Cancer Survivors’ Health Behaviors and Outcomes: A Population-Based Study of Sexual and Gender Minorities

BACKGROUND: Most case-control studies compare cancer survivors with general population controls without considering sexual orientation or gender identity. This case-control analysis compared health risk behaviors and health outcomes among sexual and gender minority cancer survivors to those of matched sexual and gender minority participants without cancer (controls). METHODS: Using data from the 2014-2021 Behavioral Risk Factor Surveillance System, a population-based sample of 4507 cancer survivors who self-identified as transgender, gay men, bisexual men, lesbian women, or bisexual women were 1:1 propensity score matched, using age at survey, race and ethnicity, marital status, education, access to health care, and US census region. Within each sexual and gender minority group, behaviors and outcomes were compared between survivors and participants without cancer, and survivors\u27 odds ratios and 95% confidence intervals calculated. RESULTS: Gay male survivors had higher odds of depression, poor mental health, limited usual activities, difficulty concentrating, and fair or poor health. Few differences were observed between bisexual male survivors and participants without cancer. Compared with controls, lesbian female survivors had greater odds of overweight-obese status, depression, poor physical health, and fair or poor health. Bisexual female survivors had the highest rates of current smoking, depression, poor mental health, and difficulty concentrating across all sexual and gender minority groups. Statistically significantly different from transgender controls, transgender survivors had greater odds of heavy alcohol use, physical inactivity, and fair or poor health. CONCLUSIONS: This analysis revealed an urgent need to address the high prevalence of engaging in multiple health risk behaviors and not following guidelines to avoid second cancers, additional adverse outcomes, and cancer recurrences among sexual and gender minority cancer survivors

Quantitative Imaging Features Predict Response of Immunotherapy in Non-Small Cell Lung Cancer Patients

[No Abstract Available

Occupational Exposure to Hydrazine and Subsequent Risk of Lung Cancer: 50-Year Follow-Up

Hydrazine is carcinogenic in animals, but there is inadequate evidence to determine if it is carcinogenic in humans. This study aimed to evaluate the association between hydrazine exposure and the risk of lung cancer.The cause specific mortality rates of a cohort of 427 men who were employed at an English factory that produced hydrazine between 1945 and 1971 were compared with national mortality rates.By the end of December 2012 205 deaths had occurred. For men in the highest exposure category with greater than two years exposure and after more than ten years since first exposure the relative risks compared with national rates were: 0.85 (95% CI: 0.18-2.48) for lung cancer, 0.61 (95% CI: 0.07-2.21) for cancers of the digestive system, and 0.44 (95% CI: 0.05-1.57) for other cancers.After 50 years of follow up, the results provide no evidence of an increased risk of death from lung cancer or death from any other cause

Clinical characteristics and treatment patterns of patients with NTRK fusion-positive solid tumors: A multisite cohort study at US academic cancer centers.

BACKGROUND: Neurotrophic tyrosine receptor kinase (NTRK) gene fusions are rare oncogenic drivers prevalent in 0.3% of solid tumors. They are most common in salivary gland cancer (2.6%), thyroid cancer (1.6%), and soft-tissue sarcoma (1.5%). Currently, there are 2 US Food and Drug Administration-approved targeted therapies for NTRK gene fusions: larotrectinib, approved in 2018, and entrectinib, approved in 2019. To date, the real-world uptake of tyrosine receptor kinase inhibitor (TRKi) use for NTRK-positive solid tumors in academic cancer centers remains largely unknown. OBJECTIVE: To describe the demographics, clinical and genomic characteristics, and testing and treatment patterns of patients with NTRK-positive solid tumors treated at US academic cancer centers. METHODS: This was a retrospective chart review study conducted in academic cancer centers in the United States. All patients diagnosed with an NTRK fusion-positive (NTRK1, NTRK2, NTRK3) solid tumor (any stage) and who received cancer treatment at participating sites between January 1, 2012, and July 1, 2023, were included in this study. Patient demographics, clinical characteristics, genomic characteristics, NTRK testing data, and treatment patterns were collected from electronic medical records and analyzed using descriptive statistics as appropriate. RESULTS: In total, 6 centers contributed data for 55 patients with NTRK-positive tumors. The mean age was 49.3 (SD = 20.5) years, 51% patients were female, and the majority were White (78%). The median duration of time from cancer diagnosis to NTRK testing was 85 days (IQR = 44-978). At the time of NTRK testing, 64% of patients had stage IV disease, compared with 33% at cancer diagnosis. Prevalent cancer types in the overall cohort included head and neck (15%), thyroid (15%), brain (13%), lung (13%), and colorectal (11%). NTRK1 fusions were most common (45%), followed by NTRK3 (40%) and NTRK2 (15%). Across all lines of therapy, 51% of patients (n = 28) received a TRKi. Among TRKi-treated patients, 71% had stage IV disease at TRKi initiation. The median time from positive NTRK test to initiation of TRKi was 48 days (IQR = 9-207). TRKis were commonly given as first-line (30%) or second-line (48%) therapies. Median duration of therapy was 610 (IQR = 182-764) days for TRKi use and 207.5 (IQR = 42-539) days for all other first-line therapies. CONCLUSIONS: This study reports on contemporary real-world NTRK testing patterns and use of TRKis in solid tumors, including time between NTRK testing and initiation of TRKi therapy and duration of TRKi therapy

Functional signaling pathway analysis of lung adenocarcinomas identifies novel therapeutic targets for KRAS mutant tumors

Little is known about the complex signaling architecture of KRAS and the interconnected RAS-driven protein-protein interactions, especially as it occurs in human clinical specimens. This study explored the activated and interconnected signaling network of KRAS mutant lung adenocarcinomas (AD) to identify novel therapeutic targets. Thirty-four KRAS mutant (MT) and twenty-four KRAS wild-type (WT) frozen biospecimens were obtained from surgically treated lung ADs. Samples were subjected to Laser Capture Microdissection and Reverse Phase Protein Microarray analysis to explore the expression/activation levels of 150 signaling proteins along with coactivation concordance mapping. An independent set of 90 non-small cell lung cancers (NSCLC) was used to validate selected findings by immunohistochemistry (IHC). Compared to KRAS WT tumors, the signaling architecture of KRAS MT ADs revealed significant interactions between KRAS downstream substrates, the AKT/mTOR pathway, and a number of Receptor Tyrosine Kinases (RTK). Approximately one-third of the KRAS MT tumors had ERK activation greater than the WT counterpart (p < 0.01). Notably 18% of the KRAS MT tumors had elevated activation of the Estrogen Receptor alpha (ER-α) (p=0.02).This finding was verified in an independent population by IHC (p=0.03). KRAS MT lung ADs appear to have a more intricate RAS linked signaling network than WT tumors with linkage to many RTKs and to the AKT-mTOR pathway. Combination therapy targeting different nodes of this network may be necessary to treat this group of patients. In addition, for patients with KRAS MT tumors and activation of the ER-α, anti-estrogen therapy may have important clinical implications

cAMP/CREB-regulated LINC00473 marks LKB1-inactivated lung cancer and mediates tumor growth

The LKB1 tumor suppressor gene is frequently mutated and inactivated in non–small cell lung cancer (NSCLC). Loss of LKB1 promotes cancer progression and influences therapeutic responses in preclinical studies; however, specific targeted therapies for lung cancer with LKB1 inactivation are currently unavailable. Here, we have identified a long noncoding RNA (lncRNA) signature that is associated with the loss of LKB1 function. We discovered that LINC00473 is consistently the most highly induced gene in LKB1-inactivated human primary NSCLC samples and derived cell lines. Elevated LINC00473 expression correlated with poor prognosis, and sustained LINC00473 expression was required for the growth and survival of LKB1-inactivated NSCLC cells. Mechanistically, LINC00473 was induced by LKB1 inactivation and subsequent cyclic AMP–responsive element–binding protein (CREB)/CREB-regulated transcription coactivator (CRTC) activation. We determined that LINC00473 is a nuclear lncRNA and interacts with NONO, a component of the cAMP signaling pathway, thereby facilitating CRTC/CREB-mediated transcription. Collectively, our study demonstrates that LINC00473 expression potentially serves as a robust biomarker for tumor LKB1 functional status that can be integrated into clinical trials for patient selection and treatment evaluation, and implicates LINC00473 as a therapeutic target for LKB1-inactivated NSCLC

Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival Among Patients with Lung Adenocarcinoma

Lung cancer is the most common cause of cancer-related deaths in the USA. It can be detected and diagnosed using computed tomography images. For an automated classifier, identifying predictive features from medical images is a key concern. Deep feature extraction using pretrained convolutional neural networks (CNNs) has recently been successfully applied in some image domains. Here, we applied a pretrained CNN to extract deep features from 40 computed tomography images, with contrast, of non-small cell adenocarcinoma lung cancer, and combined deep features with traditional image features and trained classifiers to predict short-and long-term survivors. We experimented with several pretrained CNNs and several feature selection strategies. The best previously reported accuracy when using traditional quantitative features was 77.5% (area under the curve [AUC], 0.712), which was achieved by a decision tree classifier. The best reported accuracy from transfer learning and deep features was 77.5% (AUC, 0.713) using a decision tree classifier. When extracted deep neural network features were combined with traditional quantitative features, we obtained an accuracy of 90% (AUC, 0.935) with the 5 best post-rectified linear unit features extracted from a vgg-f pretrained CNN and the 5 best traditional features. The best results were achieved with the symmetric uncertainty feature ranking algorithm followed by a random forests classifier

I need more knowledge : Qualitative Analysis of Oncology Providers\u27 Experiences with Sexual and Gender Minority Patients

Background: While societal acceptance for sexual and gender minority (SGM) individuals is increasing, this group continues to face barriers to quality healthcare. Little is known about clinicians\u27 experiences with SGM patients in the oncology setting. To address this, a mixed method survey was administered to members of the ECOG-ACRIN Cancer Research Group. Materials and methods: We report results from the open-ended portion of the survey. Four questions asked clinicians to describe experiences with SGM patients, reservations in caring for them, suggestions for improvement in SGM cancer care, and additional comments. Data were analyzed using content analysis and the constant comparison method. Results: The majority of respondents noted they had no or little familiarity with SGM patients. A minority of respondents noted experience with gay and lesbian patients, but not transgender patients; many who reported experience with transgender patients also noted difficulty navigating the correct use of pronouns. Many respondents also highlighted positive experiences with SGM patients. Suggestions for improvement in SGM cancer care included providing widespread training, attending to unique end-of-life care issues among SGM patients, and engaging in efforts to build trust. Conclusion: Clinicians have minimal experiences with SGM patients with cancer but desire training. Training the entire workforce may improve trust with, outreach efforts to, and cancer care delivery to the SGM community

Common \u3cem\u3eTDP1\u3c/em\u3e Polymorphisms in Relation to Survival Among Small Cell Lung Cancer Patients: A Multicenter Study from the International Lung Cancer Consortium

Background—DNA topoisomerase inhibitors are commonly used for treating small-cell lung cancer (SCLC). Tyrosyl-DNA phosphodiesterase (TDP1) repairs DNA damage caused by this class of drugs and may therefore influence treatment outcome. In this study, we investigated whether common TDP1 single-nucleotide polymorphisms (SNP) are associated with overall survival among SCLC patients. Methods—Two TDP1 SNPs (rs942190 and rs2401863) were analyzed in 890 patients from 10 studies in the International Lung Cancer Consortium (ILCCO). The Kaplan–Meier method and Cox regression analyses were used to evaluate genotype associations with overall mortality at 36 months postdiagnosis, adjusting for age, sex, race, and tumor stage. Results—Patients homozygous for the minor allele (GG) of rs942190 had poorer survival compared with those carrying AA alleles, with a HR of 1.36 [95% confidence interval (CI): 1.08–1.72, P = 0.01), but no association with survival was observed for patients carrying the AG genotype (HR = 1.04, 95% CI, 0.84–1.29, P = 0.72). For rs2401863, patients homozygous for the minor allele (CC) tended to have better survival than patients carrying AA alleles (HR = 0.79; 95% CI, 0.61–1.02, P = 0.07). Results from the Genotype Tissue Expression (GTEx) Project, the Encyclopedia of DNA Elements (ENCODE), and the ePOSSUM web application support the potential function of rs942190. Conclusions—We found the rs942190 GG genotype to be associated with relatively poor survival among SCLC patients. Further investigation is needed to confirm the result and to determine whether this genotype may be a predictive marker for treatment efficacy of DNA topoisomerase inhibitors