Clinically high-risk breast cancer displays markedly discordant molecular risk predictions between the MammaPrint and EndoPredict tests

Jahn, Stephan; Bösl, Andreas; Tsybrovskyy, Oleksiy; Gruber-Rossipal, Christine; Helfgott, Ruth; Fitzal, Florian; Knauer, Michael; Balić, Marija; Jasarevic, Zerina; Offner, Felix; Moinfar, Farid

doi:10.1038/s41416-020-0838-2

Cited by 10 publications

(10 citation statements)

References 9 publications

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“…Four further molecular subtypes (luminal A, luminal B, HER2-enriched and basal-like) are identified by PAM50 classification [ 10 ] based on the expression of 50-gene signatures, with most basal-like carcinomas, which are usually triple negative, having aggressive phenotype and high relapse rates. Despite significant progress in understanding disease subtypes, the challenge of matching patient clinical characteristics and tissue molecular patterns to prognosis or to a therapeutic strategy remains pertinent [ 11 , 12 ]. Predictive models of molecular profiling are urgently needed to prevent the underdiagnosis of high-risk tumours and to minimise the over-treatment of low-risk disease, which may help reduce the need for aggressive systemic therapies [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Cancer Grade Model: a multi-gene machine learning-based risk classification for improving prognosis in breast cancer

et al. 2021

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Background Prognostic stratification of breast cancers remains a challenge to improve clinical decision making. We employ machine learning on breast cancer transcriptomics from multiple studies to link the expression of specific genes to histological grade and classify tumours into a more or less aggressive prognostic type. Materials and methods Microarray data of 5031 untreated breast tumours spanning 33 published datasets and corresponding clinical data were integrated. A machine learning model based on gradient boosted trees was trained on histological grade-1 and grade-3 samples. The resulting predictive model (Cancer Grade Model, CGM) was applied on samples of grade-2 and unknown-grade (3029) for prognostic risk classification. Results A 70-gene signature for assessing clinical risk was identified and was shown to be 90% accurate when tested on known histological-grade samples. The predictive framework was validated through survival analysis and showed robust prognostic performance. CGM was cross-referenced with existing genomic tests and demonstrated the competitive predictive power of tumour risk. Conclusions CGM is able to classify tumours into better-defined prognostic categories without employing information on tumour size, stage, or subgroups. The model offers means to improve prognosis and support the clinical decision and precision treatments, thereby potentially contributing to preventing underdiagnosis of high-risk tumours and minimising over-treatment of low-risk disease.

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Section: Introductionmentioning

confidence: 99%

Cancer Grade Model: a multi-gene machine learning-based risk classification for improving prognosis in breast cancer

et al. 2021

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“…RNA and microRNA profiling of pretreatment biopsies and serum have shown potential for identification of predictive signatures associated with response to therapy [ 18 , 26 , 27 , 28 , 29 , 30 ].…”

Section: Resultsmentioning

confidence: 99%

Towards Personalized Treatment Strategies for Esophageal Adenocarcinoma; A Review on the Molecular Characterization of Esophageal Adenocarcinoma and Current Research Efforts on Individualized Curative Treatment Regimens

Hoefnagel

Boonstra

Russchen³

et al. 2021

Cancers

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Esophageal cancers confer a major health challenge and are highly aggressive malignancies with poor prognosis. Esophageal adenocarcinoma (EAC) is one of the two major histopathological subtypes of esophageal cancer. Despite advances in treatment modalities, the prognosis of patients with EAC remains poor, with a 5-year survival rate that rarely exceeds 30% in patients treated with curative intent. Chemoradiotherapy followed by resection is the treatment of choice for EAC patients, which are deemed to be curable. Current patient stratification and treatments are based on outcomes from clinical trials. Unfortunately, the molecular heterogeneity of EAC which determines the chemo- and radiosensitivity of these cancers are not taken into account. A more personalized approach in the treatment of EAC could improve patient outcomes. This review aims at summarizing literature on translational and clinical research in the field of EAC which could be of importance to develop personalized approaches. As suggested by the TCGA, expression data features molecular classifications by different platforms, including miRNA, genomic mutations and reverse-phase protein arrays. Here, we summarize literature on transcriptomic, data-driven approaches to identify distinct subtypes of EAC associated with molecular features. These novel classifications may determine the responsiveness to chemo(radio)therapy and help to identify novel molecular targets within cell signaling pathways. Moreover, we discuss the current clinical research efforts on tailored treatment regimens for patients with EAC taking into account the heterogeneous response to chemoradiotherapy. We summarize the evidence regarding active surveillance instead of immediate surgical resection after application of neoadjuvant chemo(radio)therapy in EAC. We consider that in future patients with complete response to chemo(radio)therapy, predicted by (transcriptomic) biomarkers, might benefit most from this approach. Finally, challenges to overcome for current findings to be implemented in clinical practice and move the field forward are being discussed.

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“…Some patients with little benefit from CT might be included in this study because multigenomic assays used to identify patients at increased risk for distant recurrence could not be performed during this study period. However, clinically high-risk tumors with node-positive disease were more frequently high-risk by multigenomic assays 33 .…”

Section: Discussionmentioning

confidence: 97%

Effect of radiotherapy sequence on long-term outcome in patients with node-positive breast cancer: a retrospective study

Woo

Moon

Kwon

et al. 2022

Sci Rep

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The optimal sequence of chemotherapy (CT) and radiotherapy (RT) after surgery in breast cancer patients is unclear. There is a lack of literature on RT given between anthracycline and taxane administration. We evaluated the effect of RT sequence on long-term outcome in breast cancer. Two hundred patients who underwent surgery between January 2009 and December 2012 for node-positive breast cancers were evaluated retrospectively. All patients were treated with doxorubicin and cyclophosphamide (AC) followed by taxane. Sandwich RT group that received RT between AC and taxane was compared to the group that received RT after CT. The mean follow-up period was 105.4 months. The locoregional recurrence (LRR) rate was lower in sandwich RT group (P = 0.012) and there was no significant difference in distant metastasis between the two groups. The RT sequence was an important predictor for LRR in multivariable analysis (P = 0.017). For luminal A subtype, disease-free survival (DFS) was better in sandwich RT group than in CT followed by RT group (P = 0.001). The overall survival did not correlated with RT sequence regardless of subtype. Sandwich RT can offer DFS benefit in luminal A subtype breast cancer. A tailored approach of sequencing chemotherapy and radiotherapy would be needed considering the factors that can influence outcome.

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Clinically high-risk breast cancer displays markedly discordant molecular risk predictions between the MammaPrint and EndoPredict tests

Cited by 10 publications

References 9 publications

Cancer Grade Model: a multi-gene machine learning-based risk classification for improving prognosis in breast cancer

Cancer Grade Model: a multi-gene machine learning-based risk classification for improving prognosis in breast cancer

Towards Personalized Treatment Strategies for Esophageal Adenocarcinoma; A Review on the Molecular Characterization of Esophageal Adenocarcinoma and Current Research Efforts on Individualized Curative Treatment Regimens

Effect of radiotherapy sequence on long-term outcome in patients with node-positive breast cancer: a retrospective study

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