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Advances in cancer biology and sequencing technology have enabled the selection of targeted and more effective treatments for individual patients with various types of solid tumour. However, only three molecular biomarkers have thus far been demonstrated to predict a response to targeted therapies in patients with gastric and/or gastro-oesophageal junction (G/GEJ) cancers: HER2 positivity for trastuzumab and trastuzumab deruxtecan, and microsatellite instability (MSI) status and PD-L1 expression for pembrolizumab. Despite this lack of clinically relevant biomarkers, distinct molecular subtypes of G/GEJ cancers have been identified and have informed the development of novel agents, including receptor tyrosine kinase inhibitors and monoclonal antibodies, several of which are currently being tested in ongoing trials. Many of these trials include biomarker stratification, and some include analysis of circulating tumour DNA (ctDNA), which both enables the noninvasive assessment of biomarker expression and provides an indication of the contributions of intratumoural heterogeneity to response and resistance. The results of these studies might help to optimize the selection of patients to receive targeted therapies, thus facilitating precision medicine approaches for patients with G/GEJ cancers. In this Review, we describe the current evidence supporting the use of targeted therapies in patients with G/GEJ cancers and provide guidance on future research directions.Despite considerable progress in the development of targeted therapies, only three biomarkers are currently used to guide the treatment of patients with gastric or gastro-oesophageal junction cancers using approved targeted therapies. Nonetheless, owing to advances in our understanding of tumour biology and sequencing technologies, several novel therapies are expected to soon become available. In this Review, the authors describe current and future biomarker-guided therapies for patients with G/GEJ cancers.

Immune-checkpoint inhibitors targeting PD-1 or PD-L1 have already substantially improved the outcomes of patients with many types of cancer, although only 20–40% of patients derive benefit from these new therapies. PD-L1, quantified using immunohistochemistry assays, is currently the most widely validated, used and accepted biomarker to guide the selection of patients to receive anti-PD-1 or anti-PD-L1 antibodies. However, many challenges remain in the clinical use of these assays, including the necessity of using different companion diagnostic assays for specific agents, high levels of inter-assay variability in terms of both performance and cut-off points, and a lack of prospective comparisons of how PD-L1+ disease diagnosed using each assay relates to clinical outcomes. In this Review, we describe the current role of PD-L1 immunohistochemistry assays used to inform the selection of patients to receive anti-PD-1 or anti-PD-L1 antibodies, we discuss the various technical and clinical challenges associated with these assays, including regulatory issues, and we provide some perspective on how to optimize PD-L1 as a selection biomarker for the future treatment of patients with solid tumours.PD-L1 expression is currently the best available biomarker for the prediction of responsiveness to immune-checkpoint inhibitors. However, several immunohistochemical assays are now approved for clinical use in various settings, despite imperfect inter-assay concordance, with important implications for pathology services and, potentially, for clinical outcomes. In this Review, the authors compare the performance of the various FDA-approved PD-L1 assays, discuss the varying implications of PD-L1 expression across different tumour types and provide guidance on possible novel approaches that might optimize the clinical utility of PD-L1 as a biomarker.

Mounting evidence suggests that function and connectivity of the striatum is disrupted in schizophrenia 1 – 5 . We have developed a new hypothesis-driven neuroimaging biomarker for schizophrenia identification, prognosis and subtyping based on functional striatal abnormalities (FSA). FSA scores provide a personalized index of striatal dysfunction, ranging from normal to highly pathological. Using inter-site cross-validation on functional magnetic resonance images acquired from seven independent scanners ( n  = 1,100), FSA distinguished individuals with schizophrenia from healthy controls with an accuracy exceeding 80% (sensitivity, 79.3%; specificity, 81.5%). In two longitudinal cohorts, inter-individual variation in baseline FSA scores was significantly associated with antipsychotic treatment response. FSA revealed a spectrum of severity in striatal dysfunction across neuropsychiatric disorders, where dysfunction was most severe in schizophrenia, milder in bipolar disorder, and indistinguishable from healthy individuals in depression, obsessive-compulsive disorder and attention-deficit hyperactivity disorder. Loci of striatal hyperactivity recapitulated the spatial distribution of dopaminergic function and the expression profiles of polygenic risk for schizophrenia. In conclusion, we have developed a new biomarker to index striatal dysfunction and established its utility in predicting antipsychotic treatment response, clinical stratification and elucidating striatal dysfunction in neuropsychiatric disorders. A new cross-validated neuroimaging biomarker that reflects striatal dysfunctioning can be used to distinguish patients with schizophrenia from healthy controls, and is associated with treatment response to antipsychotics.

The impact of ctDNA changes after chemotherapy on the clinical outcomes of patients with metastatic colorectal cancer (mCRC) remains unclear. The present study evaluated the clinical implications of the early change in ctDNA levels as a predictor of objective response and clinical outcome in mCRC patients who received chemotherapy. We investigated the effects of after/before ratio of ctDNA levels 2 and 8 weeks after initiation of second-line chemotherapy, on objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). ctDNA was detected using amplicon-based deep sequencing with a molecular barcode encompassing >240 hotspot mutations in 14 colon cancer-related genes. In multivariate analysis, as compared to baseline, patients with lower ctDNA level (≤50%) 8 weeks after initiation of chemotherapy showed significantly longer PFS and OS than the patients with higher (>50%) ctDNA level. In patients achieving a partial response or stable disease, the after/before ratio of ctDNA level 8 weeks after initiation of chemotherapy was significantly lower than those in patients with progressive disease. The present study suggests that an early change in the ctDNA level might serve as a biomarker to predict the chemotherapeutic efficacy and clinical outcomes in patients with mCRC.

Background Limited accessibility of the tumour precludes longitudinal characterisation for therapy guidance in pancreatic ductal adenocarcinoma (PDAC). Methods We utilised dielectrophoresis-field flow fractionation (DEP-FFF) to isolate circulating tumour cells (CTCs) in 272 blood draws from 74 PDAC patients (41 localised, 33 metastatic) to non-invasively monitor disease progression. Results Analysis using multiplex imaging flow cytometry revealed four distinct sub-populations of CTCs: epithelial (E-CTC), mesenchymal (M-CTC), partial epithelial-mesenchymal transition (pEMT-CTC) and stem cell-like (SC-CTC). Overall, CTC detection rate was 76.8% (209/272 draws) and total CTC counts did not correlate with any clinicopathological variables. However, the proportion of pEMT-CTCs (prop-pEMT) was correlated with advanced disease, worse progression-free and overall survival in all patients, and earlier recurrence after resection. Conclusion Our results underscore the importance of immunophenotyping and quantifying specific CTC sub-populations in PDAC.

Deregulation of cell cycle, via cyclin D/CDK/pRb pathway, is frequently observed in breast cancer lending support to the development of drugs targeting the cell cycle control machinery, like the inhibitors of the cycline-dependent kinases (CDK) 4 and 6. Up to now, three CDK4/6 inhibitors have been approved by FDA for the treatment of hormone receptor-positive (HR+), HER2-negative metastatic breast cancer. These agents have been effective in improving the clinical outcomes, but the development of intrinsic or acquired resistance can limit the efficacy of these treatments. Clinical and translational research is now focused on investigation of the mechanism of sensitivity/resistance to CDK4/6 inhibition and novel therapeutic strategies aimed to improve clinical outcomes. This review summarizes the available knowledge regarding CDK4/6 inhibitor, the discovery of new biomarkers of response, and the biological rationale for new combination strategies of treatment.

Abstract Context The tumor immune microenvironment is associated with clinical outcomes and immunotherapy responsiveness. Objective To investigate the intratumoral immune profile of pituitary adenomas (PAs) and its clinical relevance and to explore a novel immune classification for predicting immunotherapy responsiveness. Design, Patients, and Methods The transcriptomic data from 259 PAs and 20 normal pituitaries were included for analysis. The ImmuCellAI algorithm was used to estimate the abundance of 24 types of tumor-infiltrating immune cells (TIICs) and the expression of immune checkpoint molecules (ICMs). Results The distributions of TIICs differed between PAs and normal pituitaries and varied among PA subtypes. T cells dominated the immune microenvironment across all subtypes of PAs. The tumor size and patient age were correlated with the TIIC abundance, and the ubiquitin-specific protease 8 (USP8) mutation in corticotroph adenomas influenced the intratumoral TIIC distributions. Three immune clusters were identified across PAs based on the TIIC distributions. Each cluster of PAs showed unique features of ICM expression that were correlated with distinct pathways related to tumor development and progression. CTLA4/CD86 expression was upregulated in cluster 1, whereas programmed cell death protein 1/programmed cell death 1 ligand 2 (PD1/PD-L2) expression was upregulated in cluster 2. Clusters 1 and 2 exhibited a “hot” immune microenvironment and were predicted to exhibit higher immunotherapy responsiveness than cluster 3, which exhibited an overall “cold” immune microenvironment. Conclusions We summarized the immune profile of PAs and identified 3 novel immune clusters. These findings establish a foundation for further immune studies on PAs and provide new insights into immunotherapy strategies for PAs.

Abstract Context First-generation somatostatin receptor ligands (fg-SRLs) represent the mainstay of medical therapy for acromegaly, but they provide biochemical control of disease in only a subset of patients. Various pretreatment biomarkers might affect biochemical response to fg-SRLs. Objective To identify clinical predictors of the biochemical response to fg-SRLs monotherapy defined as biochemical response (insulin-like growth factor (IGF)-1 ≤ 1.3 × ULN (upper limit of normal)), partial response (>20% relative IGF-1 reduction without normalization), and nonresponse (≤20% relative IGF-1 reduction), and IGF-1 reduction. Design Retrospective multicenter study. Setting Eight participating European centers. Methods We performed a meta-analysis of participant data from 2 cohorts (Rotterdam and Liège acromegaly survey, 622 out of 3520 patients). Multivariable regression models were used to identify predictors of biochemical response to fg-SRL monotherapy. Results Lower IGF-1 concentration at baseline (odds ratio (OR) = 0.82, 95% confidence interval (CI) 0.72–0.95 IGF-1 ULN, P = .0073) and lower bodyweight (OR = 0.99, 95% CI 0.98–0.99 kg, P = .038) were associated with biochemical response. Higher IGF-1 concentration at baseline (OR = 1.40, (1.19–1.65) IGF-1 ULN, P ≤ .0001), the presence of type 2 diabetes (oral medication OR = 2.48, (1.43–4.29), P = .0013; insulin therapy OR = 2.65, (1.02–6.70), P = .045), and higher bodyweight (OR = 1.02, (1.01–1.04) kg, P = .0023) were associated with achieving partial response. Younger patients at diagnosis are more likely to achieve nonresponse (OR = 0.96, (0.94–0.99) year, P = .0070). Baseline IGF-1 and growth hormone concentration at diagnosis were associated with absolute IGF-1 reduction (β = 0.90, standard error (SE) = 0.02, P ≤ .0001 and β  = 0.002, SE = 0.001, P = .014, respectively). Conclusion Baseline IGF-1 concentration was the best predictor of biochemical response to fg-SRL, followed by bodyweight, while younger patients were more likely to achieve nonresponse.

There is an urgent and unmet need for accurate biomarkers in Amyotrophic Lateral Sclerosis. A pharmaco-metabolomics study was conducted using plasma samples from the TRO19622 (olesoxime) trial to assess the link between early metabolomic profiles and clinical outcomes. Patients included in this trial were randomized into either Group O receiving olesoxime (n = 38) or Group P receiving placebo (n = 36). The metabolomic profile was assessed at time-point one (V1) and 12 months (V12) after the initiation of the treatment. High performance liquid chromatography coupled with tandem mass spectrometry was used to quantify 188 metabolites (Biocrates® commercial kit). Multivariate analysis based on machine learning approaches (i.e. Biosigner algorithm) was performed. Metabolomic profiles at V1 and V12 and changes in metabolomic profiles between V1 and V12 accurately discriminated between Groups O and P (p<5×10-6), and identified glycine, kynurenine and citrulline/arginine as the best predictors of group membership. Changes in metabolomic profiles were closely linked to clinical progression, and correlated with glutamine levels in Group P and amino acids, lipids and spermidine levels in Group O. Multivariate models accurately predicted disease progression and highlighted the discriminant role of sphingomyelins (SM C22:3, SM C24:1, SM OH C22:2, SM C16:1). To predict SVC from SM C24:1 in group O and SVC from SM OH C22:2 and SM C16:1 in group P+O, we noted a median sensitivity between 67% and 100%, a specificity between 66.7 and 71.4%, a positive predictive value between 66 and 75% and a negative predictive value between 70% and 100% in the test sets. This proof-of-concept study demonstrates that the metabolomics has a role in evaluating the biological effect of an investigational drug and may be a candidate biomarker as a secondary outcome measure in clinical trials.

Small-cell lung cancer (SCLC) is an aggressive malignancy that exhibits a rapid doubling time, a high growth fraction, and the early development of widespread metastases. The addition of immune checkpoint inhibitors to first-line chemotherapy represents the first significant improvement of systemic therapy in several decades. However, in contrast to its effects on non-SCLC, the advantageous effects of immunotherapy addition are modest in SCLC. In particular, only a small number of SCLC patients benefit from immune checkpoint inhibitors. Additionally, biomarkers selection is lacking for SCLC, with clinical trials largely focusing on unselected populations. Here, we review the data concerning the major biomarkers for immunotherapy, namely, programmed death ligand 1 expression and tumour mutational burden. Furthermore, we explore other potential biomarkers, including the role of the immune microenvironment in SCLC, the role of genetic alterations, and the potential links between neurological paraneoplastic syndromes, serum anti-neuronal nuclear antibodies, and outcomes in SCLC patients treated with immunotherapy.