Stanford University

Identifying Methylation-driven Genes in Colorectal Cancer

We applied MethylMix on the colon and rectal cancer data from The Cancer Genome Atlas. This allowed to identify hyper and hypomethylated genes across colorectal cancer. We specifically focused on hypo-methylated genes and identified 58 genes that are significantly hypo-methylated, have a strong negative correlation between gene expression and DNA methylation, and are highly prevalent in the colorectal TCGA cohort.

Experimental Approaches

To identify key methylation-driven genes, we developed a model-based method called MethylMix that addresses three criteria. First, MethylMix uses a univariate Beta mixture model to identify “methylation states” for each CpG site (or cluster of correlated CpG sites). Each methylation state is defined by a statistically similar methylation pattern across a large number of patients without relying on arbitrary thresholds and is associated with its nearest gene. Secondly, MethylMix compares the DNA methylation of cancer with normal tissue to determine if a specific gene is differentially methylated in cancer. Since the normal state of DNA methylation is tissue specific, MethylMix incorporates the DNA methylation of normal tissue obtained from a subset of patients in the same tissue to determine if a specific gene is hyper or hypomethylated across each cancer.

Next, MethylMix produces a new metric called the “Differential Methylation value” or “DM-value” to identify genes that are differentially methylated in cancer relative to normal by leveraging the CpG methylation distribution across an entire patient cohort. Finally, MethylMix defines the methylation state of a gene as “functional” if its gene expression can sufficiently be predicted by methylation of its CpG sites using a linear regression model.

Organoid Modeling of the Tumor Immune Microenvironment

CTD² scientists at the Stanford University demonstrated that air-liquid interface patient-derived tumor organoid models retain the original tumor immune cells.

This model system enables testing of personalized immunotherapy in cancer.

Experimental Approaches

Single-cell gene expression and V(D)J profiling from the same samples were measured using the Chromium Single Cell Immune Profiling.

Quantifying the Timing of Metastatic Progression from Patient Genomic Data

CTD² scientists at Stanford University developed Spatial Computational Inference of MEtastatic Timing (SCIMET), an analytical framework that provides quantitative measurement of dynamics of metastasis in a patient-specific manner.

This study demonstrates early dissemination in colorectal cancer and highlights opportunities for improved patient stratification and the earlier detection.

Experimental Approaches

To reconstruct the evolutionary history of metastatic colorectal cancer, genomic analysis (whole-exome sequencing) was performed on the primary and metastatic colorectal tumors from the same patients. Raw-data is made available through the European Genotype Phenotype Archive (EGA) under accession number EGAS00001003573.

Single-cell Genomic Characterization of the Tumor Microenvironment of Gastric Cancer

We used single-cell genomics to characterize heterogenous cell types and states in the gastric cancer tumor microenvironment, in comparison to normal gastric tissue from the same patients. Our goal was to understand tumor biology and identify new treatment targets.

Experimental Approaches

Single-cell suspensions were generated by dissociating surgical resections or biopsies of patients with gastric cancer (GC), gastro-intestinal metaplasia (GIM) and matched normal tissue. PBMCs were isolated using density gradient centrifugation. Cells were subjected to single-cell RNA sequencing (10X Genomics).

Predicting DNA Methylation Patterns using Histopathology Images

The Gevaert lab at Stanford CTD² Center showed that MethylMix, a tool to identify methylation driver genes in cancer can predict DNA methylation profiles in whole slide cancer histopathology images. This study predicted genes enriched in key pathways in carcinogenesis including hypoxia in glioma and angiogenesis in renal cell carcinoma.

Experimental Approaches

Classical machine learning methods such as support vector machine and random forests were applied to morphometric features extracted from whole slide images of glioma and renal cell carcinoma cohorts of TCGA study to predict differential methylation values defined by MethylMix.

This analyses provide new insights into the link between histopathological and molecular data. The data and code used in this study is available at GitHub.

A CRISPR/Cas9-engineered ARID1A-deficient Human Gastric Cancer Organoid Model Reveals Essential and Non-essential Modes of Oncogenic Transformation

In this project, we utilize wild-type human gastric organoids to establish the first forward genetic human ARID1A-deficient oncogenic transformation model, using CRISPR/Cas9-engineered ARID1A depletion alongside mutation of TP53, a co-occurring tumor suppressor. These engineered ARID1A-deficient organoids mirror several clinicopathologic features of ARID1A-mutant gastric cancer. Coupled with a regulatory network-based analysis and high-throughput drug screening, we have leveraged this human organoid model to discover potential mechanisms underlying the role of ARID1A during oncogenic transformation of gastric epithelium.

Experimental Approaches

To establish an ARID1A-deficient human gastric cancer transformation model, we first disrupted TP53, the most frequently mutated locus (~49%) in gastric adenocarcinoma, by CRISPR/Cas9 into the same wild-type human gastric corpus organoids, followed by secondary CRISPR/Cas9 KO of ARID1A.  Transient transfection of an all-in-one construct expressing both Cas9 and sgRNA targeting TP53 exon 4 followed by a recently developed nutlin-3 functional selection yielded numerous organoid colonies, whereas no growth was seen in non-transfected cells.  After clonal expansion, a nutilin-3-resistant organoid clone harboring a 1 bp cytosine deletion (327delC; TTCCG to TTCG) within TP53 exon 4 was chosen for further analysis.

We next applied a dual lentiviral system to ARID1A genetic knockout in the same TP53-null organoids. Briefly, the TP53 KO organoids were transducted with a lentiviral Cas9 construct conferring blasticidin resistance followed by a secondary transduction targeting of ARID1A exon 1 in a lentiviral sgRNA vector with BFP reporter. We established a spectrum of clonal TP53/ARID1A DKO organoid lines. The loss of ARID1A was further confirmed by Sanger sequencing, Western blotting and immunohistochemical (IHC) staining. In parallel, an empty lentiviral sgRNA-BFP vector was transduced into the same Cas9-TP53 KO organoids, and represented the control. The TP53 KO organoids (control) and TP53/ARID1A DKO organoids (ARID1A KO) were established, grown, maintained and passaged using identical culture conditions throughout this study. In this dataset, we investigated ARID1A-associated transcripts by bulk RNA-sequencing (RNA-seq) in the control TP53 KO and two of the TP53/ARID1A DKO organoid lines.

Tumor Microenvironment of Metastatic Colorectal Cancer

Colorectal cancer commonly metastasizes to the liver. We used single-cell RNA sequencing to characterize differences in the tumor microenvironment of metastatic colorectal cancer to the liver, compared with paired normal liver tissue. We identified a transcriptional cell state in tumor macrophages, which was distinct from normal liver macrophages.

Experimental Approaches

Single-cell suspensions were generated by dissociating surgical resections of patients with metastatic colorectal cancer to the liver or matched normal liver tissue. PBMCs were isolated using density gradient centrifugation. Cells were subjected to single-cell RNA sequencing (10X Genomics).

Functional Screening of Amplification Outlier Oncogenes in Organoid Models of Early Tumorigenesis

Cancers of all types exhibit somatic copy number gains, yet their roles in oncogenesis remain unclear. We evaluated candidate oncogenic loci identified via integrative computational analysis of extreme copy gains overlapping with extreme expression dysregulation in The Cancer Genome Atlas using organoid modeling. A subset of "outlier" candidates was contextually screened with lentiviral libraries of tissue-specific cDNAs within cognate esophageal, oral cavity, colon, stomach, pancreas, and pulmonary organoids bearing initial mutations that were attributable to cancer. As a result of iterative analysis, the kinase DYRK2 at 12q15 was identified as an oncogene amplified in p53-/- oral mucosal organoids. Similarly, FGF3, which is amplified at 11q13 in 41% of esophageal squamous carcinomas, promoted p53-/- esophageal organoid growth that is reversible by antagonizing the FGFRs via small molecules and soluble receptor.

Experimental Approaches

Detailed information can be found via the following link: https://pubmed.ncbi.nlm.nih.gov/37922313/.