An artificial intelligence-powered, patient-centric digital tool for self-management of chronic pain: a prospective, multicenter clinical trial.

OBJECTIVE: To investigate how a behavioral health, artificial intelligence (AI)-powered, digital self-management tool affects the daily functions in adults with chronic back and neck pain. DESIGN: Eligible subjects were enrolled in a 12-week prospective, multicenter, single-arm, open-label study and instructed to use the digital coach daily. Primary outcome was a change in Patient-Reported Outcomes Measurement Information Systems (PROMIS) scores for pain interference. Secondary outcomes were changes in PROMIS physical function, anxiety, depression, pain intensity scores and pain catastrophizing scale (PCS) scores. METHODS: Subjects logged daily activities, using PainDrainerTM, and data analyzed by the AI engine. Questionnaire and web-based data were collected at 6 and 12 weeks and compared to subjects' baseline. RESULTS: Subjects completed the 6- (n = 41) and 12-week (n = 34) questionnaires. A statistically significant Minimal Important Difference (MID) for pain interference was demonstrated in 57.5% of the subjects. Similarly, MID for physical function was demonstrated in 72.5% of the subjects. A pre- to post-intervention improvement in depression score was also statistically significant, observed in 100% of subjects, as was the improvement in anxiety scores, evident in 81.3% of the subjects. PCS mean scores was also significantly decreased at 12 weeks. CONCLUSION: Chronic pain self-management, using an AI-powered, digital coach anchored in behavioral health principles significantly improved subjects' pain interference, physical function, depression, anxiety, and pain catastrophizing over the 12-week study period.

One-year recovery from breast cancer: Importance of tumor and treatment-related factors, resilience, and sociodemographic factors for health-related quality of life.

Aim: This study investigated the changes in health-related quality of life from diagnosis to 1 year after diagnosis in breast cancer (BC) patients and the influence of clinical, psychological, and sociodemographic variables. An additional aim was to explore the mediating and moderating effects of resilience on changes in health-related quality of life. Methods: A longitudinal population-based study was conducted in southern Sweden. Newly diagnosed BC patients filled in measures of health-related quality of life, resilience, and sociodemographic variables at diagnosis (N = 980) and 1 year post-diagnosis (N = 780). Clinical variables were extracted from the Swedish national breast cancer quality registry. Mixed-model analyses were performed. Results: Most health-related quality of life outcomes declined from diagnosis to 1 year post-diagnosis. Role limitations due to emotional problems remained the same, whereas mental health improved. Lower health-related quality of life outcomes were associated with symptomatic detection and axillary dissection. Patients with a higher TNM stage and histologic grade and estrogen receptor (ER)-negative and human epidermal growth factor 2 (HER2)-positive status, who received chemotherapy, antibody therapy, or bisphosphonate therapy, had a steeper decline in outcomes. Changes in resilience were positively associated with all outcomes but did not mediate or moderate changes in any. Resilience at baseline moderated changes in bodily pain, vitality, and mental health, with higher baseline resilience being associated with a steeper decline, possibly due to floor or ceiling effects. Patients with lower socioeconomic status, educational level, and older age had a lower health-related quality of life. Conclusion: Physical health-related quality of life among breast cancer patients declined 1 year post-diagnosis, whereas mental health-related quality of life improved. Low resilient patients may be especially vulnerable at diagnosis. Biopsychosocial assessment at diagnosis can help identify patients who may require additional support. A multidimensional treatment plan should be started early to help overcome the problems in everyday activities.

Transcriptional profiling demonstrates altered characteristics of CD8+ cytotoxic T-cells and regulatory T-cells in TP53-mutated acute myeloid leukemia.

BACKGROUND: Acute myeloid leukemia (AML) patients have limited effect from T-cell-based therapies, such as PD-1 and CTLA-4 blockade. However, recent data indicate that AML patients with TP53 mutation have higher immune infiltration and other immunomodulatory therapies could thus potentially be effective. Here, we performed the transcriptional analysis of distinct T-cell subpopulations from TP53-mutated AML to identify gene expression signatures suggestive of altered functional properties. METHODS: CD8+ cytotoxic T lymphocytes (CTLs), conventional helper T cells (Th), and regulatory T cells (Tregs) were sorted from peripheral blood of AML patients with TP53 mutation (n = 5) and healthy donors (n = 3), using FACS, and the different subpopulations were subsequently subjected to RNA-sequencing. Differentially expressed genes were identified and gene set enrichment analysis (GSEA) was performed to outline altered pathways and exhaustion status. Also, expression levels for a set of genes encoding established and emerging immuno-oncological targets were defined. RESULTS: The results showed altered transcriptional profiles for each of the T-cell subpopulations from TP53-mutated AML as compared to control subjects. IFN-α and IFN-γ signaling were stronger in TP53-mutated AML for both CTLs and Tregs. Furthermore, in TP53-mutated AML as compared to healthy controls, Tregs showed gene expression signatures suggestive of metabolic adaptation to their environment, whereas CTLs exhibited features of exhaustion/dysfunction with a stronger expression of TIM3 as well as enrichment of a gene set related to exhaustion. CONCLUSIONS: The results provide insights on mechanisms underlying the inadequate immune response to leukemic cells in TP53-mutated AML and open up for further exploration toward novel treatment regimens for these patients.

Comprehending the Proteomic Landscape of Ovarian Cancer: A Road to the Discovery of Disease Biomarkers.

Despite recent technological advancements allowing the characterization of cancers at a molecular level along with biomarkers for cancer diagnosis, the management of ovarian cancers (OC) remains challenging. Proteins assume functions encoded by the genome and the complete set of proteins, termed the proteome, reflects the health state. Comprehending the circulatory proteomic profiles for OC subtypes, therefore, has the potential to reveal biomarkers with clinical utility concerning early diagnosis or to predict response to specific therapies. Furthermore, characterization of the proteomic landscape of tumor-derived tissue, cell lines, and PDX models has led to the molecular stratification of patient groups, with implications for personalized therapy and management of drug resistance. Here, we review single and multiple marker panels that have been identified through proteomic investigations of patient sera, effusions, and other biospecimens. We discuss their clinical utility and implementation into clinical practice.

Identification of a serum biomarker signature associated with metastatic prostate cancer.

PURPOSE: Improved early diagnosis and determination of aggressiveness of prostate cancer (PC) is important to select suitable treatment options and to decrease over-treatment. The conventional marker is total prostate specific antigen (PSA) levels in blood, but lacks specificity and ability to accurately discriminate indolent from aggressive disease. EXPERIMENTAL DESIGN: In this study, we sought to identify a serum biomarker signature associated with metastatic PC. We measured 157 analytes in 363 serum samples from healthy subjects, patients with non-metastatic PC and patients with metastatic PC, using a recombinant antibody microarray. RESULTS: A signature consisting of 69 proteins differentiating metastatic PC patients from healthy controls was identified. CONCLUSIONS AND CLINICAL RELEVANCE: The clinical value of this biomarker signature requires validation in larger independent patient cohorts before providing a new prospect for detection of metastatic PC.

Fluorescence Signal Enhancement in Antibody Microarrays Using Lightguiding Nanowires.

Fluorescence-based detection assays play an essential role in the life sciences and medicine. To offer better detection sensitivity and lower limits of detection (LOD), there is a growing need for novel platforms with an improved readout capacity. In this context, substrates containing semiconductor nanowires may offer significant advantages, due to their proven light-emission enhancing, waveguiding properties, and increased surface area. To demonstrate and evaluate the potential of such nanowires in the context of diagnostic assays, we have in this work adopted a well-established single-chain fragment antibody-based assay, based on a protocol previously designed for biomarker detection using planar microarrays, to freestanding, SiO2-coated gallium phosphide nanowires. The assay was used for the detection of protein biomarkers in highly complex human serum at high dilution. The signal quality was quantified and compared with results obtained on conventional flat silicon and plastic substrates used in the established microarray applications. Our results show that using the nanowire-sensor platform in combination with conventional readout methods, improves the signal intensity, contrast, and signal-to-noise by more than one order of magnitude compared to flat surfaces. The results confirm the potential of lightguiding nanowires for signal enhancement and their capacity to improve the LOD of standard diagnostic assays.

The Value of a New Diagnostic Test for Prostate Cancer: A Cost-Utility Analysis in Early Stage of Development.

BACKGROUND: Standard biopsy for prostate cancer diagnosis is an unpleasant and sometimes painful procedure with a detection rate as low as around 50%. Consequently, an accurate blood-based test would be highly desirable to improve the predictive accuracy. However, the clinical value of a new blood test for diagnosing prostate cancer depends on its sensitivity and specificity, in relation to the selected target population. OBJECTIVE: The aim of this analysis was to investigate the health-economic value of introducing a new and more accurate diagnostic blood-based test to identify men in need of a biopsy to diagnose prostate cancer. METHOD: We developed a Discrete Event Simulation Model with outputs including number of biopsies, cancer diagnosis, treatments and prostate cancer deaths. The analysis was performed from a health care perspective. It used epidemiologic data, treatment patterns, and health care costs from the Swedish region Skåne (population of 1.3 million). A 90% sensitivity and specificity of the new test was assumed. RESULTS: Among 31,250 men, age 50-69 years, 16.4% had a PSA between 3.0 and 9.9 µg/L and 28.9% a PSA of 2.0-9.9 µg/L. Testing men with PSA 3.0-9.9 µg/L, as in current clinical practice, decreased the number of biopsies by 3595, detected 61 more cancers, resulting in and two more fatalities and subsequently a loss of 14 QALYs. Cost offsets could justify a test value of SEK 4996. Testing a larger population, PSA 2.0-9.9 µg/L prevented 6 deaths, added 50 QALYs, and could justify a value of the test of SEK 5165, given a value of health of SEK 500,000 per QALY. CONCLUSION: A new blood-based test for prostate cancer has a significant potential to reduce the number of biopsies needed, resulting in reduced health care costs and improve patient care.

Multiplex profiling of serum proteins in solution using barcoded antibody fragments and next generation sequencing.

The composition of serum proteins is reflecting the current health status and can, with the right tools, be used to detect early signs of disease, such as an emerging cancer. An earlier diagnosis of cancer would greatly increase the chance of an improved outcome for the patients. However, there is still an unmet need for proficient tools to decipher the information in the blood proteome, which calls for further technological development. Here, we present a proof-of-concept study that demonstrates an alternative approach for multiplexed protein profiling of serum samples in solution, using DNA barcoded scFv antibody fragments and next generation sequencing. The outcome shows high accuracy when discriminating samples derived from pancreatic cancer patients and healthy controls and represents a scalable alternative for serum analysis.

Psychometric properties of the Connor-Davidson Resilience Scale (CD-RISC) in a non-clinical population in Sweden.

BACKGROUND: The Connor-Davidson Resilience Scale (CD-RISC) is the most widely used scale which assesses psychological resilience. Although it is recommended to be applied as a unidimensional scale, its factor structure, reliability, as well as discriminant and predictive validity need to be assessed when used in a new context. Moreover, the original five-factor structure has not been replicated in previous investigations. This study aimed to explore psychometric properties of the scale in a Swedish context. METHODS: Construct validity of the five-factor model of CD-RISC was assessed using Exploratory and Confirmatory Factor Analyses. Its discriminant validity was assessed in relation to a measure of emotion regulation (Brief Version of the Difficulties in Emotion Regulation Scale) using a Confirmatory Factor Analysis. Predictive validity of CD-RISC was assessed in relation to measures of physical and mental health-related quality of life (The 12-Item Short Form Survey) using hierarchical multiple regression analyses. A population based sample cohort was employed (N = 2599). RESULTS: Exploratory and Confirmatory Factor Analyses suggested a 22-item unidimensional model of CD-RISC. Psychological resilience was found to be independent from the measure of emotion regulation. It was shown to predict both physical and mental health-related quality of life, being especially strongly associated with mental health aspects. CONCLUSIONS: The study showed that the Swedish version of CD-RISC is an instrument with high discriminant and predictive validity, although the original factor structure does not apply in this context. CD-RISC can thus be used to identify individuals with a higher need of psychosocial support, especially relating to mental health needs.

Skin Sensitization Testing-What's Next?

There is an increasing demand for alternative in vitro methods to replace animal testing, and, to succeed, new methods are required to be at least as accurate as existing in vivo tests. However, skin sensitization is a complex process requiring coordinated and tightly regulated interactions between a variety of cells and molecules. Consequently, there is considerable difficulty in reproducing this level of biological complexity in vitro, and as a result the development of non-animal methods has posed a major challenge. However, with the use of a relevant biological system, the high information content of whole genome expression, and comprehensive bioinformatics, assays for most complex biological processes can be achieved. We propose that the Genomic Allergen Rapid Detection (GARD™) assay, developed to create a holistic data-driven in vitro model with high informational content, could be such an example. Based on the genomic expression of a mature human dendritic cell line and state-of-the-art machine learning techniques, GARD™ can today accurately predict skin sensitizers and correctly categorize skin sensitizing potency. Consequently, by utilizing advanced processing tools in combination with high information genomic or proteomic data, we can take the next step toward alternative methods with the same predictive accuracy as today's in vivo methods-and beyond.

Serum Biomarker Signature-Based Liquid Biopsy for Diagnosis of Early-Stage Pancreatic Cancer.

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, with a 5-year survival of < 10% because of diffuse symptoms leading to late-stage diagnosis. That survival could increase significantly if localized tumors could be detected early. Therefore, we used multiparametric analysis of blood samples to obtain a novel biomarker signature of early-stage PDAC. The signature was derived from a large patient cohort, including patients with well-defined early-stage (I and II) PDAC. This biomarker signature was validated subsequently in an independent patient cohort. PATIENTS AND METHODS: The biomarker signature was derived from a case-control study, using a Scandinavian cohort, consisting of 16 patients with stage I, 132 patients with stage II, 65 patients with stage III, and 230 patients with stage IV PDAC, and 888 controls. This signature was validated subsequently in an independent case-control cohort in the United States with 15 patients with stage I, 75 patients with stage II, 15 patients with stage III, and 38 patients with stage IV PDAC, and 219 controls. An antibody microarray platform was used to identify the serum biomarker signature associated with early-stage PDAC. RESULTS: Using the Scandinavian case-control study, a biomarker signature was created, discriminating samples derived from patients with stage I and II from those from controls with a receiver operating characteristic area under the curve value of 0.96. This signature, consisting of 29 biomarkers, was then validated in an independent case-control study in the United States. The biomarker signature could discriminate patients with stage I and II PDAC from controls in this independent patient cohort with a receiver operating characteristic area under the curve value of 0.96. CONCLUSION: This serum biomarker signature might represent a tenable approach to detecting early-stage, localized PDAC if these findings are supported by a prospective validation study.

A multicenter study investigating the molecular fingerprint of psychological resilience in breast cancer patients: study protocol of the SCAN-B resilience study.

BACKGROUND: Individual patients differ in their psychological response when receiving a cancer diagnosis, in this case breast cancer. Given the same disease burden, some patients master the situation well, while others experience a great deal of stress, depression and lowered quality of life. Patients with high psychological resilience are likely to experience fewer stress reactions and better adapt to and manage the life threat and the demanding treatment that follows the diagnosis. If this phenomenon of mastering difficult situations is reflected also in biomolecular processes is not much studied, nor has its capacity for impacting the cancer prognosis been addressed. This project specifically aims, for the first time, to investigate how a breast cancer patient's psychological resilience is coupled to biomolecular parameters using advanced "omics" and, as a secondary aim, whether it relates to prognosis and quality of life one year after diagnosis. METHOD: The study population consists of newly diagnosed breast cancer patients enrolled in the Sweden Cancerome Analysis Network - Breast (SCAN-B) at four hospitals in Sweden. At the time of cancer diagnosis, the patient fills out the standardized method to measure psychological resilience, the "Connor-Davidson Resilience scale" (CD-RISC), the quality of life measure SF-36, as well as providing social and socioeconomic variables. In addition, one blood sample is collected. At the one-year follow-up, the patient will be subjected to the same assessments, and we also collect information regarding smoking, exercise habits, and BMI, as well as patients' trust in the treatment and their satisfaction with the care and treatment. DISCUSSION: This explorative hypothesis-generating project will pave the way for larger validation studies, potentially leading to a standardized method of measuring psychological resilience as an important parameter in cancer care. Revealing the body-mind interaction, in terms of psychological resilience and quality of life, will herald the development of truly personalized psychosocial care and cancer intervention treatment strategies. TRIAL REGISTRATION: This is a retrospectively registered trial at ClinicalTrials.gov, ID: NCT03430492 on February 6, 2018.

The Interface of Pancreatic Cancer With Diabetes, Obesity, and Inflammation: Research Gaps and Opportunities: Summary of a National Institute of Diabetes and Digestive and Kidney Diseases Workshop.

A workshop on "The Interface of Pancreatic Cancer with Diabetes, Obesity, and Inflammation: Research Gaps and Opportunities" was held by the National Institute of Diabetes and Digestive and Kidney Diseases on October 12, 2017. The purpose of the workshop was to explore the relationship and possible mechanisms of the increased risk of pancreatic ductal adenocarcinoma (PDAC) related to diabetes, the role of altered intracellular energy metabolism in PDAC, the mechanisms and biomarkers of diabetes caused by PDAC, the mechanisms of the increased risk of PDAC associated with obesity, and the role of inflammatory events and mediators as contributing causes of the development of PDAC. Workshop faculty reviewed the state of the current knowledge in these areas and made recommendations for future research efforts. Further knowledge is needed to elucidate the basic mechanisms contributing to the role of hyperinsulinemia, hyperglycemia, adipokines, and acute and chronic inflammatory events on the development of PDAC.

Tumor tissue protein signatures reflect histological grade of breast cancer.

Histological grade is one of the most commonly used prognostic factors for patients diagnosed with breast cancer. However, conventional grading has proven technically challenging, and up to 60% of the tumors are classified as histological grade 2, which represents a heterogeneous cohort less informative for clinical decision making. In an attempt to study and extend the molecular puzzle of histologically graded breast cancer, we have in this pilot project searched for additional protein biomarkers in a new space of the proteome. To this end, we have for the first time performed protein expression profiling of breast cancer tumor tissue, using recombinant antibody microarrays, targeting mainly immunoregulatory proteins. Thus, we have explored the immune system as a disease-specific sensor (clinical immunoproteomics). Uniquely, the results showed that several biologically relevant proteins reflecting histological grade could be delineated. In more detail, the tentative biomarker panels could be used to i) build a candidate model classifying grade 1 vs. grade 3 tumors, ii) demonstrate the molecular heterogeneity among grade 2 tumors, and iii) potentially re-classify several of the grade 2 tumors to more like grade 1 or grade 3 tumors. This could, in the long-term run, lead to improved prognosis, by which the patients could benefit from improved tailored care.

Precision diagnostics: moving towards protein biomarker signatures of clinical utility in cancer.

Interest in precision diagnostics has been fuelled by the concept that early detection of cancer would benefit patients; that is, if detected early, more tumours should be resectable and treatment more efficacious. Serum contains massive amounts of potentially diagnostic information, and affinity proteomics has risen as an accurate approach to decipher this, to generate actionable information that should result in more precise and evidence-based options to manage cancer. To achieve this, we need to move from single to multiplex biomarkers, a so-called signature, that can provide significantly increased diagnostic accuracy. This Opinion article focuses on the progress being made in identifying protein biomarker signatures of clinical utility, using blood-based proteomics.

Testing Human Skin and Respiratory Sensitizers-What Is Good Enough?

Alternative methods for accurate in vitro assessment of skin and respiratory sensitizers are urgently needed. Sensitization is a complex biological process that cannot be evaluated accurately using single events or biomarkers, since the information content is too restricted in these measurements. On the contrary, if the tremendous information content harbored in DNA/mRNA could be mined, most complex biological processes could be elucidated. Genomic technologies available today, including transcriptional profiling and next generation sequencing, have the power to decipher sensitization, when used in the right context. Thus, a genomic test platform has been developed, denoted the Genomic Allergen Rapid Detection (GARD) assay. Due to the high informational content of the GARD test, accurate predictions of both the skin and respiratory sensitizing capacity of chemicals, have been demonstrated. Based on a matured dendritic cell line, acting as a human-like reporter system, information about potency has also been acquired. Consequently, multiparametric diagnostic technologies are disruptive test principles that can change the way in which the next generation of alternative methods are designed.

Evaluation of Solid Supports for Slide- and Well-Based Recombinant Antibody Microarrays.

Antibody microarrays have emerged as an important tool within proteomics, enabling multiplexed protein expression profiling in both health and disease. The design and performance of antibody microarrays and how they are processed are dependent on several factors, of which the interplay between the antibodies and the solid surfaces plays a central role. In this study, we have taken on the first comprehensive view and evaluated the overall impact of solid surfaces on the recombinant antibody microarray design. The results clearly demonstrated the importance of the surface-antibody interaction and showed the effect of the solid supports on the printing process, the array format of planar arrays (slide- and well-based), the assay performance (spot features, reproducibility, specificity and sensitivity) and assay processing (degree of automation). In the end, two high-end recombinant antibody microarray technology platforms were designed, based on slide-based (black polymer) and well-based (clear polymer) arrays, paving the way for future large-scale protein expression profiling efforts.