Search

Risk factors of poor prognosis in cutaneous squamous cell carcinoma (CSCC) include large tumour diameter, large-calibre perineural invasion, deep tumour invasion, and poorly differentiated histology.1 However, other risk factors likely play a role in tumour aggressiveness.2 Low peritumoural inflammation3 and desmoplasia4 (features of the tumour microenvironment [TMe]) and tumour budding5, 6 (TB) have been associated with poor prognosis in CSCC in a few studies. The present study examines whether these three attributes (considered collectively based on a risk score) improve CSCC risk stratification. The study was approved by the University Hospital of Salamanca's Ethics Board and complied with STROBE recommendations. In total, 124 high-risk CSCCs (HR-CSCCs), defined as T3/T4-AJCC8, treated with clear-margin surgical excision were included. Cases with microscopical residual disease, bone involvement, or lost in follow-up were excluded. Low peritumoural inflammation, the presence of desmoplasia,4 and the presence of tumour budding (TB)5, 6 were utilized to derive the tumoural microenvironment and budding (TMeB) risk score: 1, no risk factors present; 2, one or two risk factors present; and 3, all three risk factors present. Peritumoural inflammation was defined as brisk/intense (peritumoural continuous), non-brisk/moderate (peritumoural discontinuous), scarce (dotted inflammatory cells), and absent, and it was dichotomized to distinguish low peritumoural inflammation (absent, scarce) from high (brisk, non-brisk). Desmoplasia was present when at least one-third of the tumour showed desmoplastic stroma.4 Tumour budding was defined by isolated tumour cells, or ≥4 groups of ≥5 tumour cells along the invasion front5, 6 (best observed at magnification >100×; Figure 1). Disease-free and event-free survival were assessed using R (v.3.4.1) packages survival (v.2.41-3), survminer (v.0.4.2) and cmprsk (v.2.2_7). Primary endpoints were local recurrence (LR), nodal metastasis (NM), Depto. de Estadística e Investigación Operativa, Fac. de Farmacia, TRUE, pub

Background: Luminal A tumours generally have a favourable prognosis but possess the highest 10‐year recurrence risk among breast cancers. Additionally, a quarter of the recurrence cases occur within 5 years post‐diagnosis. Identifying such patients is crucial as long‐term relapsers could benefit from extended hormone therapy, while early relapsers might require more aggressive treatment. Methods: We conducted a study to explore non‐structural chromosome maintenance condensin I complex subunit H’s (NCAPH) role in luminal A breast cancer pathogenesis, both in vitro and in vivo, aiming to identify an intratumoural gene expression signature, with a focus on elevated NCAPH levels, as a potential marker for unfavourable progression. Our analysis included transgenic mouse models overexpressing NCAPH and a genetically diverse mouse cohort generated by backcrossing. A least absolute shrinkage and selection operator (LASSO) multivariate regression analysis was performed on transcripts associated with elevated intratumoural NCAPH levels. Results: We found that NCAPH contributes to adverse luminal A breast cancer progression. The intratumoural gene expression signature associated with elevated NCAPH levels emerged as a potential risk identifier. Transgenic mice overexpressing NCAPH developed breast tumours with extended latency, and in Mouse Mammary Tumor Virus (MMTV)‐NCAPH ErbB2 double‐transgenic mice, luminal tumours showed increased aggressiveness. High intratumoural Ncaph levels correlated with worse breast cancer outcome and subpar chemotherapy response. A 10‐gene risk score, termed Gene Signature for Luminal A 10 (GSLA10), was derived from the LASSO analysis, correlating with adverse luminal A breast cancer progression. Conclusions: The GSLA10 signature outperformed the Oncotype DX signature in discerning tumours with unfavourable outcomes, previously categorised as luminal A by Prediction Analysis of Microarray 50 (PAM50) across three independent human cohorts. This new, Ministry of Science and Innovation/State Research Agency (MCIN/AEI), European Regional Development Fund (ERDF) “A way of making Europe”, Carlos III Health Institute, Regional Government of Castile and León, Depto. de Estadística e Investigación Operativa, Fac. de Farmacia, TRUE, pub

Cardiotoxicity due to anthracyclines (CDA) affects cancer patients, but we cannot predict who may suffer from this complication. CDA is a complex trait with a polygenic component that is mainly unidentified. We propose that levels of intermediate molecular phenotypes (IMPs) in the myocardium associated with histopathological damage could explain CDA susceptibility, so variants of genes encoding these IMPs could identify patients susceptible to this complication. Thus, a genetically heterogeneous cohort of mice (n = 165) generated by backcrossing were treated with doxorubicin and docetaxel. We quantified heart fibrosis using an Ariol slide scanner and intramyocardial levels of IMPs using multiplex bead arrays and QPCR. We identified quantitative trait loci linked to IMPs (ipQTLs) and cdaQTLs via linkage analysis. In three cancer patient cohorts, CDA was quantified using echocardiography or Cardiac Magnetic Resonance. CDA behaves as a complex trait in the mouse cohort. IMP levels in the myocardium were associated with CDA. ipQTLs integrated into genetic models with cdaQTLs account for more CDA phenotypic variation than that explained by cda-QTLs alone. Allelic forms of genes encoding IMPs associated with CDA in mice, including AKT1, MAPK14, MAPK8, STAT3, CAS3, and TP53, are genetic determinants of CDA in patients. Two genetic risk scores for pediatric patients (n = 71) and women with breast cancer (n = 420) were generated using machine-learning Least Absolute Shrinkage and Selection Operator (LASSO) regression. Thus, IMPs associated with heart damage identify genetic markers of CDA risk, thereby allowing more personalized patient management., Ministerio de Ciencia, Innovación y Universidades (España), European Comission, Junta de Castilla y León, Instituto de Salud Carlos III, Ministerio de Economía, Comercio y Empresa (España), Federación Española de Enfermedades Raras, Departamento de Defensa (Estados Unidos), National Institutes of Health (Estados Unidos), Universidad de California (Estados Unidos), Instituto Nacional del Cáncer (Estados Unidos), Fundación "la Caixa", Agencia Estatal de Investigación, Depto. de Estadística e Investigación Operativa, Fac. de Farmacia, TRUE, pub

Cardiotoxicity due to anthracyclines (CDA) affects cancer patients, but we cannot predict who may suffer from this complication. CDA is a complex trait with a polygenic component that is mainly unidentified. We propose that levels of intermediate molecular phenotypes (IMPs) in the myocardium associated with histopathological damage could explain CDA susceptibility, so variants of genes encoding these IMPs could identify patients susceptible to this complication. Thus, a genetically heterogeneous cohort of mice (n = 165) generated by backcrossing were treated with doxorubicin and docetaxel. We quantified heart fibrosis using an Ariol slide scanner and intramyocardial levels of IMPs using multiplex bead arrays and QPCR. We identified quantitative trait loci linked to IMPs (ipQTLs) and cdaQTLs via linkage analysis. In three cancer patient cohorts, CDA was quantified using echocardiography or Cardiac Magnetic Resonance. CDA behaves as a complex trait in the mouse cohort. IMP levels in the myocardium were associated with CDA. ipQTLs integrated into genetic models with cdaQTLs account for more CDA phenotypic variation than that explained by cda-QTLs alone. Allelic forms of genes encoding IMPs associated with CDA in mice, including AKT1, MAPK14, MAPK8, STAT3, CAS3, and TP53, are genetic determinants of CDA in patients. Two genetic risk scores for pediatric patients (n = 71) and women with breast cancer (n = 420) were generated using machine-learning Least Absolute Shrinkage and Selection Operator (LASSO) regression. Thus, IMPs associated with heart damage identify genetic markers of CDA risk, thereby allowing more personalized patient management.

Metabolic changes that facilitate tumor growth are one of the hallmarks of cancer. The triggers of these metabolic changes are located in the tumor parenchymal cells, where oncogenic mutations induce an imperative need to proliferate and cause tumor initiation and progression. Cancer cells undergo significant metabolic reorganization during disease progression that is tailored to their energy demands and fluctuating environmental conditions. Oxidative stress plays an essential role as a trigger under such conditions. These metabolic changes are the consequence of the interaction between tumor cells and stromal myofibroblasts. The metabolic changes in tumor cells include protein anabolism and the synthesis of cell membranes and nucleic acids, which all facilitate cell proliferation. They are linked to catabolism and autophagy in stromal myofibroblasts, causing the release of nutrients for the cells of the tumor parenchyma. Metabolic changes lead to an interstitium deficient in nutrients, such as glucose and amino acids, and acidification by lactic acid. Together with hypoxia, they produce functional changes in other cells of the tumor stroma, such as many immune subpopulations and endothelial cells, which lead to tumor growth. Thus, immune cells favor tissue growth through changes in immunosuppression. This review considers some of the metabolic changes described in breast cancer., Ministerio de Ciencia e Innovación (España), Instituto de Salud Carlos III, Junta de Castilla y León, Federación Española de Enfermedades Raras, Depto. de Bioquímica y Biología Molecular, Fac. de Ciencias Químicas, TRUE, pub

Mouse models of cancer provide a powerful tool for investigating all aspects of cancer biology. In this study, we used our recently developed machine learning approach to identify the cellular morphometric biomarkers (CMB) from digital images of hematoxylin and eosin (H&E) micrographs of orthotopic Trp53-null mammary tumors (n = 154) and to discover the corresponding cellular morphometric subtypes (CMS). Of the two CMS identified, CMS-2 was significantly associated with shorter survival (p = 0.0084). We then evaluated the learned CMB and corresponding CMS model in MMTV-Erbb2 transgenic mouse mammary tumors (n = 53) in which CMS-2 was significantly correlated with the presence of metastasis (p = 0.004). We next evaluated the mouse CMB and CMS model on The Cancer Genome Atlas breast cancer (TCGA-BRCA) cohort (n = 1017). Kaplan–Meier analysis showed significantly shorter overall survival (OS) of CMS-2 patients compared to CMS-1 patients (p = 0.024) and added significant prognostic value in multi-variable analysis of clinical and molecular factors, namely, age, pathological stage, and PAM50 molecular subtype. Thus, application of CMS to digital images of routine workflow H&E preparations can provide unbiased biological stratification to inform patient care.

Mouse models of cancer provide a powerful tool for investigating all aspects of cancer biology. In this study, we used our recently developed machine learning approach to identify the cellular morphometric biomarkers (CMB) from digital images of hematoxylin and eosin (H&E) micrographs of orthotopic Trp53-null mammary tumors (n = 154) and to discover the corresponding cellular morphometric subtypes (CMS). Of the two CMS identified, CMS-2 was significantly associated with shorter survival (p = 0.0084). We then evaluated the learned CMB and corresponding CMS model in MMTV-Erbb2 transgenic mouse mammary tumors (n = 53) in which CMS-2 was significantly correlated with the presence of metastasis (p = 0.004). We next evaluated the mouse CMB and CMS model on The Cancer Genome Atlas breast cancer (TCGA-BRCA) cohort (n = 1017). Kaplan–Meier analysis showed significantly shorter overall survival (OS) of CMS-2 patients compared to CMS-1 patients (p = 0.024) and added significant prognostic value in multi-variable analysis of clinical and molecular factors, namely, age, pathological stage, and PAM50 molecular subtype. Thus, application of CMS to digital images of routine workflow H&E preparations can provide unbiased biological stratification to inform patient care.

Metabolic changes that facilitate tumor growth are one of the hallmarks of cancer. These changes are not specific to tumors but also take place during the physiological growth of tissues. Indeed, the cellular and tissue mechanisms present in the tumor have their physiological counterpart in the repair of tissue lesions and wound healing. These molecular mechanisms have been acquired during metazoan evolution, first to eliminate the infection of the tissue injury, then to enter an effective regenerative phase. Cancer itself could be considered a phenomenon of antagonistic pleiotropy of the genes involved in effective tissue repair. Cancer and tissue repair are complex traits that share many intermediate phenotypes at the molecular, cellular, and tissue levels, and all of these are integrated within a Systems Biology structure. Complex traits are influenced by a multitude of common genes, each with a weak effect. This polygenic component of complex traits is mainly unknown and so makes up part of the missing heritability. Here, we try to integrate these different perspectives from the point of view of the metabolic changes observed in cancer.

Metabolic changes that facilitate tumor growth are one of the hallmarks of cancer. The triggers of these metabolic changes are located in the tumor parenchymal cells, where oncogenic mutations induce an imperative need to proliferate and cause tumor initiation and progression. Cancer cells undergo significant metabolic reorganization during disease progression that is tailored to their energy demands and fluctuating environmental conditions. Oxidative stress plays an essential role as a trigger under such conditions. These metabolic changes are the consequence of the interaction between tumor cells and stromal myofibroblasts. The metabolic changes in tumor cells include protein anabolism and the synthesis of cell membranes and nucleic acids, which all facilitate cell proliferation. They are linked to catabolism and autophagy in stromal myofibroblasts, causing the release of nutrients for the cells of the tumor parenchyma. Metabolic changes lead to an interstitium deficient in nutrients, such as glucose and amino acids, and acidification by lactic acid. Together with hypoxia, they produce functional changes in other cells of the tumor stroma, such as many immune subpopulations and endothelial cells, which lead to tumor growth. Thus, immune cells favor tissue growth through changes in immunosuppression. This review considers some of the metabolic changes described in breast cancer.

Mouse models of cancer provide a powerful tool for investigating all aspects of cancer biology. In this study, we used our recently developed machine learning approach to identify the cellular morphometric biomarkers (CMB) from digital images of hematoxylin and eosin (H&E) micrographs of orthotopic Trp53-null mammary tumors (n = 154) and to discover the corresponding cellular morphometric subtypes (CMS). Of the two CMS identified, CMS-2 was significantly associated with shorter survival (p = 0.0084). We then evaluated the learned CMB and corresponding CMS model in MMTV-Erbb2 transgenic mouse mammary tumors (n = 53) in which CMS-2 was significantly correlated with the presence of metastasis (p = 0.004). We next evaluated the mouse CMB and CMS model on The Cancer Genome Atlas breast cancer (TCGA-BRCA) cohort (n = 1017). Kaplan–Meier analysis showed significantly shorter overall survival (OS) of CMS-2 patients compared to CMS-1 patients (p = 0.024) and added significant prognostic value in multi-variable analysis of clinical and molecular factors, namely, age, pathological stage, and PAM50 molecular subtype. Thus, application of CMS to digital images of routine workflow H&E preparations can provide unbiased biological stratification to inform patient care.

Mouse models of cancer provide a powerful tool for investigating all aspects of cancer biology. In this study, we used our recently developed machine learning approach to identify the cellular morphometric biomarkers (CMB) from digital images of hematoxylin and eosin (H&E) micrographs of orthotopic Trp53-null mammary tumors (n = 154) and to discover the corresponding cellular morphometric subtypes (CMS). Of the two CMS identified, CMS-2 was significantly associated with shorter survival (p = 0.0084). We then evaluated the learned CMB and corresponding CMS model in MMTV-Erbb2 transgenic mouse mammary tumors (n = 53) in which CMS-2 was significantly correlated with the presence of metastasis (p = 0.004). We next evaluated the mouse CMB and CMS model on The Cancer Genome Atlas breast cancer (TCGA-BRCA) cohort (n = 1017). Kaplan-Meier analysis showed significantly shorter overall survival (OS) of CMS-2 patients compared to CMS-1 patients (p = 0.024) and added significant prognostic value in multi-variable analysis of clinical and molecular factors, namely, age, pathological stage, and PAM50 molecular subtype. Thus, application of CMS to digital images of routine workflow H&E preparations can provide unbiased biological stratification to inform patient care.

[Introduction]: The Brigham and Women's Hospital and the Tübingen cutaneous squamous cell carcinoma (SCC) stratification systems propose different criteria from the American Joint Committee on Cancer, eighth edition. Our group identified prognostic subgroups within T3 stage according to the American Joint Committee on Cancer eighth edition's classification, the most common classification for high-risk cutaneous SCCs., [Objective]: To compare the performance and prognostic accuracy of these staging systems in a subset of high-risk cutaneous SCCs., [Methods]: Homogeneity, monotonicity, and McNemar tests for pairwise comparisons were assessed. Distinctiveness and relative risk of poor outcome were calculated by stage. Prognostic accuracy was compared with respect to quality (Akaike and Bayesian information criteria), concordance (Harrell C-index and Gönen and Heller concordance probability estimate), and predictive accuracy (sensitivity, specificity, negative predictive value, positive predictive value, and global accuracy)., [Results]: The Brigham and Women's Hospital and Salamanca systems were more distinctive, homogeneous, and monotonic than the Tübingen system. The Tübingen system was the most specific, whereas the Salamanca and Brigham and Women's Hospital systems were more sensitive. Negative predictive value was high in all 3 systems, but positive predictive value and accuracy were low overall., [Conclusions]: Alternative staging systems may partially overcome the heterogeneity and low prognostic accuracy of the American Joint Committee on Cancer, eighth edition and enable high-risk cutaneous SCCs to be stratified more reliably, but their prognostic accuracy is still low. Considering the accumulation of risk factors may improve high-risk cutaneous SCC risk stratification.

Here we present a series of supplemental datasets that complement our study entitled "A Systems Genetics approach to identify genetic markers of cardiotoxicity due to anthracyclines in cancer patients." The datasets presented here were used to generate the main and supplementary figures and tables of the indicated study. The study consists of the identification of genetic markers of cardiotoxicity due to anthracyclines (CDA). CDA is a complex genesis disease or complex trait, and because of this, there is a component of missing heritability. Therefore, it is not possible to identify genetic markers associated with CDA risk. Here, we propose that molecular subphenotypes associated with the CDA may be a strategy for identifying some of this missing heritability and risk markers associated with it. A similar strategy could be applied to identify markers of other diseases of complex genesis. This study is done using a genetically heterogeneous cohort of mice that developed breast cancer and was treated with doxorubicin or a combined treatment of doxorubicin and docetaxel. The mouse cohort was generated by backcrossing, so each mouse is genetically unique. Post-chemotherapy heart damage was assessed by quantifying fibrosis's cardiac area and the thickness of myocardial fibers. The genetic regions associated with CDA were assessed by massive genotyping and genetic linkage analysis. Several molecular subphenotypes were quantified in the myocardium, and their association with the CDA was evaluated. Subsequently, we identified which of them were most statistically associated with CDA in multivariate models. Moreover, which complex trait loci (QTLs) associated with molecular subphenotypes best explained CDA. This strategy served to identify in the cohort of mice genes whose allelic forms could be candidates for the risk of CDA. Allelic variants of these genes were evaluated in four cohorts of cancer patients treated with anthracyclines and whose CDA was evaluated by echocardi

[Background]: Although the eighth edition of the American Joint Committee on Cancer staging system (AJCC8) provides improved prognosis stratification of cutaneous squamous cell carcinoma (CSCC) over AJCC7, T3 has a variable prognosis., [Objective]: To define prognostic subgroups in T3-AJCC8 CSCC., [Methods]: Retrospective cohort study of 196 primary T3-AJCC8 CSCCs. We conducted multidimensional scaling analysis using the 6 risk factors that define T3 CSCCs. The prognoses of the groups obtained were analyzed by means of competing risk analysis., [Results]: Group 1 was characterized by a tumor thickness greater than 6 mm (without invasion beyond the subcutaneous fat), alone or in combination with a tumor width of at least 4 cm. Group 2 was characterized by the presence of either invasion beyond the subcutaneous fat or by the involvement of nerves (≥0.1 mm, or deeper than the dermis). Group 3 was characterized by the combination of both T3b risk factors, or of 3 or more risk factors. Group 3 (tentatively named T3c) patients had the worst prognosis for disease-specific poor outcome events and major events, Group 2 (T3b) had intermediate risk, and Group 1 (T3a) had the best prognosis (disease-specific poor outcome events: hazard ratio [HR], 1.94; P = .00009; major events: HR, 2.55; P = .00001; disease-specific death: HR, 10.25; P = .0009)., [Limitations]: Retrospective study., [Conclusions]: There is statistically significant evidence that T3-AJCC8 may be classified into distinct prognostic subgroups.

Advances in virology and skin cancer over recent decades have produced achievements that have been recognized not only in the field of dermatology, but also in other areas of medicine. They have modified the therapeutic and preventive solutions that can be offered to some patients and represent a significant step forward in our knowledge of the biology of skin cancer. In this paper, we review the viral agents responsible for different types of skin cancer, especially for solid skin tumors. We focus on human papillomavirus and squamous cell cancers, Merkel cell polyomavirus and Merkel cell carcinoma, and human herpesvirus 8 and Kaposi’s sarcoma.

Cutaneous squamous cell carcinoma (CSCC) is the second most frequent cancer in humans, and is now responsible for as many deaths as melanoma. Immunotherapy has changed the therapeutic landscape of advanced CSCC after the FDA approval of anti-PD1 molecules for the treatment of locally advanced and metastatic CSCC. However, roughly 50% of patients will not respond to this systemic treatment and even those who do respond can develop resistance over time. The etiologies of primary and secondary resistance to immunotherapy involve changes in the neoplastic cells and the tumor microenvironment. Indirect modulation of immune system activation with new therapies, such as vaccines, oncolytic viruses, and new immunotherapeutic agents, and direct modulation of tumor immunogenicity using other systemic treatments or radiotherapy are now under evaluation in combined regimens. The identification of predictors of response is an important area of research. In this review, we focus on the features associated with the response to immunotherapy, and the evaluation of combination treatments and new molecules, a more thorough knowledge of which is likely to improve the survival of patients with advanced CSCC.

SNAI2 overexpression appears to be associated with poor prognosis in breast cancer, yet it remains unclear in which breast cancer subtypes this occurs. Here we show that excess SNAI2 is associated with a poor prognosis of luminal B HER2+/ERBB2+ breast cancers in which SNAI2 expression in the stroma but not the epithelium correlates with tumor proliferation. To determine how stromal SNAI2 might influence HER2+ tumor behavior, Snai2-deficient mice were crossed with a mouse line carrying the ErbB2/Neu protooncogene to generate HER2+/ERBB2+ breast cancer. Tumors generated in this model expressed SNAI2 in the stroma but not the epithelium, allowing for the role of stromal SNAI2 to be studied without interference from the epithelial compartment. The absence of SNAI2 in the stroma of HER2+/ERBB2+ tumors is associated with: (i) lower levels of cyclin D1 (CCND1) and reduced tumor epithelium proliferation; (ii) higher levels of AKT and a lower incidence of metastasis; (iii) lower levels of angiopoietin-2 (ANGPT2), and more necrosis. Together, these results indicate that the loss of SNAI2 in cancer-associated fibroblasts limits the production of some cytokines, which influences AKT/ERK tumor signaling and subsequent proliferative and metastatic capacity of ERBB2+ breast cancer cells. Accordingly, SNAI2 expression in the stroma enhanced the tumorigenicity of luminal B HER2+/ERBB2+ breast cancers. This work emphasizes the importance of stromal SNAI2 in breast cancer progression and patients' prognosis., Ministerio de Economía y Competitividad (España), Instituto de Salud Carlos III, Junta de Castilla y León, National Institutes of Health, Sandra Ibarra Foundation for Solidarity against Cancer, “We can be heroes” Foundation, Federación Española de Enfermedades Raras, Sección Deptal. de Bioquímica y Biología Molecular (Biológicas), Depto. de Bioquímica y Biología Molecular, Fac. de Ciencias Biológicas, Fac. de Ciencias Químicas, TRUE, pub

SNAI2 overexpression appears to be associated with poor prognosis in breast cancer, yet it remains unclear in which breast cancer subtypes this occurs. Here we show that excess SNAI2 is associated with a poor prognosis of luminal B HER2+/ERBB2+ breast cancers in which SNAI2 expression in the stroma but not the epithelium correlates with tumor proliferation. To determine how stromal SNAI2 might influence HER2+ tumor behavior, Snai2-deficient mice were crossed with a mouse line carrying the ErbB2/Neu protooncogene to generate HER2+/ERBB2+ breast cancer. Tumors generated in this model expressed SNAI2 in the stroma but not the epithelium, allowing for the role of stromal SNAI2 to be studied without interference from the epithelial compartment. The absence of SNAI2 in the stroma of HER2+/ERBB2+ tumors is associated with: (i) lower levels of cyclin D1 (CCND1) and reduced tumor epithelium proliferation; (ii) higher levels of AKT and a lower incidence of metastasis; (iii) lower levels of angiopoietin-2 (ANGPT2), and more necrosis. Together, these results indicate that the loss of SNAI2 in cancer-associated fibroblasts limits the production of some cytokines, which influences AKT/ERK tumor signaling and subsequent proliferative and metastatic capacity of ERBB2+ breast cancer cells. Accordingly, SNAI2 expression in the stroma enhanced the tumorigenicity of luminal B HER2+/ERBB2+ breast cancers. This work emphasizes the importance of stromal SNAI2 in breast cancer progression and patients' prognosis.

Cutaneous squamous cell carcinoma (CSCC) is the second most frequent cancer in humans and its incidence continues to rise. Although CSCC usually display a benign clinical behavior, it can be both locally invasive and metastatic. The signaling pathways involved in CSCC development have given rise to targetable molecules in recent decades. In addition, the high mutational burden and increased risk of CSCC in patients under immunosuppression were part of the rationale for developing the immunotherapy for CSCC that has changed the therapeutic landscape. This review focuses on the molecular basis of CSCC and the current biology-based approaches of targeted therapies and immune checkpoint inhibitors. Another purpose of this review is to explore the landscape of drugs that may induce or contribute to the development of CSCC. Beginning with the pathogenetic basis of these drug-induced CSCCs, we move on to consider potential therapeutic opportunities for overcoming this adverse effect.

Cutaneous squamous cell carcinoma (CSCC) is the second most frequent cancer in humans and it can be locally invasive and metastatic to distant sites. MicroRNAs (miRNAs or miRs) are endogenous, small, non-coding RNAs of 19–25 nucleotides in length, that are involved in regulating gene expression at a post-transcriptional level. MicroRNAs have been implicated in diverse biological functions and diseases. In cancer, miRNAs can proceed either as oncogenic miRNAs (onco-miRs) or as tumor suppressor miRNAs (oncosuppressor-miRs), depending on the pathway in which they are involved. Dysregulation of miRNA expression has been shown in most of the tumors evaluated. MiRNA dysregulation is known to be involved in the development of cutaneous squamous cell carcinoma (CSCC). In this review, we focus on the recent evidence about the role of miRNAs in the development of CSCC and in the prognosis of this form of skin cancer.