In recent years, head and neck cancer therapy has been revolutionized by immunotherapy, which aims to restore the immune system’s anticancer activity suppressed by cancer cells. The major concern is that only a subset of patients responds to this therapy. Having specific biomarkers that could help identify the patients who are most likely to benefit from immunotherapies would be extremely useful. This review first explains the current immunotherapies tested in head and neck cancer, to then delve into the current prognostic and predictive biomarkers that have been investigated in this field to predict their responses. In our opinion, a broader selection of biomarkers could further improve who will respond best to immunotherapy.

Recent advancements in cancer multi-omics have transformed our understanding of cancer biology by integrating genomics, transcriptomics, proteomics, and metabolomics. These integrative approaches have led to the identification of novel biomarkers and therapeutic targets, offering deeper insights into the molecular intricacies of various cancers, including breast, lung, gastric, pancreatic, and glioblastoma. Despite these advances, challenges remain, such as the integration of disparate data types and the interpretation of complex biological interactions. However, developments in proteogenomics and mass spectrometry have enhanced the correlation between molecular profiles and clinical features, refining the prediction of therapeutic responses. Future research in cancer drug discovery is poised to benefit from multi-omics approaches, improving the precision and efficacy of personalized therapies. By developing integrative network-based models, researchers aim to address challenges related to heterogeneity, reproducibility, and data interpretation. A standardized framework for multi-omics data integration could revolutionize cancer research, optimizing the identification of novel drug targets and enhancing our understanding of cancer biology. This complete approach holds the promise of advancing personalized therapies by fully characterizing the molecular landscape of cancer, ultimately improving patient outcomes through more effective and targeted treatment strategies. This narrative review underscores the potential of multi-omics approaches to transform cancer research and improve patient outcomes through more precise and effective treatments.

Metastatic urothelial carcinoma (mUC) is a challenging malignancy with historically limited treatment options. Advances in understanding its biology have enabled the development of innovative therapies, including immune checkpoint inhibitors and antibody-drug conjugates (ADCs). ADCs, such as enfortumab vedotin, sacituzumab govitecan, and trastuzumab deruxtecan, represent transformative advancements, offering targeted delivery of cytotoxic agents. This review highlights the evolving role of ADCs in mUC, examining their mechanisms, clinical efficacy, patient selection criteria, genetic insights, and future directions in personalized treatment strategies.

Artificial intelligence (AI) has been influencing the way oncology has been practiced. Major issues constituting a bottleneck are the lack of data for training purposes, confidentiality preventing development, or the absence of transparency in clarifying how models operate to generate decisions. With explainable AI, trust and utilization barriers among clinicians, researchers, and patients can be removed. With the implementation of federated learning, multiple institutions could contribute crucial dataset’s learning information, without breaching confidentiality, to build more accurate and precise models, that would be otherwise impossible to generate. Precise diagnosis and prescription of the right drug are essential in preventing unnecessary life losses, and economical burden to the underling system. Antibody-Drug Conjugates (ADC) offer efficient targeted delivery of cytotoxic payloads specific to cancer cells. The new AI models discussed in this review could make ADC selection methods thrive towards an impeccable selection method for patients, coupled with seeming monitoring of treatment and recovery.

Background: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer, able to thrive in a challenging tumor microenvironment. Current standard therapies, including surgery, radiation, chemotherapy, and chemoradiation, have shown a dismal survival prognosis, resulting in less than a year of life in the metastatic setting. Methods: The pressing need to find better therapeutic methods brought about the discovery of new targeted therapies against the infamous KRAS mutations, the major oncological drivers of PDAC. Results: The most common KRAS mutation is KRASG12D, which causes a conformational change in the protein that constitutively activates downstream signaling pathways driving cancer hallmarks. Novel anti-KRASG12D therapies have been developed for solid-organ tumors, including small compounds, pan-RAS inhibitors, protease inhibitors, chimeric T cell receptors, and therapeutic vaccines. Conclusions: This comprehensive review summarizes current knowledge on the biology of KRAS-driven PDAC, the latest therapeutic options that have been experimentally validated, and developments in ongoing clinical trials.

Cancer evades therapy by multiple mechanisms, leading to uncontrolled cell growth and metastasis. Targeted therapies have shown promise in treating cancer by focusing on pathways within cancer cells. The PEAK family, comprising PEAK1 (SgK269), PEAK2 (SgK223/Pragmin), and the latest addition, PEAK3 (C19orf35), plays a crucial role in modulating cellular processes. Dysregulation and hyperactivity of these proteins, through overexpression or mutations, are associated with a wide range of cancers. This review delves into the different roles of the PEAK family members in regulating cell signaling pathways and highlights their potential in cancer therapy.

Introduction Artificial intelligence (AI) has been influencing the way oncology has been practiced. Major issues constituting a bottleneck are the lack of data for training purposes, confidentiality preventing development, or the absence of transparency in clarifying how models operate to generate decisions. Novel Models With explainable AI, trust and utilization barriers among clinicians, researchers, and patients can be removed. With the implementation of federated learning, multiple institutions could contribute to crucial dataset’s learning information. Precise diagnosis and prescription of the right drug are essential in preventing unnecessary life losses, and economic burden to the underling system. Focus This review focuses on new AI models that could make medical diagnosis more precise, quicker and convenient, as well as help with the discovery of new drugs and better selection of certain cancer therapies, in particular for those drugs whose results have been inconsistent across different groups of patients. We then speculate on the transformative role AI could play in predicting ADC therapy efficacy. This would ultimately bestow the medical field of an impeccable selection tool. Such colossal methodology, coupled with seeming monitoring of treatment and recovery, may be granting remedies that have been so longed, and deemed necessary.

Research on cancer therapies has benefited from predictive tools capable of simulating treatment response and other disease characteristics in a personalized manner, in particular three-dimensional cell culture models. Such models include tumor-derived spheroids, multicellular spheroids including organotypic multicellular spheroids, and tumor-derived organoids. Additionally, organoids can be grown from various cancer cell types, such as pluripotent stem cells and induced pluripotent stem cells, progenitor cells, and adult stem cells. Although patient-derived xenografts and genetically engineered mouse models replicate human disease in vivo, organoids are less expensive, less labor intensive, and less time-consuming, all-important aspects in high-throughput settings. Like in vivo models, organoids mimic the three-dimensional structure, cellular heterogeneity, and functions of primary tissues, with the advantage of representing the normal oxygen conditions of patient organs. In this review, we summarize the use of organoids in disease modeling, drug discovery, toxicity testing, and precision oncology. We also summarize the current clinical trials using organoids.

The correlation between cancer and venous thromboembolism (VTE) is solid, whereas the knowledge about cancer-related arterial thromboembolism (ATE) still needs a deeper investigation to clarify its pathogenesis. We describe two cases that represent useful hints for a comprehensive review of the thrombotic issue. A 75-year-old man with advanced rectal cancer treated with fluoropyrimidines suffered two catheter-related VTE events managed according to current guidelines. There was no indication for "extended" anticoagulant therapy for him, but during antithrombotic wash-out and fluoropyrimidines plus panitumumab regimen, he suffered a massive right coronary artery (RCA) thrombosis. Another patient with no cardiovascular (CV) risk factors and affected by advanced bladder cancer was treated with a platinum-containing regimen and suffered an acute inferior myocardial infarction 2 days after chemotherapy administration. He was successfully treated with primary Percutaneous Transluminal Coronary Angioplasty of RCA, discontinuing platinum-based therapy. Our observations raise the issue of cancer-associated thrombosis (CAT) complexity and the potential correlation between arterial and venous thrombotic events. Moreover, physicians should be aware of the thrombotic risk associated with anticancer therapies, suggesting that an appropriate prophylaxis should be considered.

The medical research field has been tremendously galvanized to improve the prediction of therapy efficacy by the revolution in artificial intelligence (AI). An earnest desire to find better ways to predict the effectiveness of therapy with the use of AI has propelled the evolution of new models in which it can become more applicable in clinical settings such as breast cancer detection. However, in some instances, the U.S. Food and Drug Administration was obliged to back some previously approved inaccurate models for AI-based prognostic models because they eventually produce inaccurate prognoses for specific patients who might be at risk of heart failure. In light of instances in which the medical research community has often evolved some unrealistic expectations regarding the advances in AI and its potential use for medical purposes, implementing standard procedures for AI-based cancer models is critical. Specifically, models would have to meet some general parameters for standardization, transparency of their logistic modules, and avoidance of algorithm biases. In this review, we summarize the current knowledge about AI-based prognostic methods and describe how they may be used in the future for predicting antibody-drug conjugate efficacy in cancer patients. We also summarize the findings of recent late-phase clinical trials using these conjugates for cancer therapy.

After the discovery of deoxyribonucleic acid (DNA) in the 1950s, the medical field has markedly changed. The first Sanger sequencing method used DNA synthesis machinery and four labels for the four dideoxynucleotides. Since then, second-generation sequencing methods such as Illumina, Ion Torrent, and SOLiD have replaced the first-generation. These methods rely on single nucleotide addition, cyclic reversible termination, sequencing by ligation, and real-time sequencing. They all produce readings of short sequences. In contrast, third-generation sequencing methods, such as PacBio and Nanopore, give long reads, making it possible to read long repetitive regions of the genome with a single run. While the first Sanger method took nearly 13 years to complete reading the whole human genome, today's latest technologies can do the same in less than one hour. Such development has a significant clinical impact on cancer patients, enabling clinicians to guide personalized therapies based on molecular alterations in the patient's tissue detected via liquid biopsy. Interestingly, after the emergence of high-throughput sequencing (HTS) instruments for whole genome sequencing, diagnostic and predictive biomarkers have been discovered to help the treatment of complex diseases like cancer. The use of HTS revealed how nucleotide polymorphism, copy number variants, tumor mutation burden, microsatellite instability, and immune gene expression panels play an important role as biomarkers in cancer diagnosis and therapy. This chapter will describe the emergence of next-generation sequencing methods used for whole genome sequencing. A particular focus on cancer indicates their resourcefulness in the medical field.

Mutations of the von Hippel–Lindau (VHL) tumor suppressor gene occur frequently in clear cell renal cell carcinoma (RCC), the predominant histology of kidney cancer, and have been associated with its pathogenesis and progression. Alterations of VHL lead to impaired degradation of hypoxia-inducible factor 1α (HIF1α) and HIF2α promoting neoangiogenesis, which is pivotal for cancer growth. As such, targeting the VHL-HIF axis holds relevant potential for therapeutic purposes. Belzutifan, an HIF-2α inhibitor, has been recently indicated for metastatic RCC and other antiangiogenic drugs directed against HIF-2α are currently under investigation. Further, clinical and preclinical studies of combination approaches for metastatic RCC including belzutifan with cyclin-dependent kinase 4–6 inhibitors, tyrosine kinase inhibitors, or immune checkpoint inhibitors achieved promising results or are ongoing. This review aims to summarize the existing evidence regarding the VHL/HIF pathway, and the approved and emerging treatment strategies that target this pivotal molecular axis and their mechanisms of resistance.

The medical research field has been tremendously galvanized to improve the prediction of therapy efficacy by the revolution in artificial intelligence (AI). An earnest desire to find better ways to predict the effectiveness of therapy with the use of AI has propelled the evolution of new models in which it can become more applicable in clinical settings such as breast cancer detection. However, in some instances, the U.S. Food and Drug Administration was obliged to back some previously approved inaccurate models for AI-based prognostic models because they eventually produce inaccurate prognoses for specific patients who might be at risk of heart failure. In light of instances in which the medical research community has often evolved some unrealistic expectations regarding the advances in AI and its potential use for medical purposes, implementing standard procedures for AI-based cancer models is critical. Specifically, models would have to meet some general parameters for standardization, transparency of their logistic modules, and avoidance of algorithm biases. In this review, we summarize the current knowledge about AI-based prognostic methods and describe how they may be used in the future for predicting antibody-drug conjugate efficacy in cancer patients. We also summarize findings of recent late-phase clinical trials using these conjugates for cancer therapy.

Current cancer therapeutic strategies for the treatment of cancer are often unsuccessful due to unwanted side effects and drug resistance. Therefore, the design and development of potent, new anticancer platforms, such as stem-cell treatments, have attracted much attention. Distinctive biological properties of stem cells include their capacity to secrete bioactive factors, their limited immunogenicity, and their capacity for renewing themselves. Mesenchymal stem cells (MSCs) are one of several kinds of stem cells that are conveniently extracted and are able to be cultivated in vitro utilizing various sources. The secretome of stem cells contains many trophic factors, including cytokines, chemokines, growth factors, and microRNA molecules that can either promote or inhibit the formation of tumors, based on the cell environment. In the current review, we focused on the secretome of mesenchymal stem cells. These stem cells act as a double-edged sword in the regulation of cell signal transduction pathways in that they can either suppress or promote tumors.

Since 2019, the world has been experiencing an outbreak of a novel beta-coronavirus, severe acute respiratory syndrome coronavirus (SARS-CoV)-2. The worldwide spread of this virus has been a severe challenge for public health, and the World Health Organization declared the outbreak a public health emergency of international concern. As of June 8, 2023, the virus' rapid spread had caused over 767 million infections and more than 6.94 million deaths worldwide. Unlike previous SARS-CoV-1 and Middle East respiratory syndrome coronavirus outbreaks, the COVID-19 outbreak has led to a high death rate in infected patients; this has been caused by multiorgan failure, which might be due to the widespread presence of angiotensin-converting enzyme 2 (ACE2) receptors—functional receptors of SARS-CoV-2—in multiple organs. Patients with cancer may be particularly susceptible to COVID-19 because cancer treatments (e.g., chemotherapy, immunotherapy) suppress the immune system. Thus, patients with cancer and COVID-19 may have a poor prognosis. Knowing how to manage the treatment of patients with cancer who may be infected with SARS-CoV-2 is essential. Treatment decisions must be made on a case-by-case basis, and patient stratification is necessary during COVID-19 outbreaks. Here, we review the management of COVID-19 in patients with cancer and focus on the measures that should be adopted for these patients on the basis of the organs or tissues affected by cancer and by the tumor stage.

The phosphoinositide 3 kinase (PI3K)-protein kinase B (PKB/AKT)-mammalian target of the rapamycin (mTOR) axis is a key signal transduction system that links oncogenes and multiple receptor classes which are involved in many essential cellular functions. Aberrant PI3K signalling is one of the most commonly mutated pathways in cancer. Consequently, more than 40 compounds targeting key components of this signalling network have been tested in clinical trials among various types of cancer. As the oncogenic activation of the PI3K/AKT/mTOR pathway often occurs alongside mutations in other signalling networks, combination therapy should be considered. In this review, we highlight recent advances in the knowledge of the PI3K pathway and discuss the current state and future challenges of targeting this pathway in clinical practice.

Objective:The incidence of Wilms'tumor(WT)among adult individuals accounts for less than 1％ of kidney cancer cases,with a prognosis usually less favorable when compared to younger individuals and an overall survival rate of 70％for the adult patients versus 90％ for the pediatric cases.The diagnosis and treatment of WT are complex in the preoperative setting;neoadjuvant chemotherapy(NAC)or robotic surgery has rarely been described.This study aimed to review the literature of robotic surgery in WT and report the first adult WT man-agement using both NAC and robotic strategy. Methods:We reported a case of WT managed in a multidisciplinary setting.Furthermore,ac-cording to Preferred Reporting Items for Systematic reviews and Meta-Analyses recommenda-tions,a systematic review of the literature until August 2020 of WT treated with a robotic approach was carried out. Results:A 33-year-old female had a diagnosis of WT.She was scheduled to NAC,and according to the clinical and radiological response to a robotic radical nephrectomy with aortic lymph nodes dissection,she was managed with no intraoperative rupture,a favorable surgical outcome,and a follow-up of 25 months,which did not show any recurrence.The systematic review identified a total number of 230 cases of minimally invasive surgery reported in the literature for WT.Of these,approximately 15 patients were carried out using robotic surgery in adolescents while none in adults.Moreover,NAC has not been administered before mini-mally invasive surgery in adults up until now. Conclusion:WT is a rare condition in adults with only a few cases treated with either NAC or minimally invasive approach so far.The advantage of NAC followed by the robotic approach could lead to favorable outcomes in this complex scenario.Notwithstanding,additional cases of adult WT need to be identified and investigated to improve the oncological outcome.

A 48-year-old gentleman who had recently commenced chemotherapy for diffuse B-cell lymphoma was admitted to hospital with nausea and generalised weakness. He developed abdominal pain and oliguric acute kidney injury with multiple electrolyte derangements and was transferred to the intensive care unit (ICU). His condition deteriorated, requiring endotracheal intubation and renal replacement therapy (RRT). Tumour lysis syndrome (TLS) is a common and life-threatening complication of chemotherapy and represents an oncological emergency. TLS affects multiple organ systems and is best managed in the ICU with closer monitoring of fluid balance, serum electrolytes, cardiorespiratory and renal function. TLS patients may go on to require mechanical ventilation and RRT. TLS patients require input from a large multidisciplinary team of clinicians and allied health professionals.

The immune system plays a central role in the onset and progression of cancer. A better understanding of transcriptional changes in immune cell-related genes associated with cancer progression, and their significance in disease prognosis, is therefore needed. NanoString-based targeted gene expression profiling has advantages for deployment in a clinical setting over RNA-seq technologies. We analysed NanoString PanCancer Immune Profiling panel gene expression data encompassing 770 genes, and overall survival data, from multiple previous studies covering 10 different cancer types, including solid and blood malignancies, across 515 patients. This analysis revealed an immune gene signature comprising 39 genes that were upregulated in those patients with shorter overall survival; of these 39 genes, three (MAGEC2, SSX1 and ULBP2) were common to both solid and blood malignancies. Most of the genes identified have previously been reported as relevant in one or more cancer types. Using Cibersort, we investigated immune cell levels within individual cancer types and across groups of cancers, as well as in shorter and longer overall survival groups. Patients with shorter survival had a higher proportion of M2 macrophages and γδ T cells. Patients with longer overall survival had a higher proportion of CD8+ T cells, CD4+ T memory cells, NK cells and, unexpectedly, T regulatory cells. Using a transcriptomics platform with certain advantages for deployment in a clinical setting, our multi-cancer meta-analysis of immune gene expression and overall survival data has identified a specific transcriptional profile associated with poor overall survival.