Co-occurring genomic alterations and immunotherapy efficacy in NSCLC

Antithetical impacts of deleterious LRP1B mutations in non-squamous and squamous NSCLCs on predicting benefits from immune checkpoint inhibitor alone or with chemotherapy over chemotherapy alone: retrospective analyses of the POPLAR/OAK and CHOICE-01 trials

In non-small cell lung cancers, the non-squamous and squamous subtypes (nsqNSCLC and sqNSCLC) exhibit disparities in pathophysiology, tumor immunology, and potential genomic correlates affecting responses to immune checkpoint inhibitor (ICI)-based treatments. In our in-house training cohort (n=85), the presence of the LRP1B deleterious mutation (LRP1B-del) was associated with longer and shorter progression-free survival (PFS) on ICIs alone in nsqNSCLCs and sqNSCLCs, respectively (Pinteraction=0.008). These results were validated using a larger public ICI cohort (n=208, Pinteraction<0.001). Multiplex immunofluorescence staining revealed an association between LRP1B-del and increased and decreased numbers of tumor-infiltrating CD8+ T cells in nsqNSCLCs (P=0.040) and sqNSCLCs (P=0.014), respectively. In the POPLAR/OAK cohort, nsqNSCLCs with LRP1B-del demonstrated improved PFS benefits from atezolizumab over docetaxel (hazard ratio (HR) =0.70, P=0.046), whereas this benefit was negligible in those without LRP1B-del (HR=1.05, P=0.64). Conversely, sqNSCLCs without LRP1B-del benefited more from atezolizumab (HR=0.60, P=0.002) than those with LRP1B-del (HR=1.30, P=0.31). Consistent results were observed in the in-house CHOICE-01 cohort, in which nsqNSCLCs with LRP1B-del and sqNSCLCs without LRP1B-del benefited more from toripalimab plus chemotherapy than from chemotherapy alone (Pinteraction=0.008). This multi-cohort study delineates the antithetical impacts of LRP1B-del in nsqNSCLCs and sqNSCLCs on predicting the benefits from ICI alone or with chemotherapy over chemotherapy alone. Our findings highlight the distinct clinical utility of LRP1B-del in guiding treatment choices for nsqNSCLCs and sqNSCLCs, emphasizing the necessity for a detailed analysis based on pathological subtypes when investigating biomarkers for cancer therapeutics.

Proceedings of the 1st biannual bridging the gaps in lung cancer conference

Lung cancer is the leading cause of cancer death in the US and globally. The mortality from lung cancer has been declining, due to a reduction in incidence and advances in treatment. Although recent success in developing targeted and immunotherapies for lung cancer has benefitted patients, it has also expanded the complexity of potential treatment options for health care providers. To aid in reducing such complexity, experts in oncology convened a conference (Bridging the Gaps in Lung Cancer) to identify current knowledge gaps and controversies in the diagnosis, treatment, and outcomes of various lung cancer scenarios, as described here. Such scenarios relate to biomarkers and testing in lung cancer, small cell lung cancer, EGFR mutations and targeted therapy in non-small cell lung cancer (NSCLC), early-stage NSCLC, KRAS/BRAF/MET and other genomic alterations in NSCLC, and immunotherapy in advanced NSCLC.

Integrating knowledge graphs into machine learning models for survival prediction and biomarker discovery in patients with non-small-cell lung cancer

Accurate survival prediction for Non-Small Cell Lung Cancer (NSCLC) patients remains a significant challenge for the scientific and clinical community despite decades of advanced analytics. Addressing this challenge not only helps inform the critical aspects of clinical study design and biomarker discovery but also ensures that the 'right patient' receives the 'right treatment'. However, survival prediction is a highly complex task, given the large number of 'omics; and clinical features, as well as the high degree of freedom that drive patient survival. Prior knowledge could play a critical role in uncovering the complexity of a disease and understanding the driving factors affecting a patient's survival. We introduce a methodology for incorporating prior knowledge into machine learning-based models for prediction of patient survival through Knowledge Graphs, demonstrating the advantage of such an approach for NSCLC patients. Using data from patients treated with immuno-oncologic therapies in the POPLAR (NCT01903993) and OAK (NCT02008227) clinical trials, we found that the use of knowledge graphs yielded significantly improved hazard ratios, including in the POPLAR cohort, for models based on biomarker tumor mutation burden compared with those based on knowledge graphs. Use of a model-defined mutational 10-gene signature led to significant overall survival differentiation for both trials. We provide parameterized code for incorporating knowledge graphs into survival analyses for use by the wider scientific community.

KRAS and TP53 co-mutation predicts benefit of immune checkpoint blockade in lung adenocarcinoma

BACKGROUND

Predictive biomarkers in use for immunotherapy in advanced non-small cell lung cancer are of limited sensitivity and specificity. We analysed the potential of activating KRAS and pathogenic TP53 mutations to provide additional predictive information.

METHODS

The study cohort included 713 consecutive immunotherapy patients with advanced lung adenocarcinomas, negative for actionable genetic alterations. Additionally, two previously published immunotherapy and two surgical patient cohorts were analyzed. Therapy benefit was stratified by KRAS and TP53 mutations. Molecular characteristics underlying KRASmut/TP53mut tumours were revealed by the analysis of TCGA data.

RESULTS

An interaction between KRAS and TP53 mutations was observed in univariate and multivariate analyses of overall survival (Hazard ratio [HR] = 0.56, p = 0.0044 and HR = 0.53, p = 0.0021) resulting in a stronger benefit for KRASmut/TP53mut tumours (HR = 0.71, CI 0.55-0.92). This observation was confirmed in immunotherapy cohorts but not observed in surgical cohorts. Tumour mutational burden, proliferation, and PD-L1 mRNA were significantly higher in TP53-mutated tumours, regardless of KRAS status. Genome-wide expression analysis revealed 64 genes, including CX3CL1 (fractalkine), as specific transcriptomic characteristic of KRASmut/TP53mut tumours.

CONCLUSIONS

KRAS/TP53 co-mutation predicts ICI benefit in univariate and multivariate survival analyses and is associated with unique molecular tumour features. Mutation testing of the two genes can be easily implemented using small NGS panels.

Real-world data on ALK rearrangement test in Chinese advanced non-small cell lung cancer (RATICAL): a nationwide multicenter retrospective study

BACKGROUND

Anaplastic lymphoma kinase (ALK) test in advanced non-small cell lung cancer (NSCLC) can help physicians provide target therapies for patients harboring ALK gene rearrangement. This study aimed to investigate the real-world test patterns and positive rates of ALK gene rearrangements in advanced NSCLC.

METHODS

In this real-world study (ChiCTR2000030266), patients with advanced NSCLC who underwent an ALK rearrangement test in 30 medical centers in China between October 1, 2018 and December 31, 2019 were retrospectively analyzed. Interpretation training was conducted before the study was initiated. Quality controls were performed at participating centers using immunohistochemistry (IHC)-VENTANA-D5F3. The positive ALK gene rearrangement rate and consistency rate were calculated. The associated clinicopathological characteristics of ALK gene rearrangement were investigated as well.

RESULTS

The overall ALK gene rearrangement rate was 6.7% in 23,689 patients with advanced NSCLC and 8.2% in 17,436 patients with advanced lung adenocarcinoma. The quality control analysis of IHC-VENTANA-D5F3 revealed an intra-hospital consistency rate of 98.2% (879/895) and an inter-hospital consistency rate of 99.2% (646/651). IHC-VENTANA-D5F3 was used in 53.6%, real-time polymerase chain reaction (RT-PCR) in 25.4%, next-generation sequencing (NGS) in 18.3%, and fluorescence in-situ hybridization (FISH) in 15.9% in the adenocarcinoma subgroup. For specimens tested with multiple methods, the consistency rates confirmed by IHC-VENTANA-D5F3 were 98.0% (822/839) for FISH, 98.7% (1,222/1,238) for NGS, and 91.3% (146/160) for RT-PCR. The overall ALK gene rearrangement rates were higher in females, patients of ≤ 35 years old, never smokers, tumor cellularity of > 50, and metastatic specimens used for testing in the total NSCLC population and adenocarcinoma subgroup (all P < 0.05).

CONCLUSIONS

This study highlights the real-world variability and challenges of ALK test in advanced NSCLC, demonstrating a predominant use of IHC-VENTANA-D5F3 with high consistency and distinct clinicopathological features in ALK-positive patients. These findings underscore the need for a consensus on optimal test practices and support the development of refined ALK test strategies to enhance diagnostic accuracy and therapeutic decision-making in NSCLC.

Outcome of First-Line Treatment With Pembrolizumab According to KRAS/TP53 Mutational Status for Nonsquamous Programmed Death-Ligand 1-High (≥50%) NSCLC in the German National Network Genomic Medicine Lung Cancer

INTRODUCTION

Programmed death-ligand 1 expression currently represents the only validated predictive biomarker for immune checkpoint inhibition in metastatic NSCLC in the clinical routine, but it has limited value in distinguishing responses. Assessment of KRAS and TP53 mutations (mut) as surrogate for an immunosupportive tumor microenvironment (TME) might help to close this gap.

METHODS

A total of 696 consecutive patients with programmed death-ligand 1-high (≥50%), nonsquamous NSCLC, having received molecular testing within the German National Network Genomic Medicine Lung Cancer between 2017 and 2020, with Eastern Cooperative Oncology Group performance status less than or equal to 1 and pembrolizumab as first-line palliative treatment, were included into this retrospective cohort analysis. Treatment efficacy and outcome according to KRAS/TP53 status were correlated with TME composition and gene expression analysis of The Cancer Genome Atlas lung adenocarcinoma cohort.

RESULTS

Proportion of KRASmut and TP53mut was 53% (G12C 25%, non-G12C 28%) and 51%, respectively. In KRASmut patients, TP53 comutations increased response rates (G12C: 69.7% versus 46.5% [TP53mut versus wild-type (wt)], p = 0.004; non-G12C: 55.4% versus 39.5%, p = 0.03), progression-free survival (G12C: hazard ratio [HR] = 0.59, p = 0.009, non-G12C: HR = 0.7, p = 0.047), and overall survival (G12C: HR = 0.72, p = 0.16, non-G12C: HR = 0.56, p = 0.002), whereas no differences were observed in KRASwt patients. After a median follow-up of 41 months, G12C/TP53mut patients experienced the longest progression-free survival and overall survival (33.7 and 65.3 mo), which correlated with high tumor-infiltrating lymphocyte densities in the TME and up-regulation of interferon gamma target genes. Proinflammatory pathways according to TP53 status (mut versus wt) were less enhanced and not different in non-G12C and KRASwt, respectively.

CONCLUSIONS

G12C/TP53 comutations identify a subset of patients with a very favorable long-term survival with immune checkpoint inhibitor monotherapy, mediated by highly active interferon gamma signaling in a proinflammatory TME.

Mechanisms of resistance to targeted therapy and immunotherapy in non-small cell lung cancer: promising strategies to overcoming challenges

Resistance to targeted therapy and immunotherapy in non-small cell lung cancer (NSCLC) is a significant challenge in the treatment of this disease. The mechanisms of resistance are multifactorial and include molecular target alterations and activation of alternative pathways, tumor heterogeneity and tumor microenvironment change, immune evasion, and immunosuppression. Promising strategies for overcoming resistance include the development of combination therapies, understanding the resistance mechanisms to better use novel drug targets, the identification of biomarkers, the modulation of the tumor microenvironment and so on. Ongoing research into the mechanisms of resistance and the development of new therapeutic approaches hold great promise for improving outcomes for patients with NSCLC. Here, we summarize diverse mechanisms driving resistance to targeted therapy and immunotherapy in NSCLC and the latest potential and promising strategies to overcome the resistance to help patients who suffer from NSCLC.

Impact of PIK3CA and cell cycle pathway genetic alterations on durvalumab efficacy in patients with head and neck squamous cell carcinoma: Post hoc analysis of TRIUMPH study

OBJECTIVES

This study aimed to investigate whether genetic alterations in PI3KCA and the cell cycle pathways influence the efficacy of durvalumab, an immune checkpoint inhibitor, in patients with head and neck squamous cell carcinoma (HNSCC) who had previously failed platinum-based treatment.

MATERIALS AND METHODS

We obtained data from a phase II umbrella trial of patients with HNSCC who failed platinum-based treatment (TRIUMPH, NCT03292250). Patients receiving durvalumab treatment comprised those with PIK3CA alterations (Group A), those with cell cycle pathway alterations such as CDKN2A (Group B), and those with no druggable genetic alterations (Group C). We analyzed the overall response rate (ORR), progression-free survival (PFS), and overall survival (OS) in each group and evaluated the potential predictive factors for durvalumab.

RESULTS

We analyzed the data of 87 patients: 18, 12, and 57 in groups A, B, and C, respectively. The ORRs were 27.8 %, 8.3 %, and 15.8 % in Groups A, B, and C, respectively (P = 0.329), and the median PFS for each group was 2.3, 1.6, and 1.7 months, respectively, with no significant differences between the groups (P = 0.24). Notably, patients with lower neutrophil-lymphocyte ratio (NLR) (≤5.8) had longer PFS (median, 2.8 vs 1.6 months, P < 0.001), while those with lower platelet-lymphocyte ratio (PLR) (≤491.2) exhibited longer PFS (median, 1.8 vs 1.2 months, P < 0.001).

CONCLUSION

Durvalumab's efficacy was similar, irrespective of the presence of PIK3CA or cell cycle pathway genetic alterations in patients with platinum-resistant HNSCC. The NLR and PLR may be promising predictive biomarkers.

Prediction uncertainty estimates elucidate the limitation of current NSCLC subtype classification in representing mutational heterogeneity

The heterogeneous pathogenesis and treatment response of non-small cell lung cancer (NSCLC) has led clinical treatment decisions to be guided by NSCLC subtypes, with lung adenocarcinoma and lung squamous cell carcinoma being the most common subtypes. While histology-based subtyping remains challenging, NSCLC subtypes were found to be distinct at the transcriptomic level. However, unlike genomic alterations, gene expression is generally not assessed in clinical routine. Since subtyping of NSCLC has remained elusive using mutational data, we aimed at developing a neural network model that simultaneously learns from adenocarcinoma and squamous cell carcinoma samples of other tissue types and is regularized using a neural network model trained from gene expression data. While substructures of the expression-based manifold were captured in the mutation-based manifold, NSCLC classification accuracy did not significantly improve. However, performance was increased when rejecting inconclusive samples using an ensemble-based approach capturing prediction uncertainty. Importantly, SHAP analysis of misclassified samples identified co-occurring mutations indicative of both NSCLC subtypes, questioning the current NSCLC subtype classification to adequately represent inherent mutational heterogeneity. Since our model captures mutational patterns linked to clinical heterogeneity, we anticipate it to be suited as foundational model of genomic data for clinically relevant prognostic or predictive downstream tasks.

First-Line Dual Immunotherapy, a Treatment Option in First-Line Metastatic Non-Small-Cell Lung Cancer: Are We Ready to Use It?

This article provides valuable insights into the use of dual immunotherapy for patients with metastatic NSCLC.

Notch activation defines immune-suppressive subsets of ccRCCs with unfavorable benefits from immunotherapy over VEGFR/mTOR inhibitors

The evolutionarily conserved Notch pathway, involved in cancer stem cell capacity and cancer immunity, may predict the benefit from immune checkpoint inhibitors (ICIs) in clear cell renal cell carcinoma (ccRCC). In the TCGA dataset, mRNA expression of Notch pathway genes identified three clusters with different prognoses and molecular characteristics. Based on the differentially expressed Notch pathway genes between clusters, we constructed the Notch-score, correlated with Notch activation, angiogenesis, PI3K-AKT-mTOR activity, and sensitivities to VEGFR/mTOR inhibitors. A high Notch-score was linked with more "resting"/"anti-inflammatory" rather than "activated"/"pro-inflammatory" tumor-infiltrating immune cells, inactivated immune pathways, and scarce any benefits from ICI-based therapies over VEGFR/mTOR inhibitors in the JAVELIN Renal 101 (avelumab plus axitinib vs. sunitinib) and the CheckMate-009/010/025 trials (nivolumab vs. everolimus). For the Notch-activated ccRCCs, ICIs provide limited advantages and might not be strongly recommended, by which the cost-effectiveness of treatments in ccRCCs may be potentially improved.

Clinicopathological Features of Non-Small Cell Lung Carcinoma with BRAF Mutation

(1) Background: BRAF mutations affect 4-5% of lung adenocarcinomas. This study aimed to analyze the clinicopathological features of lung carcinomas with BRAF mutations, focusing on V600E vs. non-V600E and the presence of co-mutations. (2) Methods: All BRAF-mutated lung carcinomas were retrieved from a molecular diagnostic unit (the reference unit for four different hospitals). The samples were analyzed using next-generation sequencing. Statistical analyses included log-rank tests for overall survival (OS) and progression-free survival (PFS). (3) Results: In total, 60 BRAF-mutated lung carcinomas were retrieved: 24 (40.0%) with V600E and 36 (60.0%) with non-V600E mutations, and 21 (35.0%) with other co-mutations and 39 (65.0%) with only BRAF mutations. Survival data were available for 54/60 (90.0%) cases. Targeted therapy was documented in 11 cases. Patients with V600E mutations exhibited a better prognosis than patients with non-V600E mutations (p = 0.008 for OS, p = 0.018 for PFS); this was confirmed in PFS (p = 0.036) when considering only patients who received no targeted therapy. Patients with co-mutations displayed no prognostic difference compared to patients carrying only BRAF mutations (p = 0.590 for OS, p = 0.938 for PFS). (4) Conclusions: BRAF-mutated lung carcinomas with V600E (40.0%) had a better prognosis than those without V600E. Concomitant co-mutations (35.0%) did not affect the prognosis.

Pan-cancer analysis of NFE2L2 mutations identifies a subset of lung cancers with distinct genomic and improved immunotherapy outcomes

BACKGROUND

Mutations in the KEAP1-NFE2L2 signaling pathway were linked to increased tumorigenesis and aggressiveness. Interestingly, not all hotspot mutations on NFE2L2 were damaging; some even were activating. However, there was conflicting evidence about the association between NFE2L2 mutation and Nrf2-activating mutation and responsiveness to immune checkpoint inhibitors (ICIs) in non-small cell lung cancer (NSCLC) and other multiple cancers.

METHODS

The study with the largest sample size (n = 49,533) explored the landscape of NFE2L2 mutations and their impact response/resistance to ICIs using public cohorts. In addition, the in-house WXPH cohort was used to validate the efficacy of immunotherapy in the NFE2L2 mutated patients with NSCLC.

RESULTS

In two pan-cancer cohorts, Nrf2-activating mutation was associated with higher TMB value compared to wild-type. We identified a significant association between Nrf2-activating mutation and shorter overall survival in pan-cancer patients and NSCLC patients but not in those undergoing ICIs treatment. Similar findings were obtained in cancer patients carrying the NFE2L2 mutation. Furthermore, in NSCLC and other cancer cohorts, patients with NFE2L2 mutation demonstrated more objective responses to ICIs than patients with wild type. Our in-house WXPH cohort further confirmed the efficacy of immunotherapy in the NFE2L2 mutated patients with NSCLC. Lastly, decreased inflammatory signaling pathways and immune-depleted immunological microenvironments were enriched in Nrf2-activating mutation patients with NSCLC.

CONCLUSIONS

Our study found that patients with Nrf2-activating mutation had improved immunotherapy outcomes than patients with wild type in NSCLC and other tumor cohorts, implying that Nrf2-activating mutation defined a distinct subset of pan-cancers and might have implications as a biomarker for guiding ICI treatment, especially NSCLC.

Utility of public knowledge bases for the interpretation of comprehensive tumor molecular profiling results

With the growing use of comprehensive tumor molecular profiling (CTMP), the therapeutic landscape of cancer is rapidly evolving. NGS produces large amounts of genomic data requiring complex analysis and subsequent interpretation. We sought to determine the utility of publicly available knowledge bases (KB) for the interpretation of the cancer mutational profile in clinical practice. Analysis was performed across patients who previously underwent CTMP. Independent interpretation of the CTMP was performed manually, and then, the recommendations were compared to ones present in KBs (OncoKB, CIViC, CGI, CGA, VICC, MolecularMatch). A total of 222 CTMP reports from 222 patients with 932 genomic alterations (GA) were identified. For 368 targetable GA identified in 171 (77%) of the patients, 1381 therapy recommendations were compiled. Except for CGA, therapy ESCAT LOE I, II, IIIA and IIIB therapy options were equally represented in the majority of KB. Personalized treatment options with ESCAT LOE I-II were provided for 35 patients (16%); MolecularMatch/CIViC allowed to collect ESCAT I-II treatment options for 34 of them (97%), OncoKB/CGI-for 33 of them (94%). Employing VICC and CGA 6 (17%) and 20 (57%) of patients were left without ESCAT I or II treatment options. For 88 patients with ESCAT level III-B therapy recommendations: only 2 (2%), 3 (3%), 4 (5%) and 6 (7%) of patients were left without options with CIViC, MolecularMatch, CGI and OncoKB, and with VICC-12 (14%). Highest overlap ratio was observed for IIIA (0.81) biomarkers, with the comparable results for LOE I-II. Meanwhile, overlap ratio for ESCAT LOE IV was 0.22. Public KBs provide substantial information on ESCAT-I/R1 biomarkers, but the information on ESCAT II-IV and resistance biomarkers is underrepresented. Manual curation should be considered the gold standard for the CTMP interpretation.

PTPRD mutation is a prognostic biomarker for sensitivity to ICIs treatment in advanced non-small cell lung cancer

BACKGROUND

Immune checkpoint inhibitors (ICIs) have become the standard treatment for advanced non-small cell lung cancer (NSCLC). ICIs can provide durable responses and prolong survival for some patients. With the increasing routine of next-generation sequencing (NGS) in clinical practice, it is essential to integrate prognostic factors to establish novel nomograms to improve clinical prediction ability in NSCLC with ICIs treatment.

METHODS

Clinical information, response data, and genome data of advanced NSCLC treated ICIs were obtained from cBioPortal. The top 20 gene alterations in durable clinical benefit (DCB) were compared with those genes in no durable benefit (NDB). Survival analyses were performed using the Kaplan-Meier plot method and selected clinical variables to develop a novel nomogram.

RESULTS

The mutation of PTPRD was significantly related to progression free survival (PFS) and overall survival (OS) in advanced NSCLC with ICIs treatment (PFS: p = 0.0441, OS: p = 0.0086). The PTPRD mutation was closely related to tumor mutational burden (TMB) and tumor-infiltrating immune cells (TIICs). Two novel nomograms were built to predict the PFS and OS of advanced NSCLC patients with ICIs treatment.

CONCLUSIONS

Our study suggested that PTPRD mutations could serve as a predictive biomarker for the sensitivity to ICIs treatment and PFS and OS in advanced NSCLC with ICIs. Our systematic nomograms showed great potential value in clinical application to predict the PFS and OS for advanced NSCLC patients with ICIs.

Development of a Somatic Variant Registry in a National Cancer Center: towards Molecular Real World Data preparedness

The Biomedical Research field is currently advancing to develop Clinical Trials and translational projects based on Real World Evidence. To make this transition feasible, clinical centers need to work toward Data Accessibility and Interoperability. This task is particularly challenging when applied to Genomics, that entered in routinary screening in the last years via mostly amplicon-based Next-Generation Sequencing panels. Said experiments produce up to hundreds of features per patient, and their summarized results are often stored in static clinical reports, making critical information inaccessible to automated access and Federated Search consortia. In this study, we present a reanalysis of 4620 solid tumor sequencing samples in five different histology settings. Furthermore, we describe all the Bioinformatics and Data Engineering processes that were put in place in order to create a Somatic Variant Registry able to deal with the large biotechnological variability of routinary Genomics Profiling.

BAP1-related signature predicts benefits from immunotherapy over VEGFR/mTOR inhibitors in ccRCC: a retrospective analysis of JAVELIN Renal 101 and checkmate-009/010/025 trials

BACKGROUND

In patients with advanced clear cell renal cell carcinoma, despite the undoubted benefits from immune checkpoint inhibitor (ICI)-based therapies over monotherapies of angiogenic/mTOR inhibitors in the intention-to-treat population, approximately a quarter of the patients can scarcely gain advantage from ICIs, prompting the search for predictive biomarkers for patient selection.

METHODS

Clinical and multi-omic data of 2428 ccRCC patients were obtained from The Cancer Genome Atlas (TCGA, n = 537), JAVELIN Renal 101 (avelumab plus axitinib vs. sunitinib, n = 885), and CheckMate-009/010/025 (nivolumab vs. everolimus, n = 1006).

RESULTS

BAP1 mutations were associated with large progression-free survival (PFS) benefits from ICI-based immunotherapies over sunitinib/everolimus (pooled estimate of interaction HR = 0.71, 95% CI 0.51-0.99, P = 0.045). Using the top 20 BAP1 mutation-associated differentially expressed genes (DEGs) generated from the TCGA cohort, we developed the BAP1-score, negatively correlated with angiogenesis and positively correlated with multiple immune-related signatures concerning immune cell infiltration, antigen presentation, B/T cell receptor, interleukin, programmed death-1, and interferon. A high BAP1-score indicated remarkable PFS benefits from ICI-based immunotherapies over angiogenic/mTOR inhibitors (avelumab plus axitinib vs. sunitinib: HR = 0.55, 95% CI 0.43-0.70, P < 0.001; nivolumab vs. everolimus: HR = 0.72, 95% CI 0.52-1.00, P = 0.045), while these benefits were negligible in the low BAP1-score subgroup (HR = 1.16 and 1.02, respectively).

CONCLUSION

In advanced ccRCCs, the BAP1-score is a biologically and clinically significant predictor of immune microenvironment and the clinical benefits from ICI-based immunotherapies over angiogenic/mTOR inhibitors, demonstrating its potential utility in optimizing the personalized therapeutic strategies in patients with advanced ccRCC.

Development and validation of a mutation-based model to predict immunotherapeutic efficacy in NSCLC

Background

Immunotherapy has become increasingly important in the perioperative period of non-small-cell lung cancer (NSCLC). In this study, we intended to develop a mutation-based model to predict the therapeutic effificacy of immune checkpoint inhibitors (ICIs) in patients with NSCLC.

Methods

Random Forest (RF) classifiers were generated to identify tumor gene mutated features associated with immunotherapy outcomes. Then the best classifier with the highest accuracy served for the development of the predictive model. The correlations of some reported biomarkers with the model were analyzed, such as TMB, PD-(L)1, KEAP1-driven co-mutations, and immune subtypes. The training cohort and validation cohorts performed survival analyses to estimate the predictive efficiency independently.

Results

An 18-gene set was selected using random forest (RF) classififiers. A predictive model was developed based on the number of mutant genes among the candidate genes, and patients were divided into the MT group (mutant gene ≥ 2) and WT group (mutant gene < 2). The MT group (N = 54) had better overall survival (OS) compared to the WT group (N = 290); the median OS was not reached vs. nine months (P < 0.0001, AUC = 0.73). The robust predictive performance was confifirmed in three validation cohorts, with an AUC of 0.70, 0.57, and 0.64 (P < 0.05). The MT group was characterized by high tumor neoantigen burden (TNB), increased immune infifiltration cells such as CD8 T and macrophage cells, and upregulated immune checkpoint molecules, suggesting potential biological advantages in ICIs therapy.

Conclusions

The predictive model could precisely predict the immunotherapeutic efficacy in NSCLC based on the mutant genes within the model. Furthermore, some immune-related features and cell expression could support robust efficiency.

A Metabolism-Related Gene Prognostic Index for Prediction of Response to Immunotherapy in Lung Adenocarcinoma

Immunotherapy, such as immune checkpoint inhibitors (ICIs), is a validated strategy for treating lung adenocarcinoma (LUAD) patients. One of the main challenges in ICIs treatment is the lack of efficient biomarkers for predicting response or resistance. Metabolic reprogramming has been proven to remodel the tumor microenvironment, altering the response to ICIs. We constructed a prognostic model as metabolism-related gene (MRG) of four genes by using weighted gene co-expression network analysis (WGCNA), the nonnegative matrix factorization (NMF), and Cox regression analysis of a LUAD dataset (n = 500) from The Cancer Genome Atlas (TCGA), which was validated with three Gene Expression Omnibus (GEO) datasets (n = 442, n = 226 and n = 127). The MRG was constructed based on BIRC5, PLK1, CDKN3, and CYP4B1 genes. MRG-high patients had a worse survival probability than MRG-low patients. Furthermore, the MRG-high subgroup was more associated with cell cycle-related pathways; high infiltration of activated memory CD4+T cells, M0 macrophages, and neutrophils; and showed better response to ICIs. Contrarily, the MRG-low subgroup was associated with fatty acid metabolism, high infiltration of dendric cells, and resting mast cells, and showed poor response to ICIs. MRG is a promising prognostic index for predicting survival and response to ICIs and other therapeutic agents in LUAD, which might provide insights on strategies with ICIs alone or combined with other agents.

PTPRD/PTPRT mutation as a predictive biomarker of immune checkpoint inhibitors across multiple cancer types

Background

Immune checkpoint inhibitors (ICIs) are dramatically changing the treatment landscape of a variety of cancers. Nevertheless, the variability in ICI responses highlight the importance in identifying predictive biomarkers. PTPRD and PTPRT (PTPRD/PTPRT) are the phosphatases of JAK-STAT signaling, a critical pathway in anti-cancer immunity regulation. However, the pan-cancer association between PTPRD/PTPRT mutation and the efficacy of ICIs remains unclear across pan-cancer patients.

Methods

We analyzed the association between PTPRD/PTPRT mutations and patient outcomes using clinical data and genomic mutations from TCGA pan-cancer cohort. Furthermore, the ICI-treatment cohort was used to evaluate the relationship between PTPRD/PTPRT mutation and the efficacy of ICIs. Another ICIs-treatment cohort was used to validate the findings. The TCGA pan-cancer dataset was analyzed to explore the correlation between PTPRD/PTPRT mutations and immune signatures. Moreover, we combined four factors to construct a nomogram model that could be used to predict the survival of pan-cancer patients receiving ICI treatment. The calibration curves and area under the curve were applied to assess the performance of the model.

Results

PTPRD/PTPRT mutations were shown to be associated with a worse prognosis in TCGA cohort (P < 0.05). In the Samstein cohort, prolonged overall survival (OS) was observed in PTPRD/PTPRT mutant cancers, compared with wild-type cancers (mOS: 40.00 vs 16.00 months, HR = 0.570, 95%CI: 0.479-0.679, P < 0.0001). In the validation cohort, significant OS advantage was observed in PTPRD/PTPRT mutant patients (mOS: 31.32 vs 15.53 months, HR = 0.658, 95%CI: 0.464-0.934, P = 0.0292). Furthermore, PTPRD/PTPRT mutations were associated with a higher tumor mutational burden, MSI score, and TCR score (P < 0.0001). Enhanced immune signatures were found in the PTPRD/PTPRT mutant cancers (P < 0.05). Finally, we successfully established a nomogram model that could be used to predict the survival of NSCLC patients who received ICI treatment. Based on the risk score of the model, patients in the low-risk group showed a better mOS than those in the high-risk group (mOS: 2.75 vs 1.08 years, HR = 0.567, 95%CI: 0.492-0.654; P < 0.001).

Conclusions

PTPRD/PTPRT mutations may be a potential biomarker for predicting ICI treatment responsiveness in multiple cancer types.

The efficacy of immune checkpoint inhibitors in advanced EGFR-Mutated non-small cell lung cancer after resistance to EGFR-TKIs: Real-World evidence from a multicenter retrospective study

Background

The efficacy of immune checkpoint inhibitors (ICIs) in pretreated EGFR-mutated non-small cell lung cancer (NSCLC) patients is controversial. We conducted this multicenter retrospective study to examine the efficacy of ICIs in a real world setting.

Patients and methods

We collected 116 consecutive NSCLC patients with sensitive EGFR mutations who received ICIs alone or in combination after failure to respond to EGFR tyrosine kinase inhibitors (EGFR-TKIs), and 99 patients were included for final analysis. The impacts of ICIs on the patients’ objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), and overall survival (OS) were assessed. The relationships between outcomes and clinical characteristics were analyzed.

Results

The ORR in patients with target lesions was 31.25% (95% CI: 22.18-41.52), and the DCR in all patients was 65.66% (95% CI: 55.44-74.91). The overall median PFS was 5.0 months (95% CI: 3.0-6.6), and the median OS was 15.9 months (95% CI: 10.8-23.8). The outcomes were better in patients receiving combination therapy with ECOG scores of 0-1 and no more than 2 lines of prior therapy, with a median PFS of 7.4 months (95% CI: 3.0-13.3) and a median OS of 29.0 months (95% CI: 11.7-NE). Primary EGFR mutation type and treatment mode were found to have a notable impact on clinical outcomes. Both median PFS and OS in patients with EGFR L858R mutation were significantly shorter than those in patients with EGFR exon 19 deletion (19del) (PFS: 2.5 versus 6.7 months, HR: 1.80, log-rank P=0.011; OS: 9.8 versus 26.9 months, HR: 2.48, log-rank P=0.002). Patients receiving combination therapy had notably longer median PFS and OS than those receiving monotherapy (PFS: 5.2 versus 3.0 months, HR: 0.54, log-rank P=0.020; OS: 19.0 versus 7.4 months, HR: 0.46, log-rank P=0.009).

Conclusions

Our study suggests that ICI-based combination therapy is a potential strategy for EGFR-mutated NSCLC patients after EGFR-TKI failure. The efficacy may differ according to EGFR subtypes.

Comprehensive analysis of T cell receptor repertoire in patients with KRAS mutant non-small cell lung cancer

Background

Kirsten rat sarcoma viral oncogene homolog (KRAS) is one of the most frequently mutated oncogenes in non-small cell lung cancer (NSCLC). The administration of immunotherapy has demonstrated significant efficacy in prolonging the overall survival of patients with KRAS mutation in recent years. However, the efficacy of immunotherapy in KRAS mutant NSCLC is variable. Analysis of T cell receptor (TCR) repertoire may contribute to a better understanding of the mechanisms behind such differential outcomes.

Methods

A total of 47 patients with KRAS mutant NSCLC were enrolled in this study. Deep sequencing of the TCR β chain complementarity-determining regions in tumor tissue and paired peripheral blood specimens was conducted. Comprehensive analysis of TCR repertoire metrics was performed with different KRAS mutation subtypes and concomitant mutations. Moreover, the associations between TCR repertoire metrics and tumor mutation burden (TMB), as well as programmed death-ligand 1 were explored, respectively.

Results

TCR repertoire metrics, including Shannon index, Clonality, and Morisita index (MOI), showed no significant differences among different KRAS mutation subtypes. The similar results were observed between patients with tumor protein p53 (TP53) mutation and those with wild-type TP53. In contrast, although no significant differences were found in Shannon index and Clonality, patients with KRAS/serine/threonine kinase 11 (STK11) comutation showed a significantly higher MOI compared to their STK11 wild-type counterparts (P=0.012). In addition, TCR repertoire metrics were neither associated with TMB nor programmed death-ligand 1 expression in KRAS mutant NSCLC.

Conclusions

This retrospective study comprehensively described the TCR repertoire in KRAS mutant NSCLC. A higher MOI represented more overlap of the TCR repertoire between tumor tissue and paired peripheral blood, indicating distinctive immunological features in NSCLC with KRAS/STK11 comutation.

State-of-the-art combination treatment strategies for advanced stage non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is the most abundant type of epithelial lung cancer being diagnosed after 40% of invasions of excrescence in pulmonary tissues. According to WHO, 30% of NSCLC patients can be cured if diagnosed and treated early. Mutations play an important role in advanced stage NSCLC treatment, which includes critical proteins necessary for cellular growth and replication. Restricting such mutations may improve survival in lung cancer patients. Newer technologies include endoscopic bronchial ultrasonography and esophageal ultrasonography. Currently, policymaking or decision-making for treatment regimens merely depends on the genomic alterations and mutations. DNA sequencing, methylation, protein, and fragmented DNA analysis do NSCLC screening. Achievement of these goals requires consideration of available therapeutics in current anticancer approaches for improving quality of life and treatment outcomes for NSCLC patient. The specific goals of this review are to discuss first-line and second-line therapies for advanced-stage NSCLC and molecularly targeted therapy including thoughtful discussion on precise role of treatment strategies in specific tumors. Also, concerned diagnostics, new clinical trial designs, and pursuing appropriate combinations of radiotherapy and/or chemotherapy with biological therapy for exceptional cases considering resistance mechanisms and palliative care will be discussed.