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bioRxiv2017

Applying expression profile similarity for discovery of patient-specific functional mutations

The progress of cancer genome sequencing projects yields unprecedented information of mutations for numerous patients. However, the complexity of mutation profiles of patients hinders the further understanding of mechanisms of oncogenesis. One basic question is how to uncover mutations with functional impacts. In this work, we introduce a computational method to predict functional somatic mutations for each of patient by integrating mutation recurrence with similarity of expression profiles of patients. With this method, the functional mutations are determined by checking the mutation enrichment among a group of patients with similar expression profiles. We applied this method to three cancer types and identified the functional mutations. Comparison of the predictions for three cancer types suggested that most of the functional mutations were cancer-type-specific with one exception to p53. By checking prediction results, we found that our method effectively filtered non-functional mutations resulting from large protein sizes. In addition, this methods can also perform functional annotation to each patient to describe their association with signalling pathways or biological processes. In breast cancer, we predicted \"cell adhesion\" and other mutated gene associated terms to be significantly enriched among patients.

bioinformatics↗

bioRxiv2018

The Evolutionary Landscape of Pan-Cancer Drives Clinical Aggression

Although cancer mechanisms differ from occurrence and development, some of them have similar oncogenesis, which leads to similar clinical phenotypes. Most existing genotyping studies look at \"omics\" data, but intentionally or unintentionally avoided that cancer is a time-dependent evolutionary process, biologically represented by the time evolution of tumor clones. We used the Bayesian mutation landscape approach to reconstruct the evolutionary process of cancer by acquiring somatic mutation data consisting of 21 cancer types. Four representative evolution patterns of pan-cancer have been discovered: trees, chaos, biconvex, and Cambrian, and a strong correlation between these four evolutionary patterns and clinical aggressivity. We further explained the characteristics of the corresponding biological systems in the evolution of pan cancer by analyzing the function of differentially expressed protein-protein interaction networks. Our results explained the difference in clinical aggressivity between cancer evolution patterns from the evolution of tumor clones and exposed the functional mechanism behind.

cancer biology↗

bioRxiv2018

NF1 deficiency correlates with estrogen receptor signaling and diminished survival in breast cancer

The key negative regulatory gene of the RAS pathway, NF1, is mutated or deleted in numerous cancer types and is associated with increased cancer risk and drug resistance. Even though women with neurofibromatosis (germline NF1 mutations) have a substantially increased breast cancer risk at a young age and NF1 is commonly mutated in sporadic breast cancers, we have a limited understanding of the role of NF1 in breast cancer. Much of our understanding of the mechanisms underlying the functional loss of NF1 comes from mouse models that do not completely recapitulate the phenotypes of human NF1. We utilized CRISPR-Cas9 gene editing to create Nf1 rat models to evaluate the effect of Nf1 deficiency on tumorigenesis. The resulting Nf1 indels induced highly penetrant, aggressive mammary adenocarcinomas that express estrogen receptor and progesterone receptor. We identified distinct Nf1 isoforms that were altered during tumorigenesis.\n\nTo evaluate NF1 in human breast cancer, we analyzed genomic changes in a breast cancer dataset of 2,000 clinically annotated breast cancers. We found NF1 shallow deletions in 25% of sporadic breast cancers, which correlated with poor clinical outcome. To identify biological networks impacted by NF1 deficiency, we constructed gene co-expression networks using weighted gene correlation network analysis (WGCNA) and identified a network connected to ESR1 (estrogen receptor). Moreover, NF1-deficient cancers correlated with established RAS activation signatures. Estrogen-dependence was verified by estrogen-ablation in Nf1 rats where rapid tumor regression was observed. These results demonstrated the significant role NF1 plays in both NF1-related breast cancer and sporadic breast cancer.

cancer biology↗

bioRxiv2016

PICS: Pathway Informed Classification System for cancer analysis using gene expression data

We introduce PICS (Pathway Informed Classification System) for classifying cancers based on tumor sample gene expression levels. PICS is a computational method capable of expeditiously elucidating both known and novel biological pathway involvement specific to various cancers, and uses that learned pathway information to separate patients into distinct classes. The method clearly separates a pan-cancer dataset into their tissue of origin and is also able to sub-classify individual cancer datasets into distinct survival classes. Gene expression values are collapsed into pathway scores that reveal which biological activities are most useful for clustering cancer cohorts into sub-types. Variants of the method allow it to be used on datasets that do and do not contain non-cancerous samples. Activity levels of all types of pathways, broadly grouped into metabolic, cellular processes and signaling, and immune system, are useful for separating the pan-cancer cohort. In the clustering of specific cancer types, certain pathway types become more valuable depending on the site being studied. For lung cancer, signaling pathways dominate, for pancreatic cancer signaling and metabolic pathways, and for melanoma immune system pathways are the most useful. This work suggests the utility of pathway level genomic analysis and points in the direction of using pathway classification for predicting the efficacy and side effects of drugs and radiation.

Genomics↗

bioRxiv2018

Subnetwork-based prognostic biomarkers exhibit performance and robustness superior to gene-based biomarkers in breast cancer

BackgroundEffective classification of cancer patients into groups with differential survival remains an important and unsolved challenge. Biomarkers have been developed based on mRNA abundance data, but their replicability and clinical utility is modest. Integrating functional information, such as pathway data, has been suggested to improve biomarker performance. To date, however, the advantages of subnetwork-based biomarkers have not been quantified.\n\nResultsWe deeply sampled the population of prognostic gene-based and subnetwork-based biomarkers in a breast cancer meta-dataset of 4,960 patients. Analysing the performance and robustness of 22,000,000 gene biomarkers and 6,250,000 subnetwork biomarkers across twenty different training:testing cohort partitions of the meta-dataset revealed that subnetwork biomarkers exhibit superior overall performance and higher concordance across partitions. We find evidence of an upper bound for optimal biomarker size of [~]200 genes or [~]100 subnetworks. Additionally, with both biomarker feature types, larger biomarkers tend to show less consistency in performance across partitions, suggestive of over-fitting. Finally, an evaluation of varying training cohort sizes quantifies the effects of training cohort size.\n\nConclusionsMany groups are developing techniques for exploiting network-based representations of biological pathways to characterize cancer and other diseases. By considering the distribution of gene- and subnetwork-based biomarkers, we show that pathway data improves performance and replicability, and that smaller biomarkers are more robust across patient cohorts. These insights may facilitate development of clinically useful biomarkers.

bioinformatics↗

bioRxiv2017

MultiDCoX: Multi-factor Analysis of Differential Co-expression

BackgroundDifferential co-expression signifies change in degree of co-expression of a set of genes among different biological conditions. It has been used to identify differential co-expression networks or interactomes. Many algorithms have been developed for single-factor differential co-expression analysis and applied in a variety of studies. However, in many studies, the samples are characterized by multiple factors such as genetic markers, clinical variables and treatments. No algorithm or methodology is available for multi-factor analysis of differential co-expression.\n\nResultsWe developed a novel formulation and a computationally efficient greedy search algorithm called MultiDCoX to perform multi-factor differential co-expression analysis of transcriptomic data. Simulated data analysis demonstrates that the algorithm can effectively elicit differentially co-expressed (DCX) gene sets and quantify the influence of each factor on co-expression. MultiDCoX analysis of a breast cancer dataset identified interesting biologically meaningful differentially coexpressed (DCX) gene sets along with genetic and clinical factors that influenced the respective differential co-expression.\n\nConclusionsMultiDCoX is a space and time efficient procedure to identify differentially co-expressed gene sets and successfully identify influence of individual factors on differential co-expression.\n\nSoftwareR function will be available upon request.

bioinformatics↗

bioRxiv2017

Activation of the Androgen Receptor gene by BORIS/CTCFL in prostate cancer cells

BORIS/CTCFL, a paralogue of the chromatin architectural protein CTCF, is a member of the cancer-testis antigen family, normally present in the testes. BORIS is expressed in various tumours, including prostate cancers, however the function of BORIS in cancer cells is not well defined. The androgen receptor (AR) plays a critical role in the normal development of a human prostate gland and pathogenesis of prostate cancer. In our previous study we described a positive correlation between elevated levels of BORIS and AR in prostate cancers, and activation of the AR gene by BORIS in prostate cancer cells. Elucidation of the mechanisms involved in the modulation of AR activity is important to understand prostate tumourigenesis and investigation of transcriptional regulation of the AR gene by BORIS may provide new insights into this issue. Here we report the ability of BORIS to not only positively regulate AR in androgen-dependent prostate cancer (ADPC) cells, but re-activate epigenetically silenced AR in androgen-independent prostate cancer (AIPC) cells leading to the production of biologically active AR protein. CTCF, on the other hand, had repressive effects on the AR. In both, ADPC and AIPC cells, introduction of ectopic BORIS was associated with the reduction in the AR promoter methylation, increase in active and decrease in repressive chromatin marks, and decrease in CTCF occupancies at the two main upstream BORIS/CTCF binding sites. We propose a model of epigenetic regulation of AR by BORIS in prostate cells whereby BORIS remodels the chromatin at the AR promoter leading to transcriptional activation.

cancer biology↗

bioRxiv2018

Validation of Prostate Cancer Risk Variants by CRISPR/Cas9 Mediated Genome Editing

GWAS have identified numerous SNPs associated with prostate cancer risk. One such SNP is rs10993994. It is located in the MSMB promoter, associates with MSMB encoded {beta}-microseminoprotein prostate secretion levels, and is associated with mRNA expression changes in MSMB and the adjacent gene NCOA4. In addition, our previous work showed a second SNP, rs7098889, is in LD with rs10993994 and associated with MSMB expression independent of rs10993994. Here, we generate a series of clones with single alleles removed by double guide RNA (gRNA) mediated CRISPR/Cas9 deletions, through which we demonstrate that each of these SNPs independently and greatly alters MSMB expression in an allele-specific manner. We further show that these SNPs have no substantial effect on the expression of NCOA4. These data demonstrate that a single SNP can have a large effect on gene expression and illustrate the importance of functional validation to deconvolute observed correlations. The method we have developed is generally applicable to test any SNP for which a relevant heterozygous cell line is available.\n\nAuthor summaryIn pursuing the underlying biological mechanism of prostate cancer pathogenesis, scientists utilized the existence of common single nucleotide polymorphisms (SNPs) in human genome as genetic markers to perform large scale genome wide association studies (GWAS) and have so far identified more than a hundred prostate cancer risk variants. Such variants provide an unbiased and systematic new venue to study the disease mechanism, and the next big challenge is to translate these genetic associations to the causal role of altered gene function in oncogenesis. The majority of these variants are waiting to be studied and lots of them may act in oncogenesis through gene expression regulation. To prove the concept, we took rs10993994 and its linked rs7098889 as an example and engineered single cell clones by allelic-specific CRISPR/Cas9 deletion to separate the effect of each allele. We observed that a single nucleotide difference would lead to surprisingly high level of MSMB gene expression change in a gene specific and tissue specific manner. Our study strongly supports the notion that differential level of gene expression caused by risk variants and their associated genetic locus play a major role in oncogenesis and also highlights the importance of studying the function of MSMB encoded {beta}-MSP in prostate cancer pathogenesis.

genetics↗

bioRxiv2017

Pericyte ontogeny: the use of chimeras to track a cell lineage of diverse germ line origins

The goal of lineage tracing is to understand body formation over time by discovering which cells are the progeny of a specific, identified, ancestral progenitor. Subsidiary questions include unequivocal identification of what they have become, how many descendants develop, whether they live or die, and where they are located in the tissue or body at the end of the window examined. A classical approach in experimental embryology, lineage tracing continues to be used in developmental biology, stem cell and cancer research, wherever cellular potential and behavior need to be studied in multiple dimensions, of which one is time. Each technical approach has its advantages and drawbacks. This chapter, with some previously unpublished data, will concentrate non-exclusively on the use of interspecies chimeras to explore the origins of perivascular (or mural) cells, of which those adjacent to the vascular endothelium are termed pericytes for this purpose. These studies laid the groundwork for our understanding that pericytes derive from progenitor mesenchymal pools of multiple origins in the vertebrate embryo, some of which persist into adulthood. The results obtained through xenografting, like in the methodology described here, complement those obtained through genetic lineage tracing techniques within a given species.

developmental biology↗

bioRxiv2017

Modeling heterogeneous populations using Boolean networks

Certain biological processes such as cancer development and immune activation are controlled by rare cellular events that are difficult to capture computationally through simulations of individual cells. Here we show that when cellular states are described using a Boolean network model, one can exactly simulate the dynamics of non-interacting, highly heterogeneous populations directly, without having to model the various subpopulations. This strategy captures even the rarest outcomes of the model with no sampling error. Our method can incorporate heterogeneity in both cell state and, by augmenting the model, the underlying rules of the network as well (i.e. mutations). We demonstrate our method by using it to simulate a heterogeneous population of Boolean networks modeling the T-cell receptor, spanning ~ 1020 distinct cellular states and mutational profiles.

systems biology↗

bioRxiv2018

Detection of early stage pancreatic cancer using 5-hydroxymethylcytosine signatures in circulating cell free DNA

Pancreatic cancers are typically diagnosed at late stage where disease prognosis is poor as exemplified by a 5-year survival rate of 8.2%. Earlier diagnosis would be beneficial by enabling surgical resection or earlier application of therapeutic regimens. We investigated the detection of pancreatic ductal adenocarcinoma (PDAC) in a non-invasive manner by interrogating changes in 5-hydroxymethylation cytosine status (5hmC) of circulating cell free DNA in the plasma of a PDAC cohort (n=51) in comparison with a non-cancer cohort (n=41). We found that 5hmC sites are enriched in a disease and stage specific manner in exons, 3UTRs and transcription termination sites. Our data show that 5hmC density is reduced in promoters and histone H3K4me3-associated sites with progressive disease suggesting increased transcriptional activity. 5hmC density is differentially represented in thousands of genes, and a stringently filtered set of the most significant genes points to biology related to pancreas (GATA4, GATA6, PROX1, ONECUT1) and/or cancer development (YAP1, TEAD1, PROX1, ONECUT1, ONECUT2, IGF1 and IGF2). Regularized regression models were built using 5hmC densities in statistically filtered genes or a comprehensive set of highly variable 5hmC counts in genes and performed with an AUC = 0.94-0.96 on training data. We were able to test the ability to classify PDAC and non-cancer samples with the Elastic net and Lasso models on two external pancreatic cancer 5hmC data sets and found validation performance to be AUC = 0.74-0.97. The findings suggest that 5hmC changes enable classification of PDAC patients with high fidelity and are worthy of further investigation on larger cohorts of patient samples.

cancer biology↗

bioRxiv2017

Network analysis of mass spectrometry imaging data from colorectal cancer identifies key metabolites common to metastatic development.

A deeper understanding of inter-tumor and intra-tumor heterogeneity is a critical factor for the advancement of next generation strategies against cancer. The heterogeneous morphology exhibited by solid tumors is mirrored by their metabolic heterogeneity. Defining the basic biological mechanisms that underlie tumor cell variability will be fundamental to the development of personalized cancer treatments. Variability in the molecular signatures found in local regions of cancer tissues can be captured through an untargeted analysis of their metabolic constituents. Here we demonstrate that DESI mass spectrometry imaging (MSI) combined with network analysis can provide detailed insight into the metabolic heterogeneity of colorectal cancer (CRC). We show that network modules capture signatures which differentiate tumor metabolism in the core and in the surrounding region. Moreover, module preservation analysis of network modules between patients with and without metastatic recurrence explains the inter-subject metabolic differences associated with diverse clinical outcomes such as metastatic recurrence.\n\nSignificanceNetwork analysis of DESI-MSI data from CRC human tissue reveals clinically relevant co-expression ion patterns associated with metastatic susceptibility. This delineates a more complex picture of tumor heterogeneity than conventional hard segmentation algorithms. Using tissue sections from central regions and at a distance from the tumor center, ion co-expression patterns reveal common features among patients who developed metastases (up of > 5 years) not preserved in patients who did not develop metastases. This offers insight into the nature of the complex molecular interactions associated with cancer recurrence. Presently, predicting CRC relapse is challenging, and histopathologically like-for-like cancers frequently manifest widely varying metastatic tendencies. Thus, the methodology introduced here more robustly defines the risk of metastases based on tumor biochemical heterogeneity.\n\nAuthor contributionsP.I., Z.T., R.C.G.: designed the study, developed the workflow, analyzed the data, interpreted the results, wrote the paper; N.S. collected the MS, performed the H...E staining, wrote the paper; L.D.: interpreted the results, wrote the paper; A.M.: collected the MS; A.S.: histological assessment; L.P.: collected the tissue specimens and clinical metadata; A.D.: collected the MS; H.K.: performed the H...E staining; R.M.: collected the tissue specimens and clinical metadata. R.G.: histological assessment; J.K.N: designed the study, edited the paper.

cancer biology↗

bioRxiv2018

DUSP28 is a novel biomarker responsible for aggravating malignancy via the autocrine signaling pathways in metastatic pancreatic cancer

Pancreatic cancer remains one of the most dangerous cancers with a grave prognosis. We previously reported that pancreatic cancer cells can secrete dual specificity phosphatise 28 (DUSP28) to the cultured medium. However, its biological function is poorly understood. Here, we have identified the function of DUSP28 in human metastatic pancreatic cancer. Treatment with recombinant DUSP28 (rDUSP28) significantly increased the migration, invasion, and viability of metastatic pancreatic cancer cells through the activation of CREB, AKT, and ERK1/2 signaling pathways. Furthermore, rDUSP28 acted as an oncogenic reagent through the interaction with integrin 1 in metastatic pancreatic cancer cells. In addition, rDUSP28 induced pro-angiogenic effects in human umbilical vein endothelial cells (HUVECs). Administration of rDUSP28 also produced tumor growth in vivo. Notably, sDUSP28 can easily be detected by immunoassay. The results establish the rationale for sDUSP28 as a promising therapeutic target and biomarker for metastatic pancreatic cancer patients.

cancer biology↗

bioRxiv2016

Long Range Force Transmission in Fibrous Matrices Enabled by Tension-Driven Alignment of Fibers

Cells can sense and respond to mechanical signals over relatively long distances across fibrous extracellular matrices. Recently proposed models suggest that long-range force transmission can be attributed to the nonlinear elasticity or fibrous nature of collagen matrices, yet the mechanism whereby fibers align remains unknown. Moreover, cell shape and anisotropy of cellular contraction are not considered in existing models, although recent experiments have shown that they play crucial roles. Here, we explore all of the key factors that influence long-range force transmission in cell-populated collagen matrices: alignment of collagen fibers, responses to applied force, strain stiffening properties of the aligned fibers, aspect ratios of the cells, and the polarization of cellular contraction. A constitutive law accounting for mechanically-driven collagen fiber reorientation is proposed. We systematically investigate the range of collagen fiber alignment using both finite element simulations and analytical calculations. Our results show that tension-driven collagen fiber alignment plays a crucial role in force transmission. Small critical stretch for fiber alignment, large fiber stiffness and fiber strain-hardening behavior enable long-range interaction. Furthermore, the range of collagen fiber alignment for elliptical cells with polarized contraction is much larger than that for spherical cells with diagonal contraction. A phase diagram showing the range of force transmission as a function of cell shape and polarization and matrix properties is presented. Our results are in good agreement with recent experiments, and highlight the factors that influence long-range force transmission, in particular tension-driven alignment of fibers. Our work has important relevance to biological processes including development, cancer metastasis and wound healing, suggesting conditions whereby cells communicate over long distances.

Bioengineering↗

bioRxiv2016

Acidic pH is a Metabolic Switch for 2‐Hydroxyglutarate Generation and Signaling

2-hydroxyglutarate (2-HG) is an important epigenetic regulator, with potential roles in cancer and stem cell biology. The D (R) enantiomer (D-2-HG) is an oncometabolite generated from ketoglutarate (-KG) by mutant isocitrate dehydrogenase (ICDH), while L (S) 2-HG is generated by lactate dehydrogenase (LDH) and malate dehydrogenase (MDH) in response to hypoxia. Since acidic pH is a common feature of hypoxia, as well as tumor and stem cell microenvironments, we hypothesized that pH may regulate cellular 2-HG levels. Herein we report that cytosolic acidification under normoxia moderately elevated 2-HG in cells, and boosting endogenous substrate -KG levels further stimulated this elevation. Studies with isolated LDH-1 and MDH-2 revealed that generation of 2-HG by both enzymes was stimulated several-fold at acidic pH, relative to normal physiologic pH. In addition, acidic pH was found to inhibit the activity of the mitochondrial L-2-HG removal enzyme L-2-HG dehydrogenase, and to stimulate the reverse reaction of ICDH (carboxylation of KG to isocitrate). Furthermore, since acidic pH is known to stabilize hypoxia-inducible factor (HIF), and 2-HG is a known inhibitor of HIF prolyl hydroxylases, we hypothesized that 2-HG may be required for acid-induced HIF stabilization. Accordingly, cells stably over-expressing L-2HGDH exhibited a blunted HIF response to acid. Together these results suggest that acidosis is an important and previously overlooked regulator of 2-HG accumulation and other oncometabolic events, with implications for HIF signaling.

Biochemistry↗

bioRxiv2017

Network Curvature as a Hallmark of Brain Structural Connectivity

Studies show that while brain networks are remarkably robust to a variety of adverse events, such as injuries and lesions due to accidents or disease, they may be fragile when the disturbance takes place in specific locations. This seems to be the case for diseases in which accumulated changes in network topology dramatically affect certain sensitive areas. To this end, previous attempts have been made to quantify robustness and fragility of brain functionality in two broadly defined ways: (i) utilizing model-based techniques to predict lesion effects, and (ii) studying empirical effects from brain lesions due to injury or disease. Both directions aim at assessing functional connectivity changes resulting from structural network variations. In the present work, we follow a more geometric viewpoint that is based on a notion of curvature of networks, the so-called Ollivier-Ricci curvature. A similar approach has been used in recent studies to quantify financial market robustness as well as to differentiate biological networks corresponding to cancer cells from normal cells. The same notion of curvature, defined at the node level for brain networks obtained from MRI data, may help identify and characterize the effects of diseases on specific brain regions. In the present paper, we apply the Ollivier-Ricci curvature to brain structural networks to: i) Demonstrate its unique ability to identify robust (or fragile) brain regions in healthy subjects. We compare our results to previously published work which identified a unique set of regions (called structural core) of the human cerebral cortex. This novel characterization of brain networks, complementary to measures such as degree, strength, clustering or efficiency, may be particularly useful to detect and monitor candidate areas for targeting by surgery (e.g. deep brain stimulation) or pharmaco-therapeutic agents; ii) Illustrate the power our curvature-derived measures to track changes in brain connectivity with healthy development/aging and; iii) Detect changes in brain structural connectivity in people with Autism Spectrum Disorders (ASD) which are in agreement with previous morphometric MRI studies.

neuroscience↗

bioRxiv2018

ModulOmics: Integrating Multi-Omics Data to Identify Cancer Driver Modules

The identification of molecular pathways driving cancer progression is a fundamental unsolved problem in tumorigenesis, which can substantially further our understanding of cancer mechanisms and inform the development of targeted therapies. Most current approaches to address this problem use primarily somatic mutations, not fully exploiting additional layers of biological information. Here, we describe ModulOmics, a method to de novo identify cancer driver pathways, or modules, by integrating multiple data types (protein-protein interactions, mutual exclusivity of mutations or copy number alterations, transcriptional co-regulation, and RNA co-expression) into a single probabilistic model. To efficiently search the exponential space of candidate modules, ModulOmics employs a two-step optimization procedure that combines integer linear programming with stochastic search. Across several cancer types, ModulOmics identifies highly functionally connected modules enriched with cancer driver genes, outperforming state-of-the-art methods. For breast cancer subtypes, the inferred modules recapitulate known molecular mechanisms and suggest novel subtype-specific functionalities. These findings are supported by an independent patient cohort, as well as independent proteomic and phosphoproteomic datasets.

cancer biology↗

bioRxiv2016

Multi-omic analysis of urothelial cancer patients treated with PD-L1 blockade demonstrates the contribution of both systemic and somatic factors to the biology of response and resistance

BackgroundInhibition of programmed death-ligand one (PD-L1) with atezolizumab can induce durable clinical benefit (DCB) in patients with metastatic urothelial cancers, including complete remissions in patients with chemotherapy refractory disease. Although mutation load and PD-L1 immune cell (IC) staining have been associated with response, they lack sufficient sensitivity and specificity for clinical use. Thus, there is a need to evaluate the peripheral blood immune environment and to conduct detailed analyses of mutation load, predicted neoantigens and immune cellular infiltration in tumors to enhance our understanding of the biologic underpinnings of response and resistance.\n\nMethods and FindingsThe goals of this study were to (1) evaluate the association of mutation load and predicted neoantigen load with therapeutic benefit, and (2) determine whether intratumoral and peripheral blood T cell receptor (TCR) clonality inform clinical outcomes in urothelial carcinoma treated with atezolizumab. We hypothesized that an elevated mutation load in combination with T cell clonal dominance among intratumoral lymphocytes prior to treatment or among peripheral T cells after treatment would be associated with effective tumor control upon treatment with anti-PD-L1 therapy. We performed whole exome sequencing (WES), RNA sequencing (RNA-seq), and T cell receptor sequencing (TCR-seq) of pre-treatment tumor samples as well as TCR sequencing of matched, serially collected peripheral blood collected before and after treatment with atezolizumab. These parameters were assessed for correlation with DCB (defined as progression free survival (PFS) > 6 months), PFS, and overall survival (OS), both alone and in the context of clinical and intratumoral parameters known to be predictive of survival in this disease state.\n\nPatients with DCB displayed a higher proportion of tumor infiltrating T lymphocytes (TIL) (n=24, Mann-Whitney p=0.047). Pre-treatment peripheral blood TCR clonality below the median was associated with improved PFS (n=29, log-rank p=0.048) and OS (n=29, log-rank p=0.011). Patients with DCB also demonstrated more substantial expansion of tumor-associated TCR clones in the peripheral blood 3 weeks after starting treatment (n=22, Mann-Whitney p=0.022). The combination of high pre-treatment peripheral blood TCR clonality with elevated PD-L1 IC staining in tumor tissue was strongly associated with poor clinical outcomes (n=10, HR (mean)=89.88, HR (median)=23.41, 95% CI (2.43, 506.94), p(HR>1)=0.0014). Marked variations in mutation loads were seen with different somatic variant calling methodologies, which in turn impacted associations with clinical outcomes. Missense mutation load, predicted neoantigen load and expressed neoantigen load did not demonstrate significant association with DCB (n=25, Mann-Whitney p=0.22, n=25, Mann-Whitney p=0.55, and n=25, Mann-Whitney p=0.29 respectively). Instead, we found evidence of time-varying effects of somatic mutation load on progression-free survival in this cohort (n=25, p=0.044). A limitation of our study is its small sample size (n=29), a subset of the patients treated on IMvigor 210 (NCT02108652). Given the number of exploratory analyses performed, we intend for these results to be hypothesis-generating.\n\nConclusionsThese results demonstrate the complex nature of immune response to checkpoint blockade and the compelling need for greater interrogation and data integration of both host and tumor factors. Incorporating these variables in prospective studies will facilitate identification and treatment of resistant patients.

cancer biology↗