An AI-assisted integrated, scalable, single-cell phenomic-transcriptomic platform to elucidate intratumor heterogeneity against immune response.

We present a novel framework combining single-cell phenotypic data with single-cell transcriptomic analysis to identify factors underpinning heterogeneity in antitumor immune response. We developed a pairwise, tumor-immune discretized interaction assay between natural killer (NK-92MI) cells and patient-derived head and neck squamous cell carcinoma (HNSCC) cell lines on a microfluidic cell-trapping platform. Furthermore we generated a deep-learning computer vision algorithm that is capable of automating the acquisition and analysis of a large, live-cell imaging data set (>1 million) of paired tumor-immune interactions spanning a time course of 24 h across multiple HNSCC lines (n = 10). Finally, we combined the response data measured by Kaplan-Meier survival analysis against NK-mediated killing with downstream single-cell transcriptomic analysis to interrogate molecular signatures associated with NK-effector response. As proof-of-concept for the proposed framework, we efficiently identified MHC class I-driven cytotoxic resistance as a key mechanism for immune evasion in nonresponders, while enhanced expression of cell adhesion molecules was found to be correlated with sensitivity against NK-mediated cytotoxicity. We conclude that this integrated, data-driven phenotypic approach holds tremendous promise in advancing the rapid identification of new mechanisms and therapeutic targets related to immune evasion and response.

Evolution of precision oncology-guided treatment paradigms.

Cancer treatment is gradually evolving from the classical use of nonspecific cytotoxic drugs targeting generic mechanisms of cell growth and proliferation. Instead, new "patient-specific treatment paradigms" that are based on an individual patient's tumor-specific molecular features are emerging, and these include "druggable" genomic alterations such as oncogenic driver mutations, downstream activities of cancer-signaling pathways, and the expression of specific genes involved in tumorigenesis and cancer progression. This evolving landscape of making evidence-based treatment decisions forms the foundation of precision oncology, which aims to deliver "the right drug, to the right patient and at the right time". The long-term vision for this approach is to maximize the treatment efficacy while minimizing exposure to ineffective therapy and reducing co-morbidity-related side effects. Successful clinical translation and implementation of this vision have the potential to revolutionize treatment paradigms from predominantly reactive, to more evidence-based, proactive and predictive care. In this article, we review the past and current approaches in precision oncology, and describe their remarkable power and limitations. We also speculate on the evolution of newly emerging methodologies of the future that can be used to address some of the key challenges associated with the existing paradigms. This article is categorized under: Cancer > Genetics/Genomics/Epigenetics Cancer > Molecular and Cellular Physiology Cancer > Computational Models.

A comparative study of tumour-on-chip models with patient-derived xenografts for predicting chemotherapy efficacy in colorectal cancer patients.

Inter-patient and intra-tumour heterogeneity (ITH) have prompted the need for a more personalised approach to cancer therapy. Although patient-derived xenograft (PDX) models can generate drug response specific to patients, they are not sustainable in terms of cost and time and have limited scalability. Tumour Organ-on-Chip (OoC) models are in vitro alternatives that can recapitulate some aspects of the 3D tumour microenvironment and can be scaled up for drug screening. While many tumour OoC systems have been developed to date, there have been limited validation studies to ascertain whether drug responses obtained from tumour OoCs are comparable to those predicted from patient-derived xenograft (PDX) models. In this study, we established a multiplexed tumour OoC device, that consists of an 8 × 4 array (32-plex) of culture chamber coupled to a concentration gradient generator. The device enabled perfusion culture of primary PDX-derived tumour spheroids to obtain dose-dependent response of 5 distinct standard-of-care (SOC) chemotherapeutic drugs for 3 colorectal cancer (CRC) patients. The in vitro efficacies of the chemotherapeutic drugs were rank-ordered for individual patients and compared to the in vivo efficacy obtained from matched PDX models. We show that quantitative correlation analysis between the drug efficacies predicted via the microfluidic perfusion culture is predictive of response in animal PDX models. This is a first study showing a comparative framework to quantitatively correlate the drug response predictions made by a microfluidic tumour organ-on-chip (OoC) model with that of PDX animal models.

Single-cell and bulk transcriptome sequencing identifies two epithelial tumor cell states and refines the consensus molecular classification of colorectal cancer.

The consensus molecular subtype (CMS) classification of colorectal cancer is based on bulk transcriptomics. The underlying epithelial cell diversity remains unclear. We analyzed 373,058 single-cell transcriptomes from 63 patients, focusing on 49,155 epithelial cells. We identified a pervasive genetic and transcriptomic dichotomy of malignant cells, based on distinct gene expression, DNA copy number and gene regulatory network. We recapitulated these subtypes in bulk transcriptomes from 3,614 patients. The two intrinsic subtypes, iCMS2 and iCMS3, refine CMS. iCMS3 comprises microsatellite unstable (MSI-H) cancers and one-third of microsatellite-stable (MSS) tumors. iCMS3 MSS cancers are transcriptomically more similar to MSI-H cancers than to other MSS cancers. CMS4 cancers had either iCMS2 or iCMS3 epithelium; the latter had the worst prognosis. We defined the intrinsic epithelial axis of colorectal cancer and propose a refined 'IMF' classification with five subtypes, combining intrinsic epithelial subtype (I), microsatellite instability status (M) and fibrosis (F).

Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures.

While understanding molecular heterogeneity across patients underpins precision oncology, there is increasing appreciation for taking intra-tumor heterogeneity into account. Based on large-scale analysis of cancer omics datasets, we highlight the importance of intra-tumor transcriptomic heterogeneity (ITTH) for predicting clinical outcomes. Leveraging single-cell RNA-seq (scRNA-seq) with a recommender system (CaDRReS-Sc), we show that heterogeneous gene-expression signatures can predict drug response with high accuracy (80%). Using patient-proximal cell lines, we established the validity of CaDRReS-Sc's monotherapy (Pearson r>0.6) and combinatorial predictions targeting clone-specific vulnerabilities (>10% improvement). Applying CaDRReS-Sc to rapidly expanding scRNA-seq compendiums can serve as in silico screen to accelerate drug-repurposing studies. Availability: https://github.com/CSB5/CaDRReS-Sc .

Cyclical phosphorylation of LRAP35a and CLASP2 by GSK3ß and CK1δ regulates EB1-dependent MT dynamics in cell migration.

Mammalian cell cytoskeletal reorganization for efficient directional movement requires tight coordination of actomyosin and microtubule networks. In this study, we show that LRAP35a potentiates microtubule stabilization by promoting CLASP2/EB1 interaction besides its complex formation with MRCK/MYO18A for retrograde actin flow. The alternate regulation of these two networks by LRAP35a is tightly regulated by a series of phosphorylation events that dictated its specificity. Sequential phosphorylation of LRAP35a by Protein Kinase A (PKA) and Glycogen Synthase Kinase-3ß (GSK3ß) initiates the association of LRAP35a with CLASP2, while subsequent binding and further phosphorylation by Casein Kinase 1δ (CK1δ) induce their dissociation, which facilitates LRAP35a/MRCK association in driving lamellar actomyosin flow. Importantly, microtubule dynamics is directly moderated by CK1δ activity on CLASP2 to regulate GSK3ß phosphorylation of the SxIP motifs that blocks EB1 binding, an event countered by LRAP35a interaction and its competition for CK1δ activity. Overall this study reveals an essential role for LRAP35a in coordinating lamellar contractility and microtubule polarization in cell migration.

Author Correction: Single-cell analysis of EphA clustering phenotypes to probe cancer cell heterogeneity.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Two high-yield complementary methods to sort cell populations by their 2D or 3D migration speed.

The potential to migrate is one of the most fundamental functions for various epithelial, mesenchymal, and immune cells. Image analysis of motile cell populations, both primary and cultured, typically reveals an intercellular variability in migration speeds. However, cell migration chromatography, the sorting of large populations of cells based on their migratory characteristics, cannot be easily performed. The lack of such methods has hindered our understanding of the direct correlation between the capacity to migrate and other cellular properties. Here, we report two novel, easily implementable and readily scalable methods to sort millions of live migratory cancer and immune cells based on their spontaneous migration in two-dimensional and three-dimensional microenvironments, respectively. Correlative downstream transcriptomic, molecular and functional tests reveal marked differences between the fast and slow subpopulations in patient-derived cancer cells. We further employ our method to reveal that sorting the most migratory cytotoxic T lymphocytes yields a pool of cells with enhanced cytotoxicity against cancer cells. This phenotypic assay opens new avenues for the precise characterization of the mechanisms underlying hither to unexplained heterogeneities in migratory phenotypes within a cell population, and for the targeted enrichment of the most potent migratory leukocytes in immunotherapies.

Author Correction: Single-cell analysis of EphA clustering phenotypes to probe cancer cell heterogeneity.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Single-cell analysis of EphA clustering phenotypes to probe cancer cell heterogeneity.

The Eph family of receptor tyrosine kinases is crucial for assembly and maintenance of healthy tissues. Dysfunction in Eph signaling is causally associated with cancer progression. In breast cancer cells, dysregulated Eph signaling has been linked to alterations in receptor clustering abilities. Here, we implemented a single-cell assay and a scoring scheme to systematically probe the spatial organization of activated EphA receptors in multiple carcinoma cells. We show that cancer cells retain EphA clustering phenotype over several generations, and the degree of clustering reported for migration potential both at population and single-cell levels. Finally, using patient-derived cancer lines, we probed the evolution of EphA signalling in cell populations that underwent metastatic transformation and acquisition of drug resistance. Taken together, our scalable approach provides a reliable scoring scheme for EphA clustering that is consistent over multiple carcinomas and can assay heterogeneity of cancer cell populations in a cost- and time-effective manner.

Longitudinal single-cell RNA sequencing of patient-derived primary cells reveals drug-induced infidelity in stem cell hierarchy.

Chemo-resistance is one of the major causes of cancer-related deaths. Here we used single-cell transcriptomics to investigate divergent modes of chemo-resistance in tumor cells. We observed that higher degree of phenotypic intra-tumor heterogeneity (ITH) favors selection of pre-existing drug-resistant cells, whereas phenotypically homogeneous cells engage covert epigenetic mechanisms to trans-differentiate under drug-selection. This adaptation was driven by selection-induced gain of H3K27ac marks on bivalently poised resistance-associated chromatin, and therefore not expressed in the treatment-naïve setting. Mechanistic interrogation of this phenomenon revealed that drug-induced adaptation was acquired upon the loss of stem factor SOX2, and a concomitant gain of SOX9. Strikingly we observed an enrichment of SOX9 at drug-induced H3K27ac sites, suggesting that tumor evolution could be driven by stem cell-switch-mediated epigenetic plasticity. Importantly, JQ1 mediated inhibition of BRD4 could reverse drug-induced adaptation. These results provide mechanistic insights into the modes of therapy-induced cellular plasticity and underscore the use of epigenetic inhibitors in targeting tumor evolution.

Phenotype-driven precision oncology as a guide for clinical decisions one patient at a time.

Genomics-driven cancer therapeutics has gained prominence in personalized cancer treatment. However, its utility in indications lacking biomarker-driven treatment strategies remains limited. Here we present a "phenotype-driven precision-oncology" approach, based on the notion that biological response to perturbations, chemical or genetic, in ex vivo patient-individualized models can serve as predictive biomarkers for therapeutic response in the clinic. We generated a library of "screenable" patient-derived primary cultures (PDCs) for head and neck squamous cell carcinomas that reproducibly predicted treatment response in matched patient-derived-xenograft models. Importantly, PDCs could guide clinical practice and predict tumour progression in two n = 1 co-clinical trials. Comprehensive "-omics" interrogation of PDCs derived from one of these models revealed YAP1 as a putative biomarker for treatment response and survival in ~24% of oral squamous cell carcinoma. We envision that scaling of the proposed PDC approach could uncover biomarkers for therapeutic stratification and guide real-time therapeutic decisions in the future.Treatment response in patient-derived models may serve as a biomarker for response in the clinic. Here, the authors use paired patient-derived mouse xenografts and patient-derived primary culture models from head and neck squamous cell carcinomas, including metastasis, as models for high-throughput screening of anti-cancer drugs.