Clinical trial design in the era of precision medicine

Navigating the Immune Maze: Pioneering Strategies for Unshackling Cancer Immunotherapy Resistance

Cancer immunotherapy has ushered in a transformative era in oncology, offering unprecedented promise and opportunities. Despite its remarkable breakthroughs, the field continues to grapple with the persistent challenge of treatment resistance. This resistance not only undermines the widespread efficacy of these pioneering treatments, but also underscores the pressing need for further research. Our exploration into the intricate realm of cancer immunotherapy resistance reveals various mechanisms at play, from primary and secondary resistance to the significant impact of genetic and epigenetic factors, as well as the crucial role of the tumor microenvironment (TME). Furthermore, we stress the importance of devising innovative strategies to counteract this resistance, such as employing combination therapies, tailoring immune checkpoints, and implementing real-time monitoring. By championing these state-of-the-art methods, we anticipate a paradigm that blends personalized healthcare with improved treatment options and is firmly committed to patient welfare. Through a comprehensive and multifaceted approach, we strive to tackle the challenges of resistance, aspiring to elevate cancer immunotherapy as a beacon of hope for patients around the world.

BMC3PM: bioinformatics multidrug combination protocol for personalized precision medicine and its application in cancer treatment

BACKGROUND

In recent years, drug screening has been one of the most significant challenges in the field of personalized medicine, particularly in cancer treatment. However, several new platforms have been introduced to address this issue, providing reliable solutions for personalized drug validation and safety testing. In this study, we developed a personalized drug combination protocol as the primary input to such platforms.

METHODS

To achieve this, we utilized data from whole-genome expression profiles of 6173 breast cancer patients, 312 healthy individuals, and 691 drugs. Our approach involved developing an individual pattern of perturbed gene expression (IPPGE) for each patient, which was used as the basis for drug selection. An algorithm was designed to extract personalized drug combinations by comparing the IPPGE and drug signatures. Additionally, we employed the concept of drug repurposing, searching for new benefits of existing drugs that may regulate the desired genes.

RESULTS

Our study revealed that drug combinations obtained from both specialized and non-specialized cancer medicines were more effective than those extracted from only specialized medicines. Furthermore, we observed that the individual pattern of perturbed gene expression (IPPGE) was unique to each patient, akin to a fingerprint.

CONCLUSIONS

The personalized drug combination protocol developed in this study offers a methodological interface between drug repurposing and combination drug therapy in cancer treatment. This protocol enables personalized drug combinations to be extracted from hundreds of drugs and thousands of drug combinations, potentially offering more effective treatment options for cancer patients.

Multifactor assessment of ovarian cancer reveals immunologically interpretable molecular subtypes with distinct prognoses

Background

Ovarian cancer (OC) is a highly heterogeneous and malignant gynecological cancer, thereby leading to poor clinical outcomes. The study aims to identify and characterize clinically relevant subtypes in OC and develop a diagnostic model that can precisely stratify OC patients, providing more diagnostic clues for OC patients to access focused therapeutic and preventative strategies.

Methods

Gene expression datasets of OC were retrieved from TCGA and GEO databases. To evaluate immune cell infiltration, the ESTIMATE algorithm was applied. A univariate Cox analysis and the two-sided log-rank test were used to screen OC risk factors. We adopted the ConsensusClusterPlus algorithm to determine OC subtypes. Enrichment analysis based on KEGG and GO was performed to determine enriched pathways of signature genes for each subtype. The machine learning algorithm, support vector machine (SVM) was used to select the feature gene and develop a diagnostic model. A ROC curve was depicted to evaluate the model performance.

Results

A total of 1,273 survival-related genes (SRGs) were firstly determined and used to clarify OC samples into different subtypes based on their different molecular pattern. SRGs were successfully stratified in OC patients into three robust subtypes, designated S-I (Immunoreactive and DNA Damage repair), S-II (Mixed), and S-III (Proliferative and Invasive). S-I had more favorable OS and DFS, whereas S-III had the worst prognosis and was enriched with OC patients at advanced stages. Meanwhile, comprehensive functional analysis highlighted differences in biological pathways: genes associated with immune function and DNA damage repair including CXCL9, CXCL10, CXCL11, APEX, APEX2, and RBX1 were enriched in S-I; S-II combined multiple gene signatures including genes associated with metabolism and transcription; and the gene signature of S-III was extensively involved in pathways reflecting malignancies, including many core kinases and transcription factors involved in cancer such as CDK6, ERBB2, JAK1, DAPK1, FOXO1, and RXRA. The SVM model showed superior diagnostic performance with AUC values of 0.922 and 0.901, respectively. Furthermore, a new dataset of the independent cohort could be automatically analyzed by this innovative pipeline and yield similar results.

Conclusion

This study exploited an innovative approach to construct previously unexplored robust subtypes significantly related to different clinical and molecular features for OC and a diagnostic model using SVM to aid in clinical diagnosis and treatment. This investigation also illustrated the importance of targeting innate immune suppression together with DNA damage in OC, offering novel insights for further experimental exploration and clinical trial.

Artificial intelligence revolutionizing drug development: Exploring opportunities and challenges

By harnessing artificial intelligence (AI) algorithms and machine learning techniques, the entire drug discovery process stands to undergo a profound transformation, offering a myriad of advantages. Foremost among these is the ability of AI to conduct swift and efficient screenings of expansive compound libraries, significantly augmenting the identification of potential drug candidates. Moreover, AI algorithms can prove instrumental in predicting the efficacy and safety profiles of candidate compounds, thus endowing invaluable insights and reducing reliance on extensive preclinical and clinical testing. This predictive capacity of AI has the potential to streamline the drug development pipeline and enhance the success rate of clinical trials, ultimately resulting in the emergence of more efficacious and safer therapeutic agents. However, the deployment of AI in drug discovery introduces certain challenges that warrant attention. A primary hurdle entails the imperative acquisition of high-quality and diverse data. Furthermore, ensuring the interpretability of AI models assumes critical importance in securing regulatory endorsement and cultivating trust within scientific and medical communities. Addressing ethical considerations, including data privacy and mitigating bias, represents an additional momentous challenge, requiring assiduous navigation. In this review, we provide an intricate and comprehensive overview of the multifaceted challenges intrinsic to conventional drug development paradigms, while simultaneously interrogating the efficacy of AI in effectively surmounting these formidable obstacles.

An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized clinical trials

Randomized clinical trials (RCT) represent the cornerstone of evidence-based medicine but are resource-intensive. We propose and evaluate a machine learning (ML) strategy of adaptive predictive enrichment through computational trial phenomaps to optimize RCT enrollment. In simulated group sequential analyses of two large cardiovascular outcomes RCTs of (1) a therapeutic drug (pioglitazone versus placebo; Insulin Resistance Intervention after Stroke (IRIS) trial), and (2) a disease management strategy (intensive versus standard systolic blood pressure reduction in the Systolic Blood Pressure Intervention Trial (SPRINT)), we constructed dynamic phenotypic representations to infer response profiles during interim analyses and examined their association with study outcomes. Across three interim timepoints, our strategy learned dynamic phenotypic signatures predictive of individualized cardiovascular benefit. By conditioning a prospective candidate's probability of enrollment on their predicted benefit, we estimate that our approach would have enabled a reduction in the final trial size across ten simulations (IRIS: -14.8% ± 3.1%, pone-sample t-test = 0.001; SPRINT: -17.6% ± 3.6%, pone-sample t-test < 0.001), while preserving the original average treatment effect (IRIS: hazard ratio of 0.73 ± 0.01 for pioglitazone vs placebo, vs 0.76 in the original trial; SPRINT: hazard ratio of 0.72 ± 0.01 for intensive vs standard systolic blood pressure, vs 0.75 in the original trial; all simulations with Cox regression-derived p value of  < 0.01 for the effect of the intervention on the respective primary outcome). This adaptive framework has the potential to maximize RCT enrollment efficiency.

Protein-based nutritional strategies to manage the development of diabetes: evidence and challenges in human studies

Type 2 diabetes mellitus (T2DM) is one of the most prevalent diseases in modern society, governed by both genetic and environmental factors, such as nutritional habits. This metabolic disorder is characterized by insulin resistance, which is related to high blood glucose levels, implying negative health effects in humans, hindering the healthy ageing of people. The relationship between food and health is clear, and the ingestion of specific nutrients modulates some physiological processes, potentially implying biologically relevant changes, which can translate into a health benefit. This review aims to summarize human studies published in which the purpose was to investigate the effect of protein ingestion (in native state or as hydrolysates) on human metabolism. Overall, several studies showed how protein ingestion might induce a decrease of glucose concentration in the postprandial state (area under the curve), although it is highly dependent on the source and the dose. Other studies showed no biological effects upon protein consumption, mostly with fish-derived products. In addition, the major challenges and perspectives in this research field are highlighted, suggesting the future directions, towards which scientists should focus on. The dietary intake of proteins has been proven to likely exert a beneficial effect on diabetes-related parameters, which can have a biological relevance in the prevention and pre-treatment of diabetes. However, the number of well-designed human studies carried out to date to demonstrate the effects of specific proteins or protein hydrolysates in vivo is still scarce.

A Strategy Utilizing Protein-Protein Interaction Hubs for the Treatment of Cancer Diseases

We describe a strategy for the development of a rational approach of neoplastic disease therapy based on the demonstration that scale-free networks are susceptible to specific attacks directed against its connective hubs. This strategy involves the (i) selection of up-regulated hubs of connectivity in the tumors interactome, (ii) drug repurposing of these hubs, (iii) RNA silencing of non-druggable hubs, (iv) in vitro hub validation, (v) tumor-on-a-chip, (vi) in vivo validation, and (vii) clinical trial. Hubs are protein targets that are assessed as targets for rational therapy of cancer in the context of personalized oncology. We confirmed the existence of a negative correlation between malignant cell aggressivity and the target number needed for specific drugs or RNA interference (RNAi) to maximize the benefit to the patient's overall survival. Interestingly, we found that some additional proteins not generally targeted by drug treatments might justify the addition of inhibitors designed against them in order to improve therapeutic outcomes. However, many proteins are not druggable, or the available pharmacopeia for these targets is limited, which justifies a therapy based on encapsulated RNAi.

An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized controlled trials

Randomized controlled trials (RCT) represent the cornerstone of evidence-based medicine but are resource-intensive. We propose and evaluate a machine learning (ML) strategy of adaptive predictive enrichment through computational trial phenomaps to optimize RCT enrollment. In simulated group sequential analyses of two large cardiovascular outcomes RCTs of (1) a therapeutic drug (pioglitazone versus placebo; Insulin Resistance Intervention after Stroke (IRIS) trial), and (2) a disease management strategy (intensive versus standard systolic blood pressure reduction in the Systolic Blood Pressure Intervention Trial (SPRINT)), we constructed dynamic phenotypic representations to infer response profiles during interim analyses and examined their association with study outcomes. Across three interim timepoints, our strategy learned dynamic phenotypic signatures predictive of individualized cardiovascular benefit. By conditioning a prospective candidate's probability of enrollment on their predicted benefit, we estimate that our approach would have enabled a reduction in the final trial size across ten simulations (IRIS: -14.8% ± 3.1%, pone-sample t-test=0.001; SPRINT: -17.6% ± 3.6%, pone-sample t-test<0.001), while preserving the original average treatment effect (IRIS: hazard ratio of 0.73 ± 0.01 for pioglitazone vs placebo, vs 0.76 in the original trial; SPRINT: hazard ratio of 0.72 ± 0.01 for intensive vs standard systolic blood pressure, vs 0.75 in the original trial; all with pone-sample t-test<0.01). This adaptive framework has the potential to maximize RCT enrollment efficiency.

Precision oncology medicines and the need for real world evidence acceptance in health technology assessment: Importance of patient involvement in sustainable healthcare

Precision oncology has made remarkable strides in improving clinical outcomes, offering hope to patients with historically difficult-to-treat, as well as rare or neglected cancers. However, despite rapid advancement, precision oncology has reached a critical juncture, where patient access to these life-saving medicines may be hampered by strict requirements by Health Technology Assessment (HTA) bodies for randomised controlled trials (RCTs) for assessing new medicines against appropriate comparator. The very nature of precision oncology-matching a tumour's unique molecular alterations to targeted therapies predicted to elicit response-can make the use of RCTs very difficult, as only a very small number of patients might qualify for a given therapy within a traditional clinical trial setting. Real-world evidence (RWE) has been accepted for regulatory decision-making but has yet to reach widespread acceptance by HTA bodies. As the oncology treatment landscape has evolved towards favouring the concept of precision oncology, there is a growing need for flexibility in the way HTA bodies evaluate new medicines. We must acknowledge that current assessment methodologies can limit access to life-changing medicines for many patients who have no alternative options and that a growing number of precision oncology medicines with proven clinical benefits in rare tumours cannot be reasonably evaluated using traditional methodologies. The objectives of this paper are to advocate a change in mindset regarding best practices in drug assessment models and to propose alternative approaches when considering indications for which RWE is the most compelling data source available.

BCL2 Inhibition Reveals a Dendritic Cell-Specific Immune Checkpoint That Controls Tumor Immunosurveillance

We developed a phenotypic screening platform for the functional exploration of dendritic cells (DC). Here, we report a genome-wide CRISPR screen that revealed BCL2 as an endogenous inhibitor of DC function. Knockout of BCL2 enhanced DC antigen presentation and activation as well as the capacity of DCs to control tumors and to synergize with PD-1 blockade. The pharmacologic BCL2 inhibitors venetoclax and navitoclax phenocopied these effects and caused a cDC1-dependent regression of orthotopic lung cancers and fibrosarcomas. Thus, solid tumors failed to respond to BCL2 inhibition in mice constitutively devoid of cDC1, and this was reversed by the infusion of DCs. Moreover, cDC1 depletion reduced the therapeutic efficacy of BCL2 inhibitors alone or in combination with PD-1 blockade and treatment with venetoclax caused cDC1 activation, both in mice and in patients. In conclusion, genetic and pharmacologic BCL2 inhibition unveils a DC-specific immune checkpoint that restrains tumor immunosurveillance.

SIGNIFICANCE

BCL2 inhibition improves the capacity of DCs to stimulate anticancer immunity and restrain cancer growth in an immunocompetent context but not in mice lacking cDC1 or mature T cells. This study indicates that BCL2 blockade can be used to sensitize solid cancers to PD-1/PD-L1-targeting immunotherapy. This article is featured in Selected Articles from This Issue, p. 2293.

Exploring challenges in the management and treatment of inclusion body myositis

PURPOSE OF REVIEW

This review provides an overview of the management and treatment landscape of inclusion body myositis (IBM), while highlighting the current challenges and future directions.

RECENT FINDINGS

IBM is a slowly progressive myopathy that predominantly affects patients over the age of 40, leading to increased morbidity and mortality. Unfortunately, a definitive cure for IBM remains elusive. Various clinical trials targeting inflammatory and some of the noninflammatory pathways have failed. The search for effective disease-modifying treatments faces numerous hurdles including variability in presentation, diagnostic challenges, poor understanding of pathogenesis, scarcity of disease models, a lack of validated outcome measures, and challenges related to clinical trial design. Close monitoring of swallowing and respiratory function, adapting an exercise routine, and addressing mobility issues are the mainstay of management at this time.

SUMMARY

Addressing the obstacles encountered by patients with IBM and the medical community presents a multitude of challenges. Effectively surmounting these hurdles requires embracing cutting-edge research strategies aimed at enhancing the management and treatment of IBM, while elevating the quality of life for those affected.

Comparison of the Latin America Regulation Landscape and International Reference Health Authorities to Hasten Drug Registration and Clinical Research Applications

INTRODUCTION

Promptly providing new drugs to fulfill unmet medical needs requires changes in drug development and registration processes. Health Authorities (HAs) considered as reference due to their experience and acknowledgement (Food and Drug Administration [FDA] among others) already consider innovative clinical trial (CT) designs and flexible approval procedures, but Latin America (LATAM) regulations are still far. A comparison was performed to identify gaps.

MATERIALS AND METHODS

CT requirements for drug Marketing Authorization Application (MAA) and CT approval regulations were compared between LATAM and reference HAs (FDA/European Medicines Agency [EMA]/Health-Canada/Swissmedic/Therapeutic Goods Administration [TGA]/Pharmaceuticals and Medical Devices Agency [PMDA]), as of August 2022. Procedure included reference HAs regulations review, item selection, identification in LATAM regulations, and International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) guidelines (ICH-E6[R2]/ICH-E8[R1]) implementation revision.

RESULTS

For MAA, specific application requirements or ICH guideline M4(R4) on common technical document (CTD) adoption are generally stated, and phase-I/III performance is mandatory (explicitly/implicitly). Faster patient access procedures are infrequent: Priority-drug programs, conditional authorizations, or expedited procedures are scarce or non-existent. Regulatory reliance procedures are adopted through different pathways. Regarding CT approval, innovative/complex CT designs are not prohibited but usually omitted. Some countries implemented adapted CT conducting during the COVID-19 pandemic. Early scientific advice meetings (HA-sponsor) are occasionally considered. Most countries are not formally ICH-joined.

CONCLUSIONS

LATAM regulations must adapt to new regulatory standards (FDA/EMA/ICH) through implementation of frequent updates, reliance/expedited procedures, early HA-sponsor interactions, innovative/complex CTs, mandatory phase-III reaching elimination, and decentralized elements for CT conducting.

Precision medicine in regulatory decision making: Biomarkers used for patient selection in European Public Assessment Reports from 2018 to 2020

Biomarkers can guide precision medicine in clinical trials and practice. They can increase clinical trials' efficiency through selection of study populations more likely to benefit from treatment, thus increasing statistical power and reducing sample size requirements or study duration. We performed a narrative synthesis to explore biomarker utilization for patient selection to guide precision medicine trials in marketing authorization dossiers of centrally approved medicines in Europe between 2018 and 2020 and analyzed in-depth those that eventually included biomarkers in the medicines' indications. From 119 eligible products, 26 included a biomarker in the indication, of which most were oncology products (n = 15). Included biomarkers were often known from literature or from previously approved products in the European Union or the United States. Additionally, 52 dossiers mentioned one or more biomarkers for patient selection in their clinical efficacy and safety information. Although these were not always included in the medicines' indication, they were often implicitly embedded in condition definitions adopted from clinical guidelines or practice. In 15 out of the 26 medicines with a biomarker-guided indication, only biomarker-positive populations were included in the main clinical studies supporting the marketing authorization. These studies were mostly randomized controlled trials or single-arm trials; only two products were studied for multiple indications in an innovative basket trial. Definitions of biomarkers could be subject of debate and needed adaptation after post hoc analyses requested by the assessment committee in four cases, stressing the importance of thorough justification of these definitions to include the right population for an optimal benefit-risk balance, enabling precise medicine.

Precision cancer medicine: Concepts, current practice, and future developments

Precision cancer medicine is a multidisciplinary team effort that requires involvement and commitment of many stakeholders including the society at large. Building on the success of significant advances in precision therapy for oncological patients over the last two decades, future developments will be significantly shaped by improvements in scalable molecular diagnostics in which increasingly complex multilayered datasets require transformation into clinically useful information guiding patient management at fast turnaround times. Adaptive profiling strategies involving tissue- and liquid-based testing that account for the immense plasticity of cancer during the patient's journey and also include early detection approaches are already finding their way into clinical routine and will become paramount. A second major driver is the development of smart clinical trials and trial concepts which, complemented by real-world evidence, rapidly broaden the spectrum of therapeutic options. Tight coordination with regulatory agencies and health technology assessment bodies is crucial in this context. Multicentric networks operating nationally and internationally are key in implementing precision oncology in clinical practice and support developing and improving the ecosystem and framework needed to turn invocation into benefits for patients. The review provides an overview of the diagnostic tools, innovative clinical studies, and collaborative efforts needed to realize precision cancer medicine.

Mortality of COVID-19 in patients with hematological malignancies versus solid tumors: a systematic literature review and meta-analysis

Cancer patients are more vulnerable to COVID-19 compared to the general population, but it remains unclear which types of cancer have the highest risk of COVID-19-related mortality. This study examines mortality rates for those with hematological malignancies (Hem) versus solid tumors (Tumor). PubMed and Embase were systematically searched for relevant articles using Nested Knowledge software (Nested Knowledge, St Paul, MN). Articles were eligible for inclusion if they reported mortality for Hem or Tumor patients with COVID-19. Articles were excluded if they were not published in English, non-clinical studies, had insufficient population/outcomes reporting, or were irrelevant. Baseline characteristics collected included age, sex, and comorbidities. Primary outcomes were all-cause and COVID-19-related in-hospital mortality. Secondary outcomes included rates of invasive mechanical ventilation (IMV) and intensive care unit (ICU) admission. Effect sizes from each study were computed as logarithmically transformed odds ratios (ORs) with random-effects, Mantel-Haenszel weighting. The between-study variance component of random-effects models was computed using restricted effects maximum likelihood estimation, and 95% confidence intervals (CIs) around pooled effect sizes were calculated using Hartung-Knapp adjustments. In total, 12,057 patients were included in the analysis, with 2,714 (22.5%) patients in the Hem group and 9,343 (77.5%) patients in the Tumor group. The overall unadjusted odds of all-cause mortality were 1.64 times higher in the Hem group compared to the Tumor group (95% CI: 1.30-2.09). This finding was consistent with multivariable models presented in moderate- and high-quality cohort studies, suggestive of a causal effect of cancer type on in-hospital mortality. Additionally, the Hem group had increased odds of COVID-19-related mortality compared to the Tumor group (OR = 1.86 [95% CI: 1.38-2.49]). There was no significant difference in odds of IMV or ICU admission between cancer groups (OR = 1.13 [95% CI: 0.64-2.00] and OR = 1.59 [95% CI: 0.95-2.66], respectively). Cancer is a serious comorbidity associated with severe outcomes in COVID-19 patients, with especially alarming mortality rates in patients with hematological malignancies, which are typically higher compared to patients with solid tumors. A meta-analysis of individual patient data is needed to better assess the impact of specific cancer types on patient outcomes and to identify optimal treatment strategies.

Inflammation across tissues: can shared cell biology help design smarter trials?

Immune-mediated inflammatory diseases (IMIDs) are responsible for substantial global disease burden and associated health-care costs. Traditional models of research and service delivery silo their management within organ-based medical disciplines. Very often patients with disease in one organ have comorbid involvement in another, suggesting shared pathogenic pathways. Moreover, different IMIDs are often treated with the same drugs (including glucocorticoids, immunoregulators and biologics). Unlocking the cellular basis of these diseases remains a major challenge, leading us to ask why, if these diseases have so much in common, they are not investigated in a common manner. A tissue-based, cellular understanding of inflammation might pave the way for cross-disease, cross-discipline basket trials (testing one drug across two or more diseases) to reduce the risk of failure of early-phase drug development in IMIDs. This new approach will enable rapid assessment of the efficacy of new therapeutic agents in cross-disease translational research in humans.

Integrative bioinformatics analysis of WDHD1: a potential biomarker for pan-cancer prognosis, diagnosis, and immunotherapy

BACKGROUND

Although WD repeat and high-mobility group box DNA binding protein 1 (WDHD1) played an essential role in DNA replication, chromosome stability, and DNA damage repair, the panoramic picture of WDHD1 in human tumors remains unclear. Hence, this study aims to comprehensively characterize WDHD1 across 33 human cancers.

METHODS

Based on publicly available databases such as TCGA, GTEx, and HPA, we used a bioinformatics approach to systematically explore the genomic features and biological functions of WDHD1 in pan-cancer.

RESULTS

WDHD1 mRNA levels were significantly increased in more than 20 types of tumor tissues. Elevated WDHD1 expression was associated with significantly shorter overall survival (OS) in 10 tumors. Furthermore, in uterine corpus endometrial carcinoma (UCEC) and liver hepatocellular carcinoma (LIHC), WDHD1 expression was significantly associated with higher histological grades and pathological stages. In addition, WDHD1 had a high diagnostic value among 16 tumors (area under the ROC curve [AUC] > 0.9). Functional enrichment analyses suggested that WDHD1 probably participated in many oncogenic pathways such as E2F and MYC targets (false discovery rate [FDR] < 0.05), and it was involved in the processes of DNA replication and DNA damage repair (p.adjust < 0.05). WDHD1 expression also correlated with the half-maximal inhibitory concentrations (IC50) of rapamycin (4 out of 10 cancers) and paclitaxel (10 out of 10 cancers). Overall, WDHD1 was negatively associated with immune cell infiltration and might promote tumor immune escape. Our analysis of genomic alterations suggested that WDHD1 was altered in 1.5% of pan-cancer cohorts and the "mutation" was the predominant type of alteration. Finally, through correlation analysis, we found that WDHD1 might be closely associated with tumor heterogeneity, tumor stemness, mismatch repair (MMR), and RNA methylation modification, which were all processes associated with the tumor progression.

CONCLUSIONS

Our pan-cancer analysis of WDHD1 provides valuable insights into the genomic characterization and biological functions of WDHD1 in human cancers and offers some theoretical support for the future use of WDHD1-targeted therapies, immunotherapies, and chemotherapeutic combinations for the management of tumors.

Challenges and opportunities in building a health economic framework for personalized medicine in oncology

Personalized medicine has allowed for knowledge at an individual level for several diseases and this has led to improvements in prevention and treatment of various types of neoplasms. Despite the greater availability of tests, the costs of genomic testing and targeted therapies are still high for most patients, especially in low- and middle-income countries. Although value frameworks and health technology assessment are fundamental to allow decision-making by policymakers, there are several concerns in terms of personalized medicine pharmacoeconomics. A global effort may improve these tools in order to allow access to personalized medicine for an increasing number of patients with cancer.

Types and progress of clinical trial design for breast cancer: a narrative review

Background and Objective

In recent years, the field of breast cancer diagnosis and therapy has witnessed rapid technological advances. Concurrently, the emergence of molecular biology and novel detection methodologies has facilitated the transition of breast cancer management into the precision medicine era. The primary objective of this review is to discuss the transformation in the research and development paradigm for breast cancer therapies and strategies.

Methods

We systematically searched PubMed, EMBASE and Cochrane databases for relevant studies published over the past 20 years using keywords including "breast cancer", "clinical trial", "seamless", "master protocol", "umbrella", "basket", "platform", and "precision medicine". Articles were screened for eligibility and key data extracted. The search was limited to English-language publications.

Key Content and Findings

The review identifies three core innovations in breast cancer trial methodology: (I) in terms of research speed, the traditional three-stage drug development models are being substituted by "seamless designs" as exemplified by the immunotherapy combination study NCT0328056. (II) Addressing research breadth, "master protocols" such as basket trials (IMMU-132-01), umbrella trials (FUTURE), and platform trials (I-SPY 2) have been introduced, allowing the simultaneous assessment of multiple treatments or disease subtypes within a singular framework. (III) Pertaining to research precision, newer designs utilize biomarkers such as "enrichment" (seen in EMBRACA and OlympiA trials) and "marker stratification" (as in the SOLAR-1 trial), enabling the identification of appropriate patient subgroups and the provision of tailored therapy strategies, a stark contrast to traditional histopathology-based evaluations.

Conclusions

Clinical trial design in breast cancer research has been revolutionized, moving towards more efficient and targeted strategies. Despite the presence of ethical, logistical, and data complexities, it is anticipated that ongoing technological and regulatory enhancements will pave the way for even more refined research approaches, subsequently influencing future research, clinical practices, and policymaking in breast cancer care.

Clinical Network Systems Biology: Traversing the Cancer Multiverse

In recent decades, cancer biology and medicine have ushered in a new age of precision medicine through high-throughput approaches that led to the development of novel targeted therapies and immunotherapies for different cancers. The availability of multifaceted high-throughput omics data has revealed that cancer, beyond its genomic heterogeneity, is a complex system of microenvironments, sub-clonal tumor populations, and a variety of other cell types that impinge on the genetic and non-genetic mechanisms underlying the disease. Thus, a systems approach to cancer biology has become instrumental in identifying the key components of tumor initiation, progression, and the eventual emergence of drug resistance. Through the union of clinical medicine and basic sciences, there has been a revolution in the development and approval of cancer therapeutic drug options including tyrosine kinase inhibitors, antibody-drug conjugates, and immunotherapy. This 'Team Medicine' approach within the cancer systems biology framework can be further improved upon through the development of high-throughput clinical trial models that utilize machine learning models, rapid sample processing to grow patient tumor cell cultures, test multiple therapeutic options and assign appropriate therapy to individual patients quickly and efficiently. The integration of systems biology into the clinical network would allow for rapid advances in personalized medicine that are often hindered by a lack of drug development and drug testing.

Medical imaging and multimodal artificial intelligence models for streamlining and enhancing cancer care: opportunities and challenges

INTRODUCTION

Artificial intelligence (AI) has the potential to transform oncologic care. There have been significant developments in AI applications in medical imaging and increasing interest in multimodal models. These are likely to enable improved oncologic care through more precise diagnosis, increasingly in a more personalized and less invasive manner. In this review, we provide an overview of the current state and challenges that clinicians, administrative personnel and policy makers need to be aware of and mitigate for the technology to reach its full potential.

AREAS COVERED

The article provides a brief targeted overview of AI, a high-level review of the current state and future potential AI applications in diagnostic radiology and to a lesser extent digital pathology, focusing on oncologic applications. This is followed by a discussion of emerging approaches, including multimodal models. The article concludes with a discussion of technical, regulatory challenges and infrastructure needs for AI to realize its full potential.

EXPERT OPINION

There is a large volume of promising research, and steadily increasing commercially available tools using AI. For the most advanced and promising precision diagnostic applications of AI to be used clinically, robust and comprehensive quality monitoring systems and informatics platforms will likely be required.

The impact of the multi-disciplinary molecular tumour board and integrative next generation sequencing on clinical outcomes in advanced solid tumours

Background

The integration of next-generation sequencing (NGS) comprehensive gene profiling (CGP) into clinical practice is playing an increasingly important role in oncology. Therefore, the HKU-HKSH Multi-disciplinary Molecular Tumour Board (MTB) was established to advance precision oncology in Hong Kong. A multicenter retrospective study investigated the feasibility of the HKU-HKSH MTB in determining genome-guided therapy for treatment-refractory solid cancers in Hong Kong.

Methods

Patients who were presented at the HKU-HKSH MTB between August 2018 and June 2022 were included in this study. The primary study endpoints were the proportion of patients who receive MTB-guided therapy based on genomic analysis and overall survival (OS). Secondary endpoints included the proportion of patients with actionable genomic alterations, objective response rate (ORR), and disease control rate (DCR). The Kaplan-Meier method was used in the survival analyses, and hazard ratios were calculated using univariate Cox regression.

Findings

122 patients were reviewed at the HKU-HKSH MTB, and 63% (n = 77) adopted treatment per the MTB recommendations. These patients achieved a significantly longer median OS than those who did not receive MTB-guided therapy (12.7 months vs. 5.2 months, P = 0.0073). Their ORR and DCR were 29% and 65%, respectively.

Interpretation

Our study demonstrated that among patients with heavily pre-treated advanced solid cancers, MTB-guided treatment could positively impact survival outcomes, thus illustrating the applicability of NGS CGPs in real-world clinical practice.

Funding

The study was supported by the Li Shu Pui Medical Foundation. Dr Aya El Helali was supported by the Li Shu Pui Medical Foundation Fellowship grant from the Li Shu Pui Medical Foundation. Funders had no role in study design, data collection, data analysis, interpretation, or writing of the report.

The tumor immune microenvironment in pancreatic cancer and its potential in the identification of immunotherapy biomarkers

INTRODUCTION

Pancreatic cancer (PC) has an extremely poor prognosis, even with surgical resection and triplet chemotherapy treatment. Cancer immunotherapy has been recently approved for tumor-agnostic treatment with genome analysis, including in PC. However, it has limited efficacy.

AREAS COVERED

In addition to the low tumor mutation burden, one of the difficulties of immunotherapy in PC is the presence of abundant stromal cells in its microenvironment. Among stromal cells, cancer-associated fibroblasts (CAFs) play a major role in immunotherapy resistance, and CAF-targeted therapies are currently under development, including those in combination with immunotherapies. Meanwhile, microbiomes and tumor-derived exosomes (TDEs) have been shown to alter the behavior of distant receptor cells in PC. This review discusses the role of CAFs, microbiomes, and TDEs in PC tumor immunity.

EXPERT OPINION

Elucidating the mechanisms by which CAFs, microbiomes, and TDEs are involved in the tumorigenesis of PC will be helpful for developing novel immunotherapeutic strategies and identifying companion biomarkers for immunotherapy. Spatial single-cell analysis of the tumor microenvironment will be useful for identifying biomarkers of PC immunity. Furthermore, given the complexity of immune mechanisms, artificial intelligence models will be beneficial for predicting the efficacy of immunotherapy.

Exploring the Role of Enhancer-Mediated Transcriptional Regulation in Precision Biology

The emergence of precision biology has been driven by the development of advanced technologies and techniques in high-resolution biological research systems. Enhancer-mediated transcriptional regulation, a complex network of gene expression and regulation in eukaryotes, has attracted significant attention as a promising avenue for investigating the underlying mechanisms of biological processes and diseases. To address biological problems with precision, large amounts of data, functional information, and research on the mechanisms of action of biological molecules is required to address biological problems with precision. Enhancers, including typical enhancers and super enhancers, play a crucial role in gene expression and regulation within this network. The identification and targeting of disease-associated enhancers hold the potential to advance precision medicine. In this review, we present the concepts, progress, importance, and challenges in precision biology, transcription regulation, and enhancers. Furthermore, we propose a model of transcriptional regulation for multi-enhancers and provide examples of their mechanisms in mammalian cells, thereby enhancing our understanding of how enhancers achieve precise regulation of gene expression in life processes. Precision biology holds promise in providing new tools and platforms for discovering insights into gene expression and disease occurrence, ultimately benefiting individuals and society as a whole.

The Application of Evidence-Based Medicine in Individualized Medicine

The fundamental aim of healthcare is to improve overall health of the population by providing state-of-the-art healthcare for individuals at an affordable cost. The foundation for this system is largely referred to as "evidence-based medicine". Too often, evidence-based medicine is based solely on so-called "best research evidence", collected through randomized controlled trials while disregarding clinical expertise and patient expectations. As healthcare gravitates towards personalized and individualized medicine, such external clinical (research) evidence can inform, but never replace, individual clinical expertise. This applies in particular to orphan diseases, for which clinical trials are methodologically particularly problematic, and evidence derived from them is often questionable. Evidence-based medicine constitutes a complex process to allow doctors and patients to select the best possible solutions for each individual based on rapidly developing new therapeutic directions. This requires a revisit of the foundations of evidence-based medicine. A proposition as to how to manage evidence-based data in individualized immune-oncology is presented here.

Evidence of Immunomodulatory Food-Protein Derived Peptides in Human Nutritional Interventions: Review on the Outcomes and Potential Limitations

The immune system is somehow related to all the metabolic pathways, in a bidirectional way, and the nutritional interventions affecting these pathways might have a relevant impact on the inflammatory status of the individuals. Food-derived peptides have been demonstrated to exert several bioactivities by in vitro or animal studies. Their potential to be used as functional food is promising, considering the simplicity of their production and the high value of the products obtained. However, the number of human studies performed until now to demonstrate effects in vivo is still scarce. Several factors must be taken into consideration to carry out a high-quality human study to demonstrate immunomodulatory-promoting properties of a test item. This review aims to summarize the recent human studies published in which the purpose was to demonstrate bioactivity of protein hydrolysates, highlighting the main results and the limitations that can restrict the relevance of the studies. Results collected are promising, although in some studies, physiological changes could not be observed. When responses were observed, they sometimes did not refer to relevant parameters and the immunomodulatory properties could not be clearly established with the current evidence. Well-designed clinical trials are needed in order to evaluate the role of protein hydrolysates in immunonutrition.

Metastatic lymph node targeted CTLA4 blockade: a potent intervention for local and distant metastases with minimal ICI-induced pneumonia

BACKGROUND

Immune checkpoint blockade (ICB) elicits a strong and durable therapeutic response, but its application is limited by disparate responses and its associated immune-related adverse events (irAEs). Previously, in a murine model of lymph node (LN) metastasis, we showed that intranodal administration of chemotherapeutic agents using a lymphatic drug delivery system (LDDS) elicits stronger therapeutic responses in comparison to systemic drug delivery approaches, while minimizing systemic toxicity, due to its improved pharmacokinetic profile at the intended site. Importantly, the LN is a reservoir of immunotherapeutic targets. We therefore hypothesized that metastatic LN-targeted ICB can amplify anti-tumor response and uncouple it from ICB-induced irAEs.

METHODS

To test our hypothesis, models of LN and distant metastases were established with luciferase expressing LM8 cells in MXH10/Mo-lpr/lpr mice, a recombinant inbred strain of mice capable of recapitulating ICB-induced interstitial pneumonia. This model was used to interrogate ICB-associated therapeutic response and immune related adverse events (irAEs) by in vivo imaging, high-frequency ultrasound imaging and histopathology. qPCR and flowcytometry were utilized to uncover the mediators of anti-tumor immunity.

RESULTS

Tumor-bearing LN (tbLN)-directed CTLA4 blockade generated robust anti-tumor response against local and systemic metastases, thereby improving survival. The anti-tumor effects were accompanied by an upregulation of effector CD8T cells in the tumor-microenvironment and periphery. In comparison, non-specific CTLA4 blockade was found to elicit weaker anti-tumor effect and exacerbated ICI-induced irAEs, especially interstitial pneumonia. Together these data highlight the importance of tbLN-targeted checkpoint blockade for efficacious response.

CONCLUSIONS

Intranodal delivery of immune checkpoint inhibitors to metastatic LN can potentiate therapeutic response while minimizing irAEs stemming from systemic lowering of immune activation threshold.

Targeted combination therapies in oncology: challenging regulatory frameworks designed for monotherapies in Europe

The pharmaceutical value chain, including clinical trials, pricing, access, and reimbursement, is designed for classical monotherapies. Although there has been a paradigm shift that increases the relevance of targeted combination therapies (TCTs), regulation and common practice have been slow to adapt. We explored access to 23 TCTs for advanced melanoma and lung cancer as reported by 19 specialists from 17 leading cancer institutions in nine European countries. We find heterogeneous patient access to TCTs between countries, differences in country-specific regulations, and differences in the clinical practice of melanoma and lung cancer. Regulation that is better tailored to the context of combinational therapies can increase equity in access across Europe and promote an evidence-based and authorized use of combinations.

Dietary Oncopharmacognosy as a Crosswalk between Precision Oncology and Precision Nutrition

While diet and nutrition are modifiable risk factors for many chronic and infectious diseases, their role in cancer prevention and control remains under investigation. The lack of clarity of some diet-cancer relationships reflects the ongoing debate about the relative contribution of genetic factors, environmental exposures, and replicative errors in stem cell division as determinate drivers of cancer risk. In addition, dietary guidance has often been based upon research assuming that the effects of diet and nutrition on carcinogenesis would be uniform across populations and for various tumor types arising in a specific organ, i.e., that one size fits all. Herein, we present a paradigm for investigating precision dietary patterns that leverages the approaches that led to successful small-molecule inhibitors in cancer treatment, namely understanding the pharmacokinetics and pharmacodynamics of small molecules for targeting carcinogenic mechanisms. We challenge the scientific community to refine the paradigm presented and to conduct proof-in-concept experiments that integrate existing knowledge (drug development, natural products, and the food metabolome) with developments in artificial intelligence to design and then test dietary patterns predicted to elicit drug-like effects on target tissues for cancer prevention and control. We refer to this precision approach as dietary oncopharmacognosy and envision it as the crosswalk between the currently defined fields of precision oncology and precision nutrition with the goal of reducing cancer deaths.

Agnostic Approvals in Oncology: Getting the Right Drug to the Right Patient with the Right Genomics

(1) Background: The oncology field has drastically changed with the advent of precision medicine, led by the discovery of druggable genes or immune targets assessed through next-generation sequencing. Biomarker-based treatments are increasingly emerging, and currently, six tissue-agnostic therapies are FDA-approved. (2) Methods: We performed a review of the literature and reported the trials that led to the approval of tissue-agnostic treatments and ongoing clinical trials currently investigating novel biomarker-based approaches. (3) Results: We discussed the approval of agnostic treatments: pembrolizumab and dostarlimab for MMRd/MSI-H, pembrolizumab for TMB-H, larotrectinib and entrectinib for NTRK-fusions, dabrafenib plus trametinib for BRAF V600E mutation, and selpercatinib for RET fusions. In addition, we reported novel clinical trials of biomarker-based approaches, including ALK, HER2, FGFR, and NRG1. (4) Conclusions: Precision medicine is constantly evolving, and with the improvement of diagnostic tools that allow a wider genomic definition of the tumor, tissue-agnostic targeted therapies are a promising treatment strategy tailored to the specific tumor genomic profile, leading to improved survival outcomes.

Precision Oncology: Evolving Clinical Trials across Tumor Types

Advances in molecular technologies and targeted therapeutics have accelerated the implementation of precision oncology, resulting in improved clinical outcomes in selected patients. The use of next-generation sequencing and assessments of immune and other biomarkers helps optimize patient treatment selection. In this review, selected precision oncology trials including the IMPACT, SHIVA, IMPACT2, NCI-MPACT, TAPUR, DRUP, and NCI-MATCH studies are summarized, and their challenges and opportunities are discussed. Brief summaries of the new ComboMATCH, MyeloMATCH, and iMATCH studies, which follow the example of NCI-MATCH, are also included. Despite the progress made, precision oncology is inaccessible to many patients with cancer. Some patients' tumors may not respond to these treatments, owing to the complexity of carcinogenesis, the use of ineffective therapies, or unknown mechanisms of tumor resistance to treatment. The implementation of artificial intelligence, machine learning, and bioinformatic analyses of complex multi-omic data may improve the accuracy of tumor characterization, and if used strategically with caution, may accelerate the implementation of precision medicine. Clinical trials in precision oncology continue to evolve, improving outcomes and expediting the identification of curative strategies for patients with cancer. Despite the existing challenges, significant progress has been made in the past twenty years, demonstrating the benefit of precision oncology in many patients with advanced cancer.

Addressing Transcriptional Dysregulation in Cancer through CDK9 Inhibition

Undermining transcriptional addiction, the dependence of cancers on selected transcriptional programs, is critically important for addressing cancers with high unmet clinical need. Cyclin-dependent kinase 9 (CDK9) has long been considered an actionable therapeutic target for modulating transcription in many diseases. This appeal is due to its role in coordinating the biochemical events that regulate RNA polymerase II (RNA Pol II) pause-release state, one that offers a way for attenuating transcriptional dysregulation driven by amplified or overexpressed transcription factors implicated in cancer. However, targeting CDK9 in the clinic has historically proven elusive, a challenge that stems from the often highly intolerable cytotoxicity attributed to its essentiality across many cell lineages and the polypharmacology of the first generation of pan-CDK inhibitors to reach the clinic. A new wave of highly selective molecules progressing through the early stages of clinical evaluation offers renewed hope.

Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade

Anti-cancer drug design has been acknowledged as a complicated, expensive, time-consuming, and challenging task. How to reduce the research costs and speed up the development process of anti-cancer drug designs has become a challenging and urgent question for the pharmaceutical industry. Computer-aided drug design methods have played a major role in the development of cancer treatments for over three decades. Recently, artificial intelligence has emerged as a powerful and promising technology for faster, cheaper, and more effective anti-cancer drug designs. This study is a narrative review that reviews a wide range of applications of artificial intelligence-based methods in anti-cancer drug design. We further clarify the fundamental principles of these methods, along with their advantages and disadvantages. Furthermore, we collate a large number of databases, including the omics database, the epigenomics database, the chemical compound database, and drug databases. Other researchers can consider them and adapt them to their own requirements.

Targeting ATR in Cancer Medicine

As a key component of the DNA Damage Response, the Ataxia telangiectasia and Rad3-related (ATR) protein is a promising druggable target that is currently widely evaluated in phase I-II-III clinical trials as monotherapy and in combinations with other rational antitumor agents, including immunotherapy, DNA repair inhibitors, chemo- and radiotherapy. Ongoing clinical studies for this drug class must address the optimization of the therapeutic window to limit overlapping toxicities and refine the target population that will most likely benefit from ATR inhibition. With advances in the development of personalized treatment strategies for patients with advanced solid tumors, many ongoing ATR inhibitor trials have been recruiting patients based on their germline and somatic molecular alterations, rather than relying solely on specific tumor subtypes. Although a spectrum of molecular alterations have already been identified as potential predictive biomarkers of response that may sensitize to ATR inhibition, these biomarkers must be analytically validated and feasible to measure robustly to allow for successful integration into the clinic. While several ATR inhibitors in development are poised to address a clinically unmet need, no ATR inhibitor has yet received FDA-approval. This chapter details the underlying rationale for targeting ATR and summarizes the current preclinical and clinical landscape of ATR inhibitors currently in evaluation, as their regulatory approval potentially lies close in sight.

Artificial Intelligence in Public Health: Current Trends and Future Possibilities

Artificial intelligence (AI) is a discipline that studies whether and how intelligent computer systems that can simulate the capacity and behaviour of human thought can be created [...]