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Determining breast cancer histological grade from RNA-sequencing data.

Wang M, Klevebring D, Lindberg J, Czene K, Grönberg H, Rantalainen M - Breast Cancer Res. (2016)

Bottom Line: However, grade 2 is associated with an intermediate risk of recurrence, and carries limited information for clinical decision-making.Differentially expressed genes and isoforms associated with HGs were analysed using linear models.We identified a large number of novel genes and isoforms associated with HG.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Vag 12A, Stockholm, 171 77, Sweden.

ABSTRACT

Background: The histologic grade (HG) of breast cancer is an established prognostic factor. The grade is usually reported on a scale ranging from 1 to 3, where grade 3 tumours are the most aggressive. However, grade 2 is associated with an intermediate risk of recurrence, and carries limited information for clinical decision-making. Patients classified as grade 2 are at risk of both under- and over-treatment.

Methods: RNA-sequencing analysis was conducted in a cohort of 275 women diagnosed with invasive breast cancer. Multivariate prediction models were developed to classify tumours into high and low transcriptomic grade (TG) based on gene- and isoform-level expression data from RNA-sequencing. HG2 tumours were reclassified according to the prediction model and a recurrence-free survival analysis was performed by the multivariate Cox proportional hazards regression model to assess to what extent the TG model could be used to stratify patients. The prediction model was validated in N=487 breast cancer cases from the The Cancer Genome Atlas (TCGA) data set. Differentially expressed genes and isoforms associated with HGs were analysed using linear models.

Results: The classification of grade 1 and grade 3 tumours based on RNA-sequencing data achieved high accuracy (area under the receiver operating characteristic curve = 0.97). The association between recurrence-free survival rate and HGs was confirmed in the study population (hazard ratio of grade 3 versus 1 was 2.62 with 95 % confidence interval = 1.04-6.61). The TG model enabled us to reclassify grade 2 tumours as high TG and low TG gene or isoform grade. The risk of recurrence in the high TG group of grade 2 tumours was higher than in low TG group (hazard ratio = 2.43, 95 % confidence interval = 1.13-5.20). We found 8200 genes and 13,809 isoforms that were differentially expressed between HG1 and HG3 breast cancer tumours.

Conclusions: Gene- and isoform-level expression data from RNA-sequencing could be utilised to differentiate HG1 and HG3 tumours with high accuracy. We identified a large number of novel genes and isoforms associated with HG. Grade 2 tumours could be reclassified as high and low TG, which has the potential to reduce over- and under-treatment if implemented clinically.

No MeSH data available.


Related in: MedlinePlus

TG-Gene model predictions in HG2 tumours (Clinseq and TCGA data sets combined). a Kaplan–Meir curves of RFS by HGs. b Kaplan–Meir curves of RFS between groups predicted by the TG-Gene model (HG2-High and HG2-Low). c PAM50 subtype distribution of HGs and predicted groups in HG2. d KI67 distribution. HG histologic grade, RFS recurrence-free survival
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Fig2: TG-Gene model predictions in HG2 tumours (Clinseq and TCGA data sets combined). a Kaplan–Meir curves of RFS by HGs. b Kaplan–Meir curves of RFS between groups predicted by the TG-Gene model (HG2-High and HG2-Low). c PAM50 subtype distribution of HGs and predicted groups in HG2. d KI67 distribution. HG histologic grade, RFS recurrence-free survival

Mentions: To evaluate if the RFS rate was associated with HGs, we compared RFS between HG groups (Fig. 2a). The survival analysis was carried out on the Clinseq and TCGA data sets combined. Forest plots from the univariate and multivariate Cox regression models for each data set are displayed in Additional file 1: Figures S10 and S11. No obvious bias was found between the two cohorts. The median follow-up time was 3.6 years. The RFS rate was found to be different between HG groups (p=0.017, log-rank test). In the Cox regression model, the unadjusted HR of grade 3 against 1 was 2.62 (95 % CI = 1.04–6.61). The adjusted HR comparing grade 3 with grade 1 was not statistically significant (Table 3).Fig. 2


Determining breast cancer histological grade from RNA-sequencing data.

Wang M, Klevebring D, Lindberg J, Czene K, Grönberg H, Rantalainen M - Breast Cancer Res. (2016)

TG-Gene model predictions in HG2 tumours (Clinseq and TCGA data sets combined). a Kaplan–Meir curves of RFS by HGs. b Kaplan–Meir curves of RFS between groups predicted by the TG-Gene model (HG2-High and HG2-Low). c PAM50 subtype distribution of HGs and predicted groups in HG2. d KI67 distribution. HG histologic grade, RFS recurrence-free survival
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4862203&req=5

Fig2: TG-Gene model predictions in HG2 tumours (Clinseq and TCGA data sets combined). a Kaplan–Meir curves of RFS by HGs. b Kaplan–Meir curves of RFS between groups predicted by the TG-Gene model (HG2-High and HG2-Low). c PAM50 subtype distribution of HGs and predicted groups in HG2. d KI67 distribution. HG histologic grade, RFS recurrence-free survival
Mentions: To evaluate if the RFS rate was associated with HGs, we compared RFS between HG groups (Fig. 2a). The survival analysis was carried out on the Clinseq and TCGA data sets combined. Forest plots from the univariate and multivariate Cox regression models for each data set are displayed in Additional file 1: Figures S10 and S11. No obvious bias was found between the two cohorts. The median follow-up time was 3.6 years. The RFS rate was found to be different between HG groups (p=0.017, log-rank test). In the Cox regression model, the unadjusted HR of grade 3 against 1 was 2.62 (95 % CI = 1.04–6.61). The adjusted HR comparing grade 3 with grade 1 was not statistically significant (Table 3).Fig. 2

Bottom Line: However, grade 2 is associated with an intermediate risk of recurrence, and carries limited information for clinical decision-making.Differentially expressed genes and isoforms associated with HGs were analysed using linear models.We identified a large number of novel genes and isoforms associated with HG.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels Vag 12A, Stockholm, 171 77, Sweden.

ABSTRACT

Background: The histologic grade (HG) of breast cancer is an established prognostic factor. The grade is usually reported on a scale ranging from 1 to 3, where grade 3 tumours are the most aggressive. However, grade 2 is associated with an intermediate risk of recurrence, and carries limited information for clinical decision-making. Patients classified as grade 2 are at risk of both under- and over-treatment.

Methods: RNA-sequencing analysis was conducted in a cohort of 275 women diagnosed with invasive breast cancer. Multivariate prediction models were developed to classify tumours into high and low transcriptomic grade (TG) based on gene- and isoform-level expression data from RNA-sequencing. HG2 tumours were reclassified according to the prediction model and a recurrence-free survival analysis was performed by the multivariate Cox proportional hazards regression model to assess to what extent the TG model could be used to stratify patients. The prediction model was validated in N=487 breast cancer cases from the The Cancer Genome Atlas (TCGA) data set. Differentially expressed genes and isoforms associated with HGs were analysed using linear models.

Results: The classification of grade 1 and grade 3 tumours based on RNA-sequencing data achieved high accuracy (area under the receiver operating characteristic curve = 0.97). The association between recurrence-free survival rate and HGs was confirmed in the study population (hazard ratio of grade 3 versus 1 was 2.62 with 95 % confidence interval = 1.04-6.61). The TG model enabled us to reclassify grade 2 tumours as high TG and low TG gene or isoform grade. The risk of recurrence in the high TG group of grade 2 tumours was higher than in low TG group (hazard ratio = 2.43, 95 % confidence interval = 1.13-5.20). We found 8200 genes and 13,809 isoforms that were differentially expressed between HG1 and HG3 breast cancer tumours.

Conclusions: Gene- and isoform-level expression data from RNA-sequencing could be utilised to differentiate HG1 and HG3 tumours with high accuracy. We identified a large number of novel genes and isoforms associated with HG. Grade 2 tumours could be reclassified as high and low TG, which has the potential to reduce over- and under-treatment if implemented clinically.

No MeSH data available.


Related in: MedlinePlus