Personalized Medicine: Recent Progress in Cancer Therapy

Polybodies: Next-generation clinical antibodies

Conventional antibodies [full-length and fragments: F(ab')2, fragment antigen-binding (Fab), single-chain variable fragment (scFv), variable heavy domain of heavy chain (VHH)] are monospecific first-generation antibodies that have dominated the biopharmaceuticals field. However, advanced protein engineering technology has led to the advent of the next-generation polybody, which is a significant improvement over the conventional antibody. Polybodies comprise polyspecific and polyvalent antibodies that enable a single antibody to target multiple specific antigens simultaneously. Polybodies are superior to first-generation antibodies (more efficacious, broad-spectrum, resistance resistant, customizable, etc.) and provide a cost-effective healthcare solution. This review thoroughly addresses developments in polybodies, highlighting their superiority over conventional antibodies and offering future perspectives to encourage the generation of innovative immunotherapies.

From Biosensors to Robotics: Pioneering Advances in Breast Cancer Management

Breast cancer stands as the most prevalent form of cancer amongst females, constituting more than one-third of all cancer cases affecting women. It causes aberrant cell development, which can assault or spread to other sections of the body, perhaps leading to the patient's death. Based on research findings, timely detection can diminish the likelihood of mortality and enhance the quality of healthcare provided for the illness. However, current technologies can only identify cancer at an advanced stage. Consequently, there is a substantial demand for rapid and productive approaches to detecting breast cancer. Researchers are actively pursuing precise and timely methods for the diagnosis of breast cancer, aiming to achieve enhanced accuracy and early detection. Biosensor technology can allow for the speedy and accurate diagnosis of cancer-related cells, as well as a more sensitive and specialized technique for generating them. Additionally, numerous treatments for breast cancer are depicted such as herbal therapy, nanomaterial-based drug delivery, miRNA targeting, CRISPR technology, immunotherapy, and precision medicine. Early detection and efficient therapy are necessary to manage such a severe illness properly.

Empowering precision medicine: regenerative AI in breast cancer

Regenerative AI is transforming breast cancer diagnosis and treatment through enhanced imaging analysis, personalized medicine, drug discovery, and remote patient monitoring. AI algorithms can detect subtle patterns in mammograms and other imaging modalities with high accuracy, potentially leading to earlier diagnoses. In treatment planning, AI integrates patient-specific data to predict individual responses and optimize therapies. For drug discovery, generative AI models rapidly design and screen novel molecules targeting breast cancer pathways. Remote monitoring tools powered by AI provide real-time insights to guide care. Examples include Google's LYNA for analyzing pathology slides, Kheiron's Mia for mammogram interpretation, and Tempus's platform for integrating clinical and genomic data. While promising, challenges remain, including limited high-quality training data, integration into clinical workflows, interpretability of AI decisions, and regulatory/ethical concerns. Strategies to address these include collaborative data-sharing initiatives, user-centered design, explainable AI techniques, and robust oversight frameworks. In developing countries, AI tools like MammoAssist and Niramai's thermal imaging system are improving access to screening. Overall, regenerative AI offers significant potential to enhance breast cancer care, but judicious implementation with awareness of limitations is crucial. Coordinated efforts across the healthcare ecosystem are needed to fully realize AI's benefits while addressing challenges.

Programmable bionanomaterials for revolutionizing cancer immunotherapy

Cancer immunotherapy involves a cutting-edge method that utilizes the immune system to detect and eliminate cancer cells. It has shown substantial effectiveness in treating different types of cancer. As a result, its growing importance is due to its distinct benefits and potential for sustained recovery. However, the general deployment of this treatment is hindered by ongoing issues in maintaining minimal toxicity, high specificity, and prolonged effectiveness. Nanotechnology offers promising solutions to these challenges due to its notable attributes, including expansive precise surface areas, accurate ability to deliver drugs and controlled surface chemistry. This review explores the current advancements in the application of nanomaterials in cancer immunotherapy, focusing on three primary areas: monoclonal antibodies, therapeutic cancer vaccines, and adoptive cell treatment. In adoptive cell therapy, nanomaterials enhance the expansion and targeting capabilities of immune cells, such as T cells, thereby improving their ability to locate and destroy cancer cells. For therapeutic cancer vaccines, nanoparticles serve as delivery vehicles that protect antigens from degradation and enhance their uptake by antigen-presenting cells, boosting the immune response against cancer. Monoclonal antibodies benefit from nanotechnology through improved delivery mechanisms and reduced off-target effects, which increase their specificity and effectiveness. By highlighting the intersection of nanotechnology and immunotherapy, we aim to underscore the transformative potential of nanomaterials in enhancing the effectiveness and safety of cancer immunotherapies. Nanoparticles' ability to deliver drugs and biomolecules precisely to tumor sites reduces systemic toxicity and enhances therapeutic outcomes.

Cancer Treatment: New Drugs and Strategies

Cancer is a significant global health problem with complex treatment challenges [...].

Emerging and Biological Concepts in Pediatric High-Grade Gliomas

Primary central nervous system tumors are the most frequent solid tumors in children, accounting for over 40% of all childhood brain tumor deaths, specifically high-grade gliomas. Compared with pediatric low-grade gliomas (pLGGs), pediatric high-grade gliomas (pHGGs) have an abysmal survival rate. The WHO CNS classification identifies four subtypes of pHGGs, including Grade 4 Diffuse midline glioma H3K27-altered, Grade 4 Diffuse hemispheric gliomas H3-G34-mutant, Grade 4 pediatric-type high-grade glioma H3-wildtype and IDH-wildtype, and infant-type hemispheric gliomas. In recent years, we have seen promising advancements in treatment strategies for pediatric high-grade gliomas, including immunotherapy, CAR-T cell therapy, and vaccine approaches, which are currently undergoing clinical trials. These therapies are underscored by the integration of molecular features that further stratify HGG subtypes. Herein, we will discuss the molecular features of pediatric high-grade gliomas and the evolving landscape for treating these challenging tumors.

Dihydropyrimidine Dehydrogenase Polymorphism c.2194G>A Screening Is a Useful Tool for Decreasing Gastrointestinal and Hematological Adverse Drug Reaction Risk in Fluoropyrimidine-Treated Patients

Although the risk of fluoropyrimidine toxicity may be decreased by identifying poor metabolizers with a preemptive dihydropyrimidine dehydrogenase (DPYD) test, following international standards, many patients with wild-type (WT) genotypes for classic variations may still exhibit adverse drug reactions (ADRs). Therefore, the safety of fluoropyrimidine therapy could be improved by identifying new DPYD polymorphisms associated with ADRs. This study was carried out to assess whether testing for the underestimated c.2194G>A (DPYD*6 polymorphism, rs1801160) is useful, in addition to other well-known variants, in reducing the risk of ADRs in patients undergoing chemotherapy treatment. This retrospective study included 132 patients treated with fluoropyrimidine-containing regimens who experienced ADRs such as gastrointestinal, dermatological, hematological, and neurological. All subjects were screened for DPYD variants DPYD2A (IVS14+1G>A, c.1905+1G>A, rs3918290), DPYD13 (c.1679T>G, rs55886062), c.2846A>T (rs67376798), c.1236G>A (rs56038477), and c.2194G>A by real-time polymerase chain reaction (RT-PCR). In this cohort, the heterozygous c.2194G>A variant was present in 26 patients, while 106 individuals were WT; both subgroups were compared for the incidence of ADRs. This assessment revealed a high incidence of gastrointestinal and hematological ADRs in DPYD6 carriers compared to WT. Moreover, we have shown a higher prevalence of ADRs in females compared to males when stratifying c.2194G>A carrier individuals. Considering that c.2194G>A was linked to clinically relevant ADRs, we suggest that this variant should also be assessed preventively to reduce the risk of fluoropyrimidine-related ADRs.

Designing intelligent bioorthogonal nanozymes: Recent advances of stimuli-responsive catalytic systems for biomedical applications

Bioorthogonal nanozymes have emerged as a potent tool in biomedicine due to their unique ability to perform enzymatic reactions that do not interfere with native biochemical processes. The integration of stimuli-responsive mechanisms into these nanozymes has further expanded their potential, allowing for controlled activation and targeted delivery. As such, intelligent bioorthogonal nanozymes have received more and more attention in developing therapeutic approaches. This review provides a comprehensive overview of the recent advances in the development and application of stimuli-responsive bioorthogonal nanozymes. By summarizing the design outlines for anchoring bioorthogonal nanozymes with stimuli-responsive capability, this review seeks to offer valuable insights and guidance for the rational design of these remarkable materials. This review highlights the significant progress made in this exciting field with different types of stimuli and the various applications. Additionally, it also examines the current challenges and limitations in the design, synthesis, and application of these systems, and proposes potential solutions and research directions. This review aims to stimulate further research toward the development of more efficient and versatile stimuli-responsive bioorthogonal nanozymes for biomedical applications.

Metabolic vulnerabilities in cancer: A new therapeutic strategy

Cancer metabolism is now a key area for therapeutic intervention, targeting unique metabolic reprogramming crucial for tumor growth and survival. This article reviews the therapeutic potential of addressing metabolic vulnerabilities through glycolysis and glutaminase inhibitors, which disrupt cancer cell metabolism. Challenges such as tumor heterogeneity and adaptive resistance are discussed, with strategies including personalized medicine and predictive biomarkers to enhance treatment efficacy. Additionally, integrating diet and lifestyle changes with metabolic targeting underscores a holistic approach to improving therapy outcomes. The article also examines the benefits of incorporating these strategies into standard care, highlighting the potential for more tailored, safer treatments. In conclusion, exploiting metabolic vulnerabilities promises a new era in oncology, positioning metabolic targeting at the forefront of personalized cancer therapy and transforming patient care.

The molecular basis of the anticancer effect of statins

Statins, widely used cardiovascular drugs that lower cholesterol by inhibiting HMG-CoA reductase, have been increasingly recognized for their potential anticancer properties. This study elucidates the underlying mechanism, revealing that statins exploit Synthetic Lethality, a principle where the co-occurrence of two non-lethal events leads to cell death. Our computational analysis of approximately 37,000 SL pairs identified statins as potential drugs targeting genes involved in SL pairs with metastatic genes. In vitro validation on various cancer cell lines confirmed the anticancer efficacy of statins. This data-driven drug repurposing strategy provides a molecular basis for the anticancer effects of statins, offering translational opportunities in oncology.

Unveil Intrahepatic Cholangiocarcinoma Heterogeneity through the Lens of Omics and Multi-Omics Approaches

Intrahepatic cholangiocarcinoma (iCCA) is recognized worldwide as the second leading cause of morbidity and mortality among primary liver cancers, showing a continuously increasing incidence rate in recent years. iCCA aggressiveness is revealed through its rapid and silent intrahepatic expansion and spread through the lymphatic system leading to late diagnosis and poor prognoses. Multi-omics studies have aggregated information derived from single-omics data, providing a more comprehensive understanding of the phenomena being studied. These approaches are gradually becoming powerful tools for investigating the intricate pathobiology of iCCA, facilitating the correlation between molecular signature and phenotypic manifestation. Consequently, preliminary stratifications of iCCA patients have been proposed according to their "omics" features opening the possibility of identifying potential biomarkers for early diagnosis and developing new therapies based on personalized medicine (PM). The focus of this review is to provide new and advanced insight into the molecular pathobiology of the iCCA, starting from single- to the latest multi-omics approaches, paving the way for translating new basic research into therapeutic practices.

Chitosan Nanoparticles for Targeted Cancer Therapy: A Review of Stimuli-Responsive, Passive, and Active Targeting Strategies

Despite all major advancements in drug discovery and development in the pharmaceutical industry, cancer is still one of the most arduous challenges for the scientific community. The implications of nanotechnology have certainly resolved major issues related to conventional anticancer modalities; however, the undesired recognition of nanoparticles (NPs) by the mononuclear phagocyte system (MPS), their poor stability in biological fluids, premature release of payload, and low biocompatibility have restricted their clinical translation. In recent decades, chitosan (CS)-based nanodelivery systems (eg, polymeric NPs, micelles, liposomes, dendrimers, conjugates, solid lipid nanoparticles, etc.) have attained promising recognition from researchers for improving the pharmacokinetics and pharmacodynamics of chemotherapeutics. However, the specialty of this review is to mainly focus on and critically discuss the targeting potential of various CS-based NPs for treatment of different types of cancer. Based on their delivery mechanisms, we classified CS-based NPs into stimuli-responsive, passive, or active targeting nanosystems. Moreover, various functionalization strategies (eg, grafting with polyethylene glycol (PEG), hydrophobic substitution, tethering of stimuli-responsive linkers, and conjugation of targeting ligands) adapted to the architecture of CS-NPs for target-specific delivery of chemotherapeutics have also been considered. Nevertheless, CS-NPs based therapeutics hold great promise for improving therapeutic outcomes while mitigating the off-target effects of chemotherapeutics, a long-term safety profile and clinical testing in humans are warranted for their successful clinical translation.

Novel therapeutic agents in clinical trials: emerging approaches in cancer therapy

Novel therapeutic agents in clinical trials offer a paradigm shift in the approach to battling this prevalent and destructive disease, and the area of cancer therapy is on the precipice of a trans formative revolution. Despite the importance of tried-and-true cancer treatments like surgery, radiation, and chemotherapy, the disease continues to evolve and adapt, making new, more potent methods necessary. The field of cancer therapy is currently witnessing the emergence of a wide range of innovative approaches. Immunotherapy, including checkpoint inhibitors, CAR-T cell treatment, and cancer vaccines, utilizes the host's immune system to selectively target and eradicate malignant cells while minimizing harm to normal tissue. The development of targeted medicines like kinase inhibitors and monoclonal antibodies has allowed for more targeted and less harmful approaches to treating cancer. With the help of genomics and molecular profiling, "precision medicine" customizes therapies to each patient's unique genetic makeup to maximize therapeutic efficacy while minimizing unwanted side effects. Epigenetic therapies, metabolic interventions, radio-pharmaceuticals, and an increasing emphasis on combination therapy with synergistic effects further broaden the therapeutic landscape. Multiple-stage clinical trials are essential for determining the safety and efficacy of these novel drugs, allowing patients to gain access to novel treatments while also furthering scientific understanding. The future of cancer therapy is rife with promise, as the integration of artificial intelligence and big data has the potential to revolutionize early detection and prevention. Collaboration among researchers, and healthcare providers, and the active involvement of patients remain the bedrock of the ongoing battle against cancer. In conclusion, the dynamic and evolving landscape of cancer therapy provides hope for improved treatment outcomes, emphasizing a patient-centered, data-driven, and ethically grounded approach as we collectively strive towards a cancer-free world.

Identification of Time-Series Pattern Marker in Its Application to Mortality Analysis of Pneumonia Patients in Intensive Care Unit

Electronic Health Records (EHRs) are a significant source of big data used to track health variables over time. The analysis of EHR data can uncover medical markers or risk factors, aiding in the diagnosis and monitoring of diseases. We introduce a novel method for identifying markers with various temporal trend patterns, including monotonic and fluctuating trends, using machine learning models such as Long Short-Term Memory (LSTM). By applying our method to pneumonia patients in the intensive care unit using the MIMIC-III dataset, we identified markers exhibiting both monotonic and fluctuating trends. Specifically, monotonic markers such as red cell distribution width, urea nitrogen, creatinine, calcium, morphine sulfate, bicarbonate, sodium, troponin T, albumin, and prothrombin time were more frequently observed in the mortality group compared to the recovery group throughout the 10-day period before discharge. Conversely, fluctuating trend markers such as dextrose in sterile water, polystyrene sulfonate, free calcium, and glucose were more frequently observed in the mortality group as the discharge date approached. Our study presents a method for detecting time-series pattern markers in EHR data that respond differently according to disease progression. These markers can contribute to monitoring disease progression and enable stage-specific treatment, thereby advancing precision medicine.

Novel role of lncRNAs regulatory network in papillary thyroid cancer

Papillary thyroid cancer (PTC) is the most common endocrine malignancy. The incidence of PTC has increased annually worldwide. Thus, PTC diagnosis and treatment attract more attention. Noncoding RNAs (lncRNAs) play crucial roles in PTC progression and act as prognostic biomarkers. Moreover, microRNAs (miRNAs) and epithelial-mesenchymal transition (EMT)-associated proteins have potential biomarkers for diagnosing and treating PTC. However, the correlation of lncRNAs with miRNAs and EMT-associated proteins needs further clarification. The present review highlights the recent advances of lncRNAs in PTC. We significantly summarized the two molecular regulatory mechanisms in PTC progress, including lncRNAs-miRNAs-protein signaling axes and lncRNAs-EMT pathways. This review will help our understanding of the association between lncRNAs and PTC and may assist us in evaluating the prognosis for PTC patients. Taken together, targeting the lncRNAs regulatory network has promising applications in diagnosing and treating PTC.

Clinical Practice Recommendations for the Use of Next-Generation Sequencing in Patients with Solid Cancer: A Joint Report from KSMO and KSP

In recent years, next-generation sequencing (NGS)-based genetic testing has become crucial in cancer care. While its primary objective is to identify actionable genetic alterations to guide treatment decisions, its scope has broadened to encompass aiding in pathological diagnosis and exploring resistance mechanisms. With the ongoing expansion in NGS application and reliance, a compelling necessity arises for expert consensus on its application in solid cancers. To address this demand, the forthcoming recommendations not only provide pragmatic guidance for the clinical use of NGS but also systematically classify actionable genes based on specific cancer types. Additionally, these recommendations will incorporate expert perspectives on crucial biomarkers, ensuring informed decisions regarding circulating tumor DNA panel testing.

Clinical practice recommendations for the use of next-generation sequencing in patients with solid cancer: a joint report from KSMO and KSP

In recent years, next-generation sequencing (NGS)-based genetic testing has become crucial in cancer care. While its primary objective is to identify actionable genetic alterations to guide treatment decisions, its scope has broadened to encompass aiding in pathological diagnosis and exploring resistance mechanisms. With the ongoing expansion in NGS application and reliance, a compelling necessity arises for expert consensus on its application in solid cancers. To address this demand, the forthcoming recommendations not only provide pragmatic guidance for the clinical use of NGS but also systematically classify actionable genes based on specific cancer types. Additionally, these recommendations will incorporate expert perspectives on crucial biomarkers, ensuring informed decisions regarding circulating tumor DNA panel testing.

Hallmarks of cancer resistance

This review explores the hallmarks of cancer resistance, including drug efflux mediated by ATP-binding cassette (ABC) transporters, metabolic reprogramming characterized by the Warburg effect, and the dynamic interplay between cancer cells and mitochondria. The role of cancer stem cells (CSCs) in treatment resistance and the regulatory influence of non-coding RNAs, such as long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and circular RNAs (circRNAs), are studied. The chapter emphasizes future directions, encompassing advancements in immunotherapy, strategies to counter adaptive resistance, integration of artificial intelligence for predictive modeling, and the identification of biomarkers for personalized treatment. The comprehensive exploration of these hallmarks provides a foundation for innovative therapeutic approaches, aiming to navigate the complex landscape of cancer resistance and enhance patient outcomes.

RNA biomarkers in cancer therapeutics: The promise of personalized oncology

Cancer therapy is a rapidly evolving and constantly expanding field. Current approaches include surgery, conventional chemotherapy and novel biologic agents as in immunotherapy, that together compose a wide armamentarium. The plethora of choices can, however, be clinically challenging in prescribing the most suitable treatment for any given patient. Fortunately, biomarkers can greatly facilitate the most appropriate selection. In recent years, RNA-based biomarkers have proven most promising. These molecules that range from small noncoding RNAs to protein coding gene transcripts can be valuable in cancer management and especially in cancer therapeutics. Compared to their DNA counterparts which are stable throughout treatment, RNA-biomarkers are dynamic. This allows prediction of success prior to treatment start and can identify alterations in expression that could reflect response. Moreover, improved nucleic acid technology allows RNA to be extracted from practically every biofluid/matrix and evaluated with exceedingly high analytic sensitivity. In addition, samples are largely obtained by minimally invasive procedures and as such can be used serially to assess treatment response real-time. This chapter provides the reader insight on currently known RNA biomarkers, the latest research employing Artificial Intelligence in the identification of such molecules and in clinical decisions driving forward the era of personalized oncology.

LUNGBANK: a novel biorepository strategy tailored for comprehensive multiomics analysis and P-medicine applications in lung cancer

Background/aim

LUNGBANK was established as part of Project LUNGMARK, pioneering a biorepository dedicated exclusively to lung cancer research. It employs cutting-edge technologies to streamline the handling of biospecimens, ensuring the acquisition of high-quality samples. This infrastructure is fortified with robust data management capabilities, enabling seamless integration of diverse datasets. LUNGBANK functions not merely as a repository but as a sophisticated platform crucial for advancing lung cancer research, poised to facilitate significant discoveries.

Materials and methods

LUNGBANK was meticulously designed to optimize every stage of biospecimen handling, from collection and storage to processing. Rigorous standard operating procedures and stringent quality control measures guarantee the integrity of collected biospecimens. Advanced data management protocols facilitate the efficient integration and analysis of various datasets, enhancing the depth and breadth of research possibilities in lung cancer.

Results

LUNGBANK has amassed a comprehensive collection of biospecimens essential for unraveling the intricate molecular mechanisms of lung cancer. The integration of state-of-the-art technologies ensures the acquisition of top-tier data, fostering breakthroughs in translational and histological research. Moreover, the establishment of patient-derived systems by LUNGBANK underscores its pivotal role in personalized medicine approaches.

Conclusion

The establishment of LUNGBANK marks a significant milestone in addressing the critical challenges of lung cancer research. By providing researchers with high-quality biospecimens and advanced research tools, LUNGBANK not only supports Project LUNGMARK's objectives but also contributes extensively to the broader landscape of personalized medicine. It promises to enhance our understanding of lung cancer initiation, progression, and therapeutic interventions tailored to individual patient needs, thereby advancing the field towards more effective diagnostic and therapeutic strategies.

Bridging the Divide: A Review on the Implementation of Personalized Cancer Medicine

The shift towards personalized cancer medicine (PCM) represents a significant transformation in cancer care, emphasizing tailored treatments based on the genetic understanding of cancer at the cellular level. This review draws on recent literature to explore key factors influencing PCM implementation, highlighting the role of innovative leadership, interdisciplinary collaboration, and coordinated funding and regulatory strategies. Success in PCM relies on overcoming challenges such as integrating diverse medical disciplines, securing sustainable investment for shared infrastructures, and navigating complex regulatory landscapes. Effective leadership is crucial for fostering a culture of innovation and teamwork, essential for translating complex biological insights into personalized treatment strategies. The transition to PCM necessitates not only organizational adaptation but also the development of new professional roles and training programs, underscoring the need for a multidisciplinary approach and the importance of team science in overcoming the limitations of traditional medical paradigms. The conclusion underscores that PCM's success hinges on creating collaborative environments that support innovation, adaptability, and shared vision among all stakeholders involved in cancer care.

Investigation toward the economic feasibility of personalized medicine for healthcare service providers: the case of bladder cancer

In today's complex healthcare landscape, the pursuit of delivering optimal patient care while navigating intricate economic dynamics poses a significant challenge for healthcare service providers (HSPs). In this already complex dynamic, the emergence of clinically promising personalized medicine-based treatment aims to revolutionize medicine. While personalized medicine holds tremendous potential for enhancing therapeutic outcomes, its integration within resource-constrained HSPs presents formidable challenges. In this study, we investigate the economic feasibility of implementing personalized medicine. The central objective is to strike a balance between catering to individual patient needs and making economically viable decisions. Unlike conventional binary approaches to personalized treatment, we propose a more nuanced perspective by treating personalization as a spectrum. This approach allows for greater flexibility in decision-making and resource allocation. To this end, we propose a mathematical framework to investigate our proposal, focusing on Bladder Cancer (BC) as a case study. Our results show that while it is feasible to introduce personalized medicine, a highly efficient but highly expensive one would be short-lived relative to its less effective but cheaper alternative as the latter can be provided to a larger cohort of patients, optimizing the HSP's objective better.

Initiation of Cancer: The Journey From Mutations in Somatic Cells to Epigenetic Changes in Tissue-resident VSELs

Multiple theories exist to explain cancer initiation, although a consensus on this is crucial for developing effective therapies. 'Somatic mutation theory' suggests that mutations in somatic cells during DNA repair initiates cancer but this concept has several attached paradoxes. Research efforts to identify quiescent cancer stem cells (CSCs) that survive therapy and result in metastasis and recurrence have remained futile. In solid cancers, CSCs are suggested to appear during epithelial-mesenchymal transition by the dedifferentiation and reprogramming of epithelial cells. Pluripotent and quiescent very small embryonic-like stem cells (VSELs) exist in multiple tissues but remain elusive owing to their small size and scarce nature. VSELs are developmentally connected to primordial germ cells, undergo rare, asymmetrical cell divisions and are responsible for the regular turnover of cells to maintain tissue homeostasis throughout life. VSELs are directly vulnerable to extrinsic endocrine insults because they express gonadal and gonadotropin hormone receptors. VSELs undergo epigenetic changes due to endocrine insults and transform into CSCs. CSCs exhibit genomic instability and develop mutations due to errors during DNA replication while undergoing excessive proliferation and clonal expansion to form spheroids. Thus tissue-resident VSELs offer a connection between extrinsic insults and variations in cancer incidence reported in various body tissues. To conclude, cancer is indeed a stem cell disease with mutations occurring as a consequence. In addition to immunotherapy, targeting mutations, and Lgr5 + organoids for developing new therapeutics, targeting CSCs (epigenetically altered VSELs) by improving their niche and epigenetic status could serve as a promising strategy to treat cancer.

Engineering Biomaterials to Model Immune-Tumor Interactions In Vitro

Engineered biomaterial scaffolds are becoming more prominent in research laboratories to study drug efficacy for oncological applications in vitro, but do they have a place in pharmaceutical drug screening pipelines? The low efficacy of cancer drugs in phase II/III clinical trials suggests that there are critical mechanisms not properly accounted for in the pre-clinical evaluation of drug candidates. Immune cells associated with the tumor may account for some of these failures given recent successes with cancer immunotherapies; however, there are few representative platforms to study immune cells in the context of cancer as traditional 2D culture is typically monocultures and humanized animal models have a weakened immune composition. Biomaterials that replicate tumor microenvironmental cues may provide a more relevant model with greater in vitro complexity. In this review, the authors explore the pertinent microenvironmental cues that drive tumor progression in the context of the immune system, discuss how these cues can be incorporated into hydrogel design to culture immune cells, and describe progress toward precision oncological drug screening with engineered tissues.

The biomarkers ATLAS: An audit on 1100 non-small cell lung cancer from an Italian knowledge-based database

AIMS

To date, precision medicine has revolutionized the clinical management of Non-Small Cell Lung Cancer (NSCLC). International societies approved a rapidly improved mandatory testing biomarkers panel for the clinical stratification of NSCLC patients, but harmonized procedures are required to optimize the diagnostic workflow. In this context a knowledge-based database (Biomarkers ATLAS, https://biomarkersatlas.com/) was developed by a supervising group of expert pathologists and thoracic oncologists collecting updated clinical and molecular records from about 80 referral Italian institutions. Here, we audit molecular and clinical data from n = 1100 NSCLC patients collected from January 2019 to December 2020.

METHODS

Clinical and molecular records from NSCLC patients were retrospectively collected from the two coordinating institutions (University of Turin and University of Naples). Molecular biomarkers (KRAS, EGFR, BRAF, ROS1, ALK, RET, NTRK, MET) and clinical data (sex, age, histological type, smoker status, PD-L1 expression, therapy) were collected and harmonized.

RESULTS

Clinical and molecular data from 1100 (n = 552 mutated and n = 548 wild-type) NSCLC patients were systematized and annotated in the ATLAS knowledge-database. Molecular records from biomarkers testing were matched with main patients' clinical variables.

CONCLUSIONS

Biomarkers ATLAS (https://biomarkersatlas.com/) represents a unique, easily managing, and reliable diagnostic tool aiming to integrate clinical records with molecular alterations of NSCLC patients in the real-word Italian scenario.

From molecular pathogenesis to therapy: Unraveling non-coding RNAs/DNMT3A axis in human cancers

Cancer is a comprehensive classification encompassing more than 100 forms of malignancies that manifest in diverse tissues within the human body. Recent studies have provided evidence that aberrant epigenetic modifications are pivotal indicators of cancer. Epigenetics encapsulates DNA methyltransferases as a crucial class of modifiers. DNMTs, including DNMT3A, assume central roles in DNA methylation processes that orchestrate normal biological functions, such as gene transcription, predominantly in mammals. Typically, deviations in DNMT3A function engender distortions in factors that drive tumor growth and progression, thereby exacerbating the malignant phenotype of tumors. Consequently, such abnormalities pose significant challenges in cancer therapy because they impede treatment efficacy. Non-coding RNAs (ncRNAs) represent a group of RNA molecules that cannot encode functional proteins. Recent investigation attests to the crucial significance of regulatory ncRNAs in epigenetic regulation. Notably, recent reports have illuminated the complex interplay between ncRNA expression and epigenetic regulatory machinery, including DNMT3A, particularly in cancer. Recent findings have demonstrated that miRNAs, namely miR-770-5p, miR-101, and miR-145 exhibit the capability to target DNMT3A directly, and their aberration is implicated in diverse cellular abnormalities that predispose to cancer development. This review aims to articulate the interplay between DNMT3A and the ncRNAs, focusing on its impact on the development and progression of cancer, cancer therapy resistance, cancer stem cells, and prognosis. Importantly, the emergence of such reports that suggest a connection between DNMT3A and ncRNAs in several cancers indicates that this connecting axis offers a valuable target with significant therapeutic potential that might be exploited for cancer management.

Invisible cancers: Seeing, knowing, enacting and proving cancers in Haiti

In Haiti, pathological confirmation of a cancer diagnosis is often delayed or impossible, imaging is expensive and imperfect, and many tests are unavailable. Physicians frequently struggle to establish cancers at a level of certainty required by "evidence based" standards, delaying definitive diagnosis and rendering some cancers permanently "suspected." I use 22 months of participant observation at the two largest cancer treatment programs in Haiti, as well as throughout Haiti's fragmented healthcare system, to look closely at processes of diagnosis and management of suspected 'cancers' which may never fully come to be. I argue that as global oncology becomes increasingly standardized, local practices are forced into alignment with a global knowledge basis that governs the knowability/unknowability of cancer. Using three case studies drawn from this work, I examine relationships among visibility, power, expertise and the replication of inequity through the governance of knowledge production. Finally, I examine the implications of these processes for cancer care in the global south.

The importance of protein domain mutations in cancer therapy

Cancer is a complex disease that is caused by multiple genetic factors. Researchers have been studying protein domain mutations to understand how they affect the progression and treatment of cancer. These mutations can significantly impact the development and spread of cancer by changing the protein structure, function, and signalling pathways. As a result, there is a growing interest in how these mutations can be used as prognostic indicators for cancer prognosis. Recent studies have shown that protein domain mutations can provide valuable information about the severity of the disease and the patient's response to treatment. They may also be used to predict the response and resistance to targeted therapy in cancer treatment. The clinical implications of protein domain mutations in cancer are significant, and they are regarded as essential biomarkers in oncology. However, additional techniques and approaches are required to characterize changes in protein domains and predict their functional effects. Machine learning and other computational tools offer promising solutions to this challenge, enabling the prediction of the impact of mutations on protein structure and function. Such predictions can aid in the clinical interpretation of genetic information. Furthermore, the development of genome editing tools like CRISPR/Cas9 has made it possible to validate the functional significance of mutants more efficiently and accurately. In conclusion, protein domain mutations hold great promise as prognostic and predictive biomarkers in cancer. Overall, considerable research is still needed to better define genetic and molecular heterogeneity and to resolve the challenges that remain, so that their full potential can be realized.

Text-mining-based feature selection for anticancer drug response prediction

Motivation

Predicting anticancer treatment response from baseline genomic data is a critical obstacle in personalized medicine. Machine learning methods are commonly used for predicting drug response from gene expression data. In the process of constructing these machine learning models, one of the most significant challenges is identifying appropriate features among a massive number of genes.

Results

In this study, we utilize features (genes) extracted using the text-mining of scientific literatures. Using two independent cancer pharmacogenomic datasets, we demonstrate that text-mining-based features outperform traditional feature selection techniques in machine learning tasks. In addition, our analysis reveals that text-mining feature-based machine learning models trained on in vitro data also perform well when predicting the response of in vivo cancer models. Our results demonstrate that text-mining-based feature selection is an easy to implement approach that is suitable for building machine learning models for anticancer drug response prediction.

Availability and implementation

https://github.com/merlab/text_features.

Molecular targeted therapies for cutaneous squamous cell carcinoma: recent developments and clinical implications

Cutaneous Squamous Cell Carcinoma (cSCC) is a common and potentially fatal type of skin cancer that poses a significant threat to public health and has a high prevalence rate. Exposure to ultraviolet radiation on the skin surface increases the risk of cSCC, especially in those with genetic syndromes like xerodermapigmentosum and epidermolysis bullosa. Therefore, understanding the molecular pathogenesis of cSCC is critical for developing personalized treatment approaches that are effective in cSCC. This article provides a comprehensive overview of current knowledge of cSCC pathogenesis, emphasizing dysregulated signaling pathways and the significance of molecular profiling. Several limitations and challenges associated with conventional therapies, however, are identified, stressing the need for novel therapeutic strategies. The article further discusses molecular targets and therapeutic approaches, i.e., epidermal growth factor receptor inhibitors, hedgehog pathway inhibitors, and PI3K/AKT/mTOR pathway inhibitors, as well as emerging molecular targets and therapeutic agents. The manuscript explores resistance mechanisms to molecularly targeted therapies and proposes methods to overcome them, including combination strategies, rational design, and optimization. The clinical implications and patient outcomes of molecular-targeted treatments are assessed, including response rates and survival outcomes. The management of adverse events and toxicities in molecular-targeted therapies is crucial and requires careful monitoring and control. The paper further discusses future directions for therapeutic advancement and research in this area, as well as the difficulties and constraints associated with conventional therapies.

Natural Language Processing for Radiation Oncology: Personalizing Treatment Pathways

Natural language processing (NLP), a technology that translates human language into machine-readable data, is revolutionizing numerous sectors, including cancer care. This review outlines the evolution of NLP and its potential for crafting personalized treatment pathways for cancer patients. Leveraging NLP's ability to transform unstructured medical data into structured learnable formats, researchers can tap into the potential of big data for clinical and research applications. Significant advancements in NLP have spurred interest in developing tools that automate information extraction from clinical text, potentially transforming medical research and clinical practices in radiation oncology. Applications discussed include symptom and toxicity monitoring, identification of social determinants of health, improving patient-physician communication, patient education, and predictive modeling. However, several challenges impede the full realization of NLP's benefits, such as privacy and security concerns, biases in NLP models, and the interpretability and generalizability of these models. Overcoming these challenges necessitates a collaborative effort between computer scientists and the radiation oncology community. This paper serves as a comprehensive guide to understanding the intricacies of NLP algorithms, their performance assessment, past research contributions, and the future of NLP in radiation oncology research and clinics.

Barriers And Challenges Of Multidisciplinary Teams In Oncology Management: A Scoping Review Protocol

INTRODUCTION

Multidisciplinary teams (MDTs) are integral to oncology management, involving specialised healthcare professionals who collaborate to develop individualised treatment plans for patients. However, as cancer care grows more complex, MDTs must continually adapt to better address patient needs. This scoping review will explore barriers and challenges MDTs have encountered in the past decade; and propose strategies for optimising their utilisation to overcome these obstacles and improve patient care.

METHODS AND ANALYSIS

The scoping review will follow Arksey and O'Malley's framework and begin with a literature search using keywords in electronic databases such as PubMed/MEDLINE, Scopus and PsychINFO, covering the period from January 2013 to December 2022 and limited to English language publications. Four independent reviewers will screen titles and abstracts based on predefined inclusion criteria, followed by full-text review of selected titles. Relevant references cited in the publications will also be examined. A Preferred Reporting Items for Systematic reviews and Meta-Analyses flow diagram will be utilised to illustrate the methodology. Data from selected publications will be extracted, analysed, and categorised for further analysis.

ETHICS AND DISSEMINATION

The results of the scoping review will provide a comprehensive overview of the barriers and challenges encountered by oncology MDTs over the past decade. These findings will contribute to the existing literature and provide insights into areas that require improvement in the functioning of MDTs in oncology management. The results will be disseminated through publication in a scientific journal, which will help to share the findings with the wider healthcare community and facilitate further research and discussion in this field.

TRIAL REGISTRATION DETAILS

The protocol for this scoping review is registered with Open Science Framework, available at DOI 10.17605/OSF.IO/R3Y8U.

Current stage of preclinical and clinical development of guggulsterone in cancers: Challenges and promises

Throughout human history, the utilization of medicinal herbs has been recognized as a crucial defense against various ailments, including cancer. Natural products with potential anticancer properties, capable of inducing apoptosis in cancer cells, have garnered substantial attention. One such agent under investigation is guggulsterone (GS), a phytosterol derived from the gum resin of the Commiphora mukul tree. This review aims to provide a comprehensive summary of recent studies elucidating the anticancer molecular mechanisms and molecular targets of GS, guiding future research and potential applications as an adjuvant drug in cancer therapy. Recent in vivo and in vitro studies have explored the biological activities of the active ingredients in Commiphora mukul. Specifically, GS emerges as a potential cancer chemopreventive and therapeutic agent. The investigations delve into the impact of GS on constitutively activated survival pathways, including Janus kinase/signal transducer and activator of transcription (JAK/STAT), nuclear factor-kappa B (NF-kB), and PI3-kinase/AKT signaling pathways. These pathways regulate antiapoptotic and proinflammatory genes, exerting control over growth and inflammatory responses. The findings highlight the potential of GS in disrupting survival pathways crucial for cancer cell viability. The inhibition of JAK/STAT, NF-kB, and PI3-kinase/AKT signaling pathways positions GS as a promising candidate for cancer therapy. The review synthesizes evidence from diverse studies, underscoring the multifaceted biological activities of GS in cancer prevention and treatment. To advance our understanding, future clinical and translational studies are imperative to determine effective doses in humans. Additionally, there is a need for the development of new pharmaceutical forms of GS to optimize therapeutic effects. This comprehensive review provides a foundation for ongoing research, offering insights into the potential of GS as a valuable addition to the armamentarium against cancer.

Community Outreach and Engagement in Cancer Research Through a Biobank Clinic at Shaukat Khanum Memorial Cancer Hospital and Research Centre, Pakistan

INTRODUCTION

Cancer's increasing prevalence across the globe emphasizes the urgency for continued research, prevention, and accessible healthcare to mitigate its impact on individuals and communities. While there have been significant advances made towards controlling cancer morbidity and mortality in recent decades, Pakistan continues to experience a markedly elevated burden of the disease. With this study, we aim to raise awareness about biobank research within the cancer patient community, fostering participation and collaboration to advance the fight against cancer through vital research contributions.

METHODS

In October 2022, we initiated the biobank clinic at Shaukat Khanum Memorial Cancer Hospital and Research Centre (SKMCH&RC). Here, patients underwent screening and received invitations to voluntarily participate in biobank research. During these interactions, we engaged patients in discussions about the significance of biobank research, addressed their concerns, and encouraged their participation in advancing our research endeavors. Two-sample independent t-tests were performed to compare the mean number of participants in pre-clinic and post-clinic cohorts.

RESULTS

This research involved a total of 958 participants, with 312 participants enrolled before the clinic and 646 participants enrolled after the clinic. We have observed a noticeable increase in the participation of cancer patients in our research endeavors since the inception of the biobank clinic (p-value<0.001). Over an 11-month time frame, we scheduled appointments for 759 patients, and out of those, 656 patients availed themselves to visit the clinic. Impressively, we achieved the enrollment of 646 patients into the clinic, reflecting an exceptional consent rate of 98.47% for their active involvement in our research initiatives. This underscores our commitment to conducting comprehensive discussions and providing thorough explanations regarding the ethical and procedural aspects of our research.

CONCLUSION

Biobank clinic plays a pivotal role in raising cancer awareness and fostering research participation, especially in regions with limited healthcare infrastructure and lower literacy rates. It emerges as a community-engagement model that aligns research with local needs, ensuring its relevance and benefit to the population.

Edaravone: A Novel Possible Drug for Cancer Treatment?

Despite significant advancements in understanding the causes and progression of tumors, cancer remains one of the leading causes of death worldwide. In light of advances in cancer therapy, there has been a growing interest in drug repurposing, which involves exploring new uses for medications that are already approved for clinical use. One such medication is edaravone, which is currently used to manage patients with cerebral infarction and amyotrophic lateral sclerosis. Due to its antioxidant and anti-inflammatory properties, edaravone has also been investigated for its potential activities in treating cancer, notably as an anti-proliferative and cytoprotective drug against side effects induced by traditional cancer therapies. This comprehensive review aims to provide updates on the various applications of edaravone in cancer therapy. It explores its potential as a standalone antitumor drug, either used alone or in combination with other medications, as well as its role as an adjuvant to mitigate the side effects of conventional anticancer treatments.

Recent advances in stimuli-responsive tailored nanogels for cancer therapy; from bench to personalized treatment

To improve the quality of health in a personalized manner, better control over pharmacologically relevant cargo formulation, organ-specific targeted delivery, and on-demand release of therapeutic agents is crucial. Significant work has been put into designing and developing revolutionary nanotherapeutics approaches for the effective monitoring and personalized treatment of disease. Nanogel (NG) has attracted significant interest because of its tremendous potential in cancer therapy and its environmental stimuli responsiveness. NG is considered a next-generation delivery technology due to its benefits like as size tunability, high loading, stimuli responsiveness, prolonged drug release via in situ gelling mechanisms, stability, and its potential to provide personalized therapy from the investigation of human genes and the genes in various types of cancers and its association with a selective anticancer drug. Stimuli-responsive NGs can be used as smart nanomedicines to detect and treat cancer and can be tuned as personalized medicine as well. This comprehensive review article's major objectives include the challenges of NGs' clinical translation for cancer treatment as well as its early preclinical successes and prospects.

Single Nucleotide Polymorphisms (SNPs) in the Shadows: Uncovering their Function in Non-Coding Region of Esophageal Cancer

Esophageal cancer is a complex disease influenced by genetic and environmental factors. Single nucleotide polymorphisms (SNPs) in non-coding regions of the genome have emerged as crucial contributors to esophageal cancer susceptibility. This review provides a comprehensive overview of the role of SNPs in non-coding regions and their association with esophageal cancer. The accumulation of SNPs in the genome has been implicated in esophageal cancer risk. Various studies have identified specific locations in the genome where SNPs are more likely to occur, suggesting a location-specific response. Chromatin conformational studies have shed light on the localization of SNPs and their impact on gene transcription, posttranscriptional modifications, gene expression regulation, and histone modification. Furthermore, miRNA-related SNPs have been found to play a significant role in esophageal squamous cell carcinoma (ESCC). These SNPs can affect miRNA binding sites, thereby altering target gene regulation and contributing to ESCC development. Additionally, the risk of ESCC has been linked to base excision repair, suggesting that SNPs in this pathway may influence disease susceptibility. Somatic DNA segment alterations and modified expression quantitative trait loci (eQTL) have also been associated with ESCC. These alterations can lead to disrupted gene expression and cellular processes, ultimately contributing to cancer development and progression. Moreover, SNPs have been found to be associated with the long non-coding RNA HOTAIR, which plays a crucial role in ESCC pathogenesis. This review concludes with a discussion of the current and future perspectives in the field of SNPs in non-coding regions and their relevance to esophageal cancer. Understanding the functional implications of these SNPs may lead to the identification of novel therapeutic targets and the development of personalized approaches for esophageal cancer prevention and treatment.

A Review on Anaplastic Lymphoma Kinase (ALK) Rearrangements and Mutations: Implications for Gastric Carcinogenesis and Target Therapy

Gastric adenocarcinoma is a complex disease with diverse genetic modifications, including Anaplastic Lymphoma Kinase (ALK) gene changes. The ALK gene is located on chromosome 2p23 and encodes a receptor tyrosine kinase that plays a crucial role in embryonic development and cellular differentiation. ALK alterations can result from gene fusion, mutation, amplification, or overexpression in gastric adenocarcinoma. Fusion occurs when the ALK gene fuses with another gene, resulting in a chimeric protein with constitutive kinase activity and promoting oncogenesis. ALK mutations are less common but can also result in the activation of ALK signaling pathways. Targeted therapies for ALK variations in gastric adenocarcinoma have been developed, including ALK inhibitors that have shown promising results in pre-clinical studies. Future studies are needed to elucidate the ALK role in gastric cancer and to identify predictive biomarkers to improve patient selection for targeted therapy. Overall, ALK alterations are a relevant biomarker for gastric adenocarcinoma treatment and targeted therapies for ALK may improve patients' overall survival.

Machine Learning-Assisted Short-Wave InfraRed (SWIR) Techniques for Biomedical Applications: Towards Personalized Medicine

Personalized medicine transforms healthcare by adapting interventions to individuals' unique genetic, molecular, and clinical profiles. To maximize diagnostic and/or therapeutic efficacy, personalized medicine requires advanced imaging devices and sensors for accurate assessment and monitoring of individual patient conditions or responses to therapeutics. In the field of biomedical optics, short-wave infrared (SWIR) techniques offer an array of capabilities that hold promise to significantly enhance diagnostics, imaging, and therapeutic interventions. SWIR techniques provide in vivo information, which was previously inaccessible, by making use of its capacity to penetrate biological tissues with reduced attenuation and enable researchers and clinicians to delve deeper into anatomical structures, physiological processes, and molecular interactions. Combining SWIR techniques with machine learning (ML), which is a powerful tool for analyzing information, holds the potential to provide unprecedented accuracy for disease detection, precision in treatment guidance, and correlations of complex biological features, opening the way for the data-driven personalized medicine field. Despite numerous biomedical demonstrations that utilize cutting-edge SWIR techniques, the clinical potential of this approach has remained significantly underexplored. This paper demonstrates how the synergy between SWIR imaging and ML is reshaping biomedical research and clinical applications. As the paper showcases the growing significance of SWIR imaging techniques that are empowered by ML, it calls for continued collaboration between researchers, engineers, and clinicians to boost the translation of this technology into clinics, ultimately bridging the gap between cutting-edge technology and its potential for personalized medicine.

Simple droplet microfluidics platform for drug screening on cancer spheroids

3D in vitro biological systems are progressively replacing 2D systems to increase the physiological relevance of cellular studies. Microfluidics-based approaches can be powerful tools towards such biomimetic systems, but often require high-end complicated and expensive processes and equipment for microfabrication. Herein, a drug screening platform is proposed, minimizing technicality and manufacturing steps. It provides an alternate way of spheroid generation in droplets in tubes. Droplet microfluidics then elicit multiple droplets merging events at programmable times, to submit sequentially the spheroids to chemotherapy and to reagents for cytotoxicity screening. After a comprehensive study of tumorogenesis within the droplets, the system is validated for drug screening (IC50) with chemotherapies in cancer cell lines as well as cells from a patient-derived-xenografts (PDX). As compared to microtiter plates methods, our system reduces the initial number of cells up to 10 times and opens new avenues towards primary tumors drug screening approaches.

Implications of Agile Values in Software Engineering for Agility in Breast Cancer Treatment: Protocol for a Comparative Study

BACKGROUND

Breast cancer treatment has been described as a dynamic and patient-centered approach that emphasizes adaptability and flexibility throughout the treatment process. Breast cancer is complex, with varying subtypes and stages, making it important to tailor treatment plans to each patient's unique circumstances. Breast cancer treatment delivery relies on a multidisciplinary team of health care professionals who collaborate to provide personalized care and quick adaptation to changing conditions to optimize outcomes while minimizing side effects and maintaining the patient's quality of life. However, agility in breast cancer treatment has not been defined according to common agile values and described in language comprehensible to breast cancer professionals. In the rapidly evolving landscape of breast cancer treatment, the incorporation of agile values from software engineering promises to enhance patient care.

OBJECTIVE

Our objective is to propose agile values for breast cancer treatment adopted and adapted from software engineering. We also aim to validate how these values conform to the concept of agility in the breast cancer context through referencing past work.

METHODS

We applied a structured research methodology to identify and validate 4 agile values for breast cancer treatment. In the elicitation phase, through 2 interviews, we identified 4 agile values and described them in language that resonates with breast cancer treatment professionals. The values were then validated by a domain expert and discussed in the context of supporting work from the literature. Final validation entailed a domain expert conducting a walkthrough of the 4 identified agile values to adjust them as per the reported literature.

RESULTS

Four agile values were identified for breast cancer treatment, and among them, we validated 3 that conformed to the concept of agility. The fourth value, documentation and the quality of documentation, is vital for breast cancer treatment planning and management. This does not conform to agility. However, its nonagility is vital for the agility of the other values. None of the identified agile values were validated as partially conforming to the concept of agility.

CONCLUSIONS

This work makes a novel contribution to knowledge in identifying the first set of agile values in breast cancer treatment through multidisciplinary research. Three of these values were evaluated as conforming to the concept of agility, and although 1 value did not meet the concept of agility, it enhanced the agility of the other values. It is anticipated that these 4 agile values can drive oncology practice, strategies, policies, protocols, and procedures to enhance delivery of care. Moreover, the identified values contribute to identifying quality assurance and control practices to assess the concept of agility in oncology practice and breast cancer treatment and adjust corresponding actions. We conclude that breast cancer treatment agile values are not limited to 4.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR1-10.2196/53124.

Personalized tumor combination therapy optimization using the single-cell transcriptome

BACKGROUND

The precise characterization of individual tumors and immune microenvironments using transcriptome sequencing has provided a great opportunity for successful personalized cancer treatment. However, the cancer treatment response is often characterized by in vitro assays or bulk transcriptomes that neglect the heterogeneity of malignant tumors in vivo and the immune microenvironment, motivating the need to use single-cell transcriptomes for personalized cancer treatment.

METHODS

Here, we present comboSC, a computational proof-of-concept study to explore the feasibility of personalized cancer combination therapy optimization using single-cell transcriptomes. ComboSC provides a workable solution to stratify individual patient samples based on quantitative evaluation of their personalized immune microenvironment with single-cell RNA sequencing and maximize the translational potential of in vitro cellular response to unify the identification of synergistic drug/small molecule combinations or small molecules that can be paired with immune checkpoint inhibitors to boost immunotherapy from a large collection of small molecules and drugs, and finally prioritize them for personalized clinical use based on bipartition graph optimization.

RESULTS

We apply comboSC to publicly available 119 single-cell transcriptome data from a comprehensive set of 119 tumor samples from 15 cancer types and validate the predicted drug combination with literature evidence, mining clinical trial data, perturbation of patient-derived cell line data, and finally in-vivo samples.

CONCLUSIONS

Overall, comboSC provides a feasible and one-stop computational prototype and a proof-of-concept study to predict potential drug combinations for further experimental validation and clinical usage using the single-cell transcriptome, which will facilitate and accelerate personalized tumor treatment by reducing screening time from a large drug combination space and saving valuable treatment time for individual patients. A user-friendly web server of comboSC for both clinical and research users is available at www.combosc.top . The source code is also available on GitHub at https://github.com/bm2-lab/comboSC .

Intratumoral heterogeneity after targeted therapy in murine cancer models with differing degrees of malignancy

INTRODUCTION

Conventional morphologic and volumetric assessment of treatment response is not suitable for adequately assessing responses to targeted cancer therapy. The aim of this study was to evaluate changes in tumor composition after targeted therapy in murine models of breast cancer with differing degrees of malignancy via non-invasive magnetic resonance imaging (MRI).

MATERIALS AND METHODS

Mice bearing highly malignant 4T1 tumors or low malignant 67NR tumors were treated with either a combination of two immune checkpoint inhibitors (ICI, anti-PD1 and anti-CTLA-4) or the multi-tyrosine kinase inhibitor sorafenib, following experiments with macrophage-depleting clodronate-loaded liposomes and vessel-stabilizing angiopoietin-1. Mice were imaged on a 9.4 T small animal MRI system with a multiparametric (mp) protocol, comprising T1 and T2 mapping and diffusion-weighted imaging. Tumors were analyzed ex vivo with histology.

RESULTS AND DISCUSSIONS

All treatments led to an increase in non-viable areas, but therapy-induced intratumoral changes differed between the two tumor models and the different targeted treatments. While ICI treatment led to intratumoral hemorrhage, sorafenib treatment mainly induced intratumoral necrosis. Treated 4T1 tumors showed increasing and extensive areas of necrosis, in comparison to 67NR tumors with only small, but also increasing, necrotic areas. After either of the applied treatments, intratumoral heterogeneity, was increased in both tumor models, and confirmed ex vivo by histology. Apparent diffusion coefficient with subsequent histogram analysis proved to be the most sensitive MRI sequence. In conclusion, mp MRI enables to assess dedicated therapy-related intratumoral changes and may serve as a biomarker for treatment response assessment.

Oncological Horizons: The Synergy of Medical and Surgical Innovations in Cancer Treatment

The landscape of cancer treatment has witnessed a remarkable transformation in recent years, marked by the convergence of medical and surgical innovations. Historically, cancer therapy faced challenges, including limited efficacy and severe side effects. This narrative review explores the historical progression of cancer treatments, shedding light on significant breakthroughs in both medical and surgical oncology. It comprehensively addresses the medical domain, covering chemotherapy, targeted therapies, immunotherapy, hormonal treatments, and radiological procedures. Simultaneously, it delves into the surgical realm, discussing the evolution of surgical techniques, minimally invasive procedures, and the role of surgery across various stages of cancer. The article emphasizes the fusion of medical and surgical approaches, highlighting neoadjuvant and adjuvant therapies and the significance of multidisciplinary tumor boards. It also addresses innovations, challenges, and the pivotal role of patient-centered care. Furthermore, it offers insights into the future directions and forecasts in the constantly evolving field of integrated oncological care. This review provides a comprehensive understanding of the dynamic and transformative nature of cancer treatment, reflecting the unwavering commitment of the medical and surgical communities in the ongoing fight against cancer.

The liquid biopsy series: A cytopathological perspective

In the modern era of personalised medicine, the correct assessment of the genomic landscape for patients affected by cancer with haematological malignancies is crucial. In this scenario, liquid biopsy represents not only an alternative but a complementary source of tumour nucleic acids to tissue in order to guarantee the best clinical management for these patients.

Comprehensive Genomic Profiling in Predictive Testing of Cancer

Despite the rapid progress in the field of personalized medicine and the efforts to apply specific treatment strategies to patients based on the presence of pathogenic variants in one, two, or three genes, patient response to the treatment in terms of positive benefit and overall survival remains heterogeneous. However, advances in sequencing and bioinformatics technologies have facilitated the simultaneous examination of somatic variants in tens to thousands of genes in tumor tissue, enabling the determination of personalized management based on the patient's comprehensive genomic profile (CGP). CGP has the potential to enhance clinical decision-making and personalize innovative treatments for individual patients, by providing oncologists with a more comprehensive molecular characterization of tumors. This study aimed to highlight the utility of CGP in routine clinical practice. Here we present three patient cases with various advanced cancer indicated for CGP analysis using a combination of SOPHiA Solid Tumor Solution (STS, 42 genes) for DNA and SOPHiA RNAtarget Oncology Solution (ROS, 45 genes and 17 gene fusions with any random partners) for RNA. We were able to identify actionable genomic alterations in all three cases, thereby presenting valuable information for future management of these patients. This approach has the potential to transform clinical practice and greatly improve patient outcomes in the field of oncology.

Circulating exosomal miRNAs as a promising diagnostic biomarker in cancer

Cancer belongs to multifactorial diseases characterized by uncontrolled growth and proliferation of abnormal cells. Breast cancer, non-small cell lung cancer, and colorectal cancer are the most frequently diagnosed malignancies with a high mortality rate. These carcinomas typically contain multiple genetically distinct subpopulations of tumor cells leading to tumor heterogeneity, which promotes the aggressiveness of the disease. Early diagnosis is necessary to increase patient progression-free survival. Particularly, miRNAs present in exosomes derived from tumors represent potential biomarkers suitable for early cancer diagnosis. Identification of miRNAs by liquid biopsy enables a personalized approach with the subsequent better clinical management of patients. This review article highlights the potential of circulating exosomal miRNAs in early breast, non-small cell lung, and colorectal cancer diagnosis.

Unveiling the genetic and epigenetic landscape of colorectal cancer: new insights into pathogenic pathways

Colorectal cancer (CRC) is a complex disease characterized by genetic and epigenetic alterations, playing a crucial role in its development and progression. This review aims to provide insights into the emerging landscape of these alterations in CRC pathogenesis to develop effective diagnostic tools and targeted therapies. Genetic alterations in signaling pathways such as Wnt/β-catenin, and PI3K/Akt/mTOR are pivotal in CRC development. Genetic profiling has identified distinct molecular subtypes, enabling personalized treatment strategies. Epigenetic modifications, including DNA methylation and histone modifications, also contribute to CRC pathogenesis by influencing critical cellular processes through gene silencing or activation. Non-coding RNAs have emerged as essential players in epigenetic regulation and CRC progression. Recent research highlights the interplay between genetic and epigenetic alterations in CRC. Genetic mutations can affect epigenetic modifications, leading to dysregulated gene expression and signaling cascades. Conversely, epigenetic changes can modulate genetic expression, amplifying or dampening the effects of genetic alterations. Advancements in understanding pathogenic pathways have potential clinical applications. Identifying genetic and epigenetic markers as diagnostic and prognostic biomarkers promises more accurate risk assessment and early detection. Challenges remain, including validating biomarkers and developing robust therapeutic strategies through extensive research and clinical trials. The dynamic nature of genetic and epigenetic alterations necessitates a comprehensive understanding of their temporal and spatial patterns during CRC progression. In conclusion, the genetic and epigenetic landscape of CRC is increasingly being unraveled, providing valuable insights into its pathogenesis. Integrating genetic and epigenetic knowledge holds great potential for improving diagnostics, prognostics, and personalized therapies in CRC. Continued research efforts are vital to translate these findings into clinical practice, ultimately improving patient outcomes.

European multidisciplinary tumor boards support cross-border networking and increase treatment options for patients with rare gynecological tumors

OBJECTIVE

To evaluate outcomes of European cross-border multidisciplinary tumor boards in terms of participation, adherence to treatment recommendations, and access to novel treatment strategies.

METHODS

The European reference network for rare gynecological tumors (EURACAN G2 domain) aims to improve the diagnosis, management, and treatment of patients with these cancers. Cross-border multidisciplinary tumor boards were initiated to facilitate intercollegiate clinical discussions across Europe and increase patients' access to specialist treatment recommendations and clinical trials. All G2 healthcare providers were invited to participate in monthly multidisciplinary meetings. Patient data were collected using a standardized form and case summaries were distributed before each meeting. After each tumor board, a meeting summary with treatment recommendations was sent to all participants and the project manager at the coordinating center. The multidisciplinary tumor board format and outcomes were regularly discussed at G2 domain meetings. Anonymized clinical data and treatment recommendations were registered in a prospective database. For this report, clinical data were collected between November 2017 and December 2020 and follow-up data retrieved until May 2021.

RESULTS

During the 3-year period, 31 multidisciplinary tumor boards were held with participants from 10 countries and 20 centers. 91 individual patients were discussed between one and six times for a total of 109 case discussions. Follow-up data were retrieved from 64 patients and 80 case discussions. Adherence to treatment recommendations was 99%. Multidisciplinary tumor board recommendations resulted in 11 patients getting access to off-label treatment and one patient being enrolled in a clinical trial in another European country. 14/91 patients were recommended for surveillance only when additional treatment had been considered locally.

CONCLUSION

Cross-border multidisciplinary tumor boards enable networking and clinical collaboration between healthcare professionals in different countries. Surveillance strategies, off-label drug use, and increased participation in clinical trials are possible benefits to patients with rare gynecological tumors.

Using cfDNA and ctDNA as Oncologic Markers: A Path to Clinical Validation

The detection of circulating tumor DNA (ctDNA) in liquid biopsy samples as an oncological marker is being used in clinical trials at every step of clinical management. As ctDNA-based liquid biopsy kits are developed and used in clinics, companies work towards increased convenience, accuracy, and cost over solid biopsies and other oncological markers. The technology used to differentiate ctDNA and cell-free DNA (cfDNA) continues to improve with new tests and methodologies being able to detect down to mutant allele frequencies of 0.001% or 1/100,000 copies. Recognizing this development in technology, the FDA has recently given pre-market approval and breakthrough device designations to multiple companies. The purpose of this review is to look at the utility of measuring total cfDNA, techniques used to differentiate ctDNA from cfDNA, and the utility of different ctDNA-based liquid biopsy kits using relevant articles from PubMed, clinicaltrials.gov, FDA approvals, and company newsletters. Measuring total cfDNA could be a cost-effective, viable prognostic marker, but various factors do not favor it as a monitoring tool during chemotherapy. While there may be a place in the clinic for measuring total cfDNA in the future, the lack of standardization means that it is difficult to move forward with large-scale clinical validation studies currently. While the detection of ctDNA has promising standardized liquid biopsy kits from various companies with large clinical trials ongoing, their applications in screening and minimal residual disease can suffer from lower sensitivity. However, researchers are working towards solutions to these issues with innovations in technology, multi-omics, and sampling. With great promise, further research is needed before liquid biopsies can be recommended for everyday clinical management.