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.

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.

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.

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.

Molecular design of recombinant scFv antibodies for site-specific photocoupling to ß-cyclodextrin in solution and onto solid support.

The ability to design and tailor-make antibodies to meet the biophysical demands required by the vast range of current and future antibody-based applications within biotechnology and biomedicine will be essential. In this proof-of-concept study, we have for the first time tailored human recombinant scFv antibodies for site-specific photocoupling through the use of an unnatural amino acid (UAA) and the dock'n'flash technology. In more detail, we have successfully explored the possibility to expand the genetic code of E. coli and introduced the photoreactive UAA p-benzoyl-L-phenylalanine (pBpa), and showed that the mutated scFv antibody could be expressed in E. coli with retained structural and functional properties, as well as binding affinity. The pBpa group was then used for affinity capture of the mutated antibody by ß-cyclodextrin (ß-CD), which provided the hydrogen atoms to be abstracted in the subsequent photocoupling process upon irradiation at 365nm. The results showed that the pBpa mutated antibody could be site-specifically photocoupled to free and surface (array) immobilized ß-CD. Taken together, this paves the way for novel means of tailoring recombinant scFv antibodies for site-specific photochemical-based tagging, functionalization and immobilization in numerous applications.

Grading breast cancer tissues using molecular portraits.

Tumor progression and prognosis in breast cancer patients are difficult to assess using current clinical and laboratory parameters, where a pathological grading is indicative of tumor aggressiveness. This grading is based on assessments of nuclear grade, tubule formation, and mitotic rate. We report here the first protein signatures associated with histological grades of breast cancer, determined using a novel affinity proteomics approach. We profiled 52 breast cancer tissue samples by combining nine antibodies and label-free LC-MS/MS, which generated detailed quantified proteomic maps representing 1,388 proteins. The results showed that we could define in-depth molecular portraits of histologically graded breast cancer tumors. Consequently, a 49-plex candidate tissue protein signature was defined that discriminated between histological grades 1, 2, and 3 of breast cancer tumors with high accuracy. Highly biologically relevant proteins were identified, and the differentially expressed proteins indicated further support for the current hypothesis regarding remodeling of the tumor microenvironment during tumor progression. The protein signature was corroborated using meta-analysis of transcriptional profiling data from an independent patient cohort. In addition, the potential for using the markers to estimate the likelihood of long-term metastasis-free survival was also indicated. Taken together, these molecular portraits could pave the way for improved classification and prognostication of breast cancer.

Quantitative proteomics targeting classes of motif-containing peptides using immunoaffinity-based mass spectrometry.

The development of high-performance technology platforms for generating detailed protein expression profiles, or protein atlases, is essential. Recently, we presented a novel platform that we termed global proteome survey, where we combined the best features of affinity proteomics and mass spectrometry, to probe any proteome in a species independent manner while still using a limited set of antibodies. We used so called context-independent-motif-specific antibodies, directed against short amino acid motifs. This enabled enrichment of motif-containing peptides from a digested proteome, which then were detected and identified by mass spectrometry. In this study, we have demonstrated the quantitative capability, reproducibility, sensitivity, and coverage of the global proteome survey technology by targeting stable isotope labeling with amino acids in cell culture-labeled yeast cultures cultivated in glucose or ethanol. The data showed that a wide range of motif-containing peptides (proteins) could be detected, identified, and quantified in a highly reproducible manner. On average, each of six different motif-specific antibodies was found to target about 75 different motif-containing proteins. Furthermore, peptides originating from proteins spanning in abundance from over a million down to less than 50 copies per cell, could be targeted. It is worth noting that a significant set of peptides previously not reported in the PeptideAtlas database was among the profiled targets. The quantitative data corroborated well with the corresponding data generated after conventional strong cation exchange fractionation of the same samples. Finally, several differentially expressed proteins, with both known and unknown functions, many relevant for the central carbon metabolism, could be detected in the glucose- versus ethanol-cultivated yeast. Taken together, the study demonstrated the potential of our immunoaffinity-based mass spectrometry platform for reproducible quantitative proteomics targeting classes of motif-containing peptides.

Molecular serum portraits in patients with primary breast cancer predict the development of distant metastases.

The risk of distant recurrence in breast cancer patients is difficult to assess with current clinical and histopathological parameters, and no validated serum biomarkers currently exist. Using a recently developed recombinant antibody microarray platform containing 135 antibodies against 65 mainly immunoregulatory proteins, we screened 240 sera from 64 patients with primary breast cancer. This unique longitudinal sample material was collected from each patient between 0 and 36 mo after the primary operation. The velocity for each serum protein was determined by comparing the samples collected at the primary operation and then 3-6 mo later. A 21-protein signature was identified, using leave-one-out cross-validation together with a backward elimination strategy in a training cohort. This signature was tested and evaluated subsequently in an independent test cohort (prevalidation). The risk of developing distant recurrence after primary operation could be assessed for each patient, using her molecular portraits. The results from this prevalidation study showed that patients could be classified into high- versus low-risk groups for developing metastatic breast cancer with a receiver operating characteristic area under the curve of 0.85. This risk assessment was not dependent on the type of adjuvant therapy received by the patients. Even more importantly, we demonstrated that this protein signature provided an added value compared with conventional clinical parameters. Consequently, we present here a candidate serum biomarker signature able to classify patients with primary breast cancer according to their risk of developing distant recurrence, with an accuracy outperforming current procedures.

Distinct Infiltration of T Cell Populations in Bladder Cancer Molecular Subtypes.

Bladder cancer is a heterogenous disease, and molecular subtyping is a promising method to capture this variability. Currently, the immune compartment in relation to subtypes is poorly characterized. Here, we analyzed the immune compartment in bladder tumors and normal bladder urothelium with a focus on T cell subpopulations using flow cytometry and RNA sequencing. The results were investigated in relation to tumor invasiveness (NMIBC/MIBC) and molecular subtypes according to the Lund Taxonomy system. Whereas the NMIBC/MIBC differed in the overall immune infiltration only, the molecular subtypes differed both in terms of immune infiltration and immune compartment compositions. The Basal/Squamous (Ba/Sq) and genomically unstable (GU) tumors displayed increased immune infiltration compared to urothelial-like (Uro) tumors. Additionally, the GU tumors had a higher proportion of regulatory T cells within the immune compartment compared to Uro tumors. Furthermore, sequencing showed higher levels of exhaustion in CD8+ T cells from GU tumors compared to both Uro tumors and the control. Although no such difference was detected at the transcriptomic level in Uro tumors compared to the controls, CD8+ T cells in Uro tumors showed higher expression of several exhaustion markers at the protein level. Taken together, our findings indicate that depending on the molecular subtype, different immunotherapeutic interventions might be warranted.

Protein expression profiling of formalin-fixed paraffin-embedded tissue using recombinant antibody microarrays.

Proteomics, the large-scale analysis of proteins, is a rapidly evolving field with an increasing number of key clinical applications, such as diagnosis, prognosis, and classification. In order to generate complete protein expression profiles, or protein atlases, any crude sample format must be addressable in a rapid, multiplex, and sensitive manner. A common and clinically central sample format, formalin-fixed, paraffin-embedded (FFPE) tissue material, holds great potential as a source for disease-associated biomarker signatures. However, despite major efforts, extraction and subsequent profiling of proteins from FFPE tissue has proven to be challenging. In this proof-of-concept study, we have demonstrated for the first time that proteins could be extracted, labeled, and subsequently profiled in a multiplex, sensitive, and reproducible manner using recombinant scFv antibody microarrays. Thus, we have added FFPE samples to the list of sample formats available for high-throughput analysis by affinity proteomics, paving the way for the next generation of biomarker-driven discovery projects.

Modelling the benefits of early diagnosis of pancreatic cancer using a biomarker signature.

Pancreatic cancer (PC) has a poor prognosis, with a 5-year survival of 3-4%. This is mainly due to late diagnosis because of diffuse symptoms, where 80-85% of the patients are inoperable. Consequently, early diagnosis would be of significant benefit, resulting in a potential 5-year survival of 30-40%. However, new technologies must be carefully evaluated concerning effectiveness and healthcare costs. We have developed a framework for modelling cost and health effects from early detection of PC, which for the first time allowed us to analyse its cost-effectiveness. A probabilistic cohort model for estimating costs and quality adjusted life-years (QALY) arising from screening for PC, compared to a "wait-and-see"-approach, was designed. The test accuracy, Swedish survival and costs by tumour stage, expected life gain from early detection and pretest probabilities in risk groups, were retrieved from previous investigations. In a cohort of newly diagnosed diabetic patient (incidence 0.71%) the incremental cost per QALY gained (ICER) was €13,500, which is considered cost-effective in Europe. Results were mainly sensitive to the incidence with the ICER ranging from €315 to €204,000 (familial PC 35% and general population 0.046%, respectively). This is the first study focusing on clinical implementation of advanced testing and what is required for novel technologies in cancer care to be cost-effective. The model clearly demonstrated the potential of multiplexed proteomic-testing of PC and also identified the requirements for test accuracy. Consequently, it can serve as a model for assessing the possibilities to introduce advanced test platforms also for other cancer indications.

Proteomic analysis and discovery using affinity proteomics and mass spectrometry.

Antibody-based microarrays are a rapidly evolving affinity-proteomic methodology that recently has shown great promise in clinical applications. The resolution of these proteomic analyses is, however, directly related to the number of data-points, i.e. antibodies, included on the array. Currently, this is a key bottleneck because of limited availability of numerous highly characterized antibodies. Here, we present a conceptually new method, denoted global proteome survey, opening up the possibility to probe any proteome in a species-independent manner while still using a limited set of antibodies. We use context-independent-motif-specific antibodies directed against short amino acid motifs, where each motif is present in up to a few hundred different proteins. First, the digested proteome is exposed to these antibodies, whereby motif-containing peptides are enriched, which then are detected and identified by mass spectrometry. In this study, we profiled extracts from human colon tissue, yeast cells lysate, and mouse liver tissue to demonstrate proof-of-concept.

Serum protein profiling of systemic lupus erythematosus and systemic sclerosis using recombinant antibody microarrays.

Systemic lupus erythematosus (SLE) and systemic sclerosis (SSc) are two severe autoimmune connective tissue diseases. The fundamental knowledge about their etiology is limited and the conditions display complex pathogenesis, multifaceted presentations, and unpredictable courses. Despite significant efforts, the lack of fully validated biomarkers enabling diagnosis, classification, and monitoring of disease activity represents significant unmet clinical needs. In this discovery study, we have for the first time used recombinant antibody microarrays for miniaturized, multiplexed serum protein profiling of SLE and SSc, targeting mainly immunoregulatory proteins. The data showed that several candidate SLE-associated multiplexed serum biomarker signatures were delineated, reflecting disease (diagnosis), disease severity (phenotypic subsets), and disease activity. Selected differentially expressed markers were validated using orthogonal assays and a second, independent patient cohort. Further, biomarker signatures differentiating SLE versus SSc were demonstrated, and the observed differences increased with severity of SLE. In contrast, the data showed that the serum profiles of SSc versus healthy controls were more similar. Hence, we have shown that affinity proteomics could be used to de-convolute crude, nonfractionated serum proteomes, extracting molecular portraits of SLE and SSc, further enhancing our fundamental understanding of these complex autoimmune conditions.

A community standard format for the representation of protein affinity reagents.

Protein affinity reagents (PARs), most commonly antibodies, are essential reagents for protein characterization in basic research, biotechnology, and diagnostics as well as the fastest growing class of therapeutics. Large numbers of PARs are available commercially; however, their quality is often uncertain. In addition, currently available PARs cover only a fraction of the human proteome, and their cost is prohibitive for proteome scale applications. This situation has triggered several initiatives involving large scale generation and validation of antibodies, for example the Swedish Human Protein Atlas and the German Antibody Factory. Antibodies targeting specific subproteomes are being pursued by members of Human Proteome Organisation (plasma and liver proteome projects) and the United States National Cancer Institute (cancer-associated antigens). ProteomeBinders, a European consortium, aims to set up a resource of consistently quality-controlled protein-binding reagents for the whole human proteome. An ultimate PAR database resource would allow consumers to visit one on-line warehouse and find all available affinity reagents from different providers together with documentation that facilitates easy comparison of their cost and quality. However, in contrast to, for example, nucleotide databases among which data are synchronized between the major data providers, current PAR producers, quality control centers, and commercial companies all use incompatible formats, hindering data exchange. Here we propose Proteomics Standards Initiative (PSI)-PAR as a global community standard format for the representation and exchange of protein affinity reagent data. The PSI-PAR format is maintained by the Human Proteome Organisation PSI and was developed within the context of ProteomeBinders by building on a mature proteomics standard format, PSI-molecular interaction, which is a widely accepted and established community standard for molecular interaction data. Further information and documentation are available on the PSI-PAR web site.

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.

Design of recombinant antibody microarrays for membrane protein profiling of cell lysates and tissue extracts.

Generating global protein expression profiles, including also membrane proteins, will be crucial for our understanding of biological processes in health and disease. In this study, we have expanded our antibody microarray technology platform and designed the first human recombinant antibody microarray for membrane proteins targeting crude cell lysates and tissue extracts. We have optimized all key technological parameters and successfully developed a setup for extracting, labeling and analyzing non-fractionated membrane proteomes under non-denaturing conditions. Finally, the platform was also extended and shown to be compatible with simultaneous profiling of both membrane proteins and water-soluble proteins.