Feasibility of wearable sensor signals and self-reported symptoms to prompt at-home testing for acute respiratory viruses in the USA (DETECT-AHEAD): a decentralised, randomised controlled trial.

BACKGROUND: Early identification of an acute respiratory infection is important for reducing transmission and enabling earlier therapeutic intervention. We aimed to prospectively evaluate the feasibility of home-based diagnostic self-testing of viral pathogens in individuals prompted to do so on the basis of self-reported symptoms or individual changes in physiological parameters detected via a wearable sensor. METHODS: DETECT-AHEAD was a prospective, decentralised, randomised controlled trial carried out in a subpopulation of an existing cohort (DETECT) of individuals enrolled in a digital-only observational study in the USA. Participants aged 18 years or older were randomly assigned (1:1:1) with a block randomisation scheme stratified by under-represented in biomedical research status. All participants were offered a wearable sensor (Fitbit Sense smartwatch). Participants in groups 1 and 2 received an at-home self-test kit (Alveo be.well) for two acute respiratory viral pathogens: SARS-CoV-2 and respiratory syncytial virus. Participants in group 1 could be alerted through the DETECT study app to take the at-home test on the basis of changes in their physiological data (as detected by our algorithm) or due to self-reported symptoms; those in group 2 were prompted via the app to self-test only due to symptoms. Group 3 served as the control group, without alerts or home testing capability. The primary endpoints, assessed on an intention-to-treat basis, were the number of acute respiratory infections presented (self-reported) and diagnosed (electronic health record), and the number of participants using at-home testing in groups 1 and 2. This trial is registered with ClinicalTrials.gov, NCT04336020. FINDINGS: Between Sept 28 and Dec 30, 2021, 450 participants were recruited and randomly assigned to group 1 (n=149), group 2 (n=151), or group 3 (n=150). 179 (40%) participants were male, 264 (59%) were female, and seven (2%) identified as other. 232 (52%) were from populations historically under-represented in biomedical research. 118 (39%) of the 300 participants in groups 1 and 2 were prompted to self-test, with 61 (52%) successfully completing self-testing. Participants were prompted to home-test more frequently due to symptoms (41 [28%] in group 1 and 51 [34%] in group 2) than due to detected physiological changes (26 [17%] in group 1). Significantly more participants in group 1 received alerts to test than did those in group 2 (67 [45%] vs 51 [34%]; p=0·047). Of the 61 individuals who were prompted to test and successfully did so, 19 (31%) tested positive for a viral pathogen-all for SARS-CoV-2. The individuals diagnosed as positive for SARS-CoV-2 in the electronic health record were eight (5%) in group 1, four (3%) in group 2, and two (1%) in group 3, but it was difficult to confirm if they were tied to symptomatic episodes documented in the trial. There were no adverse events. INTERPRETATION: In this direct-to-participant trial, we showed early feasibility of a decentralised programme to prompt individuals to use a viral pathogen diagnostic test based on symptoms tracked in the study app or physiological changes detected using a wearable sensor. Barriers to adequate participation and performance were also identified, which would need to be addressed before large-scale implementation. FUNDING: Janssen Pharmaceuticals.

Gene based prediction of clinically localized prostate cancer progression after radical prostatectomy.

PURPOSE: Accurate estimates of recurrence risk are needed for optimal treatment of patients with clinically localized prostate cancer. We combined an established nomogram and what to our knowledge are novel molecular predictors into a new prognostic model of prostate specific antigen recurrence. MATERIALS AND METHODS: We analyzed gene expression profiles from formalin fixed, paraffin embedded, localized prostate cancer tissues to identify genes associated with prostate specific antigen recurrence. Profiles of the identified markers were reproduced by reverse transcriptase-polymerase chain reaction. We used the profiles of 3 of these genes along with output from the Kattan postoperative nomogram to produce a predictive model of prostate specific antigen recurrence. RESULTS: After variable selection we built a model of prostate specific antigen recurrence combining expression values of 3 genes and the postoperative nomogram. The 3-gene plus nomogram model predicted 5-year prostate specific antigen recurrence with a concordance index of 0.77 in a validation set compared to a concordance index of 0.67 for the nomogram. This model identified a subgroup of patients at high risk for recurrence that was not identified by the nomogram. CONCLUSIONS: This new gene based classifier has superior predictive power compared to that of the 5-year nomogram to assess the risk of prostate specific antigen recurrence in patients with organ confined prostate cancer. Our classifier should provide more accurate stratification of patients into high and low risk groups for treatment decisions and adjuvant clinical trials.

Robust multi-tissue gene panel for cancer detection.

BACKGROUND: We have identified a set of genes whose relative mRNA expression levels in various solid tumors can be used to robustly distinguish cancer from matching normal tissue. Our current feature set consists of 113 gene probes for 104 unique genes, originally identified as differentially expressed in solid primary tumors in microarray data on Affymetrix HG-U133A platform in five tissue types: breast, colon, lung, prostate and ovary. For each dataset, we first identified a set of genes significantly differentially expressed in tumor vs. normal tissue at p-value = 0.05 using an experimentally derived error model. Our common cancer gene panel is the intersection of these sets of significantly dysregulated genes and can distinguish tumors from normal tissue on all these five tissue types. METHODS: Frozen tumor specimens were obtained from two commercial vendors Clinomics (Pittsfield, MA) and Asterand (Detroit, MI). Biotinylated targets were prepared using published methods (Affymetrix, CA) and hybridized to Affymetrix U133A GeneChips (Affymetrix, CA). Expression values for each gene were calculated using Affymetrix GeneChip analysis software MAS 5.0. We then used a software package called Genes@Work for differential expression discovery, and SVM light linear kernel for building classification models. RESULTS: We validate the predictability of this gene list on several publicly available data sets generated on the same platform. Of note, when analysing the lung cancer data set of Spira et al, using an SVM linear kernel classifier, our gene panel had 94.7% leave-one-out accuracy compared to 87.8% using the gene panel in the original paper. In addition, we performed high-throughput validation on the Dana Farber Cancer Institute GCOD database and several GEO datasets. CONCLUSIONS: Our result showed the potential for this panel as a robust classification tool for multiple tumor types on the Affymetrix platform, as well as other whole genome arrays. Apart from possible use in diagnosis of early tumorigenesis, some other potential uses of our methodology and gene panel would be in assisting pathologists in diagnosis of pre-cancerous lesions, determining tumor boundaries, assessing levels of contamination in cell populations in vitro and identifying transformations in cell cultures after multiple passages. Moreover, based on the robustness of this gene panel in identifying normal vs. tumor, mislabelled or misinterpreted samples can be pinpointed with high confidence.

The 76-gene signature defines high-risk patients that benefit from adjuvant tamoxifen therapy.

PURPOSE: To assess the benefit from adjuvant systemic tamoxifen therapy in breast cancer risk groups identified by the previously established prognostic 76-gene signature. METHODS: In 300 lymph node-negative (LNN), estrogen receptor-positive (ER+) breast cancer patients (136 treated with adjuvant tamoxifen, 164 having received no systemic adjuvant therapy), distant metastasis-free survival (DMFS) as a function of the 76-gene signature was determined in a multicenter fashion. RESULTS: In 136 tamoxifen-treated patients, the 76-gene signature identified a group of patients with a poor prognosis [hazard ratio (HR), 4.62; P = 0.0248]. These patients showed a 12.3% absolute benefit of tamoxifen in 10-year DMFS (HR, 0.52; P = 0.0318) compared with untreated high-risk patients. This represented a 71% increase in relative benefit compared with the 7.2% absolute benefit observed for all 300 patients without using the gene signature. In the low-risk group there was no significant 10-year DMFS benefit of tamoxifen. CONCLUSIONS: The 76-gene signature defines high-risk patients who benefit from adjuvant tamoxifen therapy. Although we did not study the value of chemotherapy in this study, low-risk patients identified by the 76-gene signature have a prognosis good enough that chemotherapy would be difficult to justify. The prognosis of these patients is sufficiently good, in fact, that a disease-free benefit for tamoxifen therapy is difficult to prove, though benefits in terms of loco-regional relapse and a reduction in risk for contralateral breast cancer might justify hormonal therapy in these patients.

Development of a clinically feasible molecular assay to predict recurrence of stage II colon cancer.

The 5-year survival rate for patients with Stage II colon cancer is approximately 75%. However, there is no clinical test available to identify the 25% of patients at high risk of recurrence. We have previously identified a 23-gene signature that predicts individual risk for recurrence. The present study tested this gene signature in an independent group of 123 Stage II patients, and the 23-gene signature was highly informative in identifying patients with distant recurrence in both univariate (hazard ratio [HR] 2.51) and multivariate analyses (HR, 2.40). The composition of this representative patient group also allowed us to refine the 23-gene signature to a 7-gene signature that exhibited a similar prognostic power in both univariate (HR, 2.77) and multivariate analyses (HR, 2.87). Furthermore, we developed this prognostic signature into a clinically feasible test with real-time quantitative PCR using standard fixed paraffin-embedded tumor tissues. When a 110-patient cohort was evaluated with the PCR assay, the 7-gene signature, demonstrated to be a strong prognostic factor in both univariate (HR, 6.89) and multivariate analyses (HR, 14.2). These results clearly show the prognostic value of the predefined gene signature for Stage II colon cancer patients. The ability to identify colon cancer patients with an unfavorable outcome may help patients at high risk for recurrence to seek more aggressive therapy.

Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer.

BACKGROUND: Genome-wide measures of gene expression can identify patterns of gene activity that subclassify tumours and might provide a better means than is currently available for individual risk assessment in patients with lymph-node-negative breast cancer. METHODS: We analysed, with Affymetrix Human U133a GeneChips, the expression of 22000 transcripts from total RNA of frozen tumour samples from 286 lymph-node-negative patients who had not received adjuvant systemic treatment. FINDINGS: In a training set of 115 tumours, we identified a 76-gene signature consisting of 60 genes for patients positive for oestrogen receptors (ER) and 16 genes for ER-negative patients. This signature showed 93% sensitivity and 48% specificity in a subsequent independent testing set of 171 lymph-node-negative patients. The gene profile was highly informative in identifying patients who developed distant metastases within 5 years (hazard ratio 5.67 [95% CI 2.59-12.4]), even when corrected for traditional prognostic factors in multivariate analysis (5.55 [2.46-12.5]). The 76-gene profile also represented a strong prognostic factor for the development of metastasis in the subgroups of 84 premenopausal patients (9.60 [2.28-40.5]), 87 postmenopausal patients (4.04 [1.57-10.4]), and 79 patients with tumours of 10-20 mm (14.1 [3.34-59.2]), a group of patients for whom prediction of prognosis is especially difficult. INTERPRETATION: The identified signature provides a powerful tool for identification of patients at high risk of distant recurrence. The ability to identify patients who have a favourable prognosis could, after independent confirmation, allow clinicians to avoid adjuvant systemic therapy or to choose less aggressive therapeutic options.

Novel genes associated with malignant melanoma but not benign melanocytic lesions.

PURPOSE: Cutaneous melanoma is a common, aggressive cancer with increasing incidence. The identification of melanoma-specific deregulated genes could provide molecular markers for lymph node staging assays and further insight into melanoma tumorigenesis. EXPERIMENTAL DESIGN: Total RNA isolated from 45 primary melanoma, 18 benign skin nevi, and 7 normal skin tissue specimens were analyzed on an Affymetrix Hu133A microarray containing 22,000 probe sets. RESULTS: Hierarchical clustering revealed a distinct separation of the melanoma samples from the benign and normal specimens. Novel genes associated with malignant melanoma were identified. Differential gene expression of two melanoma-specific genes, PLAB and L1CAM, were tested by a one-step quantitative reverse transcription-PCR assay on primary malignant melanoma, benign nevi, and normal skin samples, as well as on malignant melanoma lymph node metastasis and melanoma-free lymph nodes. The performance of the markers was compared with conventional melanoma markers such as tyrosinase, gp100, and MART1. CONCLUSION: Our study systematically identified novel melanoma-specific genes and showed the feasibility of using a combination of PLAB and L1CAM in a reverse transcription-PCR assay to differentiate clinically relevant samples containing benign or malignant melanocytes.

Gene expression profiles and molecular markers to predict recurrence of Dukes' B colon cancer.

PURPOSE: The 5-year survival rate of patients with Dukes' B colon cancer is approximately 75%. Identification of the patients at high risk of recurrence in this group would allow better staging and more informed use of adjuvant chemotherapy. In this study, we used DNA chip technology to systematically identify new prognostic markers for tumor relapse in Dukes' B patients. PATIENTS AND METHODS: Using Affymetrix U133a GeneChip containing approximately 22,000 transcripts (Affymetrix, Santa Clara, CA), RNA samples from 74 patients with Dukes' B colon cancer were analyzed. Thirty-one patients developed tumor relapse in less than 3 years, whereas 43 patients remained disease-free for more than 3 years after surgery. Two supervised class prediction approaches were used to identify gene markers that can best discriminate between patients who would experience relapse and patients who would remain disease-free. A multivariate Cox model was built to predict recurrence. RESULTS: Gene expression profiling identified a 23-gene signature that predicts recurrence in Dukes' B patients. This signature was validated in 36 independent patients. The overall performance accuracy was 78%. Thirteen of 18 relapse patients and 15 of 18 disease-free patients were predicted correctly, giving an odds ratio of 13 (95% CI, 2.6 to 65; P =.003). The log-rank test indicated a significant difference in disease-free time between the predicted relapse and disease-free patients (P =.0001). CONCLUSION: The clinical value of these markers is that the patients at a high predicted risk of relapse (13-fold risk) could be upstaged to receive adjuvant therapy, similar to Dukes' C patients. Our data highlight the feasibility of a prognostic assay that could focus more intensive treatment for localized colon cancer.

Molecular profiling of carcinoma of unknown primary and correlation with clinical evaluation.

PURPOSE: To evaluate the feasibility of a 10-gene reverse transcriptase polymerase chain reaction assay to identify the tissue of origin in patients with carcinoma of unknown primary (CUP) site. PATIENTS AND METHODS: Diagnostic biopsy formalin-fixed, paraffin-embedded (FFPE) specimens from 120 patients with CUP were collected retrospectively from Sarah Cannon Research Institute, Nashville, TN, and prospectively from The University of Texas M. D. Anderson Cancer Center, Houston, TX. Tissue of origin assignments by the assay were correlated with clinical and pathologic features and with response to therapy. RESULTS: The assay was successfully performed in 104 patients (87%), and a tissue of origin was assigned in 63 patients (61%). In the remaining 41 patients (39%), the molecular profiles were not specific for the six tumor types detectable by this assay. The tissues of origin most commonly identified were lung, pancreas, and colon; most of these patients had clinical and pathologic features consistent with these diagnoses. Patients with lung and pancreas profiles had poor response to treatment. Patients with colon cancer profiles had better response to colon cancer-specific therapies than they did to empiric CUP therapy with taxane/platinum regimens. Patients with ovarian cancer profiles were atypical, with widespread visceral metastases and a paucity of overt peritoneal involvement. CONCLUSION: This gene expression profiling assay was feasible using FFPE biopsy specimens and identified a putative tissue of origin in 61% of patients with CUP. In most patients, the assigned tissue of origin was compatible with clinicopathologic features and response to treatment. Prospective studies in which assay results are used to direct therapy are indicated.

Multicenter validation of a gene expression-based prognostic signature in lymph node-negative primary breast cancer.

PURPOSE: We previously identified in a single-center study a 76-gene prognostic signature for lymph node-negative (LNN) breast cancer patients. The aim of this study was to validate this gene signature in an independent more diverse population of LNN patients from multiple institutions. PATIENTS AND METHODS: Using custom-designed DNA chips we analyzed the expression of the 76 genes in RNA of frozen tumor samples from 180 LNN patients who did not receive adjuvant systemic treatment. RESULTS: In this independent validation, the 76-gene signature was highly informative in identifying patients with distant metastasis within 5 years (hazard ratio, [HR], 7.41; 95% CI, 2.63 to 20.9), even when corrected for traditional prognostic factors in multivariate analysis (HR, 11.36; 95% CI, 2.67 to 48.4). The actuarial 5- and 10-year distant metastasis-free survival were 96% (95% CI, 89% to 99%) and 94% (95% CI, 83% to 98%), respectively, for the good profile group and 74% (95% CI, 64% to 81%) and 65% (53% to 74%), respectively for the poor profile group. The sensitivity for 5-yr distant metastasis-free survival was 90%, and the specificity was 50%. The positive and negative predictive values were 38% (95% CI, 29% to 47%) and 94% (95% CI, 86% to 97%), respectively. The 76-gene signature was confirmed as a strong prognostic factor in subgroups of estrogen receptor-positive patients, pre- and postmenopausal patients, and patients with tumor sizes 20 mm or smaller. The subgroup of patients with estrogen receptor-negative tumors was considered too small to perform a separate analysis. CONCLUSION: Our data provide a strong methodologic and clinical multicenter validation of the predefined prognostic 76-gene signature in LNN breast cancer patients.