Development of a fertility risk calculator to predict individualized chance of ovarian failure after chemotherapy.

PURPOSE: To develop an innovative machine learning (ML) model that predicts personalized risk of primary ovarian insufficiency (POI) after chemotherapy for reproductive-aged women. Currently, individualized prediction of a patient's risk of POI is challenging. METHODS: Authors of published studies examining POI after gonadotoxic therapy were contacted, and six authors shared their de-identified data (N = 435). A composite outcome for POI was determined for each patient and validated by 3 authors. The primary dataset was partitioned into training and test sets; random forest binary classifiers were trained, and mean prediction scores were computed. Institutional data collected from a cross-sectional survey of cancer survivors (N = 117) was used as another independent validation set. RESULTS: Our model predicted individualized risk of POI with an accuracy of 88% (area under the ROC 0.87, 95% CI: 0.77-0.96; p < 0.001). Mean prediction scores for patients who developed POI and who did not were 0.60 and 0.38 (t-test p < 0.001), respectively. Highly weighted variables included age, chemotherapy dose, prior treatment, smoking, and baseline diminished ovarian reserve. CONCLUSION: We developed an ML-based model to estimate personalized risk of POI after chemotherapy. Our web-based calculator will be a user-friendly decision aid for individualizing risk prediction in oncofertility consultations.

Mapping eQTL by leveraging multiple tissues and DNA methylation.

BACKGROUND: DNA methylation is an important tissue-specific epigenetic event that influences transcriptional regulation of gene expression. Differentially methylated CpG sites may act as mediators between genetic variation and gene expression, and this relationship can be exploited while mapping multi-tissue expression quantitative trait loci (eQTL). Current multi-tissue eQTL mapping techniques are limited to only exploiting gene expression patterns across multiple tissues either in a joint tissue or tissue-by-tissue frameworks. We present a new statistical approach that enables us to model the effect of germ-line variation on tissue-specific gene expression in the presence of effects due to DNA methylation. RESULTS: Our method efficiently models genetic and epigenetic variation to identify genomic regions of interest containing combinations of mRNA transcripts, CpG sites, and SNPs by jointly testing for genotypic effect and higher order interaction effects between genotype, methylation and tissues. We demonstrate using Monte Carlo simulations that our approach, in the presence of both genetic and DNA methylation effects, gives an improved performance (in terms of statistical power) to detect eQTLs over the current eQTL mapping approaches. When applied to an array-based dataset from 150 neuropathologically normal adult human brains, our method identifies eQTLs that were undetected using standard tissue-by-tissue or joint tissue eQTL mapping techniques. As an example, our method identifies eQTLs by leveraging methylated CpG sites in a LIM homeobox member gene (LHX9), which may have a role in the neural development. CONCLUSIONS: Our score test-based approach does not need parameter estimation under the alternative hypothesis. As a result, our model parameters are estimated only once for each mRNA - CpG pair. Our model specifically studies the effects of non-coding regions of DNA (in this case, CpG sites) on mapping eQTLs. However, we can easily model micro-RNAs instead of CpG sites to study the effects of post-transcriptional events in mapping eQTL. Our model's flexible framework also allows us to investigate other genomic events such as alternative gene splicing by extending our model to include gene isoform-specific data.

Exploiting expression patterns across multiple tissues to map expression quantitative trait loci.

BACKGROUND: In order to better understand complex diseases, it is important to understand how genetic variation in the regulatory regions affects gene expression. Genetic variants found in these regulatory regions have been shown to activate transcription in a tissue-specific manner. Therefore, it is important to map the aforementioned expression quantitative trait loci (eQTL) using a statistically disciplined approach that jointly models all the tissues and makes use of all the information available to maximize the power of eQTL mapping. In this context, we are proposing a score test-based approach where we model tissue-specificity as a random effect and investigate an overall shift in the gene expression combined with tissue-specific effects due to genetic variants. RESULTS: Our approach has 1) a distinct computational edge, and 2) comparable performance in terms of statistical power over other currently existing joint modeling approaches such as MetaTissue eQTL and eQTL-BMA. Using simulations, we show that our method increases the power to detect eQTLs when compared to a tissue-by-tissue approach and can exceed the performance, in terms of computational speed, of MetaTissue eQTL and eQTL-BMA. We apply our method to two publicly available expression datasets from normal human brains, one comprised of four brain regions from 150 neuropathologically normal samples and another comprised of ten brain regions from 134 neuropathologically normal samples, and show that by using our method and jointly analyzing multiple brain regions, we identify eQTLs within more genes when compared to three often used existing methods. CONCLUSIONS: Since we employ a score test-based approach, there is no need for parameter estimation under the alternative hypothesis. As a result, model parameters only have to be estimated once per genome, significantly decreasing computation time. Our method also accommodates the analysis of next- generation sequencing data. As an example, by modeling gene transcripts in an analogous fashion to tissues in our current formulation one would be able to test for both a variant overall effect across all isoforms of a gene as well as transcript-specific effects. We implement our approach within the R package JAGUAR, which is now available at the Comprehensive R Archive Network repository.

A single-cell atlas characterizes dysregulation of the bone marrow immune microenvironment associated with outcomes in multiple myeloma.

Multiple Myeloma (MM) remains incurable despite advances in treatment options. Although tumor subtypes and specific DNA abnormalities are linked to worse prognosis, the impact of immune dysfunction on disease emergence and/or treatment sensitivity remains unclear. We established a harmonized consortium to generate an Immune Atlas of MM aimed at informing disease etiology, risk stratification, and potential therapeutic strategies. We generated a transcriptome profile of 1,149,344 single cells from the bone marrow of 263 newly diagnosed patients enrolled in the CoMMpass study and characterized immune and hematopoietic cell populations. Associating cell abundances and gene expression with disease progression revealed the presence of a proinflammatory immune senescence-associated secretory phenotype in rapidly progressing patients. Furthermore, signaling analyses suggested active intercellular communication involving APRIL-BCMA, potentially promoting tumor growth and survival. Finally, we demonstrate that integrating immune cell levels with genetic information can significantly improve patient stratification.

Dormant tumors circumvent tumor-specific adaptive immunity by establishing a Treg-dominated niche via DKK3.

Approximately 30% of breast cancer survivors deemed free of disease will experience locoregional or metastatic recurrence even up to 30 years after initial diagnosis, yet how residual/dormant tumor cells escape immunity elicited by the primary tumor remains unclear. We demonstrate that intrinsically dormant tumor cells are indeed recognized and lysed by antigen-specific T cells in vitro and elicit robust immune responses in vivo. However, despite close proximity to CD8+ killer T cells, dormant tumor cells themselves support early accumulation of protective FoxP3+ T regulatory cells (Tregs), which can be targeted to reduce tumor burden. These intrinsically dormant tumor cells maintain a hybrid epithelial/mesenchymal state that is associated with immune dysfunction, and we find that the tumor-derived, stem cell/basal cell protein Dickkopf WNT signaling pathway inhibitor 3 (DKK3) is critical for Treg inhibition of CD8+ T cells. We also demonstrate that DKK3 promotes immune-mediated progression of proliferative tumors and is significantly associated with poor survival and immunosuppression in human breast cancers. Together, these findings reveal that latent tumors can use fundamental mechanisms of tolerance to alter the T cell microenvironment and subvert immune detection. Thus, targeting these pathways, such as DKK3, may help render dormant tumors susceptible to immunotherapies.

Genetic heterogeneity of Myc-induced mammary tumors reflecting diverse phenotypes including metastatic potential.

Human cancers result from a complex series of genetic alterations, resulting in heterogeneous disease states. Dissecting this heterogeneity is critical for understanding underlying mechanisms and providing opportunities for therapeutics matching the complexity. Mouse models of cancer have generally been used to reduce this complexity and focus on the role of single genes. Nevertheless, our analysis of tumors arising in the MMTV-Myc model of mammary carcinogenesis reveals substantial heterogeneity, seen in both histological and expression phenotypes. One contribution to this heterogeneity is the substantial frequency of activating Ras mutations. Additionally, we show that these Myc-induced mammary tumors exhibit even greater heterogeneity, revealed by distinct histological subtypes as well as distinct patterns of gene expression, than many other mouse models of tumorigenesis. Two of the major histological subtypes are characterized by differential patterns of cellular signaling pathways, including beta-catenin and Stat3 activities. We also demonstrate that one of the MMTV-Myc mammary tumor subgroups exhibits metastatic capacity and that the signature derived from the subgroup can predict metastatic potential of human breast cancer. Together, these data reveal that a combination of histological and genomic analyses can uncover substantial heterogeneity in mammary tumor formation and therefore highlight aspects of tumor phenotype not evident in the population as a whole.

Characterizing the developmental pathways TTF-1, NKX2-8, and PAX9 in lung cancer.

We investigated the clinical implications of lung developmental transcription factors (TTF-1, NKX2-8, and PAX9) that we recently discovered as cooperating oncogenes activated by way of gene amplification at chromosome 14q13 in lung cancer. Using stable transfectants of human bronchial epithelial cells, RNA expression profiles (signatures) representing activation of the biological pathways defined by each of the 3 genes were determined and used to risk stratify a non-small-cell lung cancer (NSCLC) clinical data set consisting of 91 early stage tumors. Coactivation of the TTF-1 and NKX2-8 pathways identified a cluster of patients with poor survival, representing approximately 20% of patients with early stage NSCLC, whereas activation of individual pathways did not reveal significant prognostic power. Importantly, the poor prognosis associated with coactivation of TTF-1 and NKX2-8 was validated in 2 other independent clinical data sets. Furthermore, lung cancer cell lines showing coactivation of the TTF-1 and NKX2-8 pathways were shown to exhibit resistance to cisplatin, the standard of care for the treatment of NSCLC. This suggests that the cohort of patients with coactivation of TTF-1 and NKX2-8 pathways appears to be resistant to standard cisplatin therapy, suggesting the need for alternative therapies in this cohort of high-risk patients.

Sensitizing immune unresponsive colorectal cancers to immune checkpoint inhibitors through MAVS overexpression.

BACKGROUND: The majority of colorectal carcinomas (CRCs) are insensitive to programmed death protein-1/programmed death-ligand 1 (anti-PD-1/PD-L1) immune checkpoint inhibitor (ICI) antibodies. While there are many causes for ICI insensitivity, recent studies suggest that suppression of innate immune gene expression in tumor cells could be a root cause of this insensitivity and an important factor in the evolution of tumor immunosuppression. METHODS: We first assessed the reduction of mitochondrial antiviral signaling gene (MAVS) and related RIG-I pathway gene expression in several patient RNA expression datasets. We then engineered MAVS expressing tumor cells and tested their ability to elicit innate and adaptive anti-tumor immunity using both in vitro and in vivo approaches, which we then confirmed using MAVS expressing viral vectors. Finally, we observed that MAVS stimulated PD-L1 expression in multiple cell types and then assessed the combination of PD-L1 ICI antibodies with MAVS tumor expression in vivo. RESULTS: MAVS was significantly downregulated in CRCs, but its re-expression could stimulate broad cellular interferon-related responses, in both murine and patient-derived CRCs. In vivo, local MAVS expression elicited significant anti-tumor responses in both immune-sensitive and insensitive CRC models, through the stimulation of an interferon responsive axis that provoked tumor antigen-specific adaptive immunity. Critically, we found that tumor-intrinsic MAVS expression triggered systemic adaptive immune responses that enabled abscopal CD8 +T cell cytotoxicity against distant CRCs. As MAVS also induced PD-L1 expression, we further found synergistic anti-tumor responses in combination with anti-PD-L1 ICIs. CONCLUSION: These data demonstrate that intratumoral MAVS expression results in local and systemic tumor antigen-specific T cell responses, which could be combined with PD-L1 ICI to permit effective anti-tumor immunotherapy in ICI resistant cancers.

Combination of a novel heat shock protein 90-targeted photodynamic therapy with PD-1/PD-L1 blockade induces potent systemic antitumor efficacy and abscopal effect against breast cancers.

BACKGROUND: We previously demonstrated potent antitumor activity against human breast cancer xenografts using photodynamic therapy (PDT) targeting a novel tumor-specific photosensitizer (HS201), which binds heat shock protein 90 (HS201-PDT). However, induction of systemic antitumor immunity by HS201-PDT alone or by the combination strategy with immune checkpoint blockade has yet to be determined. METHODS: Using unilateral and bilateral implantation models of syngeneic breast tumors (E0771, MM3MG-HER2, and JC-HER3) in mice, we assessed whether HS201-PDT could induce local and systemic antitumor immunity. In an attempt to achieve a stronger abscopal effect for distant tumors, the combination strategy with anti-PD-L1 antibody was tested. Tumor-infiltrating leukocytes were analyzed by single cell RNA-sequencing and receptor-ligand interactome analysis to characterize in more detailed the mechanisms of action of the treatment and key signaling pathways involved. RESULTS: HS201-PDT demonstrated greater tumor control and survival in immune competent mice than in immunocompromised mice, suggesting the role of induced antitumor immunity; however, survival was modest and an abscopal effect on distant implanted tumor was weak. A combination of HS201-PDT with anti-PD-L1 antibody demonstrated the greatest antigen-specific immune response, tumor growth suppression, prolonged mouse survival time and abscopal effect. The most significant increase of intratumoral, activated CD8+T cells and decrease of exhausted CD8+T cells occurred following combination treatment compared with HS201-PDT monotherapy. Receptor-ligand interactome analysis showed marked enhancement of several pathways, such as CXCL, GALECTIN, GITRL, PECAM1 and NOTCH, associated with CD8+T cell activation in the combination group. Notably, the expression of the CXCR3 gene signature was the highest in the combination group, possibly explaining the enhanced tumor infiltration by T cells. CONCLUSIONS: The increased antitumor activity and upregulated CXCR3 gene signature induced by the combination of anti-PD-L1 antibody with HS201-PDT warrants the clinical testing of HS201-PDT combined with PD-1/PD-L1 blockade in patients with breast cancer, and the use of the CXCR3 gene signature as a biomarker.

Combination of ultrasound-based mechanical disruption of tumor with immune checkpoint blockade modifies tumor microenvironment and augments systemic antitumor immunity.

BACKGROUND: Despite multimodal adjuvant management with radiotherapy, chemotherapy and hormonal therapies, most surgically resected primary breast cancers relapse or metastasize. A potential solution to late and distant recurrence is to augment systemic antitumor immunity, in part by appropriately presenting tumor antigens, but also by modulating the immunosuppressive tumor microenvironment (TME). We previously validated this concept in models of murine carcinoma treated with a novel predominately microcavitating version of high-intensity focused ultrasound (HIFU), mechanical high-intensity focused ultrasound (M-HIFU). Here we elucidated the mechanisms of enhanced antitumor immunity by M-HIFU over conventional thermal high-intensity focused ultrasound (T-HIFU) and investigated the potential of the combinatorial strategy with an immune checkpoint inhibitor, anti-PD-L1 antibody. METHODS: The antitumor efficacy of treatments was investigated in syngeneic murine breast cancer models using triple-negative (E0771) or human ErbB-2 (HER2) expressing (MM3MG-HER2) tumors in C57BL/6 or BALB/c mice, respectively. Induction of systemic antitumor immunity by the treatments was tested using bilateral tumor implantation models. Flow cytometry, immunohistochemistry, and single-cell RNA sequencing were performed to elucidate detailed effects of HIFU treatments or combination treatment on TME, including the activation status of CD8 T cells and polarization of tumor-associated macrophages (TAMs). RESULTS: More potent systemic antitumor immunity and tumor growth suppression were induced by M-HIFU compared with T-HIFU. Molecular characterization of the TME after M-HIFU by single-cell RNA sequencing demonstrated repolarization of TAM to the immunostimulatory M1 subtype compared with TME post-T-HIFU. Concurrent anti-PD-L1 antibody administration or depletion of CD4+ T cells containing a population of regulatory T cells markedly increased T cell-mediated antitumor immunity and tumor growth suppression at distant, untreated tumor sites in M-HIFU treated mice compared with M-HIFU monotherapy. CD8 T and natural killer cells played major roles as effector cells in the combination treatment. CONCLUSIONS: Physical disruption of the TME by M-HIFU repolarizes TAM, enhances T-cell infiltration, and, when combined with anti-PD-L1 antibody, mediates superior systemic antitumor immune responses and distant tumor growth suppression. These findings suggest M-HIFU combined with anti-PD-L1 may be useful in reducing late recurrence or metastasis when applied to primary tumors.

HSP90-Specific nIR Probe Identifies Aggressive Prostate Cancers: Translation from Preclinical Models to a Human Phase I Study.

A noninvasive test to discriminate indolent prostate cancers from lethal ones would focus treatment where necessary while reducing overtreatment. We exploited the known activity of heat shock protein 90 (Hsp90) as a chaperone critical for the function of numerous oncogenic drivers, including the androgen receptor and its variants, to detect aggressive prostate cancer. We linked a near-infrared fluorescing molecule to an HSP90 binding drug and demonstrated that this probe (designated HS196) was highly sensitive and specific for detecting implanted prostate cancer cell lines with greater uptake by more aggressive subtypes. In a phase I human study, systemically administered HS196 could be detected in malignant nodules within prostatectomy specimens. Single-cell RNA sequencing identified uptake of HS196 by malignant prostate epithelium from the peripheral zone (AMACR+ERG+EPCAM+ cells), including SYP+ neuroendocrine cells that are associated with therapeutic resistance and metastatic progression. A theranostic version of this molecule is under clinical testing.

A genomic approach to colon cancer risk stratification yields biologic insights into therapeutic opportunities.

Gene expression profiles provide an opportunity to dissect the heterogeneity of solid tumors, including colon cancer, to improve prognosis and predict response to therapies. Bayesian binary regression methods were used to generate a signature of disease recurrence in patients with resected early stage colon cancer validated in an independent cohort. A 50-gene signature was developed that effectively distinguished early stage colon cancer patients with a low or high risk of disease recurrence. RT-PCR analysis of the 50-gene signature validated 9 of the top 10 differentially expressed genes. When applied to two independent validation cohorts of 55 and 73 patients, the 50-gene model accurately predicted recurrence. Standard Kaplan-Meier survival analysis confirmed the prognostic accuracy (P < 0.01, log rank), as did multivariate Cox proportional hazard models. We tested potential targeted therapeutic options for patients at high risk for disease recurrence and found a clinically important relationship between sensitivity to celecoxib, LY-294002 (PI3kinase inhibitor), retinol, and sulindac in colon cancer cell lines expressing the poor prognostic phenotype (P < 0.01, t test), which performed better than standard chemotherapy (5-FU and oxaliplatin). We present a genomic strategy in early stage colon cancer to identify patients at highest risk of recurrence. An ability to move beyond current staging by refining the estimation of prognosis in early stage colon cancer also has implications for individualized therapy.

Intratumoral Plasmid IL12 Expands CD8+ T Cells and Induces a CXCR3 Gene Signature in Triple-negative Breast Tumors that Sensitizes Patients to Anti-PD-1 Therapy.

PURPOSE: Triple-negative breast cancer (TNBC) is an aggressive disease with limited therapeutic options. Antibodies targeting programmed cell death protein 1 (PD-1)/PD-1 ligand 1 (PD-L1) have entered the therapeutic landscape in TNBC, but only a minority of patients benefit. A way to reliably enhance immunogenicity, T-cell infiltration, and predict responsiveness is critically needed. PATIENTS AND METHODS: Using mouse models of TNBC, we evaluate immune activation and tumor targeting of intratumoral IL12 plasmid followed by electroporation (tavokinogene telseplasmid; Tavo). We further present a single-arm, prospective clinical trial of Tavo monotherapy in patients with treatment refractory, advanced TNBC (OMS-I140). Finally, we expand these findings using publicly available breast cancer and melanoma datasets. RESULTS: Single-cell RNA sequencing of murine tumors identified a CXCR3 gene signature (CXCR3-GS) following Tavo treatment associated with enhanced antigen presentation, T-cell infiltration and expansion, and PD-1/PD-L1 expression. Assessment of pretreatment and posttreatment tissue from patients confirms enrichment of this CXCR3-GS in tumors from patients that exhibited an enhancement of CD8+ T-cell infiltration following treatment. One patient, previously unresponsive to anti-PD-L1 therapy, but who exhibited an increased CXCR3-GS after Tavo treatment, went on to receive additional anti-PD-1 therapy as their immediate next treatment after OMS-I140, and demonstrated a significant clinical response. CONCLUSIONS: These data show a safe, effective intratumoral therapy that can enhance antigen presentation and recruit CD8 T cells, which are required for the antitumor efficacy. We identify a Tavo treatment-related gene signature associated with improved outcomes and conversion of nonresponsive tumors, potentially even beyond TNBC.

HER2 Isoforms Uniquely Program Intratumor Heterogeneity and Predetermine Breast Cancer Trajectories During the Occult Tumorigenic Phase.

HER2-positive breast cancers are among the most heterogeneous breast cancer subtypes. The early amplification of HER2 and its known oncogenic isoforms provide a plausible mechanism in which distinct programs of tumor heterogeneity could be traced to the initial oncogenic event. Here a Cancer rainbow mouse simultaneously expressing fluorescently barcoded wildtype (WTHER2), exon-16 null (d16HER2), and N-terminally truncated (p95HER2) HER2 isoforms is used to trace tumorigenesis from initiation to invasion. Tumorigenesis was visualized using whole-gland fluorescent lineage tracing and single-cell molecular pathology. We demonstrate that within weeks of expression, morphologic aberrations were already present and unique to each HER2 isoform. Although WTHER2 cells were abundant throughout the mammary ducts, detectable lesions were exceptionally rare. In contrast, d16HER2 and p95HER2 induced rapid tumor development. d16HER2 incited homogenous and proliferative luminal-like lesions which infrequently progressed to invasive phenotypes whereas p95HER2 lesions were heterogenous and invasive at the smallest detectable stage. Distinct cancer trajectories were observed for d16HER2 and p95HER2 tumors as evidenced by oncogene-dependent changes in epithelial specification and the tumor microenvironment. These data provide direct experimental evidence that intratumor heterogeneity programs begin very early and well in advance of screen or clinically detectable breast cancer. IMPLICATIONS: Although all HER2 breast cancers are treated equally, we show a mechanism by which clinically undetected HER2 isoforms program heterogenous cancer phenotypes through biased epithelial specification and adaptations within the tumor microenvironment.

Immune signatures predict prognosis in localized cancer.

The host immune response can impact cancer growth, prognosis, and response to therapy. In colorectal cancer, the presence of cells involved with T-cell-mediated adaptive immunity predicts survival better than the current staging method. We used the expression of genes recently associated with host immune responses (T(H1)-mediated adaptive immunity, inflammation, and immune suppression) to perform hierarchical clustering of multiple large cohorts of cancer specimens to determine if immune-related gene expression resulted in clinical significant groupings of tumors. Microarray data from prostate cancer (n = 79), breast cancer (n = 132), lung cancer (n = 84), glioblastoma multiforme (n = 120), and lymphoma (n = 127) were analyzed. Among adenocarcinomas, the T(H1)-mediated adaptive immunity genes were consistently associated with better prognosis, while genes associated with inflammation and immune suppression were variably associated with outcome. Specifically, increased expression of the T(H1)-mediated adaptive immunity genes was associated with good prognosis in breast cancer patients under 45 years of age (p = .04, hazard ratio [HR] = 0.42) and in prostate cancer patients (p = .03, HR = 0.36) but not in lung cancer patients (p = 0.45, HR = 1.37). In lymphoma, patients with increased expression of inflammation and immune suppression genes had better prognosis than those expressing the T(H1)-mediated adaptive immunity genes (p = .01, HR = 0.43) and in glioblastoma multiforme, the expression of inflammation genes conferred improved prognosis than those expressing immune suppression genes (p = 0.05, HR = 0.62). In aggregate, the gene expression signatures implicating specific components of the immune response hold prognostic import across solid tumors.

An age-independent gene signature for monitoring acute rejection in kidney transplantation.

Acute rejection (AR) remains a significant problem that negatively impacts long-term renal allograft survival. Numerous therapies are used to prevent AR that differ by center and recipient age. This variability confounds diagnostic methods. Methods: To develop an age-independent gene signature for AR effective across a broad array of immunosuppressive regimens, we compiled kidney transplant biopsy (n=1091) and peripheral blood (n=392) gene expression profiles from 12 independent public datasets. After removing genes differentially expressed in pediatric and adult patients, we compared gene expression profiles from biopsy and peripheral blood samples of patients with AR to those who were stable (STA), using Mann-Whitney U Tests with validation in independent testing datasets. We confirmed this signature in pediatric and adult patients (42 AR and 47 STA) from our institutional biorepository. Results: We identified a novel age-independent gene network that identified AR from both kidney and blood samples. We developed a 90-probe set signature targeting 76 genes that differentiated AR from STA and found an 8 gene subset (DIP2C, ENOSF1, FBXO21, KCTD6, PDXDC1, REXO2, HLA-E, and RAB31) that was associated with AR. Conclusion: We used publicly available datasets to create a gene signature of AR that identified AR irrespective of immunosuppression regimen or recipient age. This study highlights a novel model to screen and validate biomarkers across multiple treatment regimens.

Stimulation of Oncogene-Specific Tumor-Infiltrating T Cells through Combined Vaccine and αPD-1 Enable Sustained Antitumor Responses against Established HER2 Breast Cancer.

PURPOSE: Despite promising advances in breast cancer immunotherapy, augmenting T-cell infiltration has remained a significant challenge. Although neither individual vaccines nor immune checkpoint blockade (ICB) have had broad success as monotherapies, we hypothesized that targeted vaccination against an oncogenic driver in combination with ICB could direct and enable antitumor immunity in advanced cancers. EXPERIMENTAL DESIGN: Our models of HER2+ breast cancer exhibit molecular signatures that are reflective of advanced human HER2+ breast cancer, with a small numbers of neoepitopes and elevated immunosuppressive markers. Using these, we vaccinated against the oncogenic HER2Δ16 isoform, a nondriver tumor-associated gene (GFP), and specific neoepitopes. We further tested the effect of vaccination or anti-PD-1, alone and in combination. RESULTS: We found that only vaccination targeting HER2Δ16, a driver of oncogenicity and HER2-therapeutic resistance, could elicit significant antitumor responses, while vaccines targeting a nondriver tumor-specific antigen or tumor neoepitopes did not. Vaccine-induced HER2-specific CD8+ T cells were essential for responses, which were more effective early in tumor development. Long-term tumor control of advanced cancers occurred only when HER2Δ16 vaccination was combined with αPD-1. Single-cell RNA sequencing of tumor-infiltrating T cells revealed that while vaccination expanded CD8 T cells, only the combination of vaccine with αPD-1 induced functional gene expression signatures in those CD8 T cells. Furthermore, we show that expanded clones are HER2-reactive, conclusively demonstrating the efficacy of this vaccination strategy in targeting HER2. CONCLUSIONS: Combining oncogenic driver targeted vaccines with selective ICB offers a rational paradigm for precision immunotherapy, which we are clinically evaluating in a phase II trial (NCT03632941).

An integration of complementary strategies for gene-expression analysis to reveal novel therapeutic opportunities for breast cancer.

INTRODUCTION: Perhaps the major challenge in developing more effective therapeutic strategies for the treatment of breast cancer patients is confronting the heterogeneity of the disease, recognizing that breast cancer is not one disease but multiple disorders with distinct underlying mechanisms. Gene-expression profiling studies have been used to dissect this complexity, and our previous studies identified a series of intrinsic subtypes of breast cancer that define distinct populations of patients with respect to survival. Additional work has also used signatures of oncogenic pathway deregulation to dissect breast cancer heterogeneity as well as to suggest therapeutic opportunities linked to pathway activation. METHODS: We used genomic analyses to identify relations between breast cancer subtypes, pathway deregulation, and drug sensitivity. For these studies, we use three independent breast cancer gene-expression data sets to measure an individual tumor phenotype. Correlation between pathway status and subtype are examined and linked to predictions for response to conventional chemotherapies. RESULTS: We reveal patterns of pathway activation characteristic of each molecular breast cancer subtype, including within the more aggressive subtypes in which novel therapeutic opportunities are critically needed. Whereas some oncogenic pathways have high correlations to breast cancer subtype (RAS, CTNNB1, p53, HER1), others have high variability of activity within a specific subtype (MYC, E2F3, SRC), reflecting biology independent of common clinical factors. Additionally, we combined these analyses with predictions of sensitivity to commonly used cytotoxic chemotherapies to provide additional opportunities for therapeutics specific to the intrinsic subtype that might be better aligned with the characteristics of the individual patient. CONCLUSIONS: Genomic analyses can be used to dissect the heterogeneity of breast cancer. We use an integrated analysis of breast cancer that combines independent methods of genomic analyses to highlight the complexity of signaling pathways underlying different breast cancer phenotypes and to identify optimal therapeutic opportunities.

Gene expression signatures, clinicopathological features, and individualized therapy in breast cancer.

CONTEXT: Gene expression profiling may be useful for prognostic and therapeutic strategies in breast carcinoma. OBJECTIVES: To demonstrate the value in integrating genomic information with clinical and pathological risk factors, to refine prognosis, and to improve therapeutic strategies for early stage breast cancer. DESIGN, SETTING, AND PATIENTS: Retrospective study of patients with early stage breast carcinoma who were candidates for adjuvant chemotherapy; 964 clinically annotated breast tumor samples (573 in the initial discovery set and 391 in the validation cohort) with corresponding microarray data were used. All patients were assigned relapse risk scores based on their respective clinicopathological features. Signatures representing oncogenic pathway activation and tumor biology/microenvironment status were applied to these samples to obtain patterns of deregulation that correspond with relapse risk scores to refine prognosis with the clinicopathological prognostic model alone. Predictors of chemotherapeutic response were also applied to further characterize clinically relevant heterogeneity in early stage breast cancer. MAIN OUTCOME MEASURES: Gene expression signatures and clinicopathological variables in early stage breast cancer to determine a refined estimation of relapse-free survival and sensitivity to chemotherapy. RESULTS: In the initial data set of 573 patients, prognostically significant clusters representing patterns of oncogenic pathway activation and tumor biology/microenvironment states were identified within the low-risk (log-rank P = .004), intermediate-risk (log-rank P = .01), and high-risk (log-rank P = .003) model cohorts, representing clinically important genomic subphenotypes of breast cancer. As an example, in the low-risk cohort, of 6 prognostically significant clusters, patients in cluster 4 had an inferior relapse-free survival vs patients in cluster 1 (log-rank P = .004) and cluster 5 (log-rank P = .03). Median relapse-free survival for patients in cluster 4 was 16 months less than for patients in cluster 1 (95% CI, 7.5-24.5 months) and 19 months less than for patients in cluster 5 (95% CI, 10.5-27.5 months). Multivariate analyses confirmed the independent prognostic value of the genomic clusters (low risk, P = .05; high risk, P = .02). The reproducibility and validity of these patterns of pathway deregulation in predicting relapse risk was established using related but not identical clusters in the independent validation cohort. The prognostic clinicogenomic clusters also have unique sensitivity patterns to commonly used cytotoxic therapies. CONCLUSIONS: These results provide preliminary evidence that incorporation of gene expression signatures into clinical risk stratification can refine prognosis. Prospective studies are needed to determine the value of this approach for individualizing therapeutic strategies.