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Abstract

Immune checkpoint inhibitors (ICIs) have revealed significant clinical values in different solid tumors and hematological malignancy, changing the landscape for the treatment of multiple types of cancer. However, only a subpopulation of patients has obvious tumor response and long-term survival after ICIs treatment, and many patients may experience other undesirable clinical features. Therefore, biomarkers are critical for patients to choose exact optimum therapy. Here, we reviewed existing preclinical and clinical biomarkers of immunotherapeutic efficacy and immune-related adverse events (irAEs). Based on efficacy prediction, pseudoprogression, hyperprogressive disease, or irAEs, these biomarkers were divided into cancer cell-derived biomarkers, tumor microenvironment-derived biomarkers, host-derived biomarkers, peripheral blood biomarkers, and multi-modal model and artificial intelligence assessment-based biomarkers. Furthermore, we describe the relation between ICIs efficacy and irAEs. This review provides the overall perspective of biomarkers of immunotherapeutic outcome and irAEs prediction during ICIs treatment.

Keywords

biomarker hyperprogressive disease immune checkpoint inhibitors immune-related adverse events pseudoprogression

Introduction

Immune checkpoint inhibitors (ICIs), represented by cytotoxic T lymphocyte-associated antigen 4 (CTLA-4), programmed cell death protein 1 (PD-1), and programmed cell death ligand 1 (PD-L1) antibodies, have shown significant clinical benefits in different solid tumors and hematological malignancies, changing the landscape of the treatment of multiple cancer types. However, only one patient sub- population have obvious tumor response and long-term survival rate after ICIs treatment. A large number of patients may experience other undesirable clinical features, such as pseudoprogression (PsP), immune-related adverse events (irAEs), or even hyperprogressive disease (HPD). Therefore, biomarkers are critical for patients to receive optimum ICIs treatment and avoid irAEs. Here, we reviewed the research progress on the biomarkers including efficacy, HPD, PsP, and irAEs of ICIs (Figure 1).

Figure 1.

The biomarkers of ICIs efficacy, PsP, HPD, and irAEs.

Cancer cell-derived biomarkers

PD-L1

PD-L1 is the most widely studied and reported biomarker for ICIs efficacy. Theoretically, the more PD-L1 is expressed, the stronger the immunosuppressive effect will be generated, and the greater benefit will be achieved from ICIs treatment. Previous studies have supported this point. In the KEYNOTE-001 study, the objective response rate (ORR), median progression-free survival (PFS), and overall survival (OS) were significantly better in patients with PD-L1 expression greater than 50% than in those lower than 50%.¹ CheckMate 012 study revealed that ORR was higher in PD-L1-positive patients.² However, other studies showed that PD-L1-negative patients can also benefit from ICIs.³ This may result from the lack of a consensus on PD-L1 detection, for example, different detection antibodies, detection platforms, tissue fixation, tissue sources, and scoring methods.^4–6 In addition to immunohistochemical detection of PD-L1 expression in tissues, enzyme-linked immunosorbent assay and fluorescence immunoassay can be used to detect PD-L1 in peripheral circulation.^7,8 Therefore, it is necessary to establish the standard process of PD-L1 detection and develop a reasonable scoring method in the future.

Tumor mutational burden

Tumor mutational burden (TMB) refers to the total number of mutations per megabase, that is, the total number of somatic mutations in the tumor genome after removing germline mutations. It can be assessed by computed tomography (CT) scan, fluorodeoxyglucose-positron emission tomography (FDG-PET)/CT, circulating tumor cells, circulating tumor DNA (ctDNA), serum lactate dehydrogenase (LDH), and serum tumor markers.⁹ A retrospective study of 27 cancers demonstrated that the higher TMB was, the better efficacy ICIs treatment would achieve and the higher ORR would be.¹⁰ Subsequently, a prospective study showed that patients with TMB ⩾ 10 achieved a significantly higher 1-year PFS rate (42.6% versus 13.2%) and a longer PFS (7.2 versus 5.0 months) than those with lower TMB.¹¹ CheckMate 568 study confirmed that the median PFS of patients with TMB ⩾ 10 was significantly longer than those with TMB < 10 (7.1 versus 2.6 months), and it was not related to the expression of PD-L1.¹² Recently, Jacob et al. reported that TMB ⩾ 20 mut/MB were significantly associated with better clinical efficacy of ICIs. In the state of high TMB (TMB-H), specific oncogenic signaling pathway mutations are associated with poor response to ICIs in advanced non-small-cell lung cancer (NSCLC). Sands et al.¹³ reported that among the TMB-H patients, TP53 (p = 0.026), PIK3CA (p = 0.025), and ROS1 (p = 0.057) gave worse response. When genetic pathways were assessed, mutations in the TP53 pathway were associated with poorer response (p = 0.018). Cao et al.¹⁴ demonstrated that TMB is a promising biomarker for the prognosis of ICIs using meta-analysis and bioinformatic analysis, as it is positively correlated with ORR, PFS, and OS, but the evaluation method and cutoff value of TMB need to be further standardized.

Deficient mismatch repair and microsatellite instability-high

In a prospective study of patients with mismatch repair deficiency in 12 types of advanced solid tumors receiving PD-1 antibodies, objective radiographic responses were observed in 53% of patients, and complete disease remission was assessed in 21%, suggesting that a large proportion of deficient mismatch repair (dMMR) cancer tissues generate neoantigens to make the tissues sensitive to ICIs, regardless of the origin of the cancer tissue. The mutation rate is 10–100 times higher in dMMR tumors than in proficient mismatch repair tumors.¹⁵ Moreover, it was reported that dMMR tumors were easy to mutate in repetitive DNA sequences, possibly leading to microsatellite instability-high (MSI-H).^16,17 Therefore, FDA-approved pembrolizumab treatment in patients with metastatic or unresectable solid tumors was characterized by MSI-H or dMMR. This is the first pan-cancer ICI drug based not on cancer type but on genetic alterations.¹⁸

DNA damage repair-related genes

DNA damage repair (DDR)-related genes, including ERCC2, BRCA1, BRCA2, FANCA, RAD51C, and MSH2, play an important role in DDR.¹⁹ Studies have shown that DDR-related genes can also predict the efficacy of ICIs. BRCA2 mutations can predict the efficacy of PD-1 antibody therapy in patients with advanced melanoma.²⁰ POLE gene mutations can rapidly accumulate a large number of somatic mutations, which can promote the production of tumor-specific neoantigens, resulting in enhanced immunogenicity and high mutation load.^21,22 Recent studies have reported that POLE gene mutations can be used to predict the efficacy of ICIs for endometrial cancer and colorectal cancer.^23,24 TP53 is probably the most widely studied tumor suppressor gene, and patients with TP53 mutations always have a poor prognosis. Some studies have found that TP53 mutations reduce genomic stability and are associated with DDR defects, suggesting that tumors with TP53 mutations may have a higher TMB.^25–27 TP53/STK11 often occurs with KRAS mutations in NSCLC patients, and PD-L1 expression levels are high, which can be used to guide ICI usage.²⁷

Genetic variation

Genetic variation can serve as a biomarker to predict ICIs efficacy, irAEs, and HPD. The CTLA-4 gene variant −1661A > G predicts endocrine adverse events in patients with metastatic melanoma and treated with ipilimumab, and SNPPDCD1804C > T is associated with lower incidence of irAEs.^28,29 Kato et al. analyzed the genomic profile of 155 patients by next-generation sequencing (NGS) and found that MDM2/4 (p = 0.02) and EGFR (p = 0.02) were associated with poor efficacy and 67% of patients with MDM2/4 amplification developed HPD. The possible mechanism is that ICIs trigger MDM2 amplification by promoting the expression of interferon regulatory factor 8 (IRF-8), which binds to the MDM2 promoter via JAK-STAT signaling. Thus, the amplification of MDM2/4 may be a biomarker for HPD, and MDM2 inhibitors may be potential therapy for HPD. EGFR-sensitive mutations may also be biomarkers for the development of HPD. One study showed that 8 of 10 patients with EGFR mutations had time-to-treatment failure (TTF) less than 2 months, and 2 of them had HPD.³⁰ Another study showed HPD in 20% (2/10) of patients with EGFR changed. These findings can be possibly explained by EGFR activation leading to the upregulation of PD-1/PD-L1, which, in turn, drives immune escape and leads to HPD.³¹

Tumor microenvironment-derived biomarkers

Tumor-infiltrating lymphocytes

The phenotype, distribution, and infiltration of tumor-infiltrating lymphocytes (TILs) are the focus of many ongoing studies. It has been reported that higher score of CD8⁺ TILs has better clinical benefit.^32,33 A recent study demonstrated that the model of TILs density calculated from digital H & E images can predict the objective response to PD-1 inhibitor in patients with advanced lung adenocarcinoma. But the area under the curve was not high (0.61). TILs need to combine with other biomarkers, such as PD-L1 and TMB.³⁴ However, CD8⁺ TILs are highly heterogeneous. Only a small fraction of the population can recognize tumor mutation-associated antigens. CD8⁺ TILs in the stromal and invasive marginal compartments of the tumor have better clinical outcomes than those in the intra-tumoral compartment.^33,35

Super-exhausted T cells

Super-exhausted T cells were characterized by co-expression of key immune checkpoints, including PD-1, LAG3, TIGIT, and TIM3. From 435 patients with solid tumors, tissue samples were collected prior to the initiation of ICIs therapy. Multiplex immunofluorescence using CD3/PD1/TIM3/LAG3/TIGIT/CTLA-4 panels and multispectral image analysis were employed for immune cell characterization. The association between co-expression of T-cell-associated exhausted markers and clinical response rate, PFS, and OS was investigated by the COX proportional hazards model. The results indicate that super-exhausted T-cell-positive tumor has higher ORR and better PFS and OS than the control.³⁶

HLA-I

HLA molecules are expressed on the surface of different immune cells and play a crucial role in antigen presentation and immune signal transduction. Disruption of the HLA-I antigen processing and presentation mediates immune evasion, being one of the mechanisms of acquired resistance to ICIs. Several HLA genotypes have been reported as biomarkers for ICIs. For example, HLA-a * 03 allele is a predictor of lower ORR and shorter PFS and OS and loss of heterozygosity patients have shorter OS. HLA-B44 and HLA-A02 supertypes indicate longer OS in melanoma patients.^32,37,38

Host-derived biomarkers

Microbiome and antibiotics

The dynamic balance of intestinal microbiota plays an active role in maintaining the homeostasis of the immune system. A study has shown that the intestinal microbiome can influence cancer immune reference point by inducing specific memory T cells through CD4⁺ and CD8⁺ T cells secreting Interferon gamma (IFN-γ), which is related to good prognosis after ICI treatment.³⁹ Another study showed that hepatocellular carcinoma patients who responded to ICIs had higher bacterial flora richness in their fecal samples than non-responders, and there was a significant difference in the dissimilarity of beta diversity at week 6 of treatment. Among non-responders, Proteobacteria predominated at week 12, while Ackermanella and Ruminococcaceae increased significantly among responders.⁴⁰ Similarly, a prospective study showed that abundant Ruminococcaceae UCG 13 and Agathobacter may predict better ORR and longer PFS. Enriched Ruminococcaceae UCG 13 was associated with longer OS. Moreover, microbiome was different between patients experiencing different grade of irAEs.^41,42

A retrospective study evaluating the effect of antibiotics on OS and PFS in the subsequent treatment of NSCLC showed a significant reduction in OS in patients receiving antibiotics in the atezolizumab group, but no association between antibiotic usage and survival time in the docetaxel group.⁴³ However, antibiotics before ICIs treatment did not affect the OS of NSCLC patients treated with first-line ICIs combined with chemotherapy.⁴⁴ Studies have shown that intestinal flora and the effect of ICIs are related to the type of cancer and patient subpopulation. Further studies are needed to explore the mechanism of intestinal flora in affecting the immune microenvironment and thus the effect of ICIs.

Performance status

Recent studies have reported that first-line ICIs combination therapy in cancer patients with excellent Eastern Cooperative Oncology Group performance status (ECOG PS = 0) has a better prognosis, with improved PFS and OS.⁴⁵ PS can also predict HPD. It was reported that HPD is more likely to occur in patients with Royal Marsden Hospital score ⩾ 2, ECOG performance status ⩾ 2, more than two metastases, recurrent metastatic head and neck squamous cell carcinoma of the primary oral cavity, and PD-L1 positive combined score ⩾ 10 in liver metastasis of gastric cancer.^46–49

BMI

Higher body mass index (BMI) may be associated with better response to ICI therapy. ORR for patients with normal BMI and obesity treated with ICIs was 28% and 42%, respectively (p = 0.03). Obese patients had significantly improved median PFS (36.4 versus 23.5 months, p < 0.0001) and OS (HR: 0.64, 95% CI: 0.44–0.91, p = 0.014) compared with normal BMI patients.⁵⁰

Age

Studies have shown that age is a biomarker for HPD, because immunosenescence is age related and characterized by a decline in cell-mediated immune function and a decline in humoral immune response.⁵¹ Some studies have shown that in NSCLC and cervical squamous cell carcinoma, the HPD patients are younger.^52–54 Therefore, age cannot be used as an independent biomarker to predict HPD. As for irAEs, some studies reported that severe irAEs more frequently occur in younger patients, while others showed the opposite results.^55–57 Therefore, age could not be used as an independent biomarker, instead it needs to be combined with other biomarkers.

Autoimmune disease history

irAEs are most common in the skin, endocrine glands, gastrointestinal system, and liver, and can affect almost any organ system, including the cardiovascular, pulmonary, musculoskeletal, ocular, and central nervous systems.⁵⁸ Compared with chemotherapy-related adverse reactions, irAEs have the characteristics of long duration and late onset. Therefore, it is important to recognize and manage irAEs early in clinical practice.⁵⁹ Studies have revealed that autoimmune disease history is associated with the development of irAEs.^60,61 Researchers had analyzed autoimmune diseases and irAEs at the molecular level, and found that the frequency of HLA-DRB1 shared epitope alleles was higher in patients with immune-mediated arthritis, and HLA-DR4 alleles were associated with ICIs-related type I diabetes.⁶² In addition, some studies have shown that autoantibodies before ICI treatment are closely related to the occurrence of irAEs, such as antinuclear antibodies, anti-thyroglobulin, and anti-thyroid peroxidase.⁶³ To sum up, if patients have autoimmune diseases before treatment, they should be particularly alert to the occurrence of irAEs during ICIs.

Viral infection

Some tumors are related to viral infections, such as cervical cancer and HPV, gastric cancer, and EB virus. Studies have shown that viral infection can be also used as a potential biomarker for the efficacy of ICIs. A prospective study of advanced gastric cancer treated with pembrolizumab demonstrated that the ORR of EBV-positive patients was 100%, while another meta-analysis found that the ORR and survival time of HPV-positive patients with head and neck squamous cell carcinoma and being treated with PD-1 inhibitors were better than those HPV-negative patients.^64,65

Corticosteroids

Corticosteroids are widely used due to their potent anti-inflammatory and immunosuppressive effects. Unfortunately, studies have shown that corticosteroid use is associated with poorer clinical outcomes. A retrospective study discovered that baseline corticosteroid use of ⩾10 mg prednisone was associated with worse outcomes in patients with NSCLC and treated with PD-L1 inhibitors.⁶⁶

Imaging examination

Studies have reported that PET/CT can predict the occurrence of thyroiditis in patients with lung cancer, which is manifested by increased uptake of FDG in the thyroid gland of patients on PET images, and this uptake increase occurs before the increase in serum TSH.⁶⁷ In addition, CT-based radiomics methods can accurately predict immune-associated pneumonia.⁶⁸ At present, it is difficult to find efficient biomarkers to predict the occurrence of irAEs in the early stage. Reported biomarkers for irAEs are often based on retrospective studies. Specific types of adverse reactions need to be clarified and expensive detection techniques uncommonly used in clinical practice are required. Therefore, more large-scale, and prospective studies should be conducted to validate the above biomarkers.

Peripheral blood biomarkers

Peripheral blood cell count

Peripheral blood detection is a hot field of biomarker study because of its strong feasibility, easy receptivity, and non-invasive characteristics. It was reported that neutrophil-to-lymphocyte ratio (NLR) was associated with poor immunotherapeutic outcomes.^69,70 A meta-analysis found that a higher level of NLR indicated poorer PFS and OS in melanoma, NSCLC, and genitourinary cancers.⁷¹ In patients with stage III–IV melanoma treated with nivolumab, a high absolute lymphocyte count (⩾1000/µL) and a low absolute neutrophil count (<4000/µL) early in the course of therapy were significantly associated with better OS.⁷² Other studies constructed risk blood biomarkers by combining white blood cell count and NLR.⁷³ Meanwhile, high eosinophil count (⩾1.5%) and relative lymphocyte count (⩾17.5%) were proved to be independent baseline characteristics associated with longer OS in melanoma patients treated with pembrolizumab.⁷⁴ Kiriu et al.⁷⁵ found that patients with PsP have significantly lower NLR than patients with true tumor progression before and after treatment.

Derived NLR (dNLR) and platelet counts were higher in HPD patients with NSCLC or head and neck squamous cell carcinoma.^47,76,77 It was reported that ΔNLR > 75% indicates the occurrence of HPD in NSCLC at week 4 with an accuracy of 86.1%.⁷⁸

In addition, baseline NLR was reported to be an independent predictor of irAEs in patients with NSCLC.⁷⁹ Also, baseline circulating eosinophils are associated with higher immune pneumonia.⁸⁰

In conclusion, NLR is a promising biomarker that indicates efficacy, HPD, PsP, and irAEs. However, more prospective studies are needed to confirm the role of this biomarker.

CtDNA, cfDNA, and cfMeDIP

ctDNA helps to recognize tumor-specific abnormalities and can be used for diagnosis, follow-up therapy, and prognosis. Recently, a meta-analysis carried out 17 trials to explore the relationship between changes in ctDNA levels and prognosis in patients with advanced solid tumors treated with ICIs. ctDNA clearance was defined as a reduction in ctDNA greater than 50% or to an undetectable level. The results showed that in non-selected advanced solid tumors, a significant decrease in ctDNA after ICIs was associated with significant improvement in PFS and OS.⁸¹ In a prospective study, 125 patients with advanced melanoma received single or dual ICIs, among whom 29 patients (23.2%) developed PsP, which occurred in patients with good ctDNA profile. ctDNA was not detectable at baseline, or decreased by 10 times or not detectable within 12 weeks of treatment.⁸² Similar results were reported by Guibert et al.,⁸³ who used ddPCR to detect plasma ctDNA to monitor the response to anti PD-1 therapy in patients with KRAS-mutant lung adenocarcinoma. Methylated circulating tumor DNA (cfMeDIP) can be used as a predictive marker for the efficacy of pembrolizumab.⁸⁴ One study evaluated 56 patients for cfDNA sequencing, using the chromosome number instability score to quantify chromosomal instability, and showed that quantification of chromosomal instability can be used as an early indicator of response to ICIs.⁸⁵ When tumor tissue cannot be obtained, due to deep tumor location or complications, ctDNA is a good choice. However, the ideal time to evaluate the change in ctDNA level and the diagnostic criteria of PsP need further study.

Cytokines and chemokines

Cytokines activate immunity and chemokines attract CD8⁺ T cells, which are prerequisites for ICIs. IFN-γ is a cytokine that plays a role in innate and adaptive immunity. A study showed that patients with highly expressed IFN-γ before ICIs had higher ORR, PFS, and OS. POPLAR and other studies found that higher IFN-γ expression indicated better OS.^86–88 Interleukin (IL)-8 is a member of the CXC chemokine family and was originally identified as a chemokine for neutrophils. Retrospective studies on advanced melanoma or NSCLC have revealed that early elevation of serum IL-8 level is a predictor for poor outcome, while higher IL-6 at baseline is associated with better clinical outcome.^89,90 However, in a recent prospective real-world study of 78 patients with advanced melanoma and NSCLC treated with ICIs, a higher median relative increase of IL-6, IL-8, and IL-8 at T2 was associated with a significantly lower rate of disease control.⁹¹ A study of melanoma and NSCLC demonstrated that target lesion volume increased but serum IL-8 decreased during imaging assessment of PsP. Target lesion volumes subsequently remained below baseline and serum IL-8 steadily increased with disease progression. This suggests that serum IL-8 can accurately reflect true tumor response and can be used to distinguish PsP from true progression.⁸⁹ Khan et al.⁹² found that baseline cytokine levels were low in patients with irAEs and increased after treatment, suggesting that immune dysregulation may be associated with a higher risk of irAEs, and that the pattern of inducible CXCL9, 10, 11, and 13 levels are most strongly associated with irAEs. Low baseline IL-6 serum levels are associated with high incidence of colitis and psoriasis.^42,90,93 Serum IL-17 levels before treatment are associated with grade 3 or higher colitis.⁹⁴ Fujimura et al.⁹⁵ reported that soluble CD163 and CXCL5 can predict the occurrence of irAEs. A prospective study found that patients with a decrease in circulating B cells (70% of baseline) and a greater than twofold increase in CD21loB cells and plasmablasts are more likely to develop irAEs, and that the severity of the early decline in B cell numbers after treatment is directly related to the time of toxicity onset.⁹⁶ It has also been reported that T cell-activated lymphocyte cytosolic protein 1 combined with ADP-dependent glucokinase can accurately predict the occurrence of immune pneumonia in lung cancer patients.⁹⁷ For chemokines, a recent study showed that cancer patients with high expression of four chemokines (CCL4, CCL5, CXCL9, and CXLC10) had a longer median progression time (104 days versus 71 days, p = 0.013) and longer OS (391 days versus 195 days, p = 0.016).⁹⁸ Matsuo et al.⁹⁹ revealed that decreased plasma CXCL12 levels and increased MMP2 levels after anti-PD-1 treatment are significantly associated with an improvement in PFS. Furthermore, CXCL12 levels in patients with PsP are consistently lower than baseline levels after pretreatment, and MMP2 levels are consistently higher than baseline levels.

Peripheral blood biochemical index

LDH, one of the key enzymes in the glycolytic pathway, has been considered a negative biomarker of ICI therapy.¹⁰⁰ A high LDH level may predict poorer response and worse PFS and OS after nivolumab treatment in patients with advanced NSCLC.¹⁰¹ Similarly, normal baseline LDH level may indicate longer OS in melanoma received nivolumab.¹⁰² Furthermore, baseline serum LDH has also been reported to be the most crucially factor associated with OS in patients with melanoma and treated with nivolumab plus ipilimumab. Patients with higher baseline serum LDH had a median OS of 17.4 months compared with 60 months in patients with normal serum LDH.¹⁰³ In a large retrospective study of ICIs for advanced melanoma, high baseline serum LDH in patients with HPD suggested aggressive tumor biology.¹⁰⁴ It has also been reported that rapid elevation of carcinoembryonic antigen in colorectal cancer and carbohydrate antigen 19-9 in pancreatic and cholangiocarcinoma during the first month of ICIs indicates the development of HPD.¹⁰⁵

Multi-modal models and artificial intelligence assessment

Multi-modal models based on expert-guided learning machines can predict the immune response of NSCLC patients more accurately.¹⁰⁶ A study demonstrated that numerical quantification of TILs in HE images by machine learning models may be used to analyze NSCLC responses to ICIs.¹⁰⁷ Another study showed that deep network machine learning analysis (AE-SDN) can screen out the main genes related to immunity, carcinogenesis, and tumor suppression. This model improved the prediction quality of OS by 20% compared with using the immune score.¹⁰⁸ Furthermore, a study revealed that automated tumor immunophenotyping and spatial statistics based on metric learning can successfully link spatial features to manual immunophenotyping and link patient response to treatment.¹⁰⁹ A recently published retrospective study established a nomogram model based on LDH/MCHC/ESR for predicting the occurrence of HPD with a concordance index of 0.899.¹¹⁰ Using big data and databases to construct various prediction models is the future trend in HPD biomarkers research.

Relationship between efficacy and irAEs

A systematic review has shown a positive association between the development of irAEs and ORR, PFS, and OS in patients receiving ICIs, regardless of tumor site, type of ICIs, and type of irAEs. Moreover, irAEs at grade 3 or higher level have better ORR but poorer OS.¹¹¹ Zhou et al.¹¹² demonstrated that the occurrence of irAEs is positively correlated with the efficacy of ICIs, especially endocrine, skin, and low-grade irAEs. The results of these studies are based on systematic reviews and meta-analyses, therefore requiring further confirmation by large-scale prospective trials.

Conclusion and prospect

At present, there are many biomarkers for efficiency of immunotherapeutic outcome, HPD, PsP, and irAEs, but most of them still need to be confirmed by large-scale prospective clinical trials (Table 1). How to convert non-targeted population into targeted population and how to make ICIs more accurate, controllable, and have lower toxicity and longer-term benefits will be the focus of future research on biomarkers of efficacy and adverse reactions of ICIs.

Table 1.

Significance of biomarkers of ICIs efficacy, PsP, HPD, and irAEs.

Biomarkers		Significance	Reference
Efficacy	PD-L1	Higher PD-L1 expression may be associated with better prognosis	Garon et al.,¹ Hellmann et al.²
	TMB	Higher TMB may be associated with better prognosis	Dall’Olio et al.,⁹ Yarchoan et al.,¹⁰ Hellmann et al.,¹¹ Readyet al.,¹² Sands et al.,¹³ Cao et al.¹⁴
	dMMR/MSI-H	dMMR/MSI-H response better to ICIs	Dudley et al.,¹⁵ Timmermann et al.,¹⁶ The Cancer Genome Atlas Network,¹⁷ Le et al.¹⁸
	HLA-I	HLA-a * 03 allele predict lower ORR and worse PFS and OS; LOH predicts shorter OS. HLA-B44 and HLA-A02 supertypes mean longer OS	Chen and Mellman,³² Naranbhai et al.,³⁷ Chowell et al.³⁸
	TIL	CD8 + TILs may be associated with better prognosis	Chen et al.,³² Geng et al.,³³ Corredor et al.,³⁴ Tumeh et al.³⁵
	Super-exhausted T cells	Super-exhausted T-cell-positive tumor had higher ORR and better PFS and OS	Peyraud et al.³⁶
	Cytokines and chemokines	IL-8 predicts poor outcome, higher IL-6 at baseline, four chemokines (CCL4, CCL5, CXCL9, and CXLC10), IFN-γ are associated with better clinical prognosis	Fehrenbacher et al.,⁸⁶ Sharma et al.,⁸⁷ Socinski et al.,⁸⁸ Sanmamed et al.,⁸⁹ Valpione et al.,⁹⁰ Pasello et al.⁹¹
	Microbiome and antibiotic	Proteobacteria, Ackermanella, and ruminococcaceae increased significantly among responders	Fluckiger et al.,³⁹ Chalabi et al.,⁴³ Cortellini et al.⁴⁴
	DDR-related genes	POLE gene mutations in endometrial cancer and colorectal cancer, TP53/STK11 in KRAS mutant NSCLC patients predict better efficacy	Mouw et al.,¹⁹ Hugo et al.,²⁰ Shlien et al.,²¹ Andrianova et al.,²² Howitt et al.,²³ Hwang et al.,²⁴ Cortez et al.,²⁵ Ji et al.,²⁶ Jeanson et al.²⁷
	Viral infection	EBV positive predicts better ORR in advanced gastric cancer, HPV positive predicts better ORR and survival time in head and neck squamous cell carcinoma	Wang et al.,⁶⁴ Kim et al.⁶⁵
	Corticosteroids	More than 10 mg prednisone is associated with worse outcomes	Arbour et al.⁶⁶
	Personal factor	PS scored 0, BMI high predicts better prognosis	Grohe et al.,⁴⁵ Wang et al.⁵⁰
	Multimodal models and AI assessment	Positive/negative	Rakaee et al.,¹⁰⁷ Ghasemi Saghand et al.,¹⁰⁸ Orlova et al.,¹⁰⁹ Cao et al.¹¹⁰
PsP	ctDNA	Good ctDNA profile develop PsP	Lee et al.,⁸² Guibert et al.⁸³
	IL-8	IL-8 can accurately reflect true tumor response	Sanmamed et al.,⁸⁹ Valpione et al.,⁹⁰ Pasello et al.⁹¹
	NLR	Lower NLR before and after ICIs treatment	Kiriu et al.⁷⁵
	CXCL-12 and MMP2	Lower CXCL12 and higher MMP2 levels after anti-PD-1 treatment predicts PsP	Mezquita et al.¹⁰⁰
HPD	Clinicopathological features	Younger, PS scored more than 2, more than 2 metastases, primary oral cavity with recurrent mHNSCC, gastric cancer liver metastasis with positive combined PD-L1 score more than 10	Lin et al.,⁴⁶ Castello et al.,⁴⁷ Hagi et al.,⁴⁸ Han et al.,⁴⁹ Park et al.,⁵² Choi et al.,⁵³ Economopoulou et al.⁵⁴
	Biochemical indexes	High LDH, dNLR, and platelet count; rapid elevation of CEA, CA19-9 may predict HPD	Cortellini et al.,⁴⁴ Sanchez-Gastaldo et al.,⁷³ Weide et al.,⁷⁴ Kiriu et al.,⁷⁵ Mezquita et al.,¹⁰⁰ Agullo-Ortuno et al.¹⁰¹
	cfDNA	CNI score	Weiss et al.⁸⁵
	MDM2/4 amplification	Negative	Wang et al.³¹
	EGFR mutation	Negative	Queirolo et al.,²⁸ Bins et al.²⁹
	nomogram model	LDH/MCHC/ESR nomogram model	Cao et al.¹¹⁰
irAEs	Autoimmune disease history	Negative	Postow et al.,⁵⁸ Puzanov et al.,⁵⁹ Abdel-Wahab et al.,⁶⁰ Michailidou et al.,⁶¹ Stamatouli et al.,⁶² Toi et al.⁶³
	Circulating cytokines and immune cells	Inducible CXCL9, 10, 11, and 13 levels; twofold increase in CD21loB cells and plasmablasts; low baseline IL-6 predicts colitis and psoriasis; serum IL-17 predicts colitis; LCP1 and ADPGK predict immune pneumonia	Chaput et al.,⁴² Tanaka et al.,⁹³ Tarhini et al.,⁹⁴ Fujimura et al.,⁹⁵ Das et al.,⁹⁶ Jing et al.,⁹⁷ Romero et al.,⁹⁸ Matsuo et al.⁹⁹
	Genetic variation	CTLA-4 gene variant −1661A > G predict higher incidence of irAEs, while SNPPDCD1804C > T predicts lower incidence	Queirolo et al.,²⁸ Bins et al.²⁹
	Microbiome	Fecal bacilli and other Firmicutes predict high incidence rate of colitis, while Bacteroides predict lower incidence rate	Hakozaki et al.,⁴¹ Chaput et al.⁴²
	Imaging examination	PET-CT to predict thyroiditis, CT to immune-associated pneumonia	Eshghi et al.,⁶⁷ Colen et al.⁶⁸

ADPGK, ADP-dependent glucokinase; AI, artificial intelligence; BMI, body mass index; CA19-9, carbohydrate antigen 19-9; CEA, carcinoembryonic antigen; cfDNA, cell-free DNA; CNI, chromosome number instability; CT, computed tomography; ctDNA, circulating tumor DNA; CTLA-4, cytotoxic T lymphocyte-associated antigen 4; DDR, DNA damage repair; dMMR, deficient mismatch repair; dNLR, derived neutrophil-to-lymphocyte ratio; ESR, erythrocyte sedimentation rate; ICIs, immune checkpoint inhibitors; IFN-γ, interferon gamma; IL, interleukin; LCP1, lymphocyte cytosolic protein 1; LDH, lactate dehydrogenase; LOH, loss of heterozygosity; MSI-H, microsatellite instability-high; NLR, neutrophil-to-lymphocyte ratio; NSCLC, non-small-cell lung cancer; OS, overall survival; PD-1, programmed cell death protein 1; PD-L1, programmed cell death ligand 1; PET, positron emission tomography; PFS, progression-free survival; TILs, tumor-infiltrating lymphocytes; TMB, tumor mutational burden.

Footnotes

Acknowledgements

Not applicable.

Declarations

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Correlation of preclinical and clinical biomarkers with efficacy and toxicity of cancer immunotherapy

Abstract

Keywords

Introduction

Cancer cell-derived biomarkers

PD-L1

Tumor mutational burden

Deficient mismatch repair and microsatellite instability-high

DNA damage repair-related genes

Genetic variation

Tumor microenvironment-derived biomarkers

Tumor-infiltrating lymphocytes

Super-exhausted T cells

HLA-I

Host-derived biomarkers

Microbiome and antibiotics

Performance status

BMI

Age

Autoimmune disease history

Viral infection

Corticosteroids

Imaging examination

Peripheral blood biomarkers

Peripheral blood cell count

CtDNA, cfDNA, and cfMeDIP

Cytokines and chemokines

Peripheral blood biochemical index

Multi-modal models and artificial intelligence assessment

Relationship between efficacy and irAEs

Conclusion and prospect

Footnotes

Acknowledgements

Declarations

References