Gene expression has more power for predicting<i>in vitro</i>cancer cell vulnerabilities than genomics

Dempster, Joshua M.; Jm, Krill-Burger; Jm, McFarland; Warren, Allison; Js, Boehm; Vázquez, Francisca; Wc, Hahn; Golub, Todd R.; Tsherniak, Aviad

doi:10.1101/2020.02.21.959627

Cited by 34 publications

(56 citation statements)

References 42 publications

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“…Comparisons based on information-rich gene expression profiles are a promising alternative 20 , given their demonstrated utility for resolving clinically relevant tumor (sub)types [21][22][23][24][25] , as well as predicting genetic 2 and chemical vulnerabilities of cancer cells 5,26 . However, a key challenge is that gene expression measurements from bulk tumor biopsy samples are confounded by the presence of stromal and immune cell populations not found in cell lines, often comprising a substantial fraction of the cellular makeup of each sample 27,28 .…”

Section: Introductionmentioning

confidence: 99%

Global computational alignment of tumor and cell line transcriptional profiles

Warren

Jones

Shibue

et al. 2020

Preprint

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Cell lines are key tools for preclinical cancer research, but it remains unclear how well they represent patient tumor samples. Identifying cell line models that best represent the features of particular tumor samples, as well as tumor types that lack in vitro model representation, remain important challenges. Gene expression has been shown to provide rich information that can be used to identify tumor subtypes, as well as predict the genetic dependencies and chemical vulnerabilities of cell lines. However, direct comparisons of tumor and cell line transcriptional profiles are complicated by systematic differences, such as the presence of immune and stromal cells in tumor samples and differences in the cancer-type composition of cell line and tumor expression datasets. To address these challenges, we developed an unsupervised alignment method (Celligner) and applied it to integrate several large-scale cell line and tumor RNA-Seq datasets. While our method aligns the majority of cell lines with tumor samples of the same cancer type, it also reveals large differences in tumor/cell line similarity across disease types. Furthermore, Celligner identifies a distinct group of several hundred cell lines from diverse lineages that present a more mesenchymal and undifferentiated transcriptional state and which exhibit distinct chemical and genetic dependencies. This method could thus be used to guide the selection of cell lines that more closely resemble patient tumors and improve the clinical translation of insights gained from cell line models.

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Section: Introductionmentioning

confidence: 99%

Global computational alignment of tumor and cell line transcriptional profiles

Warren

Jones

Shibue

et al. 2020

Preprint

Self Cite

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“…A recent systematic analysis of hundreds of CRISPR screens in cancer cell lines with comprehensive multi-omic profiling demonstrated that transcript expression markers were the best predictors of gene dependency (Dempster et al, 2020), providing rationale for the use of pre-treatment-omic profiling as a means to study the biological impact of synthetic lethal hits. Hence, to prioritize the 46 common synthetic lethal genes for validation and detailed mechanistic understanding, we performed RNA sequencing and mass spectrometry-based proteomic profiling on cell lysates of all cell lines grown in drug-free media ( Figure 1A ).…”

Section: Resultsmentioning

confidence: 99%

Section: Npepps Is a Novel Determinant Of Response To Cisplatinmentioning

confidence: 99%

NPEPPS is a novel and druggable driver of platinum resistance

Jones

Goodspeed

Akbarzadeh³

et al. 2021

Preprint

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Platinum-based chemotherapeutics are used in many combination regimens in cancer. Despite extensive use across diverse cancer types, there is room for improved efficacy and patient selection for treatment. Here, we use bladder cancer to address both issues. A multi-omic assessment of five human bladder cancer cell lines and their chemotherapy resistant derivatives, coupled with in vitro whole-genome CRISPR screens were used to define functional drivers of treatment resistance. We identified 46 genes that sensitized the resistant cell lines to cisplatin plus gemcitabine (GemCis), a standard combination therapy in bladder cancer. Most genes were involved with DNA damage and repair pathways, which have previously been associated with enhanced sensitivity to cisplatin. Evaluating expression of the 46 genes in the whole transcriptome and proteome data in parental and resistant lines identified the puromycin sensitive aminopeptidase, NPEPPS, as a novel hit. Depletion of NPEPPS resulted in sensitizing resistant bladder cancer cells to cisplatin in vitro and in xenograft experiments. Pharmacologic inhibition of NPEPPS with tosedostat in cells and in chemoresistant, bladder cancer patient-derived tumoroids improved response to cisplatin. Prior work found NPEPPS in a protein complex with volume regulated anion channels (VRACs) in several cell line models. Interestingly, depletion of two VRAC subunits, LRRC8A and LRRC8D, known importers of intracellular cisplatin, enhanced resistance to cisplatin. Our findings support NPEPPS as a novel and druggable driver of cisplatin resistance with the potential for rapid translation to clinical investigation.

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“…These data are available online at depmap.org/downloads. In Figure 1a, the selectivity (NormLRT 52 ) and predictability 5,12 was determined as previously reported. “Highly Predictable” genes are indicated if the pearson correlation coefficient between the experimental data and the top predictive model is greater than 0.4.…”

Section: Methodsmentioning

confidence: 97%

“…We next pursued the molecular basis of the selective dependency on XPR1. Using more than 100,000 molecular features of the cancer cell lines 10 , we built multivariate models - potential “biomarkers” of response - to predict XPR1 dependency 11,12 .…”

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confidence: 99%

Phosphate dysregulation via the XPR1:KIDINS220 protein complex is a therapeutic vulnerability in ovarian cancer

Bondeson

Paolella

Asfaw

et al. 2020

Preprint

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Clinical outcomes for patients with ovarian and uterine cancers have not improved greatly in the past twenty years. To identify ovarian and uterine cancer vulnerabilities, we analyzed genome-scale CRISPR/Cas9 loss-of-function screens across 739 human cancer cell lines. We found that many ovarian cancer cell lines overexpress the phosphate importer SLC34A2, which renders them sensitive to loss of the phosphate exporter XPR1. We extensively validated the XPR1 vulnerability in cancer cell lines and found that the XPR1 dependency was retained in vivo. Overexpression of SLC34A2 is frequently observed in tumor samples and is regulated by PAX8 - a transcription factor required for ovarian cancer survival. XPR1 overexpression and copy number amplifications are also frequently observed. Mechanistically, SLC34A2 overexpression and impaired phosphate efflux leads to the accumulation of intracellular phosphate and cell death. We further show that proper localization and phosphate efflux by XPR1 requires a novel binding partner, KIDINS220. Loss of either XPR1 or KIDINS220 results in acidic vacuolar structures which precede cell death. These data point to the XPR1:KIDINS220 complex - and phosphate dysregulation more broadly - as a therapeutic vulnerability in ovarian cancer.

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Gene expression has more power for predictingin vitrocancer cell vulnerabilities than genomics

Cited by 34 publications

References 42 publications

Global computational alignment of tumor and cell line transcriptional profiles

Global computational alignment of tumor and cell line transcriptional profiles

NPEPPS is a novel and druggable driver of platinum resistance

Phosphate dysregulation via the XPR1:KIDINS220 protein complex is a therapeutic vulnerability in ovarian cancer

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