Context

Welcome to our regular closed dataset based weekend hackathon. In this weekend hackathon, we are challenging all the machinehackers to predict the melanoma tumor size based on various attributes. Melanomas present in many different shapes, sizes, and colors. That’s why it’s tricky to provide a comprehensive set of warning signs. Melanoma, also known as malignant melanoma, is a type of skin cancer that develops from the pigment-producing cells known as melanocytes. The primary cause of melanoma is ultraviolet light (UV) exposure in those with low levels of the skin pigment melanin. The UV light may be from the sun or other sources, such as tanning devices.

Melanoma is the most dangerous type of skin cancer. Globally, in 2012, it newly occurred in 232,000 people. In 2015, there were 3.1 million people with active disease, which resulted in 59,800 deaths. Australia and New Zealand have the highest rates of melanoma in the world. There are also high rates in Northern Europe and North America, while it is less common in Asia, Africa, and Latin America. In the United States melanoma occurs about 1.6 times more often in men than women.

Content

Train.csv - 9146 rows x 9 columns Test.csv - 36584 rows x 8 columns Sample Submission - Acceptable submission format

Attributes

Acknowledgements

Machine Hack: Melanoma Tumor Size Prediction

This dataset presents the footprint of cancer mortality statistics in Australia for all cancers combined and the 6 top cancer groupings (colorectal, leukaemia, lung, lymphoma, melanoma of the skin and pancreas) and their respective ICD-10 codes. The data spans the years 2009-2013 and is aggregated to Greater Capital City Statistical Areas (GCCSA) from the 2011 Australian Statistical Geography Standard (ASGS).

Mortality data refer to the number of deaths due to cancer in a given time period. Cancer deaths data are sourced from the Australian Institute of Health and Welfare (AIHW) 2013 National Mortality Database (NMD).

For further information about this dataset, please visit:

• Australian Institute of Health and Welfare - Cancer Incidence and Mortality Across Regions (CIMAR) books.

• Australian Institute of Health and Welfare - 2013 National Mortality Database.

Please note:

• AURIN has spatially enabled the original data.

• Due to changes in geographic classifications over time, long-term trends are not available.

• Values assigned to "n.p." in the original data have been removed from the data.

• The Australian and jurisdictional totals include people who could not be assigned a GCCSA. The number of people who could not be assigned a GCCSA is less than 1% of the total.

• The Australian total also includes residents of Other Territories (Cocos (Keeling) Islands, Christmas Island and Jervis Bay Territory).

• Cause of Death Unit Record File data are provided to the AIHW by the Registries of Births, Deaths and Marriages and the National Coronial Information System (managed by the Victorian Department of Justice) and include cause of death coded by the Australian Bureau of Statistics (ABS). The data are maintained by the AIHW in the NMD.

• Year refers to year of occurrence of death for years up to and including 2012, and year of registration of death for 2013. Deaths registered in 2011 and earlier are based on the final version of cause of death data; deaths registered in 2012 and 2013 are based on revised and preliminary versions, respectively and are subject to further revision by the ABS.

• Cause of death information are based on underlying cause of death and are classified according to the International Classification of Diseases and Related Health Problems (ICD). Deaths registered in 1997 onwards are classified according to the 10th revision (ICD-10).

• Colorectal deaths presented are underestimates. For further information, refer to "Complexities in the measurement of bowel cancer in Australia" in Causes of Death, Australia (ABS cat. no. 3303.0).

This dataset presents the footprint of cancer mortality statistics in Australia for all cancers combined and the 6 top cancer groupings (colorectal, leukaemia, lung, lymphoma, melanoma of the skin and pancreas) and their respective ICD-10 codes. The data spans the years 2006-2010 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS).

Mortality data refer to the number of deaths due to cancer in a given time period. Cancer deaths data are sourced from the Australian Institute of Health and Welfare (AIHW) 2013 National Mortality Database (NMD).

For further information about this dataset, please visit:

• Australian Institute of Health and Welfare - Cancer Incidence and Mortality Across Regions (CIMAR) books.

• Australian Institute of Health and Welfare - 2013 National Mortality Database.

Please note:

• AURIN has spatially enabled the original data using the Department of Health - PHN Areas.

• Due to changes in geographic classifications over time, long-term trends are not available.

• Values assigned to "n.p." in the original data have been removed from the data.

• The Australian and jurisdictional totals include people who could not be assigned a PHN. The number of people who could not be assigned a PHN is less than 1% of the total.

• The Australian total also includes residents of Other Territories (Cocos (Keeling) Islands, Christmas Island and Jervis Bay Territory).

• Cause of Death Unit Record File data are provided to the AIHW by the Registries of Births, Deaths and Marriages and the National Coronial Information System (managed by the Victorian Department of Justice) and include cause of death coded by the Australian Bureau of Statistics (ABS). The data are maintained by the AIHW in the NMD.

• Year refers to year of occurrence of death for years up to and including 2012, and year of registration of death for 2013. Deaths registered in 2011 and earlier are based on the final version of cause of death data; deaths registered in 2012 and 2013 are based on revised and preliminary versions, respectively and are subject to further revision by the ABS.

• Cause of death information are based on underlying cause of death and are classified according to the International Classification of Diseases and Related Health Problems (ICD). Deaths registered in 1997 onwards are classified according to the 10th revision (ICD-10).

• Colorectal deaths presented are underestimates. For further information, refer to "Complexities in the measurement of bowel cancer in Australia" in Causes of Death, Australia (ABS cat. no. 3303.0).

This dataset presents the footprint of male cancer mortality statistics in Australia for all cancers combined and the 11 top cancer groupings (bladder, colorectal, head and neck, kidney, leukaemia, lung, lymphoma, melanoma of the skin, pancreas, prostate and stomach) and their respective ICD-10 codes. The data spans the years 2009-2013 and is aggregated to Greater Capital City Statistical Areas (GCCSA) from the 2011 Australian Statistical Geography Standard (ASGS). Mortality data refer to the number of deaths due to cancer in a given time period. Cancer deaths data are sourced from the Australian Institute of Health and Welfare (AIHW) 2013 National Mortality Database (NMD). For further information about this dataset, please visit: Australian Institute of Health and Welfare - Cancer Incidence and Mortality Across Regions (CIMAR) books. Australian Institute of Health and Welfare - 2013 National Mortality Database. Please note: AURIN has spatially enabled the original data. Due to changes in geographic classifications over time, long-term trends are not available. Values assigned to "n.p." in the original data have been removed from the data. The Australian and jurisdictional totals include people who could not be assigned a GCCSA. The number of people who could not be assigned a GCCSA is less than 1% of the total. The Australian total also includes residents of Other Territories (Cocos (Keeling) Islands, Christmas Island and Jervis Bay Territory). Cause of Death Unit Record File data are provided to the AIHW by the Registries of Births, Deaths and Marriages and the National Coronial Information System (managed by the Victorian Department of Justice) and include cause of death coded by the Australian Bureau of Statistics (ABS). The data are maintained by the AIHW in the NMD. Year refers to year of occurrence of death for years up to and including 2012, and year of registration of death for 2013. Deaths registered in 2011 and earlier are based on the final version of cause of death data; deaths registered in 2012 and 2013 are based on revised and preliminary versions, respectively and are subject to further revision by the ABS. Cause of death information are based on underlying cause of death and are classified according to the International Classification of Diseases and Related Health Problems (ICD). Deaths registered in 1997 onwards are classified according to the 10th revision (ICD-10). Colorectal deaths presented are underestimates. For further information, refer to "Complexities in the measurement of bowel cancer in Australia" in Causes of Death, Australia (ABS cat. no. 3303.0).

This dataset presents the footprint of cancer mortality statistics in Australia for all cancers combined and the 5 top cancer groupings (breast - female only, colorectal, lung, melanoma of the skin and prostate) and their respective ICD-10 codes. The data spans the years 2009-2013 and is aggregated to Statistical Area Level 3 (SA3) from the 2011 Australian Statistical Geography Standard (ASGS).

Mortality data refer to the number of deaths due to cancer in a given time period. Cancer deaths data are sourced from the Australian Institute of Health and Welfare (AIHW) 2013 National Mortality Database (NMD).

For further information about this dataset, please visit:

• Australian Institute of Health and Welfare - Cancer Incidence and Mortality Across Regions (CIMAR) books.

• Australian Institute of Health and Welfare - 2013 National Mortality Database.

Please note:

• AURIN has spatially enabled the original data.

• Due to changes in geographic classifications over time, long-term trends are not available.

• Values assigned to "n.p." in the original data have been removed from the data.

• The Australian and jurisdictional totals include people who could not be assigned a SA3. The number of people who could not be assigned a SA3 is less than 1% of the total.

• The Australian total also includes residents of Other Territories (Cocos (Keeling) Islands, Christmas Island and Jervis Bay Territory).

• Cause of Death Unit Record File data are provided to the AIHW by the Registries of Births, Deaths and Marriages and the National Coronial Information System (managed by the Victorian Department of Justice) and include cause of death coded by the Australian Bureau of Statistics (ABS). The data are maintained by the AIHW in the NMD.

• Year refers to year of occurrence of death for years up to and including 2012, and year of registration of death for 2013. Deaths registered in 2011 and earlier are based on the final version of cause of death data; deaths registered in 2012 and 2013 are based on revised and preliminary versions, respectively and are subject to further revision by the ABS.

• Cause of death information are based on underlying cause of death and are classified according to the International Classification of Diseases and Related Health Problems (ICD). Deaths registered in 1997 onwards are classified according to the 10th revision (ICD-10).

• Colorectal deaths presented are underestimates. For further information, refer to "Complexities in the measurement of bowel cancer in Australia" in Causes of Death, Australia (ABS cat. no. 3303.0).

This dataset presents the footprint of cancer mortality statistics in Australia for all cancers combined and the 6 top cancer groupings (colorectal, leukaemia, lung, lymphoma, melanoma of the skin …Show full descriptionThis dataset presents the footprint of cancer mortality statistics in Australia for all cancers combined and the 6 top cancer groupings (colorectal, leukaemia, lung, lymphoma, melanoma of the skin and pancreas) and their respective ICD-10 codes. The data spans the years 2009-2013 and is aggregated to Statistical Area Level 4 (SA4) from the 2011 Australian Statistical Geography Standard (ASGS). Mortality data refer to the number of deaths due to cancer in a given time period. Cancer deaths data are sourced from the Australian Institute of Health and Welfare (AIHW) 2013 National Mortality Database (NMD). For further information about this dataset, please visit: Australian Institute of Health and Welfare - Cancer Incidence and Mortality Across Regions (CIMAR) books. Australian Institute of Health and Welfare - 2013 National Mortality Database. Please note: AURIN has spatially enabled the original data. Due to changes in geographic classifications over time, long-term trends are not available. Values assigned to "n.p." in the original data have been removed from the data. The Australian and jurisdictional totals include people who could not be assigned a SA4. The number of people who could not be assigned a SA4 is less than 1% of the total. The Australian total also includes residents of Other Territories (Cocos (Keeling) Islands, Christmas Island and Jervis Bay Territory). Cause of Death Unit Record File data are provided to the AIHW by the Registries of Births, Deaths and Marriages and the National Coronial Information System (managed by the Victorian Department of Justice) and include cause of death coded by the Australian Bureau of Statistics (ABS). The data are maintained by the AIHW in the NMD. Year refers to year of occurrence of death for years up to and including 2012, and year of registration of death for 2013. Deaths registered in 2011 and earlier are based on the final version of cause of death data; deaths registered in 2012 and 2013 are based on revised and preliminary versions, respectively and are subject to further revision by the ABS. Cause of death information are based on underlying cause of death and are classified according to the International Classification of Diseases and Related Health Problems (ICD). Deaths registered in 1997 onwards are classified according to the 10th revision (ICD-10). Colorectal deaths presented are underestimates. For further information, refer to "Complexities in the measurement of bowel cancer in Australia" in Causes of Death, Australia (ABS cat. no. 3303.0). Copyright attribution: Government of the Commonwealth of Australia - Australian Institute of Health and Welfare, (2016): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution 3.0 Australia (CC BY 3.0 AU)

An attempt has been made to develop a self-drug-delivery system against melanoma from a series of metallogelators derived from coordination polymers. Thus, a series of coordination polymers (CP1–CP6) derived from a nitrile-containing terpyridyl ligand (L) and transition metal salts (Cu(I)/Zn(II)) have been synthesized and thoroughly characterized by a number of physicochemical techniques including single crystal X-ray diffraction. Reactions of the ingredients of the coordination polymers guided by their single crystal structures produced four metallogels (CPG2–CPG5) which were characterized by dynamic rheology and TEM. The metallogelator CPG3 turned out to be the best suited for further studies as revealed from MTT assay against melanoma (B16-F10) and macrophage (RAW 264.7) cells. Various experiments (scratch, cell cycle, nuclear condensation, annexin V-FITC/PI, mitochondrial membrane potential, Ho-efflux assays) not only supported the “druglike” action against melanoma B16-F10 cells but also suggested that the mechanism of cancer cell death was via mitochondrial membrane potential depolarization-driven apoptosis. Because melanoma B16-F10 is a model cell line for human skin cancer, the metallogel CPG3 may, therefore, be further developed for such treatment.

This dataset presents the footprint of female cancer mortality statistics in Australia for all cancers combined and the 11 top cancer groupings (breast, cervical, colorectal, leukaemia, lung, lymphoma, melanoma of the skin, ovary, pancreas, thyroid and uterus) and their respective ICD-10 codes. The data spans the years 2009-2013 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). Mortality data refer to the number of deaths due to cancer in a given time period. Cancer deaths data are sourced from the Australian Institute of Health and Welfare (AIHW) 2013 National Mortality Database (NMD). For further information about this dataset, please visit:

Australian Institute of Health and Welfare - Cancer Incidence and Mortality Across Regions (CIMAR) books.

Australian Institute of Health and Welfare - 2013 National Mortality Database.

Please note:

AURIN has spatially enabled the original data.

Due to changes in geographic classifications over time, long-term trends are not available.

Values assigned to "n.p." in the original data have been removed from the data.

The Australian and jurisdictional totals include people who could not be assigned a PHN. The number of people who could not be assigned a PHN is less than 1% of the total.

The Australian total also includes residents of Other Territories (Cocos (Keeling) Islands, Christmas Island and Jervis Bay Territory).

Cause of Death Unit Record File data are provided to the AIHW by the Registries of Births, Deaths and Marriages and the National Coronial Information System (managed by the Victorian Department of Justice) and include cause of death coded by the Australian Bureau of Statistics (ABS). The data are maintained by the AIHW in the NMD.

Year refers to year of occurrence of death for years up to and including 2012, and year of registration of death for 2013. Deaths registered in 2011 and earlier are based on the final version of cause of death data; deaths registered in 2012 and 2013 are based on revised and preliminary versions, respectively and are subject to further revision by the ABS.

Cause of death information are based on underlying cause of death and are classified according to the International Classification of Diseases and Related Health Problems (ICD). Deaths registered in 1997 onwards are classified according to the 10th revision (ICD-10).

Colorectal deaths presented are underestimates. For further information, refer to "Complexities in the measurement of bowel cancer in Australia" in Causes of Death, Australia (ABS cat. no. 3303.0).

This dataset presents the footprint of male cancer mortality statistics in Australia for all cancers combined and the 11 top cancer groupings (bladder, colorectal, head and neck, kidney, leukaemia, lung, lymphoma, melanoma of the skin, pancreas, prostate and stomach) and their respective ICD-10 codes. The data spans the years 2009-2013 and is aggregated to Statistical Area Level 4 (SA4) from the 2011 Australian Statistical Geography Standard (ASGS).

Mortality data refer to the number of deaths due to cancer in a given time period. Cancer deaths data are sourced from the Australian Institute of Health and Welfare (AIHW) 2013 National Mortality Database (NMD).

For further information about this dataset, please visit:

• Australian Institute of Health and Welfare - Cancer Incidence and Mortality Across Regions (CIMAR) books.

• Australian Institute of Health and Welfare - 2013 National Mortality Database.

Please note:

• AURIN has spatially enabled the original data.

• Due to changes in geographic classifications over time, long-term trends are not available.

• Values assigned to "n.p." in the original data have been removed from the data.

• The Australian and jurisdictional totals include people who could not be assigned a SA4. The number of people who could not be assigned a SA4 is less than 1% of the total.

• The Australian total also includes residents of Other Territories (Cocos (Keeling) Islands, Christmas Island and Jervis Bay Territory).

• Cause of Death Unit Record File data are provided to the AIHW by the Registries of Births, Deaths and Marriages and the National Coronial Information System (managed by the Victorian Department of Justice) and include cause of death coded by the Australian Bureau of Statistics (ABS). The data are maintained by the AIHW in the NMD.

• Year refers to year of occurrence of death for years up to and including 2012, and year of registration of death for 2013. Deaths registered in 2011 and earlier are based on the final version of cause of death data; deaths registered in 2012 and 2013 are based on revised and preliminary versions, respectively and are subject to further revision by the ABS.

• Cause of death information are based on underlying cause of death and are classified according to the International Classification of Diseases and Related Health Problems (ICD). Deaths registered in 1997 onwards are classified according to the 10th revision (ICD-10).

• Colorectal deaths presented are underestimates. For further information, refer to "Complexities in the measurement of bowel cancer in Australia" in Causes of Death, Australia (ABS cat. no. 3303.0).

*Individuals with missing neurodegenerative disorders (ND) status were excluded in all models.CVD = cardiovascular diseases; Cancer includes all sites but skin; Non-prostate indicates non-skin cancers apart from prostate neoplasm in men; Non-breast indicates non-skin cancers apart from breast neoplasm in women;RR = relative risk; CI = Confidence interval; Ntotal and Ndied denote the total number of genotyped individuals and the number of deaths among them, respectively.All models were adjusted for birth cohorts, an indicator of the FHS or FHSO, and additive contribution of CVD, ND, and cancer, as applicable, e.g., the model for samples stratified by CVD was adjusted by cancer and ND.

The identification of galectin-7 as a p53-induced gene and its ability to induce apoptosis in many cell types support the hypothesis that galectin-7 has strong antitumor activity. This has been well documented in colon cancer. However, in some cases, such as breast cancer and lymphoma, its high expression level correlates with aggressive subtypes of cancer, suggesting that galectin-7 may have a dual role in cancer progression. In fact, in breast cancer, overexpression of galectin-7 alone is sufficient to promote metastasis to the bone and lung. In the present work, we investigated the expression and function of galectin-7 in melanoma. An analysis of datasets obtained from whole-genome profiling of human melanoma tissues revealed that galectin-7 mRNA was detected in more than 90% of biopsies of patients with nevi while its expression was more rarely found in biopsies collected from patients with malignant melanoma. This frequency, however, was likely due to the presence of normal epidermis tissues in biopsies, as shown our studies at the protein level by immunohistochemical analysis. Using the experimental melanoma B16F1 cell line, we found that melanoma cells can express galectin-7 at the primary tumor site and in lung metastasis. Moreover, we found that overexpression of galectin-7 increased the resistance of melanoma cells to apoptosis while inducing de novo egr-1 expression. Overexpression of galectin-7, however, was insufficient to modulate the growth of tumors induced by the subcutaneous injection of B16F1 cells. It also failed to modulate the dissemination of B16F1 cells to the lung.

BackgroundSex is frequently underestimated as a prognostic biomarker in cancer. In this study, we evaluated a large cohort of patients and public datasets to determine the influence of sex on clinical outcomes, mutational status, and activation of immune pathways in different types of cancer.MethodsA cohort of 13,619 Oncosalud-affiliated patients bearing sex-unrelated cancers was followed over a 20-year period. Hazard ratios (HRs) for death were estimated for female vs. male patients for each cancer type and then pooled in a meta-analysis to obtain an overall HR. In addition, the mutational status of the main actionable genes in melanoma (MEL), colorectal cancer (CRC), and lung cancer was compared between sexes. Finally, a gene set enrichment analysis (GSEA) of publicly available data was conducted, to assess differences in immune processes between sexes in MEL, gastric adenocarcinoma (GC), head and neck cancer (HNC), colon cancer (CC), liver cancer (LC), pancreatic cancer (PC), thyroid cancer (TC), and clear renal cell carcinoma (CCRCC).ResultsOverall, women had a decreased risk of death (HR = 0.73, CI95: 8%–42%), with improved overall survival (OS) in HNC, leukemia, lung cancer, lymphoma, MEL, multiple myeloma (MM), and non-melanoma skin cancer. Regarding the analysis of actionable mutations, only differences in EGFR alterations were observed (27.7% for men vs. 34.4% for women, p = 0.035). The number of differentially activated immune processes was higher in women with HNC, LC, CC, GC, MEL, PC, and TC and included cellular processes, responses to different stimuli, immune system development, immune response activation, multiorganism processes, and localization of immune cells. Only in CCRCC was a higher activation of immune pathways observed in men.ConclusionsThe study shows an improved survival rate, increased activation of immune system pathways, and an enrichment of EGFR alterations in female patients of our cohort. Enhancement of the immune response in female cancer patients is a phenomenon that should be further explored to improve the efficacy of immunotherapy.

Metastatic and drug-resistant melanoma are leading causes of skin cancer–associated death. Mitogen-associated protein kinase (MAPK) pathway inhibitors and immunotherapies have provided substantial benefits to patients with melanoma. However, long-term therapeutic efficacy has been limited due to emergence of treatment resistance. Despite the identification of several molecular mechanisms underlying the development of resistant phenotypes, significant progress has still not been made toward the effective treatment of drug-resistant melanoma. Therefore, the identification of new targets and mechanisms driving drug resistance in melanoma represents an unmet medical need. In this study, we performed unbiased RNA-sequencing (RNA-seq) and assay for transposase-accessible chromatin with sequencing (ATAC-seq) to identify new targets and mechanisms that drive resistance to MAPK pathway inhibitors targeting BRAF and MAPK kinase (MEK) in BRAF-mutant melanoma cells. An integrative analysis of ATAC-seq combined with RNA-seq showed that global changes in chromatin accessibility affected the mRNA expression levels of several known and novel genes, which consequently modulated multiple oncogenic signaling pathways to promote resistance to MAPK pathway inhibitors in melanoma cells. Many of these genes were also associated with prognosis predictions in melanoma patients. This study resulted in the identification of new genes and signaling pathways that might be targeted to treat MEK or BRAF inhibitors resistant melanoma patients. The present study applied new and advanced approaches to identify unique changes in chromatin accessibility regions that modulate gene expression associated with pathways to promote the development of resistance to MAPK pathway inhibitors.

Ferroptosis is a non-apoptotic regulated cell death process, and much research has indicated that ferroptosis can induce the non-apoptotic death of tumor cells. Ferroptosis-related genes are expected to become a biological target for cancer treatment. However, the regulation of ferroptosis-related genes in skin cutaneous melanoma (SKCM) has not been well studied. In the present study, we conducted a systematic analysis of SKCM based on RNA sequencing data and clinical data obtained from The Cancer Genome Atlas (TCGA) database and the FerrD database. SKCM patients from the GSE78220 and MSKCC cohorts were used for external validation. Applying consensus clustering on RNA sequencing data from TCGA the generated ferroptosis subclasses of SKCM, which were analyzed based on the set of differentially expressed ferroptosis-related genes. Then, a least absolute shrinkage and selection operator (LASSO)-Cox regression was used to construct an eight gene survival-related linear signature. The median cut-off risk score was used to divide patients into high- and low-risk groups. The time-dependent receiver operating characteristic curve was used to examine the predictive power of the model. The areas under the curve of the signature at 1, 3, and 5 years were 0.673, 0.716, and 0.746, respectively. Kaplan-Meier survival analysis showed that the prognosis of high-risk patients was worse than that of low-risk patients. Univariate and multivariate Cox regression analyses showed that the risk signature was a robust independent prognostic indicator. By incorporating risk scores with tumor staging, a nomogram was constructed to predict prognostic outcomes for SKCM patients. In addition, the immunological analysis showed different immune cell infiltration patterns. Programmed-death-1 (PD-1) immunotherapy showed more significant benefits in the low-risk group than in the high-risk group. In summary, a model based on ferroptosis-related genes can predict the prognosis of SKCM and could have a potential role in guiding targeted therapy of SKCM.

Melanoma is one of the most aggressive cancers and its incidence is increasing worldwide. So far there are no curable therapies especially after metastasis. Due to frequent mutations in members of the mitogen-activated protein kinase (MAPK) signaling pathway, this pathway is constitutively active in melanoma. It has been shown that the SONIC HEDGEHOG (SHH)-GLI and MAPK signaling pathway regulate cell growth in many tumors including melanoma and interact with each other in the regulation of cell proliferation and survival.Here we show that the SHH-GLI pathway is active in human melanoma cell lines as they express downstream target of this pathway GLI1. Expression of GLI1 was significantly higher in human primary melanoma tissues harboring BRAFV600E mutation than those with wild type BRAF. Pharmacologic inhibition of BRAFV600E in human melanoma cell lines resulted in decreased expression of GLI1 thus demonstrating interaction of SHH-GLI and MAPK pathways. Inhibition of SHH-GLI pathway by the novel small molecule inhibitor of smoothened NVP-LDE225 was followed by inhibition of cell growth and induction of apoptosis in human melanoma cell lines, interestingly with both BRAFV600E and BRAFWild Type status. NVP-LDE225 was potent in reducing cell proliferation and inducing tumor growth arrest in vitro and in vivo, respectively and these effects were superior to the natural compound cyclopamine.Finally, we conclude that inhibition of SHH-GLI signaling pathway in human melanoma by the specific smoothened inhibitor NVP-LDE225 could have potential therapeutic application in human melanoma even in the absence of BRAFV600E mutation and warrants further investigations.

ObjectiveTonsillar squamous cell carcinoma (TSCC) and second primary malignancies (SPMs) are the most common causes of mortality in patients with primary TSCC. However, the competing data on TSCC-specific death (TSD) or SPM-related death in patients with TSCC have not been evaluated. This study aimed to analyze the mortality patterns and formulate prediction models of mortality risk caused by TSCC and SPMs.MethodsData on patients with a first diagnosis of TSCC were extracted as the training cohort from the 18 registries comprising the Surveillance, Epidemiology, and End Results (SEER) database. A competing risk approach of cumulation incidence function was used to estimate cumulative incidence curves. Fine and gray proportional sub-distributed hazard model analyses were performed to investigate the risk factors of TSD and SPMs. A nomogram was developed to predict the 5- and 10-year risk probabilities of death caused by TSCC and SPMs. Moreover, data from the 22 registries of the SEER database were also extracted to validate the nomograms.ResultsIn the training cohort, we identified 14,530 patients with primary TSCC, with TSCC (46.84%) as the leading cause of death, followed by SPMs (26.86%) among all causes of death. In the proportion of SPMs, the lungs and bronchus (22.64%) were the most common sites for SPM-related deaths, followed by the larynx (9.99%), esophagus (8.46%), and Non-Melanoma skin (6.82%). Multivariate competing risk model showed that age, ethnicity, marital status, primary site, summary stage, radiotherapy, and surgery were independently associated with mortality caused by TSCC and SPMs. Such risk factors were selected to formulate prognostic nomograms. The nomograms showed preferable discrimination and calibration in both the training and validation cohorts.ConclusionPatients with primary TSCC have a high mortality risk of SPMs, and the competing risk nomogram has an ideal performance for predicting TSD and SPMs-related mortality. Routine follow-up care for TSCC survivors should be expanded to monitor SPMs.