A New Hierarchy of Research Evidence for Tumor Pathology: A Delphi Study to Define Levels of Evidence in Tumor Pathology

The hierarchy of evidence is a fundamental concept in evidence-based medicine, but existing models can be challenging to apply in laboratory-based health care disciplines, such as pathology, where the types of evidence and contexts are significantly different from interventional medicine. This project aimed to define a comprehensive and complementary framework of new levels of evidence for evaluating research in tumor pathology-introducing a novel Hierarchy of Research Evidence for Tumor Pathology collaboratively designed by pathologists with help from epidemiologists, public health professionals, oncologists, and scientists, specifically tailored for use by pathologists-and to aid in the production of the World Health Organization Classification of Tumors (WCT) evidence gap maps. To achieve this, we adopted a modified Delphi approach, encompassing iterative online surveys, expert oversight, and external peer review, to establish the criteria for evidence in tumor pathology, determine the optimal structure for the new hierarchy, and ascertain the levels of confidence for each type of evidence. Over a span of 4 months and 3 survey rounds, we collected 1104 survey responses, culminating in a 3-day hybrid meeting in 2023, where a new hierarchy was unanimously agreed upon. The hierarchy is organized into 5 research theme groupings closely aligned with the subheadings of the WCT, and it consists of 5 levels of evidence-level P1 representing evidence types that merit the greatest level of confidence and level P5 reflecting the greatest risk of bias. For the first time, an international collaboration of pathology experts, supported by the International Agency for Research on Cancer, has successfully united to establish a standardized approach for evaluating evidence in tumor pathology. We intend to implement this novel Hierarchy of Research Evidence for Tumor Pathology to map the available evidence, thereby enriching and informing the WCT effectively.The overall project, International Agency for Research on Cancer, and beneficiaries (German Heart Centre Munich, Maria Sklodowska-Curie National Research Institute of Oncology, and Instituto de Salud Carlos III) are funded by the European Commission (HORIZON grant no. 101057127). R.C. and F.C. are funded by UK Research and Innovation. S.H. has received research funding or honoraria from Roche, BMS, Merck, Sysmex, Thermo, Volition, Trillium, Medica, and Instand and is a founder of SFZ BioCoDE and CEBIO. P.H.T. has received honoraria from AstraZeneca.S

Municipal distribution of ovarian cancer mortality in Spain

<p>Abstract</p> <p>Background</p> <p>Spain was the country that registered the greatest increases in ovarian cancer mortality in Europe. This study describes the municipal distribution of ovarian cancer mortality in Spain using spatial models for small-area analysis.</p> <p>Methods</p> <p>Smoothed relative risks of ovarian cancer mortality were obtained, using the Besag, York and Molliè autoregressive spatial model. Standardised mortality ratios, smoothed relative risks, and distribution of the posterior probability of relative risks being greater than 1 were depicted on municipal maps.</p> <p>Results</p> <p>During the study period (1989–1998), 13,869 ovarian cancer deaths were registered in 2,718 Spanish towns, accounting for 4% of all cancer-related deaths among women. The highest relative risks were mainly concentrated in three areas, i.e., the interior of Barcelona and Gerona (north-east Spain), the north of Lugo and Asturias (north-west Spain) and along the Seville-Huelva boundary (in the south-west). Eivissa (Balearic Islands) and El Hierro (Canary Islands) also registered increased risks.</p> <p>Conclusion</p> <p>Well established ovarian cancer risk factors might not contribute significantly to the municipal distribution of ovarian cancer mortality. Environmental and occupational exposures possibly linked to this pattern and prevalent in specific regions, are discussed in this paper. Small-area geographical studies are effective instruments for detecting risk areas that may otherwise remain concealed on a more reduced scale.</p

Municipal distribution of breast cancer mortality among women in Spain

<p>Abstract</p> <p>Background</p> <p>Spain has one of the lowest rates of breast cancer in Europe, though estimated incidence has risen substantially in recent decades. Some years ago, the Spanish Cancer Mortality Atlas showed Spain as having a heterogeneous distribution of breast cancer mortality at a provincial level. This paper describes the municipal distribution of breast cancer mortality in Spain and its relationship with socio-economic indicators.</p> <p>Methods</p> <p>Breast cancer mortality was modelled using the Besag-York-Molliè autoregressive spatial model, including socio-economic level, rurality and percentage of population over 64 years of age as surrogates of reproductive and lifestyle risk factors. Municipal relative risks (RRs) were independently estimated for women aged under 50 years and for those aged 50 years and over. Maps were plotted depicting smoothed RR estimates and the distribution of the posterior probability of RR>1.</p> <p>Results</p> <p>In women aged 50 years and over, mortality increased with socio-economic level, and was lower in rural areas and municipalities with higher proportion of old persons. Among women aged under 50 years, rurality was the only statistically significant explanatory variable.</p> <p>For women older than 49 years, the highest relative risks were mainly registered for municipalities located in the Canary Islands, Balearic Islands, the Mediterranean coast of Catalonia and Valencia, plus others around the Ebro River. In premenopausal women, the pattern was similar but tended to be more homogeneous. In mainland Spain, a group of municipalities with high RRs were located in Andalusia, near the left bank of the Guadalquivir River.</p> <p>Conclusion</p> <p>As previously observed in other contexts, mortality rates are positively related with socio-economic status and negatively associated with rurality and the presence of a higher proportion of people over age 64 years. Taken together, these variables represent the influence of lifestyle factors which have determined the increase in breast cancer frequency over recent decades. The results for the younger group of women suggest an attenuation of the socio-economic gradient in breast cancer mortality in Spain. The geographical variation essentially suggests the influence of other environmental variables, yet the descriptive nature of this study does not allow for the main determinants to be established.</p

Development and evaluation of a machine learning-based in-hospital COVID-19 disease outcome predictor (CODOP): A multicontinental retrospective study

New SARS-CoV-2 variants, breakthrough infections, waning immunity, and sub-optimal vaccination rates account for surges of hospitalizations and deaths. There is an urgent need for clinically valuable and generalizable triage tools assisting the allocation of hospital resources, particularly in resource-limited countries. We developed and validate CODOP, a machine learning-based tool for predicting the clinical outcome of hospitalized COVID-19 patients. CODOP was trained, tested and validated with six cohorts encompassing 29223 COVID-19 patients from more than 150 hospitals in Spain, the USA and Latin America during 2020-22. CODOP uses 12 clinical parameters commonly measured at hospital admission for reaching high discriminative ability up to 9 days before clinical resolution (AUROC: 0.90-0.96), it is well calibrated, and it enables an effective dynamic risk stratification during hospitalization. Furthermore, CODOP maintains its predictive ability independently of the virus variant and the vaccination status. To reckon with the fluctuating pressure levels in hospitals during the pandemic, we offer two online CODOP calculators, suited for undertriage or overtriage scenarios, validated with a cohort of patients from 42 hospitals in three Latin American countries (78-100% sensitivity and 89-97% specificity). The performance of CODOP in heterogeneous and geographically disperse patient cohorts and the easiness of use strongly suggest its clinical utility, particularly in resource-limited countries

Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19)

Objectives: The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters. Methods: A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs). Results: In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22-0.47; p < 0.001) for tocilizumab, 0.82 (0.71-1.30; p 0.82) for IHDC, 0.61 (0.43-0.86; p 0.006) for PDC, and 1.17 (0.86-1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02-0.17; p < 0.001). Conclusions: Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situatio

Development and validation of COEWS (COVID-19 Early Warning Score) for hospitalized COVID-19 with laboratory features: A multicontinental retrospective study

Background: The emergence of new SARS-CoV-2 variants with significant immune-evasiveness, the relaxation of measures for reducing the number of infections, the waning of immune protection (particularly in high-risk population groups), and the low uptake of new vaccine boosters, forecast new waves of hospitalizations and admission to intensive care units. There is an urgent need for easily implementable and clinically effective Early Warning Scores (EWSs) that can predict the risk of complications within the next 24–48 hr. Although EWSs have been used in the evaluation of COVID-19 patients, there are several clinical limitations to their use. Moreover, no models have been tested on geographically distinct populations or population groups with varying levels of immune protection. Methods: We developed and validated COVID-19 Early Warning Score (COEWS), an EWS that is automatically calculated solely from laboratory parameters that are widely available and affordable. We benchmarked COEWS against the widely used NEWS2. We also evaluated the predictive performance of vaccinated and unvaccinated patients. Results: The variables of the COEWS predictive model were selected based on their predictive coefficients and on the wide availability of these laboratory variables. The final model included complete blood count, blood glucose, and oxygen saturation features. To make COEWS more actionable in real clinical situations, we transformed the predictive coefficients of the COEWS model into individual scores for each selected feature. The global score serves as an easy-to-calculate measure indicating the risk of a patient developing the combined outcome of mechanical ventilation or death within the next 48 hr. Conclusions: The COEWS score predicts death or MV within the next 48 hr based on routine and widely available laboratory measurements. The extensive external validation, its high performance, its ease of use, and its positive benchmark in comparison with the widely used NEWS2 position COEWS as a new reference tool for assisting clinical decisions and improving patient care in the upcoming pandemic waves. Funding: University of Vienna

Development and evaluation of a machine learning-based in-hospital COVID-19 disease outcome predictor (CODOP): A multicontinental retrospective study.

New SARS-CoV-2 variants, breakthrough infections, waning immunity, and sub-optimal vaccination rates account for surges of hospitalizations and deaths. There is an urgent need for clinically valuable and generalizable triage tools assisting the allocation of hospital resources, particularly in resource-limited countries. We developed and validate CODOP, a machine learning-based tool for predicting the clinical outcome of hospitalized COVID-19 patients. CODOP was trained, tested and validated with six cohorts encompassing 29223 COVID-19 patients from more than 150 hospitals in Spain, the USA and Latin America during 2020-22. CODOP uses 12 clinical parameters commonly measured at hospital admission for reaching high discriminative ability up to 9 days before clinical resolution (AUROC: 0·90-0·96), it is well calibrated, and it enables an effective dynamic risk stratification during hospitalization. Furthermore, CODOP maintains its predictive ability independently of the virus variant and the vaccination status. To reckon with the fluctuating pressure levels in hospitals during the pandemic, we offer two online CODOP calculators, suited for undertriage or overtriage scenarios, validated with a cohort of patients from 42 hospitals in three Latin American countries (78-100% sensitivity and 89-97% specificity). The performance of CODOP in heterogeneous and geographically disperse patient cohorts and the easiness of use strongly suggest its clinical utility, particularly in resource-limited countries

A Prognostic DNA Methylation Signature for Stage I Non–Small-Cell Lung Cancer

PURPOSE: Non-small-cell lung cancer (NSCLC) is a tumor in which only small improvements in clinical outcome have been achieved. The issue is critical for stage I patients for whom there are no available biomarkers that indicate which high-risk patients should receive adjuvant chemotherapy. We aimed to find DNA methylation markers that could be helpful in this regard.PATIENTS AND METHODS: A DNA methylation microarray that analyzes 450,000 CpG sites was used to study tumoral DNA obtained from 444 patients with NSCLC that included 237 stage I tumors. The prognostic DNA methylation markers were validated by a single-methylation pyrosequencing assay in an independent cohort of 143 patients with stage I NSCLC.RESULTS: Unsupervised clustering of the 10,000 most variable DNA methylation sites in the discovery cohort identified patients with high-risk stage I NSCLC who had shorter relapse-free survival (RFS; hazard ratio [HR], 2.35; 95% CI, 1.29 to 4.28; P = .004). The study in the validation cohort of the significant methylated sites from the discovery cohort found that hypermethylation of five genes was significantly associated with shorter RFS in stage I NSCLC: HIST1H4F, PCDHGB6, NPBWR1, ALX1, and HOXA9. A signature based on the number of hypermethylated events distinguished patients with high- and low-risk stage I NSCLC (HR, 3.24; 95% CI, 1.61 to 6.54; P = .001).CONCLUSION: The DNA methylation signature of NSCLC affects the outcome of stage I patients, and it can be practically determined by user-friendly polymerase chain reaction assays. The analysis of the best DNA methylation biomarkers improved prognostic accuracy beyond standard staging.</p

Adherence to nutrition-based cancer prevention guidelines and breast, prostate and colorectal cancer risk in the MCC-Spain case-control study

Prostate, breast and colorectal cancer are the most common tumours in Spain. The aim of the present study was to evaluate the association between adherence to nutrition-based guidelines for cancer prevention and prostate, breast and colorectal cancer, in the MCC-Spain case-control study. A total of 1,718 colorectal, 1,343 breast and 864 prostate cancer cases and 3,431 population-based controls recruited between 2007 and 2012, were included in the present study. The World Cancer Research Fund/American Institute for Cancer Research (WCRC/AICR) score based on six recommendations for cancer prevention (on body fatness, physical activity, foods and drinks that promote weight gain, plant foods, animal foods and alcoholic drinks; score range 0-6) was constructed. We used unconditional logistic regression analysis adjusting for potential confounders. One-point increment in the WCRF/AICR score was associated with 25% (95% CI 19-30%) lower risk of colorectal, and 15% (95% CI 7-22%) lower risk of breast cancer; no association with prostate cancer was detected, except for cases with a Gleason score ≥7 (poorly differentiated/undifferentiated tumours) (OR 0.87, 95% CI 0.76-0.99). These results add to the wealth of evidence indicating that a great proportion of common cancer cases could be avoided by adopting healthy lifestyle habits.This study was supported by Acción Transversal del Cancer and Instituto de Salud Carlos III-FEDER; Grant numbers: PI08/1770, PI08/0533, PI08/1359, PS09/00773, PS09/01286, PS09/01903, PS09/02078, PS09/01662, PI11/01403, PI11/01889, PI11/00226, PI11/01810, PI11/02213, PI12/ 00488, PI12/00265, PI12/01270, PI12/00715, PI12/00150; Grant sponsor: Fundación Marqués de Valdecilla; Grant number: API 10/09; Grant sponsors: ICGC International Cancer Genome Consortium CLL and Junta de Castilla y León; Grant number: LE22A10-2; Grant sponsor: Consejería de Salud of the Junta de Andalucía; Grant number: PI-0571; Grant sponsor: Conselleria de Sanitat of the Generalitat Valenciana; Grant number: AP 061/10; Grant sponsor: Recercaixa; Grant number: 2010ACUP 00310; Grant sponsors: Regional Government of the Basque Country and European Commission; Grant number: FOOD-CT-2006–036224-HIWATE; Grant sponsors: Spanish Association Against Cancer (AECC) Scientific Foundation and The Catalan Government DURSI Grant; Grant number: 2009SGR1489; Grant sponsors: Ministerio de Economía y Competitividad, Spain and European Regional Development Fund; Grant number: RYC-2011–0879

A prognostic DNA methylation signature for stage I non-small-cell lung cancer

Purpose Non-small-cell lung cancer (NSCLC) is a tumor in which only small improvements in clinical outcome have been achieved. The issue is critical for stage I patients for whom there are no available biomarkers that indicate which high-risk patients should receive adjuvant chemotherapy. We aimed to find DNA methylation markers that could be helpful in this regard. Patients and Methods A DNA methylation microarray that analyzes 450,000 CpG sites was used to study tumoral DNA obtained from 444 patients with NSCLC that included 237 stage I tumors. The prognostic DNA methylation markers were validated by a single-methylation pyrosequencing assay in an independent cohort of 143 patients with stage I NSCLC. Results Unsupervised clustering of the 10,000 most variable DNA methylation sites in the discovery cohort identified patients with high-risk stage I NSCLC who had shorter relapse-free survival (RFS; hazard ratio [HR], 2.35; 95% CI, 1.29 to 4.28; P = .004). The study in the validation cohort of the significant methylated sites from the discovery cohort found that hypermethylation of five genes was significantly associated with shorter RFS in stage I NSCLC: HIST1H4F, PCDHGB6, NPBWR1, ALX1, and HOXA9. A signature based on the number of hypermethylated events distinguished patients with high-and low-risk stage I NSCLC (HR, 3.24; 95% CI, 1.61 to 6.54; P = .001). Conclusion The DNA methylation signature of NSCLC affects the outcome of stage I patients, and it can be practically determined by user-friendly polymerase chain reaction assays. The analysis of the best DNA methylation biomarkers improved prognostic accuracy beyond standard staging. (C) 2013 by American Society of Clinical Oncology