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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Combination therapy with lenvatinib and radiation significantly inhibits thyroid cancer growth by uptake of tyrosine kinase inhibitor

Highlights: • Lenvatinib combined with radiation significantly inhibits thyroid cancer growth. • Combined therapy induced apoptosis and G2/M phase cell cycle arrest. • Irradiation increased cellular uptake of lenvatinib into cancer cells. • Combined therapy suggests a new therapeutic strategy for advanced thyroid cancer. Although surgical treatment cures >90% of differentiated thyroid cancer (DTC) patients, the remaining patients, including advanced DTC cases, have poor clinical outcomes. These patients with inoperable disease have only two choices of radioactive iodine therapy and tyrosine kinase inhibitors such as lenvatinib, which have a high incidence of treatment-related adverse events and can only prolong progression free survival by approximately 5–15 months. In this study, we investigated the antitumor effects of combination therapy with lenvatinib and radiation (CTLR) for DTC. CTLR synergistically inhibited cell replication and colony formation in vitro and tumor growth in nude mice without apparent toxicities and suppressed the expression of proliferation marker (Ki-67). CTLR also induced apoptosis and G2/M phase cell cycle arrest. Moreover, quantitative analysis of the intracellular uptake of lenvatinib using liquid chromatography and mass spectrometry demonstrated that intracellular uptake of lenvatinib was significantly increased 48 h following irradiation. These data suggest that increased membrane permeability caused by irradiation increases the intracellular concentration of levatinib, contributing to the synergistic effect. This mechanism-based potential of combination therapy suggests a powerful new therapeutic strategy for advanced thyroid cancer with fewer side effects and might be a milestone for developing a regimen in clinical practice.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

PCGF3 promotes the proliferation and migration of non-small cell lung cancer cells via the PI3K/AKT signaling pathway

Highlights: • PCGF3 is highly expressed in non-small cell lung cancer (NSCLC) tissues. • PCGF3 overexpression positively correlates with lymph node metastasis and p-TNM staging. • PCGF3 promotes NSCLC cells proliferation by regulating CyclinB1, CyclinD1, and CDK4. • PCGF3 promotes NSCLC cells migration by regulating RhoA, RhoC, and CDC42. • PCGF3 promotes malignant behavior in NSCLC by positively regulating the PI3K/AKT signaling pathway. The Polycomb Group Ring Finger 3 (PCGF3) protein has been reported to be significantly upregulated in pancreatic islet tumors and related to signal transduction; however, its detailed mechanisms and biological roles in other tumors, including non-small cell lung cancer (NSCLC), remain unclear. This study investigated the function of PCGF3 in NSCLC and further elucidated its mechanism of action. The immunohistochemical analysis of 86 selected lung cancer tissues revealed that PCGF3 was highly expressed in NSCLC tissues and positively correlated with lymph node metastasis and p-TNM staging. Additionally, PCGF3 promoted cell proliferation in lung cancer by regulating CyclinB1, CyclinD1, and CDK4 expression, and also promoting their migration by regulating RhoA, RhoC, and CDC42. Furthermore, PCGF3 affected both the proliferation and migration of lung cancer cells by regulating the PI3K/AKT pathway, as verified by inhibiting this pathway using LY294002. The findings of this study suggested that PCGF3 is associated with poor prognosis in patients with NSCLC and could therefore be an important biomarker for treating and preventing NSCLC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Balancing DNA repair to prevent ageing and cancer

DNA damage is a constant stressor to the cell. Persistent damage to the DNA over time results in an increased risk of mutation and an accumulation of mutations with age. Loss of efficient DNA damage repair can lead to accelerated ageing phenotypes or an increased cancer risk, and the trade-off between cancer susceptibility and longevity is often driven by the cell's response to DNA damage. High levels of mutations in DNA repair mutants often leads to excessive cell death and stem cell exhaustion which may promote premature ageing. Stem cells themselves have distinct characteristics that enable them to retain low mutation rates. However, when mutations do arise, stem cell clonal expansion can also contribute to age-related tissue dysfunction as well as heightened cancer risk. In this review, we will highlight increasing DNA damage and mutation accumulation as hallmarks common to both ageing and cancer. We will propose that anti-ageing interventions might be cancer preventative and discuss the mechanisms through which they may act.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Preclinical tumor organoid models in personalized cancer therapy: Not everyone fits the mold

Highlights: • In personalized cancer medicine, each patient receives a specific treatment. • The tumor microenvironment and interpersonal differences can affect treatment. • Organoid culture systems fully recapitulate the tumor microenvironments. In contrast to conventional cancer treatment, in personalized cancer medicine each patient receives a specific treatment. The response to therapy, clinical outcomes, and tumor behavior such as metastases, tumor progression, carcinogenesis can be significantly affected by the heterogeneous tumor microenvironment (TME) and interpersonal differences. Therefore, using native tumor microenvironment mimicking models is necessary to improving personalized cancer therapy. Both in vitro 2D cell culture and in vivo animal models poorly recapitulate the heterogeneous tumor (immune) microenvironments of native tumors. The development of 3D culture models, native tumor microenvironment mimicking models, made it possible to evaluate the chemoresistance of tumor tissue and the functionality of drugs in the presence of cell-extracellular matrix and cell-cell interactions in a 3D construction. Various personalized tumor models have been designed to preserving the native tumor microenvironment, including patient-derived tumor xenografts and organoid culture strategies. In this review, we will discuss the patient-derived organoids as a native tumor microenvironment mimicking model in personalized cancer therapy. In addition, we will also review the potential and the limitations of organoid culture systems for predicting patient outcomes and preclinical drug screening. Finally, we will discuss immunotherapy drug screening in tumor organoids by using microfluidic technology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

PDRG1 predicts a poor prognosis and facilitates the proliferation and metastasis of colorectal cancer

Highlights: • PDRG1 is overexpressed in colorectal cancer tissues and is associated with poor prognosis of colorectal cancer patients. • PDRG1 regulates the proliferation, apoptosis, cell cycle progression and metastasis of colorectal cancer cells. • PDRG1 promotes the proliferation, migration and invasion of colorectal cancer via p21-mediated cell cycle progression. The incidence and mortality of colorectal cancer (CRC) is increasing yearly and CRC patients are becoming younger in global. Evidences have revealed the carcinogenic effect of p53 and DNA damage-regulated gene 1 (PDRG1) in several types of tumors. However, its biological function is yet to be investigated in CRC. This study aimed to unveil the prooncogenic role of PDRG1 in CRC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Unconventional isoquinoline-based SERMs elicit fulvestrant-like transcriptional programs in ER+ breast cancer cells

Estrogen receptor alpha (ERα) is a ligand-dependent master transcriptional regulator and key driver of breast cancer pathology. Small molecule hormones and competitive antagonists favor unique ERα conformational ensembles that elicit ligand-specific transcriptional programs in breast cancer and other hormone-responsive tissues. By affecting disparate ligand binding domain structural features, unconventional ligand scaffolds can redirect ERα genomic binding patterns to engage novel therapeutic transcriptional programs. To improve our understanding of these ERα structure-transcriptional relationships, we develop a series of chemically unconventional antagonists based on the antiestrogens elacestrant and lasofoxifene. High-resolution x-ray co-crystal structures show that these molecules affect both classical and unique structural motifs within the ERα ligand binding pocket. They show moderately reduced antagonistic potencies on ERα genomic activities but are effective anti-proliferative agents in luminal breast cancer cells. Interestingly, they favor a 4-hydroxytamoxifen-like accumulation of ERα in breast cancer cells but lack uterotrophic activities in an endometrial cell line. Importantly, RNA sequencing shows that the lead molecules engage transcriptional pathways similar to the selective estrogen receptor degrader fulvestrant. This advance shows that fulvestrant-like genomic activities can be achieved without affecting ERα accumulation in breast cancer cells.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Visible light-activatable platinum( IV ) prodrugs harnessing CD36 for ovarian cancer therapy

We hereby engineered photoactivatable Pt( IV ) metallodrugs that harness CD36 to target ovarian cancer cells. Pt( IV ) compounds mimic the structure of fatty acids and take advantage of CD36 as a “Trojan horse” to gain entry into the cells. We confirmed that CD36-dependent entry occurs using graphite furnace atomic absorption spectroscopy with ovarian cancer cells expressing different levels of CD36 and a CD36 inhibitor, SSO. Once the Pt( IV ) metallodrugs enter the cancer cells, they can be activated to form Pt(II) with characteristics of cisplatin under visible light (490 nm) irradiation, promoting photoinduced electron transfer from the attached fluorophore to the metal center. Finally, this light-induced activation can increase the cytotoxicity of the Pt( IV ) metallodrugs by up to 20 times toward ovarian cancer cells, inducing DNA damage and enabling efficient elimination of drug-resistant cancer cells.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Using ensembles and distillation to optimize the deployment of deep learning models for the classification of electronic cancer pathology reports

One of the goals of the Surveillance, Epidemiology, and End Results (SEER) program is to estimate incidence, prevalence, and mortality of all cancers. To that end, cancer registries across the country maintain a massive database of cancer pathology reports which contain rich information to understand cancer trends. However, these reports are stored in the form of unstructured text, and human annotators are required to read and extract relevant information. In this article, we show that existing deep learning models for automating information extraction from cancer pathology reports can be significantly improved by using ensemble model distillation. We found that by training multiple predictive models and transferring their knowledge to a single, low-resource model, we can reduce the number of highly confident wrong predictions. Our results show that our implemented methods could save 1000s of manual annotation hours.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Antitumor T-cell Immunity Contributes to Pancreatic Cancer Immune Resistance

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer death in the United States. Pancreatic tumors are minimally infiltrated by T cells and are largely refractory to immunotherapy. Accordingly, the role of T-cell immunity in pancreatic cancer has been somewhat overlooked. Here, we hypothesized that immune resistance in pancreatic cancer was induced in response to antitumor T-cell immune responses and that understanding how pancreatic tumors respond to immune attack may facilitate the development of more effective therapeutic strategies. We now provide evidence that T-cell–dependent host immune responses induce a PDAC-derived myeloid mimicry phenomenon and stimulate immune resistance. Three KPC mouse models of pancreatic cancer were used: the mT3-2D (Kras +/LSL-G12D ; Trp53 +/LSL-R172H ; Pdx1-Cre) subcutaneous and orthotopic models, as well as the KP1 (p48-CRE/LSL-Kras/Trp53 flox/flox ) subcutaneous model. KPC cancer cells were grown in immunocompetent and immunodeficient C57BL/6 mice and analyzed to determine the impact of adaptive immunity on malignant epithelial cells, as well as on whole tumors. We found that induced T-cell antitumor immunity, via signal transducer and activator of transcription 1 (STAT1), stimulated malignant epithelial pancreatic cells to induce the expression of genes typically expressed by myeloid cells and altered intratumoral immunosuppressive myeloid cell profiles. Targeting the Janus Kinase (JAK)/STAT signaling pathway using the FDA-approved drug ruxolitinib overcame these tumor-protective responses and improved anti–PD-1 therapeutic efficacy. Overall, these findings provide future directions for treatments that specifically disable this mechanism of resistance in PDAC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Proteogenomic characterization of difficult-to-treat breast cancer with tumor cells enriched through laser microdissection

Abstract Background Breast cancer (BC) is the most commonly diagnosed cancer and the leading cause of cancer death among women globally. Despite advances, there is considerable variation in clinical outcomes for patients with non-luminal A tumors, classified as difficult-to-treat breast cancers (DTBC). This study aims to delineate the proteogenomic landscape of DTBC tumors compared to luminal A (LumA) tumors. Methods We retrospectively collected a total of 117 untreated primary breast tumor specimens, focusing on DTBC subtypes. Breast tumors were processed by laser microdissection (LMD) to enrich tumor cells. DNA, RNA, and protein were simultaneously extracted from each tumor preparation, followed by whole genome sequencing, paired-end RNA sequencing, global proteomics and phosphoproteomics. Differential feature analysis, pathway analysis and survival analysis were performed to better understand DTBC and investigate biomarkers. Results We observed distinct variations in gene mutations, structural variations, and chromosomal alterations between DTBC and LumA breast tumors. DTBC tumors predominantly had more mutations inTP53,PLXNB3, Zinc finger genes, and fewer mutations inSDC2,CDH1,PIK3CA,SVIL, andPTEN. Notably, Cytoband 1q21, which contains numerous cell proliferation-related genes, was significantly amplified in the DTBC tumors. LMD successfully minimized stromal components and increased RNA–protein concordance, as evidenced by stromal score comparisons and proteomic analysis. Distinct DTBC and LumA-enriched clusters were observed by proteomic and phosphoproteomic clustering analysis, some with survival differences. Phosphoproteomics identified two distinct phosphoproteomic profiles for high relapse-risk and low relapse-risk basal-like tumors, involving several genes known to be associated with breast cancer oncogenesis and progression, includingKIAA1522,DCK,FOXO3,MYO9B,ARID1A,EPRS,ZC3HAV1, andRBM14. Lastly, an integrated pathway analysis of multi-omics data highlighted a robust enrichment of proliferation pathways in DTBC tumors. Conclusions This study provides an integrated proteogenomic characterization of DTBC vs LumA with tumor cells enriched through laser microdissection. We identified many common features of DTBC tumors and the phosphopeptides that could serve as potential biomarkers for high/low relapse-risk basal-like BC and possibly guide treatment selections.

Oncology↗
DOE OSTI2024

Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine

Cancer exhibits substantial heterogeneity, manifesting as distinct morphological and molecular variations across tumors, which frequently undermines the efficacy of conventional oncological treatments. Developments in multiomics and sequencing technologies have paved the way for unraveling this heterogeneity. Nevertheless, the complexity of the data gathered from these methods cannot be fully interpreted through multimodal data analysis alone. Mathematical modeling plays a crucial role in delineating the underlying mechanisms to explain sources of heterogeneity using patient-specific data. Intra-tumoral diversity necessitates the development of precision oncology therapies utilizing multiphysics, multiscale mathematical models for cancer. This review discusses recent advancements in computational methodologies for precision oncology, highlighting the potential of cancer digital twins to enhance patient-specific decision-making in clinical settings. We review computational efforts in building patient-informed cellular and tissue-level models for cancer and propose a computational framework that utilizes agent-based modeling as an effective conduit to integrate cancer systems models that encode signaling at the cellular scale with digital twin models that predict tissue-level response in a tumor microenvironment customized to patient information. Furthermore, we discuss machine learning approaches to building surrogates for these complex mathematical models. These surrogates can potentially be used to conduct sensitivity analysis, verification, validation, and uncertainty quantification, which is especially important for tumor studies due to their dynamic nature.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Comparative Molecular Analysis of Cancer Behavior Cultured In Vitro, In Vivo, and Ex Vivo

Current pre-clinical models of cancer fail to recapitulate the cancer cell behavior in primary tumors primarily because of the lack of a deeper understanding of the effects that the microenvironment has on cancer cell phenotype. Transcriptomic profiling of 4T1 murine mammary carcinoma cells from 2D and 3D cultures, subcutaneous or orthotopic allografts (from immunocompetent or immunodeficient mice), as well as ex vivo tumoroids, revealed differences in molecular signatures including altered expression of genes involved in cell cycle progression, cell signaling and extracellular matrix remodeling. The 3D culture platforms had more in vivo-like transcriptional profiles than 2D cultures. In vivo tumors had more cells undergoing epithelial-to-mesenchymal transition (EMT) while in vitro cultures had cells residing primarily in an epithelial or mesenchymal state. Ex vivo tumoroids incorporated aspects of in vivo and in vitro culturing, retaining higher abundance of cells undergoing EMT while shifting cancer cell fate towards a more mesenchymal state. Cellular heterogeneity surveyed by scRNA-seq revealed that ex vivo tumoroids, while rapidly expanding cancer and fibroblast populations, lose a significant proportion of immune components. This study emphasizes the need to improve in vitro culture systems and preserve syngeneic-like tumor composition by maintaining similar EMT heterogeneity as well as inclusion of stromal subpopulations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Machine learning-assisted elucidation of CD81–CD44 interactions in promoting cancer stemness and extracellular vesicle integrity

Tumor-initiating cells with reprogramming plasticity or stem-progenitor cell properties (stemness) are thought to be essential for cancer development and metastatic regeneration in many cancers; however, elucidation of the underlying molecular network and pathways remains demanding. Combining machine learning and experimental investigation, here we report CD81, a tetraspanin transmembrane protein known to be enriched in extracellular vesicles (EVs), as a newly identified driver of breast cancer stemness and metastasis. Using protein structure modeling and interface prediction-guided mutagenesis, we demonstrate that membrane CD81 interacts with CD44 through their extracellular regions in promoting tumor cell cluster formation and lung metastasis of triple negative breast cancer (TNBC) in human and mouse models. In-depth global and phosphoproteomic analyses of tumor cells deficient with CD81 or CD44 unveils endocytosis-related pathway alterations, leading to further identification of a quality-keeping role of CD44 and CD81 in EV secretion as well as in EV-associated stemness-promoting function. CD81 is coexpressed along with CD44 in human circulating tumor cells (CTCs) and enriched in clustered CTCs that promote cancer stemness and metastasis, supporting the clinical significance of CD81 in association with patient outcomes. Our study highlights machine learning as a powerful tool in facilitating the molecular understanding of new molecular targets in regulating stemness and metastasis of TNBC.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

CD46 targeted 212 Pb alpha particle radioimmunotherapy for prostate cancer treatment

We recently identified CD46 as a novel prostate cancer cell surface antigen that shows lineage independent expression in both adenocarcinoma and small cell neuroendocrine subtypes of metastatic castration resistant prostate cancer (mCRPC), discovered an internalizing human monoclonal antibody YS5 that binds to a tumor selective CD46 epitope, and developed a microtubule inhibitor-based antibody drug conjugate that is in a multi-center phase I trial for mCRPC (NCT03575819). Here we report the development of a novel CD46-targeted alpha therapy based on YS5. We conjugated 212 Pb, an in vivo generator of alpha-emitting 212 Bi and 212 Po, to YS5 through the chelator TCMC to create the radioimmunoconjugate, 212 Pb-TCMC-YS5. We characterized 212 Pb-TCMC-YS5 in vitro and established a safe dose in vivo. We next studied therapeutic efficacy of a single dose of 212 Pb-TCMC-YS5 using three prostate cancer small animal models: a subcutaneous mCRPC cell line-derived xenograft (CDX) model (subcu-CDX), an orthotopically grafted mCRPC CDX model (ortho-CDX), and a prostate cancer patient-derived xenograft model (PDX). In all three models, a single dose of 0.74 MBq (20 µCi) 212 Pb-TCMC-YS5 was well tolerated and caused potent and sustained inhibition of established tumors, with significant increases of survival in treated animals. A lower dose (0.37 MBq or 10 µCi 212 Pb-TCMC-YS5) was also studied on the PDX model, which also showed a significant effect on tumor growth inhibition and prolongation of animal survival. These results demonstrate that 212 Pb-TCMC-YS5 has an excellent therapeutic window in preclinical models including PDXs, opening a direct path for clinical translation of this novel CD46-targeted alpha radioimmunotherapy for mCRPC treatment.

212Pb↗
DOE OSTI2022

Investigating key cell types and molecules dynamics in PyMT mice model of breast cancer through a mathematical model

The most common kind of cancer among women is breast cancer. Understanding the tumor microenvironment and the interactions between individual cells and cytokines assists us in arriving at more effective treatments. Here, we develop a data-driven mathematical model to investigate the dynamics of key cell types and cytokines involved in breast cancer development. We use time-course gene expression profiles of a mouse model to estimate the relative abundance of cells and cytokines. We then employ a least-squares optimization method to evaluate the model’s parameters based on the mice data. The resulting dynamics of the cells and cytokines obtained from the optimal set of parameters exhibit a decent agreement between the data and predictions. We perform a sensitivity analysis to identify the crucial parameters of the model and then perform a local bifurcation on them. The results reveal a strong connection between adipocytes, IL6, and the cancer population, suggesting them as potential targets for therapies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

High Accuracy Indicators of Androgen Suppression Therapy Failure for Prostate Cancer—A Modeling Study

Prostate cancer is a serious public health concern in the United States. The primary obstacle to effective long-term management for prostate cancer patients is the eventual development of treatment resistance. Due to the uniquely chaotic nature of the neoplastic genome, it is difficult to determine the evolution of tumor composition over the course of treatment. Hence, a drug is often applied continuously past the point of effectiveness, thereby losing any potential treatment combination with that drug permanently to resistance. If a clinician is aware of the timing of resistance to a particular drug, then they may have a crucial opportunity to adjust the treatment to retain the drug’s usefulness in a potential treatment combination or strategy. In this study, we investigate new methods of predicting treatment failure due to treatment resistance using a novel mechanistic model built on an evolutionary interpretation of Droop cell quota theory. We analyze our proposed methods using patient PSA and androgen data from a clinical trial of intermittent treatment with androgen deprivation therapy. Our results produce two indicators of treatment failure. The first indicator, proposed from the evolutionary nature of the cancer population, is calculated using our mathematical model with a predictive accuracy of 87.3% (sensitivity: 96.1%, specificity: 65%). The second indicator, conjectured from the implication of the first indicator, is calculated directly from serum androgen and PSA data with a predictive accuracy of 88.7% (sensitivity: 90.2%, specificity: 85%). Our results demonstrate the potential and feasibility of using an evolutionary tumor dynamics model in combination with the appropriate data to aid in the adaptive management of prostate cancer.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Floor and ceiling effects in the EORTC QLQ-C30 Physical Functioning subscale among patients with advanced or metastatic breast cancer

The European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire Core 30 Physical Functioning subscale is a widely used patient-reported outcome measure that quantifies cancer patients' physical functioning. Strong floor/ceiling effects can affect a scale's sensitivity to change. The aim of this study was to characterize floor/ceiling effects of the physical functioning domain in patients with advanced/metastatic breast cancer enrolled in commercial clinical trials and a community-based trial. The clinical trial cohort comprised patients from 5 registrational trials submitted to the Food and Drug Administration for review (2010-2017). The community cohort comprised a subgroup of patients from the Alliance Patient Reported Outcomes to Enhance Cancer Treatment (PRO-TECT) trial. The distribution of patient responses to Physical Functioning items and the summed score were assessed at the baseline and 3-month follow-up for both cohorts. Descriptive statistics were used to determine floor/ceiling effects at the item and scale levels. The clinical trial cohort and the community cohort consisted of 2407 and 178 patients, respectively. Twenty-four percent or more of the respondents reported “not at all” for having trouble/needing help with each Physical Functioning item across both cohorts and measurement time points. Fourteen to twenty percent of the patients scored perfectly (100 of 100) on the Physical Functioning subscale summary measure (where higher scores indicated better physical functioning) across both cohorts and time points. Minor floor effects and notable ceiling effects were found at the item and scale levels of the Physical Functioning subscale, regardless of cohort, and this creates some uncertainty about its ability to detect changes in physical functioning among high-functioning patients. In conclusion, investigators may consider adding additional high-functioning items from the EORTC's item library to more accurately describe the impact of anticancer treatment on patients' physical functioning.

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↗