Topological plots for APWG05PF7 (PDX.1B) and V1-organoid (PDO) from the same patient (171881–09-R) in KEGG Cell Adhesion Molecules.

(A) APWG05 (PDX.1B). (B) V1-organoid (PDO.1). (TIF)</p

Cross-cancer validation of CASCAM.

(A) Genome-wide preselection identifies 5 out of 24 liver cell lines as ILC. (B) Pathway-based heatmap reveals that these 5 preselected cell lines significantly diverge from the ILC tumor center in terms of differentially expressed genes and associated pathways. (TIF)</p

Flowchart of CASCAM for congruence quantification and selection.

Tumor and cancer model gene expression data are first harmonized (Module 1). Transparent machine learning by sparse discriminant analysis (SDA) is applied by combining predication accuracy and SDA-based deviance score for pre-selecting candidate cancer models (Module 2). Pathway-specific mechanistic explorations are iteratively investigated to conclude the final representative cancer model (Module 3). Blue frames represent input data, orange frames for essential output results, parallelogram frames for intermediate results, rectangular frames for analysis process, bullet-shaped frames for visualization, and rhombus frames for decision making.</p

Violin plot for CAMA1 and CDK4 in KEGG Cell Adhesion Molecules.

Violin plot for CAMA1 and CDK4 in KEGG Cell Adhesion Molecules.</p

Heatmap of pathway-specific deviance scores (<i>DS</i>_<i>path</i>) with 53 pathways (rows) and 9 genome-wide pre-selected cell lines +1 manually selected cell line (MDA-MB-134VI) (columns).

The genome-wide SDA projected deviance score (DSSDA) is shown on the top sidebar and the pathway size and normalized enrichment score (NES) are on the left. (TIF)</p

UMAP of Celligner alignment between tumors and PDX/PDO models.

(A) Three distinct clusters were observed. The small cluster on the left consists of a seemingly rare breast cancer subtype, the upper-right cluster includes mostly non-basal samples, and the lower-right cluster includes mostly basal samples. (B) UMAP is redrawn when the small cluster in (A) is removed. (TIF)</p

Summary of relevant publications.

Cancer models are instrumental as a substitute for human studies and to expedite basic, translational, and clinical cancer research. For a given cancer type, a wide selection of models, such as cell lines, patient-derived xenografts, organoids and genetically modified murine models, are often available to researchers. However, how to quantify their congruence to human tumors and to select the most appropriate cancer model is a largely unsolved issue. Here, we present Congruence Analysis and Selection of CAncer Models (CASCAM), a statistical and machine learning framework for authenticating and selecting the most representative cancer models in a pathway-specific manner using transcriptomic data. CASCAM provides harmonization between human tumor and cancer model omics data, systematic congruence quantification, and pathway-based topological visualization to determine the most appropriate cancer model selection. The systems approach is presented using invasive lobular breast carcinoma (ILC) subtype and suggesting CAMA1 followed by UACC3133 as the most representative cell lines for ILC research. Two additional case studies for triple negative breast cancer (TNBC) and patient-derived xenograft/organoid (PDX/PDO) are further investigated. CASCAM is generalizable to any cancer subtype and will authenticate cancer models for faithful non-human preclinical research towards precision medicine.</div

Summary table of the 11 PDO and 136 PDX BC models.

Summary table of the 11 PDO and 136 PDX BC models.</p

Pathway- and gene-specific analysis for selection of representative cell line(s).

(A) Heatmap of pathway-specific deviance scores (DSpath) with 14 unbiased-selected and 1 manually-included pathways (30 size NES| > 1.5; shown on the rows) and 9 unbiased-selected and 1 manually-included cell lines (columns). The genome-wide SDA projected deviance score (DSSDA) is shown on the top side-bar and the pathway size and normalized enrichment score (NES) are on the left. Positive (negative) NES indicates up-regulation (down-regulation) in ILC compared to IDC. Average of the 14 pathways and the pre-selected “KEGG Cell Adhesion Molecules” pathway are shown at the bottom. The p-values of DSpath are annotated in the heatmap (one circle: p − value p − value p − value DSgene for the 10 selected cell lines and 22 DE genes in “KEGG Cell Adhesion Molecules” pathway. (C) Part of KEGG PathView topological networks for BCK4 (DSpath = 1.323) for the “KEGG Cell Adhesion Molecules” pathway. The result shows discordance of 10 genes in BCK4 (orange stars showing up-regulation compared to ILC tumors and blue start showing down-regulation).</p

Selecting representative PDO/PDX for ILC.

(A) SDA projected positions for PDO and PDX models from PDMR. Four models (three PDXs and one PDO; red circles) from the same patient (171881–019-R) were identified as candidate ILC models. Six models from this patient are labeled with the sample ID. High consistency was observed between SDA deviance scores and passages among PDX models. (B) Six models originated from the same patient were used for pathway-specific analysis. Six models show high congruence in the majority of 14 pathways and the Cell Adhesion pathway. (C) Violin plot shows the position of PDO.1 and PDX.1B on the six genes on which PDO.1 is discordant with.</p