Dataset Information

A model integrating tonic and antigen-triggered BCR signals to predict the survival of primary B cells.

ABSTRACT: The BCR constitutively transmits a "tonic" survival signal in the absence of exogenous antigen-binding. However, the strength of tonic BCR signal and its relationship with antigen-triggered survival signal are poorly understood. We found that primary B cells expressing high levels of BCR had elevated BCR tonic signal and increased survival compared with those expressing low levels of BCR. In addition, we found that crosslinking BCR with low doses of F(ab')₂ ?-IgM antibodies did not enhance, but rather decreased, B cell survival and that only when most of the BCR were occupied by F(ab')₂ ?-IgM antibodies was B cell survival enhanced. Based on these experimental results, we present a mathematical model integrating tonic and antigen-triggered BCR signals. Our model indicates that the signal generated from crosslinked BCR is 4.3 times as strong as the tonic signal generated from free BCR and that the threshold of B cell activation corresponds to the signal generated by crosslinking 61% of the surface BCR. This model also allows the prediction of the survival probability of a B cell based on its initial BCR level and the strength and duration of antigen stimulation, and fits with the mechanism of B cell tolerance.

SUBMITTER: Yasuda S

PROVIDER: S-EPMC5668375 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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A model integrating tonic and antigen-triggered BCR signals to predict the survival of primary B cells.

Yasuda Shoya S Zhou Yang Y Wang Yanqing Y Yamamura Masayuki M Wang Ji-Yang JY

Scientific reports 20171102 1

The BCR constitutively transmits a "tonic" survival signal in the absence of exogenous antigen-binding. However, the strength of tonic BCR signal and its relationship with antigen-triggered survival signal are poorly understood. We found that primary B cells expressing high levels of BCR had elevated BCR tonic signal and increased survival compared with those expressing low levels of BCR. In addition, we found that crosslinking BCR with low doses of F(ab')<sub>2</sub> α-IgM antibodies did not en ...[more]

PMID: 29097663

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Project description:BackgroundThe pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) of breast cancer is closely related to a better prognosis. However, there are no reliable indicators to accurately identify which patients will achieve pCR before surgery, and a model for predicting pCR to NAC is required.MethodsA total of 269 breast cancer patients in Shandong Cancer Hospital and Liaocheng People's Hospital receiving anthracycline and taxane-based NAC were prospectively enrolled. Expression profiling using a 457 cancer-related gene sequencing panel (DNA sequencing) covering genes recurrently mutated in breast cancer was carried out on 243 formalin-fixed paraffin-embedded tumor biopsies samples before NAC from 243 patients. The unique personalized panel of nine individual somatic mutation genes from the constructed model was used to detect and analyze ctDNA on 216 blood samples. Blood samples were collected at indicated time points including before chemotherapy initiation, after the 1st NAC and before the 2nd NAC cycle, during intermediate evaluation, and prior to surgery. In this study, we characterized the value of gene profile mutation and circulating tumor DNA (ctDNA) in combination with clinical characteristics in the prediction of pCR before surgery and investigated the prognostic prediction. The median follow-up time for survival analysis was 898 days.ResultsFirstly, we constructed a predictive NAC response model including five single nucleotide variant (SNV) mutations (TP53, SETBP1, PIK3CA, NOTCH4 and MSH2) and four copy number variation (CNV) mutations (FOXP1-gain, EGFR-gain, IL7R-gain, and NFKB1A-gain) in the breast tumor, combined with three clinical factors (luminal A, Her2 and Ki67 status). The tumor prediction model showed good discrimination of chemotherapy sensitivity for pCR and non-pCR with an AUC of 0.871 (95% CI, 0.797-0.927) in the training set, 0.771 (95% CI, 0.649-0.883) in the test set, and 0.726 (95% CI, 0.556-0.865) in an extra test set. This tumor prediction model can also effectively predict the prognosis of disease-free survival (DFS) with an AUC of 0.749 at 1 year and 0.830 at 3 years. We further screened the genes from the tumor prediction model to establish a unique personalized panel consisting of 9 individual somatic mutation genes to detect and analyze ctDNA. It was found that ctDNA positivity decreased with the passage of time during NAC, and ctDNA status can predict NAC response and metastasis recurrence. Finally, we constructed the chemotherapy prediction model combined with the tumor prediction model and pretreatment ctDNA levels, which has a better prediction effect of pCR with the AUC value of 0.961.ConclusionsIn this study, we established a chemotherapy predictive model with a non-invasive tool that is built based on genomic features, ctDNA status, as well as clinical characteristics for predicting pCR to recognize the responders and non-responders to NAC, and also predicting prognosis for DFS in breast cancer. Adding pretreatment ctDNA levels to a model containing gene profile mutation and clinical characteristics significantly improves stratification over the clinical variables alone.

Dataset Information

A model integrating tonic and antigen-triggered BCR signals to predict the survival of primary B cells.

Publications

A model integrating tonic and antigen-triggered BCR signals to predict the survival of primary B cells.

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