Pan-cancer proteogenomic investigations identify post-transcriptional kinase targets

AbstractIdentifying genomic alterations of cancer proteins has guided the development of targeted therapies, but proteomic analyses are required to validate and reveal new treatment opportunities. Herein, we develop a new algorithm, OPPTI, to discover overexpressed kinase proteins across 10 cancer types using global mass spectrometry proteomics data of 1,071 cases. OPPTI outperforms existing methods by leveraging multiple co-expressed markers to identify targets overexpressed in a subset of tumors. OPPTI-identified overexpression of ERBB2 and EGFR proteins correlates with genomic amplifications, while CDK4/6, PDK1, and MET protein overexpression frequently occur without corresponding DNA- and RNA-level alterations. Analyzing CRISPR screen data, we confirm expression-driven dependencies of multiple currently-druggable and new target kinases whose expressions are validated by immunochemistry. Identified kinases are further associated with up-regulated phosphorylation levels of corresponding signaling pathways. Collectively, our results reveal protein-level aberrations—sometimes not observed by genomics—represent cancer vulnerabilities that may be targeted in precision oncology.

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Identification of pan-cancer Ras pathway activation with deep learning

Briefings in Bioinformatics ◽

10.1093/bib/bbaa258 ◽

2020 ◽

Author(s):

Xiangtao Li ◽

Shaochuan Li ◽

Yunhe Wang ◽

Shixiong Zhang ◽

Ka-Chun Wong

Keyword(s):

Deep Learning ◽

Superior Performance ◽

Recent Attempt ◽

Precision Oncology ◽

Pathway Activity ◽

Ras Pathway ◽

Cancer Data ◽

Pathway Activation ◽

Cancer Types ◽

Pan Cancer

Abstract The identification of hidden responders is often an essential challenge in precision oncology. A recent attempt based on machine learning has been proposed for classifying aberrant pathway activity from multiomic cancer data. However, we note several critical limitations there, such as high-dimensionality, data sparsity and model performance. Given the central importance and broad impact of precision oncology, we propose nature-inspired deep Ras activation pan-cancer (NatDRAP), a deep neural network (DNN) model, to address those restrictions for the identification of hidden responders. In this study, we develop the nature-inspired deep learning model that integrates bulk RNA sequencing, copy number and mutation data from PanCanAltas to detect pan-cancer Ras pathway activation. In NatDRAP, we propose to synergize the nature-inspired artificial bee colony algorithm with different gradient-based optimizers in one framework for optimizing DNNs in a collaborative manner. Multiple experiments were conducted on 33 different cancer types across PanCanAtlas. The experimental results demonstrate that the proposed NatDRAP can provide superior performance over other benchmark methods with strong robustness towards diagnosing RAS aberrant pathway activity across different cancer types. In addition, gene ontology enrichment and pathological analysis are conducted to reveal novel insights into the RAS aberrant pathway activity identification and characterization. NatDRAP is written in Python and available at https://github.com/lixt314/NatDRAP1.

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Pan-cancer detection of driver genes at the single-patient resolution

Genome Medicine ◽

10.1186/s13073-021-00830-0 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Joel Nulsen ◽

Hrvoje Misetic ◽

Christopher Yau ◽

Francesca D. Ciccarelli

Keyword(s):

False Positive Rate ◽

Genetic Alterations ◽

Therapeutic Interventions ◽

Precision Oncology ◽

Cancer Type ◽

Rare Cancer ◽

Driver Genes ◽

Cancer Data ◽

Cancer Types ◽

Pan Cancer

Abstract Background Identifying the complete repertoire of genes that drive cancer in individual patients is crucial for precision oncology. Most established methods identify driver genes that are recurrently altered across patient cohorts. However, mapping these genes back to patients leaves a sizeable fraction with few or no drivers, hindering our understanding of cancer mechanisms and limiting the choice of therapeutic interventions. Results We present sysSVM2, a machine learning software that integrates cancer genetic alterations with gene systems-level properties to predict drivers in individual patients. Using simulated pan-cancer data, we optimise sysSVM2 for application to any cancer type. We benchmark its performance on real cancer data and validate its applicability to a rare cancer type with few known driver genes. We show that drivers predicted by sysSVM2 have a low false-positive rate, are stable and disrupt well-known cancer-related pathways. Conclusions sysSVM2 can be used to identify driver alterations in patients lacking sufficient canonical drivers or belonging to rare cancer types for which assembling a large enough cohort is challenging, furthering the goals of precision oncology. As resources for the community, we provide the code to implement sysSVM2 and the pre-trained models in all TCGA cancer types (https://github.com/ciccalab/sysSVM2).

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Pan-cancer detection of driver genes at the single-patient resolution

10.1101/2020.06.12.147983 ◽

2020 ◽

Author(s):

Joel Nulsen ◽

Hrvoje Misetic ◽

Christopher Yau ◽

Francesca D. Ciccarelli

Keyword(s):

False Positive Rate ◽

Genetic Alterations ◽

Therapeutic Interventions ◽

Precision Oncology ◽

Cancer Type ◽

Rare Cancer ◽

Driver Genes ◽

Cancer Data ◽

Cancer Types ◽

Pan Cancer

ABSTRACTBackgroundIdentifying the complete repertoire of genes that drive cancer in individual patients is crucial for precision oncology. Most established methods identify driver genes that are recurrently altered across patient cohorts. However, mapping these genes back to patients leaves a sizeable fraction with few or no drivers, hindering our understanding of cancer mechanisms and limiting the choice of therapeutic interventions.ResultsWe present sysSVM2, a machine learning software that integrates cancer genetic alterations with gene systems-level properties to predict drivers in individual patients. Using simulated pan-cancer data, we optimise sysSVM2 for application to any cancer type. We benchmark its performance on real cancer data and validate its applicability to a rare cancer type with few known driver genes. We show that drivers predicted by sysSVM2 have a low false-positive rate, are stable and disrupt well-known cancer-related pathways.ConclusionssysSVM2 can be used to identify driver alterations in patients lacking sufficient canonical drivers or belonging to rare cancer types for which assembling a large enough cohort is challenging, furthering the goals of precision oncology. As resources for the community, we provide the code to implement sysSVM2 and the pre-trained models in all TCGA cancer types (https://github.com/ciccalab/sysSVM2).

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Immunotherapy and potential predictive biomarkers in the treatment of neuroendocrine neoplasia

Future Oncology ◽

10.2217/fon-2020-0703 ◽

2020 ◽

Author(s):

Guanghui Xu ◽

Yuhao Wang ◽

Hushan Zhang ◽

Xueke She ◽

Jianjun Yang

Keyword(s):

Current Knowledge ◽

Checkpoint Inhibitors ◽

Treatment Options ◽

Predictive Biomarkers ◽

The Body ◽

Low Grade ◽

Ablative Therapies ◽

Cancer Types ◽

New Treatment ◽

Well Differentiated

Neuroendocrine neoplasias (NENs) are a heterogeneous group of rare tumors scattered throughout the body. Surgery, locoregional or ablative therapies as well as maintenance treatments are applied in well-differentiated, low-grade NENs, whereas cytotoxic chemotherapy is usually applied in high-grade neuroendocrine carcinomas. However, treatment options for patients with advanced or metastatic NENs are limited. Immunotherapy has provided new treatment approaches for many cancer types, including neuroendocrine tumors, but predictive biomarkers of immune checkpoint inhibitors (ICIs) in the treatment of NENs have not been fully reported. By reviewing the literature and international congress abstracts, we summarize the current knowledge of ICIs, potential predicative biomarkers in the treatment of NENs, implications and efficacy of ICIs as well as biomarkers for NENs of gastroenteropancreatic system, lung NENs and Merkel cell carcinoma in clinical practice.

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Characterization of the Survival Influential Genes in Carcinogenesis

International Journal of Molecular Sciences ◽

10.3390/ijms22094384 ◽

2021 ◽

Vol 22 (9) ◽

pp. 4384

Author(s):

Divya Sahu ◽

Yu-Lin Chang ◽

Yin-Chen Lin ◽

Chen-Ching Lin

Keyword(s):

Cell Cycle ◽

Cox Regression ◽

Monte Carlo Algorithm ◽

Functional Modules ◽

Protective Genes ◽

Cancer Types ◽

Underlying Mechanisms ◽

Patient Prognosis ◽

Pan Cancer

The genes influencing cancer patient mortality have been studied by survival analysis for many years. However, most studies utilized them only to support their findings associated with patient prognosis: their roles in carcinogenesis have not yet been revealed. Herein, we applied an in silico approach, integrating the Cox regression model with effect size estimated by the Monte Carlo algorithm, to screen survival-influential genes in more than 6000 tumor samples across 16 cancer types. We observed that the survival-influential genes had cancer-dependent properties. Moreover, the functional modules formed by the harmful genes were consistently associated with cell cycle in 12 out of the 16 cancer types and pan-cancer, showing that dysregulation of the cell cycle could harm patient prognosis in cancer. The functional modules formed by the protective genes are more diverse in cancers; the most prevalent functions are relevant for immune response, implying that patients with different cancer types might develop different mechanisms against carcinogenesis. We also identified a harmful set of 10 genes, with potential as prognostic biomarkers in pan-cancer. Briefly, our results demonstrated that the survival-influential genes could reveal underlying mechanisms in carcinogenesis and might provide clues for developing therapeutic targets for cancers.

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Oncology Clinicians’ Challenges to Providing Palliative Cancer Care—A Theoretical Domains Framework, Pan-Cancer System Survey

Current Oncology ◽

10.3390/curroncol28020140 ◽

2021 ◽

Vol 28 (2) ◽

pp. 1483-1494

Author(s):

Sharlette Dunn ◽

Madelene A. Earp ◽

Patricia Biondo ◽

Winson Y. Cheung ◽

Marc Kerba ◽

...

Keyword(s):

Online Survey ◽

Social Issues ◽

Theoretical Domains Framework ◽

Advanced Cancer Patients ◽

Behaviour Change Wheel ◽

Negative Perceptions ◽

Cancer Types ◽

Multifaceted Interventions ◽

Competing Priorities ◽

Pan Cancer

Despite the known benefits, healthcare systems struggle to provide early, integrated palliative care (PC) for advanced cancer patients. Understanding the barriers to providing PC from the perspective of oncology clinicians is an important first step in improving care. A 33-item online survey was emailed to all oncology clinicians working with all cancer types in Alberta, Canada, from November 2017 to January 2018. Questions were informed by Michie’s Theoretical Domains Framework and Behaviour Change Wheel (BCW) and queried (a) PC provision in oncology clinics, (b) specialist PC consultation referrals, and (c) working with PC consultants and home care. Respondents (n = 263) were nurses (41%), physicians (25%), and allied healthcare professionals (18%). Barriers most frequently identified were “clinicians’ limited time/competing priorities” (64%), “patients’ negative perceptions of PC” (63%), and clinicians’ capability to manage patients’ social issues (63%). These factors mapped to all three BCW domains: motivation, opportunity, and capability. In contrast, the least frequently identified barriers were clinician motivation and perceived PC benefits. Oncology clinicians’ perceptions of barriers to early PC were comparable across tumour types and specialties but varied by professional role. The main challenges to early integrated PC include all three BCW domains. Notably, motivation is not a barrier for oncology clinicians; however, opportunity and capability barriers were identified. Multifaceted interventions using these findings have been developed, such as tip sheets to enhance capability, reframing PC with patients, and earlier specialist PC nursing access, to enhance clinicians’ use of and patients’ benefits from an early PC approach.

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Chromosomal copy number heterogeneity predicts survival rates across cancers

Nature Communications ◽

10.1038/s41467-021-23384-6 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Erik van Dijk ◽

Tom van den Bosch ◽

Kristiaan J. Lenos ◽

Khalid El Makrini ◽

Lisanne E. Nijman ◽

...

Keyword(s):

Copy Number ◽

Cancer Survival ◽

Survival Rates ◽

Cancer Prognosis ◽

Disease Outcomes ◽

Distinct Disease ◽

Cancer Types ◽

Chromosomal Copy Number ◽

Chromosomal Copy ◽

Pan Cancer

AbstractSurvival rates of cancer patients vary widely within and between malignancies. While genetic aberrations are at the root of all cancers, individual genomic features cannot explain these distinct disease outcomes. In contrast, intra-tumour heterogeneity (ITH) has the potential to elucidate pan-cancer survival rates and the biology that drives cancer prognosis. Unfortunately, a comprehensive and effective framework to measure ITH across cancers is missing. Here, we introduce a scalable measure of chromosomal copy number heterogeneity (CNH) that predicts patient survival across cancers. We show that the level of ITH can be derived from a single-sample copy number profile. Using gene-expression data and live cell imaging we demonstrate that ongoing chromosomal instability underlies the observed heterogeneity. Analysing 11,534 primary cancer samples from 37 different malignancies, we find that copy number heterogeneity can be accurately deduced and predicts cancer survival across tissues of origin and stages of disease. Our results provide a unifying molecular explanation for the different survival rates observed between cancer types.

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Pan-Cancer Transcriptional Models Predicting Chemosensitivity in Human Tumors

Cancer Informatics ◽

10.1177/11769351211002494 ◽

2021 ◽

Vol 20 ◽

pp. 117693512110024

Author(s):

Jason D Wells ◽

Jacqueline R Griffin ◽

Todd W Miller

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Human Tumors ◽

Molecular Characteristics ◽

Survival Times ◽

Chemotherapy Drugs ◽

Testing Dataset ◽

Cancer Models ◽

Cancer Types ◽

Pan Cancer

Motivation: Despite increasing understanding of the molecular characteristics of cancer, chemotherapy success rates remain low for many cancer types. Studies have attempted to identify patient and tumor characteristics that predict sensitivity or resistance to different types of conventional chemotherapies, yet a concise model that predicts chemosensitivity based on gene expression profiles across cancer types remains to be formulated. We attempted to generate pan-cancer models predictive of chemosensitivity and chemoresistance. Such models may increase the likelihood of identifying the type of chemotherapy most likely to be effective for a given patient based on the overall gene expression of their tumor. Results: Gene expression and drug sensitivity data from solid tumor cell lines were used to build predictive models for 11 individual chemotherapy drugs. Models were validated using datasets from solid tumors from patients. For all drug models, accuracy ranged from 0.81 to 0.93 when applied to all relevant cancer types in the testing dataset. When considering how well the models predicted chemosensitivity or chemoresistance within individual cancer types in the testing dataset, accuracy was as high as 0.98. Cell line–derived pan-cancer models were able to statistically significantly predict sensitivity in human tumors in some instances; for example, a pan-cancer model predicting sensitivity in patients with bladder cancer treated with cisplatin was able to significantly segregate sensitive and resistant patients based on recurrence-free survival times ( P = .048) and in patients with pancreatic cancer treated with gemcitabine ( P = .038). These models can predict chemosensitivity and chemoresistance across cancer types with clinically useful levels of accuracy.

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Pan-Cancer Molecular Patterns and Biological Implications Associated with a Tumor-Specific Molecular Signature

Cells ◽

10.3390/cells10010045 ◽

2020 ◽

Vol 10 (1) ◽

pp. 45

Author(s):

Darío Rocha ◽

Iris A. García ◽

Aldana González Montoro ◽

Andrea Llera ◽

Laura Prato ◽

...

Keyword(s):

Cell Cycle ◽

Cell Cycle Progression ◽

Uncertainty Assessment ◽

Cell Cycle Checkpoints ◽

Molecular Signature ◽

Cancer Subtypes ◽

United States Food ◽

Cancer Types ◽

Molecular Patterns ◽

Pan Cancer

Studying tissue-independent components of cancer and defining pan-cancer subtypes could be addressed using tissue-specific molecular signatures if classification errors are controlled. Since PAM50 is a well-known, United States Food and Drug Administration (FDA)-approved and commercially available breast cancer signature, we applied it with uncertainty assessment to classify tumor samples from over 33 cancer types, discarded unassigned samples, and studied the emerging tumor-agnostic molecular patterns. The percentage of unassigned samples ranged between 55.5% and 86.9% in non-breast tissues, and gene set analysis suggested that the remaining samples could be grouped into two classes (named C1 and C2) regardless of the tissue. The C2 class was more dedifferentiated, more proliferative, with higher centrosome amplification, and potentially more TP53 and RB1 mutations. We identified 28 gene sets and 95 genes mainly associated with cell-cycle progression, cell-cycle checkpoints, and DNA damage that were consistently exacerbated in the C2 class. In some cancer types, the C1/C2 classification was associated with survival and drug sensitivity, and modulated the prognostic meaning of the immune infiltrate. Our results suggest that PAM50 could be repurposed for a pan-cancer context when paired with uncertainty assessment, resulting in two classes with molecular, biological, and clinical implications.

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Exome-Wide Pan-Cancer Analysis of Germline Variants in 8,719 Individuals Finds Little Evidence of Rare Variant Associations

Human Heredity ◽

10.1159/000519355 ◽

2021 ◽

pp. 1-10

Author(s):

Zoe Guan ◽

Ronglai Shen ◽

Colin B. Begg

Keyword(s):

Rare Variant ◽

Rare Variants ◽

Association Studies ◽

The Cancer Genome Atlas ◽

Considerable Proportion ◽

Genome Wide Association Studies ◽

Sequencing Data ◽

Risk Variants ◽

Cancer Types ◽

Pan Cancer

Background: Many cancer types show considerable heritability, and extensive research has been done to identify germline susceptibility variants. Linkage studies have discovered many rare high-risk variants, and genome-wide association studies (GWAS) have discovered many common low-risk variants. However, it is believed that a considerable proportion of the heritability of cancer remains unexplained by known susceptibility variants. The “rare variant hypothesis” proposes that much of the missing heritability lies in rare variants that cannot reliably be detected by linkage analysis or GWAS. Until recently, high sequencing costs have precluded extensive surveys of rare variants, but technological advances have now made it possible to analyze rare variants on a much greater scale. Objectives: In this study, we investigated associations between rare variants and 14 cancer types. Methods: We ran association tests using whole-exome sequencing data from The Cancer Genome Atlas (TCGA) and validated the findings using data from the Pan-Cancer Analysis of Whole Genomes Consortium (PCAWG). Results: We identified four significant associations in TCGA, only one of which was replicated in PCAWG (BRCA1 and ovarian cancer). Conclusions: Our results provide little evidence in favor of the rare variant hypothesis. Much larger sample sizes may be needed to detect undiscovered rare cancer variants.

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