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Progressive tumor growth is associated with deficits in the immunity generated against tumor antigens. Vaccines targeting tumor neoepitopes have the potential to address qualitative defects; however, additional mechanisms of immune failure may underlie tumor progression. In such cases, patients would benefit from additional immune-oncology agents targeting potential mechanisms of immune failure. This study explores the identification of neoepitopes in the MC38 colon carcinoma model by comparison of tumor to normal DNA and tumor RNA sequencing technology, as well as neoepitope delivery by both peptide- and adenovirus-based vaccination strategies. To improve antitumor efficacies, we combined the vaccine with a group of rationally selected immune-oncology agents. We utilized an IL15 superagonist to enhance the development of antigen-specific immunity initiated by the neoepitope vaccine, PD-L1 blockade to reduce tumor immunosuppression, and a tumor-targeted IL12 molecule to facilitate T-cell function within the tumor microenvironment. Analysis of tumor-infiltrating leukocytes demonstrated this multifaceted treatment regimen was required to promote the influx of CD8+ T cells and enhance the expression of transcripts relating to T-cell activation/effector function. Tumor-targeted IL12 resulted in a marked increase in clonality of T-cell repertoire infiltrating the tumor, which when sculpted with the addition of either a peptide or adenoviral neoepitope vaccine promoted efficient tumor clearance. In addition, the neoepitope vaccine induced the spread of immunity to neoepitopes expressed by the tumor but not contained within the vaccine. These results demonstrate the importance of combining neoepitope-targeting vaccines with a multifaceted treatment regimen to generate effective antitumor immunity.

Background: Tumor immune markers such as tumor infiltrating lymphocytes (TILs) or expression-based profiles have been correlated with both response to neoadjuvant chemotherapy and prognosis in early breast cancer (BC) patients. Some chemotherapies, such as paclitaxel, lead to the development of TILs and in some cases, suppression of regulatory T-cells. Therefore, assessment of the tumor microenvironment (TME) could provide important information for clinical decision-making. The aim of this study was to test if RNAseq-based TME classification of BC tumors is predictive of pathological complete Response (pCR) and prognosis in the neoadjuvant GeparSepto (G7) trial (NCT01583426). Methods: We performed a retrospective-prospective analysis of a subset of 810 subjects of the total of 1207 patients of the G7 trial. In G7 HER2-negative early high-risk BC patients were studied to determine if nab-paclitaxel is superior to solvent-based paclitaxel. In addition to the taxane paclitaxel, both treatment arms received epirubicin plus cyclophosphamide before surgery. For this analysis, a subset of 279 HER2 negative patients with sufficient quality of pretherapeutic core biopsies to perform whole-transcriptome RNAseq (~200x106 reads per tumor) was used. Based on RNAseq data, immune activity classification was provided by ImmunityBio (Culver City, CA) by comparison of expression of 23 immune-cell-specific gene signatures as described by Bindea et al. (Immunity, 2013) to those from a reference population of 1467 similarly-profiled unselected tumor samples from a large tumor database (NantOmics, Culver City, CA). Unsupervised hierarchical clustering of inferred immune activities revealed 3 distinct groups termed “hot”, “warm”, and “cold” clusters. Logistic regression analysis based on age, trial arm, tumor size, nodal status, Ki-67, hormone-receptor (HR) status and immune activity cluster (hot/warm vs. cold) as independent variables was performed to predict pCR (ypT0/ypN0). Cox regression analysis with the same covariates was also performed to predict disease-free survival (DFS) and overall survival (OS). Results: Of the 279 patients, 67 had a pCR (24%). The analyzed subset was similar to the main HER2 negative population (pCR-rate: 22%). Patients with a “hot/warm” or “cold” immune activity assessment had a pCR in 30% and 13% of the cases, respectively. The odds-ratio of the multivariate logistic regression analysis was 2.17 (95% CI: 1.00-4.71, p=0.0512). With regard to DFS and OS, T follicular helper cell B-cell, and T-cell signatures seemed to play a prominent role, and the hazard ratios (also “hot/warm” vs. “cold”) for the multivariate analyses were 0.38 (95% CI: 0.21-0.66; p=0.0007) and 0.34 (95%CI: 0.16-0.72, p= 0.0045), respectively. Within the 23 individual immune-cell-specific gene signatures, CD56dimNatural Killer (NK), type 1 helper T-cells, and CD8+ T-cell signatures seemed to be closely associated with achievement of a pCR. RNAseq-based deconvolution of immune-cell activity was corroborated by IHC-based TIL scoring. Immune-hot/warm patients had more intratumoral lymphocytes compared to cold tumors (mean: 11.6% vs. 4.9%, p Citation Format: Peter A Fasching, Carsten Denkert, Stephen Benz, Karsten E Weber, Christopher Szeto, Jan Budczies, Andreas Schneeweiss, Elmar Stickeler, Sabine Schmatloch, Christian Jackisch, Thomas Karn, Hans Peter Sinn, Mathias Warm, Marion van Mackelenbergh, Sharooz Rabizadeh, Christian Schem, Ernst Heinmöller, Volkmar Müller, Frederik Marmé, Patrick Soon-Shiong, Valentina Nekljudova, Michael Untch, Sibylle Loibl. Tumor immune-cell activity assessed by RNAseq is an independent predictor of therapy response and prognosis after neoadjuvant chemotherapy in HER2 negative breast cancer patients - An analysis of the GeparSepto trial [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr PD5-08.

Background: Previous studies of breast samples from cosmetic surgeries, benign biopsies associated with abnormal mammograms or cancer-adjacent tissue have identified at least two different transcriptional phenotypes of “normal” human breast tissue (Troester, 2009; Haakensen, 2011), including an “Active” phenotype linked to increased risk of later breast cancer mortality (Roman-Perez, 2012; Troester, 2016). This study compares breast histology and transcriptional phenotypes from healthy parous women with no prior history of breast disease who donated breast core biopsies for research and supplied reproductive histories enabling breast cancer risk calculation. Since the Active transcriptome phenotype was recently associated with increased mammary adipocyte content, we focused on the possibility that adipocyte activation contributes to the Active transcriptome and drives breast cancer risk. Methods: RNA from paraffin-embedded tissue sections sufficient for RNAseq analysis (~100ng) was extracted from 151/200 core biopsies donated to the Komen Tissue Bank by healthy, parous white women (age range: 27-66, median = 45) with no history of breast cancer. Questionnaire data enabled breast cancer risk (Gail) score calculation; and digitized H&E images were used for histologic analyses. A previously validated classifying signature was used in unsupervised hierarchical clustering to identify samples with Active (78/151) vs. Inactive (73/151) transcriptome phenotypes for comparison with donor risk factors, breast tissue composition, and expression of candidate genes and gene signatures. Results: Mean (+/-SD) BMI and Gail score values were 29.60 (+/-7.92) and 1.27 (+/- 1.34), respectively; BMI scores were not significantly different by phenotype, but Gail scores were significantly higher for donors with an Active phenotype (1.46 vs. 1.18; p=0.007, Wilcoxon rank-sum). Active normal breast tissue samples possessed significantly more (%) adipocyte nuclei (p=3.9e-11) and greater adipocyte size (p80% of this donor cohort would not qualify for breast cancer chemoprevention, those with Active transcriptomes had significantly higher Gail scores supporting their increased future risk for breast cancer development. The Active breast transcriptome is strongly associated with increased adipocyte content, size, and overexpression of signatures and genes (including those previously linked to breast cancer progression) indicating a differentially activated adipocyte population. This dysregulated mammary adipocyte microenvironment not only appears to underlie the Active transcriptome phenotype but also precedes and potentially predicts the future histologic development of breast cancer. Citation Format: Christopher C Benz, Taekyu Kang, Christina Yau, Chris Wong, Yulia Newton, Charlie Vaske, Stephen C Benz, Gregor Krings, Roman Camarda, Jill E Henry, Josh Stuart, Mark Powell. The normal breast Active transcriptome associated with future breast cancer risk is driven by a dysregulated adipocyte microenvironment [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P3-08-20.

Background Breast cancer intrinsic molecular subtype (IMS) as classified by the expression-based PAM50 assay is considered a strong prognostic feature, even when controlled for by standard clinicopathological features such as age, grade, and nodal status, yet the molecular testing required to elucidate these subtypes is not routinely performed. Furthermore, when such bulk assays as RNA sequencing are performed, intratumoral heterogeneity that may affect prognosis and therapeutic decision-making can be missed. Methods As a more facile and readily available method for determining IMS in breast cancer, we developed a deep learning approach for approximating PAM50 intrinsic subtyping using only whole-slide images of H&E-stained breast biopsy tissue sections. This algorithm was trained on images from 443 tumors that had previously undergone PAM50 subtyping to classify small patches of the images into four major molecular subtypes—Basal-like, HER2-enriched, Luminal A, and Luminal B—as well as Basal vs. non-Basal. The algorithm was subsequently used for subtype classification of a held-out set of 222 tumors. Results This deep learning image-based classifier correctly subtyped the majority of samples in the held-out set of tumors. However, in many cases, significant heterogeneity was observed in assigned subtypes across patches from within a single whole-slide image. We performed further analysis of heterogeneity, focusing on contrasting Luminal A and Basal-like subtypes because classifications from our deep learning algorithm—similar to PAM50—are associated with significant differences in survival between these two subtypes. Patients with tumors classified as heterogeneous were found to have survival intermediate between Luminal A and Basal patients, as well as more varied levels of hormone receptor expression patterns. Conclusions Here, we present a method for minimizing manual work required to identify cancer-rich patches among all multiscale patches in H&E-stained WSIs that can be generalized to any indication. These results suggest that advanced deep machine learning methods that use only routinely collected whole-slide images can approximate RNA-seq-based molecular tests such as PAM50 and, importantly, may increase detection of heterogeneous tumors that may require more detailed subtype analysis.

Background Metastatic triple negative breast cancer (mTNBC) is an inherently diverse disease and while molecular classification of mTNBC has assisted in treatment decisions, if based on only an initial biopsy, it does not take into account the evolution of metastatic cancer. Characterization of emerging metastases is needed to reveal both new resistance or sensitivity to available therapeutics. The goal of “Intensive Trial of OMics in Cancer (ITOMIC) - Intensive Longitudinal Monitoring in Subjects With Triple-Negative Breast Cancer” (NCT01957514) - was to determine the feasibility of longitudinal collection of patient biopsies that would be subjected to molecular analysis to provide actionable, relevant and timely information to guide treatment decisions.Methods Multiple biopsies were collected longitudinally, including pre- and post-treatment, from 29 mTNBC patients enrolled in the ITOMIC study and subjected to multi-dimensional molecular profiling including WES, WGS, cancer gene panel sequencing, RNA-seq, and proteomics and/or IHC for tumor biomarkers. This information was used to guide iterative, patient- and tumor- individualized treatment recommendations made by a multi-institutional ITOMIC Tumor Board (ITB) and conveyed to each subject’s oncologist.Results Longitudinal biopsy collection was found to be safe. Molecular profiling revealed that 2 of an original 31 enrolled subjects likely had lung cancer rather than mTNBC, supporting the merit of repeated tissue analysis. While the other 29 subjects had all been given a diagnosis of mTNBC before entering the trial, estrogen receptor, progesterone receptor, and/or HER2 were found to be over-expressed in at least one sample for 12 subjects; appearance of receptor positivity suggests targeted therapy may be effective. Tumor evolution in response to the first on-study treatment for most subjects (cisplatin) was revealed by copy number alterations, changes in single nucleotide variants, and insertions/deletions in pre-/post-treatment biopsies. Over the course of the study, the ITB convened 54 times and 39 of 182 recommended treatments were evaluated and accessed through either an existing clinical trial, a single patient IND, approved off label or label indication. While not all ITB treatment recommendations were followed, 24 subjects did receive at least one ITB-recommended drug, frequently as part of a clinical trial. Currently, for 27 subjects (2 withdrew) median survival is ~31 months. There are 4 surviving patients in treatment with a remarkable median survival of >51 months.Conclusion Collection and molecular analysis of multiple biopsies during the course of patient’s disease, shown here to be safe and feasible, provides information vital to appropriate treatment choice and reveals new targets for and resistance to therapy in metastatic TNBC. Citation Format: Kimberly A Burton, Eric Q Konnick, Sibel Blau, Michael O Dorschner, Julie Gralow, Rahul Parulkar, Elisabeth Mahen, Patricia Spilman, Stephanie Parker, Francis M Senecal, Colin Pritchard, Christopher Szeto, Jing Zhu, Vijayakrishna K Gadi, Stephen C Benz, Shahrooz Rabizadeh, Patrick Soon-Shiong, Carl Anthony Blau. Multidimensional molecular profiling of repeated metastatic TNBC biopsies in the intensive trial of omics safely guides treatment decisions [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS11-13.

The homeostatic mechanisms that fail to restrain chronic tissue inflammation in diseases, such as psoriasis vulgaris, remain incompletely understood. We profiled transcriptomes and epitopes of single psoriatic and normal skin-resident T cells, revealing a gradated transcriptional program of coordinately regulated inflammation-suppressive genes. This program, which is sharply suppressed in lesional skin, strikingly restricts Th17/Tc17 cytokine and other inflammatory mediators on the single-cell level. CRISPR-based deactivation of two core components of this inflammation-suppressive program, ZFP36L2 and ZFP36, replicates the interleukin-17A (IL-17A), granulocyte macrophage-colony-stimulating factor (GM-CSF), and interferon gamma (IFNγ) elevation in psoriatic memory T cells deficient in these transcripts, functionally validating their influence. Combinatoric expression analysis indicates the suppression of specific inflammatory mediators by individual program members. Finally, we find that therapeutic IL-23 blockade reduces Th17/Tc17 cell frequency in lesional skin but fails to normalize this inflammatory-suppressive program, suggesting how treated lesions may be primed for recurrence after withdrawal of treatment.

BackgroundTherapeutic regimens designed to augment the immunological response of a patient with breast cancer (BC) to tumor tissue are critically informed by tumor mutational burden and the antigenicity of expressed neoepitopes. Herein we describe a neoepitope and cognate neoepitope-reactive T-cell identification and validation program that supports the development of next-generation immunotherapies.MethodsUsing GPS Cancer, NantOmics research, and The Cancer Genome Atlas databases, we developed a novel bioinformatic-based approach which assesses mutational load, neoepitope expression, human leukocyte antigen (HLA)-binding prediction, and in vitro confirmation of T-cell recognition to preferentially identify targetable neoepitopes. This program was validated by application to a BC cell line and confirmed using tumor biopsies from two patients with BC enrolled in the Tumor-Infiltrating Lymphocytes and Genomics (TILGen) study.ResultsThe antigenicity and HLA-A2 restriction of the BC cell line predicted neoepitopes were determined by reactivity of T cells from HLA-A2-expressing healthy donors. For the TILGen subjects, tumor-infiltrating lymphocytes (TILs) recognized the predicted neoepitopes both as peptides and on retroviral expression in HLA-matched Epstein-Barr virus–lymphoblastoid cell line and BC cell line MCF-7 cells; PCR clonotyping revealed the presence of T cells in the periphery with T-cell receptors for the predicted neoepitopes. These high-avidity immune responses were polyclonal, mutation-specific and restricted to either HLA class I or II. Interestingly, we observed the persistence and expansion of polyclonal T-cell responses following neoadjuvant chemotherapy.ConclusionsWe demonstrate our neoepitope prediction program allows for the successful identification of neoepitopes targeted by TILs in patients with BC, providing a means to identify tumor-specific immunogenic targets for individualized treatment, including vaccines or adoptively transferred cellular therapies.

Summary Inflammatory response heterogeneity has impeded high-resolution dissection of diverse immune cell populations during activation. We characterize mouse cutaneous immune cells by single-cell RNA sequencing, after inducing inflammation using imiquimod and oxazolone dermatitis models. We identify 13 CD45+ subpopulations, which broadly represent most functionally characterized immune cell types. Oxazolone pervasively upregulates Jak2/Stat3 expression across T cells and antigen-presenting cells (APCs). Oxazolone also induces Il4/Il13 expression in newly infiltrating basophils, and Il4ra and Ccl24, most prominently in APCs. In contrast, imiquimod broadly upregulates Il17/Il22 and Ccl4/Ccl5. A comparative analysis of single-cell inflammatory transcriptional responses reveals that APC response to oxazolone is tightly restricted by cell identity, whereas imiquimod enforces shared programs on multiple APC populations in parallel. These global molecular patterns not only contrast immune responses on a systems level but also suggest that the mechanisms of new sources of inflammation can eventually be deduced by comparison to known signatures., Graphical Abstract, Highlights • Oxazolone pervasively upregulates Jak2/Stat3 expression across T cells and APCs • Il4/Il13 induction in skin by oxazolone is dominated by infiltrating basophils • Imiquimod broadly increases Il17/Il22 and Ccl4/Ccl5, extending to non-T cells • Oxazolone induces more highly compartmentalized immune cell responses than imiquimod, Immunology; Systems Biology

Transcriptome profiling can provide information of great value in clinical decision-making, yet RNA from readily available formalin-fixed paraffin-embedded (FFPE) tissue is often too degraded for quality sequencing. To assess the clinical utility of FFPE-derived RNA, we performed ribo-deplete RNA extractions on > 3200 FFPE slide samples; 25 of these had direct FFPE vs. fresh frozen (FF) replicates, 57 were sequenced in 2 different labs, 87 underwent multiple library analyses, and 16 had direct microdissected vs. macrodissected replicates. Poly-A versus ribo-depletion RNA extraction methods were compared using transcriptomes of TCGA cohort and 3116 FFPE samples. Compared to FF, FFPE transcripts coding for nuclear/cytoplasmic proteins involved in DNA packaging, replication, and protein synthesis were detected at lower rates and zinc finger family transcripts were of poorer quality. The greatest difference in extraction methods was in histone transcripts which typically lack poly-A tails. Encouragingly, the overall sequencing success rate was 81%. Exome coverage was highly concordant in direct FFPE and FF replicates, with 98% agreement in coding exon coverage and a median correlation of whole transcriptome profiles of 0.95. We provide strong rationale for clinical use of FFPE-derived RNA based on the robustness, reproducibility, and consistency of whole transcriptome profiling.

Different endogenous and exogenous mutational processes act over the evolutionary history of a malignant tumor, driven by abnormal DNA editing, mutagens or age-related DNA alterations, among others, to generate the specific mutational landscape of each individual tumor. The signatures of these mutational processes can be identified in large genomic datasets. We investigated the hypothesis that genomic patterns of mutational signatures are associated with the clinical behavior of breast cancer, in particular chemotherapy response and survival, with a particular focus on therapy-resistant disease.Whole exome sequencing was carried out in 405 pretherapeutic samples from the prospective neoadjuvant multicenter GeparSepto study. We analyzed 11 mutational signatures including biological processes such as APOBEC-mutagenesis, homologous recombination deficiency (HRD), mismatch repair deficiency and also age-related or tobacco-induced alterations.Different subgroups of breast carcinomas were defined mainly by differences in HRD-related and APOBEC-related mutational signatures and significant differences between hormone-receptor (HR)-negative and HR-positive tumors as well as correlations with age, Ki-67 and immunological parameters were observed. We could identify mutational processes that were linked to increased pathological complete response rates to neoadjuvant chemotherapy with high significance. In univariate analyses for HR-positive tumors signatures, S3 (HRD, P0.001) and S13 (APOBEC, P = 0.001) as well as exonic mutation rate (P = 0.002) were significantly correlated with increased pathological complete response rates. The signatures S3 (HRD, P = 0.006) and S4 (tobacco, P = 0.011) were prognostic for reduced disease-free survival of patients with chemotherapy-resistant tumors.The results of this investigation suggest that the clinical behavior of a tumor, in particular, response to neoadjuvant chemotherapy and disease-free survival of therapy-resistant tumors, could be predicted by the composition of mutational signatures as an indicator of the individual genomic history of a tumor. After additional validations, mutational signatures might be used to identify tumors with an increased response rate to neoadjuvant chemotherapy and to define therapy-resistant subgroups for future therapeutic interventions.

Immature teratoma is a subtype of malignant germ cell tumor of the ovary that occurs most commonly in the first three decades of life, frequently with bilateral ovarian disease. Despite being the second most common malignant germ cell tumor of the ovary, little is known about its genetic underpinnings. Here we performed multiregion whole-exome sequencing to interrogate the genetic zygosity, clonal relationship, DNA copy number, and mutational status of 52 pathologically distinct tumor components from ten females with ovarian immature teratomas, with bilateral tumors present in five cases and peritoneal dissemination in seven cases. We found that ovarian immature teratomas are genetically characterized by 2N near-diploid genomes with extensive loss of heterozygosity and an absence of genes harboring recurrent somatic mutations or known oncogenic variants. All components within a single ovarian tumor (immature teratoma, mature teratoma with different histologic patterns of differentiation, and yolk sac tumor) were found to harbor an identical pattern of loss of heterozygosity across the genome, indicating a shared clonal origin. In contrast, the four analyzed bilateral teratomas showed distinct patterns of zygosity changes in the right versus left sided tumors, indicating independent clonal origins. All disseminated teratoma components within the peritoneum (including gliomatosis peritonei) shared a clonal pattern of loss of heterozygosity with either the right or left primary ovarian tumor. The observed genomic loss of heterozygosity patterns indicate that diverse meiotic errors contribute to the formation of ovarian immature teratomas, with 11 out of the 15 genetically distinct clones determined to result from nondisjunction errors during meiosis I or II. Overall, these findings suggest that copy-neutral loss of heterozygosity resulting from meiotic abnormalities may be sufficient to generate ovarian immature teratomas from germ cells.

Summary: Perturbations in the transcriptional programs specifying epidermal differentiation cause diverse skin pathologies ranging from impaired barrier function to inflammatory skin disease. However, the global scope and organization of this complex cellular program remain undefined. Here we report single-cell RNA sequencing profiles of 92,889 human epidermal cells from 9 normal and 3 inflamed skin samples. Transcriptomics-derived keratinocyte subpopulations reflect classic epidermal strata but also sharply compartmentalize epithelial functions such as cell-cell communication, inflammation, and WNT pathway modulation. In keratinocytes, ∼12% of assessed transcript expression varies in coordinate patterns, revealing undescribed gene expression programs governing epidermal homeostasis. We also identify molecular fingerprints of inflammatory skin states, including S100 activation in the interfollicular epidermis of normal scalp, enrichment of a CD1C+CD301A+ myeloid dendritic cell population in psoriatic epidermis, and IL1βhiCCL3hiCD14+ monocyte-derived macrophages enriched in foreskin. This compendium of RNA profiles provides a critical step toward elucidating epidermal diseases of development, differentiation, and inflammation. : Cheng et al. report single-cell RNA sequencing of normal and inflamed human epidermis, revealing a discrete set of specialized keratinocytes that exhibit a distinct composition at different anatomic sites. Myeloid dendritic cells and macrophages also vary sharply with epidermal anatomic site and inflammation, indicating dynamic programming of antigen-presenting cells. Keywords: epidermis, single-cell RNA-seq, keratinocyte, skin

Sebaceous carcinomas (SeC) are cutaneous malignancies that, in rare cases, metastasize and prove fatal. Here we report whole-exome sequencing on 32 SeC, revealing distinct mutational classes that explain both cancer ontogeny and clinical course. A UV-damage signature predominates in 10/32 samples, while nine show microsatellite instability (MSI) profiles. UV-damage SeC exhibited poorly differentiated, infiltrative histopathology compared to MSI signature SeC (p = 0.003), features previously associated with dissemination. Moreover, UV-damage SeC transcriptomes and anatomic distribution closely resemble those of cutaneous squamous cell carcinomas (SCC), implicating sun-exposed keratinocytes as a cell of origin. Like SCC, this UV-damage subclass harbors a high somatic mutation burden with >50 mutations per Mb, predicting immunotherapeutic response. In contrast, ocular SeC acquires far fewer mutations without a dominant signature, but show frequent truncations in the ZNF750 epidermal differentiation regulator. Our data exemplify how different mutational processes convergently drive histopathologically related but clinically distinct cancers., Sebaceous carcinomas (SeC) are cutaneous malignancies that sometimes metastasize and cause death. Here the authors perform whole-exome sequencing on 32 SeC and report distinct mutational classes that may explain cancer ontogeny and clinical outcome.

A CMS approved test for lung cancer is based on tumor-only analysis of a targeted 35 gene panel, specifically excluding the use of the patient’s normal germline tissue. However, this tumor-only approach increases the risk of mistakenly identifying germline single nucleotide polymorphisms (SNPs) as somatically-derived cancer driver mutations (false positives). 621 patients with 30 different cancer types, including lung cancer, were studied to compare the precision of tumor somatic variant calling in 35 genes using tumor-only DNA sequencing versus tumor-normal DNA plus RNA sequencing. When sequencing of lung cancer was performed using tumor genomes alone without normal germline controls, 94% of variants identified were SNPs and thus false positives. Filtering for common SNPs still resulted in as high as 48% false positive variant calling. With tumor-only sequencing, 29% of lung cancer patients had a false positive variant call in at least one of twelve genes with directly targetable drugs. RNA analysis showed 18% of true somatic variants were not expressed. Thus, sequencing and analysis of both normal germline and tumor genomes is necessary for accurate identification of molecular targets. Treatment decisions based on tumor-only analysis may result in the administration of ineffective therapies while also increasing the risk of negative drug-related side effects.

Background: Recent studies have identified at least two different transcriptional subtypes of benign human breast tissue (Haakensen et al., 2011), including an “active” subtype associated with increased risk of later life mortality from breast cancer (Troester et al., 2016). However, previous studies used "normal" breast samples from cosmetic surgery, biopsies from abnormal mammograms, or cancer-adjacent tissue. The present study evaluates normal breast transcriptome phenotypes from healthy women who donated for this study purpose only. Methods: 200 formalin-fixed paraffin-embedded (FFPE) breast tissue biopsy samples were analyzed from healthy, parous non-Hispanic white women ranging in age from 27–66 (median = 45) with no prior history of breast cancer ( Results: As expected, the normal histologic composition of these KTB samples varied considerably with mean % adipose area twice that of fibrous area, and epithelial content averaging Conclusion: Over 30% of healthy adult women with histologically normal breast tissue carry an “active” breast transcriptome phenotype previously linked to co-existent breast cancer and now shown to be associated with increased future risk of developing breast cancer assessed by Gail score. This “active” transcriptome phenotype is characterized by increased endogenous IGF-1 activity, a known breast cancer promoting growth factor, along with an inverse reduction in IGF1R expression and activity as previously seen in some breast cancers. Further morphologic and molecular characterization of this risk-associated subset of normal breast tissues is underway. Citation Format: Benz C, Yau C, Benz S, Krings G, Powell M. Normal breast biopsies reveal an "active" transcriptome associating with higher breast cancer risk (Gail) scores and increased IGF-1 growth factor expression [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P3-10-05.

Background: Tumor microenvironment (TME) characteristics are gaining acceptance as important biomarkers across all subtypes of breast cancer (BC). For example, stromal Tumor Infiltrating Lymphocytes (sTILs) have been demonstrated to be predictive in triple-negative and HER2+ BC on immune therapies (Hudeček, 2020), and TILs have been associated with recurrence-risk in HER2- BC (Kolberg-Liedtke, 2020). However, standardizing TME biomarkers remains a challenge to these prognostic & predictive studies (Kos, 2020). Methods: Diagnostic H&E-stained pathology images from 506 HER2- breast cancer patients were acquired from TCGA sources. Pre-trained convolutional neural networks were used to classify each 100μm2 region as containing tumoral, stromal, and lymphocyte-infiltrated tissue as well as map their spatial co-distribution. Nine TME summary features were derived from these spatial maps, including total lymphocyte area, tumor-infiltrating lymphocytes (iTILs), stromal-infiltrating lymphocytes (sTILs), and tumor-adjacent lymphocytes (aTILs). Prognostic models relating these TME features to risk were fitted using Cox multiple-regression trained on 60% of patients and tested in the remaining 40%. Additional Cox models that incorporate seven standard clinicopathological features such as TNM staging, age, ethnicity, treatment type, and hormone-receptor status were also analyzed to establish independence of the TME features. Results: A prognostic model developed using 9 TME-summarizing features accurately stratified unseen patients (HR=0.67 p=0.002) into high-risk (N=95) and low-risk (N=107) categories. Interestingly, tumor-adjacent stroma was significantly associated with higher risk (proportional HR=1.02, p=0.005) whereas tumor-infiltrating stroma was associated with lower risk (proportional HR=0.97, p=0.02). Incorporating standard clinicopathological features increased prognostic performance in test patients (HR=0.63, p=0.0005). The prognostic effect of tumor-adjacent and tumor-infiltrating stroma remained significant in multiple regression with clinicopathological features (p=0.003 & p=0.02 respectively). Conclusions: Here we present using machine-vision to automate and standardize describing the TME, as well as demonstrate the prognostic potential of these descriptions by successfully stratifying risk in HER2- breast cancer. These TME descriptions add independent prognostic power to standard clinicopathological features. Machine-vision tools that produce interpretable features such as this can inform current pathology practices, as well as provide facile and scalable biomarkers for clinical studies going forward. Citation Format: Mustafa I. Jaber, Liudmila Beziaeva, Stephen C. Benz, Shahrooz Rabizadeh, Patrick Soon-Shiong, Christopher W. Szeto. Deep-learning image-based features stratify risk in HER2- breast cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3171.

Immature teratoma is a subtype of malignant germ cell tumor of the ovary that occurs most commonly in the first three decades of life, frequently with bilateral ovarian disease. Despite being the second most common malignant germ cell tumor of the ovary, little is known about its genetic underpinnings. Here we performed multi-region whole exome sequencing to interrogate the genetic zygosity, clonal relationship, DNA copy number, and mutational status of 52 pathologically distinct tumor components from 10 females with ovarian immature teratomas, with bilateral tumors present in 5 cases and peritoneal dissemination in 7 cases. We found that ovarian immature teratomas are genetically characterized by 2N near-diploid genomes with extensive loss of heterozygosity and an absence of genes harboring recurrent somatic mutations or known oncogenic variants. All components within a single ovarian tumor (immature teratoma, mature teratoma with different histologic patterns of differentiation, and yolk sac tumor) were found to harbor an identical pattern of loss of heterozygosity across the genome, indicating a shared clonal origin. In contrast, the 4 analyzed bilateral teratomas showed distinct patterns of zygosity changes in the right versus left sided tumors, indicating independent clonal origins. All disseminated teratoma components within the peritoneum (including gliomatosis peritonei) shared a clonal pattern of loss of heterozygosity with either the right or left primary ovarian tumor. The observed genomic loss of heterozygosity patterns indicate that diverse meiotic errors contribute to the formation of ovarian immature teratomas, with 11 out of the 15 genetically distinct clones determined to result from the failure of meiosis I or II. Overall, these findings suggest that copy-neutral loss of heterozygosity resulting from meiotic abnormalities may be sufficient to generate ovarian immature teratomas from germ cells.

Background Sebaceous carcinoma (SeC) is an uncommon malignancy arising from sebaceous glands of the conjunctiva and skin. Recurrent mutations in the ZNF750 were recently identified in ocular SeC. We assessed whether ZNF750 loss is a specific feature of ocular SeC or a general feature of sebaceous tumors. Methods Immunostaining for ZNF750 expression was performed in 54 benign and malignant sebocytic proliferations. Staining for ZNF750 was scored on a three-tier scale: positive (>75%), partially positive (5%-74%), and negative ( Results ZNF750 expression was negative in 4/11 ocular SeC, and partially positive in 4/11 ocular SeC and 6/13 cutaneous SeC. No extraocular tumors were negative. No loss was found in sebaceous adenoma or sebaceous hyperplasia. In nine previously sequenced ocular SeCs, two lacked detectable somatic mutations in ZNF750, but showed complete loss of staining, indicating non-mutational inactivation of ZNF750. Conclusion We show complete loss of the ZNF750 epidermal differentiation regulator in about half of ocular SeC, highlighting the most common genetic defect in this cancer type. Loss of ZNF750 expression is seen even in tumors without truncating mutations and reduced in many of the remaining ocular and cutaneous SeC. In contrast, no ZNF750 loss was detected in benign sebaceous proliferations.

Progressive tumor growth is associated with deficits in the immunity generated against tumor antigens. Vaccines targeting tumor neoepitopes have the potential to address qualitative defects; however, additional mechanisms of immune failure may underlie tumor progression. In such cases, patients would benefit from additional immune-oncology agents targeting potential mechanisms of immune failure. This study explores the identification of neoepitopes in the MC38 colon carcinoma model by comparison of tumor to normal DNA and tumor RNA sequencing technology, as well as neoepitope delivery by both peptide- and adenovirus-based vaccination strategies. To improve antitumor efficacies, we combined the vaccine with a group of rationally selected immune-oncology agents. We utilized an IL15 superagonist to enhance the development of antigen-specific immunity initiated by the neoepitope vaccine, PD-L1 blockade to reduce tumor immunosuppression, and a tumor-targeted IL12 molecule to facilitate T-cell function within the tumor microenvironment. Analysis of tumor-infiltrating leukocytes demonstrated this multifaceted treatment regimen was required to promote the influx of CD8+ T cells and enhance the expression of transcripts relating to T-cell activation/effector function. Tumor-targeted IL12 resulted in a marked increase in clonality of T-cell repertoire infiltrating the tumor, which when sculpted with the addition of either a peptide or adenoviral neoepitope vaccine promoted efficient tumor clearance. In addition, the neoepitope vaccine induced the spread of immunity to neoepitopes expressed by the tumor but not contained within the vaccine. These results demonstrate the importance of combining neoepitope-targeting vaccines with a multifaceted treatment regimen to generate effective antitumor immunity.

Genetic variation in the NF-κB inhibitors, ABIN1 and A20, increase risk for psoriasis. While critical for hematopoietic immune cell function, these genes are believed to additionally inhibit psoriasis by dampening inflammatory signaling in keratinocytes. We dissected ABIN1 and A20's regulatory role in human keratinocyte inflammation using an RNA sequencing-based comparative genomic approach. Here we show subsets of the IL-17 and tumor necrosis factor-α signaling pathways are robustly restricted by A20 overexpression. In contrast, ABIN1 overexpression inhibits these genes more modestly for IL-17, and weakly for tumor necrosis factor-α. Our genome-scale analysis also indicates that inflammatory program suppression appears to be the major transcriptional influence of A20/ABIN1 overexpression, without obvious influence on keratinocyte viability genes. Our findings thus enable dissection of the differing anti-inflammatory mechanisms of two distinct psoriasis modifiers, which may be directly exploited for therapeutic purposes. Importantly, we report that IL-17-induced targets of A20 show similar aberrant epidermal layer-specific transcriptional upregulation in keratinocytes from diseases as diverse as psoriasis, atopic dermatitis, and erythrokeratodermia variabilis, suggesting a contributory role for epidermal inflammation in a broad spectrum of rashes.

Background: Stromal and lymphocyte density have each been implicated in differential survival in pancreatic cancer (Torphy, 2018 & Orhan, 2020). In this study, we developed an automated deep-learning system to provide risk-assessment upon spatial relationships between tumor, stroma, and lymphocyte regions in pancreatic pathology images. Methods: Diagnostic H&E-stained pathology images from 82 pancreatic adenocarcinoma patients who underwent chemotherapy were acquired from TCGA sources. Thirty-two patients were held out for testing purposes. Tumor, stroma, and lymphocytes image masks were generated using pre-trained convolutional neural networks, and their co-distribution was summarized in nine numerical image-based features. Optimal thresholds in these image-based features were identified using 2-way Gaussian mixture models. This process found four spatial image features that significantly contributed to low-risk of early death: Low lymphocyte count, lymphocytes adjacent to tumor regions, and stromal adjacency to tumor regions. Ability to separate patients based on these features was evaluated using silhouette score, concordance index, and Cox proportional hazards ratios (HR). Results: Without using image-based features, exhaustive search of this cohort’s clinicopathological features found an optimal Cox proportional hazards model can yield a HR = 0.22 (p = 0.02) in 50 training examples and HR = 0.41 (p = 0.29) on 32 unseen test patients, ultimately utilizing just pathologically-determined T, and N information. The developed image-based risk predictor improved performance with HR = 0.51 (p = 0.06) on training data and HR = 0.52 (p = 0.09) on unseen test data. Combining the image-based risk models to selected clinicopathological features enhanced performance further to HR = 0.25 (p = 0.01) on the training set and HR = 0.37 (p = 0.07) on unseen test patients. Conclusions: Our interpretable image-based risk predictor shows high-risk pancreatic cancer patients have higher lymphocyte count overall but proportionally fewer tumor-infiltrating lymphocytes (TILs). In addition, this system shows high-risk patients have less stromal tissue within 100um from tumor compared to low risk patients. By aggregating both standard clinicopathological features with the proposed image-based risk assessment, superior separation in survival curves was achieved for both training and testing sets compared to either risk-model alone. Thus, our study demonstrates that image-based risk-associated features are independently prognostic of clinicopathological features. Despite the very limited sample-size of similarly-treated patients within the training dataset, these results trend towards significance and warrant further study within a larger cohort. Citation Format: Mustafa I. Jaber, Liudmila Beziaeva, Robert J. Torphy, Stephen C. Benz, Shahrooz Rabizadeh, Patrick Soon-Shiong, Christopher W Szeto. Deep-learning image-based tumor, stroma, and lymphocytes spatial relationships and clinicopathological features that affect survival in pancreatic cancer patients [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2020 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2020;80(22 Suppl):Abstract nr PO-030.

Background: Even within patients with high tumor mutational burden (TMB) and/or PD-L1 expression, objective response rates to checkpoint therapies remain below 50% (e.g. Checkmate 227). Identifying predictive biomarkers for immune checkpoint inhibition therapy is an active area of research. Tumor infiltrating lymphocytes (TILs) show potential as a predictive biomarker for immune-oncology therapies. Rearrangement of the T-cell receptor (TCR) complementarity-determining region 3 (CDR3) serves as a marker for T-cell clones, and can be detected by RNA-seq of the region. The standard mechanism of tissue storage, formalin fixation and paraffin embedding (FFPE), degrades nucleic acids and makes sequencing more challenging. We present detection of TCR CDR3 of TILs from FFPE RNA-seq as a potential biomarker. Methods: FFPE Tumors from 65 head and neck squamous cell cancer (HNSCC), 233 colorectal carcinoma (CRC), and 217 non-small cell lung cancer (NSCLC) patients were profiled using deep full-transcriptome RNA-seq. Patients were assigned a TCR score by first aligning RNA-seq reads to TCRα and TCRβ CDR3 sequences, then counting overall CDR3 read support. Gene expression profiles were also used to predict consensus molecular subtype (CMS) and estimate immune cell abundance. To assess tumor sample purity, deep whole genome (N=244) or whole exome (N=271) DNA sequencing was performed on both tumor and normal blood samples from each patient. TMB was calculated directly by counting somatic non-synonymous exonic mutations. Results: Immune populations revealed high correlation (Pearson's r = .84, p = 3.6e-169) between TCR CDR3 counts and independent gene expression-based estimates of T-cell activity. Using the CMS of colon cancer samples, we found that CMS2 patients had a significantly lower average number of reads aligned to CDR3 (46.5 vs 91.6, p = 1.5e-7) which agrees with previous reports of low immune activity in that subtype. We compared CDR3 read count against TMB and found significant correlation (Spearman's rho = 0.34, p = 0.0048) in head and neck tumors where the median TMB was 98, but no association in lung or colon tumors where TMB is higher (median TMB 169 and 132 respectively). Finally, tumor purity estimated via DNA-seq was anti-correlated with CDR3 read count (Spearman's ρ = -0.39, p = 3.3e-21). Conclusions: TCR CDR3 read support from bulk RNA-seq on 515 solid tumor FFPE samples provides a related but distinct measure of T-cell infiltration to gene expression-based estimates. TMB and TCR support are generally independent biomarkers, especially in those tissues with higher average mutation rate. TCR support has potential to complement TMB as a predictive biomarker of immune checkpoint therapy response. Citation Format: Andrew J. Sedgewick, Jacob J. Adashek, Christopher W. Szeto, Charles J. Vaske, Philippe E. Spiess, Stephen C. Benz. T-cell receptor clonotyping and immune infiltrate quantification with whole transcriptome RNA-seq from FFPE [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4980.

3578 Background: Pathologist inspection of biopsy slides is the gold-standard for diagnosis and is crucial for effective therapy decisions. However, expert shortage is resulting in turnaround times exceeding College of American Pathologists’ (CAP) standards (Alshieban, 2015). Further, discrepancy between diagnoses can exceed 4% (Mukhopadhyay, 2018), and 2% of cases are designated as ‘carcinoma of unknown primary’ (CUP) negatively affect outcomes due to difficulty selecting therapies (Rassy, 2020). Here we sought to aid in diagnosing patients from whole-slide images (WSIs) using deep neural networks. Methods: > 6.3K high-resolution H&E-stained diagnostic WSI of formalin-fixed paraffin-embedded (FFPE) tumor block slices were selected from TCGA sources. Slide images were obtained from 30 different cancer subtypes including 368 Breast (5.6%), 324 Colon (5.12%), 287 Lung Adenocarcinoma (LUAD) (4.5%), Lung Squamous-Cell carcinoma (LUSC) (4.5%), and Stomach Adenocarcinoma (4.3%). Local regions containing tumor tissue were automatically identified by training an Inception V3 deep-learning network as previously presented. A separate Inception V3 network was trained to classify the primary tissue of 200mm2 tumor regions in 60% of the images, which was validated in the remaining 40% testing cohort. Results: The proposed deep-learning model was 92.7% accurate in identifying the primary tissue within the test set of WSIs. As expected, most misclassification occurred in highly-related tissue-types: Rectal cancers misclassified as colon (25%) and vice versa (4.8%), uveal melanomas misclassified as cutaneous melanomas (18.6%), cholangiocarcinomas as hepatocellular carcinomas (8.6%), and LUSC misclassified as LUAD (6.0%) and vice versa (3.4%). Combining related tissues, the classifier achieves 94.6% accuracy across 24 primary types. Unexpectedly, cutaneous melanomas samples were misclassified as breast (9.1%) and LUSC (5.6%), suggestive of related molecular phenotypes. Conclusions: By focusing machine-vision attention on tumor regions, the automated system approaches pathologist accuracy. Used in conjunction with molecular profiling, rates of CUP or misdiagnosis can feasibly be minimized to improve patient care. This system is currently being validated in an external set of > 4K unselected clinical cases from the NantHealth database.

3131 Background: Tumor mutation burden (TMB) and PD1/L1 expression are independent biomarkers for immune checkpoint blockade therapy, as seen in the Checkmate227 trial. Here we explore whether chemokine activity, an intermediate step between neoantigen presentation and immune-infiltration, can resolve this lack of association between existing biomarkers. We further use this novel biomarker to corroborate recent findings from Crowther et al. for a role of MHC class 1-related gene ( MR1) downregulation in immune evasion. Methods: 1,395 clinical samples from the NantHealth database with matched tumor:normal whole exomes and deep whole-transcriptomic sequencing (> 200M reads) were available for analysis. The most common indications in the cohort were Breast (18%), Colon (9.8%), Lung (7.8%), Soft-tissue/Sarcomas (7.7%), and Pancreatic (6.1%). TMB was calculated by counting non-synonymous exonic mutations as per Rizvi, 2015. Immune-infiltration and chemokine signaling were inferred from RNAseq expression of published immune-cell-specific genesets (Bindea, 2013) and chemokine ligands (Nagarsheth, 2017) respectively. Significant associations between TMB, chemokine activity, immune-infiltration, and checkpoint expression were analyzed by ranksums test and corrected for multiple-hypothesis testing using Benjamini-Hochberg adjustment. Results: As expected, TMB and PD1/L1 mRNA expression were not correlated in this cohort (r = 0.08 and r = 0.07 respectively). 36.3% of patients classified as highly immune-infiltrated by unsupervised clustering of immune-cell scores, and this subgroup significantly overexpressed all 11 targetable checkpoint genes analyzed including PD1, PDL1, CTLA4, IDO1, and VISTA (adj. p 9.7e-68 to 4e-168). There was no association between immune-infiltrated samples and TMB (t = 0.9, p = 0.35). Twice as many patients classified as chemokine-active (70.0%) and there was significant agreement between immune-infiltrated and chemokine-active patients (OR = 34.8, p = 6.5e-81). Interestingly, there was a weak but significant association between high chemokine-activity and increased TMB (t = 3.3, p = 0.001). Within patients that were chemokine-active but lacked immune-infiltration, MR1 expression was significantly depleted (t = -10.7, p = 1e-26). Conclusions: Chemokine signatures can help resolve discordance between TMB and checkpoint expression. Analysis of discordance between chemokine-active but immune-depleted tumors may aid in identifying targets for converting from cold to hot tumor microenvironments.

BackgroundNatural killer (NK) cells are immune cells capable of killing virally infected cells and tumor cells without the need for antigen stimulation. Tumors, however, can create a suppressive microenvironment that decreases NK function. A feature of many tumors is hypoxia (low oxygen perfusion), which has been previously shown to decrease NK function. A high affinity NK (haNK) cell has been engineered to express a high affinity CD16 receptor as well as internal interleukin (IL)-2 for increased antibody-dependent cellular cytotoxicity (ADCC) and activation, respectively. We sought to investigate the tolerance of NK cells versus haNK cells to hypoxia.MethodsWe exposed healthy donor (HD) NK and X-irradiated haNK cells to normoxia (20% oxygen) as well as hypoxia (0% oxygen) and investigated their ability to kill prostate, breast and lung tumor cell lines after 5 hours. We also used monoclonal antibodies cetuximab (anti-EGFR) or avelumab (antiprogrammed death-ligand 1) to investigate the effects of hypoxia on NK ADCC. Genomic and proteomic analyzes were done to determine the effect of hypoxia on the expression of factors important to NK cell function.ResultsWhile HD NK cell cytolytic abilities were markedly and significantly impaired under hypoxic conditions, haNK cells maintained killing capacity under hypoxic conditions. NK killing, serial killing and ADCC were maintained under hypoxia in haNK cells. IL-2 has been previously implicated in serial killing and perforin regeneration and thus the endogenous IL-2 produced by haNK cells is likely a driver of the maintained killing capacity of haNK cells under hypoxic conditions. Activation of signal transducer and activator of transcription 3 (STAT3) is not seen in haNKs under hypoxia but is significant in HD NK cells. Pharmaceutical activation of STAT3 in haNKs led to reduced killing, implicating active STAT3 in reduced NK cell function.ConclusionsIn contrast to HD NK cells, haNK cells are resistant to acute hypoxia. The potent cytolytic function of haNK cells was maintained in an environment comparable to what would be encountered in a tumor. The data presented here provide an additional mechanism of action for haNK cells that are currently being evaluated in clinical trials for several tumor types.

Introduction: Laser microdissection (LMD) is a valuable method to isolate target populations of cells for molecular analysis. LMD of breast tumor samples can isolate breast tumor cells whereas bulk processing of tumor tissue will incorporate surrounding non-cancerous cells and bias tumor expression profiling. Here, we evaluated the advantage of using LMD breast tumors for RNA-Seq over bulk processing. Methods: Tissue samples for the in-house dataset were from breast cancer patients consented by a HIPAA-compliant, IRB-approved protocol of the Clinical Breast Care Project. A total of 118 primary breast tumors embedded in OCT (Optimum Cutting Temperature) were selected and processed by LMD. Total RNA and protein were extracted using the illustra triplePrep kit. Paired-end RNA sequencing of 118 cases was performed using the Illumina HiSeq platform and the reads were preprocessed using a PERL-based pipeline involving PRINSEQ, GSNAP and HTSeq. The Cancer Genome Atlas (TCGA) primary breast cancer RNA-Seq data for 1097 tumors, bulk processed was downloaded. Differential expression of genes (DEG) was assessed using DESeq2. Significance was described for DEG with fold change >2 and p-adjusted value of 0.05. Results: A total of 24,518 genes with a mean expression of ≥ 10 (~9%) raw counts across 118 tumor samples were identified in the in-house LMD dataset. In TCGA breast cancer RNA-Seq, 14,281 genes with a mean expression of ≥ 100 (~9%) raw counts across 1097 tumor samples were identified. The conventional PAM50 classifier was used for intrinsic subtyping of in-house data, yielding 36 Basal-like, 14 HER2-enriched, 43 Luminal A, 22 Luminal B and 3 Normal-like calls. The provided PAM50 calls for TCGA were 192 Basal-like, 82 HER2-enriched, 566 Luminal A, 217 Luminal B and 40 Normal-like calls. Within commonly expressed 13,165 genes, LMD (in-house) and bulk (TCGA) processing exhibited approximately 40-78% non-overlap in significantly differentially expressed genes (SDEG) among the conventional intrinsic subtypes. 21 unique stromal genes were present in SDEG unique to TCGA whereas there were only 5 SDEG unique to in-house dataset. We validated the results with 34 patients that had both LMD and bulk processing RNA-Seq data and found the non-overlap genes percentage to be even greater from 46-85%. The observed percentages of non-overlapping genes in the whole datasets were also validated in the 34 overlapping cases when using IHC subtypes. Overall high positive correlation is observed among the stromal genes present in SDEG unique to TCGA suggesting strong stromal contribution in bulk processing. Pathway analysis of SDEG unique to LMD data suggested alterations in known cancer pathways (B-cell immune response, RNA metabolism and splicing, phagocytosis, and signaling components). Conclusion: Analysis of The Cancer Genome Atlas breast cancer RNA-Seq data set (based on bulk tissue processing) suggested contribution of stromal signature genes and important differences from LMD specimens. Thus, tumor selection via LMD may allow us to unveil signals that are more specific to cancer cells. Disclaimer: The contents of this publication are the sole responsibility of the author(s) and do not necessarily reflect the views, opinions or policies of Uniformed Services University of the Health Sciences (USUHS), The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., the Department of Defense (DoD), the Departments of the Army, Navy, or Air Force. Mention of trade names, commercial products, or organizations does not imply endorsement by the U.S. Government. Citation Format: Praveen-Kumar Raj-Kumar, Lori A. Sturtz, Albert J. Kovatich, Brenda Deyarmin, Jeffrey A. Hooke, Leigh Fantacone-Campbell, Anupama Praveen-Kumar, Jianfang Liu, James Craig, Leonid Kvecher, Jennifer Kane, Jennifer Melley, Stella Somiari, Stephen C. Benz, Justin Golovato, Shahrooz Rabizadeh, Patrick Soon-Shiong, Richard Mural, Craig D. Shriver, Hai Hu. Evaluation of laser microdissected primary breast tumors for RNA Seq over bulk processing and validated with cohort control [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P6-06-09.

Recessive dystrophic epidermolysis bullosa (RDEB) is a rare inherited skin and mucous membrane fragility disorder complicated by early-onset, highly malignant cutaneous squamous cell carcinomas (SCCs). The molecular etiology of RDEB SCC, which arises at sites of sustained tissue damage, is unknown. We performed detailed molecular analysis using whole-exome, whole-genome, and RNA sequencing of 27 RDEB SCC tumors, including multiple tumors from the same patient and multiple regions from five individual tumors. We report that driver mutations were shared with spontaneous, ultraviolet (UV) light-induced cutaneous SCC (UV SCC) and head and neck SCC (HNSCC) and did not explain the early presentation or aggressive nature of RDEB SCC. Instead, endogenousmutation processes associated with apolipoprotein B mRNA-editing enzymecatalytic polypeptide-like (APOBEC) deaminases dominated RDEB SCC. APOBEC mutation signatures were enhanced throughout RDEB SCC tumor evolution, relative to spontaneous UV SCC and HNSCCmutation profiles. Sixty-seven percent of RDEB SCC driver mutations was found to emerge as a result of APOBEC and other endogenous mutational processes previously associated with age, potentially explaining a > 1000-fold increased incidence and the early onset of these SCCs. Human papillomavirus-negative basal and mesenchymal subtypes of HNSCC harbored enhanced APOBEC mutational signatures and transcriptomes similar to those of RDEB SCC, suggesting thatAPOBECdeaminases drive other subtypes of SCC. Collectively, these data establish specific mutagenic mechanisms associated with chronic tissue damage. Our findings reveal a cause for cancers arising at sites of persistent inflammation and identify potential therapeutic avenues to treat RDEB SCC.

Immune heterogeneity within the tumor microenvironment undoubtedly adds several layers of complexity to our understanding of drug sensitivity and patient prognosis across various cancer types. Within the tumor microenvironment, immunogenicity is a favorable clinical feature in part driven by the antitumor activity of CD8+ T cells. However, tumors often inhibit this antitumor activity by exploiting the suppressive function of regulatory T cells (Tregs), thus suppressing the adaptive immune response. Despite the seemingly intuitive immunosuppressive biology of Tregs, prognostic studies have produced contradictory results regarding the relationship between Treg enrichment and survival. We therefore analyzed RNA-seq data of Treg-enriched tumor samples to derive a pan-cancer gene signature able to help reconcile the inconsistent results of Treg studies, by better understanding the variable clinical association of Tregs across alternative tumor contexts. We show that increased expression of a 32-gene signature in Treg-enriched tumor samples (n = 135) is able to distinguish a cohort of patients associated with chemosensitivity and overall survival. This cohort is also enriched for CD8+ T cell abundance, as well as the antitumor M1 macrophage subtype. With a subsequent validation in a larger TCGA pool of Treg-enriched patients (n = 626), our results reveal a gene signature able to produce unsupervised clusters of Treg-enriched patients, with one cluster of patients uniquely representative of an immunogenic tumor microenvironment. Ultimately, these results support the proposed gene signature as a putative biomarker to identify certain Treg-enriched patients with immunogenic tumors that are more likely to be associated with features of favorable clinical outcome.

The Cancer Genome Atlas profiled 279 head and neck squamous cell carcinomas (HNSCCs) to provide a comprehensive landscape of somatic genomic alterations. Here we show that human-papillomavirus-associated tumours are dominated by helical domain mutations of the oncogene PIK3CA, novel alterations involving loss of TRAF3, and amplification of the cell cycle gene E2F1. Smoking-related HNSCCs demonstrate near universal loss-of-function TP53 mutations and CDKN2A inactivation with frequent copy number alterations including amplification of 3q26/28 and 11q13/22. A subgroup of oral cavity tumours with favourable clinical outcomes displayed infrequent copy number alterations in conjunction with activating mutations of HRAS or PIK3CA, coupled with inactivating mutations of CASP8, NOTCH1 and TP53. Other distinct subgroups contained loss-of-function alterations of the chromatin modifier NSD1, WNT pathway genes AJUBA and FAT1, and activation of oxidative stress factor NFE2L2, mainly in laryngeal tumours. Therapeutic candidate alterations were identified in most HNSCCs.

Adipocytes are the predominant cell population in the normal breast and while recent attention has pointed to adipocyte-tumor cell crosstalk as a driver of breast cancer biology there have been few reports on the potential role of adipocytes in driving breast cancer initiation. Because normal breast tissue studies have invariably used reduction mammoplasty, benign biopsy or cancer-adjacent tissues, we studied random breast core biopsy samples donated by 145 healthy, parous, non-obese, white women (median age = 45, range 27-66 y) without any history of breast cancer. Using questionnaire data to calculate future breast cancer risk (Gail scores), we compared digitized microscopic breast tissue (H&E) images with whole genome transcriptome profiling (RNAseq) from FFPE-extracted RNA. We used unsupervised hierarchical clustering of 1487 genes (normalized, median centered, log2-scaled RSEM values) to identify 32% of normal samples with an “Active” (vs. 68% “Inactive”) transcriptome phenotype previously associated with later-life risk of death from breast cancer. Despite slightly lower BMI values, donors with the Active transcriptome phenotype showed significantly higher Gail scores as well as higher mammary adipocyte nuclei counts (median 80% vs. 60%, p=2.3e-6). Tissue resident leukocytes were uncommon but Active transcriptome tissues expressed significantly altered immune modules enriched in TGFβ, interferon and macrophage gene signatures (including single gene increase in CD68) and depleted (relative to Inactive samples) of CD8+ T-cell and serum response/inflammation/wound healing signatures. Active samples were not enriched in cell senescence, SASP or DNA damage response gene signatures but were enriched in an autophagy-to-senescence-transition (AST) signature with increased CAV1 (caveolin-1, p=2.7e-12) and BNIP3 (Bcl2 interacting protein-3, p=4.7e-05) expression, genes that also regulate lipoprotein digestion/mobilization and adipocyte remodeling. Strongest among significant associations linking Active with adipocyte-enriched normal breast samples were increases in two adipokine growth factors, IGF-1 (p=2.2e-16) and FGF2 (p=3.0e-11), the adipokine (resistin) receptor CAP1 (p=0.04) recently linked to poor breast cancer outcomes, and a cAMP-dependent pro-lipolytic signature (p=0.01) known to drive breast cancer progression which, in these samples, correlated positively with average adipocyte area values. Altogether, the collective histologic and molecular features characterizing the normal breast tissue of >30% of healthy parous and non-obese women with increased predicted breast cancer risk seem to implicate a dysregulated mammary adipocyte microenvironment similar to but distinct from that associated with established breast tumors, that precedes microscopic and clinical evidence of breast tumorigenesis. Citation Format: Taekyu Kang, Christina Yau, Stephen Benz, Gregor Krings, Roman Camarda, Jill E. Henry, Mark Powell, Christopher C. Benz. Normal breast tissue at risk for cancer development: A breast cancer initiating role for mammary adipocytes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3314.

Introduction: RNA-Seq based gene expression profiling of breast tumor samples is widely used to subgroup patients and to identify gene signatures of prognostic value. However, tumor samples are highly heterogeneous, and so bulk processing of tumor tissue will consist of several different cell types. Here, we evaluated the advantage of using laser microdissected (LMD) breast tumors for RNA-Seq over bulk processing. Methods: Patients for the in-house dataset were duly consented under an IRB-approved protocol of the Clinical Breast Care Project. A total of 118 primary breast tumors embedded in OCT (Optimum Cutting Temperature) were selected and processed by LMD. Total RNA and protein were extracted using the Illustra triplePrep kit. Paired-end RNA sequencing of 118 cases was performed using the Illumina HiSeq platform and the reads were preprocessed using a PERL-based pipeline involving PRINSEQ, GSNAP and HTSeq. The Cancer Genome Atlas (TCGA) primary breast cancer RNA-Seq data for 1097 samples was downloaded. Differential expression of genes (DEG) was assessed using DESeq2. Significance was described for DEG with fold change >2 and p-adjusted value of 0.05. Results: A total of 24,518 genes with a mean expression of ≥ 10 raw counts across 118 tumor samples were identified in the in-house LMD dataset. In TCGA breast cancer RNA-Seq, 14,281 genes with a mean expression of ≥ 100 raw counts across 1097 tumor samples were identified. The conventional PAM50 classifier was used for intrinsic subtyping of in-house data, yielding 36 Basal-like, 14 HER2-enriched, 43 Luminal A, 22 Luminal B and 3 Normal-like calls. The provided PAM50 calls were used for TCGA which are 192 Basal-like, 82 HER2-enriched, 566 Luminal A, 217 Luminal B and 40 Normal-like calls. Within commonly expressed 13,165 genes, LMD and bulk processing exhibited approximately 40-78% non-overlap in significantly differentially expressed genes (SDEG) among the intrinsic subtypes. 21 unique stromal genes were present in SDEG unique to TCGA whereas there were only 5 SDEG unique to in-house dataset. Overall high positive correlation is observed among the stromal genes present in SDEG unique to TCGA suggesting strong stromal contribution in bulk processing. Pathway analysis of SDEG unique to LMD data suggested alterations in known cancer pathways (B-cell immune response, RNA metabolism and splicing, phagocytosis, and signaling components). Conclusion: Analysis of The Cancer Genome Atlas breast cancer RNA-Seq data set (based on bulk processing tissue) suggested contribution of stromal signature genes and important differences from LMD specimens. Thus, tumor selection via LMD can result in better expression profiling by RNA-Seq which has the potential to uncover many cancer genes and pathways. The views expressed in this abstract are those of the author and do not reflect the official policy of the Department of Army/Navy/Air Force, Department of Defense, or U.S. Government. Citation Format: Praveen-Kumar Raj-Kumar, Lori A. Sturtz, Albert J. Kovatich, Brenda Deyarmin, Jeffrey A. Hooke, Leigh Fantacone-Campbell, Anupama Praveen-Kumar, Jianfang Liu, James Craig, Leonid Kvecher, Jennifer Kane, Jennifer Melley, Stella Somiari, Stephen C. Benz, Justin Golovato, Shahrooz Rabizadeh, Patrick Soon-Shiong, Richard J. Mural, Craig D. Shriver, Hai Hu. Evaluation of laser microdissected primary breast tumors for RNA-Seq over bulk processing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3402.

528 Background: Dysregulation of DNA mismatch repair pathway can lead to microsatellite instability in many GI tumors, and microsatellite instability is an important diagnostic and prognostic marker. Microsatellite instable (MSI) tumors comprise about 15% of colorectal malignancies and can be found in other gastrointestinal (GI) tumor types. We present results of analysis of genomic and immune infiltration differences between MSI and microsatellite stable (MSS) GI tumors spanning multiple cancer types. Methods: A total of 521 GI patients with deep whole exome sequencing (WES) of tumor and blood samples, and whole transcriptomic sequencing (RNA-Seq) (∼200M reads per tumor) were available for this analysis from a commercial database. Variant calling was performed through joint probabilistic analysis of tumor and normal DNA reads, with germline status of variants being determined by heterozygous or homozygous alternate allele fraction in the germline sample. Results: Gene expression and pathway analysis found significantly higher immune signaling in MSI cohort and higher metabolic signaling in MSS cohort. We also found upregulation of structural cellular integrity pathways in MSI tumors. Per-sample deconvolution of immune infiltration using cell type gene markers shows some MSI samples with high CD8 T-cells. Co-expression analysis of checkpoint and TME genes shows higher correlation of FOXP3 and CTLA4 in the MSS cohort compared to the MSI samples, whereas correlation between FOXP3 and PDL1 is decreased. TIM3, LAG3, and OX40 are significantly more expressed in MSI samples than MSS samples. Within the subset of colorectal tumors, additional checkpoints are significantly differentially overexpressed in MSI malignancies. 50 somatic variants are significantly differential in MSI tumors. Conclusions: MSI tumors demonstrably exhibit higher immune signaling, with many immune and checkpoint markers expressed at higher levels in MSI tumors. Some cellular integrity pathways also appear to be up in MSI cohort. A number of potentially important somatic variants are associated with MSI samples.

610 Background: Tumor mutational burden (TMB) is emerging as an important biomarker for immune checkpoint therapy (ICT) response. Yet even in the context of high TMB, ICT are likely ineffective in an immuno-suppressed microenvironment. Here we demonstrate that a well-characterized subtype of CRC, CMS2, associated with Wnt pathway activation, is immunosuppressive despite high TMB. Methods: Tumor/normal-paired DNAseq (WGS or WES) and deep RNAseq (∼200x106reads per tumor) was performed on 464 GI tumors from a commercial database. Samples were classified as high TMB if they had > 200 non-synonymous exonic mutations as previously established (Rizvi et al, 2015). Each sample was assigned to one of the colorectal Consensus Molecular subtypes (CMS) based on RNA classification. A curated panel of 109 genes that discriminate between 22 immune subsets was identified. For each of these immune signatures, a database containing 1880 unselected tumors was used to define a distribution of expression. The study samples were then scored for their deviances within such distributions. Significant enrichment was analyzed between immune subsets, CMS types, TMB status, and somatic mutational status. Results: Compared to other subtypes, CMS1 & CMS2 were significantly high-TMB (adj. p < 3.8E-4 & p < 4.7E-3 respectively). Perplexingly, CMS2 had significantly lower expression of 11 well-established checkpoint and TME markers including LAG3 and PDL1 (adj. p 1.5E-2 & 2.9E-9 respectively), while CMS1 (MSI-enriched) expresses selected TME markers more than other subtypes (PDL1 adj. p < 4.0E-6 & LAG3 adj. p < 1.0E-6). As expected, CMS2 tumors were significantly enriched for likely pathogenic variants in the Wnt-associated gene APC (adj. p < 1.3E-8). Immune-deconvolution indicated substantial exclusion of Tem cells from CMS2 tumors, in line with Wnt/b-catenin blockade of TcmàTem maturation for immunoreactivity. Conclusions: The most common subtype of CRC, CMS2 (~37%), is highly immunosuppressive despite high TMB. ICT is only effective in an immunologically active microenvironment. TMB alone as a biomarker likely is insufficient to indicate the effectiveness of immunotherapy.

579 Background: Immune contexture shapes oncogenesis, tumor progression and response to therapy. Traditional techniques for studying the microenvironment, such as flow cytometry, are limited by low throughput. Recently, computational methods using transcriptomic data have enabled deconvolution of tumor immune landscape, particularly in colorectal cancer (CRC). However, the immune profiles of other GI and HN cancers remain unclear. We proposed that elucidating the immune compositions in those cancers would reveal biological insights and potentially therapeutic targets. Methods: 464 GI tumors with RNA-Seq data (∼200x106reads per tumor) from a commercial database were available for analysis. Tumors were categorized into CRC, gastroesophageal (GE), HN, and biliary cancer. A curated panel of 122 genes that discriminate between 28 immune subsets was identified. For each of these immune signatures, a database containing 1880 unselected tumors was used to define a distribution of expression. The study samples were then scored for their deviances within such distributions.In addition to immune deconvolution, each of the 464 tumors were assigned to one of the colorectal Consensus Molecular subtypes (CMS). Significant enrichment for immune subsets between locations and CMS was analyzed. Results: Unsupervised clustering revealed 2 clusters with distinct immune profiles, which largely separated CRC from HN/GE tumors (silhouette coefficient = 0.14). Eosinophils are more abundant in GI cancers than others. HN/GE tumors were characterized by abundant NK and Tregs (adj. p < 0.001), and were predominantly classified as the immune-active CMS1 (adj. p < 0.001). CRC was significantly associated with high eosinophils and fibroblasts (adj. p < 0.0001). Biliary tumors spanned both immune-type clusters, and were frequently classified as the stromal-rich CMS4 (adj. p < 0.0001). Immature dendritic cells were sparse in GI tumors, especially GE. Conclusions: Upper and lower GI tumors have distinct immune contextures, which may differentially impact the efficacy of immunotherapy. Comprehensive immune profiling may facilitate the identification of targetable immune subsets.

High-dimensional ‘-omics’ profiling provides a detailed molecular view of individual cancers; however, understanding the mechanisms by which tumors evade cellular defenses requires deep knowledge of the underlying cellular pathways within each cancer sample. We extended the PARADIGM algorithm (Vaske et al., 2010, Bioinformatics, 26, i237–i245), a pathway analysis method for combining multiple ‘-omics’ data types, to learn the strength and direction of 9139 gene and protein interactions curated from the literature. Using genomic and mRNA expression data from 1936 samples in The Cancer Genome Atlas (TCGA) cohort, we learned interactions that provided support for and relative strength of 7138 (78%) of the curated links. Gene set enrichment found that genes involved in the strongest interactions were significantly enriched for transcriptional regulation, apoptosis, cell cycle regulation and response to tumor cells. Within the TCGA breast cancer cohort, we assessed different interaction strengths between breast cancer subtypes, and found interactions associated with the MYC pathway and the ER alpha network to be among the most differential between basal and luminal A subtypes. PARADIGM with the Naive Bayesian assumption produced gene activity predictions that, when clustered, found groups of patients with better separation in survival than both the original version of PARADIGM and a version without the assumption. We found that this Naive Bayes assumption was valid for the vast majority of co-regulators, indicating that most co-regulators act independently on their shared target. Availability: http://paradigm.five3genomics.com Contact: moc.scimoneg3evif@eilrahc Supplementary information: Supplementary data are available at Bioinformatics online.

High-throughput data sets such as genome-wide protein–protein interactions, protein–DNA interactions and gene expression data have been published for several model systems, especially for human cancer samples. The University of California, Santa Cruz (UCSC) Interaction Browser (http://sysbio.soe.ucsc.edu/nets) is an online tool for biologists to view high-throughput data sets simultaneously for the analysis of functional relationships between biological entities. Users can access several public interaction networks and functional genomics data sets through the portal as well as upload their own networks and data sets for analysis. Users can navigate through correlative relationships for focused sets of genes belonging to biological pathways using a standard web browser. Using a new visual modality called the CircleMap, multiple ‘omics’ data sets can be viewed simultaneously within the context of curated, predicted, directed and undirected regulatory interactions. The Interaction Browser provides an integrative viewing of biological networks based on the consensus of many observations about genes and their products, which may provide new insights about normal and disease processes not obvious from any isolated data set.