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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Tumor‐pH‐value responsive non‐peripheral substituted phthalocyanines: Synthesis, investigation of photophysical and photochemical properties

Cancer is one of the diseases with the highest mortality rate worldwide. Although PDT has recently produced encouraging outcomes, there are still many areas that need to be improved. The first of these is the negative consequences faced by patients treated with the PDT method when exposed to sunlight. For this reason, a new PDT method has been developed in recent years, and it is aimed at using photosensitizer molecules that can be active in acidic conditions. Since the pH values of tumor tissues are more acidic than normal tissues, preparing molecules that act effectively in acidic conditions will allow for more effective results in treating cancer with PDT. In this context, within the scope of this study, 3‐(4‐propionylphenoxy)phthalonitrile ( 1 ) and its non‐peripheral tetra‐substituted phthalocyanine derivatives [( 2 ), ( 3 ), and ( 4 )] were prepared. With these phthalocyanine derivatives, the novel compounds ( 5 ), ( 6 ), and ( 7 ) were synthesized for the first time. The aggregation tendencies of newly synthesized phthalocyanines ( 5–7 ) were investigated in solvent media. The effects of pH changes upon UV–Vis and fluorescence spectra were performed. The electronic and emission spectra of synthesized phthalocyanine derivatives are highly sensitive to pH changes. Formation constant (Log K) values of mono‐ and di‐protonated phthalocyanine forms were calculated by the Henderson–Hasselback equation. The mono‐ and di‐protonated species' equilibrium constants (logK 1 and logK 2 ) were calculated as ~5.0. This value may be promising for pH‐sensitizing photosensitizers. Also, the photophysical and photochemical properties of synthesized metallophthalocyanine derivatives ( 2 ) and ( 5 ) were studied at different pH values. The singlet oxygen quantum yield of ( 2 ) and ( 5 ) was calculated to be 0.78 and 0.81 in DMSO, respectively. When pH = 6.4, that is, tumor‐pH‐values, this value for ( 5 ) has increased to 0.92. The newly synthesized phthalocyanines are suitable photosensitizers for PDT applications, especially with high singlet oxygen quantum yield at pH 6.4.

Genc Bilgicli, Hayriye↗
DOE OSTI2023

Breast tumor-targeted drug delivery via polymer nanocarriers: Endogenous and exogenous strategies

Breast cancer, one of the most common types of cancer worldwide, has a high degree of malignancy while lacking efficient treatments. The off-target events frequently occurring in drug administration have been a major obstacle, which causes severe side effects to patients and reduces the therapeutic efficacy. An ideal medication should precisely target the specific site with precise control over concentration and duration. In recent years, there has been immense progress in polymer nanocarriers that demonstrate great potential in the targeted delivery. These nanocarriers are constructed from materials sensitive to a wide range of endogenous (e.g., pH, enzyme, redox) and exogenous stimuli (e.g., light, ultrasound, magnetic waves), driving cargo release to the targeted tissue or cells. Here, we elucidate the mechanism of current targeting strategies and the engineering methods to construct these targeting systems or to improve their targeting efficiency. Here we capture the recent progress in the targeted nanocarriers derived from polymers which span hydrogels and micelles, demonstrating their design and applications in chemo, hyperthermia, immuno, and gene therapies.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Artificial intelligence to unlock real-world evidence in clinical oncology: A primer on recent advances

Purpose: Real world evidence is crucial to understanding the diffusion of new oncologic therapies, monitoring cancer outcomes, and detecting unexpected toxicities. In practice, real world evidence is challenging to collect rapidly and comprehensively, often requiring expensive and time-consuming manual case-finding and annotation of clinical text. In this Review, we summarise recent developments in the use of artificial intelligence to collect and analyze real world evidence in oncology. Methods: We performed a narrative review of the major current trends and recent literature in artificial intelligence applications in oncology. Results: Artificial intelligence (AI) approaches are increasingly used to efficiently phenotype patients and tumors at large scale. These tools also may provide novel biological insights and improve risk prediction through multimodal integration of radiographic, pathological, and genomic datasets. Custom language processing pipelines and large language models hold great promise for clinical prediction and phenotyping. Conclusions: Despite rapid advances, continued progress in computation, generalizability, interpretability, and reliability as well as prospective validation are needed to integrate AI approaches into routine clinical care and real-time monitoring of novel therapies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

A Systematic Assessment of US Food and Drug Administration Dosing Recommendations For Drug Development Programs Amenable to Response‐Guided Titration

A key goal in drug development is optimized dosing for patients. Interactions between drug developers and regulatory scientists throughout development are important for the optimization of dosing and serve as a forum to discuss approaches for optimal dosing, such as precision or individualized dosing. To date, there has not been a systematic assessment of the advice provided by the US Food and Drug Administration (FDA) to drug developers from an individualized dosing perspective. Here, we reviewed FDA recommendations on dose selection for efficacy trials at end‐of‐phase meetings between the FDA and drug developers for 76 new molecular entities approved between 2013 and 2017 that are considered amenable for an individualized dosing method, response‐guided titration. Forty FDA dosing recommendations were identified as specific to dose selection and design of the respective efficacy trials and subsequently: (i) characterized based on if they were supportive of individualized dosing and (ii) compared with dosing regimens used in efficacy trials and labeling at approval to evaluate if FDA recommendations were implemented. Of these 40 recommendations for efficacy trials, 35 (88%) were considered supportive of individualized dosing. Eighteen of these 40 recommendations (45%) were incorporated into efficacy trials and 11 (28%) were incorporated into labeling. This research suggests that early FDA‐sponsor interactions can support the study of doses in efficacy trials that may lead to individualized dosing strategies in labeling.

Wang, Lingshan↗
DOE OSTI2022

Calibration and Validation of a Mechanistic COVID-19 Model for Translational Quantitative Systems Pharmacology – A Proof-of-Concept Model Development for Remdesivir

With the ongoing global pandemic of coronavirus disease 2019 (COVID-19), there is an urgent need to accelerate the traditional drug development process. Many studies identified potential COVID-19 therapies based on promising nonclinical data. However, the poor translatability from nonclinical to clinical settings has led to failures of many of these drug candidates in the clinical phase. In this study, we propose a mechanism-based, quantitative framework to translate nonclinical findings to clinical outcome. Adopting a modularized approach, this framework includes an in silico disease model for COVID-19 (virus infection and human immune responses) and a pharmacological component for COVID-19 therapies. Here, the disease model was able to reproduce important longitudinal clinical data for patients with mild and severe COVID-19, including viral titer, key immunological cytokines, antibody responses, and time courses of lymphopenia. Using remdesivir as a proof-of-concept example of model development for the pharmacological component, we developed a pharmacological model that describes the conversion of intravenously administered remdesivir as a prodrug to its active metabolite nucleoside triphosphate through intracellular metabolism and connected it to the COVID-19 disease model. After being calibrated with the placebo arm data, our model was independently and quantitatively able to predict the primary endpoint (time to recovery) of the remdesivir clinical study, Adaptive Covid-19 Clinical Trial (ACTT). Our work demonstrates the possibility of quantitatively predicting clinical outcome based on nonclinical data and mechanistic understanding of the disease and provides a modularized framework to aid in candidate drug selection and clinical trial design for COVID-19 therapeutics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Development of a Translational Model to Assess the Impact of Opioid Overdose and Naloxone Dosing on Respiratory Depression and Cardiac Arrest

In response to a surge of deaths from synthetic opioid overdoses, there have been increased efforts to distribute naloxone products in community settings. Prior research has assessed the effectiveness of naloxone in the hospital setting; however, it is challenging to assess naloxone dosing regimens in the community/first‐responder setting, including reversal of respiratory depression effects of fentanyl and its derivatives (fentanyls). Here, we describe the development and validation of a mechanistic model that combines opioid mu receptor binding kinetics, opioid agonist and antagonist pharmacokinetics, and human respiratory and circulatory physiology, to evaluate naloxone dosing to reverse respiratory depression. Validation supports our model, which can quantitatively predict displacement of opioids by naloxone from opioid mu receptors in vitro , hypoxia‐induced cardiac arrest in vivo , and opioid‐induced respiratory depression in humans from different fentanyls. After validation, overdose simulations were performed with fentanyl and carfentanil followed by administration of different intramuscular naloxone products. Carfentanil induced more cardiac arrest events and was more difficult to reverse than fentanyl. Opioid receptor binding data indicated that carfentanil has substantially slower dissociation kinetics from the opioid receptor compared with nine other fentanyls tested, which likely contributes to the difficulty in reversing carfentanil. Administration of the same dose of naloxone intramuscularly from two different naloxone products with different formulations resulted in differences in the number of virtual patients experiencing cardiac arrest. This work provides a robust framework to evaluate dosing regimens of opioid receptor antagonists to reverse opioid‐induced respiratory depression, including those caused by newly emerging synthetic opioids.

Pharmacology & Pharmacy↗
DOE OSTI2023

Macrophage-derived MLKL in alcohol-associated liver disease: Regulation of phagocytosis

Mixed lineage kinase domain-like pseudokinase (MLKL), a key terminal effector of necroptosis, also plays a role in intracellular vesicle trafficking that is critical for regulating liver inflammation and injury in alcohol-associated liver disease (ALD). Although receptor interacting protein kinase 3 (Rip3) -/- mice are completely protected from ethanol-induced liver injury, Mlkl -/- mice are only partially protected. Therefore, we hypothesized that cell-specific functions of MLKL may contribute to ethanol-induced injury. Bone marrow transplants between Mlkl -/- mice and littermates were conducted to distinguish the role of myeloid versus nonmyeloid Mlkl in the Gao-binge model of ALD. Ethanol-induced hepatic injury, steatosis, and inflammation were exacerbated in Mlkl -/- →wild-type (WT) mice, whereas Mlkl deficiency in nonmyeloid cells (WT→Mlkl -/- ) had no effect on Gao-binge ethanol-induced injury. Importantly, Mlkl deficiency in myeloid cells exacerbated ethanol-mediated bacterial burden and accumulation of immune cells in livers. Mechanistically, challenging macrophages with lipopolysaccharide (LPS) induced signal transducer and activator of transcription 1–mediated expression and phosphorylation of MLKL, as well as translocation and oligomerization of MLKL to intracellular compartments, including phagosomes and lysosomes but not plasma membrane. Importantly, pharmacological or genetic inhibition of MLKL suppressed the phagocytic capability of primary mouse Kupffer cells (KCs) at baseline and in response to LPS with/without ethanol as well as peripheral monocytes isolated from both healthy controls and patients with alcohol-associated hepatitis. Further, in vivo studies revealed that KCs of Mlkl -/- mice phagocytosed fewer bioparticles than KCs of WT mice. Together, these data indicate that myeloid MLKL restricts ethanol-induced liver inflammation and injury by regulating hepatic immune cell homeostasis and macrophage phagocytosis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

The sodium channel Na X : Possible player in excitation–contraction coupling

Abstract The sodium channel Na X (encoded by the SCN7A gene) was originally identified in the heart and skeletal muscle and is structurally similar to the other voltage‐gated sodium channels but does not appear to be voltage gated. Although Na X is expressed at high levels in cardiac and skeletal muscle, little information exists on the function of Na X in these tissues. Transcriptional profiling of ion channels in the heart in a subset of patients with Brugada syndrome revealed an inverse relationship between the expression of Na X and Na V 1.5 suggesting that, in cardiac myocytes, the expression of these channels may be linked. We propose that Na X plays a role in excitation–contraction coupling based on our experimental observations. Here we show that in cardiac myocytes, Na X is expressed in a striated pattern on the sarcolemma in regions corresponding to the sarcomeric M‐line. Knocking down Na X expression decreased Na V 1.5 mRNA and protein and reduced the inward sodium current ( I Na+ ) following cell depolarization. When the expression of Na V 1.5 was knocked down, ~85% of the I Na+ was reduced consistent with the observations that Na V 1.5 is the main voltage‐gated sodium channel in cardiac muscle and that Na X likely does not directly participate in mediating the I Na+ following depolarization. Silencing Na V 1.5 expression led to significant upregulation of Na X mRNA. Similar to Na V 1.5, Na X protein levels were rapidly downregulated when the intracellular [Ca 2+ ] was increased either by CaCl 2 or caffeine. These data suggest that a relationship exists between Na X and Na V 1.5 and that Na X may play a role in excitation–contraction coupling.

Bogdanovic, Elena↗
DOE OSTI2022

Interactions Between Diabetes Mellitus and Osteoarthritis: From Animal Studies to Clinical Data

Diabetes mellitus (DM) and osteoarthritis (OA) are commonly known metabolic diseases that affect a large segment of the world population. These two conditions share several risk factors such as obesity and aging; however, there is still no consensus regarding the direct role of DM on OA development and progression. Interestingly, both animal and human studies have yielded conflicting results, with some showing a significant role for DM in promoting OA, while others found no significant interactions between these conditions. In this review, we will discuss preclinical and clinical data that assessed the interaction between DM and OA. We will also discuss possible mechanisms associated with the effect of high glucose on the articular cartilage and chondrocytes. An emerging theme dominates the breath of published work in this area: most of the studies discussed in this review do not take into consideration the role of other factors such as the type of diabetes, age, biological sex, type of animal model, body mass index, and the use of pain medications when analyzing and interpreting data. Therefore, future studies should be more rigorous when designing experiments looking at DM and its effects on OA and should carefully account for these confounding factors, so that better approaches can be developed for monitoring and treating patients at risk of OA and DM.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

US FDA Postmarketing Requirements and Commitments: A Systematic Assessment of Clinical Pharmacology Studies and Their Impact on US FDA Prescribing Information

Abstract Many of the conditions for the safe and effective use of new molecular entities (NMEs) are understood at the time of initial drug approval. However, some remaining knowledge gaps can be addressed after drug approval through postmarketing requirements (PMRs) or commitments (PMCs) established by the US Food and Drug Administration (FDA). Our objective was to conduct an assessment of clinical pharmacology–related PMRs and PMCs established at the time of approval and evaluate the impact of fulfilled PMRs and PMCs on prescription information (PI). This analysis included clinical pharmacology–related PMRs and PMCs established for NMEs approved between 2009 and 2020. Of the 1171 PMRs and PMCs, over one‐third were clinical pharmacology–related. Of these, 46% were to evaluate drug interactions, 16% were to evaluate drug dosing in patients with hepatic impairment, and 10% were related to dose. The majority (57%) of PMRs and PMCs were fulfilled at the time of analysis, with a median time to fulfillment of approximately 2.3 years. The majority (94%) of the fulfilled PMRs and PMCs, either with or without a PI revision, resulted in new or modified instructions for use or supported existing instructions for use. This is the first time that clinical pharmacology–related PMRs and PMCs have been catalogued and analyzed to understand their impact on PI. An understanding of the knowledge gaps that exist at the time of drug approval could inform the most effective and efficient methods for evidence generation prior to and after new drug approval.

Pharmacology & Pharmacy↗
DOE OSTI2022

Circadian rhythmicity of amyloid‐beta‐related molecules is disrupted in the choroid plexus of a female Alzheimer's disease mouse model

Abstract The choroid plexus (CP) is part of the blood‐cerebrospinal fluid barrier (BCSFB) and was recently described as an important component of the circadian clock system. It is the principal source of cerebrospinal fluid (CSF) and responsible for the synthesis and secretion of various neuroprotective peptides including those involved in amyloid‐β (Aβ) transport/degradation, contributing to Aβ homeostasis. Inadequate Aβ metabolic clearance and transport across the BCSFB have been associated with circadian dysfunctions in Alzheimer's disease (AD) patients. To investigate whether AD pathology influences Aβ scavengers circadian expression, we collected CP at different time points from an AD mouse model (APP/PS1) (female and male animals, aged 6‐ and 12‐months‐old) and analyzed their mRNA expression by Real‐time RT‐PCR. Only angiotensin‐converting enzyme (Ace) expression in 6‐month‐old female wild‐type mice and transthyretin (Ttr) expression in 12‐month‐old female wild‐type mice presented significant rhythmicity. The circadian rhythmicity of Ace and Ttr, prompt us to analyze the involvement of circadian rhythm in Aβ uptake. A human CP papilloma (HIBCPP) cell line was incubated with Aβ‐488 and uptake was evaluated at different time points using flow cytometry. Aβ uptake displayed circadian rhythmicity. Our results suggest that AD might affect Aβ scavengers rhythmicity and that Aβ clearance is a rhythmic process possibly regulated by the rhythmic expression of Aβ scavengers.

Furtado, André↗
DOE OSTI2020

Prenatal Ultrasound Analysis of Umbilical‐Portal‐Systemic Venous Shunts Concurrent With Trisomy 21

Objectives A classification termed umbilical‐portal‐systemic venous shunt (UPSVS) for an abnormal umbilical vein (UV), portal vein (PV), and ductus venosus (DV) was proposed recently. According to this classification, there are 3 types of UPSVSs: types I, II, and III. Trisomy 21 associated with UV‐PV‐DV anomalies has been described, but the incidence of trisomy 21 in UPSVS cases, the relationship between UPSVS types and trisomy 21, and the pregnancy outcome are poorly documented. This study aimed to address these issues. Methods All UPSVS cases diagnosed at our department from 2016 to 2019 were retrospectively studied. The English literature describing UV‐PV‐DV anomalies and trisomy 21 from 2000 to 2019 was searched, and the retrieved cases were analyzed. Results Four of 20 UPSVS cases identified by us also had trisomy 21, with 2 type I and 2 type II UPSVSs. Ultrasound markers of Down syndrome were observed in all 4 cases that underwent termination of pregnancy (TOP). The literature search retrieved 12 reports including 279 patients, with 29 also having trisomy 21, giving a pooled trisomy 21 incidence rate of 10.4%. Of the 29 cases, 16 had type I, and 9 had type II, whereas UPSVS types in 4 were undeterminable, and 22 cases underwent TOP. Conclusions There is a high incidence of trisomy 21 in UPSVS cases. Trisomy 21 is associated with a type I or II UPSVS. Most cases with the combined defect underwent TOP. These findings may be used to direct prenatal counseling and management of the combined condition.

Dong, Xiangyi↗
DOE OSTI2022

Computational models of direct and indirect X‐ray breast imaging detectors for in silico trials

Abstract Background To facilitate in silico studies that investigate digital mammography (DM) and breast tomosynthesis (DBT), models replicating the variety in imaging performance of the DM and DBT systems, observed across manufacturers are needed. Purpose The main purpose of this work is to develop generic physics models for direct and indirect detector technology used in commercially available systems, with the goal of making them available open source to manufacturers to further tweak and develop the exact in silico replicas of their systems. Methods We recently reported on an in silico version of the SIEMENS Mammomat Inspiration DM/DBT system using an open‐source GPU‐accelerated Monte Carlo x‐ray imaging simulation code (MC‐GPU). We build on the previous version of the MC‐GPU codes to mimic the imaging performances of two other Food and Drug Administration (FDA)‐approved DM/DBT systems, such as Hologic Selenia Dimensions (HSD) and the General Electric Senographe Pristina (GSP) systems. In this work, we developed a hybrid technique to model the optical spread and signal crosstalk observed in the GSP and HSD systems. MC simulations are used to track each x‐ray photon till its first interaction within the x‐ray detector. On the other hand, the signal spread in the x‐ray detectors is modeled using previously developed analytical equations. This approach allows us to preserve the modeling accuracy offered by MC methods in the patient body, while speeding up secondary carrier transport (either electron–hole pairs or optical photons) using analytical equations in the detector. The analytical optical spread model for the indirect detector includes the depth‐dependent spread and collection of optical photons and relies on a pre‐computed set of point response functions that describe the optical spread as a function of depth. To understand the capabilities of the computational x‐ray detector models, we compared image quality metrics like modulation transfer function (MTF), normalized noise power spectrum (NNPS), and detective quantum efficiency (DQE), simulated with our models against measured data. Please note that the purpose of these comparisons with measured data would be to gauge if the model developed as part of this work could replicate commercially used direct and indirect technology in general and not to achieve perfect fits with measured data. Results We found that the simulated image quality metrics such as MTF, NNPS, and DQE were in reasonable agreement with experimental data. To demonstrate the imaging performance of the three DM/DBT systems, we integrated the detector models with the VICTRE pipeline and simulated DM images of a fatty breast model containing a spiculated mass and a calcium oxalate cluster. In general, we found that the images generated using the indirect model appeared more blurred with a different noise texture and contrast as compared to the systems with direct detectors. Conclusions We have presented computational models of three commercially available FDA‐approved DM/DBT systems, which implement both direct and indirect detector technology. The updated versions of the MC‐GPU codes that can be used to replicate three systems are available in open source format through GitHub.

Sengupta, Aunnasha↗
DOE OSTI2024

Screening a knowledge‐based library of low molecular weight compounds against the proline biosynthetic enzyme 1‐pyrroline‐5‐carboxylate 1 ( PYCR1)

Abstract Δ 1 ‐pyrroline‐5‐carboxylate reductase isoform 1 (PYCR1) is the last enzyme of proline biosynthesis and catalyzes the NAD(P)H‐dependent reduction of Δ 1 ‐pyrroline‐5‐carboxylate toL‐proline. High PYCR1 gene expression is observed in many cancers and linked to poor patient outcomes and tumor aggressiveness. The knockdown of thePYCR1gene or the inhibition of PYCR1 enzyme has been shown to inhibit tumorigenesis in cancer cells and animal models of cancer, motivating inhibitor discovery. We screened a library of 71 low molecular weight compounds (average MW of 131 Da) against PYCR1 using an enzyme activity assay. Hit compounds were validated with X‐ray crystallography and kinetic assays to determine affinity parameters. The library was counter‐screened against human Δ 1 ‐pyrroline‐5‐carboxylate reductase isoform 3 and proline dehydrogenase (PRODH) to assess specificity/promiscuity. Twelve PYCR1 and one PRODH inhibitor crystal structures were determined. Three compounds inhibit PYCR1 with competitive inhibition parameter of 100 μM or lower. Among these, (S)‐tetrahydro‐2H‐pyran‐2‐carboxylic acid (70 μM) has higher affinity than the current best tool compoundN‐formyl‐l‐proline, is 30 times more specific for PYCR1 over human Δ 1 ‐pyrroline‐5‐carboxylate reductase isoform 3, and negligibly inhibits PRODH. Structure‐affinity relationships suggest that hydrogen bonding of the heteroatom of this compound is important for binding to PYCR1. The structures of PYCR1 and PRODH complexed with 1‐hydroxyethane‐1‐sulfonate demonstrate that the sulfonate group is a suitable replacement for the carboxylate anchor. This result suggests that the exploration of carboxylic acid isosteres may be a promising strategy for discovering new classes of PYCR1 and PRODH inhibitors. The structure of PYCR1 complexed withl‐pipecolate and NADH supports the hypothesis that PYCR1 has an alternative function in lysine metabolism.

Biochemistry & Molecular Biology↗
DOE OSTI2022

Case studies in bias reduction and inference for electronic health record data with selection bias and phenotype misclassification

Electronic health records (EHR) are not designed for population‐based research, but they provide easy and quick access to longitudinal health information for a large number of individuals. Many statistical methods have been proposed to account for selection bias, missing data, phenotyping errors, or other problems that arise in EHR data analysis. However, addressing multiple sources of bias simultaneously is challenging. We developed a methodological framework (R package, SAMBA ) for jointly handling both selection bias and phenotype misclassification in the EHR setting that leverages external data sources. These methods assume factors related to selection and misclassification are fully observed, but these factors may be poorly understood and partially observed in practice. As a follow‐up to the methodological work, we demonstrate how to apply these methods for two real‐world case studies, and we evaluate their performance. In both examples, we use individual patient‐level data collected through the University of Michigan Health System and various external population‐based data sources. In case study (a), we explore the impact of these methods on estimated associations between gender and cancer diagnosis. In case study (b), we compare corrected associations between previously identified genetic loci and age‐related macular degeneration with gold standard external summary estimates. These case studies illustrate how to utilize diverse auxiliary information to achieve less biased inference in EHR‐based research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Use of Longitudinal Serum Analysis and Machine Learning to Develop a Classifier for Cancer Early Detection

Early detection of solid tumors through a simple screening process, such as the proteomic analysis of biofluids, has the potential to significantly alter the management and outcomes of cancers. The application of advanced targeted proteomics measurements and data analysis strategies to uniformly collected serum or plasma samples would enable longitudinal studies of cancer risk, progression, and response to therapy that have the potential to significantly reduce cancer burden in general. In this article, we describe a generalizable workflow combining robust, multiplexed targeted proteomics measurements applied to longitudinal samples from the Department of Defense Serum Repository with a Random Forest machine learning method for developing and initially evaluating the performance of candidate biomarker panels for early detection of cancers. The effectiveness of this approach was demonstrated in a cohort of 175 head and neck squamous cell carcinoma patients. The outlined protocols include methods for sample preparation, instrument analysis, and data analysis and interpretation using this workflow.

Longitudinal analysis, machine learning, cancer, e↗