Synthetic lethality-mediated precision oncology via the tumor transcriptome

Lee, Joo Sang; Nair, Nishanth Ulhas; Dinstag, Gal; Chapman, Lesley; Chung, Youngmin; Wang, Kun; Sinha, Sanju; Cha, Hongui; Kim, Dasol; Schperberg, Alexander; Srinivasan, Ajay; Lazar, Vladimir; Rubin, Eitan; Hwang, Sohyun; Berger, Raanan; Beker, Tuvik; Ronai, Ze’ev; Hannenhalli, Sridhar; Gilbert, Mark R.; Kurzrock, Razelle; Lee, Se‐Hoon; Aldape, Kenneth; Ruppin, Eytan

doi:10.1016/j.cell.2021.03.030

Cited by 70 publications

(94 citation statements)

References 102 publications

(150 reference statements)

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“…As shown in Figure 2 , our classifier has a more general prognostic ability than other clinical information ( p < 0.05 in all datasets). We further introduced several published transcriptomic-based predictors as previous study ( Lee et al, 2021 ), including the proliferation index ( Whitfield et al, 2006 ), interferon-γ (IFNγ) signature score ( Ayers et al, 2017 ) as well as cytolytic activity score ( Rooney et al, 2015 ), and performed a multivariate Cox regression analysis with age, tumor stage, and our classifier ( Supplementary Figure S2 ). In this analysis, the proliferation index and the IFNγ signature score were estimated as ssGSEA score ( Yi et al, 2020a ) of each gene signature, respectively, and the cytolytic activity score was calculated as the mean expression level of GZMA and PRF1 ( Rooney et al, 2015 ).…”

Section: Resultsmentioning

confidence: 99%

“…As shown in Figure 2, our classifier has a more general prognostic ability than other clinical information (p < 0.05 in all datasets). We further introduced several published transcriptomic-based predictors as previous study (Lee et al, 2021), including the proliferation index FIGURE 1 | Kaplan-Meier analysis to determine the survival differences between group 2 (G2) and group 1 (G1). (Whitfield et al, 2006), interferon-γ (IFNγ) signature score (Ayers et al, 2017) as well as cytolytic activity score (Rooney et al, 2015), and performed a multivariate Cox regression analysis with age, tumor stage, and our classifier (Supplementary Figure S2).…”

Section: Evaluation Of the Performancementioning

confidence: 99%

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Identification of Pathway-Based Biomarkers with Crosstalk Analysis for Overall Survival Risk Prediction in Breast Cancer

Liu

Song

et al. 2021

Front. Genet.

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Recently, many studies have investigated the role of gene-signature on the prognostic assessment of breast cancer (BC), however, the tumor heterogeneity and sequencing noise have limited the clinical usage of these models. Pathway-based approaches are more stable to the perturbation of certain gene expression. In this study, we constructed a prognostic classifier based on survival-related pathway crosstalk analysis. We estimated pathway’s deregulation scores (PDSs) for samples collected from public databases to select survival-related pathways. After pathway crosstalk analysis, we conducted K-means clustering analysis to cluster the patients into G1 and G2 subgroups. The survival outcome of the G2 subgroup was significantly worse than the G1 subgroup. Internal and external dataset exhibits high consistency with the training dataset. Significant differences were found between G2 and G1 subgroups on pathway activity, gene mutation, immune cell infiltration levels, and in particular immune cells/pathway’s activities were significantly negatively associated with BC patient’s outcomes. In conclusion, we established a novel classifier reflecting the overall survival risk of BC and successfully validated its clinical usage on multiple BC datasets, which could offer clinicians inspiration in formulating the clinical treatment plan.

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Section: Resultsmentioning

confidence: 99%

“…As shown in Figure 2, our classifier has a more general prognostic ability than other clinical information (p < 0.05 in all datasets). We further introduced several published transcriptomic-based predictors as previous study (Lee et al, 2021), including the proliferation index FIGURE 1 | Kaplan-Meier analysis to determine the survival differences between group 2 (G2) and group 1 (G1). (Whitfield et al, 2006), interferon-γ (IFNγ) signature score (Ayers et al, 2017) as well as cytolytic activity score (Rooney et al, 2015), and performed a multivariate Cox regression analysis with age, tumor stage, and our classifier (Supplementary Figure S2).…”

Section: Evaluation Of the Performancementioning

confidence: 99%

Identification of Pathway-Based Biomarkers with Crosstalk Analysis for Overall Survival Risk Prediction in Breast Cancer

Liu

Song

et al. 2021

Front. Genet.

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“…The SL interaction analysis is a computational pipeline for identifying candidate SL interactions drawing on the experiences from several previous researches, such as DAISY [9], MiSL [10], SELECT [40]. This analysis consists of four statistical inference procedures:…”

Section: Overview Of the Sl Interaction Analysismentioning

confidence: 99%

Exploiting Synthetic Lethal Network for Precision Treatment of Clear Cell Renal Cell Carcinoma

Liu

Lin

Zhang

et al. 2021

Preprint

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Background The emerging of targeted therapies has revolutionized the treatment modalities of advanced clear cell renal cell carcinoma (ccRCC) over the past fifteen years. However, lack of personalized treatment limits the development of effective clinical guidelines and improvement of patient prognosis. In this study, large-scale genomic profiles of ccRCC cohorts were exploited for conducting an integrative analysis. Method Based on synthetic lethality (SL), a concept that simultaneous losses of two genes cause cell death while a single loss does not, we sought to develop a computational pipeline to infer potential SL partners of ccRCC. Drug response prediction were received from three pharmacological databases to select agents which are likely to be effective in precisely treating patients with target gene mutation. Results We developed a credible method to identify SL pairs and determined a list of 72 candidate pairs which might be utilized to selectively eliminate tumors with genetic aberrations through SL partners of specific mutations. Further analysis identified BRD4 and PRKDC as novel medicine targets for patients with BAP1 mutations. After mapping these target genes to comprehensive drug datasets, two agents (BI-2536 and PI-103) were found to have considerable therapeutic potential in BAP1 mutant tumors. Conclusion Overall, our findings provide insight into the overview of ccRCC mutation patterns and offer novel opportunities for improving individualized cancer treatment.

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“…Several studies identified biomarkers from bulk transcriptomic data. 9 , 10 Other studies also exploited single-cell sequencing 11 and cytometry 4 to identify cell populations associated with therapy response. Given such data with high dimensions and throughputs, user-friendly tools are essential for biologists to draw reliable conclusions, and many tools have been developed recently.…”

Section: Main Textmentioning

confidence: 99%