Reconstructing tumor history in breast cancer: signatures of mutational processes and response to neoadjuvant chemotherapy⋆

BACKGROUND

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.

PATIENTS AND METHODS

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.

RESULTS

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, P < 0.001) 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.

CONCLUSION

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.

Pressure-Driven Mitochondrial Transfer Pipeline Generates Mammalian Cells of Desired Genetic Combinations and Fates

Generating mammalian cells with desired mitochondrial DNA (mtDNA) sequences is enabling for studies of mitochondria, disease modeling, and potential regenerative therapies. MitoPunch, a high-throughput mitochondrial transfer device, produces cells with specific mtDNA-nuclear DNA (nDNA) combinations by transferring isolated mitochondria from mouse or human cells into primary or immortal mtDNA-deficient (ρ0) cells. Stable isolated mitochondrial recipient (SIMR) cells isolated in restrictive media permanently retain donor mtDNA and reacquire respiration. However, SIMR fibroblasts maintain a ρ0-like cell metabolome and transcriptome despite growth in restrictive media. We reprogrammed non-immortal SIMR fibroblasts into induced pluripotent stem cells (iPSCs) with subsequent differentiation into diverse functional cell types, including mesenchymal stem cells (MSCs), adipocytes, osteoblasts, and chondrocytes. Remarkably, after reprogramming and differentiation, SIMR fibroblasts molecularly and phenotypically resemble unmanipulated control fibroblasts carried through the same protocol. Thus, our MitoPunch "pipeline" enables the production of SIMR cells with unique mtDNA-nDNA combinations for additional studies and applications in multiple cell types.

Prediction of Benefit from Checkpoint Inhibitors in Mismatch Repair Deficient Metastatic Colorectal Cancer: Role of Tumor Infiltrating Lymphocytes

BACKGROUND

Immunotherapy with immune checkpoint inhibitors (ICIs) is highly effective in microsatellite instability-high (MSI-H) metastatic colorectal cancer (mCRC); however, specific predictive biomarkers are lacking.

PATIENTS AND METHODS

Data and samples from 85 patients with MSI-H mCRC treated with ICIs were gathered. Tumor infiltrating lymphocytes (TILs) and tumor mutational burden (TMB) were analyzed in an exploratory cohort of "super" responders and "clearly" refractory patients; TILs were then evaluated in the whole cohort of patients. Primary objectives were the correlation between the number of TILs and TMB and their role as biomarkers of ICI efficacy. Main endpoints included response rate (RR), progression-free survival (PFS), and overall survival (OS).

RESULTS

In the exploratory cohort, an increasing number of TILs correlated to higher TMB (Pearson's test, p = .0429). In the whole cohort, median number of TILs was 3.6 in responders compared with 1.8 in nonresponders (Mann-Whitney test, p = .0448). RR was 70.6% in patients with high number of TILs (TILs-H) compared with 42.9% in patients with low number of TILs (odds ratio = 3.20, p = .0291). Survival outcomes differed significantly in favor of TILs-H (PFS: hazard ratio [HR] = 0.42, p = .0278; OS: HR = 0.41, p = .0463).

CONCLUSION

A significant correlation between higher TMB and increased number of TILs was shown. A significantly higher activity and better PFS and OS with ICI in MSI-H mCRC were reported in cases with high number of TILs, thus supporting further studies of TIL count as predictive biomarker of ICI efficacy.

IMPLICATIONS FOR PRACTICE

Microsatellite instability is the result of mismatch repair protein deficiency, caused by germline mutations or somatic modifications in mismatch repair genes. In metastatic colorectal cancer (mCRC), immunotherapy (with immune checkpoint inhibitors [ICIs]) demonstrated remarkable clinical benefit in microsatellite instability-high (MSI-H) patients. ICI primary resistance has been observed in approximately 25% of patients with MSI-H mCRC, underlining the need for predictive biomarkers. In this study, tumor mutational burden (TMB) and tumor infiltrating lymphocyte (TIL) analyses were performed in an exploratory cohort of patients with MSI-H mCRC treated with ICIs, demonstrating a significant correlation between higher TMB and increased number of TILs. Results also demonstrated a significant correlation between high number of TILs and clinical responses and survival benefit in a large data set of patients with MSI-H mCRC treated with ICI. TMB and TILs could represent predictive biomarkers of ICI efficacy in MSI-H mCRC and should be incorporated in future trials testing checkpoint inhibitors in colorectal cancer.

Abstract P6-06-09: Evaluation of laser microdissected primary breast tumors for RNA Seq over bulk processing and validated with cohort control

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 &gt;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.

Transcriptional Programming of Normal and Inflamed Human Epidermis at Single-Cell Resolution

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 IL1β^hiCCL3^hiCD14⁺ 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.

APOBEC mutation drives early-onset squamous cell carcinomas in recessive dystrophic epidermolysis bullosa

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, endogenous mutation processes associated with apolipoprotein B mRNA-editing enzyme catalytic polypeptide-like (APOBEC) deaminases dominated RDEB SCC. APOBEC mutation signatures were enhanced throughout RDEB SCC tumor evolution, relative to spontaneous UV SCC and HNSCC mutation 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 that APOBEC deaminases 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.

Loss of ZNF750 in ocular and cutaneous sebaceous carcinoma

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 (<5%).

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.

Abstract 3402: Evaluation of laser microdissected primary breast tumors for RNA-Seq over bulk processing

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 &gt;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.

Genomic and immune infiltration differences between MSI and MSS GI tumors

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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.

A20 and ABIN1 Suppression of a Keratinocyte Inflammatory Program with a Shared Single-Cell Expression Signature in Diverse Human Rashes

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.

Abstract 284: Integrated proteogenomic analysis of laser microdissected primary breast tumors define proteome clusters

Introduction: Breast tumors have 4 well-established intrinsic subtypes based on transcriptome profiling. However, clusters defined by proteomics are often in disagreement with those defined by transcriptomics. Here, we report the findings of proteogenomic profiling of 118 laser microdissected (LMD) breast tumors using RNA-Seq and mass-spectrometry (MS)-based proteomic technologies.

Methods: Cases used in this study were drawn from the Clinical Breast Care Project, with patients consented using an IRB-approved protocol. A total of 118 primary breast tumors embedded in OCT 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 the preprocessing tool PRINSEQ, splice-aligner GSNAP and HTSeq for quantifying expression. Quantitative global proteomics analyses were performed on 113 cases using isobaric TMT 6-plex labeling with the “universal reference” strategy. MS data were acquired using a Q-Exactive instrument and analyzed using Proteome Discoverer with Byonic node. Sample-to-sample normalization was conducted to remove pipetting errors and ComBat was used to remove batch effect. K-means clustering was done using Bioconductor package Consensusclustering.

Results: The number of preprocessed RNA sequencing reads for the 118 cases ranged from over 43 to 295 million. An average of 83% of reads was mapped, and 24,518 genes with a mean expression of ≥ 10 counts across 118 tumor samples were identified. The PAM50 algorithm was used for intrinsic subtyping, yielding 37 Basal-like, 16 HER2-enriched, 39 Luminal A and 26 Luminal B calls. Unsupervised clustering of 3,000 highly varying genes reflected 4 intrinsic subtypes. In the global proteomics data, 840 proteins were identified across all 113 cases. Unsupervised K-means consensus clustering on all 840 or just using the top 210 highly varying proteins indicated the optimal number of clusters to be 3. These 3 clusters were identified as Basal-enriched, Luminal A-enriched and Luminal B-enriched. HER2-enriched cases were distributed among these clusters.

We did not observe a stromal-enriched cluster in this analysis of LMD-prepared samples that selected against stromal components of the tumor.

Conclusion: Analysis of LMD breast tumors using proteogenomic technologies resulted in 3 clusters for proteome data: basal-enriched, luminal A-enriched and luminal B-enriched. Unlike a recent report on proteomics clustering using bulk processing of tumors, a stromal-enriched cluster was not observed in this analysis which excluded stromal components of the samples.

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, Tao Liu, Lori A. Sturtz, Albert J. Kovatich, Marina A. Gritsenko, Vladislav A. Petyuk, Brenda Deyarmin, Viswanadham Sridhara, James Craig, Jason E. McDermott, Anil K. Shukla, Ronald J. Moore, Matthew E. Monroe, Bobbie-Jo M. Webb-Robertson, Jeffrey A. Hooke, J.Leigh Fantacone-Campbell, Leonid Kvecher, Jianfang Liu, Jennifer Kane, Jennifer Melley, Stella Somiari, Stephen C. Benz, Justin Golovato, Shahrooz Rabizadeh, Patrick Soon-Shiong, Richard D. Smith, Richard J. Mural, Karin D. Rodland, Craig D. Shriver, Hai Hu. Integrated proteogenomic analysis of laser microdissected primary breast tumors define proteome clusters [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 284.

Abstract 3398: Analysis of whole transcriptome RNA-seq of large numbers of clinical FFPE samples

Background: We evaluate the feasibility, quality, and analytical potential of whole-transcriptome RNA-seq on over one thousand clinical FFPE tumor samples. Transcriptional profiling of RNA is used for clinical decision making in many tumor types. Clinical analysis of RNA is complicated by the common use of formalin-fixed paraffin-embedded (FFPE) tissue storage, which can cause low yield and RNA degradation.

Methods: RNA was extracted from FFPE material using commercially available kits using an RNAseH based ribodeplete. Multiple libraries per sample are sequenced using standard Illumina sequencing. Bowtie2, RSEM, and custom software are used for alignment, transcript quantification, fusion detection, and variant expression analysis.

Results: We observe a &gt;85% success rate on whole transcriptome RNA-seq on our cohort of more than 1000 samples. We find reliable transcript quantification upon successful sequencing, and on a subset of samples quantify differences between FFPE and fresh-frozen material when using ribodeplete. We also compare transcriptional profiles of clinical FFPE samples to an independent set of fresh frozen, poly-A capture samples from The Cancer Genome Atlas (TCGA) and show differences between poly-A capture and ribodeplete RNA isolation methods as well as FFPE vs. FF effects. We present a robust mapping methodology for comparison of public FF poly-A dataset to our FFPE ribodeplete samples. We demonstrate that we are able to utilize this joint gene expression space to perform site of origin prediction on FFPE samples. This is especially important for clinical application in analyzing Cancer of Unknown Primary (CUP) samples as well as detecting outlier samples whose molecular features may suggest additional therapeutic avenues. Finally, we show reliable detection of fusion transcripts from FFPE RNA-seq material from whole-transcriptome analysis and expression of somatic tumor variants detected from DNA sequencing.

Conclusions: Large scale sequencing of RNA from clinical FFPE materials provides reliable transcriptomic results comparable to existing public databases of RNA, enabling research on cohorts of tumors that are FFPE-banked and unavailable as frozen tissue.

Citation Format: Yulia Newton, Justin Golovato, Mark Johnson, Shahrooz Rabizadeh, Zack Sanborn, Steve Benz, Charles J. Vaske. Analysis of whole transcriptome RNA-seq of large numbers of clinical FFPE samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3398.

Abstract 213: Integrated proteogenomic analysis of laser capture microdissected breast tumors

Introduction: Molecular characteristics of breast tumors play an important role in determining patients’ survival outcome. Here, we report preliminary findings of proteogenomic profiling of 50 breast tumors using RNA-Seq and mass-spectrometry (MS) based proteomic technologies. An additional 60 tumors are being analyzed, including WGS for all samples. We are also collecting patient survival outcome data.

Methods: Cases used in this study were drawn from the Clinical Breast Care Project, where patients were consented using an IRB-approved protocol. A total of 50 breast tumors were selected and processed by laser capture microdissection (LCM). This cohort includes 36 Caucasian Americans (CA) and 8 African Americans (AA), and the age of the patients is 57 ± 13 years. Protein and RNA were extracted using the Illustra triplePrep kit, which isolates DNA from the same cells as well. Quantitative global proteomics and phosphoproteomics analyses were performed using isobaric TMT 6-plex labeling with the “universal reference” strategy and IMAC enrichment of phosphopeptides. Mass spectrometry data were acquired using a Q-Exactive instrument and analyzed using Proteome Discoverer with Byonic node. Phosphopeptide abundance was normalized to abundance measurements of the parent protein for all of the phosphorylation analyses. Phosphoproteomic data was also searched for the presence of O-GlcNAc modifications. RNA-Seq analyses were done on Illumina HiSeq and the data were analyzed using GSNAP.

Results: There were 19 Luminal A, 7 Luminal B, 8 HER2-enriched, and 16 basal-like subtypes based on the PAM50 algorithm. In the global proteomics data, we were able to quantitate &gt;8600 proteins. Unsupervised clustering on the highly varying proteins across the samples resulted in two primary clusters, with one being luminal-enriched. The other cluster contains a basal-like tumor sub-cluster and a sub-cluster of mixed subtypes. Differential protein expression analyses between the two primary clusters confirmed known markers (e.g., overexpression of KRT8/KRT18 in luminal-enriched cluster). The luminal-enriched cluster is primarily CA with post-menopausal status.

A similar search of the phosphoproteomic data yielded quantitation of &gt;12500 phosphopeptides. Unsupervised clustering of the phosphoproteins resulted in four primary groups, with one being basal-enriched and another being luminal-enriched. We also observed &gt;50 overexpressed phosphopeptides. While some of these phosphosites have been previously reported (e.g., on RANBP2), other phosphosites appeared to be novel (e.g., on IRF2BP2).

Conclusion: Analysis of LCM breast tumors using proteogenomic technologies resulted in basal- and luminal-enriched clusters, thus enabling us to study protein and phosphopeptide markers across multiple platforms.

The views expressed in this article are those of the author and do not reflect the official policy of the Department of Defense, or U.S. Government.

Citation Format: Viswanadham Sridhara, Tao Liu, Marina A. Gritsenko, Lori A. Sturtz, Albert J. Kovatich, Vladislav A. Petyuk, Brenda Deyarmin, Jason E. McDermott, Anil K. Shukla, Ronald J. Moore, Matthew E. Monroe, Bobbie-Jo M. Webb-Robertson, Jeffrey A. Hooke, Leigh Fantacone-Campbell, Praveen Kumar Raj Kumar, Leonid Kvecher, Jianfang Liu, Jennifer Kane, Jennifer Melley, Stella Somiari, Joji Iida, Stephen C. Benz, Justin Golovato, Shahrooz Rabizadeh, Patrick Soon-Shiong, Richard D. Smith, Richard J. Mural, Craig D. Shriver, Hai Hu, Karin D. Rodland. Integrated proteogenomic analysis of laser capture microdissected breast tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 213. doi:10.1158/1538-7445.AM2017-213

Abstract P6-04-14: Integrating whole genome sequencing data with RNAseq, pathway analysis, and quantitative proteomics to determine prognosis after standard adjuvant treatment with trastuzumab and chemotherapy in primary breast cancer patients

Background: Despite improvements in the treatment of HER2+ breast cancer (BC), almost all patients (pts) progress in the metastatic setting. Three examples of resistance mechanisms are: PI3K mutations, lack of ADCC, or low expression of HER2. We recently showed that among 237 pts who had HER2 amplifications, 49% had normal or low levels of HER2 RNA. In addition, quantification of HER2 protein by selected reaction monitoring mass spectrometry (SRM-MS) accurately predicted HER2 expression status compared with IHC (3+)/ISH (≥2.0). Here we report a comprehensive panomic approach that integrates whole genome sequencing (WGS), RNASeq, quantitative proteomics, and pathway analysis to determine associations between tumor molecular profiles and prognosis among HER2+ pts.

Methods: Matched tumor-normal samples (FFPE tumors and blood) were obtained from 58 pts with HER2+ BC who had received standard adjuvant chemotherapy and trastuzumab. Pts were divided into 2 groups: those who had no recurrence after 5 years and those who had developed metastases. The HER2 status of each pt was previously determined using IHC/FISH. Samples underwent WGS and RNASeq according to NantOmics CLIA-approved assay specifications. WGS data were processed using Contraster; RNASeq data confirmed the presence of gene mutations and was used to identify mutational and transcript abundance. PARADIGM was used to reveal associations between gene mutations and pathway levels. SRM-MS was used for proteomics analysis of a panel of 53 proteins. Tumor areas from FFPE tissue sections were analyzed after laser microdissection. Absolute protein quantitation was accomplished through simultaneous detection of endogenous target and synthetic labeled heavy peptide identical to analytical targets. Genetic alterations in germline and tumor DNA were compared in pts with vs without recurrence.

Results: There was no statistically significant difference in the mean concentration of HER2 in the tumors of pts with vs without recurrence: 2.34 fmol/µL vs 2.56 fmol/µL. Other analyzed proteins did not appear to be associated with recurrence; however, expected correlations between pt and tumor characteristics and protein expression were found. With regard to clinically relevant mutations, we found one germline BRCA2 mutation in a pt with no family history of this mutation. The most commonly found somatic mutations were in TP53 (11 pts), AMBRA1 (11 pts), MORC4 (10 pts), SETD2 (8 pts), CDC27 (6 pts), BCLAF1 (5 pts), ZNF479 (4 pts) , PIK3CA (3 pts), PIK3R1 (3 pts), RUNX1 (3 pts), and GATA3 (3 pts).

Conclusion: Whereas HER2 expression status was predictive of OS and PFS in pts treated with trastuzumab (Nuciforo et al. Mol Onc. 2015), in this small exploratory study of HER2+ BC pts, HER2 expression status was not predictive of recurrence. To better understand the molecular mechanisms driving recurrence beyond HER2 status alone, genomic sequencing may define a signature of recurrence after anti-HER2 therapy.

Citation Format: Benz SC, Rabizadeh S, Cecchi F, Beckman MW, Brucker SY, Hartmann A, Golovato J, Hembrough T, Janni W, Rack B, Sanborn JZ, Schneeweiss A, Vaske CJ, Soon-Shiong P, Fasching PA. Integrating whole genome sequencing data with RNAseq, pathway analysis, and quantitative proteomics to determine prognosis after standard adjuvant treatment with trastuzumab and chemotherapy in primary breast cancer patients. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P6-04-14.