The Pan-Cancer Landscape of Prognostic Germline Variants in 10,582 Patients

Chatrath, Ajay; Przanowska, Roza; Kiran, Shashi; Su, Zhangli; Saha, Shekhar; Wilson, Briana; Takemoto, Tsunematsu; Ahn, Ji-Hye; Lee, Kyung Yong; Paulsen, Teressa; Sobierajska, Ewelina; Kiran, Manjari; Tang, Xiwei; Li, Tianxi; Kumar, Pankaj; Dutta, Anindya

doi:10.1101/19010264

Cited by 5 publications

(7 citation statements)

References 63 publications

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“…Identifying patients with multiple clinically actionable mutations has important implications for patients and their family members, yet much remains unclear regarding the consequence of carrying more than one pathogenic mutation. Our understanding of how combinations of mutations may interact to alter the ultimate profile of cancers in a patient, or their family, is currently very limited [32][33][34]. Certain combinations of mutations may be detrimental in that they increase overall risk of malignancy while others may reduce overall cancer risk, relative to inheritance of the single gene mutation alone.…”

Section: Multiple Pathogenic Mutationsmentioning

confidence: 99%

Clinical challenges in interpreting multiple pathogenic mutations in single patients

Slaught

Berry

Bacik

et al. 2021

Hered Cancer Clin Pract

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Background In the past two decades, genetic testing for cancer risk assessment has entered mainstream clinical practice due to the availability of low-cost panels of multiple cancer-associated genes. However, the clinical value of multiple-gene panels for cancer susceptibility is not well established, especially in cases where panel testing identifies more than one pathogenic variant. The risk for specific malignancies as a result of a mutated gene is complex and likely influenced by superimposed modifier variants and/or environmental effects. Recent data suggests that the combination of multiple pathogenic variants may be fewer than reported by chance, suggesting that some mutation combinations may be detrimental. Management of patients with “incidentally” discovered mutations can be particularly challenging, especially when established guidelines call for radical procedures (e.g. total gastrectomy in CDH1) in patients and families without a classic clinical history concerning for that cancer predisposition syndrome. Case presentation We present two cases, one of an individual and one of a family, with multiple pathogenic mutations detected by multi-gene panel testing to highlight challenges practitioners face in counseling patients about pathogenic variants and determining preventive and therapeutic interventions. Conclusions Ongoing investigation is needed to improve our understanding of inherited susceptibility to disease in general and cancer predisposition syndromes, as this information has the potential to lead to the development of more precise and patient-specific counseling and surveillance strategies. The real-world adoption of new or improved technologies into clinical practice frequently requires medical decision-making in the absence of established understanding of gene-gene interactions. In the meantime, practitioners must be prepared to apply a rationale based on currently available knowledge to clinical decision-making. Current practice is evolving to rely heavily on clinical concordance with personal and family history in making specific therapeutic decisions.

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Section: Multiple Pathogenic Mutationsmentioning

confidence: 99%

Clinical challenges in interpreting multiple pathogenic mutations in single patients

Slaught

Berry

Bacik

et al. 2021

Hered Cancer Clin Pract

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“…The identification of tumor‐type agnostic markers to predict outcome in cancer patients is a challenge. However, pan‐cancer studies have reported germline variants associated with outcome across different tumor types 1,10 . In addition, the identification of biomarkers that can indicate which patients would benefit or not from treatment with bevacizumab regardless of tumor type is even more challenging.…”

Section: Introductionmentioning

confidence: 99%

“…Germline variants can significantly improve predictions of the survival of cancer patients compared to clinical variables alone. 1 In addition to their prognostic potential, germline variants are also useful to individualize the selection of the most effective treatment. 2,3 This is clearly demonstrated by the example of deficient mismatch repair of germline DNA, which creates tumors responsive to checkpoint inhibition.…”

Section: Introductionmentioning

confidence: 99%

Genome‐wide association studies of survival in 1520 cancer patients treated with bevacizumab‐containing regimens

Quintanilha

Wang

Sibley

et al. 2021

Intl Journal of Cancer

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Germline variants might predict cancer progression. Bevacizumab improves overall survival (OS) in patients with advanced cancers. No biomarkers are available to identify patients that benefit from bevacizumab. A meta‐analysis of genome‐wide association studies (GWAS) was conducted in 1,520 patients from Phase III trials (CALGB 80303, 40503, 80405 and ICON7), where bevacizumab was randomized to treatment without bevacizumab. We aimed to identify genes and single nucleotide polymorphisms (SNPs) associated with survival independently of bevacizumab treatment or through interaction with bevacizumab. A cause‐specific Cox model was used to test the SNP‐OS association in both arms combined (prognostic), and the effect of SNPs‐bevacizumab interaction on OS (predictive) in each study. The SNP effects across studies were combined using inverse variance. Findings were tested for replication in advanced colorectal and ovarian cancer patients from The Cancer Genome Atlas (TGCA). In the GWAS meta‐analysis, patients with rs680949 in PRUNE2 experienced shorter OS compared to patients without it (P = 1.02 × 10−7, hazard ratio [HR] = 1.57, 95% confidence interval [CI] 1.33‐1.86), as well as in TCGA (P = .0219, HR = 1.58, 95% CI 1.07‐2.35). In the GWAS meta‐analysis, patients with rs16852804 in BARD1 experienced shorter OS compared to patients without it (P = 1.40 × 10−5, HR = 1.51, 95% CI 1.25‐1.82) as well as in TCGA (P = 1.39 × 10−4, HR = 3.09, 95% CI 1.73‐5.51). Patients with rs3795897 in AGAP1 experienced shorter OS in the bevacizumab arm compared to the nonbevacizumab arm (P = 1.43 × 10−5). The largest GWAS meta‐analysis of bevacizumab treated patients identified PRUNE2 and BARD1 (tumor suppressor genes) as prognostic genes of colorectal and ovarian cancer, respectively, and AGAP1 as a potentially predictive gene that interacts with bevacizumab with respect to patient survival.

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“…Second, partial dependence interpretation is less straightforward than marginal dependence and thus appears less frequently in biological and medical studies. Third, due to the noisy nature of data, medical researchers typically prefer to adopt robust versions of dependence metrics [Chatrath et al, 2020], such as the Spearman correlation coefficient; less is known about statistical methods related to robust partial dependence. Finally, differential analysis based on partial dependence tends to be computationally expensive and unable to tackle the scale of our problem.…”

Section: Introduction 1differential Expression and Co-expression Anal...mentioning

confidence: 99%

Compressed spectral screening for large-scale differential correlation analysis with application in selecting Glioblastoma gene modules

Li¹,

Tang²,

Chatrath³

2021

Preprint

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Differential co-expression analysis has been widely applied by scientists in understanding the biological mechanisms of diseases. However, the unknown differential patterns are often complicated; thus, models based on simplified parametric assumptions can be ineffective in identifying the differences. Meanwhile, the gene expression data involved in such analysis are in extremely high dimensions by nature, whose correlation matrices may not even be computable. Such a large scale seriously limits the application of most well-studied statistical methods. This paper introduces a simple yet powerful approach to the differential correlation analysis problem called compressed spectral screening. By leveraging spectral structures and random sampling techniques, our approach could achieve a highly accurate screening of features with complicated differential patterns while maintaining the scalability to analyze correlation matrices of 10 4 -10 5 variables within a few minutes on a standard personal computer. We have applied this screening approach in comparing a TCGA data set about Glioblastoma with normal subjects. Our analysis successfully identifies multiple functional modules of genes that exhibit different co-expression patterns. The findings reveal new insights about Glioblastoma's evolving mechanism. The validity of our approach is also justified by a theoretical analysis, showing that the compressed spectral analysis can achieve variable screening consistency.

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The Pan-Cancer Landscape of Prognostic Germline Variants in 10,582 Patients

Cited by 5 publications

References 63 publications

Clinical challenges in interpreting multiple pathogenic mutations in single patients

Clinical challenges in interpreting multiple pathogenic mutations in single patients

Genome‐wide association studies of survival in 1520 cancer patients treated with bevacizumab‐containing regimens

Compressed spectral screening for large-scale differential correlation analysis with application in selecting Glioblastoma gene modules

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