Shifting Gears in Precision Oncology—Challenges and Opportunities of Integrative Data Analysis

For decades, research relating to modification of host immunity towards antitumor response activation has been ongoing, with the breakthrough discovery of immune-checkpoint blockers. Several biomarkers with potential predictive value have been reported in recent studies for these novel therapies. However, with the plethora of therapeutic options existing for a given cancer entity, modern oncology is now being confronted with multifactorial interpretation to devise “the best therapy” for the individual patient. Into the bargain come the multiverse guidelines for established and emerging diagnostic biomarkers, as well as the complex interplay between cancer cells and tumor microenvironment, provoking immense challenges in the therapy decision-making process. Through this review, we present various molecular diagnostic modalities and techniques, such as genomics, immunohistochemistry and quantitative image analysis, which have the potential of becoming powerful tools in the development of an optimal treatment regime when analogized with patient characteristics. We will summarize the underlying complexities of these methods and shed light upon the necessary considerations and requirements for data integration. It is our hope to provide compelling evidence to emphasize on the need for inclusion of integrative data analysis in modern cancer therapy, and thereupon paving a path towards precision medicine and better patient outcomes.

Mind-Body Practices and Self-Enhancement: Direct Replications of Gebauer et al.’s (2018) Experiments 1 and 2

Mind-body practices such as yoga and meditation are often believed to instill a “quiet ego,” entailing less self-enhancement. In two experiments, however, Gebauer et al. (2018) demonstrated that mind-body practices may actually increase self-enhancement, particularly because such practices become self-central bases for self-esteem. We conducted preregistered replications of both of Gebauer et al.’s experiments. Experiment 1 was a field study of Canadian yoga students ( N = 97), and Experiment 2 was a multiwave meditation intervention among Canadian university students ( N = 300). Our results supported Gebauer et al.’s original conclusions that mind-body practices increase self-enhancement. Although the self-centrality effects were not clearly replicated in either experiment, we found evidence that measurement and sampling differences may explain this discrepancy. Moreover, an integrative data analysis of the original and the replication data strongly supported all of Gebauer et al.’s conclusions. In short, we provide new evidence against the ego-quieting perspective and in support of the self-centrality interpretation of mind-body practices.

MEASUREMENT PROPERTIES OF A STANDARDIZED ELICITED IMITATION TEST: AN INTEGRATIVE DATA ANALYSIS

Elicited Imitation Tests (EITs) are commonly used in second language acquisition (SLA)/bilingualism research contexts to assess the general oral proficiency of study participants. While previous studies have provided valuable EIT construct-related validity evidence, some key gaps remain. This study uses an integrative data analysis to further probe the validity of the Korean EIT score interpretations by examining the performances of 318 Korean learners (198 second language, 79 foreign language, and 41 heritage) on the Korean EIT scored by five different raters. Expanding on previous EIT validation efforts, this study (a) examined both inter-rater reliability and differences in rater severity, (b) explored measurement bias across subpopulations of language learners, (c) identified relevant linguistic features which relate to item difficulty, and (d) provided a norm-referenced interpretation for Korean EIT scores. Overall, findings suggest that the Korean EIT can be used in diverse SLA/bilingualism research contexts, as it measures ability similarly across subgroups and raters.

Leveraging Novel Integrated Single-Cell Analyses to Define HIV-1 Latency Reversal

While suppressive antiretroviral therapy can effectively limit HIV-1 replication and evolution, it leaves behind a residual pool of integrated viral genomes that persist in a state of reversible nonproductive infection, referred to as the HIV-1 reservoir. HIV-1 infection models were established to investigate HIV-1 latency and its reversal; recent work began to probe the dynamics of HIV-1 latency reversal at single-cell resolution. Signals that establish HIV-1 latency and govern its reactivation are complex and may not be completely resolved at the cellular and regulatory levels by the aggregated measurements of bulk cellular-sequencing methods. High-throughput single-cell technologies that characterize and quantify changes to the epigenome, transcriptome, and proteome continue to rapidly evolve. Combinations of single-cell techniques, in conjunction with novel computational approaches to analyze these data, were developed and provide an opportunity to improve the resolution of the heterogeneity that may exist in HIV-1 reactivation. In this review, we summarize the published single-cell HIV-1 transcriptomic work and explore how cutting-edge advances in single-cell techniques and integrative data-analysis tools may be leveraged to define the mechanisms that control the reversal of HIV-1 latency.