TIM-3 transcriptomic landscape with clinical and immunomic correlates in cancer.

TIM-3, an inhibitory checkpoint receptor, may invoke anti-PD-1/anti-PD-L1 immune checkpoint inhibitor (ICI) resistance. The predictive impact of TIM-3 RNA expression in various advanced solid tumors among patients treated with ICIs is yet to be determined, and their prognostic significance also remains unexplored. We investigated TIM-3 transcriptomic expression and clinical outcomes. We examined TIM-3 RNA expression data through the OmniSeq database. TIM-3 transcriptomic patterns were calibrated against a reference population (735 tumors), adjusted to internal housekeeping genes, and calculated as percentiles. Overall, 514 patients (31 cancer types; 489 patients with advanced/metastatic disease and clinical annotation) were assessed. Ninety tumors (17.5% of 514) had high (≥75th percentile RNA rank) TIM-3 expression. Pancreatic cancer had the greatest proportion of TIM-3 high expressors (36% of 55 patients). Still, there was variability within cancer types with, for instance, 12.7% of pancreatic cancers harboring low TIM-3 (<25th percentile) levels. High TIM-3 expression independently and significantly correlated with high PD-L2 RNA expression (odds ratio (OR) 9.63, 95% confidence interval (CI) 4.91-19.4, P<0.001) and high VISTA RNA expression (OR 2.71, 95% CI 1.43-5.13, P=0.002), all in multivariate analysis. High TIM-3 RNA did not correlate with overall survival (OS) from time of metastatic disease in the 272 patients who never received ICIs, suggesting that it is not a prognostic factor. However, high TIM-3 expression predicted longer median OS (but not progression-free survival) in 217 ICI-treated patients (P=0.0033; median OS, 2.84 versus 1.21 years (high versus not-high TIM-3)), albeit not retained in multivariable analysis. In summary, TIM-3 RNA expression was variable between and within malignancies, and high levels associated with high PD-L2 and VISTA checkpoints and with pancreatic cancer. Individual tumor immunomic assessment and co-targeting co-expressed checkpoints merits exploration in prospective trials as part of a precision immunotherapy strategy.

The transcriptomic expression pattern of immune checkpoints shows heterogeneity between and within cancer types.

Transcriptomic expression profiles of immune checkpoint markers are of interest in order to decipher the mechanisms of immunotherapy response and resistance. Overall, 514 patients with various solid tumors were retrospectively analyzed in this study. The RNA expression levels of tumor checkpoint markers (ADORA2A, BTLA, CD276, CTLA4, IDO1, IDO2, LAG3, NOS2, PD-1, PD-L1, PD-L2, PVR, TIGIT, TIM3, VISTA, and VTCN) were ranked from 0-100 percentile based on a reference population. The expression of each checkpoint was correlated with cancer type, microsatellite instability (MSI), tumor mutational burden (TMB), and programmed death-ligand 1 (PD-L1) by immunohistochemistry (IHC). The cohort included 30 different tumor types, with colorectal cancer being the most common (27%). When RNA percentile rank values were categorized as "Low" (0-24), "Intermediate" (25-74), and "High" (75-100), each patient had a distinctive portfolio of the categorical expression of 16 checkpoint markers. Association between some checkpoint markers and cancer types were observed; NOS2 showed significantly higher expression in colorectal and stomach cancer (P < 0.001). Principal component analysis demonstrated no clear association between combined RNA expression patterns of 16 checkpoint markers and cancer types, TMB, MSI or PD-L1 IHC. Immune checkpoint RNA expression varies from patient to patient, both within and between tumor types, though colorectal and stomach cancer showed the highest levels of NOS2, a mediator of inflammation and immunosuppression. There were no specific combined expression patterns correlated with MSI, TMB or PD-L1 IHC. Next generation immunotherapy trials may benefit from individual analysis of patient tumors as selection criteria for specific immunomodulatory approaches.

Transcriptomic analysis of GITR and GITR ligand reveals cancer immune heterogeneity with implications for GITR targeting.

Glucocorticoid-induced tumor necrosis factor related protein (GITR) is a transmembrane protein expressed mostly on CD25+CD4+ regulatory T-cells (Tregs) and upregulated on all T-cells upon activation. It is a T-cell co-stimulatory receptor and has demonstrated promising anti-tumor activity in pre-clinical studies. To date, however, the efficacy of GITR agonism has been discouraging in clinical trials. This study explores GITR and GITR ligand (GITR-L) ribonucleic acid (RNA) expression in solid tumors in an attempt to delineate causes for variable responses to GITR agonists. RNA expression levels of 514 patients with a variety of cancer types were normalized to internal housekeeping gene profiles and ranked as percentiles. 99/514 patients (19.3%) had high GITR expression (defined as ≥ 75th percentile). Breast and lung cancer had the highest proportion of patients with high GITR expression (39% and 35%, respectively). The expression of concomitant high GITR and low-moderate GITR-L expression (defined as <75th percentile) was present in 31% and 30% of patients with breast and lung cancer respectively. High GITR expression also showed a significant independent association with high RNA expression of other immune modulator proteins, namely, PD-L1 immunohistochemistry (IHC) ≥1 (odds ratio (OR) 2.15, P=0.008), CTLA4 (OR=2.17, P=0.05) and OX40 high RNA expression (OR=2.64, P=0.001). Overall, these results suggest that breast and lung cancer have a high proportion of patients with a GITR and GITR-L RNA expression profile that merits further investigation in GITR agonism studies. The association of high GITR expression with high CTLA4 and OX40 RNA expression, as well as positive PD-L1 IHC, provides a rationale for a combination approach targeting these specific immune modulator proteins in patients whose tumors show such co-expression.

Clinical and Biologic Correlates of ADORA2A Transcriptomic Expression in Cancer.

ADORA2A (adenosine A2a receptor) and ADORA2B propagate immunoregulatory signals, including restricting both innate and adaptive immunity, though recent data also suggest a tumor suppressor effect in certain settings. We evaluated the RNA expression from 514 tumors in a clinical-grade laboratory; 489 patients with advanced/metastatic disease had clinical outcome correlates. Transcript expression was standardized to internal housekeeping genes and ranked (0-100 scale) relative to 735 specimens from 35 different cancer types. Transcript abundance rank values were defined as "low/moderate" (0-74) or "high" (75-100) percentile RNA expression ranks. Overall, 20.8% of tumors had high ADORA2A (≥75 percentile RNA rank). The greatest proportion of high ADORA2A expressors was found in neuroendocrine and breast cancers and sarcomas, whereas the lowest was found in colorectal and ovarian cancers, albeit with patient-to-patient variability. In multivariable logistic regression analysis, there was a significant positive correlation between high ADORA2A RNA expression and a high expression of the immune checkpoint-related molecules PD-1 (p = 0.015), VISTA (p ≤ 0.001), CD38 (p = 0.031), and CD39 (p ≤ 0.001). In 217 immunotherapy-treated patients, high ADORA2A did not correlate significantly with progression-free (p = 0.51) or overall survival (OS) (p = 0.09) from the initiation of the checkpoint blockade. However, high versus not-high ADORA2A transcript expression correlated with longer OS from the time of advanced/metastatic disease (N = 489 patients; (HR 0.69 (95% CI 0.51-0.95) (p = 0.02)). Therefore, high ADORA2A transcript levels may be a favorable prognostic factor, unrelated to immunotherapy. Importantly, ascertaining co-expression patterns of ADORA2A with PD-1 and VISTA in individual tumors as a basis for the precision co-targeting of ADORA2A and these other checkpoint-related molecules warrants investigation in clinical trials.

High indoleamine 2,3-dioxygenase transcript levels predict better outcome after front-line cancer immunotherapy.

Indoleamine 2,3-dioxygenase 1 (IDO1), which catabolizes tryptophan, is a potential target to unlock the immunosuppressive tumor microenvironment. Correlations between IDO1 and immune checkpoint inhibitor (ICI) efficacy remain unclear. Herein, we investigated IDO1 transcript expression across cancers and clinical outcome correlations. High IDO1 transcripts were more frequent in uterine (54.2%) and ovarian cancer (37.2%) but varied between and within malignancies. High IDO1 RNA expression was associated with high expression of PD-L1 (immune checkpoint ligand), CXCL10 (an effector T cell recruitment chemokine), and STAT1 (a component of the JAK-STAT pathway) (all multivariable p < 0.05). PIK3CA and CTCF alterations were more frequent in the high IDO1 group. High IDO1 expression was an independent predictor of progression-free survival (adjusted HR = 0.44, 95% CI 0.20-0.99, p = 0.049) and overall survival (adjusted HR = 0.31, 95% CI 0.11-0.87, p = 0.026) after front-line ICIs. IDO1 expression warrants further exploration as a predictive biomarker for immunotherapy. Moreover, co-expressed immunoregulatory molecules merit exploration for co-targeting.

The Impact of Prior Single-Gene Testing on Comprehensive Genomic Profiling Results for Patients with Non-Small Cell Lung Cancer.

INTRODUCTION: Tissue-based broad molecular profiling of guideline-recommended biomarkers is advised for the therapeutic management of patients with non-small cell lung cancer (NSCLC). However, practice variation can affect whether all indicated biomarkers are tested. We aimed to evaluate the impact of common single-gene testing (SGT) on subsequent comprehensive genomic profiling (CGP) test outcomes and results in NSCLC. METHODS: Oncologists who ordered SGT for guideline-recommended biomarkers in NSCLC patients were prospectively contacted (May-December 2022) and offered CGP (DNA and RNA sequencing), either following receipt of negative SGT findings, or instead of SGT for each patient. We describe SGT patterns and compare CGP completion rates, turnaround time, and recommended biomarker detection for NSCLC patients with and without prior negative SGT results. RESULTS: Oncologists in > 80 community practices ordered CGP for 561 NSCLC patients; 135 patients (27%) first had negative results from 30 different SGT combinations; 84% included ALK, EGFR and PD-L1, while only 3% of orders included all available SGTs for guideline-recommended genes. Among patients with negative SGT results, CGP was attempted using the same tissue specimen 90% of the time. There were also significantly more CGP order cancellations due to tissue insufficiency (17% vs. 7%), DNA sequencing failures (13% vs. 8%), and turnaround time > 14 days (62% vs. 29%) than among patients who only had CGP. Forty-six percent of patients with negative prior SGT had positive CGP results for recommended biomarkers, including targetable genomic variants in genes beyond ALK and EGFR, such as ERBB2, KRAS (non-G12C), MET (exon 14 skipping), NTRK2/3, and RET . CONCLUSION: For patients with NSCLC, initial use of SGT increases subsequent CGP test cancellations, turnaround time, and the likelihood of incomplete molecular profiling for guideline-recommended biomarkers due to tissue insufficiency.

Patients with non-small cell lung cancer (NSCLC) should have their tumor tissue tested for all recommended biomarkers that can help identify their best treatment options. Traditional tests look at gene biomarkers one by one (single-gene testing), and doctors can order some or all these tests individually or in a group. However, some recommended biomarkers cannot be tested by traditional single-gene tests at all. Newer technology (next-generation sequencing) covers all current recommended treatment biomarkers in one test (comprehensive genomic profiling), but this testing is more expensive and can take more time. Our study shows that NSCLC patients do not get all recommended treatment biomarkers tested when a single-gene testing approach is taken. Single-gene testing also used up some patients' tumor tissue entirely, such that further testing by comprehensive genomic profiling could not be done at all (17% vs. 7% for patients with no prior single-gene tests), resulted in more sequencing failures (13% vs. 8%), and had turnaround time for results greater than 14 days for more patients (62% vs. 29%). When comprehensive genomic profiling was completed, 46% of patients with negative results from prior single-gene testing had positive results for recommended treatment biomarkers that were not included in the initial single-gene tests. To ensure that NSCLC patients receive testing for all recommended biomarkers, comprehensive genomic profiling must be performed first.

OX40/OX40 ligand and its role in precision immune oncology.

Immune checkpoint inhibitors have changed the treatment landscape for various malignancies; however, their benefit is limited to a subset of patients. The immune machinery includes both mediators of suppression/immune evasion, such as PD-1, PD-L1, CTLA-4, and LAG-3, all of which can be inhibited by specific antibodies, and immune-stimulatory molecules, such as T-cell co-stimulatory receptors that belong to the tumor necrosis factor receptor superfamily (TNFRSF), including OX40 receptor (CD134; TNFRSF4), 4-1BB (CD137; TNFRSF9), and glucocorticoid-induced TNFR-related (GITR) protein (CD357; TNFRSF18). In particular, OX40 and its binding ligand OX40L (CD134L; TNFSF4; CD252) are critical for immunoregulation. When OX40 on activated T cells binds OX40L on antigen-presenting cells, T-cell activation and immune stimulation are initiated via enhanced T-cell survival, proliferation and cytotoxicity, memory T-cell formation, and abrogation of regulatory T cell (Treg) immunosuppressive functions. OX40 agonists are in clinical trials both as monotherapy and in combination with other immunotherapy agents, in particular specific checkpoint inhibitors, for cancer treatment. To date, however, only a minority of patients respond. Transcriptomic profiling reveals that OX40 and OX40L expression vary between and within tumor types, and that only ~ 17% of cancer patients have high OX40 and low OX40L, one of the expression patterns that might be theoretically amenable to OX40 agonist enhancement. Taken together, the data suggest that the OX40/OX40L machinery is a critical part of the immune stimulatory system and that understanding endogenous expression patterns of these molecules and co-existing checkpoints merits further investigation in the context of a precision immunotherapy strategy for cancer therapy.

Cancer testis antigen burden (CTAB): a novel biomarker of tumor-associated antigens in lung cancer.

BACKGROUND: Cancer-testis antigens (CTAs) are tumor antigens that are normally expressed in the testes but are aberrantly expressed in several cancers. CTA overexpression drives the metastasis and progression of lung cancer, and is associated with poor prognosis. To improve lung cancer diagnosis, prognostic prediction, and drug discovery, robust CTA identification and quantitation is needed. In this study, we examined and quantified the co-expression of CTAs in lung cancer to derive cancer testis antigen burden (CTAB), a novel biomarker of immunotherapy response. METHODS: Formalin fixed paraffin embedded (FFPE) tumor samples in discovery cohort (n = 5250) and immunotherapy and combination therapy treated non-small cell lung cancer (NSCLC) retrospective (n = 250) cohorts were tested by comprehensive genomic and immune profiling (CGIP), including tumor mutational burden (TMB) and the mRNA expression of 17 CTAs. PD-L1 expression was evaluated by IHC. CTA expression was summed to derive the CTAB score. The median CTAB score for the discovery cohort of 170 was applied to the retrospective cohort as cutoff for CTAB "high" and "low". Biomarker and gene expression correlation was measured by Spearman correlation. Kaplan-Meier survival analyses were used to detect overall survival (OS) differences, and objective response rate (ORR) based on RECIST criteria was compared using Fisher's exact test. RESULTS: The CTAs were highly co-expressed (p < 0.05) in the discovery cohort. There was no correlation between CTAB and PD-L1 expression (R = 0.011, p = 0.45) but some correlation with TMB (R = 0.11, p = 9.2 × 10-14). Kaplan-Meier survival analysis of the immunotherapy-treated NSCLC cohort revealed better OS for the pembrolizumab monotherapy treated patients with high CTAB (p = 0.027). The combination group demonstrated improved OS compared to pembrolizumab monotherapy group (p = 0.04). The pembrolizumab monotherapy patients with high CTAB had a greater ORR than the combination therapy group (p = 0.02). CONCLUSIONS: CTA co-expression can be reliably measured using CGIP in solid tumors. As a biomarker, CTAB appears to be independent from PD-L1 expression, suggesting that CTAB represents aspects of tumor immunogenicity not measured by current standard of care testing. Improved OS and ORR for high CTAB NSCLC patients treated with pembrolizumab monotherapy suggests a unique underlying aspect of immune response to these tumor antigens that needs further investigation.

LAG-3 transcriptomic expression correlates linearly with other checkpoints, but not with clinical outcomes.

Immune checkpoint inhibitors have revolutionized the treatment landscape for patients with cancer. Multi-omics, including next-generation DNA and RNA sequencing, have enabled the identification of exploitable targets and the evaluation of immune mediator expression. There is one FDA-approved LAG-3 inhibitor and multiple in clinical trials for numerous cancers. We analyzed LAG-3 transcriptomic expression among 514 patients with diverse cancers, including 489 patients with clinical annotation for their advanced malignancies. Transcriptomic LAG-3 expression was highly variable between histologies/cancer types and within the same histology/cancer type. LAG-3 RNA levels correlated linearly, albeit weakly, with high RNA levels of other checkpoints, including PD-L1 (Pearson's R2 = 0.21 (P < 0.001)), PD-1 (R2 = 0.24 (P < 0.001)) and CTLA-4 (R2 = 0.19 (P < 0.001)); when examined for Spearman correlation, significance did not change. LAG-3 expression (dichotomized at ≥ 75th (high) versus < 75th (moderate/low) RNA percentile level) was not a prognostic factor for overall survival (OS) in 272 immunotherapy-naïve patients with advanced/metastatic disease (Kaplan Meier analysis; P = 0.54). High LAG-3 levels correlated with longer OS after anti-PD-1/PD-L1-based checkpoint blockade (univariate (P = 0.003), but not multivariate analysis (hazard ratio, 95% confidence interval = 0.80 (0.46-1.40) (P = 0.44))); correlation with longer progression-free survival showed a weak univariate trend (P = 0.13). Taken together, these results suggest that high LAG-3 levels in and of themselves do not predict resistance to anti-PD-1/PD-L1 checkpoint blockade. Even so, since LAG-3 is often co-expressed with PD-1, PD-L1 and/or CTLA-4, selecting patients for combinations of checkpoint blockade based on immunomic co-expression patterns is a strategy that merits exploration.

High CTLA-4 transcriptomic expression correlates with high expression of other checkpoints and with immunotherapy outcome.

Background: CTLA-4 impedes the immune system's antitumor response. There are two Food and Drug Administration-approved anti-CTLA-4 agents - ipilimumab and tremelimumab - both used together with anti-PD-1/PD-L1 agents. Objective: To assess the prognostic implications and immunologic correlates of high CTLA-4 in tumors of patients on immunotherapy and those on non-immunotherapy treatments. Design/methods: We evaluated RNA expression levels in a clinical-grade laboratory and clinical correlates of CTLA-4 and other immune checkpoints in 514 tumors, including 489 patients with advanced/metastatic cancers and full outcome annotation. A reference population (735 tumors; 35 histologies) was used to normalize and rank transcript abundance (0-100 percentile) to internal housekeeping gene profiles. Results: The most common tumor types were colorectal (140/514, 27%), pancreatic (55/514, 11%), breast (49/514, 10%), and ovarian cancers (43/514, 8%). Overall, 87 of 514 tumors (16.9%) had high CTLA-4 transcript expression (⩾75th percentile rank). Cancers with the largest proportion of high CTLA-4 transcripts were cervical cancer (80% of patients), small intestine cancer (33.3%), and melanoma (33.3%). High CTLA-4 RNA independently/significantly correlated with high PD-1, PD- L2, and LAG3 RNA levels (and with high PD-L1 in univariate analysis). High CTLA-4 RNA expression was not correlated with survival from the time of metastatic disease [N = 272 patients who never received immune checkpoint inhibitors (ICIs)]. However, in 217 patients treated with ICIs (mostly anti-PD-1/anti-PD- L1), progression-free survival (PFS) and overall survival (OS) were significantly longer among patients with high versus non-high CTLA-4 expression [hazard ratio, 95% confidence interval: 0.6 (0.4-0.9) p = 0.008; and 0.5 (0.3-0.8) p = 0.002, respectively]; results were unchanged when 18 patients who received anti-CTLA-4 were omitted. Patients whose tumors had high CTLA-4 and high PD-L1 did best; those with high PD-L1 but non-high CTLA-4 and/or other expression patterns had poorer outcomes for PFS (p = 0.004) and OS (p = 0.009) after immunotherapy. Conclusion: High CTLA-4, especially when combined with high PD-L1 transcript expression, was a significant positive predictive biomarker for better outcomes (PFS and OS) in patients on immunotherapy.

High CTLA-4 expression and immunotherapy outcome High CTLA-4 expression was not a prognostic factor for survival in patients not receiving ICIs but was a significant positive predictive biomarker for better outcome (PFS and OS) in patients on immunotherapy, perhaps because it correlated with expression of other checkpoints such as PD-1 and PD-L2.

Pan-cancer analysis of TIM-3 transcriptomic expression reveals high levels in pancreatic cancer and interpatient heterogeneity.

BACKGROUND: T-cell immunoglobulin and mucin domain-containing protein 3 (TIM-3), an immune checkpoint receptor, dampens immune function. TIM-3 antagonists have entered the clinic. METHODS: We analyzed TIM-3 transcriptomic expression in 514 diverse cancers. Transcript abundance was normalized to internal housekeeping genes and ranked (0-100 percentile) to a reference population (735 tumors; 35 histologies [high≥75 percentile rank]). Ninety tumors (17.5%) demonstrated high TIM-3 expression. RESULTS: TIM-3 expression varied between and within tumor types. However, high TIM-3 expression was more common in pancreatic cancer (20/55 tumors, 36.4%; odds ratio, 95% confidence interval (pancreatic vs. other tumors) = 3.176 (1.733-5.818; p < 0.001, multivariate]). High TIM-3 also significantly and independently correlated with high PD-L1 (p = 0.014) and high CTLA-4 (p < 0.001) transcriptomic expression (multivariate). CONCLUSIONS: These observations indicate that TIM-3 RNA expression is heterogeneous, but more common in pancreatic cancer and in tumors exploiting PD-L1 and CTLA-4 checkpoints. Clinical trials with patient selection for matched immune-targeted combinations may be warranted.

PD-L1 Expression by RNA-Sequencing in Non-Small Cell Lung Cancer: Concordance with Immunohistochemistry and Associations with Pembrolizumab Treatment Outcomes.

Programmed cell death ligand (PD-L1) expression by immunohistochemistry (IHC) lacks sensitivity for pembrolizumab immunotherapy selection in non-small cell lung cancer (NSCLC), particularly for tumors with low expression. We retrospectively evaluated transcriptomic PD-L1 by mRNA next-generation sequencing (RNA-seq). In an unselected NSCLC patient cohort (n = 3168) tested during standard care (2017-2021), PD-L1 IHC and RNA-seq demonstrated moderate concordance, with 80% agreement overall. Most discordant cases were either low or negative for PD-L1 expression by IHC but high by RNA-seq. RNA-seq accurately discriminated PD-L1 IHC high from low tumors by receiver operator curve (ROC) analysis but could not distinguish PD-L1 IHC low from negative tumors. In a separate pembrolizumab monotherapy cohort (n = 102), NSCLC tumors classified as PD-L1 high versus not high by RNA-seq had significantly improved response, progression-free survival, and overall survival as an individual measure and in combination with IHC high or low status. PD-L1 IHC status (high or low) trended toward but had no significant associations with improved outcomes. Conventional PD-L1 IHC testing has inherent limitations, making it an imperfect reference standard for evaluating novel testing technologies. RNA-seq offers an objective PD-L1 measure that could represent a complementary method to IHC to improve NSCLC patient selection for immunotherapy.

Cancer-Immunity Marker RNA Expression Levels across Gynecologic Cancers: Implications for Immunotherapy.

Our objective was to characterize cancer-immunity marker expression in gynecologic cancers and compare immune landscapes between gynecologic tumor subtypes and with nongynecologic solid tumors. RNA expression levels of 51 cancer-immunity markers were analyzed in patients with gynecologic cancers versus nongynecologic cancers, and normalized to a reference population of 735 control cancers, ranked from 0 to 100, and categorized as low (0-24), moderate (25-74), or high (75-100) percentile rank. Of the 72 patients studied, 43 (60%) had ovarian, 24 (33%) uterine, and 5 (7%) cervical cancer. No two immune profiles were identical according to expression rank (0-100) or rank level (low, moderate, or high). Patients with cervical cancer had significantly higher expression level ranks of immune activating, proinflammatory, tumor-infiltrating lymphocyte markers, and checkpoints than patients with uterine or ovarian cancer (P < 0.001 for all comparisons). However, there were no significant differences in immune marker expression between uterine and ovarian cancers. Tumors with PD-L1 tumor proportional score (TPS) ≥1% versus 0% had significantly higher expression levels of proinflammatory markers (58 vs. 49%, P = 0.0004). Compared to patients with nongynecologic cancers, more patients with gynecologic cancers express high levels of IDO-1 (44 vs. 13%, P < 0.001), LAG3 (35 vs. 21%, P = 0.008), and IL10 (31 vs. 15%, P = 0.002.) Patients with gynecologic cancers have complex and heterogeneous immune landscapes that are distinct from patient to patient and from other solid tumors. High levels of IDO1 and LAG3 suggest that clinical trials with IDO1 inhibitors or LAG3 inhibitors, respectively, may be warranted in gynecologic cancers.

T-cell priming transcriptomic markers: implications of immunome heterogeneity for precision immunotherapy.

Immune checkpoint blockade is effective for only a subset of cancers. Targeting T-cell priming markers (TPMs) may enhance activity, but proper application of these agents in the clinic is challenging due to immune complexity and heterogeneity. We interrogated transcriptomics of 15 TPMs (CD137, CD27, CD28, CD80, CD86, CD40, CD40LG, GITR, ICOS, ICOSLG, OX40, OX40LG, GZMB, IFNG, and TBX21) in a pan-cancer cohort (N = 514 patients, 30 types of cancer). TPM expression was analyzed for correlation with histological type, microsatellite instability high (MSI-H), tumor mutational burden (TMB), and programmed death-ligand 1 (PD-L1) expression. Among 514 patients, the most common histological types were colorectal (27%), pancreatic (11%), and breast cancer (10%). No statistically significant association between histological type and TPM expression was seen. In contrast, expression of GZMB (granzyme B, a serine protease stored in activated T and NK cells that induces cancer cell apoptosis) and IFNG (activates cytotoxic T cells) were significantly higher in tumors with MSI-H, TMB ≥ 10 mutations/mb and PD-L1 ≥ 1%. PD-L1 ≥ 1% was also associated with significantly higher CD137, GITR, and ICOS expression. Patients' tumors were classified into "Hot", "Mixed", or "Cold" clusters based on TPM expression using hierarchical clustering. The cold cluster showed a significantly lower proportion of tumors with PD-L1 ≥ 1%. Overall, 502 patients (98%) had individually distinct patterns of TPM expression. Diverse expression patterns of TPMs independent of histological type but correlating with other immunotherapy biomarkers (PD-L1 ≥ 1%, MSI-H and TMB ≥ 10 mutations/mb) were observed. Individualized selection of patients based on TPM immunomic profiles may potentially help with immunotherapy optimization.

LAG-3 transcriptomic expression patterns across malignancies: Implications for precision immunotherapeutics.

BACKGROUND: Lymphocyte activation gene 3 (LAG-3) or CD223 is a transmembrane protein that serves as an immune checkpoint which attenuates T-cell activation. Many clinical trials of LAG-3 inhibitors have had modest effects, but recent data indicate that the LAG-3 antibody relatlimab, together with nivolumab (anti-PD-1), provided greater benefit than nivolumab alone in patients with melanoma. METHODS: In this study, the RNA expression levels of 397 genes were assessed in 514 diverse cancers at a clinical-grade laboratory (OmniSeq: https://www.omniseq.com/). Transcript abundance was normalized to internal housekeeping gene profiles and ranked (0-100 percentile) using a reference population (735 tumors; 35 histologies). RESULTS: A total of 116 of 514 tumors (22.6%) had high LAG-3 transcript expression (≥75 percentile rank). Cancers with the greatest proportion of high LAG-3 transcripts were neuroendocrine (47% of patients) and uterine (42%); colorectal had among the lowest proportion of high LAG-3 expression (15% of patients) (all p < 0.05 multivariate); 50% of melanomas were high LAG-3 expressors. There was significant independent association between high LAG-3 expression and high expression of other checkpoints, including programmed death-ligand 1 (PD-L1), PD-1, and CTLA-4, as well as high tumor mutational burden (TMB) ≥10 mutations/megabase, a marker for immunotherapy response (all p < 0.05 multivariate). However, within all tumor types, there was inter-patient variability in LAG-3 expression level. CONCLUSIONS: Prospective studies are therefore needed to determine if high levels of the LAG-3 checkpoint are responsible for resistance to anti-PD-1/PD-L1 or anti-CTLA-4 antibodies. Furthermore, a precision/personalized immunotherapy approach may require interrogating individual tumor immunograms to match patients to the right combination of immunotherapeutic agents for their malignancy.

Cancer immunity marker RNA expression levels across gynecologic cancers: Implications for immunotherapy.

Background: Our objective was to characterize cancer immunity marker expression in gynecologic cancers and compare immune landscapes between gynecologic tumor subtypes and with non-gynecologic solid tumors. Methods: RNA expression levels of 51 cancer-immunity markers were analyzed in patients with gynecologic cancers vs. non-gynecologic cancers, and normalized to a reference population of 735 control cancers, ranked from 0-100, and categorized as low (0-24), moderate (25-74), or high (75-100) percentile rank. Results: Of the 72 patients studied, 43 (60%) had ovarian, 24 (33%) uterine, and 5 (7%) cervical cancer. No two immune profiles were identical according to expression rank (0-100) or rank level (low, moderate, or high). Patients with cervical cancer had significantly higher expression level ranks of immune activating, pro-inflammatory, tumor infiltrating lymphocyte markers and checkpoints than patients with uterine or ovarian cancer (p<0.001 for all comparisons). However, there were no significant differences in immune marker expression between uterine and ovarian cancers. Tumors with PD-L1 TPS =>1% versus 0% had significantly higher expression levels of pro-inflammatory markers (58 vs. 49%, p=0.0004). Compared to patients with non-gynecologic cancers, more patients with gynecologic cancers express high levels of IDO-1 (44 vs. 13%, p<0.001), LAG3 (35 vs. 21%, p=0.008) and IL10 (31 vs. 15%, p=0.002.) Conclusions: Patients with gynecologic cancers have complex and heterogeneous immune landscapes that are distinct from patient to patient and from other solid tumors. High levels of IDO1 and LAG3 suggest that clinical trials with IDO1 inhibitors or LAG3 inhibitors, respectively, may be warranted in gynecologic cancers.

Indoleamine 2,3-dioxygenase (IDO) inhibitors and cancer immunotherapy.

Strategies for unlocking immunosuppression in the tumor microenvironment have been investigated to overcome resistance to first-generation immune checkpoint blockade with anti- programmed cell death protein 1 (PD-1)/ programmed death-ligand 1 (PD-L1) and anti-cytotoxic T-lymphocyte associated protein 4 (CTLA-4) agents. Indoleamine 2,3-dioxygenase (IDO) 1, an enzyme catabolizing tryptophan to kynurenine, creates an immunosuppressive environment in preclinical studies. Early phase clinical trials investigating inhibition of IDO1, especially together with checkpoint blockade, provided promising results. Unfortunately, the phase 3 trial of the IDO1 inhibitor epacadostat combined with the PD-1 inhibitor pembrolizumab did not show clinical benefit when compared with pembrolizumab monotherapy in patients with advanced malignant melanoma, which dampened enthusiasm for IDO inhibitors. Even so, several molecules, such as the aryl hydrocarbon receptor and tryptophan 2,3-dioxygenase, were reported as additional potential targets for the modulation of the tryptophan pathway, which might enhance clinical effectiveness. Furthermore, the combination of IDO pathway blockade with agents inhibiting other signals, such as those generated by PIK3CA mutations that may accompany IDO1 upregulation, may be a novel way to enhance activity. Importantly, IDO1 expression level varies by tumor type and among patients with the same tumor type, suggesting that patient selection based on expression levels of IDO1 may be warranted in clinical trials.

A scalable high-throughput targeted next-generation sequencing assay for comprehensive genomic profiling of solid tumors.

Timely and accurate identification of molecular alterations in solid tumors is essential for proper management of patients with advanced cancers. This has created a need for rapid, scalable comprehensive genomic profiling (CGP) systems that detect an increasing number of therapeutically-relevant variant types and molecular signatures. In this study, we assessed the analytical performance of the TruSight Oncology 500 High-Throughput assay for detection of somatic alterations from formalin-fixed paraffin-embedded tissue specimens. In parallel, we developed supporting software and automated sample preparation systems designed to process up to 70 clinical samples in a single NovaSeq 6000TM sequencing run with a turnaround time of <7 days from specimen receipt to report. The results demonstrate that the scalable assay accurately and reproducibly detects small variants, copy number alterations, microsatellite instability (MSI) and tumor mutational burden (TMB) from 40ng DNA, and multiple gene fusions, including known and unknown partners and splice variants from 20ng RNA. 717 tumor samples and reference materials with previously known alterations in 96 cancer-related genes were sequenced to evaluate assay performance. All variant classes were reliably detected at consistent and reportable variant allele percentages with >99% overall accuracy and precision. Our results demonstrate that the high-throughput CGP assay is a reliable method for accurate detection of molecular alterations in support of precision therapeutics in oncology. The supporting systems and scalable workflow allow for efficient interpretation and prompt reporting of hundreds of patient cancer genomes per week with excellent analytical performance.

Integration of tumor inflammation, cell proliferation, and traditional biomarkers improves prediction of immunotherapy resistance and response.

BACKGROUND: Contemporary to the rapidly evolving landscape of cancer immunotherapy is the equally changing understanding of immune tumor microenvironments (TMEs) which is crucial to the success of these therapies. Their reliance on a robust host immune response necessitates clinical grade measurements of immune TMEs at diagnosis. In this study, we describe a stable tumor immunogenic profile describing immune TMEs in multiple tumor types with ability to predict clinical benefit from immune checkpoint inhibitors (ICIs). METHODS: A tumor immunogenic signature (TIGS) was derived from targeted RNA-sequencing (RNA-seq) and gene expression analysis of 1323 clinical solid tumor cases spanning 35 histologies using unsupervised analysis. TIGS correlation with ICI response and survival was assessed in a retrospective cohort of NSCLC, melanoma and RCC tumor blocks, alone and combined with TMB, PD-L1 IHC and cell proliferation biomarkers. RESULTS: Unsupervised clustering of RNA-seq profiles uncovered a 161 gene signature where T cell and B cell activation, IFNg, chemokine, cytokine and interleukin pathways are over-represented. Mean expression of these genes produced three distinct TIGS score categories: strong (n = 384/1323; 29.02%), moderate (n = 354/1323; 26.76%), and weak (n = 585/1323; 44.22%). Strong TIGS tumors presented an improved ICI response rate of 37% (30/81); with highest response rate advantage occurring in NSCLC (ORR = 36.6%; 16/44; p = 0.051). Similarly, overall survival for strong TIGS tumors trended upward (median = 25 months; p = 0.19). Integrating the TIGS score categories with neoplastic influence quantified via cell proliferation showed highly proliferative and strong TIGS tumors correlate with significantly higher ICI ORR than poorly proliferative and weak TIGS tumors [14.28%; p = 0.0006]. Importantly, we noted that strong TIGS and highly [median = not achieved; p = 0.025] or moderately [median = 16.2 months; p = 0.025] proliferative tumors had significantly better survival compared to weak TIGS, highly proliferative tumors [median = 7.03 months]. Importantly, TIGS discriminates subpopulations of potential ICI responders that were considered negative for response by TMB and PD-L1. CONCLUSIONS: TIGS is a comprehensive and informative measurement of immune TME that effectively characterizes host immune response to ICIs in multiple tumors. The results indicate that when combined with PD-L1, TMB and cell proliferation, TIGS provides greater context of both immune and neoplastic influences on the TME for implementation into clinical practice.

Immune profiling and immunotherapeutic targets in pancreatic cancer.

BACKGROUND: Immunotherapeutic approaches for pancreatic ductal adenocarcinoma (PDAC) are less successful as compared to many other tumor types. In this study, comprehensive immune profiling was performed in order to identify novel, potentially actionable targets for immunotherapy. METHODS: Formalin-fixed paraffin embedded (FFPE) specimens from 68 patients were evaluated for expression of 395 immune-related markers (RNA-seq), mutational burden by complete exon sequencing of 409 genes, PD-L1 expression by immunohistochemistry (IHC), pattern of tumor infiltrating lymphocytes (TILs) infiltration by CD8 IHC, and PD-L1/L2 copy number by fluorescent in situ hybridization (FISH). RESULTS: The seven classes of actionable genes capturing myeloid immunosuppression, metabolic immunosuppression, alternative checkpoint blockade, CTLA-4 immune checkpoint, immune infiltrate, and programmed cell death 1 (PD-1) axis immune checkpoint, discerned 5 unique clinically relevant immunosuppression expression profiles (from most to least common): (I) combined myeloid and metabolic immunosuppression [affecting 25 of 68 patients (36.8%)], (II) multiple immunosuppressive mechanisms (29.4%), (III) PD-L1 positive (20.6%), (IV) highly inflamed PD-L1 negative (10.3%); and (V) immune desert (2.9%). The Wilcoxon rank-sum test was used to compare the PDAC cohort with a comparison cohort (n=1,416 patients) for the mean expressions of the 409 genes evaluated. Multiple genes including TIM3, VISTA, CCL2, CCR2, TGFB1, CD73, and CD39 had significantly higher mean expression versus the comparison cohort, while three genes (LAG3, GITR, CD38) had significantly lower mean expression. CONCLUSIONS: This study demonstrates that a clinically relevant unique profile of immune markers can be identified in PDAC and be used as a roadmap for personalized immunotherapeutic decision-making strategies.