Characterization of immune infiltrate along the leading edge of differentiated thyroid cancer.

BACKGROUND: While the impact of tumor-immune infiltrate has been reported on differentiated thyroid cancer behavior (DTC), the expression of immune checkpoints [programmed cell death protein 1 (PD-1) and its ligand (PD-L1)] alone has not been able to predict response to immunotherapies. We aimed to identify tumor-infiltrating immune cells and checkpoints associated with DTC. METHODS: We performed multiplex immunofluorescence on de-paraffinized thyroid tissue collected at thyroidectomy from 17 adults with DTC to characterize the tumor immune microenvironment for leukocytes (CD45+), T cells (CD3+), T regulatory cells (Tregs) (CD3+FOXP3+), CD4+ T cells (CD3+CD4+), CD8+ T cells (CD3+CD8+), macrophages (CD68+), M2 macrophages (CD68+CD163+), M1 Macrophages (CD68+iNOS+) and immune checkpoints PD-1 and PD-L1. We compared the mean percentage expression of immune markers between tumor and adjacent thyroid tissue from the same patient by paired t-test and performed spatial analysis along the tumor's leading edge. RESULTS: Immune checkpoints PD-1 and PD-L1 showed a significant increase in expression intratumorally as compared to adjacent thyroid tissue (p<0.05). A higher trend for M2 macrophages was observed intratumorally compared to adjacent tissue. Along the leading edge, PD-L1 expression correlated negatively with CD45 and positively with CD163 intratumorally. On exploratory analysis, there was a non-significant trend for higher FOXP3 but less CD8 and iNOS expression in tumor from DTC with (n=3) versus without distant metastases (n=14). There was a non-significant trend for higher CD58 and iNOS expression in DTC with (n=7) than without thyroiditis (n=10). CONCLUSIONS: Higher tumoral PD-1 and PD-L1 expression indicate their role in DTC occurrence. A trend for more Tregs and M2 macrophages but less M1 macrophages intratumorally in patients with distant metastatic DTC, suggests their potential role as prognostic biomarkers. Future studies with larger sample sizes are needed to compare various clinicopathologic severities to harness tumor microenvironment for cancer prognostication and therapy.

Relaxin-2 is a novel biomarker for differentiated thyroid carcinoma in humans.

Relaxin's role in differentiated thyroid cancer (DTC) has been suggested but its characterization in a large clinical sample remains limited. We performed immunohistochemistry for relaxin-2 (RLN2), CD68 (total macrophages), CD163 (M2 macrophages) on tissue microarrays from 181 subjects with non-distant metastatic DTC, and 185 subjects with benign thyroid tissue. Mean pixels/area for each marker was compared between tumor and adjacent tissue via paired-t test and between DTC and benign subjects via t-test assuming unequal variances. RNA qPCR was performed for expression of RLN2, RLN1, and RXFP1 in cell lines. Amongst 181 cases, the mean age was 46 years, 75 % were females. Tumoral tissue amongst the DTC cases demonstrated higher mean expression of RLN2 (53.04 vs. 9.79; p < 0.0001) compared to tumor-adjacent tissue. DTC tissue also demonstrated higher mean expression of CD68 (14.46 vs. 4.79; p < 0.0001), and CD163 (23.13 vs. -0.73; p < 0.0001) than benign thyroid. These markers did not differ between tumor-adjacent and benign thyroid tissue groups; and amongst cases, did not differ by demographic or clinicopathologic features. RLN1 and RXFP1 expression was detected in a minority of the cell lines, while RLN2 was expressed by 6/7 cell lines. In conclusion, widespread RLN2 expression in DTC tissue and most cell lines demonstrates that RLN2 acts in a paracrine manner, and that RLN1 and RXFP1 are probably not involved in thyroid cancer cell signaling. RLN2 is a biomarker for thyroid carcinogenesis, being associated with but not secreted by immunosuppressive macrophages. These findings will guide further investigations for therapeutic avenues against thyroid cancer.

The mutational landscape of a US Midwestern breast cancer cohort reveals subtype-specific cancer drivers and prognostic markers.

BACKGROUND: Female breast cancer remains the second leading cause of cancer-related death in the USA. The heterogeneity in the tumor morphology across the cohort and within patients can lead to unpredictable therapy resistance, metastasis, and clinical outcome. Hence, supplementing classic pathological markers with intrinsic tumor molecular markers can help identify novel molecular subtypes and the discovery of actionable biomarkers. METHODS: We conducted a large multi-institutional genomic analysis of paired normal and tumor samples from breast cancer patients to profile the complex genomic architecture of breast tumors. Long-term patient follow-up, therapeutic regimens, and treatment response for this cohort are documented using the Breast Cancer Collaborative Registry. The majority of the patients in this study were at tumor stage 1 (51.4%) and stage 2 (36.3%) at the time of diagnosis. Whole-exome sequencing data from 554 patients were used for mutational profiling and identifying cancer drivers. RESULTS: We identified 54 tumors having at least 1000 mutations and 185 tumors with less than 100 mutations. Tumor mutational burden varied across the classified subtypes, and the top ten mutated genes include MUC4, MUC16, PIK3CA, TTN, TP53, NBPF10, NBPF1, CDC27, AHNAK2, and MUC2. Patients were classified based on seven biological and tumor-specific parameters, including grade, stage, hormone receptor status, histological subtype, Ki67 expression, lymph node status, race, and mutational profiles compared across different subtypes. Mutual exclusion of mutations in PIK3CA and TP53 was pronounced across different tumor grades. Cancer drivers specific to each subtype include TP53, PIK3CA, CDC27, CDH1, STK39, CBFB, MAP3K1, and GATA3, and mutations associated with patient survival were identified in our cohort. CONCLUSIONS: This extensive study has revealed tumor burden, driver genes, co-occurrence, mutual exclusivity, and survival effects of mutations on a US Midwestern breast cancer cohort, paving the way for developing personalized therapeutic strategies.

Receptor for hyaluronan-mediated motility (RHAMM) defines an invasive niche associated with tumor progression and predicts poor outcomes in breast cancer patients.

Breast cancer invasion and metastasis result from a complex interplay between tumor cells and the tumor microenvironment (TME). Key oncogenic changes in the TME include aberrant synthesis, processing, and signaling of hyaluronan (HA). Hyaluronan-mediated motility receptor (RHAMM, CD168; HMMR) is an HA receptor enabling tumor cells to sense and respond to this aberrant TME during breast cancer progression. Previous studies have associated RHAMM expression with breast tumor progression; however, cause and effect mechanisms are incompletely established. Focused gene expression analysis of an internal breast cancer patient cohort confirmed that increased RHAMM expression correlates with aggressive clinicopathological features. To probe mechanisms, we developed a novel 27-gene RHAMM-related signature (RRS) by intersecting differentially expressed genes in lymph node (LN)-positive patient cases with the transcriptome of a RHAMM-dependent model of cell transformation, which we validated in an independent cohort. We demonstrate that the RRS predicts for poor survival and is enriched for cell cycle and TME-interaction pathways. Further analyses using CRISPR/Cas9-generated RHAMM-/- breast cancer cells provided direct evidence that RHAMM promotes invasion in vitro and in vivo. Immunohistochemistry studies highlighted heterogeneous RHAMM protein expression, and spatial transcriptomics associated the RRS with RHAMM-high microanatomic foci. We conclude that RHAMM upregulation leads to the formation of 'invasive niches', which are enriched in RRS-related pathways that drive invasion and could be targeted to limit invasive progression and improve patient outcomes. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

The impact of the affordable care act on breast cancer care in the USA: A multi-institutional analysis.

There are less data available on the effect of the ACA on breast cancer care beyond the screening level. A retrospective review at participating iCaRe2/BCCR institutions was completed before and after ACA. Post-ACA, patients were older, more urban, and more likely to be insured through Medicaid. Increased imaging use was noted post-ACA. These patients were less likely to be diagnosed with late-stage cancers, received fewer mastectomies, and were more likely to have radiation.

Thyroid Cancer and Tumor Collaborative Registry (TCCR).

A multicenter, web-based Thyroid Cancer and Tumor Collaborative Registry (TCCR, http://tccr.unmc.edu) allows for the collection and management of various data on thyroid cancer (TC) and thyroid nodule (TN) patients. The TCCR is coupled with OpenSpecimen, an open-source biobank management system, to annotate biospecimens obtained from the TCCR subjects. The demographic, lifestyle, physical activity, dietary habits, family history, medical history, and quality of life data are provided and may be entered into the registry by subjects. Information on diagnosis, treatment, and outcome is entered by the clinical personnel. The TCCR uses advanced technical and organizational practices, such as (i) metadata-driven software architecture (design); (ii) modern standards and best practices for data sharing and interoperability (standardization); (iii) Agile methodology (project management); (iv) Software as a Service (SaaS) as a software distribution model (operation); and (v) the confederation principle as a business model (governance). This allowed us to create a secure, reliable, user-friendly, and self-sustainable system for TC and TN data collection and management that is compatible with various end-user devices and easily adaptable to a rapidly changing environment. Currently, the TCCR contains data on 2,261 subjects and data on more than 28,000 biospecimens. Data and biological samples collected by the TCCR are used in developing diagnostic, prevention, treatment, and survivorship strategies against TC.

Web tool for estimating the cancer hazard rates in aging.

A computational approach for estimating the overall, population, and individual cancer hazard rates was developed. The population rates characterize a risk of getting cancer of a specific site/type, occurring within an age-specific group of individuals from a specified population during a distinct time period. The individual rates characterize an analogous risk but only for the individuals susceptible to cancer. The approach uses a novel regularization and anchoring technique to solve an identifiability problem that occurs while determining the age, period, and cohort (APC) effects. These effects are used to estimate the overall rate, and to estimate the population and individual cancer hazard rates. To estimate the APC effects, as well as the population and individual rates, a new web-based computing tool, called the CancerHazard@Age, was developed. The tool uses data on the past and current history of cancer incidences collected during a long time period from the surveillance databases. The utility of the tool was demonstrated using data on the female lung cancers diagnosed during 1975-2009 in nine geographic areas within the USA. The developed tool can be applied equally well to process data on other cancer sites. The data obtained by this tool can be used to develop novel carcinogenic models and strategies for cancer prevention and treatment, as well as to project future cancer burden.

Prognostic estimator of survival for patients with localized and extended pancreatic ductal adenocarcinoma.

The 18,352 pancreatic ductal adenocarcinoma (PDAC) cases from the Surveillance Epidemiology and End Results (SEER) database were analyzed using the Kaplan-Meier method for the following variables: race, gender, marital status, year of diagnosis, age at diagnosis, pancreatic subsite, T-stage, N-stage, M-stage, tumor size, tumor grade, performed surgery, and radiation therapy. Because the T-stage variable did not satisfy the proportional hazards assumption, the cases were divided into cases with T1- and T2-stages (localized tumor) and cases with T3- and T4-stages (extended tumor). For estimating survival and conditional survival probabilities in each group, a multivariate Cox regression model adjusted for the remaining covariates was developed. Testing the reproducibility of model parameters and generalizability of these models showed that the models are well calibrated and have concordance indexes equal to 0.702 and 0.712, respectively. Based on these models, a prognostic estimator of survival for patients diagnosed with PDAC was developed and implemented as a computerized web-based tool.

Alcohol and tobacco lower the age of presentation in sporadic pancreatic cancer in a dose-dependent manner: a multicenter study.

OBJECTIVES: The objective of this study was to examine the association between tobacco and alcohol dose and type and the age of onset of pancreatic adenocarcinoma (PancCa). METHODS: Prospective data from the Pancreatic Cancer Collaborative Registry were used to examine the association between age of onset and variables of interest including: gender, race, birth country, educational status, family history of PancCa, diabetes status, and tobacco and alcohol use. Statistical analysis included logistic and linear regression, Cox proportional hazard regression, and time-to-event analysis. RESULTS: The median age to diagnosis for PancCa was 66.3 years (95% confidence intervals (CIs), 64.5-68.0). Males were more likely than females to be smokers (77% vs. 69%, P=0.0002) and heavy alcohol and beer consumers (19% vs. 6%, 34% vs. 19%, P<0.0001). In univariate analysis for effects on PancCa presentation age, the following were significant: gender, alcohol and tobacco use (amount, status and type), family history of PancCa, and body mass index. Both alcohol and tobacco had dose-dependent effects. In multivariate analysis, alcohol status and dose were independently associated with increased risk for earlier PancCa onset with greatest risk occurring in heavy drinkers (HR 1.62, 95% CI 1.04-2.54). Smoking status had the highest risk for earlier onset pancreatic cancer with a HR of 2.69 (95% CI, 1.97-3.68) for active smokers and independent effects for dose (P=0.019). The deleterious effects for alcohol and tobacco appear to resolve after 10 years of abstinence. CONCLUSIONS: Alcohol and tobacco use are associated with a dose-related increased risk for earlier age of onset of PancCa. Although beer drinkers develop pancreatic cancer at an earlier age than nondrinkers, alcohol type did not have a significant effect after controlling for alcohol dose.

Multicenter breast cancer collaborative registry.

The Breast Cancer Collaborative Registry (BCCR) is a multicenter web-based system that efficiently collects and manages a variety of data on breast cancer (BC) patients and BC survivors. This registry is designed as a multi-tier web application that utilizes Java Servlet/JSP technology and has an Oracle 11g database as a back-end. The BCCR questionnaire has accommodated standards accepted in breast cancer research and healthcare. By harmonizing the controlled vocabulary with the NCI Thesaurus (NCIt) or Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT), the BCCR provides a standardized approach to data collection and reporting. The BCCR has been recently certified by the National Cancer Institute's Center for Biomedical Informatics and Information Technology (NCI CBIIT) as a cancer Biomedical Informatics Grid (caBIG(®)) Bronze Compatible product.The BCCR is aimed at facilitating rapid and uniform collection of critical information and biological samples to be used in developing diagnostic, prevention, treatment, and survivorship strategies against breast cancer. Currently, seven cancer institutions are participating in the BCCR that contains data on almost 900 subjects (BC patients and survivors, as well as individuals at high risk of getting BC).

PCCR: Pancreatic Cancer Collaborative Registry.

The Pancreatic Cancer Collaborative Registry (PCCR) is a multi-institutional web-based system aimed to collect a variety of data on pancreatic cancer patients and high-risk subjects in a standard and efficient way. The PCCR was initiated by a group of experts in medical oncology, gastroenterology, genetics, pathology, epidemiology, nutrition, and computer science with the goal of facilitating rapid and uniform collection of critical information and biological samples to be used in developing diagnostic, prevention and treatment strategies against pancreatic cancer. The PCCR is a multi-tier web application that utilizes Java/JSP technology and has Oracle 10 g database as a back-end. The PCCR uses a "confederation model" that encourages participation of any interested center, irrespective of its size or location. The PCCR utilizes a standardized approach to data collection and reporting, and uses extensive validation procedures to prevent entering erroneous data. The PCCR controlled vocabulary is harmonized with the NCI Thesaurus (NCIt) or Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT). The PCCR questionnaire has accommodated standards accepted in cancer research and healthcare. Currently, seven cancer centers in the USA, as well as one center in Italy are participating in the PCCR. At present, the PCCR database contains data on more than 2,700 subjects (PC patients and individuals at high risk of getting this disease). The PCCR has been certified by the NCI Center for Biomedical Informatics and Information Technology as a cancer Biomedical Informatics Grid (caBIG(®)) Bronze Compatible product. The PCCR provides a foundation for collaborative PC research. It has all the necessary prerequisites for subsequent evolution of the developed infrastructure from simply gathering PC-related data into a biomedical computing platform vital for successful PC studies, care and treatment. Studies utilizing data collected in the PCCR may engender new approaches to disease prognosis, risk factor assessment, and therapeutic interventions.