Yang, Ping

11118 Background: Lung cancer survivors experience more physical and mental challenges, heavier symptom burden, and poorer quality of life than survivors of other cancers. Many symptoms persist or worsen for survivors beyond 5 years of cancer diagnosis, referred as long-term lung cancer survivors (LTLCS). Social determinants of health (SDoH) factors negatively impact patient reported outcomes (PROs) among cancer survivors, but their role in outcomes is under reported in lung cancer survivors and unknown in LTLCS. We investigated the impact of SDoH on PROs in a 20-year prospective lung cancer cohort. Methods: Our cohort includes 19,251 lung cancer patients (cases) diagnosed from 1997 through 2016 and followed up to 27 years, of whom 4799 (25%) became LTLCS. SDoH information was obtained by a lung cancer study PRO questionnaire or an institution SDoH questionnaire. Analyzed in this report are 2982 cases and 480 controls who had a low-dose chest CT scan and were ruled out for cancer, all answered at least one PRO questionnaire (first response); and 1366 LTLCS answered 5 years after diagnosis (long-term response). Along with fatigue and dyspnea, 5 SDoH variables, i.e., financial concerns, legal concerns, family and friends support, spiritual well-being, and social activity were scored on a 0 (worst) - 10 (best) scale and dichotomized as 0-5 (deficit/worse/lower) or 6-10 (no deficit/better/higher). We also assessed employment and health-related work experience. A difference of 1.0 unit or more in score was considered clinically meaningful along with a 2-sided test P-value less than 0.05; Chi-square or Fisher’s Exact Test and Kruskal-Wallis test were used accordingly. Results: Compared to controls, cases were younger (mean age: 62 vs. 65 years, P<0.05); more women than men reported worse financial concerns (56% vs. 38%, P<0.01) and lower spiritual well-being (43% vs. 26%, P=0.04) at first response; this sex disparity reduced but remained significant at long-term. Worse dyspnea (4.4-4.8 vs. 5.6) and fatigue (4.7-4.8 vs. 5.8-5.9) were seen in cases with more financial and legal concerns compared to those with fewer concerns (Ps<0.01). Cases with higher family and friends support, spiritual well-being and social activity had less dyspnea (5.7-6.0 vs. 4.2-4.5) and fatigue (5.5-5.9 vs. 3.9-4.4) than those with lower scores (Ps<0.04). Employment and health-related work experience data were provided by 2213 responders: Comparing 1735 cases to 478 controls, a lower proportion of cases was employed (28% vs. 39%), more cases “often felt tired after work” (49% vs. 28%), and more cases had “quite a lot” of difficulty in daily work or could not work at all” (26% vs. 9%, Ps<0.01). Conclusions: We found that in lung cancer survivors, more women than men reported worse financial concerns, and lower SDoH status was correlated with worse dyspnea and fatigue, indicating SDoH-driven interventions are important to improve lung cancer survivors’ PROs.

Background Analysis of the real-world data of low-dose spiral CT screening of healthy physical examination population in Southwest China from 2006 to 2017, discussed the standard of low-dose spiral CT screening for high-risk population of lung cancer in China. Methods Incorporate all team subjects who have undergone LDCT screenings at the Health Management Center of West China Hospital of Sichuan University between 2006 and 2017, and analyze the cumulative incidence and age-standardized (Segi population) incidence of lung cancer in the whole population; finally, analysis the age-specific, smoking and non-smokers’ lung cancer incidence. Results A total of 15,996 people who met the enrollment conditions and participated in the LDCT lung cancer screening were included. The average screening interval was 1.12±0.26 years, and the average follow-up time was 5.43±0.82 years; The cumulative number of lung cancer cases with negative baseline screening was 56 cases, and the cumulative incidence rate of lung cancer was 122.1/100,000 (the world standard rate is 60.6/100,000), of which males were 100.0/100,000 (the world standard rate is 45.3/100,000) and women were 156.6 /100,000 (the world standard rate is 94.7/100,000); the cumulative incidence of lung cancer in the smoking group is higher than that in the non-smoking group for both males and females, but the cumulative incidence of lung cancer in the non-smokers of the female population (146.3/100,000) is higher than that of the male population (86.5/10 Million). Conclusion In the real world study of lung cancer screening in Chinese population, we should consider setting different standards for high-risk groups of lung cancer by gender or smoking and non-smokers.

Abstract Background: Clinical, molecular, and genetic epidemiology studies displayed remarkable differences between ever- and never-smoking lung cancer. Methods: We conducted a stratified multi-population (European, East Asian, and African descent) association study on 44,823 ever-smokers and 20,074 never-smokers to identify novel variants that were missed in the non-stratified analysis. Functional analysis including expression quantitative trait loci (eQTL) colocalization and DNA damage assays, and annotation studies were conducted to evaluate the functional roles of the variants. We further evaluated the impact of smoking quantity on lung cancer risk for the variants associated with ever-smoking lung cancer. Results: Five novel independent loci, GABRA4, intergenic region 12q24.33, LRRC4C, LINC01088, and LCNL1 were identified with the association at two or three populations (P &lt; 5 × 10−8). Further functional analysis provided multiple lines of evidence suggesting the variants affect lung cancer risk through excessive DNA damage (GABRA4) or cis-regulation of gene expression (LCNL1). The risk of variants from 12 independent regions, including the well-known CHRNA5, associated with ever-smoking lung cancer was evaluated for never-smokers, light-smokers (packyear ≤ 20), and moderate-to-heavy-smokers (packyear &gt; 20). Different risk patterns were observed for the variants among the different groups by smoking behavior. Conclusions: We identified novel variants associated with lung cancer in only ever- or never-smoking groups that were missed by prior main-effect association studies. Impact: Our study highlights the genetic heterogeneity between ever- and never-smoking lung cancer and provides etiologic insights into the complicated genetic architecture of this deadly cancer.

AbstractBackgroundRecent therapeutic advances and screening technologies have improved survival among patients with lung cancer, who are now at high risk of developing second primary lung cancer (SPLC). Recently, an SPLC risk‐prediction model (called SPLC‐RAT) was developed and validated using data from population‐based epidemiological cohorts and clinical trials, but real‐world validation has been lacking. The predictive performance of SPLC‐RAT was evaluated in a hospital‐based cohort of lung cancer survivors.MethodsThe authors analyzed data from 8448 ever‐smoking patients diagnosed with initial primary lung cancer (IPLC) in 1997–2006 at Mayo Clinic, with each patient followed for SPLC through 2018. The predictive performance of SPLC‐RAT and further explored the potential of improving SPLC detection through risk model‐based surveillance using SPLC‐RAT versus existing clinical surveillance guidelines.ResultsOf 8448 IPLC patients, 483 (5.7%) developed SPLC over 26,470 person‐years. The application of SPLC‐RAT showed high discrimination area under the receiver operating characteristics curve: 0.81). When the cohort was stratified by a 10‐year risk threshold of ≥5.6% (i.e., 80th percentile from the SPLC‐RAT development cohort), the observed SPLC incidence was significantly elevated in the high‐risk versus low‐risk subgroup (13.1% vs. 1.1%, p < 1 × 10–6). The risk‐based surveillance through SPLC‐RAT (≥5.6% threshold) outperformed the National Comprehensive Cancer Network guidelines with higher sensitivity (86.4% vs. 79.4%) and specificity (38.9% vs. 30.4%) and required 20% fewer computed tomography follow‐ups needed to detect one SPLC (162 vs. 202).ConclusionIn a large, hospital‐based cohort, the authors validated the predictive performance of SPLC‐RAT in identifying high‐risk survivors of SPLC and showed its potential to improve SPLC detection through risk‐based surveillance.Plain Language Summary Lung cancer survivors have a high risk of developing second primary lung cancer (SPLC). However, no evidence‐based guidelines for SPLC surveillance are available for lung cancer survivors. Recently, an SPLC risk‐prediction model was developed and validated using data from population‐based epidemiological cohorts and clinical trials, but real‐world validation has been lacking. Using a large, real‐world cohort of lung cancer survivors, we showed the high predictive accuracy and risk‐stratification ability of the SPLC risk‐prediction model. Furthermore, we demonstrated the potential to enhance efficiency in detecting SPLC using risk model‐based surveillance strategies compared to the existing consensus‐based clinical guidelines, including the National Comprehensive Cancer Network.

e16326 Background: Cholelithiasis or Gallstone disease (GSD), is one of the most common and costly digestive disorders in adults worldwide, presenting 10-20% of the general population but in greater than 30% of Latina and Hispanic American women aged 40 years or older. Moreover, GSD individuals contribute significantly to the incidence and mortality of gallbladder and other digestive system cancers; specifically, the excessive risk of gallbladder cancer is 3-10-fold. Parallel to gallbladder cancer, an appalling female preponderance of GSD has been documented for decades; thus, precisely defining high-risk populations and properly managing patients with GSD would lead to reduced disease burden from GSD and multiple cancers. Several genome-wide association studies (GWAS) in the past have identified genes associated with GSD but few were focused on Latinx population. Methods: To identify genetic risk factors for GSD in Latinx population, we used the Sangre Por Salud cohort (SPS) where participants are Latinx adult individuals aged 18-85 years who were current patients in the Mountain Park Health Center in Arizona. This sample represents historically underrepresented population with low socioeconomic status that are primarily Spanish-speaking, majority being born outside of the US, with low rates of health insurance coverage. A total of 172 GSD cases were identified, each matched with 6 controls (n = 1032) who were people from the same community but without history of GSD diagnosis; matching variables are age at enrollment to SPS, sex, height and weight, dyslipidemia, and HbA1C level indicative of type 2 diabetes. We characterized genome-wide interrogation for biomarkers predicting GSD and contributing to the observed gender disparity by performing a GWAS using a matched case-control design. Results: We identified several novel loci associated with GSD and validated few loci that were reported in previous GWAS. The top 3 loci (MATN2, GPRIN3, GPC6) were strongly associated with GSD in our overall analysis; follow-up pathway enrichment analysis suggests enrichment of GO terms that are associated with immunological pathways, enzymatic processes in gallbladder, liver and gastrointestinal tract and GSD pathology. In a sex-specific analysis, CDHR3 and RPL31P35-SEC61G were identified for females, which have been associated respectively with rhinovirus C infection and HbA1c level in previous studies. Conclusions: Our findings suggest directions towards better and deeper understanding of sex differences in GSD pathology, biological mechanisms, and disease progression among Latinx people. Knowing approximately 80% of gallbladder cancer patients harbored gallstones, our optimal goal is to provide evidence for an innovative strategy to early detect and effectively intervene the modifiable risk factors to reduce gallbladder cancer burden in high risk populations.

AbstractBackgroundThere is a knowledge gap regarding lobar versus sublobar resection for atypical carcinoid (AC) of the lung. As such, the authors sought to understand and analyze the outcomes of sublobar resection versus lobectomy in this patient population.MethodsA retrospective analysis using the National Cancer Database was performed to compare overall survival (OS) between patients treated with lobectomy and patients treated with sublobar resection for AC of the lung between the years 2004 and 2016. Patient characteristics were compared with χ2 tests. The Kaplan–Meier method was used to estimate OS distributions, and the log‐rank test was used to compare distributions by treatment strategy. A multivariable Cox regression model was used to assess associations between the treatment strategy and OS. A propensity score matching method was also implemented to further eliminate treatment selection bias in the study sample.ResultsThe database identified 669 patients with T1–T4 and N0–N3 lung ACs that were surgically resected. Unadjusted Kaplan–Meier survival curves did not demonstrate an OS difference between lobectomy and sublobar resection (p = .094). After propensity score matching, curves demonstrated a numerical improvement in OS with lobectomy; however, it was not statistically significant (p = .5). In a subgroup analysis, lobectomy and node‐negative disease were associated with the best OS, whereas sublobar resection and node‐positive disease were associated with the worst OS (p < .0001). Nodal involvement was associated with worse survival, regardless of surgical treatment (p < .0001).ConclusionsIn patients with T1–T4 and N0–N3 ACs of the lung, lobectomy was not associated with an improvement in OS in comparison with sublobar resection.

Abstract The recent development of imaging and sequencing technologies enables systematic advances in the clinical study of lung cancer. Meanwhile, the human mind is limited in effectively handling and fully utilizing the accumulation of such enormous amounts of data. Machine learning-based approaches play a critical role in integrating and analyzing these large and complex datasets, which have extensively characterized lung cancer through the use of different perspectives from these accrued data. In this review, we provide an overview of machine learning-based approaches that strengthen the varying aspects of lung cancer diagnosis and therapy, including early detection, auxiliary diagnosis, prognosis prediction, and immunotherapy practice. Moreover, we highlight the challenges and opportunities for future applications of machine learning in lung cancer.

PURPOSEThe advancement of natural language processing (NLP) has promoted the use of detailed textual data in electronic health records (EHRs) to support cancer research and to facilitate patient care. In this review, we aim to assess EHR for cancer research and patient care by using the Minimal Common Oncology Data Elements (mCODE), which is a community-driven effort to define a minimal set of data elements for cancer research and practice. Specifically, we aim to assess the alignment of NLP-extracted data elements with mCODE and review existing NLP methodologies for extracting said data elements.METHODSPublished literature studies were searched to retrieve cancer-related NLP articles that were written in English and published between January 2010 and September 2020 from main literature databases. After the retrieval, articles with EHRs as the data source were manually identified. A charting form was developed for relevant study analysis and used to categorize data including four main topics: metadata, EHR data and targeted cancer types, NLP methodology, and oncology data elements and standards.RESULTSA total of 123 publications were selected finally and included in our analysis. We found that cancer research and patient care require some data elements beyond mCODE as expected. Transparency and reproductivity are not sufficient in NLP methods, and inconsistency in NLP evaluation exists.CONCLUSIONWe conducted a comprehensive review of cancer NLP for research and patient care using EHRs data. Issues and barriers for wide adoption of cancer NLP were identified and discussed.