Obesity paradox in patients with cancer: A systematic review and meta-analysis of 6,320,365 patients

BACKGROUND. Obesity, defined as a body mass index (BMI) > 30 kg/m2, is associated with a significant increase in risk of many cancers. In last years, various studies suggested that obese cancer patients have better outcomes than non-obese patients. This phenomenon, also known as “ the obesity paradox ”, is not well understood and presents controversial explanations. We performed a systematic review and meta-analysis to assess the association between obesity and outcome after a diagnosis of cancer. PATIENTS AND METHODS. PubMed, the Cochrane Library, and EMBASE were searched from inception to January 2020, for studies reporting prognosis of patients with obesity and cancer. Risk of death, cancer-specific survival (CSS) and progression were pooled to provide an adjusted hazard ratio with a 95% confidence interval (HR 95%CI). The primary outcome of the study refers to overall survival (OS) in obese vs non-obese patients with malignancies. Secondary endpoints were CSS and progression- or disease-free survival (PFS or RFS). RESULTS. Mortality and relapse associated with obesity in patients with cancer were evaluated among n=6,320,365 participants (n=203 studies). Overall, association of obesity and cancer was associated with a reduced OS (HR =1.14, 95% CI: 1.09 –1.19; P<.01) and CSS (HR=1.17, 95%CI 1.12-1.23; P<.01). Patients were also at increased risk for relapse (HR=1.13, 95%CI 1.07-1.19; P<.01). Patients with breast, colorectal and uterine tumors were at increased risk of death. Conversely, obese with lung cancer, renal cell carcinoma and melanoma survived longer that non-obese. CONCLUSIONS. In many cancer patients, obesity reduces survival and increases the risk of relapse. In lung cancer, renal cell carcinoma and melanoma obesity was protective in terms of outcome. More intensive follow up , adequate dosing of oncological treatments , calories intake restrictions, physical activity and monitoring of obesity-related complications are effective measures for reducing mortality in these subjects.

obesity, cancer, survival, meta-analysis All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 2, 2020. ; https://doi.org/10.1101/2020.04.28.20082800doi: medRxiv preprint 4

Obesity, defined as a body mass index (BMI) greater than 30 kg/m2, is a chronic disease with an increasing prevalence around the world, largely contributing to important health issues in most countries. Despite body fat is a general risk factor for serious illness (e.g. metabolic syndrome), greater cardiometabolic risk has also been associated with the localization of excess fat in the visceral adipose tissue and ectopic deposits1. Several large epidemiologic studies have evaluated the link between obesity and mortality. In particular, a meta-analysis of 230 cohort studies including over 30 million individuals, found that both obesity and overweight were associated with an increased risk of all-cause mortality2. Despite the evidence that excess mortality increases with increasing BMI, some studies have reached the conclusion that elevated BMI may improve survival in subjects with cardiovascular disease, a phenomenon called the "obesity paradox"3. Increased BMI is also associated with an increased risk of multiple cancer types4. In addition, obesity and overweight may increase cancer mortality5. During last decades we also observed a more rapid increase in obesity among adult cancer survivors compared with the general population6. The mechanisms contributing to higher cancer incidence and mortality may include alterations in sex hormone metabolism, insulin and insulin-like growth factor levels, and adipokine pathways78. In last years, various studies suggested that, in cancers, patients with a normal BMI have worse outcomes than obese patients. This phenomenon (the obesity paradox) in cancer, is not well understood and presents controversial explanations91011. Three different meta- All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 2, 2020. ; https://doi.org/10.1101/2020.04.28.20082800doi: medRxiv preprint 5 analyses led to different results in particular in lung and renal cell carcinomas121314. In lung cancer, obesity has favourable effects on long-term survival of surgical patients. Moreover, in renal cell carcinoma an inconsistent effect of BMI on cancer-specific survival was found. Conversely, breast, ovarian and colorectal cancer are invariably associated with increased mortality in obese patients151617. The main explanations proposed to answer these observations include the general poor health status of patients with very low BMI. Additionally, weight loss may usually be associated with frailty and other risk factors (e.g. smoke)10. In cancer, obesity is also associated with increased efficacy of PD-1/PD-L1 blockade in both tumor-bearing mice and patients11. This updated systematic review and meta-analysis was conducted to evaluate the prognosis of obese vs non-obese cancer patients.

We followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) and Meta-analysis of Observational Studies in Epidemiology (MOOSE) reporting guidelines1819. Search strategy and inclusion criteria A systematic search was conducted of the EMBASE, Pubmed and The Cochrane Library published from inception until January 2020. The following search terms were used: ((carcinoma or cancer or sarcoma or melanoma or ("Neoplasms"[Mesh])) AND (obese OR obesity OR 30 kg/m2 OR “body mass index”) AND (hazard ratio) AND survival AND (multivariate OR cox) . The reference lists of identified articles were then manually searched to identify potentially relevant omitted citations Articles that were not published in the English language were not included in this study. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 2, 2020. ; https://doi.org/10.1101/2020.04.28.20082800doi: medRxiv preprint 6 Retrospective and observational studies (cohort, case control) or prospective trials were selected when they reported the association of obesity, defined as a BMI >30 kg/m2, with the risk of death (OS), cancer-specific death (CSS) or disease-progression (PFS or DFS) in patients with cancer. We placed no restrictions on study setting, size, race or country. The inclusion was limited to studies reporting hazard ratios (HRs) and their corresponding 95% CIs. Studies were restricted to adult patients with solid tumors. Hematologic malignancies were excluded. The most up-to-date versions of full-text publications were included. Study selection was performed in 2 stages, first, titles and abstracts were screened; then, selected full-text articles were included according to the eligibility criteria. Screening was performed independently by 10 authors (MG, GT, AG, AI, AC, ON, VR, AI, LD, MS) and conflicts were handled by consensus with a senior author (FP). Data Collection and quality assessment Data were collected independently by using a predesigned spreadsheet (Excel [Microsoft]. Collected data items included authors, year of publication, study setting and design, median follow up, treatments received, outcomes, and type of analysis. The primary outcome was OS, secondary endpoints were CSS and PFS/DFS. Along with data extraction, 1 author (FP) assessed study quality according to a modified Newcastle Ottawa Scale20. Statistical analysis First, pooled HRs with 95% CIs were estimated using random effects meta-analysis with the generic inverse-variance method for studies that provided fully adjusted HRs. Inconsistency across studies was measured with the I2 method. Cutoff values of 25%, 50%, and 75% indicated low, moderate, and high heterogeneity, respectively. When I2 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 2, 2020. ; https://doi.org/10.1101/2020.04.28.20082800doi: medRxiv preprint 7 was larger than 50%, a random effects model was used; otherwise, the fixed effects model was used. Second, to examine heterogeneity, we performed analyses of predefined subgroups based on type of disease. Additionally, to address potential bias and verify our results, we performed sensitivity analysis using a leave-one-out method and the Trim and Fill method. Finally, to investigate the risk of publication bias, we applied the Egger test and visually inspected the funnel plots (begg’s test). All analyses were carried out using Comprehensive Meta-Analysis software,

Our literature search yielded 1,892 articles, of which 203 met the inclusion criteria for our overall systematic review of the effect of obesity on cancer outcome21,22,31,121–130,32,131– 140,33,141–150,34,151–160,35,161–170,36,171–180,37,181–190,38,191–200,39,201–210,40,211–220,23,41,221–223,42–50,24,51– 60,25,61–70,26,71–80,27,81–90,28,91–100,29,101–110,30,111–120 (Fig. 1). Most excluded studies did not use the cut-off value for obesity (BMI values different from 30 kg/m2) or used different cut-off for risk of death (e.g. 1 kg/m2 of increase in BMI). Of the 203 articles, 170 were eligible for inclusion in the systematic review of the risk of obesity with OS, 109 for correlation with CSS, and 79 for correlation with DFS/PFS. Descriptive data for studies included in our meta-analysis are listed in Table 1. Overall, the included studies encompassed a total of 6,320,365 patients. Sample sizes ranged from 41 to 1,096,492 with a median of 1,543. Most studies were retrospective in nature (n=132), the minority were prospective cohort/observational studies (n=63) or pooled analysis/randomized trials (n=8). Overall survival and obesity in cancer patients N=170 studies reported data on OS. Because the heterogeneity test showed a high level of heterogeneity (I2 =79.7%, P<.01) among studies, a random-effects model was used for the analysis. OS of obese patients was significantly worse (HR =1.14, 95% CI: 1.09–1.19; P<01; Figure 2) compared to those of not obese patients. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 2, 2020. ; https://doi.org/10.1101/2020.04.28.20082800doi: medRxiv preprint 8 Cancer-specific survival and obesity in cancer patients Similarly, obesity reduced CSS in n=109 studies (HR=1.17, 95%CI 1.12-1.23; P<.01; Figure 3), which reported data on cancer mortality. Heterogeneity was high (I2=73.9%), so a random effect model was used Progression-free survival and obesity in cancer patients Obese patients with cancer (n=79 studies) have a worse DFS/PFS than non-obese subjects (HR=1.13, 95%CI 1.07-1.19; P<0.01). Heterogeneity was high (I2=73.7%), so a random effect model was used. Subgroup analysis A subgroup analysis for OS was performed according to type of disease (Suppl. file 1), type of study (retrospective vs prospective: HR=1.07, 95%CI 1.07-1.18, P<.01 vs HR=1.14, 95%CI 1.05-1.23, P vs < 10 years HR=1.16, 95%CI 0.86-1.58, P=.08 vs HR=1.23, 95%CI 0.84-3; P=.09), race (Non asiatic HR= 1.22, 95%CI 0.86-1.6, P=.06 vs HR=1.22, 0.74-1.72, P=.09) and stage of disease (early vs advanced: HR=1.20, 95%CI 0.99-1.25, P=.072 vs HR=1.2, 95% 1.12-1.28, P=.01). Publication bias A funnel plot was used to assess publication bias in the studies evaluating OS in obese versus not obese cancer patients. No publication bias was detected (Begg’s test P=.23). Egger’s test was instead significant (P=0.03) (Suppl. file 2). According to the Trim and Fill

n=18 studies were placed to the left of the mean, and according the random effect model the final result was similar (HR=1.08, 95%CI 1.03-1.13). After the one study removed procedure the HR for ranged from 1.14 to 1.15.

All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 2, 2020. ; https://doi.org/10.1101/2020.04.28.20082800doi: medRxiv preprint 9 This meta-analysis of published (observational and retrospective) studies shows that overall mortality is increased in obese patients with breast, colorectal and uterine cancers. Cancer mortality is increased in breast, colorectal, prostate and pancreatic cancers. Finally, relapse rate is increased in breast, colorectal, prostate and gastroesophageal cancers. The “obesity paradox”, that means the apparent better cancer mortality in obese patients, is observed in lung cancer and in melanoma, despite these data derive from n=12 studies only. Various factors are potentially linked to increased cancer mortality in some malignancies. Hormonal factors , reduced physical activity, more lethal/aggressive disease behavior, metabolic syndrome and a potential undertreatment in obese subjects are possible reasons. It is well known that postmenopausal women with higher BMI have an increased breast cancer risk due to higher estrogen levels resulting from the peripheral conversion of estrogen precursors (from adipose tissue) to estrogen224. In these patients weight loss and exercise may reduce cancer risk through lowering exposure to breast cancer biomarkers225. In colorectal cancer, BMI before diagnosis was associated with increased all-cause, cardiovascular and colorectal cancer specific mortality226. The reason of this association is presently not completely understood although insulin, insulin-like growth factors, their binding proteins, chronic inflammation, oxidative stress, and impaired immune surveillance have been supposed to be causative factors227. In pancreatic cancer higher prediagnostic BMI was associated with more advanced stage at diagnosis, with 72.5% of obese patients presenting with metastatic disease versus 59.4% of healthy- weight patients (P = .02) in 2 large prospective cohort studies222. Lastly, in prostate cancer obesity may a consequence of androgen deprivation therapy but seems also related with more aggressive disease (i.e., Gleason score ≥7)228 or more advanced disease at diagnosis229. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 2, 2020. ; https://doi.org/10.1101/2020.04.28.20082800doi: medRxiv preprint 10 Our results showed that obese lung cancer patients had significantly prolonged cancer specific survival and overall survival compared to non-obese patients. When considering these findings, we must take into account that 9 out of 11 evaluated studies included advanced/metastatic patients. Cancer cachexia mechanisms are not completely defined yet, but several evidences showed that the systemic inflammatory status induced either by the tumor and host-response is a key moment in its developing230. Lung cancers are indeed known to be aggressive diseases, and advanced patients are usually accompanied by poorer performance status and significant weight loss at the time of diagnosis, which underlies a systemic inflammatory response231. According to this view, a post-hoc analysis from three international phase III studies including advanced non-small cell lung cancer patients who received a platinum-based, first-line chemotherapy, revealed that weight gain during treatment was significantly associated with better clinical outcomes232. Intriguingly, a recent study showed that a decrease in BMI at lung cancer diagnosis (from the early adulthood) is a consistent marker of poor survival183. Considering these observations, an increased BMI might be considered a protective feature and potentially a marker of functional reserve for lung cancer patients. Recent evidences suggest a positive predictive role of an increased BMI for cancer patients receiving immune checkpoint inhibitors across different malignancies, including lung cancer54. Interestingly, the only study with lung cancer patients who received an immune checkpoint inhibitor (atezolizumab), revealed that the OS benefit for obese patients was significantly higher within the atezolizumab- treated cohort, compared to the doxetaxel treated cohort110. However, we have to remark that no significant OS difference was found across BMI categories within the docetaxel treated patients. Similar mechanisms can be applied to the finding of a survival benefit for obese melanoma patients from the single included study. Several evidences indeed confirmed that a high BMI might be considered a surrogate marker of clinical benefit from immune checkpoint inhibitors for advanced melanoma patients233234. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 2, 2020. ; https://doi.org/10.1101/2020.04.28.20082800doi: medRxiv preprint 11 Interestingly, also obese RCC patients revealed to have a significantly longer OS compared to the respective non-obese cohorts. It has been already hypotesized that the perinephric white adipose tissue act as a reservoir of activated immune cells with increased characteristics of hypoxia, infiltration of Th1 cells, regulatory T cells, dendritic cells, and type 1 macrophages. However, only one out of six studies inclued patient receiving immunotherapy235236. Intriguingly, we found that the association between obesity and better clinical outcomes seems to be confirmed for those malignancies in which immune checkpoint inhibitors have first (and strongly) proved to be effective, despite studies involving patients receiving immune checkpoint inhibitors are poorly represented in this meta-analysis. Such results, might probably be an epiphenomenon, however we are allowed to speculate that the white adipose tissue could be considered an immune organ, which somehow plays a role in the anti-tumor immune response. It has been observed that the adipocyte-derived hormone leptin could alter T-cell function, resulting in improved response to anti-PD-1 therapy11. Moreover, another preclinical study reported that white adipose tissue acts as a reservoir for a peculiar population of memory T cells, which elicitate some effective responses in case of antigenic re-exposure during infections (and why not in case of exposure to cancer-specific antigens?)237. Obese patients are also at increased risk of reduced physical activity. Various studies highlighted this concept. Physical activity declines over time in obese patients238239. In particular, physical activity is strictly correlated with breast cancer and colorectal cancer mortality240241. Therefore physical (in)activity should be a major target of obesity prevention and treatment in particular in cancer patients. Type 2 diabetes mellitus is strongly associated with obesity in the metabolic syndrome. More than 80 percent of cases of type 2 diabetes can be attributed to obesity, which may also account for many diabetes-related All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 2, 2020. ; https://doi.org/10.1101/2020.04.28.20082800doi: medRxiv preprint 12 deaths. The association between BMI and cause-specific mortality was also illustrated in the Prospective Studies Collaboration analysis242. In the upper BMI range (25 to 50 kg/m2), each 5 kg/m2 increase in BMI was associated with a significant increase in mortality from coronary heart disease (CHD), stroke, diabetes mellitus, chronic kidney disease, and many cancers. In the same analysis, subjects with BMI below 22.5 kg/m2 had higher mortality compared with subjects with a BMI of 22.5 to 25 kg/m2. The excess mortality was predominantly due to smoking-related diseases (respiratory and cancer). Despite there are no clear recommendation about dosing of chemotherapy in obese patients, caution is recommended for high risk regimens243. The hypothesis that a reduced dose according to ideal body weight may lead to a reduced outcome cannot be confirmed by prospective studies but may be considered as potential reason for the observed results in some settings ( e.g. breast cancer). In a pooled analysis of toxicity in obese vs non- obese rates of toxic effects were similar or lower in obese patients244. However, this meta‐analysis has several limitations. First, we combined data of obese patients and compared prognosis with different non-obese subjects (normal weight or normal weight + overweight). Second, accurate measures of weight and height are always a challenge in observational studies. The evaluation is often before diagnosis but in some studies timing of obesity diagnosis is not described. Obese subjects have a general poor prognosis in term of overall mortality and non-cancer mortality, so it seems obvious that their prognosis is dismal. However, almost all studies provided a multivariate analysis according to main prognostic factor for oncological outcome so that obesity remain generally an independent prognostic factor in cancer patients. Duration of follow up, treatments received, and countries are heterogeneous even if subgroup analysis does not explain results with these different variables. Lastly, this meta‐analysis compared mortality All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 2, 2020. ; https://doi.org/10.1101/2020.04.28.20082800doi: medRxiv preprint 13 between patients belonging to a fixed category of obesity (>30 kg/m2) and thus we are not able to provide effect size per unit increment of kg/m2. In conclusion, results of this meta-analysis confirm and reinforce the notion that obesity is a competing risk factor for overall and cancer specific mortality and relapse in various cancers treated with curative intent or for metastatic disease except for lung cancer and melanoma where it has an apparent protective effect (obesity paradox). It is advised that in the future, oncologists increase their efforts to manage patients into multidisciplinary teams for care and cure of both cancer and obesity. Improving lifestyle (e.g., physical activity, calories intake, care/prevention of cardiovascular complications), more intensive follow- ups of cancer in obese, and adequate dose of medical therapies are all proved measures that may improve prognosis of obese cancer patients.

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The copyright holder for this preprintthis version posted May 2, 2020. ; https://doi.org/10.1101/2020.04.28.20082800doi: medRxiv preprint 47 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 2, 2020. ; https://doi.org/10.1101/2020.04.28.20082800doi: medRxiv preprint Author, Year N° pts N° of obese (%) Study type Country Disease Median Follow Up (months) Disease stage Treatment Type of analysis OS CSS DFS/PFS Quality (NOS- METRICS) Abdel- Rahman/201921 145544 18131 (23.7) Population-based randomized US Lung 135 Early + advanced - MVA ✓ - - 12 Abdullah/201122 5036 567 (11) Retrospective (cohort) Various Various - - - MVA - ✓ - 5 Abrahamson/200623 1254 - Retrospective US Breast - Early + advanced - MVA ✓ - - 5 Abukabar/201824 3012 433 (13) Retrospective Malaysia Breast 24 Early + advanced S ± RT ± CT MVA ✓ - ✓ 6 Ademuyiawa/201125 418 164 (39.2) Retrospective US Breast 37.2 Early S MVA ✓ - ✓ 6 Akinyemiju/201826 22514 8786 (39) Prospective US Various 78 - - MVA - ✓ - 8 Aldrich/201328 501 126 (25.1) Prospective (cohort) US NSCLC 16 Early + advanced - MVA ✓ - - 7 Alsaker/201129 2640 432 (16.3) Retrospective Norway Breast 69 Early + advanced - MVA - ✓ - 7 Arce-Salinas/201430 819 596 (74) Retrospective Mexico Breast 28 Advanced CT UVA ✓ - ✓ 6 Arem/201331 1400 610 (43) Retrospective US Uterus 61.2 Early + advanced - MVA ✓ ✓ - 7 Ata/201832 8352 2841 (34) Retrospective US HCC 60 - Liver transplantation MVA ✓ ✓ - 7 Bandera/201533 1846 547 (29.6) Cohort study US Ovarian - Early + advanced CT MVA ✓ ✓ - 5 Bassett/201235 16525 247 (18) Prospective (cohort) Australia Prostate 180 - - MVA - ✓ - 12 Beasley/201227 13302 2330 (17.5) Metanalysis US, China Breast - Early All MVA ✓ ✓ ✓ - Blair/201936 859 195 (22.7) Cohort study US Breast 94 Early + advanced CT, RT, HT MVA - ✓ - 8 Boggs/201137 51695 23656 (46) Prospective US Various - - - MVA - ✓ - 8 Bonn/201438 4376 483 (11) Retrospective Sweden Prostate 48 Early S, RT MVA ✓ ✓ ✓ 6 Boyle/201339 879 258 (29.4) Retrospective Australia CRC 67.2 Early - MVA ✓ ✓ ✓ 7 Braithwaite/201040 2202 500 (22.7) Retrospective US Breast 88 Early - MVA ✓ ✓ - 8 Campbell/201544 5615 1483 (26.4) Prospective Various CRC - Early + advanced S, CT MVA ✓ ✓ - 5 Caan/200843 1692 409 (24.2) Retrospective US Breast - Early S / adjuvant MVA ✓ ✓ ✓ 5 Carr/201845 521 - Retrospective Italy HCC - Early + advanced - MVA ✓ - - 5 Cecchini/201646 5265 1794 (34) Phase III (NSABP-B30) US Breast 108 Early CT MVA ✓ - - - Cecchini/201646 2102 664 (31.6) Phase III (NSABP-B31) US Breast 99.6 Early CT + TTZ MVA ✓ - - - Cecchini/201646 3311 1186 (35.8) Phase III (NSABP-B34) US Breast 100.8 Early BPS vs Placebo* MVA ✓ - - - Cecchini/201646 4860 1917 (39.4) Phase III (NSABP-B38) US Breast 70.8 Early CT MVA ✓ - - - Cespedes Feliciano/201747 2470 - Retrospective US CRC 72 Early - MVA ✓ ✓ - 7 Chang/200048 177 64 (36.2) Retrospective US Breast 100 Advanced Induction CT / S MVA ✓ - - 8 Table All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 2, 2020. ; https://doi.org/10.1101/2020.04.28.20082800doi: medRxiv preprint Chen/201049 5042 283 (5.6) Retrospective China Breast 6.5 Early + advanced S, CT, RT, HT MVA ✓ ✓ - 6 Hung/201834 33551 2362 (7.1) Retrospective (cohort) Taiwan Solid cancers 43.8 Early + advanced S MVA ✓ ✓ - 6 Chromecki/201250 4118 1739 (42.2) Retrospective Various Bladder 44 Early S ± adjuvant CT MVA ✓ ✓ ✓ 7 Chung/201751 8742 75 (8.5) Retrospective South Korea Breast 92 Early All MVA ✓ ✓ - 9 Clark/201341 99 40 (41) Retrospective US CRC 39.4 Early CT + RT MVA ✓ - ✓ 6 Cleveland/201242 1447 319 (22) Prospective (case-control) US Breast - - - MVA ✓ ✓ - 8 Connor/201652 2478 142 (5.7) Prospective (registry) US Breast 129.6 Early + advanced - MVA ✓ ✓ - 12 Conroy/201153 3842 901 (23) Prospective (cohort) US Breast 74.4 Early + advanced S ± CT/HT and/or RT MVA ✓ ✓ - 11 Copson/201456 2843 533 (18.7) Prospective UK Breast 70.4 Early + advanced S, RT, CT, HT MVA ✓ - ✓ 11 Cortellini/201954 976 377 (38.6) Retrospective Italy Various 17.2 Advanced Anti-PD-1/PD-L1 MVA ✓ - ✓ 6 Crozier 201355 3017 1298 (43) Retrospective US Breast 63.6 Early - MVA - ✓ ✓ 8 Dahdaleh/201857 1543 529 (34.3) Retrospective (cohort) US CRC 30.9 Early S, adjuvant CT MVA ✓ - ✓ 6 Dalal/201259 41 8 (20) Prospective US Pancreas - Advanced CTRT MVA ✓ - - 7 Dal Maso/200858 1453 172 (11.8) Retrospective Italy Breast - Early S / adjuvant UVA ✓ ✓ - 5 Dawood/201260 2311 825 (35.7) Retrospective US Breast (TN) 39 Early S MVA ✓ - ✓ 6 Dickerman/201761 5158 564 (10.9) Retrospective US Prostate - Early All MVA - ✓ - 5 Dignam/200363 3385 395 (11.67) Retrospective US Breast - Early S + HT MVA ✓ ✓ ✓ 5 Dignam/200664 4077 1056 (25.9) Retrospective US Breast - - - MVA ✓ ✓ ✓ 5 Dignam/200665 4288 812 (9.5) Retrospective North America CRC - Early CT MVA ✓ ✓ ✓ 5 Doria-Rose/200666 633 96 (15.2) Retrospective US CRC (women) 112.8 Early S / others MVA ✓ ✓ - 8 Drake/201767 7061 3220 (45.6) Prospective (cohort) Sweden Various 202 Early + advanced All MVA ✓ ✓ - 12 Efstathiou/200862 945 145 (15.3) Prospective US Prostate 97.2 Advanced RT +/- Goserelin MVA ✓ ✓ - 11 Emaus/200969 1364 147 (10.78) Retrospective Norway Breast 98.4 Early + advanced - MVA ✓ ✓ - 8 Farris/201871 987 192 (19.4) Prospective (cohort) Canada Prostate 228 Early + advanced S, RT, HT MVA ✓ ✓ - 12 Fedirko/201472 3924 689 (17.5) Prospective Western Europe Colorectal 49 Early + advanced - MVA ✓ ✓ - 7 Feliciano/201773 1559 471 (30.2) Retrospective US Breast 108 Early All MVA - ✓ ✓ 8 Ferro/201874 1155 224 (21.4) Retrospective Italy Bladder 48 Early TURBT + BCG MVA - ✓ ✓ 7 Froehner/201475 2131 356 (16.7) Retrospective Germany Prostate 110 Early All UVA, MVA ✓ ✓ - 9 Frumovitz/201476 3086 1026 (33.2) Retrospective US Cervical 133 Early + advanced S, RT MVA ✓ ✓ - 9 Gama/2017121 1279 243 (21) Retrospective Canada H&N 30 Early + advanced RT, S, CT MVA ✓ - ✓ 6 Genkinger/201577 1096492 - Cohort study US Pancreas 152.4 - - MVA - ✓ - 11 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 2, 2020. ; https://doi.org/10.1101/2020.04.28.20082800doi: medRxiv preprint Gong/200778 752 128 (17) Retrospective US Prostate 116.4 Early + advanced S / ADT / RT / others MVA - ✓ - 9 Gong/201270 510 51 (10) Retrospective US Pancreas 121.2 Early + advanced All MVA ✓ - - 9 Goodwin/201279 535 - Retrospective Canada Breast 145.2 Early S ± adjuvant CT and/or HT MVA ✓ - ✓ 9 Grossberg/201680 190 65 (34.2) Retrospective US H&N 68.6 Early CT + RT MVA ✓ ✓ ✓ 7 Halabi/200782 1296 405 (31.2) Retrospective US Prostate (androgen- independent) 33.8 Early + advanced - MVA ✓ ✓ - 6 Han/201084 2511 211 (8.4) Retrospective US Prostate 156 Early All UVA ✓ - - 9 Han/201485 13901 708 (5.09) Retrospective US Various - - - MVA - ✓ - 5 Xu /2018215 644 92 (14.3) Retrospective China Upper tract urothelial 39 Early S MVA ✓ ✓ ✓ 6 He/201286 1983 546 (27.5) Retrospective US Breast 47.6 Early + advanced All MVA ✓ - - 7 Hellmann/201087 528 76 (14.4) Prospective Denmark Breast 93.6 Early + advanced - MVA ✓ ✓ - 10 His/201688 3160 194 (6.1) Prospective France Breast 109.2 Early - MVA ✓ ✓ ✓ 11 Ho/201289 1038 337 (32.4) Retrospective US Prostate 41 Early + advanced S MVA - - ✓ 6 Houdek/201990 261 71 (8.6) Retrospective - STS 48 - RT / none MVA ✓ - ✓ 6 Iyengar/201494 155 30 (19) Retrospective US Tongue - Early S MVA ✓ ✓ ✓ 5 Izumida/201995 10824 235 (2) Cohort study China Various 220.8 - - MVA - ✓ - 12 Janssen/201596 927 - Retrospective US Various - Early + advanced - MVA ✓ - - 5 Jayasekara/201897 724 164 (22.7) Cohort study Australia CRC 108 Early - MVA ✓ ✓ - 11 Jenkins/201898 502631 12539 (24.9) Cohort study UK Various 93.6 - - MVA ✓ - - 7 Jeon/201599 41021 1632 (4) Prospective (registry) Korea Breast 92 Early CT, HT MVA ✓ ✓ - 11 Jiralerspong/2013100 6342 1779 (30) Retrospective US Breast 64.8 Early - MVA ✓ ✓ ✓ 7 Kaidar- Person/2015101 184 46 (25) Retrospective Israel CRC 27.6 Advanced CT, Bevacizumab MVA ✓ - ✓ 6 Kalb/2019102 612 127 (20.7) Retrospective Germany CRC 58 Early CT, RT, S MVA ✓ - ✓ 7 Katzmarzyk/2012103 10522 1972 (19) Retrospective Canada Various 168 Early + advanced All MVA - ✓ - 8 Kawai/2016104 20090 897 (4.5) Prospective (registry) Japan Breast 80.4 Early CT, HT MVA ✓ ✓ ✓ 10 Keegan/2010105 4153 127 (3) Retrospective US Breast - - - MVA ✓ - - 5 Keizman/2014106 278 67 (24.1) Retrospective Israel RCC 55 Advanced TKI MVA ✓ - ✓ 7 Kelly/2017107 7822 1612 (20.6) Retrospective US Prostate 156 Early + advanced All MVA - ✓ - 9 Kenfield/2016108 112185 9984 (8.9) Prospective US Prostate 170 - - MVA - ✓ - 12 Khan/2017109 822 - Retrospective US Prostate 60 Early + advanced All MVA - - ✓ 7 Kichenadasse/20191 10 1434 239 (7) Prospective Various NSCLC - Advanced Atezolizumab vs Docetaxel UVA ✓ - ✓ 7 Kitahara/2014111 313575 9564 (3.04) Retrospective Various Various - - - MVA - ✓ - 5 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 2, 2020. ; https://doi.org/10.1101/2020.04.28.20082800doi: medRxiv preprint Kotsopoulos/2012113 1423 230 (17.5) Retrospective Canada Ovarian 120 Early + advanced All MVA - ✓ - 9 Kristensen/2017114 4330 - Retrospective Denmark Endometrial - Early + advanced S MVA ✓ - - 5 Kwan/2012115 14948 2440 (16.3) Prospective (cohort) US Breast 93.6 Early S ± adjuvant CT and/or HT and/or RT MVA ✓ ✓ - 11 Kwan/2013116 11351 3405 (30) 3 pooled case- control, 3 prospective cohort US Breast 132 Early + advanced All MVA ✓ ✓ - - Ladoire/2014117 5009 666 (13.3) Pooled analysis (2 phase III) France Breast 70.8 Early CT MVA ✓ - ✓ - Larsen/2015118 1229 167 (14) Prospective (cohort) Denmark Breast 115.2 Early - MVA ✓ - - 12 Lee/2010120 2750 120 (4.3) Retrospective South Corea RCC 34.8 Early S MVA - ✓ ✓ 6 Leung/2011122 58931 3520 (5.97) Prospective Japan Lung - - - MVA - ✓ - 7 Li/2009123 841 163 (19.38) Retrospective US Pancreas - Early + advanced - MVA ✓ - - 5 Lin/2013124 799542 19988 (2.5) Retrospective Various Pancreas 37.2 - - MVA - ✓ - 6 Loi/2005125 1101 131 (11.9) Retrospective Australia Breast 60 Early S / adjuvant MVA ✓ - ✓ 6 Ma/2008126 2546 87 (3.4) Retrospective US Prostate 84 Early + advanced - MVA ✓ ✓ - 7 Maj-Hes/2017127 6519 2462 (37.7) Retrospective Austria Prostate 28 Early S MVA - - ✓ 6 Martini/2020128 90 23 (25.5) Retrospective US Various - Advanced IT MVA ✓ - ✓ 5 Maskarinec/2011129 382 71 (18.5) Prospective (cohort) Hawaii Breast 158.4 Early + advanced S ± adjuvant CT and/or HT MVA ✓ ✓ - 12 Mathur/2010130 279 97 (35) Retrospective US Various 31 Advanced Hepatectomy MVA ✓ - ✓ 6 Matsuo/2016131 665 459 (69) Retrospective US Uterus 36.4 Early + advanced S + CTRT MVA ✓ - ✓ 6 Mazzarella/2013133 1250 101 (8.1) Retrospective Europe Breast - Early S, RT, CT MVA ✓ - ✓ 5 McCullough/2005134 430236 5433 (1.3) Prospective US Breast 240 Early + advanced All MVA - ✓ - 12 McCullough/2016135 1308 301 (23) Population-based US Breast 180 Early - MVA ✓ ✓ - 12 McMahon/201783 1080 - Retrospective US Liver 123.6 Early + advanced All MVA - ✓ - 9 McQuade/2019136 1918 513 (26.7) Pooled analysis US Melanoma - Advanced CT, IT, TT MVA ✓ - ✓ - Melhem- Bertrandt/2011138 1413 460 (33) Retrospective US Breast (TN) 59 Early S ± CT MVA ✓ - ✓ 7 Meyer/2015139 35703 2,820 (7.9) Population-based Switzerland Various - Early + advanced - MVA ✓ - - 7 Meyerhardt/2003140 3561 500 (16.8) Cohort study US CRC 112 Early S + CT MVA ✓ - ✓ 12 Meyerhardt/2004141 1688 306 (18) Cohort study US CRC 118 Early S + CT MVA ✓ - ✓ 12 Meyerhardt/2008142 1043 236 (22.6) Prospective US and Canada CRC 63.6 Early S / adjuvant MVA ✓ - ✓ 8 Minlikeeva/2019112 7022 1557 (22.2) Retrospective (pool data) US and Australia Ovarian - Early + advanced - MVA ✓ - ✓ 5 Moller/2014143 26877 4140 (15) Cohort study UK Prostate 43.2 Early + advanced - MVA - ✓ - 11 All rights reserved. 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The copyright holder for this preprintthis version posted May 2, 2020. ; https://doi.org/10.1101/2020.04.28.20082800doi: medRxiv preprint Montgomery/2007144 1006 160 (16) Retrospective US Prostate (androgen- dependent) - Advanced Bilateral orchiectomy ± Flutamide MVA ✓ - ✓ 5 Montgomery/2007144 671 253 (38) Retrospective US Prostate (androgen- independent) - Advanced Mitoxantrone/Pred nisone vs Docetaxel/Estram ustine MVA ✓ - ✓ 5 Morikawa/2011145 1060 200 (19) Prospective (cohort) US CRC 162 Early + advanced All MVA ✓ ✓ - 12 Nagle/2018148 1359 568 (41.7) Retrospective Australia Endometrial 85.2 Early + advanced S ± CT MVA ✓ ✓ - 8 Nakagawa/2016149 1311 25 (1.9) Retrospective Japan NSCLC 59 Early S MVA ✓ - ✓ 7 Nechuta/2010150 71243 8264 (11.6) Cohort study UK Various 109.2 - - MVA - ✓ - 12 Nicholas/2014151 490 203 (41.4) Retrospective US Endometrial 54 Early + advanced S MVA ✓ - - 6 Nichols/2009152 3993 639 (16) Retrospective US Breast 76.8 Early S MVA ✓ ✓ - 7 Nonemaker/2009153 2054 50 (12.5) / 46 (8.4) Retrospective Africa / Europe and America Lung - - - MVA ✓ ✓ - 5 Nur/2019154 49259 202 (4.1) Retrospective Sweden Breast 91.6 Early S MVA ✓ ✓ - 8 Ogino/2009155 546 84 (16) Retrospective US CRC - Early + advanced - MVA ✓ ✓ - 5 Oh/2011156 747 251 (33.7) Cohort study Korea Breast 62.2 Early S ± CT MVA - - ✓ 10 Olson/2010157 475 108 (23) Retrospective US Pancreas - Early + advanced S MVA ✓ ✓ - 5 Oudanonh/2019158 3747 1790 (47.8) Retrospective (registry) Canada Breast - Early HT, CT, RT, antiHER2 MVA ✓ - - 5 Pajares/2013159 5683 1376 (24.4) Retrospective Spain Breast - Early CT, HT, S MVA ✓ ✓ ✓ 5 Parker/2006160 970 336 (34.6) Retrospective US RCC 56.4 Early S MVA ✓ ✓ - 7 Parr 2010161 401215 16978 (4.23) Retrospective All Various - - - MVA ✓ - - 5 Patel/2015162 1174 462 (39.4) Retrospective Australia CRC - Advanced CT MVA ✓ - - 5 Pelser/2014163 5727 - Retrospective US CRC - Early + advanced S, RT, CT MVA ✓ ✓ - 5 Pfeiler/2013164 1509 315 (21) Retrospective Austria Breast 60 Early - MVA ✓ ✓ ✓ 7 Pierce/2007166 1490 380 (25.5) Prospective US Breast 80.4 Early S / adjuvant MVA ✓ - - 8 Potharaju/2018167 392 40 (10.2) Retrospective India GBM 48.6 - S + RT + TMZ MVA ✓ - - 6 Previs/2014168 81 28 (34) Retrospective US Ovarian - Early + advanced S, RT MVA ✓ ✓ ✓ 5 Prizment/2010169 1096 295 (26.9) Retrospective (registry) US CRC 240 Early + advanced CT, RT, S MVA ✓ ✓ - 9 Probst- Hensch/2010170 855 72 (19.8) Retrospective Switzerland Breast 43.8 Early S ± HT MVA ✓ - - 6 Psutka/2015171 387 166 (43) Retrospective US RCC 86.4 Early S MVA ✓ ✓ ✓ 8 Qi/2009172 420 79 (22.7) Retrospective US Lung - Advanced All MVA ✓ - - 5 Qinchao Hu/201991 576 33 (5,7) Retrospective China H&N 64 Early S MVA - ✓ ✓ 7 Roque/2016173 128 72 (56.3) Retrospective US Leiomyosarcom a 49 Early + advanced All MVA ✓ - ✓ 6 Rosenberg/201092 2640 376 (14.24) Retrospective Sweden Breast - Early + advanced S, CT, HT, RT MVA - ✓ - 5 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 2, 2020. ; https://doi.org/10.1101/2020.04.28.20082800doi: medRxiv preprint Rudman/2016174 273 59 (21.6) Retrospective UK Prostate 139.2 Early HT MVA ✓ ✓ - 9 Ruterbusch/201493 627 184 (29) Retrospective US Uterus - Early + advanced S ± CT MVA ✓ ✓ - 5 Sanchez/2019175 730 249 (34) Cohort US RCC - Early + advanced Systemic therapy + S MVA ✓ - - 6 Sasazuki/2011176 353422 7327 (2) Prospective (cohort) Japan Various 150 - - MVA - ✓ - 12 Schiffmann/2017177 16014 2403 (15) Retrospective Germany Prostate 36.4 Early S MVA - - ✓ 6 Schlesinger/2014178 2143 397 (19) Prospective Germany CRC 42 Early + advanced - MVA ✓ - - 6 Seidelin/2016179 3638 984 (27) Population-based Denmark Uterus - Early + advanced - MVA ✓ - - 7 Senie/1992180 923 207 (22) Prospective US Breast 120 Early - MVA - - ✓ 12 Shah/2015181 242 59 (24.5) Prospective US CRC - Advanced S, CT MVA ✓ - - 7 Shepshelovich/20191 83 29217 418 (1.4) Pooled analysis Canada Lung - Early + advanced - MVA ✓ - - - Siegel/2013119 853 216 (25) Prospective US Brain 19 Early - MVA ✓ - - 8 Silventoinen/2014184 734438 9187 (1.2) Retrospective Finland, Sweden Various 403.2 - - MVA ✓ ✓ - 9 Sinicrope/2012185 2693 630 (23) Randomized trial US CRC - Early S ± CT MVA ✓ - ✓ 5 Sinicrope/201381 25291 4463 (17,6) Retrospective US CRC 93.6 Early - MVA ✓ - ✓ 8 Song/2012132 135745 - Prospective Europe Various 201.6 - - MVA - ✓ - 12 Sorbye/2012137 342 67 (19.5) Retrospective Europe CRC - Advanced S ± CT MVA - - ✓ Spangler/2007186 924 286 (31) Prospective Various Prostate 36 Early S MVA - - ✓ 10 Sparano/2012187 4817 1745 (46) Retrospective (phase III) US Breast 95 Early S + CT + HT MVA ✓ ✓ ✓ - Sparano/2012188 6885 2547 (37) Retrospective (3 phase III) US Breast 95 Early S ± CT ± HT MVA ✓ ✓ ✓ 9 Spiess/2012189 99 43 (43) Retrospective US RCC 44.4 Early + advanced S MVA ✓ - - 6 Spreafico/2017190 564 76 (13) Retrospective Canada Esophagus 32.5 Early + advanced All MVA ✓ - ✓ 6 Su/2013191 1030 312 (30.3) Randomized study US Breast - Early S ± CT ± HT MVA - - ✓ - Sun/2015192 1109 410 (37) Population-based US Breast 162 Early - MVA ✓ ✓ - 8 Sun/2018193 1017 192 (18.9) Retrospective China Breast 80 Early CT, HT, RT, S MVA ✓ - ✓ 8 Sundelof/2009194 580 55 (9.5) Retrospective Sweden Esophagus - Early + advanced - MVA ✓ - - 5 Taghizadeh/2015195 8645 683 (7.9) Cohort study Netherlands Various 480 - - MVA ✓ - - 12 Tait/2014196 501 202 (40.3) Retrospective US Breast 40.1 Early + advanced S, CT MVA ✓ - ✓ 6 Thrift/2013197 783 263 (33) Retrospective Australia Esophagus / Gastric 76.8 Early + advanced S ± CTRT/BSC MVA ✓ - - 8 Todo/2014198 716 99 (13.8) Retrospective Japan Endometrial 74 Early + advanced S, CT MVA ✓ ✓ - 7 Trivers/2005200 1142 156 (4.2) Retrospective US Esophagus / Gastric - Early + advanced S / others MVA ✓ - ✓ 5 Tsai/2010201 795 103 (13) Retrospective US Pancreatic - Early + advanced S MVA ✓ - - 5 Tseng/2013146 89056 - Prospective Taiwan Various 144 Early + advanced All MVA - ✓ -. 12 Tseng/2016202 92546 - Retrospective Taiwan Various 204 Early + advanced All MVA - ✓ - 9 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 2, 2020. ; https://doi.org/10.1101/2020.04.28.20082800doi: medRxiv preprint Turner/2011203 188699 22054 (12.4) Prospective US Lung 312 - - MVA - ✓ - 12 Tyler/2012204 425 28 (6.6) Prospective (case-control) US Ovarian 116.4 Early + advanced All MVA ✓ ✓ - 12 Valentijn/2013205 10247 1851 (18) Retrospective The Netherlands Various 64.8 - - MVA - ✓ - 7 Vidal/2017206 4268 1372 (32.1) Retrospective US Prostate 81.6 Early S MVA - ✓ ✓ 8 Wang/2017208 1452 - Retrospective China CRC 40.8 Early + advanced All MVA - ✓ - 6 Wang/2019209 250 81 (12) Retrospective US Various - Advanced IT MVA ✓ - ✓ 5 Warren/2016210 878 - Retrospective US Breast 129.6 Early S UVA - - ✓ 9 Widschwendter/2015 211 3754 788 (21) Phase III (SUCCESS A) Germany Breast - Early CT + HT MVA ✓ - ✓ - Wu/2015212 333 118 (35.4) Retrospective US Prostate - Advanced CT MVA ✓ - - 5 Xiao/2014213 5785 1680 (29) Retrospective China Breast 70 Early CT, HT MVA ✓ - - 7 Xie/2017147 624 - Retrospective China Lung 63.2 Early S MVA ✓ - - 6 Xu/2018214 6197 1885 (30.4) Cohort study US Various 204 - - MVA - ✓ - 12 Yang/2008216 635 81 (12.8) Prospective Europe Ovarian 96 Early + advanced - MVA - ✓ - 10 Yang/2019217 2442 86 (3.5) Retrospective US HCC 50.5 Early S MVA ✓ - ✓ 8 Yano/2013218 3641 792 (21.7) Prospective Japan Various 122 - - MVA - ✓ - 12 Yoon/2011219 778 46 (19) Retrospective US Esophagus (adeno) 154.8 Early S ± adjuvant CT ± RT ± CTRT MVA ✓ ✓ ✓ 9 Yoon/2015220 2987 417 (13.9) Retrospective (cohort) US Endometrial - Early + advanced S, CT, RT MVA ✓ - - 5 You/2015221 1314 - Prospective (cohort) China Various 52.7 Early + advanced - MVA ✓ - ✓ 10 Yu/1991165 360 44 (12) Retrospective US RCC 53 Early S MVA ✓ - - 7 Yuan/2013222 902 136 (15) Prospective (cohort) US Pancreas 480 Early + advanced - MVA ✓ - - 12 Zheng/2015223 226 52 (23) Cohort study China CRC - Early + advanced - MVA ✓ - ✓ 5 *Clinical Trial Randomizing Breast Cancer Patients To Receive BFS Vs Placebo; Patients Received Concomitant Systemic Anticancer Treatment According To Physicians’ Decision (Following Institutional Guidelines). OS, Overall Survival ; CSS, Cancer Specific Survival; DFS, Disease-Free Survival; PFS, Progression-Free Survival; DDFI, Distant Disease Free Interval; US, United States Of America; UK, United Kingdom; NSCLC, Non-Small Cell Lung Cancer; HCC, Hepatocellular Carcinoma; RCC, Renal Cell Carcinoma; CRC, Colorectal Cancer; H&N, Head and Neck Tumors; STS, Soft Tissue Sarcoma; SCC, Squamous Cell Carcinoma; GBM, Glioblastoma; TN, Triple Negative; HR, Hormone Receptor; MVA, Multivariate Analysis; UVA, Univariate Analysis; S, Surgery; CT, Chemotherapy; HT, Hormone Therapy; IT, Immunotherapy; TKI, Tyrosine Kinase Inhibitor; TT, Targeted Therapy; TTZ, Trastuzumab; PD-1, Programmed Cell Death 1; PD-L1, Programmed Cell Death Ligand 1; ADT, Androgen Deprivation Therapy; BPS, Bisphosphonate; TMZ, Temozolomide; BCG, Bacillus Calmette-Guérin; RT, Radiotherapy; TURBT, Transurethral Resection of Bladder Tumor; BSC, Best Supportive Care Tab.1: characteristics of included studies All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 2, 2020. ; https://doi.org/10.1101/2020.04.28.20082800doi: medRxiv preprint Fig.1 flow diagram of included studies Records identified through Pubmed searching (n = 815 ) Screening Included Eligibility Identification Additional records identified through other sources (n = 1077 ) Records after duplicates removed (n = 1056 ) Records screened (n = 1056 ) Records excluded because they did not correlate obesity with outcome (n = 732) Full-text articles assessed for eligibility (n = 324) Full-text articles excluded, because they did not reported hazard ratios or other survival data (n = 121 ) Studies included in qualitative synthesis (n = 203 ) Studies included in quantitative synthesis (meta-analysis) (n = 203 ) All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 2, 2020. ; https://doi.org/10.1101/2020.04.28.20082800doi: medRxiv preprint Study nam e Subgroup within study Outcom e Statistics for each study Hazard ratio and 95% CI Hazard L o wer Up p er ratio limit limit Z -Valu e p -Valu e Abubakar 2018 Breast pfs 1,180 0,690 2,018 0,605 0,545 Adem uyiwa 2011 Breast pfs 0,810 0,490 1,339 -0,822 0,411 Beasley 2012 Breast pfs 0,960 0,770 1,197 -0,363 0,717 Bonn 2014 Prostate pfs 1,110 0,810 1,521 0,649 0,516 Buono 2017 Breast pfs 1,510 0,570 4,000 0,829 0,407 Caan 2008 Breast pfs 1,000 0,700 1,429 0,000 1,000 Cespedes-F eliciano 2017Breast pfs 0,990 0,750 1,307 -0,071 0,943 Chrom ecki 2012 Bladder/UT UC pfs 1,160 1,020 1,319 2,262 0,024 Clark 2013 CRC pfs 1,520 0,630 3,667 0,932 0,351 Copson 2014 Breast pfs 1,210 1,000 1,464 1,960 0,050 Cortellini 2019 Various pfs 0,720 0,490 1,058 -1,673 0,094 Crozier 2013 Breast pfs 1,280 1,050 1,560 2,443 0,015 Cullen 2017 Prostate pfs 1,390 0,970 1,992 1,794 0,073 Dahdaleh 2018 CRC pfs 1,670 1,290 2,162 3,893 0,000 Dawood 2012 Breast pfs 1,020 0,860 1,210 0,227 0,820 Dignam 2003 Breast pfs 0,980 0,800 1,201 -0,195 0,845 Dignam 2006 Breast pfs 1,170 0,980 1,397 1,737 0,082 Elwood 2018 Breast pfs 1,300 0,810 2,086 1,087 0,277 F erro 2018 Bladder/UT UC pfs 2,510 1,760 3,580 5,081 0,000 Gam a 2017 Head & Neck pfs 1,100 0,700 1,729 0,413 0,679 Gong 2007 Prostate pfs 3,610 1,730 7,533 3,420 0,001 Goodwin 2012 Breast pfs 1,150 0,850 1,556 0,906 0,365 Grossberg 2016 Head & Neck pfs 0,390 0,150 1,014 -1,931 0,053 Halabi 2007 Prostate pfs 0,890 0,760 1,042 -1,446 0,148 His 2016 Breast pfs 0,920 0,570 1,485 -0,341 0,733 Ho 2012 Prostate pfs 1,730 1,120 2,672 2,471 0,013 Houdek 2019 Sarcom a pfs 0,890 0,470 1,685 -0,358 0,721 Hu 2019 Head & Neck pfs 2,016 1,100 3,695 2,268 0,023 Jiralerspong 2013 Breast pfs 1,130 0,980 1,303 1,682 0,093 Kaidar-Person 2015 CRC pfs 0,940 0,680 1,299 -0,375 0,708 Kawai 2016 Breast pfs 1,150 0,950 1,392 1,434 0,152 Keizm an 2014 RCC pfs 0,810 0,530 1,238 -0,974 0,330 Kichenadasse 2019 Lung pfs 0,860 0,730 1,013 -1,804 0,071 Kwan 2012 Breast pfs 1,060 0,940 1,195 0,951 0,342 Ladoire 2014 Breast pfs 1,130 0,930 1,373 1,230 0,219 Lee 2010 RCC pfs 0,215 0,053 0,872 -2,151 0,031 Loi 2005 Breast pfs 1,500 1,070 2,103 2,353 0,019 M aj-Hes 2017 Prostate pfs 2,150 1,710 2,703 6,552 0,000 M athur 2010 Hepatobiliary pfs 0,900 0,860 0,942 -4,542 0,000 M atsuo 2016 Utherine pfs 1,440 0,860 2,411 1,386 0,166 M ayerhardt 2008 CRC pfs 1,120 0,760 1,651 0,573 0,567 M azzarella 2013 Breast pfs 1,050 0,730 1,510 0,263 0,792 M cQuade 2018 M elanom a pfs 0,790 0,690 0,904 -3,414 0,001 M elhem -Bertrandt 2011 Breast pfs 1,160 0,900 1,495 1,146 0,252 M eyerhardt 2003 CRC pfs 1,240 0,980 1,569 1,792 0,073 M eyerhardt 2004 CRC pfs 1,100 0,910 1,330 0,985 0,325 M eyerhardt/2008 CRC dfs 1,120 0,780 1,608 0,614 0,539 M inlikeeva 2019 Ovarian pfs 1,040 0,920 1,176 0,627 0,531 M ontgom ery 2007 Prostate pfs 1,000 0,800 1,250 0,000 1,000 M utschler 2018 Breast pfs 1,380 1,030 1,849 2,158 0,031 Nagakawa 2016 Lung pfs 0,280 0,100 0,784 -2,423 0,015 Oh 2011 Breast pfs 1,020 0,505 2,060 0,055 0,956 Pajares 2013 Breast pfs 1,130 0,980 1,303 1,682 0,093 Pfeiler 2013 Breast pfs 1,450 1,030 2,041 2,129 0,033 Previs 2014 Ovarian pfs 1,470 0,080 27,011 0,259 0,795 Psutka 2015 RCC pfs 1,090 0,740 1,606 0,436 0,663 Roque 2016 Utherine pfs 0,660 0,370 1,177 -1,407 0,159 Schiffm ann 2017 Prostate pfs 0,980 0,860 1,117 -0,303 0,762 Senie 1992 Breast pfs 1,290 1,000 1,664 1,960 0,050 Sinicrope 2012 CRC pfs 1,370 1,140 1,646 3,357 0,001 Sinicrope 2013 CRC pfs 1,060 1,000 1,124 1,960 0,050 Sorbye 2012 CRC pfs 1,040 0,750 1,442 0,235 0,814 Spangler 2007 Prostate pfs 1,790 1,290 2,484 3,484 0,000 Sparano 2012 Breast pfs 1,170 1,040 1,316 2,613 0,009 Sparano 2012 (2) Breast pfs 1,240 1,080 1,424 3,052 0,002 Spreafico 2017 Gastroesophageal pfs 1,620 1,130 2,322 2,625 0,009 Su 2013 Breast pfs 1,000 0,740 1,351 0,000 1,000 Sun 2018 Breast pfs 1,880 1,280 2,761 3,219 0,001 T ait 2014 Breast pfs 0,945 0,605 1,476 -0,249 0,804 Vidal 2017 Prostate pfs 1,110 0,940 1,311 1,230 0,219 Vidal 2018 Prostate pfs 1,060 0,840 1,338 0,491 0,623 Wang 2019 CRC pfs 0,610 0,420 0,886 -2,596 0,009 Warren 2016 Breast pfs 1,440 0,970 2,138 1,809 0,070 Widschwendter 2015 Breast pfs 1,380 0,760 2,506 1,058 0,290 Xu 2018 Bladder/UT UC pfs 1,060 0,780 1,441 0,372 0,710 Yang 2019 Hepatobiliary pfs 1,310 1,030 1,666 2,201 0,028 Yoon 2011 Breast pfs 1,210 0,960 1,525 1,614 0,106 You 2015 CRC pfs 1,030 0,730 1,453 0,168 0,866 Yu 1991 RCC pfs 0,430 0,190 0,973 -2,025 0,043 Z heng 2015 CRC pfs 1,590 1,140 2,218 2,732 0,006 1,136 1,079 1,195 4,887 0,000 0,01 0,1 1 10 100 F avo u rs A F avo u rs B Meta Analysis Figure All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 2, 2020. ; https://doi.org/10.1101/2020.04.28.20082800doi: medRxiv preprint All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 2, 2020. ; https://doi.org/10.1101/2020.04.28.20082800doi: medRxiv preprint Study nam e Subgroup within study Outcom e Statistics for each study Hazard ratio and 95% CI Hazard L o wer Up p er ratio limit limit Z -Valu e p -Valu e Abdullah 2011 Various css 1,030 1,010 1,050 2,955 0,003 Akinyem iju 2018 Various css 0,760 0,610 0,947 -2,447 0,014 Alsaker 2011 Breast css 1,520 1,250 1,848 4,196 0,000 Arem 2013 Utherine css 1,790 1,200 2,670 2,854 0,004 Bandera 2015 Ovarian css 0,670 0,400 1,122 -1,522 0,128 Barroso 2017 Various css 3,550 0,530 23,778 1,306 0,192 Bassett 2011 Prostate css 1,520 0,890 2,596 1,533 0,125 Beasley 2012 Breast css 0,720 0,610 0,850 -3,884 0,000 Blair 2019 Breast css 1,330 0,950 1,862 1,661 0,097 Boggs 2011 Various css 1,120 0,860 1,459 0,841 0,400 Bonn 2014 Prostate css 1,290 0,670 2,484 0,762 0,446 Boyle 2013 CRC css 1,330 0,890 1,988 1,391 0,164 Braithwaite 2010 Breast css 0,720 0,340 1,525 -0,858 0,391 Caan 2008 Breast css 1,200 0,700 2,057 0,663 0,507 Cam pbell 2015 CRC css 1,200 1,060 1,358 2,881 0,004 Cespedes F eliciano 2017CRC css 2,290 1,350 3,885 3,073 0,002 Cespedes-F eliciano 2017Breast css 1,010 0,790 1,291 0,079 0,937 Chen 2010 Breast css 1,440 1,020 2,033 2,073 0,038 Chrom ecki 2012 Bladder/UT UC css 1,260 1,100 1,443 3,336 0,001 Chung 2017 Breast css 1,200 0,750 1,920 0,760 0,447 Cleveland 2011 Breast css 1,220 0,510 2,918 0,447 0,655 Connor 2016 Breast css 0,970 0,570 1,651 -0,112 0,911 Conroy 2011 Breast css 1,450 1,050 2,002 2,256 0,024 Dal M aso 2008 Breast css 1,380 1,020 1,867 2,088 0,037 Dickerm an 2017 Prostate css 1,090 0,760 1,563 0,468 0,640 Dignam 2003 Breast css 1,200 0,970 1,485 1,679 0,093 Dignam 2006 Breast css 1,070 0,820 1,396 0,498 0,618 Dignam 2006 CRC css 1,220 0,980 1,519 1,779 0,075 Doria-Rose 2006 CRC css 1,500 0,900 2,500 1,556 0,120 Drake 2017 Various css 1,450 1,170 1,797 3,394 0,001 Efstathiou 2008 Prostate css 1,640 1,010 2,663 2,000 0,045 Elwood 2018 Breast css 0,980 0,690 1,392 -0,113 0,910 Em aus 2009 Breast css 1,430 1,010 2,025 2,016 0,044 Eskelinen 2017 RCC css 1,020 0,770 1,351 0,138 0,890 F arris 2018 Prostate css 1,130 0,800 1,596 0,694 0,488 F edirko 2014 CRC css 1,260 1,040 1,527 2,361 0,018 F rum ovitz 2014 Utherine css 1,240 1,060 1,451 2,688 0,007 Genkinger 2015 Pancreas css 1,480 1,270 1,725 5,021 0,000 Gong 2007 Prostate css 2,640 1,180 5,906 2,363 0,018 Grossberg 2016 Head & Neck css 0,260 0,110 0,615 -3,069 0,002 Halabi 2007 Prostate css 0,810 0,680 0,965 -2,361 0,018 Han 2014 Breast css 1,240 0,940 1,636 1,522 0,128 Han 2014 Various css 1,290 0,980 1,698 1,816 0,069 Hellm ann 2010 Breast css 1,820 1,110 2,984 2,374 0,018 His 2016 Breast css 1,720 0,790 3,745 1,366 0,172 Hu 2019 Head & Neck css 2,022 1,040 3,931 2,076 0,038 Hynes 2017 Gastroesophageal css 0,890 0,570 1,390 -0,513 0,608 Iyengar 2014 Head & Neck css 5,010 1,690 14,852 2,906 0,004 Izum ida 2019 Various css 2,720 1,340 5,521 2,770 0,006 Jayasekara 2018 CRC css 1,410 0,900 2,209 1,500 0,134 Jeon 2015 Breast css 1,210 1,020 1,435 2,187 0,029 Jiralerspong 2013 Breast css 1,230 1,000 1,513 1,960 0,050 Katzm arzyk 2012 Various css 1,490 1,190 1,866 3,476 0,001 Kawai 2016 Breast css 1,470 1,110 1,947 2,688 0,007 Kelly 2017 Prostate css 1,460 1,020 2,090 2,068 0,039 Kenfield 2016 Prostate css 1,070 0,818 1,400 0,493 0,622 Khan 2017 Prostate css 1,400 0,990 1,980 1,903 0,057 Kocarnik 2017 CRC css 1,860 0,670 5,164 1,191 0,234 Kotsopoulos 2012 Ovarian css 1,110 0,870 1,416 0,840 0,401 Kwan 2012 Breast css 1,100 0,950 1,274 1,274 0,203 Kwan 2013 Breast css 1,100 0,970 1,247 1,485 0,137 Landm an 2008 Various css 0,570 0,320 1,015 -1,908 0,056 Lee 2018 Gastroesophageal css 0,750 0,440 1,278 -1,057 0,290 Leung 2010 Lung css 0,550 0,100 3,025 -0,687 0,492 Lin 2013 Pancreas css 0,960 0,670 1,376 -0,222 0,824 Liss 2016 RCC css 2,840 1,290 6,252 2,592 0,010 M a 2008 Prostate css 1,950 1,170 3,250 2,562 0,010 M askarinec 2011 Breast css 2,990 1,220 7,328 2,395 0,017 M atsuo 2018 Utherine css 0,220 0,060 0,807 -2,284 0,022 M cCullough 2005 Breast css 1,470 1,130 1,912 2,871 0,004 M cCullough 2016 Breast css 1,630 1,150 2,310 2,745 0,006 M cM ahon 2017 Hepatobiliary css 0,790 0,500 1,248 -1,010 0,312 M cT iernan 2018 Breast css 1,350 1,240 1,470 6,920 0,000 M oller 2014 Prostate css 1,270 0,900 1,792 1,360 0,174 M orikawa 2012 CRC css 1,100 0,810 1,494 0,610 0,542 M urakam i 2018 Bladder/UT UC css 3,460 1,380 8,675 2,647 0,008 Nagle 2018 Utherine css 0,840 0,560 1,260 -0,843 0,399 Nechuta 2010 Various css 0,820 0,620 1,085 -1,391 0,164 Nichols 2009 Breast css 1,420 0,860 2,345 1,370 0,171 Nonem aker 2009 Lung css 0,500 0,160 1,563 -1,192 0,233 Nur 2019 Breast css 1,450 0,810 2,596 1,251 0,211 Ogino 2009 CRC css 1,100 0,240 5,042 0,123 0,902 Oudanonh 2019 Breast css 1,330 0,910 1,944 1,473 0,141 Pajares 2013 Breast css 1,130 0,940 1,358 1,301 0,193 Parker 2006 RCC css 0,480 0,360 0,640 -5,000 0,000 Pelser 2014 CRC css 1,150 0,960 1,378 1,517 0,129 Previs 2014 Ovarian css 1,660 0,570 4,834 0,929 0,353 Prizm ent 2010 CRC css 1,300 0,950 1,779 1,639 0,101 Psutka 2015 RCC css 1,310 0,800 2,145 1,073 0,283 Rosenberg 2009 Breast css 1,400 1,100 1,782 2,735 0,006 Rudm an 2016 Prostate css 1,020 0,550 1,892 0,063 0,950 Ruterbusch 2014 Utherine css 0,900 0,580 1,397 -0,470 0,638 Sasazuki 2011 Various css 1,250 1,070 1,460 2,813 0,005 Silventoinen 2014 Various css 1,540 1,270 1,867 4,390 0,000 Sparano 2012 (2) Breast css 1,450 1,160 1,813 3,264 0,001 Sun 2015 Breast css 0,970 0,680 1,384 -0,168 0,867 T odo 2014 Utherine css 0,880 0,510 1,518 -0,459 0,646 T seng 2013 Various css 0,830 0,730 0,944 -2,845 0,004 T seng 2016 Various css 1,012 0,918 1,116 0,240 0,810 T urner 2011 Lung css 0,540 0,270 1,080 -1,742 0,081 Valentijn 2013 Various css 0,630 0,460 0,863 -2,879 0,004 Vidal 2017 Prostate css 2,050 1,040 4,041 2,073 0,038 Vidal 2018 Prostate css 1,110 0,860 1,433 0,802 0,423 Wang 2017 CRC css 1,510 1,080 2,111 2,410 0,016 Xu 2018 Bladder/UT UC css 1,130 0,770 1,658 0,624 0,532 Xu 2018 Various css 1,150 0,970 1,363 1,609 0,108 Yang 2008 Ovarian css 1,230 0,870 1,739 1,172 0,241 Yoon 2011 Breast css 1,140 0,880 1,477 0,992 0,321 Yuan 2013 Pancreas css 1,280 1,060 1,546 2,566 0,010 1,179 1,125 1,236 6,856 0,000 0,01 0,1 1 10 100 F avo u rs A F avo u rs B Meta Analysis All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 2, 2020. ; https://doi.org/10.1101/2020.04.28.20082800doi: medRxiv preprint Suppl. file 2: Funnel plot of publication bias for overall survival analysis -2,0 -1,5 -1,0 -0,5 0,0 0,5 1,0 1,5 2,0 0,0 0,5 1,0 1,5 2,0 Standard Error Log hazard ratio Funnel Plot of Standard Error by Log hazard ratio All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted May 2, 2020. ; https://doi.org/10.1101/2020.04.28.20082800doi: medRxiv preprint