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Analysis of gene expression levels and their impact on survival in 31 cancer-types patients identifies novel prognostic markers and suggests unexplored immunotherapy treatment options in a wide range of malignancies

Abstract

Background

Immunotherapy has dramatically improved cancer treatment by inhibiting or activating specific cell receptors, thus unleashing the host anti-tumor response. However, the engagement of the three main immune checkpoints so far identified, CTLA4, PD-1 and PD-L1, is effective in a fraction of patients, therefore novel targets must be identified and tested.

Methods

We focused our attention on the following nine highly relevant immune checkpoint (ICR) receptors: CTLA4, PD1, PD-L1, LAG3, TIM3, OX40, GITR, 4-1BB and TIGIT. All of them are targets of existing drugs currently under clinical scrutiny in several malignancies. Their expression levels were evaluated in patient tissues of 31 different cancer types vs. proper controls, in a total of 15,038 individuals. This analysis was carried out by interrogating public databases available on GEPIA2 portal and UALCAN portal. By the Principal Component Analysis (PCA) their ability to effectively discriminate patients form controls was then investigated. Expression of the nine ICRs was also related to overall survival in 31 cancer types and expressed as Hazard Ratio, on the GEPIA2 portal and validated, for melanoma patients, in patients-datasets available on PROGgene V2 portal.

Results

Significant differential expression was observed for each ICR molecule in many cancer types. A 7-molecules profile was found to specifically discriminate melanoma patients from controls, while two different 6-molecules profiles discriminate pancreatic cancer patients and Testicular Germ Cell Tumors from matched controls. Highly significant survival improvement was found to be related to the expression levels of all nine ICRs in a wide spectrum of malignancies. For melanoma analysis, the relation with survival observed in TCGA datasets was validated in independent GSE melanoma datasets.

Conclusion

Analysis the nine ICR molecules demonstrates that their expression patterns may be considered as markers of disease and strong survival predictors in a variety of malignancies frequently associated to poor prognosis. Thus, the present findings are strongly advocating that exploratory clinical trials are worth to be performed, using available drugs, targeting these molecules.

Background

Developing immunotherapy treatments based on the activation or inhibition of molecules that orchestrate the host immune response is opening new horizons in the treatment options. Indeed, inhibitory- or agonistic- drugs targeting these molecules are currently under common use or scrutiny in several clinical trials with impressive results in different cancer types [1,2,3,4]) and have substantially modified survival in an increasing fraction of patients bearing different tumor types, with less severe side effects [5,6,7]. The most important inhibitory targets so far identified are CTL4 [8, 9], PD-1 and its ligand PD-L1 (CD-274) [10, 11]; many other co-inhibitory molecules were identified such as Adenosine A2A receptors [12], B7-H3 (CD-276) [13], B7-H4 (VTCN1) [14], LAG3, TIM3, and TIGIT [15, 16], as well as co-stimulatory molecules such as CD27 [17], CD28 [18], CD40 [19], OX40 (TNFRSF4), GITR (TNFRSF18), 41BB (TNFRSF9) [20,21,22]. Despite the remarkable results obtained so far, new targets and new drugs need to be developed and tested, since many patients do not respond to the treatments or even developed resistance in some cases.

One way to identify cancer markers or potential therapeutic targets is to analyze the expression level of potential targets in the blood as well in tumors vs. normal tissue counterparts. By using this approach, the aim of the present study was to identify new prognostic markers able to predict response to therapy, or new potential targets of currently available treatments. Since all molecules under investigation in the present study are either targets of existing drugs, or under clinical investigation, the results collected in the present study may be of timely translational relevance. Based on our previous experience in the field of melanoma markers- and therapeutic targets identification [23,24,25,26,27,28,29,30,31,32], in the present study we selected nine molecules referred to as immune checkpoint related (ICR) molecules (namely: inhibitory targets such as CTLA4, PD-1, PD-L1, LAG3, TIM3, and TIGIT, and co-stimulatory molecules such as OX40 (TNFRSF4), GITR (TNFRSF18), 4-1BB (TNFRSF9). All such ICRs are relevant players in the immune-response control and are all targets of existing drugs under clinical use or clinical investigation in several cancers. The gene expression levels of the ICR were then evaluated in cancer vs. control patients, in 31 different cancer types, in a total of 15,038 individuals, and relation to diseases state and patient survival was investigated.

Methods

The specific aim of the current study was to select novel molecular targets showing, on the base of their gene-expression levels, a statistically significant ability to discriminate patients from healthy controls and to predict prognosis and response to therapy. Expression data and survival data analysis were carried out on public portals for cancer patient data, namely GEPIA2 portal (at http://gepia2.cancer-pku.cn/#index), PROGgene V2 portal (at http://www.progtools.net/gene/index.php), UALCAN portal (at http://ualcan.path.uab.edu/analysis.html) and EBI expression atlas at https://www.ebi.ac.uk/gxa/home.

The list of 31 different cancer types under investigation is reported in Table 1, indicating for each cancer type: the full name, the acronym, and the patients-sample numerosity. Cancer types were the ones investigated in GEPIA2 cancer portal which refers to TCGA datasets and GTEx datasets.

Table 1 The list of 31 different cancer types under investigation, indicating the acronyms and the sample numerosity for each cancer type and for each corresponding control

Expression levels of the ICR molecules CTL4, PD-1 (PCD1), PD-L1 (CD274), LAG3, TIM3 (HAVCR2), OX40 (TNFRSF4), GITR (TNFRSF18), 4-1BB (TNFRSF9) and TIGIT were addressed in biopsies of each cancer type vs. the matched healthy tissue, with a significance threshold set at p < 0.0001, according to the tool: Expression analysis / Expression DIY/ Box plot, in GEPIA2 portal.

The principal component analysis (PCA) was carried out at the GEPIA2 portal according to the tool “Dimensionality reduction”, retrieving the 2D plots. Expression of the ICR molecules in the metastatic phase vs. primary tumor, in melanoma patients, was investigated in the cancer portal UALCAN (at http://ualcan.path.uab.edu/analysis.html).

Survival analysis was carried out at GEPIA2 portal using the tool: “Survival analysis”, on Overall Survival data and Median expression, retrieving Hazar Ration (HR) with 95% confidence interval.

Validation of expression data in melanoma human samples for the nine ICRs was achieved by accessing data on the EBI Expression Atlas at https://www.ebi.ac.uk/gxa/home.

Validation of survival data in melanoma patients was achieved on GSE datasets, (i.e. datasets independent from the TCGA datasets used by GEPIA2), at the PROGgene V2 portal (http://www.progtools.net/gene/index.php).

Statistical analyses implemented within the GEPIA2, PROGgene V2 and UALCAN portals were exploited. Significance threshold of p < 0.0001 was chosen.

Results

Expression levels of nine immune checkpoint-related (ICR) molecules in 31 cancer types

In the present study we addressed the role Immune Checkpoint-Related (ICR) molecules, either as markers of disease or as markers of response to therapy/therapeutic targets, in 31 cancer types listed in Table 1. This analysis was carried out on expression data derived from the public cancer portal GEPIA2 (http://gepia2.cancer-pku.cn/), which refers to TCGA and GTEx datasets. Namely, CTL4, PD-1 (PCD1), PD-L1 (CD274), LAG3, TIM3 (HAVCR2), OX40 (TNFRSF4), GITR (TNFRSF18), 4-1BB (TNFRSF9) and TIGIT were investigated. Selection of these nine ICR molecules was based on a preliminary analysis, indicating that the selected ICR molecules are all currently investigated in clinical trials involving cancer and non-cancer patients (see Table 1 not shown).

Additional file 1: Figures S1 to S9 show expression levels of the ICR molecules in 31 cancer types vs. the corresponding controls, in 9498 cancer samples and 5540 matched-healthy controls. Numerosity of cancer samples and corresponding controls for each cancer type is reported in Table 1. Boxes in Additional file 1: Figures S1 to S9 highlight the cases where a significantly different expression (p < 0.0001) is found in the given cancer vs. the corresponding controls. Data reported in Additional file 1: Figures S1 to S9 are summarized in Fig. 1 reporting the expression data in a heatmap modality. Cancer types are sorted vertically from the top (higher number of significant differences observed) to the bottom (lower number of significant differences observed). ICR molecules are sorted horizontally from the left (higher number of significant differences found) to the right (lower number of significant differences). Red spots indicate significant up-regulation and blue spots indicate significant down-regulation. Figure 1 shows that Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (DLBC) is at the top first position since DLBC patients show expression of all nine molecules up-regulated in cancer vs. controls. Skin Cutaneous Melanoma (SKCM) is at the second top position, since SKCM patients show 7 out 9 molecules up-regulated. Such 7 molecules represent the “Profile-A” investigated later in Fig. 2A. Pancreatic Adenocarcinoma (PAAD) and Testicular Germ Cell Tumors (TGCT) are at the third and fourth top position, since PAAD and TGCT patients show 6 out of 9 molecules up-regulated. Such two 6-molecules combinations are the “Profile-B” and “Profile-C”, investigated later in Fig. 2B, C. Figure 1 then reports all other cancer types showing 5, 4, 3, 2, 1 and 0 ICR molecules differently expressed, according to the vertical sort. It is noteworthy that 23 cancer types out of 31 show at least 1 significantly different expression. ICRs expression data reported in Fig. 1 and Additional file 1: Figures S1 to S9 were from TCGA datasets analyzed in GEPIA2. Expression data of ICRs in melanoma, in pancreatic adenocarcinoma and in thyroid carcinoma were validated in the EBI expression atlas, from independent datasets, as reported in Additional file 1: Table S3 not shown, Additional file 1: Table S4 not shown and Additional file 1: Table S5 not shown, respectively.

Fig. 1
figure 1

Immune-check point molecules differently expressed in different cancer-types. Red spots indicate significant up-regulation; blue spots indicate significant down-regulation

Fig. 2
figure 2

Principal Component Analysis of the Profile-A, Profile-B and Profile-C in SKCM, PAAD and TGCT cancers, respectively. The 3 profiles effectively discriminate patients from controls, in SKCM, PAAD and TGCT, respectively, while they are ineffective in LUAD, PRAD and THCA cancers

Figure 1 also shows that TIM3 (HAVCR2) and OX40 (TNFRSF4) are at the most left positions; they are differently expressed in 15 and 14 cancers types respectively, out of 31, followed by GITR (TNFRSF18), LAG3 and TIGIT, differently expressed in 12 and 10 and 9 cancers, respectively. The other ICR molecules are then reported according to the left-to-right horizontal sort.

The large number of significant differences reported in Fig. 1 indicate that such ICR molecules may have a potential role in several cancer types, either as diagnostic/prognostic markers or as therapeutic targets. We then hypothesized that ICRs expression may represent a profile able to effectively discriminate cancer samples from the corresponding healthy controls. This hypothesis was verified using the Dimensionality Reduction tool of GEPIA2, which performs a PCA analysis on the combined expression data. PCA analysis of all nine ICRs in Lymphoid Neoplasm Diffuse Large B-cell Lymphoma patients (DLBC) was not possible, due to the lack of a proper control dataset to be used in PCA calculation. However, PCA analysis of the “Profile-A” was possible in melanoma (SKCM) patients. In fact, Fig. 2A shows that the 7 molecules differently expressed in SKCM samples (i.e., TIM3 (HAVCR2), OX40 (TNFRSF4), GITR (TNFRRSF18), TIGIT, LAG3, PD-1 (PDCD1) and CTL4), show a clear separation of patients from the corresponding controls. Figure 2A also shows that Profile-A is not able to discriminate LUAD, PRAD and THCA cancer patients from the corresponding controls, indicating that such profile appears to be specific for melanoma patients (SKCM).

“Profile-B” is made by the 6 molecules differently expressed in Pancreatic adenocarcinoma (PAAD) patients (i.e., TIM3 (HAVCR2), OX40 (TNFRSF4), GITR (TNFRRSF18), TIGIT, LAG3 and CTLA4). PCA analysis of Profile-B expression shows a clear separation of PAAD patients form the corresponding controls (Fig. 2B), while the same profile is not able to discriminate LUAD, PRAD and THCA cancer patients from the corresponding controls, indicating that such profile appears to be specific for PAAD patients. Similarly, when the “Profile-C” expression (i.e., TIM3 (HAVCR2), OX40 (TNFRSF4), GITR (TNFRSF18), TIGIT, LAG3 and PDCD1) was investigated in Testicular Germ Cells Tumor (TGCT) samples, a clear separation from the corresponding controls was found (Fig. 2C), while the same profile does not discriminate LUAD, PRAD and THCA cancer patients from the corresponding controls, indicating that such profile appears to be specific for TGCT patients. Figure 2 shows that the 7 molecules differently expressed in SKCM, and the six molecules differently expressed in PAAD and in TGCT are specific expression-profiles able to clearly discriminate cancers form controls samples, according to PCA analysis, showing a relevant marker potential for specific cancer types.

We then investigated the role the nine ICR molecules may play in the metastatic phase of melanoma, by accessing the expression data in 104 melanoma primary patients vs. 368 melanoma metastatic patients, at the UALCAN public portal (http://ualcan.path.uab.edu/analysis.html). All ICRs investigated, except GITR (TNFRSF18), show a significantly different expression in primary vs. metastatic patients, as reported in Fig. 3, suggesting that such molecules (or drugs targeting these molecules) may have a role in metastatic melanoma diagnosis or control.

Fig. 3
figure 3

Gene expression levels of the 9 ICRs in primary (N = 104) and metastatic (N = 368) melanoma patients

Patients survival analysis

We then addressed the hypothesis that the nine ICRs may represent potential suitable therapeutic targets or markers effectively predicting prognosis or response to therapy. We investigated such hypothesis by analyzing how their expression relates to patient survival. This analysis may indicate molecules with a potential therapeutic application, irrespective of their marker ability to identify cancer patients. Table 2 reports survival data expressed as Hazard Ratio (HR) as a function of expression values of each ICR molecules, according to GEPIA2 database. Expression levels were categorized as “high-level” (i.e., above the median value) and “low-level” (i.e., below the median value). In Table 2 only HR ≤ 0.7 or ≥ 1.5 are reported, where HR < 1 indicates improved survival in high-level expressing patients, while HR > 1 indicates improved survival in low-level expressing patients. Data in Table 2 are both vertically and horizontally sorted, similarly to Fig. 1. Surprisingly, horizontal sorting shows that in Skin Cancer Melanoma (SKCM), in Testicular Germ Cell Tumors (TGCT) and in Thymoma (THYM) the expression of all nine ICR molecules is associated with relevant survival improvement. In TGCT and THYM, survival improvement is almost invariantly associated with low-expression levels of all ICRs except PD-1 (PDCD1), while improved survival in SKCM is always associated with high expression levels of all nine molecules. Notably, SKCM and TGCT are in the very top positions both in Fig. 1 (expression data) and in Table 2 (survival data).

Table 2 Survival Hazard Ratio (HR) calculated according to expression values of 9 ICR molecules (only HR ≤ 0.7 ≥ 1.5 are reported)

On the other hand, Table 2 shows that the highest number of cancer types with improved survival associates with expression levels of TIM3 and OX40 (14 out of 31 cancer types and 13 out of 31 cancer types, respectively). Most interestingly, TIM3, OX40 and TIGIT are in the very left positions both in expression and in survival analyses (Fig. 1 and Table 3, respectively).

Table 3 Survival in melanoma patients, according to the expression values, in melanoma GSE datasets (http://www.progtools.net/gene/index.php)

Table 2 shows that in several cases HR was found to be smaller than 0.5 or largely higher than 1.5, suggesting a very strong association of the expression to the survival, as for instance for PD-1 (PDCD1) expression in Uterine Corpus Endometrial Carcinoma (UCEC) (see the corresponding Kaplan–Meier plots in Fig. 4A), or for TIGIT expression in Thymoma (THYM) (see the corresponding Kaplan–Meier plots in Fig. 4B). Interestingly, Fig. 4 shows a striking complete survival in patients expressing low-levels of TIGIT as compared to high-levels expressing patients showing a time-dependent loss of survival. Such data indicate that drugs inhibiting TIGIT may exert a potential strong protective role (Fig. 4B) and that PD-1 agonists may have an impact on UCEC patients-survival (Fig. 4A).

Fig. 4
figure 4

Kaplan–Meier plots indicating survival in UCEC patients (A) and in THYM patients (B) associated with high- and low-expression levels of PD-1 and TIGIT, respectively. Continuous lines indicate high-level expressing patients; dashed lines indicate low-level expressing patients

Data reported above indicate that expression of several molecules associate with strong survival improvement in melanoma, in Testicular Germ Cell Tumors, in Thymoma as well as in other cancer types, introducing the hypothesis that targeting such molecules may represent an effective therapeutic option in different cancer types.

Validation of survival data

The nine ICRs showing a relevant survival association in melanoma patients according to data reported in TCGA datasets (Table 2), were all validated by analyzing independent GSE datasets, at the PROGgene V2 portal (http://www.progtools.net/gene/index.php). Figure 5 reports the corresponding Kaplan–Meier survival plots and Table 3 reports the corresponding numerical analysis.

Fig. 5
figure 5

Kaplan–Meier plots indicating survival in melanoma patients according to data reported in PROGgene V2 portal (http://www.progtools.net/gene/index.php). Continuous lines indicate high-level expressing patients; dashed lines indicate low-level expressing patients

Validation achieved on GSE datasets confirm the interest of all such molecules as potential therapeutic targets in melanoma patients.

Discussion

Immunotherapy represents one of the most important advancements in the cancer therapy field, since inhibitors of immune checkpoints CTLA4, PD-1 and PD-L1 dramatically improved therapeutic results [33]. However, as underlined [34], despite the strong positive impact of these drugs in many patients, many others do not receive relevant advantages, pointing toward the need to identify additional targets and drugs. Furthermore, as reported in Table 2 not shown, the Clinicaltrials.gov portal reports several “ongoing” or “completed” immunotherapy-based clinical trials addressing melanoma, lung cancers, breast cancers, and renal cancers, while only a few clinical immunotherapy-based trials are reported for other cancers such as testis cancers, adrenocortical cancers, thymoma, pheochromocytoma and paraganglioma, and thyroid cancers. Cancers response to immunotherapy has been shown to be related to their immune landscape, i.e., “active”, or “deserted” or “excluded” immune landscape [35] or to a simpler two-levels landscape, i.e., immune-active and immune-silent background [36]. The immune landscape relates to the balance between immune-activating and immune-suppressing mechanisms, ultimately controlling tumor-escape, as recently investigated in cancers undergoing anti-CTLA4 and anti PD-1 antibodies treatment [37]. The presence of the immune-active or -silent environment is related to five mechanisms (i.e. tissue-specific destruction, autoimmunity, tumor rejection, pathogen clearance, allograft rejection) requiring the coordinated involvement of three major players, i.e., T cell response, self- non-self-recognition and innate immunity [38]. Indeed, nineteen upstream molecular mechanisms and signatures control the way cancers comply or escape to the immune response, including WNT/βCatenin, MAPK, Immunogenic Cell Death, Regulatory T cells, IL23-Th17 axis, PI3K signature, SHC1 signature, IDO/NOS signature and others [36].

Besides CTLA4, PD-1 and PD-L1, several additional immune checkpoints related (ICR) molecules have been identified such as LAG3, TIM3 (HAVCR2), OX40 (TNFRSF4), GITR (TNFRSF18), 4-1BB (TNFRSF9), TIGIT, currently under active clinical investigation. The present study aimed at obtaining clinical relevant information by addressing whether prognostic correlations exist between expression of ICR molecules and patients’ survival, as also investigated in previous studies [39, 40]. We hypothesized that the nine molecules investigated, while being immune-check point related molecules all strongly related to each other, may be also involved in many different functions which coordinately converge toward the control of cancer growth. This hypothesis was supported by a preliminary interactome analysis with STRING (at https://string-db.org/), which indicated functional/molecular interactions of the ICRs with the previously mentioned models controlling immune response to cancers [36] and with the signature underlying the Immunologic Constant of Rejection, which controls innate and adaptative immune effectors-mediated tissue-specific destruction, autoimmunity, pathogen-bearing cells clearance, acute allograft rejection, and rejection of cancer [36, 38]. Namely, STRING analysis found functional/molecular interactions between CTLA4 and βCatenin, between PD-L1 and βCatenin, between CTLA4 and IL23, between PD-L1 and SHC1, between PD-L1 and PI3K, between OX40 and NOS, between GITR and NOS, thus suggesting some nodes functionally linking ICRs to the Immunologic Constant of Rejection.

The novelty of this study is related to the strong translational potential of the results, supported by: (a) the nine molecules investigated are all targets of drugs already under clinical evaluation; (b) a large representative number of samples (15,038 individuals) was studied; (c) the screening phase performed on TCGA datasets, was paralleled, when possible, by a validation phase in independent cancer datasets; (d) a more in-depth validation was performed in melanoma. As a possible limitation, the current analysis does not consider prognostic factors such as age or gender or tumor-stage, which refer to data unavailable in the GEPIA2 portal. However, analyses here reported provide clinically informative results. Namely, gene expression level of all nine ICRs is significantly changed in Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (DLBC) patients; expression of 7 out of 9 ICRs is significantly modified in melanoma (SKCM) while expression of 6 out of 9 is altered in Pancreatic adenocarcinoma (PAAD) and in Testicular Germ Cell Tumors TGCT patients. Such data (reported in Fig. 1 and Additional file 1: Figures S1 to S9), suggest that these molecules may represent markers profiles (Profile-A, Profile-B and Profile-C), able to effectively discriminate patients from controls. This hypothesis was supported by PCA analysis reported in Fig. 2, indicating the actual ability of the 3 profiles to discriminate SKCM, PAAD and TGCT from controls, respectively, as well as their in-ability to discriminate LUAD, PAAD and THCA cancers patients from controls. Further, according to Fig. 3, expression levels of all nine ICRs (except GITR) may also represent a hallmark of metastatic phase in melanoma patients.

Analyses evaluating the overall survival, based on the gene expression values in patients irrespective of the expression values in controls, identified potential therapeutic targets or markers able to predict response to therapy. Survival analysis revealed relevant data in several cancer types and were validated in melanoma patients in independents datasets (see Fig. 5).

ICRs showing the highest survival impact in several cancer types are individually discussed below.

CTLA4 (Cytotoxic T-Lymphocyte Associated Protein 4) is one of the most important negative regulators of T cells and one of the main inhibitory immune checkpoints. It is the target of a few drugs such as ipilimumab and tremelimumab. Clinical indications of CTLA4 inhibitors, often in combination with PD-1/PD-L1 inhibitors, include several advanced or metastatic cancers [41, 42]. Survival data reported in Table 3 of the current study indicate that low CTLA4 expression levels associate with strong survival in TCGT and in THYM patients (HR > 10 and HR = 5, respectively), thus suggesting that CTLA4 inhibitors may have beneficial effects in these malignancies. To date (June 2022) a very limited number of studies is reported in Clinicaltrials.gov portal, investigating CTLA4 inhibitors in testis cancers or in thymic cancer patients.

PD-1 (PDCD1) (Programmed Cell Death Protein 1) is another key inhibitory receptor expressed on T-cells, inducing immune tolerance. It is the target of antibodies drugs such as nivolumab, pembrolizumab, sintilimab, which reinforce the immune response by inhibiting PD-1. Clinical indications of PD-1 inhibitors include several cancers in advanced stage [43, 44]. Table 2 indicates that low PD-1 (PDCD1) expression levels associate with strong survival improvement in Testicular Germ Cell Tumors (TCGT), Brain Lower Grade Glioma (LGG) and Acute Myeloid Leukemia (LAML), (HR 2, 1.9 and 2.2, respectively), thus suggesting that drugs inhibiting this molecule may be beneficial in such patients. While several studies are reported addressing the effect of PD-1 (PDCD1) inhibitors in gliomas and leukemia patients, to date only one study is reported in Clinicaltrials.gov investigating PD-1 inhibitors (namely nivolumab) in testis cancers [45]. On the other hand, PD-1 (PDCD1) high-levels expression associate with substantially improved survival in a few other cancer types, such as UCEC, as reported in Table 2 and Fig. 2A, therefore we suggest that PD-1 (PDCD1) agonists may have beneficial effects in these patients.

PD-L1 (CD274) (Programmed Cell Death 1 Ligand 1) is another key T cells negative regulator able to induce immune tolerance. PD-L1 inhibiting drugs, such as atezolizumab, durvalumab and avelumab, are currently used in cancer therapy to burst the immune response. Clinical indications of PD-L1 inhibitors include several advanced solid and blood cancers [46]. Table 2 of the current study indicates that low expression levels of PD-L1 (CD274) associate with strong survival improvement in TGCT, THYM, LGG, DLBC and LAML, suggesting that inhibitors of this molecule may have a beneficial effect in these tumors. Consistently with these data, several clinical trials are reported in Clinicaltrials.gov on PD-L1 and glioma, or lymphoma, or leukemia. On the contrary, only a few studies are reported on other cancer types, namely testis cancer and thymoma.

LAG3 (Lymphocyte Activating 3) is a key immune checkpoint molecule negatively controlling immune response in cancer, infectious diseases and autoimmunity [47, 48]. It is the target of different drugs including relatlimab, an anti-LAG3 antibody recently approved by FDA for clinical use in melanoma, in combination with nivolumab. In fact, results of the RELATIVITY-047 study [49] indicate a significant improvement of Progression-Free Survival (PFS) in melanoma patients treated with nivolumab + relatlimab, where the relatlimab combination with anti-PD-1 drugs is a strategy to overcome drug resistance [50]. Table 2 of the present study indicates that low expression levels of LAG3 is associated with favorable survival in several cancer types including TGCT, THYM, KIRC, KIRP, LGG, ACC, LAML and ESCA. This suggests that LAG3 inhibitors may have a therapeutic potential in these cancers. Nevertheless, Clinicaltrials.gov portal reports only a few ongoing studies regarding LAG3 inhibitors in renal and esophageal tumors, in glioma, in leukemia, in testis cancer. No studies at all are reported on LAG3 and thymoma, nor LAG3 and adrenocortical carcinoma.

TIM3 (HAVCR2) (Hepatitis A Virus Cellular Receptor 2) is a key checkpoint molecule inhibiting the innate and adaptive immune response in cancer. It is the target of LY-3321367, an anti-TIM3 (HAVCR2) antibody. It is currently investigated in solid tumors under progression (such as NSCLC, gastric and urothelial carcinoma [51] and it is suggested as a potential therapeutic target in Acute Myeloid Leukemia [52]. Table 2 indicates that TIM3 (HAVCR2) low expression levels associate with improved survival in TGCT, THYM and LGG, thus suggesting the use of TIM3 (HAVCR2) inhibitors in such patients. Nevertheless, only a few studies investigate TIM3 (HAVCR2) inhibitors in glioma, testis cancer, and thymoma. Alternatively, consistently with the reported dual role of TIM3 (HAVCR2) [53], survival data in Table 2 also indicate that high expression levels of TIM3 (HAVCR2) associate with improved survival in 10 cancer types, including SKCM, KIRC, ACC, DLBC, UCEC, CHOL and others. This may suggest that TIM3 (HAVCR2) agonists may have a role in these cancer types. Nevertheless, according to the Clinicaltrials.gov portal, TIM3 (HAVCR2) related drugs are poorly investigated in kidney cancers, in adrenocortical cancer, in uterus cancer and in cholangiocarcinoma.

OX40 (Tumor necrosis factor receptor superfamily, member 4 TNFRSF4) is a costimulatory immune checkpoint molecule. OX40 agonistic monoclonal antibodies recognize OX40, leading to T cell activation. Several OX40 (TNFRSF4) targeting molecules, such as ivuxolimab, vonlerolizumab and tavolimab, show in clinical trials preliminary anti-cancer action in locally advanced or metastatic cancers [54]. According to Table 2 of the present study, OX40 (TNFRSF4) expression levels associate with an improved survival in as many as 14 different cancer types. Improved survival is observed in high-level expressing patients of SKCM, CESC, HNASC, PAAD, PCPG, and PRAD, therefore, OX40 (TNFRSF4) agonists may be useful in such patients. On the contrary, improved survival is observed in low-levels expressing patients of TGCT, THYM, LGG, ACC, KICH, KIRP, THCA and LIHC, suggesting the use of OX40 (TNFRSF4) antagonists in these patients. However, according to clinicaltrials.gov portal, almost no studies are reported for OX40 (TNFRSF4) related molecules in pancreas cancers, prostate cancers, pheochromocytoma cancers, testis cancer, thymoma, glioma, thyroid cancers nor in adrenocortical cancers. Considering the poor prognosis of most of these cancers, these data may represent a valuable indication for future clinical studies.

TIGIT is a T-cell immunoreceptor with Ig and immunoreceptor tyrosine-based inhibitory motif (ITIM), with negative regulatory action on T-cells. The use of a TGIT inhibitor is currently evaluated in glioblastoma, glioma, and other advanced solid tumors [55]. According to Table 2, TIGIT expression levels associate with significantly improved survival in 10 out of 31 cancer types. In most cases, survival is improved in high expression-levels patients, for instance in SKCM, ACC, PRAD, READ, UCEC or BRCA. TIGIT inhibitors may therefore worth to be tested in these patients. However, Clinicaltrials.gov reports very few or no studies on TIGIT targeting drugs in prostate cancers, nor in adrenocortical carcinoma, nor rectum cancer, endometrial cancers, breast cancers. The strong survival improvement associated with TIGIT expression levels (reported in Table 2 and in Fig. 4B) suggests that TIGIT-inhibitors drugs may be clinically beneficial in thymoma and Testicular Germ Cell Tumors, while TIGIT-agonists may improve survival in SKCM, READ, UCEC, ACC, BRCA, CESC, PRAD and HNSC patients. Presently, only 3 clinical trials are reported on TIGIT-related drugs in lymphoma, and no studies relate TIGIT drugs to thymoma nor to testis cancers.

GITR (TNFRSF18) (TNF Receptor Superfamily Member 18) is a key positive regulator of the immune response. Agonists drugs of this molecule, such as TRX-518, are likely to improve immune response against cancers [56]. Survival data of Table 2 indicate cancer types where high-expression levels of GITR associate with strongly improved survival, namely SKCM, KICH, READ, BRCA, CHOL and PCPG. On the other hand, low-expression levels of GITR associate with strongly improved survival in TGCT, THYM and KIRC. Therefore, GITR inhibitors and GITR agonists may have interesting effects in several cancers, while Clinicaltrials.gov portal shows no clinical trials on GITR-related drugs in cholangiocarcinoma, pheochromocytoma, testis cancers nor in thymoma.

4-1BB (TNFRSF9) (TNF Receptor Superfamily Member 9) is a costimulatory molecule, key controller of immune response. It is the target of different activating or inhibitory monoclonal antibodies, such as urelumab and utomilumab, respectively. They are both currently under investigation in blood- as well as solid malignancies. Table 2 indicates an impressive survival improvement related to 4-1BB expression levels, in several cancer types; consistently, several studies are reported on Clinicaltrials.gov, but very few are reported on Glioma/Glioblastoma, and none on testis cancer nor on Thymoma.

In conclusion, the expression levels of the nine ICR molecules investigated in the present study show strong and significant differences in several cancers vs. controls, as well as relevant association with overall survival, in several cancer types. In many of these malignancies no or very-few studies are reported regarding drugs targeting these molecules, as for instance in testis cancer, thymoma, gliomas, adrenocortical carcinoma, cholangiocarcinoma. Data reported in the present study indicate potential prognostic markers and may suggest that several drugs, already under clinical use or under clinical evaluation, may have relevance in cancer types such as testis cancer, thymoma, glioma, cholangiocarcinoma, pheochromocytoma, adrenocortical cancer, kidney cancer and uterus cancers. The cartoon reported in Fig. 6 summarizes FDA-approved immunotherapy treatments according to AACR Cancer Progress Report 2021 [57] (Fig. 6A), while cancers and targets selected in the present study are reported in Fig. 6B.

Fig. 6
figure 6

Immunotherapy based treatments approved by FDA (A) and new options and molecular targets selected in the present study (B)

Availability of data and materials

All data analyzed come from public databases, available at links clearly indicated in the manuscript.

References

  1. Mar N, Uchio E, Kalebasty AR. Use of immunotherapy in clinical management of genitourinary cancers - a review. Cancer Treat Res Commun. 2022. https://doi.org/10.1016/j.ctarc.2022.100564.

    Article  PubMed  Google Scholar 

  2. Kang S, El-Rayes BF, Akce M. Evolving role of immunotherapy in advanced biliary tract cancers. Cancers (Basel). 2022. https://doi.org/10.3390/cancers14071748.

    Article  PubMed Central  Google Scholar 

  3. Hu LF, Lan HR, Huang D, Li XM, Jin KT. Personalized immunotherapy in colorectal cancers: where do we stand? Front Oncol. 2021. https://doi.org/10.3389/fonc.2021.769305.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Huang AC, Zappasodi R. A decade of checkpoint blockade immunotherapy in melanoma: understanding the molecular basis for immune sensitivity and resistance. Nat Immunol. 2022. https://doi.org/10.1038/s41590-022-01141-1.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Gupta SL, Basu S, Soni V, Jaiswal RK. Immunotherapy: an alternative promising therapeutic approach against cancers. Mol Biol Rep. 2022. https://doi.org/10.1007/s11033-022-07525-8.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Halima A, Vuong W, Chan TA. Next-generation sequencing: unraveling genetic mechanisms that shape cancer immunotherapy efficacy. J Clin Invest. 2022. https://doi.org/10.1172/JCI154945.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Rahman MM, Behl T, Islam MR, Alam MN, Islam MM, Albarrati A, Albratty M, Meraya AM, Bungau SG. Emerging management approach for the adverse events of immunotherapy of cancer. Molecules. 2022. https://doi.org/10.3390/molecules27123798.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Lisi L, Lacal PM, Martire M, Navarra P, Graziani G. Clinical experience with CTLA-4 blockade for cancer immunotherapy: From the monospecific monoclonal antibody ipilimumab to probodies and bispecific molecules targeting the tumor microenvironment. Pharmacol Res. 2022. https://doi.org/10.1016/j.phrs.2021.105997.

    Article  PubMed  Google Scholar 

  9. Sobhani N, Tardiel-Cyril DR, Davtyan A, Generali D, Roudi R, Li Y. CTLA-4 in Regulatory T Cells for Cancer Immunotherapy. Cancers (Basel). 2021. https://doi.org/10.3390/cancers13061440.

    Article  Google Scholar 

  10. Borst J, Busselaar J, Bosma DMT, Ossendorp F. Mechanism of action of PD-1 receptor/ligand targeted cancer immunotherapy. Eur J Immunol. 2021. https://doi.org/10.1002/eji.202048994.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Gou Q, Dong C, Xu H, Khan B, Jin J, Liu Q, et al. PD-L1 degradation pathway and immunotherapy for Cancer. Cell Death Dis. 2020;11(11):955.

    Article  CAS  Google Scholar 

  12. Leone RD, Lo YC, Powell JD. A2aR antagonists: Next generation checkpoint blockade for cancer immunotherapy. Comput Struct Biotechnol J. 2015. https://doi.org/10.1016/j.csbj.2015.03.008.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Leitner J, Klauser C, Pickl WF, Stöckl J, Majdic O, Bardet AF, et al. B7–H3 is a potent inhibitor of human T-cell activation: No evidence for B7–H3 and TREML2 interaction. Eur J Immunol. 2009. https://doi.org/10.1002/eji.200839028.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Dangaj D, Lanitis E, Zhao A, Joshi S, Cheng Y, Sandaltzopoulos R, et al. Novel recombinant human b7–h4 antibodies overcome tumoral immune escape to potentiate T-cell antitumor responses. Can Res. 2013. https://doi.org/10.1158/0008-5472.can-12-3457.

    Article  Google Scholar 

  15. Anderson AC, Joller N, Kuchroo VK. Lag-3, Tim-3, and TIGIT: Co-inhibitory Receptors with Specialized Functions in Immune Regulation. Immunity. 2016. https://doi.org/10.1016/j.immuni.2016.05.001.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Dai E, Zhu Z, Wahed S, Qu Z, Walter J, Storkus WJ, et al. Epigenetic modulation of antitumor immunity for improved cancer immunotherapy. Mol Cancer. 2021. https://doi.org/10.1186/s12943-021-01464-x.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Coquet JM, Middendorp S, van der Horst G, Kind J, Veraar EA, Xiao Y, et al. The CD27 and CD70 costimulatory pathway inhibits effector function of T helper 17 cells and attenuates associated autoimmunity. Immunity. 2013. https://doi.org/10.1016/j.immuni.2012.09.009.PMID23159439).

    Article  PubMed  Google Scholar 

  18. Eastwood D, Findlay L, Poole S, Bird C, Wadhwa M, Moore M, et al. Monoclonal antibody TGN1412 trial failure explained by species differences in CD28 expression on CD4+ effector memory T-cells. Br J Pharmacol. 2010. https://doi.org/10.1111/j.1476-5381.2010.00922.x.

    Article  PubMed  PubMed Central  Google Scholar 

  19. O’Sullivan B, Thomas R. CD40 and dendritic cell function. Crit Rev Immunol. 2003. https://doi.org/10.1615/critrevimmunol.v23.i12.50.

    Article  PubMed  Google Scholar 

  20. Brennick CA, George MM, Corwin WL, Srivastava PK, Ebrahimi-Nik H. Neoepitopes as cancer immunotherapy targets: key challenges and opportunities. Immunotherapy. 2017. https://doi.org/10.2217/imt-2016-0146.

    Article  PubMed  Google Scholar 

  21. Kumar P, Arbieva ZH, Maienschein-Cline M, Ganesh BB, Ramasamy S, Prabhakar BS. Induction of Antigen-Independent Proliferation of Regulatory T-Cells by TNF Superfamily Ligands OX40L and GITRL. Methods Mol Biol. 2021. https://doi.org/10.1007/978-1-0716-1130-2_4.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Suzuki S, Ogawa T, Sano R, Takahara T, Inukai D, Akira S, et al. Immune-checkpoint molecules on regulatory T-cells as a potential therapeutic target in head and neck squamous cell cancers. Cancer Sci. 2020. https://doi.org/10.1111/cas.14422.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Arcangelo D, Scatozza F, Giampietri C, Marchetti P, Facchiano F, Facchiano A. Ion Channel Expression in Human Melanoma Samples: In Silico Identification and Experimental Validation of Molecular Targets. Cancers. 2019. Doi https://doi.org/10.3390/cancers11040446.

  24. Biasiotta A, D’Arcangelo D, Passarelli F, Nicodemi EM, Facchiano A. Ion channels expression and function are strongly modified in solid tumors and vascular malformations. J Transl Med. 2016. https://doi.org/10.1186/s12967-016-1038-y.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Scatozza F, D’Arcangelo D, Giampietri C, Facchiano F, Facchiano A. Melanogenesis and autophagy in melanoma. Melanoma Res. 2020. https://doi.org/10.1097/CMR.0000000000000615.

    Article  PubMed  Google Scholar 

  26. D’Arcangelo D, Giampietri C, Muscio M, Scatozza F, Facchiano F, Facchiano A. WIPI1, BAG1, and PEX3 autophagy-related genes are relevant melanoma markers. Oxid Med Cell Longev. 2018. https://doi.org/10.1155/2018/1471682.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Giampietri C, Petrungaro S, Cordella M, Tabolacci C, Tomaipitinca L, Facchiano A, Eramo A, Filippini A, Facchiano F, Ziparo E. Lipid storage and autophagy in melanoma cancer cells. Int J Mol Sci. 2017. https://doi.org/10.3390/ijms18061271.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Tabolacci C, Cordella M, Mariotti S, Rossi S, Senatore C, Lintas C, et al. Melanoma cell resistance to vemurafenib modifies inter-cellular communication signals. Biomedicines. 2021. https://doi.org/10.3390/biomedicines9010079.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Cesati M, Scatozza F, D’Arcangelo D, Antonini-Cappellini GC, Rossi S, Tabolacci C, et al. Investigating serum and tissue expression identified a cytokine/chemokine signature as a highly effective melanoma marker. Cancers (Basel). 2020. https://doi.org/10.3390/cancers12123680.

    Article  Google Scholar 

  30. Giampietri C, Tomaipitinca L, Scatozza F, Facchiano A. Expression of genes related to lipid handling and the obesity paradox in melanoma: database analysis. JMIR Cancer. 2020. https://doi.org/10.2196/16974.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Scatozza F, Moschella F, D’Arcangelo D, Rossi S, Tabolacci C, Giampietri C, et al. Nicotinamide inhibits melanoma in vitro and in vivo. J Exp Clin Cancer Res. 2020. https://doi.org/10.1186/s13046-020-01719-3.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Facchiano A, Facchiano F, Facchiano A. An investigation into the molecular basis of cancer comorbidities in coronavirus infection. FEBS Open Bio. 2020. https://doi.org/10.1002/2211-5463.12984.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Wojtukiewicz MZ, Rek MM, Karpowicz K, Górska M, Polityńska B, Wojtukiewicz AM, et al. Inhibitors of immune checkpoints—PD-1, PD-L1, CTLA-4—new opportunities for cancer patients and a new challenge for internists and general practitioners. Cancer Metastasis Rev. 2021. https://doi.org/10.1007/s10555-021-09976-0.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Vijayan D, Young A, Teng MWL, Smyth MJ. Targeting immunosuppressive adenosine in cancer. Nat Rev Cancer. 2017. https://doi.org/10.1038/nrc.2017.86.

    Article  PubMed  Google Scholar 

  35. Chen DS, Mellman I. Elements of cancer immunity and the cancer-immune set point. Nature. 2017. https://doi.org/10.1038/nature21349.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Turan T, Kannan D, Patel M, Barnes M, Tanlimco SG, Lu R, et al. Immune oncology, immune responsiveness and the theory of everything. J Immunother Cancer. 2018. https://doi.org/10.1186/s40425-018-0355-5.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D, et al. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep. 2017. https://doi.org/10.1016/j.celrep.2016.12.019.

    Article  PubMed  Google Scholar 

  38. Wang E, Worschech A, Marincola F. The immunologic constant of rejection. Trends Immunol. 2008. https://doi.org/10.1016/j.it.2008.03.002.

    Article  PubMed  Google Scholar 

  39. Thorsson V,L Gibbs LD, Brown SD, Wolf D, Bortone DS, Yang THO, et al. The immune landscape of cancer. Immunity. 2018. https://doi.org/10.1016/j.immuni.2018.03.023.

    Article  Google Scholar 

  40. Tu L, Guan R, Yang H, Zhou Y, Hong W, Ma L, Zhao G, Yu M. Assessment of the expression of the immune checkpoint molecules PD-1, CTLA4, TIM-3 and LAG-3 across different cancers in relation to treatment response, tumor-infiltrating immune cells and survival. Int J Cancer. 2020. https://doi.org/10.1002/ijc.32785.

    Article  PubMed  Google Scholar 

  41. Rotte A. Combination of CTLA-4 and PD-1 blockers for treatment of cancer. J Exp Clin Cancer Res. 2019. https://doi.org/10.1186/s13046-019-1259-z.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Ipilimumab. Dosing and uses. https://reference.medscape.com/drug/yervoy-ipilimumab-999636

  43. Nivolumab. Dosing and uses. https://reference.medscape.com/drug/opdivo-nivolumab-999989)

  44. Pembrolizumab. Dosing and uses. https://reference.medscape.com/drug/keytruda-pembrolizumab-999962

  45. Nivolumab and Ipilimumab in Treating Patients With Rare Tumors. https://www.clinicaltrials.gov/ct2/show/NCT02834013?term=nivolumab&cond=Testicular+Germ+Cell+Tumor+Non-Seminomatous&draw=2&rank=1

  46. Atezolisumab. Dosing and uses. https://reference.medscape.com/drug/tecentriq-atezolizumab-1000098

  47. Graydon CG, Mohideen S, Fowke KR. LAG3’s Enigmatic Mechanism of Action. Front Immunol. 2021. https://doi.org/10.3389/fimmu.2020.615317.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Aroldi F, Saleh R, Jafferji I, Barreto C, Saberian C, Middleton MR. Lag3: From Bench to Bedside. Cancer Treat Res. 2022. https://doi.org/10.1007/978-3-030-96376-7_6.

    Article  PubMed  Google Scholar 

  49. Sidaway P. LAG3 inhibition improves outcomes. Nat Rev Clin Oncol. 2022. https://doi.org/10.1038/s41571-022-00602-8.

    Article  PubMed  Google Scholar 

  50. We Y, Li Z. LAG3-PD-1 Combo Overcome the Disadvantage of Drug Resistance. Front Oncol. 2022. https://doi.org/10.3389/fonc.2022.831407.

    Article  Google Scholar 

  51. Harding JJ, Moreno V, Bang YJ, Hong M, Patnaik A, Trigo J, et al. Blocking TIM-3 in Treatment-refractory Advanced Solid Tumors: A Phase Ia/b Study of LY3321367 with or without an Anti-PD-L1 Antibody. Clin Cancer Res. 2021. https://doi.org/10.1158/1078-0432.CCR-20-4405.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Astor L. TIM3 Checkpoint Is a Potential Target for Acute Myeloid Leukemia Treatment. Targeted Ther Oncol. 2022;11(3):60–9.

    Google Scholar 

  53. Leavy O. TIM3: dual role in immunity. Nat Rev Immunol. 2008. https://doi.org/10.1038/nri2239.

    Article  Google Scholar 

  54. Diab A, Hamid O, Thompson JA, Ros W, Eskens FA, Dodi T, Siwen et al. A Phase I, Open-Label, Dose-Escalation Study of the OX40 Agonist Ivuxolimab in Patients with Locally Advanced or Metastatic Cancers. Clin Cancer Res. 2022. doi: https://doi.org/10.1158/1078-0432.CCR-21-0845.

  55. Shirasuna K, Koelsch G, Seidel-Dugan C, Salmeron A, Steiner P, Winston WM, et al. Characterization of ASP8374, a fully-human, antagonistic anti-TIGIT monoclonal antibody. Cancer Treat Res Commun. 2021. https://doi.org/10.1016/j.ctarc.2021.100433.

    Article  PubMed  Google Scholar 

  56. Killock D. GITR agonism—combination is key. Nat Rev Clin Oncol. 2019. https://doi.org/10.1038/s41571-019-0221-5.

    Article  PubMed  Google Scholar 

  57. AACR Cancer Progress Report; 2021. http://www.cancerprogressreport.org. Accessed 20 June 2022

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Acknowledgements

The technological support from the “Complex Protein Mixture (CPM) Analysis” Facility at the Department OMM, Istituto Superiore di Sanità, Rome, Italy, is kindly acknowledged. Authors are grateful to LILT- Roma for the support.

Funding

The study was supported in part by Ricerca Corrente 2021, Line 3.4, from Italian Ministry of Health, to AF.

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CG: conception, design of the work, acquisition, analysis, interpretation of data, writing; FS: acquisition, analysis; EC: acquisition, analysis; VVB: acquisition, analysis; PGN: interpretation of data; writing; AF: conception; design of the work, analysis, interpretation of data, writing. All Authors approve the current submitted version and agree with the submission. have agreed both to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature. All authors read and approved the final manuscript.

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Correspondence to Antonio Facchiano.

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Giampietri, C., Scatozza, F., Crecca, E. et al. Analysis of gene expression levels and their impact on survival in 31 cancer-types patients identifies novel prognostic markers and suggests unexplored immunotherapy treatment options in a wide range of malignancies. J Transl Med 20, 467 (2022). https://doi.org/10.1186/s12967-022-03670-7

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Keywords

  • Melanoma
  • Cholangiocarcinoma
  • Thymoma
  • Testis cancer
  • LAG3
  • TIM3
  • OX40
  • GITR
  • 4-1BB
  • TIGIT