Dynamics of breast-cancer relapse reveal late-recurring ER-positive genomic subgroups

Rueda, Oscar M.; Sammut, Stephen‐John; Seoane, José A.; Chin, Suet-Feung; Caswell-Jin, Jennifer L.; Callari, Maurizio; Batra, R N; Pereira, Bernard; Bruna, Alejandra; Ali, H. Raza; Provenzano, Elena; Liu, Bin; Parisien, Michelle; Gillett, Cheryl; McKinney, Steven; Green, Andrew; Murphy, Leigh C.; Purushotham, Arnie; Ellis, Ian O.; Pharoah, Paul D.P.; Rueda, Cristina; Aparicio, Samuel; Caldas, Carlos; Curtis, Christina

doi:10.1038/s41586-019-1007-8

Cited by 306 publications

(303 citation statements)

References 54 publications

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“…Of note, HER2positive breast cancers tended to disseminate earlier than HER2-negative breast cancers ( Fig. S18) consistent with this subgroup having the highest risk of distant metastasis before the routine use of the HER2-targeted therapy, trastuzumab, which has revolutionized the treatment of this disease 46 .…”

Section: Chronology Of Metastatic Seedingmentioning

confidence: 62%

Multi-cancer analysis of clonality and the timing of systemic spread in paired primary tumors and metastases

et al. 2019

Preprint

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Metastasis is the primary cause of cancer-related deaths, but the natural history, clonal evolution and patterns of systemic spread are poorly understood. We analyzed exome sequencing data from 458 paired primary tumors (P) or metastasis (M) samples from 136 breast, colorectal and lung cancer patients, including both untreated (n=98) and treated (n=101) metastases. We find that treated metastases often harbored private driver gene mutations whereas untreated metastases did not, suggesting that treatment promotes clonal evolution. Polyclonal seeding was common in lymph node metastases (n=19/35, 54%; mostly untreated) and untreated distant metastases (n=20/70, 29%), but less frequent in treated metastases (n=9/90, 10%). The low number of metastasis-private clonal mutations is consistent with early metastatic seeding, which commonly occurred several years prior to diagnosis in breast (2.4 years, range 0−3.3), lung (3.6 years, range 2.8 − 3.7) and colorectal (4.1 years, range 3.1 − 4.6) cancers. Thus, this pan-cancer analysis reveals early systemic spread in three common cancer types. Further, these data suggest that the natural course of metastasis is selectively relaxed relative to early tumor development and that metastasis-private mutations are not drivers of cancer spread but are instead associated with drug resistance.

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Section: Chronology Of Metastatic Seedingmentioning

confidence: 62%

Multi-cancer analysis of clonality and the timing of systemic spread in paired primary tumors and metastases

et al. 2019

Preprint

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“…In this study, the risk of severe progression assessed without considering the competition would be overestimated because the patients who would never progress (those who discharged from hospital without progression) were treated as if they could progress. The extent of such bias and its adjustment by competing risks modeling have been evaluated in clinical virology and oncology research [10][11][12][13] . We incorporated high-dimensional variable selection techniques into the competing risks modeling so that quantitative image features can be extensively evaluated according to their contribution to risk prediction.…”

Section: Discussionmentioning

confidence: 99%

Risk assessment of progression to severe conditions for patients with COVID-19 pneumonia: a single-center retrospective study

Zeng

Li²,

Liao

et al. 2020

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BackgroundManagement of high mortality risk due to significant progression requires prior assessment of time-to-progression. However, few related methods are available for COVID-19 pneumonia. MethodsWe retrospectively enrolled 338 adult patients admitted to one hospital between Jan 11, 2020 to Feb 29, 2020. The final follow-up date was March 8, 2020. We compared characteristics between patients with severe and non-severe outcome, and used multivariate survival analyses to assess the risk of progression to severe conditions. ResultsA total of 76 (31.9%) patients progressed to severe conditions and 3 (0.9%) died. The mean time from hospital admission to severity onset is 3.7 days. Age, body mass index (BMI), fever symptom on admission, co-existing hypertension or diabetes are associated with severe progression. Compared to non-severe group, the severe group already demonstrated, at an early stage, abnormalities in biomarkers indicating organ function, inflammatory responses, blood oxygen and coagulation function. The cohort is characterized with increasing cumulative incidences of severe progression up to 10 days after admission. Competing risks survival model incorporating CT imaging and baseline information showed an improved performance for predicting severity onset (mean time-dependent AUC = 0.880). : medRxiv preprint ConclusionsMultiple predisposition factors can be utilized to assess the risk of progression to severe conditions at an early stage. Multivariate survival models can reasonably analyze the progression risk based on early-stage CT images that would otherwise be misjudged by artificial analysis.

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“…The approach uses the Integrative Clustering (IC) method (Shen et al, 2009) which produces clusters from a multi-omic joint latent embedding. These clusters are then utilised for identifying mutation-driver genes (Pereira et al, 2016) and survival analyses (Rueda et al, 2019). In this context, the work presented in this paper can be readily applied to similar tasks.…”

Section: Discussionmentioning

confidence: 99%

Variational autoencoders for cancer data integration: design principles and computational practice

Simidjievski

Bodnar

Tariq

et al. 2019

Preprint

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International initiatives such as the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) are collecting multiple data sets at different genome-scales with the aim to identify novel cancer bio-markers and predict patient survival. To analyse such data, several machine learning, bioinformatics and statistical methods have been applied, among them neural networks such as autoencoders. Although these models provide a good statistical learning framework to analyse multi-omic and/or clinical data, there is a distinct lack of work on how to integrate diverse patient data and identify the optimal design best suited to the available data.In this paper, we investigate several autoencoder architectures that integrate a variety of cancer patient data types (e.g., multi-omics and clinical data). We perform extensive analyses of these approaches and provide a clear methodological and computational framework for designing systems that enable clinicians to investigate cancer traits and translate the results into clinical applications. We demonstrate how these networks can be designed, built and, in particular, applied to tasks of integrative analyses of heterogeneous breast cancer data. The results show that these approaches yield relevant data representations that, in turn, lead to accurate and stable diagnosis.

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Dynamics of breast-cancer relapse reveal late-recurring ER-positive genomic subgroups

Cited by 306 publications

References 54 publications

Multi-cancer analysis of clonality and the timing of systemic spread in paired primary tumors and metastases

Multi-cancer analysis of clonality and the timing of systemic spread in paired primary tumors and metastases

Risk assessment of progression to severe conditions for patients with COVID-19 pneumonia: a single-center retrospective study

Variational autoencoders for cancer data integration: design principles and computational practice

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