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There is a paucity of studies describing malignancy-related mortality after kidney transplantation. To help quantify this, we extracted data for all kidney-alone transplant procedures performed in England between April 2001 and March 2012. Data linkage analysis was performed between Hospital Episode Statistics and the Office for National Statistics to identify all deaths occurring in this cohort. Among 19,103 kidney transplant procedures analyzed (median follow-up 4.4 years), 2085 deaths occurred, of which 376 (18.0%) were due to malignancy (crude mortality rate 361 malignancy-related deaths per 100,000 person-years). Common sites of malignancy-related death were lymphoma (18.4%), followed by lung (17.6%) and renal (9.8%), with 14.1% unspecified. The risk of malignancy-related death increased with age: under 50 (0.8%), 50–59 (2.5%), 60–69 (4.8%), 70–79 (6.5%) and over 80 years (9.1%). Age- and gender-stratified malignancy-related mortality risk difference was higher in the transplant compared with the general population. Cox proportional hazard models identified increased age, pretransplant history of malignancy and deceased-donor kidney transplantation to be independently associated with risk for post-transplant death from malignancy. Thus, malignancy as a cause of post-kidney transplantation death is common and requires heightened surveillance.

Stakeholders in healthcare are increasingly turning to real world evidence (RWE) to inform their decisions, alongside evidence from randomized controlled trials. RWE is generated by analysing data gathered from routine clinical practice, and can be used across the product lifecycle, providing insights into areas including disease epidemiology, treatment effectiveness and safety, and health economic value and impact. Recently, the US Food and Drug Administration and the European Medicines Agency have stated their ambition for greater use of RWE to support applications for new indications, and are now consulting with their stakeholders to formalize standards and expected methods for generating RWE. Pharmaceutical companies are responding to the increasing demands for RWE by developing standards and processes for each stage of the evidence generation pathway. Some conventions are already in place for assuring quality, whereas other processes are specific to the research question and data sources available. As evidence generation increasingly becomes a core role of medical affairs divisions in large pharmaceutical companies, standards of rigour will continue to evolve and improve. Senior pharmaceutical leaders can drive this change by making RWE a core element of their corporate strategy, providing top-level direction on how their respective companies should approach RWE for maximum quality. Here, we describe the current and future areas of RWE application within the pharmaceutical industry, necessary access to data to generate RWE, and the challenges in communicating RWE. Supporting and building on viewpoints from industry and publicly funded research, our perspective is that at each stage of RWE generation, quality will be critical to the impact that RWE has on healthcare decision-makers; not only where RWE is an established and evolving tool, but also in new areas that have the potential to disrupt and to improve drug development pathways.

In the new era of healthcare digitalization, there is a golden opportunity in the overlap between digital health and Real-World Evidence (RWE). In this commentary, we define RWE and digital health and investigate their intersection. We describe the stages in the RWE value chain critical to the evidence generation process, how these stages change with new digital technologies and the opportunities and challenges that arise from how these stages evolve—including their application for stakeholders such as patients, physicians and regulators. We also discuss the current published guidelines and frameworks regarding digital health. We categorise these publications in terms of their clarity as “Extensive”, “Intermediate” or “Basic” and according to whether they encompass all levels of digital health or are more focussed in their guidance. Finally, we provide recommendations to increase synergy between RWE and digital health.

This commentary is authored by several industry real-world evidence (RWE) experts, with support from IQVIA, as part of the 'RWE Leadership Forum': a group of Industry Leaders who have come together as non-competitive partners to understand and respond to RWD/E challenges and opportunities with a single expert voice. Here, the forum discusses the value in bridging the industry disconnect between RTCs and RWE, with a view to promoting the use of RWE in the RCT environment. RCT endpoints are explored along several axes including their clinical relevance and their measure of direct patient benefit, and then compared with their real-world counterparts to identify suitable paths, or gaps, for assimilating RWE endpoints into the RCT environment.

BackgroundWhy some patients fail or have short lived response to immune checkpoint blockade (ICB) immunotherapy remains largely unknown. While baseline molecular assessments have provided clues to prognostic factors, insights into resistance drivers remains elusive. This is partially due to the difficulty in getting access to post progression samples from patients that were either primary resistant or developed acquired resistance after an initial response to ICB. Thus, the tumour-intrinsic and -extrinsic features that are selected for during progression and potentially drive primary and acquired resistance to immunotherapy remain underexplored.MethodsTo compare clinical features and immunogenomic drivers of acquired and primary resistance to ICB across major cancers, we analysed and annotated de-identified patient records in the Tempus real-world database1 2 (figure 1). We built an immuno-oncology cohort consisting of >2500 multimodal (DNA, RNA and clinical outcome data) pre-treatment baseline with >1500 post-treatment tumour biopsy samples from mainly NSCLC, TNBC, HNC and Bladder cancer patients. We used bulk RNA-seq data to estimate activation of the hallmark oncogenic pathways3 and immune cell composition4 and used panel DNA-seq data (>500 genes) to quantify mutation selection at the gene and pathway levels using dndscv.5 ResultsCompared to acquired, primary resistant patients tended to have a higher observation of liver lesions at progression. Post-ICB, acquired resistant NSCLC and HNC patients showed a significantly inflamed tumour microenvironment (TME) characterised by higher estimation of infiltration of T cells and myeloid cells and higher activation of interferon gamma (IFNg) signalling as compared to primary resistant patients. In addition, in post-ICB acquired resistance in NSCLC we observed selection for mutations in genes involved in known immunomodulatory pathways, including loss-of-function mutations in B2M in the antigen processing and presentation machinery (APM) pathway and APC in the Wnt pathway. Consistently, acquired resistance patients showed stronger selection for mutations in APM, IFN, WNT, MYC, and Notch pathways as compared to primary resistance patients across NSCLC, HNC and bladder cancer post-ICB.ConclusionsAcquired and primary ICB resistant patients have distinct clinical and molecular features at progression. Their tumours’ TME is fundamentally different with acquired resistance TMEs being infiltrated with immune cells albeit escaped post progression. In addition, ICB selects mutations that potentially activate immunosuppressive pathways such as Wnt and Myc. This multi-modal Real-World Data with post therapy biopsies has given insights for patient selection strategies and provides rational into combination treatment options for acquired resistant patients.References1. Fernandes LE, Epstein CG, Bobe AM, Bell JSK, Stumpe MC, Salazar ME, Salahudeen AA, Pe Benito RA, McCarter C, Leibowitz BD, Kase M, Igartua C, Huether R, Hafez A, Beaubier N, Axelson MD, Pegram MD, Sammons SL, O’Shaughnessy JA, Palmer GA. Real-world evidence of diagnostic testing and treatment patterns in US patients with breast cancer with implications for treatment biomarkers from RNA sequencing data. Clin Breast Cancer. 2021 Aug;21(4):e340-e361. doi: 10.1016/j.clbc.2020.11.012. Epub 2020 Dec 18. PMID: 33446413.2. Rivera DR, Henk HJ, Garrett-Mayer E, Christian JB, Belli AJ, Bruinooge SS, Espirito JL, Sweetnam C, Izano MA, Natanzon Y, Robert NJ, Walker MS, Cohen AB, Boyd M, Enewold L, Hansen E, Honnold R, Kushi L, Mishra Kalyani PS, Pe Benito R, Sakoda LC, Sharon E, Tymejczyk O, Valice E, Wagner J, Lasiter L, Allen JD. The friends of cancer research real-world data collaboration pilot 2.0: methodological recommendations from oncology case studies. Clin Pharmacol Ther. 2022 Jan;111(1):283–292. doi: 10.1002/cpt.2453. Epub 2021 Nov 11. PMID: 34664259; PMCID: PMC9298732.3. Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The molecular signatures database (MSigDB) hallmark gene set collection. Cell Syst. 2015 Dec 23;1(6):417–425. doi: 10.1016/j.cels.2015.12.004. PMID: 26771021; PMCID: PMC4707969.4. Jiménez-Sánchez A, Cast O, Miller ML. Comprehensive benchmarking and integration of tumor microenvironment cell estimation methods. Cancer Res. 2019 Dec 15;79(24):6238–6246. doi: 10.1158/0008–5472.CAN-18–3560. Epub 2019 Oct 22. PMID: 31641033.5. Martincorena I, Raine KM, Gerstung M, Dawson KJ, Haase K, Van Loo P, Davies H, Stratton MR, Campbell PJ. Universal patterns of selection in cancer and somatic tissues. Cell. 2017 Nov 16;171(5):1029–1041.e21. doi: 10.1016/j.cell.2017.09.042. Epub 2017 Oct 19. Erratum in: Cell. 2018 Jun 14;173(7):1823. PMID: 29056346; PMCID: PMC5720395.Ethics ApprovalAll ethics and consent have been obtained in accordance with Tempus Labs IRB approval supporting the use of de-identified data.ConsentAll ethics and consent have been obtained in accordance with Tempus Labs IRB approval supporting the use of de-identified data.Abstract 620 Figure 1Clinical and immunogenomic features of acquired and primary resistance to ICB

Stakeholders in healthcare are increasingly turning to real world evidence (RWE) to inform their decisions, alongside evidence from randomized controlled trials. RWE is generated by analysing data gathered from routine clinical practice, and can be used across the product lifecycle, providing insights into areas including disease epidemiology, treatment effectiveness and safety, and health economic value and impact. Recently, the US Food and Drug Administration and the European Medicines Agency have stated their ambition for greater use of RWE to support applications for new indications, and are now consulting with their stakeholders to formalize standards and expected methods for generating RWE. Pharmaceutical companies are responding to the increasing demands for RWE by developing standards and processes for each stage of the evidence generation pathway. Some conventions are already in place for assuring quality, whereas other processes are specific to the research question and data sources available. As evidence generation increasingly becomes a core role of medical affairs divisions in large pharmaceutical companies, standards of rigour will continue to evolve and improve. Senior pharmaceutical leaders can drive this change by making RWE a core element of their corporate strategy, providing top-level direction on how their respective companies should approach RWE for maximum quality. Here, we describe the current and future areas of RWE application within the pharmaceutical industry, necessary access to data to generate RWE, and the challenges in communicating RWE. Supporting and building on viewpoints from industry and publicly funded research, our perspective is that at each stage of RWE generation, quality will be critical to the impact that RWE has on healthcare decision-makers; not only where RWE is an established and evolving tool, but also in new areas that have the potential to disrupt and to improve drug development pathways.

For patients with non-small-cell lung cancer (NSCLC) tumors without currently targetable molecular alterations, standard-of-care treatment is immunotherapy with anti-PD-(L)1 checkpoint inhibitors, alone or with platinum-doublet therapy. However, not all patients derive durable benefit and resistance to immune checkpoint blockade is common. Understanding mechanisms of resistance—which can include defects in DNA damage response and repair pathways, alterations or functional mutations in STK11 /LKB1, alterations in antigen-presentation pathways, and immunosuppressive cellular subsets within the tumor microenvironment—and developing effective therapies to overcome them, remains an unmet need. Here the phase 2 umbrella HUDSON study evaluated rational combination regimens for advanced NSCLC following failure of anti-PD-(L)1-containing immunotherapy and platinum-doublet therapy. A total of 268 patients received durvalumab (anti-PD-L1 monoclonal antibody)–ceralasertib (ATR kinase inhibitor), durvalumab–olaparib (PARP inhibitor), durvalumab–danvatirsen (STAT3 antisense oligonucleotide) or durvalumab–oleclumab (anti-CD73 monoclonal antibody). Greatest clinical benefit was observed with durvalumab–ceralasertib; objective response rate (primary outcome) was 13.9% (11/79) versus 2.6% (5/189) with other regimens, pooled, median progression-free survival (secondary outcome) was 5.8 (80% confidence interval 4.6–7.4) versus 2.7 (1.8–2.8) months, and median overall survival (secondary outcome) was 17.4 (14.1–20.3) versus 9.4 (7.5–10.6) months. Benefit with durvalumab–ceralasertib was consistent across known immunotherapy-refractory subgroups. In ATM -altered patients hypothesized to harbor vulnerability to ATR inhibition, objective response rate was 26.1% (6/23) and median progression-free survival/median overall survival were 8.4/22.8 months. Durvalumab–ceralasertib safety/tolerability profile was manageable. Biomarker analyses suggested that anti-PD-L1/ATR inhibition induced immune changes that reinvigorated antitumor immunity. Durvalumab–ceralasertib is under further investigation in immunotherapy-refractory NSCLC. ClinicalTrials.gov identifier: NCT03334617 In the phase 2 HUDSON study, patients with advanced non-small-cell lung cancer received anti-PD-L1 combined with biomarker-guided therapy targeting ATR kinase, PARP, STAT3 or CD73, leading to encouraging clinical benefit in response to combination of the ATR kinase inhibitor ceralasertib with durvalumab.

The association between area socioeconomic deprivation and mortality post kidney transplantation is unclear. To clarify this, we obtained data from 19,103 kidney transplant procedures performed in England from April 2001 to March 2012. Patient demographics included age, gender, donor type (living or deceased), ethnicity, transplant year, allograft failure, medical comorbidities, and area socioeconomic deprivation (Index of Multiple Deprivation (2010)). Primary and secondary outcome measures were 1- and 5-year mortality with Cox proportional hazard models performed to identify independent factors associated with mortality. Data were broken down into quintiles of patients by area socioeconomic deprivation 1 to 5 (most to least deprived, respectively). At 1 year post transplant, 566 deaths were recorded, with infection being the most common cause of death. Compared with the most deprived individuals (reference point), the least deprived recipients had significantly decreased risk of death at 1 and 5 years post kidney transplant (hazard ratio 0.66, 95% CI (0.57–0.76) and hazard ratio 0.65, 95% CI (0.54–0.77), respectively). Thus, socioeconomic deprivation is independently associated with increased mortality post kidney transplantation.

Immune checkpoint blockade (ICB) has revolutionized cancer therapy, yet resistance-both primary and acquired-remains a significant obstacle, affecting the majority of patients. Here, we leverage a large-scale, real-world clinicogenomic dataset to systematically explore the molecular underpinnings of ICB resistance in the post-progression setting. We analyze over 5,000 pan-cancer patients with clinical and pre-/post-treatment genomic and transcriptomic data and systematically compare the clinical and molecular features of acquired versus primary ICB resistance. Post-ICB progression, acquired resistance showed extended survival compared to primary resistance across all cancer types. This clinical phenotype was paralleled by a universally immune-inflamed, albeit dysfunctional, tumor microenvironment (TME) at the onset of acquired resistance, with sustained or ICB-induced inflammatory and interferon responses. We confirm previously described mechanisms of acquired resistance, including loss-of-function (LoF) in non-small cell lung cancer (NSCLC), and identify novel potential mediators, including LoF of in NSCLC, in head and neck cancer, and in triple-negative breast cancer. Further supporting their involvement in resistance, these acquired ICB alterations associated with immune-escaped TMEs, characterized by active immunomodulatory oncogenic signaling, hyperproliferation and invasiveness, or altered tumor metabolism. These findings emphasize the heterogeneity of molecular drivers of acquired resistance to ICB within and across cancers, and highlight the potential for personalized therapeutic interventions post-progression to improve patient outcomes.