Predictive Biomarkers: Progress on the Road to Personalized Cancer Immunotherapy

MRTX-500 Phase 2 Trial: Sitravatinib With Nivolumab in Patients With Nonsquamous NSCLC Progressing On or After Checkpoint Inhibitor Therapy or Chemotherapy

INTRODUCTION

Sitravatinib, a receptor tyrosine kinase inhibitor targeting TYRO3, AXL, MERTK receptors, and vascular epithelial growth factor receptor 2, can shift the tumor microenvironment toward an immunostimulatory state. Combining sitravatinib with checkpoint inhibitors (CPIs) may augment antitumor activity.

METHODS

The phase 2 MRTX-500 study evaluated sitravatinib (120 mg daily) with nivolumab (every 2 or 4 wk) in patients with advanced nonsquamous NSCLC who progressed on or after previous CPI (CPI-experienced) or chemotherapy (CPI-naive). CPI-experienced patients had a previous clinical benefit (PCB) (complete response, partial response, or stable disease for at least 12 weeks then disease progression) or no PCB (NPCB) from CPI. The primary end point was objective response rate (ORR); secondary objectives included safety and secondary efficacy end points.

RESULTS

Overall, 124 CPI-experienced (NPCB, n = 35; PCB, n = 89) and 32 CPI-naive patients were treated. Investigator-assessed ORR was 11.4% in patients with NPCB, 16.9% with PCB, and 25.0% in CPI-naive. The median progression-free survival was 3.7, 5.6, and 7.1 months with NPCB, PCB, and CPI-naive, respectively; the median overall survival was 7.9 and 13.6 months with NPCB and PCB, respectively (not reached in CPI-naive patients; median follow-up 20.4 mo). Overall, (N = 156), any grade treatment-related adverse events (TRAEs) occurred in 93.6%; grade 3/4 in 58.3%. One grade 5 TRAE occurred in a CPI-naive patient. TRAEs led to treatment discontinuation in 14.1% and dose reduction or interruption in 42.9%. Biomarker analyses supported an immunostimulatory mechanism of action.

CONCLUSIONS

Sitravatinib with nivolumab had a manageable safety profile. Although ORR was not met, this combination exhibited antitumor activity and encouraged survival in CPI-experienced patients with nonsquamous NSCLC.

Inferencing Bulk Tumor and Single-Cell Multi-Omics Regulatory Networks for Discovery of Biomarkers and Therapeutic Targets

There are insufficient accurate biomarkers and effective therapeutic targets in current cancer treatment. Multi-omics regulatory networks in patient bulk tumors and single cells can shed light on molecular disease mechanisms. Integration of multi-omics data with large-scale patient electronic medical records (EMRs) can lead to the discovery of biomarkers and therapeutic targets. In this review, multi-omics data harmonization methods were introduced, and common approaches to molecular network inference were summarized. Our Prediction Logic Boolean Implication Networks (PLBINs) have advantages over other methods in constructing genome-scale multi-omics networks in bulk tumors and single cells in terms of computational efficiency, scalability, and accuracy. Based on the constructed multi-modal regulatory networks, graph theory network centrality metrics can be used in the prioritization of candidates for discovering biomarkers and therapeutic targets. Our approach to integrating multi-omics profiles in a patient cohort with large-scale patient EMRs such as the SEER-Medicare cancer registry combined with extensive external validation can identify potential biomarkers applicable in large patient populations. These methodologies form a conceptually innovative framework to analyze various available information from research laboratories and healthcare systems, accelerating the discovery of biomarkers and therapeutic targets to ultimately improve cancer patient survival outcomes.

Hub Genes in Non-Small Cell Lung Cancer Regulatory Networks

There are currently no accurate biomarkers for optimal treatment selection in early-stage non-small cell lung cancer (NSCLC). Novel therapeutic targets are needed to improve NSCLC survival outcomes. This study systematically evaluated the association between genome-scale regulatory network centralities and NSCLC tumorigenesis, proliferation, and survival in early-stage NSCLC patients. Boolean implication networks were used to construct multimodal networks using patient DNA copy number variation, mRNA, and protein expression profiles. T statistics of differential gene/protein expression in tumors versus non-cancerous adjacent tissues, dependency scores in in vitro CRISPR-Cas9/RNA interference (RNAi) screening of human NSCLC cell lines, and hazard ratios in univariate Cox modeling of the Cancer Genome Atlas (TCGA) NSCLC patients were correlated with graph theory centrality metrics. Hub genes in multi-omics networks involving gene/protein expression were associated with oncogenic, proliferative potentials and poor patient survival outcomes (p < 0.05, Pearson's correlation). Immunotherapy targets PD1, PDL1, CTLA4, and CD27 were ranked as top hub genes within the 10th percentile in most constructed multi-omics networks. BUB3, DNM1L, EIF2S1, KPNB1, NMT1, PGAM1, and STRAP were discovered as important hub genes in NSCLC proliferation with oncogenic potential. These results support the importance of hub genes in NSCLC tumorigenesis, proliferation, and prognosis, with implications in prioritizing therapeutic targets to improve patient survival outcomes.

Identification of APC Mutation as a Potential Predictor for Immunotherapy in Colorectal Cancer

To date, anticancer immunotherapy has presented some clinical benefits to most of advanced mismatch repair deficient (dMMR)/microsatellite instability-high (MSI-H) colorectal cancer (CRC) patients. In addition to MSI status, we aimed to reveal the potential predictive value of adenomatous polyposis coli (APC) gene mutations in CRC patients. A total of 238 Chinese CRC patients was retrospectively identified and analyzed for clinical features and gene alternations in APC-mutant type (MT) and APC-wild-type (WT) groups. Clinical responses were then evaluated from the public TCGA database and MSKCC immunotherapy database. Although programmed cell death ligand 1 (PD-L1) level, MSI status, loss of heterogeneity at the human leukocyte antigen (HLA LOH), and tumor neoantigen burden (TNB) level were not statistically different between the APC-MT group and APC-WT group, tumor mutation burden (TMB) level was significantly higher in APC-MT patients (P < 0.05). Furthermore, comutation analysis for APC mutations revealed co-occurring genomic alterations of PCDHB7 and exclusive mutations of CTNNB1, BRAF, AFF3, and SNX25 (P < 0.05). Besides, overall survival from MSKCC-CRC cohort was longer in the APC-WT group than in the APC-MT group (HR 2.26 (95% CI 1.05–4.88), P < 0.05). Furthermore, most of patients in the APC-WT group were detected as high-grade immune subtypes (C2–C4) comparing with those in the APC-MT group. In addition, the percentages of NK T cells, Treg cells, and fibroblasts cells were higher in APC-WT patients than in APC-MT patients (P < 0.05). In summary, APC mutations might be associated with poor outcomes for immunotherapy in CRC patients regardless of MSI status. This study suggested APC gene mutations might be a potential predictor for immunotherapy in CRC.

Single B Cell Gene Co-Expression Networks Implicated in Prognosis, Proliferation, and Therapeutic Responses in Non-Small Cell Lung Cancer Bulk Tumors

Simple Summary

This study presents novel insights on dysregulated B cell proliferation networks in non-small cell lung cancer (NSCLC). Within this network, a nine-gene signature demonstrated prognostic and predictive indications in more than 1400 NSCLC patients using their gene and protein expression profiles in bulk tumors. Furthermore, novel therapeutic candidates are identified to improve NSCLC treatment outcomes.

Abstract

In NSCLC, there is a pressing need for immunotherapy predictive biomarkers. The processes underlying B-cell dysfunction, as well as their prognostic importance in NSCLC, are unknown. Tumor-specific B-cell gene co-expression networks were constructed by comparing the Boolean implication modeling of single-cell RNA sequencing of NSCLC tumor B cells and normal B cells. Proliferation genes were selected from the networks using in vitro CRISPR-Cas9/RNA interfering (RNAi) screening data in more than 92 human NSCLC epithelial cell lines. The prognostic and predictive evaluation was performed using public NSCLC transcriptome and proteome profiles. A B cell proliferation and prognostic gene co-expression network was present only in normal lung B cells and missing in NSCLC tumor B cells. A nine-gene signature was identified from this B cell network that provided accurate prognostic stratification using bulk NSCLC tumor transcriptome (n = 1313) and proteome profiles (n = 103). Multiple genes (HLA-DRA, HLA-DRB1, OAS1, and CD74) differentially expressed in NSCLC B cells, peripheral blood lymphocytes, and tumor T cells had concordant prognostic indications at the mRNA and protein expression levels. The selected genes were associated with drug sensitivity/resistance to 10 commonly used NSCLC therapeutic regimens. Lestaurtinib was discovered as a potential repositioning drug for treating NSCLC.

Combined angiogenesis and PD-1 inhibition for immunomodulatory TNBC: concept exploration and biomarker analysis in the FUTURE-C-Plus trial

BACKGROUND

Immune checkpoint inhibitors had a great effect in triple-negative breast cancer (TNBC); however, they benefited only a subset of patients, underscoring the need to co-target alternative pathways and select optimal patients. Herein, we investigated patient subpopulations more likely to benefit from immunotherapy and inform more effective combination regimens for TNBC patients.

METHODS

We conducted exploratory analyses in the FUSCC cohort to characterize a novel patient selection method and actionable targets for TNBC immunotherapy. We investigated this in vivo and launched a phase 2 trial to assess the clinical value of such criteria and combination regimen. Furthermore, we collected clinicopathological and next-generation sequencing data to illustrate biomarkers for patient outcomes.

RESULTS

CD8-positivity could identify an immunomodulatory subpopulation of TNBCs with higher possibilities to benefit from immunotherapy, and angiogenesis was an actionable target to facilitate checkpoint blockade. We conducted the phase II FUTURE-C-Plus trial to assess the feasibility of combining famitinib (an angiogenesis inhibitor), camrelizumab (a PD-1 monoclonal antibody) and chemotherapy in advanced immunomodulatory TNBC patients. Within 48 enrolled patients, the objective response rate was 81.3% (95% CI, 70.2-92.3), and the median progression-free survival was 13.6 months (95% CI, 8.4-18.8). No treatment-related deaths were reported. Patients with CD8- and/or PD-L1- positive tumors benefit more from this regimen. PKD1 somatic mutation indicates worse progression-free and overall survival.

CONCLUSION

This study confirms the efficacy and safety of the triplet regimen in immunomodulatory TNBC and reveals the potential of combining CD8, PD-L1 and somatic mutations to guide clinical decision-making and treatments.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT04129996 . Registered 11 October 2019.

Immune-Omics Networks of CD27, PD1, and PDL1 in Non-Small Cell Lung Cancer

To date, there are no prognostic/predictive biomarkers to select chemotherapy, immunotherapy, and radiotherapy in individual non-small cell lung cancer (NSCLC) patients. Major immune-checkpoint inhibitors (ICIs) have more DNA copy number variations (CNV) than mutations in The Cancer Genome Atlas (TCGA) NSCLC tumors. Nevertheless, CNV-mediated dysregulated gene expression in NSCLC is not well understood. Integrated CNV and transcriptional profiles in NSCLC tumors (n = 371) were analyzed using Boolean implication networks for the identification of a multi-omics CD27, PD1, and PDL1 network, containing novel prognostic genes and proliferation genes. A 5-gene (EIF2AK3, F2RL3, FOSL1, SLC25A26, and SPP1) prognostic model was developed and validated for patient stratification (p < 0.02, Kaplan-Meier analyses) in NSCLC tumors (n = 1163). A total of 13 genes (COPA, CSE1L, EIF2B3, LSM3, MCM5, PMPCB, POLR1B, POLR2F, PSMC3, PSMD11, RPL32, RPS18, and SNRPE) had a significant impact on proliferation in 100% of the NSCLC cell lines in both CRISPR-Cas9 (n = 78) and RNA interference (RNAi) assays (n = 92). Multiple identified genes were associated with chemoresponse and radiotherapy response in NSCLC cell lines (n = 117) and patient tumors (n = 966). Repurposing drugs were discovered based on this immune-omics network to improve NSCLC treatment.