The RNA world is changing our views about sensing and resolution of DNA damage. Here, we develop single-molecule DNA/RNA analysis approaches to visualize how nascent RNA facilitates the repair of DNA double-strand breaks (DSBs). RNA polymerase II (RNAPII) is crucial for DSB resolution in human cells. DSB-flanking, RNAPII-generated nascent RNA forms RNA:DNA hybrids, guiding the upstream DNA repair steps towards favouring the error-free Homologous Recombination (HR) pathway over Non-Homologous End Joining. Specific RNAPII inhibitor, THZ1, impairs recruitment of essential HR proteins to DSBs, implicating nascent RNA in DNA end resection, initiation and execution of HR repair. We further propose that resection factor CtIP interacts with and helps re-activate RNAPII when paused by the RNA:DNA hybrids, collectively promoting faithful repair of chromosome breaks to maintain genomic integrity. RNA has been implicated in DNA repair. This work shows that the interplay of RNAPII-generated nascent RNA, RNA:DNA hybrids and the resection factor CtIP guide DNA double strand break repair pathway choice towards error-free homologous recombination. To compensate for RNA instability, DNA appeared later during the evolution to better preserve genetic information, followed by fidelity mechanisms to maintain genome stability1. Recently, RNA has emerged as a major factor in essential mechanisms regulating gene expression3 and contributing actively to DNA repair processes9. Arguably the most cytotoxic genomic lesions are DNA double-strand breaks (DSBs), lesions repaired mainly by either of the two major pathways: non-homologous end-joining (NHEJ) and homologous recombination (HR)14. While numerous protein components of these two pathways have been discovered over time, only recently RNA has been implicated in DSB repair as well. For example, recent evidence showed that DSBs in transcriptionally active genomic regions are more prone to be repaired by HR15.The concept of an RNA world postulates that RNA was essential for molecular processes and biochemical reactions implicated in the origin of life on Earth16, yet it is unclear, unlikely perhaps, that HR is restricted to these genomic regions only. Hence, chromatin structure at transcriptionally active sites and the influence of diverse, relevant mechanisms, including DNA repair pathways, are currently subject to intense investigation to elucidate to what extent and how RNA impacts DSB repair. So far, such efforts generated controversial results. On the one hand, global RNA transcription is inhibited after DNA damage to avoid conflicts between repair and other DNA metabolic processes such as replication17. RNA involvement in DNA repair processes has also been demonstrated to contribute to genomic instability by forming RNA:DNA hybrid structures18.Interestingly, only 2-8% of the human genome gets transcribed19, exemplified by the DNA damage response RNAs (DDRNAs), necessary for DDR activation22. Furthermore, recruitment of general transcription factors to DNA damage sites was reported, and an active role for RNA polymerase II (RNAPII) in DNA repair was proposed23. Recently, RNA polymerase III was reported to be actively recruited to DSBs by the MRN complex and mediate RNA synthesis, promoting HR repair24. However, any role of RNAPII, the major mammalian RNA polymerase, in this context remains unknown. Any potential mechanistic contribution to DNA repair pathway choice could also inspire cancer treatment strategies to be combined with standard-of-care DNA damaging radio-chemotherapy.On the other hand, the formation of RNA:DNA hybrids regulates DNA repair in diverse organismsAs the repair mode is critical for genomic integrity and thereby inheritance, evolution, organismal development, and tissue homeostasis, the emerging evidence for RNA involvement raises the crucial questions of whether and how could RNA guide the choice between HR and NHEJ in DSB repair, an issue that we address in our present study.Here, we elucidate the role played by de novo RNA synthesis by RNAPII in DSB repair via HR in human cells. We found that RNA presence during different cell cycle phases impacts the decision between the HR and NHEJ repair pathways, combined with the homologous sequences of sister chromatids. Using our single-molecule analysis approaches, we observed nascent RNA overlapping with ssDNA resection tracts generated during DNA end resection, indicating that RNAs, mainly synthesized by RNAPII, are essential to initiate DNA end resection and thereby shift the choice of DSB repair towards using the more faithful HR over NHEJ. Indeed, RNA:DNA hybrid formation is essential for resection processing and as a repair regulatory step in the HR pathway. Moreover, RNAPII inhibition, using a specific CDK7 inhibitor THZ1, impairs HR factor recruitment to DSB. We further demonstrate a previously unsuspected function of CtIP as a transcription re-activator of RNAPII paused transiently by the RNA:DNA hybrids during the early stage response to DSBs, thereby promoting DNA resection and skewing DSB repair balance towards HR.25, in human U2OS osteosarcoma cells. We focused on the time period early after irradiation since that is the time critical for the cell’s choice to repair DSBs via either NHEJ or HR. Hence our experiments were designed to analyze particularly the initial 30 min post-radiation exposure sure Fig.. In contsure Fig.. The ovesure Fig.. Such RNsure Fig.. While Hsure Fig.. Althougons Fig. . For sucmin Fig. , the nasmin Fig. .Fig. 1DN27. CtIP depletion negatively affected nascent RNA predominantly in the late S and G2, having less impact in the early S phase  upon IR. To perform this analysis, HeLa cells were treated with BrdU for 24 h, followed by a 30 min EU pulse immediately after IR exposure, before stretching the NA fibers , the critical step of HR repair  fibers approach. Our technique, called R-SMART (RNA-SMART) to acknowledge a modification of the SMART assayers Fig. . The maiers Fig. . We obseers Fig. . Using ters Fig. . Hence, ers Fig.  and can ers Fig. . Notablyers Fig. . To bettair Fig. .Fig. 2Na29. RNaseH1 expression reduced the extent of post-IR interaction between ssDNA and nascent RNA , consistent with a scenario that ssDNA tracts may indeed be covered or protected in some regions from degradation by nascent complementary RNA molecules  gets recruited to DSBslls Fig. . THZ1-mells Fig. , suggestlls Fig. . These d 36 Fig. . Further 36 Fig. . Taken tNext, we wished to further extend the concept emerging from our present results, namely that new RNAPII-mediated transcription at DSB sites is required to direct the cell’s choice of DSB repair process by promoting accrual of essential HR factors. First, we demonstrated that recruitment of RPA, which is required to protect ssDNA stretches created during DNA end resection, diminished at DSBs during the first 10 min (600 s) after microirradiation Fig. . Next, wFurthermore, we tested RIF1, as a 53BP1 interactor when phosphorylated by ATM to promote NHEJ, in THZ1-treated cells and showed that RNAPII inhibition resulted in increased RIF1 foci formation upon IR as well  with high glucose plus GlutaMax supplemented with 10% FBS, 100 μg/ml streptomycin and 100 U/ml penicillin at 37 °C in 5% CO2. siRNAs against CtIP (GCUAAAACAGGAACGAAUC), BRCA1 (GGAACCUGUCTCCACAAAG), RAD52 , 53BP1 (GAAGGACGGAGTACTAATA) and a non-target control sequence (Sigma Aldrich) were transfected with the RNAiMax lipofectamine reagent mix (Life Technologies), according to the manufacturer’s instructions. Cells were cultured in the presence of THZ1 , Triptolide  or Pladionalide-B (CAS Number: 445493-23-2) using concentrations mentioned in each figure.U2OS cells were grown on coverslips and pre-extracted for 3 min on ice using 0.2% Triton X-100 in PBS, then fixed with 4% paraformaldehyde (w/v) in PBS for 10 min, washed three times with PBS, and blocked for at least 1 h with 5% FBS diluted in PBS. Cells were incubated with adequate primary antibodies  for further analysis. Statistical significance was determined with Ordinary one-way and two-way ANOVA tests using multiple comparison using PRISM 8 software (Graphpad Software Inc). Statistically significant differences were labeled with one, two, or three asterisks if p < 0.05, p < 0.01 or p < 0.001, respectively.Quantitative image-based cytometry (QIBC) was performed as previously describedNascent RNA was visualized using metabolic labeling using Invitrogen™ Click-iT™ RNA Alexa Fluor™ 594 imaging kit, with modifications. Briefly, cells were cultured in complete media and pulsed for 30 min with EU at a final concentration of 1 mM, before fixation with 4% paraformaldehyde (PFA) for 10 min. After fixation, cells were permeabilized with 0.5% Triton X-100 and washed 3 times with Tris buffered saline (TBS) . The click reaction master mix was then prepared as follows: 5 µM Alexa Fluor 488 azide, 2 mM CuSO4, 100 mM sodium ascorbate before sample preparation for acquisition using QIBC. The mean intensity of the nucleoli EU was analyzed using a spot detector tool and was excluded from the EU intensity of the nucleus to determine the EU intensity of the nucleoplasm.HeLa and HeLa-RNAseH1 cells down-regulated for the indicated genes were grown in the presence of 10 μM bromodeoxyuridine  for 24 h. The cultures were then irradiated (5 Gy) and incubated with 100 μM RNA precursor 5-ethynyluridine (EU) and harvested after 30 min. Cells were lysed using Spreading Buffer . 2000 cells were used to stretch nucleic acid fibers on coverslips using a 15° angle of incline for 8 min and fixed with cold methanol:acetic acid (3:1) for 10 min at room temperature. Five slides were stretched for all experimental conditions and two or three slides for each condition were stained. Nascent RNA was detected using the Click-it RNA Alexa Fluor 488 imaging kit (ThermoFisher Scientific) following the manufacturer’s instructions. The samples were then incubated directly without denaturation with an anti-BrdU mouse monoclonal antibody . Secondary antibodies were DayLight 555 antimouse (Thermo Fisher Scientific).DNA fiber images were acquired using an LSM800 confocal microscope (Carl Zeiss) and a Plan-Apochromat 63×/1.4 numerical aperture (NA) oil immersion objective (Carl Zeiss). The labeled RNA and DNA fibers were analyzed using LSM800 Zeiss ZEN software (Blue edition), using a colocalization tool to measure pixel staining for both fluorescence. More than 20 fields or more than 100 fibers were scored during measurements on each slide for each repeated experiment. The percentage of RNA and DNA staining pixels and their colocalization was presented from the experiments.HeLa and HeLa-RNAseH1 cells were treated with 10 μM bromodeoxyuridine  for 24 h. The cultures were then irradiated (5 Gy) harvested after 1 h. Cells were lysed using Spreading Buffer . 2000 cells were used to stretch and fix nucleic acid fibers as described in the R-SMART section. The samples were then incubated directly without denaturation with an anti-BrdU rat monoclonal antibody  and a S9.6 mouse monoclonal antibody . Secondary antibodies were DayLight 550 anti-rat and 488 antimouse. Five slides were stretched for all experimental conditions and two or three slides for each condition were stained. Images were taken as described above for the R-SMART technique. The percentage of pixels of RNA:DNA hybrids and DNA resection staining, and their colocalization, were analyzed as in the R-SMART technique and presented from both experiments. High resolution images were acquired with STELLARIS 8 Confocal Microscope (Leica Microsystems).Cells were seeded in 1 glass bottom well chamber one day before analysis and pre-sensitized with 10 μM bromodeoxyuridine  for 24 h. Before laser irradiation, cells were incubated with 1 µM THZ1 (1604810-83-4) for 1 h, at 37 °C and 5% CO2. The cells were then maintained under the same conditions using the Temperature Control Chamber (PerkinElmer UltraView VoX), and images were taken with a Nikon Eclipse Ti microscope equipped with a 63x oil objective and equipped with a Perkin Elmer spinning disk. Images were collected every 4 s for 10 min after irradiation. Cellular nuclei were irradiated with a 355 nm UV ablation laser at a power setting of 0.15, a repetition rate of 200 Hz, a pulse energy >60 μJ, pulse length< 4 ns (Rapp OptoElectronic). The intensity of the GFP signal was measured using ImageJ software for at least 20 cells per condition from three biological replicates.Cells were seeded on coverslips and on the day of the BrUTP incorporation assay were 70-75% confluent. Cells were first irradiated and incubated for the indicated period of time . Coverslips were washed once with PBS at each of the above time points and incubated with permeabilization buffer  for 2 min at RT for every time point. Next, the permeabilization buffer was removed from the coverslips and transcription buffer  was added. The coverslips were incubated for additional 8–10 min at 37 °C. After removing the transcription buffer, the coverslips were gently washed with cold PBS and fixed in 4% paraformaldehyde for 10 min at RT. Indirect immunofluorescence was performed using mouse anti-BrdU antibody (BD347580) to detect the incorporation of the BrUTP analog in nascent transcripts. The images were acquired using an LSM800 confocal microscope (Carl Zeiss) and a Plan-Apochromat ×63/1.4 numerical aperture (NA) oil immersion objective (Carl Zeiss). The labeled nascent RNAs were analyzed using LSM800 ZEN software (Blue edition). The mean intensity of BrUTP incorporation was measured via ZEN blue (Zeiss) at different sites of the nucleus of the cell avoiding the nucleolar compartment. Data were processed using PRISM 8 software (Graphpad software Inc.) and statistical significance was determined using multiple comparison with Ordinary One-Way ANOVA.Whole-cell line extracts were prepared in Laemmli buffer , and proteins were resolved using SDS-PAGE and transferred to nitrocellulose membranes, followed by immunoblotting. Western blot analysis was performed using the antibodies listed in Table U2OS cells were irradiated and incubated for specific time points at 7, 15, 30, 60, and 120 min. Nuclear protein fractions were prepared using the Nuclear Complex co-IP kit (Active Motif #5400), according to the manufacturer. Subsequently, the nuclear extracts were diluted in the appropriate volume of IP buffer  supplemented with protease and phosphatase inhibitors. The diluted nuclear extracts were incubated with anti-RNA polymerase II CTD repeat YSPTSPS antibody (8WG16)- ChIP grade  overnight at 4 °C. The protein extracts were then incubated with 25 μl of protein G dynabeads  for 1 h at 4 °C. The beads were washed 5 times with 250 μl of IP buffer and 20 μl of Laemmli sample buffer was added combined with heating at 97 °C for 5 min to elute the precipitated protein fraction from the beads. Western blot analysis was performed using the antibodies listed in Table Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Movie 1Reporting Summary


In this paper, we respond to the calls for context-specific scholarship and research on human resource management (HRM) in Asia. We provide an overview of and key insights into HRM in five Asian countries, representing five key regions: China (East Asia), India (South Asia), Kazakhstan , United Arab Emirates (West Asia), and Vietnam (Southeast Asia). Based on our comprehensive, pan-Asian review, we develop a reflective, comparative, and relational understanding of HRM practices. In doing so, we group the myriad contextual influences on the shaping of HRM practices at three broad levels: macro-, meso-, and microlevel influences. Specifically, we propose that influences from regionalization of economies, national business systems, industry, multinational enterprises, and individual-level predispositions collectively shape and variously influence the nature and extent of HRM practices. By considering the findings of prior research on convergence and divergence, we offer a nuanced perspective wherein each country and region in Asia possesses a distinct amalgam of national business systems, and where HRM practices respond to multilevel influences in varied ways. Recently, the contribution of the Asian region to global economic activity has continued to grow when other developed markets’ trade outputs have shrunk : China, India, Vietnam, Kazakhstan, and the United Arab Emirates (UAE). While Asian countries, to some scholars, might offer opportunities for greater convergence than divergence, we take a nuanced view wherein each country and region of Asia offers a distinct amalgam of national business systems and ways in which HRM practices respond to multilevel influences.When considering the myriad contextual influences affecting the shaping of HRM practices, we group these at three broad levels: Macro-, meso-, and microlevel influences. Specifically, we assert that influences from the regionalization of economies, national business systems , industry, global multinational enterprises (MNEs), and finally, individual-level predispositions collectively shape and variously influence the nature and extent of HRM practices. A view that these multiple levels of influence prevail has been widely acknowledged, for example, in studies on the influence of regionalization of economies , and concerning which thematic areas. More specifically, we used ProQuest to search for articles and studies related to the key HRM themes and terms relevant to our analysis (based on the three research questions we framed above). The preconditions for this analysis were that each paper was peer-reviewed and available in the full-text format. In practice, we ran a number of Boolean filter searches using relevant terms for the topic of interest and thematic area, accounting for regional variation in the appropriate terms. To address the two other parameters of quality and recentness, we further refined our search by targeting articles published in journals listed by the Chartered Association of Business Schools (CABS), Australian Business Dean Council (ABDC), the UK’s Association of Business Schools (ABS), and the Social Science Citation Index (SSCI). We extended our search to a variety of sources including ProQuest, ESBCO, books, webpages, and relevant journal articles. The original search was conducted in 2018 and updated in 2022.This article is reflective in nature as it is not only This section considers the three multilevel influences  that shape HRM practices in Asian contexts. These levels interact with each other and represent manifestations of practices or actions driven by the values and behaviors key decision-makers consider to be important for the operation of their business. Before delving into those, however, we state herewith the commonly understood goals of HRM, and investigate the extent to which these are found in an Asian context. Boxall and Purcell  offer anIn the seminal work in this field, Whitley  noted a Keeping in mind the pervasive impacts of globalization, we also employ in this paper the convergence–divergence–crossvergence theoretical lens proposed by Ralston and colleagues  perspective, it also created challenges around integrating and embedding these practices and values with local employees Budhwar, . The extEarly economic activity while still under British rule also saw a mirroring of UK common law, including the application of some employment legislation such as the Trade Unions Act in the early 1900s. Given the diversity of economic and political ideologies in India, and the scale of the workers to represent, several hundred local and regional trade unions were registered to represent the rights of their members. The country has over ten national unions, which typically have a highly adversarial stance in bargaining processes and are invariably linked to a major political party in the country . Following the economic liberalization programs of 1991, the Indian personnel management and industrial relations system witnessed a major set of reforms to allow businesses and industries flexibility in managing and organizing their labor policies. A case in point is the highly successful Indian software, information technology (IT), and business process outsourcing (BPO) industry, which employs a range of modern and globally comparable HRM practices  growth rates of the late 1990s and mid-2000s that major transformative pressure was placed on the Indian HRM function to become more responsive, proactive, market-oriented, and innovative  and egalitarianism  can be seen to be related to managerial philosophies and practices in China. Guan-xi is said to influence organizational knowledge-sharing, which may affect employees' values and behavior. De, meanwhile, is commonly regarded as a key performance criterion, and morality and personal responsibility are also critical to social harmony and the equality system  and joint ventures (JVs). The rapidly increasing number of such foreign-invested companies has put greater pressure on indigenous private companies and state-owned enterprises (SOEs), which must attend to their efficiency if they are to remain competitive  Free Trade Area (AFTA), US–Vietnam Bilateral Trade Agreement, and WTO membership has necessitated rapid and comprehensive changes at both the macro- and microlevels to enhance their competitiveness. In this context, the skilled, flexible, and well-educated workforce and its management, which are considered the most important sources of competitive advantage at both economic and organizational levels . Yet, the quality of human capital, which is fundamental to how these countries will advance in the new economic environment, remains low. Despite the high number of university graduates, there is a continual lack of qualified applicants for work positions . An individual possessing the desired knowledge and skills may receive two or three job offers at once, which contributes to a high level of job-hopping, especially in regions with high FDI. A further challenge is that the new realities of business environments  require 21st-century skills and significantly higher levels of human capital than in the past—something the Central Asian education system cannot really offer. Most of its higher education institutions have been privatized and are now largely concerned with profit generation, while considering faculty development a low priority.In Central Asia, HRM—as business professionals in Western societies understand it—has only recently started to take hold in the management discourse and in practice. As in all other Asian countries, MNCs have certainly played a significant role in reshaping the HRM practice in the Central Asian region, especially in terms of the emergence of mimetic pressures to adopt “best practices” and new approaches , and numerous nonmonetary benefits . As Fey et al. .y et al.  stated, y et al. . Notablycannot learn much from the Chinese”) compared to the previous owners. Yet, the situation and attitudes changed over time. Now the total trade between China and Central Asian countries has surpassed Central Asia’s trade with Russia, with commodities dominating. Chinese firms learned from their early experiences in Central Asia and gradually adjusted their approaches to managing human resources in Central Asian subsidiaries.As in other Asian countries, evidence of multivergence , the UAE is classified as a high-income developing economy, one of the most developed and economically important countries in the Middle East. The GDP per capita in the UAE was last estimated at 38,661 USD in 2020 IMF, .The government’s and leadership’s primary objective for the UAE is to build and develop an innovative and modern knowledge-based economy. An amalgam of resources and competitive advantages offered by each of the Emirates has helped the UAE to pursue this strategy. For example, Abu Dhabi’s considerable oil and gas resources, alongside a deliberate effort to develop and expand the public infrastructure and services throughout the country, have helped stimulate growth in the region Suliman, . Dubai, Article 20 of the UAE Constitution specifically commits the national government to “endeavour to ensure that employment is available for citizens and to train them so that they are prepared for it.” Furthermore, UAE Vision 2021 lists “skilled human capital” as the first of seven strategic enablers, consistent with the role of HR, which is described as “building strategic capabilities” to achieve strategic goals (Scott-Jackson et al., The most unique and unusual characteristics of the UAE labor market are its extensive reliance on expatriates and its dual nature. As the UAE began its modernization and strategic economic development, the existing skills, learning, training, development, and education levels of locals could not meet the rapidly increasing needs of a fast-changing and expanding country, which led to high demand for skilled and knowledgeable foreign labor (Fasano-Filho & Goyal, Workers from the Indian subcontinent including Bangladeshi, Nepalese, and Sri Lankan migrants, alongside those from India and Pakistan, who came to collectively make up 60% of the lower end of the labor market. Interestingly, workers from other Arab countries only came to represent 13% of the UAE workforce, with local Emirati nationals even fewer. Expatriate workers at the lower end of the market were, and still are, contracted for a fixed period and sponsored for a work visa by their employer, with limited mobility. By 2010, native citizens made up only 12% of the total population and 4% of the UAE workforce (Forstenlechner & Rutledge, Local Emirati nationals prefer jobs that offer high wage levels, short working hours, and good benefits, such as government jobs. In desirable, employee-friendly job positions, workers enjoy certain facilities and privileges, such as decent remuneration and benefits packages and guaranteed job security, even in the private sector. Given the lack of these for others, local Emirati workers and expatriate workers are not comparable. However, it is not necessarily better to be among the former, for the phenomenal growth in the number of expatriates has not created sufficient opportunities for locals, meaning they face heightened unemployment. As a result, Emiratis are under increasing pressure to train professionally and gain relevant skills, knowledge, and qualifications. An estimated 200,000 young Emirati locals will have entered the labor market over the past ten years armed with such qualifications, just what the country needed Toledo, . Today, The UAE has the highest number of published research articles on HRM among the Arab countries, which signals that HRM is attracting growing and evolving attention from both researchers and business leaders. The growth of both local and multinational corporations in the UAE has crucially underpinned the country’s modern and innovative HRM practices. Furthermore, a growing number of foreign and local universities enroll students on campuses in the UAE, which led to an increase in experienced HR professionals in the country. However, so far, the HRM function in the UAE has not demonstrated its value, nor effectively contributed to the achievement of organizations’ strategic goals. Many generally accepted HR practices have yet to be implemented fully, and there are significant variations in the ways in which HRM strategies and practices are formulated and implemented in the UAE.Our critical review of the literature leads us to conclude that the HRM function faces four key challenges in the UAE, which are common to other Arab countries (Budhwar et al., Having separately analyzed HRM in five countries, we now provide an integrated understanding of overall HRM in Asia. To begin with, our literature review suggests significant diversity and also certain overlaps in terms of the goals of HRM functions. These goals share similarities with Boxall’s and Purcell’s  classifiOur review also revealed significant diversity in terms of the multiple levels of influence that shape the nature and extent of HRM practices. At the macrolevel, we classified the influences into indigenous or modern, which we found a useful distinction to make as the early stages of economic growth and evolution of HRM in these countries were reflective of the dominant indigenous philosophical and ideological approaches. Similar macrolevel indigenous influences were noted in China, Vietnam, and the UAE, whereas India and Kazakhstan had somewhat different macrolevel factors shaping their HRM practices (see Table At the next level of analysis, we delved into how four key aspects of a country’s dominant NBS Whitley,  have shaMeanwhile, the extent of unionization and its nature will directly determine the rules of employment in each country. Today, China has a single trade union that is regulated and managed by the state, while in India, a democratic country, unions have different political and ideological affiliations, different densities and membership sizes by industry, and are generally perceived as independent of the state. As such, in a bargaining setting, India’s trade unions deliver different outcomes for their members compared to those in China. In India, one can expect varied employment rule-making for different industry sectors, and strong adversarial stances during the bargaining process, whereas China, in contrast, is characterized by homogeneous employment laws and regulations and compliance with the rule of the state.A final factor to note is that the social sphere, which held relevance in all Asian countries studied, but played diverse roles. Kazakhstan, for instance, has inherited socialist Soviet approaches of artificially re-engineering or “beefing up” employment numbers to achieve social legitimacy. Aside from this, social capital and relational business practices were noted to be employed across the board, though the actual practices reported and the extent of their effect on HR varied.The purpose of our study was to contribute to context-specific scholarship and research. We assert that thorough, descriptive understandings of each Asian region will provide the basis for developing a thematic and relational understanding of each country in terms of how their multiple levels of influences shape and affect indigenous HRM practices to converge, diverge, or form some novel amalgam of HRM practices. Table Our analysis suggests weak convergence of HRM practices for the two fastest-growing global economies of China and India. Conversely, for Vietnam and Kazakhstan, we observed medium overall convergence, while in the UAE, convergence was paired with MNE influences on traditional industries. When it came to divergence of HRM practices, China seemed to experience a strong level of divergence, whereas, not surprisingly, with greater diversity, inter-regional divergence was found. Likewise, there was evidence of strong divergence in Vietnam due to the highly heterogeneous nature of HRM practices in the country, though only weak divergence was seen for startups and private-sector firms. In comparison, for Kazakhstan, we found evidence of strong levels of divergence, which could have arisen due to the significant local indigenous influences. Interestingly, for the UAE, there was strong evidence of divergence in the public sector and among local firms and SMEs, which could be attributed to localization effort, that is, Emiratization. Lastly, when it came to crossvergence/multivergence, China showed evidence of inter-regional crossvergence, which could be strongly attributed to the levels of influence of MNEs, paired with local indigenous practices that are still strong and divergent in certain parts of China. In comparison, for India, there was evidence of certain industry-specific multivergence, especially in the IT/BPO industry (Malik et al., In sum, based on the critical review and analysis presented above, we can conclude that the main influences on HRM practices in Asia are the regionalization of economies, national business systems, industry, global MNEs, and individual-level predispositions, which collectively shaped and variously influenced the nature and extent of HRM practices in the Asian countries studied. We found a mix of convergence, divergence, and crossvergence/multivergence as each country and region in Asia offered a distinctive amalgam of national business systems, and the varied ways in which HRM practices responded to multilevel influences were complex and evolving. This work contributes to the literature by presenting a comparative and relational understanding of HRM practices in five key Asian regions. In the future, more such studies should be conducted to further unravel and separate out this complex, evolving, and dynamic research area, that is, HRM in Asia.


Succinate dehydrogenase-deficient and fumarate hydratase-deficient renal cell carcinomas (SDHRCC and FHRCC) are rare kidney cancers driven by loss of TCA cycle enzymes.To define and compare the genomic and metabolomic hallmarks of SDHRCC and FHRCC.SDHA/B/C/D/AF2 or FH.We analyzed SDHRCC and FHRCC tumors with either immunohistochemical evidence of loss of protein expression or genomically confirmed biallelic inactivation of Somatic alterations were identified using clinical pipelines, with allele-specific copy number alterations (CNAs) identified using FACETS. Mass spectrometry–based metabolomic profiling was performed on available SDHRCC and FHRCC tumors.SDHB, whereas only 17/22 FHRCCs had pathogenic germline FH variants. SDHRCCs had a lower mutation burden (p = 0.02) and CNA burden (p = 0.0002) than FHRCCs. All SDHRCCs presented with deletion of chromosome 1p (overlapping SDHB), whereas FHRCCs demonstrated high but not ubiquitous loss of 1q (FH locus). Both SDHRCCs and FHRCCs exhibited significant idiopathic accumulation of the metabolite guanine. FHRCC tumors had elevated levels of urea cycle metabolites , whereas SDHRCC tumors had elevation of numerous acylcarnitines. These characteristic metabolic changes allowed identification of a previously unrecognized SDH-deficient RCC.Tumors were analyzed for 42 patients . In the germline analysis, 16/17 SDHRCCs harbored a germline alteration in Despite sharing similar genetic etiology, SDHRCC and FHRCC represent distinct molecular entities with unique genetic and metabolic abnormalities.Kidney cancers driven by loss of the gene encoding either the succinate dehydrogenase or fumarate hydratase enzyme are rare. We sought to define and compare the genetic and metabolic features of these cancer entities. SDH andvely) [2 , while Fvely) [2 .FH or SDHA suggest that complete loss of either enzyme produces a block in the TCA cycle and consequent accumulation of upstream metabolic intermediates (succinate in SDHRCC and fumarate in FHRCC) [FH or SDH mutation are predisposed to developing additional syndromic features; FHRCC patients can develop cutaneous and uterine leiomyomas [Owing to their rarity, comprehensive evaluation of the genetic and metabolic features of SDHRCC and FHRCC tumors has been limited . In vitrn FHRCC) . Both fun FHRCC) , and botn FHRCC) ,8, suggeiomyomas  comprisiiomyomas  and gastiomyomas .+ T-cell infiltration with PD-L1 expression in tumors, and a highly depleted and mutant mitochondrial genome [SDH mutant alleles, but did not describe the full genomic landscape of SDHRCC [The functional similarity of the SDH and FH enzymes and the similar downstream epigenetic changes caused by accumulation of the associated oncometabolites suggest that SDHRCC and FHRCC may bear genomic and metabolic similarities. Three recent studies, including one by our group, have described several genetic hallmarks of FHRCC, including a characteristically low tumor mutation burden, distinct tumor microenvironments characterized by CD8l genome ,12,13. Bf SDHRCC ,14,15. Hf SDHRCC .To characterize and compare the molecular characteristics of SDHRCC in relation to FHRCC, we assembled a multi-institutional genomically defined cohort of SDHRCC and FHRCC tumors. We describe two novel discoveries. First, we report that SDHRCCs exhibit a recurrent pattern of somatic genetic alterations, most prominently characterized by near-universal loss of heterozygosity on chromosome 1p. Second, by applying metabolomic profiling to a subset of SDHRCC and FHRCC cases, we identify characteristic metabolic alterations associated with TCA-cyclemutated kidney cancers, including large accumulation of the metabolite guanine. We then apply metabolomic profiling as an exploratory exercise in a previously cohort of unclassified RCC tumors and identify a previously unrecognized SDHRCC patient within the cohort.2.2.1.For patients for whom targeted sequencing was carried out, tumor and matched normal samples were sent for targeted sequencing using our previously validated sequencing panel (MSK-IMPACT), of which three versions exist, targeting 341, 410, or 468 actionable cancer-associated genes, respectively . DNA fro2.2.https://ccstempo.netlify.app/). In brief, demultiplexed FASTQ files were aligned to the b37 assembly of the human reference genome from the GATK bundle using BWA mem (v0.7.17). Aligned reads were converted and sorted into BAM files using samtools (v1.9) and marked for PCR duplicates using GATK MarkDuplicates (v3.8-1). Somatic mutations  were called in tumor-normal pairs using MuTect2 (v4.1.0.0) and Strelka2 (v2.9.10), and structural variants were detected using Delly (v0.8.2) and Manta (v1.5.0). Variants were annotated and filtered for recurrent artifacts and false positives using methods as previously described [Whole-exome sequencing (WES) samples  were processed and analyzed using the TEMPO pipeline  institutional review board, partial or radical nephrectomies were performed at MSKCC and tissue was stored at the MSK Translational Kidney Research Program. Samples were flash frozen and stored at −80 °C before metabolomic characterization. Clinical metadata were recorded for all tumor samples. Samples were thawed and extracted according to the standard Metabolon protocol to remove proteins, dislodge small molecules bound to proteins or physically trapped in the protein matrix, and recover a wide range of chemically diverse metabolites. Samples were then frozen, dried under vacuum, and prepared for liquid chromatography (LC)/mass spectrometry (MS).The sample extract was split in two and reconstituted in acidic and basic LC-compatible solvents. Samples were eluted with a methanol/water gradient; the solvent contained 0.1% formic acid for the acidic extracts and 6.5 mM ammonium bicarbonate for the basic extracts. One aliquot was analyzed using conditions optimized for acidic positive ions and the other using conditions optimized for basic negative ions. The two aliquots were independently injected onto separate dedicated columns. The MS analysis alternated between MS and data-dependent MS/MS scans using dynamic exclusion. The LC/MS portion of the platform was based on a Waters ACQUITY UPLC and a Thermo-Finnigan LTQ-FT mass spectrometer, which had a linear ion-trap at the front end and a Fourier-transform ion cyclotron resonance mass spectrometer at the back end. Accurate mass measurements could be performed for ions with counts greater than 2 million. The average mass error was <5 ppm. Greater effort was required to characterize ions with a count of less than 2 million. Typically, fragmentation spectra (MS/MS) were generated in a data-dependent manner, but targeted MS/MS could be used if necessary, such as in cases of lower signal levels.2 normalized. When metabolite levels were below the level of detection, the lowest measured abundance of that compound across all samples was imputed. All statistical tests were performed in R. Distributions were compared using the Wilcoxon test. All statistical analyses were two-sided and Benjamini-Hochberg correction was applied to p values.Data were extracted from the raw MS files, which were loaded into a relational database. The information was then examined and appropriate quality control limits were imposed. The Metabolon proprietary peak integration software was used to identify peaks, and results for components were stored in a separate data structure. Metabolites were compared to an in-house library of standards from Metabolon. Data for each of these standards were based on retention index, mass-to-charge ratio, and MS/MS spectra. These parameters for each feature for each compound in the metabolomic data were compared to analogous parameters in the library. As described by Evans et al. , compoun3.3.1.FH or SDHA/SDHB/SDHC/SDHD/SDHAF2) or immunohistochemistry (IHC) criteria  and had comparable gender ratios. Other demographic and clinical information is provided in SDHRCC and FHRCC patients presented with disease at a young age , FAT1 (n = 3), PTPRT (n = 3), and EP300 , while two out of three FAT1, PTPRT, and EP300 mutations each were approximately clonal. Most of these mutations  were accompanied by LOH and therefore represent bona fide biallelic loss of these tumor suppressors. SDHRCC tumors tended to have few, if any, accompanying somatic mutations, with a single case showing a subclonal NF2 mutation. Both SDHRCC and FHRCC tumors lacked a significant number of mutations in common ccRCC-associated genes, including PBRM1 (n = 1), SETD2 (n = 0), TERT (n = 1), and TP53 (n = 1).We next evaluated whether secondary somatic mutations in other cancer-associated genes present in SDHRCC and FHRCC tumors converged on common molecular pathways. FHRCC tumors were in general more highly mutated than SDHRCC tumors, with a significantly higher TMB  tissues also demonstrated LOH on chromosome 1p. In ~50% of cases, 1p LOH was accompanied by single-copy amplification of chromosome 1q. Besides chromosome 1, there were no highly recurrent CNAs among SDHRCC patients. By contrast, FHRCC patients usually—although not always—had LOH on chromosome 1q, particularly in the region overlapping FH. FHRCC tumors showed a higher incidence of other CNAs, including gains on chromosomes 16 and 17, which occurred in >40% of FHRCC patients. FHRCC patients were also more likely to have gains on chromosome 2, with approximately one-third having this alteration, while only 1/11 SDHRCC patients had a gain on chromosome 2.In contrast to somatic mutations, SDHRCC and FHRCC tumors demonstrated numerous large-scale CNAs . Althoug3.3.Complete loss of SDH and FH in vitro leads to accumulation of the upstream metabolites succinate and fumarate, respectively, and disruption of metabolic pathways dependent on normal TCA cycle function. To study the metabolic alterations underlying SDHRCC and FHRCC in vivo, we identified three SDHRCC and two FHRCC tumors from our genomic cohort with suitable fresh-frozen tissue for metabolomic profiling. For comparison, we also profiled 14 ccRCC samples, four unclassified RCC (uRCC) samples, and 19 matched normal kidney tissue samples. Principal component analysis (PCA) separated both FHRCC and SDHRCC from ccRCC, indicating that these entities broadly display unique metabolic phenotypes relative to ccRCC .2 fold-change 3.41; p = 0.001, q = 0.08; 2 fold-change 1.69; p = 0.02, q = 0.34). By contrast, fumarate levels were uniquely elevated in FHRCC, consistent with the position of SDH upstream of FH. Despite complete loss of FH, FHRCC samples demonstrated only an approximately twofold increase in fumarate levels that did not reach statistical significance , in contrast to the ~50-fold increase observed when FH is ablated in vitro [Of the eight TCA cycle metabolites quantified, succinate exhibited the largest increase in abundance compared to normal tissue when grouping FHRCC and SDHRCC samples together, but succinate accumulation was greater in SDHRCC , but no change in purine-derived adenine or adenosine, and no change in the catabolic product of guanine, xanthine. Importantly, guanine was also elevated ~20-fold relative to ccRCC tumors  with a statistically significant difference in abundance between FHRCC/SDHRCC and normal kidney  and citrulline . Other urea cycle metabolites did not show significant changes in abundance  did not allow de novo discovery of statistically significant differences in metabolite levels. Instead, we examined putative metabolomic phenotypes specific to either SDHRCC or FHRCC as identified by prior in vitro studies. Consistent with prior reports suggesting that excess fumarate can disrupt normal flux in the urea cycle ,29, we fbundance .3.5.2 fold-change 5.30) and succinate (log2 fold-change 1.95), but not fumarate (log2 fold-change −0.60) when compared to ccRCC (As noted earlier, four uRCC tumors included in our metabolomics data clustered closely with the SDHRCC and FHRCC tumors, but separately from ccRCC on PCA . Althougto ccRCC . This suto ccRCC , which wSDH or FH via MSK-IMPACT testing. The germline status of FH and SDH subunits was unknown. Given the presence of 1p LOH and the characteristic metabolic features seen, we hypothesized that RCC260 may be a SDHRCC tumor. IHC analysis showed loss of SDHB protein expression in neoplastic cells, but retention in stromal, endothelial, and inflammatory cells (Patient RCC260 was a 44-yr-old male who presented with a kidney mass measuring 14.6 cm on imaging along with multiple bone lesions. Genitourinary pathology review of a bone biopsy noted a high-grade uRCC subtype with suspicion of distal nephron differentiation. He was treated with first-line pazopanib and passed away 10 months later. Critically, this patient was among the first to consent to MSK-IMPACT sequencing, several years before SDHRCC and FHRCC were recognized as distinct entities. There was no evidence of somatic mutation of either ry cells , D. Geno4.SDHB and CNAs, FHRCC demonstrates more genomic diversity. SDHRCC tumors had a lower TMB and lower FGA than FHRCC tumors. The comparative genomic stability of SDHRCC relative to FHRCC suggests that from an evolutionary perspective, SDHRCC tumors fail to genomically diversify and that biallelic loss of SDHB is sufficient to drive tumorigenesis.Despite superficially similar pathophysiology, SDHRCC and FHRCC are genomically distinct entities. While SDHRCC appears to be mainly driven by biallelic loss of SDHB or FH via one of two genetic mechanisms: either an initial germline mutation followed by a somatic second hit [FH and SDHB are haplosufficient and require two hits to produce metabolomic changes. Because of the n-of-1 nature of the purely somatic SDHRCC case, it is difficult to conclude if purely somatic SDHRCC is molecularly or clinically distinguishable from SDHRCC in patients with germline mutation, and these results should motivate future investigation into identifying phenotypic differences between these entities.Our data establish that SDHRCC and FHRCC arise through biallelic loss of cond hit , or altecond hit . In compBecause complete loss of SDH or FH disrupts the normally high metabolic flux through the TCA cycle, largescale changes to metabolism are expected . There wAs the field moves towards molecular stratification of RCCs , integraOur study has limitations inherent to studies on rare tumors. To increase our sample size, we included patients from an external multi-institutional cohort that had limited clinical information available. We acknowledge that there is heterogeneity in the type and depth of genetic sequencing and metabolomic analyses. Despite the statistical limitations and the heterogeneous data, our genomic and metabolomic analysis of FHRCC and SDHRCC provides insights into the defining molecular phenotypes of these entities and serves as a foundational resource for future investigation.5.In conclusion, our sequencing and metabolomic analyses showed that despite sharing similar genetic etiology, SDHRCC and FHRCC represent distinct molecular entities with unique genetic and metabolic abnormalities.3125467


Adverse events(AEs) related to hepatotoxicity have been reported in patients treated with immune checkpoint inhibitors (ICIs). As the number of adverse events increases, it is necessary to assess the differences in each immune checkpoint inhibitor regimen. The purpose of this study was to examine the relationship between ICIs and hepatotoxicity in a scientific and systematic manner. Data were obtained from the FDA Adverse Event Reporting System database (FAERS) and included data from the first quarter of 2014 to the fourth quarter of 2021. Disproportionality analysis assessed the association between drugs and adverse reactions based on the reporting odds ratio (ROR) and information components (IC). 9,806 liver adverse events were reported in the FAERS database. A strong signal was detected in older patients (≥65 years) associated with ICIs. hepatic adverse events were most frequently reported with Nivolumab (36.17%). Abnormal liver function, hepatitis, and autoimmune hepatitis were most frequently reported, and hepatitis and immune-mediated hepatitis signals were generated in all regimens. In clinical use, patients should be alert to these adverse effects, especially in elderly patients, who may be aggravated by the use of ICI. Immune checkpoint inhibitors (ICIs) are a new class of cancer therapeutics that include programmed death receptor 1 monoclonal antibodies , cellular programmed death ligand 1 monoclonal antibodies  and Cytotoxic T-lymphocyte-associated protein 4 monoclonal antibodies  –4.In recent studies, ICIs have shown significant efficacy, changing the outlook for the treatment of non-small cell lung cancer(NSCLC) , squamouFAERS is a spontaneous reporting system that includes all adverse drug reactions and medication error information collected by the US Food and Drug Administration (FDA) and provides an important basis for post-marketing safety risk monitoring and evaluation of drugs. There is no safety analysis of ICIs-related hepatotoxicity in a large data sample. Therefore, this study provides a reference for rational clinical use of drugs by data mining and analysis of safety signals of ICIs in the FAERS database, the adverse event reporting system of the FDA.Data for the retrospective pharmacovigilance study were obtained from the FAERS database. FAERS collects data not only from the United States, but also from other countries and regions. A total of 36 quarterly reporting files were screened from the first quarter of 2014 to the fourth quarter of 2021.FAERS data files contain seven types of data sets: patient demographic and administrative information (DEMO), drug/biologic information (DRUG), adverse events (REAC), patient outcomes (OUTC), reporting source (RPSR), reporting drug therapy start date and end date (THER) and indication (INDI). As recommended by the FDA, we removed duplicate records prior to statistical analysis by selecting the most recent EVENT_DT when the CASEID was the same and the higher PRIMARYID when the CASEID and EVENT_DT were the same. All data downloaded from the FDA website were processed by SAS 9.4 and further analyzed using R software.We used the trade names and generic names of drugs included in the National Center for Biotechnology Information(NCBI) to search the FAERS database for ICIs that have been approved for marketing by the FDA, including CTLA-4 , PD-1  and PD-L1  .Adverse events with ICIs related to hepatotoxicity were defined as cases in the FAERS database where the treatment regimen included drugs in the ICIs class and a liver-related adverse reaction in the SOC classification occurred. AEs in the FAERS database are coded according to the preferred terms (PTs) in the Medical Dictionary of Regulatory Activities (MedDRA). According to MedDRA version 23.0, our study includes all liver and hepatobiliary-like diseases (MedDRA code 10019654) and all tumors of the hepatobiliary system (MedDRA code 10019811). In addition, based on the structure and variables of the FAERS database, a single adverse event report of ICIs related to hepatotoxicity was recorded as one case of data, even if more than one adverse event report was reported by the same patient.We assessed the time to occur of ICIs-related hepatotoxic AEs. The time of occurrence was the time interval between START_DT (treatment start date) and EVENT_DT (date of adverse event). We excluded inaccurate data entry, missing specific data and incorrectly entered reports (EVENT_DT earlier than START_DT). Reported hepatotoxic adverse events were used as positive endpoint events of interest, with the time of initiation of treatment in the case as the starting point of the event and the time of reporting of AEs as the positive endpoint of interest. Kaplan-Meier curves were used to calculate the median survival time and to describe the course of AE occurrence in terms of survival curves. The Kruskal-Wallis test was used to determine whether the time to AE onset was statistically different between ICI treatment regimens. A two-by-two comparison of each drug was then performed by Wilcoxon rank sum test.Disproportionality analysis is a data mining method that is now widely used in adverse drug reaction monitoring, and this method uses the number of times a drug is reported in association with an event in the adverse drug event reporting database as a basis to study the statistical relationship between the target drug and the target event in the database . There aAE reports for suspected drugs and other drugs were calculated using a 2×2 columnar table . Two datThe formula for calculating the ROR is asThe 95% confidence interval for the ROR can be calculated byP(Drug) obey a Beta distribution with α1, α2 as prior parameters, P(ADR) obey a Beta distribution with β1, β2 as prior parameters and P obey a joint Beta distribution with γ1, γ2 as prior parameters. When the Bayesian prior probabilities are uninformative priors, the Beta distribution is represented asAccording to Bayes’ theorem, the calculation of IC can be obtained asE(IC) and V(IC) can be expressed in a more understandable and computer-friendly way byThe 95% confidence interval for IC was calculated byBy processing, a total of 53,000,662 data were recorded in the FAERS database between 2014 and 2021, of which 269,090 ICI-related adverse events were reported, of which 9,806 were reported to be related to hepatotoxicity. We summarized the clinical characteristics of the patients, which are described in A greater proportion of all reports of hepatotoxicity associated with ICIs were in males than in females (57.65% vs. 32.77%). By further analysis, signals were detected in both males and females . According to the United Nations definition of the age of the elderly, developed countries define 65 years or older as the elderly, so 65 years was used as the cut-off for classification. The results of the study showed significant differences between age subgroups, with a smaller proportion of elderly (≥65) than non-elderly (<65) (36.78% vs. 44.50%), but further testing produced a significant signal , which may be attributed to the effect of cancer survival and degenerative changes in the elderly organism. The most frequently reported regressions were other serious medical events, hospitalization and death. Hospitalizations , deaths  associated with liver AEs following ICI treatment were reported, indicating the threatening nature of potential life-ICIs-related hepatotoxicity. The most reported countries were Japan (29.57%), followed by the USA (18.05%), France (13.15%), Germany (5.65%) and China (3.24%).In all reports of ICIs-related hepatotoxicity, we analyzed the relationship between each class of ICIs and age separately . atezoliDifferences in various specific hepatotoxic adverse events were observed in all ICIs regimens. Of all hepatotoxic adverse events reported, abnormal liver function , hepatitis , liver disease , autoimmune hepatitis , Drug-induced liver injury , immune-mediated hepatitis  and ascites (521 5.31%) were the most commonly reported . These rIn general, not all ICIs were associated with hepatic AEs. However, signals were detected when each drug was analyzed separately with hepatotoxic AEs. Tremelimumab had the strongest statistical association with ICIs-associated hepatotoxic AEs in the analysis of overall and individual ICIs .Figs A total of 1,768 ICI-associated hepatotoxic AEs were reported at time of onset. The Kaplan-Meier curves of AE onset time for different ICIs are shown in The median time to onset of action was 42 days (interquartile range (IQR) 16–98 days). A two-by-two comparison of the different drugs by Wilcoxon rank sum test revealed that the time interval to AEs onset was significantly shorter for Ipilimumab than Durvalumab, Nivolumab and Pembrolizumab; Atezolizumab had a significantly shorter time to AEs onset than Nivolumab . Of thesOur study is the most systematic and comprehensive to date, based on the FAERS pharmacovigilance database, for comparing the association between ICIs and hepatic adverse reactions. We analyzed the FAERS database for adverse events associated with ICIs by measuring disproportionality and identified characteristics and differences between ICIs and associated hepatotoxic AEs in order to update safety information. We used two methods, the Bayesian Confidence Neural Network method (BCPNN)  and the Most clinical trials of ICIs have evaluated clinical effects other than AEs, and only brief descriptions of these severe or even fatal AEs have been provided. Previous studies have shown that ICIs increase the risk of organ system toxicity, such as endocrine, hematological, otologic and vagotoxicity , 17, 18.Our results show that most ICIs-associated hepatotoxic AEs occur in the first few weeks after dosing, and then the likelihood decreases. However, it is still possible for patients to develop AEs during subsequent treatment, even over months or years . Based oIn addition, hepatitis and immune-mediated hepatitis have the strongest adverse effect signals and the widest distribution. the severity of ICI-associated immune-mediated hepatitis (IMH) varied. The risk of ICIs-induced liver injury may be influenced by the specific checkpoint molecules targeted, the dose level of the ICIs, and the preexisting autoimmune qualities, chronic infection or tumor cells infiltrating the liver parenchyma. The impact of ICIs therapy may be influenced by the presence of specific checkpoint molecules, ICIs dose levels and pre-existing autoimmune qualities, chronic infection or tumor cells infiltrating liver parenchyma. When patients experience liver injury during ICIs therapy, the cause of the injury should be promptly assessed and steps taken to best manage adverse events . In addiLiver metastases were detected as a signal in four drugs , however, it is generally believed that liver metastases are not caused by ICIs, that liver metastases are associated with poor prognosis and that ICIs therapy is less effective in patients with liver metastases. One study reported a shorter median progression-free survival time (MPFS) and lower disease control rate (DCR) in patients with liver metastases in NSCLC patients treated with Nivolumab .One possThe mechanisms by which ICIs cause hepatotoxicity are currently being described differently. The hemi-antigen hypothesis mentions that reactive metabolites bind to cellular proteins to form neoantigens, called "haptens", which then move to major histocompatibility complex molecules on antigen-presenting cells and activate cytotoxic T lymphocytes, B cells and natural killer cells, stimulating an immune response against hepatocytes. The haptens may also induce autoantibodies to cytochrome p450 enzymes, leading to cellular damage and death , 28. In There are some certain limitations remain in our study. First, although safety issues can be assessed through the FAERS database, the database is limited by the lack of detailed clinical data. Further study needs carry out to construct the relationship between safety issue findings and practical clinical scenario. Moreover, due to lack of follow-up/censoring data, it is difficult to determine the causal relationship between ICI and adverse events in the hepatobiliary system. Finally, we compared ICIs with non-ICIs in the FAERS database using the proportional imbalance method in this article. In the future, we will compare AE signals with non-ICI drugs to enhance this research.This study comprehensively assessed the association of ICIs with hepatotoxicity in real-world practice. Overall, a significant association was detected between ICIs and liver AEs and a relatively strong signal was detected in several ICIs immunotherapy regimens. Some of the results were consistent with previous literature. Immune-mediated hepatitis was associated with each drug and, in addition, autoimmune hepatitis was associated with most drugs (except Avelumab). In the clinical use of ICIs, patients should be alerted to the development of hepatotoxic AEs, especially in elderly patients. Clinicians need to be alerted to AEs associated with hepatotoxicity. The delayed onset and prolonged duration of AEs associated with ICIs makes timely recognition and early individualized management important.S1 Table(TIF)Click here for additional data file.


Cutaneous squamous cell carcinoma (cSCC) harbors metastatic potential and causes mortality. However, clinical assessment of metastasis risk is challenging. We approached this challenge by harnessing artificial intelligence (AI) algorithm to identify metastatic primary cSCCs. Residual neural network-architectures were trained with cross-validation to identify metastatic tumors on clinician annotated, hematoxylin and eosin-stained whole slide images representing primary non-metastatic and metastatic cSCCs (n = 104). Metastatic primary tumors were divided into two subgroups, which metastasize rapidly (≤ 180 days) (n = 22) or slowly (> 180 days) (n = 23) after primary tumor detection. Final model was able to predict whether primary tumor was non-metastatic or rapidly metastatic with slide-level area under the receiver operating characteristic curve (AUROC) of 0.747. Furthermore, risk factor (RF) model including prediction by AI, Clark’s level and tumor diameter provided higher AUROC (0.917) than other RF models and predicted high 5-year disease specific survival (DSS) for patients with cSCC with 0 or 1 RFs (100% and 95.7%) and poor DSS for patients with cSCCs with 2 or 3 RFs (41.7% and 40.0%). These results indicate, that AI recognizes unknown morphological features associated with metastasis and may provide added value to clinical assessment of metastasis risk and prognosis of primary cSCC. The overall rate of metastasis of cSCC has been estimated as 1–4% and it accounts for at least 20% of all skin cancer-related mortality4. The prognosis of patients with metastatic disease is generally poor with 3-year overall survival (OS) of 29–46% and mortality is associated primarily with nodal metastases5. Metastasis occurs relatively rapidly with every other patient developing detectable metastasis within 6 months of the diagnosis of primary metastatic cSCC (mcSCC)3.Cutaneous squamous cell carcinoma (cSCC) is the most common metastatic skin cancer with increasing incidence worldwideth edition of AJCC (AJCC-8) staging systems6. However, current staging systems are unsatisfactory in predicting the risk of progression of primary cSCC to metastatic disease7. In addition, to date, there are no clinically established biomarkers for assessment of metastasis risk of primary cSCC.Established tumor staging systems by American Joint Committee on Cancer (AJCC) and Brigham and Women’s Hospital (BWH) are utilized in the clinical risk assessment of cSCC patients. Tumor diameter, invasion depth, and perineural invasion are associated with the risk of metastasis and are pivotal factors in both the BWH and the 88. ML represents a subfield of artificial intelligence (AI) and can be utilized via supervised, unsupervised, or reinforcement learning10. Supervised learning dominates medical applications and is based on training with data with known outcomes with the aim of finding a mathematical function that can map input data into output predictions11. In general, ML algorithms include regression and more advanced artificial neural networks (ANNs), which are computing programs inspired by biological neural networks12. ML is called deep learning (DL), when ANN includes multiple (> 1) hidden layers between the input and output layers and it represents more recent evolution of ML with the ability to handle large datasets13. DL requires less manual preprocessing and can learn more complex features with higher efficiency enabling more successful pattern recognition and computer vision14. Numerous specialized deep neural networks, such as convolutional neural networks (CNNs) have been developed for different tasks15.Current digitalization of pathology, especially whole slide imaging enables utilization of machine learning (ML) in the analysis of tissue specimens9. Connectivity pattern between the neurons of the CNN resembles the organization of the animal visual cortex and demonstrates higher performance regarding spatial features crucial for the performance of computer vision such as object recognition, identification and classification10. Residual neural network-18 (ResNet-18) is an 18-layer deep and ResNet-50 a 50-layer deep CNN, which adds residual learning to the traditional CNN16. ResNets resemble pyramidal cells in cerebral cortex and utilize skip connections to jump over layers in order to solve the problem of gradient dispersion and accuracy degradation16.CNNs are deep neural networks, which contain hidden convolutional layer(s) in the architecture17. In dermatopathology, CNNs have been shown to successfully identify basal cell carcinomas, dermal nevi and seborrheic keratoses, to distinguish Spitz nevi from conventional melanocytic lesions and to predict the prognosis of primary melanoma20.To date, CNNs have been studied as diagnostic tools in clinical dermatology in dermoscopy and dermatopathology3. Furthermore, a risk factor model (RFM), which utilized prediction by AI and conventional histopathological features was generated.In this study, we have used AI algorithm to identify primary cSCCs with risk for metastasis. ResNet architectures were trained with cross-validation and fine-tuned on tumor tiles extracted from clinician annotated, hematoxylin and eosin-stained whole slide images representing a cohort of primary mcSCCs and a cohort of non-metastatic cSCCs (non-mcSCCs)For testing of AI-models we utilized input data from 45 whole slide images (WSIs) representing mcSCCs and 59 WSIs representing non-mcSCCs. For rapid metastasis -AI-model, WSIs of a subcohort of 22 rapidly ≤ 180 days) metastatic mcSCCs was used. Tumor characteristics of the rapid metastasis -AI-model are shown in Table 80 days mIn single tile -AI-model, a slide level area under the receiver operating characteristic curve (AUROC) of 0.689 was reached at best. However, the model was dramatically overfitting to the training data despite heavy regulation and data augmentation and ultimately the model could not reliably reproduce the results between different sampling of folds. Invasive front -AI-model produced results inferior to the single tile -AI-model with tile level AUROC of 0.629 at best, regardless of whether tiles inside the annotation, outside the annotation or both were included. Multi-tile -AI-model did not produce more convincing results than the previous approaches (AUROC 0.672 at best) on stack tile level. This may be due to the fact that using n tiles as input effectively reduced the size of the dataset by n-fold, since we retained from showing a tile in different stacks multiple times.Rapid metastasis -AI-model generated most convincing results from the beginning. Finally, AUROCs of 0.754–0.814 were reached on tile level depending on the fold  of 5.63. In comparison, pathologist reached an AUROC of 0.694 and an OR of 5.71. Clark’s level provided higher OR (13.75) and AUROC (0.788) than invasion beyond fat, and diameter provided highest AUROC of 0.804 of the individual clinicopathological variables. AJCC-8 and BWH tumor staging systems provided slightly higher AUROCs , but in logistic regression analysis the increase of risk was non-linear Table .Table 2ANext, it was evaluated, whether prediction as metastatic by AI could act as an individual risk factor in multifactorial risk factor model (RFM) and improve the clinical risk assessment. For comparison, a RFM  taking into account tumor diameter ≥ 30 mm and Clark’s level 5 as risk factors provided higher AUROC 0.862) than AJCC-8 or BWH staging systems. Furthermore, a RFM containing prediction by AI as metastatic and Clark’s level 5 as risk factors provided even higher AUROC of 0.872. Finally, a RFM including prediction by AI as metastatic, Clark’s level 5 and diameter ≥ 30 mm as risk factors (AI-RFM) produced an AUROC of 0.917. Similar RFMs that took into account prediction by pathologist instead of AI resulted in lower AUROCs of 0.807 and 0.841 respectively  (Data not shown).Survival analyses were conducted to further evaluate the discriminative power of AI and AI-RFM from prognostic point of view. First, survival between slow and rapid metastasis cohorts, as well as non-metastatic cohort was analyzed. Survival was higher in slow metastasis cohort up to approximately 4 years until slow metastasis cohort reached the level of rapid metastasis cohort with 50% of patients alive at 1.2 years for rapid metastasis and 3.4 years for slow metastasis cohort, when OS was considered and 1.3 and 4.1 years, respectively, when disease-specific survival (DSS) was considered  and Auria Biobank’s Scientific Steering Committee (AB15-9721). The research was carried out according to the Declaration of Helsinki. Registry study approval for collection and use of clinicopathological data was obtained from the Turku University Hospital Clinical Research Centre . Informed consent was obtained from all subjects involved in the study.Hematoxylin and eosin-stained archived formalin-fixed and paraffin-embedded tissue specimens representing primary non-mcSCC and mcSCC tumors were scanned and digitalized into WSIs. Each tumor was obtained for diagnostic purpose from patients from the area served by Turku University Hospital. Eventually, one tumor per patient and one WSI representing each cSCC was utilized to train and test the AI algorithm, and in blind analysis by dermatopathologist. Altogether 45 mcSCC from 45 and 59 non-mcSCCs from 59 individual patients, who did not develop cSCC metastasis during at least 5-year follow-up were included in the analyses. mcSCCs were further divided into two subcohorts: one with primary mcSCCs, which developed metastasis within 180 days (rapid metastasis cohort) n = 22) and another with primary mcSCCs that developed metastasis after 180 days from the initial diagnosis of primary tumor (slow metastasis cohort) (n = 23). Clinical, histopathological, follow-up and demographic data were gathered manually from patient records and pathology reports . Both unambiguous OS and deduced DSS were utilized in survival analyses. Survival time was calculated from the date of initial diagnosis of the primary cSCC with 5 years of follow-up. Death represented the end of follow-up.Tissue specimens (slides) were scanned with 3DHistech scanner  and annotated using CaseViewer . The WSIs representing cSCCs were manually annotated. Annotated area included whole tumor represented on the WSI, including tumor cells, intratumoral and peritumoral stroma, as well as intratumoral inflammatory cells  from the annotated tumor areas. The tissue content of the tiles was further analyzed by using a combination of binary and Otsu thresholding: a tile was considered valid if it contained more than 50% of tissue pixels as defined by the thresholding algorithm. Similarly, a tile was considered a tumor tile if it contained more than 50% pixels from the tumor annotation mask. All tiles inherited the cohort label from the original slide. Every tile was then resized into 299 × 299 pixels before training the algorithms.We analyzed the task of distinguishing mcSCCs from non-mcSCCs as a binary classification problem on the level of a single tumor tile. Each tumor tile was assigned to a single cohort and the binary classification problem was to classify the tiles into these two classes. As the data set is relatively small we approached the task with cross-validation, in which data is sectioned into subsets and one subset at a time is used for testing and rest for training. Thus, several models are trained and the performance of each model is tested with alternating validation subset.Final reported results of the rapid metastasis -AI-model are based on the ResNet18 architecture with a custom head consisting of average pooling and two dense layers with heavy dropout to combat overfitting. More complex ResNet models (ResNet50) were also tested but were more prone to overfitting, which lead to the choice of a simpler model for the final rapid metastasis -AI-model. The models were trained using threefold cross-validation (3-CV) with different extraction tile sizes until the final rapid metastasis -AI-model in which fourfold cross-validation (4-CV) was used. The loss function used was the binary cross-entropy loss. In training, it was confirmed that the tiles from a given patient and slide were exclusively sampled into one of the cross-validation-folds to prevent the leakage of information between the training and validation sets. With every fold the model was trained 10 or 20 epochs.In addition to analyzing the cross-validation accuracy and loss, we created the out-of-the-fold (OOF) tile level receiver operating characteristic (ROC) curves and calculated area under the curve (AUC) values. The OOF tile level predictions were also mapped to slide level. Namely, each tile and the corresponding location in the WSI was assigned a prediction probability between 0 and 1 for developing a metastasis. This probability was scaled to  and then shifted to the interval − 50, 50] for each tile. The scores on WSI were then spatially smoothed using a median filter with window size 2. This meant that a window of 2 × 2 tiles was moved along the slide and the tile value was replaced with the median of the tile values within the window. The idea was to remove noise according to our hypothesis, that the features representing metastasis risk would vary smoothly across the tissue slide. To date, there is no way to scientifically select a correct size for the smoothing window. Our selection was based on visualizations of the results. Probability maps of the annotated tumors were generated for metastasis score visualization . Hypothetically, metastatic characteristics of the tumor are more likely visualized in the invasive front and focusing on this area was expected to lead to less noisy training labels. Third approach was to use stack of tiles (multi-tile -AI-model) as an input data in order to reduce the noise in the labels. Instead of just one tile, randomly selected sample of stack of n tiles from the same WSI was used as the input of the classifier. In above mentioned approaches ResNet50 architecture was used with 3-CV and single tiles from annotated WSIs representing the two cohorts (primary mcSCCs and primary non-mcSCCs) were used as input data. In multi-tile -AI-model instead of single tile a stack of tiles and in invasive front -AI-model only tiles adjacent to the edge of the annotation (either inside and/or outside of the annotated area) from annotated WSIs were used as input data.3 we subdivided the mcSCC cohort into cases that metastasize rapidly and slowly. Furthermore, we hypothesized that features characteristic for the metastatic tumor would be more prominent or more probably already present at the date of tissue specimen in primary tumors, which metastasize rapidly. Therefore, only tumors, which metastasized rapidly were utilized in the rapid metastasis -AI-model. Both stack of tiles and single tiles from annotated WSIs representing two cohorts (primary non-mcSCCs and primary rapid mcSCCs) were used as input data. Fourfold cross-validation and ResNet18 architecture were ultimately used. To further prevent overfitting into the more exclusive dataset, we used a “zoomed-in” approach in which 512 × 512 pixels instead of 1024 × 1024 pixels tiles were used.Due to previous notion of rapid development of metastasisRegarding technical execution of the study, during repeated runs of 3 × or 4 × CV analysis on all of the available slides for training, it seemed that depending on the choice of the CV split, the results varied considerably. Often it seemed that one fold was much more difficult for the model to analyze than the others, leading to considerably lower AUROC values. We took this as a sign that the dataset contained some examples that were difficult for the model to analyze, for example by being very atypical cases of rapid metastasis tumors. More careful analysis of the metadata revealed, however, that the difficult cases appeared in folds where the slide data had been scanned in 2020 instead of 2016. Most of the rapid metastasis cases were scanned earlier because various research projects have been conducted with the samples in the past. The scanner used was the same, but we hypothesized that the scanning software, image packing algorithms or the physical components of the scanner itself could have been updated affecting the image colors, noise patterns or other qualities. We used heavy color augmentations in training of the algorithm, but to study the possible effect of slides scanned at different times we tried and restricted the data to old samples only. This reduced the size of the dataset available, but was useful in ruling out at least one more possible source of error. This affected the AUROC results somewhat and brought the average AUROC value up.For comparison purposes, every WSI included in the final rapid metastasis -AI-model was analyzed by experienced dermatopathologist (LT). Only tissue sample ID and information, whether the specimen represented biopsy or resection was provided to the pathologist in accordance with access to CaseCenter folder including 81 WSIs representing non-mcSCCs and rapid metastasis mcSCCs without the knowledge of the proportion of cases. The histological criteria used by the pathologist for predicting metastasis in this series included invasion depth, histological grade, tumor size, perineural invasion and also the subjective interpretation of the pattern of invasion . Although for instance invasion depth is a part of staging systems, no staging system as such was used. The pathologist also had the option not to classify the specimen into the cohorts, if assessment was not possible, as was the case with biopsy samples.p values < 0.05 and 95% confidence intervals (95% CIs) of odds ratios (ORs) not including 1.00 were considered statistically significant. Baseline tumor characteristics were analyzed using descriptive statistics mainly crosstabs and frequency tabulation. Statistical analyses were conducted with Pearson χ2 test and Fisher's exact test. Binary logistic regression analyses with 95% CIs were performed in order to determine ORs regarding the risk of metastasis. For every risk factor and risk factor combination an AUROC was calculated in order to further visualize results in relation to AI prediction. Pearson correlation was conducted in order to visualize multicollinearity and to examine whether predictions correlated with some of the clinicopathological variables. The Kaplan–Meier method was applied to generate survival estimate curves and define survival probabilities.All statistical analyses were conducted using IBM SPSS Statistics for Windows, version 25.0 . Bidirectional Supplementary Information.