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New Era of Cancer Research: From Large-Scale Cohorts to Big-Data

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A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Epidemiology and Prevention".

Deadline for manuscript submissions: 10 August 2024 | Viewed by 13066

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Special Issue Editors

Dr. Dimitra Sifaki-Pistolla

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Guest Editor

1. Cancer Registry of Crete, School of Medicine, University of Crete, 700 13 Heraklion, Greece
2. Department of Medical Oncology, University General Hospital of Heraklion, 715 00 Heraklion, Greece
Interests: environmental epidemiology; epidemiology; cancer epidemiology; public health; risk factors; environmental exposures; spatial statistics; geo-epidemiology; respiratory diseases; chronic diseases; COVID-19; prediction models; biostatistics
Special Issues, Collections and Topics in MDPI journals

Dr. Georgia Pistolla

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Guest Editor

1. Cancer Registry of Crete, Clinic of Social and Family Medicine, School of Medicine, University of Crete, Heraklion, Greece
2. 7th Health Region of Crete, Heraklion, Greece
Interests: medicine; public health; epidemiology; mathematical analysis in health data; biostatistics

Special Issue Information

Dear Colleagues,

Cancer is the second leading cause of incidence and mortality globally, leading to a wide range of inequalities within countries. Cancer rates are predicted to increase even further in the next decades. Data on cancer incidence, mortality, survival and clinical outcomes have been gathered for many years as an essential resource for planning cancer control programmes that focus on primary prevention, early detection, screening and treatment. Population-based cancer cohorts and registries constituted the first step towards building reliable and comprehensive cancer data that could be translated into clinical practices, public health policies and methodological frameworks for chronic diseases. Nowadays, the discussion has turned towards big data, linking all types of data with these large-scale cohorts.

Modern biomedical research and clinical care on cancer involve more data than ever. Big data provide an unprecedented opportunity to understand cancer and contribute to treatment decisions based on evidence and knowledge extracted from these massive collections of data. Data-mining techniques, spatial statistics and geographical information systems were previously among the most useful tools for managing large-scale cohorts and population-based data. With the evolution of big data, machine learning and artificial intelligence approaches are now at the forefront of these tools and methodologies. Combining all these techniques will help in rapidly and precisely analyzing complex biomedical data and finding hidden patterns.

This Special Issue will collect studies from different settings and multidisciplinary teams worldwide. It aims to “map” recent trends in cancer epidemiology and clinical research, derived from large-scale cohorts, population-based registrations and, most importantly, big data.

Dr. Dimitra Sifaki-Pistolla
Dr. Georgia Pistolla
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

cancer epidemiology
cancer control
cancer data
biomedical research
data-mining techniques
spatial statistics
geographical information system
artificial intelligence

Published Papers (8 papers)

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Research

11 pages, 509 KiB

Open AccessArticle

Clinical and Genomic Features of Patients with Renal Cell Carcinoma and Advanced Chronic Kidney Disease: Analysis of a Multi-Institutional Database

by Corbin J. Eule, Junxiao Hu, Dale Hedges, Alkesh Jani, Thomas Pshak, Brandon J. Manley, Alejandro Sanchez, Robert Dreicer, Zin W. Myint, Yousef Zakharia and Elaine T. Lam

Cancers 2024, 16(10), 1920; https://doi.org/10.3390/cancers16101920 - 18 May 2024

Viewed by 249

Abstract

Background: Patients with advanced chronic kidney disease (ACKD) are at an increased risk of developing renal cell carcinoma (RCC), but molecular alterations in RCC specimens arising from ACKD and overall survival (OS) in affected patients are not well defined. Patients and Methods: Using the Oncology Research Information Exchange Network (ORIEN) Total Cancer Care^® protocol, 296 consented adult patients with RCC and somatic tumor whole exome sequencing were included. Patients with ACKD were defined as those with serum creatinine ≥1.5 mg/dL prior to RCC diagnosis. Results: Of 296 patients with RCC, 61 met the criteria for ACKD. The most common somatic mutations in the overall cohort were in VHL (126, 42.6%), PBRM1 (102, 34.5%), and SETD2 (54, 18.2%). BAP1 had a decreased mutational frequency in RCC specimens from patients without ACKD as compared to those with ACKD (10.6% versus 1.6%), but this was not statistically significant in univariable (OR 0.14, p = 0.056) or multivariable (OR 0.15, p = 0.067) analysis. Median OS was not reached in either cohort. Conclusions: Using the clinicogenomic ORIEN database, our study found lower rates of BAP1 mutations in RCC specimens from patients with ACKD, which may reflect a BAP1-independent mutational driver of RCC in patients with ACKD. Full article

(This article belongs to the Special Issue New Era of Cancer Research: From Large-Scale Cohorts to Big-Data)

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15 pages, 851 KiB

Open AccessArticle

Implementation and Evaluation of a Breast Cancer Disease Model Using Real-World Claims Data in Germany from 2010 to 2020

by Dominik Dannehl, Alexandra von Au, Tobias Engler, Léa Louise Volmer, Raphael Gutsfeld, Johannes Felix Englisch, Markus Hahn, Sabine Hawighorst-Knapstein, Ariane Chaudhuri, Armin Bauer, Markus Wallwiener, Florin-Andrei Taran, Diethelm Wallwiener, Sara Yvonne Brucker, Stephanie Wallwiener, Andreas Daniel Hartkopf and Tjeerd Maarten Hein Dijkstra

Cancers 2024, 16(8), 1490; https://doi.org/10.3390/cancers16081490 - 13 Apr 2024

Viewed by 614

Abstract

Breast cancer is the leading cause of cancer-related mortality among women in Germany and worldwide. This retrospective claims data analysis utilizing data from AOK Baden-Wuerttemberg, a major statutory German health insurance provider, aimed to construct and assess a real-world data breast cancer disease model. The study included 27,869 female breast cancer patients and 55,738 age-matched controls, analyzing data from 2010 to 2020. Three distinct breast cancer stages were analyzed: Stage A (early breast cancer without lymph node involvement), Stage B (early breast cancer with lymph node involvement), and Stage C (primary distant metastatic breast cancer). Tumor subtypes were estimated based on the prescription of antihormonal or HER2-targeted therapy. The study established that 77.9% of patients had HR+ breast cancer and 9.8% HER2+; HR+/HER2− was the most common subtype (70.9%). Overall survival (OS) analysis demonstrated significantly lower survival rates for stages B and C than for controls, with 5-year OS rates ranging from 79.3% for stage B to 35.4% for stage C. OS rates were further stratified by tumor subtype and stage, revealing varying prognoses. Distant recurrence-free survival (DRFS) analysis showed higher recurrence rates in stage B than in stage A, with HR−/HER2− displaying the worst DRFS. This study, the first to model breast cancer subtypes, stages, and outcomes using German claims data, provides valuable insights into real-world breast cancer epidemiology and demonstrates that this breast cancer disease model has the potential to be representative of treatment outcomes. Full article

(This article belongs to the Special Issue New Era of Cancer Research: From Large-Scale Cohorts to Big-Data)

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14 pages, 1725 KiB

Open AccessArticle

Stage Shifting by Modifying the Determinants of Breast Cancer Stage at Diagnosis: A Simulation Study

by Gyanendra Pokharel, Qinggang Wang, Momtafin Khan, Paula J. Robson, Lorraine Shack and Karen A. Kopciuk

Cancers 2024, 16(6), 1201; https://doi.org/10.3390/cancers16061201 - 19 Mar 2024

Viewed by 866

Abstract

Background: Breast cancer is the most common cancer in Canadian women; nearly 25% of women diagnosed with cancer have breast cancer. The early detection of breast cancer is a major challenge because tumours often grow without causing symptom. The diagnosis of breast cancer at an early stage (stages I and II) improves survival outcomes because treatments are more effective and better tolerated. To better inform the prevention of and screening for breast cancer, simulations using modifiable rather than non-modifiable risk factors may be helpful in shifting the stage at diagnosis downward. Methods: Breast cancer stages were simulated using the data distributions from Alberta’s Tomorrow Project participants who developed breast cancer. Using multivariable partial proportional odds regression models, modifiable lifestyle factors associated with the stage of cancer at diagnosis were evaluated. The proportions or mean levels of these lifestyle factors in the simulated population were systematically changed, then multiplied by their corresponding estimated odds ratios from the real data example. The effects of these changes were evaluated singly as well as cumulatively. Results: Increasing total dietary protein (g/day) intake was the single most important lifestyle factor in shifting the breast cancer stage downwards followed by decreasing total dietary energy intake (kcal/day). Increasing the proportion of women who spend time in the sun between 11 am and 4 pm in the summer months, who have had a mammogram, who have been pregnant or reducing the proportion who are in stressful situations had much smaller effects. The percentage of Stage I diagnoses could be increased by approximately 12% with small modifications of these lifestyle factors. Conclusion: Shifting the breast cancer stage at diagnosis of a population may be achieved through changes to lifestyle factors. This proof of principle study that evaluated multiple factors associated with the stage at diagnosis in a population can be expanded to other cancers as well, providing opportunities for cancer prevention programs to target specific factors and identify populations at higher risk. Full article

(This article belongs to the Special Issue New Era of Cancer Research: From Large-Scale Cohorts to Big-Data)

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14 pages, 1570 KiB

Open AccessArticle

A Propensity-Matched Retrospective Comparative Study with Historical Control to Determine the Real-World Effectiveness of Durvalumab after Concurrent Chemoradiotherapy in Unresectable Stage III Non-Small Cell Lung Cancer

by Cheol-Kyu Park, Nakyung Jeon, Hwa-Kyung Park, Hyung-Joo Oh, Young-Chul Kim, Ha-Lim Jeon, Yong-Hyub Kim, Sung-Ja Ahn and In-Jae Oh

Cancers 2023, 15(5), 1606; https://doi.org/10.3390/cancers15051606 - 5 Mar 2023

Cited by 3 | Viewed by 1992

Abstract

This study aimed to add real-world evidence to the literature regarding the effectiveness and safety of durvalumab consolidation (DC) after concurrent chemoradiotherapy (CCRT) in the treatment of unresectable stage III non-small cell lung cancer (NSCLC). Using a hospital-based NSCLC patient registry and propensity score matching in a 2:1 ratio, we conducted a retrospective cohort study of patients with unresectable stage III NSCLC who completed CCRT with and without DC. The co-primary endpoints were 2-year progression-free survival and overall survival. For the safety evaluation, we evaluated the risk of any adverse events requiring systemic antibiotics or steroids. Of 386 eligible patients, 222 patients—including 74 in the DC group—were included in the analysis after propensity score matching. Compared with CCRT alone, CCRT with DC was associated with increased progression-free survival (median: 13.3 vs. 7.6 months, hazard ratio[HR]: 0.63, 95% confidence interval[CI]: 0.42–0.96) and overall survival (HR: 0.47, 95% CI: 0.27–0.82) without an increased risk of adverse events requiring systemic antibiotics or steroids. While there were differences in patient characteristics between the present real-world study and the pivotal randomized controlled trial, we demonstrated significant survival benefits and tolerable safety with DC after the completion of CCRT. Full article

(This article belongs to the Special Issue New Era of Cancer Research: From Large-Scale Cohorts to Big-Data)

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10 pages, 1123 KiB

Open AccessArticle

Nutritional Intake, Hospital Readmissions and Length of Stay in Hospitalised Oncology Patients

by Cecelia MacFarling Meure, Belinda Steer and Judi Porter

Cancers 2023, 15(5), 1488; https://doi.org/10.3390/cancers15051488 - 27 Feb 2023

Cited by 3 | Viewed by 1393

Abstract

Background: Poor food intake is an independent risk factor for malnutrition in oncology patients, and achieving adequate nutrition is essential for optimal clinical and health outcomes. This study investigated interrelationships between nutritional intake and clinical outcomes in hospitalised adult oncology patients. Methods: Estimated nutrition intake data were obtained from patients admitted to a 117-bed tertiary cancer centre during May–July 2022. Clinical healthcare data, including length of stay (LOS) and 30-day hospital readmissions, were obtained from patient medical records. Statistical analysis, including multivariable regression analysis, assessed whether poor nutritional intake was predictive of LOS and readmissions. Results: No relationships between nutritional intake and clinical outcomes were evident. Patients at risk of malnutrition had lower mean daily energy (−898.9 kJ, p = 0.001) and protein (−10.34 g, p = 0.015) intakes. Increased malnutrition risk at admission prolonged LOS (1.33 days, p = 0.008). Hospital readmission rates were 20.2%, and associated with age (r = −0.133, p = 0.015), presence of metastases (r = 0.125, p = 0.02) and longer LOS (1.34 days, r = 0.145, p = 0.02). Sarcoma (43.5%), gynaecological (36.8%) and lung (40.0%) cancers had the highest readmission rates. Conclusions: Despite research showing the benefits of nutritional intake during hospitalisation, evidence continues to emerge on the relationship between nutritional intake and LOS and readmissions that may be confounded by malnutrition risk and cancer diagnosis. Full article

(This article belongs to the Special Issue New Era of Cancer Research: From Large-Scale Cohorts to Big-Data)

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12 pages, 284 KiB

Open AccessArticle

Measuring the Wellbeing of Cancer Patients with Generic and Disease-Specific Instruments

by Gang Chen, Norma B. Bulamu, Ellen McGrane and Jeff Richardson

Cancers 2023, 15(4), 1351; https://doi.org/10.3390/cancers15041351 - 20 Feb 2023

Viewed by 1669

Abstract

Different wellbeing measures have been used among cancer patients. This study aimed to first investigate the sensitivity of health state utility (HSU), capability, and subjective wellbeing (SWB) instruments in cancer. A cancer-specific instrument (QLQ-C30) was included and transferred onto the cancer-specific HSU scores. Furthermore, it examined the relative importance of key life domains explaining overall life satisfaction. Data were drawn from the Multi-instrument Comparison survey. Linear regression was used to explore the extent to which the QLQ-C30 sub-scales explain HSU and SWB. Kernel-based Regularized Least Squares (KRLS), a machine learning method, was used to explore the life domain importance of cancer patients. As expected, the QLQ-C30 sub-scales explained the vast majority of the variance in its derived cancer-specific HSU (R² = 0.96), followed by generic HSU instruments (R² of 0.65–0.73) and SWB and capability instruments (R² of 0.33–0.48). The cancer-specific measure was more closely correlated with generic HSU than SWB measures, owing to the construction of these instruments. In addition to health, life achievements, relationships, the standard of living, and future security all play an important role in explaining the overall life satisfaction of cancer patients. Full article

(This article belongs to the Special Issue New Era of Cancer Research: From Large-Scale Cohorts to Big-Data)

9 pages, 1450 KiB

Open AccessArticle

Lung Cancer Screening in Greece: A Modelling Study to Estimate the Impact on Lung Cancer Life Years

by Kyriakos Souliotis, Christina Golna, Pavlos Golnas, Ioannis-Anestis Markakis, Helena Linardou, Dimitra Sifaki-Pistolla and Evi Hatziandreou

Cancers 2022, 14(22), 5484; https://doi.org/10.3390/cancers14225484 - 8 Nov 2022

Cited by 3 | Viewed by 1787

Abstract

(1) Background: Lung cancer causes a substantial epidemiological burden in Greece. Yet, no formal national lung cancer screening program has been introduced to date. This study modeled the impact on lung cancer life years (LCLY) of a hypothetical scenario of comprehensive screening for lung cancer with low-dose computed tomography (LDCT) of the high-risk population in Greece, as defined by the US Preventive Services Taskforce, would be screened and linked to care (SLTC) for lung cancer versus the current scenario of background (opportunistic) screening only; (2) Methods: A stochastic model was built to monitor a hypothetical cohort of 100,000 high-risk men and women as they transitioned between health states (without cancer, with cancer, alive, dead) over 5 years. Transition probabilities were based on clinical expert opinion. Cancer cases, cancer-related deaths, and LCLYs lost were modeled in current and hypothetical scenarios. The difference in outcomes between the two scenarios was calculated. 150 iterations of simulation scenarios were conducted for 100,000 persons; (3) Results: Increasing SLTC to a hypothetical 100% of eligible high-risk people in Greece leads to a statistically significant reduction in deaths and in total years lost due to lung cancer, when compared with the current SLTC paradigm. Over 5 years, the model predicted a difference of 339 deaths and 944 lost years between the hypothetical and current scenario. More specifically, the hypothetical scenario led to fewer deaths (−24.56%, p < 0.001) and fewer life years lost (−31.01%, p < 0.001). It also led to a shift to lower-stage cancers at the time of diagnosis; (4) Conclusions: Our study suggests that applying a 100% screening strategy amongst high-risk adults aged 50–80, would result in additional averted deaths and LCLYs gained over 5 years in Greece. Full article

(This article belongs to the Special Issue New Era of Cancer Research: From Large-Scale Cohorts to Big-Data)

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13 pages, 2050 KiB

Open AccessArticle

Significant Rise of Colorectal Cancer Incidence in Younger Adults and Strong Determinants: 30 Years Longitudinal Differences between under and over 50s

by Dimitra Sifaki-Pistolla, Viktoria Poimenaki, Ilektra Fotopoulou, Emmanouil Saloustros, Dimitrios Mavroudis, Lampros Vamvakas and Christos Lionis

Cancers 2022, 14(19), 4799; https://doi.org/10.3390/cancers14194799 - 30 Sep 2022

Cited by 17 | Viewed by 3649

Abstract

(1) Background: There is evidence in the recent literature that the incidence patterns of colorectal cancer (CRC) have changed considerably over the years, tending to rise rapidly in individuals under 50 years old compared with those over 50 years. The current study aimed to assess the incidence of CRC in Crete from 1992–2021 and compare them among younger and older adults. (2) Methods: Data on malignant neoplasms of colon, rectosigmoid junction, and rectum have been extracted from the database of the Regional Cancer Registry of Crete. (3) Results: The number of these cases for the period 1992–2021 was 3857 (n = 2895 colon and n = 962 rectum). The mean age-specific incidence rate (ASpIR/100,000/year) of colon cancer patients <50 years was 7.2 (95% CI 5.1–9.7), while for patients ≥50 years the ASpIR was 149 (95% CI 146.2–153.4). ASpIR presented a 29.6% increase from 2001 to 2011 in the age group of 20–34 years and further increase is expected from 2022–2030 (projected change, 42.8%). The main risk factors were the pack years (p = 0.01), alcohol consumption (0.02), and farmer occupation (0.04), especially during 2012–2021. (4) Conclusions: We confirmed an increased incidence of CRC in young adults <50 in a European population with low cancer incidence in the past and a worrisome prediction for the near future. The observed trends clearly indicate that starting CRC screening at an earlier age may be essential. Full article

(This article belongs to the Special Issue New Era of Cancer Research: From Large-Scale Cohorts to Big-Data)

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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Assessment of patient perceptions of counselling on oral antineoplastic agents by a dedicated Cancer Services Pharmacist in an outpatient cancer clinic
Authors: Petra Czarniak
Affiliation: Curtin University

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