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  • 1
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    Online Resource
    Abstract 1993: A comprehensive benchmarking of paired whole exome and transcriptome biomarker analysis of response to cancer immunotherapy
    American Association for Cancer Research (AACR) ; 2020
    In:  Cancer Research Vol. 80, No. 16_Supplement ( 2020-08-15), p. 1993-1993
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 80, No. 16_Supplement ( 2020-08-15), p. 1993-1993
    Abstract: Recent advances in cancer immunotherapy have revolutionized cancer treatment. Notably, pembrolizumab - an anti-PD1 checkpoint inhibitor - has been approved as first-line treatment for metastatic non-small cell lung cancer. Clinical response to checkpoint inhibitors has been demonstrated in other cancer types; however, durable response is not observed in all patients. As a result, development of new biomarkers for response to checkpoint inhibitors has become an active area of research. Due to the complexity of the anti-tumor immune response, a single biomarker may not accurately predict patient response to immunotherapy. In parallel, dramatic decreases in the costs of next generation sequencing (NGS) have enabled broad whole exome sequencing (WES) and whole transcriptome sequencing (WTS)-based studies, enabling a new phase in both academic and clinical cancer research. Despite the proven utility of NGS-based biomarker analysis, the accuracy of combined WES/WTS results compared to other assays, specifically in clinical tumor tissues, has not been demonstrated. In this study, we perform comprehensive benchmarking of several immuno-oncology (IO) biomarkers on cell lines, fresh frozen tumor tissues, and formalin-fixed paraffin-embedded (FFPE) tumor tissues. WES and WTS libraries were generated using Illumina Nextera Flex for Enrichment, TruSight Oncology, and TruSeq Stranded Total RNA library prep methods, and sequenced on a NovaSeq 6000. Using an internally developed bioinformatics pipeline, the accuracy of several IO biomarker measurements was evaluated. Specifically, we investigated the performance of tumor-only (T) and/or paired tumor-normal (TN) WES/WTS in assessing tumor mutational burden (TMB), microsatellite instability (MSI), tumor purity, copy number variants (CNV), human leukocyte antigen (HLA) type, fusions, and tumor infiltrating lymphocytes (TILs) levels, among other features. Our benchmarking results demonstrated correlations of 0.99 and 0.98 for TMB as measured by TN and T, respectively (truth: FOCR reported TMB); 100.0% PPV and NPV for MSI status as measured by both TN and T (truth: MSI-PCR); 0.86 correlation for tumor purity estimates based on TN (0.64 based on T; truth: cell-line titration levels); 100% PPV for both TN and T CNV calling (truth: ddPCR); 92% accuracy for both TN and T HLA typing (truth: IHW HLA types); 92.9% sensitivity for fusion calling (truth: ddPCR); and R2 values of 0.87, 0.70, and 0.48 for CD19+, CD4+, and CD8+ TIL levels, respectively (truth: FACS). Taken together, this study demonstrates WES/WTS can not only be utilized for broad exome- and transcriptome-wide biomarker discovery, but can also be an accurate, comprehensive alternative to iteratively testing IO biomarkers. Future studies should further standardize assay and analysis approaches for NGS-based biomarker measurements to ensure consistent and accurate reporting. Citation Format: Mahdi Golkaram, Michael Salmans, Raakhee Vijayaraghavan, Shannon Kaplan, Robert Haigis, Joyee Yao, Kristina Kruglyak, Li Liu, Traci Pawlowski, Sven Bilke, Shile Zhang. A comprehensive benchmarking of paired whole exome and transcriptome biomarker analysis of response to cancer immunotherapy [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1993.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2020-1993
    RVK:
    XA 36000
    RVK:
    XA 10000
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2020
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  • 2
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    Identification of biomarkers predictive of metastasis development in early-stage colorectal cancer using network-based regularization
    Springer Science and Business Media LLC ; 2023
    In:  BMC Bioinformatics Vol. 24, No. 1 ( 2023-01-16)
    Details
    In: BMC Bioinformatics, Springer Science and Business Media LLC, Vol. 24, No. 1 ( 2023-01-16)
    Abstract: Colorectal cancer (CRC) is the third most common cancer and the second most deathly worldwide. It is a very heterogeneous disease that can develop via distinct pathways where metastasis is the primary cause of death. Therefore, it is crucial to understand the molecular mechanisms underlying metastasis. RNA-sequencing is an essential tool used for studying the transcriptional landscape. However, the high-dimensionality of gene expression data makes selecting novel metastatic biomarkers problematic. To distinguish early-stage CRC patients at risk of developing metastasis from those that are not, three types of binary classification approaches were used: (1) classification methods (decision trees, linear and radial kernel support vector machines, logistic regression, and random forest) using differentially expressed genes (DEGs) as input features; (2) regularized logistic regression based on the Elastic Net penalty and the proposed iTwiner—a network-based regularizer accounting for gene correlation information; and (3) classification methods based on the genes pre-selected using regularized logistic regression. Classifiers using the DEGs as features showed similar results, with random forest showing the highest accuracy. Using regularized logistic regression on the full dataset yielded no improvement in the methods’ accuracy. Further classification using the pre-selected genes found by different penalty factors, instead of the DEGs, significantly improved the accuracy of the binary classifiers. Moreover, the use of network-based correlation information (iTwiner) for gene selection produced the best classification results and the identification of more stable and robust gene sets. Some are known to be tumor suppressor genes ( OPCML-IT2 ), to be related to resistance to cancer therapies ( RAC1P3 ), or to be involved in several cancer processes such as genome stability ( XRCC6P2 ), tumor growth and metastasis ( MIR602 ) and regulation of gene transcription ( NME2P2 ). We show that the classification of CRC patients based on pre-selected features by regularized logistic regression is a valuable alternative to using DEGs, significantly increasing the models’ predictive performance. Moreover, the use of correlation-based penalization for biomarker selection stands as a promising strategy for predicting patients’ groups based on RNA-seq data.
    Type of Medium: Online Resource
    ISSN: 1471-2105
    URL: Article
    DOI: 10.1186/s12859-022-05104-z
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
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    SSG: 12
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  • 3
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    Online Resource
    Genetic and microenvironmental intra-tumor heterogeneity impacts colorectal cancer evolution and metastatic development
    Springer Science and Business Media LLC ; 2022
    In:  Communications Biology Vol. 5, No. 1 ( 2022-09-09)
    Details
    In: Communications Biology, Springer Science and Business Media LLC, Vol. 5, No. 1 ( 2022-09-09)
    Abstract: Colorectal cancer (CRC) is a highly diverse disease, where different genomic instability pathways shape genetic clonal diversity and tumor microenvironment. Although intra-tumor heterogeneity has been characterized in primary tumors, its origin and consequences in CRC outcome is not fully understood. Therefore, we assessed intra- and inter-tumor heterogeneity of a prospective cohort of 136 CRC samples. We demonstrate that CRC diversity is forged by asynchronous forms of molecular alterations, where mutational and chromosomal instability collectively boost CRC genetic and microenvironment intra-tumor heterogeneity. We were able to depict predictor signatures of cancer-related genes that can foresee heterogeneity levels across the different tumor consensus molecular subtypes (CMS) and primary tumor location. Finally, we show that high genetic and microenvironment heterogeneity are associated with lower metastatic potential, whereas late-emerging copy number variations favor metastasis development and polyclonal seeding. This study provides an exhaustive portrait of the interplay between genetic and microenvironment intra-tumor heterogeneity across CMS subtypes, depicting molecular events with predictive value of CRC progression and metastasis development.
    Type of Medium: Online Resource
    ISSN: 2399-3642
    URL: Article
    DOI: 10.1038/s42003-022-03884-x
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2919698-X
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  • 4
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    Online Resource
    Abstract 175: Illumina RNA-sequencing for biomarker analysis from FFPE and fresh frozen tumor specimens
    American Association for Cancer Research (AACR) ; 2020
    In:  Cancer Research Vol. 80, No. 16_Supplement ( 2020-08-15), p. 175-175
    Details
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 80, No. 16_Supplement ( 2020-08-15), p. 175-175
    Abstract: Biomarkers developed from DNA sequencing have improved the accuracy of selecting treatment regimens for oncology patients, such as tumor mutational burden to predict immune checkpoint inhibitor (ICI) response in metastatic non-small cell lung cancer and melanoma. However, mounting evidence demonstrates the need for additional biomarkers to identify patients that could benefit from ICIs. Paired DNA and RNA sequencing have the potential to improve patient diagnosis and treatment selection by providing a more comprehensive view of the tumor biology than DNA sequencing alone. Most next-generation sequencing (NGS) diagnostics have primarily been DNA-based assays with limited, if any, scope in biomarkers derived from RNA. Here, tumor RNA profiling for gene expression and gene fusion detection are evaluated with two Illumina RNA-seq applications: RNA exome enrichment and whole transcriptome sequencing (WTS). RNA exome enriched libraries were prepared with Illumina® TruSeq™ RNA Exome or a modified version of the Illumina TruSight™ Oncology RNA library preparation. WTS libraries were prepared with Illumina TruSeq Stranded Total RNA, a workflow that enables sequencing coding and non-coding transcripts. RNA of variable quality derived from FFPE and fresh frozen tumor tissue, and commercial RNA controls were titrated to determine optimal input quantity. Stranded and non-stranded RNA library preparation workflows were evaluated for differences in gene expression. Gene expression and fusion calling performance were evaluated for each RNA-seq application. A comparison of RNA exome to WTS demonstrated robust performance for tumor RNA profiling. Optimal RNA input was 40ng for RNA Exome and 100ng for WTS, regardless of input type. Gene expression values for exome-enriched and whole transcriptome libraries were reproducible (r & gt; 0.99 for technical replicates), with minimal differentially expressed genes between coding regions of both RNA-seq workflows (r & gt; 0.83). WTS yielded up to 2-fold more transcripts with the addition of non-coding RNAs that were not captured by the RNA coding exome panel. Commercial RNA controls and FFPE tumor RNAs with validated fusions were used for evaluating fusion calling performance from RNA exome and whole transcriptome libraries. Both workflows yielded adequate library diversity for calling clinically relevant fusions. However, RNA exome enrichment fusion calling sensitivity (84.4%) was impacted when one or both fusion partners were not targeted by the capture panel, a caveat that can be resolved with WTS (92.9% sensitivity). Collectively, these data demonstrate the feasibility of RNA-seq for gene expression and fusion calling applications. These assays can be instrumental in the development of novel RNA biomarkers to supplement DNA-derived biomarkers for improved prediction of response to a variety of treatment regimens. For Research Use Only. Not for use in diagnostic procedures. Citation Format: Michael Salmans, Mahdi Golkaram, Joyee Yao, Shile Zhang, Li Liu, Traci Pawlowski. Illumina RNA-sequencing for biomarker analysis from FFPE and fresh frozen tumor specimens [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 175.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    URL: Article
    DOI: 10.1158/1538-7445.AM2020-175
    RVK:
    XA 36000
    RVK:
    XA 10000
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2020
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    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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