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Today I will share with you an article recently published in the International Journal of Biological Sciences (IF: 4.
858) on Basal-like breast cancer.
This article focuses on two pathways related to breast cancer and uses a large sample (more than 40 data sets).
) Analysis of pathway activity is worth watching.
Shengxin people provide novel and reproducible life information analysis.
Interested to scan the code to contact Basal-like breast cancer with low TGFβ and high TNFα pathway activity is rich in activated memory CD4 T cells and has a good prognosis.
Low TGFβ and high TNFα pathways Active basal-like breast cancer is rich in activated memory CD4 T cells, and the prognosis is good.
Let us first look at the main content of the article: Basal-like breast cancer (BLBC) is a type of breast cancer that is prone to recurrence and metastasis.
Highly invasive breast cancer with poor survival.
Immune signals play an important role in promoting the progression and metastasis of BLBC.
Previously, the author has studied a variety of cancer-related pathway signals in colon cancer and breast cancer; five of them, including IFNα, IFNγ, STAT3, TGFβ and TNFα signaling pathways, are all related to cancer immunity.
On this basis, this article continues to analyze the relationship between these characteristics and the prognosis of breast cancer subtypes based on pam50.
The author collected 42 Affymetrix U133 microarray data sets, including 6381 patients.
Combine 42 microarray data sets into 4 cohorts.
Cohort 1 contains a data set of 15 patients with neoadjuvant response information, which is used to study the relationship between pathway characteristics and BC neoadjuvant response, and the rest are used to check pathway characteristics and BC recurrence Relationship.
Cohort 2 contains 16 data sets, of which the transfer date is available; Cohort 3 contains 8 data sets with only recurrence date information; the remaining 6 data sets with RFS or DMFS information have a much shorter follow-up time than Cohort 2 and Cohort 3.
Merge into queue 4.
The BinReg method is used to find pathway characteristics in the training set and predict the pathway activity of the sample in the test set.
Finally, survival analysis is used to detect the correlation between pathway activity and clinical results, so as to discover the relationship between immune-related pathways and the prognosis of breast cancer.
In addition, the author also predicted the internal subtypes of breast cancer and analyzed the immune cell composition in tumor cells.
Next, let us walk into this article together.
Results show: 1.
In BLBC, the IFNα, IFNγ and TNFα pathways are up-regulated, and the TGFβ pathway is down-regulated.
The authors used the BinReg method to analyze the gene expression patterns of the two sets of samples (in each of the two groups, one pathway is "on" and "off" ) To identify pathway-specific genes, and then use principal component analysis to calculate the weight of each characteristic gene, so that the weighted average gene expression level can be used to distinguish pathway "on" and "off" groups.
By applying a binary regression model to the principal components of the gene expression data set of the test sample, the probability score of the pathway activity of the sample is generated.
The activities of IFNα, IFNγ, TNFα, TGFβ and STAT3 signaling pathways in five subtypes of breast cancer based on pam50 were compared.
The results showed that the first four pathways showed different expression patterns in different BC subtypes.
In BLBC, the IFNα, IFNγ, and TNFα pathways have the highest activity, and the TGFβ pathway has the lowest activity.
Among the five PAM50 subtypes, there was no significant difference in STAT3 pathway activity (Figure 1).
Figure 1.
Comparison of IFNα, IFNγ, TNFα, TGFβ and STAT3 pathway activity in the intrinsic subtypes of breast cancer based on pam50.
Two: IFNα, IFNγ, TNFα and TGFβ signals are correlated with the risk of BLBC recurrence using univariate Cox regression analysis 5 The relationship between various immune-related pathways and the risk of breast cancer recurrence, the forest plot shows the total HR of the risk of recurrence.
When Cox regression is performed with the DFS of basal breast cancer and the activity of each pathway, the IFNγ (Figure 2B) and TNFα (Figure 2C) pathways are significant favorable factors, while the TGFβ pathway is a significant disadvantage.
The IFNα pathway is also a prognostic factor (Figure 2A), but its effect is weaker than the IFNγ and TNFα pathways.
Figure 2 Cox regression analysis of IFNα, IFNγ, TNFα and TGFβ pathways and different subtypes of breast cancer recurrence risk.
According to IFNγ, TNFα or TGFβ pathway activity, BLBC can be further divided into 3 subgroups with significantly different recurrence risks (Figure 3).
Similar to the results of Cox regression analysis, BLBC's IFNγ and TNFα pathways are more active, and the risk of recurrence is significantly reduced in all three BC cohorts (Figure 3).
In addition, in the subgroups with high IFNα pathway activity and low TGFβ pathway activity, the risk of BLBC recurrence is also lower.
Figure 3 Kaplan-Meier analysis of IFNα, IFNγ, TNFα and TGFβ pathway activity and the risk of BLBC recurrence 3: Combined application of TNFα and TGFβ pathway activity can improve the characteristic scores of the predictive pathway of BLBC recurrence.
First perform Z-score conversion; then use them separately The characteristic values of IFNα, IFNγ and TNFα are subtracted from the characteristic values of TGFβ (for example, TNFα/TGFβ binding value = TNFα characteristic value-TGFβ characteristic value) to calculate the score of TGFβ binding to IFNα, IFNγ and TNFα.
Pathway combinations were tested in all PAM50 subtypes, and Cox regression analysis showed that the synergistic effect of pathway combinations on recurrence prediction was only observed in BLBC and HER2-enriched BC (Figure 4A).
In BLBC, the combination of TGFβ pathway and IFNα, IFNγ or TNFα pathway has a higher total HR in relation to cancer recurrence than the four separate pathways (Figure 4A).
In addition, the combined effect of these three combinations in the prediction of BLBC recurrence was observed in all three independent cohorts (Figure 4B, Figure 2).
The combined effect only appeared in the second group, while the other groups did not observe this.
Situation (Figure 4C).
Figure 5 shows the survival curve of the combined effect of pathways.
According to the median of predicted pathway activity or pathway combined score in each cohort, patients are divided into high and low groups.
Figures A, D, and G are stratification based on TNFα pathway activity, and Figures B, E, and H are based on TGFβ pathway.
The stratification of activity, Figures C, F, I are based on the combined scoring of TNFα and TGFβ pathways.
The results showed that the risk of recurrence between subgroups stratified based on the combined scores of TGFβ and TNFα was much greater than the risk of recurrence between subgroups stratified based on the TGFβ or TNFα pathway alone.
Figure 4 Pathway combination can improve the prediction of BLBC prognosis Figure 5 Kaplan-Meier analysis of the synergistic effect of TNFα and TGFβ pathway and BLBC prognosis 4: Combined use of TNFα and TGFβ signaling pathway to improve the recurrence prediction of BLBC treated with adjuvant chemotherapy Cohort 1 BC analysis showed that among the 5 subtypes, basal-like and HER2-enriched BC had the best response to neoadjuvant chemotherapy (Figure 6A).
The Cox regression analysis of BLBC in this cohort showed that these 4 pathways (IFNα, IFNγ, TNFα and TGFβ) were not related to neoadjuvant response.
The author continued to analyze the impact of the above four pathways on the risk of BLBC recurrence in adjuvant chemotherapy.
Cox regression analysis showed that only the TGFβ pathway was significantly associated with BLBC recurrence after chemotherapy (Figure 6C).
In cohort 2, the combination of TGFβ and IFNγ or TNFα increased this correlation (Figure 6C).
Figure 6 The relationship between the IFNα, IFNγ, TNFα and TGFβ pathways and the prognostic prediction of chemotherapy.
Five: activated memory CD4 T cells The BLBC enriched in high TNFα and low TGFβ pathway activity has a heat map between the activity of TNFα and TGFβ pathways, the activity of 6 BLBC-related pathways, and the infiltration of 4 immune cell subgroups; each row represents a pathway or one A subset of immune cells, each column represents a BLBC sample.
As can be seen from the figure, higher TNFα signal in cancer cells is associated with higher activated Tm cell levels, lower M2 and higher M1 macrophage levels, while higher TGFβ signal is associated with higher M0 Correlation at the level of macrophages.
Only activated Tm cells in these 4 cell subgroups showed specific enrichment in BLBC with TNFα+TGFβ- or TNFα-TGFβ+.
Figure 7 TNFα and TGFβ pathway activity correlates with the activity of six BLBC-related pathways and the infiltration of four immune cell subpopulations.
That’s all for this article.
In summary, the author used 4 large independent breast cancer data sets to analyze the relationship between 5 immune-related pathways and the prognosis of breast cancer; they obtained very meaningful results and were consistent with previously reported articles.
The author's future research will focus on expanding these findings in more BLBC patient cohorts and applying them to the clinic, aiming to protect low-risk BLBC patients from harmful and unnecessary adjuvant chemotherapy.
Scan the code and fill in the form to get first-hand ideas
858) on Basal-like breast cancer.
This article focuses on two pathways related to breast cancer and uses a large sample (more than 40 data sets).
) Analysis of pathway activity is worth watching.
Shengxin people provide novel and reproducible life information analysis.
Interested to scan the code to contact Basal-like breast cancer with low TGFβ and high TNFα pathway activity is rich in activated memory CD4 T cells and has a good prognosis.
Low TGFβ and high TNFα pathways Active basal-like breast cancer is rich in activated memory CD4 T cells, and the prognosis is good.
Let us first look at the main content of the article: Basal-like breast cancer (BLBC) is a type of breast cancer that is prone to recurrence and metastasis.
Highly invasive breast cancer with poor survival.
Immune signals play an important role in promoting the progression and metastasis of BLBC.
Previously, the author has studied a variety of cancer-related pathway signals in colon cancer and breast cancer; five of them, including IFNα, IFNγ, STAT3, TGFβ and TNFα signaling pathways, are all related to cancer immunity.
On this basis, this article continues to analyze the relationship between these characteristics and the prognosis of breast cancer subtypes based on pam50.
The author collected 42 Affymetrix U133 microarray data sets, including 6381 patients.
Combine 42 microarray data sets into 4 cohorts.
Cohort 1 contains a data set of 15 patients with neoadjuvant response information, which is used to study the relationship between pathway characteristics and BC neoadjuvant response, and the rest are used to check pathway characteristics and BC recurrence Relationship.
Cohort 2 contains 16 data sets, of which the transfer date is available; Cohort 3 contains 8 data sets with only recurrence date information; the remaining 6 data sets with RFS or DMFS information have a much shorter follow-up time than Cohort 2 and Cohort 3.
Merge into queue 4.
The BinReg method is used to find pathway characteristics in the training set and predict the pathway activity of the sample in the test set.
Finally, survival analysis is used to detect the correlation between pathway activity and clinical results, so as to discover the relationship between immune-related pathways and the prognosis of breast cancer.
In addition, the author also predicted the internal subtypes of breast cancer and analyzed the immune cell composition in tumor cells.
Next, let us walk into this article together.
Results show: 1.
In BLBC, the IFNα, IFNγ and TNFα pathways are up-regulated, and the TGFβ pathway is down-regulated.
The authors used the BinReg method to analyze the gene expression patterns of the two sets of samples (in each of the two groups, one pathway is "on" and "off" ) To identify pathway-specific genes, and then use principal component analysis to calculate the weight of each characteristic gene, so that the weighted average gene expression level can be used to distinguish pathway "on" and "off" groups.
By applying a binary regression model to the principal components of the gene expression data set of the test sample, the probability score of the pathway activity of the sample is generated.
The activities of IFNα, IFNγ, TNFα, TGFβ and STAT3 signaling pathways in five subtypes of breast cancer based on pam50 were compared.
The results showed that the first four pathways showed different expression patterns in different BC subtypes.
In BLBC, the IFNα, IFNγ, and TNFα pathways have the highest activity, and the TGFβ pathway has the lowest activity.
Among the five PAM50 subtypes, there was no significant difference in STAT3 pathway activity (Figure 1).
Figure 1.
Comparison of IFNα, IFNγ, TNFα, TGFβ and STAT3 pathway activity in the intrinsic subtypes of breast cancer based on pam50.
Two: IFNα, IFNγ, TNFα and TGFβ signals are correlated with the risk of BLBC recurrence using univariate Cox regression analysis 5 The relationship between various immune-related pathways and the risk of breast cancer recurrence, the forest plot shows the total HR of the risk of recurrence.
When Cox regression is performed with the DFS of basal breast cancer and the activity of each pathway, the IFNγ (Figure 2B) and TNFα (Figure 2C) pathways are significant favorable factors, while the TGFβ pathway is a significant disadvantage.
The IFNα pathway is also a prognostic factor (Figure 2A), but its effect is weaker than the IFNγ and TNFα pathways.
Figure 2 Cox regression analysis of IFNα, IFNγ, TNFα and TGFβ pathways and different subtypes of breast cancer recurrence risk.
According to IFNγ, TNFα or TGFβ pathway activity, BLBC can be further divided into 3 subgroups with significantly different recurrence risks (Figure 3).
Similar to the results of Cox regression analysis, BLBC's IFNγ and TNFα pathways are more active, and the risk of recurrence is significantly reduced in all three BC cohorts (Figure 3).
In addition, in the subgroups with high IFNα pathway activity and low TGFβ pathway activity, the risk of BLBC recurrence is also lower.
Figure 3 Kaplan-Meier analysis of IFNα, IFNγ, TNFα and TGFβ pathway activity and the risk of BLBC recurrence 3: Combined application of TNFα and TGFβ pathway activity can improve the characteristic scores of the predictive pathway of BLBC recurrence.
First perform Z-score conversion; then use them separately The characteristic values of IFNα, IFNγ and TNFα are subtracted from the characteristic values of TGFβ (for example, TNFα/TGFβ binding value = TNFα characteristic value-TGFβ characteristic value) to calculate the score of TGFβ binding to IFNα, IFNγ and TNFα.
Pathway combinations were tested in all PAM50 subtypes, and Cox regression analysis showed that the synergistic effect of pathway combinations on recurrence prediction was only observed in BLBC and HER2-enriched BC (Figure 4A).
In BLBC, the combination of TGFβ pathway and IFNα, IFNγ or TNFα pathway has a higher total HR in relation to cancer recurrence than the four separate pathways (Figure 4A).
In addition, the combined effect of these three combinations in the prediction of BLBC recurrence was observed in all three independent cohorts (Figure 4B, Figure 2).
The combined effect only appeared in the second group, while the other groups did not observe this.
Situation (Figure 4C).
Figure 5 shows the survival curve of the combined effect of pathways.
According to the median of predicted pathway activity or pathway combined score in each cohort, patients are divided into high and low groups.
Figures A, D, and G are stratification based on TNFα pathway activity, and Figures B, E, and H are based on TGFβ pathway.
The stratification of activity, Figures C, F, I are based on the combined scoring of TNFα and TGFβ pathways.
The results showed that the risk of recurrence between subgroups stratified based on the combined scores of TGFβ and TNFα was much greater than the risk of recurrence between subgroups stratified based on the TGFβ or TNFα pathway alone.
Figure 4 Pathway combination can improve the prediction of BLBC prognosis Figure 5 Kaplan-Meier analysis of the synergistic effect of TNFα and TGFβ pathway and BLBC prognosis 4: Combined use of TNFα and TGFβ signaling pathway to improve the recurrence prediction of BLBC treated with adjuvant chemotherapy Cohort 1 BC analysis showed that among the 5 subtypes, basal-like and HER2-enriched BC had the best response to neoadjuvant chemotherapy (Figure 6A).
The Cox regression analysis of BLBC in this cohort showed that these 4 pathways (IFNα, IFNγ, TNFα and TGFβ) were not related to neoadjuvant response.
The author continued to analyze the impact of the above four pathways on the risk of BLBC recurrence in adjuvant chemotherapy.
Cox regression analysis showed that only the TGFβ pathway was significantly associated with BLBC recurrence after chemotherapy (Figure 6C).
In cohort 2, the combination of TGFβ and IFNγ or TNFα increased this correlation (Figure 6C).
Figure 6 The relationship between the IFNα, IFNγ, TNFα and TGFβ pathways and the prognostic prediction of chemotherapy.
Five: activated memory CD4 T cells The BLBC enriched in high TNFα and low TGFβ pathway activity has a heat map between the activity of TNFα and TGFβ pathways, the activity of 6 BLBC-related pathways, and the infiltration of 4 immune cell subgroups; each row represents a pathway or one A subset of immune cells, each column represents a BLBC sample.
As can be seen from the figure, higher TNFα signal in cancer cells is associated with higher activated Tm cell levels, lower M2 and higher M1 macrophage levels, while higher TGFβ signal is associated with higher M0 Correlation at the level of macrophages.
Only activated Tm cells in these 4 cell subgroups showed specific enrichment in BLBC with TNFα+TGFβ- or TNFα-TGFβ+.
Figure 7 TNFα and TGFβ pathway activity correlates with the activity of six BLBC-related pathways and the infiltration of four immune cell subpopulations.
That’s all for this article.
In summary, the author used 4 large independent breast cancer data sets to analyze the relationship between 5 immune-related pathways and the prognosis of breast cancer; they obtained very meaningful results and were consistent with previously reported articles.
The author's future research will focus on expanding these findings in more BLBC patient cohorts and applying them to the clinic, aiming to protect low-risk BLBC patients from harmful and unnecessary adjuvant chemotherapy.
Scan the code and fill in the form to get first-hand ideas
