Roche

Purpose Gallbladder cancers (GBC), unique to certain geographical regions, are lethal digestive tract cancers, disproportionately affecting women, with limited information on risk factors. Methods We evaluated the association between household cooking fuel and GBC risk in a hospital-based case–control study conducted in the North-East and East Indian states of Assam and Bihar. We explored the potential mediation by diet, fire-vents, ‘daily exposure duration’ and parity (among women). We recruited biopsy-confirmed GBC (n = 214) men and women aged 30–69 years between 2019 and 2021, and controls frequency-matched by age, sex and region (n = 166). Information about cooking fuel, lifestyle, personal and family history, female reproductive factors, socio-demographics, and anthropometrics was collected. We tested associations using multivariable logistic regression analyses. Results All participants (73.4% women) were categorised based on predominant cooking fuel use. Group-1: LPG (Liquefied Petroleum Gas) users in the previous 20 years and above without concurrent biomass use (26.15%); Group-2: LPG users in the previous 20 years and above with concurrent secondary biomass use (15.9%); Group-3: Biomass users for ≥ 20 years (57.95%). Compared to group-1, accounting for confounders, GBC risk was higher in group-2 [OR: 2.02; 95% CI: 1.00–4.07] and group-3 [OR: 2.01; 95% CI: 1.08–3.73] (p-trend:0.020). These associations strengthened among women that attenuated with high daily consumption of fruits-vegetables but not with fire-vents, ‘daily exposure duration’ or parity. Conclusion Biomass burning was associated with a high-risk for GBC and should be considered as a modifiable risk factor for GBC. Clean cooking fuel can potentially mitigate, and a healthy diet can partially reduce the risk among women.

Introduction: Many people with cognitive complaints or impairment never receive an accurate diagnosis of the underlying condition, potentially impacting their access to appropriate treatment. To address this unmet need, plasma biomarker tests are being developed for use in Alzheimer's disease (AD). Plasma biomarker tests span various stages of development, including in vitro diagnostic devices (or tests) (IVDs), laboratory-developed tests (LDTs) and research use only devices (or tests) (RUOs). Understanding the differences between each test type is important for appropriate implementation into the AD diagnostic pathway and care continuum. Methods: Authors reviewed scientific literature (PubMed, meeting abstracts and presentations, company press releases and websites) on AD plasma biomarkers. Results: This article defines IVDs, LDTs, and RUOs, discusses potential clinical applications and highlights the steps necessary for their clinical implementation. Discussion: Plasma biomarkers could revolutionize many areas of the AD diagnostic pathway and care continuum, but further research is needed. Highlights: There is a need for a minimally invasive Alzheimer's disease (AD) diagnostic tool. AD plasma biomarker tests exist at various stages of commercial development. Understanding the development stage of a test is important for its appropriate use. Plasma biomarker tests could function as a triage tool to streamline AD diagnosis. Further steps remain before AD plasma biomarkers can be used routinely.

Introduction: We studied how biomarkers of reactive astrogliosis mediate the pathogenic cascade in the earliest Alzheimer's disease (AD) stages. Methods: We performed path analysis on data from 384 cognitively unimpaired individuals from the ALzheimer and FAmilies (ALFA)+ study using structural equation modeling to quantify the relationships between biomarkers of reactive astrogliosis and the AD pathological cascade. Results: Cerebrospinal fluid (CSF) amyloid beta (Aβ)42/40 was associated with Aβ aggregation on positron emission tomography (PET) and with CSF p-tau181 , which was in turn directly associated with CSF neurofilament light (NfL). Plasma glial fibrillary acidic protein (GFAP) mediated the relationship between CSF Aβ42/40 and Aβ-PET, and CSF YKL-40 partly explained the association between Aβ-PET, p-tau181 , and NfL. Discussion: Our results suggest that reactive astrogliosis, as indicated by different fluid biomarkers, influences the pathogenic cascade during the preclinical stage of AD. While plasma GFAP mediates the early association between soluble and insoluble Aβ, CSF YKL-40 mediates the latter association between Aβ and downstream Aβ-induced tau pathology and tau-induced neuronal injury. Highlights: Lower CSF Aβ42/40 was directly linked to higher plasma GFAP concentrations. Plasma GFAP partially explained the relationship between soluble Aβ and insoluble Aβ. CSF YKL-40 mediated Aβ-induced tau phosphorylation and tau-induced neuronal injury.

Vascularized composite allotransplantation can improve quality of life and restore functionality. However, the complex tissue composition of vascularized composite allografts (VCAs) presents unique clinical challenges that increase the likelihood of transplant rejection. Under prolonged static cold storage, highly damage-susceptible tissues such as muscle and nerve undergo irreversible degradation that may render allografts non-functional. Skin-containing VCA elicits an immunogenic response that increases the risk of recipient allograft rejection. The development of quantitative metrics to evaluate VCAs prior to and following transplantation are key to mitigating allograft rejection. Correspondingly, a broad range of bioanalytical methods have emerged to assess the progression of VCA rejection and characterize transplantation outcomes. To consolidate the current range of relevant technologies and expand on potential for development, methods to evaluate ex vivo VCA status are herein reviewed and comparatively assessed. The use of implantable physiological status monitoring biochips, non-invasive bioimpedance monitoring to assess edema, and deep learning algorithms to fuse disparate inputs to stratify VCAs are identified.

Surface functionalization has a prominent influence on tuning/manipulating the physicochemical properties of nanometer scaled materials. Ultrasmall sized nanoclusters with very few atoms have received enormous attention due to their bright fluorescence, biocompatibility, lower toxicity, good colloidal stability and strong photostability. These properties make them suitable for diagnostic applications. In this work, we intend to study the effect of surface functional ligands on their biodistribution both in vitro and in vivo organelle systems for bioimaging applications.

Objective: To evaluate blood-based biomarkers to detect endometriosis and/or adenomyosis across nine European centers (June 2014-April 2018). Methods: This prospective, non-interventional study assessed the diagnostic accuracy of 54 blood-based biomarker immunoassays in samples from 919 women (aged 18-45 years) with suspicion of endometriosis and/or adenomyosis versus symptomatic controls. Endometriosis was stratified by revised American Society for Reproductive Medicine stage. Symptomatic controls were "pathologic symptomatic controls" or "pathology-free symptomatic controls". The main outcome measure was receiver operating characteristic-area under the curve (ROC-AUC) and Wilcoxon P values corrected for multiple testing (q values). Results: CA-125 performed best in "all endometriosis cases" versus "all symptomatic controls" (AUC 0.645, 95% confidence interval [CI] 0.600-0.690, q < 0.001) and increased (P < 0.001) with disease stage. In "all endometriosis cases" versus "pathology-free symptomatic controls", S100-A12 performed best (AUC 0.692, 95% CI 0.614-0.769, q = 0.001) followed by CA-125 (AUC 0.649, 95% CI 0.569-0.729, q = 0.021). In "adenomyosis only cases" versus "symptomatic controls" or "pathology-free symptomatic controls", respectively, the top-performing biomarkers were sFRP-4 (AUC 0.615, 95% CI 0.551-0.678, q = 0.045) and S100-A12 (AUC 0.701, 95% CI 0.611-0.792, q = 0.004). Conclusion: This study concluded that no biomarkers tested could diagnose or rule out endometriosis/adenomyosis with high certainty.

Purpose of review: The role of the gut microbiota in modulating blood pressure is increasingly being recognized, currently. The purpose of this review is to summarize recent findings about the mechanisms involved in hypertension with regard to the phenomenon of "gut dysbiosis." Recent findings: Gut dysbiosis, i.e., the imbalance between the gut microbiota and the host, is characterized by a disruption of the tight junction proteins, such as occludins, claudins, and JAMs (junctional adhesion molecules), resulting in increased gut permeability or the so called "leaky gut." Due to the influence of genetic as well as environmental factors, various metabolites produced by the gut microbiota, such as indole and p-cresol, are increased. Thereby, uremic toxins, such as indoxyl sulfates and p-cresol sulfates, accumulate in the blood and the urine, causing damage in the podocytes and the tubular cells. In addition, immunological mechanisms are implicated as well. In particular, a switch from M2 macrophages to M1 macrophages, which produce pro-inflammatory cytokines, occurs. Moreover, a higher level of Th17 cells, releasing large amounts of interleukin-17 (IL-17), has been reported, when a diet rich in salt is consumed. Therefore, apart from the aggravation of uremic toxins, which may account for direct harmful effects on the kidney, there is inflammation not only in the gut, but in the kidneys as well. This crosstalk between the gut and the kidney is suggested to play a crucial role in hypertension. Notably, the brain is also implicated, with an increasing sympathetic output. The brain-gut-kidney axis seems to be deeply involved in the development of hypertension and chronic kidney disease (CKD). The notion that, by modulating the gut microbiota, we could regulate blood pressure is strongly supported by the current evidence. A healthy diet, low in animal protein and fat, and low in salt, together with the utilization of probiotics, prebiotics, synbiotics, or postbiotics, may contribute to our fight against hypertension.

Background: Lipoprotein(a) is a risk factor for cardiovascular events and modifies the benefit of pcsk9 inhibitors (pcsk9i). Lipoprotein(a) concentration can be measured with immunoassays reporting mass or molar concentration or a reference measurement system employing mass spectrometry. Whether the relationships between lipoprotein(a) concentrations and cardiovascular events in a high-risk cohort differ across lipoprotein(a) methods is unknown. We compared the prognostic and predictive value of these types of lipoprotein(a) tests for major adverse cardiovascular events (mace). Methods: The odyssey outcomes trial compared the pcsk9i alirocumab with placebo in patients with recent acute coronary syndrome (acs). We compared risk of mace in the placebo group and mace risk reduction with alirocumab according to baseline lipoprotein(a) concentration measured by siemens n-latex nephelometric immunoassay (ia-mass, mg/dl), roche tina-quant® turbidimetric immunoassay (ia-molar, nmol/l), and a non-commercial mass spectrometry-based test (ms, nmol/l). Lipoprotein(a) values were transformed into percentiles for comparative modeling. Natural cubic splines estimated continuous relationships between baseline lipoprotein(a) and outcomes in each treatment group. Event rates were also determined across baseline lipoprotein(a) quartiles defined by each assay. Results: Among 11,970 trial participants with results from all 3 tests, baseline median (q1, q3) lipoprotein(a) concentrations were 21.8 (6.9, 60.0) mg/dl, 45.0 (13.2, 153.8) nmol/l, and 42.2 (14.3, 143.1) nmol/l for ia-mass, ia-molar, and ms, respectively. The strongest correlation was between ia-molar and ms (r=0.990), with nominally weaker correlations between ia-mass and ms (r=0.967) and ia-mass and ia-molar (r=0.972). Relationships of lipoprotein(a) with mace risk in the placebo group were nearly identical with each test with estimated cumulative incidences differing by ≤0.4% across lipoprotein(a) percentiles, and all were incrementally prognostic after accounting for ldl-c (all spline p≤0.0003). Predicted alirocumab treatment effects were also nearly identical for each of the three tests, with estimated treatment hazard ratios (hrs) differing by ≤0.07 between tests across percentiles and nominally less relative risk reduction by alirocumab at lower percentiles for all three tests. Absolute risk reduction with alirocumab increased with increasing lipoprotein(a) measured by each test, with significant linear trends across quartiles. Conclusions: In patients with recent acs, three lipoprotein(a) tests were similarly prognostic for mace in the placebo group and predictive of mace reductions with alirocumab at the cohort level.

Reference intervals are essential for interpreting laboratory test results. Continuous reference intervals precisely capture physiological age-specific dynamics that occur throughout life, and thus have the potential to improve clinical decision-making. However, established approaches for estimating continuous reference intervals require samples from healthy individuals, and are therefore substantially restricted. Indirect methods operating on routine measurements enable the estimation of one-dimensional reference intervals, however, no automated approach exists that integrates the dependency on a continuous covariate like age. We propose an integrated pipeline for the fully automated estimation of continuous reference intervals expressed as a generalized additive model for location, scale and shape based on discrete model estimates using an indirect method (refineR). The results are free of subjective user-input, enable conversion of test results into z-scores and can be integrated into laboratory information systems. Comparison of our results to established and validated reference intervals from the CALIPER and PEDREF studies and manufacturers’ package inserts shows good agreement of reference limits, indicating that the proposed pipeline generates high-quality results. In conclusion, the developed pipeline enables the generation of high-precision percentile charts and continuous reference intervals. It represents the first parameter-less and fully automated solution for the indirect estimation of continuous reference intervals.

Multiple Myeloma is an incurable plasma cell malignancy with a poor survival rate that is usually treated with immunomodulatory drugs (iMiDs) and proteosome inhibitors (PIs). The malignant plasma cells quickly become resistant to these agents causing relapse and uncontrolled growth of resistant clones. From whole genome sequencing (WGS) and RNA sequencing (RNA-seq) studies, different high-risk translocation, copy number, mutational, and transcriptional markers have been identified. One of these markers, PHF19, epigenetically regulates cell cycle and other processes and has already been studied using RNA-seq. In this study a massive (325,025 cells and 49 patients) single cell multiomic dataset was generated with jointly quantified ATAC- and RNA-seq for each cell and matched genomic profiles for each patient. We identified an association between one plasma cell subtype with myeloma progression that we have called relapsed/refractory plasma cells (RRPCs). These cells are associated with 1q alterations, TP53 mutations, and higher expression of PHF19. We also identified downstream regulation of cell cycle inhibitors in these cells, possible regulation of the transcription factor (TF) PBX1 on 1q, and determined that PHF19 may be acting primarily through this subset of cells.

Preclinical biomedical research depends on accurately detecting and quantifying rodent behavior. Current data-driven approaches, which segment free exploratory behavior into clusters, suffer from low statistical power due to multiple-testing, exhibit poor transferability across experiments, and fail to exploit the rich behavioral profiles of individual animals. We introduce a pipeline to capture each animal’s behavioral flow, yielding a single metric based on all observed transitions between clusters. By stabilizing these clusters through machine learning, we ensure data transferability, while dimensionality reduction techniques facilitate detailed analysis of individual animals. We provide a unique dataset of 443 behavior recordings of freely moving mice - including stress exposures, pharmacological and brain circuit interventions - to identify hidden treatment effects, reveal subtle variations on the level of individual animals, and detect brain processes underlying specific interventions. Our pipeline, compatible with popular clustering methods, significantly enhances statistical power and enables predictions of an animal’s future behavior.

Background: Wordlist and story recall tests are routinely employed in clinical practice for dementia diagnosis. In this study, our aim was to establish how well-standard clinical metrics compared to process scores derived from wordlist and story recall tests in predicting biomarker determined Alzheimer's disease, as defined by CSF ptau/Aβ42 ratio. Methods: Data from 295 participants (mean age = 65 ± 9.) were drawn from the University of Wisconsin - Madison Alzheimer's Disease Research Center (ADRC) and Wisconsin Registry for Alzheimer's Prevention (WRAP). Rey's Auditory Verbal Learning Test (AVLT; wordlist) and Logical Memory Test (LMT; story) data were used. Bayesian linear regression analyses were carried out with CSF ptau/Aβ42 ratio as outcome. Sensitivity analyses were carried out with logistic regressions to assess diagnosticity. Results: LMT generally outperformed AVLT. Notably, the best predictors were primacy ratio, a process score indexing loss of information learned early during test administration, and recency ratio, which tracks loss of recently learned information. Sensitivity analyses confirmed this conclusion. Conclusions: Our study shows that story recall tests may be better than wordlist tests for detection of dementia, especially when employing process scores alongside conventional clinical scores.

Background: Patients with non-small cell lung cancer (NSCLC) and stable disease (SD) have an unmet clinical need to help guide early treatment adjustments. Objective: To evaluate the potential of tumor biomarkers to inform on survival outcomes in NSCLC SD patients. Methods: This post hoc analysis included 480 patients from the IMpower150 study with metastatic NSCLC, treated with chemotherapy, atezolizumab and bevacizumab combinations, who had SD at first CT scan (post-treatment initiation). Patients were stratified into high- and low-risk groups (overall survival [OS] and progression-free survival [PFS] outcomes) based on serum tumor biomarker levels. Results: The CYFRA 21-1 and CA 125 biomarker combination predicted OS and PFS in patients with SD. Risk of death was ~4-fold higher for the biomarker-stratified high-risk versus low-risk SD patients (hazard ratio [HR] 3.80; 95% confidence interval [CI] 3.02-4.78; p < 0.0001). OS in patients with the low- and high-risk SD was comparable to that in patients with the CT-defined partial response (PR; HR 1.10; 95% CI 0.898-1.34) and progressive disease (PD) (HR 1.05; 95% CI 0.621-1.77), respectively. The findings were similar with PFS, and consistent across treatment arms. Conclusions: Biomarker testing shows potential for providing prognostic information to help direct treatment in NSCLC patients with SD. Prospective clinical studies are warranted. Clinicaltrials: gov: NCT02366143.

Cloud-based regulatory platforms have the potential to substantially transform how regulatory submissions are developed, transmitted, and reviewed across the full life cycle of drug development. The benefits of cloud-based submission and review include accelerating critical therapies to patients in need globally and efficiency gains for both drug developers and regulators. The key challenge is turning the theoretical promise of cloud-based regulatory platforms into reality to further the application of technology in the regulatory processes. In this publication we outline regulatory policy journeys needed to effect the changes in the external environment that would allow for use of a cloud-based technology, discuss the prerequisites to successfully navigate the policy journeys, and elaborate on future possibilities when adoption of cloud-based regulatory technologies is achieved.

Background: Genetic scores for late-onset Alzheimer's disease (LOAD) have been associated with preclinical cognitive decline and biomarker variations. Compared with an overall polygenic risk score (PRS), a pathway-specific PRS (p-PRS) may be more appropriate in predicting a specific biomarker or cognitive component underlying LOAD pathology earlier in the lifespan. Objective: In this study, we leveraged longitudinal data from the Wisconsin Registry for Alzheimer's Prevention and explored changing patterns in cognition and biomarkers at various age points along six biological pathways. Methods: PRS and p-PRSs with and without APOE were constructed separately based on the significant SNPs associated with LOAD in a recent genome-wide association study meta-analysis and compared to APOE alone. We used a linear mixed-effects model to assess the association between PRS/p-PRSs and cognitive trajectories among 1,175 individuals. We also applied the model to the outcomes of cerebrospinal fluid biomarkers in a subset. Replication analyses were performed in an independent sample. Results: We found p-PRSs and the overall PRS can predict preclinical changes in cognition and biomarkers. The effects of PRS/p-PRSs on rate of change in cognition, amyloid-β, and tau outcomes are dependent on age and appear earlier in the lifespan when APOE is included in these risk scores compared to when APOE is excluded. Conclusion: In addition to APOE, the p-PRSs can predict age-dependent changes in amyloid-β, tau, and cognition. Once validated, they could be used to identify individuals with an elevated genetic risk of accumulating amyloid-β and tau, long before the onset of clinical symptoms.

Background Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) are the most common notifiable sexually transmitted infections (STIs) in the United States. Because symptoms of these infections often overlap with other urogenital infections, misdiagnosis and incorrect treatment can occur unless appropriate STI diagnostic testing is performed in clinical settings. The objective of this study was to describe STI diagnostic testing and antimicrobial treatment patterns and trends among adolescent and adult men and women with lower genitourinary tract symptoms (LGUTS). Methods We analyzed insurance claims data from the IBM® MarketScan® Research Databases. Patients included were between 14 and 64 years old with LGUTS as determined by selected International Classification of Diseases codes between January 2010 and December 2019. Testing of STIs and relevant drug claims were captured, and distribution of testing patterns and drug claims were described. Results In total, 23,537,812 episodes with LGUTS (87.4% from women; 12.6% from men) were analyzed from 12,341,154 patients. CT/NG testing occurred in only 17.6% of all episodes. For episodes where patients received treatment within 2 weeks of the visit date, 89.3% received treatment within the first 3 days (likely indicating presumptive treatment), and 77.7% received it on the first day. For women with pelvic inflammatory disease and men with orchitis/epididymitis and acute prostatitis, ≤ 15% received CT/NG testing, and around one-half received antibiotic treatment within 3 days. Conclusions Our study revealed low CT/NG testing rates, even in patients diagnosed with complications commonly associated with these STIs, along with high levels of potentially inappropriate presumptive treatment. This highlights the need for timely and accurate STI diagnosis in patients with LGUTS to inform appropriate treatment recommendations.

Background The insulin-like growth factors (IGF) play a crucial role in regulating cellular proliferation, apoptosis, and key metabolic pathways. The ratio of IGF-1 to IGF binding protein-3 (IGFBP-3) is an important factor in determining IGF-1 bioactivity. We sought to investigate the association of IGF-1 and IGFBP-3 with cardio-renal outcomes among persons with type 2 diabetes. Methods Samples were available from 2627 individuals with type 2 diabetes and chronic kidney disease that were randomized to receive canagliflozin or placebo and were followed up for incident cardio-renal events. Primary outcome was defined as a composite of end-stage kidney disease, doubling of the serum creatinine level, or renal/cardiovascular death. IGF-1 and IGFBP-3 were measured at baseline, Year-1 and Year-3. Elevated IGF-1 level was defined according to age-specific cutoffs. Cox proportional hazard regression was used to investigate the association between IGF-1 level, IGFBP-3, and the ratio of IGF-1/IGFBP-3 with clinical outcomes. Results Elevated IGF-1 was associated with lower glomerular filtration rate at baseline. Treatment with canagliflozin did not significantly change IGF-1 and IGFBP-3 concentrations by 3 years (p-value > 0.05). In multivariable models, elevated IGF-1 (above vs below age-specific cutoffs) was associated with the primary composite outcome (incidence rate:17.8% vs. 12.7% with a hazard ratio [HR]: 1.52; 95% confidence interval CI 1.09–2.13;P: 0.01), renal composite outcome (HR: 1.65; 95% CI 1.14–2.41; P: 0.01), and all-cause mortality (HR: 1.52; 95% CI 1.00–2.32; P; 0.05). Elevations in log IGFBP-3 did not associate with any clinical outcomes. Increase in log IGF-1/IGFBP-3 ratio was also associated with a higher risk of the primary composite outcome (HR per unit increase: 1.57; 95% CI 1.09–2.26; P; 0.01). Conclusions These results further suggest potential importance of IGF biology in the risk for cardio-renal outcomes in type 2 diabetes. SGLT2 inhibition has no impact on the biology of IGF despite its significant influence on outcomes. Trial registration: CREDENCE; ClinicalTrials.gov Identifier: NCT02065791.

The measurement of Epstein-Barr virus (EBV) deoxyribonucleic acid (DNA) is key to diagnosing and managing EBV-associated complications in transplant recipients. The performance of the new Conformité Européenne (CE) and Food and Drug Administration (FDA)-cleared quantitative Roche cobas EBV real-time PCR assay was determined by using EDTA-plasma dilution panels and clinical samples that were spiked with either the World Health Organization's EBV international standard or high-titer EBV lambda stock. Correlation with the Abbott Realtime EBV assay was assessed in clinical specimens and conducted at two independent laboratories. An in silico analysis revealed that the dual-target test (EBNA1 and BMRF2) was 100% inclusive for the known diversity of EBV. The overall limit of detection (LoD) was 16.6 IU/mL for genotype 1 (GT1). GT2 LoD was verified at 18.8 IU/mL. The linear ranges were from 1.40 × 101 to 2.30 × 108 IU/mL and from 2.97 × 101 to 9.90 × 107 IU/mL for GT1 and GT2, respectively. Accuracy was confirmed across the linear range (mean difference not exceeding ±0.18 log10). Precision was not influenced by the factors analyzed (standard deviation of 0.02 to 0.17 log10), including the presence of potentially interfering endogenous or exogenous substances. Plasma samples were stable under several conditions (variable time points, storage, and freeze/thaw cycles). In clinical EBV DNA-positive samples, correlation between the cobas EBV test and the comparator was high (n = 126 valid results; R2 = 0.96) with a 0.1 mean log10 titer difference. The cobas EBV test is an accurate, sensitive, specific, and reproducible assay for the detection of EBV DNAemia in plasma. In general, high levels of automation and calibration to the international standard will lead to improvements in the harmonization of quantitative EBV DNA test results across laboratories.

Background A modeling framework was previously developed to simulate overall survival (OS) using tumor growth inhibition (TGI) data from six randomized phase 2/3 atezolizumab monotherapy or combination studies in non-small-cell lung cancer (NSCLC). We aimed to externally validate this framework to simulate OS in patients with treatment-naive advanced anaplastic lymphoma kinase (ALK)-positive NSCLC in the alectinib ALEX study. Methods TGI metrics were estimated from a biexponential model using longitudinal tumor size data from a Phase 3 study evaluating alectinib compared with crizotinib in patients with treatment-naive ALK-positive advanced NSCLC. Baseline prognostic factors and TGI metric estimates were used to predict OS. Results 286 patients were evaluable (at least baseline and one post-baseline tumor size measurements) out of 303 (94%) followed for up to 5 years (cut-off: 29 November 2019). The tumor growth rate estimate and baseline prognostic factors (inflammatory status, tumor burden, Eastern Cooperative Oncology Group performance status, race, line of therapy, and sex) were used to simulate OS in ALEX study. Observed survival distributions for alectinib and crizotinib were within model 95% prediction intervals (PI) for approximately 2 years. Predicted hazard ratio (HR) between alectinib and crizotinib was in agreement with the observed HR (predicted HR 0.612, 95% PI 0.480–0.770 vs. 0.625 observed HR). Conclusion The TGI-OS model based on unselected or PD-L1 selected NSCLC patients included in atezolizumab trials is externally validated to predict treatment effect (HR) in a biomarker-selected (ALK-positive) population included in alectinib ALEX trial suggesting that TGI-OS models may be treatment independent.