Cancer types with high numbers of driver events are largely preventable

Population attributable fractions data

Population attributable fractions (PAFs) combining all risk factors were obtained directly from published open-access articles separately for each cancer type and sex.

PAFs for USA were obtained from Table 2 in Islami et al. (2018). Briefly, Islami et al. applied a simulation method in which numbers from repeated draws were generated for all relative risks, exposure levels, and numbers of cancer cases and deaths, allowing for uncertainty in the data. The simulation process was replicated 1000 times for each sex and age-group stratum. The numbers from repeated draws were used to calculate the proportion and number of attributable cancer cases and deaths and their 95% confidence intervals. By using exposure prevalence (Pi) at the exposure category i and the corresponding relative risks (RRi), PAFs for categorical exposure variables for each stratum of sex and age group were calculated using the following formula: ${\displaystyle PAF=\frac{\sum P_i\left(R{R}_i-1\right)}{\sum P_i\left(R{R}_i-1\right)+1}}$

Islami et al. used the above approximate formula for all associations, with a few exceptions. All cervical cancers were attributed to human papillomavirus infection and all Kaposi sarcomas to Kaposi sarcoma herpesvirus/human herpesvirus 8 infection. Because of the lack of data on anal human papillomavirus infection, 88% of anal cancers were attributed to human papillomavirus 10 before applying the simulation method. PAFs for excess ultraviolet radiation-associated melanomas were estimated using the difference between observed melanoma incidence rates by sex and age group in the general population and the rates in blacks. To calculate the overall attributable proportion and number of cancer cases or deaths for a given cancer type when there were several risk factors, it was assumed that the risk factors had no interactions.

PAFs for England were obtained from Table 2 in Brown et al. (2018). Briefly, Brown et al. calculated PAFs for most risk factors using the same standard formula as Islami et al. PAFs for Epstein–Barr virus, human papillomavirus, Kaposi sarcoma herpesvirus/human herpesvirus 8, and diagnostic radiation were obtained by Brown et al. from other published studies. PAFs for all risk factors combined, for each cancer type and for all cancers combined, were obtained by first applying the first relevant PAF to the total number of observed cases, to obtain the number of cases attributable to that factor only. Each subsequent PAF was applied only to the number of observed cases not yet explained by the risk factors applied earlier. This aggregation method avoids overestimating PAFs for all risk factors combined but does not account for cases caused by exposure to risk factors in combination, e.g., the synergistic effect.

PAFs for Australia were obtained from Table 2 in Whiteman et al. (2015a). Briefly, Whiteman et al. calculated PAFs using the same standard formula as Islami et al. and Brown et al. for all risk factors with the exception of some infections, smoking and ultaviolet radiation (Whiteman et al., 2015b). This method does not permit estimation of the fractions of cancers arising through synergistic effects of causal factors (Whiteman et al., 2015b). For Epstein–Barr virus and human papillomavirus, where mechanistic knowledge strongly suggests that the presence of infection in a cancer is sufficient to infer that infection caused the cancer, the PAF was assumed to be equivalent to the prevalence of viral DNA in tumour cells (Antonsson et al., 2015). Kaposi sarcoma herpesvirus is recognised as a necessary cause of Kaposi sarcoma, and thus the PAF was assumed to be 100% (Antonsson et al., 2015). The number of lung cancer cases expected in Australian adults in the absence of smoking was calculated by applying the estimated incidence rates of lung cancer in never smokers in the CPS II study to the population of Australia (Pandeya et al., 2015). The number and percentage of lung cancer cases attributable to smoking was then calculated by subtracting the expected number of cases from those actually observed (Pandeya et al., 2015). For the primary melanoma analysis, the difference was estimated between the observed numbers of melanoma cases in Australian residents (i.e., ‘exposed’ to high ambient ultraviolet radiation in Australia) and the expected number of cases assuming the population was exposed to levels of ambient ultraviolet radiation experienced by an ‘ancestral’ population for many Australians - the UK population (Olsen et al., 2015).

No modification or processing of PAF data was performed.

USA incidence data

United States Cancer Statistics Public Information Data: Incidence 1999–2012 was downloaded from the Centers for Disease Control and Prevention Wide-ranging OnLine Data for Epidemiologic Research (CDC WONDER) online database (http://wonder.cdc.gov/cancer-v2012.HTML). The United States Cancer Statistics (USCS) are the official federal statistics on cancer incidence from registries having high-quality data for 50 states and the District of Columbia. Data are provided by The Centers for Disease Control and Prevention National Program of Cancer Registries (NPCR) and The National Cancer Institute Surveillance, Epidemiology and End Results (SEER) program. Results were grouped by 5-year Age Groups and Crude Rates were selected as output. Crude Rates are calculated as the number of new cancer cases reported each calendar year per 100 000 population in each 5-year age group. The data were downloaded separately for males and females for each cancer type listed in Table 2 in the publication by Islami et al. (2018).

England incidence data

England cancer incidence data were downloaded from the European Cancer Information System (ECIS) Data explorer (https://ecis.jrc.ec.europa.eu/explorer.php?0-11-UK2-2244-1,23-All6-5,845-1999,20127-2CRatesByCancerX0_10-ASR_EU_NEW). The ECIS database contains the aggregated output and the results computed from data submitted by population-based European cancer registries participating in Europe to the European Network of Cancer Registries – Joint Research Centre (ENCR-JRC) project on ”Cancer Incidence and Mortality in Europe”. Years of observation were limited to 1999–2012 period, to match the USA data. Incidence is calculated as the number of new cancer cases reported each calendar year per 100,000 population in each 5-year age group. The data were downloaded separately for males and females for each cancer type listed in Table 2 in the publication by Brown et al. (2018), except for vulva and vagina cancers, as their selection was not possible in ECIS Data explorer.

Australia incidence data

Australia cancer incidence data were downloaded from the Cancer Incidence in Five Continents (CI5) Volume XI Age-specific curves Online Analysis tool (http://ci5.iarc.fr/CI5-XI/Pages/age-specific-curves_sel.aspx). CI5 is published approximately every five years by the International Agency for Research on Cancer (IARC) and the International Association of Cancer Registries (IACR) and provides comparable high quality statistics on the incidence of cancer from cancer registries around the world. Volume XI contains information from 343 cancer registries in 65 countries for cancers diagnosed from 2008 to 2012. Incidence is calculated as the number of new cancer cases reported each calendar year per 100 000 population in each 5-year age group. The data were downloaded separately for males and females for each cancer type listed in Table 2 in the publication by Whiteman et al. (2015a).

Estimation of the number of driver events per tumor

For analysis, the incidence data were imported into GraphPad Prism 9 (http://www.graphpad.com/). The following age groups were selected: “5–9 years”, “10–14 years”, “15–19 years”, “20–24 years”, “25–29 years”, “30–34 years”, “35–39 years”, “40–44 years”, “45–49 years”, “50–54 years”, “55–59 years”, “60–64 years “, “65–69 years”, “70–74 years”, “75–79 years” and “80–84 years”. Prior age groups were excluded due to possible contamination by childhood subtype incidence, and “85+ years” was excluded due to an undefined age interval. If in the first several age groups (“5–9 years”, “10–14 years”, “15–19 years”) incidence initially decreased with age, reflecting contamination by childhood subtype incidence, these values were removed until a steady increase in incidence was detected. The middle age of each age group was used for the x values, e.g., 17.5 for the “15–19 years” age group. Incidence (new cancer cases per calendar year per 100,000 population) for each age group and each cancer type was used for the y values. Data for different countries, as well as for males and females, were analyzed separately. Data were analyzed with Nonlinear regression using the following User-defined equation for the gamma distribution: ${\displaystyle Y=A\ast \left(x\wedge \left(k-1\right)\right)\ast \left(\text{exp}\left(-x/b\right)\right)/\left(\left(b\wedge k\right)\ast \text{gamma}\left(k\right)\right)}$

The amplitude parameter A was constrained to “Must be between zero and 100000.0” and scale and shape parameters b and k to “Must be greater than 0.0”. “Initial values, to be fit” for all parameters were set to 50. All other settings were kept at default values, e.g., Least squares fit and No weighting.

The numerical value of the shape parameter k rounded to the nearest integer was interpreted as the number of driver events per tumor (Belikov, 2017).

Correlation of the predicted numbers of driver events per tumor with PAFs

Obtained k values were correlated to population attributable fractions (PAFs) in GraphPad Prism 6 using the inbuilt Correlation tool at default settings, e.g., Pearson correlation with two-tailed P value. Cancer types were sorted into two classes, and correlation was performed separately for each class. Cancer types in which infection (Helicobacter pylori, hepatitis B virus, hepatitis C virus, Kaposi sarcoma herpesvirus/ human herpesvirus 8, human immunodeficiency virus and human papillomavirus) or ultraviolet radiation contributed to more than 30% of cases, for a given country according to the published PAF data (Whiteman et al., 2015a; Brown et al., 2018; Islami et al., 2018), were assigned to Class 2 (non-anthropogenic). The rest were assigned to Class 1 (anthropogenic), which included cancers with substantial contribution from air pollution, occupational exposure, exposure to ionizing radiation, smoking and exposure to secondhand smoke, alcohol intake, poor diet (red and processed meat, insufficient fiber, vegetables, fruit and calcium), excess body weight, insufficient physical activity, insufficient breastfeeding, postmenopausal hormone therapy and oral contraceptives, according to the published PAF data (Whiteman et al., 2015a; Brown et al., 2018; Islami et al., 2018).