Prognostic mortality factors in advanced light chain cardiac amyloidosis: A prospective cohort study

AIMS

Predicting mortality in severe AL cardiac amyloidosis is challenging due to elevated biomarker levels and limited thresholds for stratifying severe cardiac damage.

METHODS AND RESULTS

This prospective, observational, cohort study included de novo, confirmed cardiac AL amyloidosis patients at the Henri Mondor National Reference Centre. The goal was to identify predictors of mortality to enhance prognostic stratification and improve informed decision-making regarding therapy. Over the 12-year study period, among the 233 patients included, 133 were NYHA III-IV and 179 Mayo 2004 III. The independent predictors for mortality identified were hsTnT, NT-proBNP, cardiac output, and conjugated bilirubin. A novel prognostic, conditional stratification, Mondor amyloidosis cardiac staging (MACS) was developed with biomarker cut-off values for Stage 1: hsTnT ≤ 107 ng/L and NT-proBNP ≤ 3867 ng/L (n = 77; 33%); for stage 2 NT-proBNP > 3867 ng/L (n = 72; 30%). For stage 3, if troponin >107 ng/L, regardless of NT-proBNP then CB 4 μmol/L, was added (n = 41; 17.5%) and stage 4: CB > 4 μmol/L (n = 43; 18.5%). The median overall survival was 8 months 95% CI [2-24]. At 1 year, 102 (44%) patients died and the Kaplan-Meier median survival with MACS Stage 1 was not reached, while stage 2 was 15.2 months (95% CI [11-18]) and stage 3, 6.6 months (95% CI [1-13]). Notably, among European stage II patients, 17.1%, n = 8 were MACS stage 3 and European stage IIIb 21.4% (n = 23) were MACS stage 4. Importantly, among European stage IIIb patients 42.2% (n = 29) were classified MACS stage 4 and 12.5% n = 9 were only MACS stage 2.

CONCLUSIONS

The Mondor prognostic staging system, including conjugate bilirubin may significantly improve prognostic stratification for patients with severe cardiac amyloidosis.

Identification of endophenotypes supporting outcome prediction in hemodialysis patients based on mechanistic markers of statin treatment

Background

Statins are widely used to reduce the risk of cardiovascular disease (CVD). Patients with end-stage renal disease (ESRD) on hemodialysis have significantly increased risk of developing CVD. Statin treatment in these patients however did not show a statistically significant benefit in large trials on a patient cohort level.

Methods

We generated gene expression profiles for statins to investigate the impact on cellular programs in human renal proximal tubular cells and mesangial cells in-vitro. We subsequently selected biomarkers from key statin-affected molecular pathways and assessed these biomarkers in plasma samples from the AURORA cohort, a double-blind, randomized, multi-center study of patients on hemodialysis or hemofiltration that have been treated with rosuvastatin. Patient clusters (phenotypes) were created based on the identified biomarkers using Latent Class Model clustering and the associations with outcome for the generated phenotypes were assessed using Cox proportional hazards regression models. The multivariable models were adjusted for clinical and biological covariates based on previously published data in AURORA.

Results

The impact of statin treatment on mesangial cells was larger as compared with tubular cells with a large overlap of differentially expressed genes identified for atorvastatin and rosuvastatin indicating a predominant drug class effect. Affected molecular pathways included TGFB-, TNF-, and MAPK-signaling and focal adhesion among others. Four patient clusters were identified based on the baseline plasma concentrations of the eight biomarkers. Phenotype 1 was characterized by low to medium levels of the hepatocyte growth factor (HGF) and high levels of interleukin 6 (IL6) or matrix metalloproteinase 2 (MMP2) and it was significantly associated with outcome showing increased risk of developing major adverse cardiovascular events (MACE) or cardiovascular death. Phenotype 2 had high HGF but low Fas cell surface death receptor (FAS) levels and it was associated with significantly better outcome at 1 year.

Conclusions

In this translational study, we identified patient subgroups based on mechanistic markers of statin therapy that are associated with disease outcome in patients on hemodialysis.

Circulating biomarkers as predictors of response to immune checkpoint inhibitors in NSCLC: Are we on the right path?

Immune checkpoints inhibitors (ICIs) have markedly improved the therapeutic management of advanced NSCLC and, more recently, they have demonstrated efficacy also in the early-stage disease. Despite better survival outcomes with ICIs compared to standard chemotherapy, a large proportion of patients can derive limited clinical benefit from these agents. So far, few predictive biomarkers, including the programmed death-ligand 1 (PD-L1), have been introduced in clinical practice. Therefore, there is an urgent need to identify novel biomarkers to select patients for immunotherapy, to improve efficacy and avoid unnecessary toxicity. A deeper understanding of the mechanisms involved in antitumor immunity and advances in the field of liquid biopsy have led to the identification of a wide range of circulating biomarkers that could potentially predict response to immunotherapy. Herein, we provide an updated overview of these circulating biomarkers, focusing on emerging data from clinical studies and describing modern technologies used for their detection.

Novel Associations Between Mid-Pregnancy Cardiovascular Biomarkers and Preeclampsia: An Explorative Nested Case-Control Study

Prediction of women at high risk of preeclampsia is important for prevention and increased surveillance of the disease. Current prediction models need improvement, particularly with regard to late-onset preeclampsia. Preeclampsia shares pathophysiological entities with cardiovascular disease; thus, cardiovascular biomarkers may contribute to improving prediction models. In this nested case-control study, we explored the predictive importance of mid-pregnancy cardiovascular biomarkers for subsequent preeclampsia. We included healthy women with singleton pregnancies who had donated blood in mid-pregnancy (~ 18 weeks' gestation). Cases were women with subsequent preeclampsia (n = 296, 10% of whom had early-onset preeclampsia [< 34 weeks]). Controls were women who had healthy pregnancies (n = 333). We collected data on maternal, pregnancy, and infant characteristics from medical records. We used the Olink cardiovascular II panel immunoassay to measure 92 biomarkers in the mid-pregnancy plasma samples. The Boruta algorithm was used to determine the predictive importance of the investigated biomarkers and first-trimester pregnancy characteristics for the development of preeclampsia. The following biomarkers had confirmed associations with early-onset preeclampsia (in descending order of importance): placental growth factor (PlGF), matrix metalloproteinase (MMP-12), lectin-like oxidized LDL receptor 1, carcinoembryonic antigen-related cell adhesion molecule 8, serine protease 27, pro-interleukin-16, and poly (ADP-ribose) polymerase 1. The biomarkers that were associated with late-onset preeclampsia were BNP, MMP-12, alpha-L-iduronidase (IDUA), PlGF, low-affinity immunoglobulin gamma Fc region receptor II-b, and T cell surface glycoprotein. Our results suggest that MMP-12 is a promising novel preeclampsia biomarker. Moreover, BNP and IDUA may be of value in enhancing prediction of late-onset preeclampsia.

Tumor mutational burden in colorectal cancer: Implications for treatment

Although immune checkpoint inhibitors have revolutionized the treatment of several advanced solid cancers, in colorectal cancer, the transformative benefit of these innovative medicines is currently limited to those with deficient mismatch repair or high microsatellite instability. Tumor mutational burden (TMB) has emerged as a potential predictor of immunotherapy benefit, but the lack of standardization in its assessment and reporting has hindered the introduction of this biomarker in routine clinical practice. Here, we compiled 45 colorectal cancer studies utilizing numerical thresholds for high-TMB. In this group of studies, TMB cut-offs ranged from 6.88 to 41 mut/Mb and were most often set at 10, 17, or 20 mut/Mb. Additionally, we observed divergent TMB definitions and inconsistent disclosure of specific methodological details, which collectively emphasize the substantial lack of harmonization within the field. Ongoing efforts to harmonize TMB assessment will be critical to validate TMB as a predictive marker of immunotherapy response.

The significance of m6A RNA methylation regulators in diagnosis and subtype classification of HBV-related hepatocellular carcinoma

In recent years, abnormal m6A alteration in hepatocellular carcinoma (HCC) has been a focus on investigating the biological implications. In this study, our objective is to determine whether m6A modification contributes to the progression of HBV-related HCC. To achieve this, we employed a random forest model to screen top 8 characteristic m6A regulators from 19 candidate genes. Subsequently, we developed a nomogram model that utilizes these 8 characteristic m6A regulators to predict the prevalence of HBV-related HCC. According to decision curve analysis, patients may benefit from the nomogram model. The clinical impact curves exhibited a robust predictive capability of the nomogram models. Additionally, consensus molecular subtyping was employed to identify m6A modification patterns and m6A-related gene signature. The quantification of immune cell subsets was accomplished through the implementation of ssGSEA algorithms. PCA algorithms were developed to compute the m6A score for individual tumors. Two distinct m6A modification patterns, namely cluster A and cluster B, exhibited significant correlations with distinct immune infiltration patterns and biological pathways. Notably, patients belonging to cluster B demonstrated higher m6A scores compared to those in cluster A, as determined by the m6A score metric. Furthermore, the expression of IGFBP3 proteins was validated through immunofluorescence, revealing their pronounced lower expression in tumor tissues. In summary, our study underscores the importance of m6A modification in the advancement of HBV-related HCC. This research has the potential to yield novel prognostic biomarkers and therapeutic targets for the identification of HBV-related HCC.

Overall haemostatic potential assay for prediction of outcomes in venous and arterial thrombosis and thrombo-inflammatory diseases

Thromboembolic diseases including arterial and venous thrombosis are common causes of morbidity and mortality globally. Thrombosis frequently recurs and can also complicate many inflammatory conditions through the process of 'thrombo-inflammation,' as evidenced during the COVID-19 pandemic. Current candidate biomarkers for thrombosis prediction, such as D-dimer, have poor predictive efficacy. This limits our capacity to tailor anticoagulation duration individually and may expose lower risk individuals to undue bleeding risk. Global coagulation assays, such as the Overall Haemostatic Potential (OHP) assay, that investigate fibrin generation and fibrinolysis, may provide a more accurate and functional assessment of hypercoagulability. We present a review of fibrin's critical role as a central modulator of thrombotic risk. The results of our studies demonstrating the OHP assay as a predictive biomarker in venous thromboembolism, chronic renal disease, diabetes mellitus, post-thrombotic syndrome, and COVID-19 are discussed. As a comprehensive and global measurement of fibrin generation and fibrinolytic capacity, the OHP assay may be a valuable addition to future multi-modal predictive tools in thrombosis.

Preterm birth, a consequence of immune deviation mediated hyperinflammation

Preterm birth represents a multifaceted syndrome with intricacies still present in our comprehension of its etiology. In the context of a semi-allograft, the prosperity from implantation to pregnancy to delivery hinges on the establishment of a favorable maternal-fetal immune microenvironment and a successful trilogy of immune activation, immune tolerance and then immune activation transitions. The occurrence of spontaneous preterm birth could be related to abnormalities within the immune trilogy, stemming from deviation in maternal and fetal immunity. These immune deviations, characterized by insufficient immune tolerance and early immune activation, ultimately culminated in an unsustainable pregnancy. In this review, we accentuated the role of both innate and adaptive immune reason in promoting spontaneous preterm birth, reviewed the risk of preterm birth from vaginal microbiome mediated by immune changes and the potential of vaginal microbiomes and metabolites as a new predictive marker, and discuss the changes in the role of progesterone and its interaction with immune cells in a preterm birth population. Our objective was to contribute to the growing body of knowledge in the field, shedding light on the immunologic reason of spontaneous preterm birth and effective biomarkers for early prediction, providing a roadmap for forthcoming investigations.

A paclitaxel-hyaluronan conjugate (ONCOFID-P-B™) in patients with BCG-unresponsive carcinoma in situ of the bladder: a dynamic assessment of the tumor microenvironment

BACKGROUND

The intravesical instillation of the paclitaxel-hyaluronan conjugate ONCOFID-P-B™ in patients with bacillus Calmette-Guérin (BCG)-unresponsive bladder carcinoma in situ (CIS; NCT04798703 phase I study), induced 75 and 40% of complete response (CR) after 12 weeks of intensive phase and 12 months of maintenance phase, respectively. The aim of this study was to provide a detailed description of the tumor microenvironment (TME) of ONCOFID-P-B™-treated BCG-unresponsive bladder CIS patients enrolled in the NCT04798703 phase I study, in order to identify predictive biomarkers of response.

METHODS

The composition and spatial interactions of tumor-infiltrating immune cells and the expression of the most relevant hyaluronic acid (HA) receptors on cancer cells, were analyzed in biopsies from the 20 patients enrolled in the NCT04798703 phase I study collected before starting ONCOFID-P-B™ therapy (baseline), and after the intensive and the maintenance phases. Clinical data were correlated with cell densities, cell distribution and cell interactions. Associations between immune populations or HA receptors expression and outcome were analyzed using univariate Cox regression and log-rank analysis.

RESULTS

In baseline biopsies, patients achieving CR after the intensive phase had a lower density of intra-tumoral CD8+ cytotoxic T lymphocytes (CTL), but also fewer interactions between CTL and macrophages or T-regulatory cells, as compared to non-responders (NR). NR expressed higher levels of the HA receptors CD44v6, ICAM-1 and RHAMM. The intra-tumoral macrophage density was positively correlated with the expression of the pro-metastatic and aggressive variant CD44v6, and the combined score of intra-tumoral macrophage density and CD44v6 expression had an AUC of 0.85 (95% CI 0.68-1.00) for patient response prediction.

CONCLUSIONS

The clinical response to ONCOFID-P-B™ in bladder CIS likely relies on several components of the TME, and the combined evaluation of intra-tumoral macrophages density and CD44v6 expression is a potentially new predictive biomarker for patient response. Overall, our data allow to advance a potential rationale for combinatorial treatments targeting the immune infiltrate such as immune checkpoint inhibitors, to make bladder CIS more responsive to ONCOFID-P-B™ treatment.

177Lu-PSMA therapy in metastatic prostate cancer: An updated review of prognostic and predictive biomarkers

177Lu-PSMA has been approved for the treatment of PSMA-positive metastatic castration-resistant (mCRPC) patients who progressed to androgen receptor pathway inhibitors (ARPIs) and taxane-based chemotherapy. However, a higher proportion of patients do not respond to this type of radioligand therapy (RLT). To date, there is a lack of validated prognostic and predictive biomarkers for 177Lu-PSMA therapy in prostate cancer. Several studies have investigated the prognostic and predictive role of clinical and molecular factors and also the metabolic features of PET imaging. In this review, we aim to take stock of the current scenario, focusing on new emerging data from retrospective/prospective series and clinical trials. Given the high costs and the possibility of primary resistance, it seems essential to identify clinical and molecular characteristics that could allow clinicians to choose the right patient to treat with 177Lu-PSMA. Biomarker-based clinical trials are urgently needed in this field.

A 14-gene B-cell immune signature in early-stage triple-negative breast cancer (TNBC): a pooled analysis of seven studies

BACKGROUND

Early-stage triple-negative breast cancer (TNBC) displays clinical and biological diversity. From a biological standpoint, immune infiltration plays a crucial role in TNBC prognosis. Currently, there is a lack of genomic tools aiding in treatment decisions for TNBC. This study aims to assess the effectiveness of a B-cell/immunoglobulin signature (IGG) alone, or in combination with tumor burden, in predicting prognosis and treatment response in patients with TNBC.

METHODS

Genomic and clinical data were retrieved from 7 cohorts: SCAN-B (N = 874), BrighTNess (n = 482), CALGB-40603 (n = 389), METABRIC (n = 267), TCGA (n = 118), GSE58812 (n = 107), GSE21653 (n = 67). IGG and a risk score integrating IGG with tumor/nodal staging (IGG-Clin) were assessed for event-free survival (EFS) and overall survival (OS) in each cohort. Random effects model was used to derive pooled effect sizes. Association of IGG with pathological complete response (pCR) was assessed in CALGB-40603 and BrighTNess. Immune significance of IGG was estimated through CIBERSORTx and EcoTyper.

FINDINGS

IGG was associated with improved EFS (pooled HR = 0.77, [95% CI = 0.70-0.85], I2 = 18%) and OS (pooled HR = 0.79, [0.73-0.85], I2 = 0%) across cohorts, and was predictive of pCR in CALGB-40603 (OR 1.25, [1.10-1.50]) and BrighTNess (OR 1.57 [1.25-1.98]). IGG-Clin was predictive of recurrence (pooled HR = 2.11, [1.75-2.55], I2 = 0%) and death (pooled HR = 1.99, 95% [0.84-4.73], I2 = 79%) across cohorts. IGG was associated with adaptive immune response at CIBERSORTx and EcoTyper analysis.

INTERPRETATION

IGG is linked to improved prognosis and pCR in early-stage TNBC. The integration of IGG alongside tumor and nodal staging holds promise as an approach to identify patients benefitting from intensified or de-intensified treatments.

FUNDING

This study received funding from: Associació Beca Marta Santamaria, European Union's Horizon 2020 research and innovation and Marie Skłodowska-Curie Actions programs, Fundación FERO, Fundación CRIS contra el cáncer, Agència de Gestó d'Ajuts Universitaris i de Recerca, Instituto de Salud Carlos III, Fundación Contigo, Asociación Cáncer de Mama Metastásico IV, Breast Cancer Research Foundation, RESCUER, Fundación científica AECC and FSEOM.

Treatment Patterns and Clinical Outcomes Among Patients With Metastatic Prostate Cancer Harboring Homologous Recombination Repair Mutations

BACKGROUND

There is currently limited literature assessing the real-world treatment patterns and clinical outcomes of patients with metastatic castration-resistant prostate cancer (mCRPC) and homologous recombination repair (HRR) mutations.

METHODS

Medical charts were abstracted for mCRPC patients with ≥ 1 of 12 HRR somatic gene alterations treated at US oncology centers participating in the American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange. Treatment patterns and clinical outcomes were assessed from the initiation of first-line or later (1L+) mCRPC therapy received on or after July 1, 2014.

RESULTS

Among 138 patients included in the study, the most common somatic HRR mutations were CDK12 (47.8%), BRCA2 (22.5%), and ATM (21.0%). Novel hormonal therapy and taxane chemotherapy were most commonly used in 1L; taxane use increased in later lines. Median overall survival (95% confidence interval [CI]) was 36.3 (30.7-47.8) months from initiation of 1L therapy and decreased for subsequent lines. Similarly, there was a trend of decreasing progression-free survival and prostate-specific antigen response from 1L to 4L+ therapy.

CONCLUSIONS

Treatment patterns identified in this study were similar to those among patients with mCRPC regardless of tumor HRR mutation status in the literature.

Prognostic and predictive role of immune microenvironment in colorectal cancer

Colorectal cancer (CRC) represents a molecularly heterogeneous disease and one of the most frequent causes of cancer-related death worldwide. The traditional classification of CRC is based on pathomorphological and molecular characteristics of tumor cells (mucinous, ring-cell carcinomas, etc.), analysis of mechanisms of carcinogenesis involved (chromosomal instability, microsatellite instability, CpG island methylator phenotype) and mutational statuses of commonly altered genes (KRAS, NRAS, BRAF, APC, etc.), as well as expression signatures (CMS 1-4). It is also suggested that the tumor microenvironment is a key player in tumor progression and metastasis in CRC. According to the latest data, the immune microenvironment can also be predictive of the response to immune checkpoint inhibitors. In this review, we highlight how the immune environment influences CRC prognosis and sensitivity to systemic therapy.

Predictive response and outcome of peripheral CD4+ T cell subpopulations to combined immunotherapy and chemotherapy in advanced gastric cancer patients

BACKGROUND

The identification of predictive biomarkers for patient stratification in immunotherapy is of utmost importance, given the limited benefit observed in certain populations. However, only limited information is so far available on the association between peripheral CD4+ T cell subpopulations and immunotherapy for advanced gastric cancer. Our current report aimed to investigate the predictive value of peripheral CD4+ T cell subpopulations in advanced gastric cancer patients treated with immunotherapy.

METHODS

A retrospective cohort analysis of 169 advanced gastric cancer patients treated with sintilimab combined with capecitabine and oxaliplatin in The Affiliated Xinghua People's Hospital, Medical School of Yangzhou University (Xinghua, China) between June 2019 and October 2022 was conducted. Clinical outcomes of peripheral CD4+ T cell subpopulations were analyzed by receiver operating characteristic (ROC) curve, chi-square test, Kaplan-Meier method and the univariate and multivariate Cox proportional hazards regression models.

RESULTS

The optimal cutoff values for percentages of CD4+ T cells, naive CD4+ T cells (CD4+ Tn), memory CD4+ T cells (CD4+ Tm), central memory CD4+ T cells (CD4+ Tcm) and effector memory CD4+ T cells (CD4+ Tem) expressing PD-1 were 30.16 %, 17.79 %, 42.49 %, 31.54 % and 74.64 %, respectively. It was found that the percentages of CD4+ T, CD4+ Tn, CD4+ Tm, CD4+ Tcm and CD4+ Tem expressing PD-1 were significantly higher in responder (R) than non-responder (NonR) advanced gastric cancer patients associated with a longer progression free survival (PFS) and overall survival (OS). This correlation was also observed in the PD-L1 combined positive score (CPS) ≥ 5 populations. Univariate and multivariate Cox regression analyses indicated that lower CD4+ T, CD4+ Tn, CD4+ Tm, CD4+ Tcm and CD4+ Tem expressing PD-1 were independent risk factors of PFS and OS in advanced gastric cancer patients treated with combined immunotherapy and chemotherapy.

CONCLUSION

The peripheral CD4+ T cell subpopulations demonstrated the high predictive value for therapeutic response and prolonged survival outcomes in advanced gastric cancer patients. CD4+ T cell subpopulations have the potential in predicting and screening benefit populations in advanced gastric cancer patients.

Sex-related differences in serum biomarker levels predict the activity and efficacy of immune checkpoint inhibitors in advanced melanoma and non-small cell lung cancer patients

BACKGROUND

Immune Checkpoint Inhibitors (ICIs) lead to durable response and a significant increase in long-term survival in patients with advanced malignant melanoma (MM) and Non-Small Cell Lung Cancer (NSCLC). The identification of serum cytokines that can predict their activity and efficacy, and their sex interaction, could improve treatment personalization.

METHODS

In this prospective study, we enrolled immunotherapy-naïve patients affected by advanced MM and NSCLC treated with ICIs. The primary endpoint was to dissect the potential sex correlations between serum cytokines (IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, GM-CSF, MCP-1, TNF-ɑ, IP-10, VEGF, sPD-L1) and the objective response rate (ORR). Secondly, we analyzed biomarker changes during treatment related to ORR, disease control rate (DCR), progression free survival (PFS) and overall survival (OS). Blood samples, collected at baseline and during treatment until disease progression (PD) or up to 2 years, were analyzed using Luminex xMAP or ELLA technologies.

RESULTS

Serum samples from 161 patients (98 males/63 females; 92 MM/69 NSCLC) were analyzed for treatment response. At baseline, IL-6 was significantly lower in females (F) versus males (M); lower levels of IL-4 in F and of IL-6 in both sexes significantly correlated with a better ORR, while higher IL-4 and TNF-ɑ values were predictive of a lower ORR in F versus M. One hundred and sixty-five patients were evaluable for survival analysis: at multiple Cox regression, an increased risk of PD was observed in F with higher baseline values of IL-4, sPD-L1 and IL-10, while higher IL-6 was a negative predictor in males. In males, higher levels of GM-CSF predict a longer survival, whereas higher IL-1β predicts a shorter survival. Regardless of sex, high baseline IL-8 values were associated with an increased risk of both PD and death, and high IL-6 levels only with shorter OS.

CONCLUSIONS

Serum IL-1β, IL-4, IL-6, IL-10, GM-CSF, TNF-ɑ, and sPD-L1 had a significant sex-related predictive impact on ORR, PFS and OS in melanoma and NSCLC patients treated with ICIs. These results will potentially pave the way for new ICI combinations, designed according to baseline and early changes of these cytokines and stratified by sex.

Clinical utility of Next Generation Sequencing of plasma cell-free DNA for the molecular profiling of patients with NSCLC at diagnosis and disease progression

BACKGROUND

The present study evaluates the utility of NGS analysis of circulating free DNA (cfDNA), which incorporates small amounts of tumor DNA (ctDNA), at diagnosis or at disease progression (PD) in NSCLC patients.

METHODS

Comprehensive genomic profiling on cfDNA by NGS were performed in NSCLC patients at diagnosis (if tissue was unavailable/insufficient) or at PD to investigate potential druggable molecular aberrations. Blood samples were collected as routinary diagnostic procedures, DNA was extracted, and the NextSeq 550 Illumina platform was used to run the Roche Avenio ctDNA Expanded Kit for molecular analyses. Gene variants were classified accordingly to the ESCAT score.

RESULTS

A total of 106 patients were included in this study; 44 % of cases were requested because of tissue unavailability at the diagnosis and 56 % were requested at the PD. At least one driver alteration was observed in 62 % of cases at diagnosis. Driver druggable variants classified as ESCAT level I were detected in 34 % of patients, including ALK-EML4, ROS1-CD74, EGFR, BRAF, KRAS p.G12C, PI3KCA. In the PD group, most patients were EGFR-positive, progressing to a first line-therapy. Sixty-three percent of patients had at least one driver alteration detected in blood and 17 % of patients had a known biological mechanism of resistance allowing further therapeutic decisions.

CONCLUSIONS

The present study confirms the potential of liquid biopsy to detect tumour molecular heterogeneity in NSCLC patients at the diagnosis and at PD, demonstrating that a significant number of druggable mutations and mechanisms of resistance can be detected by NGS analysis on ctDNA.

Assessment of the predictive value of plasma calprotectin in the evolution of SARS-Cov-2 primo-infection

BACKGROUND

The COVID-19 epidemic still calls for anticipation aimed at preventing the overloading of critical care services. With this in mind, the predictive value of easily accessible biomarkers is to be assessed.

OBJECTIVE

Secretion of calprotectin is stimulated during an inflammatory process, especially in the cytokine storm. We tried to determine whether early plasma concentration of calprotectin in patients with primary SARS-CoV-2 infection could predict an adverse outcome in cases of COVID-19.

METHODS

We included 308 patients with a primary diagnosis of SARS-CoV-2 confirmed by PCR. Heparinized tube samples, collected within the first 24 h of hospitalization, were used for biomarker assays, in which plasma calprotectin was included. Data from the patients' medical records and severity groups established subsequent to diagnosis at the end of hospitalization were collected.

RESULTS

Early plasma calprotectin concentration is significantly associated with progression to a severe form of COVID-19 in patients with primary infection (Relative Risk: 2.2 [1.6-2.7]). In multivariate analysis, however, it does not appear to provide additional information compared to other parameters (age, GFR, CRP…).

CONCLUSION

Our study shows that while an early single blood test for calprotectin could help to predict the progression of a primary SARS-CoV-2 infection, it is not superior to the other parameters currently used in emergency medicine. However, it paves the way for future considerations, such as the interest of this biomarker for high-risk infected patients (immunocompromised individuals…). Finally, the usefulness of early serial measurements of plasma calprotectin to assess progression towards severity of COVID-19 requires further assessment.

Current challenges and practical aspects of molecular pathology for non-small cell lung cancers

The continuing evolution of treatment options in thoracic oncology requires the pathologist to regularly update diagnostic algorithms for management of tumor samples. It is essential to decide on the best way to use tissue biopsies, cytological samples, as well as liquid biopsies to identify the different mandatory predictive biomarkers of lung cancers in a short turnaround time. However, biological resources and laboratory member workforce are limited and may be not sufficient for the increased complexity of molecular pathological analyses and for complementary translational research development. In this context, the surgical pathologist is the only one who makes the decisions whether or not to send specimens to immunohistochemical and molecular pathology platforms. Moreover, the pathologist can rapidly contact the oncologist to obtain a new tissue biopsy and/or a liquid biopsy if he/she considers that the biological material is not sufficient in quantity or quality for assessment of predictive biomarkers. Inadequate control of algorithms and sampling workflow may lead to false negative, inconclusive, and incomplete findings, resulting in inappropriate choice of therapeutic strategy and potentially poor outcome for patients. International guidelines for lung cancer treatment are based on the results of the expression of different proteins and on genomic alterations. These guidelines have been established taking into consideration the best practices to be set up in clinical and molecular pathology laboratories. This review addresses the current predictive biomarkers and algorithms for use in thoracic oncology molecular pathology as well as the central role of the pathologist, notably in the molecular tumor board and her/his participation in the treatment decision-making. The perspectives in this setting will be discussed.

Biomarkers predictive of a response to extended endocrine therapy in breast cancer: a systematic review and meta-analysis

PURPOSE

Extension of adjuvant endocrine therapy beyond five years confers only modest survival benefit in breast cancer patients and carries risk of toxicities. This systematic review investigates the role of biomarker tests in predicting the clinical response to an extension of endocrine therapy.

METHODS

We searched Ovid MEDLINE, Ovid Embase, Global Index Medicus, and the Cochrane Central Register of Controlled Trials using an iterative approach to identify full-text articles related to breast cancer, endocrine therapy, and biomarkers.

RESULTS

Of the 1,217 unique reports identified, five studies were deemed eligible. Four investigated the Breast Cancer Index (BCI) assay in three distinct study populations. These studies consistently showed that BCI score was predictive of response to extended endocrine therapy among 1,946 combined patients, who were predominately non-Hispanic white and postmenopausal.

CONCLUSIONS

Evidence in the setting of predictive tests for extended endocrine therapy is sparse. Most relevant studies investigated the use of BCI, but these study populations were largely restricted to a single age, race, and ethnicity group. Future studies should evaluate a variety of biomarkers in diverse populations. Without sufficient evidence, physicians and patients face a difficult decision in balancing the benefits and risks of endocrine therapy extension.

[Standardized and quality-assured predictive PD-L1 testing in the upper gastrointestinal tract. German version]

As a result of the high approval dynamics and the growing number of immuno-oncological therapy concepts, the complexity of therapy decisions and control in the area of carcinomas of the esophagus, gastroesophageal junction and stomach is constantly increasing. Since the treatment indication for PD‑1 inhibitors that are currently approved in the European Union is often linked to the expression of PD-L1 (programmed cell death-ligand 1), the evaluation of tissue-based predictive markers by the pathologist is of crucial importance for treatment stratification. Even though the immunohistochemical analysis of the PD-L1 expression status is one of the best studied, therapy-relevant biomarkers for an immuno-oncological treatment, due to the high heterogeneity of carcinomas of the upper gastrointestinal tract, there are challenges in daily clinical diagnostic work with regard to implementation, standardization and interpretation of testing. An interdisciplinary group of experts from Germany has taken a position on relevant questions from daily pathological and clinical practice, which concern the starting material, quality-assured testing and the interpretation of pathological findings, and has developed recommendations for structured reporting.

A novel model for predicting prognosis and response to immunotherapy in nasopharyngeal carcinoma patients

Blood-based biomarkers of immune checkpoint inhibitors (ICIs) response in patients with nasopharyngeal carcinoma (NPC) are lacking, so it is necessary to identify biomarkers to select NPC patients who will benefit most or least from ICIs. The absolute values of lymphocyte subpopulations, biochemical indexes, and blood routine tests were determined before ICIs-based treatments in the training cohort (n = 130). Then, the least absolute shrinkage and selection operator (Lasso) Cox regression analysis was developed to construct a prediction model. The performances of the prediction model were compared to TNM stage, treatment, and Epstein-Barr virus (EBV) DNA using the concordance index (C-index). Progression-free survival (PFS) was estimated by Kaplan-Meier (K-M) survival curve. Other 63 patients were used for validation cohort. The novel model composed of histologic subtypes, CD19+ B cells, natural killer (NK) cells, regulatory T cells, red blood cells (RBC), AST/ALT ratio (SLR), apolipoprotein B (Apo B), and lactic dehydrogenase (LDH). The C-index of this model was 0.784 in the training cohort and 0.735 in the validation cohort. K-M survival curve showed patients with high-risk scores had shorter PFS compared to the low-risk groups. For predicting immune therapy responses, the receiver operating characteristic (ROC), decision curve analysis (DCA), net reclassifcation improvement index (NRI) and integrated discrimination improvement index (IDI) of this model showed better predictive ability compared to EBV DNA. In this study, we constructed a novel model for prognostic prediction and immunotherapeutic response prediction in NPC patients, which may provide clinical assistance in selecting those patients who are likely to gain long-lasting clinical benefits to anti-PD-1 therapy.

A promising subgroup identification method based on a genetic algorithm for censored survival data

Modern precision medicine requires drug development to account for patients' heterogeneity, as only a subgroup of the patient population is likely to benefit from the targeted therapy. In this paper, we propose a novel method for subgroup identification based on a genetic algorithm. The proposed method can detect promising subgroups defined by predictive biomarkers in which the treatment effects are much higher than the population average. The main idea is to search for the subgroup with the greatest predictive ability in the entire subgroup space via a genetic algorithm. We design a real-valued representation of subgroups that evolves according to a genetic algorithm and derive an objective function that properly evaluates the predictive ability of the subgroups. Compared with model- or tree-based subgroup identification methods, the distinctive search strategy of this new approach offers an improved capability to explore subgroups defined by multiple predictive biomarkers. By embedding a resampling scheme, the multiplicity and complexity issues inherent in subgroup identification methods can be addressed flexibly. We evaluate the performance of the proposed method in comparison with two other methods using simulation studies and a real-world example. The results show that the proposed method exhibits good properties in terms of multiplicity and complexity control, and the subgroups identified are much more accurate. Although we focus on the implementation of censored survival data, this method could easily be extended for the realization of continuous and categorical endpoints.

Whether serum leptin and insulin-like growth factor-1 are predictive biomarkers for post-stroke depression: A meta-analysis and systematic review

Leptin and insulin-like growth factor-1 (IGF-1) may play a role in clinical identification of post-stroke depression (PSD). Here, eight databases (including CNKI, Wanfang, SinoMed, VIP, PubMed, the Cochrane Library, Embase, and the Web of Science) were employed to search for studies on serum leptin and insulin-like growth factor-1 expression levels in patients with PSD. In total, 13 articles were included, of which 6 studies investigated the expression level of serum leptin in patients with PSD, 7 studies explored the serum IGF-1 in PSD patients. Then, the RevMan 5.4 software was used for meta-analysis. The results showed that serum leptin levels were significantly higher in PSD patients than in patients without PSD (SMD = 1.54, 95% CI: 0.84, 2.23; P = 0.006). The result of subgroup analysis showed that the serum leptin levels in PSD patients were significantly higher than those without PSD in acute phase (SMD = 1.38, 95% CI: 0.04, 2.71; P = 0.04), subacute phase (SMD = 2.31, 95% CI: 0.88, 3.73; P = 0.001), and chronic phase (SMD = 1.02, 95% CI: 0.43, 1.60; P = 0.0007); There was no significant difference in serum IGF-1 level between PSD patients and patients without PSD (SMD = 0.49, 95% CI: -0.55, 1.52; P = 0.36). Moreover, the subgroup analysis also showed that there was no statistical difference in acute stage (SMD = 0.36, 95% CI: 0.89, 1.60; P = 0.57). Our study provides evidence to prove that serum leptin level has potential clinical application value as biomarkers for identifying PSD.

Selecting Optimal First-Line Treatment for Microsatellite Stable and Non-Mutated RAS/BRAF Metastatic Colorectal Cancer

OPINION STATEMENT

Standard frontline treatment of metastatic colorectal cancer (CRC) is cytotoxic chemotherapy plus a biologic agent such as an anti-EGFR monoclonal antibody (cetuximab or panitumumab) or anti-VEGF antibody (bevacizumab). Predictive biomarkers include mismatch repair (MMR) status, and RAS and BRAF mutation status; and important factors in treatment selection include primary tumor location, intent of therapy, and potential toxicity, as well as patient age, comorbidities, and patient preference. To date, single-, double-, or triple-agent cytotoxic chemotherapy all have important roles in appropriately selected patients, with the addition of anti-VEGF or anti-EGFR antibody therapy based on the relevant predictive biomarker. Data indicate that patients with proficient MMR, RAS/BRAF wt mCRC are candidates for an anti-EGFR antibody plus doublet chemotherapy if they have a left-sided primary tumor, or for anti-VEGF (bevacizumab) plus doublet or triplet chemotherapy if they have a right-sided primary tumor. Future studies may provide more predictive biomarkers to further personalize therapy for this heterogeneous disease.

A Review of AI-Based Radiomics and Computational Pathology Approaches in Triple-Negative Breast Cancer: Current Applications and Perspectives

Breast cancer is one of the most common and deadly cancers worldwide. Approximately, 20% of all breast cancers are characterized as triple negative (TNBC). TNBC typically is associated with a poorer prognosis relative to other breast cancer subtypes. Due to its aggressiveness and lack of response to hormonal therapy, conventional cytotoxic chemotherapy is the usual treatment; however, this treatment is not always effective, and an important percentage of patients develop recurrence. More recently, immunotherapy has started to be used on some populations with TNBC showing promising results. Unfortunately, immunotherapy is only applicable to a minority of patients and responses in metastatic TNBC have overall been modest in comparison to other cancer types. This situation evidences the need for developing effective biomarkers that help to stratify and personalize patient management. Thanks to recent advances in artificial intelligence (AI), there has been an increasing interest in its use for medical applications aiming at supporting clinical decision making. Several works have used AI in combination with diagnostic medical imaging, more specifically radiology and digitized histopathological tissue samples, aiming to extract disease-specific information that is difficult to quantify by the human eye. These works have demonstrated that analysis of such images in the context of TNBC has great potential for (1) risk-stratifying patients to identify those patients who are more likely to experience disease recurrence or die from the disease and (2) predicting pathologic complete response. In this manuscript, we present an overview on AI and its integration with radiology and histopathological images for developing prognostic and predictive approaches for TNBC. We present state of the art approaches in the literature and discuss the opportunities and challenges with developing AI algorithms regarding further development and clinical deployment, including identifying those patients who may benefit from certain treatments (e.g., adjuvant chemotherapy) from those who may not and thereby should be directed toward other therapies, discovering potential differences between populations, and identifying disease subtypes.

Immune checkpoint inhibitors in sarcomas: a systematic review

Sarcomas are tumors that originate from mesenchymal cells. The variety of sarcomas' response to chemotherapy and the wide range of prognosis reflect their heterogeneity. In order to improve the rates of response, the research has been orientated toward other forms of therapy, such as targeted therapies and immunotherapy or toward combinations of them. Immune checkpoint inhibitors (ICIs) have been the highlight of immunotherapy in the last decade. Although ICIs are already included in the guidelines of different malignancies, their clinical benefit in sarcomas is still under study. Alveolar soft part sarcomas, undifferentiated pleomorphic sarcomas and other subtypes of sarcoma with high presence of tertiary lymphoid structures tend to respond to ICIs, but further investigation is still needed. Furthermore, the search of predictive biomarkers to determine the type of sarcomas that are sensitive to ICIs is still very challenging. This review will focus on the results of clinical trials, which examine the effect of ICIs and their combination with chemotherapy, targeted therapies and other forms of immunotherapy in sarcomas.

Circulating memory PD-1+CD8+ T cells and PD-1+CD8+T/PD-1+CD4+T cell ratio predict response and outcome to immunotherapy in advanced gastric cancer patients

BACKGROUND

Limited benefit population of immunotherapy makes it urgent to select effective biomarkers for screening appropriate treatment population. Herein, we have investigated the predictive values of circulating CD8+ T cells and CD8+T/CD4+T cell ratio in advanced gastric cancer patients receiving immunotherapy.

METHODS

A retrospective cohort analysis of 187 advanced gastric cancer patients receiving sintilimab combined with oxaliplatin and capecitabine therapy in The Affiliated Xinghua People's Hospital, Medical School of Yangzhou University between December 2019 and February 2023 was conducted. The corresponding clinical outcomes of the variables were analyzed by receiver operating characteristic (ROC) curve, chi-square test, Kaplan-Meier methods and Cox proportional hazards regression models.

RESULTS

The optimal cutoff values for percentages of CD8+ T cells, naive CD8+ T cells (CD8+ Tn) and memory CD8+ T cells (CD8+ Tm) expressing programmed cell death -1(PD-1) as well as PD-1+CD8+T/PD-1+CD4+T cell ratio were 21.0, 21.5, 64.3 and 0.669, respectively. It was found that the mean percentages of CD8+ T and CD8+ Tm expressing PD-1 as well as PD-1+CD8+T/PD-1+CD4+T cell ratio were significantly higher in responder (R) than non-responder (NonR) advanced gastric cancer patients associated with a longer progression free survival (PFS) and overall survival (OS). We also observed this correlation in programmed cell death-ligand 1(PD-L1) combined positive score (CPS) ≥ 5 subgroups. Univariate and multivariate Cox regression analyses demonstrated that lower CD8+ T and CD8+ Tm expressing PD-1 as well as PD-1+CD8+T/PD-1+CD4+T cell ratio were independent risk factors in advanced gastric cancer patients receiving immunotherapy plus chemotherapy.

CONCLUSION

The circulating memory PD-1+CD8+ T cells and PD-1+CD8+T/PD-1+CD4+T cell ratio revealed high predictive values for response and prolonged survival outcomes in advanced gastric cancer patients receiving immunotherapy. Memory PD-1+CD8+ T cells and PD-1+CD8+T/PD-1+CD4+T cell ratio might be effective for screening benefit population of immunotherapy in advanced gastric cancer patients based on this preliminary evidence.

Standardized and quality-assured predictive PD-L1 testing in the upper gastrointestinal tract

As a result of the high approval dynamics and the growing number of immuno-oncological concepts, the complexity of treatment decisions and control in the area of cancers of the esophagus, gastroesophageal junction and stomach is constantly increasing. Since the treatment indication for PD-1 inhibitors that are currently approved in the European Union is often linked to the expression of PD-L1 (programmed cell death-ligand 1), the evaluation of tissue-based predictive markers by the pathologist is of crucial importance for treatment stratification. Even though the immunohistochemical analysis of the PD-L1 expression status is one of the best studied, therapy-relevant biomarkers for an immuno-oncological treatment, due to the high heterogeneity of carcinomas of the upper gastrointestinal tract, there are challenges in daily clinical diagnostic work with regard to implementation, standardization and interpretation of testing. An interdisciplinary group of experts from Germany has taken a position on relevant questions from daily pathological and clinical practice, which concern the starting material, quality-assured testing and the interpretation of pathological findings, and has developed recommendations for structured reporting.

Predictors of thyroid adverse events during cancer immunotherapy: a real-life experience at a single center

BACKGROUND

Thyroid dysfunction is among the most common immune-related adverse events (irAEs) of immune checkpoint inhibitors (ICIs) therapy. Data regarding potential predictors of the development of thyroid irAEs are still limited and sometimes conflicting.

PATIENTS AND METHODS

We assessed potential risk factors and clinical outcomes associated with the onset of thyroid irAEs in a cohort of patients with different types of cancer treated with ICIs at a single center. Clinical and biochemical data, including thyroid function tests and autoantibodies at baseline and during treatment, were collected, and the onset of thyroid irAEs was recorded. Patients with thyroid dysfunction and/or under levothyroxine therapy before starting ICI were excluded.

RESULTS

110 patients (80 M, 30 F, aged 32-85 years; 56.4% non-small-cell lung cancer, 87% treated with anti-PD-1) with complete information were included in the study. Among them, 32 (29%) developed thyroid irAEs during ICIs therapy. Primary hypothyroidism was the most common irAEs, occurring in 31 patients (28.18% of the whole cohort), including 14 patients who experienced a transient thyrotoxicosis. About 60% of irAEs occurred within the first 8 weeks of therapy. At multivariate analysis, anti-thyroid autoantibodies positivity at baseline (OR 18.471, p = 0.022), a pre-existing (autoimmune and non-autoimmune) thyroid disorder (OR 16.307, p < 0.001), and a family history of thyroid diseases (OR = 9.287, p = 0.002) were independent predictors of the development of thyroid irAEs.

CONCLUSION

Our data confirm the high frequency of thyroid dysfunctions (mostly hypothyroidism) during ICIs, and provide data on valuable predictors of thyroid toxicities that may help clinicians in identifying patients at risk for developing irAEs.

A flexible approach for predictive biomarker discovery

An endeavor central to precision medicine is predictive biomarker discovery; they define patient subpopulations which stand to benefit most, or least, from a given treatment. The identification of these biomarkers is often the byproduct of the related but fundamentally different task of treatment rule estimation. Using treatment rule estimation methods to identify predictive biomarkers in clinical trials where the number of covariates exceeds the number of participants often results in high false discovery rates. The higher than expected number of false positives translates to wasted resources when conducting follow-up experiments for drug target identification and diagnostic assay development. Patient outcomes are in turn negatively affected. We propose a variable importance parameter for directly assessing the importance of potentially predictive biomarkers and develop a flexible nonparametric inference procedure for this estimand. We prove that our estimator is double robust and asymptotically linear under loose conditions in the data-generating process, permitting valid inference about the importance metric. The statistical guarantees of the method are verified in a thorough simulation study representative of randomized control trials with moderate and high-dimensional covariate vectors. Our procedure is then used to discover predictive biomarkers from among the tumor gene expression data of metastatic renal cell carcinoma patients enrolled in recently completed clinical trials. We find that our approach more readily discerns predictive from nonpredictive biomarkers than procedures whose primary purpose is treatment rule estimation. An open-source software implementation of the methodology, the uniCATE R package, is briefly introduced.

Evolution of predictive risk factor analysis for chemotherapy-related toxicity

The causes of variation in toxicity to the same treatment regimen among seemingly similar patients remain largely unknown. There was tremendous optimism that the patient's germline genome would be strongly predictive of treatment-related toxicity and could be used to personalize treatment and improve therapeutic outcomes. However, there has been limited success in discovering robust pharmacogenetic predictors of treatment-related toxicity and even less progress in translating the few validated predictors into clinical practice. It is apparent that identification of toxicity predictors that can be used to predict and prevent treatment-related toxicity will require thinking beyond germline genomics. To that end, we propose an integrated biomarker discovery approach that recognizes that a patient's toxicity risk is determined by the cumulative effects of a broad range of "omic" and non-omic factors. This commentary describes the limited success in discovering and translating clinical and pharmacogenetic toxicity predictors into clinical practice. We illustrate the evolution of cancer toxicity biomarker discovery and translation through studies of taxane-induced peripheral neuropathy, which is one of the most common and debilitating side effects of cancer treatment. We then discuss the opportunities for discovering non-genomic (e.g., metabolomic, lipidomic, transcriptomic, proteomic, microbiomic, medical, behavioral, environmental) and integrated biomarkers that may be more strongly predictive of toxicity risk and the potential challenges with translating integrated biomarkers into clinical practice. This integrated biomarker discovery approach may circumvent some of the major limitations in toxicity biomarker science and move precision oncology treatment forward so that patients receive maximum treatment benefit with minimal toxicity.

An evidence-based debate on epigenetics and immunosenescence in COVID-19

Immunosenescence contributes to the decline of immune function leading to a reduced ability to respond to severe coronavirus disease 2019 (COVID-19) in elderly patients. Clinical course of COVID-19 is widely heterogeneous and guided by the possible interplay between genetic background and epigenetic-sensitive mechanisms underlying the immunosenescence which could explain, at least in part, the higher percentage of disease severity in elderly individuals. The most convincing evidence regards the hypomethylation of the angiotensin-converting enzyme 2 (ACE2) promoter gene in lungs as well as the citrullination of histone H3 in neutrophils which have been associated with worsening of COVID-19 outcome in elderly patients. In contrast, centenarians who have showed milder symptoms have been associated to a younger "epigenetic age" based on DNA methylation profiles at specific genomic sites (epigenetic clock). Some large prospective studies showed that the acceleration of epigenetic aging as well as the shortening of telomeres were significantly associated with lymphopenia and poor outcome suggesting prognostic biomarkers in elderly COVID-19 patients. Furthermore, randomized clinical trials showed that statins, L-arginine, and resveratrol could mediate anti-inflammatory effects via indirect epigenetic interference and might improve COVID-19 outcome. Here, we discuss the epigenetic-sensitive events which might contribute to increase the risk of severity and mortality in older subjects and possible targeted therapies to counteract immunosenescence.

The progresses of relevant factors on the efficacy of immune checkpoint inhibitors in the non-small cell lung cancer patients

Lung cancer has the highest mortality rate of all cancers worldwide. Although immune checkpoint inhibitor (ICI)-based therapy can improve the survival of patients with lung cancer, its efficacy is affected by many factors. Therefore, it is necessary to identify factors that affect the efficacy of ICI-based treatment and establish a model for predicting drug response and resistance before and during treatment for individualized and accurate treatment of patients. This review summarizes the clinical and biological factors related to ICI-based treatment of non-small cell lung cancer (NSCLC) and the recent research progress of predictive models for assessing ICI efficacy.

Biomarkers for immune checkpoint inhibition in sarcomas - are we close to clinical implementation?

Sarcomas are a group of diverse and complex cancers of mesenchymal origin that remains poorly understood. Recent developments in cancer immunotherapy have demonstrated a potential for better outcomes with immune checkpoint inhibition in some sarcomas compared to conventional chemotherapy. Immune checkpoint inhibitors (ICIs) are key agents in cancer immunotherapy, demonstrating improved outcomes in many tumor types. However, most patients with sarcoma do not benefit from treatment, highlighting the need for identification and development of predictive biomarkers for response to ICIs. In this review, we first discuss United States (US) Food and Drug Administration (FDA)-approved and European Medicines Agency (EMA)-approved biomarkers, as well as the limitations of their use in sarcomas. We then review eight potential predictive biomarkers and rationalize their utility in sarcomas. These include gene expression signatures (GES), circulating neutrophil-to-lymphocyte ratio (NLR), indoleamine 2,3-dioxygenase (IDO), lymphocyte activation gene 3 (LAG-3), T cell immunoglobin and mucin domain-containing protein 3 (TIM-3), TP53 mutation status, B cells, and tertiary lymphoid structures (TLS). Finally, we discuss the potential for TLS as both a predictive and prognostic biomarker for ICI response in sarcomas to be implemented in the clinic.

Laboratory scoring system to predict hepatic indocyanine green clearance ability during fluorescence imaging-guided laparoscopic hepatectomy

BACKGROUND

Indocyanine green (ICG) fluorescence played an important role in tumor localization and margin delineation in hepatobiliary surgery. However, the preoperative regimen of ICG administration was still controversial. Factors associated with tumor fluorescence staining effect were unclear.

AIM

To investigate the preoperative laboratory indexes corelated with ICG fluorescence staining effect and establish a novel laboratory scoring system to screen specifical patients who need ICG dose adjustment.

METHODS

To investigate the predictive indicators of ICG fluorescence characteristics in patients undergoing laparoscopic hepatectomy from January 2018 to January 2021 were included. Blood laboratory tests were completed within 1 wk before surgery. All patients received 5 mg ICG injection 24 h before surgery for preliminary tumor imaging. ImageJ software was used to measure the fluorescence intensity values of regions of interest. Correlation analysis was used to identify risk factors. A laboratory risk model was established to identify individuals at high risk for high liver background fluorescence.

RESULTS

There were 110 patients who were enrolled in this study from January 2019 to January 2021. The mean values of fluorescence intensity of liver background (FI-LB), fluorescence intensity of gallbladder, and fluorescence intensity of target area were 18.87 ± 17.06, 54.84 ± 33.29, and 68.56 ± 36.11, respectively. The receiver operating characteristic (ROC) curve showed that FI-LB was a good indicator for liver clearance ability [area under the ROC curve (AUC) = 0.984]. Correlation analysis found pre-operative aspartate aminotransferase, alanine aminotransferase, gamma-glutamyl transpeptidase, adenosine deaminase, and lactate dehydrogenase were positively associated with FI-LB and red blood cell, cholinesterase, and were negatively associated with FI-LB. Total laboratory risk score (TLRS) was calculated according to ROC curve (AUC = 0.848, sensitivity = 0.773, specificity = 0.885). When TLRS was greater than 6.5, the liver clearance ability of ICG was considered as poor.

CONCLUSION

Preoperative laboratory blood indicators can predict hepatic ICG clearance ability. Surgeons can adjust the dose and timing of ICG preoperatively to achieve better liver fluorescent staining.

Deep learning-based scoring of tumour-infiltrating lymphocytes is prognostic in primary melanoma and predictive to PD-1 checkpoint inhibition in melanoma metastases

BACKGROUND

Recent advances in digital pathology have enabled accurate and standardised enumeration of tumour-infiltrating lymphocytes (TILs). Here, we aim to evaluate TILs as a percentage electronic TIL score (eTILs) and investigate its prognostic and predictive relevance in cutaneous melanoma.

METHODS

We included stage I to IV cutaneous melanoma patients and used hematoxylin-eosin-stained slides for TIL analysis. We assessed eTILs as a continuous and categorical variable using the published cut-off of 16.6% and applied Cox regression models to evaluate associations of eTILs with relapse-free, distant metastasis-free, and overall survival. We compared eTILs of the primaries with matched metastasis. Moreover, we assessed the predictive relevance of eTILs in therapy-naïve metastases according to the first-line therapy.

FINDINGS

We analysed 321 primary cutaneous melanomas and 191 metastatic samples. In simple Cox regression, tumour thickness (p < 0.0001), presence of ulceration (p = 0.0001) and eTILs ≤16.6% (p = 0.0012) were found to be significant unfavourable prognostic factors for RFS. In multiple Cox regression, eTILs ≤16.6% (p = 0.0161) remained significant and downgraded the current staging. Lower eTILs in the primary tissue was associated with unfavourable relapse-free (p = 0.0014) and distant metastasis-free survival (p = 0.0056). In multiple Cox regression adjusted for tumour thickness and ulceration, eTILs as continuous remained significant (p = 0.019). When comparing TILs in primary tissue and corresponding metastasis of the same patient, eTILs in metastases was lower than in primary melanomas (p < 0.0001). In therapy-naïve metastases, an eTILs >12.2% was associated with longer progression-free survival (p = 0.037) and melanoma-specific survival (p = 0.0038) in patients treated with anti-PD-1-based immunotherapy. In multiple Cox regression, lactate dehydrogenase (p < 0.0001) and eTILs ≤12.2% (p = 0.0130) were significantly associated with unfavourable melanoma-specific survival.

INTERPRETATION

Assessment of TILs is prognostic in primary melanoma samples, and the eTILs complements staging. In therapy-naïve metastases, eTILs ≤12.2% is predictive of unfavourable survival outcomes in patients receiving anti-PD-1-based therapy.

FUNDING

See a detailed list of funding bodies in the Acknowledgements section at the end of the manuscript.

BluePrint molecular subtypes predict response to neoadjuvant pertuzumab in HER2-positive breast cancer

BACKGROUND

The introduction of pertuzumab has greatly improved pathological complete response (pCR) rates in HER2-positive breast cancer, yet effects on long-term survival have been limited and it is uncertain which patients derive most benefit. In this study, we determine the prognostic value of BluePrint subtyping in HER2-positive breast cancer. Additionally, we evaluate its use as a biomarker for predicting response to trastuzumab-containing neoadjuvant chemotherapy with or without pertuzumab.

METHODS

From a cohort of patients with stage II-III HER2-positive breast cancer who were treated with neoadjuvant chemotherapy and trastuzumab with or without pertuzumab, 836 patients were selected for microarray gene expression analysis, followed by readout of BluePrint standard (HER2, Basal and Luminal) and dual subtypes (HER2-single, Basal-single, Luminal-single, HER2-Basal, Luminal-HER2, Luminal-HER2-Basal). The associations between subtypes and pathological complete response (pCR), overall survival (OS) and breast cancer-specific survival (BCSS) were assessed, and pertuzumab benefit was evaluated within the BluePrint subgroups.

RESULTS

BluePrint results were available for 719 patients. In patients with HER2-type tumors, the pCR rate was 71.9% in patients who received pertuzumab versus 43.5% in patients who did not (adjusted Odds Ratio 3.43, 95% CI 2.36-4.96). Additionally, a significantly decreased hazard was observed for both OS (adjusted hazard ratio [aHR] 0.45, 95% CI 0.25-0.80) and BCSS (aHR 0.46, 95% CI 0.24-0.86) with pertuzumab treatment. Findings were similar in the HER2-single subgroup. No significant benefit of pertuzumab was seen in other subtypes.

CONCLUSIONS

In patients with HER2-type or HER2-single-type tumors, pertuzumab significantly improved the pCR rate and decreased the risk of breast cancer mortality, which was not observed in other subtypes. BluePrint subtyping may be valuable in future studies to identify patients that are likely to be highly sensitive to HER2-targeting agents.

Immune checkpoint blockade therapy for BRAF mutant metastatic colorectal cancer: the efficacy, new strategies, and potential biomarkers

BRAF mutant metastatic colorectal cancer has long been considered a tumor with a poor prognosis and a poor response to chemotherapy. Despite the efficacy of targeted therapy with multi-targeted blockade of the mitogen-activated protein kinase (MAPK) signaling pathway has brought a glimmer of hope to this group of patients, the need to improve treatment efficacy remains unmet, especially for the microsatellite stability/DNA proficient mismatch repair (MSS/pMMR) subtype. BRAF mutant colorectal cancer patients with high microsatellite instability/DNA deficient mismatch repair (MSI-H/dMMR) have high tumor mutation burden and abundant neoantigen, who are deemed as ones that could receive expected efficacy from immunotherapy. Generally, it is believed that MSS/pMMR colorectal cancer is an immunologically "cold" tumor that is insensitive to immunotherapy. However, targeted therapy combined with immune checkpoint blockade therapy seems to bring light to BRAF mutant colorectal cancer patients. In this review, we provide an overview of clinical efficacy and evolving new strategies concerning immune checkpoint blockade therapy for both MSI-H/dMMR and MSS/pMMR BRAF mutant metastatic colorectal cancer and discuss the potential biomarkers in the tumor immune microenvironment for predicting immunotherapeutic response in BRAF mutant colorectal cancer.

Predictive plasma biomarkers of long-term increase in hepatic steatosis index after HCV eradication in HIV/HCV-coinfected patients

Hepatic steatosis is a common condition found in the liver of hepatitis C virus (HCV)-infected patients, contributing to more severe forms of liver disease. In addition, the human immunodeficiency virus (HIV) may accelerate this process. Alternatively, several immune checkpoint proteins have been reported to be upregulated and correlated with disease progression during HCV and HIV infections. In steatosis, a detrimental immune system activation has been established; however, the role of the immune checkpoints has not been addressed so far. Thus, this study aimed to evaluate the association between plasma immune checkpoint proteins at baseline (before antiviral therapy) with hepatic steatosis index (HSI) increase at the end of follow-up (∼ five years after sustained virologic response (SVR)). We performed a multicenter retrospective study in 62 patients coinfected with HIV/HCV who started antiviral therapy. Immune checkpoint proteins were analyzed at baseline using a Luminex 200TM analyzer. The statistical association analysis was carried out using Generalized Linear Models (GLM) and Partial Least Squares Discriminant Analysis (PLS-DA). Fifty-three percent of the patients showed HSI increase from baseline to the end of follow-up. Higher immune checkpoint protein levels of BTLA, CD137(4-1BB), CD80, GITR, LAG-3, and PD-L1 before HCV therapy were associated with a long-term increase in HSI after successful HCV therapy, suggesting a potential predictive role for early detection of progression towards steatosis in HIV/HCV-coinfected patients.

Development and validation of a simple general population lung cancer risk model including AHRR-methylation

INTRODUCTION

Screening reduces lung cancer mortality of high-risk populations. Currently proposed screening eligibility criteria only identify half of those individuals, who later develop lung cancer. This study aimed to develop and validate a sensitive and simple model for predicting 10-year lung cancer risk.

METHODS

Using the 1991-94 examination of The Copenhagen City Heart Study in Denmark, 6,820 former or current smokers from the general population were followed for lung cancer within 10 years after examination. Logistic regression of baseline variables (age, sex, education, chronic obstructive pulmonary disease, family history of lung cancer, smoking status and cumulative smoking, secondhand smoking, occupational exposures to dust and fume, body mass index, lung function, plasma C-reactive protein, and AHRR(cg05575921) methylation) identified the best predictive model. The model was validated among 3,740 former or current smokers from the 2001-03 examination, also followed for 10 years. A simple risk chart was developed with Poisson regression.

RESULTS

Age, sex, education, smoking status, cumulative smoking, and AHRR(cg05575921) methylation identified 65 of 88 individuals who developed lung cancer in the validation cohort. The highest risk group, consisting of less educated men aged >65 with current smoking status and cumulative smoking >20 pack-years, had absolute 10-year risks varying from 4% to 16% by AHRR(cg05575921) methylation.

CONCLUSION

A simple risk chart including age, sex, education, smoking status, cumulative smoking, and AHRR(cg05575921) methylation, identifies individuals with 10-year lung cancer risk from below 1% to 16%. Including AHRR(cg05575921) methylation in the eligibility criteria for screening identifies smokers who would benefit the most from screening.

A pilot-study to examine motor cortical plasticity as a neuro-marker of antipsychotic treatment response in schizophrenia

In a pilot study on 15 untreated first-episode schizophrenia participants, we examined how pre-treatment motor cortical plasticity - the brain's ability to change in response to an external perturbation - induced with intermittent theta burst stimulation, predicted prospectively ascertained (in 4-to-6 weeks) response to antipsychotic medications. We observed that participants with cortical plasticity in the opposite direction (possibly compensatory) had significantly greater improvements in their positive symptoms. This association persisted after correcting for multiple comparisons and controlling for potential confounders via linear regression. Inter-individual variability in cortical plasticity is a potential predictive biomarker in schizophrenia requiring further investigation and replication.

Association of clinical, laboratory and imaging biomarkers with the occurrence of acute myocardial infarction in patients without standard modifiable risk factors - rationale and design of the "Beyond-SMuRFs Study"

BACKGROUND

Acute myocardial infarction (AMI) remains the leading cause of mortality worldwide. The majority of patients who suffer an AMI have a history of at least one of the standard modifiable risk factors (SMuRFs): smoking, hypertension, dyslipidemia, and diabetes mellitus. However, emerging scientific evidence recognizes a clinically significant and increasing proportion of patients presenting with AMI without any SMuRF (SMuRF-less patients). To date, there are no adequate data to define specific risk factors or biomarkers associated with the development of AMIs in these patients.

METHODS

The ''Beyond-SMuRFs Study'' is a prospective, non-interventional cohort trial designed to enroll patients with AMI and no previous coronary intervention history, who undergo coronary angiography in two academic hospitals in Thessaloniki, Greece. The rationale of the study is to investigate potential relations between SMuRF-less AMIs and the clinical, laboratory and imaging profile of patients, by comparing parameters between patients with and without SMuRFs. Complete demographic and comprehensive clinical data will be recorded, Venous blood samples will be collected before coronary angiography and the following parameters will be measured: total blood count, standard biochemistry parameters, coagulation tests, hormone levels, glycosylated hemoglobin, N- terminal pro-B-type natriuretic peptide and high-sensitivity troponin T levels- as well as serum levels of novel atherosclerosis indicators and pro-inflammatory biomarkers. Furthermore, all participants will undergo a complete and comprehensive transthoracic echocardiographic assessment according to a pre-specified protocol within 24 h from admission. Among others, 2D-speckle-tracking echocardiographic analysis of cardiac chambers and non-invasive calculation of myocardial work indices for the left ventricle will be performed. Moreover, all patients will be assessed for angiographic parameters and the complexity of coronary artery disease using the SYNTAX score. Multivariable linear and logistic regression models will be used to phenotypically characterize SMuRF-less patients and investigate independent clinical, laboratory, echocardiographic and angiographic biomarkers-predictors of SMuRF-less status in AMI.The first patient was enrolled in March 2022 and completion of enrollment is expected until December 2023.

DISCUSSION

The ''Beyond-SmuRFs'' study is an ongoing prospective trial aiming to investigate potential clinical, laboratory and imaging biomarkers associated with the occurrence of AMIs in SMuRF-less patients. The configuration of these patients' profiles could lead to the development of personalized risk-stratification models predicting the occurrence of cardiovascular events in SΜuRF-less individuals.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT05535582 / September 10, 2022.

The EU-funded I3LUNG Project: Integrative Science, Intelligent Data Platform for Individualized LUNG Cancer Care With Immunotherapy

Although immunotherapy (IO) has changed the paradigm for the treatment of patients with advanced non-small cell lung cancers (aNSCLC), only around 30% to 50% of treated patients experience a long-term benefit from IO. Furthermore, the identification of the 30 to 50% of patients who respond remains a major challenge, as programmed Death-Ligand 1 (PD-L1) is currently the only biomarker used to predict the outcome of IO in NSCLC patients despite its limited efficacy. Considering the dynamic complexity of the immune system-tumor microenvironment (TME) and its interaction with the host's and patient's behavior, it is unlikely that a single biomarker will accurately predict a patient's outcomes. In this scenario, Artificial Intelligence (AI) and Machine Learning (ML) are becoming essential to the development of powerful decision-making tools that are able to deal with this high-complexity and provide individualized predictions to better match treatments to individual patients and thus improve patient outcomes and reduce the economic burden of aNSCLC on healthcare systems. I³LUNG is an international, multicenter, retrospective and prospective, observational study of patients with aNSCLC treated with IO, entirely funded by European Union (EU) under the Horizon 2020 (H2020) program. Using AI-based tools, the aim of this study is to promote individualized treatment in aNSCLC, with the goals of improving survival and quality of life, minimizing or preventing undue toxicity and promoting efficient resource allocation. The final objective of the project is the construction of a novel, integrated, AI-assisted data storage and elaboration platform to guide IO administration in aNSCLC, ensuring easy access and cost-effective use by healthcare providers and patients.

External quality assessment (EQA) for tumor mutational burden: results of an international IQN path feasibility pilot scheme

Tumor mutational burden (TMB) has recently been approved as an agnostic biomarker for immune checkpoint inhibitors. However, methods for TMB testing have not yet been standardized. The International Quality Network for Pathology (IQNPath) organized a pilot external quality assessment (EQA) scheme for TMB testing. The aim of this program was the validation of the materials and the procedures for the EQA of this complex biomarker. Five formalin-fixed paraffin-embedded (FFPE) cell lines were selected to mimic the various TMB values observed in clinical practice. The FFPE samples were tested with the FoundationOne CDx (F1CDx) assay as the reference test and three commercially available targeted sequencing panels. Following this internal validation, the five cell lines were sent to 29 laboratories selected on the basis of a previous survey. Nineteen of the 23 laboratories that submitted results (82.6%) used targeted sequencing for TMB estimation. Only two laboratories performed whole exome sequencing (WES) and two assessed TMB by clinical exome. A high variability in the reported TMB values was observed. The variability was higher for samples with the highest TMB value according to the F1CDx test. However, good reproducibility of the TMB score was shown by laboratories using the same panel. The majority of laboratories did not indicate a TMB cut-off value for clinical interpretation. In conclusion, this pilot EQA scheme suggests that it is feasible to run such an EQA program for TMB assessment. However, the results of our pilot highlight the numerous challenges for the standardization of this test.

Predictive Biomarkers of Response to Neoadjuvant Therapy in Muscle Invasive Bladder Cancer

Neoadjuvant cisplatin-based chemotherapy is recommended prior to surgical removal of the bladder for patients with non-metastatic muscle invasive bladder cancer. Despite a survival benefit, approximately half of patients do not respond to chemotherapy and are exposed potentially unnecessarily to substantial toxicity and delay in surgery. Therefore, biomarkers to identify likely responders before initiating chemotherapy would be a helpful clinical tool. Furthermore, biomarkers may be able to identify patients who do not need subsequent surgery after clinical complete response to chemotherapy. To date, there are no clinically approved predictive biomarkers of response to neoadjuvant therapy. Recent advances in the molecular characterization of bladder cancer have shown the potential role for DNA damage repair (DDR) gene alterations and molecular subtypes to guide therapy, but these need validation from prospective clinical trials. This chapter reviews candidate predictive biomarkers of response to neoadjuvant therapy in muscle invasive bladder cancer.

Immune biology of NSCLC revealed by single-cell technologies: implications for the development of biomarkers in patients treated with immunotherapy

First-line immunotherapy in non-small-cell lung cancer largely improved patients' survival. PD-L1 testing is required before immune checkpoint inhibitor initiation. However, this biomarker fails to accurately predict patients' response. On the other hand, immunotherapy exposes patients to immune-related toxicity, the mechanisms of which are still unclear. Hence, there is an unmet need to develop clinically approved predictive biomarkers to better select patients who will benefit the most from immune checkpoint inhibitors and improve risk management. Single-cell technologies provide unprecedented insight into the tumor and its microenvironment, leading to the discovery of immune cells involved in immune checkpoint inhibitor response or toxicity. In this review, we will underscore the potential of the single-cell approach to identify candidate biomarkers improving non-small-cell lung cancer patients' care.

Clinical implications of the intrinsic molecular subtypes in hormone receptor-positive and HER2-negative metastatic breast cancer

Traditionally, the classification of breast cancer relies on the expression of immunohistochemical (IHC) biomarkers readily available in clinical practice. Using highly standardized and reproducible assays across patient cohorts, intrinsic molecular subtypes of breast cancer - also called "intrinsic subtypes" (IS) - have been identified based on the expression of 50 genes. Although IHC-based subgroups and IS moderately correlate to each other, they are not superimposable. In fact, non-luminal biology has been detected in a substantial proportion (5-20%) of hormone receptor-positive (HoR+) tumors, has prognostic value, and identifies reduced and increased sensitivity to endocrine therapy and chemotherapy, respectively. During tumor progression, a shift toward a non-luminal estrogen-independent and more aggressive phenotype has been demonstrated. Intrinsic genomic instability and cell plasticity, alone or combined with external constraints deriving from treatment selective pressure or interplay with the tumor microenvironment, may represent the determinants of such biological diversity between primary and metastatic disease, and during metastatic tumor evolution. In this review, we describe the distribution and the clinical behavior of IS as the disease progresses, focusing on HoR+/HER2-negative advanced breast cancer. In addition, we provide an overview of the ongoing clinical trials aiming to validate the predictive and prognostic value of IS towards their incorporation into routine care.

Predictive genomic tools in disease stratification and targeted prevention: a recent update in personalized therapy advancements

In the current era of medical revolution, genomic testing has guided the healthcare fraternity to develop predictive, preventive, and personalized medicine. Predictive screening involves sequencing a whole genome to comprehensively deliver patient care via enhanced diagnostic sensitivity and specific therapeutic targeting. The best example is the application of whole-exome sequencing when identifying aberrant fetuses with healthy karyotypes and chromosomal microarray analysis in complicated pregnancies. To fit into today's clinical practice needs, experimental system biology like genomic technologies, and system biology viz., the use of artificial intelligence and machine learning is required to be attuned to the development of preventive and personalized medicine. As diagnostic techniques are advancing, the selection of medical intervention can gradually be influenced by a person's genetic composition or the cellular profiling of the affected tissue. Clinical genetic practitioners can learn a lot about several conditions from their distinct facial traits. Current research indicates that in terms of diagnosing syndromes, facial analysis techniques are on par with those of qualified therapists. Employing deep learning and computer vision techniques, the face image assessment software DeepGestalt measures resemblances to numerous of disorders. Biomarkers are essential for diagnostic, prognostic, and selection systems for developing personalized medicine viz. DNA from chromosome 21 is counted in prenatal blood as part of the Down's syndrome biomarker screening. This review is based on a detailed analysis of the scientific literature via a vigilant approach to highlight the applicability of predictive diagnostics for the development of preventive, targeted, personalized medicine for clinical application in the framework of predictive, preventive, and personalized medicine (PPPM/3 PM). Additionally, targeted prevention has also been elaborated in terms of gene-environment interactions and next-generation DNA sequencing. The application of 3 PM has been highlighted by an in-depth analysis of cancer and cardiovascular diseases. The real-time challenges of genome sequencing and personalized medicine have also been discussed.

Circulating β2 and α1 microglobulins predict progression of nephropathy in diabetic patients: a meta-analysis of prospective cohort studies

AIMS

To study the association of circulating β2 (B2M) and α1 microglobulins (A1M) with diabetic nephropathy (DN) progression, a meta-analysis was performed on the prospective cohort studies.

METHODS

Up to October 2021, a comprehensive search of the PubMed, EMBASE, Scopus, Web of Science, and Cochrane Library databases was performed. The primary outcome (progression of DN) was defined as a decrease in eGFR or the occurrence of end stage renal disease or DN-related mortality. Eligible studies were included in a pooled analysis that used either fixed-effect or random-effect models to compensate for variation in measurement standards between studies. The funnel plot and Egger's test were used to assess publication bias.

RESULTS

The meta-analysis included 4398 people from 9 prospective trials (8 cohorts) for B2M and 3110 people from 4 prospective trials (3 cohorts) for A1M. Diabetic individuals with higher B2M levels had an increased risk for DN (relative risk [RR]: 1.81, 95% confidence interval [CI]: 1.56-2.09). Likewise, higher A1M was associated with augmented probability of DN (RR: 1.96, 95% CI: 1.46-2.62). The funnel plot and Egger's tests indicated no publication bias for A1M. Additionally, to compensate for putative publication bias for B2M, using trim and fill analysis, four studies were filled for this marker and the results remained significant (RR: 1.62, 95% CI: 1.37-1.92).

CONCLUSIONS

The elevated serum levels of B2M and A1M could be considered as potential predictors of DN progression in diabetic patients.

PROTOCOL REGISTRATION

PROSPERO CRD42021278300.