Kinome-Focused CRISPR-Cas9 Screens in African Ancestry Patient-Derived Breast Cancer Organoids Identify Essential Kinases and Synergy of EGFR and FGFR1 Inhibition

Abstract

Precision medicine approaches to cancer treatment aim to exploit genomic alterations that are specific to individual patients to tailor therapeutic strategies. Yet, some targetable genes and pathways are essential for tumor cell viability even in the absence of direct genomic alterations. In underrepresented populations, the mutational landscape and determinants of response to existing therapies are poorly characterized because of limited inclusion in clinical trials and studies. One way to reveal tumor essential genes is with genetic screens. Most screens are conducted on cell lines that bear little resemblance to patient tumors, after years of culture under nonphysiologic conditions. To address this problem, we aimed to develop a CRISPR screening pipeline in three-dimensionally grown patient-derived tumor organoid (PDTO) models. A breast cancer PDTO biobank that focused on underrepresented populations, including West African patients, was established and used to conduct a negative-selection kinome-focused CRISPR screen to identify kinases essential for organoid growth and potential targets for combination therapy with EGFR or MEK inhibitors. The screen identified several previously unidentified kinase targets, and the combination of FGFR1 and EGFR inhibitors synergized to block organoid proliferation. Together, these data demonstrate the feasibility of CRISPR-based genetic screens in patient-derived tumor models, including PDTOs from underrepresented patients with cancer, and identify targets for cancer therapy. Significance: Generation of a breast cancer patient-derived tumor organoid biobank focused on underrepresented populations enabled kinome-focused CRISPR screening that identified essential kinases and potential targets for combination therapy with EGFR or MEK inhibitors. See related commentary by Trembath and Spanheimer, p. 407.

Figure 1.. Patient-derived Tumor Organoid characterization.

A) Histology and Hormone receptor status of breast PDTO. H&E and immunohistochemical images for breast cancer related markers (ER, PR, and HER2) of tumors and PDTO IHC. Original magnification 20X, scale bar = 50μM. B) Oncoplots of COSMIC breast cancer Tier I and II genes. Somatic mutations and copy number alterations from organoids (O) and parent tumor specimens (T) where available (i.e. O1 matches T1, O2 matches T2). Organoids and tumors are columns, and mutated genes are rows. Total tumor mutational burden (TMB) by organoid or tumor specimen is indicated in the top stacked bar chart, where the proportion of specific variant type is indicated. For each row, the total percent mutation across the samples and proportion of variant type is indicated on the right stacked bar chart. Events coded in black indicate multiple hits, where _multiple single-nucleotide variant types are detected. Events coded in gray indicate complex events, where both single nucleotide mutations and copy number alterations are detected. Color map column indicates data source for somatic mutation calls as well as molecular subtype of the organoid or parent tumor specimen determined from IHC.

Figure 2.. Kinase domain-focused CRISPR-Cas9 Screening in breast cancer organoids.

A) Kinase domain-focused CRISPR-Cas9 screening strategy. B) Dot-plot representation of the log10 gRNA counts of DMSO treated PDTO at final time point (D39 or D31) vs initial post-gRNA library transduction (D4 or D3). Indicated are positive (red) and negative (blue) controls for gRNA depletion. gRNAs targeting pan-cancer essential kinases (CHEK1, ATR, AURKA) are depicted in green. C) Volcano plots from the kinome CRISPR screens performed in ICSBCS002 (left) and ICSBCS007 (right). For each gene, the x axis shows log2 fold change comparing DMSO treated vs initial time point, and the y axis shows statistical significance as measured by the p value. The horizontal dashed line represents a p value threshold of 0.05. D) Venn diagrams of non-pancancer essential kinase genes identified in ICSBCS002 and ICSBCS007. E) Heatmap showing gene effect for essential hits obtained in CRISPR screening in PDTO in breast cancer cell lines (DepMap data). A lower score means that a gene is more likely to be dependent in each cell line. A score of 0 is equivalent to a gene that is not essential whereas a score of −1 corresponds to the median of all common essential genes.

Figure 3.. Validation of potential kinase hits in breast cancer organoids and analysis of specific inhibitors.

A) Growth assay. ICSBCS002 and ICSBCS007 PDTO were transduced with different individual gRNAs against CDK2, PTK2 and PRKDC, empty gRNA control vector (EV) or a positive control with gRNA targeting RPA3 essential gene (RPA) and they were grown and imaged over time to calculate organoid area. B) Representative images of ICSBCS002 and ICSBCS007 PDTOs transduced with EV or specific gRNAs. C) Dose response curves to inhibitors targeting potential hits for essential kinases in ICSBCS002 and ICSBCS007. Inhibitors targeting shared hits between the 2 lines (upper panels), ICSBCS002 exclusive hits (middle panel) and ICSBCS007 exclusive hits (lower panel).

Figure 4.. Effect of inhibitors targeting potential essential hits in different breast PDTOs.

A) Dose response curves to inhibitors targeting potential essential hits identified in CRISPR screens. TNBC PDTO (WCM3103B, HCM-CSH-0655-C50) and ER+ PDTO lines (WCM2968, WCM2108_1, WCM2438, WCM2137_2, ICSBCS014, ICSBCS016, WCM3150_1, HCM-CSH-0366-C50) were analyzed. B) Heatmap with normalized area under the curve (nAUC) values for the different inhibitors tested in the PDTO lines.

Figure 5.. Synergistic interactions with gefitinib.

A) Rank plot with normalized Z scores for ICSBCS002 PDTO treated with gefitinib. B) Dose response curves to gefitinib of ICSBCS002 PDTO transduced with individual gRNAs against FGFR1 or empty vector (EV). C) ICSBCS002 drug full dose curves of FGFR1 inhibitors (PD173074, CH5183284 and pemigatinib) and Gefitinib. D) Synergy maps for FGFR1 inhibitors-gefitinib were calculated using the SynergyFinder+ web application with the ZIP synergy model (red indicates a synergistic effect, white an additive effect, and green an antagonistic effect). E) EGFR and FGFR1 gene expression comparison among PDTO samples with and without observed synergy to EGFR and FGFR1 inhibitors. Gene expression of EGFR (light blue) and FGFR1 (dark blue) among PDTO samples. Samples with observed synergy of EGFR and FGFR1 inhibitors contained either EGFR1 amplifications or FGFR1 amplifications.

Figure 6.. High-throughput drug screening in the presence of EGFR inhibitor gefitinib validates CRISPR screening result and identifies potential synergies.

A) The graphs show the average HSA score across the 156 drugs screened in combination with 0.1 μM gefitinib. B) Synergy maps for Tucatinib-gefitinib (left) and Copanlisib dihydrochloride-gefitinib (middle) in ICSBCS002 and Copanlisib dihydrochloride-gefitinib in ICSBCS007 (right) were calculated using the SynergyFinder+ web application with the HSA synergy model (red indicates a synergistic effect, white an additive effect, and green an antagonistic effect).