Biology subjects

Miller, M. L.

Publications and source records attributed to Miller, M. L..

10 recordsLinked to original sources

bioRxiv2025

Pan-cancer analysis in the real-world setting uncovers immunogenomic drivers of acquired resistance post-immunotherapy

Immune checkpoint blockade (ICB) has revolutionised cancer therapy, yet resistance -- both primary and acquired -- remains a significant obstacle, affecting the majority of patients. Here, we leveraged a large-scale, real-world clinicogenomic dataset to systematically explore the molecular underpinnings of ICB resistance in the post-progression setting. Analysing over 5,000 pan-cancer patients with clinical and pre-/post-treatment genomic and transcriptomic data, we identify distinct immunogenomic drivers of acquired vs. primary ICB resistance. Post-ICB progression, acquired resistance showed extended survival compared to primary resistance across all cancer types. The acquired resistance clinical phenotype was paralleled by a universally immune-inflamed, albeit dysfunctional, tumour microenvironment (TME) at the onset of acquired resistance, with sustained or ICB-induced inflammatory and interferon responses. We confirm previously described mechanisms of acquired resistance, including B2M loss-of-function (LoF) in non-small cell lung cancer (NSCLC), and identify novel potential mediators, including LoF of TGFBR2 in NSCLC, CYLD in head and neck cancer, and RUNX1 in triple negative breast cancer. Further supporting their involvement in resistance, these acquired ICB alterations associated with immune-escaped TMEs, characterised by active immunomodulatory oncogenic signalling, hyperproliferation and invasiveness, or altered tumour metabolism. These findings emphasise the heterogeneity of molecular drivers of acquired resistance to ICB within and across cancers, and highlight the potential for personalised therapeutic interventions post-progression to improve patient outcomes.

cancer biology↗

bioRxiv2024

Defective glycosylation and ELFN1 binding of mGluR6 congenital stationary night blindness mutants

Synaptic transmission from photoreceptors to ON-bipolar cells (BCs) requires the postsynaptic metabotropic glutamate receptor mGluR6, located at BC dendritic tips. Binding of the neurotransmitter glutamate initiates G-protein signaling that regulates the TRPM1 transduction channel. mGluR6 also interacts with presynaptic ELFN adhesion proteins, and these interactions are important for mGluR6 synaptic localization. The mechanisms of mGluR6 trafficking and synaptic targeting remain poorly understood. In this study, we investigated mGluR6 missense mutations from patients with congenital stationary night blindness (CSNB), which is associated with loss of synaptic transmission to ON-BCs. We found that multiple CSNB mutations in the extracellular ligand-binding domain of mGluR6 impart a trafficking defect leading to lack of complex N-glycosylation but efficient plasma membrane insertion, suggesting a Golgi bypass mechanism. These mutants fail to bind ELFN1, consistent with lack of a necessary modification normally acquired in the Golgi. The same mutants were mislocalized in bipolar cells, explaining the loss of function in CSNB. The results reveal a key role of Golgi trafficking in mGluR6 function, and suggest a role for the extracellular domain in Golgi sorting.

cell biology↗

bioRxiv2024

Enhancing tumor-infiltrating T cells with an exclusive fuel source

Solid tumors harbor immunosuppressive microenvironments that inhibit tumor-infiltrating lymphocytes (TILs) through the voracious consumption of glucose. We sought to restore TIL function by providing them with an exclusive fuel source. The glucose disaccharide cellobiose, which is the building block of cellulose, contains a {beta}-1,4-glycosidic bond that that animals (or their tumors) cannot hydrolyze, but microbes have evolved enzymes to catabolize cellobiose into useful glucose. We equipped mouse T cells and human CAR-T cells with two proteins enabling import and hydrolysis of cellobiose and demonstrated that cellobiose supplementation during glucose withdrawal restores key anti-tumor T-cell functions: viability, proliferation, cytokine production, and cytotoxic killing. Engineered T cells offered cellobiose suppress murine tumor growth and prolong survival. Offering exclusive access to a natural disaccharide is a new tool that augments cancer immunotherapies. This approach could be used to answer questions about glucose metabolism across many cell types, biological processes, and diseases.

immunology↗

bioRxiv2021

Clinical and molecular features of acquired resistance to immunotherapy in non-small cell lung cancer

Although cancer immunotherapy with PD-(L)1 blockade is now routine treatment for patients with lung cancer, remarkably little is known about acquired resistance. We examined 1,201 patients with NSCLC treated with PD-(L)1 blockade to clinically characterize acquired resistance, finding it to be common (occurring in more than 60% of initial responders), with persistent but diminishing risk over time, and with distinct metastatic and survival patterns compared to primary resistance. To examine the molecular phenotype and potential mechanisms of acquired resistance, we performed whole transcriptome and exome tumor profiling in a subset of NSCLC patients (n=29) with acquired resistance. Systematic immunogenomic analysis revealed that tumors with acquired resistance generally had enriched signals of inflammation (including IFN{gamma} signaling and inferred CD8+ T cells) and could be separated into IFN{gamma} upregulated and stable subsets. IFN{gamma} upregulated tumors had putative routes of resistance with signatures of dysfunctional interferon signaling and mutations in antigen presentation genes. Transcriptomic profiling of cancer cells from a murine model of acquired resistance to PD-(L)1 blockade also showed evidence of dysfunctional interferon signaling and acquired insensitivity to in vitro interferon gamma treatment. In summary, we characterized clinical and molecular features of acquired resistance to PD-(L)1 blockade in NSCLC and found evidence of ongoing but dysfunctional IFN response. The persistently inflamed, rather than excluded or deserted, tumor microenvironment of acquired resistance informs therapeutic strategies to effectively reprogram and reverse acquired resistance.

cancer biology↗

bioRxiv2019

Multi-site clonality analyses uncovers pervasive subclonal heterogeneity and branching evolution across melanoma metastases

Metastatic melanoma carries a poor prognosis despite modern systemic therapies. Understanding the evolution of the disease could help inform patient management. Through whole-genome sequencing of 13 melanoma metastases sampled at autopsy from a treatment naive patient and by leveraging the analytical power of multi-sample analyses, we reveal that metastatic cells may depart the primary tumour very early in the disease course and follow a branched pattern of evolution. Truncal UV-induced mutations that often swamp downstream analyses of heterogeneity, were found to be replaced by APOBEC-associated mutations in the branches of the evolutionary tree. Multi-sample analyses from a further 7 patients confirmed that branched evolution was pervasive, representing an important mode of melanoma dissemination. Our analyses illustrate that combining cancer cell fraction estimates across multiple metastases provides higher resolution phylogenetic reconstructions relative to single sample analyses and highlights the limitations of accurately inferring inter-tumoural heterogeneity from a single biopsy.

genomics↗

bioRxiv2019

Computed Tomography Measures of Inter-site tumor Heterogeneity for Classifying Outcomes in High-Grade Serous Ovarian Carcinoma: a Retrospective Study

AbstractO_ST_ABSBackgroundC_ST_ABSHigh grade serous ovarian carcinoma shows marked intra-tumoral heterogeneity which is associated with decreased survival and resistance to platinum-based chemotherapy. Pre-treatment quantification of spatial tumor heterogeneity by multiple tissue sampling is not clinically feasible. Using standard-of-care CT imaging to non-invasively quantify heterogeneity could have high clinical utility and would be highly cost-effective. Texture analysis measures local variations in computed tomography (CT) image intensity. Haralick texture methods are typically used to capture the heterogeneity of entire lesions; however, this neglects the possible presence of texture habitats within the lesion, and the differences between metastatic sites. The primary aim of this study was to develop texture analysis of intra-site and inter-site spatial heterogeneity from standard-of-care CT images and to correlate these measures with clinical and genomic features in patients with HGSOC. Methods and findingsWe analyzed the data from a retrospective cohort of 84 patients with HGSOC consisting of 46 patients from Memorial Sloan Kettering Cancer Center (MSKCC) and 38 non-MSKCC cases selected from The Cancer Imaging Archive (TCIA). Inclusion criteria consisted of FIGO stage II-IV HGSOC, attempted primary cytoreductive surgery, intravenous contrast-enhanced CT of abdomen and pelvis performed prior to surgery and availability of molecular tumor data analysed as per the Cancer Genome Atlas (TCGA) Research Network ovarian cancer project. Manual segmentation and image analysis was performed on 463 metastatic tumor sites from 84 patients. In the MSKCC cohort the median number of tumor sites was 7 (interquartile range 5-9) and 4 (interquartile range 3-4) in the TCIA patients. Sub-regions were produced within each tumor site by grouping voxels with similar Haralick texture using the Kernel K-means method. We derived statistical measures of intra- and inter-site tumor heterogeneity (IISTH) including cluster sites entropy (cSE), cluster sites standard deviation (cluDev) and cluster sites dissimilarity (cluDiss) from sub-regions identified within and between individual tumor sites. Unsupervised clustering was used to group patient IISTH measures into low, medium, high, and ultra-high heterogeneity clusters from each cohort. The IISTH measure cluDiss was an independent predictor of progression-free survival (PFS) in multivariable analysis in both datasets (MSKCC hazard ratio [HR] 1.04, 95% CI 1.01-1.06, P = 0.002; TCIA HR 1.05, 95% CI 1.00-1.10, P = 0.049). Low and medium IISTH clusters were associated with longer PFS in multivariable analysis (MSKCC HR 2.94, 90% CI 1.29-6.70, P = 0.009, TCIA HR 5.94, 95% CI 1.05-33.6, P = 0.044). IISTH measures were robust to differences in the CT imaging systems. Average Haralick textures contrast (TCIA HR 1.08, 95% CI 1.01-1.10, P = 0.019) and homogeneity (TCIA HR 1.09, 95% CI 1.02-1.16, P = 0.008) were associated with PFS in mutivariate analysis only in the TCIA dataset. All other average Haralick textures and total tumor volume were not associated with PFS in either dataset. ConclusionsTexture measures of intra- and inter-site tumor heterogeneity from standard of care CT images are correlated with shorter PFS in HGSOC patients. These quantitative methods are independent of the CT imaging system and can thus be applied in clinical practice. The methodology proposed here enables the non-invasive quantification of intra-tumoral heterogeneity and disease stratification for future experimental medicine studies and clinical trials, particularly in cases where total tumour volume and averaged textures have low predictive power. Author summaryO_ST_ABSWhy was this study done?C_ST_ABSO_LITumor heterogeneity is a feature of many solid malignancies including ovarian cancer. C_LIO_LIRecent genomic research suggests that intra-site tumor heterogeneity (heterogeneity within a single tumor site) and inter-site tumor heterogeneity (heterogeneity between different metastatic sites in the same patient) correlate with clinical outcome in HGSOC. C_LI What did the researchers do and find?O_LIWe developed quantitative and non-invasive image-analysis based measures for predicting outcome in HGSOC patients by combining image-based information from within and between multiple tumor sites. C_LIO_LIUsing datasets from two sources, we demonstrate that these image-based tumor heterogeneity measures predict progression free survival in patients with HGSOC. C_LI What do these findings mean?O_LINon-invasive measures of CT image heterogeneity may predict outcomes in HGSOC patients. C_LIO_LIWider application of these CT image heterogeneity measures could prove useful for stratifying patients to different therapies given that total tumour volume and averaged textures have low predictive power. C_LI

bioinformatics↗

bioRxiv2019

Copy number aberrations drive kinase re-wiring leading to genetic vulnerabilities in cancer

Somatic DNA copy number variations (CNVs) are prevalent in cancer and can drive cancer progression albeit with often uncharacterized roles in altering cell signaling states. Here, we integrated genomic and proteomic data for 5598 tumor samples to identify CNVs leading to aberrant signal transduction. The resulting associations recapitulated known kinase-substrate relationships and further network analysis prioritized likely driver genes. A total of 44 robust pan-cancer gene-phosphosite associations were replicated in cell line samples. Of these, ARHGEF17, a predicted regulator of hippo-signaling, was further studied through (phospho)proteomics analysis where ARHGEF17 knockdown cells showed dys-regulation of hippo- and p38 signaling as well as immune related pathways. Using, RNAi, CRISPR and drug screening data we find evidence of kinase addiction in cancer cell lines identifying inhibitors for targeting of kinase-dependent cell lines. We propose copy number status of genes as useful predictors of differential impact of kinase inhibition, a strategy that may be of use in the future for anticancer therapies.

genomics↗

bioRxiv2018

Resolving Tumour Clonal Heterogeneity and Spatial Complexity using Nuclear Tandem Epitope Protein (nTEP) Barcoding

Tumours are composed of an array of unique cancer cell clones along with many non-tumour cells such as immune cells, fibroblasts and endothelial cells, which make up the complex tumour microenvironment. To better understand the co-evolution of tumour clones and cells of the tumour microenvironment, we require tools to spatially resolve heterotypic cellular interactions at the single cell level. We present a novel protein-based barcoding technology termed nuclear tandem epitope protein (nTEP) barcoding, which can be designed to combinatorially encode and track dozens to hundreds of tumour clones in their spatial context within complex cellular mixtures using multiplexed antibody-based imaging. Here we provide proof-of-principle of nTEP barcoding and develop the technology, which relies on lentiviral - based stable expression of a nuclear-localised fluorophore that contains unique combinations of protein epitope tags that can be decoded by a limited set of antibodies. By generating a series of cell lines expressing unique nTEP barcodes, we were able to robustly identify and spatially deconvolve specific clones present within highly complex cell mixtures at the single cell level using state-of-the-art iterative indirect immunofluorescence imaging (4i). We define the utility of nTEP-barcoding as a powerful tool for visualising and resolving tumour heterogeneity at the cellular level, and envision its usage in mouse tumour models for understanding how tumour clones modulate and interact with stromal- and immune cells in cancer.

cancer biology↗

bioRxiv2018

Unraveling Tumor-Immune Heterogeneity in Advanced Ovarian Cancer Uncovers Immunogenic Effect of Chemotherapy

In metastatic cancer, the role of heterogeneity at the tumor-immune microenvironment, its molecular underpinnings and clinical relevance remain largely unexplored. To understand tumor-immune dynamics at baseline and upon chemotherapy treatment, we performed unbiased pathway and cell type-specific immunogenomics analysis of treatment-naive (38 samples from 8 patients) and paired chemotherapy treated (80 paired samples from 40 patients) high-grade serous ovarian cancer (HGSOC) samples. Whole transcriptome analysis and image-based quantification of T cells from treatment-naive tumors revealed ubiquitous variability in immune signaling and distinct immune microenvironments co-existing within the same individuals and within tumor deposits at diagnosis. To systematically explore cell type composition of the tumor microenvironment using bulk mRNA, we derived consensus immune and stromal cell gene signatures by intersecting state-of-the-art deconvolution methods, providing improved accuracy and sensitivity when compared to HGSOC immunostaining and leukocyte methylation data sets. Cell-type deconvolution and pathway analyses revealed that Myc and Wnt signaling associate with immune cell exclusion in untreated HGSOC. To evaluate the effect of chemotherapy on the intrinsic tumor-immune heterogeneity, we compared site-matched and site-unmatched tumors before and after neoadjuvant chemotherapy. Transcriptomic and T-cell receptor sequencing analyses showed that site-matched samples had increased cytotoxic immune activation and oligoclonal expansion of T cells after chemotherapy, which was not seen in site-unmatched samples where heterogeneity could not be accounted for. These results demonstrate that the tumor-immune interface in advanced HGSOC is intrinsically heterogeneous, and thus requires site-specific analysis to reliably unmask the impact of therapy on the tumor-immune microenvironment.

cancer biology↗

bioRxiv2017

The Evolution of Fair Offers with Low Rejection Thresholds in the Ultimatum Game

The ultimatum game (UG) is widely used in economic and anthropological research to investigate fairness by how one player proposes to divide a resource with a second player who can reject the offer. In these contexts, fairness is understood as offers that are more generous than predicted by the subgame perfect Nash equilibrium (SPNE). A surprising and robust result of UG experiments is that proposers offer much more than the SPNE. These results have spawned many models aimed at explaining why players do not conform to the SPNE by showing how Nash equilibrium strategies can evolve far from the SPNE. However, empirical data from UG experiments indicate that players do not use Nash equilibrium strategies, but rather make generous offers while rejecting only very low offers. To better understand why people behave this way, we developed an agent-based model to investigate how generous strategies could evolve in the UG. Using agents with generic biological properties, we found that fair offers can readily evolve in structured populations even while rejection thresholds remain relatively low. We explain the evolution of fairness as a problem of the efficient conversion of resources into the production of offspring at the level of the group.\n\nSignificance StatementHuman generosity is widespread and far exceeds that of other social animals. Generosity is often studied experimentally with the ultimatum game, in which a proposer offers a split and a responder can either accept it or cancel the whole deal. A surprising result of ultimatum game experiments is that players are much more generous than predicted while only rejecting very low offers. This has presented a theoretical puzzle, since mathematical models have generally relied on high rejection levels--just below offer levels--to maintain generosity. Using evolutionary simulations, we explain both generous offers and the rejection of only low offers as a solution to the problem of how groups can efficiently convert resources into the production of offspring.

evolutionary biology↗