Integration of peripheral blood- and tissue-based biomarkers of response to immune checkpoint blockade in urothelial carcinoma

As predictive biomarkers of response to immune checkpoint inhibitors (ICIs) remain a major unmet clinical need in patients with urothelial carcinoma (UC), we sought to identify tissue-based immune biomarkers of clinical benefit to ICIs using multiplex immunofluorescence and to integrate these findings with previously identified peripheral blood biomarkers of response. Fifty-five pretreatment and 12 paired on-treatment UC specimens were identified from patients treated with nivolumab with or without ipilimumab. Whole tissue sections were stained with a 12-plex mIF panel, including CD8, PD-1/CD279, PD-L1/CD274, CD68, CD3, CD4, FoxP3, TCF1/7, Ki67, LAG-3, MHC-II/HLA-DR, and pancytokeratin+SOX10 to identify over three million cells. Immune tissue densities were compared to progression-free survival (PFS) and best overall response (BOR) by RECIST version 1.1. Correlation coefficients were calculated between tissue-based and circulating immune populations. The frequency of intratumoral CD3+ LAG-3+ cells was higher in responders compared to nonresponders (p = 0.0001). LAG-3+ cellular aggregates were associated with response, including CD3+ LAG-3+ in proximity to CD3+ (p = 0.01). Exploratory multivariate modeling showed an association between intratumoral CD3+ LAG-3+ cells and improved PFS independent of prognostic clinical factors (log HR -7.0; 95% confidence interval [CI] -12.7 to -1.4), as well as established biomarkers predictive of ICI response (log HR -5.0; 95% CI -9.8 to -0.2). Intratumoral LAG-3+ immune cell populations warrant further study as a predictive biomarker of clinical benefit to ICIs. Differences in LAG-3+ lymphocyte populations across the intratumoral and peripheral compartments may provide complementary information that could inform the future development of multimodal composite biomarkers of ICI response. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

aWCluster: A Novel Integrative Network-Based Clustering of Multiomics for Subtype Analysis of Cancer Data

The remarkable growth of multi-platform genomic profiles has led to the challenge of multiomics data integration. In this study, we present a novel network-based multiomics clustering founded on the Wasserstein distance from optimal mass transport. This distance has many important geometric properties making it a suitable choice for application in machine learning and clustering. Our proposed method of aggregating multiomics and Wasserstein distance clustering (aWCluster) is applied to breast carcinoma as well as bladder carcinoma, colorectal adenocarcinoma, renal carcinoma, lung non-small cell adenocarcinoma, and endometrial carcinoma from The Cancer Genome Atlas project. Subtypes were characterized by the concordant effect of mRNA expression, DNA copy number alteration, and DNA methylation of genes and their neighbors in the interaction network. aWCluster successfully clusters all cancer types into classes with significantly different survival rates. Also, a gene ontology enrichment analysis of significant genes in the low survival subgroup of breast cancer leads to the well-known phenomenon of tumor hypoxia and the transcription factor ETS1 whose expression is induced by hypoxia. We believe aWCluster has the potential to discover novel subtypes and biomarkers by accentuating the genes that have concordant multiomics measurements in their interaction network, which are challenging to find without the network inference or with single omics analysis.

Pan-Cancer Prediction of Cell-Line Drug Sensitivity Using Network-Based Methods

The development of reliable predictive models for individual cancer cell lines to identify an optimal cancer drug is a crucial step to accelerate personalized medicine, but vast differences in cancer cell lines and drug characteristics make it quite challenging to develop predictive models that result in high predictive power and explain the similarity of cell lines or drugs. Our study proposes a novel network-based methodology that breaks the problem into smaller, more interpretable problems to improve the predictive power of anti-cancer drug responses in cell lines. For the drug-sensitivity study, we used the GDSC database for 915 cell lines and 200 drugs. The theory of optimal mass transport was first used to separately cluster cell lines and drugs, using gene-expression profiles and extensive cheminformatic drug features, represented in a form of data networks. To predict cell-line specific drug responses, random forest regression modeling was separately performed for each cell-line drug cluster pair. Post-modeling biological analysis was further performed to identify potential biological correlates associated with drug responses. The network-based clustering method resulted in 30 distinct cell-line drug cluster pairs. Predictive modeling on each cell-line-drug cluster outperformed alternative computational methods in predicting drug responses. We found that among the four drugs top-ranked with respect to prediction performance, three targeted the PI3K/mTOR signaling pathway. Predictive modeling on clustered subsets of cell lines and drugs improved the prediction accuracy of cell-line specific drug responses. Post-modeling analysis identified plausible biological processes associated with drug responses.

Cardiolipin is required for membrane docking of mitochondrial ribosomes and protein synthesis

The mitochondrial inner membrane contains a unique phospholipid known as cardiolipin (CL), which stabilises the protein complexes embedded in the membrane and supports its overall structure. Recent evidence indicates that the mitochondrial ribosome may associate with the inner membrane to facilitate co-translational insertion of the hydrophobic oxidative phosphorylation (OXPHOS) proteins into the inner membrane. We generated three mutant knockout cell lines for the CL biosynthesis gene Crls1 to investigate the effects of CL loss on mitochondrial protein synthesis. Reduced CL levels caused altered mitochondrial morphology and transcriptome-wide changes that were accompanied by uncoordinated mitochondrial translation rates and impaired respiratory chain supercomplex formation. Aberrant protein synthesis was caused by impaired formation and distribution of mitochondrial ribosomes. Reduction or loss of CL resulted in divergent mitochondrial and endoplasmic reticulum stress responses. We show that CL is required to stabilise the interaction of the mitochondrial ribosome with the membrane via its association with OXA1 (also known as OXA1L) during active translation. This interaction facilitates insertion of newly synthesised mitochondrial proteins into the inner membrane and stabilises the respiratory supercomplexes.

Molecular phenotyping using networks, diffusion, and topology: soft tissue sarcoma

Many biological datasets are high-dimensional yet manifest an underlying order. In this paper, we describe an unsupervised data analysis methodology that operates in the setting of a multivariate dataset and a network which expresses influence between the variables of the given set. The technique involves network geometry employing the Wasserstein distance, global spectral analysis in the form of diffusion maps, and topological data analysis using the Mapper algorithm. The prototypical application is to gene expression profiles obtained from RNA-Seq experiments on a collection of tissue samples, considering only genes whose protein products participate in a known pathway or network of interest. Employing the technique, we discern several coherent states or signatures displayed by the gene expression profiles of the sarcomas in the Cancer Genome Atlas along the TP53 (p53) signaling network. The signatures substantially recover the leiomyosarcoma, dedifferentiated liposarcoma (DDLPS), and synovial sarcoma histological subtype diagnoses, and they also include a new signature defined by activation and inactivation of about a dozen genes, including activation of serine endopeptidase inhibitor SERPINE1 and inactivation of TP53-family tumor suppressor gene TP73.

Robust and interpretable PAM50 reclassification exhibits survival advantage for myoepithelial and immune phenotypes

We introduce a classification of breast tumors into seven classes which are more clearly defined by interpretable mRNA signatures along the PAM50 gene set than the five traditional PAM50 intrinsic subtypes. Each intrinsic subtype is partially concordant with one of our classes, and the two additional classes correspond to division of the classes concordant with the Luminal B and the Normal intrinsic subtypes along expression of the Her2 gene group. Our Normal class shows similarity with the myoepithelial mammary cell phenotype, including TP63 expression (specificity: 80.8% and sensitivity: 82.8%), and exhibits the best overall survival (89.6% at 5 years). Though Luminal A tumors are traditionally considered the least aggressive, our analysis shows that only the Luminal A tumors which are now classified as myoepithelial have this phenotype, while tumors in our luminal class (concordant with Luminal A) may be more aggressive than previously thought. We also find that patients with basal tumors surviving to 48 months exhibit favorable continued survival rates when certain markers for B lymphocytes are present and poor survival rates when they are absent, which is consistent with recent findings.

An Artificial Yeast Genetic Circuit Enables Deep Mutational Scanning of an Antimicrobial Resistance Protein

Understanding the molecular mechanisms underlying antibiotic resistance requires concerted efforts in enzymology and medicinal chemistry. Here we describe a new synthetic biology approach to antibiotic development, where the presence of tetracycline antibiotics is linked to a life-death selection in Saccharomyces cerevisiae. This artificial genetic circuit allowed the deep mutational scanning of the tetracycline inactivating enzyme TetX, revealing key functional residues. We used both positive and negative selections to confirm the importance of different residues for TetX activity, and profiled activity hotspots for different tetracyclines to reveal substrate-specific activity determinants. We found that precise positioning of FAD and hydrophobic shielding of the tetracycline are critical for enzymatic inactivation of doxycycline. However, positioning of FAD is suboptimal in the case of anhydrotetracycline, potentially explaining its comparatively poor degradation and potential as an inhibitor for this family of enzymes. By combining artificial genetic circuits whose function can be modulated by antimicrobial resistance determinants, we establish a framework to select for the next generation of antibiotics.

Inhibiting monoacylglycerol acyltransferase 1 ameliorates hepatic metabolic abnormalities but not inflammation and injury in mice

Abnormalities in hepatic lipid metabolism and insulin action are believed to play a critical role in the etiology of nonalcoholic steatohepatitis. Monoacylglycerol acyltransferase (MGAT) enzymes convert monoacylglycerol to diacylglycerol, which is the penultimate step in one pathway for triacylglycerol synthesis. Hepatic expression of Mogat1, which encodes an MGAT enzyme, is increased in the livers of mice with hepatic steatosis, and knocking down Mogat1 improves glucose metabolism and hepatic insulin signaling, but whether increased MGAT activity plays a role in the etiology of nonalcoholic steatohepatitis is unclear. To examine this issue, mice were placed on a diet containing high levels of trans fatty acids, fructose, and cholesterol (HTF-C diet) or a low fat control diet for 4 weeks. Mice were injected with antisense oligonucleotides (ASOs) to knockdown Mogat1 or a scrambled ASO control for 12 weeks while remaining on diet. The HTF-C diet caused glucose intolerance, hepatic steatosis, and induced hepatic gene expression markers of inflammation, macrophage infiltration, and stellate cell activation. Mogat1 ASO treatment, which suppressed Mogat1 expression in liver and adipose tissue, attenuated weight gain, improved glucose tolerance, improved hepatic insulin signaling, and decreased hepatic triacylglycerol content compared with control ASO-treated mice on HTF-C chow. However, Mogat1 ASO treatment did not reduce hepatic diacylglycerol, cholesterol, or free fatty acid content; improve histologic measures of liver injury; or reduce expression of markers of stellate cell activation, liver inflammation, and injury. In conclusion, inhibition of hepatic Mogat1 in HTF-C diet-fed mice improves hepatic metabolic abnormalities without attenuating liver inflammation and injury.

Assessment of quantitative computed tomographic cerebral perfusion imaging with H2(15)O positron emission tomography

Assessment of quantitative cerebral blood flow on a conventional fast CT machine without the use of specialized equipment may be valuable in the investigation of acute stroke and head injury. We aimed to compare a single slice CT perfusion sequence with H2(15)O positron emission tomography using the sagittal sinus as an input function, a method that avoids unnecessary orbital irradiation. Eight patients were studied, two patients with gliomas, and six with arteriovenous malformations. The dynamic CT perfusion sequence was performed by acquiring the same 10 mm slice 10 times over 30 sec during a 50 ml bolus of intravenous contrast medium given at a rate of 7.5 ml sec-1 using a power injector. The CT perfusion studies were completed without complication. Co-registration was sub-optimal in one patient. Overall the correlation between the two methodologies was encouraging with an average r2 value of 0.524 for individual analyses. When two patients with high flow arteriovenous malformations were excluded the average r2 value increased to 0.640. The results of this CT perfusion methodology are encouraging. Having shown its feasibility, further studies in conditions with lower rates of cerebral blood flow are warranted.