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.

Tumor Immune Microenvironment and Response to Neoadjuvant Chemotherapy in Hormone Receptor/HER2+ Early Stage Breast Cancer.

BACKGROUND: Pathologic response at the time of surgery after neoadjuvant therapy for HER2 positive early breast cancer impacts both prognosis and subsequent adjuvant therapy. Comprehensive descriptions of the tumor microenvironment (TME) in patients with HER2 positive early breast cancer is not well described. We utilized standard stromal pathologist-assessed tumor infiltrating lymphocyte (TIL) quantification, quantitative multiplex immunofluorescence, and RNA-based gene pathway signatures to assess pretreatment TME characteristics associated pathologic complete response in patients with hormone receptor positive, HER2 positive early breast cancer treated in the neoadjuvant setting. METHODS: We utilized standard stromal pathologist-assessed TIL quantification, quantitative multiplex immunofluorescence, and RNA-based gene pathway signatures to assess pretreatment TME characteristics associated pathologic complete response in 28 patients with hormone receptor positive, HER2 positive early breast cancer treated in the neoadjuvant setting. RESULTS: Pathologist-assessed stromal TILs were significantly associated with pathologic complete response (pCR). By quantitative multiplex immunofluorescence, univariate analysis revealed significant increases in CD3+, CD3+CD8-FOXP3-, CD8+ and FOXP3+ T-cell densities as well as increased immune cell aggregates in pCR patients. In subsets of paired pre/post-treatment samples, we observed significant changes in gene expression signatures in non-pCR patients and significant decreases in CD8+ densities after treatment in pCR patients. No RNA based pathway signature was associated with pCR. CONCLUSION: TME characterization HER2 positive breast cancer patients revealed several stromal T-cell densities and immune cell aggregates associated with pCR. These results demonstrate the feasibility of these novel methods in TME evaluation and contribute to ongoing investigations of the TME in HER2+ early breast cancer to identify robust biomarkers to best identify patients eligible for systemic de-escalation strategies.

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.

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.

Functional network analysis reveals an immune tolerance mechanism in cancer.

We present a technique to construct a simplification of a feature network which can be used for interactive data exploration, biological hypothesis generation, and the detection of communities or modules of cofunctional features. These are modules of features that are not necessarily correlated, but nevertheless exhibit common function in their network context as measured by similarity of relationships with neighboring features. In the case of genetic networks, traditional pathway analyses tend to assume that, ideally, all genes in a module exhibit very similar function, independent of relationships with other genes. The proposed technique explicitly relaxes this assumption by employing the comparison of relational profiles. For example, two genes which always activate a third gene are grouped together even if they never do so concurrently. They have common, but not identical, function. The comparison is driven by an average of a certain computationally efficient comparison metric between Gaussian mixture models. The method has its basis in the local connection structure of the network and the collection of joint distributions of the data associated with nodal neighborhoods. It is benchmarked on networks with known community structures. As the main application, we analyzed the gene regulatory network in lung adenocarcinoma, finding a cofunctional module of genes including the pregnancy-specific glycoproteins (PSGs). About 20% of patients with lung, breast, uterus, and colon cancer in The Cancer Genome Atlas (TCGA) have an elevated PSG+ signature, with associated poor group prognosis. In conjunction with previous results relating PSGs to tolerance in the immune system, these findings implicate the PSGs in a potential immune tolerance mechanism of cancers.

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.

Building artificial genetic circuits to understand protein function.

Intrinsic protein properties that may not be apparent by only examining three-dimensional structures can be revealed by careful analysis of mutant protein variants. Deep mutational scanning is a technique that allows the functional analysis of millions of protein variants in a single experiment. To enable this high-throughput technique, the mutant genotype of protein variants must be coupled to a selectable function. This chapter outlines how artificial genetic circuits in the yeast Saccharomyces cerevisiae can maintain the genotype-phenotype link, thus enabling the general application of this approach. To do this, we describe how to engineer genetic selections in yeast, methods to construct mutant libraries, and how to analyze sequencing data. We investigate the structure-function relationships of the antimicrobial resistance protein TetX to illustrate this process. In doing so, we demonstrate that deep mutational scanning is a powerful method to dissect the importance of individual residues for the inactivation of antibiotic analogues, with consequences for the rational design of new drugs to combat antimicrobial resistance.

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.

Characterizing Cancer Drug Response and Biological Correlates: A Geometric Network Approach.

In the present work, we apply a geometric network approach to study common biological features of anticancer drug response. We use for this purpose the panel of 60 human cell lines (NCI-60) provided by the National Cancer Institute. Our study suggests that mathematical tools for network-based analysis can provide novel insights into drug response and cancer biology. We adopted a discrete notion of Ricci curvature to measure, via a link between Ricci curvature and network robustness established by the theory of optimal mass transport, the robustness of biological networks constructed with a pre-treatment gene expression dataset and coupled the results with the GI50 response of the cell lines to the drugs. Based on the resulting drug response ranking, we assessed the impact of genes that are likely associated with individual drug response. For genes identified as important, we performed a gene ontology enrichment analysis using a curated bioinformatics database which resulted in biological processes associated with drug response across cell lines and tissue types which are plausible from the point of view of the biological literature. These results demonstrate the potential of using the mathematical network analysis in assessing drug response and in identifying relevant genomic biomarkers and biological processes for precision medicine.

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.