Predictive Biomarkers Of Response Research Articles

Selection of Chemotherapy in Advanced Poorly Differentiated Extra-Pulmonary Neuroendocrine Carcinoma.

Extra-pulmonary poorly differentiated neuroendocrine carcinoma is rare, and evidence for treatment has been limited. In this article, the evidence behind the cytotoxic chemotherapy choices used for metastatic or unresectable EP-PD-NEC is reviewed. In the first-line setting, etoposide and platinum chemotherapy or irinotecan and platinum have been demonstrated to be equivalent in a large phase III trial. Questions remain regarding the optimal number of cycles, mode of delivery, and the precise definition of platinum resistance in this setting. In the second-line setting, FOLFIRI has emerged as an option, with randomized phase 2 trials demonstrating modest, but significant, response rates. Beyond this, data are extremely limited, and several regimens have been used. Heterogeneity in biological behaviour is a major barrier to optimal EP-PD-NEC management. Available data support the potential role of the Ki-67 index as a predictive biomarker for chemotherapy response. A more personalised approach to management in future studies will be essential, and comprehensive multi-omic approaches are required to understand tumour somatic genetic changes in relation to their effects on the surrounding microenvironment.

High levels of CRBN isoform lacking IMiDs binding domain predicts for a worse response to IMiDs-based upfront therapy in newly diagnosed myeloma patients

In recent years, the immunoderivative (IMiD) agents have been extensively used for the treatment of multiple myeloma (MM). IMiDs and their newer derivatives CRBN E3 ligase modulator bind the E3 ligase substrate recognition adapter protein cereblon (CRBN), which has been recognized as one of the IMiDs’ direct target proteins, and it is essential for the therapeutic effect of these agents.High expression of CRBN was associated with improved clinical response in patients with MM treated with IMiDs, further confirming that the expression of IMiDs’ direct target protein CRBN is required for the anti-MM activity. CRBN’s central role as a target of IMiDs suggests potential utility as a predictive biomarker of response or resistance to IMiDs therapy. Additionally, the presence of alternatively spliced variants of CRBN in MM cells, especially those lacking the drug-binding domain for IMiDs, raise questions concerning their potential biological function, making difficult the transcript measurement, which leads to inaccurate overestimation of full-length CRBN transcripts. In sight of this, in the present study, we evaluated the CRBN expression, both full-length and spliced isoforms, by using real-time assay data from 87 patients and RNA sequencing data from 50 patients (n = 137 newly diagnosed MM patients), aiming at defining CRBN’s role as a predictive biomarker for response to IMiDs-based induction therapy. We found that the expression level of the spliced isoform tends to be higher in not-responding patients, confirming that the presence of a more CRBN spliced transcript predicts for lack of IMiDs response.

1121P Interferon-gamma (IFNy) gene signature as a predictive biomarker for response in lactate dehydrogenase (LDH) low advanced melanoma patients

1121P Interferon-gamma (IFNy) gene signature as a predictive biomarker for response in lactate dehydrogenase (LDH) low advanced melanoma patients

Radiomic-Based Prediction of Lesion-Specific Systemic Treatment Response in Metastatic Disease.

Cancer is a heterogenous, dynamic target. Individual patients, lesions, and cell populations may present with different characteristics and varying sensitivities to anticancer therapies. The purpose of this study was to investigate the utility of image-derived (radiomic) biomarkers for prediction of lesion-specific treatment response. Using a database of serial imaging from a Phase III clinical trial (NCT01440088), all visible lung metastases were contoured on chest computed tomography performed at baseline and after 2 cycles of chemotherapy. Metastatic leiomyosarcoma patients received either doxorubicin monotherapy (DM) or doxorubicin plus evofosfamide (DE). Percent change in volume with respect to baseline was evaluated as a metric of treatment response. Lesions that experienced a positive volume change greater than 50% were labeled as 'progressors'; all other lesions were labeled as 'non-progressors'. Radiomic features were extracted at baseline from the whole lesion and three peritumoral regions using PyRadiomics (version 3.0.1). Features were compared using the Mann-Whitney U-test and further used to classify between response categories. Logistic classification models were evaluated with 10-fold cross-validation using Area Under the Curve for the Receiver Operating Characteristic (AUROC) and Precision-Recall (AUPRC) curves as measures of model performance. Significance was evaluated by permutation tests. All code used to derive features is open source and available for download. In total, 204 lung metastases from 80 patients were evaluated. The fraction of progressing lesions was 19% and 27% for the DM and DE regimes, respectively. Differences in individual lesion response were observed in 20% of patients with multiple lung metastases. Significant differences in entropy were observed between response categories (false discovery rate < 5% after multiple testing correction): interior regions for lesions receiving DM, and peripheral regions for lesions receiving DE. The best performing models for the DM and DE regimens had AUROC of 0.87 and 0.76 and AUPRC 0.60 and 0.50, respectively (p < 0.05). Predicting lesion-specific responses using radiomic features represents a paradigm shift. Lesion-specific radiomic models indicate a 2 to 3-fold increase in predictive capacity in comparison to a no-skill classifier. These models, although preliminary, achieved a strong predictive value and could be used to predict lesion-specific treatment response.

H1Innovative approaches to combat anti-cancer drug resistance: Targeting lncRNA and autophagy.

To date, standardizing clinical predictive biomarkers for assessing the response to immunotherapy remains challenging due to variations in personal genetic signatures, tumour microenvironment complexities and epigenetic onco-mechanisms. Early monitoring of key non-coding RNA (ncRNA) biomarkers may help in predicting the clinical efficacy of cancer immunotherapy and come up with standard predictive ncRNA biomarkers. For instance, reduced miR-125b-5p level in the plasma of non-small cell lung cancer patients treated with anti-PD-1 predicts a positive outcome. The level of miR-153 in the plasma of colorectal cancer patients treated with chimeric antigen receptor T lymphocyte (CAR-T) cell therapy may indicate the activation of T-cell killing activity. miR-148a-3p and miR-375 levels may forecast favourable responses to CAR-T-cell therapy in B-cell acute lymphoblastic leukaemia. In cancer patients treated with the GPC3 peptide vaccine, serum levels of miR-1228-5p, miR-193a-5p and miR-375-3p were reported as predictive biomarkers of good response and improved overall survival. Therefore, there is a critical need for further studies to elaborate on the key ncRNA biomarkers that have the potential to predict early clinical responses to immunotherapy. This review summarises important predictive ncRNA biomarkers that were reported in cancer patients treated with different immunotherapeutic modalities including monoclonal antibodies, small molecule inhibitors, cancer vaccines and CAR-T cells. In addition, a concise discussion on forthcoming perspectives is provided, outlining technical approaches for the optimal utilisation of immune-modulatory ncRNA biomarkers as predictive tools and therapeutic targets.

148P Identification of potential predictive biomarkers for ovarian cancer chemotherapy response

148P Identification of potential predictive biomarkers for ovarian cancer chemotherapy response

Transmembrane serine protease 2, a SARS-CoV-2 internalization protease, correlates with clinical outcome, molecular features, and immunotherapy response in colorectal cancer.

Transmembrane serine protease 2 (TMPRSS2) mediates the entry of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into host cells. The relevant research indicates the intestine to be a target of SARS-CoV-2 infection, and thus we aimed to investigate the correlation between TMPRSS2 expression and the prognosis, molecular features, and immunotherapy response in patients with colorectal cancer (CRC). The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used in this study and a total of 1,385 patients were identified. The CIBERSORT algorithms were used to evaluate the relative infiltration levels of immune cell types in the tumor microenvironment (TME). The correlation between TMPRSS2 expression and immunotherapy response rate was assessed in another 2 independent cohorts. TMPRSS2 expression was significantly downregulated in cancer tissue compared to the adjacent normal tissue, and patients with CRC with lower TMPRSS2 expression showed notably poorer prognosis. Functional enrichment analysis found that low TMPRSS2 expression was significantly associated with cancer metastasis-related pathways. Further analysis based on the miRWalk tool and JASPAR database identified a list of microRNAs (miRNAs) and transcriptional factors targeting TMPRSS2. Distinct differences in immune cell infiltration and tumor purity reflected by estimate and mutant-allele tumor heterogeneity score were observed between patients with low and high TMPRSS2 expression levels. Interestingly, patients with a low TMPRSS2 expression level showed a higher response rate to immunotherapy. CRC cells may be more resistant to SARS-CoV-2 infection due to the decreased expression of TMPRSS2, which could be a newly identified biomarker for prognosis and immunotherapy response prediction in patients with CRC.

Updated insights into predictive biomarkers for response to cardiac resynchronization therapy - A literature review

Abstract Cardiac resynchronization therapy (CRT) was shown to greatly improve outcomes in specific patients with heart failure, low ejection fraction and wide QRS complex; however post-therapeutic response is heterogeneous and currently difficult to predict. There is an increasing interest in identifying humoral biomarkers which could help stratify prognosis, and better identify responders. The aim of this review was to provide an overview of recent data regarding the predictive value of biomarkers for evaluating response to CRT. A definitive conclusion cannot currently be drawn due to disparate results, varying methodologies, relatively small-scale studies and lack of consensus in defining CRT response. More extensive comparable research is paramount to facilitate progress in this field.

Analyzing the Predictive Effects of PD-L1 Expression, Early Changes of bTMB and Circulated CD8+T Cells during Treatment for Responses of RT Combined with ICI in NSCLC

The beneficial role of immunotherapy and the clinical relevance of current biomarkers remain inconclusive; thus, appropriate strategies and reliable predictors need further definition. A rational combination of biomarkers is needed. Here, we estimated potential predictive factors for responses of radiotherapy (RT) combined with immune checkpoint inhibitor (ICI) in a phase II trial to determine the efficacy and safety of combination of moderate hypofractionated RT with ICI in patients with oligometastatic NSCLC (NCT03557411). Pretreatment tumor tissue samples and longitudinal blood were collected for immune and tumor biomarker analysis. We examined pre-treatment (pre-ICI) PD-L1 expression in tumor cells. Circulating tumor cell (CTC), PD-L1+CTC, blood tumor mutation burden (bTMB), CD8+T cells, CD4+T cells, NK cells, B cells in circulation were acquired pre-ICI and 1 month after ICI starting (1-mth). In addition, early changes of CTC (CTC), PD-L1+CTC (PD-L1+CTC), bTMB (bTMB), CD8+T cells (CD8+T cells), CD4+T cells (CD4+T cells), NK cells (NK cells), B cells (B cells) were also analyzed to estimate the predictive effects for treatment. High pre-ICI bTMB and increased CD8+T cells at 1 month was associated with better PFS (p = 0.016; p = 0.006). Interaction analyses revealed that each combination of two markers in the 5 markers including PD-L1, pre-ICI bTMB, 1-mth bTMB, 1-mth CD8+T cells and CD8+T cells was significantly associated with PFS, except for CTC, PD-L1+CTC, CD4+T cells, NK cells and B cells in circulation due to low power. Unsupervised cluster analysis based on these markers revealed three sub-cohorts. Cohort-1 was overrepresented by patients with progressive disease (81%) of whom were negative for 3-4 of the 5 biomarkers. Cohort-3 was overrepresented by patients with partial response (70%) of whom were positive for 3-4 of the 5 biomarkers. Survival analyses of the 3 cohorts indicated a significant association with PFS (p = 0.017). This study suggests that a combination of PD-L1 expression, early changes of bTMB and circulated CD8+T cells as a better predictive biomarker for response to RT combined with ICI. Consequently, refinement of this set of biomarkers and validation in a larger set of patients is warranted.

Circulating extracellular vesicles are monitoring biomarkers of anti-PD1 response and enhancer of tumor progression and immunosuppression in metastatic melanoma

BackgroundClinical drawback in checkpoint inhibitors immunotherapy (ICI) of metastatic melanoma (MM) is monitoring clinical benefit. Soluble forms of PD1(sPD1) and PD-L1(sPD-L1) and extracellular vesicles (EVs) expressing PD1 and PD-L1 have recently emerged as predictive biomarkers of response. As factors released in the blood, EVs and soluble forms could be relevant in monitoring treatment efficacy and adaptive resistance to ICI.MethodsWe used pre-therapy plasma samples of 110 MM patients and longitudinal samples of 46 patients. Elisa assay and flow cytometry (FCM) were used to measure sPD-L1 and sPD1 concentrations and the percentage of PD1+ EVs and PD-L1+ EVs, released from tumor and immune cells in patients subsets. Transwell assays were conducted to investigate the impact of EVs of each patient subset on MM cells invasion and interaction between tumor cells and macrophages or dendritic cells. Viability assays were performed to assess EVs effect on MM cells and organoids sensitivity to anti-PD1. FCM was used to investigate immunosuppressive markers in EVs and immune cells.ResultsThe concentrations of sPD1 and sPD-L1 in pre-treatment and longitudinal samples did not correlate with anti-PD1 response, instead only tumor-derived PD1+ EVs decreased in long responders while increased during disease progression in responders. Notably, we observed reduction of T cell derived EVs expressing LAG3+ and PD1+ in long responders and their increase in responders experiencing progression. By investigating the impact of EVs on disease progression, we found that those isolated from non-responders and from patients with progression disease accelerated tumor cells invasiveness and migration towards macrophages, while EVs of long responders reduced the metastatic potential of MM cells and neo-angiogenesis. Additionally, the EVs of non-responders and of progression disease patients subset reduced the sensitivity of MM cells and organoids of responder to anti-PD1 and the recruitment of dendritic cells, while the EVs of progression disease subset skewed macrophages to express higher level of PDL-1.ConclusionCollectively, we suggest that the detection of tumor-derived PD1 + EVs may represent a useful tool for monitoring the response to anti-PD1 and a role for EVs shed by tumor and immune cells in promoting tumor progression and immune dysfunction.Graphical

Predictive modelling of response to neoadjuvant therapy in HER2+ breast cancer

HER2-positive (HER2+) breast cancer accounts for 20–25% of all breast cancers. Predictive biomarkers of neoadjuvant therapy response are needed to better identify patients with early stage disease who may benefit from tailored treatments in the adjuvant setting. As part of the TCHL phase-II clinical trial (ICORG10–05/NCT01485926) whole exome DNA sequencing was carried out on normal-tumour pairs collected from 22 patients. Here we report predictive modelling of neoadjuvant therapy response using clinicopathological and genomic features of pre-treatment tumour biopsies identified age, estrogen receptor (ER) status and level of immune cell infiltration may together be important for predicting response. Clonal evolution analysis of longitudinally collected tumour samples show subclonal diversity and dynamics are evident with potential therapy resistant subclones detected. The sources of greater pre-treatment immunogenicity associated with a pathological complete response is largely unexplored in HER2+ tumours. However, here we point to the possibility of APOBEC associated mutagenesis, specifically in the ER-neg/HER2+ subtype as a potential mediator of this immunogenic phenotype.

Role of DNA Methylation Profiles as Potential Biomarkers and Novel Therapeutic Targets in Head and Neck Cancer.

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide and is associated with high mortality. The main reasons for treatment failure are a low rate of early diagnosis, high relapse rates, and distant metastasis with poor outcomes. These are largely due to a lack of diagnostic, prognostic, and predictive biomarkers in HNSCC. DNA methylation has been demonstrated to play an important role in the pathogenesis of HNSCC, and recent studies have also valued DNA methylation as a potential biomarker in HNSCC. This review summarizes the current knowledge on DNA methylation profiles in HPV-positive and HPV-negative HNSCC and how these may contribute to the pathogenesis of HNSCC. It also summarizes the potential value of DNA methylation as a biomarker in the diagnosis, prognosis, and prediction of the response to therapy. With the recent immunotherapy era in head and neck treatment, new strategies to improve immune responses by modulating TIMEs have been intensely investigated in early-phase trials. Therefore, this study additionally summarizes the role of DNA methylation in the regulation of TIMEs and potential predictive immunotherapy response biomarkers. Finally, this study reviews ongoing clinical trials using DNA methylation inhibitors in HNSCC.

RANK Expression as an Independent Predictor for Response to Neoadjuvant Chemotherapy in Luminal-Like Breast Cancer: A Translational Insight from the GeparX Trial.

The GeparX study investigated whether denosumab as add-on treatment to nab-paclitaxel-based neoadjuvant chemotherapy (NACT) with two different schedules (125 mg/m² weekly vs. day 1, 8 every 22 days) may increase pathologic complete response (pCR) rate. The addition of denosumab to NACT did not improve pCR rates as recently published. In this study, we investigated whether receptor activator of nuclear factor-kappa B (RANK) expression, as part of the denosumab target pathway: (i) may retrospectively identify a subgroup of patients with additional clinical benefit of denosumab or (ii) may predict response to nab-paclitaxel NACT. RANK protein was IHC-stained on pre-therapeutic core biopsies from patients of the GeparX study (n = 667) with the antibody RANK/Envision System HRP (DAB) and was analyzed for the percentage of membranous RANK tumor cell staining (>5% RANKhigh vs. ≤5% RANKlow). We could not identify any patient subgroup with differential response under denosumab add-on treatment in patients with RANKhigh expression [139/667, 20.8%; OR, 0.86; 95% confidence interval (CI), 0.44-1.68; P = 0.667] or RANKlow expression (528/667 (79.2%) OR, 1.10; 95% CI, 0.78-1.56; P = 0.589; Pinteraction = 0.528). However, the pCR rate was higher in the RANKhigh subgroup compared with RANKlow (50% vs. 39%; OR, 1.52; 95% CI, 1.04-2.21; P = 0.037). RANK expression constituted an independent predictor of response to NACT frequently in patients with luminal-like subtype (HR+/HER2-; OR, 2.98; 95% CI, 1.30-6.79; P = 0.010). No predictive value of RANK expression among the different nab-paclitaxel regimens was observed. We report RANK expression to be an independent predictive biomarker for response to NACT in patients with luminal-like breast cancer.

PLK1 Regulates MicroRNA Biogenesis through Drosha Phosphorylation.

Polo-Like Kinase 1 (PLK1), a key mediator of cell-cycle progression, is associated with poor prognosis and is a therapeutic target in a number of malignancies. Putative phosphorylation sites for PLK1 have been identified on Drosha, the main catalytic component of the microprocessor responsible for miR biogenesis. Several kinases, including GSK3β, p70 S6 kinase, ABL, PAK5, p38 MAPK, CSNK1A1 and ANKRD52-PPP6C, have been shown to phosphorylate components of the miR biogenesis machinery, altering their activity and/or localisation, and therefore the biogenesis of distinct miR subsets. We hypothesised that PLK1 regulates miR biogenesis through Drosha phosphorylation. In vitro kinase assays confirmed PLK1 phosphorylation of Drosha at S300 and/or S302. PLK1 inhibition reduced serine-phosphorylated levels of Drosha and its RNA-dependent association with DGCR8. In contrast, a "phospho-mimic" Drosha mutant showed increased association with DGCR8. PLK1 phosphorylation of Drosha alters Drosha Microprocessor complex subcellular localisation, since PLK1 inhibition increased cytosolic protein levels of both DGCR8 and Drosha, whilst nuclear levels were decreased. Importantly, the above effects are independent of PLK1's cell cycle-regulatory role, since altered Drosha:DGCR8 localisation upon PLK1 inhibition occurred prior to significant accumulation of cells in M-phase, and PLK1-regulated miRs were not increased in M-phase-arrested cells. Small RNA sequencing and qPCR validation were used to assess downstream consequences of PLK1 activity on miR biogenesis, identifying a set of ten miRs (miR-1248, miR-1306-5p, miR-2277-5p, miR-29c-5p, miR-93-3p, miR-152-3p, miR-509-3-5p, miR-511-5p, miR-891a-5p and miR-892a) whose expression levels were statistically significantly downregulated by two pharmacological PLK1 kinase domain inhibitors, RO-5203280 and GSK461364. Opposingly, increased levels of these miRs were observed upon transfection of wild-type or constitutively active PLK1. Importantly, pre-miR levels were reduced upon PLK1 inhibition, and pri-miR levels decreased upon PLK1 activation, and hence, PLK1 Drosha phosphorylation regulates MiR biogenesis at the level of pri-miR-to-pre-miR processing. In combination with prior studies, this work identifies Drosha S300 and S302 as major integration points for signalling by several kinases, whose relative activities will determine the relative biogenesis efficiency of different miR subsets. Identified kinase-regulated miRs have potential for use as kinase inhibitor response-predictive biomarkers, in cancer and other diseases.

Amivantamab plus lazertinib in osimertinib-relapsed EGFR-mutant advanced non-small cell lung cancer: a phase 1 trial

Patients with epidermal growth factor receptor (EGFR)-mutated non-small cell lung cancer (NSCLC) often develop resistance to current standard third-generation EGFR tyrosine kinase inhibitors (TKIs); no targeted treatments are approved in the osimertinib-relapsed setting. In this open-label, dose-escalation and dose-expansion phase 1 trial, the potential for improved anti-tumor activity by combining amivantamab, an EGFR-MET bispecific antibody, with lazertinib, a third-generation EGFR TKI, was evaluated in patients with EGFR-mutant NSCLC whose disease progressed on third-generation TKI monotherapy but were chemotherapy naive (CHRYSALIS cohort E). In the dose-escalation phase, the recommended phase 2 combination dose was established; in the dose-expansion phase, the primary endpoints were safety and overall response rate, and key secondary endpoints included progression-free survival and overall survival. The safety profile of amivantamab and lazertinib was generally consistent with previous experience of each agent alone, with 4% experiencing grade ≥3 events; no new safety signals were identified. In an exploratory cohort of 45 patients who were enrolled without biomarker selection, the primary endpoint of investigator-assessed overall response rate was 36% (95% confidence interval, 22–51). The median duration of response was 9.6 months, and the median progression-free survival was 4.9 months. Next-generation sequencing and immunohistochemistry analyses identified high EGFR and/or MET expression as potential predictive biomarkers of response, which will need to be validated with prospective assessment. ClinicalTrials.gov identifier: NCT02609776.

Stratum Corneum Hydration As a Potential Marker of Response to Dupilumab in Atopic Dermatitis®: A Prospective Observational Study.

Background: Dupilumab is an effective treatment for atopic Dermatitis® (AD) and it also restores skin barrier function. Nevertheless, early changes in epidermal barrier parameters related to sustained treatment response or treatment failure are not known. So, the objective of this study is to evaluate whether changes in skin barrier function after 16 weeks dupilumab treatment could predict sustained treatment response or treatment failure. Materials and Methods: A prospective observational study was conducted that included patients with AD starting dupilumab. Clinical scores, patient-reported outcome measures (PROMs), and skin barrier function parameters were assessed at baseline and after 16 weeks treatment. Patients were followed until they failed to dupilumab or until the end of the study period. Participants were divided into 2 groups: patients with treatment failure and those with sustained treatment response. Results: In total, 32 patients with AD were included in the study, with a mean age of 28.03 years (standard deviation 10.65), being 20 (60.6%) females. In total, 22 (66.7%) patients sustained dupilumab response during the study period and only 10 (33.3%) failed to treatment. After 16 weeks treatment, clinical scores were improved in both groups. Patients with sustained treatment response increased stratum corneum hydration (SCH) on noninvolved skin (34.25 arbitrary units [AU] vs 44.90AU, P = 0.001) and on eczematous lesions (20.71 AU vs 40.94 AU, P < 0.001) and also decreased transepidermal water loss (TEWL) on eczematous lesions (28.22 g/[m2·h] vs 14.83 g/[m2·h], P = 0.002). Patients with treatment failure did not change TEWL or SCH. SCH after 16 weeks treatment on noninvolved skin (odds ratio [OR] = 0.83, P = 0.018) and SCH after 16 weeks treatment on eczematous lesions (OR = 0.86, P = 0.028) were related to dupilumab failure. Conclusion: SCH could be used as a predictive biomarker of dupilumab response in patients with AD.

Integration of peripheral blood- and tissue-based biomarkers of response to immune checkpoint blockade in urothelial carcinoma.

As predictive biomarkers of response to immune checkpoint inhibitors (ICIs) remain a major unmet clinical need in patients with urothelial carcinoma (UC), we sought to identify tissue-based immune biomarkers of clinical benefit to ICIs using multiplex immunofluorescence and to integrate these findings with previously identified peripheral blood biomarkers of response. Fifty-five pretreatment and 12 paired on-treatment UC specimens were identified from patients treated with nivolumab with or without ipilimumab. Whole tissue sections were stained with a 12-plex mIF panel, including CD8, PD-1/CD279, PD-L1/CD274, CD68, CD3, CD4, FoxP3, TCF1/7, Ki67, LAG-3, MHC-II/HLA-DR, and pancytokeratin+SOX10 to identify over three million cells. Immune tissue densities were compared to progression-free survival (PFS) and best overall response (BOR) by RECIST version 1.1. Correlation coefficients were calculated between tissue-based and circulating immune populations. The frequency of intratumoral CD3+ LAG-3+ cells was higher in responders compared to nonresponders (p = 0.0001). LAG-3+ cellular aggregates were associated with response, including CD3+ LAG-3+ in proximity to CD3+ (p = 0.01). Exploratory multivariate modeling showed an association between intratumoral CD3+ LAG-3+ cells and improved PFS independent of prognostic clinical factors (log HR -7.0; 95% confidence interval [CI] -12.7 to -1.4), as well as established biomarkers predictive of ICI response (log HR -5.0; 95% CI -9.8 to -0.2). Intratumoral LAG-3+ immune cell populations warrant further study as a predictive biomarker of clinical benefit to ICIs. Differences in LAG-3+ lymphocyte populations across the intratumoral and peripheral compartments may provide complementary information that could inform the future development of multimodal composite biomarkers of ICI response. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

ZEB1 promotes non-homologous end joining double-strand break repair.

Repair of DSB induced by IR is primarily carried out by Non-Homologous End Joining (NHEJ), a pathway in which 53BP1 plays a key role. We have discovered that the EMT-inducing transcriptional repressor ZEB1 (i) interacts with 53BP1 and that this interaction occurs rapidly and is significantly amplified following exposure of cells to IR; (ii) is required for the localization of 53BP1 to a subset of double-stranded breaks, and for physiological DSB repair; (iii) co-localizes with 53BP1 at IR-induced foci (IRIF); (iv) promotes NHEJ and inhibits Homologous Recombination (HR); (v) depletion increases resection at DSBs and (vi) confers PARP inhibitor (PARPi) sensitivity on BRCA1-deficient cells. Lastly, ZEB1's effects on repair pathway choice, resection, and PARPi sensitivity all rely on its homeodomain. In contrast to the well-characterized therapeutic resistance of high ZEB1-expressing cancer cells, the novel ZEB1-53BP1-shieldin resection axis described here exposes a therapeutic vulnerability: ZEB1 levels in BRCA1-deficient tumors may serve as a predictive biomarker of response to PARPis.

Utility of Smartphone-Based Digital Phenotyping Biomarkers in Assessing Treatment Response to Transcranial Magnetic Stimulation in Depression: Proof-of-Concept Study.

Identifying biomarkers of response to transcranial magnetic stimulation (TMS) in treatment-resistant depression is a priority for personalizing care. Clinical and neurobiological determinants of treatment response to TMS, while promising, have limited scalability. Therefore, evaluating novel, technologically driven, and potentially scalable biomarkers, such as digital phenotyping, is necessary. This study aimed to examine the potential of smartphone-based digital phenotyping and its feasibility as a predictive biomarker of treatment response to TMS in depression. We assessed the feasibility of digital phenotyping by examining the adherence and retention rates. We used smartphone data from passive sensors as well as active symptom surveys to determine treatment response in a naturalistic course of TMS treatment for treatment-resistant depression. We applied a scikit-learn logistic regression model (l1 ratio=0.5; 2-fold cross-validation) using both active and passive data. We analyzed related variance metrics throughout the entire treatment duration and on a weekly basis to predict responders and nonresponders to TMS, defined as ≥50% reduction in clinician-rated symptom severity from baseline. The adherence rate was 89.47%, and the retention rate was 73%. The area under the curve for correct classification of TMS response ranged from 0.59 (passive data alone) to 0.911 (both passive and active data) for data collected throughout the treatment course. Importantly, a model using the average of all features (passive and active) for the first week had an area under the curve of 0.7375 in predicting responder status at the end of the treatment. The results of our study suggest that it is feasible to use digital phenotyping data to assess response to TMS in depression. Early changes in digital phenotyping biomarkers, such as predicting response from the first week of data, as shown in our results, may also help guide the treatment course.

Non-coding RNAs in cancer immunotherapy: Predictive biomarkers and targets.

To date, standardising clinical predictive biomarkers for assessing the response to immunotherapy remains challenging due to variations in personal genetic signatures, tumour microenvironment complexities and epigenetic onco-mechanisms. Early monitoring of key non-coding RNA (ncRNA) biomarkers may help in predicting the clinical efficacy of cancer immunotherapy and come up with standard predictive ncRNA biomarkers. For instance, reduced miR-125b-5p level in the plasma of non-small cell lung cancer patients treated with anti-PD-1 predicts a positive outcome. The level of miR-153 in the plasma of colorectal cancer patients treated with chimeric antigen receptor T lymphocyte (CAR-T) cell therapy may indicate the activation of T-cell killing activity. miR-148a-3p and miR-375 levels may forecast favourable responses to CAR-T-cell therapy in B-cell acute lymphoblastic leukaemia. In cancer patients treated with the GPC3 peptide vaccine, serum levels of miR-1228-5p, miR-193a-5p and miR-375-3p were reported as predictive biomarkers of good response and improved overall survival. Therefore, there is a critical need for further studies to elaborate on the key ncRNA biomarkers that have the potential to predict early clinical responses to immunotherapy. This review summarises important predictive ncRNA biomarkers that were reported in cancer patients treated with different immunotherapeutic modalities, including monoclonal antibodies, small molecule inhibitors, cancer vaccines and CAR-T cells. In addition, a concise discussion on forthcoming perspectives is provided, outlining technical approaches for the optimal utilisation of immunomodulatory ncRNA biomarkers as predictive tools and therapeutic targets.