Research Status and Molecular Mechanism of the Traditional Chinese Medicine and Antitumor Therapy Combined Strategy Based on Tumor Microenvironment

Treatment of malignant tumors encompasses multidisciplinary comprehensive diagnosis and treatment and reasonable combination and arrangement of multidisciplinary treatment, which is not a simple superimposition of multiple treatment methods, but a comprehensive consideration of the characteristics and specific conditions of the patients and the tumor. The mechanism of tumor elimination by restoring the body's immune ability is consistent with the concept of "nourishing positive accumulation and eliminating cancer by itself" in traditional Chinese medicine (TCM). The formation and dynamic changes in the tumor microenvironment (TME) involve many different types of cells and multiple signaling pathways. Those changes are similar to the multitarget and bidirectional regulation of immunity by TCM. Discussing the relationship and mutual influence of TCM and antitumor therapy on the TME is a current research hotspot. TCM has been applied in the treatment of more than 70% of cancer patients in China. Data have shown that TCM can significantly enhance the sensitivity to chemotherapeutic drugs, enhance tumor-suppressing effects, and significantly improve cancer-related fatigue, bone marrow suppression, and other adverse reactions. TCM treatments include the application of Chinese medicine monomers, extracts, classic traditional compound prescriptions, listed compound drugs, self-made compound prescriptions, as well as acupuncture and moxibustion. Studies have shown that the TCM functional mechanism related to the positive regulation of cytotoxic T cells, natural killer cells, dendritic cells, and interleukin-12, while negatively regulating of regulatory T cells, tumor-associated macrophages, myeloid-derived suppressive cells, PD-1/PD-L1, and other immune regulatory factors. However, the application of TCM in cancer therapy needs further study and confirmation. This article summarizes the existing research on the molecular mechanism of TCM regulation of the TME and provides a theoretical basis for further screening of the predominant population. Moreover, it predicts the effects of the combination of TCM and antitumor therapy and proposes further developments in clinical practice to optimize the combined strategy.

A Pre-Test-Post-Test Trial of a Breast Cancer Risk Report for Women in Their 40s

INTRODUCTION

Guidelines recommend individualized breast cancer screening and prevention interventions for women in their 40s. Yet, few primary care clinicians assess breast cancer risk.

STUDY DESIGN

Pretest-Posttest trial.

SETTING/PARTICIPANTS

Women aged 40-49 years were recruited from one large Boston-based academic primary care practice between July 2017 and April 2019.

INTERVENTION

Participants completed a pretest, received a personalized breast cancer risk report, saw their primary care clinician, and completed a posttest.

MAIN OUTCOME MEASURES

Using mixed effects models, changes in screening intentions (0-100 scale [0=will not screen to 100=will screen]), mammography knowledge, decisional conflict, and receipt of screening were examined. Analyses were conducted from June 2019 to February 2020.

RESULTS

Patient (n=337) mean age was 44.1 (SD=2.9) years, 61.4% were non-Hispanic white, and 76.6% were college graduates; 306 (90.5%) completed follow-up (203 with 5-year breast cancer risk <1.1%). Screening intentions declined from pre- to post-visit (79.3 to 68.0, p<0.0001), especially for women with 5-year risk <1.1% (77.2 to 63.3, p<0.0001), but still favored screening. In the 2 years prior, 37.6% had screening mammography compared with 41.8% over a mean 16 months follow-up (p=0.17). Mammography knowledge increased and decisional conflict declined. Eleven (3.3%) women met criteria for breast cancer prevention medications (ten discussed medications with their clinicians), 22 (6.5%) for MRI (19 discussed MRI with their clinician), and 67 (19.8%) for genetic counseling (47 discussed with the clinician).

CONCLUSIONS

Receipt of a personalized breast cancer report was associated with women in their 40s making more-informed and less-conflicted mammography screening decisions and with high-risk women discussing breast cancer prevention interventions with clinicians.

TRIAL REGISTRATION

This study is registered at www.clinicaltrials.govNCT03180086.

Management of women at increased risk for breast cancer secondary to high-risk proliferative lesions and family history of the disease

Women with breast biopsies showing high-risk proliferative lesions such as atypical hyperplasia (AH) and lobular carcinoma in situ (LCIS) have an increased risk of developing breast cancer. Other factors including age, family history of breast cancer, and extent of AH may play a role in increasing breast cancer risk. In addition to women with AH, there is a subset of women with a positive family history of breast cancer, without a known germline mutation, which places them also at an increased risk for breast cancer. Clinical management, screening, chemoprevention, and surgical risk-reduction are discussed in this review to inform the management of these high-risk women. Advanced imaging technology, pharmacologic research into different targets, and innovations in breast reconstruction are changing the way in which patients are counseled of their individual risk.

Prognosis in pathology: Are we "prognosticating" or only establishing correlations between independent variables and survival? A study with various analytics cautions about the overinterpretation of statistical results

Survival data from 225 patients with resected pulmonary typical carcinoids were analyzed with Kaplan-Meier statistics (K-M) and "deep learning" methods to illustrate the difference between establishing "correlations" and "prognostications". Cases were stratified into G1 and G2 classes using a ≤5% Ki-67% cut-point. Overall survival, number of patients at risk and 95% confidence intervals (CI) were estimated for the two classes. Seven neural network models (NN) were developed with GMDH Shell 3.8.2 and Statgraphics Centurion 18.1 software, using variable prior probabilities and different numbers of training vs testing cases. The NNs used age, sex, and pTNM, G1 and G2 as input neurons and "alive" and "dead" as output neurons. Areas under the curve (AUC) and other performance measures were evaluated for all models. Log-rank test showed a significant difference in overall survival between G1 and G2 (p < 0.001). However, 95% CI estimates showed considerable variability in survival at different time intervals. Including the number of patients at risk at different time intervals showed that most G2 patients had been censored by 100 weeks. The NN models provided variable "prognostications", with AUC ranging from 0.5 to 1 and variability in the sensitivity, specificity, and other performance measures. The results illustrate the limitations of survival statistics and NNs in predicting the prognosis of individual patients. The need for pathologists not to overinterpret the finding of significant correlations as "prognostic" or "predictive" for individual patients is discussed.