Perceived Benefits and Barriers of a COVID-19 Test to Stay Program in a School District Serving Black or African American People With Low Income, December 2021.

OBJECTIVES: Quarantine after exposure to COVID-19 has resulted in substantial loss of in-person learning in schools from prekindergarten through grade 12. Test to Stay (TTS), a strategy that limits the spread of SARS-CoV-2 while prioritizing in-person learning, requires substantial investment in resources. The objective of this study was to assess the perceived benefits, barriers, and facilitators of implementing TTS in an urban school district in the Midwest serving primarily Black or African American people with low income. METHODS: In December 2021, we used a concurrent mixed-methods approach to understand perceived benefits, barriers, and facilitators of implementing TTS by combining quantitative analysis of telephone surveys conducted with parents (n = 124) and a qualitative inquiry involving key informants from the school district and local health department (n = 22). We analyzed quantitative data using descriptive statistics. We used thematic analysis to analyze qualitative data. RESULTS: Quantitative findings showed that parents supported TTS because it was convenient (n = 83, 97%) and effective (n = 82, 95%) in keeping students learning in person (n = 82, 95%) and preventing the spread of COVID-19 (n = 80, 93%). Qualitative interviews with informants found that having a clear protocol and assigning staff to specified tasks allowed for successful TTS implementation. However, insufficient staffing and testing resources, parent mistrust of testing, and lack of communication from schools were perceived barriers. CONCLUSION: The school community strongly supported TTS despite the many implementation challenges faced. This study emphasized the importance of ensuring resources for equitable implementation of COVID-19 prevention strategies and the critical role of communication.

Alterations in patient plasma microRNA expression profiles following resection of metastatic melanoma.

BACKGROUND AND OBJECTIVES: MicroRNAs (miRs) are noncoding RNAs that regulate protein translation and melanoma progression. Changes in plasma miR expression following surgical resection of metastatic melanoma are under-investigated. We hypothesize differences in miR expression exist following complete surgical resection of metastatic melanoma. METHODS: Blood collection pre- and post-surgical resection was performed in six individuals with solitary melanoma metastases. miR expression in extracted RNA was quantified using the NanoString nCounter Digital Analyzer. RESULTS: Pre- and post-surgical plasma samples contained 216 miRs with expression above baseline. Comparison of postsurgical to preresection samples revealed differential expression of 25 miRs: miR-let-7a, miR-let7g, miR-15a, miR-16, miR-22, miR-30b, miR-126, miR-140, miR-145, miR-148a, miR-150-5p, miR-191, miR-378i, miR-449c, miR-494, miR-513b, miR-548aa, miR-571, miR-587, miR-891b, miR-1260a, miR 1268a, miR-1976, miR-4268, miR-4454 (P < 0.05). Utilizing P < 0.0046 as a cutoff to control for one false positive among the 216 miRs revealed that postsurgical melanoma plasma samples had upregulation of miR-1260a (P = 0.0007) and downregulation of miR-150-5p (P = 0.0026) relative to pre-surgical samples. CONCLUSIONS: Differential expression of miR-150-5p and miR-1260a is present in plasma following surgical resection of metastatic melanoma in this small sample (n = 6) of melanoma patients. Therefore, further investigation of these plasma miRs as noninvasive biomarkers for melanoma is warranted.

MD-Miner: a network-based approach for personalized drug repositioning.

BACKGROUND: Due to advances in next generation sequencing technologies and corresponding reductions in cost, it is now attainable to investigate genome-wide gene expression and variants at a patient-level, so as to better understand and anticipate heterogeneous responses to therapy. Consequently, it is feasible to inform personalized drug treatment decisions using personal genomics data. However, these efforts are limited due to a lack of reliable computational approaches for predicting effective drugs for individual patients. The reverse gene set enrichment analysis (i.e., connectivity mapping) approach and its variants have been widely and successfully used for drug prediction. However, the performance of these methods is limited by undefined mechanism of action (MoA) of drugs and reliance on cohorts of patients rather than personalized predictions for individual patients. RESULTS: In this study, we have developed and evaluated a computational approach, known as Mechanism and Drug Miner (MD-Miner), using a network-based computational approach to predict effective drugs and reveal potential drug mechanisms of action at the level of signaling pathways. Specifically, the patient-specific signaling network is constructed by integrating known disease associated genes with patient-derived gene expression profiles. In parallel, a drug mechanism of action network is constructed by integrating drug targets and z-score profiles of drug-induced gene expression (pre vs. post-drug treatment). Potentially effective candidate drugs are prioritized according to the number of common genes between the patient-specific dysfunctional signaling network and drug MoA network. We evaluated the MD-Miner method on the PC-3 prostate cancer cell line, and showed that it significantly improved the success rate of discovering effective drugs compared with the random selection, and could provide insight into potential mechanisms of action. CONCLUSIONS: This work provides a signaling network-based drug repositioning approach. Compared with the reverse gene signature based drug repositioning approaches, the proposed method can provide clues of mechanism of action in terms of signaling transduction networks.

Identification of NRAS isoform 2 overexpression as a mechanism facilitating BRAF inhibitor resistance in malignant melanoma.

Activating mutations in BRAF are found in 50% of melanomas and although treatment with BRAF inhibitors (BRAFi) is effective, resistance often develops. We now show that recently discovered NRAS isoform 2 is up-regulated in the setting of BRAF inhibitor resistance in melanoma, in both cell lines and patient tumor tissues. When isoform 2 was overexpressed in BRAF mutant melanoma cell lines, melanoma cell proliferation and in vivo tumor growth were significantly increased in the presence of BRAFi treatment. shRNA-mediated knockdown of isoform 2 in BRAFi resistant cells restored sensitivity to BRAFi compared with controls. Signaling analysis indicated decreased mitogen-activated protein kinase (MAPK) pathway signaling and increased phosphoinositol-3-kinase (PI3K) pathway signaling in isoform 2 overexpressing cells compared with isoform 1 overexpressing cells. Immunoprecipitation of isoform 2 validated a binding affinity of this isoform to both PI3K and BRAF/RAF1. The addition of an AKT inhibitor to BRAFi treatment resulted in a partial restoration of BRAFi sensitivity in cells expressing high levels of isoform 2. NRAS isoform 2 may contribute to resistance to BRAFi by facilitating PI3K pathway activation.

Integrative network and transcriptomics-based approach predicts genotype- specific drug combinations for melanoma.

Computational methods for drug combination predictions are needed to identify effective therapies that improve durability and prevent drug resistance in an efficient manner. In this paper, we present SynGeNet, a computational method that integrates transcriptomics data characterizing disease and drug z-score profiles with network mining algorithms in order to predict synergistic drug combinations. We compare SynGeNet to other available transcriptomics-based tools to predict drug combinations validated across melanoma cell lines in three genotype groups: BRAF-mutant, NRAS-mutant and combined. We showed that SynGeNet outperforms other available tools in predicting validated drug combinations and single agents tested as part of additional drug pairs. Interestingly, we observed that the performance of SynGeNet decreased when the network construction step was removed and improved when the proportion of matched-genotype validation cell lines increased. These results suggest that delineating functional information from transcriptomics data via network mining and genomic features can improve drug combination predictions.

Plasma MicroRNA Levels Following Resection of Metastatic Melanoma.

Melanoma remains the leading cause of skin cancer-related deaths. Surgical resection and adjuvant therapies can result in disease-free intervals for stage III and stage IV disease; however, recurrence is common. Understanding microRNA (miR) dynamics following surgical resection of melanomas is critical to accurately interpret miR changes suggestive of melanoma recurrence. Plasma of 6 patients with stage III (n = 2) and stage IV (n = 4) melanoma was evaluated using the NanoString platform to determine pre- and postsurgical miR expression profiles, enabling analysis of more than 800 miRs simultaneously in 12 samples. Principal component analysis detected underlying patterns of miR expression between pre- vs postsurgical patients. Group A contained 3 of 4 patients with stage IV disease (pre- and postsurgical samples) and 2 patients with stage III disease (postsurgical samples only). The corresponding preoperative samples to both individuals with stage III disease were contained in group B along with 1 individual with stage IV disease (pre- and postsurgical samples). Group A was distinguished from group B by statistically significant analysis of variance changes in miR expression (P < <0001). This analysis revealed that group A vs group B had downregulation of let-7b-5p, miR-520f, miR-720, miR-4454, miR-21-5p, miR-22-3p, miR-151a-3p, miR-378e, and miR-1283 and upregulation of miR-126-3p, miR-223-3p, miR-451a, let-7a-5p, let-7g-5p, miR-15b-5p, miR-16-5p, miR-20a-5p, miR-20b-5p, miR-23a-3p, miR-26a-5p, miR-106a-5p, miR-17-5p, miR-130a-3p, miR-142-3p, miR-150-5p, miR-191-5p, miR-199a-3p, miR-199b-3p, and miR-1976. Changes in miR expression were not readily evident in individuals with distant metastatic disease (stage IV) as these individuals may have prolonged inflammatory responses. Thus, inflammatory-driven miRs coinciding with tumor-derived miRs can blunt anticipated changes in expression profiles following surgical resection.

MicroRNA profiling of patient plasma for clinical trials using bioinformatics and biostatistical approaches.

BACKGROUND: MicroRNAs (miRNAs) are short noncoding RNAs that function to repress translation of mRNA transcripts and contribute to the development of cancer. We hypothesized that miRNA array-based technologies work best for miRNA profiling of patient-derived plasma samples when the techniques and patient populations are precisely defined. METHODS: Plasma samples were obtained from five sources: melanoma clinical trial of interferon and bortezomib (12), purchased normal donor plasma samples (four), gastrointestinal tumor bank (nine), melanoma tumor bank (ten), or aged-matched normal donors (eight) for the tumor bank samples. Plasma samples were purified for miRNAs and quantified using NanoString® arrays or by the company Exiqon. Standard biostatistical array approaches were utilized for data analysis and compared to a rank-based analytical approach. RESULTS: With the prospectively collected samples, fewer plasma samples demonstrated visible hemolysis due to increased attention to eliminating factors, such as increased pressure during phlebotomy, small gauge needles, and multiple punctures. Cancer patients enrolled in a melanoma clinical study exhibited the clearest pattern of miRNA expression as compared to normal donors in both the rank-based analytical method and standard biostatistical array approaches. For the patients from the tumor banks, fewer miRNAs (<5) were found to be differentially expressed and the false positive rate was relatively high. CONCLUSION: In order to obtain consistent results for NanoString miRNA arrays, it is imperative that patient cohorts have similar clinical characteristics with a uniform sample preparation procedure. A clinical workflow has been optimized to collect patient samples to study plasma miRNAs.

Global microRNA profiling for diagnostic appraisal of melanocytic Spitz tumors.

BACKGROUND: Melanoma skin cancer remains the leading cause of skin cancer-related deaths. Spitz lesions represent a subset of melanocytic skin lesions characterized by epithelioid or spindled melanocytes organized in nests. These lesions occupy a spectrum ranging from benign Spitz and atypical Spitz lesions all the way to malignant Spitz tumors. Appropriate management is reliant on accurate diagnostic classification, yet this effort remains challenging using current light microscopic techniques. The discovery of novel biomarkers such as microRNAs (miR) may ultimately be a useful diagnostic adjunct for the evaluation of Spitz lesions. miR expression profiles have been suggested for non-Spitz melanomas but have yet to be ascribed to Spitz lesions. We hypothesized that distinct miR expression profiles would be associated with different lesions along the Spitz spectrum. MATERIALS AND METHODS: RNAs extracted from paraffin-embedded, formalin-fixed tissues of 11 resected skin lesions including benign nevi (n = 2), benign Spitz lesions (n = 3), atypical Spitz lesions (n = 3), and malignant Spitz tumors (n = 3) were analyzed by the NanoString platform for simultaneous evaluation of over 800 miRs in each patient sample. RESULTS: Benign Spitz lesions had increased expression of miR-21-5p and miR-363-3p compared with those of benign nevi. Malignant Spitz lesions exhibited overexpression of miR-21-5p, miR-155-5p, and miR-1283 relative to both benign nevi and benign Spitz tumors. Notably, atypical Spitz tumors had increased expression of miR-451a and decreased expression of miR-155-5p expression relative to malignant Spitz lesions. Conversely, atypical Spitz lesions had increased expression of miR-21-5p, miR-34a-5p, miR-451a, miR-1283, and miR-1260a relative to benign Spitz tumors. CONCLUSIONS: Overall, distinct miR profiles are suggested among Spitz lesions of varying malignant potential with some similarities to non-Spitz melanoma tumors. This work demonstrates the feasibility of this analytic method and forms the basis for further validation studies.

'RE:fine drugs': an interactive dashboard to access drug repurposing opportunities.

The process of discovering new drugs has been extremely costly and slow in the last decades despite enormous investment in pharmaceutical research. Drug repurposing enables researchers to speed up the process of discovering other conditions that existing drugs can effectively treat, with low cost and fast FDA approval. Here, we introduce 'RE:fine Drugs', a freely available interactive website for integrated search and discovery of drug repurposing candidates from GWAS and PheWAS repurposing datasets constructed using previously reported methods in Nature Biotechnology. 'RE:fine Drugs' demonstrates the possibilities to identify and prioritize novelty of candidates for drug repurposing based on the theory of transitive Drug-Gene-Disease triads. This public website provides a starting point for research, industry, clinical and regulatory communities to accelerate the investigation and validation of new therapeutic use of old drugs.Database URL: http://drug-repurposing.nationwidechildrens.org.

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System.

The promise of drug repurposing is that existing drugs may be used for new disease indications in order to curb the high costs and time for approval. The goal of computational methods for drug repurposing is to enable solutions for safer, cheaper and faster drug discovery. Towards this end, we developed a novel method that integrates genetic and clinical phenotype data from large-scale GWAS and PheWAS studies with detailed drug information on the concept of transitive Drug-Gene-Disease triads. We created "RE:fine Drugs," a freely available, interactive dashboard that automates gene, disease and drug-based searches to identify drug repurposing candidates. This web-based tool supports a user-friendly interface that includes an array of advanced search and export options. Results can be prioritized in a variety of ways, including but not limited to, biomedical literature support, strength and statistical significance of GWAS and/or PheWAS associations, disease indications and molecular drug targets. Here we provide a protocol that illustrates the functionalities available in the "RE:fine Drugs" system and explores the different advanced options through a case study.

Automatic data source identification for clinical trial eligibility criteria resolution.

Clinical trial coordinators refer to both structured and unstructured sources of data when evaluating a subject for eligibility. While some eligibility criteria can be resolved using structured data, some require manual review of clinical notes. An important step in automating the trial screening process is to be able to identify the right data source for resolving each criterion. In this work, we discuss the creation of an eligibility criteria dataset for clinical trials for patients with two disparate diseases, annotated with the preferred data source for each criterion (i.e., structured or unstructured) by annotators with medical training. The dataset includes 50 heart-failure trials with a total of 766 eligibility criteria and 50 trials for chronic lymphocytic leukemia (CLL) with 677 criteria. Further, we developed machine learning models to predict the preferred data source: kernel methods outperform simpler learning models when used with a combination of lexical, syntactic, semantic, and surface features. Evaluation of these models indicates that the performance is consistent across data from both diagnoses, indicating generalizability of our method. Our findings are an important step towards ongoing efforts for automation of clinical trial screening.

Gene expression profiling of the human natural killer cell response to Fc receptor activation: unique enhancement in the presence of interleukin-12.

BACKGROUND: Traditionally, the CD56(dim)CD16(+) subset of Natural Killer (NK) cells has been thought to mediate cellular cytotoxicity with modest cytokine secretion capacity. However, studies have suggested that this subset may exert a more diverse array of immunological functions. There exists a lack of well-developed functional models to describe the behavior of activated NK cells, and the interactions between signaling pathways that facilitate effector functions are not well understood. In the present study, a combination of genome-wide microarray analyses and systems-level bioinformatics approaches were utilized to elucidate the transcriptional landscape of NK cells activated via interactions with antibody-coated targets in the presence of interleukin-12 (IL-12). METHODS: We conducted differential gene expression analysis of CD56(dim)CD16(+) NK cells following FcR stimulation in the presence or absence of IL-12. Next, we functionally characterized gene sets according to patterns of gene expression and validated representative genes using RT-PCR. IPA was utilized for biological pathway analysis, and an enriched network of interacting genes was generated using GeneMANIA. Furthermore, PAJEK and the HITS algorithm were employed to identify important genes in the network according to betweeness centrality, hub, and authority node metrics. RESULTS: Analyses revealed that CD56(dim)CD16(+) NK cells co-stimulated via the Fc receptor (FcR) and IL-12R led to the expression of a unique set of genes, including genes encoding cytotoxicity receptors, apoptotic proteins, intracellular signaling molecules, and cytokines that may mediate enhanced cytotoxicity and interactions with other immune cells within inflammatory tissues. Network analyses identified a novel set of connected key players, BATF, IRF4, TBX21, and IFNG, within an integrated network composed of differentially expressed genes in NK cells stimulated by various conditions (immobilized IgG, IL-12, or the combination of IgG and IL-12). CONCLUSIONS: These results are the first to address the global mechanisms by which NK cells mediate their biological functions when encountering antibody-coated targets within inflammatory sites. Moreover, this study has identified a set of high-priority targets for subsequent investigation into strategies to combat cancer by enhancing the anti-tumor activity of CD56(dim)CD16(+) NK cells.

Domain Analysis of Integrated Data to Reduce Cost Associated with Liver Disease.

Liver cancer, the fifth most common cancer and second leading cause of cancer-related death among men worldwide, is plagued by not only lack of clinical research, but informatics tools for early detection. Consequently, it presents a major health and cost burden. Among the different types of liver cancer, hepatocellular carcinoma (HCC) is the most common and deadly form, arising from underlying liver disease. Current models for predicting risk of HCC and liver disease are limited to clinical data. A domain analysis of existing research related to screening for HCC and liver disease suggests that metabolic syndrome (MetS) may present oppportunites to detect early signs of liver disease. The purpose of this paper is to (i) provide a domain analysis of the relationship between HCC, liver disease, and metabolic syndrome, (ii) a review of the current disparate sources of data available for MetS diagnosis, and (iii) recommend informatics solutions for the diagnosis of MetS from available administrative (Biometrics, PHA, claims) and laboratory data, towards early prediction of liver disease. Our domain analysis and recommendations incorporate best practices to make meaningful use of available data with the goal of reducing cost associated with liver disease.

Conceptual Knowledge Discovery in Databases for Drug Combinations Predictions in Malignant Melanoma.

The worldwide incidence of melanoma is rising faster than any other cancer, and prognosis for patients with metastatic disease is poor. Current targeted therapies are limited in their durability and/or effect size in certain patient populations due to acquired mechanisms of resistance. Thus, the development of synergistic combinatorial treatment regimens holds great promise to improve patient outcomes. We have previously shown that a model for in-silico knowledge discovery, Translational Ontology-anchored Knowledge Discovery Engine (TOKEn), is able to generate valid relationships between bimolecular and clinical phenotypes. In this study, we have aggregated observational and canonical knowledge consisting of melanoma-related biomolecular entities and targeted therapeutics in a computationally tractable model. We demonstrate here that the explicit linkage of therapeutic modalities with biomolecular underpinnings of melanoma utilizing the TOKEn pipeline yield a set of informed relationships that have the potential to generate combination therapy strategies.

In Silico cancer cell versus stroma cellularity index computed from species-specific human and mouse transcriptome of xenograft models: towards accurate stroma targeting therapy assessment.

BACKGROUND: The current state of the art for measuring stromal response to targeted therapy requires burdensome and rate limiting quantitative histology. Transcriptome measures are increasingly affordable and provide an opportunity for developing a stromal versus cancer ratio in xenograft models. In these models, human cancer cells are transplanted into mouse host tissues (stroma) and together coevolve into a tumour microenvironment. However, profiling the mouse or human component separately remains problematic. Indeed, laser capture microdissection is labour intensive. Moreover, gene expression using commercial microarrays introduces significant and underreported cross-species hybridization errors that are commonly overlooked by biologists. METHOD: We developed a customized dual-species array, H&M array, and performed cross-species and species-specific hybridization measurements. We validated a new methodology for establishing the stroma vs cancer ratio using transcriptomic data. RESULTS: In the biological validation of the H&M array, cross-species hybridization of human and mouse probes was significantly reduced (4.5 and 9.4 fold reduction, respectively; p < 2x10-16 for both, Mann-Whitney test). We confirmed the capability of the H&M array to determine the stromal to cancer cells ratio based on the estimation of cellularity index of mouse/human mRNA content in vitro. This new metrics enable to investigate more efficiently the stroma-cancer cell interactions (e.g. cellularity) bypassing labour intensive requirement and biases of laser capture microdissection. CONCLUSION: These results provide the initial evidence of improved and cost-efficient analytics for the investigation of cancer cell microenvironment, using species-specificity arrays specifically designed for xenografts models.

Network models of genome-wide association studies uncover the topological centrality of protein interactions in complex diseases.

BACKGROUND: While genome-wide association studies (GWAS) of complex traits have revealed thousands of reproducible genetic associations to date, these loci collectively confer very little of the heritability of their respective diseases and, in general, have contributed little to our understanding the underlying disease biology. Physical protein interactions have been utilized to increase our understanding of human Mendelian disease loci but have yet to be fully exploited for complex traits. METHODS: We hypothesized that protein interaction modeling of GWAS findings could highlight important disease-associated loci and unveil the role of their network topology in the genetic architecture of diseases with complex inheritance. RESULTS: Network modeling of proteins associated with the intragenic single nucleotide polymorphisms of the National Human Genome Research Institute catalog of complex trait GWAS revealed that complex trait associated loci are more likely to be hub and bottleneck genes in available, albeit incomplete, networks (OR=1.59, Fisher's exact test p < 2.24 × 10(-12)). Network modeling also prioritized novel type 2 diabetes (T2D) genetic variations from the Finland-USA Investigation of Non-Insulin-Dependent Diabetes Mellitus Genetics and the Wellcome Trust GWAS data, and demonstrated the enrichment of hubs and bottlenecks in prioritized T2D GWAS genes. The potential biological relevance of the T2D hub and bottleneck genes was revealed by their increased number of first degree protein interactions with known T2D genes according to several independent sources (p<0.01, probability of being first interactors of known T2D genes). CONCLUSION: Virtually all common diseases are complex human traits, and thus the topological centrality in protein networks of complex trait genes has implications in genetics, personal genomics, and therapy.

Oligo- and polymetastatic progression in lung metastasis(es) patients is associated with specific microRNAs.

RATIONALE: Strategies to stage and treat cancer rely on a presumption of either localized or widespread metastatic disease. An intermediate state of metastasis termed oligometastasis(es) characterized by limited progression has been proposed. Oligometastases are amenable to treatment by surgical resection or radiotherapy. METHODS: We analyzed microRNA expression patterns from lung metastasis samples of patients with ≤ 5 initial metastases resected with curative intent. RESULTS: Patients were stratified into subgroups based on their rate of metastatic progression. We prioritized microRNAs between patients with the highest and lowest rates of recurrence. We designated these as high rate of progression (HRP) and low rate of progression (LRP); the latter group included patients with no recurrences. The prioritized microRNAs distinguished HRP from LRP and were associated with rate of metastatic progression and survival in an independent validation dataset. CONCLUSION: Oligo- and poly- metastasis are distinct entities at the clinical and molecular level.

Environmental diel variation, parasite loads, and local population structuring of a mixed-mating mangrove fish.

Genetic variation within populations depends on population size, spatial structuring, and environmental variation, but is also influenced by mating system. Mangroves are some of the most productive and threatened ecosystems on earth and harbor a large proportion of species with mixed-mating (self-fertilization and outcrossing). Understanding population structuring in mixed-mating species is critical for conserving and managing these complex ecosystems. Kryptolebias marmoratus is a unique mixed-mating vertebrate inhabiting mangrove swamps under highly variable tidal regimes and environmental conditions. We hypothesized that geographical isolation and ecological pressures influence outcrossing rates and genetic diversity, and ultimately determine the local population structuring of K. marmoratus. By comparing genetic variation at 32 microsatellites, diel fluctuations of environmental parameters, and parasite loads among four locations with different degrees of isolation, we found significant differences in genetic diversity and genotypic composition but little evidence of isolation by distance. Locations also differed in environmental diel fluctuation and parasite composition. Our results suggest that mating system, influenced by environmental instability and parasites, underpins local population structuring of K. marmoratus. More generally, we discuss how the conservation of selfing species inhabiting mangroves and other biodiversity hotspots may benefit from knowledge of mating strategies and population structuring at small spatial scales.

Single sample expression-anchored mechanisms predict survival in head and neck cancer.

Gene expression signatures that are predictive of therapeutic response or prognosis are increasingly useful in clinical care; however, mechanistic (and intuitive) interpretation of expression arrays remains an unmet challenge. Additionally, there is surprisingly little gene overlap among distinct clinically validated expression signatures. These "causality challenges" hinder the adoption of signatures as compared to functionally well-characterized single gene biomarkers. To increase the utility of multi-gene signatures in survival studies, we developed a novel approach to generate "personal mechanism signatures" of molecular pathways and functions from gene expression arrays. FAIME, the Functional Analysis of Individual Microarray Expression, computes mechanism scores using rank-weighted gene expression of an individual sample. By comparing head and neck squamous cell carcinoma (HNSCC) samples with non-tumor control tissues, the precision and recall of deregulated FAIME-derived mechanisms of pathways and molecular functions are comparable to those produced by conventional cohort-wide methods (e.g. GSEA). The overlap of "Oncogenic FAIME Features of HNSCC" (statistically significant and differentially regulated FAIME-derived genesets representing GO functions or KEGG pathways derived from HNSCC tissue) among three distinct HNSCC datasets (pathways:46%, p<0.001) is more significant than the gene overlap (genes:4%). These Oncogenic FAIME Features of HNSCC can accurately discriminate tumors from control tissues in two additional HNSCC datasets (n = 35 and 91, F-accuracy = 100% and 97%, empirical p<0.001, area under the receiver operating characteristic curves = 99% and 92%), and stratify recurrence-free survival in patients from two independent studies (p = 0.0018 and p = 0.032, log-rank). Previous approaches depending on group assignment of individual samples before selecting features or learning a classifier are limited by design to discrete-class prediction. In contrast, FAIME calculates mechanism profiles for individual patients without requiring group assignment in validation sets. FAIME is more amenable for clinical deployment since it translates the gene-level measurements of each given sample into pathways and molecular function profiles that can be applied to analyze continuous phenotypes in clinical outcome studies (e.g. survival time, tumor volume).