The global burden of adolescent and young adult cancer in 2019: a systematic analysis for the Global Burden of Disease Study 2019

Background: In estimating the global burden of cancer, adolescents and young adults with cancer are often overlooked, despite being a distinct subgroup with unique epidemiology, clinical care needs, and societal impact. Comprehensive estimates of the global cancer burden in adolescents and young adults (aged 15–39 years) are lacking. To address this gap, we analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, with a focus on the outcome of disability-adjusted life-years (DALYs), to inform global cancer control measures in adolescents and young adults. Methods: Using the GBD 2019 methodology, international mortality data were collected from vital registration systems, verbal autopsies, and population-based cancer registry inputs modelled with mortality-to-incidence ratios (MIRs). Incidence was computed with mortality estimates and corresponding MIRs. Prevalence estimates were calculated using modelled survival and multiplied by disability weights to obtain years lived with disability (YLDs). Years of life lost (YLLs) were calculated as age-specific cancer deaths multiplied by the standard life expectancy at the age of death. The main outcome was DALYs (the sum of YLLs and YLDs). Estimates were presented globally and by Socio-demographic Index (SDI) quintiles (countries ranked and divided into five equal SDI groups), and all estimates were presented with corresponding 95% uncertainty intervals (UIs). For this analysis, we used the age range of 15–39 years to define adolescents and young adults. Findings: There were 1·19 million (95% UI 1·11–1·28) incident cancer cases and 396 000 (370 000–425 000) deaths due to cancer among people aged 15–39 years worldwide in 2019. The highest age-standardised incidence rates occurred in high SDI (59·6 [54·5–65·7] per 100 000 person-years) and high-middle SDI countries (53·2 [48·8–57·9] per 100 000 person-years), while the highest age-standardised mortality rates were in low-middle SDI (14·2 [12·9–15·6] per 100 000 person-years) and middle SDI (13·6 [12·6–14·8] per 100 000 person-years) countries. In 2019, adolescent and young adult cancers contributed 23·5 million (21·9–25·2) DALYs to the global burden of disease, of which 2·7% (1·9–3·6) came from YLDs and 97·3% (96·4–98·1) from YLLs. Cancer was the fourth leading cause of death and tenth leading cause of DALYs in adolescents and young adults globally. Interpretation: Adolescent and young adult cancers contributed substantially to the overall adolescent and young adult disease burden globally in 2019. These results provide new insights into the distribution and magnitude of the adolescent and young adult cancer burden around the world. With notable differences observed across SDI settings, these estimates can inform global and country-level cancer control efforts. Funding: Bill & Melinda Gates Foundation, American Lebanese Syrian Associated Charities, St Baldrick's Foundation, and the National Cancer Institute

The global burden of adolescent and young adult cancer in 2019 : a systematic analysis for the Global Burden of Disease Study 2019

Background In estimating the global burden of cancer, adolescents and young adults with cancer are often overlooked, despite being a distinct subgroup with unique epidemiology, clinical care needs, and societal impact. Comprehensive estimates of the global cancer burden in adolescents and young adults (aged 15-39 years) are lacking. To address this gap, we analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, with a focus on the outcome of disability-adjusted life-years (DALYs), to inform global cancer control measures in adolescents and young adults. Methods Using the GBD 2019 methodology, international mortality data were collected from vital registration systems, verbal autopsies, and population-based cancer registry inputs modelled with mortality-to-incidence ratios (MIRs). Incidence was computed with mortality estimates and corresponding MIRs. Prevalence estimates were calculated using modelled survival and multiplied by disability weights to obtain years lived with disability (YLDs). Years of life lost (YLLs) were calculated as age-specific cancer deaths multiplied by the standard life expectancy at the age of death. The main outcome was DALYs (the sum of YLLs and YLDs). Estimates were presented globally and by Socio-demographic Index (SDI) quintiles (countries ranked and divided into five equal SDI groups), and all estimates were presented with corresponding 95% uncertainty intervals (UIs). For this analysis, we used the age range of 15-39 years to define adolescents and young adults. Findings There were 1.19 million (95% UI 1.11-1.28) incident cancer cases and 396 000 (370 000-425 000) deaths due to cancer among people aged 15-39 years worldwide in 2019. The highest age-standardised incidence rates occurred in high SDI (59.6 [54.5-65.7] per 100 000 person-years) and high-middle SDI countries (53.2 [48.8-57.9] per 100 000 person-years), while the highest age-standardised mortality rates were in low-middle SDI (14.2 [12.9-15.6] per 100 000 person-years) and middle SDI (13.6 [12.6-14.8] per 100 000 person-years) countries. In 2019, adolescent and young adult cancers contributed 23.5 million (21.9-25.2) DALYs to the global burden of disease, of which 2.7% (1.9-3.6) came from YLDs and 97.3% (96.4-98.1) from YLLs. Cancer was the fourth leading cause of death and tenth leading cause of DALYs in adolescents and young adults globally. Interpretation Adolescent and young adult cancers contributed substantially to the overall adolescent and young adult disease burden globally in 2019. These results provide new insights into the distribution and magnitude of the adolescent and young adult cancer burden around the world. With notable differences observed across SDI settings, these estimates can inform global and country-level cancer control efforts. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd.Peer reviewe

Data from: An analysis toolbox to explore mesenchymal migration heterogeneity reveals adaptive switching between distinct modes

Mesenchymal (lamellipodial) migration is heterogeneous, although whether this reflects progressive variability or discrete, 'switchable' migration modalities, remains unclear. We present an analytical toolbox, based on quantitative single-cell imaging data, to interrogate this heterogeneity. Integrating supervised behavioral classification with multivariate analyses of cell motion, membrane dynamics, cell-matrix adhesion status and F-actin organization, this toolbox here enables the detection and characterization of two quantitatively distinct mesenchymal migration modes, termed 'Continuous' and 'Discontinuous'. Quantitative mode comparisons reveal differences in cell motion, spatiotemporal coordination of membrane protrusion/retraction, and how cells within each mode reorganize with changed cell speed. These modes thus represent distinctive migratory strategies. Additional analyses illuminate the macromolecular- and cellular-scale effects of molecular targeting (fibronectin, talin, ROCK), including 'adaptive switching' between Continuous (favored at high adhesion/full contraction) and Discontinuous (low adhesion/inhibited contraction) modes. Overall, this analytical toolbox now facilitates the exploration of both spontaneous and adaptive heterogeneity in mesenchymal migration

Analysis of relationship structures between features and Cell Speed.

<p>(A) A Venn diagram summarizes the frequencies of particular relationship structures between features and changing values of Cell Speed. Each circle of the Venn diagram contains two colors, indicating the Cell Speed quintiles (red, slow; yellow, moderate; green, fast) between which feature values were compared via pairwise testing (slow <i>versus</i> moderate; moderate <i>versus</i> fast; slow <i>versus</i> fast). Segments of the Venn diagram indicate which combinations of pairwise tests (Wilcoxon rank sum test with Bonferroni correction) resulted in statistically discernable differences. The number of features is indicated for which a given combination of tests showed significance. To aid interpretation, schematic archetypes are included to indicate the type of correspondence that is observed between each feature and Cell Speed. Where boxes do not overlap in the Y-axis, statistically significant differences were detected between feature values in the corresponding Cell Speed groups. Note that the actual sign of feature responses may be inverted compared to these generalized archetypes. (B-K) The observed archetypes from (A) are illustrated to the left and examples of features corresponding to each archetype are shown in boxes to the right. Comparison brackets in each panel indicate significant differences (P<0.001 after Bonferroni correction, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135204#sec012" target="_blank">Materials and Methods</a>). Box plots show quartiles. Outliers are not shown. Notches are placed at the median value , where <i>n</i> is the number of observations in each quintile (approximation of the 95% confidence interval of the median). (B) Mean Cell-Matrix Adhesion Complex (CMAC) Lifetime and (C) median of CMAC Mean paxillin intensity per cell both show stably monotonic decreases across all Cell Speeds. (D) Quartile dispersion (QD) of CMAC compactness is significantly lower in fast than in slow cells. (E) The median rate of change in CMAC area is negative meaning that CMACs are shrinking. This shrinking is more rapid in fast than in slow cells. (F) Cell major Axis is significantly higher in moderate than in slow cell observations, but not between any other groups. (G) QD of CMAC to cell border distance at each time point increases significantly between slow and moderate but not between moderate and fast cells; (H) paxillin-actin colocalization on a cell level decreases in a corresponding way. (I) Coefficient of variation (CoV) of CMAC Speed is significantly lower in fast than in moderate cell observations. (J) The number of CMACs per cell at each time point and (K) the total area of the cell at each time point both show a monotonic decrease between moderate and fast cells.</p

Quantitative imaging and analysis reveals that slow migrating cells increase in area while fast migrating cells decrease in area.

<p>(A) Live H1299 P/L cells expressing EGFP-paxillin and RubyRed-LifeAct were imaged and segmented in order to identify individual cells and their cohort of Cell-Matrix Adhesion Complexes (CMACs). EGFP-paxillin and RubyRed-LifeAct channels are displayed in inverted gray scale (high intensity is black). The segmentation image shows the EGFP-paxillin channel with the cell border identified in blue, CMAC borders in red and CMAC major axes in cyan. Scale bar: 10 μm. (B) By comparing consecutive frames, protrusions (green), retractions (red), short-lived (blue) and stable (gray) regions were identified, as described in Materials and Methods. White circles indicate the locations of CMACs. (C) The average size of each type of dynamic cell region (per frame) in the dataset was calculated and stratified per quintile of Cell Speed. The total height of each bar (the sum of protrusion, retraction and short-lived areas per frame) corresponds to the Dynamic Cell Area. We observed that this quantity increases with Cell Speed. (D) Scatter Plot: The net value of protrusion minus retraction areas (delta (Δ) Cell Area, μm²) is shown as a function of Cell Speed. The density of observations at a given Cell Speed (Cell Speed conditional density) is color-coded following log transformation, enabling better observation of trends in Δ Cell Area values given changing Cell Speed. A linear fit (Pearson’s correlation coefficient <i>r</i> = -0.27, <i>P</i> = 4.39·10⁻²³³) of the relationship is indicated (black line). Probability distributions: of Cell Speed (X axis), and; Δ Cell Area (Y axis, right) show a heavy-tailed distribution of Cell Speed where most cells are slow moving and few are fast moving, while Cell Area changes are approximately symmetrical overall. Notice, however, that the relatively few fast moving cells are decreasing in area substantially (retraction area is much larger than protrusion area), while the numerous slow moving cells only grow slightly, on average. (E) Mean Cell Speed autocorrelation coefficients (Y axis) are plotted conditioned upon the mean Cell Speed (X axis) of each cell. Autocorrelation values were calculated per cell trajectory with a maximum time lag of 1 h (12 frames). A linear fit (Pearson’s correlation coefficient <i>r</i> = -0.32, <i>P</i> = 0.00037) of the relationship indicates that autocorrelation of Cell Speed is lower in cells with higher average Cell Speeds. This indicates that the temporal persistence of Cell Speed correlates negatively to Cell Speed itself. See also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135204#pone.0135204.s005" target="_blank">S1 Movie</a>, showing the same cell as in A-B.</p

Methodological overview.

<p>(A) Schematic of live H1299 cells expressing EGFP-paxillin (Green, upper left) and RubyRed-LifeAct (red, upper right) that were imaged at 5 min intervals for 8 h. (Lower left) Segmentation identified the cell border (blue) and Cell-Matrix Adhesion Complexes (CMACs) (red). These were tracked over time, allowing extraction of static and dynamic <i>features</i> describing cell, CMAC and F-actin characteristics per cell, per time point. (Lower right) Consecutive frames were compared and protrusive (green), retractive (red), short-lived (blue) and stable (gray) regions were identified. The identification of these regions allowed for per cell quantification of both <i>processes</i> of interest, membrane dynamics and cell migration. (B) Dynamic Cell Area, defined as the total non-stable area (total area of protrusions, retractions and short-lived regions), is linearly dependent on Cell Speed. Observations were stratified into equally sized Cell Speed groups: slow (red); moderate (yellow); or fast (green). (C) To establish a Cell Speed-independent measure of membrane activity, Corrected Membrane Dynamics (CMD) was calculated by subtracting the linear dependence between Cell Speed and Dynamic Cell Area. CMD data was also stratified into equal groups with: low (blue); intermediate (gray), or; high (pink) activity. (D-E) Venn diagrams summarize the frequencies of particular relationship structures between features and changing values of Cell Speed (D) or CMD (E). Each circle of the Venn diagrams contain two colors (as defined in B-C), indicating the pairs of Cell Speed or CMD groups between which feature values were statistically compared, e.g. slow (red) vs moderate (yellow) migrating cells in D. Segments of the Venn diagram indicate which combinations of these pairwise statistical comparisons revealed significant differences in feature values. To aid interpretation, schematic archetypes (small graphical insets) are included to indicate the generalized relationship structure that is observed between each feature (Y-axes) and either Cell Speed or CMD (X-axes). Where boxes do not overlap in the Y-axis, statistically significant differences were detected between feature values in the corresponding Cell Speed or CMD groups. For example, features associated with the category represented in the lower segment of D would have significantly different values when comparing cells migrating slow (red) <i>versus</i> fast (green), but not between slow and moderate, or moderate and fast migrating cells. Hence, change in the values of such features would proceed slowly but progressively over the full range of observed Cell Speeds. Note that the actual sign of feature responses may be inverted compared to these generalized archetypes. (F) Finally, Cell Speed- and CMD-related features were identified and compared using a stringent approach. This resulted in lists of features that are related to Cell Speed, to CMD or to both processes. Of those features related to both processes, some showed equivalent responses (feature 1 example), while others showed distinct and even opposite dependencies to each process (feature 2 example).</p

Speed and Corrected Membrane Dynamics are independent.

<p>(A) Values for Cell Speed and Dynamic Cell Area are plotted. The density of observations at a given Cell Speed (Cell Speed conditional density) is color-coded following log transformation, enabling better observation of trends in Dynamic Cell Area values given changing Cell Speed. A linear fit (Pearson’s correlation coefficient <i>r</i> = 0.74) of the relationship between Cell Speed and Dynamic Cell Area is indicated (black line). (B) Scatter plot of Cell Speed versus Dynamic Cell Area. Cell observations are divided into Cell Speed quintiles as indicated by color scaling: red (slow, 0%-20%); orange (20%-40%); yellow (moderate, 40%-60%); light-green (60%-80%); green (fast, 80%-100%). (C) Box plots show the median and variability of Dynamic Cell Area per speed quintile. The box shows the quartiles and the whiskers show 1.5 times the interquartile range (IQR). Outliers are not shown. Notches are placed at the median value , where <i>n</i> is the number of observations in each quintile (approximation of the 95% confidence interval of the median). By comparing these measures for each Cell Speed quintile we observed a monotonic increase of Dynamic Cell Area with Cell Speed. Colors as in (B). (D) Corrected Membrane Dynamics (CMD) is defined by subtracting the linear relationship between Cell Speed and Dynamic Cell Area from all observations. Conditional density color-coding and linear fit are calculated and displayed as in (A). (E) Scatter plot of Cell Speed versus CMD, color-coded by Cell Speed quintiles as in B. (F) Box plots as in (C) based on Cell Speed quintiles show that there is no trend in CMD as a function of Cell Speed. Box plots structured as in C. Colors as in (B). (G) Scatter plot of Cell Speed versus CMD. The observations were divided into equally sized quintiles of CMD as indicated by color scaling: blue (low, 0%-20%); purple (20%-40%); grey (intermediate, 40%-60%); dark-pink (60%-80%); pink (high, 80%-100%). (H) Box plots as in (C) showing median and variability of Cell Speed per CMD quintile. Colors as in (G). (I) Box plots as in (C) showing median and variability of Dynamic Cell Area per CMD quintile. Colors as in (G).</p

Stringent selection of features related to Cell Speed and or Corrected Membrane Dynamics.

<p>Canonical Vector Analysis (CVA) was used for multivariate separation of slow (red), moderate (yellow) and fast (green) Cell Speed groups (A), and low (blue), intermediate (gray) and high (pink) Corrected Membrane Dynamics (CMD) groups (B), respectively. (C) The features were categorized by whether they contributed to each separation, as well as whether they showed a significant difference between groups (determined via Kruskal-Wallis multiple group testing). According to this two-step criteria, 15 variables contributed to a difference only between Cell Speed related groups, 7 variables contributed to the difference between CMD related groups only and 33 variables contributed to both Cell Speed and CMD related differences. (D-E) Cell Speed related responses. (D) The coefficient of variation indicates heterogeneity in Cell-Matrix Adhesion Complex (CMAC) perimeter distribution. This heterogeneity decreases with Cell Speed but is not significantly changed with CMD. (E) The median rate of change in CMAC paxillin intensity (frame-to-frame difference) shows a concerted decrease in relation to increased Cell Speed, but not in response to changing CMD. (F-G) Responses related to CMD. (F) Median LifeAct intensity per CMAC. This feature is independent of Cell Speed, but decreases with higher CMD. (G) Mean LifeAct intensity per cell is also independent of Cell Speed but decreases with increased CMD. (H-I) Responses related to both Cell Speed and CMD. (H) Median of mean CMAC paxillin intensity per CMAC decreases with both increased Cell Speed and CMD. (I) Number of CMACs per cell decreases with increased Cell Speed but increases with CMD.</p