Definitions of disease burden across the spectrum of metastatic castration-sensitive prostate cancer: comparison by disease outcomes and genomics

BACKGROUND: Several definitions have attempted to stratify metastatic castrate-sensitive prostate cancer (mCSPC) into low and high-volume states. However, at this time, comparison of these definitions is limited. Here we aim to compare definitions of metastatic volume in mCSPC with respect to clinical outcomes and mutational profiles. METHODS: We performed a retrospective review of patients with biochemically recurrent or mCSPC whose tumors underwent somatic targeted sequencing. 294 patients were included with median follow-up of 58.3 months. Patients were classified into low and high-volume disease per CHAARTED, STAMPEDE, and two numeric (≤3 and ≤5) definitions. Endpoints including radiographic progression-free survival (rPFS), time to development of castration resistance (tdCRPC), and overall survival (OS) were evaluated with Kaplan-Meier survival curves and log-rank test. The incidence of driver mutations between definitions were compared. RESULTS: Median OS and tdCRPC were shorter for high-volume than low-volume disease for all four definitions. In the majority of patients (84.7%) metastatic volume classification did not change across all four definitions. High volume disease was significantly associated with worse OS for all four definitions (CHAARTED: HR 2.89; p < 0.01, STAMPEDE: HR 3.82; p < 0.01, numeric ≤3: HR 4.67; p < 0.01, numeric ≤5: HR 3.76; p < 0.01) however, were similar for high (p = 0.95) and low volume (p = 0.79) disease across all four definitions. Those with discordant classification tended to have more aggressive clinical behavior and mutational profiles. Patients with low-volume disease and TP53 mutation experienced a more aggressive course with rPFS more closely mirroring high-volume disease. CONCLUSIONS: The spectrum of mCSPC was confirmed across four different metastatic definitions for clinical endpoints and genetics. All definitions were generally similar in classification of patients, outcomes, and genetic makeup. Given these findings, the simplicity of numerical definitions might be preferred, especially when integrating metastasis directed therapy. Incorporation of tumor genetics may allow further refinement of current metastatic definitions

It doesn’t always pay to be fit : success landscapes

Landscapes play an important role in many areas of biology, in which biological lives are deeply entangled. Here we discuss a form of landscape in evolutionary biology which takes into account (1) initial growth rates, (2) mutation rates, (3) resource consumption by organisms, and (4) cyclic changes in the resources with time. The long-term equilibrium number of surviving organisms as a function of these four parameters forms what we call a success landscape, a landscape we would claim is qualitatively different from fitness landscapes which commonly do not include mutations or resource consumption/changes in mapping genomes to the final number of survivors. Although our analysis is purely theoretical, we believe the results have possibly strong connections to how we might treat diseases such as cancer in the future with a deeper understanding of the interplay between resource degradation, mutation, and uncontrolled cell growth

Promoting data harmonization to evaluate vaccine hesitancy in LMICs: approach and applications

Abstract Background Factors influencing the health of populations are subjects of interdisciplinary study. However, datasets relevant to public health often lack interdisciplinary breath. It is difficult to combine data on health outcomes with datasets on potentially important contextual factors, like political violence or development, due to incompatible levels of geographic support; differing data formats and structures; differences in sampling procedures and wording; and the stability of temporal trends. We present a computational package to combine spatially misaligned datasets, and provide an illustrative analysis of multi-dimensional factors in health outcomes. Methods We rely on a new software toolkit, Sub-National Geospatial Data Archive (SUNGEO), to combine data across disciplinary domains and demonstrate a use case on vaccine hesitancy in Low and Middle-Income Countries (LMICs). We use data from the World Bank’s High Frequency Phone Surveys (HFPS) from Kenya, Indonesia, and Malawi. We curate and combine these surveys with data on political violence, elections, economic development, and other contextual factors, using SUNGEO. We then develop a stochastic model to analyze the integrated data and evaluate 1) the stability of vaccination preferences in all three countries over time, and 2) the association between local contextual factors and vaccination preferences. Results In all three countries, vaccine-acceptance is more persistent than vaccine-hesitancy from round to round: the long-run probability of staying vaccine-acceptant (hesitant) was 0.96 (0.65) in Indonesia, 0.89 (0.21) in Kenya, and 0.76 (0.40) in Malawi. However, vaccine acceptance was significantly less durable in areas exposed to political violence, with percentage point differences (ppd) in vaccine acceptance of -10 (Indonesia), -5 (Kenya), and -64 (Malawi). In Indonesia and Kenya, although not Malawi, vaccine acceptance was also significantly less durable in locations without competitive elections (-19 and -6 ppd, respectively) and in locations with more limited transportation infrastructure (-11 and -8 ppd). Conclusion With SUNGEO, researchers can combine spatially misaligned and incompatible datasets. As an illustrative example, we find that vaccination hesitancy is correlated with political violence, electoral uncompetitiveness and limited access to public goods, consistent with past results that vaccination hesitancy is associated with government distrust

Novel Structured Reporting Systems for Theranostic Radiotracers

Standardized reporting is more and more routinely implemented in clinical practice and such structured reports have a major impact on a large variety of medical fields, e.g. laboratory medicine, pathology, and, recently, radiology. Notably, the field of nuclear medicine is constantly evolving, as novel radiotracers for numerous clinical applications are developed. Thus, framework systems for standardized reporting in this field may a) increase clinical acceptance of new radiotracers, b) allow for inter- and intra-center comparisons for quality assurance, and c) may be used in (global) multi-center studies to ensure comparable results and enable efficient data abstraction. In the last two years, several standardized framework systems for positron emission tomography (PET) radiotracers with potential theranostic applications have been proposed. These include systems for prostate-specific membrane antigen (PSMA)-targeted PET agents for the diagnosis and treatment of prostate cancer (PCa) and somatostatin receptor (SSTR)-targeted PET agents for the diagnosis and treatment of neuroendocrine neoplasias. In the present review, those standardized framework systems for PSMA- and SSTR-targeted PET will be briefly introduced followed by an overview of their advantages and limitations. In addition, potential applications will be defined, approaches to validate such concepts will be proposed, and future perspectives will be discussed

Additional file 1 of Promoting data harmonization to evaluate vaccine hesitancy in LMICs: approach and applications

Additional file 1