DNA damage response alterations in clear cell renal cell carcinoma: clinical, molecular, and prognostic implications

BACKGROUND

DNA damage repair (DDR) pathways modulate cancer risk, progression, and therapeutic responses. Nonetheless, the characteristics and significance of DDR alterations in clear cell renal cell carcinoma (ccRCC) remain undefined. This study aimed to explore the predictive role, molecular mechanism, and tumor immune profile of DDR genes in ccRCC.

METHODS

We prospectively sequenced 757 tumors and matched blood DNA samples from Chinese patients with ccRCC using next-generation sequencing (NGS) and analyzed data from 537 patients from The Cancer Genome Atlas (TCGA). A comprehensive analysis was performed.

RESULTS

Fifty-two percent of Chinese patients with ccRCC harbored DDR gene mutations and 57% of TCGA patients. The immunotherapy treatment prognosis of patients with DDR gene mutations was superior to that of patients without DDR gene mutations (p = 0.047). DDR gene mutations were associated with more gene mutations and a higher tumor mutation load (TMB, p < 0.001). Moreover, patients with DDR gene mutations have a distinct mutational signature compared with those with wild-type DDR. Furthermore, the DDR-mut group had elevated neoantigen load (including single-nucleotide variants (SNV) and indel neoantigen load, p = 0.037 and p = 0.002, respectively), TCR Shannon (p = 0.025), and neutrophils (p = 0.010). DDR gene mutations exhibited a distinct immune profile with significantly higher expression levels of TNFSF9, CD70, ICAM1, and indoleamine-2,3-dioxygenase (IDO) and lower expression levels of VTCN1 and IL12A.

CONCLUSIONS

Our data suggest that the detection of somatic mutations in DDR genes can predict the efficacy of immunotherapy in patients with ccRCC. Furthermore, we revealed the unique molecular and immune mechanisms underlying ccRCC with DDR gene mutations.

Association of Molecular Profiles and Mutational Status With Distinct Histological Lung Adenocarcinoma Subtypes. An Analysis of the LACE-Bio Data

BACKGROUND

Adjuvant chemotherapy (AC) is indicated for stage II and stage III lung adenocarcinomas (ADC). Using the LACE Bio II database, we analyzed the distribution of various mutations across the subtypes of ADCs and studied the prognostic and predictive roles of PD-L1, TMB, and Tumor Infiltrating Lymphocytes (TILs).

MATERIALS AND METHODS

Clinical and genomic data from the LACE Bio II data were extracted. Patients were divided into ADC subtypes, in which the grouping was done based on their known clinical behavior (Lepidic [LEP], Acinar/Papillary [ACI or PAP], Micropapillary/Solid [MIP or SOL], Mucinous [MUC] and Others). Kaplan-Meier (KM) and log-rank test were used to compare survival based on PD-L1, TMB, TILs and combinations of TMB with PD-L1 and TILs. Adjusted Hazard Ratios (HR) were analyzed with Overall Survival (OS), Disease-Free Survival (DFS) and Lung Cancer-Specific Survival (LCSS) as endpoints.

RESULTS

A total of 375 ADC patients were identified. MIP/SOL was the subtype most commonly positive for various biomarkers. PD-L1 Negative/high TMB was associated with better outcomes in terms of OS (HR = 0.46 [0.23-0.89], P = .021) and DFS (HR = 0.52 [0.30-0.90], P = .02), relative to PD-L1 Negative/low TMB. High TMB predicted worse outcome with AC use in terms of OS (ratio of hazard ratio rHR = 2.75 [1.07-7.04], P = .035). Marked TILs had better outcome with AC for DFS (rHR = 0.22 [0.06-0.87], P = .031 and LCSS (rHR = 0.08 [0.01-0.66], P = .019) respectively. There was also a beneficial effect of AC among patients with Marked TILs/low TMB in terms of DFS (rHR = 0.06 [0.01-0.53], P = .011).

CONCLUSION

High TMB has a prognostic role in resectable lung ADC. The high TMB group had a poor outcome with AC, suggesting that this group may be better served with immune checkpoint therapy.

Convergence of Precision Oncology and Liquid Biopsy in Non-Small Cell Lung Cancer

This review article illuminates the role of liquid biopsy in the continuum of care for non-small cell lung cancer (NSCLC). We discuss its current application in advanced-stage NSCLC at the time of diagnosis and at progression. We highlight research showing that concurrent testing of blood and tissue yields faster, more informative, and cheaper answers than the standard stepwise approach. We also describe future applications for liquid biopsy including treatment response monitoring and testing for minimal residual disease. Lastly, we discuss the emerging role of liquid biopsy for screening and early detection.

A prognostic model for oral squamous cell carcinoma using 7 genes related to tumor mutational burden

Background

Oral squamous cell carcinoma (OSCC) is a rising problem in global public health. The traditional physical and imageological examinations are invasive and radioactive. There is a need for less harmful new biomarkers. Tumor mutational burden (TMB) is a novel prognostic biomarker for various cancers. We intended to explore the relationship between TMB-related genes and the prognosis of OSCC and to construct a prognostic model.

Methods

TMB-related differential expressed genes (DEGs) were screened by differential analysis and optimized via the univariate Cox and LASSO Cox analyses. Risk Score model was constructed by expression values of screened genes multiplying coefficient of LASSO Cox.

Results

Seven TMB-related DEGs (CTSG, COL6A5, GRIA3, CCL21, ZNF662, TDRD5 and GSDMB) were screened. Patients in high-risk group (Risk Score >  − 0.684511507) had worse prognosis compared to the low-risk group (Risk Score <  − 0.684511507). Survival rates of patients in the high-risk group were lower in the gender, age and degrees of differentiation subgroups compared to the low-risk group.

Conclusions

The Risk Score model constructed by 7 TMB-related genes may be a reliable biomarker for predicting the prognosis of OSCC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12903-022-02193-3.

Multiomics analysis of tumor mutational burden across cancer types

Whether tumor mutational burden (TMB) is related to improved survival outcomes or the promotion of immunotherapy in various malignant tumors remains controversial, and we lack a comprehensive understanding of TMB across cancers. Based on the data obtained from The Cancer Genome Atlas (TCGA), we conducted a multiomics analysis of TMB across 21 cancer types to identify characteristics related to TMB and determine the mechanism as it relates to prognosis, gene expression, gene mutation and signaling pathways. In our study, TMB was found to have a significant relationship with prognosis for 21 tumors, and the relationship was different in different tumors. TMB may also be related to different outcomes for patients with different tumor subtypes. TMB was confirmed to be correlated with clinical information, such as age and sex. Mutations in GATA3 and MAP3K1 in beast invasive carcinoma (BRCA), TCF7L2 in colon adenocarcinoma (COAD), NFE2L2 in esophageal carcinoma (ESCA), CIC and IDH1 in brain lower grade glioma (LGG), CDH1 in stomach adenocarcinoma (STAD), and TP53 in uterine corpus endometrial carcinoma (UCEC) were demonstrated to be correlated with lower TMB. Moreover, we identified differentially expressed genes (DEGs) and differentially methylated regions (DMRs) according to different TMB levels in 21 cancers. We also investigated the correlation between enrichment of signaling pathways, immune cell infiltration and TMB. In conclusion, we identified multiomic characteristics related to the TMB in 21 tumors, providing support for a comprehensive understanding of the role of TMB in different tumors.

Estimating tumor mutational burden across multiple cancer types using whole-exome sequencing

Background

Tumor mutational burden (TMB) is emerging as a promising biomarker in immune checkpoint inhibitor (ICI) therapy. Despite whole-exome sequencing (WES) being the gold standard for quantifying TMB, TMB is determined by selected targeted panels in most cases, and WES-derived TMB data are lacking due to the greater cost and complexity. Determining TMB thresholds is another issue that needs attention.

Methods

A total of 309 patients who had received ICI therapy, representing five cancers (listed in “Results”), were recruited. Among them, 269 patients were evaluable for survival analysis. Tumor and matched blood samples from the patients were analyzed using WES and somatic mutations were determined. TMB is defined as the total number of somatic nonsynonymous mutations in the tumor exome in our study. The patients were divided into different TMB subgroups according to a common fixed number (10 mutations/Mb) or the top tertile within each tumor type.

Results

The distribution of WES-derived median TMBs was highly variable across different tumor types, ranging from 2.71 (cholangiocarcinoma) to 2.97 (nervous system tumor), 3.69 (gastric cancer), 4.31 (hepatocellular carcinoma), and 4.64 [colorectal cancer (CRC)] mutations/Mb. In CRC, the survival benefit of TMB-high patients was significant using both the top tertile and the 10 mutations/Mb threshold. In hepatocellular carcinoma, the 10 mutations/Mb threshold showed an advantage over the top tertile threshold. Among patients with nervous system tumors, cholangiocarcinoma, and gastric cancer, no obvious survival differences were observed between the TMB-high and TMB-low groups with either TMB stratification approach.

Conclusions

The TMB threshold criterion may vary for different cancers. Our data suggest that TMB is unable to predict ICI benefit across all cancer types in Chinese patients. However, it may be an effective biomarker for predicting the clinical benefit of ICI therapy for patients with CRC.

High tumor mutational burden and T-cell activation are associated with long-term response to anti-PD1 therapy in Lynch syndrome recurrent glioblastoma patient

BACKGROUND

Glioblastomas (GBMs) in patients harboring somatic or germinal mutations of mismatch-repair (MMR) genes exhibit a hypermutable phenotype. Here, we describe a GBM patient with increased tumor mutational burden and germline MMR mutations, treated using anti-PD1 therapy.

METHODS

A woman with newly diagnosed GBM (nGBM) was treated by surgery, radiotherapy, and temozolomide. The tumor recurred after 13 months leading to a second surgery and treatment with nivolumab. Whole-exome sequencing was performed on the nGBM, recurrent GBM (rGBM), and blood. Immune infiltration was investigated by immunohistochemistry and the immune response in the blood during treatment was analyzed by flow cytometry.

RESULTS

High density of infiltrating CD163 + cells was found in both GBM specimens. Large numbers of CD3 + and CD8 + T cells were homogeneously distributed in the nGBM. The infiltration of CD4 + T cells and a different CD8 + T cell density were observed in the rGBM. Both GBM shared 12,431 somatic mutations, with 113 substitutions specific to the nGBM and 1,683 specific to the rGBM. Germline variants included pathogenic mutation in the MSH2 (R359S) gene, suggesting the diagnosis of Lynch syndrome. Systemic immunophenotyping revealed the generation of CD8 + T memory cells and persistent activation of CD4 + T cells. The patient is still receiving nivolumab 68 months after the second surgery.

CONCLUSIONS

Our observations indicate that the hypermutator phenotype associated with germinal mutations of MMR genes and abundant T-cell infiltration contributes to a durable clinical benefit sustained by a persistent and robust immune response during anti-PD1 therapy.

Translational Biomarkers and Rationale Strategies to Overcome Resistance to Immune Checkpoint Inhibitors in Solid Tumors

Immune checkpoint inhibitors (ICIs) targeting the programed cell-death protein 1 (PD-1) or its ligand PD-L1 and cytotoxic T-lymphocyte antigen 4 (CTLA-4) pathways have improved the survival for patients with solid tumors. Unfortunately, durable clinical responses are seen in only 10-40% of patients at the cost of potential immune-related adverse events. In the tumor microenvironment (TME), tumor cells can influence the microenvironment by releasing extracellular signals and generating peripheral immune tolerance, while the immune cells can affect the initiation, growth, proliferation, and evolution of cancer cells. Currently, translational biomarkers that predict responses to ICIs include high PD-L1 tumor proportion score, defective DNA mismatch repair, high microsatellite instability, and possibly high tumor mutational burden. Characterization of immune cells in the TME, such as tumor-infiltrating lymphocytes, T-cell gene expression profile, T-cell receptor sequencing, and peripheral blood biomarkers are being explored as promising biomarkers. Recent neoadjuvant studies have integrated the real-time assessment of both molecular and immune biomarkers using the tissue and blood specimens simultaneously and longitudinally. This review summarizes the current knowledge and progress in developing translational biomarkers and rational combinational strategies to improve the efficacy of ICIs tailored to individual cancer patients.

MiRNA expression patterns are associated with tumor mutational burden in lung adenocarcinoma

Background: Tumor mutational burden (TMB) has emerged as an independent biomarker to predict patient responses to treatment with immune checkpoint inhibitors (ICIs) for lung adenocarcinoma (LUAD). MicroRNAs (miRNAs) have a crucial role in the regulation of anticancer immune responses, but the association of miRNA expression patterns and TMB is not clear in LUAD. Methods: Differentially expressed miRNAs in samples with high TMB and low TMB samples were screened in the LUAD dataset in The Cancer Genome Atlas. The least absolute shrinkage and selection operator (LASSO) method was applied to develop a miRNA-based signature classifier for predicting TMB levels in the training set. An test set was used to validate this classifier. The correlation between the miRNA-based classifier index and the expression of three immune checkpoints (PD-1, PD-L1, and CTLA-4) were explored. Functional enrichment analysis was carried out of the miRNAs included in the miRNA-based signature classifier. Results: Twenty-five differentially expressed miRNAs were used to establish a miRNA-based signature classifier for predicting TMB level. The accuracy of the 25-miRNA-based signature classifier was 0.850 in the training set, 0.810 in the test set and 0.840 in the total set. This miRNA-based signature classifier index showed a low correlation with PD-1 and PD-L1, and no correlation with CTLA-4. Enrichment analysis for these 25 miRNA revealed they are involved in many immune-related biological processes and cancer-related pathways. Conclusion: MiRNA expression patterns are associated with tumor mutational burden and a miRNA-based signature classifier may serve as a biomarker for prediction of TMB levels in LUAD.

Toward a comprehensive view of cancer immune responsiveness: a synopsis from the SITC workshop

Tumor immunology has changed the landscape of cancer treatment. Yet, not all patients benefit as cancer immune responsiveness (CIR) remains a limitation in a considerable proportion of cases. The multifactorial determinants of CIR include the genetic makeup of the patient, the genomic instability central to cancer development, the evolutionary emergence of cancer phenotypes under the influence of immune editing, and external modifiers such as demographics, environment, treatment potency, co-morbidities and cancer-independent alterations including immune homeostasis and polymorphisms in the major and minor histocompatibility molecules, cytokines, and chemokines. Based on the premise that cancer is fundamentally a disorder of the genes arising within a cell biologic process, whose deviations from normality determine the rules of engagement with the host's response, the Society for Immunotherapy of Cancer (SITC) convened a task force of experts from various disciplines including, immunology, oncology, biophysics, structural biology, molecular and cellular biology, genetics, and bioinformatics to address the complexity of CIR from a holistic view. The task force was launched by a workshop held in San Francisco on May 14-15, 2018 aimed at two preeminent goals: 1) to identify the fundamental questions related to CIR and 2) to create an interactive community of experts that could guide scientific and research priorities by forming a logical progression supported by multiple perspectives to uncover mechanisms of CIR. This workshop was a first step toward a second meeting where the focus would be to address the actionability of some of the questions identified by working groups. In this event, five working groups aimed at defining a path to test hypotheses according to their relevance to human cancer and identifying experimental models closest to human biology, which include: 1) Germline-Genetic, 2) Somatic-Genetic and 3) Genomic-Transcriptional contributions to CIR, 4) Determinant(s) of Immunogenic Cell Death that modulate CIR, and 5) Experimental Models that best represent CIR and its conversion to an immune responsive state. This manuscript summarizes the contributions from each group and should be considered as a first milestone in the path toward a more contemporary understanding of CIR. We appreciate that this effort is far from comprehensive and that other relevant aspects related to CIR such as the microbiome, the individual's recombined T cell and B cell receptors, and the metabolic status of cancer and immune cells were not fully included. These and other important factors will be included in future activities of the taskforce. The taskforce will focus on prioritization and specific actionable approach to answer the identified questions and implementing the collaborations in the follow-up workshop, which will be held in Houston on September 4-5, 2019.

Clinical Implications of Circulating Tumor DNA Tumor Mutational Burden (ctDNA TMB) in Non-Small Cell Lung Cancer

BACKGROUND

Tissue tumor mutational burden (TMB) has emerged as a potential biomarker predicting response to anti-programmed cell death-1 protein receptor (PD-1)/programmed cell death-1 protein ligand (PD-L1) therapy, but few studies have explored using circulating tumor DNA (ctDNA) TMB in non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS

A total of 136 patients with NSCLC with ctDNA testing were retrospectively evaluated from a single institution, along with a validation cohort from a second institution. ctDNA TMB was derived using the number of detected mutations over the DNA sequencing length.

RESULTS

Higher ctDNA TMB was significantly correlated with smoking history (p < .05, chi-squared test). Among patients treated with immune checkpoint inhibitors (n = 20), higher ctDNA TMB was significantly correlated with shorter progressive free survival (PFS) and overall survival (OS; 45 vs. 355 days; hazard ratio [HR], 5.6; 95% confidence interval [CI], 1.3-24.6; p < .01, and OS 106 days vs. not reached; HR, 6.0; 95% CI, 1.3-27.1; p < .01, respectively). In a small independent validation cohort (n = 12), there was a nonsignificant numerical difference for higher ctDNA TMB predicting shorter OS but not PFS. ctDNA TMB was not correlated with RECIST tumor burden estimation in the subset of patients treated with immune checkpoint blockade.

CONCLUSION

The findings indicate that higher ctDNA TMB, at the current commercial sequencing length, reflects worse clinical outcomes.

IMPLICATIONS FOR PRACTICE

Biomarkers to identify patients who will respond to immune checkpoint blockade are critical. Tissue tumor mutational burden (TMB) has emerged as a viable biomarker to predict response to anti-PD-1/PD-L1 therapy, but few studies have explored the meaning and potential clinical significance of noninvasive, blood-based TMB. Here, we investigated circulating tumor DNA (ctDNA) TMB and present data demonstrating that current ctDNA TMB may reflect tumor burden and that ctDNA panels with a greater number of mutations may be necessary to more accurately reflect tissue TMB.

Current views on tumor mutational burden in patients with non-small cell lung cancer treated by immune checkpoint inhibitors

In the last few years, the treatment of patients with non-small cell lung cancer (NSCLC) has impressively benefitted from immunotherapy, in particular from the inhibition of immune checkpoints such as programmed cell death-1 (PD-1) and programmed cell death ligand-1 (PD-L1). However, despite the significant survival benefit for some patients with advanced NSCLC, the objective response rates (ORRs) remain relatively low no more than 20-30% with a large proportion of patients demonstrating primary resistance. Although the selection of NSCLC patients for the first-line treatment is currently guided by the expression of PD-L1 in tumor cells as detected by immunohistochemistry, this is not the case for the second-line setting. Moreover, the sensitivity and specificity of PD-L1 expression is modest which has prompted the search for additional predictive biomarkers. In this context, the assessment of the tumor mutational burden (TMB), defined as the total number of nonsynonymous mutations in the coding regions of genes, has recently emerged as an additional powerful biomarker to select patients for immunotherapy. The purpose of our review is to highlight the recent advances as well as the challenges and perspectives in the field of TMB and immunotherapy for patients with NSCLC.

The clinical utility of tumor mutational burden in non-small cell lung cancer

Despite advances made during the last two decades, lung cancer remains the leading cause of cancer-related death worldwide. Recently, immune checkpoint inhibitors (ICIs) became available for the treatment of advanced non-small cell lung cancer (NSCLC) patients. Although ICIs showed a survival advantage in comparison with chemotherapy in the second and first-line setting, overall response rate is only around 20% and a large proportion of patients will undergo disease progression within the first weeks of treatment. For this reason, there is a need for biomarkers to predict the efficacy of ICIs in NSCLC and to identify patients who will benefit from ICIs. The first biomarker developed was programmed cell death ligand 1 (PD-L1) expression. However, the predictive role of PD-L1 expression varied from one clinical trial to another, due to the multiple assays used, with different antibodies, different platforms, and different thresholds. Moreover, PD-L1 expression is highly heterogeneous. For these reasons, PD-L1 expression alone is not a good biomarker to predict the efficacy of ICIs and there is a need for the identification of other biomarkers. Tumor mutational burden (TMB) is defined as the number of mutations per DNA megabases. It was first assessed as a biomarker for ICI based on the observation of successful immune checkpoint inhibition in solid tumors with high TMB such as NSCLC, melanoma or bladder cancer. Pre-clinical data suggested that the association between TMB and ICIs efficacy could be explained by the creation of neoantigens induced by mutations acquisition, increasing tumor immunogenicity and response to ICIs. Preliminary observations of TMB role as a predictive biomarker for the efficacy of ICIs in patients with advanced NSCLC led to the assessment of TMB clinical utility in phase III clinical trials. This review reports the clinical features and prognostic role of TMB in NSCLC. This review also focuses on TMB predictive role for the treatment of NSCLC patients with single-agent programmed cell death 1 (PD-1) and PD-L1 inhibitors such as pembrolizumab, nivolumab and atezolizumab or ICIs combination.

Implementing tumor mutational burden (TMB) analysis in routine diagnostics-a primer for molecular pathologists and clinicians

Tumor mutational burden (TMB) is a new biomarker for prediction of response to PD-(L)1 treatment. Comprehensive sequencing approaches (i.e., whole exome and whole genome sequencing) are ideally suited to measure TMB directly. However, as their applicability in routine diagnostics is currently limited by high costs, long turnaround times and poor availability of fresh tissue, targeted next-generation sequencing (NGS) of formalin-fixed and paraffin-embedded (FFPE) samples appears to be a more feasible and straight-forward approach for TMB approximation, which can be seamlessly integrated in already existing diagnostic workflows and pipelines. In this work, we provide an overview of the clinical implications of TMB testing and highlight key parameters including pre-analysis, analysis and post-analytical steps that influence and shape TMB approximation by panel sequencing. Collectively, the data will not only serve as a field guide and state of the art knowledge source for molecular pathologists who consider implementation of TMB measurement in their lab, but also enable clinicians in understanding the specific parameters influencing TMB test results and reporting.

Methods of measurement for tumor mutational burden in tumor tissue

Immunotherapies based on immune checkpoint inhibitors are emerging as an innovative treatment for different types of advanced cancers. While the utility of immune checkpoint inhibitors has been clearly demonstrated, the response rate is highly variable across individuals. Due to the cost and toxicity of these immunotherapies, a critical challenge in this field is the identification of predictive biomarkers to discriminate which patients may respond to immunotherapy. Recently, a high tumor mutational burden (TMB) has been identified as a genetic signature that is associated with a favorable outcome for immune checkpoint inhibitor therapy. The TMB is defined as the total number of nonsynonymous mutations per coding area of a tumor genome. Initially, it was determined using whole exome sequencing, but due to the high costs and long turnaround time of this method, targeted panel sequencing is currently being explored to measure TMB. In the near future, TMB evaluation may play an important role in immuno-oncology, but its implementation in a routine setting involves robust analytical and clinical validation. Standardization is also needed in order to make informed decisions about patients. This review presents the methodologies employed for determining TMB and discusses the factors that may have an impact on its measurement.

Approach to evaluating tumor mutational burden in routine clinical practice

Immune checkpoint inhibition with monoclonal antibodies has emerged as a promising therapeutic approach but in most tumor types responses are unpredictable and observed in a minority of treated patients. Positive and negative predictive biomarkers for efficacy of these costly drugs are desperately needed. Immunohistochemistry (IHC) for programmed death ligand (PD-L1) expression in tumor and inflammatory infiltrate has emerged as one predictive biomarker of some value. However, multiple confounders including those inherent to any IHC and the unique complexities of the biology of the immune response have limited its utility. Tumor mutational burden (TMB) has emerged as a seemingly more promising predictive biomarker for immunotherapy with checkpoint inhibitors in several tumor types and is likely to be incorporated into future treatment algorithms for these agents. Given this, the need to define and standardize key parameters of the most promising biomarkers becomes essential to allow all stakeholders to make meaningful observations and inferences as to the efficacy of ostensibly similar agents and combinations in various settings. This review briefly summarizes approaches to measurement of TMB and ongoing efforts to achieve harmonization of this key biomarker.

Clinical implication of tumor mutational burden in patients with HER2-positive refractory metastatic breast cancer

This study explored the clinical implications of tumor mutational burden (TMB) in a well-defined HER2-positive metastatic breast cancer (MBC) patient population who had been previously treated but had subsequent disease progression. Whole exome sequencing was performed on formalin-fixed paraffin-embedded tumor samples and matched normal tissue. Among the 46 patients, 13 (28.3%) were estrogen receptor-positive and nine (19.6%) were progesterone receptor-positive by immunohistochemistry analysis. Twenty patients (43.5%) had recurrent MBC compared with de novo MBC (n = 26, 56.5%). Sixteen patients (34.6%) demonstrated more than 100 somatic non-synonymous SNV mutations, which was predefined as a high TMB. The median follow-up duration was 57.5 months. The median overall survival (mOS) differed significantly between low and high TMB status (44.9 months vs. 85.8 months, respectively, p = 0.016). In a multivariate Cox regression analysis, TMB was the only independent prognostic factor for good metastatic overall survival after adjusting for age and recurrence (Hazard ratio [HR] = 0.32, 95% confidence interval [CI], 0.103-0.998, p = 0.049). These data suggest that high TMB may be a prognostic marker for predicting good overall survival for patients undergoing conventional HER2-directed treatments and chemotherapy. Further, future clinical trials harnessing TMB may benefit by identifying an appropriate population who may have a favorable response to immunotherapy after recurrence following HER2-directed treatments.

Discovery of putative breast cancer antigens using an integrative platform of genomics-driven immunoproteomics

Recent advances in cancer immuno-therapeutics such as checkpoint inhibitors, chimeric antigen-receptor T cells, and tumor infiltrating T cells (TIL) are now significantly impacting cancer patients in a positive manner. Although very promising, reports indicate no more than 25% of cases result in complete remission. One of the limitations of these treatments is the identity of putative cancer antigens in each patient, as it is technically challenging to identify cancer antigens in a rapid fashion. Thus, identification of cancer antigens followed by targeted treatment will increase the efficacy of cancer immunotherapies. To achieve this goal, a combined technologies platform of deep genomic sequencing and personalized immune assessment was devised, termed Genomics Driven Immunoproteomics (GDI). Using this technological platform, we report the discovery of 149 tumor antigens from human breast cancer patients. Significant number of these putative cancer antigens arise from single nucleotide variants (SNVs), as well as insertions and deletions that results into frame-shift mutations. We propose a general model of anti-cancer immunity and suggest that the GDI platform may help identify patient-specific tumor antigens in a timely fashion for precision immunotherapies.