Computational features of TIL architecture are differentially prognostic of uterine cancer between African and Caucasian American women.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 5585-5585
Author(s):  
Sepideh Azarianpour Esfahani ◽  
Pingfu Fu ◽  
Haider Mahdi ◽  
Anant Madabhushi
Keyword(s):  
Cox Regression ◽  
Early Stage ◽  
Uterine Cancer ◽  
Prognostic Models ◽  
High Grade ◽  
Caucasian American ◽  
Stromal Compartment ◽  
Prognostic Features ◽  
Post Surgery ◽  
Architectural Patterns

5585 Background: Although the vast majority of endometrial cancer (EC) is early-stage and thus, curable by surgery, chemotherapy, and radiotherapy (with at least 85% 5-year OS), a fraction of them are aggressive neoplasms such as high-grade or deeply invasive lesions and thus exhibit poor prognosis. African American (AA) women are disproportionately affected by high-grade EC and have 80% higher mortality rate compared with Caucasian American (CA) women. In this work, we evaluated the prognostic ability of computational measurements of architecture of tumor-infiltrating lymphocytes (ArcTIL) from H&E slide images for EC. We also investigated the presence of morphologic differences in terms of ArcTIL features between AA and CA women and whether ArcTIL based population-specific models were more prognostic of OS in AA women compared to a population-agnostic model. Methods: The study included digitized H&E tissue slides from 445 post-surgery EC patients from TCGA, with further chemotherapy, or radiotherapy, including only the AA and CA patients, patients without reported race or from other populations were excluded. The dataset was divided into discovery (D1, n = 300), and a validation set (D2, n = 145), while ensuring population balance between two splits (D1(AA) = 65, D1(CA) = 235, D2(AA) = 37, D2(CA) = 108). A machine learning approach was employed to identify tumor regions, and tumor-associated stroma on the diagnostic slides and then used to automatically identify TILs within these compartments. Graph network theory based computational algorithms were used to capture 85 quantitative descriptors of the architectural patterns of intratumoral and stromal TILs. A multivariable Cox regression model (MCRM) was used to create population specific-prognostic models (MAA, MCA) and a population-agnostic model (MAA+CA)) to predict OS. All 3 models were evaluated on D2(AA), D2(CA), and D2. Results: MAA identified 4 prognostic features relating to interaction of TIL clusters with cancer nuclei in stromal compartment and was prognostic of OS on D2(AA) (see Table) but not prognostic in D2(CA) nor D2(AA+CA). MCA and MAA+CA identified respectively 7 and 6 prognostic features relating to interaction of TIL clusters with cancer nuclei (both in the epithelial and stromal regions) and were prognostic of OS on D2(CA) and D2, but not prognostic in D2(AA). Conclusions: Our findings suggest an important role of stromal TIL architecture in prognosticating OS in AA women with EC, while epithelial TIL features were more prognostic in CA women. These findings need to be validated in larger, multi-site validation sets.[Table: see text]

Sequencing chemotherapy and surgery in upper tract urothelial cancers.

2020 ◽  
Vol 38 (6_suppl) ◽  
pp. 521-521
Author(s):  
Saurabh Parasramka ◽  
Alex Cook ◽  
Zin Myint ◽  
Ding Xue ◽  
Jianrong Wu ◽  
...  
Keyword(s):  
Renal Pelvis ◽  
Cox Regression ◽  
Early Stage ◽  
Univariate Analysis ◽  
Disease Free Survival ◽  
Phase Iii ◽  
High Grade ◽  
Upper Tract ◽  
Significant Survival ◽  
Urothelial Bladder

521 Background: Prognosis for high grade, non-metastatic upper tract urothelial carcinoma (UTUC) (renal pelvis or ureter) has not improved in past two decades. Given improvements in disease-free survival in phase III POUT study, adjuvant chemotherapy (AC) has been the preferred approach. Neoadjuvant chemotherapy (NAC) is favored based on median survival (OS) benefit seen in urothelial bladder cancer. We studied National Cancer Database (NCDB) to answer this question. Methods: We identified adults > 18 years with non-metastatic, high grade, UTUC. All patients received surgery of the primary site and chemotherapy in the neoadjuvant or adjuvant setting. Patient’s receiving radiation therapy or who died within 90 days of surgery were not included. Descriptive statistics, log-rank tests and cox-regression tests were performed. Patients achieving complete pathological response (pCR) defined as (pTis, pT0, pTa and N0) were assessed for OS. Results: 1191 patients with complete data were identified; 225 (19%) received NAC and 966 (81%) received AC. 60% were males, median age was 68 and 73% had Charlson score (CS) of ‘0’. Median follow-up time for alive patients was 30.4 and 36.7 months in the NAC and AC groups respectively. Renal pelvis was the primary in 760 cases (63%) and ureter in 441 (37%). On univariate analysis receiving NAC, age < 75 years and CS score ‘0’ was associated with significant survival benefit (p < 0.05). Similarly on multivariate analysis receiving NAC and having CS of ‘0’ had significantly better survival with HR 0.75 (CI 0.58-0.96) and 0.8 (CI 0.65-0.96) respectively. Age > 75 years had worse survival HR 1.34 (CI 1.08-1.66). Thirty-seven patients (17%) in the NAC group achieved pCR with OS > 71.6 months which was significantly better than AC group and non-responders in the NAC group (p < 0.05). There was a trend towards more benefit with NAC compared to AC in Stage 1 and 2 UTUC than in Stage 3 and 4. Conclusions: Our study indicates that subset of early stage UTUC benefit more from NAC comparing to AC. However, randomized prospective study is warranted to further explore the role of NAC in UTUC.

scholarly journals The breast pre-cancer atlas illustrates the molecular and micro-environmental diversity of ductal carcinoma in situ

2021 ◽  
Author(s):  
Daniela Nachmanson ◽  
Adam Officer ◽  
Hidetoshi Mori ◽  
Jonathan Gordon ◽  
Mark F. Evans ◽  
...  
Keyword(s):  
Ductal Carcinoma In Situ ◽  
Carcinoma In Situ ◽  
Ductal Carcinoma ◽  
Early Stage ◽  
Prognostic Models ◽  
Low Grade ◽  
High Grade ◽  
Luminal A ◽  
Environmental Diversity

The increased detection and treatment of early stage breast cancer as well as ductal carcinoma in situ (DCIS) has not led to significant survival benefits. Therefore, the current standard treatment of DCIS is questionable. An informed evidence-based treatment strategy, and likely de-escalation from the current standards requires new prognostic models built from more comprehensive characterization with objective criteria. Parallel profiling of the molecular landscape and micro-environment in pure DCIS remains challenging due to histological heterogeneity and the inevitable reliance on small archived specimens. Leveraging recent methodological advances, we characterized the mutational, transcriptional, histological and microenvironmental landscape across multiple micro-dissected regions from 39 cases to generate a multi-modal breast precancer atlas. The histological architecture was associated with grade, adiposity, and intrinsic expression subtypes. Similar to previous findings, high-grade lesions had higher mutational burden, including TP53 mutations, while low-grade lesions had more frequent 16q losses and GATA3 mutations. Multi-region analysis revealed most somatic alterations, including whole genome duplication events, were clonal, but genetic divergence increased with distance between regions. In 7/12 evaluable cases, somatic mutations in putative driver genes affected a subset of regions only. This genetic heterogeneity often accompanied phenotypic heterogeneity and regions with low risk features (Normal-like, Luminal A) occurred earlier than those with high-risk features (Her2-like, Basal or necrosis) according to the phylogenetic analysis. The immune-environment was evaluated using multiplex immuno-histochemistry to measure relative stromal and epithelial densities of B lymphocyte (B-cell), T lymphocyte (T-cell) and regulatory T cells (T-reg) and identify 3 immune-states: Active, Suppressed and Excluded (lower epithelial density). All states included both DCIS and adjacent benign regions, and none associated with intrinsic subtypes. The Excluded state was enriched in high-grade DCIS and, compared to benign areas, more likely acquired in DCIS, showing transcriptional evidence of stronger immune-suppression and possible evasion. The breast pre-cancer atlas therefore reveals correlated levels of phenotypic and genotypic heterogeneity, including at sub-histological resolution. These uniquely integrated observations will help scope future studies, prioritize candidate markers for progression risk modelling and identify functional similarities in precursor lesions from other types of adenocarcinomas.

scholarly journals Identification of an Autophagy-related Gene Signature Predicting Overall Survival for Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Wenfang Xu ◽  
Wenke Guo ◽  
Ping Lu ◽  
Duan Ma ◽  
Lei Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Risk Model ◽  
Cox Regression ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Prognostic Models ◽  
Cox Regression Analysis ◽  
Prognostic Features ◽  
Tcga Dataset

The poor prognosis of hepatocellular carcinoma (HCC) calls for the development of accurate prognostic models. The growing number of studies indicating a correlation between autophagy activity and HCC indicates there is a commitment to finding solutions for the prognosis of HCC from the perspective of autophagy. We used a cohort in The Cancer Genome Atlas (TCGA) to evaluate the expression of autophagy-related genes in 371 HCC samples using univariate Cox and lasso Cox regression analysis, and the prognostic features were identified. A prognostic model was established by combining the expression of selected genes with the multivariate Cox regression coefficient of each gene. Eight autophagy-related genes were selected as prognostic features of HCC. We established the HCC prognostic risk model in TCGA dataset using these identified prognostic genes. The model’s stability was confirmed in two independent verification sets (GSE14520 and GSE36376). The model had a good predictive power for the overall survival (OS) of HCC (Hazard Ratio=2.32, 95% Confidence Interval=1.76–3.05, p&lt;0.001). Moreover, the risk score computed by the model did not depend on other clinical parameters. Finally, the applicability of the model was demonstrated through a nomogram (C-index=0.701). In this study, we established an autophagy-related risk model having a high prediction accuracy for OS in HCC. Our findings will contribute to the definition of prognosis and establishment of personalized therapy for HCC patients.

scholarly journals The prognostic and diagnostic significance of the neutrophil-to-lymphocyte ratio in hepatocellular carcinoma: a prospective controlled study

2021 ◽  
Author(s):  
Philip J. Johnson ◽  
Sofi Dhanaraj ◽  
Sarah Berhane ◽  
Laura Bonnett ◽  
Yuk Ting Ma
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Prognostic Factor ◽  
Cox Regression ◽  
Prognostic Models ◽  
Controlled Study ◽  
Cox Regression Analysis ◽  
Neutrophil Lymphocyte Ratio ◽  
Lymphocyte Ratio ◽  
Significant Difference

Abstract Background The neutrophil–lymphocyte ratio (NLR), a presumed measure of the balance between neutrophil-associated pro-tumour inflammation and lymphocyte-dependent antitumour immune function, has been suggested as a prognostic factor for several cancers, including hepatocellular carcinoma (HCC). Methods In this study, a prospectively accrued cohort of 781 patients (493 HCC and 288 chronic liver disease (CLD) without HCC) were followed-up for more than 6 years. NLR levels between HCC and CLD patients were compared, and the effect of baseline NLR on overall survival amongst HCC patients was assessed via multivariable Cox regression analysis. Results On entry into the study (‘baseline’), there was no clinically significant difference in the NLR values between CLD and HCC patients. Amongst HCC patients, NLR levels closest to last visit/death were significantly higher compared to baseline. Multivariable Cox regression analysis showed that NLR was an independent prognostic factor, even after adjustment for the HCC stage. Conclusion NLR is a significant independent factor influencing survival in HCC patients, hence offering an additional dimension in prognostic models.

scholarly journals Metabolic Changes in Early-Stage Non–Small Cell Lung Cancer Patients after Surgical Resection

Cancers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 3012
Author(s):  
Naseer Ahmed ◽  
Biniam Kidane ◽  
Le Wang ◽  
Zoann Nugent ◽  
Nataliya Moldovan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Surgical Resection ◽  
Cell Lung Cancer ◽  
Early Stage ◽  
Small Cell ◽  
Resonance Spectroscopy ◽  
Small Cell Lung ◽  
Serum Samples ◽  
Post Surgery

Metabolic alterations in malignant cells play a vital role in tumor initiation, proliferation, and metastasis. Biofluids from patients with non–small cell lung cancer (NSCLC) harbor metabolic biomarkers with potential clinical applications. In this study, we assessed the changes in the metabolic profile of patients with early-stage NSCLC using mass spectrometry and nuclear magnetic resonance spectroscopy before and after surgical resection. A single cohort of 35 patients provided a total of 29 and 32 pairs of urine and serum samples, respectively, pre-and post-surgery. We identified a profile of 48 metabolites that were significantly different pre- and post-surgery: 17 in urine and 31 in serum. A higher proportion of metabolites were upregulated than downregulated post-surgery (p < 0.01); however, the median fold change (FC) was higher for downregulated than upregulated metabolites (p < 0.05). Purines/pyrimidines and proteins had a larger dysregulation than other classes of metabolites (p < 0.05 for each class). Several of the dysregulated metabolites have been previously associated with cancer, including leucyl proline, asymmetric dimethylarginine, isopentenyladenine, fumaric acid (all downregulated post-surgery), as well as N6-methyladenosine and several deoxycholic acid moieties, which were upregulated post-surgery. This study establishes metabolomic analysis of biofluids as a path to non-invasive diagnostics, screening, and monitoring in NSCLC.

scholarly journals Monocarboxylate transporter-1 (MCT1) protein expression in head and neck cancer affects clinical outcome

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Martin Leu ◽  
J. Kitz ◽  
Y. Pilavakis ◽  
S. Hakroush ◽  
H. A. Wolff ◽  
...  
Keyword(s):  
Cell Membrane ◽  
Protein Expression ◽  
Head And Neck ◽  
Cox Regression ◽  
Locally Advanced ◽  
Monocarboxylate Transporter ◽  
Positive Staining ◽  
Monocarboxylate Transporter 1 ◽  
Prognostic Features ◽  
Hpv Status

AbstractTreatment of locally advanced, unresectable head and neck squamous cell carcinoma (HNSCC) often yields only modest results with radiochemotherapy (RCT) as standard of care. Prognostic features related to outcome upon RCT might be highly valuable to improve treatment. Monocarboxylate transporters-1 and -4 (MCT1/MCT4) were evaluated as potential biomarkers. A cohort of HNSCC patients without signs for distant metastases was assessed eliciting 82 individuals eligible whereof 90% were diagnosed with locally advanced stage IV. Tumor specimens were stained for MCT1 and MCT4 in the cell membrane by immunohistochemistry. Obtained data were evaluated with respect to overall (OS) and progression-free survival (PFS). Protein expression of MCT1 and MCT4 in cell membrane was detected in 16% and 85% of the tumors, respectively. Expression of both transporters was not statistically different according to the human papilloma virus (HPV) status. Positive staining for MCT1 (n = 13, negative in n = 69) strongly worsened PFS with a hazard ratio (HR) of 3.1 (95%-confidence interval 1.6–5.7, p < 0.001). OS was likewise affected with a HR of 3.8 (2.0–7.3, p < 0.001). Multivariable Cox regression confirmed these findings. We propose MCT1 as a promising biomarker in HNSCC treated by primary RCT.

scholarly journals DeepRePath: Identifying the Prognostic Features of Early-Stage Lung Adenocarcinoma Using Multi-Scale Pathology Images and Deep Convolutional Neural Networks

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3308
Author(s):  
Won Sang Shim ◽  
Kwangil Yim ◽  
Tae-Jung Kim ◽  
Yeoun Eun Sung ◽  
Gyeongyun Lee ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Early Stage ◽  
External Validation ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Deep Convolutional Neural Networks ◽  
Prognostic Features ◽  
Multi Scale ◽  
Model Based ◽  
Number Of Patients

The prognosis of patients with lung adenocarcinoma (LUAD), especially early-stage LUAD, is dependent on clinicopathological features. However, its predictive utility is limited. In this study, we developed and trained a DeepRePath model based on a deep convolutional neural network (CNN) using multi-scale pathology images to predict the prognosis of patients with early-stage LUAD. DeepRePath was pre-trained with 1067 hematoxylin and eosin-stained whole-slide images of LUAD from the Cancer Genome Atlas. DeepRePath was further trained and validated using two separate CNNs and multi-scale pathology images of 393 resected lung cancer specimens from patients with stage I and II LUAD. Of the 393 patients, 95 patients developed recurrence after surgical resection. The DeepRePath model showed average area under the curve (AUC) scores of 0.77 and 0.76 in cohort I and cohort II (external validation set), respectively. Owing to low performance, DeepRePath cannot be used as an automated tool in a clinical setting. When gradient-weighted class activation mapping was used, DeepRePath indicated the association between atypical nuclei, discohesive tumor cells, and tumor necrosis in pathology images showing recurrence. Despite the limitations associated with a relatively small number of patients, the DeepRePath model based on CNNs with transfer learning could predict recurrence after the curative resection of early-stage LUAD using multi-scale pathology images.

Patterns of striatal dopamine depletion in early Parkinson disease

Neurology ◽  
2020 ◽  
Vol 95 (3) ◽  
pp. e280-e290 ◽  
Author(s):  
Seok Jong Chung ◽  
Hye Sun Lee ◽  
Han Soo Yoo ◽  
Yang Hyun Lee ◽  
Phil Hyu Lee ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Parkinson Disease ◽  
Cox Regression ◽  
Early Stage ◽  
Freezing Of Gait ◽  
Striatal Dopamine ◽  
Dopamine Depletion ◽  
Wearing Off ◽  
Striatal Dopamine Depletion ◽  
Selective Dopamine

ObjectiveTo investigate whether the patterns of striatal dopamine depletion on dopamine transporter (DAT) scans could provide information on the long-term prognosis in Parkinson disease (PD).MethodsWe enrolled 205 drug-naive patients with early-stage PD, who underwent 18F-FP-CIT PET scans at initial assessment and received PD medications for 3 or more years. After quantifying the DAT availability in each striatal subregion, factor analysis was conducted to simplify the identification of striatal dopamine depletion patterns and to yield 4 striatal subregion factors. We assessed the effect of these factors on the development of levodopa-induced dyskinesia (LID), wearing-off, freezing of gait (FOG), and dementia during the follow-up period (6.84 ± 1.80 years).ResultsThe 4 factors indicated which striatal subregions were relatively preserved: factor 1 (caudate), factor 2 (more-affected sensorimotor striatum), factor 3 (less-affected sensorimotor striatum), and factor 4 (anterior putamen). Cox regression analyses using the composite scores of these striatal subregion factors as covariates demonstrated that selective dopamine depletion in the sensorimotor striatum was associated with a higher risk for developing LID. Selective dopamine loss in the putamen, particularly in the anterior putamen, was associated with early development of wearing-off. Selective involvement of the anterior putamen was associated with a higher risk for dementia conversion. However, the patterns of striatal dopamine depletion did not affect the risk of FOG.ConclusionsThese findings suggested that the patterns of striatal dopaminergic denervation, which were estimated by the equation derived from the factor analysis, have a prognostic implication in patients with early-stage PD.

scholarly journals High expression of fibroblast activation protein (FAP) predicts poor outcome in high-grade serous ovarian cancer

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Min Li ◽  
Xue Cheng ◽  
Rong Rong ◽  
Yan Gao ◽  
Xiuwu Tang ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cox Regression ◽  
Paraffin Section ◽  
Progression Free Survival ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Systematic Analysis ◽  
Cox Regression Analysis ◽  
Genes Expression ◽  
High Level

Abstract Background High-grade serous ovarian cancer (HGSOC) is a fatal form of ovarian cancer. Previous studies indicated some potential biomarkers for clinical evaluation of HGSOC prognosis. However, there is a lack of systematic analysis of different expression genes (DEGs) to screen and detect significant biomarkers of HGSOC. Methods TCGA database was conducted to analyze relevant genes expression in HGSOC. Outcomes of candidate genes expression, including overall survival (OS) and progression-free survival (PFS), were calculated by Cox regression analysis for hazard rates (HR). Histopathological investigation of the identified genes was carried out in 151 Chinese HGSOC patients to validate gene expression in different stages of HGSOC. Results Of all 57,331 genes that were analyzed, FAP was identified as the only novel gene that significantly contributed to both OS and PFS of HGSOC. In addition, FAP had a consistent expression profile between carcinoma-paracarcinoma and early-advanced stages of HGSOC. Immunological tests in paraffin section also confirmed that up-regulation of FAP was present in advanced stage HGSOC patients. Prediction of FAP network association suggested that FN1 could be a potential downstream gene which further influenced HGSOC survival. Conclusions High-level expression of FAP was associated with poor prognosis of HGSOC via FN1 pathway.

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