Second Primary Cancers

We included all breast cancer patients that were operated on at the Karolinska Hospital from 1 January 1994 to 31 December 1996 (n = 524), identified from the population-based Stockholm–Gotland breast cancer registry established in 1976. Available tumor material was frozen on dry ice or in liquid nitrogen and was stored in -70°C freezers. Figure 1 shows the details of various exclusions leading to the final 159 patients for analysis. The ethical committee at the Karolinska Hospital approved this microarray expression project.
The different reasons for exclusion were not influenced by age at diagnosis (Table 1). The 231 tumors that were not analyzed using expression profiling had a lower mean diameter, had fewer mean affected lymph nodes, and had fewer individuals with recurrent disease at the end of the study period (Table 1). For those excluded for other reasons, there did not seem to be a selection based on age or stage of the disease, compared with those patients included in the study (Table 1).
The Stockholm–Gotland Breast Cancer Registry, supplemented with patient records, were examined for information on the tumor size, the number of retrieved and metastatic axillary lymph nodes, the hormonal receptor status, distant metastases, the site and date of relapse, initial therapy, therapy for possible recurrences, the date and cause of death. Tumor sections from the primary tumors from patients with array profiles were classified using Elston–Ellis grading [18 (link)] by a blinded pathologist (HN).
In the adjuvant setting tamoxifen and/or goserelin is normally used for hormonal treatment, but mostly intravenous cyclophosphamide, methotrexate and 5-fluorouracil (CMF) on days 1 and 8 was used as adjuvant chemotherapy, except in high-risk patients who were offered inclusion in the Scandinavian Breast Group 9401 study [19 (link)]. After primary therapy, patients were recommended to have regular clinical examinations and yearly mammograms, in addition to laboratory and X-ray tests guided by clinical signs and symptoms. Patients were normally followed for 5 years. Patients followed up outside the Karolinska Hospital were tracked using a unique personal identification number. There was no loss to follow-up.
The relapse site, date of relapse, relapse therapy and date of death were ascertained in May 2002. The average follow-up was 6.1 years. Cause of death was coded as death due to breast cancer (including those with distant metastases but dying from other causes), death due to other malignancies and death due to nonmalignant disorders. Through the population-based Swedish Cancer Registry, second primary malignancies were identified.

The GBD study provides a standardised analytical approach for estimating incidence, prevalence, and YLDs by age, sex, cause, year, and location. We aim to use all accessible information on disease occurrence, natural history, and severity that passes minimum inclusion criteria set disease-by-disease (appendix 1, p 33). Our approach is to optimise the comparability of data collected by varying methods or different case definitions; find a consistent set of estimates between data for prevalence, incidence, and causes of death; and predict estimates for locations with sparse or absent data by borrowing information from other locations and using covariates.
In this study, we use different methods to reflect the available data and specific epidemiology of each disease. Our main approach is to combine all sources of information for a disease using the Bayesian meta-regression tool DisMod-MR 2.1.¹⁶ Subsequently, we use data for severity, the occurrence of particular consequences of diseases, or sequelae, to establish the proportion of prevalent cases experiencing each sequela. Several broad classes of alternative approaches are used within the GBD study. First, for injuries, non-fatal estimates must account for the cause of injury (eg, a fall), the nature of injury (eg, a fracture or head injury), the amount of disability arising in the short term, and permanent disability for a subset of cases. Second, cancers were estimated by assessing the association between mortality and incidence, taking into account the effect on survival of access to, and quality of, treatment for the cancer site. Third, we combined the natural history model strategy for HIV/AIDS with the DisMod-MR 2.1 modelling approach for tuberculosis as HIV infection affects outcomes in patients who also have tuberculosis. Fourth, models for malaria, hepatitis, and varicella relied on data of the presence of circulating antibodies or parasites in the blood to predict the incidence of clinical episodes for which we estimate disability. Fifth, neonatal disorders were estimated from birth prevalence data and cohort studies on the risk of death in the first month and the probability of long-term disabling outcomes. Sixth, incidence of rabies, whooping cough, diphtheria, and tetanus was estimated from cause-specific mortality rates and data on the case fatality of acute episodes (appendix 1, p 33).
Below we describe these modelling efforts organised into eight sections; the supplementary methods (appendix 1, p 1) presents a single source for additional detail of inputs, analytical processes, outputs, and methods specific to each cause. This study complies with the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) recommendations (appendix 1, p 723).¹⁷ (link)

The epithelial hypoxia signature gene list consists of 253 unique image clones on the cDNA Stanford array by selecting a gene cluster showing induction in the tested epithelial cells (HMECs and RPTECs). For gene expression analysis of renal cell carcinoma [28 (link)], breast cancers [29 (link)], and ovarian cancer, the expression value of these clones was extracted and genes were selected for further analysis for which the corresponding array elements had fluorescent hybridization signals at least 2.5-fold greater than the local background fluorescence. We further restricted our analysis to genes for which adequate data were obtained in at least 80% of experiments. The image clones in the epithelial hypoxia signature that satisfied all the above criteria were used to stratify tumors in different datasets based on their hypoxia response with hierarchical clustering. For the analysis of breast cancer samples of Netherlands Cancer Institute (NKI), 35 of 253 unique image clones could not be mapped to a Unigene cluster. The 218 remaining clones were mapped to 168 unique Unigene clusters. The 168 Unigene cluster represented 180 unique sequences on the Rosetta/NKI oligo array. Cross-checking gene names revealed 22 probes that could not confidently be contributed to genes in the original hypoxia signature. These were removed, resulting in 158 matched probes. These 158 were the matching probes to 123 unique Unigene clusters. In order to overcome possible overestimation of Unigene clones that were matched to more than one probe on the NKI array, the probes that were not uniquely match to one Unigene cluster were averaged. Genes were mean centered and clustered and visualized in TreeView. Based on genes highly expressed in the hypoxia response, two groups of patients were identified. These were called, respectively, high- and low-hypoxia response. Overall survival was defined by death from any cause. Distant metastasis-free survival was defined by a distant metastasis as a first recurrence event; data on all patients were censored on the date of the last follow-up visit, death from causes other than breast cancer, the recurrence of local or regional disease, or the development of a second primary cancer, including contralateral breast cancer.
Kaplan-Meier survival curves were compared by the Cox-Mantel log-rank test in Winstat for Excel (R. Fitch Software, Staufen, Germany). Multivariate analysis by the Cox proportional hazard method was performed using the software package SPSS 11.5 (SPSS, Chicago, Illinois, United States).

This study included 422 patients with PC, 119 patients with benign pancreatic tumors (BPT; 39 chronic pancreatitis, 56 pancreatic serous cystadenomas, and 24 pancreatic mucinous cystadenomas), 98 patients with solid pseudo-papilloma of the pancreas (SPT), 59 patients with pancreatic neuroendocrine tumors (PNET), and 392 healthy controls (HC) from January 2015 to December 2021 at the Harbin Medical University Cancer Hospital. Eight patients with PC, 11 with other pancreatic diseases (OPT; two CP, two SPT, and seven pancreatic serous or mucinous cystadenoma), and nine HC from January 2017 to December 2021 in the Municipal Hospital Affiliated to Taizhou University were also enrolled in this study. The inclusion and exclusion criteria were as follows:1) age ≥ 18 years; 2) pathologically confirmed diagnoses of PC(adenocarcinoma, pancreatic ductal adenocarcinoma, and mucinous adenocarcinoma), neuroendocrine tumor (G1, G2, and G3), solid pseudopapillary neoplasm, chronic pancreatitis, pancreatic serous cystadenoma, and pancreatic mucinous cystadenoma; 3) R0 resection (radical surgical resection); 4) PC pathology at TNM stage I—II; 5) available clinical baseline information; 6) no antitumor therapy performed before surgery; 7) no second primary cancer; 8) no history of autoimmune disorders, hepatitis, nephropathy, coagulation disorders, or HIV infection; and 9) no acute inflammation before surgery.
Each disease group and HC from Harbin Medical University Cancer Hospital were randomly divided into training and testing sets 1 at a ratio of 4:1. The patients and HC from Municipal Hospital Affiliated to Taizhou University were used as testing set 2. Ethical approval for this study was granted by the Harbin Medical University Cancer Hospital and Municipal Hospital Affiliated to Taizhou University Ethics Committee, and all participants provided signed informed consent forms.

The SEER database comprises the initial treatment information. We placed patients with the first primary PBC into two groups: those who received RT and those who did not. The former (RT group) included BC patients who had surgery as well as (neo)adjuvant RT, whereas the latter no RT group included those who underwent surgery without radiotherapy.
The primary outcome of interest was the occurrence of an SEC, defined as a malignancy developing after at least one year of the initial therapy of BC patients. Follow-up ended at the point of interest, death, or the end of follow-up, whichever came first. The secondary outcomes were the overall survival (OS) and cancer-specific survival (CSS) of SECs. The endpoint events of OS and CSS were measured from the time after EC diagnosis. OS included all deaths from any cause during the follow-up period, while patients who were still alive were right-censored. CSS was defined based on the cause of death from esophageal etiology, whereas non-SEC deaths were considered competing risks, and patients alive were right-censored. Here the "non-SEC" developed a second cancer with a different cancer diagnosis.

The National Inpatient Sample (NIS) 2019 database was utilized to conduct this study. Data was extracted by utilizing the International Classification of Diseases, 10th Revision (ICD-10) codes. The inclusion criteria for this study were patients with a primary diagnosis of agranulocytosis secondary to cancer chemotherapy (ASCC) who underwent CVC insertion during the hospitalization. Exclusion criteria for this study were patients with iatrogenic pneumothorax secondary to central line insertion, patients with blood product administration, patients with platelet product administration, and patients with sepsis. The primary outcome studied was in-hospital mortality and was identified by the variable 'DIED' from the NIS database.
Data processing and statistical analysis was done using the SPSS version 26 software package (IBM Inc.m Armonk, New York). Categorical data was compared using Pearson's Chi-squared test, and continuous variables were compared using the independent samples t-test. Multivariate regression utilizing binomial logistic regression was performed with in-hospital mortality as the primary outcome. Multivariate regression was conducted to study the effects of CVC insertion on in-hospital mortality, with covariates including age, sex, race, primary payer status, and select medical comorbidities from the Charlson comorbidity index. A p-value of <0.05 was considered significant for all statistical comparisons.