There has long been an interest in understanding how the hazards from spaceflight may trigger or exacerbate human diseases. With the goal of advancing our knowledge on physiological changes during space travel, NASA GeneLab provides an open-source repository of multi-omics data from real and simulated spaceflight studies. Alone, this data enables identification of biological changes during spaceflight, but cannot infer how that may impact an astronaut at the phenotypic level. To bridge this gap, Scalable Precision Medicine Oriented Knowledge Engine (SPOKE), a heterogeneous knowledge graph connecting biological and clinical data from over 30 databases, was used in combination with GeneLab transcriptomic data from six studies. This integration identified critical symptoms and physiological changes incurred during spaceflight.

Introduction: RNA sequencing (RNA-seq) data from space biology experiments promise to yield invaluable insights into the effects of spaceflight on terrestrial biology. However, sample numbers from each study are low due to limited crew availability, hardware, and space. To increase statistical power, spaceflight RNA-seq datasets from different missions are often aggregated together. However, this can introduce technical variation or “batch effects”, often due to differences in sample handling, sample processing, and sequencing platforms. Several computational methods have been developed to correct for technical batch effects, thereby reducing their impact on true biological signals.Methods: In this study, we combined 7 mouse liver RNA-seq datasets from NASA GeneLab (part of the NASA Open Science Data Repository) to evaluate several common batch effect correction methods (ComBat and ComBat-seq from the sva R package, and Median Polish, Empirical Bayes, and ANOVA from the MBatch R package). Principal component analysis (PCA) was used to identify library preparation method and mission as the primary sources of batch effect among the technical variables in the combined dataset. We next quantitatively evaluated the ability of each of the indicated methods to correct for each identified technical batch variable using the following criteria: BatchQC, PCA, dispersion separability criterion, log fold change correlation, and differential gene expression analysis. Each batch variable/correction method combination was then assessed using a custom scoring approach to identify the optimal correction method for the combined dataset, by geometrically probing the space of all allowable scoring functions to yield an aggregate volume-based scoring measure.Results and Discussion: Using the method described for the combined dataset in this study, the library preparation variable/ComBat correction method pair out ranked the other candidate pairs, suggesting that this combined dataset should be corrected for library preparation using the ComBat correction method prior to downstream analysis. We describe the GeneLab multi-study analysis and visualization portal which will allow users to access the publicly available space biology ‘omics data, select multiple studies to combine for analysis, and examine the presence or absence of batch effects using multiple metrics. If the user chooses to perform batch effect correction, the scoring approach described here can be implemented to identify the optimal correction method to use for their specific combined dataset prior to analysis.

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Extensive preclinical studies of several groups using tumor cells co-cultured with bone marrow stromal cells (BMSCs) has documented that the potent anti-MM activity of the proteasome inhibitor bortezomib is not suppressed by BMSCs (e.g. primary and immortalized BMSCs). Using our compartment-specific bioluminescence imaging (CS-BLI) assays, we extended these observations to larger panels of MM cell lines. We observed, however, a recurrent pattern that primary CD138+ MM tumor cells from bortezomib-refractory patients recurrently exhibited substantial in vitro response to clinically-achievable concentrations and durations of bortezomib treatment. To simulate this clinicopathological observation, MM.1R-Luc+ cells were injected i.v. in SCID-beige mice and treated with bortezomib (0.5 mg/kg s.c. twice weekly for 5 weeks): diffuse MM tumors initially responded to bortezomib, but eventually became refractory. These in vivo-resistant MM cells were isolated from the mice and were treated in vitro with bortezomib, exhibiteing similar responsinveness to this agent as their isogenic bortezomib-naive MM cells, To further address the possibility that this represents a previously underexplored form of a microenvironment-induced alteration in bortezomib responsiveness, we studied how MM cells respond to pharmacological proteasome inhibition after variable times of co-culture with BMSCs prior to bortezomib exposure. We observed that prolonged tumor-stromal co-culture (48-96hrs) prior to initiation of bortezomib treatment did not affect drug sensitivity for many of the MM cell lines (OPM2, H929, UM9, KMS11, KMS18 and RPMI-8226) tested. Notably, prolonged co-cultures with BMSCs prior to bortezomib treatment enhanced the activity of this agent for other MM cell lines (e.g. OPM1, Dox40, OCI-My5, KMS12BM or KMS18). However, MM.1S and MM.1R cells, when exposed to extended co-cultures with BMSCs prior to initiation of drug exposure, exhibited significant attenuation (2-3 fold increase of IC50 values) of their response to bortezomib in several independent replicate experiments. In support of these in vitro results, heterotypic s.c. xenografts of Luc+ MM.1S cells co-implanted with Luc-negative BMSCs did not show significant reduction in MM tumor growth with bortezomib treatment (0.5 mg/kg s.c. twice weekly for 5 weeks) compared to vehicle-treated controls (p=0.13), as quantified by bioluminescence imaging. In co-cultures with BMSCs, MM.1S and MM.1R cells also exhibited suppression of their response to carfilzomib (the degree of this stroma-induced resistance was more pronounced that in the case of bortezomib for these 2 cell lines). Consistent with these observations, in vivo administration of carfilzomib in the orthotopic model of diffuse bone lesions of MM.1R-Luc+ cells was associated with less pronounced reduction in tumor growth, compared to bortezomib treatment (p<0.03). These results suggest that the stroma-induced attenuation of activity against a subset of MM cells represents a class-effect for this group of therapeutics, despite quantitative differences between different proteasome inhibitors. Mechanistically, we determined a distinct transcriptional signature of stroma-induced transcripts which are overexpressed in refractory myeloma patients with significantly shorter overall survival (p<0.03, log-rank tests) after bortezomib treatment. Our results in vitro and in vivo support the notion that the responses of MM cells to proteasome inhibition can exhibit substantial plasticity depending on the specific microenvironmental context with which these MM cells interact. We also identify prognostically-relevant candidate molecular mediators of stroma-induced resistance to proteasome-inhibitor based therapy in MM. Disclosures: No relevant conflicts of interest to declare.