Bioethical implications of current state practices of molecular diagnostics in neuropathology

Wang, Wesley; Howard, Dana; Giglio, Pierre; Thomas, Diana; Otero, José

doi:10.1093/neuonc/noac058

Cited by 5 publications

(5 citation statements)

References 10 publications

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“…Molecular classification is also lacking or uneven in many data sets. This likely reflects more profound disparities, as recently discussed by Wang et al, in bioethical implications of current practices of molecular diagnostics in neuropathology [ 79 ].…”

Section: Omics and Biospecimen Analysismentioning

confidence: 99%

The Next Frontier in Health Disparities—A Closer Look at Exploring Sex Differences in Glioma Data and Omics Analysis, from Bench to Bedside and Back

et al. 2022

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Sex differences are increasingly being explored and reported in oncology, and glioma is no exception. As potentially meaningful sex differences are uncovered, existing gender-derived disparities mirror data generated in retrospective and prospective trials, real-world large-scale data sets, and bench work involving animals and cell lines. The resulting disparities at the data level are wide-ranging, potentially resulting in both adverse outcomes and failure to identify and exploit therapeutic benefits. We set out to analyze the literature on women’s data disparities in glioma by exploring the origins of data in this area to understand the representation of women in study samples and omics analyses. Given the current emphasis on inclusive study design and research, we wanted to explore if sex bias continues to exist in present-day data sets and how sex differences in data may impact conclusions derived from large-scale data sets, omics, biospecimen analysis, novel interventions, and standard of care management.

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Section: Omics and Biospecimen Analysismentioning

confidence: 99%

The Next Frontier in Health Disparities—A Closer Look at Exploring Sex Differences in Glioma Data and Omics Analysis, from Bench to Bedside and Back

et al. 2022

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“…Furthermore, the logistics of e ciently scheduling patients between visits with neuro-oncology, neurosurgery, neuroradiology, and radiation oncology would signi cantly bene t from improved triaging methods that prioritize the most sensitive patients. Unfortunately, the accessibility of molecular markers is not equal across healthcare ecosystems (9)(10). Current discussions in the eld of neuro-oncology have pointed to major accessibility barriers and bioethical implications of pure reliance of molecular biomarkers of disease to understand cancer (10).…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, the accessibility of molecular markers is not equal across healthcare ecosystems (9)(10). Current discussions in the eld of neuro-oncology have pointed to major accessibility barriers and bioethical implications of pure reliance of molecular biomarkers of disease to understand cancer (10). Molecular pathology testing requires batching to reduce patient costs, a need that results in signi cant costs in turn-around-time for molecular assays (11).…”

Section: Introductionmentioning

confidence: 99%

Unsupervised machine learning models reveal predictive markers of glioblastoma patient survival using white blood cell counts prior to initiating chemoradiation

Wang

Kumm

et al. 2023

Preprint

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Purpose: Glioblastoma is a malignant brain tumor requiring careful clinical monitoring even after primary management. Personalized medicine has suggested use of various molecular biomarkers as predictors of patient prognosis or factors utilized for clinical decision making. However, the accessibility of such molecular testing poses a constraint for various institutes requiring identification of low-cost predictive biomarkers to ensure equitable care. Methods: We collected retrospective data from patients seen at Ohio State University, University of Mississippi, Barretos Cancer Hospital (Brazil), and FLENI (Argentina) who were managed for glioblastoma—amounting to nearly 600 patient records documented using REDCap. Patients were evaluated using an unsupervised machine learning approach comprised of dimensionality reduction and eigenvector analysis to visualize the inter-relationship of collected clinical features. Results: We discovered that white blood cell count of a patient during baseline planning for treatment was predictive of overall survival with an over 6-month median survival difference between the upper and lower quartiles of white blood cell count. By utilizing an objective PDL-1 immunohistochemistry quantification algorithm, we were further able to identify an increase in PDL-1 expression in glioblastoma patients with high white blood cell counts. Conclusion: These findings suggest that in a subset of glioblastoma patients the incorporation of white blood cell count and PDL-1 expression in the brain tumor biopsy as simple biomarkers predicting glioblastoma patient survival. Moreover, use of machine learning models allows us to visualize complex clinical datasets to uncover novel clinical relationships.

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“…Unfortunately, molecular marker accessibility is not equal across healthcare ecosystems. 9 , 10 Current discussions in the field of neuro-oncology have pointed to major accessibility barriers and bioethical implications of pure reliance on molecular biomarkers of disease to understand cancer. 10 Molecular pathology testing requires batching to reduce patient costs, resulting in significant turn-around time delays for molecular assays.…”

mentioning

confidence: 99%

“… 9 , 10 Current discussions in the field of neuro-oncology have pointed to major accessibility barriers and bioethical implications of pure reliance on molecular biomarkers of disease to understand cancer. 10 Molecular pathology testing requires batching to reduce patient costs, resulting in significant turn-around time delays for molecular assays. 11 In consequence, groups have sought ways to evaluate outcomes of glioma patients using surrogate measures such as neuro-cognitive testing, psychiatric examination, or image analysis of histology to predict molecular phenotypes as these turn-around-times are superior to those of molecular pathology.…”

mentioning

confidence: 99%

Unsupervised machine learning models reveal predictive clinical markers of glioblastoma patient survival using white blood cell counts prior to initiating chemoradiation

Wang,

Kumm,

et al. 2023

Neuro-Oncology Advances

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Background Glioblastoma is a malignant brain tumor requiring careful clinical monitoring even after primary management. Personalized medicine has suggested use of various molecular biomarkers as predictors of patient prognosis or factors utilized for clinical decision making. However, the accessibility of such molecular testing poses a constraint for various institutes requiring identification of low-cost predictive biomarkers to ensure equitable care. Methods We collected retrospective data from patients seen at Ohio State University, University of Mississippi, Barretos Cancer Hospital (Brazil), and FLENI (Argentina) who were managed for glioblastoma—amounting to 581 patient records documented using REDCap. Patients were evaluated using an unsupervised machine learning approach comprised of dimensionality reduction and eigenvector analysis to visualize the inter-relationship of collected clinical features. Results We discovered that serum white blood cell count of a patient during baseline planning for treatment was predictive of overall survival with an over 6-month median survival difference between the upper and lower quartiles of white blood cell count. By utilizing an objective PD-L1 immunohistochemistry quantification algorithm, we were further able to identify an increase in PD-L1 expression in glioblastoma patients with high serum white blood cell counts. Conclusions These findings suggest that in a subset of glioblastoma patients the incorporation of white blood cell count and PD-L1 expression in the brain tumor biopsy as simple biomarkers predicting glioblastoma patient survival. Moreover, machine learning models allow distillation of complex clinical datasets to uncover novel and meaningful clinical relationships.

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Bioethical implications of current state practices of molecular diagnostics in neuropathology

Cited by 5 publications

References 10 publications

The Next Frontier in Health Disparities—A Closer Look at Exploring Sex Differences in Glioma Data and Omics Analysis, from Bench to Bedside and Back

The Next Frontier in Health Disparities—A Closer Look at Exploring Sex Differences in Glioma Data and Omics Analysis, from Bench to Bedside and Back

Unsupervised machine learning models reveal predictive markers of glioblastoma patient survival using white blood cell counts prior to initiating chemoradiation

Unsupervised machine learning models reveal predictive clinical markers of glioblastoma patient survival using white blood cell counts prior to initiating chemoradiation

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