from Late Prehistory through Colonial Periods. This indicates that Datura was ingested in the cave and that the rock painting represents the plant itself, serving to codify communal rituals involving this powerful entheogen. These results confirm the use of hallucinogens at a rock art site while calling into question previous assumptions concerning trance and rock art imagery.

Detection of paracetamol binding to albumin in blood serum using 2D-IR spectroscopy.

Binding of drugs to blood serum proteins can influence both therapeutic efficacy and toxicity. The ability to measure the concentrations of protein-bound drug molecules quickly and with limited sample preparation could therefore have considerable benefits in biomedical and pharmaceutical applications. Vibrational spectroscopies provide data quickly but are hampered by complex, overlapping protein amide I band profiles and water absorption. Here, we show that two-dimensional infrared (2D-IR) spectroscopy can achieve rapid detection and quantification of paracetamol binding to serum albumin in blood serum at physiologically-relevant levels with no additional sample processing. By measuring changes to the amide I band of serum albumin caused by structural and dynamic impacts of paracetamol binding we show that drug concentrations as low as 7 µM can be detected and that the availability of albumin for paracetamol binding is less than 20% in serum samples, allowing identification of paracetamol levels consistent with a patient overdose.

Measuring proteins in H2O using 2D-IR spectroscopy: pre-processing steps and applications toward a protein library.

The ability of two-dimensional infrared (2D-IR) spectroscopy to measure the amide I band of proteins in H2O rather than D2O-based solvents by evading the interfering water signals has enabled in vivo studies of proteins under physiological conditions and in biofluids. Future exploitation of 2D-IR in analytical settings, from diagnostics to protein screening, will, however, require comparisons between multiple datasets, necessitating control of data collection protocols to minimize measurement-to-measurement inconsistencies. Inspired by analytical spectroscopy applications in other disciplines, we describe a workflow for pre-processing 2D-IR data that aims to simplify spectral cross-comparisons. Our approach exploits the thermal water signal that is collected simultaneously with, but is temporally separated from the amide I response to guide custom baseline correction and spectral normalization strategies before combining them with Principal Component noise reduction tools. Case studies show that application of elements of the pre-processing workflow to previously published data enables improvements in quantification accuracy and detection limits. We subsequently apply the complete workflow in a new pilot study, testing the ability of a prototype library of 2D-IR spectra to quantify the four major protein constituents of blood serum in a single, label-free measurement. These advances show progress toward the robust data handling strategies that will be necessary for future applications of 2D-IR to pharmaceutical or biomedical problems.

Detection of Glycine as a Model Protein in Blood Serum Using 2D-IR Spectroscopy.

Glycine (Gly) is used as a model system to evaluate the ability of ultrafast two-dimensional infrared (2D-IR) spectroscopy to detect and quantify the low-molecular-weight proteinaceous components of blood serum. Combining data acquisition schemes to suppress absorption bands of H2O that overlap with the protein amide I band with analysis of peak patterns appearing in the off-diagonal region of the 2D-IR spectrum allows separation of the Gly spectral signature from that of the dominant protein fraction of serum in a transmission-mode 2D-IR measurement without any sample manipulation, e.g., filtration or drying. 2D-IR spectra of blood serum samples supplemented with varying concentrations of Gly were obtained, and a range of data analysis methods compared, leading to a detection limit of ∼3 mg/mL for Gly. The reported methodology provides a platform for a critical assessment of the sensitivity of 2D-IR for measuring the concentrations of amino acids, peptides, and low-molecular-weight proteins present in serum samples. We conclude that, in the case of several clinically relevant diagnostic molecules and their combinations, the potential exists for 2D-IR to complement IR absorption methods as the benefits of the second frequency dimension offered by 2D-IR spectroscopy outweigh the added technical complexity of the measurement.

Early economic evaluation to guide the development of a spectroscopic liquid biopsy for the detection of brain cancer.

OBJECTIVES: An early economic evaluation to inform the translation into clinical practice of a spectroscopic liquid biopsy for the detection of brain cancer. Two specific aims are (1) to update an existing economic model with results from a prospective study of diagnostic accuracy and (2) to explore the potential of brain tumor-type predictions to affect patient outcomes and healthcare costs. METHODS: A cost-effectiveness analysis from a UK NHS perspective of the use of spectroscopic liquid biopsy in primary and secondary care settings, as well as a cost-consequence analysis of the addition of tumor-type predictions was conducted. Decision tree models were constructed to represent simplified diagnostic pathways. Test diagnostic accuracy parameters were based on a prospective validation study. Four price points (GBP 50-200, EUR 57-228) for the test were considered. RESULTS: In both settings, the use of liquid biopsy produced QALY gains. In primary care, at test costs below GBP 100 (EUR 114), testing was cost saving. At GBP 100 (EUR 114) per test, the ICER was GBP 13,279 (EUR 15,145), whereas at GBP 200 (EUR 228), the ICER was GBP 78,300 (EUR 89,301). In secondary care, the ICER ranged from GBP 11,360 (EUR 12,956) to GBP 43,870 (EUR 50,034) across the range of test costs. CONCLUSIONS: The results demonstrate the potential for the technology to be cost-effective in both primary and secondary care settings. Additional studies of test use in routine primary care practice are needed to resolve the remaining issues of uncertainty-prevalence in this patient population and referral behavior.

Infrared Based Saliva Screening Test for COVID-19.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in an unprecedented need for diagnostic testing that is critical in controlling the spread of COVID-19. We propose a portable infrared spectrometer with purpose-built transflection accessory for rapid point-of-care detection of COVID-19 markers in saliva. Initially, purified virion particles were characterized with Raman spectroscopy, synchrotron infrared (IR) and AFM-IR. A data set comprising 171 transflection infrared spectra from 29 subjects testing positive for SARS-CoV-2 by RT-qPCR and 28 testing negative, was modeled using Monte Carlo Double Cross Validation with 50 randomized test and model sets. The testing sensitivity was 93 % (27/29) with a specificity of 82 % (23/28) that included positive samples on the limit of detection for RT-qPCR. Herein, we demonstrate a proof-of-concept high throughput infrared COVID-19 test that is rapid, inexpensive, portable and utilizes sample self-collection thus minimizing the risk to healthcare workers and ideally suited to mass screening.

2D-Infrared Spectroscopy of Proteins in Water: Using the Solvent Thermal Response as an Internal Standard.

Ultrafast two-dimensional infrared (2D-IR) spectra can now be obtained in a matter of seconds, opening up the possibility of high-throughput screening applications of relevance to the biomedical and pharmaceutical sectors. Determining quantitative information from 2D-IR spectra recorded on different samples and different instruments is however made difficult by variations in beam alignment, laser intensity, and sample conditions. Recently, we demonstrated that 2D-IR spectroscopy of the protein amide I band can be performed in aqueous (H2O) rather than deuterated (D2O) solvents, and we now report a method that uses the magnitude of the associated thermal response of H2O as an internal normalization standard for 2D-IR spectra. Using the water response, which is temporally separated from the protein signal, to normalize the spectra allows significant reduction of the impact of measurement-to-measurement fluctuations on the data. We demonstrate that this normalization method enables creation of calibration curves for measurement of absolute protein concentrations and facilitates reproducible difference spectroscopy methodologies. These advances make significant progress toward the robust data handling strategies that will be essential for the realization of automated spectral analysis tools for large scale 2D-IR screening studies of protein-containing solutions and biofluids.

Two-dimensional infrared spectroscopy: an emerging analytical tool?

Ultrafast two-dimensional infrared (2D-IR) spectroscopy has provided valuable insights into biomolecular structure and dynamics, but recent progress in laser technology and data analysis methods have demonstrated the potential for high throughput 2D-IR measurements and analytical applications. Using 2D-IR as an analytical tool requires a different approach to data collection and analysis compared to pure research applications however and, in this review, we highlight progress towards usage of 2D-IR spectroscopy in areas relevant to biomedical, pharmaceutical and analytical molecular science. We summarise the technical and methodological advances made to date and discuss the challenges that still face 2D-IR spectroscopy as it attempts to transition from the state-of-the-art laser laboratory to the standard suite of analytical tools.

Discrimination of fresh frozen non-tumour and tumour brain tissue using spectrochemical analyses and a classification model.

Introduction: In order for brain tumours to be successfully treated, maximal resection is beneficial. A method to detect infiltrative tumour edges intraoperatively, improving on current methods would be clinically useful. Vibrational spectroscopy offers the potential to provide a handheld, reagent-free method for tumour detection.Purpose: This study was designed to determine the ability of both Raman and Fourier-transform infrared (FTIR) spectroscopy towards differentiating between normal brain tissue, glioma or meningioma.Method: Unfixed brain tissue, which had previously only been frozen, comprising normal, glioma or meningioma tissue was placed onto calcium fluoride slides for analysis using Raman and attenuated total reflection (ATR)-FTIR spectroscopy. Matched haematoxylin and eosin slides were used to confirm tumour areas. Analyses were then conducted to generate a classification model.Results: This study demonstrates the ability of both Raman and ATR-FTIR spectroscopy to discriminate tumour from non-tumour fresh frozen brain tissue with 94% and 97.2% of cases correctly classified, with sensitivities of 98.8% and 100%, respectively. This decreases when spectroscopy is used to determine tumour type.Conclusion: The study demonstrates the ability of both Raman and ATR-FTIR spectroscopy to detect tumour tissue from non-tumour brain tissue with a high degree of accuracy. This demonstrates the ability of spectroscopy when targeted for a cancer diagnosis. However, further improvement would be required for a classification model to determine tumour type using this technology, in order to make this tool clinically viable.

Is Infrared Spectroscopy Ready for the Clinic?

Fourier transform-infrared spectroscopy (FT-IR) represents an attractive molecular diagnostic modality for translation to the clinic, where comprehensive chemical profiling of biological samples may revolutionize a myriad of pathways in clinical settings. Principally, FT-IR provides a rapid, cost-effective platform to obtain a molecular fingerprint of clinical samples based on vibrational transitions of chemical bonds upon interaction with infrared light. To date, considerable research activities have demonstrated competitive to superior performance of FT-IR strategies in comparison to conventional techniques, with particular promise for earlier, accessible disease diagnostics, thereby improving patient outcomes. However, amidst the changing healthcare landscape in times of aging populations and increased prevalence of cancer and chronic disease, routine adoption of FT-IR within clinical laboratories has remained elusive. Hence, this perspective shall outline the significant clinical potential of FT-IR diagnostics and subsequently address current barriers to translation from the perspective of all stakeholders, in the context of biofluid, histopathology, cytology, microbiology, and biomarker discovery frameworks. Thereafter, future perspectives of FT-IR for healthcare will be discussed, with consideration of recent technological advances that may facilitate future clinical translation.

Utilization of an Electrochemiluminescence Sensor for Atropine Determination in Complex Matrices.

A major challenge within forensic science is the development of accurate and robust methodologies that can be utilized on-site for detection at crime scenes and can be used for analyzing multiple sample types. The recent expansion of electrochemical sensors to tackle this hurdle requires sensors that can undergo analysis without any pretreatment. Given the vast array of samples that are submitted for forensic analysis, this can pose a major challenge for all electrochemical sensors, including electrochemiluminescent (ECL)-based sensors. Within this contribution, we demonstrate the capacity for an ECL-based sensor to address this challenge and it is potential to detect and quantify atropine from a wide range of samples directly from herbal material to spiked solutions. This portable platform demonstrates satisfactory analytical parameters with linearity across a concentration range of 0.75 to 100 µM, reproducibility of 3.0%, repeatability of 9.2%, and a detection limit of ∼0.75 µM. The sensor displays good selectivity toward alkaloid species and, in particular, the hallucinogenic tropane alkaloid functionality within complex matrices. This portable sensor provides rapid detection alongside low cost and operational simplicity, thus, providing a basis for the exploitation of ECL-based sensors within the forensic arena.

Analysis of bodily fluids using vibrational spectroscopy: a direct comparison of Raman scattering and infrared absorption techniques for the case of glucose in blood serum.

Analysis of biomarkers present in the blood stream can potentially deliver crucial information on patient health and indicate the presence of numerous pathologies. The potential of vibrational spectroscopic analysis of human serum for diagnostic purposes has been widely investigated and, in recent times, infrared absorption spectroscopy, coupled with ultra-filtration and multivariate analysis techniques, has attracted increasing attention, both clinical and commercial. However, such methods commonly employ a drying step, which may hinder the clinical work flow and thus hamper their clinical deployment. As an alternative, this study explores the use of Raman spectroscopy, similarly coupled with ultra-filtration and multivariate analysis techniques, to quantitatively monitor diagnostically relevant changes of glucose in liquid serum samples, and compares the results with similar analysis protocols using infrared spectroscopy of dried samples. The analysis protocols to detect the imbalances in glucose using Raman spectroscopy are first demonstrated for aqueous solutions and spiked serum samples. As in the case of infrared absorption studies, centrifugal filtration is utilised to deplete abundant analytes and to reveal the spectral features of Low Molecular Weight Fraction analytes in order to improve spectral sensitivity and detection limits. Improved Root Mean Square Error of Cross Validation (RMSECV) was observed for Raman prediction models, whereas slightly higher R2 values were reported for infrared absorption prediction models. Summarising, it is demonstrated that the Raman analysis protocol can yield accuracies which are comparable with those reported using infrared absorption based measurements of dried serum, without the need for additional drying steps.

Developing infrared spectroscopic detection for stratifying brain tumour patients: glioblastoma multiforme vs. lymphoma.

Over a third of brain tumour patients visit their general practitioner more than five times prior to diagnosis in the UK, leading to 62% of patients being diagnosed as emergency presentations. Unfortunately, symptoms are non-specific to brain tumours, and the majority of these patients complain of headaches on multiple occasions before being referred to a neurologist. As there are currently no methods in place for the early detection of brain cancer, the affected patients' average life expectancy is reduced by 20 years. These statistics indicate that the current pathway is ineffective, and there is a vast need for a rapid diagnostic test. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy is sensitive to the hallmarks of cancer, as it analyses the full range of macromolecular classes. The combination of serum spectroscopy and advanced data analysis has previously been shown to rapidly and objectively distinguish brain tumour severity. Recently, a novel high-throughput ATR accessory has been developed, which could be cost-effective to the National Health Service in the UK, and valuable for clinical translation. In this study, 765 blood serum samples have been collected from healthy controls and patients diagnosed with various types of brain cancer, contributing to one of the largest spectroscopic studies to date. Three robust machine learning techniques - random forest, partial least squares-discriminant analysis and support vector machine - have all provided promising results. The novel high-throughput technology has been validated by separating brain cancer and non-cancer with balanced accuracies of 90% which is comparable to the traditional fixed diamond crystal methodology. Furthermore, the differentiation of brain tumour type could be useful for neurologists, as some are difficult to distinguish through medical imaging alone. For example, the highly aggressive glioblastoma multiforme and primary cerebral lymphoma can appear similar on magnetic resonance imaging (MRI) scans, thus are often misdiagnosed. Here, we report the ability of infrared spectroscopy to distinguish between glioblastoma and lymphoma patients, at a sensitivity and specificity of 90.1% and 86.3%, respectively. A reliable serum diagnostic test could avoid the need for surgery and speed up time to definitive chemotherapy and radiotherapy.

Rapid Screening of DNA-Ligand Complexes via 2D-IR Spectroscopy and ANOVA-PCA.

Two-dimensional infrared spectroscopy (2D-IR) is well established as a specialized, high-end technique for measuring structural and solvation dynamics of biological molecules. Recent technological developments now make it possible to acquire time-resolved 2D-IR spectra within seconds, and this opens up the possibility of screening-type applications comparing spectra spanning multiple samples. However, such applications bring new challenges associated with finding accurate, efficient methodologies to analyze large data sets in a timely, informative manner. Here, we demonstrate such an application by screening 2016 2D-IR spectra of 12 double-stranded DNA oligonucleotides obtained in the presence and absence of binding therapeutic molecule Hoechst 33258. By applying analysis of variance combined with principal component analysis (ANOVA-PCA) to 2D-IR data for the first time, we demonstrate the ability to efficiently retrieve the base composition of a DNA sequence and discriminate ligand-DNA complexes from unbound sequences. We further show accurate differentiation of the induced-fit and rigid-body binding modes that is key to identifying optimal binding interactions of Hoechst 33258, while ANOVA-PCA results across the full sequence range correlate directly with thermodynamic indicators of ligand-binding strength that require significantly longer data acquisition times to obtain.