bluuebunny/crossref_metadata_2025 huggingface dataset:
New Analysis of the Seasonal Variation of the Critical Frequencies foF2 by a Proposed Formula of the Power of Solar Radiation
Authors: Abdoul-kader Segda , Allain Gnabahou Doua , Aristide Gyebre
Published: November 2023

The present article is a new analysis of the seasonal variation of the critical frequencies foF2 by establishing a formula as a tool. Thus, from the formula of the power of solar radiation proposed, by its two components, one the power linked to the angles of attack of the solar rays and the other the power imparted to the Earth-Sun distance, we will understand the asymmetries foF2 peaks at the equinoxes and the various winter and semiannual anomalies. By the power component imparted to the contribution of sunspots, we will understand the reversal of the maximum peak during the growth and waning phase of the solar cycle. Finally, by the power component linked to the transport mechanism and the reinforcement by the solar wind of the ionization, we will understand the why of the noninversion of the maximum of the peaks.
Influence of Glow Discharge Plasma Treatment on Cashew Apple Juice’s Aroma Profile and Volatile Compounds
Authors: Maia Dayanne L. H. , Rodrigues Sueli , Fernandes Fabiano A. N.
Published: November 2023

Cashew apple juice has a distinctive fruity aroma but contains an undesired balsamic/chemical note caused by the high concentration of styrene and other aromatic hydrocarbons. Cold plasma technology can induce chemical changes to fruit juices’ volatile compounds, improving the aroma of fruit juices. This study is aimed at evaluating the chemical effects of cold plasma on the volatile compounds and aroma of cashew apple juice, which is characterized by a complex mixture of compounds. Glow discharge plasma was applied to cashew apple juice, varying the plasma flow rate (10 to 30 mL/min) and processing time (10 to 20 min) at a constant voltage (80 kV). Plasma treatment induced several changes in the juice’s volatile compound composition, with a significant decrease in fatty acids and fatty acid esters (92%) and an increase in aldehydes (50%), alcohols (86%), and short-chain esters (21%). The primary reaction observed during plasma treatment was the internal scission of fatty acid and fatty acid esters, which formed short-chain esters and aldehydes. Further hydrogenation of aldehydes produced alcohols. The chemical changes induced by plasma treatment intensified cashew apple juice’s aroma by 28% while maintaining its aroma profile.
Removal of Pb(II) from Aqueous Solutions with Manganese Oxide-Modified Diatomite
Authors: Danh Huynh Thanh , Ly Nguyen Thi Truc , Nguyen Vanthan , Khieu Dinh Quang , Du Pham Dinh
Published: November 2023

In the present work, natural diatomite modified with manganese oxide (MnO2) was prepared via direct redox reaction with KMnO4 and HCl. The product was characterized by using X-ray diffraction, Fourier-transform infrared spectroscopy, scanning electron microscopy, transmission electron microscopy, energy-dispersive X-ray, and nitrogen adsorption-desorption isotherms. It was found that the nanorod manganese oxide was highly dispersed onto the diatomite porous matrix. The specific surface area of the obtained manganese oxide/diatomite ([Formula: see text]) is larger than that of natural diatomite ([Formula: see text]). It was utilized to remove Pb(II) in aqueous solutions. It exhibits an excellent Pb(II) adsorption capacity. The adsorption data fits well with the pseudo-second-order kinetics model, and the adsorption process is endothermic and spontaneous with an activation energy of 41.56 kJ mol−1 and follows the Freundlich isotherm model. The Mn/diatomite adsorption capacity for Pb(II) is 81.42 mg g−1, calculated with the Langmuir model. In addition, the adsorption mechanism of Pb(II) onto Mn/diatomite is also addressed.
Epidemiological Profile of Salivary Gland Tumors in Southern Iranian Population: A Retrospective Study of 405 Cases
Authors: Ghaderi Hamid , Kruger Estie , Ahmadvand Simin , Mohammadi Yousef , Khademi Bijan , Ghaderi Abbas
Published: November 2023

Aims. Salivary gland tumors (SGTs) are a rare and diverse group of tumors that account for 3 to 10% of all head and neck malignancies. We aimed to conduct a comprehensive epidemiological analysis of SGTs in the south of Iran and compare the findings with previous reports from Iran and other parts of the world. Methods. Using a retrospective study, 405 patients diagnosed with SGTs were observed over an eight-year period between April 2013 and October 2021 in Shiraz, Iran. Patients’ demographic and clinicopathological features were obtained from patients’ records. Quantitative and descriptive data analysis was performed using SPSS software. Results. There were 302 benign (74.5%) and 103 (25.4%) malignant SGTs. Pleomorphic adenoma and Warthin’s tumors were the most common benign SGTs (70.5% and 21.5%, respectively). The most common malignant SGTs were mucoepidermoid carcinoma and adenoid cystic carcinoma (26.2% and 22.3%, respectively). There was a statistically significant association between tumor origin and its malignancy status (

p
<
0.001

). In addition, the results indicated that benign tumors were most commonly detected in the parotid gland (

p
<
0.05

). The benign tumors were more frequently observed among the younger population (

p
=
0.006

). Conclusion. In summary, the findings of the current study were mainly consistent with the previous reports from Iran and the rest of the world. Benign tumors were the most prevalent type of SGTs, and the parotid gland was the most common site. While the majority of cases that developed from the major salivary glands were benign, all the minor SGTs were malignant. Older patients were more likely to develop malignant tumors compared to younger ones. This study provides insights into the prevalence, age-related incidence, gender distribution, and geographic variation of salivary gland tumors. This can be instrumental to develop a guideline for screening, diagnosis, and determining an optimal treatment.
A Diagnosis of Maternal 22q Duplication and Mosaic Deletion following Prenatal Cell-Free DNA Screening
Authors: Hicks Melissa A. , Lalonde Emilie , Zoladz Jessica , Gonik Bernard , Ebrahim Salah
Published: November 2023

Concurrent microduplication and microdeletion of the chromosome 22q11.2 region are a rarely reported phenomenon. We describe a case of germline 22q11.21 microduplication syndrome with concurrent mosaic 22q11.2 deletion in a pregnant patient, identified by chromosomal microarray and FISH after noninvasive prenatal genetic screening (cfDNA) results discordant with family history. The patient was referred to maternal-fetal medicine (MFM) at 14 weeks’ gestation secondary to an SNP-based cfDNA result of a suspected maternal 22q11.2 deletion and a fetal risk of 1 in 2 for 22q11.2 deletion syndrome. The patient reported a similar cfDNA result in a previous pregnancy; however postnatal chromosomal microarray on that child identified an atypical 22q11.21 microduplication. We report the maternal chromosomal microarray findings of a germline 726 kb 22q11.21 duplication and a mosaic 1.33 Mb 22q11.2 deletion and highlight the copy number variant data generated by cfDNA in this unique case. This family adds to the limited literature of concurrent 22q11.2 microduplication and microdeletion carriers.
Prevalence and Risk Assessment of Aflatoxin in Iowa Corn during a Drought Year
Authors: Branstad-Spates Emily H. , Bowers Erin L. , Hurburgh Charles R. , Dixon Philip M. , Mosher Gretchen A.
Published: November 2023

Warm temperatures and drought conditions in the United States (US) Corn Belt in 2012 raised concern for widespread aflatoxin (AFL) contamination in Iowa corn. To identify the prevalence of AFL in the 2012 corn crop, the Iowa Department of Agriculture and Land Stewardship (IDALS) conducted a sample of Iowa corn to assess the incidence and severity of AFL contamination. Samples were obtained from grain elevators in all of Iowa’s 99 counties, representing nine crop reporting districts (CRD), and 396 samples were analyzed by IDALS using rapid test methods. The statewide mean for AFL in parts per billion (ppb) was 5.57 ppb. Regions of Iowa differed in their incidence levels, with AFL levels significantly higher in the Southwest (SW; mean 15.13 ppb) and South Central (SC; mean 10.86 ppb) CRD (p<0.05) regions of Iowa. This sampling demonstrated high variability among samples collected within CRD and across the entire state of Iowa in an extreme weather event year. In years when Iowa has AFL contamination in corn, there is a need for a proactive and preventive strategy to minimize hazards in domestic and export markets.
Supplementary Table 9 from Associations between MICA and MICB Genetic Variants, Protein Levels, and Colorectal Cancer: Atherosclerosis Risk in Communities (ARIC)
Authors: Wang Shuo , Onyeaghala Guillaume C. , Pankratz Nathan , Nelson Heather H. , Thyagarajan Bharat , Tang Weihong , Norby Faye L. , Ugoji Chinenye , Joshu Corinne E. , Gomez Christian R. , Couper David J. , Coresh Josef , Platz Elizabeth A. , Prizment Anna E.
Published: November 2023

Supplemental Table 9 shows the associations between pre-selected SNPs and mean of Visit 2 and Visit 3 sMICA and sMICB levels (continuous variables).
Supplementary Table 9 from Somatic Mosaic Chromosomal Alterations and Death of Cardiovascular Disease Causes among Cancer Survivors
Authors: Sun Maxine , Cyr Marie-Christyne , Sandoval Johanna , Lemieux Perreault Louis-Philippe , Busque Lambert , Tardif Jean-Claude , Dubé Marie-Pierre
Published: November 2023

Supplementary Table 9: The effect of mosaic loss of Y chromosome on death of cardiovascular disease causes, coronary artery disease causes, from cancer and any cause of death
Supplementary Table 8 from Associations between MICA and MICB Genetic Variants, Protein Levels, and Colorectal Cancer: Atherosclerosis Risk in Communities (ARIC)
Authors: Wang Shuo , Onyeaghala Guillaume C. , Pankratz Nathan , Nelson Heather H. , Thyagarajan Bharat , Tang Weihong , Norby Faye L. , Ugoji Chinenye , Joshu Corinne E. , Gomez Christian R. , Couper David J. , Coresh Josef , Platz Elizabeth A. , Prizment Anna E.
Published: November 2023

Supplemental Table 8 shows the associations of pre-selected SNPs with Visit 2 sMICA and sMICB levels and CRC risk without adjustment for ancestry markers.
Supplementary Table 8 from Somatic Mosaic Chromosomal Alterations and Death of Cardiovascular Disease Causes among Cancer Survivors
Authors: Sun Maxine , Cyr Marie-Christyne , Sandoval Johanna , Lemieux Perreault Louis-Philippe , Busque Lambert , Tardif Jean-Claude , Dubé Marie-Pierre
Published: November 2023

Supplementary Table 8: The effect of mosaic loss of X chromosome on death of cardiovascular disease causes, coronary artery disease causes, from cancer, and any cause of death
Supplementary Table 7 from Associations between MICA and MICB Genetic Variants, Protein Levels, and Colorectal Cancer: Atherosclerosis Risk in Communities (ARIC)
Authors: Wang Shuo , Onyeaghala Guillaume C. , Pankratz Nathan , Nelson Heather H. , Thyagarajan Bharat , Tang Weihong , Norby Faye L. , Ugoji Chinenye , Joshu Corinne E. , Gomez Christian R. , Couper David J. , Coresh Josef , Platz Elizabeth A. , Prizment Anna E.
Published: November 2023

Supplemental Table 7 shows the association of sMICA and sMICB levels (continuous variables) and pre-selected SNPsa with risk of CRC stratified by family history of CRC.
Supplementary Table 7 from Somatic Mosaic Chromosomal Alterations and Death of Cardiovascular Disease Causes among Cancer Survivors
Authors: Sun Maxine , Cyr Marie-Christyne , Sandoval Johanna , Lemieux Perreault Louis-Philippe , Busque Lambert , Tardif Jean-Claude , Dubé Marie-Pierre
Published: November 2023

Supplementary Table 7: The effect of autosomal mosaic chromosomal alterations on death of cardiovascular disease causes, coronary artery disease causes, from cancer and any cause of death
Supplementary Table 6 from Associations between MICA and MICB Genetic Variants, Protein Levels, and Colorectal Cancer: Atherosclerosis Risk in Communities (ARIC)
Authors: Wang Shuo , Onyeaghala Guillaume C. , Pankratz Nathan , Nelson Heather H. , Thyagarajan Bharat , Tang Weihong , Norby Faye L. , Ugoji Chinenye , Joshu Corinne E. , Gomez Christian R. , Couper David J. , Coresh Josef , Platz Elizabeth A. , Prizment Anna E.
Published: November 2023

Supplemental Table 6 shows the associations of sMICA and sMICB levels (continuous variables) and pre-selected SNPsb with risk of CRC and subtypes of CRC.
Supplementary Table 6 from Somatic Mosaic Chromosomal Alterations and Death of Cardiovascular Disease Causes among Cancer Survivors
Authors: Sun Maxine , Cyr Marie-Christyne , Sandoval Johanna , Lemieux Perreault Louis-Philippe , Busque Lambert , Tardif Jean-Claude , Dubé Marie-Pierre
Published: November 2023

Supplementary Table 6: Cox regression analyses evaluating the effect of mosaic chromosomal alterations on the risk of death of cardiovascular disease causes, coronary artery disease causes, from cancer, and any cause of death by chemotherapy status
Supplementary Table 5 from Somatic Mosaic Chromosomal Alterations and Death of Cardiovascular Disease Causes among Cancer Survivors
Authors: Sun Maxine , Cyr Marie-Christyne , Sandoval Johanna , Lemieux Perreault Louis-Philippe , Busque Lambert , Tardif Jean-Claude , Dubé Marie-Pierre
Published: November 2023

Supplementary Table 5: Cox regression analyses evaluating the effect of mosaic chromosomal alterations on the risk of death of cardiovascular disease causes, coronary artery disease causes, from cancer, and any cause of death stratified by smoking status groups
Supplementary Table 4 from Somatic Mosaic Chromosomal Alterations and Death of Cardiovascular Disease Causes among Cancer Survivors
Authors: Sun Maxine , Cyr Marie-Christyne , Sandoval Johanna , Lemieux Perreault Louis-Philippe , Busque Lambert , Tardif Jean-Claude , Dubé Marie-Pierre
Published: November 2023

Supplementary Table 4: Multivariable competing-risks regression model for the prediction of death of CVD causes and CAD causes
Supplementary Table 10 from Associations between MICA and MICB Genetic Variants, Protein Levels, and Colorectal Cancer: Atherosclerosis Risk in Communities (ARIC)
Authors: Wang Shuo , Onyeaghala Guillaume C. , Pankratz Nathan , Nelson Heather H. , Thyagarajan Bharat , Tang Weihong , Norby Faye L. , Ugoji Chinenye , Joshu Corinne E. , Gomez Christian R. , Couper David J. , Coresh Josef , Platz Elizabeth A. , Prizment Anna E.
Published: November 2023

Supplemental Table 10 shows that associations between mean of Visit 2 and Visit 3 sMICA and sMICB levels (continuous variables) with CRC risk.
Supplementary Table 16 from Somatic Mosaic Chromosomal Alterations and Death of Cardiovascular Disease Causes among Cancer Survivors
Authors: Sun Maxine , Cyr Marie-Christyne , Sandoval Johanna , Lemieux Perreault Louis-Philippe , Busque Lambert , Tardif Jean-Claude , Dubé Marie-Pierre
Published: November 2023

Supplementary Table 16: Cox regression analyses for the effect of mosaic chromosomal alterations on the risk of incident cardiovascular endpoints aged ≥65 years old (n=15,273)
Supplementary Table 1 from Associations between MICA and MICB Genetic Variants, Protein Levels, and Colorectal Cancer: Atherosclerosis Risk in Communities (ARIC)
Authors: Wang Shuo , Onyeaghala Guillaume C. , Pankratz Nathan , Nelson Heather H. , Thyagarajan Bharat , Tang Weihong , Norby Faye L. , Ugoji Chinenye , Joshu Corinne E. , Gomez Christian R. , Couper David J. , Coresh Josef , Platz Elizabeth A. , Prizment Anna E.
Published: November 2023

Supplemental Table 1 shows the results from previous studies that found significant associations between the pre-selected SNPs and risk of cancers or autoimmune diseases.
Supplementary Table 15 from Somatic Mosaic Chromosomal Alterations and Death of Cardiovascular Disease Causes among Cancer Survivors
Authors: Sun Maxine , Cyr Marie-Christyne , Sandoval Johanna , Lemieux Perreault Louis-Philippe , Busque Lambert , Tardif Jean-Claude , Dubé Marie-Pierre
Published: November 2023

Supplementary Table 15: Cox regression analyses evaluating the effect of mosaic chromosomal alterations on the risk of any cause of death by cancer type
Supplementary Table 14 from Somatic Mosaic Chromosomal Alterations and Death of Cardiovascular Disease Causes among Cancer Survivors
Authors: Sun Maxine , Cyr Marie-Christyne , Sandoval Johanna , Lemieux Perreault Louis-Philippe , Busque Lambert , Tardif Jean-Claude , Dubé Marie-Pierre
Published: November 2023

Supplementary Table 14: Cox regression analyses of the effect of mosaic chromosomal alterations on the risk of death from cancer according to cancer type
Supplementary Table 1 from Survival of Middle Eastern and North African Individuals Diagnosed with Colorectal Cancer: A Population-Based Study in California
Authors: Zaki Timothy A. , Ziogas Argyrios , Chang Jenny , Murphy Caitlin C. , Anton-Culver Hoda
Published: November 2023

Supplementary Table 1 provides treatment quality metrics for colon and rectal cancers using adherence to National Comprehensive Cancer Network guidelines.
Supplementary Table 11 from Somatic Mosaic Chromosomal Alterations and Death of Cardiovascular Disease Causes among Cancer Survivors
Authors: Sun Maxine , Cyr Marie-Christyne , Sandoval Johanna , Lemieux Perreault Louis-Philippe , Busque Lambert , Tardif Jean-Claude , Dubé Marie-Pierre
Published: November 2023

Supplementary Table 11: The effect of mosaic chromosomal alterations (mCA) on death of cardiovascular disease causes, coronary artery disease causes, from cancer, and any-cause death with a mCA-by-cancer status interaction term in all patients
Supplemental Table 4 from Nitrated Polycyclic Aromatic Hydrocarbon (Nitro-PAH) Signatures and Somatic Mutations in Diesel Exhaust-Exposed Bladder Tumors
Authors: Gonzalez Nicole , Rao Nina , Dean Michael , Lee Donghyuk , Hurson Amber N. , Baris Dalsu , Schwenn Molly , Johnson Alison , Prokunina-Olsson Ludmila , Friesen Melissa C. , Zhu Bin , Rothman Nathaniel , Silverman Debra T. , Koutros Stella
Published: November 2023

specific TP53 mutations and ever diesel
Supplemental Table 3 from Nitrated Polycyclic Aromatic Hydrocarbon (Nitro-PAH) Signatures and Somatic Mutations in Diesel Exhaust-Exposed Bladder Tumors
Authors: Gonzalez Nicole , Rao Nina , Dean Michael , Lee Donghyuk , Hurson Amber N. , Baris Dalsu , Schwenn Molly , Johnson Alison , Prokunina-Olsson Ludmila , Friesen Melissa C. , Zhu Bin , Rothman Nathaniel , Silverman Debra T. , Koutros Stella
Published: November 2023

Case-case associations between REC and TP53 mutation, NEBCS
Supplemental Table 2 from Nitrated Polycyclic Aromatic Hydrocarbon (Nitro-PAH) Signatures and Somatic Mutations in Diesel Exhaust-Exposed Bladder Tumors
Authors: Gonzalez Nicole , Rao Nina , Dean Michael , Lee Donghyuk , Hurson Amber N. , Baris Dalsu , Schwenn Molly , Johnson Alison , Prokunina-Olsson Ludmila , Friesen Melissa C. , Zhu Bin , Rothman Nathaniel , Silverman Debra T. , Koutros Stella
Published: November 2023

study characteristics
Supplemental Table 2 from Measurement of Ovarian Tumor Immune Profiles by Multiplex Immunohistochemistry: Implications for Epidemiologic Studies
Authors: Hathaway Cassandra A. , Conejo-Garcia Jose R. , Fridley Brooke L. , Rosner Bernard , Saeed-Vafa Daryoush , Moran Segura Carlos , Nguyen Jonathan V. , Hecht Jonathan L. , Sasamoto Naoko , Terry Kathryn L. , Tworoger Shelley S. , Townsend Mary K.
Published: November 2023

Supplemental Table 2 shows the distribution of presence/absence of percentage of the marker in the tumor, by age of sample for type I and type II tumors
Supplemental Table 1 from Measurement of Ovarian Tumor Immune Profiles by Multiplex Immunohistochemistry: Implications for Epidemiologic Studies
Authors: Hathaway Cassandra A. , Conejo-Garcia Jose R. , Fridley Brooke L. , Rosner Bernard , Saeed-Vafa Daryoush , Moran Segura Carlos , Nguyen Jonathan V. , Hecht Jonathan L. , Sasamoto Naoko , Terry Kathryn L. , Tworoger Shelley S. , Townsend Mary K.
Published: November 2023

Supplemental Table 1 shows the correlations of percentages of the markers in the tumor.
Supplementary Table S5 from COVID-19 Outcomes by Cancer Status, Site, Treatment, and Vaccination
Authors: Salvatore Maxwell , Hu Miriam M. , Beesley Lauren J. , Mondul Alison M. , Pearce Celeste Leigh , Friese Christopher R. , Fritsche Lars G. , Mukherjee Bhramar
Published: November 2023

Logistic regression odds ratios (95% CI) for COVID-19 outcomes by cancer treatment type using restricted electronic health record for cancer recency analysis.
Supplemental Figure 2 from Measurement of Ovarian Tumor Immune Profiles by Multiplex Immunohistochemistry: Implications for Epidemiologic Studies
Authors: Hathaway Cassandra A. , Conejo-Garcia Jose R. , Fridley Brooke L. , Rosner Bernard , Saeed-Vafa Daryoush , Moran Segura Carlos , Nguyen Jonathan V. , Hecht Jonathan L. , Sasamoto Naoko , Terry Kathryn L. , Tworoger Shelley S. , Townsend Mary K.
Published: November 2023

Supplemental Figure 2 provides representative images from the immune checkpoint panel for high-grade serous, endometrioid, clear cell, and mucinous tumors.
Supplemental Figure 1 from Measurement of Ovarian Tumor Immune Profiles by Multiplex Immunohistochemistry: Implications for Epidemiologic Studies
Authors: Hathaway Cassandra A. , Conejo-Garcia Jose R. , Fridley Brooke L. , Rosner Bernard , Saeed-Vafa Daryoush , Moran Segura Carlos , Nguyen Jonathan V. , Hecht Jonathan L. , Sasamoto Naoko , Terry Kathryn L. , Tworoger Shelley S. , Townsend Mary K.
Published: November 2023

Supplemental Figure 1 provides representative images from the T cell panel for high-grade serous, endometrioid, clear cell, and mucinous tumors.
Table S6 from Trend in Cervical Cancer Incidence and Mortality Rates in China, 2006–2030: A Bayesian Age-Period-Cohort Modeling Study
Authors: Yuan Meiwen , Zhao Xuelian , Wang Honghao , Hu Shangying , Zhao Fanghui
Published: November 2023

Table S6 shows trends for age-specific cervical cancer incidence and mortality rates by region in China between 2017 to 2030.
Supplementary Table S2 from COVID-19 Outcomes by Cancer Status, Site, Treatment, and Vaccination
Authors: Salvatore Maxwell , Hu Miriam M. , Beesley Lauren J. , Mondul Alison M. , Pearce Celeste Leigh , Friese Christopher R. , Fritsche Lars G. , Mukherjee Bhramar
Published: November 2023

Codes used to define cancers diagnoses and treatments. Cancer sites are highlighted different colors and correspond to the highlighted rows of the same color in Table S1.
Table S5 from Trend in Cervical Cancer Incidence and Mortality Rates in China, 2006–2030: A Bayesian Age-Period-Cohort Modeling Study
Authors: Yuan Meiwen , Zhao Xuelian , Wang Honghao , Hu Shangying , Zhao Fanghui
Published: November 2023

Table S5 shows proportion of the annual number of deaths by age group between 2006 to 2016.
Supplementary Materials and Methods from COVID-19 Outcomes by Cancer Status, Site, Treatment, and Vaccination
Authors: Salvatore Maxwell , Hu Miriam M. , Beesley Lauren J. , Mondul Alison M. , Pearce Celeste Leigh , Friese Christopher R. , Fritsche Lars G. , Mukherjee Bhramar
Published: November 2023

Additional description of methods including derivation of analytic cohort, description of the neighborhood disadvantage index, and additional details of statistical analyses for transparency, replicability, and reproducibility.
Supplementary Figure S9 from COVID-19 Outcomes by Cancer Status, Site, Treatment, and Vaccination
Authors: Salvatore Maxwell , Hu Miriam M. , Beesley Lauren J. , Mondul Alison M. , Pearce Celeste Leigh , Friese Christopher R. , Fritsche Lars G. , Mukherjee Bhramar
Published: November 2023

COVID-19 hospitalization among recent cancer (within three years of positive COVID-19 test; n = 6,143) and non-cancer patients (n = 41,545) who tested positive and have non-missing covariates. The results were from the model logit P(YHospitalization = 1|X, Cancer, Covariate) = β0 + βXX + βCancerCancer + βIntX × Cancer + βCovCovariate, where Covariate = Age + Race/Ethnicity + Sex + Disadvantage Index (quartile) + Comorbidity Score. Reference groups were age: 18 to 35, sex: female, BMI: 18.5 to 25, race: white/non-Hispanic, smoking status: never. A unit change in the continuous age variable is 10 years. The comorbidity score ranges from 0 to 6 and is the sum of indicators for presence of six categories of preexisting conditions in the electronic health record.
Supplementary Figure S7 from COVID-19 Outcomes by Cancer Status, Site, Treatment, and Vaccination
Authors: Salvatore Maxwell , Hu Miriam M. , Beesley Lauren J. , Mondul Alison M. , Pearce Celeste Leigh , Friese Christopher R. , Fritsche Lars G. , Mukherjee Bhramar
Published: November 2023

COVID-19 ICU admission among cancer (n = 12,995) and non-cancer patients (n = 34,693) who tested positive and had non-missing covariates. The results were from the model logit P(YICU = 1|X, Cancer, Covariate) = β0 + βXX + βCancerCancerStatus + βIntX × CancerStatus + βCovCovariate, where Covariate = Age + Race/Ethnicity + Sex + Disadvantage Index (quartile) + Comorbidity Score. Reference groups were age: 18 to 35, sex: female, BMI: 18.5 to 25, race: white/non-Hispanic, smoking status: never. A unit change in the continuous age variable is 10 years. The comorbidity score ranges from 0 to 6 and is the sum of indicators for presence of six categories of preexisting conditions in the electronic health record.
Table S1 from Trend in Cervical Cancer Incidence and Mortality Rates in China, 2006–2030: A Bayesian Age-Period-Cohort Modeling Study
Authors: Yuan Meiwen , Zhao Xuelian , Wang Honghao , Hu Shangying , Zhao Fanghui
Published: November 2023

Table S1 shows average age and standardized average age at diagnosis for cervical cancer between 2006 to 2030.
Figure S4 from Trend in Cervical Cancer Incidence and Mortality Rates in China, 2006–2030: A Bayesian Age-Period-Cohort Modeling Study
Authors: Yuan Meiwen , Zhao Xuelian , Wang Honghao , Hu Shangying , Zhao Fanghui
Published: November 2023

Figure S4 age-period-cohort effect on cervical cancer mortality rates in China between 2006 to 2016.
Figure S3 from Trend in Cervical Cancer Incidence and Mortality Rates in China, 2006–2030: A Bayesian Age-Period-Cohort Modeling Study
Authors: Yuan Meiwen , Zhao Xuelian , Wang Honghao , Hu Shangying , Zhao Fanghui
Published: November 2023

Figure S3 shows age-period-cohort effect on cervical cancer incidence rates in China between 2006 to 2016.
Supplementary Figure S4 from COVID-19 Outcomes by Cancer Status, Site, Treatment, and Vaccination
Authors: Salvatore Maxwell , Hu Miriam M. , Beesley Lauren J. , Mondul Alison M. , Pearce Celeste Leigh , Friese Christopher R. , Fritsche Lars G. , Mukherjee Bhramar
Published: November 2023

An UpSet diagram showing the distribution of cancer treatment patterns among COVID-19 positive individuals with cancer (n = 13,752). Treatment-based analyses were performed using three mutually exclusive categories: chemotherapy (n = 2,910), radiation only (n = 690), and surgery only (n = 172). Treatment was defined according to Table S2.
Figure S2 from Trend in Cervical Cancer Incidence and Mortality Rates in China, 2006–2030: A Bayesian Age-Period-Cohort Modeling Study
Authors: Yuan Meiwen , Zhao Xuelian , Wang Honghao , Hu Shangying , Zhao Fanghui
Published: November 2023

Figure S2 shows trends for deaths during 2006−2016 and predicted from 2017 to 2030 in China.
Figure S1 from Trend in Cervical Cancer Incidence and Mortality Rates in China, 2006–2030: A Bayesian Age-Period-Cohort Modeling Study
Authors: Yuan Meiwen , Zhao Xuelian , Wang Honghao , Hu Shangying , Zhao Fanghui
Published: November 2023

Figure S1 shows trends for incidence cases during 2006−2016 and predicted from 2017 to 2030 in China.
Supplementary Figure S2 from COVID-19 Outcomes by Cancer Status, Site, Treatment, and Vaccination
Authors: Salvatore Maxwell , Hu Miriam M. , Beesley Lauren J. , Mondul Alison M. , Pearce Celeste Leigh , Friese Christopher R. , Fritsche Lars G. , Mukherjee Bhramar
Published: November 2023

Flowcharts describing the definitions of outcomes (panel A) and vaccination status (panel B).
Supplementary Figure S10 from COVID-19 Outcomes by Cancer Status, Site, Treatment, and Vaccination
Authors: Salvatore Maxwell , Hu Miriam M. , Beesley Lauren J. , Mondul Alison M. , Pearce Celeste Leigh , Friese Christopher R. , Fritsche Lars G. , Mukherjee Bhramar
Published: November 2023

COVID-19 ICU admission among recent cancer (within three years of positive COVID-19 test; n = 6,143) and non-cancer patients (n = 41,545) who tested positive and have non-missing covariates. The results were from the model logit P(YICU = 1|X, Cancer, Covariate) = β0 + βXX + βCancerCancer + βIntX × Cancer + βCovCovariate, where Covariate = Age + Race/Ethnicity + Sex + Disadvantage Index (quartile) + Comorbidity Score. Reference groups were age: 18 to 35, sex: female, BMI: 18.5 to 25, race: white/non-Hispanic, smoking status: never. A unit change in the continuous age variable is 10 years. The comorbidity score ranges from 0 to 6 and is the sum of indicators for presence of six categories of preexisting conditions in the electronic health record.
Supplementary Figure 1 from The Impact of Improved Treatments on Survival of Adult U.S. Leukemia Patients: 1990–2018
Authors: Howlader Nadia , Sharon Elad , Bhattacharya Manami , Ehrlich Lori A. , Richardson Nicholas C. , Gormley Nicole J. , de Claro R. Angelo , Wood Amy E. , Mariotto Angela B. , Cronin Kathleen A.
Published: November 2023

Supplementary Figure 1: Trends in Mortality by Leukemia Subtypes and Race Ethnicity. Total US 1992-2018. Results are shown for mortality trends for CML (panel A), ALL (panel B), and CLL (panel C), 1992-2018. For each panel, we show age-adjusted mortality rates by five mutually exclusive race-ethnicity categories: non-Hispanic White, non-Hispanic Black, non-Hispanic Asian Pacific Islander, non-Hispanic American Indian/Alaskan Natives, and Hispanic.
Table S9 from Exploring the Complex Relationship between Gut Microbiota and Risk of Colorectal Neoplasia Using Bidirectional Mendelian Randomization Analysis
Authors: Li Wanxin , Zhou Xuan , Yuan Shuai , Wang Lijuan , Yu Lili , Sun Jing , Chen Jie , Xiao Qian , Wan Zhongxiao , Zheng Ju-Sheng , Zhang Cai-Xia , Larsson Susanna C. , Farrington Susan M. , Law Philip , Houlston Richard S. , Tomlinson Ian , Ding Ke-Feng , Dunlop Malcolm G. , Theodoratou Evropi , Li Xue
Published: November 2023

Table S9 shows the reverse MR analyses of colorectal cancer, adenoma and polyps with the abundance of nine gut microbiota.
Supplementary Table 4 from Low-Carbohydrate Diet Score and the Risk of Colorectal Cancer: Findings from the Singapore Chinese Health Study
Authors: Yu Yi-Chuan , Paragomi Pedram , Jin Aizhen , Wang Renwei , Schoen Robert E. , Koh Woon-Puay , Yuan Jian-Min , Luu Hung N.
Published: November 2023

Supplementary Table S4 describes results from models without BMI variable adjustment (stratified analysis)
Table S8 from Exploring the Complex Relationship between Gut Microbiota and Risk of Colorectal Neoplasia Using Bidirectional Mendelian Randomization Analysis
Authors: Li Wanxin , Zhou Xuan , Yuan Shuai , Wang Lijuan , Yu Lili , Sun Jing , Chen Jie , Xiao Qian , Wan Zhongxiao , Zheng Ju-Sheng , Zhang Cai-Xia , Larsson Susanna C. , Farrington Susan M. , Law Philip , Houlston Richard S. , Tomlinson Ian , Ding Ke-Feng , Dunlop Malcolm G. , Theodoratou Evropi , Li Xue
Published: November 2023

Table S8 shows the forward MR analyses of the six gut microbial metabolites with the risk of colorectal cancer.
Table S7 from Exploring the Complex Relationship between Gut Microbiota and Risk of Colorectal Neoplasia Using Bidirectional Mendelian Randomization Analysis
Authors: Li Wanxin , Zhou Xuan , Yuan Shuai , Wang Lijuan , Yu Lili , Sun Jing , Chen Jie , Xiao Qian , Wan Zhongxiao , Zheng Ju-Sheng , Zhang Cai-Xia , Larsson Susanna C. , Farrington Susan M. , Law Philip , Houlston Richard S. , Tomlinson Ian , Ding Ke-Feng , Dunlop Malcolm G. , Theodoratou Evropi , Li Xue
Published: November 2023

Table S7 shows the forward MR analyses of nine gut microbiota with the risk of colorectal cancer.
Table S6 from Exploring the Complex Relationship between Gut Microbiota and Risk of Colorectal Neoplasia Using Bidirectional Mendelian Randomization Analysis
Authors: Li Wanxin , Zhou Xuan , Yuan Shuai , Wang Lijuan , Yu Lili , Sun Jing , Chen Jie , Xiao Qian , Wan Zhongxiao , Zheng Ju-Sheng , Zhang Cai-Xia , Larsson Susanna C. , Farrington Susan M. , Law Philip , Houlston Richard S. , Tomlinson Ian , Ding Ke-Feng , Dunlop Malcolm G. , Theodoratou Evropi , Li Xue
Published: November 2023

Table S6 shows the forward MR analyses of 24 gut microbiota with the risk of colorectal cancer.
Supplementary Table 1 from Low-Carbohydrate Diet Score and the Risk of Colorectal Cancer: Findings from the Singapore Chinese Health Study
Authors: Yu Yi-Chuan , Paragomi Pedram , Jin Aizhen , Wang Renwei , Schoen Robert E. , Koh Woon-Puay , Yuan Jian-Min , Luu Hung N.
Published: November 2023

Supplementary Table S1 describes baseline characteristics of participants
Table S4 from Exploring the Complex Relationship between Gut Microbiota and Risk of Colorectal Neoplasia Using Bidirectional Mendelian Randomization Analysis
Authors: Li Wanxin , Zhou Xuan , Yuan Shuai , Wang Lijuan , Yu Lili , Sun Jing , Chen Jie , Xiao Qian , Wan Zhongxiao , Zheng Ju-Sheng , Zhang Cai-Xia , Larsson Susanna C. , Farrington Susan M. , Law Philip , Houlston Richard S. , Tomlinson Ian , Ding Ke-Feng , Dunlop Malcolm G. , Theodoratou Evropi , Li Xue
Published: November 2023

Table S4 shows the SNPs for colorectal cancer.
Table S3 from Exploring the Complex Relationship between Gut Microbiota and Risk of Colorectal Neoplasia Using Bidirectional Mendelian Randomization Analysis
Authors: Li Wanxin , Zhou Xuan , Yuan Shuai , Wang Lijuan , Yu Lili , Sun Jing , Chen Jie , Xiao Qian , Wan Zhongxiao , Zheng Ju-Sheng , Zhang Cai-Xia , Larsson Susanna C. , Farrington Susan M. , Law Philip , Houlston Richard S. , Tomlinson Ian , Ding Ke-Feng , Dunlop Malcolm G. , Theodoratou Evropi , Li Xue
Published: November 2023

Table S3 shows the SNPs that associated with six gut microbial metabolites.
Table S1 from Exploring the Complex Relationship between Gut Microbiota and Risk of Colorectal Neoplasia Using Bidirectional Mendelian Randomization Analysis
Authors: Li Wanxin , Zhou Xuan , Yuan Shuai , Wang Lijuan , Yu Lili , Sun Jing , Chen Jie , Xiao Qian , Wan Zhongxiao , Zheng Ju-Sheng , Zhang Cai-Xia , Larsson Susanna C. , Farrington Susan M. , Law Philip , Houlston Richard S. , Tomlinson Ian , Ding Ke-Feng , Dunlop Malcolm G. , Theodoratou Evropi , Li Xue
Published: November 2023

Table S1 shows the SNPs that associated with 24 microbiota taxa in MiBioGen (P<5×10-8).
Supplementary Methods from Exploring the Complex Relationship between Gut Microbiota and Risk of Colorectal Neoplasia Using Bidirectional Mendelian Randomization Analysis
Authors: Li Wanxin , Zhou Xuan , Yuan Shuai , Wang Lijuan , Yu Lili , Sun Jing , Chen Jie , Xiao Qian , Wan Zhongxiao , Zheng Ju-Sheng , Zhang Cai-Xia , Larsson Susanna C. , Farrington Susan M. , Law Philip , Houlston Richard S. , Tomlinson Ian , Ding Ke-Feng , Dunlop Malcolm G. , Theodoratou Evropi , Li Xue
Published: November 2023

Supplementary Methods shows the formulas for calculating F-statistics.
Data from Five-year Survival Trends for Young Adult Cancers Diagnosed from 2002 to 2014 in Taiwan and the United States
Authors: Tsai Yu-Han , Teng Yu-Tung , Kuan Tai-Chuan , Liu Jason J.
Published: November 2023

AbstractBackground:
Young adult cancer incidence has been increasing in Taiwan, but no studies have examined their survival trends.
Methods:
We analyzed data from the Taiwan National Health Insurance Research Database and the U.S. Surveillance, Epidemiology, and End Results Program. We obtained the five-year survival estimates and trends for primary invasive cancers diagnosed at 20–39 years of age from 2002 to 2014. When analyzing specific cancers, we focused on the common young adult cancers in Taiwan. For the trend analysis, the average annual percent change (AAPC) was calculated using joinpoint Regression Program. We also obtained estimates stratified by sex or age at cancer diagnosis.
Results:
The five-year age-standardized relative survival for all young adult cancers combined significantly increased in Taiwan [AAPC = 1.4%; 95% confidence interval (CI), 1.3%–1.5%] and the United States (AAPC = 0.4%; 95% CI, 0.3%–0.6%). Cancers occurring in both sexes had similar trend directions for both sexes. Lung and bronchus cancer, liver cancer, and leukemia had the most survival improvement in both regions. However, the five-year relative survival for cervical cancer declined in Taiwan (AAPC = −0.6%; 95% CI, -1.0% to −0.1%) and did not improve in the United States (AAPC = −0.1%; 95% CI, −0.4%–0.2%).
Conclusions:
Survival has improved for most but not all of the common young adult cancer types in Taiwan. Additional studies can understand why survival has not improved for certain cancer types, and examine subtype-specific survival trends.
Impact:
This is the first study of five-year survival trends for young adult cancers in Taiwan and the United States stratified by sex or age at diagnosis.
Data from Nitrated Polycyclic Aromatic Hydrocarbon (Nitro-PAH) Signatures and Somatic Mutations in Diesel Exhaust-Exposed Bladder Tumors
Authors: Gonzalez Nicole , Rao Nina , Dean Michael , Lee Donghyuk , Hurson Amber N. , Baris Dalsu , Schwenn Molly , Johnson Alison , Prokunina-Olsson Ludmila , Friesen Melissa C. , Zhu Bin , Rothman Nathaniel , Silverman Debra T. , Koutros Stella
Published: November 2023

AbstractBackground:
Diesel exhaust is a complex mixture, including polycyclic aromatic hydrocarbons (PAH) and nitrated PAHs (nitro-PAH), many of which are potent mutagens and possible bladder carcinogens. To explore the association between diesel exposure and bladder carcinogenesis, we examined the relationship between exposure and somatic mutations and mutational signatures in bladder tumors.
Methods:
Targeted sequencing was conducted in bladder tumors from the New England Bladder Cancer Study. Using data on 797 cases and 1,418 controls, two-stage polytomous logistic regression was used to evaluate etiologic heterogeneity between bladder cancer subtypes and quantitative, lifetime estimates of respirable elemental carbon (REC), a surrogate for diesel exposure. Poisson regression was used to evaluate associations between REC and mutational signatures.
Results:
We observed significant heterogeneity in the diesel-bladder cancer risk relationship, with a strong positive association among cases with high-grade, nonmuscle invasive TP53-mutated tumors compared with controls [ORTop Tertile vs.Unexposed, 4.8; 95% confidence interval (CI), 2.2–10.5; Ptrend < 0.001; Pheterogeneity = 0.002]. In muscle-invasive tumors, we observed a positive association between diesel exposure and the nitro-PAH signatures of 1,6-dintropyrene (RR, 1.93; 95% CI, 1.28–2.92) and 3-nitrobenzoic acid (RR, 1.97; 95% CI, 1.33–2.92).
Conclusions:
The relationship between diesel exhaust and bladder cancer was heterogeneous based on the presence of TP53 mutations in tumors, further supporting the link between PAH exposure and TP53 mutations in carcinogenesis. Future studies that can identify nitro-PAH signatures in exposed tumors are warranted to add human data supporting the link between diesel and bladder cancer.
Impact:
This study provides additional insight into the etiology and possible mechanisms related to diesel exhaust-induced bladder cancer.
Data from Exploring the Complex Relationship between Gut Microbiota and Risk of Colorectal Neoplasia Using Bidirectional Mendelian Randomization Analysis
Authors: Li Wanxin , Zhou Xuan , Yuan Shuai , Wang Lijuan , Yu Lili , Sun Jing , Chen Jie , Xiao Qian , Wan Zhongxiao , Zheng Ju-Sheng , Zhang Cai-Xia , Larsson Susanna C. , Farrington Susan M. , Law Philip , Houlston Richard S. , Tomlinson Ian , Ding Ke-Feng , Dunlop Malcolm G. , Theodoratou Evropi , Li Xue
Published: November 2023

AbstractBackground:
Human gut microbiome has complex relationships with the host, contributing to metabolism, immunity, and carcinogenesis.
Methods:
Summary-level data for gut microbiota and metabolites were obtained from MiBioGen, FINRISK and human metabolome consortia. Summary-level data for colorectal cancer were derived from a genome-wide association study meta-analysis. In forward Mendelian randomization (MR), we employed genetic instrumental variables (IV) for 24 gut microbiota taxa and six bacterial metabolites to examine their causal relationship with colorectal cancer. We also used a lenient threshold for nine apriori gut microbiota taxa as secondary analyses. In reverse MR, we explored association between genetic liability to colorectal neoplasia and abundance of microbiota studied above using 95, 19, and 7 IVs for colorectal cancer, adenoma, and polyps, respectively.
Results:
Forward MR did not find evidence indicating causal relationship between any of the gut microbiota taxa or six bacterial metabolites tested and colorectal cancer risk. However, reverse MR supported genetic liability to colorectal adenomas was causally related with increased abundance of two taxa: Gammaproteobacteria (β = 0.027, which represents a 0.027 increase in log-transformed relative abundance values of Gammaproteobacteria for per one-unit increase in log OR of adenoma risk; P = 7.06×10−8), Enterobacteriaceae (β = 0.023, P = 1.29×10−5).
Conclusions:
We find genetic liability to colorectal neoplasia may be associated with abundance of certain microbiota taxa. It is more likely that subset of colorectal cancer genetic liability variants changes gut biology by influencing both gut microbiota and colorectal cancer risk.
Impact:
This study highlights the need of future complementary studies to explore causal mechanisms linking both host genetic variation with gut microbiome and colorectal cancer susceptibility.
Data from COVID-19 Outcomes by Cancer Status, Site, Treatment, and Vaccination
Authors: Salvatore Maxwell , Hu Miriam M. , Beesley Lauren J. , Mondul Alison M. , Pearce Celeste Leigh , Friese Christopher R. , Fritsche Lars G. , Mukherjee Bhramar
Published: November 2023

AbstractBackground:
Studies have shown an increased risk of severe SARS-CoV-2–related (COVID-19) disease outcome and mortality for patients with cancer, but it is not well understood whether associations vary by cancer site, cancer treatment, and vaccination status.
Methods:
Using electronic health record data from an academic medical center, we identified a retrospective cohort of 260,757 individuals tested for or diagnosed with COVID-19 from March 10, 2020, to August 1, 2022. Of these, 52,019 tested positive for COVID-19 of whom 13,752 had a cancer diagnosis. We conducted Firth-corrected logistic regression to assess the association between cancer status, site, treatment, vaccination, and four COVID-19 outcomes: hospitalization, intensive care unit admission, mortality, and a composite “severe COVID” outcome.
Results:
Cancer diagnosis was significantly associated with higher rates of severe COVID, hospitalization, and mortality. These associations were driven by patients whose most recent initial cancer diagnosis was within the past 3 years. Chemotherapy receipt, colorectal cancer, hematologic malignancies, kidney cancer, and lung cancer were significantly associated with higher rates of worse COVID-19 outcomes. Vaccinations were significantly associated with lower rates of worse COVID-19 outcomes regardless of cancer status.
Conclusions:
Patients with colorectal cancer, hematologic malignancies, kidney cancer, or lung cancer or who receive chemotherapy for treatment should be cautious because of their increased risk of worse COVID-19 outcomes, even after vaccination.
Impact:
Additional COVID-19 precautions are warranted for people with certain cancer types and treatments. Significant benefit from vaccination is noted for both cancer and cancer-free patients.
Supplementary Table 2 from Concurrent Targeting of HDAC and PI3K to Overcome Phenotypic Heterogeneity of Castration-resistant and Neuroendocrine Prostate Cancers
Authors: Zhang Ailin , Lau Nathan A. , Wong Alicia , Brown Lisha G. , Coleman Ilsa M. , De Sarkar Navonil , Li Dapei , DeLucia Diana C. , Labrecque Mark P. , Nguyen Holly M. , Conner Jennifer L. , Dumpit Ruth F. , True Lawrence D. , Lin Daniel W. , Corey Eva , Alumkal Joshi J. , Nelson Peter S. , Morrissey Colm , Lee John K.
Published: November 2023

Supplementary Table 2
Supplementary Figure 9 from Concurrent Targeting of HDAC and PI3K to Overcome Phenotypic Heterogeneity of Castration-resistant and Neuroendocrine Prostate Cancers
Authors: Zhang Ailin , Lau Nathan A. , Wong Alicia , Brown Lisha G. , Coleman Ilsa M. , De Sarkar Navonil , Li Dapei , DeLucia Diana C. , Labrecque Mark P. , Nguyen Holly M. , Conner Jennifer L. , Dumpit Ruth F. , True Lawrence D. , Lin Daniel W. , Corey Eva , Alumkal Joshi J. , Nelson Peter S. , Morrissey Colm , Lee John K.
Published: November 2023

Supplementary Figure 9. Romidepsin demonstrates broad activity across prostate cancer models with preferential potency in NEPC.
Supplementary Figure 7 from Concurrent Targeting of HDAC and PI3K to Overcome Phenotypic Heterogeneity of Castration-resistant and Neuroendocrine Prostate Cancers
Authors: Zhang Ailin , Lau Nathan A. , Wong Alicia , Brown Lisha G. , Coleman Ilsa M. , De Sarkar Navonil , Li Dapei , DeLucia Diana C. , Labrecque Mark P. , Nguyen Holly M. , Conner Jennifer L. , Dumpit Ruth F. , True Lawrence D. , Lin Daniel W. , Corey Eva , Alumkal Joshi J. , Nelson Peter S. , Morrissey Colm , Lee John K.
Published: November 2023

Supplementary Figure 7. Mouse body weights with fimepinostat or romidepsin therapy.
Supplementary Figure 5 from Concurrent Targeting of HDAC and PI3K to Overcome Phenotypic Heterogeneity of Castration-resistant and Neuroendocrine Prostate Cancers
Authors: Zhang Ailin , Lau Nathan A. , Wong Alicia , Brown Lisha G. , Coleman Ilsa M. , De Sarkar Navonil , Li Dapei , DeLucia Diana C. , Labrecque Mark P. , Nguyen Holly M. , Conner Jennifer L. , Dumpit Ruth F. , True Lawrence D. , Lin Daniel W. , Corey Eva , Alumkal Joshi J. , Nelson Peter S. , Morrissey Colm , Lee John K.
Published: November 2023

Supplementary Figure 5. Fimepinostat and other HDAC inhibitors disrupt ASCL1 expression in NEPC cell lines through the activation of Notch signaling.
Supplementary Figure 3 from Concurrent Targeting of HDAC and PI3K to Overcome Phenotypic Heterogeneity of Castration-resistant and Neuroendocrine Prostate Cancers
Authors: Zhang Ailin , Lau Nathan A. , Wong Alicia , Brown Lisha G. , Coleman Ilsa M. , De Sarkar Navonil , Li Dapei , DeLucia Diana C. , Labrecque Mark P. , Nguyen Holly M. , Conner Jennifer L. , Dumpit Ruth F. , True Lawrence D. , Lin Daniel W. , Corey Eva , Alumkal Joshi J. , Nelson Peter S. , Morrissey Colm , Lee John K.
Published: November 2023

Supplementary Figure 3. Fimepinostat therapy does not impact AR or Myc mRNA and protein stability.
Supplementary Figure 2 from Concurrent Targeting of HDAC and PI3K to Overcome Phenotypic Heterogeneity of Castration-resistant and Neuroendocrine Prostate Cancers
Authors: Zhang Ailin , Lau Nathan A. , Wong Alicia , Brown Lisha G. , Coleman Ilsa M. , De Sarkar Navonil , Li Dapei , DeLucia Diana C. , Labrecque Mark P. , Nguyen Holly M. , Conner Jennifer L. , Dumpit Ruth F. , True Lawrence D. , Lin Daniel W. , Corey Eva , Alumkal Joshi J. , Nelson Peter S. , Morrissey Colm , Lee John K.
Published: November 2023

Supplementary Figure 2. Effects of HDAC inhibitors on AR and Myc expression in prostate cancer cell lines.
Supplementary Figure 16 from Concurrent Targeting of HDAC and PI3K to Overcome Phenotypic Heterogeneity of Castration-resistant and Neuroendocrine Prostate Cancers
Authors: Zhang Ailin , Lau Nathan A. , Wong Alicia , Brown Lisha G. , Coleman Ilsa M. , De Sarkar Navonil , Li Dapei , DeLucia Diana C. , Labrecque Mark P. , Nguyen Holly M. , Conner Jennifer L. , Dumpit Ruth F. , True Lawrence D. , Lin Daniel W. , Corey Eva , Alumkal Joshi J. , Nelson Peter S. , Morrissey Colm , Lee John K.
Published: November 2023

Supplementary Figure 16. Overlapping gene expression in C4-2B and NCI-H660 cells treated with vehicle, fimepinostat, ipatasertib, romidepsin, or a combination of ipatasertib + romidepsin.