Precision medicine is an emerging practice by which clinicians aim to deliver a personalized treatment program to affected patients based on information gained from their individual clinical and biological profiles. In the context of precise cancer immunotherapy, multi-omics, high-throughput sequencing, big data, and other approaches serve to screen new predictive factors for immunotherapy response and prognosis in cancer patients.
Breast cancer and gynecological cancer (cervical cancer, endometrial cancer, and ovarian cancer) are prevalent malignancies in women worldwide. Many researchers have explored the value of predictors from the immune microenvironment, tumor genome, and liquid biopsies for cancer immunotherapy. As a result, numerous immune markers have been identified to help select appropriate immunotherapy regimens and monitor therapeutic efficacy and recurrence. However, there is no standardized protocol for their clinical application. While single markers are of limited predictive value for treatment efficacy, predictive models by the combination of multiple markers hold the potential to improve the accuracy of clinical judgments. We need to comprehensively analyze and reveal the dynamic immune responses in breast and gynecological cancers from multiple aspects, such as basic and clinical research and bioinformatics. We will then be better placed to develop novel predictive models by rational design and formulate combination strategies for cancer immunotherapy and patients’ prognosis.
The aim of this Research Topic is to discuss the latest knowledge made in the field of novel immune markers and predictive models related to immunotherapy response and prognosis of patients with breast and gynecological cancers.
We welcome submissions of Original Research and Review articles, covering but not limited to the following topics:
1. Immune markers and their regulatory mechanisms in breast and gynecological cancers;
2. Tumor immune-related predictive models and the underlying mechanisms in breast and gynecological cancers;
3. Liquid immune and metabolic predictors in breast and gynecological cancers;
4. Cancer molecular subtypes for breast and gynecological cancer immunotherapy;
5. Mechanisms of the interaction between anti-tumor therapies and the immune response in breast and gynecological cancers;
6. Novel predictors for the combination of immunotherapy and radiotherapy/chemotherapy.
Please NOTE: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of the scope for this section and will not be accepted as part of this Research Topic.
Precision medicine is an emerging practice by which clinicians aim to deliver a personalized treatment program to affected patients based on information gained from their individual clinical and biological profiles. In the context of precise cancer immunotherapy, multi-omics, high-throughput sequencing, big data, and other approaches serve to screen new predictive factors for immunotherapy response and prognosis in cancer patients.
Breast cancer and gynecological cancer (cervical cancer, endometrial cancer, and ovarian cancer) are prevalent malignancies in women worldwide. Many researchers have explored the value of predictors from the immune microenvironment, tumor genome, and liquid biopsies for cancer immunotherapy. As a result, numerous immune markers have been identified to help select appropriate immunotherapy regimens and monitor therapeutic efficacy and recurrence. However, there is no standardized protocol for their clinical application. While single markers are of limited predictive value for treatment efficacy, predictive models by the combination of multiple markers hold the potential to improve the accuracy of clinical judgments. We need to comprehensively analyze and reveal the dynamic immune responses in breast and gynecological cancers from multiple aspects, such as basic and clinical research and bioinformatics. We will then be better placed to develop novel predictive models by rational design and formulate combination strategies for cancer immunotherapy and patients’ prognosis.
The aim of this Research Topic is to discuss the latest knowledge made in the field of novel immune markers and predictive models related to immunotherapy response and prognosis of patients with breast and gynecological cancers.
We welcome submissions of Original Research and Review articles, covering but not limited to the following topics:
1. Immune markers and their regulatory mechanisms in breast and gynecological cancers;
2. Tumor immune-related predictive models and the underlying mechanisms in breast and gynecological cancers;
3. Liquid immune and metabolic predictors in breast and gynecological cancers;
4. Cancer molecular subtypes for breast and gynecological cancer immunotherapy;
5. Mechanisms of the interaction between anti-tumor therapies and the immune response in breast and gynecological cancers;
6. Novel predictors for the combination of immunotherapy and radiotherapy/chemotherapy.
Please NOTE: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of the scope for this section and will not be accepted as part of this Research Topic.
