Initial Medical Attention on Patients with Early-Stage Non-Small Cell Lung Cancer

Detection of early stage non-small cell lung cancer (NSCLC) is commonly believed to be incidental. Understanding the reasons that caused initial detection of these patients is important for early diagnosis. However, these reasons are not well studied.We retrospectively reviewed medical records of patients diagnosed with stage I or II NSCLC between 2000 and 2009 at UT MD Anderson Cancer Center. Information on suggestive LC-symptoms or other reasons that caused detection were extracted from patients' medical records. We applied univariate and multivariate analyses to evaluate the association of suggestive LC-symptoms with tumor size and patient survival.Of the 1396 early stage LC patients, 733 (52.5%) presented with suggestive LC-symptoms as chief complaint. 347 (24.9%) and 287 (20.6%) were diagnosed because of regular check-ups and evaluations for other diseases, respectively. The proportion of suggestive LC-symptom-caused detection had a linear relationship with the tumor size (correlation 0.96; with p<.0001). After age, gender, race, smoking status, therapy, and stage adjustment, the symptom-caused detection showed no significant difference in overall and LC-specific survival when compared with the other (non-symptom-caused) detection.Symptoms suggestive of LC are the number one reason that led to detection in early NSCLC. They were also associated with tumor size at diagnosis, suggesting early stage LC patients are developing symptoms. Presence of symptoms in early stages did not compromise survival. A symptom-based alerting system or guidelines may be worth of further study to benefit NSCLC high risk individuals

Hybrid Models Identified a 12-Gene Signature for Lung Cancer Prognosis and Chemoresponse Prediction

Lung cancer remains the leading cause of cancer-related deaths worldwide. The recurrence rate ranges from 35-50% among early stage non-small cell lung cancer patients. To date, there is no fully-validated and clinically applied prognostic gene signature for personalized treatment.From genome-wide mRNA expression profiles generated on 256 lung adenocarcinoma patients, a 12-gene signature was identified using combinatorial gene selection methods, and a risk score algorithm was developed with Naïve Bayes. The 12-gene model generates significant patient stratification in the training cohort HLM & UM (n = 256; log-rank P = 6.96e-7) and two independent validation sets, MSK (n = 104; log-rank P = 9.88e-4) and DFCI (n = 82; log-rank P = 2.57e-4), using Kaplan-Meier analyses. This gene signature also stratifies stage I and IB lung adenocarcinoma patients into two distinct survival groups (log-rank P<0.04). The 12-gene risk score is more significant (hazard ratio = 4.19, 95% CI: [2.08, 8.46]) than other commonly used clinical factors except tumor stage (III vs. I) in multivariate Cox analyses. The 12-gene model is more accurate than previously published lung cancer gene signatures on the same datasets. Furthermore, this signature accurately predicts chemoresistance/chemosensitivity to Cisplatin, Carboplatin, Paclitaxel, Etoposide, Erlotinib, and Gefitinib in NCI-60 cancer cell lines (P<0.017). The identified 12 genes exhibit curated interactions with major lung cancer signaling hallmarks in functional pathway analysis. The expression patterns of the signature genes have been confirmed in RT-PCR analyses of independent tumor samples.The results demonstrate the clinical utility of the identified gene signature in prognostic categorization. With this 12-gene risk score algorithm, early stage patients at high risk for tumor recurrence could be identified for adjuvant chemotherapy; whereas stage I and II patients at low risk could be spared the toxic side effects of chemotherapeutic drugs

Combining Clinical, Pathological, and Demographic Factors Refines Prognosis of Lung Cancer: A Population-Based Study

In the treatment of lung cancer, an accurate estimation of patient clinical outcome is essential for choosing an appropriate course of therapy. It is important to develop a prognostic stratification model which combines clinical, pathological and demographic factors for individualized clinical decision making.A total of 234,412 patients diagnosed with adenocarcinomas or squamous cell carcinomas of the lung or bronchus between 1988 and 2006 were retrieved from the SEER database to construct a prognostic model. A model was developed by estimating a Cox proportional hazards model on 500 bootstrapped samples. Two models, one using stage alone and another comprehensive model using additional covariates, were constructed. The comprehensive model consistently outperformed the model using stage alone in prognostic stratification and on Harrell's C, Nagelkerke's R(2), and Brier Scores in the whole patient population as well as in specific treatment modalities. Specifically, the comprehensive model generated different prognostic groups with distinct post-operative survival (log-rank P<0.001) within surgical stage IA and IB patients in Kaplan-Meier analyses. Two additional patient cohorts (n = 1,991) were used as an external validation, with the comprehensive model again outperforming the model using stage alone with regards to prognostic stratification and the three evaluated metrics.These results demonstrate the feasibility of constructing a precise prognostic model combining multiple clinical, pathologic, and demographic factors. The comprehensive model significantly improves individualized prognosis upon AJCC tumor staging and is robust across a range of treatment modalities, the spectrum of patient risk, and in novel patient cohorts

Significance of abnormal 53BP1 expression as a novel molecular pathologic parameter of follicular-shaped B-cell lymphoid lesions in human digestive tract

The digestive tract is a common site of extranodal malignant lymphomas (MLs) and benign lymphoid lesions (BLs). TP53-binding protein 1 (53BP1) expression has been widely investigated in class switch recombination but rarely in human lymphoid tissues with respect to tumorigenesis. We previously reported that immunofluorescence (IF) analysis of 53BP1 nuclear foci (NF), reflecting DNA double strand breaks, is useful for estimating genomic instability in different tumor types. In this study, we evaluated the potential of IF-based analysis of 53BP1 expression in differentiating MLs from BLs. We examined 231 biopsied tissue samples of primary MLs and BLs in the digestive tract. The 53BP1 immunoreactivity pattern was determined by multicolor IF. Compared to BLs, MLs showed a high frequency of abnormal 53BP1 expression (p < 0.0001). Statistically, abnormal 53BP1 expression is an effective test for distinguishing follicular lymphomas from BLs (specificity 98.6%, sensitivity 86.8%) and for distinguishing small B-cell lymphomas from BLs (specificity 98.3%, sensitivity 77.6%). Furthermore, a high frequency of abnormal 53BP1 expression was associated with “high-risk” MALT lymphomas, which exhibited t(11;18)(q21;21) (p = 0.0145). Collectively, these results suggest that IF-based analysis of 53BP1 expression in biopsy samples is a promising technique for diagnosing MLs in the digestive system