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The potential of real‐world data to inform clinical trial design and supplement control arms has gained much interest in recent years. The most common approach relies on reproducing control arm outcomes by matching real‐world patient cohorts to clinical trial baseline populations. However, recent studies pointed out that there is a lack of replicability, generalisability, and consensus. In this article, we propose a novel approach that aims to explore and examine these discrepancies by concomitantly investigating the impact of selection criteria and operations on the measurements of outcomes from the patient data. We tested the approach on a dataset consisting of small‐cell lung cancer patients receiving platinum‐based chemotherapy regimens from a real‐world data cohort (n = 223) and six clinical trial control arms (n = 1224). The results showed that the discrepancy between real‐world and clinical trial data potentially depends on differences in both patient populations and operational conditions (e.g., frequency of assessments, and censoring), for which further investigation is required. Discovering and accounting for confounders, including hidden effects of differences in operations related to the treatment process and clinical trial study protocol, would potentially allow for improved translation between clinical trials and real‐world data. Continued development of the method presented here to systematically explore and account for these differences could pave the way for transferring learning across clinical studies and developing mutual translation between the real‐world and clinical trials to inform clinical study design. [ABSTRACT FROM AUTHOR]

Radiotherapy (RT) is primarily considered as a palliative treatment in patients with metastatic melanoma. However, observations suggest that when RT is combined with immune checkpoint inhibitors (ICI), it can induce an immune response leading to an anti-tumoral effect also distant from the irradiated area – a phenomenon called 'abscopal effect'. The frequency and circumstances of abscopal effect among metastatic melanoma patients remains uncertain and further research is necessary. This retrospective study included all metastatic melanoma patients who received non-stereotactic RT in Stockholm, Sweden in 2015–2020. Patients were grouped depending on if RT was given at start of ICI (RT + ICI(start)), at ICI progression (RT + ICI(salvage)) or without ICI (RT(only)). Response rates in irradiated (RR(irradiated)) and overall response rates in non-irradiated (ORR(non-irradiated)) metastases were evaluated together with survival and toxicity in each cohort. In the RT + ICI(start) (n = 47), RT + ICI(salvage) (n = 41) and RT(only) (n = 55) cohorts, RR(irradiated) was 70.7%, 67.5% and 43.1% (p = 0.018) while the ORR(non-irradiated) was 36.1%, 14.8% and 0.0% (p = 0.003), and the median overall survival was 18.2, 15.0 and 7.2 months, respectively (p = 0.014). Local response to RT was in all cohorts associated with longer survival (p < 0.001). The frequency of grade ≥3 immune-related adverse events was 17.0% and 19.5% in the RT + ICI(start) and RT + ICI(salvage) cohorts. No increased frequency of RT-related adverse events was seen in the RT + ICI cohorts, compared to the RT(only) cohort. This retrospective study showed that melanoma patients receiving RT in combination with ICI had a superior antitumoral response in both irradiated and non-irradiated lesions as compared to patients receiving only RT. Additionally, a subgroup of patients receiving RT when progressing on ICI experienced tumor regression also in non-irradiated areas. [ABSTRACT FROM AUTHOR]

Stereotactic body radiotherapy (SBRT) for tumours ≥5 cm is poorly studied and its utility and feasibility is uncertain. We here report the Karolinska experience of SBRT in this setting. All patients had a gross tumour volume (GTV) ≥70 cc, a prescribed physical dose of at least 40 Gy and received treatment between 1995–2012. We included 164 patients with 175 tumours located in the thorax (n = 86), the liver (n = 27) and the abdomen (n = 62) and treated with a median prescribed dose (BEDα/β 10Gy) of 80 Gy (71.4–113). One- and 2- year local control rates were 82% and 61%. In multivariate analyses, minimum dose to the GTV and histological subtype were associated with local control. Renal cell carcinoma (RCC) histology showed the most favourable local control − 94% at 2 years for all histologies. Thirty-seven patients experienced grade 3–5 toxicity most likely related to SBRT. Seven of the ten patients with grade 5 toxicity, had a centrally located tumour in the thorax. SBRT of tumours >5 cm in diameter may be an option for peripherally located lung and abdominal tumours. Histological origin and tumour location should be considered before treatment. [ABSTRACT FROM AUTHOR]

Background: Whole-brain radiotherapy (WBRT) has been the standard of care for multiple NSCLC brain metastases but due to its toxicity and lack of survival benefit, its use in the palliative setting is being questioned. Patient and methods: This was a single institution cohort study including brain metastasized lung cancer patients who received WBRT at Karolinska University Hospital. Information about Recursive Partitioning Analysis (RPA) and Graded Prognostic Assessment (GPA) scores, demographics, histopathological results and received oncological therapy were collected. Predictors of overall survival (OS) from the time of received WBRT were identified by Cox regression analyses. OS between GPA and RPA classes were compared by pairwise log rank test. A subgroup OS analysis was performed stratified by RPA class. Results: The cohort consisted of 280 patients. RPA 1 and 2 classes had better OS compared to class 3, patients with GPA <1.5 points had better OS compared to GPA≥1.5 points and age >70 years was associated with worse OS (p<.0001 for all comparisons). In RPA class 2 subgroup analysis GPA ≥1.5 points, age ≤70 years and CNS surgery before salvage WBRT were independent positive prognostic factors. Conclusions: RPA class 3 patients should not receive WBRT, whereas RPA class 1 patients should receive WBRT if clinically indicated. RPA class 2 patients with age ≤70 years and GPA ≤1.5 points should be treated as RPA 1. WBRT should be omitted in RPA 2 patients with age >70. In RPA 2 patients with age ≤70 years and GPA <1.5 points WBRT could be a reasonable option. [ABSTRACT FROM AUTHOR]

Simple Summary: Immune checkpoint inhibitors have improved the prognosis for patients with advanced melanoma. Despite the recent success of immunotherapy, many patients still do not benefit from these treatments, and their real-life application may yield different outcomes compared to the advantage presented in clinical trials. There is therefore a need to select patients who can really benefit from these treatments. We have focused our study on a real-life retrospective analysis of metastatic melanoma patients treated with immunotherapy at a single institution—the Istituto Nazionale Tumori IRCCS Fondazione "G. Pascale" of Napoli, Italy. With the help of AI and machine learning we validated an algorithm based on clinical variables of patients—namely, the Clinical Categorization Algorithm (CLICAL)—that defines five predictable cohorts of benefit to immunotherapy with 95% accuracy. It can be a useful tool for the stratification of metastatic melanoma patients who may or may not improve from immunotherapy treatment. The real-life application of immune checkpoint inhibitors (ICIs) may yield different outcomes compared to the benefit presented in clinical trials. For this reason, there is a need to define the group of patients that may benefit from treatment. We retrospectively investigated 578 metastatic melanoma patients treated with ICIs at the Istituto Nazionale Tumori IRCCS Fondazione "G. Pascale" of Napoli, Italy (INT-NA). To compare patients' clinical variables (i.e., age, lactate dehydrogenase (LDH), neutrophil–lymphocyte ratio (NLR), eosinophil, BRAF status, previous treatment) and their predictive and prognostic power in a comprehensive, non-hierarchical manner, a clinical categorization algorithm (CLICAL) was defined and validated by the application of a machine learning algorithm—survival random forest (SRF-CLICAL). The comprehensive analysis of the clinical parameters by log risk-based algorithms resulted in predictive signatures that could identify groups of patients with great benefit or not, regardless of the ICI received. From a real-life retrospective analysis of metastatic melanoma patients, we generated and validated an algorithm based on machine learning that could assist with the clinical decision of whether or not to apply ICI therapy by defining five signatures of predictability with 95% accuracy. [ABSTRACT FROM AUTHOR]

Background: Disease-specific Graded Prognostic Assessment (DS-GPA) is the most validated prognostic tool for patients with brain metastasized lung cancer. The Lung-molGPA scoring system was recently introduced for oncogenic-driven brain metastasized lung cancer, but has not yet been validated in cohorts including only ALK-translocated tumors. Methods: We designed a retrospective cohort study consisting of 44 patients with brain metastasized ALK-positive, non-small cell lung cancer (NSCLC) who were treated between January 2009 and November 2019 at Karolinska University Hospital in Stockholm, Sweden. Information about demographics and clinicopathological parameters were collected. Predictors of overall survival (OS) were identified by Cox regression analyses. A bootstrap validation with 1000 samples was performed in order to compare the different prognostic scores. Results: The variables found to independently influence OS in the multivariate analysis, i.e., PS, sex and brain metastases at diagnosis, were used as prognostic variables in our new prognostic index (ALK-BPI). Patients were divided into two prognostic groups. The median OS was 65.7 months for the good prognostic group and 22.7 months for the poor prognostic group (p = 0.0068). In the univariate analysis of the different prognostic scores, ALK-BPI performed better than the others (HR = 3.6; 95% CI: 1.3–9.9). The mean C-statistics of the different prognostic scores were compared to each other, and no significant difference was observed. Conclusion: We propose the ALK-BPI score as a new prognostic tool that can easily be applied for ALK-positive lung cancer patients with brain metastases in daily clinical practice, as it has at least the same prognostic value as Lung-molGPA. [ABSTRACT FROM AUTHOR]

• The severity of gemcitabine/carboplatin-induced hematological toxicities varies extensively. • Severe hematological toxicities can lead to the need for postponed treatment, reduced doses and treatment discontinuation. • Genetic variability may explain and predict the variation in severe leukopenia and neutropenia. Gemcitabine/carboplatin treatment is known to cause severe adverse drug reactions which can lead to the need for reduction or cessation of chemotherapy. It would be beneficial to identify patients at risk of severe hematological toxicity in advance before treatment start. This study aims to identify genetic markers for gemcitabine/carboplatin-induced leukopenia and neutropenia in non-small cell lung cancer patients. Whole-exome sequencing was performed on 215 patients. Association analysis was performed on single-nucleotide variants (SNVs) and genes, and the validation was based on an independent genome-wide association study (GWAS). Based on the association and validation analyses the genetic variants were then selected for and used in weighted genetic risk score (wGRS) prediction models for leukopenia and neutropenia. Association analysis identified 50 and 111 SNVs, and 12 and 20 genes, for leukopenia and neutropenia, respectively. Of these SNVS 20 and 19 were partially validated for leukopenia and neutropenia, respectively. The genes SVIL (p = 2.48E-06) and EFCAB2 (p = 4.63E-06) were significantly associated with leukopenia contain the partially validated SNVs rs3740003, rs10160013, rs1547169, rs10927386 and rs10927387. The wGRS prediction models showed significantly different risk scores for high and low toxicity patients. We have identified and partially validated genetic biomarkers in SNVs and genes correlated to gemcitabine/carboplatin-induced leukopenia and neutropenia and created wGRS models for predicting the risk of chemotherapy-induced hematological toxicity. These results provide a strong foundation for further studies of chemotherapy-induced toxicity. [ABSTRACT FROM AUTHOR]

Disconnected cancer research data management and lack of information exchange about planned and ongoing research are complicating the utilisation of internationally collected medical information for improving cancer patient care. Rapidly collecting/pooling data can accelerate translational research in radiation therapy and oncology. The exchange of study data is one of the fundamental principles behind data aggregation and data mining. The possibilities of reproducing the original study results, performing further analyses on existing research data to generate new hypotheses or developing computational models to support medical decisions (e.g. risk/benefit analysis of treatment options) represent just a fraction of the potential benefits of medical data-pooling. Distributed machine learning and knowledge exchange from federated databases can be considered as one beyond other attractive approaches for knowledge generation within “Big Data”. Data interoperability between research institutions should be the major concern behind a wider collaboration. Information captured in electronic patient records (EPRs) and study case report forms (eCRFs), linked together with medical imaging and treatment planning data, are deemed to be fundamental elements for large multi-centre studies in the field of radiation therapy and oncology. To fully utilise the captured medical information, the study data have to be more than just an electronic version of a traditional (un-modifiable) paper CRF. Challenges that have to be addressed are data interoperability, utilisation of standards, data quality and privacy concerns, data ownership, rights to publish, data pooling architecture and storage. This paper discusses a framework for conceptual packages of ideas focused on a strategic development for international research data exchange in the field of radiation therapy and oncology. [ABSTRACT FROM AUTHOR]

Purpose: Cytokeratin 18 (CK18) can be used as a serum biomarker for carcinoma cell death, whereas caspase-cleaved (ccCK18) fragments reflect tumour apoptosis. We explored the potential diagnostic and prognostic role of circulating CK18 and ccCK18 in patients with non-small-cell lung cancer (NSCLC) in comparison with Cyfra 21.1, a fragment of cytokeratin 19. Methods: Subject cohorts consisted of 200 healthy blood donors (HBD), 113 patients with benign lung diseases (BLD) and 179 NSCLC cases. Plasma levels of ccCK18, total CK18 and Cyfra 21.1 were determined with ELISA assays. Results: Plasma levels of ccCK18 and total CK18 were higher in the NSCLC group compared to the HBD and BLD cohorts (p <0.0001). Using a cut-off of 104U/L for ccCK18 and 302 U/L for total CK18 (95% specificity in the HBD group) the diagnostic accuracy of both CK18 forms to distinguish between NSCLC and BLD cases was 56%, whereas it was 94% for Cyfra 21.1. Multivariate survival analysis showed that total CK18 was a stronger prognostic factor than both ccCK18 and Cyfra 21.1 (HR 0.64 for low versus high total CK18 levels, 95% confidence interval (CI) 0.50–0.82; p =0.0004) in the entire NSCLC cohort and in 78 patients with locally advanced or metastatic disease treated with chemoradiotherapy or first-line chemotherapy (HR 0.70 95% CI 0.52–0.94; p =0.018). Conclusions: Cyfra 21.1 is a useful diagnostic biomarker for NSCLC. Total CK18 shows a promising potential as prognostic marker in NSCLC patients, independently of the therapeutical intervention. In contrast, ccCK18 was not of prognostic value in NSCLC, suggesting that tumour necrosis is of particular importance in this disease. [Copyright &y& Elsevier]

Liquid biopsies based on extracellular vesicles (EVs) represent a promising tool for treatment monitoring of tumors, including non-small-cell lung cancers (NSCLC). In this study, we report on a multiplexed electrokinetic sensor for surface protein profiling of EVs from clinical samples. The method detects the difference in the streaming current generated by EV binding to the surface of a functionalized microcapillary, thereby estimating the expression level of a marker. Using multiple microchannels functionalized with different antibodies in a parallel fluidic connection, we first demonstrate the capacity for simultaneous detection of multiple surface markers in small EVs (sEVs) from NSCLC cells. To investigate the prospects of liquid biopsies based on EVs, we then apply the method to profile sEVs isolated from the pleural effusion (PE) fluids of five NSCLC patients with different genomic alterations (ALK , KRAS or EGFR) and applied treatments (chemotherapy, EGFR- or ALK-tyrosine kinase inhibitors). The vesicles were targeted against CD9, as well as EGFR and PD-L1, two treatment targets in NSCLC. The electrokinetic signals show detection of these markers on sEVs, highlighting distinct interpatient differences, e.g., increased EGFR levels in sEVs from a patient with EGFR mutation as compared to an ALK -fusion one. The sensors also detect differences in PD-L1 expressions. The analysis of sEVs from a patient prior and post ALK-TKI crizotinib treatment reveals significant increases in the expressions of some markers (EGFR and PD-L1). These results hold promise for the application of the method for tumor treatment monitoring based on sEVs from patient liquid biopsies. • The development of a multiplexed electrokinetic assay is demonstrated. • Electrokinetic sensors can be multiplexed without affecting measurement parameters. • Multiplexed detection of extracellular vesicles from pleural effusions is shown. • The possibility to profile surface proteins for different patients is demonstrated. • An application of the proposed method for tumor treatment monitoring is presented. [ABSTRACT FROM AUTHOR]