Histopathologic and proteogenomic heterogeneity reveals features of clear cell renal cell carcinoma aggressiveness

Li, Yize; Lih, Tung-Shing Mamie; Dhanasekaran, Saravana M.; Mannan, Rahul; Chen, Lijun; Cieślik, Marcin; Wu, Yige; Lu, Rita Jiu-Hsien; Clark, David; Kołodziejczak, Iga; Hong, Runyu; Chen, Siqi; Zhao, Yanyan; Chugh, Seema; Caravan, Wagma; Deen, Nataly Naser Al; Hosseini, Noshad; Newton, Chelsea J.; Krug, Karsten; Xu, Yuanwei; Cho, Kyung-Cho; Hu, Yingwei; Zhang, Yuping; Kumar-Sinha, Chandan; Ma, Wanli; Calinawan, Anna; Wyczalkowski, Matthew A.; Wendl, Michael C.; Wang, Yuefan; Guo, Shenghao; Zhang, Cissy; Le, Anne; Dagar, Aniket; Hopkins, Betsy; Cho, Hanbyul; Leprevost, Felipe da Veiga; Jing, Xiaojun; Teo, Guo Ci; Liu, Wenke; Reimers, Melissa A.; Pachynski, Russell K.; Lazar, Alexander J.; Chinnaiyan, Arul M.; Tine, Brian Andrew Van; Zhang, Bing; Rodland, Karin D.; Getz, Gad; Mani, D. R.; Wang, Pei; Chen, Feng; Hostetter, Galen; Thiagarajan, Mathangi; Linehan, W. Marston; Fenyö, David; Jewell, Scott D.; Omenn, Gilbert S.; Mehra, Rohit; Wiznerowicz, Maciej; Robles, Ana I.; Mesri, Mehdi; Hiltke, Tara; An, Eunkyung; Rodriguez, Henry; Chan, Daniel W.; Ricketts, Christopher J.; Nesvizhskii, Alexey I.; Zhang, Hui; Li, Ding; Francis, Alicia; Paulovich, Amanda G.; Antczak, Andrzej; Green, Anthony; Colaprico, Antonio; Hakimi, A. Ari; Pruetz, Barb; Hindenach, Barbara; Yadav, Birendra Kumar; Reva, Boris; Fevrier-Sullivan, Brenda; Druker, Brian J.; Szczylik, Cezary; Goldthwaite, Charles A.; Birger, Chet; Jones, Corbin D.; Rohrer, Daniel C.; Tansil, Darlene; Chesla, David; Heiman, David I.; Duffy, Elizabeth; Schadt, Eri E.; Petralia, Francesca; Bromiński, Gabriel; Quiroga‐Garza, Gabriela; Wilson, George; Li, Ginny Xiaohe; Zhao, Grace; Hsiao, Yi; Hsieh, James J.; Lubiński, Jan; Bavarva, Jasmin; Huang, Jasmine; Hafron, Jason; Eschbacher, Jennifer; Hon, Jennifer; Francis, Jesse; Freymann, John; Vo, Josh N.; Wang, Joshua; Kirby, Justin; Zaalishvili, Kakhaber; Ketchum, Karen A.; Hoadley, Katherine A.; Um, Ki Sung; Qi, Liqun; Domagalski, Marcin J.; Tobin, Matt; Dyer, Maureen A.; Anurag, Meenakshi; Borucki, Melissa; Gillette, Michael A.; Birrer, Michael J.; Ittmann, Michael; Roehrl, Michael H. A.; Schnaubelt, Michael; Smith, Michael J.; Fam, Mina; Roche, Nancy; Vatanian, Negin; Maunganidze, Nicollette; Potapova, Olga; Paklina, Oxana; VanderKolk, Pamela; Castro, Patricia; Kurzawa, Paweł; Hariharan, Pushpa; Li, Qin; Li, Qing Kay; Dhir, Rajiv; Thangudu, Ratna R.; Montgomery, Rebecca; Smith, Richard; Mareedu, Sailaja; Payne, Samuel H.; Cerda, Sandra; Cottingham, Sandra; Haynes, Sarah E.; Satpathy, Shankha; Richey, Shannon; Singh, Shilpi; Tsang, Shirley; Cai, Shuang; Cao, Song; Gabriel, Stacey; Carr, Steven A.; Liu, Tao; Bauer, Thomas; Le, Toan; Chen, Xi S.; Zhang, Xu; Shutack, Yvonne; Zhang, Zhen

doi:10.1016/j.ccell.2022.12.001

Cited by 56 publications

(30 citation statements)

References 171 publications

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“…To validate both the immune composition estimates and the inferred immune subtypes, we analyzed a subset of tumors, for which FFPE blocks were available, using alternative experimental platforms, including immunohistochemistry (IHC), multiple reaction monitoring-mass spectrometry (MRM), and tissue microarray (TMA) multiplex immunofluorescence-stained image experiments ( STAR Methods ). Additionally, we leveraged a recent proteogenomic data from an independent CCRCC cohort 3 , 34 to confirm the immune subtypes detected in CCRCC.…”

Section: Resultsmentioning

confidence: 99%

“…Analyzing proteogenomic data of 112 independent CCRCC tumors 34 ( STAR Methods ), we detected all five subtypes identified within CCRCC in the CPTAC pan-cancer cohort ( Figure S1F ). Notably, the predominant subtype, CCRCC-endothelial remained as the largest subtype in the validation cohort, suggesting the reproducibility of these subtypes across cohorts.…”

Section: Resultsmentioning

confidence: 99%

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Pan-cancer proteogenomics characterization of tumor immunity

Petralia,

Ma,

Yaron

et al. 2024

Cell

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Pan-cancer proteogenomics characterization of tumor immunity

Petralia,

Ma,

Yaron

et al. 2024

Cell

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“…CNVEX (Li et al, 2023) is used to infer copy number variations for the short arm of chromosome 6. CNVEX is also used to calculate the purity and ploidy of the test data set for validation and results.…”

Section: Non-mhc Cnv Callsmentioning

confidence: 99%

MHCnvex: Likelihood-based model for calling the copy number variations and loss of heterozygosity in MHC class I and II locus

Hosseini

Mumphrey

Cieślik

2023

Preprint

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Motivation: Accurate detection of copy number variation (CNV) and loss of heterozygosity (LOH) in the major histocompatibility complex (MHC) locus is of great significance to both clinicians and researchers since it has the potential to inform treatment decisions, particularly in the context of immunotherapy. However, due to the high level of polymorphism in this region, calling copy number variations is a challenging task and requires special methodology. To address this challenge, we have developed a tool with a wide range of applicability to call CNV and LOH in the MHC region. Results: To address the challenge mentioned above, we have developed MHCnvex, an algorithm that accurately calls haplotype level CNVs for the genes in the MHC class I and II locus. MHCnvex presents a novel approach based on likelihood models for detecting CNVs at haplotype level. Additionally, this method integrates the MHC locus with other adjacent loci from the short arm of chromosome 6 to enhance the accuracy of the calls. The incorporation of a statistical approach and the examination of the broader chromosome 6 region, rather than just the MHC locus alone, make MHCnvex less vulnerable to local coverage biases (commonly associated with MHC locus). The performance of MHCnvex has been evaluated according to different measures including concordance with MHC flanking regions and changes in the allelic expression of MHC genes due to alteration. MHCnvex has also shown to significantly reduce the variability of calculated coverage for CNV analysis. Availability: Implementation of MHCnvex algorithm is available as an R package at: https://github.com/NoshadHo/MHCnvex

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“…Furthermore, they serve as central hubs for metabolicimmune system integration, consequently contributing to anti-tumor immunity. [12][13][14] Therefore, LDs may be implicated in immune cell in ltration within KIRC tissues and possess therapeutic potential for tumor management. However, the gene set associated with LDs in KIRC has not been systematically investigated.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive analysis of LD-related genes signature for predicting prognosis and immunotherapy response in clear cell renal cell carcinoma

Yangtao,

Xinke,

Fangzheng

et al. 2024

Preprint

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Background Lipid droplets (LD) in renal clear cell carcinoma (ccRCC)play a crucial role in lipid metabolism and immune response modulation. The purpose of this study was to create a LD-related signature to predict prognosis and guide the immunotherapy and targeted therapy in ccRCC patients. Methods We conducted a comprehensive analysis using transcriptional profiles and clinical data obtained from The Cancer Genome Atlas (TCGA). LD-related genes were identified from existing literature and the GeneCards database, and differentially expressed genes were determined. Sequentially, we conducted Cox regression analysis and Lasso regression analysis, to establish a prognostic risk model. The performance of the risk model was evaluated using Kaplan-Meier (KM) analysis and time-dependent receiver operating characteristic (ROC) analysis. Additionally, gene set enrichment analysis (GSEA), ESTIMATE, CIBERSORT, and immunophenoscore (IPS) algorithm were used to assess the tumor microenvironment (TME) and treatment response. Results We constructed a risk signature with four LD-related genes in the TCGA dataset, which could be an independent prognostic factor in ccRCC patients. Then, patients were classified into two risk groups and exhibited notable differences in overall survival (OS), progression-free survival (PFS), and TME characteristics. Furthermore, we developed a comprehensive nomogram based on clinical features, which demonstrated good prognostic predictive value. According to the results of GSEA analysis, immune-related pathways were found to be significantly enriched in the high-risk group. Additionally, the high-risk group displayed high levels of immune cell infiltration, TMB and IPS scores, indicating better efficacy of immune checkpoint inhibitors (ICIs). Finally, high-risk demonstrated reduced IC50 values compared to the low-risk counterpart for specific targeted and chemotherapeutic drugs., suggesting that the patients receiving these targeted drugs in high-risk group had better treatment outcomes. Conclusions Our LD-related genes signature provides reliable prognostic predictions and can guide immunotherapy and targeted therapy in ccRCC patients.

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Histopathologic and proteogenomic heterogeneity reveals features of clear cell renal cell carcinoma aggressiveness

Cited by 56 publications

References 171 publications

Pan-cancer proteogenomics characterization of tumor immunity

Pan-cancer proteogenomics characterization of tumor immunity

MHCnvex: Likelihood-based model for calling the copy number variations and loss of heterozygosity in MHC class I and II locus

Comprehensive analysis of LD-related genes signature for predicting prognosis and immunotherapy response in clear cell renal cell carcinoma

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