Improved prognostic classification of breast cancer defined by antagonistic activation patterns of immune response pathway modules

Emerging measurements for tumor-infiltrating lymphocytes in breast cancer

Tumor-infiltrating lymphocytes are a general term for lymphocytes or immune cells infiltrating the tumor microenvironment. Numerous studies have demonstrated tumor-infiltrating lymphocytes to be robust prognostic and predictive biomarkers in breast cancer. Recently, immune checkpoint inhibitors, which directly target tumor-infiltrating lymphocytes, have become part of standard of care treatment for triple-negative breast cancer. Surprisingly, tumor-infiltrating lymphocytes quantified by conventional methods do not predict response to immune checkpoint inhibitors, which highlights the heterogeneity of tumor-infiltrating lymphocytes and the complexity of the immune network in the tumor microenvironment. Tumor-infiltrating lymphocytes are composed of diverse immune cell populations, including cytotoxic CD8-positive T lymphocytes, B cells and myeloid cells. Traditionally, tumor-infiltrating lymphocytes in tumor stroma have been evaluated by histology. However, the standardization of this approach is limited, necessitating the use of various novel technologies to elucidate the heterogeneity in the tumor microenvironment. This review outlines the evaluation methods for tumor-infiltrating lymphocytes from conventional pathological approaches that evaluate intratumoral and stromal tumor-infiltrating lymphocytes such as immunohistochemistry, to the more recent advancements in computer tissue imaging using artificial intelligence, flow cytometry sorting and multi-omics analyses using high-throughput assays to estimate tumor-infiltrating lymphocytes from bulk tumor using immune signatures or deconvolution tools. We also discuss higher resolution technologies that enable the analysis of tumor-infiltrating lymphocytes heterogeneity such as single-cell analysis and spatial transcriptomics. As we approach the era of personalized medicine, it is important for clinicians to understand these technologies.

Exploring the role of ubiquitin regulatory X domain family proteins in cancers: bioinformatics insights, mechanisms, and implications for therapy

UBXD family (UBXDF), a group of proteins containing ubiquitin regulatory X (UBX) domains, play a crucial role in the imbalance of proliferation and apoptotic in cancer. In this study, we summarised bioinformatics proof on multi-omics databases and literature on UBXDF's effects on cancer. Bioinformatics analysis revealed that Fas-associated factor 1 (FAF1) has the largest number of gene alterations in the UBXD family and has been linked to survival and cancer progression in many cancers. UBXDF may affect tumour microenvironment (TME) and drugtherapy and should be investigated in the future. We also summarised the experimental evidence of the mechanism of UBXDF in cancer, both in vitro and in vivo, as well as its application in clinical and targeted drugs. We compared bioinformatics and literature to provide a multi-omics insight into UBXDF in cancers, review proof and mechanism of UBXDF effects on cancers, and prospect future research directions in-depth. We hope that this paper will be helpful for direct cancer-related UBXDF studies.

Dissecting cellular states of infiltrating microenvironment cells in melanoma by integrating single-cell and bulk transcriptome analysis

BACKGROUND

Cellular states of different immune cells can affect the activity of the whole immune microenvironment.

METHODS

Here, leveraging reference profiles of microenvironment cell states that were constructed based on single-cell RNA-seq data of melanoma, we dissected the composition of microenvironment cell states across 463 skin cutaneous melanoma (SKCM) bulk samples through CIBERSORT-based deconvolution of gene expression profiles and revealed high heterogeneity of their distribution. Correspondence analysis on the estimated cellular fractions of melanoma bulk samples was performed to identify immune phenotypes. Based on the publicly available clinical survival and therapy data, we analyzed the relationship between immune phenotypes and clinical outcomes of melanoma.

RESULTS

By analysis of the relationships among those cell states, we further identified three distinct tumor microenvironment immune phenotypes: "immune hot/active", "immune cold-suppressive" and "immune cold-exhausted". They were characterized by markedly different patterns of cell states: most notably the CD8 T Cytotoxic state, CD8 T Mixed state, B non-regulatory state and cancer-associated fibroblasts (CAFs), depicting distinct types of antitumor immune response (or immune activity). These phenotypes had prognostic significance for progression-free survival and implications in response to immune therapy in an independent cohort of anti-PD1 treated melanoma patients.

CONCLUSIONS

The proposed strategy of leveraging single-cell data to dissect the composition of microenvironment cell states in individual bulk tumors can also extend to other cancer types, and our results highlight the importance of microenvironment cell states for the understanding of tumor immunity.

Race, Gene Expression Signatures, and Clinical Outcomes of Patients With High-Risk Early Breast Cancer

Importance

There has been little consideration of genomic risk of recurrence by breast cancer subtype despite evidence of racial disparities in breast cancer outcomes.

Objective

To evaluate associations between clinical trial end points, namely pathologic complete response (pCR) and distant recurrence-free survival (DRFS), and race and examine whether gene expression signatures are associated with outcomes by race.

Design, Setting, and Participants

This retrospective cohort study used data from the Investigation of Serial Studies to Predict Your Therapeutic Response With Imaging and Molecular Analysis 2 (I-SPY 2) multicenter clinical trial of neoadjuvant chemotherapy with novel agents and combinations for patients with previously untreated stage II/III breast cancer. Analyses were conducted of associations between race and short- and long-term outcomes, overall and by receptor subtypes, and their association with 28 expression biomarkers. The trial enrolled 990 female patients between March 30, 2010, and November 5, 2016, with a primary tumor size of 2.5 cm or greater and clinical or molecular high risk based on MammaPrint or hormone receptor (HR)-negative/ERBB2 (formerly HER2 or HER2/neu)-positive subtyping across 9 arms. This data analysis was performed between June 10, 2021, and October 20, 2022.

Exposure

Race, tumor receptor subtypes, and genomic biomarker expression of early breast cancer.

Main Outcomes and Measures

The primary outcomes were pCR and DRFS assessed by race, overall, and by tumor subtype using logistic regression and Cox proportional hazards regression models. The interaction between 28 expression biomarkers and race, considering pCR and DRFS overall and within subtypes, was also evaluated.

Results

The analytic sample included 974 participants (excluding 16 self-reporting as American Indian or Alaska Native, Native Hawaiian or Other Pacific Islander, or multiple races due to small sample sizes), including 68 Asian (7%), 120 Black (12%), and 786 White (81%) patients. Median (range) age at diagnosis was 47 (25-71) years for Asian, 49 (25-77) for Black, and 49 (23-73) years for White patients. The pCR rates were 32% (n = 22) for Asian, 30% for Black (n = 36), and 32% for White (n = 255) patients (P = .87). Black patients with HR-positive/ERBB2-negative tumors not achieving pCR had significantly worse DRFS than their White counterparts (hazard ratio, 2.28; 95% CI, 1.24-4.21; P = .01), with 5-year DRFS rates of 55% (n = 32) and 77% (n = 247), respectively. Black patients with HR-positive/ERBB2-negative tumors, compared with White patients, had higher expression of an interferon signature (mean [SD], 0.39 [0.87] and -0.10 [0.99]; P = .007) and, compared with Asian patients, had a higher mitotic score (mean [SD], 0.07 [1.08] and -0.69 [1.06]; P = .01) and lower estrogen receptor/progesterone receptor signature (mean [SD], 0.31 [0.90] and 1.08 [0.95]; P = .008). A transforming growth factor β signature had a significant association with race relative to pCR and DRFS, with a higher signature associated with lower pCR and worse DRFS outcomes among Black patients only.

Conclusions and Relevance

The findings show that women with early high-risk breast cancer who achieve pCR have similarly good outcomes regardless of race, but Black women with HR-positive/ERBB2-negative tumors without pCR may have worse DRFS than White women, highlighting the need to develop and test novel biomarker-informed therapies in diverse populations.

MDR1-EXPRESSING CD4+ T CELLS WITH TH1.17 FEATURES RESIST TO NEOADJUVANT CHEMOTHERAPY AND ARE ASSOCIATED WITH BREAST CANCER CLINICAL RESPONSE

BACKGROUND

Multidrug resistance-1 (MDR1) transporter limits the intracellular accumulation of chemotherapies (paclitaxel, anthracyclines) used in breast cancer (BC) treatment. In addition to tumor cells, MDR1 is expressed on immune cell subsets in which it confers chemoresistance. Among human T cells, MDR1 is expressed by most CD8+ T cells, and a subset of CD4+ T helper (Th) cells. Here we explored the expression, function and regulation of MDR1 on CD4+ T cells and investigated the role of this population in response to neoadjuvant chemotherapy (NAC) in BC.

METHODS

Phenotypic and functional characteristics of MDR1+ CD4 Th cells were assessed on blood from healthy donors and patients with BC by flow cytometry. These features were extended to CD4+ Th cells from untreated breast tumor by flow cytometry and RNA-sequencing (RNA-seq). We performed in vitro polarization assays to decipher MDR1 regulation on CD4 Th cells. We evaluated in vitro the impact of chemotherapy agents on MDR1+ CD4+ Th cells. We analyzed the impact of NAC treatment on MDR1+ CD4+ Th cells from blood and tumors and their association with treatment efficacy in two independent BC cohorts and in a public RNA-seq data set of BC tumor biopsies before and after NAC. Finally, we performed single cell (sc) RNAseq of blood CD4+ memory T cells from NAC-treated patients and combined them with an scRNAseq public data set.

RESULTS

MDR1+ CD4 Th cells were strongly enriched in Th1.17 polyfunctional cells but also in Th17 cells, both in blood and untreated breast tumor tissues. Mechanistically, Tumor growth factor (TGF)-β1 was required for MDR1 induction during in vitro Th17 or Th1.17 polarization. MDR1 expression conferred a selective advantage to Th1.17 and Th17 cells following paclitaxel treatment in vitro and in vivo in NAC-treated patients. scRNAseq demonstrated MDR1 association with tumor Th1.17 and Th with features of cytotoxic cells. Enrichment in MDR1+ CD4+ Th1.17 and Th17 cells, in blood and tumors positively correlated with pathological response. Absence of early modulation of Th1.17 and Th17 in NAC-resistant patients, argue for its use as a biomarker for chemotherapy regimen adjustment.

CONCLUSION

MDR1 favored the enrichment of Th1.17 and Th17 in blood and tumor after NAC that correlated to clinical response.

B-cells and regulatory T-cells in the microenvironment of HER2+ breast cancer are associated with decreased survival: a real-world analysis of women with HER2+ metastatic breast cancer

BACKGROUND

Despite major improvements in treatment of HER2-positive metastatic breast cancer (MBC), only few patients achieve complete remission and remain progression free for a prolonged time. The tumor immune microenvironment plays an important role in the response to treatment in HER2-positive breast cancer and could contain valuable prognostic information. Detailed information on the cancer-immune cell interactions in HER2-positive MBC is however still lacking. By characterizing the tumor immune microenvironment in patients with HER2-positive MBC, we aimed to get a better understanding why overall survival (OS) differs so widely and which alternative treatment approaches may improve outcome.

METHODS

We included all patients with HER2-positive MBC who were treated with trastuzumab-based palliative therapy in the Netherlands Cancer Institute between 2000 and 2014 and for whom pre-treatment tissue from the primary tumor or from metastases was available. Infiltrating immune cells and their spatial relationships to one another and to tumor cells were characterized by immunohistochemistry and multiplex immunofluorescence. We also evaluated immune signatures and other key pathways using next-generation RNA-sequencing data. With nine years median follow-up from initial diagnosis of MBC, we investigated the association between tumor and immune characteristics and outcome.

RESULTS

A total of 124 patients with 147 samples were included and evaluated. The different technologies showed high correlations between each other. T-cells were less prevalent in metastases compared to primary tumors, whereas B-cells and regulatory T-cells (Tregs) were comparable between primary tumors and metastases. Stromal tumor-infiltrating lymphocytes in general were not associated with OS. The infiltration of B-cells and Tregs in the primary tumor was associated with unfavorable OS. Four signatures classifying the extracellular matrix of primary tumors showed differential survival in the population as a whole.

CONCLUSIONS

In a real-world cohort of 124 patients with HER2-positive MBC, B-cells, and Tregs in primary tumors are associated with unfavorable survival. With this paper, we provide a comprehensive insight in the tumor immune microenvironment that could guide further research into development of novel immunomodulatory strategies.

An immune indicator based on BTK and DPEP2 identifies hot and cold tumors and clinical treatment outcomes in lung adenocarcinoma

In lung adenocarcinoma (LUAD), immune heterogeneity of hot and cold tumors has been recognized as one of the major factors affecting immunotherapy and other common treatments. However, there is still a lack of biomarkers that can effectively identify the immunophenotype of cold and hot tumors. First, the immune signatures were obtained based on literature mining, including macrophage/monocyte, IFN-γ response, TGF-β response, IL12 response, lymphocyte activation, and ECM/Dve/immune response. Subsequently, LUAD patients were further clustered into different immune phenotypes based on these immune signatures. Next, the key genes related to the immune phenotypes were screened by WGCNA analysis, univariate analysis, and lasso-cox analysis, and the risk signature was established via the key genes. In additional, we compared the clinicopathological characteristics, drug sensitivity, the abundance of immune infiltration, and the efficacy of immunotherapy and commonly used therapies between patients in the high- and low-risk groups in LUAD. LUAD patients were divided into immune hot phenotype and immune cold phenotype groups. The clinical presentation showed that patients with the immune hot phenotype had higher immunoactivity (including higher MHC, CYT, immune, stromal, ESTIMATE scores, higher abundance of immune cell infiltration, higher abundance of TIL, and enrichment of immune-enriched subtypes) and better survival outcomes than those with the immune cold phenotype. Subsequently, WGCNA analysis, univariate analysis, and lasso-cox analysis identified the genes highly associated with the immune phenotype: BTK and DPEP2. The risk signature, consisting of BTK and DPEP2, is highly correlated with the immune phenotype. High-risk scores were enriched in patients with immune cold phenotype and low-risk scores were enriched in patients with immune hot phenotype. Compared to the high-risk group, the low-risk group had better clinical performance, higher drug sensitivity, and a higher degree of immunoactivity, as well as better efficacy in receiving immunotherapy and common adjuvant therapy. This study developed an immune indicator consisting of BTK and DPEP2 based on the heterogeneity of hot and cold Immunophenotypes of the tumor microenvironment. This indicator has good efficacy in predicting prognosis and assessing the efficacy of immunotherapy, chemotherapy, and radiotherapy. It has the potential to facilitate personalized and precise treatment of LUAD in the future.

The involvement of Th1 cell differentiation in the anti-tumor effect of purified polysaccharide from Sanghuangporus vaninii in colorectal cancer via multi-omics analysis

Sanghuangporus vaninii is a medicinal mushroom, which has been used as a treatment for various diseases; however, the therapeutic potential and mechanism of action of S. vaninii in colorectal cancer (CRC) remain unknown. Herein, human colon adenocarcinoma cells were used to analyze the anti-CRC effects of the purified polysaccharide of S. vaninii (SVP-A-1) in vitro. In SVP-A-1-treated B6/JGpt-Apc^{em1Cin (Min)}/Gpt male (Apc^Min/+) mice, 16S rRNA sequencing was performed on cecal feces, metabolites were examined in serum, and LC-MS/MS protein detection was performed in colorectal tumors. Protein changes were further confirmed by various biochemical detection methods. Water-soluble SVP-A-1 with a molecular weight of 22.5 kDa was first obtained. SVP-A-1 prevented gut microbiota dysbiosis related to metabolic pathways of L-arginine biosynthesis, increased L-citrulline levels in the serum of Apc^Min/+ mice, mediated L-arginine synthesis, and improved antigen presentation in dendritic cells and activated CD4⁺ T cells; the resulting Th1 cells released IFN-γ and TNF-α to act on tumor cells and promoted the sensitivity of tumor cells to cytotoxic T lymphocytes. In summary, SVP-A-1 exerted anti-CRC effects and has excellent potential for CRC treatment.

Pan-cancer analysis reveals interleukin-17 family members as biomarkers in the prediction for immune checkpoint inhibitor curative effect

Background

The interleukin-17 (IL-17) family contains six homologous genes, IL-17A to IL-17F. Growing evidence indicates that dysregulated IL-17 family members act as major pathogenic factors in the early and late stages of cancer development and progression. However, the prevalence and predictive value of IL-17 for immune checkpoint inhibitor (ICI) therapeutic effectiveness in multiple tumor types remain largely unknown, and the associations between its expression levels and immunotherapy-associated signatures also need to be explored.

Methods

The pan-cancer dataset in The Cancer Genome Atlas (TCGA) was downloaded from UCSC Xena (http://xena.ucsc.edu/). The immunotherapeutic cohorts included IMvigor210, which were obtained from the Gene Expression Omnibus database and included in a previously published study. Other datasets, namely, the GEO dataset and PRECOG, GEO, and METABRIC databases, were also included. In 33 TCGA tumor types, a pan-cancer analysis was carried out including their expression map, clinical risk assessment, and immune subtype analysis, along with their association with the stemness indices, tumor microenvironment (TME) in pan-cancer, immune infiltration analysis, ICI-related immune indicators, and drug sensitivity. RT-PCR was also carried out to verify the gene expression levels among MCF-10A and MCF-7 cell lines.

Results

The expression of the IL-17 family is different between tumor and normal tissue in most cancers, and consistency has been observed between gene activity and gene expression. RT-PCR results show that the expression differences in the IL-17 family of human cell (MCF-10A and MCF-7) are consistent with the bioinformatics differential expression analysis. Moreover, the expression of the IL-17 family can be a sign of patients’ survival prognosis in some tumors and varies in different immune subtypes. Moreover, the expression of the IL-17 family presents a robust correlation with immune cell infiltration, ICI-related immune indicators, and drug sensitivity. High expression of the IL-17 family is significantly related to immune-relevant pathways, and the low expression of IL-17B means a better immunotherapeutic response in BLCA.

Conclusion

Collectively, IL-17 family members may act as biomarkers in predicting the prognosis of the tumor and the therapeutic effects of ICIs, which provides new guidance for cancer treatment.

Pan-Cancer HLA Gene-Mediated Tumor Immunogenicity and Immune Evasion

Human leukocyte antigen (HLA) expression contributes to the activation of antitumor immunity through interactions with T-cell receptors. Pan-cancer HLA-mediated immunogenicity and immunoediting mechanisms have not been systematically studied previously. In a retrospective analysis of 33 tumor types from the Cancer Genome Atlas (TCGA), we characterized the differential expression of HLA class I and class II genes across various oncogenic pathways and immune subtypes. While HLA I genes were upregulated in all immunogenically hot tumors, HLA II genes were upregulated in an inflammatory immune subtype associated with best prognosis and were systematically downregulated in specific oncogenic pathways. A subset of immunogenically hot tumors which upregulated HLA class I but not class II genes exploited HLA-mediated escape strategies. Furthermore, with a machine learning model, we demonstrated that HLA gene expression data can be used to predict the immune subtypes of patients receiving immune checkpoint blockade and stratify patient survival. Interestingly, tumors with the highest immune infiltration did not have the best prognosis but showed significantly higher immune exhaustion.

IMPLICATIONS

Taken together, we highlight the prognostic potential of HLA genes in immunotherapies and suggest that higher tumor immunogenicity mediated by HLA expression may sometimes lead to tumor escape under strong selective pressure.

Identification and validation of a novel 16-gene prognostic signature for patients with breast cancer

Despite increased early diagnosis and improved treatment in breast cancer (BRCA) patients, prognosis prediction is still a challenging task due to the disease heterogeneity. This study was to identify a novel gene signature that can accurately evaluate BRCA patient survival. The gene expression and clinical data of BRCA patients were collected from The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of BRCA International Consortium (METABRIC) databases. Genes associated with prognosis were determined by Kaplan–Meier survival analysis and multivariate Cox regression analysis. A prognostic 16-gene score was established with linear combination of 16 genes. The prognostic value of the signature was validated in the METABRIC and GSE202203 datasets. Gene expression analysis was performed to investigate the diagnostic values of 16 genes. The 16-gene score was associated with shortened overall survival in BRCA patients independently of clinicopathological characteristics. The signalling pathways of cell cycle, oocyte meiosis, RNA degradation, progesterone mediated oocyte maturation and DNA replication were the top five most enriched pathways in the high 16-gene score group. The 16-gene nomogram incorporating the survival‐related clinical factors showed improved prediction accuracies for 1-year, 3-year and 5‐year survival (area under curve [AUC] = 0.91, 0.79 and 0.77 respectively). MORN3, IGJ, DERL1 exhibited high accuracy in differentiating BRCA tissues from normal breast tissues (AUC > 0.80 for all cases). The 16-gene profile provides novel insights into the identification of BRCA with a high risk of death, which eventually guides treatment decision making.

Tumor Immunogenomic Features Determine Outcomes in Patients with Metastatic Colorectal Cancer Treated with Standard-of-Care Combinations of Bevacizumab and Cetuximab

PURPOSE

CALGB/SWOG 80405 was a randomized phase III trial in first-line patients with metastatic colorectal cancer treated with bevacizumab, cetuximab, or both, plus chemotherapy. We tested the effect of tumor immune features on overall survival (OS).

EXPERIMENTAL DESIGN

Primary tumors (N = 554) were profiled by RNA sequencing. Immune signatures of macrophages, lymphocytes, TGFβ, IFNγ, wound healing, and cytotoxicity were measured. CIBERSORTx scores of naive and memory B cells, plasma cells, CD8+ T cells, resting and activated memory CD4+ T cells, M0 and M2 macrophages, and activated mast cells were measured.

RESULTS

Increased M2 macrophage score [HR, 6.30; 95% confidence interval (CI), 3.0-12.15] and TGFβ signature expression (HR, 1.35; 95% CI, 1.05-1.77) were associated with shorter OS. Increased scores of plasma cells (HR, 0.55; 95% CI, 0.38-0.87) and activated memory CD4+ T cells (HR, 0.34; 95% CI, 0.16-0.65) were associated with longer OS. Using optimal cutoffs from these four features, patients were categorized as having either 4, 3, 2, or 0-1 beneficial features associated with longer OS, and the median (95% CI) OS decreased from 42.5 (35.8-47.8) to 31.0 (28.8-34.4), 25.2 (20.6-27.9), and 17.7 (13.5-20.4) months respectively (P = 3.48e-11).

CONCLUSIONS

New immune features can be further evaluated to improve patient response. They provide the rationale for more effective immunotherapy strategies.

Comprehensive Analysis of the Immune and Prognostic Implication of TRIM8 in Breast Cancer

Background: Breast cancer remains one of most lethal illnesses and the most common malignancies among women, making it important to discover novel biomarkers and therapeutic targets for the disease. Immunotherapy has become a promising therapeutic tool for breast cancer. The role of TRIM8 in breast cancer has rarely been reported.

Method: Here we identified TRIM8 expression and its potential function on survival in patients with breast cancer using TCGA (The cancer genome atlas), GEO (Gene expression omnibus) database and METABRIC (Molecular Taxonomy of Breast Cancer International Consortium). Then, TIMER and TISIDB databases were used to investigate the correlations between TRIM8 mRNA levels and immune characteristics. Using stepwise cox regression, we established an immune prognostic signature based on five differentially expression immune-related genes (DE-IRGs). Finally, a nomogram, accompanied by a calibration curve was proposed to predict 1-, 3-, and 5-year survival for breast cancer patients.

Results: We found that TRIM8 expression was dramatically lower in breast cancer tissues in comparison with normal tissues. Lower TRIM8 expression was related with worse prognosis in breast cancer. TIMER and TISIDB analysis showed that there were strong correlations between TRIM8 expression and immune characteristics. The receiver operating characteristic (ROC) curve confirmed the good performance in survival prediction and showed good accuracy of the immune prognostic signature. We demonstrated the model usefulness of predictions by nomogram and calibration curves. Our findings indicated that TRIM8 might be a potential link between progression and prognosis survival of breast cancer.

Conclusion: This is a comprehensive study to reveal that tripartite motif 8 (TRIM8) may serve as a potential prognostic biomarker associating with immune characteristics and provide a novel therapeutic target for the treatment of breast cancer.

Using a Machine Learning Approach to Identify Key Biomarkers for Renal Clear Cell Carcinoma

Background

The most common and deadly subtype of renal carcinoma is kidney renal clear cell carcinoma (KIRC), which accounts for approximately 75% of renal carcinoma. However, the main cause of death in KIRC patients is tumor metastasis. There are no obvious clinical features in the early stage of kidney cancer, and 25–30% of patients have already metastasized when they are first diagnosed. Moreover, KIRC patients whose local tumors have been removed by nephrectomy are still at high risk of metastasis and recurrence and are not sensitive to chemotherapy and radiotherapy, leading to poor prognosis. Therefore, early diagnosis and treatment of this disease are very important.

Methods

KIRC-related patient datasets were downloaded from the GEO database and TCGA database. DEG screening and GO, KEGG and GSEA enrichment analysis was firstly conducted and then the LASSO and support vector machine (SVM) RFE algorithms were adopted to identify KIRC-associated key genes in training sets and validate them in the test set. The clinical prognostic analysis including the association between the expression of key genes and the overall survival, stage, grade across KIRC, the immune infiltration difference between normal samples and cancer samples, the correlation between the key genes and immune cells, immunomodulator, immune subtypes of KIRC were investigated in this research.

Results

We finally screened out 4 key genes, including ACPP, ANGPTL4, SCNN1G, SLC22A7. The expression of key genes show difference among normal samples and tumor samples, SCNN1G and SLC22A7 could be predictor of prognosis of patients. The expression of key genes was related with the abundance of tumor infiltration immune cells and the gene expression of immune checkpoint.

Conclusion

This study screened the 4 key genes, which contributed to early diagnosis, prognosis assessment and immune target treatment of patients with KIRC.

Activation of a cGAS-STING-mediated immune response predicts response to neoadjuvant chemotherapy in early breast cancer

BACKGROUND

The DNA-damage immune-response (DDIR) signature is an immune-driven gene expression signature retrospectively validated as predicting response to anthracycline-based therapy. This feasibility study prospectively evaluates the use of this assay to predict neoadjuvant chemotherapy response in early breast cancer.

METHODS

This feasibility study assessed the integration of a novel biomarker into clinical workflows. Tumour samples were collected from patients receiving standard of care neoadjuvant chemotherapy (FEC + /-taxane and anti-HER2 therapy as appropriate) at baseline, mid- and post-chemotherapy. Baseline DDIR signature scores were correlated with pathological treatment response. RNA sequencing was used to assess chemotherapy/response-related changes in biologically linked gene signatures.

RESULTS

DDIR signature reports were available within 14 days for 97.8% of 46 patients (13 TNBC, 16 HER2 + ve, 27 ER + HER2-ve). Positive scores predicted response to treatment (odds ratio 4.67 for RCB 0-1 disease (95% CI 1.13-15.09, P = 0.032)). DDIR positivity correlated with immune infiltration and upregulated immune-checkpoint gene expression.

CONCLUSIONS

This study validates the DDIR signature as predictive of response to neoadjuvant chemotherapy which can be integrated into clinical workflows, potentially identifying a subgroup with high sensitivity to anthracycline chemotherapy. Transcriptomic data suggest induction with anthracycline-containing regimens in immune restricted, "cold" tumours may be effective for immune priming.

TRIAL REGISTRATION

Not applicable (non-interventional study). CRUK Internal Database Number 14232.

Tumor-Infiltrating Lymphocytes (TILs) as a Biomarker of Abscopal Effect of Cryoablation in Breast Cancer: A Pilot Study

Background

Morphological evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer is gaining momentum as an immunological biomarker. This experiment evaluates the role of TILs in distant tumors as a measure of abscopal effect from cryoablation of breast cancer.

Methods

BALB/c mice underwent bilateral orthotopic transplant with 4T1-12B (triple-negative) cells. At 2 weeks, left tumors were treated by either resection (standard of care group) or cryoablation (intervention group) followed by resection of the distant right tumors 1 week posttreatment. TIL scores were calculated from hematoxylin and eosin-stained sections and phenotyped for cytotoxic T-lymphocyte (CTL) markers by immunofluorescence. Primarily resected tumors served as baseline (Tbaseline), whereas resected distant right-sided served as the readout for abscopal effect (AbsRes or AbsCryo). Mice were monitored for tumor recurrence and metastasis.

Results

The AbsCryo had a significant mean (SD) increase in stromal (2.8 [1.1]%; p = 0.015) and invasive margin TILs (50 [12]%; p = 0.02) compared with TBaseline (1.0 [0]% and 31 [4.9]%, respectively). CTL phenotyping revealed a significant increase in mean (SD) CD8+ T cells (15.7 [12.1]; p = 0.02) and granzyme B (4.8 [3.6]; p = 0.048) for the AbsCryo compared with TBaseline (5.2 [4.7] and 2.4 [0.9], respectively). Posttreatment, the cryoablation group had no recurrence or metastasis, whereas the resected group showed local recurrence and lung metastasis in 40% of the mice. Postprocedure increase in TIL score of distant tumors was associated with decrease in tumor relapse (p = 0.02).

Conclusions

Cryoablation induced a robust tumor-specific TIL response compared with resection, suggesting an abscopal effect leading to the prevention of cancer recurrence and metastasis.

Tumor-Infiltrating Lymphocytes and Their Role in Solid Tumor Progression

Tumor-infiltrating lymphocytes (TIL) are an important component of the tumor environment. Their role in tumor growth and progression has been debated for decades. Today, emphasis has shifted to beneficial effects of TIL for the host and to therapies optimizing the benefits by reducing immune suppression in the tumor microenvironment. Evidence indicates that when TILs are present in the tumor as dense aggregates of activated immune cells, tumor prognosis and responses to therapy are favorable. Gene signatures and protein profiling of TIL at the population and single-cell levels provide clues not only about their phenotype and numbers but also about TIL potential functions in the tumor. Correlations of the TIL data with clinicopathological tumor characteristics, clinical outcome, and patients' survival indicate that TILs exert influence on the disease progression, especially in colorectal carcinomas and breast cancer. At the same time, the recognition that TIL signatures vary with time and cancer progression has initiated investigations of TIL as potential prognostic biomarkers. Multiple mechanisms are utilized by tumors to subvert the host immune system. The balance between pro- and antitumor responses of TIL largely depends on the tumor microenvironment, which is unique in each cancer patient. This balance is orchestrated by the tumor and thus is shifted toward the promotion of tumor growth. Changes occurring in TIL during tumor progression appear to serve as a measure of tumor aggressiveness and potentially provide a key to selecting therapeutic strategies and inform about prognosis.

A Pan-Cancer Analysis of Predictive Methylation Signatures of Response to Cancer Immunotherapy

Recently, tumor immunotherapy based on immune checkpoint inhibitors (ICI) has been introduced and widely adopted for various tumor types. Nevertheless, tumor immunotherapy has a few drawbacks, including significant uncertainty of outcome, the possibility of severe immune-related adverse events for patients receiving such treatments, and the lack of effective biomarkers to determine the ICI treatments' responsiveness. DNA methylation profiles were recently identified as an indicator of the tumor immune microenvironment. They serve as a potential hot spot for predicting responses to ICI treatment for their stability and convenience of measurement by liquid biopsy. We demonstrated the possibility of DNA methylation profiles as a predictor for responses to the ICI treatments at the pan-cancer level by analyzing DNA methylation profiles considered responsive and non-responsive to the treatments. An SVM model was built based on this differential analysis in the pan-cancer levels. The performance of the model was then assessed both at the pan-cancer level and in specific tumor types. It was also compared to the existing gene expression profile-based method. DNA methylation profiles were shown to be predictable for the responses to the ICI treatments in the TCGA cases in pan-cancer levels. The proposed SVM model was shown to have high performance in pan-cancer and specific cancer types. This performance was comparable to that of gene expression profile-based one. The combination of the two models had even higher performance, indicating the potential complementarity of the DNA methylation and gene expression profiles in the prediction of ICI treatment responses.

Epigenome signature as an immunophenotype indicator prompts durable clinical immunotherapy benefits in lung adenocarcinoma

Intertumoral immune heterogeneity is a critical reason for distinct clinical benefits of immunotherapy in lung adenocarcinoma (LUAD). Tumor immunophenotype (immune 'Hot' or 'Cold') suggests immunological individual differences and potential clinical treatment guidelines. However, employing epigenome signatures to determine tumor immunophenotypes and responsive treatment is not well understood. To delineate the tumor immunophenotype and immune heterogeneity, we first distinguished the immune 'Hot' and 'Cold' tumors of LUAD based on five immune expression signatures. In terms of clinical presentation, the immune 'Hot' tumors usually had higher immunoactivity, lower disease stages and better survival outcomes than 'Cold' tumors. At the epigenome levels, we observed that distinct DNA methylation patterns between immunophenotypes were closely associated with LUAD development. Hence, we identified a set of five CpG sites as the immunophenotype-related methylation signature (iPMS) for tumor immunophenotyping and further confirmed its efficiency based on a machine learning framework. Furthermore, we found iPMS and immunophenotype-related immune checkpoints (IPCPs) could contribute to the risk of tumor progression, implying IPCP has the potential to be a novel immunotherapy blockade target. After further parsing of the role of iPMS-predicted immunophenotypes, we found immune 'Hot' was a protective factor leading to better survival outcomes when patients received the anti-PD-1/PD-L1 immunotherapy. And iPMS was also a well-performed signature (AUC = 0.752) for predicting the durable/nondurable clinical benefits. In summary, our study explored the role of epigenome signature in clinical tumor immunophenotyping. Utilizing iPMS to characterize tumor immunophenotypes will facilitate developing personalized epigenetic anticancer approaches.

Expression and Clinical Significance of Origin Recognition Complex Subunit 6 in Breast Cancer – A Comprehensive Bioinformatics Analysis

Objective

We aimed to investigate the expression, diagnostic and prognostic values, and potential molecular mechanisms of the origin recognition complex (ORC) in breast cancer (BC).

Methods

Kaplan–Meier estimation was used to assess the prognostic value of ORC genes, and Oncomine, TCGA, GEO and ULCAN databases were used to analyze their expression in BC. Wilcoxon rank-sum tests were used to evaluate the relationship between ORC gene expression levels and BC clinicopathological features. Receiver operating characteristic (ROC) curves were used to assess the diagnostic value of ORC genes in BC. Survival analysis was performed using Kaplan–Meier estimation and Cox regression. A nomogram was constructed to predict 1-, 3-, and 5-year survival probabilities in BC. Gene set enrichment analysis (GSEA) and immune infiltration were used to investigate potential molecular mechanisms of the ORC.

Results

ORC1L and ORC6L were highly expressed in BC compared with healthy tissue, while ORC5L expression patterns were inconsistent; no significant differences in ORC2L, ORC3L or ORC4L expression were observed between BC and healthy tissues. ORC1L and ORC6L expression levels were significantly correlated with age, tumor (T) stage and molecular subtype; ORC5L expression was significantly correlated with age and number of nearby lymph nodes with cancer (N stage). ORC6L expression had the highest diagnostic value in BC and was an independent prognostic factor for poor overall survival (OS). ORC6L may be involved in cell cycle progression and may regulate cancer signaling pathways, including NF-κB, P53, and WNT, in BC. ORC6L expression was also associated with immune infiltration.

Conclusion

ORC1L and ORC6L are highly expressed in BC; ORC6L has a high diagnostic value and is an independent prognostic factor for poor OS. ORC6L may be involved in the initiation and progression of BC by regulating cell cycle progression, promoting cancer signaling pathway activation, and influencing tumor immune cell infiltration.

SLFN11 captures cancer-immunity interactions associated with platinum sensitivity in high-grade serous ovarian cancer

Large independent analyses on cancer cell lines followed by functional studies have identified Schlafen 11 (SLFN11), a putative helicase, as the strongest predictor of sensitivity to DNA-damaging agents (DDAs), including platinum. However, its role as a prognostic biomarker is undefined, partially due to the lack of validated methods to score SLFN11 in human tissues. Here, we implemented a pipeline to quantify SLFN11 in human cancer samples. By analyzing a cohort of high-grade serous ovarian carcinoma (HGSOC) specimens before platinum-based chemotherapy treatment, we show, for the first time to our knowledge, that SLFN11 density in both the neoplastic and microenvironmental components was independently associated with favorable outcome. We observed SLFN11 expression in both infiltrating innate and adaptive immune cells, and analyses in a second, independent, cohort revealed that SLFN11 was associated with immune activation in HGSOC. We found that platinum treatments activated immune-related pathways in ovarian cancer cells in an SLFN11-dependent manner, representative of tumor-immune transactivation. Moreover, SLFN11 expression was induced in activated, isolated immune cell subpopulations, hinting that SLFN11 in the immune compartment may be an indicator of immune transactivation. In summary, we propose SLFN11 is a dual biomarker capturing simultaneously interconnected immunological and cancer cell–intrinsic functional dispositions associated with sensitivity to DDA treatment.

A novel immune subtype classification of ER-positive, PR-negative and HER2-negative breast cancer based on the genomic and transcriptomic landscape

BACKGROUND

The diversity and plasticity behind ER+/PR-/HER2- breast cancer have not been widely explored. It is essential to identify heterogeneous microenvironment phenotypes and investigate specific genomic events driving the formation of these phenotypes.

METHODS

Based on the immune-related gene expression profiles of 411 ER+/PR-/HER2- breast cancers in the METABRIC cohort, we used consensus clustering to identify heterogeneous immune subtypes and assessed their reproducibility in an independent meta-cohort including 135 patients collected from GEO database. We further analyzed the differences of cellular and molecular characteristics, and potential immune escape mechanism among immune subtypes. In addition, we constructed a transcriptional trajectory to visualize the distribution of individual patient.

RESULTS

Our analysis identified and validated five reproducible immune subtypes with distinct cellular and molecular characteristics, potential immune escape mechanisms, genomic drivers, as well as clinical outcomes. An immune-cold subtype, with the least amount of lymphocyte infiltration, had a poorer prognosis. By contrast, an immune-hot subtype, which demonstrated the highest infiltration of CD8+ T cells, DCs and NK cells, and elevated IFN-γ response, had a comparatively favorable prognosis. Other subtypes showed more diverse gene expression and immune infiltration patterns with distinct clinical outcomes. Finally, our analysis revealed a complex immune landscape consisting of both discrete cluster and continuous spectrum.

CONCLUSION

Overall, this study revealed five heterogeneous immune subtypes among ER+/PR-/HER2- breast cancer, also provided important implications for clinical translations.

Selective inhibition of TGFβ1 activation overcomes primary resistance to checkpoint blockade therapy by altering tumor immune landscape

Despite breakthroughs achieved with cancer checkpoint blockade therapy (CBT), many patients do not respond to anti-programmed cell death-1 (PD-1) due to primary or acquired resistance. Human tumor profiling and preclinical studies in tumor models have recently uncovered transforming growth factor-β (TGFβ) signaling activity as a potential point of intervention to overcome primary resistance to CBT. However, the development of therapies targeting TGFβ signaling has been hindered by dose-limiting cardiotoxicities, possibly due to nonselective inhibition of multiple TGFβ isoforms. Analysis of mRNA expression data from The Cancer Genome Atlas revealed that TGFΒ1 is the most prevalent TGFβ isoform expressed in many types of human tumors, suggesting that TGFβ1 may be a key contributor to primary CBT resistance. To test whether selective TGFβ1 inhibition is sufficient to overcome CBT resistance, we generated a high-affinity, fully human antibody, SRK-181, that selectively binds to latent TGFβ1 and inhibits its activation. Coadministration of SRK-181-mIgG1 and an anti-PD-1 antibody in mice harboring syngeneic tumors refractory to anti-PD-1 treatment induced profound antitumor responses and survival benefit. Specific targeting of TGFβ1 was also effective in tumors expressing more than one TGFβ isoform. Combined SRK-181-mIgG1 and anti-PD-1 treatment resulted in increased intratumoral CD8⁺ T cells and decreased immunosuppressive myeloid cells. No cardiac valvulopathy was observed in a 4-week rat toxicology study with SRK-181, suggesting that selectively blocking TGFβ1 activation may avoid dose-limiting toxicities previously observed with pan-TGFβ inhibitors. These results establish a rationale for exploring selective TGFβ1 inhibition to overcome primary resistance to CBT.

A Signaling View into the Inflammatory Tumor Microenvironment

The development of tumors requires an initiator event, usually exposure to DNA damaging agents that cause genetic alterations such as gene mutations or chromosomal abnormalities, leading to deregulated cell proliferation. Although the mere stochastic accumulation of further mutations may cause tumor progression, it is now clear that an inflammatory microenvironment has a major tumor-promoting influence on initiated cells, in particular when a chronic inflammatory reaction already existed before the initiated tumor cell was formed. Moreover, inflammatory cells become mobilized in response to signals emanating from tumor cells. In both cases, the microenvironment provides signals that initiated tumor cells perceive by membrane receptors and transduce via downstream kinase cascades to modulate multiple cellular processes and respond with changes in cell gene expression, metabolism, and morphology. Cytokines, chemokines, and growth factors are examples of major signals secreted by immune cells, fibroblast, and endothelial cells and mediate an intricate cell-cell crosstalk in an inflammatory microenvironment, which contributes to increased cancer cell survival, phenotypic plasticity and adaptation to surrounding tissue conditions. Eventually, consequent changes in extracellular matrix stiffness and architecture, coupled with additional genetic alterations, further fortify the malignant progression of tumor cells, priming them for invasion and metastasis. Here, we provide an overview of the current knowledge on the composition of the inflammatory tumor microenvironment, with an emphasis on the major signals and signal-transducing events mediating different aspects of stromal cell-tumor cell communication that ultimately lead to malignant progression.

Profiles of immune infiltration in the tumor microenvironment of hepatocellular carcinoma

Background

Thus far, few studies have systematically analyzed the profiles of immune cells infiltrated in the tumor microenvironment (TME) of hepatocellular carcinoma (HCC). Therefore, the purpose of our study was to comprehensively analyze the 22 tumor-infiltrating immune cells (TIICs) and the immune subtypes of HCC, as well as the factors associated with the prognosis of HCC patients.

Methods

In this study, we evaluated the abundance of 22 tumor-infiltrating immunocytes of 371 HCC patients from The Cancer Genome Atlas (TCGA) database by using the CIBERSORT algorithm, and defined immune subtypes of HCC according to unsupervised cluster analysis. The immune score of HCC patients was calculated by the prognostic regression model, while the survival analysis was evaluated by the Kaplan-Meier method. In addition, the consistency index of TIICs and principal component analysis (PCA) of immunomodulator genes were estimated.

Results

The results of this study showed that three distinct immune subtypes of HCC were stratified, and the C1 subtype and C3 subtype were correlated with a good prognosis. The cellular composition of three immune subtypes was different. Moreover, immunomodulator gene and programmed cell death protein 1/programmed death-ligand 1 (PD-1/PD-L1) expression in the C1 subtype was significantly higher (P<0.05).

Conclusions

This suggested that the low immune score of HCC patients is associated with better clinical outcomes. In addition, the interaction network of cluster of differentiation CD8+ T cells was mainly concentrated in the C1 subtype. Taken together, this study showed that tumor-infiltrating immune cells can perhaps be an important determinant of clinical outcomes of patients with HCC and may provide biomarkers to reflect the immunotherapy response. Notably, the C1 subtype of HCC may be used as an important predictive factor for immunotherapy response.

Comparison of tumor microenvironment in primary and paired metastatic ER+/HER2- breast cancers: results of a pilot study

Background

Tumor microenvironment (TME) is a dynamic setting and changes in TILs and their subpopulations are potential candidates to influence the metastatic process. Aim of this pilot study is to describe the changes occurring between primary breast cancers and their paired metastases in terms of TILs composition. To assess if these changes influence the process of metastasis development, we used a control group of patients.

Methods

We retrospectively identified 18 Luminal patients, for whom primary and metastatic tissue were available (cases) and 18 paired-matched patients (controls), not relapsed after at least 9 years of follow-up, and we quantified TILs and their composition (i.e. T CD8+ and CD4+/FOXP3+). The presence of TILs was defined as ≥10%.

Results

Our results showed that the microenvironment composition of relapsed patients was poor of TILs (median = 5%, I-III quartiles = 0.6–5%), CD8+ (2.5%, 0–5%) and CD4+/FOXP3 + (0%, 0–0.6%) in the primary tumor. Comparable results were observed in their related metastases (TILs 3.8%, 0.6–5%; CD8+ 0%, 0–1.3%; CD4+/FOXP3+ 0%,0–1.9%). On the contrary, the microenvironment in the control group was richer of TILs (5%, 5–17.5%) in comparison to cases, both in primary tumor (p = 0.035) and related metastases (p = 0.018). Although CD8+ in controls were similar to cases at primary tumor (p = 0.6498), but not at metastasis (p = 0.0223), they expressed only one part on the TILs subpopulations (p = 0.0060), while TILs in the cases at primary tumor were almost completely CD8+ (p = 0.5034).

Conclusions

These findings suggest that the lack of activation of immune system in the primary tumor might influence the multifactor process of cancer progression.

Correlation of immune infiltration with clinical outcomes in breast cancer patients: The 25-gene prognostic signatures model

PURPOSE

Breast cancer is the most common cancer in women. The aim of this study was to build a prognostic signatures model based on the immune score of the ESTIMATE algorithm to predict survival of breast cancer patients.

METHODS

The RNA-seq expression data and clinical characteristics of patients were derived from TCGA and GSE88770 of GEO. The ESTIMATE algorithm was used to calculate the patients' immune scores and to obtain DEGs. The LASSO Cox regression model was applied to select prognostic genes. Survival analysis and the ROC curve were used to evaluate the predictive efficacy of the prognostic signatures model. Independent prognostic factors of breast cancer were assessed using the Cox regression analyses, and a nomogram was constructed to enhance the clinical value.

RESULTS

Based on the immune score, we found that the high-score group showed better clinical outcomes than the low-score group. Twenty-five (25) genes of 616 DEGs were confirmed as prognostic signatures through the LASSO Cox regression. The risk score for each patient was calculated according to the prognostic signatures. Survival analysis showed that the low-risk group had longer overall survival than the high-risk group. We also found that the risk score was an independent prognostic factor. To improve the clinical application value, a nomogram combing the risk score according to the 25-gene prognostic signatures and several clinicopathological prognostic factors was constructed.

CONCLUSIONS

This study revealed the significance of immune infiltration and constructed a 25-gene prognostic signatures model, that has a strong prognostic value for patients with breast cancer.

Immune microenvironment and intrinsic subtyping in hormone receptor-positive/HER2-negative breast cancer

Little is known regarding the interaction between immune microenvironment and tumor biology in hormone receptor (HR)+/HER2− breast cancer (BC). We here assess pretreatment gene-expression data from 66 HR+/HER2− early BCs from the LETLOB trial and show that non-luminal tumors (HER2-enriched, Basal-like) present higher tumor-infiltrating lymphocyte levels than luminal tumors. Moreover, significant differences in immune infiltrate composition, assessed by CIBERSORT, were observed: non-luminal tumors showed a more proinflammatory antitumor immune infiltrate composition than luminal ones.

Trial watch: Prognostic and predictive value of the immune infiltrate in cancer

Solid tumors are constituted of a variety of cellular components, including bona fide malignant cells as well as endothelial, structural and immune cells. On one hand, the tumor stroma exerts major pro-tumorigenic and immunosuppressive functions, reflecting the capacity of cancer cells to shape the microenvironment to satisfy their own metabolic and immunological needs. On the other hand, there is a component of tumor-infiltrating leucocytes (TILs) that has been specifically recruited in the attempt to control tumor growth. Along with the recognition of the critical role played by the immune system in oncogenesis, tumor progression and response to therapy, increasing attention has been attracted by the potential prognostic and/or predictive role of the immune infiltrate in this setting. Data from large clinical studies demonstrate indeed that a robust infiltration of neoplastic lesions by specific immune cell populations, including (but not limited to) CD8⁺ cytotoxic T lymphocytes, Th1 and Th17 CD4⁺ T cells, natural killer cells, dendritic cells, and M1 macrophages constitutes an independent prognostic indicator in several types of cancer. Conversely, high levels of intratumoral CD4⁺CD25⁺FOXP3⁺ regulatory T cells, Th2 CD4⁺ T cells, myeloid-derived suppressor cells, M2 macrophages and neutrophils have frequently been associated with dismal prognosis. So far, only a few studies have addressed the true predictive potential of TILs in cancer patients, generally comforting the notion that—at least in some clinical settings—the immune infiltrate can reliably predict if a specific patient will respond to therapy or not. In this Trial Watch, we will summarize the results of clinical trials that have evaluated/are evaluating the prognostic and predictive value of the immune infiltrate in the context of solid malignancies.

A Six-lncRNA Signature for Immunophenotype Prediction of Glioblastoma Multiforme

Glioblastoma multiforme (GBM) is the most aggressive primary tumor of the central nervous system. As biomedicine advances, the researcher has found the development of GBM is closely related to immunity. In this study, we evaluated the GBM tumor immunoreactivity and defined the Immune-High (IH) and Immune-Low (IL) immunophenotypes using transcriptome data from 144 tumors profiled by The Cancer Genome Atlas (TCGA) project based on the single-sample gene set enrichment analysis (ssGSEA) of five immune expression signatures (IFN-γ response, macrophages, lymphocyte infiltration, TGF-β response, and wound healing). Next, we identified six immunophenotype-related long non-coding RNA biomarkers (im-lncRNAs, USP30-AS1, HCP5, PSMB8-AS1, AL133264.2, LINC01684, and LINC01506) by employing a machine learning computational framework combining minimum redundancy maximum relevance algorithm (mRMR) and random forest model. Moreover, the expression level of identified im-lncRNAs was converted into an im-lncScore using the normalized principal component analysis. The im-lncScore showed a promising performance for distinguishing the GBM immunophenotypes with an area under the curve (AUC) of 0.928. Furthermore, the im-lncRNAs were also closely associated with the levels of tumor immune cell infiltration in GBM. In summary, the im-lncRNA signature had important clinical implications for tumor immunophenotyping and guiding immunotherapy in glioblastoma patients in future.

The molecular landscape of Asian breast cancers reveals clinically relevant population-specific differences

Molecular profiling of breast cancer has enabled the development of more robust molecular prognostic signatures and therapeutic options for breast cancer patients. However, non-Caucasian populations remain understudied. Here, we present the mutational, transcriptional, and copy number profiles of 560 Malaysian breast tumours and a comparative analysis of breast cancers arising in Asian and Caucasian women. Compared to breast tumours in Caucasian women, we show an increased prevalence of HER2-enriched molecular subtypes and higher prevalence of TP53 somatic mutations in ER+ Asian breast tumours. We also observe elevated immune scores in Asian breast tumours, suggesting potential clinical response to immune checkpoint inhibitors. Whilst HER2-subtype and enriched immune score are associated with improved survival, presence of TP53 somatic mutations is associated with poorer survival in ER+ tumours. Taken together, these population differences unveil opportunities to improve the understanding of this disease and lay the foundation for precision medicine in different populations.

PD-L1 Expression and Tumor-infiltrating Lymphocytes in Breast Cancer: Clinicopathological Analysis in Women Younger than 40 Years Old

BACKGROUND/AIM

To evaluate the association between programmed cell death ligand 1 (PD-L1) expression on both tumor cells (TC) and inflammatory cells (IC), tumor infiltrating lymphocytes (TILs), CD3⁺ and CD8⁺ lymphocytes and other clinicopathological parameters in primary infiltrative breast cancer (IBC) of young women, a population shown to have a worse prognosis.

MATERIALS AND METHODS

A retrospective study was performed collecting data from patients younger than 40 years old. Forty-five young women with IBC were included. Whole tissue sections were used to evaluate all parameters.

RESULTS

Twenty percent (20%) of cases showed PD-L1 expression by tumor cells (PDL1TC) and 44.4% showed PD-L1 expression by immune cells (PDL1IC). Furthermore, 28.88% revealed high stromal TILs. PDL1TC and PDL1IC expression were significantly associated with tumor diameter and expression of estrogen (ER) and progesterone (PR) receptors and Ki67. PDL1TC expression was also associated with grade. High TILs were associated with tumor diameter, ER and Ki67 expression. PDL1TC, PDL1IC expression and TILs were associated with the density of CD3⁺ and CD8⁺ lymphocytes.

CONCLUSION

Our results are similar to those of other age groups, as reported in the literature.

Activin A Promotes Regulatory T-cell-Mediated Immunosuppression in Irradiated Breast Cancer

Increased regulatory T cells (Treg) after radiotherapy have been reported, but the mechanisms of their induction remain incompletely understood. TGFβ is known to foster Treg differentiation within tumors and is activated following radiotherapy. Thus, we hypothesized that TGFβ blockade would result in decreased Tregs within the irradiated tumor microenvironment. We found increased Tregs in the tumors of mice treated with focal radiotherapy and TGFβ blockade. This increase was mediated by upregulation of another TGFβ family member, activin A. In vitro, activin A secretion was increased following irradiation of mouse and human breast cancer cells, and its expression was further enhanced upon TGFβ blockade. In vivo, dual blockade of activin A and TGFβ was required to decrease intratumoral Tregs in the context of radiotherapy. This resulted in an increase in CD8⁺ T-cell priming and was associated with a reduced tumor recurrence rate. Combination of immune checkpoint inhibitors with the dual blockade of activin A and TGFβ led to the development of tumor-specific memory responses in irradiated breast cancer. Supporting the translational value of activin A targeting to reduce Treg-mediated immunosuppression, retrospective analysis of a public dataset of patients with breast cancer revealed a positive correlation between activin A gene expression and Treg abundance. Overall, these results shed light on an immune escape mechanism driven by activin A and suggest that dual targeting of activin A and TGFβ may be required to optimally unleash radiation-induced antitumor immunity against breast cancer.

Impact of anatomic site on antigen-presenting cells in cancer

Checkpoint blockade immunotherapy (CBT) can induce long-term clinical benefits in patients with advanced cancer; however, response rates to CBT vary by cancer type. Cancers of the skin, lung, and kidney are largely responsive to CBT, while cancers of the pancreas, ovary, breast, and metastatic lesions to the liver respond poorly. The impact of tissue-resident immune cells on antitumor immunity is an emerging area of investigation. Recent evidence indicates that antitumor immune responses and efficacy of CBT depend on the tissue site of the tumor lesion. As myeloid cells are predominantly tissue-resident and can shape tumor-reactive T cell responses, it is conceivable that tissue-specific differences in their function underlie the tissue-site-dependent variability in CBT responses. Understanding the roles of tissue-specific myeloid cells in antitumor immunity can open new avenues for treatment design. In this review, we discuss the roles of tissue-specific antigen-presenting cells (APCs) in governing antitumor immune responses, with a particular focus on the contributions of tissue-specific dendritic cells. Using the framework of the Cancer-Immunity Cycle, we examine the contributions of tissue-specific APC in CBT-sensitive and CBT-resistant carcinomas, highlight how these cells can be therapeutically modulated, and identify gaps in knowledge that remain to be addressed.

Systematic Analysis of Alternative Splicing Landscape in Pancreatic Adenocarcinoma Reveals Regulatory Network Associated with Tumorigenesis and Immune Response

BACKGROUND Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive gastrointestinal tumors and has an extremely high mortality rate. Recent studies indicate that alternative splicing (AS), a common post-transcriptional process, has important roles in tumor biological behaviors and may provide novel immunotherapeutic targets. This study systematically analyzes AS profiles in PDAC and reveals their potential regulatory effects on cancer immune response. MATERIAL AND METHODS AS event, RNA sequencing, and splicing factor (SF) data were extracted from SpliceSeq, The Cancer Genome Atlas, and SpliceAid2, respectively. Overall survival (OS)-associated AS events and SFs were identified with univariate analysis. The LASSO method and multivariate Cox regression analysis were used to construct predictive signatures for the prediction of patient prognosis. The proportions of immune cells within PDAC samples were evaluated using the CIBERSORT algorithm. The correlations among AS events, SFs, and immune cell proportions were calculated using Spearman correlation analysis. Consensus clustering and immune classification were performed on the PDAC cohort. RESULTS A total of 4812 OS-related AS events from 3341 parent genes were identified, and 8 AS-based predictive models were constructed for PDAC. An OS-related SF-AS regulatory network was constructed. The AS events regulated by ELAVL4 exhibited strong correlations with CD8 T cells and regulatory T cells. In addition, AS-based clusters demonstrated distinct OS outcomes and immune features. CONCLUSIONS AS-based predictive models with high accuracy were constructed to facilitate prognosis prediction and treatment of PDAC. An SF-AS regulatory network was constructed, revealing the potential relationships among SF, AS, and immune response.

Identification of Prognostic Biomarkers for Multiple Solid Tumors Using a Human Villi Development Model

The processes of embryonic development that rely on epithelial-mesenchymal transition (EMT) for the implantation of trophoblast cells are co-opted by tumors, reflecting their inherent uncontrolled characteristics and leading to invasion and metastasis. Although tumorigenesis and embryogenesis have similar EMT characteristics, trophoblasts have been shown to exhibit "physiological metastasis" or be "pseudo-malignant," resulting in different outcomes. The gene co-expression network is the basis of embryonic development and tumorigenesis. We hypothesize that if the gene co-expression network in tumors is "off-track" from that in villi, it is more likely to develop into malignant tumors and have a worse prognosis, and we proposed the "off-track theory" for the first time. In this study, we examined gene co-expression networks in villi and multiple solid tumors. Through network functional enrichment analyses, we found that most tumors and villi exhibited a significantly enriched EMT, but the genes that performed this function were not identical. Then, we identified the "off-track genes" in the EMT-related gene interaction network using the "off-track theory," and through survival analysis, we discovered that the risk score of "off-track genes" was associated with poor survival of cancer patients. Our study indicated that villi development is a reliable and strictly regulated model that can illuminate the trajectory of human cancer development and that the gene co-expression networks in tumor development are "off-track" from those in villi. These "off-track genes" may have a substantial impact on tumor development and could reveal novel prognostic biomarkers.

Predicting and affecting response to cancer therapy based on pathway-level biomarkers

Identifying robust, patient-specific, and predictive biomarkers presents a major obstacle in precision oncology. To optimize patient-specific therapeutic strategies, here we couple pathway knowledge with large-scale drug sensitivity, RNAi, and CRISPR-Cas9 screening data from 460 cell lines. Pathway activity levels are found to be strong predictive biomarkers for the essentiality of 15 proteins, including the essentiality of MAD2L1 in breast cancer patients with high BRCA-pathway activity. We also find strong predictive biomarkers for the sensitivity to 31 compounds, including BCL2 and microtubule inhibitors (MTIs). Lastly, we show that Bcl-xL inhibition can modulate the activity of a predictive biomarker pathway and re-sensitize lung cancer cells and tumors to MTI therapy. Overall, our results support the use of pathways in helping to achieve the goal of precision medicine by uncovering dozens of predictive biomarkers.

Immunological Differences Between Immune-Rich Estrogen Receptor-Positive and Immune-Rich Triple-Negative Breast Cancers

PURPOSE

A subset of estrogen receptor–positive (ER-positive) breast cancer (BC) contains high levels of tumor-infiltrating lymphocytes (TILs), similar to triple-negative BC (TNBC). The majority of immuno-oncology trials target TNBCs because of the greater proportion of TIL-rich TNBCs. The extent to which the immune microenvironments of immune-rich ER-positive BC and TNBC differ is unknown.

PATIENTS AND METHODS

RNA sequencing data from The Cancer Genome Atlas (TCGA; n = 697 ER-positive BCs; n = 191 TNBCs) were used for discovery; microarray expression data from Molecular Taxonomy of Breast Cancer International Consortium (METABRIC; n = 1,186 ER-positive BCs; n = 297 TNBCs) was used for validation. Patients in the top 25th percentile of a previously published total TIL metagene score distribution were considered immune rich. We compared expression of immune cell markers, immune function metagenes, and immuno-oncology therapeutic targets among immune-rich subtypes.

RESULTS

Relative fractions of resting mast cells (TCGA P_adj = .009; METABRIC P_adj = 4.09E-15), CD8⁺ T cells (TCGA P_adj = .015; METABRIC P_adj = 0.390), and M2-like macrophages (TCGA P_adj= 4.68E-05; METABRIC P_adj = .435) were higher in immune-rich ER-positive BCs, but M0-like macrophages (TCGA P_adj = 0.015; METABRIC P_adj = .004) and M1-like macrophages (TCGA P_adj = 9.39E-08; METABRIC P_adj = 6.24E-11) were higher in immune-rich TNBCs. Ninety-one immune-related genes (eg, CXCL14, CSF3R, TGF-B3, LRRC32/GARP, TGFB-R2) and a transforming growth factor β (TGF-β) response metagene were significantly overexpressed in immune-rich ER-positive BCs, whereas 41 immune-related genes (eg, IFNG, PD-L1, CTLA4, MAGEA4) were overexpressed in immune-rich TNBCs in both discovery and validation data sets. TGF-β pathway member genes correlated negatively with expression of immune activation markers (IFNG, granzyme-B, perforin) and positively with M2-like macrophages (IL4, IL10, and MMP9) and regulatory T-cell (FOXP3) markers in both subtypes.

CONCLUSION

Different immunotherapy strategies may be optimal in immune-rich ER-positive BC and TNBC. Drugs targeting the TGF-β pathway and M2-like macrophages are promising strategies in immune-rich ER-positive BCs to augment antitumor immunity.

TP53 Status, Patient Sex, and the Immune Response as Determinants of Lung Cancer Patient Survival

Lung cancer poses the greatest cancer-related death risk and males have poorer outcomes than females, for unknown reasons. Patient sex is not a biological variable considered in lung cancer standard of care. Correlating patient genetics with outcomes is predicted to open avenues for improved management. Using a bioinformatics approach across non-small cell lung cancer (NSCLC) subtypes, we identified where patient sex, mutation of the major tumor suppressor gene, Tumour protein P53 (TP53), and immune signatures stratified outcomes in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), among datasets of The Cancer Genome Atlas (TCGA). We exposed sex and TP53 gene mutations as prognostic for LUAD survival. Longest survival in LUAD occurred among females with wild-type (wt) TP53 genes, high levels of immune infiltration and enrichment for pathway signatures of Interferon Gamma (INF-γ), Tumour Necrosis Factor (TNF) and macrophages-monocytes. In contrast, poor survival in men with LUAD and wt TP53 genes corresponded with enrichment of Transforming Growth Factor Beta 1 (TGFB1, hereafter TGF-β) and wound healing signatures. In LUAD with wt TP53 genes, elevated gene expression of immune checkpoint CD274 (hereafter: PD-L1) and also protein 53 (p53) negative-regulators of the Mouse Double Minute (MDM)-family predict novel avenues for combined immunotherapies. LUSC is dominated by male smokers with TP53 gene mutations, while a minor population of TCGA LC patients with wt TP53 genes unexpectedly had the poorest survival, suggestive of a separate etiology. We conclude that advanced approaches to LUAD and LUSC therapy lie in the consideration of patient sex, TP53 gene mutation status and immune signatures.

Dissecting expression profiles of gastric precancerous lesions and early gastric cancer to explore crucial molecules in intestinal-type gastric cancer tumorigenesis

Intestinal‐type gastric cancer (IGC) has a clear and multistep histological evolution. No studies have comprehensively explored gastric tumorigenesis from inflammation through low‐grade intraepithelial neoplasia (LGIN) and high‐grade intraepithelial neoplasia (HGIN) to early gastric cancer (EGC). We sought to investigate the characteristics participating in IGC tumorigenesis and identify related prognostic information within the process. RNA expression profiles of 94 gastroscopic biopsies from 47 patients, including gastric precancerous lesions (GPL: LGIN and HGIN), EGC, and paired controls, were detected by Agilent Microarray. During IGC tumorigenesis from LGIN through HGIN to EGC, the number of activity‐changed tumor hallmarks increased. LGIN and HGIN had similar expression profiles when compared to EGC. We observed an increase in the stemness of gastric epithelial cells in LGIN, HGIN, and EGC, and we found 27 consistent genes that might contribute to dedifferentiation, including five driver genes. Remarkably, we perceived that the immune microenvironment was more active in EGC than in GPL, especially in the infiltration of lymphocytes and macrophages. We identified a five‐gene signature from the gastric tumorigenesis process that could independently predict the overall survival and disease‐free survival of GC patients (log‐rank test: p < 0.0001), and the robustness was verified in an independent cohort (n > 300) and by comparing with two established prognostic signatures in GC. In conclusion, during IGC tumorigenesis, cancer‐like changes occur in LGIN and accumulate in HGIN and EGC. The immune microenvironment is more active in EGC than in LGIN and HGIN. The identified signature from the tumorigenesis process has robust prognostic significance for GC patients. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Prognostic significance of immune landscape in tumour microenvironment of endometrial cancer

Tumour microenvironment (TME) is crucial to tumorigenesis. This study aimed to uncover the differences in immune phenotypes of TME in endometrial cancer (EC) using Uterine Corpus Endometrial Carcinoma (UCEC) cohort and explore the prognostic significance. We employed GVSA enrichment analysis to cluster The Cancer Genome Atlas (TCGA) EC samples into immune signature cluster modelling, evaluated immune cell profiling in UCEC cohort (n = 538) and defined four immune subtypes of EC. Next, we analysed the correlation between immune subtypes and clinical data including patient prognosis. Furthermore, we analysed the expression of immunomodulators and DNA methylation modification. The profiles of immune infiltration in TCGA UCEC cohort showed significant difference among four immune subtypes of EC. Among each immune subtype, natural killer T cells (NKT), dendritic cells (DCs) and CD8⁺T cells were significantly associated with EC patients survival. Each immune subtype exhibited specific molecular classification, immune cell characterization and immunomodulators expression. Moreover, the expression immunomodulators were significantly related to DNA methylation level. In conclusion, the identification of immune subtypes in EC tissues could reveal unique immune microenvironments in EC and predict the prognosis of EC patients.

CD20+ tumor-infiltrating immune cells and CD204+ M2 macrophages are associated with prognosis in thymic carcinoma

Thymic carcinoma is a rare malignant disease with no standard systemic chemotherapy. The purpose of the present study was to investigate tumor‐infiltrating immune cells (TIIC) in the tumor microenvironment (TME), focusing on the impact of TIIC and program death‐ligand 1 (PD‐L1) expression on clinical outcomes in thymic cancer. Patients with thymic carcinoma resected between 1973 and 2017 were investigated. The tissue specimens were analyzed through immunohistochemical staining to elucidate the prognostic effects of TIIC, their ratios and PD‐L1 in a preliminary cohort (n = 10). The density of TIIC as well as PD‐L1 expression was evaluated in intraepithelial and tumor‐stromal areas on the representative whole section of tumors. The immune factors showing significant association with disease‐free survival (DFS) were evaluated in the total cohort (n = 42). TIIC in the preliminary population showed no significant difference between the two groups. However, CD8, CD20, CD204, FOXP3 and CD20/CD204 ratio demonstrated a tendency to act as predictive markers for recurrence. In the total cohort, significant differences were observed for CD8⁺, CD20⁺ and CD204⁺ cells in tumor islets, and for CD8⁺, CD20⁺ and FOXP3⁺ cells as well as the CD8/CD204 and CD20/CD204 ratios in the stroma, indicating their prognostic effect. The prognostic effect of the PD‐L1 expression in tumor cells could not be established, possibly because of intratumoral heterogeneity. CD8, CD20 and CD204 positive TIIC in stroma were identified as possible better prognostic biomarkers, considering the heterogeneity of other biomarkers. The present study paves the way for exploring strategies of combination immunotherapy targeting B cell immunity in thymic carcinoma.

Germline Features Associated with Immune Infiltration in Solid Tumors

The immune composition of the tumor microenvironment influences response and resistance to immunotherapies. While numerous studies have identified somatic correlates of immune infiltration, germline features that associate with immune infiltrates in cancers remain incompletely characterized. We analyze seven million autosomal germline variants in the TCGA cohort and test for association with established immune-related phenotypes that describe the tumor immune microenvironment. We identify one SNP associated with the amount of infiltrating follicular helper T cells; 23 candidate genes, some of which are involved in cytokine-mediated signaling and others containing cancer-risk SNPs; and networks with genes that are part of the DNA repair and transcription elongation pathways. In addition, we find a positive association between polygenic risk for rheumatoid arthritis and amount of infiltrating CD8+ T cells. Overall, we identify multiple germline genetic features associated with tumor-immune phenotypes and develop a framework for probing inherited features that contribute to differences in immune infiltration.

Development of an Immune-Related Prognostic Signature in Breast Cancer

BACKGROUND

Although increased early detection, diagnosis and treatment have improved the outcome of breast cancer patients, prognosis estimation still poses challenges due to the disease heterogeneity. Accumulating data indicated an evident correlation between tumor immune microenvironment and clinical outcomes.

OBJECTIVE

To construct an immune-related signature that can estimate disease prognosis and patient survival in breast cancer.

METHODS

Gene expression profiles and clinical data of breast cancer patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, which were further divided into a training set (n = 499), a testing set (n = 234) and a Meta-validation set (n = 519). In the training set, immune-related genes were recognized using combination of gene expression data and ESTIMATE algorithm-derived immune scores. An immune-related prognostic signature was generated with LASSO Cox regression analysis. The prognostic value of the signature was validated in the testing set and the Meta-validation set.

RESULTS

A total of 991 immune-related genes were identified. Twelve genes with non-zero coefficients in LASSO analysis were used to construct an immune-related prognostic signature. The 12-gene signature significantly stratified patients into high and low immune risk groups in terms of overall survival independent of clinical and pathologic factors. The signature also significantly stratified overall survival in clinical defined groups, including stage I/II disease. Several biological processes, such as immune response, were enriched among genes in the immune-related signature. The percentage of M2 macrophage infiltration was significantly different between low and high immune risk groups. Time-dependent ROC curves indicated good performance of our signature in predicting the 1-, 3- and 5-year overall survival for patients from the full TCGA cohort. Furthermore, the composite signature derived by integrating immune-related signature with clinical factors, provided a more accurate estimation of survival relative to molecular signature alone.

CONCLUSION

We developed a 12-gene prognostic signature, providing novel insights into the identification of breast cancer with a high risk of death and assessment of the possibility of immunotherapy incorporation in personalized breast cancer management.

A multivariate Th17 metagene for prognostic stratification in T cell non-inflamed triple negative breast cancer

A diversity of T helper (Th) subsets (Th1, Th2, Th17) has been identified in the human tumor microenvironment. In breast cancer, the role of Th subsets remains controversial, and a systematic study integrating Th subset diversity, T cell inflammation, breast cancer molecular subtypes, and patient prognosis, is lacking. In primary untreated breast cancer samples, we analyzed 19 Th cytokines at the protein level. Eight were T cell-specific, and subsequently measured in 106 prospectively-collected untreated samples. The dominant Th cytokines across all breast cancer samples were IFN-γ and IL-2. Th2 cytokines (IL-4, IL-5, IL-13) were expressed at low levels and not associated with any breast cancer subtype. Th17 cytokines (IL-17A and IL-17F) were up-regulated in triple negative breast cancer (TNBC), specifically in T cell non-inflamed tumors. In order to get insight into prognosis, we exploited the METABRIC transcriptomic dataset. We derived Th1, Th2, and Th17 metagenes based on manually curated Th signatures, and found that a high Th17 metagene was of good prognosis in T cell non-inflamed TNBC. Multivariate Cox modeling selected the Nottingham Prognostic Index (NPI), Th2 and Th17 metagenes as additive predictors of breast cancer-specific survival, which defined novel and highly distinct prognostic groups within TNBC. Our results reveal that Th17 is a novel prognostic composite biomarker in T cell non-inflamed TNBC.

Integrating immune cell and tumor molecular diversity is an efficient strategy for prognostic stratification of cancer patients.

The Immune Subtypes and Landscape of Squamous Cell Carcinoma

PURPOSE

To identify immune subtypes and investigate the immune landscape of squamous cell carcinomas (SCC), which share common etiology and histologic features.

EXPERIMENTAL DESIGN

Based on the immune gene expression profiles of 1,368 patients with SCC in the Cancer Genome Atlas (TCGA), we used consensus clustering to identify robust clusters of patients and assessed their reproducibility in an independent pan-SCC cohort of 938 patients. We further applied graph structure learning-based dimensionality reduction to the immune profiles to visualize the distribution of individual patients.

RESULTS

We identified and independently validated six reproducible immune subtypes associated with distinct molecular characteristics and clinical outcomes. An immune-cold subtype had the least amount of lymphocyte infiltration and a high level of aneuploidy, and these patients had the worst prognosis. By contrast, an immune-hot subtype demonstrated the highest infiltration of CD8⁺ T cells, activated NK cells, and elevated IFNγ response. Accordingly, these patients had the best prognosis. A third subtype was dominated by M2-polarized macrophages with potent immune-suppressive factors such as TGFβ signaling and reactive stroma, and these patients had relatively inferior prognosis. Other subtypes showed more diverse immunologic features with intermediate prognoses. Finally, our analysis revealed a complex immune landscape consisting of both discrete clusters and continuous spectrum.

CONCLUSIONS

This study provides a conceptual framework to understand the tumor immune microenvironment of SCCs. Future work is needed to evaluate its relevance in the design of combination treatment strategies and guiding optimal selection of patients for immunotherapy.

HLA-DR in Cytotoxic T Lymphocytes Predicts Breast Cancer Patients' Response to Neoadjuvant Chemotherapy

Prediction of breast cancer response to Neoadjuvant Chemotherapy (NACT) is an urgent need to promptly direct non-responder patients to alternative therapies. Infiltrating T lymphocytes, namely cytotoxic T lymphocytes (CTLs) have been appointed as predictors of response. However, cancer cells have the ability to dampen CTLs' activity and thus, the prognostic value of the CTLs, per se, is debatable. Here, we disclose that more than the occurrence of CTLs, it is their activation state, revealed by HLA-DR expression, that can accurately predict response to NACT. Flow cytometry analysis of breast cancer biopsies showed that the frequency of CTLs and other lymphocytes were similar regardless disease stage and between NACT responders and non-responders. However, only breast cancer patients without axillary lymph node metastasis and NACT responders have HLA-DR^hi CTLs. Interestingly, HLA-DR levels in tumor CTLs is correlated with HLA-DR levels in systemic CTLs. These HLA-DR+ CTLs produce IFN-γ and Granzyme B, enlightening their effector and probable anti-tumor activity profile. Moreover, the level of HLA-DR in CTLs is negatively correlated with the level of HLA-DR in T regulatory lymphocytes and with immunosuppressive and pro-tumor molecules in the tumor microenvironment. Hence, HLA-DR levels in CTLs is a highly sensitive and specific potential predictive factor of NACT-response, which can be assessed in blood to guide therapeutic decisions.

Pathway-structured predictive modeling for multi-level drug response in multiple myeloma

Motivation

Molecular analyses suggest that myeloma is composed of distinct sub-types that have different molecular pathologies and various response rates to certain treatments. Drug responses in multiple myeloma (MM) are usually recorded as a multi-level ordinal outcome. One of the goals of drug response studies is to predict which response category any patients belong to with high probability based on their clinical and molecular features. However, as most of genes have small effects, gene-based models may provide limited predictive accuracy. In that case, methods for predicting multi-level ordinal drug responses by incorporating biological pathways are desired but have not been developed yet.

Results

We propose a pathway-structured method for predicting multi-level ordinal responses using a two-stage approach. We first develop hierarchical ordinal logistic models and an efficient quasi-Newton algorithm for jointly analyzing numerous correlated variables. Our two-stage approach first obtains the linear predictor (called the pathway score) for each pathway by fitting all predictors within each pathway using the hierarchical ordinal logistic approach, and then combines the pathway scores as new predictors to build a predictive model. We applied the proposed method to two publicly available datasets for predicting multi-level ordinal drug responses in MM using large-scale gene expression data and pathway information. Our results show that our approach not only significantly improved the predictive performance compared with the corresponding gene-based model but also allowed us to identify biologically relevant pathways.

Availability and implementation

The proposed approach has been implemented in our R package BhGLM, which is freely available from the public GitHub repository https://github.com/abbyyan3/BhGLM.