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      Research-based PAM50 signature and long-term breast cancer survival

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          Abstract

          Purpose

          Multi-gene signatures provide biological insight and risk stratification in breast cancer. Intrinsic molecular subtypes defined by mRNA expression of 50 genes (PAM50) are prognostic in hormone-receptor positive postmenopausal breast cancer. Yet, for 25–40% in the PAM50 intermediate risk group, long-term risk remains uncertain. Our study aimed to (i) test the long-term prognostic value of the PAM50 signature in pre- and post-menopausal breast cancer; (ii) investigate if the PAM50 model could be improved by addition of other mRNAs implicated in oncogenesis.

          Methods

          We used archived FFPE samples from 1723 breast cancer survivors; high quality reads were obtained on 1253 samples. Transcript expression was quantified using a custom codeset with probes for > 100 targets. Cox models assessed gene signatures for breast cancer relapse and survival.

          Results

          Over 15 + years of follow-up, PAM50 subtypes were ( P < 0.01) associated with breast cancer outcomes after accounting for tumor stage, grade and age at diagnosis. Results did not differ by menopausal status at diagnosis. Women with Luminal B (versus Luminal A) subtype had a > 60% higher hazard. Addition of a 13-gene hypoxia signature improved prognostication with > 40% higher hazard in the highest vs lowest hypoxia tertiles.

          Conclusions

          PAM50 intrinsic subtypes were independently prognostic for long-term breast cancer survival, irrespective of menopausal status. Addition of hypoxia signatures improved risk prediction. If replicated, incorporating the 13-gene hypoxia signature into the existing PAM50 risk assessment tool, may refine risk stratification and further clarify treatment for breast cancer.

          Electronic supplementary material

          The online version of this article (10.1007/s10549-019-05446-y) contains supplementary material, which is available to authorized users.

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          Most cited references23

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          A comparison of PAM50 intrinsic subtyping with immunohistochemistry and clinical prognostic factors in tamoxifen-treated estrogen receptor-positive breast cancer.

          To compare clinical, immunohistochemical (IHC), and gene expression models of prognosis applicable to formalin-fixed, paraffin-embedded blocks in a large series of estrogen receptor (ER)-positive breast cancers from patients uniformly treated with adjuvant tamoxifen. Quantitative real-time reverse transcription-PCR (qRT-PCR) assays for 50 genes identifying intrinsic breast cancer subtypes were completed on 786 specimens linked to clinical (median follow-up, 11.7 years) and IHC [ER, progesterone receptor (PR), HER2, and Ki67] data. Performance of predefined intrinsic subtype and risk-of-relapse scores was assessed using multivariable Cox models and Kaplan-Meier analysis. Harrell's C-index was used to compare fixed models trained in independent data sets, including proliferation signatures. Despite clinical ER positivity, 10% of cases were assigned to nonluminal subtypes. qRT-PCR signatures for proliferation genes gave more prognostic information than clinical assays for hormone receptors or Ki67. In Cox models incorporating standard prognostic variables, hazard ratios for breast cancer disease-specific survival over the first 5 years of follow-up, relative to the most common luminal A subtype, are 1.99 [95% confidence interval (CI), 1.09-3.64] for luminal B, 3.65 (95% CI, 1.64-8.16) for HER2-enriched subtype, and 17.71 (95% CI, 1.71-183.33) for the basal-like subtype. For node-negative disease, PAM50 qRT-PCR-based risk assignment weighted for tumor size and proliferation identifies a group with >95% 10-year survival without chemotherapy. In node-positive disease, PAM50-based prognostic models were also superior. The PAM50 gene expression test for intrinsic biological subtype can be applied to large series of formalin-fixed, paraffin-embedded breast cancers, and gives more prognostic information than clinical factors and IHC using standard cut points. ©2010 AACR.
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            Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer.

            A 70-gene signature was previously shown to have prognostic value in patients with node-negative breast cancer. Our goal was to validate the signature in an independent group of patients. Patients (n = 307, with 137 events after a median follow-up of 13.6 years) from five European centers were divided into high- and low-risk groups based on the gene signature classification and on clinical risk classifications. Patients were assigned to the gene signature low-risk group if their 5-year distant metastasis-free survival probability as estimated by the gene signature was greater than 90%. Patients were assigned to the clinicopathologic low-risk group if their 10-year survival probability, as estimated by Adjuvant! software, was greater than 88% (for estrogen receptor [ER]-positive patients) or 92% (for ER-negative patients). Hazard ratios (HRs) were estimated to compare time to distant metastases, disease-free survival, and overall survival in high- versus low-risk groups. The 70-gene signature outperformed the clinicopathologic risk assessment in predicting all endpoints. For time to distant metastases, the gene signature yielded HR = 2.32 (95% confidence interval [CI] = 1.35 to 4.00) without adjustment for clinical risk and hazard ratios ranging from 2.13 to 2.15 after adjustment for various estimates of clinical risk; clinicopathologic risk using Adjuvant! software yielded an unadjusted HR = 1.68 (95% CI = 0.92 to 3.07). For overall survival, the gene signature yielded an unadjusted HR = 2.79 (95% CI = 1.60 to 4.87) and adjusted hazard ratios ranging from 2.63 to 2.89; clinicopathologic risk yielded an unadjusted HR = 1.67 (95% CI = 0.93 to 2.98). For patients in the gene signature high-risk group, 10-year overall survival was 0.69 for patients in both the low- and high-clinical risk groups; for patients in the gene signature low-risk group, the 10-year survival rates were 0.88 and 0.89, respectively. The 70-gene signature adds independent prognostic information to clinicopathologic risk assessment for patients with early breast cancer.
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              Large meta-analysis of multiple cancers reveals a common, compact and highly prognostic hypoxia metagene

              Background: There is a need to develop robust and clinically applicable gene expression signatures. Hypoxia is a key factor promoting solid tumour progression and resistance to therapy; a hypoxia signature has the potential to be not only prognostic but also to predict benefit from particular interventions. Methods: An approach for deriving signatures that combine knowledge of gene function and analysis of in vivo co-expression patterns was used to define a common hypoxia signature from three head and neck and five breast cancer studies. Previously validated hypoxia-regulated genes (seeds) were used to generate hypoxia co-expression cancer networks. Results: A common hypoxia signature, or metagene, was derived by selecting genes that were consistently co-expressed with the hypoxia seeds in multiple cancers. This was highly enriched for hypoxia-regulated pathways, and prognostic in multivariate analyses. Genes with the highest connectivity were also the most prognostic, and a reduced metagene consisting of a small number of top-ranked genes, including VEGFA, SLC2A1 and PGAM1, outperformed both a larger signature and reported signatures in independent data sets of head and neck, breast and lung cancers. Conclusion: Combined knowledge of multiple genes' function from in vitro experiments together with meta-analysis of multiple cancers can deliver compact and robust signatures suitable for clinical application.
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                Author and article information

                Contributors
                LNatarajan@ucsd.edu
                Journal
                Breast Cancer Res Treat
                Breast Cancer Res. Treat
                Breast Cancer Research and Treatment
                Springer US (New York )
                0167-6806
                1573-7217
                21 September 2019
                21 September 2019
                2020
                : 179
                : 1
                : 197-206
                Affiliations
                [1 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Moores Cancer Center, , University of California, San Diego, ; San Diego, CA USA
                [2 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Department of Family Medicine and Public Health, , University of California, San Diego, ; 3855 Health Sciences Drive #0901, La Jolla, CA 92093-0901 USA
                [3 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Department of Medicine, , Washington University St. Louis, ; St. Louis, MO USA
                [4 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Washington University St. Louis, McDonnell Genome Institute, ; St. Louis, MO USA
                [5 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Department of Medicine, , University of California, San Diego, ; San Diego, CA USA
                [6 ]GRID grid.39382.33, ISNI 0000 0001 2160 926X, Baylor College of Medicine, , Lester and Sue Smith Breast Center, ; Houston, TX USA
                [7 ]GRID grid.240344.5, ISNI 0000 0004 0392 3476, Nationwide Children’s Hospital, Institute for Genomic Medicine, ; Columbus, OH USA
                [8 ]GRID grid.65499.37, ISNI 0000 0001 2106 9910, Division of Population Sciences, Department of Medical Oncology, , Dana-Farber Cancer Institute, ; Boston, MA USA
                [9 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Epidemiology, , Harvard T.H. Chan School of Public Health, ; Boston, MA USA
                [10 ]GRID grid.474131.4, Precision for Medicine, ; San Diego, CA USA
                Author information
                http://orcid.org/0000-0001-5719-828X
                Article
                5446
                10.1007/s10549-019-05446-y
                6985186
                31542876
                108f1000-c67f-46be-ad90-6f60adea9cdc
                © The Author(s) 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 4 September 2019
                : 12 September 2019
                Funding
                Funded by: National Cancer Institute (US)
                Award ID: R01CA166293
                Award ID: P30CA023100
                Award ID: F32CA220859
                Award Recipient :
                Categories
                Epidemiology
                Custom metadata
                © Springer Science+Business Media, LLC, part of Springer Nature 2020

                Oncology & Radiotherapy
                breast cancer,long-term survival,gene signatures,hypoxia,pam50 subtypes,prognostic modeling

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