Leveraging high-resolution omics data for predicting responses and adverse events to immune checkpoint inhibitors.

A long-standing goal of personalized and precision medicine is to enable accurate prediction of the outcomes of a given treatment regimen for patients harboring a disease. Currently, many clinical trials fail to meet their endpoints due to underlying factors in the patient population that contribute to either poor responses to the drug of interest or to treatment-related adverse events. Identifying these factors beforehand and correcting for them can lead to an increased success of clinical trials. Comprehensive and large-scale data gathering efforts in biomedicine by omics profiling of the healthy and diseased individuals has led to a treasure-trove of host, disease and environmental factors that contribute to the effectiveness of drugs aiming to treat disease. With increasing omics data, artificial intelligence allows an in-depth analysis of big data and offers a wide range of applications for real-world clinical use, including improved patient selection and identification of actionable targets for companion therapeutics for improved translatability across more patients. As a blueprint for complex drug-disease-host interactions, we here discuss the challenges of utilizing omics data for predicting responses and adverse events in cancer immunotherapy with immune checkpoint inhibitors (ICIs). The omics-based methodologies for improving patient outcomes as in the ICI case have also been applied across a wide-range of complex disease settings, exemplifying the use of omics for in-depth disease profiling and clinical use.

Targeted In Vivo Mutagenesis in Yeast Using CRISPR/Cas9 and Hyperactive Cytidine and Adenine Deaminases.

Directed evolution is a preferred strategy to improve the function of proteins such as enzymes that act as bottlenecks in metabolic pathways. Common directed evolution approaches rely on error-prone PCR-based libraries where the number of possible variants is usually limited by cellular transformation efficiencies. Targeted in vivo mutagenesis can advance directed evolution approaches and help to overcome limitations in library generation. In the current study, we aimed to develop a high-efficiency time-controllable targeted mutagenesis toolkit in the yeast Saccharomyces cerevisiae by employing the CRISPR/Cas9 technology. To that end, we fused the dCas9 protein with hyperactive variants of adenine and cytidine deaminases aiming to create an inducible CRISPR-based mutagenesis tool targeting a specific DNA sequence in vivo with extended editing windows and high mutagenesis efficiency. We also investigated the effect of guide RNA multiplexing on the mutagenesis efficiency both phenotypically and on the DNA level.

Multiplexed Guide RNA Expression Leads to Increased Mutation Frequency in Targeted Window Using a CRISPR-Guided Error-Prone DNA Polymerase in Saccharomyces cerevisiae.

Clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 technology, with its ability to target a specific DNA locus using guide RNAs (gRNAs), is particularly suited for targeted mutagenesis. The targeted diversification of nucleotides in Saccharomyces cerevisiae using a CRISPR-guided error-prone DNA polymerase─called yEvolvR─was recently reported. Here, we investigate the effect of multiplexed expression of gRNAs flanking a short stretch of DNA on reversion and mutation frequencies using yEvolvR. Phenotypic assays demonstrate that higher reversion frequencies are observed when expressing multiple gRNAs simultaneously. Next generation sequencing reveals a synergistic effect of multiple gRNAs on mutation frequencies, which is more pronounced in a mutant with a partially defective DNA mismatch repair system. Additionally, we characterize a galactose-inducible yEvolvR, which enables temporal control of mutagenesis. This study demonstrates that multiplex expression of gRNAs and induction of mutagenesis greatly improves the capabilities of yEvolvR for generation of genetic libraries in vivo.

Plasma and Urine Free Glycosaminoglycans as Monitoring and Predictive Biomarkers in Metastatic Renal Cell Carcinoma: A Prospective Cohort Study.

PURPOSE: No liquid biomarkers are approved in metastatic renal cell carcinoma (mRCC) despite the need to predict and monitor response noninvasively to tailor treatment choices. Urine and plasma free glycosaminoglycan profiles (GAGomes) are promising metabolic biomarkers in mRCC. The objective of this study was to explore if GAGomes could predict and monitor response in mRCC. PATIENTS AND METHODS: We enrolled a single-center prospective cohort of patients with mRCC elected for first-line therapy (ClinicalTrials.gov identifier: NCT02732665) plus three retrospective cohorts (ClinicalTrials.gov identifiers: NCT00715442 and NCT00126594) for external validation. Response was dichotomized as progressive disease (PD) versus non-PD every 8-12 weeks. GAGomes were measured at treatment start, after 6-8 weeks, and every third month in a blinded laboratory. We correlated GAGomes with response and developed scores to classify PD versus non-PD, which were used to predict response at treatment start or after 6-8 weeks. RESULTS: Fifty patients with mRCC were prospectively included, and all received tyrosine kinase inhibitors (TKIs). PD correlated with alterations in 40% of GAGome features. We developed plasma, urine, and combined glycosaminoglycan progression scores that monitored PD at each response evaluation visit with the area under the receiving operating characteristic curve (AUC) of 0.93, 0.97, and 0.98, respectively. For internal validation, the scores predicted PD at treatment start with the AUC of 0.66, 0.68, and 0.74 and after 6-8 weeks with the AUC of 0.76, 0.66, and 0.75. For external validation, 70 patients with mRCC were retrospectively included and all received TKI-containing regimens. The plasma score predicted PD at treatment start with the AUC of 0.90 and at 6-8 weeks with the AUC of 0.89. The pooled sensitivity and specificity were 58% and 79% at treatment start. Limitations include the exploratory study design. CONCLUSION: GAGomes changed in association with mRCC response to TKIs and may provide biologic insights into mRCC mechanisms of response.

Noninvasive detection of any-stage cancer using free glycosaminoglycans.

Cancer mortality is exacerbated by late-stage diagnosis. Liquid biopsies based on genomic biomarkers can noninvasively diagnose cancers. However, validation studies have reported ~10% sensitivity to detect stage I cancer in a screening population and specific types, such as brain or genitourinary tumors, remain undetectable. We investigated urine and plasma free glycosaminoglycan profiles (GAGomes) as tumor metabolism biomarkers for multi-cancer early detection (MCED) of 14 cancer types using 2,064 samples from 1,260 cancer or healthy subjects. We observed widespread cancer-specific changes in biofluidic GAGomes recapitulated in an in vivo cancer progression model. We developed three machine learning models based on urine (Nurine = 220 cancer vs. 360 healthy) and plasma (Nplasma = 517 vs. 425) GAGomes that can detect any cancer with an area under the receiver operating characteristic curve of 0.83-0.93 with up to 62% sensitivity to stage I disease at 95% specificity. Undetected patients had a 39 to 50% lower risk of death. GAGomes predicted the putative cancer location with 89% accuracy. In a validation study on a screening-like population requiring ≥ 99% specificity, combined GAGomes predicted any cancer type with poor prognosis within 18 months with 43% sensitivity (21% in stage I; N = 121 and 49 cases). Overall, GAGomes appeared to be powerful MCED metabolic biomarkers, potentially doubling the number of stage I cancers detectable using genomic biomarkers.

Plasma and Urine Free Glycosaminoglycans as Monitoring Biomarkers in Nonmetastatic Renal Cell Carcinoma-A Prospective Cohort Study.

Background: No liquid biomarkers are approved in renal cell carcinoma (RCC), making early detection of recurrence in surgically treated nonmetastatic (M0) patients dependent on radiological imaging. Urine- and plasma free glycosaminoglycan profiles-or free GAGomes-are promising biomarkers reflective of RCC metabolism. Objective: To explore whether free GAGomes could detect M0 RCC recurrence noninvasively. Design setting and participants: Between June 2016 and February 2021, we enrolled a prospective consecutive series of patients elected for (1) partial or radical nephrectomy for clinical M0 RCC (cohort 1) or (2) first-line therapy following RCC metachronous metastatic recurrence (cohort 2) at Sahlgrenska University Hospital, Gothenburg, Sweden. The study population included M0 RCC patients with recurrent disease (RD) versus no evidence of disease (NED) in at least one follow-up visit. Plasma and urine free GAGomes-consisting of 40 chondroitin sulfate (CS), heparan sulfate, and hyaluronic acid (HA) features-were measured in a blinded central laboratory preoperatively and at each postoperative follow-up visit until recurrence or end of follow-up in cohort 1, or before treatment start in cohort 2. Outcome measurements and statistical analysis: We used Bayesian logistic regression to correlate GAGome features with RD versus NED and with various histopathological variables. We developed three recurrence scores (plasma, urine, and combined) proportional to the predicted probability of RD. We internally validated the area under the curve (AUC) using bootstrap resampling. We performed a decision curve analysis to select a cutoff and report the corresponding net benefit, sensitivity, and specificity of each score. We used univariable analyses to correlate each preoperative score with recurrence-free survival (RFS). Results and limitations: Of 127 enrolled patients in total, 62 M0 RCC patients were in the study population (median age: 63 year, 35% female, and 82% clear cell). The median follow-up time was 3 months, totaling 72 postoperative visits -17 RD and 55 NED cases. RD was compatible with alterations in 14 (52%) of the detectable GAGome features, mostly free CS. Eleven (79%) of these correlated with at least one histopathological variable. We developed a plasma, a urine, and a combined free CS RCC recurrence score to diagnose RD versus NED with AUCs 0.91, 0.93, and 0.94, respectively. At a cutoff equivalent to ≥30% predicted probability of RD, the sensitivity and specificity were, respectively, 69% and 84% in plasma, 81% and 80% in urine, and 80% and 82% when combined, and the net benefit was equivalent to finding an extra ten, 13, and 12 cases of RD per hundred patients without any unnecessary imaging for plasma, urine, and combined, respectively. The combined score was prognostic of RFS in univariable analysis (hazard ratio = 1.90, p = 0.02). Limitations include a lack of external validation. Conclusions: Free CS scores detected postsurgical recurrence noninvasively in M0 RCC with substantial net benefit. External validity is required before wider clinical implementation. Patient summary: In this study, we examined a new noninvasive blood and urine test to detect whether renal cell carcinoma recurred after surgery.

Analysis of normal levels of free glycosaminoglycans in urine and plasma in adults.

Plasma and urine glycosaminoglycans (GAGs) are long, linear sulfated polysaccharides that have been proposed as potential noninvasive biomarkers for several diseases. However, owing to the analytical complexity associated with the measurement of GAG concentration and disaccharide composition (the so-called GAGome), a reference study of the normal healthy GAGome is currently missing. Here, we prospectively enrolled 308 healthy adults and analyzed their free GAGomes in urine and plasma using a standardized ultra-high-performance liquid chromatography coupled with triple-quadrupole tandem mass spectrometry method together with comprehensive demographic and blood chemistry biomarker data. Of 25 blood chemistry biomarkers, we mainly observed weak correlations between the free GAGome and creatinine in urine and hemoglobin or erythrocyte counts in plasma. We found a higher free GAGome concentration - but not a more diverse composition - in males. Partitioned by gender, we also established reference intervals for all detectable free GAGome features in urine and plasma. Finally, we carried out a transference analysis in healthy individuals from two distinct geographical sites, including data from the Lifelines Cohort Study, which validated the reference intervals in urine. Our study is the first large-scale determination of normal free GAGomes reference intervals in plasma and urine and represents a critical resource for future physiology and biomarker research.

Meta-analysis of the gut microbiota in predicting response to cancer immunotherapy in metastatic melanoma.

BACKGROUNDIdentifying factors conferring responses to therapy in cancer is critical to select the best treatment for patients. For immune checkpoint inhibition (ICI) therapy, mounting evidence suggests that the gut microbiome can determine patient treatment outcomes. However, the extent to which gut microbial features are applicable across different patient cohorts has not been extensively explored.METHODSWe performed a meta-analysis of 4 published shotgun metagenomic studies (Ntot = 130 patients) investigating differential microbiome composition and imputed metabolic function between responders and nonresponders to ICI.RESULTSOur analysis identified both known microbial features enriched in responders, such as Faecalibacterium as the prevailing taxa, as well as additional features, including overrepresentation of Barnesiella intestinihominis and the components of vitamin B metabolism. A classifier designed to predict responders based on these features identified responders in an independent cohort of 27 patients with the area under the receiver operating characteristic curve of 0.625 (95% CI: 0.348-0.899) and was predictive of prognosis (HR = 0.35, P = 0.081).CONCLUSIONThese results suggest the existence of a fecal microbiome signature inherent across responders that may be exploited for diagnostic or therapeutic purposes.FUNDINGThis work was funded by the Knut and Alice Wallenberg Foundation, BioGaia AB, and Cancerfonden.

An atlas of human metabolism.

Genome-scale metabolic models (GEMs) are valuable tools to study metabolism and provide a scaffold for the integrative analysis of omics data. Researchers have developed increasingly comprehensive human GEMs, but the disconnect among different model sources and versions impedes further progress. We therefore integrated and extensively curated the most recent human metabolic models to construct a consensus GEM, Human1. We demonstrated the versatility of Human1 through the generation and analysis of cell- and tissue-specific models using transcriptomic, proteomic, and kinetic data. We also present an accompanying web portal, Metabolic Atlas (https://www.metabolicatlas.org/), which facilitates further exploration and visualization of Human1 content. Human1 was created using a version-controlled, open-source model development framework to enable community-driven curation and refinement. This framework allows Human1 to be an evolving shared resource for future studies of human health and disease.

Tackling Cancer with Yeast-Based Technologies.

The ability to precisely engineer yeast, coupled with its genetic and metabolic similarity to tumor cells, has enabled researchers to use this organism in cancer research. Here we review advances that leveraged yeast as a model organism for studying cancer biology, including the investigation of tumorigenic mechanisms, development of advanced technologies for drug discovery, production of anticancer drugs on an industrial scale, and delivering the next generation of immunotherapies.