Utilization of Target Lesion Heterogeneity For Treatment Efficacy assessment in Late Stage Lung Cancer

RATIONALE: Recent studies have discovered several unique tumor response subgroups outside of response classification by Response Evaluation Criteria for Solid Tumors (RECIST), such as mixed response and oligometastasis. These subtypes have a distinctive property, lesion heterogeneity defined as diversity of tumor growth profiles in RECIST target lesions. Furthermore, many cancer clinical trials have been activated to evaluate various treatment options for heterogeneity-related subgroups (e.g., 29 trials so far listed in clinicaltrials.gov for cancer patients with oligometastasis). Some of the trials have shown survival benefit by tailored treatment strategies. This evidence presents the unmet need to incorporate lesion heterogeneity to improve RECIST response classification. METHOD: An approach for Lesion Heterogeneity Classification (LeHeC) was developed using a contemporary statistical approach to assess target lesion variation, characterize patient treatment response, and translate informative evidence to improving treatment strategy. A mixed effect linear model was used to determine lesion heterogeneity. Further analysis was conducted to classify various types of lesion variation and incorporate with RECIST to enhance response classification. A study cohort of 110 target lesions from 36 lung cancer patients was used for evaluation. RESULTS: Due to small sample size issue, the result was exploratory in nature. By analyzing RECIST target lesion data, the LeHeC approach detected a high prevalence (n = 21; 58%) of lesion heterogeneity. Subgroup classification revealed several informative distinct subsets in a descending order of lesion heterogeneity: mix of progression and regression (n = 7), mix of progression and stability (n = 9), mix of regression and stability (n = 5), and non-heterogeneity (n = 15). Evaluation for association of lesion heterogeneity and RECIST best response classification showed lesion heterogeneity commonly occurred in each response group (stable disease: 16/27; 59%; partial response: 3/5; 60%; progression disease: 2/4; 50%). Survival analysis showed a differential trend of overall survival between heterogeneity and non-heterogeneity in RECIST response groups. CONCLUSION: This is the first study to evaluate lesion heterogeneity, an underappreciated metric, for RECIST application in oncology clinical trials. Results indicated lesion heterogeneity is not an uncommon event. The LeHeC approach could enhance RECIST response classification by utilizing granular lesion level discovery of heterogeneity

Patients with ClearCode34-identified molecular subtypes of clear cell renal cell carcinoma represent unique populations with distinct comorbidities

The 34-gene classifier, ClearCode34, identifies prognostically distinct molecular subtypes of clear cell renal cell carcinoma (ccRCC) termed ccA and ccB. The primary objective of this study was to describe clinical characteristics and comorbidities of relevance in patients stratified by ClearCode34

The Florida pancreas collaborative next-generation biobank: Infrastructure to reduce disparities and improve survival for a diverse cohort of patients with pancreatic cancer

Background: Well-annotated, high-quality biorepositories provide a valuable platform to support translational research. However, most biorepositories have poor representation of minority groups, limiting the ability to address health disparities. Methods: We describe the establishment of the Florida Pancreas Collaborative (FPC), the first state-wide prospective cohort study and biorepository designed to address the higher burden of pancreatic cancer (PaCa) in African Americans (AA) compared to Non-Hispanic Whites (NHW) and Hispanic/Latinx (H/L). We provide an overview of stakeholders; study eligibility and design; recruitment strategies; standard operating procedures to collect, process, store, and transfer biospecimens, medical images, and data; our cloud-based data management platform; and progress regarding recruitment and biobanking. Results: The FPC consists of multidisciplinary teams from fifteen Florida medical institutions. From March 2019 through August 2020, 350 patients were assessed for eligibility, 323 met inclusion/exclusion criteria, and 305 (94%) enrolled, including 228 NHW, 30 AA, and 47 H/L, with 94%, 100%, and 94% participation rates, respectively. A high percentage of participants have donated blood (87%), pancreatic tumor tissue (41%), computed tomography scans (76%), and questionnaires (62%). Conclusions: This biorepository addresses a critical gap in PaCa research and has potential to advance translational studies intended to minimize disparities and reduce PaCa-related morbidity and mortality

AA9int: SNP interaction pattern search using non-hierarchical additive model set.

MOTIVATION: The use of single nucleotide polymorphism (SNP) interactions to predict complex diseases is getting more attention during the past decade, but related statistical methods are still immature. We previously proposed the SNP Interaction Pattern Identifier (SIPI) approach to evaluate 45 SNP interaction patterns/patterns. SIPI is statistically powerful but suffers from a large computation burden. For large-scale studies, it is necessary to use a powerful and computation-efficient method. The objective of this study is to develop an evidence-based mini-version of SIPI as the screening tool or solitary use and to evaluate the impact of inheritance mode and model structure on detecting SNP-SNP interactions. RESULTS: We tested two candidate approaches: the 'Five-Full' and 'AA9int' method. The Five-Full approach is composed of the five full interaction models considering three inheritance modes (additive, dominant and recessive). The AA9int approach is composed of nine interaction models by considering non-hierarchical model structure and the additive mode. Our simulation results show that AA9int has similar statistical power compared to SIPI and is superior to the Five-Full approach, and the impact of the non-hierarchical model structure is greater than that of the inheritance mode in detecting SNP-SNP interactions. In summary, it is recommended that AA9int is a powerful tool to be used either alone or as the screening stage of a two-stage approach (AA9int+SIPI) for detecting SNP-SNP interactions in large-scale studies. AVAILABILITY AND IMPLEMENTATION: The 'AA9int' and 'parAA9int' functions (standard and parallel computing version) are added in the SIPI R package, which is freely available at https://linhuiyi.github.io/LinHY_Software/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

SNP interaction pattern identifier (SIPI): an intensive search for SNP-SNP interaction patterns.

MOTIVATION: Testing SNP-SNP interactions is considered as a key for overcoming bottlenecks of genetic association studies. However, related statistical methods for testing SNP-SNP interactions are underdeveloped. RESULTS: We propose the SNP Interaction Pattern Identifier (SIPI), which tests 45 biologically meaningful interaction patterns for a binary outcome. SIPI takes non-hierarchical models, inheritance modes and mode coding direction into consideration. The simulation results show that SIPI has higher power than MDR (Multifactor Dimensionality Reduction), AA_Full, Geno_Full (full interaction model with additive or genotypic mode) and SNPassoc in detecting interactions. Applying SIPI to the prostate cancer PRACTICAL consortium data with approximately 21 000 patients, the four SNP pairs in EGFR-EGFR , EGFR-MMP16 and EGFR-CSF1 were found to be associated with prostate cancer aggressiveness with the exact or similar pattern in the discovery and validation sets. A similar match for external validation of SNP-SNP interaction studies is suggested. We demonstrated that SIPI not only searches for more meaningful interaction patterns but can also overcome the unstable nature of interaction patterns. AVAILABILITY AND IMPLEMENTATION: The SIPI software is freely available at http://publichealth.lsuhsc.edu/LinSoftware/ . CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.This study was supported by the National Cancer Institute (R01CA128813, PI: Park, JY and R21CA202417, PI: Lin, HY)

Mixed effects model in negative exponential curve

Despite many researches on development in education and psychology, not often is the methodology tested with real data. A major barrier to test the growth model is that the design of study includes repeated observations and the nature of the growth is nonlinear. The repeat measurements on a nonlinear model require sophisticated statistical methods. In this study, we present mixed effects model in a negative exponential curve to describe the development of children\u27s reading skills. This model can describe the nature of the growth on children\u27s reading skills and account for intra-individual and inter-individual variation. We also apply simple techniques including cross-validation, regression, and graphical methods to determine the most appropriate curve for data, to find efficient initial values of parameters, and to select potential covariates. We illustrate with an example that motivated this research: a longitudinal study of academic skills from grade 1 to grade 12 in Connecticut public schools

Malignancy-risk signature from histologically normal breast tissue

The invention provides for malignancy-risk gene signatures that predict the risk of developing breast cancer, the recurrence of breast cancer, and/or the metastasis of breast cancer. These signatures have numerous clinical applications including assessing risk of breast cancer development following routine breast biopsy, assessing the need for adjuvant radiotherapy after lumpectomy, and determining the need for completion mastectomy following lumpectomy for the breast cancer patient and other treatment plans that are personalized for the patient

Cancer platinum resistance detection and sensitization method

The phosphorylation status of the BAD protein is a determinant of ovarian cancer cell responsiveness to platinum chemotherapy. Indirect manipulation of BAD phosphorylation status influences cisplatin sensitivity. BAD phosphorylation represents a biomarker that predicts platinum sensitivity and is a therapeutic target to increase platinum sensitivity. The methods employ phospho-specific antibody against a particular amino acid residue or site. Phospho-specific protein characterization methods include immunohistochemical (IHC), flow cytometric, immunofluorescent, capture-and-detection, or reversed phase assay

Impact of COVID-19 Pandemic on Frontline Pembrolizumab-Based Treatment for Advanced Lung Cancer

Background: Pembrolizumab monotherapy or pembrolizumab plus chemotherapy has become an important frontline treatment for advanced non-small cell lung cancer (NSCLC). To date, it remains unclear how the coronavirus disease 2019 (COVID-19) pandemic impacted the treatment outcome. Methods: A quasi-experimental study was conducted based on a real-world database, comparing pandemic with pre-pandemic patient cohorts. The pandemic cohort consisted of patients who initiated treatment from March to July 2020, with follow-up through March 2021. The pre-pandemic cohort consisted of those initiating treatment between March and July 2019.The outcome was overall real-world survival. Multivariable Cox-proportional hazard models were constructed. Results: Analyses included data from 2090 patients: 998 in the pandemic cohort and 1092 in the pre-pandemic cohort. Baseline characteristics were comparable, with 33% of patients having PD-L1 expression level ≥50% and 29% of patients receiving pembrolizumab monotherapy. Among those treated with pembrolizumab monotherapy (N = 613), there was a differential impact of the pandemic on survival by PD-L1 expression levels (p-interaction = 0.02). For those with PD-L1 level p = 0.03). However, for those with PD-L1 level ≥ 50%, survival was not better in the pandemic cohort: HR 1.17 (95% CI: 0.85–1.61, p = 0.34). We found no statistically significant impact of the pandemic on survival among patients treated with pembrolizumab plus chemotherapy. Conclusions: The COVID-19 pandemic was associated with an increase in survival among patients with lower PD-L1 expression who were treated with pembrolizumab monotherapy. This finding suggests an increased efficacy of immunotherapy due to viral exposure in this population