CD4 T cells and toxicity from immune checkpoint blockade.

Immune-related toxicities, otherwise known as immune-related adverse events (irAEs), occur in a substantial fraction of cancer patients treated with immune checkpoint inhibitors (ICIs). Ranging from asymptomatic to life-threatening, ICI-induced irAEs can result in hospital admission, high-dose corticosteroid treatment, ICI discontinuation, and in some cases, death. A deeper understanding of the factors underpinning severe irAE development will be essential for improved irAE prediction and prevention, toward maximizing the benefits and safety profiles of ICIs. In recent work, we applied mass cytometry, single-cell RNA sequencing, single-cell V(D)J sequencing, bulk RNA sequencing, and bulk T-cell receptor (TCR) sequencing to identify pretreatment determinants of severe irAE development in patients with advanced melanoma. Across 71 patients separated into three cohorts, we found that two baseline features in circulation-elevated activated CD4 effector memory T-cell abundance and TCR diversity-are associated with severe irAE development, independent of the affected organ system within 3 months of ICI treatment initiation. Here, we provide an extended perspective on this work, synthesize and discuss related literature, and summarize practical considerations for clinical translation. Collectively, these findings lay a foundation for data-driven and mechanistic insights into irAE development, with the potential to reduce ICI morbidity and mortality in the future.

Identification of a proteomic signature for predicting immunotherapy response in patients with metastatic non-small cell lung cancer.

BACKGROUND: Immunotherapy has improved survival rates in cancer patients, but identifying those who will respond to treatment remains a challenge. Recent advances in proteomic technologies have enabled the identification and quantification of nearly all expressed proteins in a single experiment. Integration of mass spectrometry with other high-throughput technologies has paved the way for comprehensive and systematic analysis of the plasma proteome in cancer, facilitating early diagnosis and personalized treatment. In this context, the objective of our study was to investigate the predictive and prognostic value of plasma proteome analysis using the SWATH-MS (Sequential Window Acquisition of All Theoretical Mass Spectra) strategy in newly diagnosed NSCLC patients who received pembrolizumab therapy. METHODS: For this purpose, 64 newly diagnosed advanced NSCLC patients treated with pembrolizumab therapy were enrolled and blood samples were collected from all patients before and during therapy. In total 171 blood samples were collected, and plasma samples were analysed employing SWATH-MS strategy. Next, we compared the plasma protein expression of metastatic NSCLC patients prior to receiving pembrolizumab treatment and divided the cohort into two groups in order to identify a proteomic signature that allow us to predict immunotherapy response. RESULTS: Proteomic analyses by SWATH-MS strategy allow us to identified 324 differentially expressed proteins between responder and non-responder patients. In addition, we developed a predictive model and found a combination of seven proteins, including ATG9A, DCDC2, HPS5, FIL1L, LZTL1, PGTA, and SPTN2, with stronger predictive value than PD-L1 expression alone. Additionally, survival analyses showed that low levels of ATG9A, DCDC2, and HPS5 were associated with longer progression-free survival (PFS) and overall survival (OS), while low levels of SPTN2 were associated with worse OS. CONCLUSIONS: Our work highlights the potential value of proteomic technologies to detect predictive biomarkers in blood samples of NSCLC patients. These analyses shed light on the correlation between the response to immunotherapy in patients with NSCLC and the set of 7 proteins.

Nasal Epithelium Transcriptomics Predict Clinical Response to Elexacaftor/Tezacaftor/Ivacaftor.

Elexacaftor/tezacaftor/ivacaftor (ETI) has made a substantial positive impact for people living with CF (pwCF). However, there can be substantial variability in efficacy, and we lack adequate biomarkers to predict individual response. We thus aimed to identify transcriptomic profiles in nasal respiratory epithelium that predict clinical response to ETI treatment. We obtained nasal epithelial samples from pwCF prior to ETI initiation and performed a transcriptome-wide analysis of baseline gene expression to predict changes in FEV1 (∆FEV1), year's best FEV1 (∆ybFEV1), and body mass index (∆BMI). Using the top differentially expressed genes (DEGs), we generated transcriptomic risk scores (TRS) and evaluated their predictive performance. The study included 40 pwCF aged ≥6 years (mean 27.7 [SD=15.1] years; 40% female). After ETI initiation, FEV1 improved ≥5% in 22 (61.1%) participants and ybFEV1 improved ≥5% in 19 (50%). TRS were constructed using top over-expressed and under-expressed genes for each. Adding the ∆FEV1 TRS for to a model with age, sex, and baseline FEV1 increased the AUC from 0.41 to 0.88; the ∆ybFEV1 TRS increased the AUC from 0.51 to 0.88; and the ∆BMI TRS increased the AUC from 0.46 to 0.92. Average accuracy was thus ~85% in predicting the response to the three outcomes. Results were similar in models further adjusted for F508del zygosity and previous CFTR modulator use. In conclusion, we identified nasal epithelial transcriptomic profiles that help accurately predict changes in FEV1 and BMI with ETI treatment. These novel TRS could serve as predictive biomarkers for clinical response to modulator treatment in pwCF.

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 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.

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.

Claudin-18.2 Immunohistochemical Evaluation in Gastric and Gastroesophageal Junction Adenocarcinomas to Direct Targeted Therapy: A Practical Approach.

Claudin-18.2 (CLDN18.2) expression evaluated by immunohistochemistry is a new biomarker for gastric and gastroesophageal junction adenocarcinomas that will soon have market authorization for implementation into routine clinical practice. Despite successful testing in the setting of clinical trials, no specific practical testing guidelines have been proposed. Several preanalytical and analytical variables may interfere with adequate CLDN18.2 staining interpretation; thus, this article provides practical guidance on CLDN18.2 testing and scoring in gastric and gastroesophageal junction adenocarcinomas to identify patients who may respond to targeted therapy with monoclonal antibodies directed against CLDN18.2. Based on available data, moderate to strong (2+/3+) membrane staining in ≥75% of adenocarcinoma cells is the proposed cutoff for clinical use of monoclonal antibody anti-CLDN18.2 (zolbetuximab).

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-1ß, 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-1ß 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-1ß, 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.

Optimizing tissue stewardship in non-small cell lung cancer to support molecular characterization and treatment selection: statement from a working group of thoracic pathologists.

Many patients with non-small cell lung cancer do not receive guideline-recommended, biomarker-directed therapy, despite the potential for improved clinical outcomes. Access to timely, accurate, and comprehensive molecular profiling, including targetable protein overexpression, is essential to allow fully informed treatment decisions to be taken. In turn, this requires optimal tissue management to protect and maximize the use of this precious finite resource. Here, a group of leading thoracic pathologists recommend factors to consider for optimal tissue management. Starting from when lung cancer is first suspected, keeping predictive biomarker testing in the front of the mind should drive the development of practices and procedures that conserve tissue appropriately to support molecular characterization and treatment selection.

Development of an algorithm combining blood-based biomarkers, fecal immunochemical test, and age for population-based colorectal cancer screening.

BACKGROUND AND AIMS: Implementation of screening modalities have reduced the burden of colorectal cancer (CRC), but high false positive rates pose a major problem for colonoscopy capacity. We aimed to create a tailored screening algorithm that expands the fecal immunochemical test (FIT) with a blood specimen and current age to improve selection of individuals for diagnostic colonoscopy. METHODS: In this prospective multi-center study, eight blood-based biomarkers (CEA, Ferritin, hsCRP, HE4, Cyfra21-1, Hepsin, IL-8 and OPG) were investigated in 1,977 FIT positive individuals from the Danish national CRC screening program undergoing follow-up colonoscopy. Specimens were analyzed on ARCHITECT i2000®, ARCHITECT c8000® or Luminex xMAP® machines. FIT analyses and blood-based biomarker data were combined with clinical data (i.e., age and colonoscopy findings) in a cross-validated logistic regression model (algorithm) benchmarked against a model solely using the FIT result (FIT model) applying different cutoffs for FIT positivity. RESULTS: The cohort included individuals with CRC (n = 240), adenomas (n = 938) or no neoplastic lesions (n = 799). The cross-validated algorithm combining the eight biomarkers, quantitative FIT result and age performed superior to the FIT model in discriminating CRC versus non-CRC individuals (AUC 0.77 versus 0.67, p < 0.001). When discriminating individuals with either CRC or high- or medium-risk adenomas versus low-risk adenomas or clean colorectum, the AUCs were 0.68 versus 0.64 for the algorithm and FIT model, respectively. CONCLUSIONS: The algorithm presented here can improve patient allocation to colonoscopy, reducing colonoscopy burden without compromising cancer and adenomas detection rates or vice versa.

Predictive biomarkers for response to TGF- ß inhibition in resensitizing chemo(radiated) esophageal adenocarcinoma.

Epithelial-mesenchymal transition (EMT) has been identified as a driver of therapy resistance, particularly in esophageal adenocarcinoma (EAC), where transforming growth factor beta (TGF-ß) can induce this process. Inhibitors of TGF-ß may counteract the occurrence of mesenchymal, resistant tumor cell populations following chemo(radio)therapy and improve treatment outcomes in EAC. Here, we aimed to identify predictive biomarkers for the response to TGF-ß targeting. In vitro approximations of neoadjuvant treatment were applied to publicly available primary EAC cell lines. TGF-ß inhibitors fresolimumab and A83-01 were employed to inhibit EMT, and mesenchymal markers were quantified via flow cytometry to assess efficacy. Our results demonstrated a robust induction of mesenchymal cell states following chemoradiation, with TGF-ß inhibition leading to variable reductions in mesenchymal markers. The cell lines were clustered into responders and non-responders. Genomic expression profiles were obtained through RNA-seq analysis. Differentially expressed gene (DEG) analysis identified 10 positively- and 23 negatively-associated hub genes, which were bioinformatically identified. Furthermore, the correlation of DEGs with response to TGF-ß inhibition was examined using public pharmacogenomic databases, revealing 9 positively associated and 11 negatively associated DEGs. Among these, ERBB2, EFNB1, and TNS4 were the most promising candidates. Our findings reveal a distinct gene expression pattern associated with the response to TGF-ß inhibition in chemo(radiated) EAC. The identified DEGs and predictive markers may assist patient selection in clinical studies investigating TGF-ß targeting.

Advances in Predictive Biomarkers for Anti-Angiogenic Therapy in Non-Small Cell Lung Cancer.

This study aimed to explore advances in biomarkers related to anti-angiogenic therapy in patients with non-small cell lung cancer (NSCLC), thereby enhancing treatment selection, advancing personalized and precision medicine to improve treatment outcomes and patient survival rates. This article reviews key discoveries in predictive biomarkers for anti-angiogenic therapy in NSCLC in recent years, such as (1) liquid biopsy predictive biomarkers: studies have identified activated circulating endothelial cells (aCECs) via liquid biopsy as potential predictive biomarkers for the efficacy of anti-angiogenic therapy; (2) imaging biomarkers: advanced imaging technologies, such as dynamic contrast-enhanced integrated magnetic resonance positron emission tomography (MR-PET), are used to assess tumor angiogenesis in patients with NSCLC and evaluate the clinical efficacy of anti-angiogenic drugs; (3) genetic predictive biomarkers: research has explored polymorphisms of Vascular Endothelial Growth Factor Receptor-1 (VEGFR-1) and vascular endothelial growth factor-A (VEGF-A), as well as how plasma levels of VEGF-A can predict the outcomes and prognosis of patients with non-squamous NSCLC undergoing chemotherapy combined with bevacizumab. Despite progress in identifying biomarkers related to anti-angiogenic therapy, several challenges remain, including limitations in clinical trials, heterogeneity in NSCLC, and technical hurdles. Future research will require extensive clinical validation and in-depth mechanistic studies to fully exploit the potential of these biomarkers for personalized treatment.

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.

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.

Skin TARC/CCL17 increase precedes the development of childhood atopic dermatitis.

BACKGROUND: It is unknown whether skin biomarkers collected in infancy can predict the onset of atopic dermatitis (AD) and be used in future prevention trials to identify children at risk. OBJECTIVES: This study sought to examine whether skin biomarkers can predict AD during the first 2 years of life. METHODS: This study enrolled 300 term and 150 preterm children at birth and followed for AD until the age of 2 years. Skin tape strips were collected at 0 to 3 days and 2 months of age and analyzed for selected immune and barrier biomarkers. Hazard ratio (HR) with 95% confidence interval (CI) using Cox regression was calculated for the risk of AD. RESULTS: The 2-year prevalence of AD was 34.6% (99 of 286) and 21.2% (25 of 118) among term and preterm children, respectively. Skin biomarkers collected at birth did not predict AD. Elevated thymus- and activation-regulated chemokine/C-C motif chemokine ligand 17 -levels collected at 2 months of age increased the overall risk of AD (HR: 2.11; 95% CI: 1.36-3.26; P = .0008) and moderate-to-severe AD (HR: 4.97; 95% CI: 2.09-11.80; P = .0003). IL-8 and IL-18 predicted moderate-to-severe AD. Low filaggrin degradation product levels increased the risk of AD (HR: 2.04; 95% CI: 1.32-3.15; P = .001). Elevated biomarker levels at 2 months predicted AD at other skin sites and many months after collection. CONCLUSIONS: This study showed that noninvasively collected skin biomarkers of barrier and immune pathways can precede the onset of AD.

Circulating Blood Prognostic Biomarker Signatures for Hemorrhagic Cerebral Cavernous Malformations (CCMs).

Cerebral cavernous malformations (CCMs) are a neurological disorder characterized by enlarged intracranial capillaries in the brain, increasing the susceptibility to hemorrhagic strokes, a major cause of death and disability worldwide. The limited treatment options for CCMs underscore the importance of prognostic biomarkers to predict the likelihood of hemorrhagic events, aiding in treatment decisions and identifying potential pharmacological targets. This study aimed to identify blood biomarkers capable of diagnosing and predicting the risk of hemorrhage in CCM1 patients, establishing an initial set of circulating biomarker signatures. By analyzing proteomic profiles from both human and mouse CCM models and conducting pathway enrichment analyses, we compared groups to identify potential blood biomarkers with statistical significance. Specific candidate biomarkers primarily associated with metabolism and blood clotting pathways were identified. These biomarkers show promise as prognostic indicators for CCM1 deficiency and the risk of hemorrhagic stroke, strongly correlating with the likelihood of hemorrhagic cerebral cavernous malformations (CCMs). This lays the groundwork for further investigation into blood biomarkers to assess the risk of hemorrhagic CCMs.

Predictive biomarkers for response to immune checkpoint inhibition.

Immune checkpoint inhibitors have transformed the prognosis and treatment paradigm of many cancer types, through the potential for durable responses. However, the majority of patients still do not benefit. Response to checkpoint inhibition is determined by dynamic host, tumour and tumour microenvironment factors that display spatial and temporal variability, but our understanding of these interactions is incomplete. Through investigating biomarkers of resistance and response, opportunities arise to discover new therapeutic targets and shape personalised treatment strategies. Here we review approved and emerging biomarkers of response to immune checkpoint inhibitors, in particular the recent rapid progress in host and tumour genomics. It is unlikely that a single biomarker will precisely predict response, but multivariate multiomic markers may provide a balanced assessment of these factors and more accurately identify patients who will benefit. Further efforts are required to translate these groundbreaking discoveries into novel therapeutics and biomarker driven clinical trials, to provide durable treatment response to a greater population of patients.

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.

Molecular prediction of clinical response to anti-PD-1/anti-PD-L1 immune checkpoint inhibitors: New perspectives for precision medicine and mass spectrometry-based investigations.

Monoclonal antibodies (mAbs) acting as immune checkpoint inhibitors (ICIs) are among the most frequently used immunotherapies in oncology. However, precision medicine approaches to adapt the treatment to the patient are still poorly exploited. Given the risk of severe adverse reactions, predicting patient eligibility for ICI therapy represents a great asset for precision medicine. Today, the extended panel of mass spectrometric approaches, accompanied by newly developed sample preparation methods is a strategy of choice for responder and non-responder stratification on a molecular basis, and early detection of resistance. In this perspective article, we review the biodisposition of mAbs, the interest in molecular stratification of patients treated with these mAbs, and the possible analytical strategies to achieve this goal, with a major emphasis on mass spectrometric approaches.

Predicting Response to Antibody Drug Conjugates: A Focus on Antigens' Targetability.

Antibody-drug conjugates (ADCs) represent a cornerstone in the treatment of many cancers nowadays. ADCs fulfill their function by binding a target on tumor cell membrane to deliver a cytotoxic payload; in addition, those moieties capable of crossing cancer cell membranes can achieve near-by cells that do not express the target antigen, exerting the so-called "bystander" cytotoxic effect. The presence of a specific target antigen expressed on cancer cells has been for long considered crucial for ADCs and commonly required for the inclusion of patients in clinical trials with ADCs. To date, only ado-trastuzumab-emtansine, fam-trastuzumab deruxtecan-nxki, and mirvetuximab soravtansine-gynx are approved according to the expression of a target antigen in solid tumors, while the clinical use of other ADCs (eg, sacituzumab govitecan) is not conditioned by the presence of a specific biomarker. Given the ever-growing number of approved ADCs and those under investigation, it is essential to find new biomarkers to guide their use, especially in those settings for which different ADCs are approved to establish the best therapeutic sequence based on robust biomarkers. Hence, this work addresses the role of target antigens in predicting response to ADCs, focusing on examples of antigens' targetability according to their expression on cancer cells' surface or to the presence of specific target aberrations (eg, mutation or over-expression). New methods for the assessment and quantification of targets' expression, like molecular imaging and in vitro assays, might be key tools to improve biomarker analysis and eventually deliver better outcomes by refined patient selection.