Sex differences in health and disease: A review of biological sex differences relevant to cancer with a spotlight on glioma

Massey, Susan Christine; Whitmire, Paula; Doyle, Tatum E; Ippolito, Joseph E.; Mrugala, Maciej M.; Hu, Leland; Canoll, Peter; Anderson, Alexander R.A.; Wilson, Melissa A.; Fitzpatrick, Susan M.; McCarthy, Margaret M.; Rubin, Joshua B.; Swanson, Kristin R.

doi:10.1016/j.canlet.2020.07.030

Cited by 34 publications

(19 citation statements)

References 142 publications

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“…A debated topic without a definitive answer today is the role of hormones [26][27][28][29][30]. Interestingly, we did note a small but significant difference related to age at diagnostic surgery, with female patients generally being older, which could suggest a role for hormonal factors.…”

Section: Discussionmentioning

confidence: 62%

Sex Differences in Glioblastoma—Findings from the Swedish National Quality Registry for Primary Brain Tumors between 1999–2018

Tavelin

Malmström

2022

JCM

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Sex disparities in glioblastoma (GBM) have received increasing attention. Sex-related differences for several molecular markers have been reported, which could impact on clinical factors and outcomes. We therefore analyzed data on all patients with GBM reported to the Swedish National Quality Registry for Primary Brain Tumors, according to sex, with a focus on prognostic factors and survival. All glioma patients registered during 20 years, from 1 January 1999 until 31 December 2018, with SNOMED codes 94403, 94413, and 94423, were analyzed. Chi2-test, log-rank test, and Kaplan–Meier analyses were performed. We identified 5243 patients, of which 2083 were females and 3160 males, resulting in a ratio of 1:1.5. We found sex related differences, with women having diagnostic surgery at a significantly higher age (p = 0.001). Women were also reported to have a worse preoperative performance status (PPS) (<0.001). There was no gender difference for the type of surgery performed. For women with radical surgery, overall survival was slightly better than for men (p = 0.045). The time period did not influence survival, neither for 1999–2005 nor 2006–2018, after temozolomide treatment was introduced (p = 0.35 and 0.10, respectively). In the multivariate analysis including sex, age, surgery, and PPS, a survival advantage was noted for women, but this was not clinically relevant (HR = 0.92, p = 0.006). For patients with GBM; sex-related differences in clinical factors could be identified in a population-based cohort. In this dataset, for survival, the only advantage noted was for women who had undergone radical surgery, although this was clinically almost negligible.

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Section: Discussionmentioning

confidence: 62%

Sex Differences in Glioblastoma—Findings from the Swedish National Quality Registry for Primary Brain Tumors between 1999–2018

Tavelin

Malmström

2022

JCM

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“…Due to the highly structured nature of the brain and heterogeneity of glioblastoma tumors, including hypoxia, necrosis, and extensive invasion through the brain, spatial considerations may be important. Finally, a growing subject of importance is understanding sex and age-based differences in the immunological responses of patient derived cell lines, and how those difference translate to an individual level in clinical applications [ 48 ]. In this work, all GBM cell lines were derived from male patients of a similar age (43, 52, and 59 years old), suggesting that future in vitro work would benefit from including a greater diversity of patients across both age and sex.…”

Section: Discussionmentioning

confidence: 99%

Dose-dependent thresholds of dexamethasone destabilize CAR T-cell treatment efficacy

Brummer

Yang

et al. 2022

PLoS Comput Biol

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Chimeric antigen receptor (CAR) T-cell therapy is potentially an effective targeted immunotherapy for glioblastoma, yet there is presently little known about the efficacy of CAR T-cell treatment when combined with the widely used anti-inflammatory and immunosuppressant glucocorticoid, dexamethasone. Here we present a mathematical model-based analysis of three patient-derived glioblastoma cell lines treated in vitro with CAR T-cells and dexamethasone. Advanced in vitro experimental cell killing assay technologies allow for highly resolved temporal dynamics of tumor cells treated with CAR T-cells and dexamethasone, making this a valuable model system for studying the rich dynamics of nonlinear biological processes with translational applications. We model the system as a nonautonomous, two-species predator-prey interaction of tumor cells and CAR T-cells, with explicit time-dependence in the clearance rate of dexamethasone. Using time as a bifurcation parameter, we show that (1) dexamethasone destabilizes coexistence equilibria between CAR T-cells and tumor cells in a dose-dependent manner and (2) as dexamethasone is cleared from the system, a stable coexistence equilibrium returns in the form of a Hopf bifurcation. With the model fit to experimental data, we demonstrate that high concentrations of dexamethasone antagonizes CAR T-cell efficacy by exhausting, or reducing the activity of CAR T-cells, and by promoting tumor cell growth. Finally, we identify a critical threshold in the ratio of CAR T-cell death to CAR T-cell proliferation rates that predicts eventual treatment success or failure that may be used to guide the dose and timing of CAR T-cell therapy in the presence of dexamethasone in patients.

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“…They have also been shown to affect data collected at all levels, clinical [ 6 ], imaging [ 13 ], and biospecimen [ 20 ], and in turn, analysis of downstream genomic and proteomic data sets ( Figure 2 ) [ 8 ]. Oncologic disciplines where this has been highlighted include large patient volume oncologic sites such as lung cancer [ 20 ], breast cancer [ 18 ], and head and neck cancer [ 21 ] but also lower volume sites such as central nervous system malignancies [ 22 , 23 ].…”

Section: Sources Of Bias and Class Imbalance In Large Scale Medical Datamentioning

confidence: 99%

Bias and Class Imbalance in Oncologic Data—Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets

Taşçı

Zhuge

Camphausen

et al. 2022

Cancers

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Recent technological developments have led to an increase in the size and types of data in the medical field derived from multiple platforms such as proteomic, genomic, imaging, and clinical data. Many machine learning models have been developed to support precision/personalized medicine initiatives such as computer-aided detection, diagnosis, prognosis, and treatment planning by using large-scale medical data. Bias and class imbalance represent two of the most pressing challenges for machine learning-based problems, particularly in medical (e.g., oncologic) data sets, due to the limitations in patient numbers, cost, privacy, and security of data sharing, and the complexity of generated data. Depending on the data set and the research question, the methods applied to address class imbalance problems can provide more effective, successful, and meaningful results. This review discusses the essential strategies for addressing and mitigating the class imbalance problems for different medical data types in the oncologic domain.

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Sex differences in health and disease: A review of biological sex differences relevant to cancer with a spotlight on glioma

Cited by 34 publications

References 142 publications

Sex Differences in Glioblastoma—Findings from the Swedish National Quality Registry for Primary Brain Tumors between 1999–2018

Sex Differences in Glioblastoma—Findings from the Swedish National Quality Registry for Primary Brain Tumors between 1999–2018

Dose-dependent thresholds of dexamethasone destabilize CAR T-cell treatment efficacy

Bias and Class Imbalance in Oncologic Data—Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets

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