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.

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.

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.

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.

Multitarget Fluorescence In Situ Hybridization Diagnostic Applications in Tumors

Multitarget fluorescence in situ hybridization (mFISH) is a technique that allows the detection of multiple target sequences on the same sample using spectrally distinct fluorophore labels. The mFISH approach is currently a useful assay in the oncologic field for the detection of predictive, prognostic, and diagnostic biomarkers. In this chapter, we summarize the application of mFISH in the identification of target genetic aberrations in formalin-fixed, paraffin-embedded (FFPE) tissue samples of several tumor types. We discuss the mFISH protocols in FFPE samples, the innovative multitarget probes used, and the critical issues related to their interpretation.

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.