Colorectal cancer (CRC) consensus molecular subtypes (CMS) have different immunological, stromal cell, and clinicopathological characteristics. Single-cell characterization of CMS subtype tumor microenvironments is required to elucidate mechanisms of tumor and stroma cell contributions to pathogenesis which may advance subtype-specific therapeutic development. We interrogate racially diverse human CRC samples and analyze multiple independent external cohorts for a total of 487,829 single cells enabling high-resolution depiction of the cellular diversity and heterogeneity within the tumor and microenvironmental cells.Tumor cells recapitulate individual CMS subgroups yet exhibit significant intratumoral CMS heterogeneity. Both CMS1 microsatellite instability (MSI-H) CRCs and microsatellite stable (MSS) CRC demonstrate similar pathway activations at the tumor epithelial level. However, CD8+ cytotoxic T cell phenotype infiltration in MSI-H CRCs may explain why these tumors respond to immune checkpoint inhibitors. Cellular transcriptomic profiles in CRC exist in a tumor immune stromal continuum in contrast to discrete subtypes proposed by studies utilizing bulk transcriptomics. We note a dichotomy in tumor microenvironments across CMS subgroups exists by which patients with high cancer-associated fibroblasts (CAFs) and C1Q+TAM content exhibit poor outcomes, providing a higher level of personalization and precision than would distinct subtypes. Additionally, we discover CAF subtypes known to be associated with immunotherapy resistance.Distinct CAFs and C1Q+ TAMs are sufficient to explain CMS predictive ability and a simpler signature based on these cellular phenotypes could stratify CRC patient prognosis with greater precision. Therapeutically targeting specific CAF subtypes and C1Q + TAMs may promote immunotherapy responses in CRC patients.
Abstract Background: Targeted therapies against specific driver mutations of cancer progression have been used to improve survival of lung adenocarcinoma patients. In KRAS mutant NSCLC specifically, however, after some initial improvement in lung cancer patients, targeted therapies often fail due to acquired drug resistance. To uncover mechanisms of resistance and to discover new drivers, genome-scale sequencing of lung cancers has identified candidate genes, but these data have not rapidly translated in preclinical validation. A major obstacle in lung cancer research has been the deficiencies of standard in vitro models. Methods: To address the deficiencies within standard models we have developed an in vitro 3-dimensional, KRAS-mutated “organoid” model of lung adenocarcinoma that surpasses both in vitro and in vivo models by possessing the tractability of cell lines and the 3-dimensional architecture and morphology of animal models. We have engineered a p53 knockout and KRAS mutation on top of normal wild-type lung epithelium to achieve oncogenicity. Result: Through an optimized growth period in the presence of drug, an organoid model of resistance has been developed through which de novo genetic events underlying acquired resistance can be studied. Conclusion: The highly defined genetic background of the KRAS-mutated 3-D organoid model serves as a tabula rasa upon which stochastic secondary genetic and epigenetic changes can be identified and mechanistically studied by forward and reverse genetics approaches in order to rapidly identify mechanisms of acquired drug resistance and validate therapeutic options. Citation Format: Navika D. Shukla, Ameen A. Salahudeen, Sukhmani K. Padda, Joel W. Neal, Heather A. Wakelee, Calvin J. Kuo. Three-dimensional organoid model for acquired drug resistance in non-small cell lung cancer [abstract]. In: Proceedings of the Fifth AACR-IASLC International Joint Conference: Lung Cancer Translational Science from the Bench to the Clinic; Jan 8-11, 2018; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(17_Suppl):Abstract nr B30.
Reproducibility of results obtained using ribonucleic acid (RNA) data across labs remains a major hurdle in cancer research. Often, molecular predictors trained on one dataset cannot be applied to another due to differences in RNA library preparation and quantification, which inhibits the validation of predictors across labs. While current RNA correction algorithms reduce these differences, they require simultaneous access to patient-level data from all datasets, which necessitates the sharing of training data for predictors when sharing predictors. Here, we describe SpinAdapt, an unsupervised RNA correction algorithm that enables the transfer of molecular models without requiring access to patient-level data. It computes data corrections only via aggregate statistics of each dataset, thereby maintaining patient data privacy. Despite an inherent trade-off between privacy and performance, SpinAdapt outperforms current correction methods, like Seurat and ComBat, on publicly available cancer studies, including TCGA and ICGC. Furthermore, SpinAdapt can correct new samples, thereby enabling unbiased evaluation on validation cohorts. We expect this novel correction paradigm to enhance research reproducibility and to preserve patient privacy.
High-content imaging of tumor organoids (TOs) treated with therapeutic agents provides detailed cell viability readouts at the organoid level. In contrast, most used protocols provide one number per well. While requiring the use of inverted microscopy with an automated stage, this protocol can provide critical information about heterogeneous responses of TOs to various treatments. This protocol describes a technique for culturing and drug testing TOs using fluorescent indicators of cell viability with high reproducibility. For complete details on the use and execution of this protocol, please refer to Larsen et al. (2021).
209 Background: Metastatic colorectal (CRC), pancreatic (PANC), and gastroesophageal cancers are the leading causes of GI cancer–related mortality (5-y survival: 15%, 3%, and 5%-6%, respectively) (ACS 2022). HLA LOH is a recurrent mechanism of immune escape observed in 15%-20% of GI cancers (Hecht R., ASCO GI 2022). The Tmod platform is a logic-gated chimeric antigen receptor (CAR) T-cell modular system, comprising a carcinoembryonic antigen (CEA)- or mesothelin (MSLN)-targeting CAR activator and a separate HLA-A*02-targeting blocker receptor. Both in vitro/in vivo, Tmod CAR T therapy kills cells with HLA-A*02 LOH (tumor) without harming cells with retained HLA-A*02 expression (normal). However, HLA-A*02 LOH can only be therapeutically exploited if patients are identifiable through a feasible and timely clinical workflow. Methods: We established a biobanking protocol (BASECAMP-1, NCT04981119) to determine whether HLA-A*02 LOH patients can be prospectively identified. Patients with CRC, PANC, or non-small cell lung cancer (NSCLC), and a high risk for incurable relapse, were screened first using a standard HLA assay. Heterozygous HLA-A*02 positive tumor samples were then assessed for LOH using a bioinformatic algorithm applied via the Tempus xT platform. Results: As of Sep 1, 2022, 83 patients were consented at 4 institutions. HLA status was obtained from 70 patients and 28 were identified as HLA-A*02:01 heterozygous (40%; expected frequency based on USA NMDP data, 27.6%). LOH results were available for 16 patients; 4 LOH-positive patients were identified (25%, 2 PANC, 2 NSCLC). The LOH assay sensitivity declines below a tumor purity of 40% (Hecht R., ASCO GI 2022). Six patients had a tumor purity of 20% (all with PANC, a tumor known for high stromal content), limiting possible LOH detection. The impact of tumor purity on LOH sensitivity was highlighted in a patient with a low initial sample tumor purity (30%) that resulted in a 41% probability of HLA-A*02:01 LOH (below positive threshold). A second sample with a higher tumor purity (70%), obtained from formalin-fixed, paraffin-embedded sections, resulted in a 92% probability of HLA-A*02:01 LOH (positive). Conclusions: BASECAMP-1 prospective identification of HLA-A*02 LOH is feasible in the real-world setting. The frequencies of the HLA-A*02 allele and of HLA-A*02 LOH in this cohort mirrored expected population frequencies. LOH results can be obtained within a clinically feasible workflow and timeframe, although samples with a < 40% tumor purity have a reduced sensitivity for LOH detection, an issue recurrently observed in patients with PANC. The BASECAMP-1 strategy enables prospective identification of appropriate patients for future therapeutic clinical trials using Tmod CEA and MSLN logic-gated CAR T cells. Clinical trial information: NCT04981119 .
SUMMARY Somatic copy number gains are pervasive in many cancer types, yet their roles in oncogenesis are often poorly explored. This lack of understanding is in part due to broad extensions of copy gains across cancer genomes spanning large chromosomal regions, obscuring causal driver loci. Here we employed a multi-tissue pan-organoid modeling approach to validate candidate oncogenic loci identified within pan-cancer TCGA data by the overlap of extreme copy number amplifications with extreme expression dysregulation for each gene. The candidate outlier loci nominated by this integrative computational analysis were functionally validated by infecting cancer type-specific barcoded full length cDNA lentiviral libraries into cognate minimally transformed human and mouse organoids bearing initial oncogenic mutations from esophagus, oral cavity, colon, stomach, pancreas and lung. Presumptive amplification oncogenes were identified by barcode enrichment as a proxy for increased proliferation. Iterative analysis validated DYRK2 at 12q15, encoding a serine-threonine kinase, as an amplified head and neck squamous carcinoma oncogene in p53 -/- oral mucosal organoids. Similarly, FGF3 , amplified at 11q13 in 41% of esophageal squamous carcinomas, was validated in p53 -/- esophageal organoids in vitro and in vivo with pharmacologic inhibition by small molecule and soluble receptor FGFR antagonists. Our studies establish the feasibility of pan-organoid contextual modeling of pan-cancer candidate genomic drivers, enabling oncogene discovery and preclinical therapeutic modeling.
ABSTRACT Colorectal cancer (CRC), a disease of high incidence and mortality, has had few treatment advances owing to a large degree of inter- and intratumoral heterogeneity. Attempts to classify subtypes of colorectal cancer to develop treatment strategies has been attempted by Consensus Molecular Subtypes (CMS) classification. However, the cellular etiology of CMS classification is incompletely understood and controversial. Here, we generated and analyzed a single-cell transcriptome atlas of 49,859 CRC cells from 16 patients, validated with an additional 31,383 cells from an independent CRC patient cohort. We describe subclonal transcriptomic heterogeneity of CRC tumor epithelial cells, as well as discrete stromal populations of cancer-associated fibroblasts (CAFs). Within CRC CAFs, we identify the transcriptional signature of specific subtypes (CAF-S1 and CAF-S4) in more than 1,500 CRC patients using bulk transcriptomic data that significantly stratifies overall survival in multiple independent cohorts. We also uncovered two CAF-S1 subpopulations, ecm-myCAF and TGFß-myCAF, known to be associated with primary resistance to immunotherapies. We demonstrate that scRNA analysis of malignant, stromal, and immune cells exhibit a more complex picture than portrayed by bulk transcriptomic-based Consensus Molecular Subtypes (CMS) classification. By demonstrating an abundant degree of heterogeneity amongst these cell types, our work shows that CRC is best represented in a transcriptomic continuum crossing traditional classification systems boundaries. Overall, this CRC cell map provides a framework to re-evaluate CRC tumor biology with implications for clinical trial design and therapeutic development.
Abstract Next-generation sequencing (NGS) of bulk cell populations is a useful and ubiquitous tool for the molecular characterization of clinical tumor samples. Bulk NGS reveals transcript abundance within a tumor sample and can further infer cell populations via deconvolution algorithms (PMID:31570899). However, it can’t ascribe the cellular context for a given gene’s expression or elucidate the spatial organization of tumor microenvironments. These additional features are critical to our understanding of tumor biology and are key to the development of immuno-oncology therapeutics. Spatial Transcriptomics (ST) is an emerging technology that characterizes gene expression within the spatial context of tissue. ST data can be generated directly from archival formalin fixed paraffin embedded samples, enabling the study of spatial gene expression in real-world clinical settings. We have studied a dataset comprising 6 samples from non-small cell lung cancer (NSCLC) patients and 1 core needle biopsy from a tumor of unknown origin. We used the 10X Visium CytAssist platform to generate ST data and additionally generated paired bulk RNAseq data. To test the interassay reliability of CytAssist on archival FFPE tissue sections, we compared ST results across 3 sample preparation conditions. We further studied the state of the tumor microenvironment by applying state-of-the-art computational approaches to deconvolve immune cell populations and produce super-resolution ST maps, validated using multiplex immunofluorescence (IF) via CODEX (PMID:30078711). We find key quality control metrics and spatial biomarkers are consistent across all 3 sample preparation conditions. When comparing deconvolution results between bulk and spatially-resolved methods we observe modest correlations for many cell types despite differences in sample preparation, supporting the idea that bulk and spatial samples contain complementary transcriptomic information. However, within samples, we find many of the correlations observed in bulk do not show strong spatial correlation. These comparisons indicate the importance of considering spatial context when studying the tumor immune microenvironment. Finally, we find an agreement between super-resolution ST and multiplex IF across key spatial biomarkers. These results demonstrate clinical archival FFPE samples yield high interassay reliability via the CytAssist platform. Results were consistent through 3 different H&E staining protocols and findings were consistent when superresolution deconvolution was utilized which further strongly correlated with high-resolution multiplex IF. Our findings demonstrate the feasibility and translational utility of ST to discover spatial signatures and the cellular context in retrospective clinical cohorts to empower discovery and translational efforts in precision oncology and therapeutic development. Citation Format: Mario G. Rosasco, Chi-Sing Ho, Tianyou Luo, Michelle M. Stein, Luca Lonini, Martin C. Stumpe, Jagadish Venkataraman, Sonal Khare, Ameen A. Salahudeen. Comparison of interassay similarity and cellular deconvolution in spatial transcriptomics data using Visum CytAssist. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4692.
