Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome.

BACKGROUND: Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers. METHODS: We present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patient's response to a variety of therapies in multiple cancer types without training on previous treatment response data. We study ENLIGHT in two translationally oriented scenarios: personalized oncology (PO), aimed at prioritizing treatments for a single patient, and clinical trial design (CTD), selecting the most likely responders in a patient cohort. FINDINGS: Evaluating ENLIGHT's performance on 21 blinded clinical trial datasets in the PO setting, we show that it can effectively predict a patient's treatment response across multiple therapies and cancer types. Its prediction accuracy is better than previously published transcriptomics-based signatures and is comparable with that of supervised predictors developed for specific indications and drugs. In combination with the interferon-γ signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, ENLIGHT can potentially enhance clinical trial success for immunotherapies and other monoclonal antibodies by excluding non-responders while overall achieving more than 90% of the response rate attainable under an optimal exclusion strategy. CONCLUSIONS: ENLIGHT demonstrably enhances the ability to predict therapeutic response across multiple cancer types from the bulk tumor transcriptome. FUNDING: This research was supported in part by the Intramural Research Program, NIH and by the Israeli Innovation Authority.

Optimal growth temperature of prokaryotes correlates with class II amino acid composition.

Partitioning of aminoacyl-tRNA synthetases and their associated amino acids into two classes allows us to distinguish between thermophilic and mesophilic species based only on amino acids composition. The CLASSDB program has been developed for amino acid content analysis in organisms treated individually or pooled together to form a pattern of characteristic properties. A strong correlation has been observed between optimal growth temperature (OGT) of organisms and class II amino acids content. Amino acid composition in organisms closely related phylogenetically but dissimilar in their OGT testifies that thermo-adaptation happens rather rapidly on the time scale of evolution.

Retinoic acid stability in stem cell cultures.

It has been reported that retinoids, such as retinoic acid (RA) and retinol (ROL), dissolved in aqueous solutions are susceptible to oxidative damage when exposed to light, air, and relatively high temperatures, conditions that are normal for culturing stem cells. Thus, questions arise regarding the interpretation of results obtained from studies of mouse embryonic stem cells exposed to retinoids because their isomerization state, their stability in culture conditions, and their interactions with other potential differentiation factors in growth media could influence developmental processes under study. Media samples were supplemented with retinoids and exposed to cell culture conditions with and without mouse embryonic stem cells (mESC), and retinoids were extracted and analyzed using HPLC. To determine whether retinoids are stable in media supplemented with fetal bovine serum (FBS) or in chemically-defined, serum-free media, mESC adapted to each type of growth media were investigated. Studies reported here indicate there was little loss or isomerization of at-RA, 9-cis-RA, 13-cis-RA, or ROL in cell cultures grown in serum-supplemented media when cell cultures were maintained in the dark and manipulated and observed under yellow light. In contrast, the stability of both at-RA and ROL were determined to be greatly reduced in serum-free media as compared with serum-supplemented media. Addition of 6 mg/ml bovine serum albumin was found to stabilize retinoids in serum-free media. It was also determined that ROL is less stable than RA in cell culture conditions.