ETV4 is a mechanical transducer linking cell crowding dynamics to lineage specification.

Dynamic changes in mechanical microenvironments, such as cell crowding, regulate lineage fates as well as cell proliferation. Although regulatory mechanisms for contact inhibition of proliferation have been extensively studied, it remains unclear how cell crowding induces lineage specification. Here we found that a well-known oncogene, ETS variant transcription factor 4 (ETV4), serves as a molecular transducer that links mechanical microenvironments and gene expression. In a growing epithelium of human embryonic stem cells, cell crowding dynamics is translated into ETV4 expression, serving as a pre-pattern for future lineage fates. A switch-like ETV4 inactivation by cell crowding derepresses the potential for neuroectoderm differentiation in human embryonic stem cell epithelia. Mechanistically, cell crowding inactivates the integrin-actomyosin pathway and blocks the endocytosis of fibroblast growth factor receptors (FGFRs). The disrupted FGFR endocytosis induces a marked decrease in ETV4 protein stability through ERK inactivation. Mathematical modelling demonstrates that the dynamics of cell density in a growing human embryonic stem cell epithelium precisely determines the spatiotemporal ETV4 expression pattern and, consequently, the timing and geometry of lineage development. Our findings suggest that cell crowding dynamics in a stem cell epithelium drives spatiotemporal lineage specification using ETV4 as a key mechanical transducer.

Towards accurate indel calling for oncopanel sequencing through an international pipeline competition at precisionFDA.

Accurately calling indels with next-generation sequencing (NGS) data is critical for clinical application. The precisionFDA team collaborated with the U.S. Food and Drug Administration's (FDA's) National Center for Toxicological Research (NCTR) and successfully completed the NCTR Indel Calling from Oncopanel Sequencing Data Challenge, to evaluate the performance of indel calling pipelines. Top performers were selected based on precision, recall, and F1-score. The performance of many other pipelines was close to the top performers, which produced a top cluster of performers. The performance was significantly higher in high confidence regions and coding regions, and significantly lower in low complexity regions. Oncopanel capture and other issues may have occurred that affected the recall rate. Indels with higher variant allele frequency (VAF) may generally be called with higher confidence. Many of the indel calling pipelines had good performance. Some of them performed generally well across all three oncopanels, while others were better for a specific oncopanel. The performance of indel calling can further be improved by restricting the calls within high confidence intervals (HCIs) and coding regions, and by excluding low complexity regions (LCR) regions. Certain VAF cut-offs could be applied according to the applications.

Clinical-genomic determinants of immune checkpoint blockade response in head and neck squamous cell carcinoma.

BACKGROUNDRecurrent and/or metastatic (R/M) head and neck squamous cell carcinoma (HNSCC) is generally an incurable disease, with patients experiencing median survival of under 10 months and significant morbidity. While immune checkpoint blockade (ICB) drugs are effective in approximately 20% of patients, the remaining experience limited clinical benefit and are exposed to potential adverse effects and financial costs. Clinically approved biomarkers, such as tumor mutational burden (TMB), have a modest predictive value in HNSCC.METHODSWe analyzed clinical and genomic features, generated using whole-exome sequencing, in 133 ICB-treated patients with R/M HNSCC, of whom 69 had virus-associated and 64 had non-virus-associated tumors.RESULTSHierarchical clustering of genomic data revealed 6 molecular subtypes characterized by a wide range of objective response rates and survival after ICB therapy. The prognostic importance of these 6 subtypes was validated in an external cohort. A random forest-based predictive model, using several clinical and genomic features, predicted progression-free survival (PFS), overall survival (OS), and response with greater accuracy than did a model based on TMB alone. Recursive partitioning analysis identified 3 features (systemic inflammatory response index, TMB, and smoking signature) that classified patients into risk groups with accurate discrimination of PFS and OS.CONCLUSIONThese findings shed light on the immunogenomic characteristics of HNSCC tumors that drive differential responses to ICB and identify a clinical-genomic classifier that outperformed the current clinically approved biomarker of TMB. This validated predictive tool may help with clinical risk stratification in patients with R/M HNSCC for whom ICB is being considered.FUNDINGFundación Alfonso Martín Escudero, NIH R01 DE027738, US Department of Defense CA210784, The Geoffrey Beene Cancer Research Center, The MSKCC Population Science Research Program, the Jayme Flowers Fund, the Sebastian Nativo Fund, and the NIH/NCI Cancer Center Support Grant P30 CA008748.

Immunometabolic coevolution defines unique microenvironmental niches in ccRCC.

Tumor cell phenotypes and anti-tumor immune responses are shaped by local metabolite availability, but intratumoral metabolite heterogeneity (IMH) and its phenotypic consequences remain poorly understood. To study IMH, we profiled tumor/normal regions from clear cell renal cell carcinoma (ccRCC) patients. A common pattern of IMH transcended all patients, characterized by correlated fluctuations in the abundance of metabolites and processes associated with ferroptosis. Analysis of intratumoral metabolite-RNA covariation revealed that the immune composition of the microenvironment, especially the abundance of myeloid cells, drove intratumoral metabolite variation. Motivated by the strength of RNA-metabolite covariation and the clinical significance of RNA biomarkers in ccRCC, we inferred metabolomic profiles from the RNA sequencing data of ccRCC patients enrolled in 7 clinical trials, and we ultimately identifyied metabolite biomarkers associated with response to anti-angiogenic agents. Local metabolic phenotypes, therefore, emerge in tandem with the immune microenvironment, influence ongoing tumor evolution, and are associated with therapeutic sensitivity.

Novel Genetic Subtypes of Urothelial Carcinoma With Differential Outcomes on Immune Checkpoint Blockade.

PURPOSE: Immune checkpoint blockade (ICB) therapy has significantly improved clinical outcomes in bladder cancer. Identification of correlates of benefit is critical to select appropriate therapy for individual patients. METHODS: To reveal genetic variables associated with benefit from ICB, we performed whole-exome sequencing on tumor specimens from 88 patients with advanced bladder cancer treated with ICB. RESULTS: We identified several genetic factors that correlated with progression-free and overall survival after ICB therapy including ARID1A mutation, tumor mutational burden, intratumoral heterogeneity, the ratio of nonsynonymous to synonymous mutations in the immunopeptidome (immune dN/dS), and tumor cell purity. In addition, we noted that neutrophil-to-lymphocyte ratio and smoking history were negatively associated with overall survival. These genetic characteristics define four molecular subtypes demonstrating differential sensitivity to ICB. We validated the association of these four subtypes with clinical benefit from ICB in an independent cohort (IMvigor210). Finally, we showed that these molecular subtypes also correlate with outcome, although with distinct relationships, among patients not treated with ICB using The Cancer Genome Atlas (TCGA) bladder cancer cohort. Using parallel RNA sequencing data, the subtypes were also shown to correlate with immune infiltration and inflammation, respectively, in the IMvigor210 and TCGA cohorts. CONCLUSION: Together, our study defines molecular subgroups of bladder cancer that influence benefit from ICB.

Identification of biomarkers predictive of metastasis development in early-stage colorectal cancer using network-based regularization.

Colorectal cancer (CRC) is the third most common cancer and the second most deathly worldwide. It is a very heterogeneous disease that can develop via distinct pathways where metastasis is the primary cause of death. Therefore, it is crucial to understand the molecular mechanisms underlying metastasis. RNA-sequencing is an essential tool used for studying the transcriptional landscape. However, the high-dimensionality of gene expression data makes selecting novel metastatic biomarkers problematic. To distinguish early-stage CRC patients at risk of developing metastasis from those that are not, three types of binary classification approaches were used: (1) classification methods (decision trees, linear and radial kernel support vector machines, logistic regression, and random forest) using differentially expressed genes (DEGs) as input features; (2) regularized logistic regression based on the Elastic Net penalty and the proposed iTwiner-a network-based regularizer accounting for gene correlation information; and (3) classification methods based on the genes pre-selected using regularized logistic regression. Classifiers using the DEGs as features showed similar results, with random forest showing the highest accuracy. Using regularized logistic regression on the full dataset yielded no improvement in the methods' accuracy. Further classification using the pre-selected genes found by different penalty factors, instead of the DEGs, significantly improved the accuracy of the binary classifiers. Moreover, the use of network-based correlation information (iTwiner) for gene selection produced the best classification results and the identification of more stable and robust gene sets. Some are known to be tumor suppressor genes (OPCML-IT2), to be related to resistance to cancer therapies (RAC1P3), or to be involved in several cancer processes such as genome stability (XRCC6P2), tumor growth and metastasis (MIR602) and regulation of gene transcription (NME2P2). We show that the classification of CRC patients based on pre-selected features by regularized logistic regression is a valuable alternative to using DEGs, significantly increasing the models' predictive performance. Moreover, the use of correlation-based penalization for biomarker selection stands as a promising strategy for predicting patients' groups based on RNA-seq data.

Comprehensive Genomic Profiling of Cell-Free Circulating Tumor DNA Detects Response to Ribociclib Plus Letrozole in a Patient with Metastatic Breast Cancer.

Analysis of cell-free circulating tumor DNA obtained by liquid biopsy is a non-invasive approach that may provide clinically actionable information when conventional tissue biopsy is inaccessible or infeasible. Here, we followed a patient with hormone receptor-positive and human epidermal growth factor receptor (HER) 2-negative breast cancer who developed bone metastases seven years after mastectomy. We analyzed circulating cell-free DNA (cfDNA) extracted from plasma using high-depth massively parallel sequencing targeting 468 cancer-associated genes, and we identified a clonal hotspot missense mutation in the PIK3CA gene (3:178952085, A > G, H1047R) and amplification of the CCND1 gene. Whole-exome sequencing revealed that both alterations were present in the primary tumor. After treatment with ribociclib plus letrozole, the genetic abnormalities were no longer detected in cfDNA. These results underscore the clinical utility of combining liquid biopsy and comprehensive genomic profiling to monitor treatment response in patients with metastasized breast cancer.

Spatiotemporal evolution of the clear cell renal cell carcinoma microenvironment links intra-tumoral heterogeneity to immune escape.

BACKGROUND: Intratumoral heterogeneity (ITH) is a hallmark of clear cell renal cell carcinoma (ccRCC) that reflects the trajectory of evolution and influences clinical prognosis. Here, we seek to elucidate how ITH and tumor evolution during immune checkpoint inhibitor (ICI) treatment can lead to therapy resistance. METHODS: Here, we completed a single-arm pilot study to examine the safety and feasibility of neoadjuvant nivolumab in patients with localized RCC. Primary endpoints were safety and feasibility of neoadjuvant nivolumab. Then, we spatiotemporally profiled the genomic and immunophenotypic characteristics of 29 ccRCC patients, including pre- and post-therapy samples from 17 ICI-treated patients. Deep multi-regional whole-exome and transcriptome sequencing were performed on 29 patients at different time points before and after ICI therapy. T cell repertoire was also monitored from tissue and peripheral blood collected from a subset of patients to study T cell clonal expansion during ICI therapy. RESULTS: Angiogenesis, lymphocytic infiltration, and myeloid infiltration varied significantly across regions of the same patient, potentially confounding their utility as biomarkers of ICI response. Elevated ITH associated with a constellation of both genomic features (HLA LOH, CDKN2A/B loss) and microenvironmental features, including elevated myeloid expression, reduced peripheral T cell receptor (TCR) diversity, and putative neoantigen depletion. Hypothesizing that ITH may itself play a role in shaping ICI response, we derived a transcriptomic signature associated with neoantigen depletion that strongly associated with response to ICI and targeted therapy treatment in several independent clinical trial cohorts. CONCLUSIONS: These results argue that genetic and immune heterogeneity jointly co-evolve and influence response to ICI in ccRCC. Our findings have implications for future biomarker development for ICI response across ccRCC and other solid tumors and highlight important features of tumor evolution under ICI treatment. TRIAL REGISTRATION: The study was registered on ClinicalTrial.gov (NCT02595918) on November 4, 2015.

Genetic and microenvironmental intra-tumor heterogeneity impacts colorectal cancer evolution and metastatic development.

Colorectal cancer (CRC) is a highly diverse disease, where different genomic instability pathways shape genetic clonal diversity and tumor microenvironment. Although intra-tumor heterogeneity has been characterized in primary tumors, its origin and consequences in CRC outcome is not fully understood. Therefore, we assessed intra- and inter-tumor heterogeneity of a prospective cohort of 136 CRC samples. We demonstrate that CRC diversity is forged by asynchronous forms of molecular alterations, where mutational and chromosomal instability collectively boost CRC genetic and microenvironment intra-tumor heterogeneity. We were able to depict predictor signatures of cancer-related genes that can foresee heterogeneity levels across the different tumor consensus molecular subtypes (CMS) and primary tumor location. Finally, we show that high genetic and microenvironment heterogeneity are associated with lower metastatic potential, whereas late-emerging copy number variations favor metastasis development and polyclonal seeding. This study provides an exhaustive portrait of the interplay between genetic and microenvironment intra-tumor heterogeneity across CMS subtypes, depicting molecular events with predictive value of CRC progression and metastasis development.

A Targetable Myeloid Inflammatory State Governs Disease Recurrence in Clear-Cell Renal Cell Carcinoma.

It is poorly understood how the tumor immune microenvironment influences disease recurrence in localized clear-cell renal cell carcinoma (ccRCC). Here we performed whole-transcriptomic profiling of 236 tumors from patients assigned to the placebo-only arm of a randomized, adjuvant clinical trial for high-risk localized ccRCC. Unbiased pathway analysis identified myeloid-derived IL6 as a key mediator. Furthermore, a novel myeloid gene signature strongly correlated with disease recurrence and overall survival on uni- and multivariate analyses and is linked to TP53 inactivation across multiple data sets. Strikingly, effector T-cell gene signatures, infiltration patterns, and exhaustion markers were not associated with disease recurrence. Targeting immunosuppressive myeloid inflammation with an adenosine A2A receptor antagonist in a novel, immunocompetent, Tp53-inactivated mouse model significantly reduced metastatic development. Our findings suggest that myeloid inflammation promotes disease recurrence in ccRCC and is targetable as well as provide a potential biomarker-based framework for the design of future immuno-oncology trials in ccRCC. SIGNIFICANCE: Improved understanding of factors that influence metastatic development in localized ccRCC is greatly needed to aid accurate prediction of disease recurrence, clinical decision-making, and future adjuvant clinical trial design. Our analysis implicates intratumoral myeloid inflammation as a key driver of metastasis in patients and a novel immunocompetent mouse model. This article is highlighted in the In This Issue feature, p. 2221.

Single-cell sequencing links multiregional immune landscapes and tissue-resident T cells in ccRCC to tumor topology and therapy efficacy.

Clear cell renal cell carcinomas (ccRCCs) are highly immune infiltrated, but the effect of immune heterogeneity on clinical outcome in ccRCC has not been fully characterized. Here we perform paired single-cell RNA (scRNA) and T cell receptor (TCR) sequencing of 167,283 cells from multiple tumor regions, lymph node, normal kidney, and peripheral blood of two immune checkpoint blockade (ICB)-naïve and four ICB-treated patients to map the ccRCC immune landscape. We detect extensive heterogeneity within and between patients, with enrichment of CD8A+ tissue-resident T cells in a patient responsive to ICB and tumor-associated macrophages (TAMs) in a resistant patient. A TCR trajectory framework suggests distinct T cell differentiation pathways between patients responding and resistant to ICB. Finally, scRNA-derived signatures of tissue-resident T cells and TAMs are associated with response to ICB and targeted therapies across multiple independent cohorts. Our study establishes a multimodal interrogation of the cellular programs underlying therapeutic efficacy in ccRCC.

HERVs establish a distinct molecular subtype in stage II/III colorectal cancer with poor outcome.

Colorectal cancer (CRC) is one of the most lethal malignancies. The extreme heterogeneity in survival rate is driving the need for new prognostic biomarkers. Human endogenous retroviruses (hERVs) have been suggested to influence tumor progression, oncogenesis and elicit an immune response. We examined multiple next-generation sequencing (NGS)-derived biomarkers in 114 CRC patients with paired whole-exome and whole-transcriptome sequencing (WES and WTS, respectively). First, we demonstrate that the median expression of hERVs can serve as a potential biomarker for prognosis, relapse, and resistance to chemotherapy in stage II and III CRC. We show that hERV expression and CD8+ tumor-infiltrating T-lymphocytes (TILs) synergistically stratify overall and relapse-free survival (OS and RFS): the median OS of the CD8-/hERV+ subgroup was 29.8 months compared with 37.5 months for other subgroups (HR = 4.4, log-rank P < 0.001). Combing NGS-based biomarkers (hERV/CD8 status) with clinicopathological factors provided a better prediction of patient survival compared to clinicopathological factors alone. Moreover, we explored the association between genomic and transcriptomic features of tumors with high hERV expression and establish this subtype as distinct from previously described consensus molecular subtypes of CRC. Overall, our results underscore a previously unknown role for hERVs in leading to a more aggressive subtype of CRC.

The interplay between cancer type, panel size and tumor mutational burden threshold in patient selection for cancer immunotherapy.

The tumor mutational burden (TMB) is increasingly recognized as an emerging biomarker that predicts improved outcomes or response to immune checkpoint inhibitors in cancer. A multitude of technical and biological factors make it difficult to compare TMB values across platforms, histologies, and treatments. Here, we present a mechanistic model that explains the association between panel size, histology, and TMB threshold with panel performance and survival outcome and demonstrate the limitations of existing methods utilized to harmonize TMB across platforms.

Control over single-cell distribution of G1 lengths by WNT governs pluripotency.

The link between single-cell variation and population-level fate choices lacks a mechanistic explanation despite extensive observations of gene expression and epigenetic variation among individual cells. Here, we found that single human embryonic stem cells (hESCs) have different and biased differentiation potentials toward either neuroectoderm or mesendoderm depending on their G1 lengths before the onset of differentiation. Single-cell variation in G1 length operates in a dynamic equilibrium that establishes a G1 length probability distribution for a population of hESCs and predicts differentiation outcome toward neuroectoderm or mesendoderm lineages. Although sister stem cells generally share G1 lengths, a variable proportion of cells have asymmetric G1 lengths, which maintains the population dispersion. Environmental Wingless-INT (WNT) levels can control the G1 length distribution, apparently as a means of priming the fate of hESC populations once they undergo differentiation. As a downstream mechanism, global 5-hydroxymethylcytosine levels are regulated by G1 length and thereby link G1 length to differentiation outcomes of hESCs. Overall, our findings suggest that intrapopulation heterogeneity in G1 length underlies the pluripotent differentiation potential of stem cell populations.

Regulation of cell-type-specific transcriptomes by microRNA networks during human brain development.

MicroRNAs (miRNAs) regulate many cellular events during brain development by interacting with hundreds of mRNA transcripts. However, miRNAs operate nonuniformly upon the transcriptional profile with an as yet unknown logic. Shortcomings in defining miRNA-mRNA networks include limited knowledge of in vivo miRNA targets and their abundance in single cells. By combining multiple complementary approaches, high-throughput sequencing of RNA isolated by cross-linking immunoprecipitation with an antibody to AGO2 (AGO2-HITS-CLIP), single-cell profiling and computational analyses using bipartite and coexpression networks, we show that miRNA-mRNA interactions operate as functional modules that often correspond to cell-type identities and undergo dynamic transitions during brain development. These networks are highly dynamic during development and over the course of evolution. One such interaction is between radial-glia-enriched ORC4 and miR-2115, a great-ape-specific miRNA, which appears to control radial glia proliferation rates during human brain development.

The Role of Chromatin Density in Cell Population Heterogeneity during Stem Cell Differentiation.

We incorporate three-dimensional (3D) conformation of chromosome (Hi-C) and single-cell RNA sequencing data together with discrete stochastic simulation, to explore the role of chromatin reorganization in determining gene expression heterogeneity during development. While previous research has emphasized the importance of chromatin architecture on activation and suppression of certain regulatory genes and gene networks, our study demonstrates how chromatin remodeling can dictate gene expression distribution by folding into distinct topological domains. We hypothesize that the local DNA density during differentiation accentuate transcriptional bursting due to the crowding effect of chromatin. This phenomenon yields a heterogeneous cell population, thereby increasing the potential of differentiation of the stem cells.

Macromolecular Crowding Regulates the Gene Expression Profile by Limiting Diffusion.

We seek to elucidate the role of macromolecular crowding in transcription and translation. It is well known that stochasticity in gene expression can lead to differential gene expression and heterogeneity in a cell population. Recent experimental observations by Tan et al. have improved our understanding of the functional role of macromolecular crowding. It can be inferred from their observations that macromolecular crowding can lead to robustness in gene expression, resulting in a more homogeneous cell population. We introduce a spatial stochastic model to provide insight into this process. Our results show that macromolecular crowding reduces noise (as measured by the kurtosis of the mRNA distribution) in a cell population by limiting the diffusion of transcription factors (i.e. removing the unstable intermediate states), and that crowding by large molecules reduces noise more efficiently than crowding by small molecules. Finally, our simulation results provide evidence that the local variation in chromatin density as well as the total volume exclusion of the chromatin in the nucleus can induce a homogenous cell population.

Multiple stiffening effects of nanoscale knobs on human red blood cells infected with Plasmodium falciparum malaria parasite.

During its asexual development within the red blood cell (RBC), Plasmodium falciparum (Pf), the most virulent human malaria parasite, exports proteins that modify the host RBC membrane. The attendant increase in cell stiffness and cytoadherence leads to sequestration of infected RBCs in microvasculature, which enables the parasite to evade the spleen, and leads to organ dysfunction in severe cases of malaria. Despite progress in understanding malaria pathogenesis, the molecular mechanisms responsible for the dramatic loss of deformability of Pf-infected RBCs have remained elusive. By recourse to a coarse-grained (CG) model that captures the molecular structures of Pf-infected RBC membrane, here we show that nanoscale surface protrusions, known as "knobs," introduce multiple stiffening mechanisms through composite strengthening, strain hardening, and knob density-dependent vertical coupling. On one hand, the knobs act as structural strengtheners for the spectrin network; on the other, the presence of knobs results in strain inhomogeneity in the spectrin network with elevated shear strain in the knob-free regions, which, given its strain-hardening property, effectively stiffens the network. From the trophozoite to the schizont stage that ensues within 24-48 h of parasite invasion into the RBC, the rise in the knob density results in the increased number of vertical constraints between the spectrin network and the lipid bilayer, which further stiffens the membrane. The shear moduli of Pf-infected RBCs predicted by the CG model at different stages of parasite maturation are in agreement with experimental results. In addition to providing a fundamental understanding of the stiffening mechanisms of Pf-infected RBCs, our simulation results suggest potential targets for antimalarial therapies.