Landscape of cancer clinical trials in India - a comprehensive analysis of the Clinical Trial Registry-India.

Background: Cancer is one of the leading causes of morbidity and mortality in India. Clinical trials are critical for driving innovation in cancer therapy, diagnosis, and prevention. This study aims to depict the evolving landscape of cancer clinical trials in India by analysing the clinical trials registered in Clinical Trial Registry-India (CTRI). Methods: We identified cancer trials registered in CTRI (between 2007 and 2021) using search terms adapted from the cancer types defined by the National Cancer Institute (USA). We then collated and analysed the publicly available information from CTRI (cancer subtypes, type of trial, treatment intent, type of intervention, sponsor type, recruitment countries) and used descriptive statistics to illustrate the overall as well as year-to-year trend. Findings: In total, we identified 1988 cancer trials, the majority of which focused on treating cancer (63%) and rest of the trials aimed at optimising the operational aspects of surgery (19%), mitigating treatment-related toxicity (10.6%), or treating cancer-related symptoms (7.8%). Focusing on trials with the intent of treating cancer, we found that most were investigating solid tumours as opposed to haematological malignancies with the most prominent cancer subtypes being breast cancer (17%), head and neck cancer (9.8%), lung cancer (9.6%), and cervical cancer (6.6%). The number of trials conducted in a given cancer subtype from our analysis overall correlated to the incidence, mortality, and 5-year prevalence of the respective cancer subtype in India; however, head and neck cancer and cervical cancer were underrepresented in trials as compared with the disease burden. The most common type of intervention was investigational drugs. The most common sponsor types were global pharmaceutical industry (26%) and research institution and hospital (26%). Despite a relatively high cancer burden, the availability of cancer trials in the Northeastern states of India was limited. Interpretation: There is a pressing need for clinical cancer research in India to be better aligned with the nation's healthcare needs and disease burden, focusing on prevalent and deadly cancers while ensuring the availability of clinical trials across geographic regions and underserved populations. Funding: Pi Health USA, a fully owned subsidiary of BeiGene Ltd.

Law and Stock Market Development in the UK over Time: An Uneasy Match.

Britain has a reputation for having a stock market-oriented corporate economy and there is an extensive literature maintaining that laws affording substantial protection to outside investors are needed for a thriving stock market. Historically, however, UK equity markets have not always flourished and, when they have, law's contribution has been open to question. This article considers the uneasy match between law and Britain's stock market development from when shares first began to trade publicly through to the present day, offering in so doing insights into the relationship between law and equity markets and current reforms intended to revive a flagging UK stock exchange.

State of cancer care in India and opportunities for innovation.

Cancer is one of the leading causes of morbidity and mortality in India. Despite recent medical and technological advances, the cancer burden in India remains high and continues to rise. Moreover, substantial regional disparities in cancer incidence and access to essential medical resources exist throughout the country. While innovative and effective cancer therapies hold promise for improving patient outcomes, several barriers hinder their development and utilization in India. Here we provide an overview of these barriers, including challenges related to patient awareness, inadequate infrastructure, scarcity of trained oncology professionals, and the high cost of cancer care. Furthermore, we discuss the limited availability of cancer clinical trials in the country, along with an examination of potential avenues to enhance cancer care in India. By confronting these hurdles head-on and implementing innovative, pragmatic solutions, we take an indispensable step toward a future where every cancer patient in the country can access quality care.

Cancer is a major cause of illness and death in India. Despite advances in medicine and technology, the number of people with cancer remains high and continues to increase. There are big differences in access to necessary medical resources across different regions of the country. This article focuses on the barriers that hinder the development and use of effective cancer treatments in India. We discuss challenges related to patient awareness, inadequate medical facilities, a shortage of trained cancer specialists and the high cost of cancer treatment. Additionally, we explore the limited availability of cancer clinical trials in India and potential ways to improve cancer care in the country. By finding innovative and practical solutions to these challenges, we can take a crucial step toward a future where all cancer patients in India have access to high-quality care.

Analysis of Protein Biomarkers From Hospitalized COVID-19 Patients Reveals Severity-Specific Signatures and Two Distinct Latent Profiles With Differential Responses to Corticosteroids.

OBJECTIVES: To identify and validate novel COVID-19 subphenotypes with potential heterogenous treatment effects (HTEs) using electronic health record (EHR) data and 33 unique biomarkers. DESIGN: Retrospective cohort study of adults presenting for acute care, with analysis of biomarkers from residual blood collected during routine clinical care. Latent profile analysis (LPA) of biomarker and EHR data identified subphenotypes of COVID-19 inpatients, which were validated using a separate cohort of patients. HTE for glucocorticoid use among subphenotypes was evaluated using both an adjusted logistic regression model and propensity matching analysis for in-hospital mortality. SETTING: Emergency departments from four medical centers. PATIENTS: Patients diagnosed with COVID-19 based on International Classification of Diseases , 10th Revision codes and laboratory test results. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Biomarker levels generally paralleled illness severity, with higher levels among more severely ill patients. LPA of 522 COVID-19 inpatients from three sites identified two profiles: profile 1 ( n = 332), with higher levels of albumin and bicarbonate, and profile 2 ( n = 190), with higher inflammatory markers. Profile 2 patients had higher median length of stay (7.4 vs 4.1 d; p < 0.001) and in-hospital mortality compared with profile 1 patients (25.8% vs 4.8%; p < 0.001). These were validated in a separate, single-site cohort ( n = 192), which demonstrated similar outcome differences. HTE was observed ( p = 0.03), with glucocorticoid treatment associated with increased mortality for profile 1 patients (odds ratio = 4.54). CONCLUSIONS: In this multicenter study combining EHR data with research biomarker analysis of patients with COVID-19, we identified novel profiles with divergent clinical outcomes and differential treatment responses.

Diagnostic and prognostic capabilities of a biomarker and EMR-based machine learning algorithm for sepsis.

Sepsis is a major cause of mortality among hospitalized patients worldwide. Shorter time to administration of broad-spectrum antibiotics is associated with improved outcomes, but early recognition of sepsis remains a major challenge. In a two-center cohort study with prospective sample collection from 1400 adult patients in emergency departments suspected of sepsis, we sought to determine the diagnostic and prognostic capabilities of a machine-learning algorithm based on clinical data and a set of uncommonly measured biomarkers. Specifically, we demonstrate that a machine-learning model developed using this dataset outputs a score with not only diagnostic capability but also prognostic power with respect to hospital length of stay (LOS), 30-day mortality, and 3-day inpatient re-admission both in our entire testing cohort and various subpopulations. The area under the receiver operating curve (AUROC) for diagnosis of sepsis was 0.83. Predicted risk scores for patients with septic shock were higher compared with patients with sepsis but without shock (p < 0.0001). Scores for patients with infection and organ dysfunction were higher compared with those without either condition (p < 0.0001). Stratification based on predicted scores of the patients into low, medium, and high-risk groups showed significant differences in LOS (p < 0.0001), 30-day mortality (p < 0.0001), and 30-day inpatient readmission (p < 0.0001). In conclusion, a machine-learning algorithm based on electronic medical record (EMR) data and three nonroutinely measured biomarkers demonstrated good diagnostic and prognostic capability at the time of initial blood culture.

Loss of ACK1 Upregulates EGFR and Mediates Resistance to BRAF Inhibition.

Targeted BRAF(V600E) suppression by selective BRAF inhibitors (BRAFis; e.g., vemurafenib and dabrafenib) has led to a sea change in the treatment of metastatic melanoma. Despite frequent upfront responses, acquired resistance has compromised long-term applicability. Among the various mechanisms of resistance, activation of multiple receptor tyrosine kinases is a known critical factor that contributes to vemurafenib resistance⁠. EGFR activation has been recurrently identified in a set of vemurafenib-resistant melanomas, but little is known about how EGFR, or possibly other receptor tyrosine kinases, becomes activated. Here, we report that ACK1, a protein kinase that modulates EGFR turnover, is downregulated in vemurafenib-resistant melanoma cells. We also found that ACK1 depletion with short hairpin RNA decreased EGFR degradation when activated by epidermal growth factor, increased EGFR protein expression, and conferred resistance to BRAFis both in vitro and in vivo. Vemurafenib resistance mediated by ACK1 inhibition can be reversed by the EGFR inhibitor gefitinib. Our data indicate that ACK1 loss may be a post-transcriptional mechanism that increases EGFR signaling and contributes to drug resistance.

Publisher Correction: Combining Biomarkers with EMR Data to Identify Patients in Different Phases of Sepsis.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Growth suppression by dual BRAF(V600E) and NRAS(Q61) oncogene expression is mediated by SPRY4 in melanoma.

The underlying forces that shape mutational patterns within any type of cancer have been poorly characterized. One of the best preserved exclusionary relationships is that between BRAF(V600E) and NRAS(Q61) in melanomas. To explore possible mechanisms which could explain this phenomenon, we overexpressed NRAS(Q61) in a set of BRAF(V600E) melanoma lines and vice versa. Controlled expression of a second activating oncogene led to growth arrest ("synthetic suppression") in a subset of cells, which was accompanied by cell cycle arrest and senescence in several melanoma cell lines along with apoptosis. Through differential gene expression analysis, we identified SPRY4 as the potential mediator of this synthetic response to dual oncogene suppression. Ectopic introduction of SPRY4 recapitulated the growth arrest phenotype of dual BRAF(V600E)/NRAS(Q61) expression while SPRY4 depletion led to a partial rescue from oncogenic antagonism. This study thus defined SPRY4 as a potential mediator of synthetic suppression, which is likely to contribute to the observed exclusivity between BRAF(V600E) and NRAS(Q61R) mutations in melanoma. Further leverage of the SPRY4 pathway may also hold therapeutic promise for NRAS(Q61) melanomas.

Clinical spectrum of cutaneous melanoma morphology.

BACKGROUND: Melanoma can mimic other cutaneous lesions, but the full spectrum and prevalence of these morphologic variants remain largely unknown. OBJECTIVE: To classify nonacral cutaneous melanomas into distinct morphologic clusters and characterize clusters' clinicopathologic features. METHODS: All pathologic melanoma diagnoses (occurring during 2011-2016) were reviewed for routine prebiopsy digital photographs (n = 400). Six dermatologists independently assigned lesions into 1 of 14 diagnostic classes on the basis of morphology. Image consensus clusters were generated by K-means; clinicopathologic features were compared with analysis of variance and χ2. RESULTS: Five morphologic clusters were identified: typical (n = 136), nevus-like (n = 81), amelanotic/nonmelanoma skin cancer (NMSC)-like (n = 70), seborrheic keratosis (SK)-like (n = 68), and lentigo/lentigo maligna (LM)-like (n = 45) melanomas. Nevus-like melanomas were found in younger patients. Nevus-like and lentigo/LM-like melanomas tended to be thinner and more likely identified on routine dermatologic examinations. NMSC-like melanomas were tender, thicker, more mitotically active, and associated with prior NMSC. Typical and SK-like melanomas had similar clinicopathologic features. LIMITATIONS: Cluster subdivision yielded diminished sample sizes. Visual assignment was performed without clinical context. CONCLUSION: When primary cutaneous melanomas were assigned into diagnostic groups and subjected to novel consensus clustering, recurrent morphologic patterns emerged. The spectrum of these morphologies was unexpectedly diverse, which might have implications for visual training and possibly clinical diagnosis.

Pleomorphic Dermal Sarcoma of the Scalp.

Pleomorphic dermal sarcoma (PDS) is a rare mesenchymal tissue tumor. Distinguishing PDS from similar conditions, such as atypical fibroxanthoma (AFX), its less aggressive tumor counterpart, is difficult, as they are clinically and histologically similar. We present a case of a 77-year-old man presenting with a large nodular scalp lesion of three weeks duration. Pathology revealed a 3.3 cm invasive pleomorphic dermal sarcoma. Surgical excision with 2 cm margins was performed with successful healing of the graft. This case highlights a rare case of a large pleomorphic dermal sarcoma and discusses the histological features and management of PDS.

Cutaneous Presentation of Mesothelioma With a Sarcomatoid Transformation.

Malignant pleural mesothelioma is a rare neoplasm of mesodermal origin. Cutaneous involvement of malignant pleural mesothelioma is a very rare entity, with only 11 cases reported in the literature. Here, we describe the case of a 75-year-old man with stage IV epithelioid pleural mesothelioma, presenting with a cutaneous eruption 5 months after initial diagnosis, which revealed sarcomatoid features on skin biopsy. Histological analysis of malignancy progression through immunohistochemical staining of the pleural, lymph node, and skin tissue revealed gradual loss of calretinin and gain of desmin, supporting a transformation from epithelioid to sarcomatoid tissue. To our knowledge, this is the first reported case of an epithelioid to sarcomatoid transformation of malignant pleural mesothelioma manifesting in a cutaneous presentation.

Combining Biomarkers with EMR Data to Identify Patients in Different Phases of Sepsis.

Sepsis is a leading cause of death and is the most expensive condition to treat in U.S. hospitals. Despite targeted efforts to automate earlier detection of sepsis, current techniques rely exclusively on using either standard clinical data or novel biomarker measurements. In this study, we apply machine learning techniques to assess the predictive power of combining multiple biomarker measurements from a single blood sample with electronic medical record data (EMR) for the identification of patients in the early to peak phase of sepsis in a large community hospital setting. Combining biomarkers and EMR data achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.81, while EMR data alone achieved an AUC of 0.75. Furthermore, a single measurement of six biomarkers (IL-6, nCD64, IL-1ra, PCT, MCP1, and G-CSF) yielded the same predictive power as collecting an additional 16 hours of EMR data(AUC of 0.80), suggesting that the biomarkers may be useful for identifying these patients earlier. Ultimately, supervised learning using a subset of biomarker and EMR data as features may be capable of identifying patients in the early to peak phase of sepsis in a diverse population and may provide a tool for more timely identification and intervention.

Somatic driver mutations in melanoma.

Melanoma has one of the highest somatic mutational burdens among solid malignancies. Although the rapid progress in genomic research has contributed immensely to our understanding of the pathogenesis of melanoma, the clinical significance of the vast array of genomic alterations discovered by next-generation sequencing is far from being fully characterized. Most mutations prevalent in melanoma are simply neutral "passengers," which accompany functionally significant "drivers" under transforming conditions. The delineation of driver mutations from passenger mutations is critical to the development of targeted therapies. Novel advances in genomic data analysis have aided in distinguishing true driver mutations involved in tumor progression. Here, the authors review the current literature on important somatic driver mutations in melanoma, along with the implications for treatment. Cancer 2017;123:2104-17. © 2017 American Cancer Society.

Rare Variant, Gene-Based Association Study of Hereditary Melanoma Using Whole-Exome Sequencing.

Background: Extraordinary progress has been made in our understanding of common variants in many diseases, including melanoma. Because the contribution of rare coding variants is not as well characterized, we performed an exome-wide, gene-based association study of familial cutaneous melanoma (CM) and ocular melanoma (OM). Methods: Using 11 990 jointly processed individual DNA samples, whole-exome sequencing was performed, followed by large-scale joint variant calling using GATK (Genome Analysis ToolKit). PLINK/SEQ was used for statistical analysis of genetic variation. Four models were used to estimate the association among different types of variants. In vitro functional validation was performed using three human melanoma cell lines in 2D and 3D proliferation assays. In vivo tumor growth was assessed using xenografts of human melanoma A375 melanoma cells in nude mice (eight mice per group). All statistical tests were two-sided. Results: Strong signals were detected for CDKN2A (Pmin = 6.16 × 10-8) in the CM cohort (n = 273) and BAP1 (Pmin = 3.83 × 10-6) in the OM (n = 99) cohort. Eleven genes that exhibited borderline association (P < 10-4) were independently validated using The Cancer Genome Atlas melanoma cohort (379 CM, 47 OM) and a matched set of 3563 European controls with CDKN2A (P = .009), BAP1 (P = .03), and EBF3 (P = 4.75 × 10-4), a candidate risk locus, all showing evidence of replication. EBF3 was then evaluated using germline data from a set of 132 familial melanoma cases and 4769 controls of UK origin (joint P = 1.37 × 10-5). Somatically, loss of EBF3 expression correlated with progression, poorer outcome, and high MITF tumors. Functionally, induction of EBF3 in melanoma cells reduced cell growth in vitro, retarded tumor formation in vivo, and reduced MITF levels. Conclusions: The results of this large rare variant germline association study further define the mutational landscape of hereditary melanoma and implicate EBF3 as a possible CM predisposition gene.

Electrical Chips for Biological Point-of-Care Detection.

As the future of health care diagnostics moves toward more portable and personalized techniques, there is immense potential to harness the power of electrical signals for biological sensing and diagnostic applications at the point of care. Electrical biochips can be used to both manipulate and sense biological entities, as they can have several inherent advantages, including on-chip sample preparation, label-free detection, reduced cost and complexity, decreased sample volumes, increased portability, and large-scale multiplexing. The advantages of fully integrated electrical biochip platforms are particularly attractive for point-of-care systems. This review summarizes these electrical lab-on-a-chip technologies and highlights opportunities to accelerate the transition from academic publications to commercial success.

Fixed Drug Eruption due to Achiote Dye.

Fixed drug eruption (FDE) is a localized type IV sensitivity reaction to a systemically introduced allergen. It usually occurs as a result of new medication, making identification and avoidance of the trigger medication straightforward; however, in a rare subset of cases no pharmacological source is identified. In such cases, the causative agent is often a food or food additive. In this report we describe a case of a FDE in a 12-year-old girl recently immigrated to the United States from Ecuador who had no medication exposure over the course of her illness. Through an exhaustive patient history and literature review, we were able to hypothesize that her presentation was caused by a dietary change of the natural achiote dye used in the preparation of yellow rice to a locally available commercial dye mix containing tartrazine, or Yellow 5, which has previously been implicated in both systemic hypersensitivity reactions and specifically in FDE. This report adds to the small body of available literature on non-pharmacological fixed hypersensitivity eruptions and illustrates an effective approach to the management of such a presentation when history is not immediately revealing.

Microfluidic differential immunocapture biochip for specific leukocyte counting.

Enumerating specific cell types from whole blood can be very useful for research and diagnostic purposes-e.g., for counting of CD4 and CD8 T cells in HIV/AIDS diagnostics. We have developed a biosensor based on a differential immunocapture technology to enumerate specific cells in 30 min using 10 µl of blood. This paper provides a comprehensive stepwise protocol to replicate our biosensor for CD4 and CD8 cell counts. The biochip can also be adapted to enumerate other specific cell types such as somatic cells or cells from tissue or liquid biopsies. Capture of other specific cells requires immobilization of their corresponding antibodies within the capture chamber. Therefore, this protocol is useful for research into areas surrounding immunocapture-based biosensor development. The biosensor production requires 24 h, a one-time cell capture optimization takes 6-9 h, and the final cell counting experiment in a laboratory environment requires 30 min to complete.