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Simple Summary: Pancreatic cancer (PCa) is a highly aggressive and deadly form of cancer with a low five-year survival rate. This paper explores the role of pharmacologic anthropology in understanding and addressing health disparities related to PCa. Pharmacologic anthropology examines how cultural, social, economic, and behavioral factors affect the use and effectiveness of pharmaceutical treatments. In the context of PCa, it helps us understand why different population groups experience disparities in PCa outcomes. By adopting this interdisciplinary approach, researchers, healthcare providers, and policymakers can better understand the complex dynamics of PCa health disparities. This understanding can lead to culturally sensitive interventions, improved communication between patients and providers, increased community engagement, and policies that enhance access to quality care for all PCa patients. Pancreatic cancer (PCa) remains a formidable global health challenge, with high mortality rates and limited treatment options. While advancements in pharmacology have led to improved outcomes for various cancers, PCa continues to exhibit significant health disparities, disproportionately affecting certain populations. This paper explores the intersection of pharmacology and anthropology in understanding the health disparities associated with PCa. By considering the socio-cultural, economic, and behavioral factors that influence the development, diagnosis, treatment, and outcomes of PCa, pharmacologic anthropology provides a comprehensive framework to address these disparities and improve patient care. [ABSTRACT FROM AUTHOR]

Simple Summary: The current evidence regarding Indigenous* women and breast cancer in Australia shows lower prevalence but higher mortality rates. There are a range of reasons for this, including co-morbidities, lack of access to health services and low health information fluency. Perhaps most importantly, breast cancer health policy and service delivery practice do not meet the needs of Indigenous women in Australia, according to Indigenous women. Talking and listening to Indigenous women about breast cancer highlight that the solutions to improve breast cancer outcomes are available and that they are not complex. Indigenous women must be involved in the improvement of policy and practice in order for these outcomes to improve. *Terminology: We respectfully refer to Aboriginal and Torres Strait Islander people as "Indigenous". In Australia, the incidence rate of breast cancer is lower in Indigenous* women than non-Indigenous women; however, the mortality rate is higher, with Indigenous women 1.2 times more likely to die from the disease. This paper provides practical and achievable solutions to improve health outcomes for Indigenous women with breast cancer in Australia. This research employed the Context–Mechanism–Outcome (CMO) framework to reveal potential mechanisms and contextual factors that influence breast cancer outcomes for Indigenous women, stratified into multiple levels, namely, micro (interpersonal), meso (systemic) and macro (policy) levels. The CMO framework allowed us to interpret evidence regarding Indigenous women and breast cancer and provides nine practical ways to improve health outcomes and survival rates. [ABSTRACT FROM AUTHOR]

Simple Summary: Many anti-cancer drugs are prescribed at a 'one-size fits all' starting dose, without considering individual differences. This is a problem for many cancer patients as this fixed dose might be too much for an individual which could lead to adverse effects and toxicity. Similarly, the fixed-dose might not be enough for some patients which could lead to therapeutic failure. Achieving the right amount of anti-cancer drugs in the body is critical, however, currently, the anti-cancer drug concentrations are not monitored in routine care. Our group used our extensive experience, existing published frameworks and recommendations to establish our own framework to implement a program that enables individualized dosing of anti-cancer drugs in routine care. This paper describes this framework with a simple 6-step process that can be utilised for the implementation of individualised dosing programs for anti-cancer drugs. Precision medicine in oncology involves identifying the 'right drug', at the 'right dose', for the right person. Currently, many orally administered anti-cancer drugs, particularly kinase inhibitors (KIs), are prescribed at a standard fixed dose. Identifying the right dose remains one of the biggest challenges to optimal patient care. Recently the Precision Dosing Group established the Accurate Dosing of Anti-cancer Patient-centred Therapies (ADAPT) Program to address individualised dosing; thus, use existing anti-cancer drugs more safely and efficiently. In this paper, we outline our framework, based on the Medical Research Council (MRC) framework, with a simple 6-step process and strategies which have led to the successful implementation of the ADAPT program in South Australia. Implementation strategies in our 6-step process involve: (1) Evaluate the evidence and identify the cancer drugs: Literature review, shadowing other experts, establishing academic partnerships, adaptability/flexibility; (2) Establishment of analytical equipment for drug assays for clinical purposes: assessment for readiness, accreditation, feasibility, obtaining formal commitments, quality assurance to all stakeholders; (3) Clinical preparation and education: educational material, conducted educational meetings, involve opinion leaders, use of mass media, promote network weaving, conduct ongoing training; (4) Blood collection, sample preparation and analyses: goods received procedures, critical control points (transport time); (5) Interpret and release results with recommendations: facilitate the relay of clinical data to providers; (6) Clinical application: providing ongoing consultation, identify early adopters, identify, and prepare champions. These strategies were selected from the 73 implementation strategies outlined in the Expert Recommendations for Implementing Change (ERIC) study. The ADAPT program currently provides routine plasma concentrations for patients on several orally administered drugs in South Australia and is currently in its evaluation phase soon to be published. Our newly established framework could provide great potential and opportunities to advance individualised dosing of oral anti-cancer drugs in routine clinical care. [ABSTRACT FROM AUTHOR]

Simple Summary: Cancer treatment during pregnancy can raise many difficult questions. Currently available clinical practice guidelines offer very limited ethical guidance for healthcare professionals. This article offers a theoretical framework and a practical ethics checklist for ethical and patient-centred care. It takes a holistic view to patient treatment, care and counselling that emphasises the need to recognise the relational context of individual patient's autonomy; balance maternal and foetal beneficence obligations; balance maternalistic and relational approaches to evidence-based personalised patient care; consider protection of the vulnerable in light of responsibilities towards the unborn; and ensure reasonable and just resource allocation. At the moment, very few studies have explored clinicians' attitudes and patients' experiences when cancer treatment is delivered during pregnancy. Therefore, future work will require patient engagement to develop ethical guidance in this setting. (1) Background: Caring for pregnant cancer patients is clinically and ethically complex. There is no structured ethical guidance for healthcare professionals caring for these patients. (2) Objective: This concept paper proposes a theoretically grounded framework to support ethical and patient-centred care of pregnant cancer patients. (3) Methodological approach: The framework development was based on ethical models applicable to cancer care during pregnancy—namely principle-based approaches (biomedical ethics principles developed by Beauchamp and Childress and the European principles in bioethics and biolaw) and relational, patient-focused approaches (relational ethics, ethics of care and medical maternalism)—and informed by a systematic review of clinical practice guidelines. (4) Results: Five foundational discussion themes, summarising the key ethical considerations that should be taken into account by healthcare professionals while discussing treatment and care options with these patients, were identified. This was further developed into a comprehensive ethics checklist that can be used during clinical appointments and highlights the need for a holistic view to patient treatment, care and counselling while providing ethical, patient-centric care. (5) Conclusion: The proposed framework was further operationalised into an ethics checklist for healthcare professionals that aims to help them anticipate and address ethical concerns that may arise when attending to pregnant cancer patients. Further studies exploring clinicians' attitudes towards cancer treatment in the course of pregnancy and patient experiences when diagnosed with cancer while pregnant and wider stakeholder engagement are needed to inform the development of further ethical, patient-centred guidance. [ABSTRACT FROM AUTHOR]

Simple Summary: Testicular cancer is a prevalent malignancy in young men aged 15 to 35 years. Sonoelastography is an emerging technique for distinguishing between non-neoplasms, benignities, and malignancies by characterizing the tissue stiffness of testes. This review provides a summary of studies on the diagnostic accuracy of sonoelastography for identifying benign and malignant lesions, as well as tumors and non-tumors. The objective of this review was to summarize the applications of sonoelastography in testicular tumor identification and inquire about their test performances. Two authors independently searched English journal articles and full conference papers from CINAHL, Embase, IEEE Xplore®, PubMed, Scopus, and Web of Science from inception and organized them into a PIRO (patient, index test, reference test, outcome) framework. Eleven studies (n = 11) were eligible for data synthesis, nine of which (n = 9) utilized strain elastography and two (n = 2) employed shear-wave elastography. Meta-analyses were performed on the distinction between neoplasm (tumor) and non-neoplasm (non-tumor) from four study arms and between malignancy and benignity from seven study arms. The pooled sensitivity of classifying malignancy and benignity was 86.0% (95%CI, 79.7% to 90.6%). There was substantial heterogeneity in the classification of neoplasm and non-neoplasm and in the specificity of classifying malignancy and benignity, which could not be addressed by the subgroup analysis of sonoelastography techniques. Heterogeneity might be associated with the high risk of bias and applicability concern, including a wide spectrum of testicular pathologies and verification bias in the reference tests. Key technical obstacles in the index test were manual compression in strain elastography, qualitative observation of non-standardized color codes, and locating the Regions of Interest (ROI), in addition to decisions in feature extractions. Future research may focus on multiparametric sonoelastography using deep learning models and ensemble learning. A decision model on the benefits–risks of surgical exploration (reference test) could also be developed to direct the test-and-treat strategy for testicular tumors. [ABSTRACT FROM AUTHOR]

Simple Summary: Adolescents and young adults (AYAs) experiencing cancer have support needs that differ from older adult cancer patients. The aim of our qualitative study was to determine the holistic needs of AYA cancer survivors and to develop a patient-centered conceptual model of AYA survivorship care. Analysis of our interview results reveals eight key domains critical to holistic patient-centered AYA survivorship care. We offer a conceptual model that differs from current conceptual models of AYA survivorship care by centering the patient and their support systems, emphasizing the need for continuing supportive navigation, and the importance of repeated support along the identified domains over time. Purpose: Conceptual models provide frameworks to illustrate relationships among patient-, provider-, system-, and community-level factors that inform care delivery and research. Existing models of cancer survivorship care focus largely on pediatric or adult populations whose needs differ from adolescents and young adults (AYAs). We developed a patient-centered conceptual model of AYA survivorship care. Methods: We conducted a narrative literature review of current conceptual and theoretical models of care. We engaged AYA cancer survivors (n = 25) in semi-structured one-hour telephone interviews. Most participants were in their 20s and 30s, and the majority (84%) were women. Recruitment was stratified by age and time since cancer diagnosis. We conducted a thematic analysis of interview transcripts to identify themes that exemplified patient-centered care. Results: Most participants identified as white and female. Leukemia and breast cancer were the most common cancer types. Main themes included the need for (1) care coordination, (2) ongoing mental health support, (3) connection to AYA peer support, (4) support during fertility preservation efforts, (5) support with financial burden, (6) support for quality of life, (7) information about and support with side effects and late effects, and (8) attention to the unique needs of young adults. Conclusions: We present a patient-centered conceptual model of AYA survivorship care needs that can inform future cancer care delivery and research. [ABSTRACT FROM AUTHOR]

Simple Summary: Sentinel lymph node (SLN) mapping for the surgical staging of endometrial and cervical cancer is commonly performed by laparoscopy, but recently, a new retroperitoneal transvaginal natural orifice transluminal endoscopic surgery approach has been described and developed by Jan Baekelandt. This technique provides easy visualization of lymphatic afferent vessels and pelvic lymph nodes, early SLN assessment, and a coherent mapping methodology following the lymphatic flow from caudal to cranial. However, only a few publications have reported it. Following the IDEAL (Idea Development Exploration Assessment Long-term follow-up) framework, research concerning this technique is in Stage 2a, with only small case series as evidence of its feasibility. Provided that the standardized description of this surgical technique appears necessary to provide the homogeneity required to move further in its investigation, here, we describe a 10-step approach for performing it successfully. This could help other surgeons approach this new technique, and it proposes a common methodology necessary for evolving through future studies. (1) Background: Sentinel lymph node (SLN) mapping represents an accurate and feasible technique for the surgical staging of endometrial and cervical cancer. This is commonly performed by conventional laparoscopy or robotic-assisted laparoscopy, but in recent years, a new retroperitoneal transvaginal natural orifice transluminal endoscopic surgery (vNOTES) approach has been described and developed by Jan Baekelandt. This technique provides easy visualization of lymphatic afferent vessels and pelvic lymph nodes, early SLN assessment, and a coherent mapping methodology following the lymphatic flow from caudal to cranial. However, only a few publications have reported it. Following the IDEAL (Idea Development Exploration Assessment Long-term follow-up) framework, research concerning this technique is in Stage 2a, with only small case series as evidence of its feasibility. Its standardized description appears necessary to provide the surgical homogeneity required to move further. (2) Methods: Description of a standardized approach for retroperitoneal pelvic SLN mapping by vNOTES. (3) Results: We describe a 10-step approach to successfully perform retroperitoneal vNOTES SLN mapping, including pre-, intra-, and postoperative management. (4) Conclusions: This IDEAL Stage 2a study could help other surgeons approach this new technique, and it proposes a common methodology necessary for evolving through future IDEAL Stage 2b (multi-center studies) and Stage 3 (randomized controlled trials) studies. [ABSTRACT FROM AUTHOR]

Simple Summary: Cancer genome analysis often reveals structural variants (SVs) involving fusion genes that are difficult to classify as drivers or passengers. Obtaining accurate AI predictions and explanations, which are crucial for a reliable diagnosis, is challenging. We developed an explainable AI (XAI) system that predicts the pathogenicity of SVs with gene fusions, providing reasons for its predictions. Our XAI achieved high accuracy, comparable to existing tools, and generated plausible explanations based on pathogenic mechanisms. This research represents a promising step towards AI-supported decision making in genomic medicine, enabling efficient and accurate diagnosis. When analyzing cancer sample genomes in clinical practice, many structural variants (SVs), other than single nucleotide variants (SNVs), have been identified. To identify driver variants, the leading candidates must be narrowed down. When fusion genes are involved, selection is particularly difficult, and highly accurate predictions from AI is important. Furthermore, we also wanted to determine how the prediction can make more reliable diagnoses. Here, we developed an explainable AI (XAI) suitable for SVs with gene fusions, based on the XAI technology we previously developed for the prediction of SNV pathogenicity. To cope with gene fusion variants, we added new data to the previous knowledge graph for SVs and we improved the algorithm. Its prediction accuracy was as high as that of existing tools. Moreover, our XAI could explain the reasons for these predictions. We used some variant examples to demonstrate that the reasons are plausible in terms of pathogenic basic mechanisms. These results can be seen as a hopeful step toward the future of genomic medicine, where efficient and correct decisions can be made with the support of AI. [ABSTRACT FROM AUTHOR]

Simple Summary: The immune system can fight foreign invaders by different means, expressed by different classes of immunity. There are two main classes: antibodies and cell-mediated immunity. Studies directed at how distinct classes of immunity are differentially generated and regulated have been ongoing for the better part of a century. Various quantitative variables of immunization, such as antigen doses, are critical, and were first identified more than fifty years ago. It was shown that lower and higher doses, respectively, lead to cell-mediated and antibody responses. The importance of these variables has stood the test of time. We argue here for a framework, the Threshold Hypothesis, that is consistent with these variables and accounts for their central role in determining the class of immunity generated. This framework leads to an understanding of many observations in the fields of tumor immunology and immunity against pathogens uniquely contained by cell-mediated attack. This understanding relies on the generalization that cancers are uniquely susceptible to cell-mediated attack. This confidence in the threshold mechanism led me to propose non-invasive and efficacious strategies to prevent and treat cancer and infectious diseases caused by pathogens uniquely susceptible to cell-mediated immunity. Different frameworks, which are currently employed to understand how immune responses are regulated, can account for different observations reported in the classical literature. I have argued that the predominant frameworks, employed over the last two/three decades to analyze the circumstances that determine whether an immune response is generated or this potential is ablated, and that determine the class of immunity an antigen induces, are inconsistent with diverse classical observations. These observations are "paradoxical" within the context of these frameworks and, consequently, tend to be ignored by most contemporary researchers. One such observation is that low and high doses of diverse types of antigen result, respectively, in cell-mediated and IgG antibody responses. I suggest these paradoxes render these frameworks implausible. An alternative framework, The Threshold Hypothesis, accounts for the paradoxical observations. Some frameworks are judged more plausible when found to be valuable in understanding findings in fields beyond their original compass. I explore here how the Threshold Hypothesis, initially based on studies with chemically well-defined and "simple antigens", most often a purified protein, can nevertheless shed light on diverse classical and more recent observations in the fields of immunity against cancer and against infectious agents, thus revealing common, immune mechanisms. Most cancers and some pathogens are best contained by cell-mediated immunity. The success of the Threshold Hypothesis has encouraged me to employ it as a basis for proposing strategies to prevent and to treat cancer and those infectious diseases caused by pathogens best contained by a cell-mediated attack. [ABSTRACT FROM AUTHOR]

Simple Summary: Liver cancer is a very well-known disease that causes an increased death rate every year. The death rate has actually doubled in recent years. In order to increase the survival rate, it is therefore most important to detect and treat the tumor at an early stage. Microwave liver ablation (MWA) is increasingly used for successful treatment. This procedure offers the possibility of minimally invasive intervention in the treatment, so that a quick recovery time after the treatment can take place. In addition, the treatment of cancer using microwaves is a cost-effective and radiation-free treatment. This work aims to introduce an alternative method for monitoring ablation zones during thermal ablation, rather than relying on computer tomography (CT)/magnetic resonance imaging (MRI). Our results demonstrate that the measurement method provides reproducible outcomes, enabling the determination of the extent of the ablation zone. This study presents a measurement principle for determining the size of the ablation zone in MWA, which could ultimately form an alternative to more expensive monitoring approaches like CT. The measurement method is based on a microwave transmission measurement. A MWA is performed experimentally on ex vivo bovine liver to determine the ablation zone. This setup uses a custom slot applicator performing the MWA at an operating frequency of 2.45 GHz and a custom bowtie antenna measuring the waves transmitted from the applicator. Furthermore, a custom measurement probe is used to determine the dielectric properties. A time-shift analysis is used to determine the radial extent of the ablation zone. Several measurements are carried out with a power of 50 W for 10 min to show the reproducibility. The results show that this method can provide reproducible outcomes to determine the ablation zone with a maximum error of 4.11%. [ABSTRACT FROM AUTHOR]

Simple Summary: Gliomas are known to impact on large-scale networks beyond the tumor location, but it is unknown how the tumor affects controllability and robustness of neural networks. We applied advanced control theory algorithms on connectivity data of structural and functional networks of prognostically differing glioma patients and healthy controls. We determined the driver nodes of the default-mode network (DMN), which are receptive to outside signals, and critical nodes as the most important elements for network controllability. Patients showed decreased network controllability and robustness mainly in the isocitratedehydrogenase (IDH) wildtype group, while additional topological shifts of driver and critical nodes were observed mainly in the prognostically more favourable IDH mutated patients. We hereby suggest a novel approach for elucidating disease evolution in brain cancer, which may aid in defining potential treatment targets under the aspects of network controllability and robustness in glioma patients. Previous studies suggest that the topological properties of structural and functional neural networks in glioma patients are altered beyond the tumor location. These alterations are due to the dynamic interactions with large-scale neural circuits. Understanding and describing these interactions may be an important step towards deciphering glioma disease evolution. In this study, we analyze structural and functional brain networks in terms of determining the correlation between network robustness and topological features regarding the default-mode network (DMN), comparing prognostically differing patient groups to healthy controls. We determine the driver nodes of these networks, which are receptive to outside signals, and the critical nodes as the most important elements for controllability since their removal will dramatically affect network controllability. Our results suggest that network controllability and robustness of the DMN is decreased in glioma patients. We found losses of driver and critical nodes in patients, especially in the prognostically less favorable IDH wildtype (IDHwt) patients, which might reflect lesion-induced network disintegration. On the other hand, topological shifts of driver and critical nodes, and even increases in the number of critical nodes, were observed mainly in IDH mutated (IDHmut) patients, which might relate to varying degrees of network plasticity accompanying the chronic disease course in some of the patients, depending on tumor growth dynamics. We hereby implement a novel approach for further exploring disease evolution in brain cancer under the aspects of neural network controllability and robustness in glioma patients. [ABSTRACT FROM AUTHOR]

Simple Summary: Prostate cancer is the second most deadly cancer in American men. The mainstay of treatment, androgen deprivation therapy, is aimed at modifying the hormonal signals involved in growth of prostate tumors. Recent research has revealed an association between these therapies and increased cardiovascular events like myocardial infarction and stroke. The goal of this review is to provide an improved framework for recognition of the cardiovascular risks of androgen deprivation therapy (ADT) and how to identify men who have an increased risk for a cardiovascular event while on ADT. Achieving this goal means physicians can more easily engage in shared decision making with patients and provide suggestions of cardiac optimization on an individual basis considering each person's baseline risk factors. We also aim to initiate discussion surrounding the impact of racial disparities on the incidence and research of these cardiovascular complications. Androgen deprivation therapy is the cornerstone of prostate cancer therapy. Recent studies have revealed an association between androgen deprivation therapy and cardiovascular adverse effects such as myocardial infarction and stroke. This review summarizes the available research on the cardiovascular risk of men using androgen deprivation therapy. We also discuss racial disparities surrounding both prostate cancer and cardiovascular disease, emphasizing the importance of biological/molecular and socioeconomic factors in assessing baseline risk in patients beginning androgen ablation. Based on the literature, we provide recommendations for monitoring patients who are at high risk for a cardiovascular adverse event while being treated on androgen deprivation therapy. This review aims to present the current research on androgen deprivation therapy and cardiovascular toxicity with an emphasis on racial disparities and provides a framework for clinicians to decrease the cardiovascular morbidity in men that are being treated with hormone therapy. [ABSTRACT FROM AUTHOR]

Simple Summary: Hyperthermia is a thermal cancer treatment that consists of a selective increase in the tumor temperature to a supra-physiological value for 60–90 min. Heating via microwaves using a phased array applicator proved to be a very useful, non-invasive energy carrier. Although hyperthermia is currently employed for many anatomical sites in combination with standard techniques, there are still open challenges that prevent its more widespread use in the clinic. The aim of this article is to review the work carried out in the framework of a national project concerning the introduction of new tools for microwave hyperthermia and to unify these approaches. The proposed methodologies are interconnected and potentially allow an improvement in treatment planning using a single device. The aim of the article is to provide a summary of the work carried out in the framework of a research project funded by the Italian Ministry of Research. The main goal of the activity was to introduce multiple tools for reliable, affordable, and high-performance microwave hyperthermia for cancer therapy. The proposed methodologies and approaches target microwave diagnostics, accurate in vivo electromagnetic parameters estimation, and improvement in treatment planning using a single device. This article provides an overview of the proposed and tested techniques and shows their complementarity and interconnection. To highlight the approach, we also present a novel combination of specific absorption rate optimization via convex programming with a temperature-based refinement method implemented to mitigate the effect of thermal boundary conditions on the final temperature map. To this purpose, numerical tests were carried out for both simple and anatomically detailed 3D scenarios for the head and neck region. These preliminary results show the potential of the combined technique and improvements in the temperature coverage of the tumor target with respect to the case wherein no refinement is adopted. [ABSTRACT FROM AUTHOR]

Simple Summary: Recent AI methods in the automated analysis of histopathological imaging data associated with cancer have trended towards less supervision by humans. Yet, there are circumstances when humans cannot lend a hand to AI. Hence, we present an unsupervised method to learn meaningful features from histopathological imaging data. We applied our method to non-small cell lung cancer subtyping as a classification prototype and breast cancer proliferation scoring as a regression prototype. Our AI method achieves high accuracy and correlation, respectively. Additional experiments aimed at reducing the amount of available data demonstrated that the learned features are robust. Overall, our AI method approaches the analysis of histopathological imaging data in a novel manner, where meaningful features can be learned from without the need for any supervision my humans. The proposed method stands to benefit the field, as it theoretically enables researchers to benefit from completely raw histopathology imaging data. Recent methods in computational pathology have trended towards semi- and weakly-supervised methods requiring only slide-level labels. Yet, even slide-level labels may be absent or irrelevant to the application of interest, such as in clinical trials. Hence, we present a fully unsupervised method to learn meaningful, compact representations of WSIs. Our method initially trains a tile-wise encoder using SimCLR, from which subsets of tile-wise embeddings are extracted and fused via an attention-based multiple-instance learning framework to yield slide-level representations. The resulting set of intra-slide-level and inter-slide-level embeddings are attracted and repelled via contrastive loss, respectively. This resulted in slide-level representations with self-supervision. We applied our method to two tasks— (1) non-small cell lung cancer subtyping (NSCLC) as a classification prototype and (2) breast cancer proliferation scoring (TUPAC16) as a regression prototype—and achieved an AUC of 0.8641 ± 0.0115 and correlation (R2) of 0.5740 ± 0.0970, respectively. Ablation experiments demonstrate that the resulting unsupervised slide-level feature space can be fine-tuned with small datasets for both tasks. Overall, our method approaches computational pathology in a novel manner, where meaningful features can be learned from whole-slide images without the need for annotations of slide-level labels. The proposed method stands to benefit computational pathology, as it theoretically enables researchers to benefit from completely unlabeled whole-slide images. [ABSTRACT FROM AUTHOR]

Simple Summary: TCIRG1, also known as V-ATPase-a3, is critical for cellular metabolism, membrane transport, and intracellular signaling through its dependent acidification. In earlier research, TCIRG1 was found to be dysregulated in several cancers and to accelerate the growth of various malignancies. The molecular mechanisms behind TCIRG1 and its possible role in the development of clear cell renal cell carcinoma are still poorly understood. Our research is the first to thoroughly examine TCIRG1's function in clear cell renal cell carcinoma prognosis, immunity, and treatment. The validity that TCIRG1 can accelerate the development of renal clear cell carcinoma was also confirmed in this work by using certain testable experiments. This establishes the theoretical framework for our future investigation into the occurrence and progression of clear cell renal cell carcinoma. Background: TCIRG1, also known as V-ATPase-a3, is critical for cellular life activities through its dependent acidification. Prior to the present research, its relationship with prognostic and tumor immunity in clear cell renal cell carcinoma (ccRCC) had not yet been investigated. Methods: We assessed TCIRG1 expression in normal and tumor tissues using data from TCGA, GEO, GTEX, and IHC. We also analyzed the relationship between TCIRG1 and somatic mutations, TMB, DNA methylation, cancer stemness, and immune infiltration. We evaluated the relevance of TCIRG1 to immunotherapy and potential drugs. Finally, we explored the effect of TCIRG1 knockdown on tumor cells. Results: TCIRG1 was overexpressed in tumor tissue and predicted a significantly unfavorable clinical outcome. High TCIRG1 expression may be associated with fewer PBRM1 and more BAP1 mutations and may reduce DNA methylation, thus leading to a poor prognosis. TCIRG1 was strongly associated with CD8+ T-cell, Treg, and CD4+ T-cell infiltration. Moreover, TCIRG1 was positively correlated with TIDE scores and many drug sensitivities. Finally, experiments showed that the knockdown of TCIRG1 inhibited the migration of ccRCC cells. Conclusions: TCIRG1 may have great potential in identifying prognostic and immunomodulatory mechanisms in tumor patients and may provide a new therapeutic strategy for ccRCC. [ABSTRACT FROM AUTHOR]

Simple Summary: The individual's ability to conceive and regulate the broad spectrum of their human emotions is closely linked to their mental health. The implications of a serious disease such as cancer represent an extraordinary burden to these internal coping mechanisms, especially in the case of young patients. Regarding their well-being and support, it is therefore of particular interest for caregivers to be able to follow the dynamics of the patient's emotional world and perceptions. Technical progress enables new possibilities for data collection through tools for digital patient self-reports while simultaneously creating new challenges. Within the scope of this article, we provide an overview of the literature on this topic, outlining the current strengths and weaknesses and possible perspectives on digital aids, especially in terms of capturing the flexibility, fluctuations and early detection of symptom changes. Emotion dysregulation is regarded as a driving mechanism for the development of mental health problems and psychopathology. The role of emotion regulation (ER) in the management of cancer distress and quality of life (QoL) has recently been recognized in psycho-oncology. The latest technological advances afford ways to assess ER, affective experiences and QoL in child, adolescent and young adult (CAYA) cancer patients through electronic patient-reported outcomes (ePRO) in their daily environment in real-time. Such tools facilitate ways to study the dynamics of affect and the flexibility of ER. However, technological advancement is not risk-free. We critically review the literature on ePRO in cancer existing models of ER in pediatric psycho-oncology and analyze strength, weaknesses, opportunities and threats of ePRO with a focus on CAYA cancer research and care. Supported by personal study-based experiences, this narrative review serves as a foundation to propose a novel methodological and metatheoretical framework based on: (a) an extended notion of ER, which includes its dynamic, adaptive and flexible nature and focuses on processes and conditions rather than fixed categorical strategies; (b) ePRO as a means to measure emotion regulation flexibility and affect dynamics; (c) identifying early warning signals for symptom change via ePRO and building forecasting models using dynamical systems theory. [ABSTRACT FROM AUTHOR]

Simple Summary: Early prediction of significant alterations of head and neck (HN) cancer volume due to radiation therapy (RT) could provide an indication for necessary planning adaptations of the RT dose, tumor and organs at risk anatomy. However, the irregularities of the underlying cancer tissue and the patient-specific responses to RT render the prognostics for the tumor's behavior exceedingly complex. In this study, a data-driven machine learning approach is proposed that incorporates the radiomic features of the low-dosage cone beam CT (CBCT) images clinically used in image-guided radiotherapy treatments in a feature selection and classification framework. The proposed model achieved high prediction performance, while able to identify indicative image characteristics for early prediction, further investigated in terms of their implications in HN cancer treated with RT. Background: During RT cycles, the tumor response pattern could affect tumor coverage and may lead to organs at risk of overdose. As such, early prediction of significant volumetric changes could therefore reduce potential radiation-related adverse effects. Nevertheless, effective machine learning approaches based on the radiomic features of the clinically used CBCT images to determine the tumor volume variations due to RT not having been implemented so far. Methods: CBCT images from 40 HN cancer patients were collected weekly during RT treatment. From the obtained images, the Clinical Target Volume (CTV) and Parotid Glands (PG) regions of interest were utilized to calculate 104 delta-radiomics features. These features were fed on a feature selection and classification procedure for the early prediction of significant volumetric alterations. Results: The proposed framework was able to achieve 0.90 classification performance accuracy while detecting a small subset of discriminative characteristics from the 1st week of RT. The selected features were further analyzed regarding their effects on temporal changes in anatomy and tumor response modeling. Conclusion: The use of machine learning algorithms offers promising perspectives for fast and reliable early prediction of large volumetric deviations as a result of RT treatment, exploiting hidden patterns in the overall anatomical characteristics. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast cancer is one of the most common cancers worldwide and the leading cause of cancer death in women. Screening, early diagnosis, and surgical techniques might increase patients' survival, leading to a rise in the health-related consequences of anti-neoplastic therapies, such as cardiotoxicity following anthracycline treatments. Alongside conventional therapies, physical activity seems to reduce treatment side effects, improving quality of life either after a breast cancer diagnosis or in the early steps post-surgery. This review offers a general framework for the role of anthracycline in the physio-pathological mechanisms of cardiotoxicity and the effect of exercise on cancer treatment side effects. Moreover, we propose the type and the timing of exercise to better assist patients and reduce the pressure on the health care system in breast cancer patients undergoing anthracycline. The increase in breast cancer (BC) survival has determined a growing survivor population that seems to develop several comorbidities and, specifically, treatment-induced cardiovascular disease (CVD), especially those patients treated with anthracyclines. Indeed, it is known that these compounds act through the induction of supraphysiological production of reactive oxygen species (ROS), which appear to be central mediators of numerous direct and indirect cardiac adverse consequences. Evidence suggests that physical exercise (PE) practised before, during or after BC treatments could represent a viable non-pharmacological strategy as it increases heart tolerance against many cardiotoxic agents, and therefore improves several functional, subclinical, and clinical parameters. At molecular level, the cardioprotective effects are mainly associated with an exercise-induced increase of stress response proteins (HSP60 and HSP70) and antioxidant (SOD activity, GSH), as well as a decrease in lipid peroxidation, and pro-apoptotic proteins such as Bax, Bax-to-Bcl-2 ratio. Moreover, this protection can potentially be explained by a preservation of myosin heavy chain (MHC) isoform distribution. Despite this knowledge, it is not clear which type of exercise should be suggested in BC patient undergoing anthracycline treatment. This highlights the lack of special guidelines on how affected patients should be managed more efficiently. This review offers a general framework for the role of anthracyclines in the physio-pathological mechanisms of cardiotoxicity and the potential protective role of PE. Finally, potential exercise-based strategies are discussed on the basis of scientific findings. [ABSTRACT FROM AUTHOR]

Simple Summary: Collaboration between oncologists and geriatricians has been shown to improve the quality of elderly cancer patient care. However, previous research has revealed how interpersonal factors might hinder this interdisciplinary work. This study aims to assess sprocessual and contextual determinants of the collaboration between these two disciplines, including shared time and routines, medical decision criteria and perceptions of age and needs of elderly patients. These aspects are important to develop a more efficient patient-centered approach in oncogeriatric care and improve collaboration between the different disciplines involved. Therapeutic challenges regarding the population of elderly cancer patients and their heterogeneity lead to the need to implement person-centered approaches in order to optimize care strategies and adapt oncology treatments to each pattern of aging. The International Society of Geriatric Oncology recommends a multidisciplinary evaluation of these patients and the use of screening tools prior to the initiation of treatments. However, previous research shows a poor implementation of these recommendations in geriatric oncology. Although some studies have identified how different perceptions of geriatric oncology might hinder routine teamwork, little is known about the impact of other factors on promoting the collaboration between the two specialties. This mixed-method exploratory study used an online questionnaire to assess the perception of a group of 22 geriatricians and oncology physicians on different determinants of oncology care and teamwork. In this sample, older oncology patients benefited from geriatric care. However, there was a variability regarding age criteria and a limited use of screening tools. The multidimensional framework for interprofessional teamwork by Reeves has been used to analyze some of the determinants of the collaboration between oncology physicians and geriatricians. This study has identified systematic issues to consider when promoting communication and common values between the two disciplines, including available resources in terms of shared time, space and routine actions. [ABSTRACT FROM AUTHOR]

Simple Summary: Although ipilimumab has been approved for the treatment of many types of cancer, most prostate cancer patients seem to not respond well to the therapy. Here, we mathematically investigate the clinical relevance of ipilimumab, as mono-therapy and in combination with the dendritic cell vaccine sipuleucel-T, for the treatment of castration-resistant prostate cancer patients. The employed optimization problem, which incorporates a function of toxicity depending on the drug concentration, establishes precise protocols of administration of ipilimumab to control or eradicate prostate tumor, and defines how changing of key parameters affects the outcome. Overall, this mathematical study can help in optimizing the clinical use of ipilimumab for the effective treatment of castration-resistant prostate cancer patients. Prostate cancer (PCa) is one of the most frequent cancer in male population. Androgen deprivation therapy is the first-line strategy for the metastatic stage of the disease, but, inevitably, PCa develops resistance to castration (CRPC), becoming incurable. In recent years, clinical trials are testing the efficacy of anti-CTLA4 on CRPC. However, this tumor seems to be resistant to immunotherapies that are very effective in other types of cancers, and, so far, only the dendritic cell vaccine sipuleucel-T has been approved. In this work, we employ a mathematical model of CRPC to determine the optimal administration protocol of ipilimumab, a particular anti-CTLA4, as single treatment or in combination with the sipuleucel-T, by considering both the effect on tumor population and the drug toxicity. To this end, we first introduce a dose-depending function of toxicity, estimated from experimental data, then we define two different optimization problems. We show the results obtained by imposing different constraints, and how these change by varying drug efficacy. Our results suggest administration of high-doses for a brief period, which is predicted to be more efficient than solutions with prolonged low-doses. The model also highlights a synergy between ipilimumab and sipuleucel-T, which leads to a better tumor control with lower doses of ipilimumab. Finally, tumor eradication is also conceivable, but it depends on patient-specific parameters. [ABSTRACT FROM AUTHOR]

Simple Summary: OmiEmbed is a unified multi-task deep learning framework for multi-omics data, supporting dimensionality reduction, multi-omics integration, tumour type classification, phenotypic feature reconstruction and survival prediction. The framework is comprised of deep embedding and downstream task modules to capture biomedical information from high-dimensional omics data. OmiEmbed outperformed state-of-the-art methods on all three types of downstream tasks: classification, regression and survival prediction. Better performance was achieved using the multi-task training strategy compared to training each downstream task individually. With the multi-task strategy, OmiEmbed learnt a comprehensive omics embedding containing information from multiple tasks. OmiEmbed is open source, well-organised and convenient to be extended to other customised input data, network structures and downstream tasks, which has promising potential to facilitate more accurate and personalised clinical decision making. High-dimensional omics data contain intrinsic biomedical information that is crucial for personalised medicine. Nevertheless, it is challenging to capture them from the genome-wide data, due to the large number of molecular features and small number of available samples, which is also called "the curse of dimensionality" in machine learning. To tackle this problem and pave the way for machine learning-aided precision medicine, we proposed a unified multi-task deep learning framework named OmiEmbed to capture biomedical information from high-dimensional omics data with the deep embedding and downstream task modules. The deep embedding module learnt an omics embedding that mapped multiple omics data types into a latent space with lower dimensionality. Based on the new representation of multi-omics data, different downstream task modules were trained simultaneously and efficiently with the multi-task strategy to predict the comprehensive phenotype profile of each sample. OmiEmbed supports multiple tasks for omics data including dimensionality reduction, tumour type classification, multi-omics integration, demographic and clinical feature reconstruction, and survival prediction. The framework outperformed other methods on all three types of downstream tasks and achieved better performance with the multi-task strategy compared to training them individually. OmiEmbed is a powerful and unified framework that can be widely adapted to various applications of high-dimensional omics data and has great potential to facilitate more accurate and personalised clinical decision making. [ABSTRACT FROM AUTHOR]

Simple Summary: Handcrafted radiomic features (HRFs) are quantitative features extracted from medical images, and they are mined for associations with different clinical endpoints. While many studies reported on the potential of HRFs to unravel clinical endpoints, the sensitivity of HRFs to variations in scanning parameters is affecting the inclusion of radiomic signatures in clinical decision-making. In this study, we investigated the effects of variations in the in-plane resolution of scans, while all other scanning parameters were fixed. Moreover, we investigated the effects of ten different image resampling methods and ComBat harmonization on the reproducibility of HRFs. Our results show that the majority of HRFs are significantly and variably affected by the differences in in-plane resolution. The majority of image resampling methods resulted in a higher number of reproducible HRFs compared to ComBat harmonization. Our developed framework guides identifying the reproducible and harmonizable HRFs in different scenarios. While handcrafted radiomic features (HRFs) have shown promise in the field of personalized medicine, many hurdles hinder its incorporation into clinical practice, including but not limited to their sensitivity to differences in acquisition and reconstruction parameters. In this study, we evaluated the effects of differences in in-plane spatial resolution (IPR) on HRFs, using a phantom dataset (n = 14) acquired on two scanner models. Furthermore, we assessed the effects of interpolation methods (IMs), the choice of a new unified in-plane resolution (NUIR), and ComBat harmonization on the reproducibility of HRFs. The reproducibility of HRFs was significantly affected by variations in IPR, with pairwise concordant HRFs, as measured by the concordance correlation coefficient (CCC), ranging from 42% to 95%. The number of concordant HRFs (CCC > 0.9) after resampling varied depending on (i) the scanner model, (ii) the IM, and (iii) the NUIR. The number of concordant HRFs after ComBat harmonization depended on the variations between the batches harmonized. The majority of IMs resulted in a higher number of concordant HRFs compared to ComBat harmonization, and the combination of IMs and ComBat harmonization did not yield a significant benefit. Our developed framework can be used to assess the reproducibility and harmonizability of RFs. [ABSTRACT FROM AUTHOR]

Simple Summary: We present an open-source AI framework for marking, training, and automated recognition of pathological features in whole-slide scans of diagnostic tissue sections. The integrated system permits high-resolution qualitative as well as quantitative morphological analyses of entire histological slides and harbors significant potential to facilitate the microscopic analysis of complex pathomorphological problems and the simultaneous mapping of immunohistochemical markers in routine slide diagnostics. We have developed a platform, termed PathoFusion, which is an integrated system for marking, training, and recognition of pathological features in whole-slide tissue sections. The platform uses a bifocal convolutional neural network (BCNN) which is designed to simultaneously capture both index and contextual feature information from shorter and longer image tiles, respectively. This is analogous to how a microscopist in pathology works, identifying a cancerous morphological feature in the tissue context using first a narrow and then a wider focus, hence bifocal. Adjacent tissue sections obtained from glioblastoma cases were processed for hematoxylin and eosin (H&E) and immunohistochemical (CD276) staining. Image tiles cropped from the digitized images based on markings made by a consultant neuropathologist were used to train the BCNN. PathoFusion demonstrated its ability to recognize malignant neuropathological features autonomously and map immunohistochemical data simultaneously. Our experiments show that PathoFusion achieved areas under the curve (AUCs) of 0.985 ± 0.011 and 0.988 ± 0.001 in patch-level recognition of six typical pathomorphological features and detection of associated immunoreactivity, respectively. On this basis, the system further correlated CD276 immunoreactivity to abnormal tumor vasculature. Corresponding feature distributions and overlaps were visualized by heatmaps, permitting high-resolution qualitative as well as quantitative morphological analyses for entire histological slides. Recognition of more user-defined pathomorphological features can be added to the system and included in future tissue analyses. Integration of PathoFusion with the day-to-day service workflow of a (neuro)pathology department is a goal. The software code for PathoFusion is made publicly available. [ABSTRACT FROM AUTHOR]

Simple Summary: Malignant pleura Mesothelioma (MPM) is an aggressive cancer arising from the mesothelial cells of the pleura. About 80% of mesothelioma cases are linked to asbestos exposure; the remainder may be related to prior chest radiation, genetic predisposition or spontaneous occurrence. Understanding the genetic alterations that drive MPM is critical for successful development of diagnostics, prognostics and personalized therapeutic modalities. Because MPM is rare, genomic studies are limited and have typically involved a small number of samples. The aim of our study was to understand the mutational landscape of MPM. Our analysis identified significantly mutated genes. This study on a rare tumor type will be important for patients "in real life". Purpose: Malignant pleural mesothelioma (MPM) is an aggressive cancer. Data are not available in prospective trials on correlations between genetic alterations and outcomes of therapies. In this study, we assessed the genetic profile of MPM patients (pts) in tissue samples. Patients and Methods: From December 2016 to July 2018 (end of enrolment), 164 pts were enrolled. We evaluated by targeted sequencing the mutational profile of a panel of 34 genes: ACTB, ACTG1, ACTG2, ACTR1A, BAP1, CDH8, CDK4, CDKN2A, CDKN2B, COL3A1, COL5A2, CUL1, DHFR, GOT1, KDR, KIT, MXRA5, NF2, NFRKB, NKX6-2, NOD2, PCBD2, PDZK1IP1, PIK3CA, PIK3CB, PSMD13, RAPGEF6, RDX, SETDB1, TAOK1, TP53, TXNRD1, UQCRC1, XRCC6. Genetic profiling was correlated with clinical and pathological variables. Results: Overall, 110 pts (67%) from both treatment arms had samples available for molecular analysis. Median age was 63 years (45–81), 25.5% (n = 28) were females, and 74.5% (n = 82) were males. Tumor histotype was 81.8% (n = 90) epithelioid and 18.2% (n = 20) non-epithelioid; 28.5% of the tumors (n = 42) were stage IV, 71.5% (n = 68) were stage III. Targeted sequencing of tissue specimens identified 275 functional somatic mutations in the 34 genes analyzed. The number of mutated genes was positively associated with higher stage and metastatic disease (p = 0.025). RDX (42%), MXRA5 (23%), BAP1 (14%), and NF2 (11%) were the most frequently mutated genes. Mutations in RAPGEF6 (p = 0.03) and ACTG1 (p = 0.02) were associated with the non-epithelioid subtype, and mutations in BAP1 (p = 0.04) were related to progression-free survival (PFS) > 6 months. Conclusions: In the Ramucirumab Mesothelioma clinical trial (RAMES), mutation of the gene BAP1 is related to a prolonged PFS for patients treated with platinum/pemetrexed regimens (p = 0.04). [ABSTRACT FROM AUTHOR]

Simple Summary: Tumours are heterogeneous that reflect variable patient prognosis and treatment responses (phenotypes). Since these variable phenotypes are outcomes of genomics, it is essential to integrate genome and phenome jointly. In this study, we report the development and application of a new machine learning tool (Phenotypic Mapping; PhenMap) to identify unsupervised clinically-relevant (functional) subtypes and biomarkers by simultaneously integrating clinical phenotypes and single omics data, mainly, transcriptome. This integrative analysis provides the opportunity to simultaneously identify robust and context-specific subtypes and associated phenotypes (that biologically explain each subtype) without statistically losing information. We demonstrate its utility using breast cancer cell lines and patient samples to identify functional subtypes associated with specific drug responses (including CD4/6 inhibitor) and prognosis. These subtypes may potentially predict clinical outcomes with further validation. This tool can be applied to other omics data such as methylomics, genomics and radiomics along with any phenotypic data. One of the major challenges in defining clinically-relevant and less heterogeneous tumor subtypes is assigning biological and/or clinical interpretations to etiological (intrinsic) subtypes. Conventional clustering/subtyping approaches often fail to define such subtypes, as they involve several discrete steps. Here we demonstrate a unique machine-learning method, phenotype mapping (PhenMap), which jointly integrates single omics data with phenotypic information using three published breast cancer datasets (n = 2045). The PhenMap framework uses a modified factor analysis method that is governed by a key assumption that, features from different omics data types are correlated due to specific "hidden/mapping" variables (context-specific mapping variables (CMV)). These variables can be simultaneously modeled with phenotypic data as covariates to yield functional subtypes and their associated features (e.g., genes) and phenotypes. In one example, we demonstrate the identification and validation of six novel "functional" (discrete) subtypes with differential responses to a cyclin-dependent kinase (CDK)4/6 inhibitor and etoposide by jointly integrating transcriptome profiles with four different drug response data from 37 breast cancer cell lines. These robust subtypes are also present in patient breast tumors with different prognosis. In another example, we modeled patient gene expression profiles and clinical covariates together to identify continuous subtypes with clinical/biological implications. Overall, this genome-phenome machine-learning integration tool, PhenMap identifies functional and phenotype-integrated discrete or continuous subtypes with clinical translational potential. [ABSTRACT FROM AUTHOR]