Value of innovation in hematologic malignancies: a systematic review of published cost-effectiveness analyses

Saret, Cayla J.; Winn, Aaron N.; Shah, Gunjan L.; Parsons, Susan K.; Lin, Pei‐Jung; Cohen, Joshua T.; Neumann, Peter J.

doi:10.1182/blood-2014-07-592832

Cited by 32 publications

(39 citation statements)

References 16 publications

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“…1 Among females, breast cancer is the most common secondary malignancy, which develops approximately 10 years after the diagnosis of HL. 2 Prior studies have suggested that age at diagnosis of HL, time from initial therapy, and radiation dose and field size may affect the cumulative risks of developing secondary breast cancer.…”

Section: Resultsmentioning

confidence: 99%

“…Recently, Saret and colleagues, using data from the Tufts CEA Registry (www.cearegistry.org), 1 systematically reviewed published CEAs of hematologic cancer drugs and found that most CEA ratios fell below $50,000 (73%) or $100,000 (86%), 2 traditional thresholds of effectiveness. Although there are surely cases of transformative drugs (imatinib, rituximab), the authors' general conclusion that "innovative treatments for hematologic malignancies may provide reasonable value" is surprising.…”

Section: Introductionmentioning

confidence: 99%

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How should we assess the value of innovative drugs in oncology? Lessons from cost-effectiveness analyses

Prasad

Mailankody

2015

Blood

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How should we assess the value of innovative drugs in oncology? Lessons from cost-effectiveness analyses

Prasad

Mailankody

2015

Blood

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“…The outcome database could thus be used to not only inform future selection of therapeutic agents and their combinations based on response, survival and toxicities, but also aid in formulating fiscally responsible clinical strategies based on cost-effectiveness models. [13]…”

Section: A Potential Solutionmentioning

confidence: 99%

“…[13] The concept of precision medicine, as heretical as it may have initially sounded in cancer therapy, is not foreign in medicine. We test for antibiotic sensitivity, and we match blood for HLA subtypes in transfusion and transplantation medicine, and it is not surprising that our cancer patients are beginning to demand the same.…”

Section: Introductionmentioning

confidence: 99%

Future paradigms for precision oncology

et al. 2016

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Research has exposed cancer to be a heterogeneous disease with a high degree of inter-tumoral and intra-tumoral variability. Individual tumors have unique profiles, and these molecular signatures make the use of traditional histology-based treatments problematic. The conventional diagnostic categories, while necessary for care, thwart the use of molecular information for treatment as molecular characteristics cross tissue types.This is compounded by the struggle to keep abreast the scientific advances made in all fields of science, and by the enormous challenge to organize, cross-reference, and apply molecular data for patient benefit. In order to supplement the site-specific, histology-driven diagnosis with genomic, proteomic and metabolomics information, a paradigm shift in diagnosis and treatment of patients is required.While most physicians are open and keen to use the emerging data for therapy, even those versed in molecular therapeutics are overwhelmed with the amount of available data. It is not surprising that even though The Human Genome Project was completed thirteen years ago, our patients have not benefited from the information. Physicians cannot, and should not be asked to process the gigabytes of genomic and proteomic information on their own in order to provide patients with safe therapies. The following consensus summary identifies the needed for practice changes, proposes potential solutions to the present crisis of informational overload, suggests ways of providing physicians with the tools necessary for interpreting patient specific molecular profiles, and facilitates the implementation of quantitative precision medicine. It also provides two case studies where this approach has been used.

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“…Current challenges for the management of MM, including bortezomib drug treatment, are resistance development to drugs, increased unsustainable cost [9,10], lack of standardization in the therapeutic steps including stem cell transplantation, and morbidity and mortality due to drugs and/or ongoing resistant incurable neoplastic myeloma disease [4,5,11,12,13]. In silico studies are effective for key decision making during clinicopathological battles against the chronic course of MM [3,7,14,15].…”

Section: Introductionmentioning

confidence: 99%

Expression Profiles of the Individual Genes Corresponding to the Genes Generated by Cytotoxicity Experiments with Bortezomib in Multiple Myeloma

Ghasemi¹,

Alpsoy²,

Turk³

et al. 2016

Tjh

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Objective:Multiple myeloma (MM) is currently incurable due to refractory disease relapse even under novel anti-myeloma treatment. In silico studies are effective for key decision making during clinicopathological battles against the chronic course of MM. The aim of this present in silico study was to identify individual genes whose expression profiles match that of the one generated by cytotoxicity experiments for bortezomib.Materials and Methods:We used an in silico literature mining approach to identify potential biomarkers by creating a summarized set of metadata derived from relevant information. The E-MTAB-783 dataset containing expression data from 789 cancer cell lines including 8 myeloma cell lines with drug screening data from the Wellcome Trust Sanger Institute database obtained from ArrayExpress was “Robust Multi-array analysis” normalized using GeneSpring v.12.5. Drug toxicity data were obtained from the Genomics of Drug Sensitivity in Cancer project. In order to identify individual genes whose expression profiles matched that of the one generated by cytotoxicity experiments for bortezomib, we used a linear regression-based approach, where we searched for statistically significant correlations between gene expression values and IC50 data. The intersections of the genes were identified in 8 cell lines and used for further analysis.Results:Our linear regression model identified 73 genes and some genes expression levels were found to very closely correlated with bortezomib IC50 values. When all 73 genes were used in a hierarchical cluster analysis, two major clusters of cells representing relatively sensitive and resistant cells could be identified. Pathway and molecular function analysis of all the significant genes was also investigated, as well as the genes involved in pathways.Conclusion:The findings of our present in silico study could be important not only for the understanding of the genomics of MM but also for the better arrangement of the targeted anti-myeloma therapies, such as bortezomib.

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Value of innovation in hematologic malignancies: a systematic review of published cost-effectiveness analyses

Cited by 32 publications

References 16 publications

How should we assess the value of innovative drugs in oncology? Lessons from cost-effectiveness analyses

How should we assess the value of innovative drugs in oncology? Lessons from cost-effectiveness analyses

Future paradigms for precision oncology

Expression Profiles of the Individual Genes Corresponding to the Genes Generated by Cytotoxicity Experiments with Bortezomib in Multiple Myeloma

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