Network-based approach elucidates critical genes in BRCA subtypes and chemotherapy response in triple negative breast cancer

Breast cancers (BRCA) exhibit substantial transcriptional heterogeneity, posing a significant clinical challenge. The global transcriptional changes in a disease context, however, are likely mediated by few key genes which reflect disease etiology better than the differentially expressed genes (DEGs). We apply our network-based tool PathExt to 1,059 BRCA tumors across 4 subtypes to identify key mediator genes in each subtype. Compared to conventional differential expression analysis, PathExt-identified genes exhibit greater concordance across tumors, revealing shared and subtype-specific biological processes; better recapitulate BRCA-associated genes in multiple benchmarks, and are more essential in BRCA subtype-specific cell lines. Single-cell transcriptomic analysis reveals a subtype-specific distribution of PathExt-identified genes in multiple cell types from the tumor microenvironment. Application of PathExt to a TNBC chemotherapy response dataset identified subtype-specific key genes and biological processes associated with resistance. We described putative drugs that target key genes potentially mediating drug resistance.

Network-based approach elucidates critical genes in BRCA subtypes and chemotherapy response in Triple Negative Breast Cancer

Breast cancers exhibit substantial transcriptional heterogeneity, posing a significant challenge to the prediction of treatment response and prognostication of outcomes. Especially, translation of TNBC subtypes to the clinic remains a work in progress, in part because of a lack of clear transcriptional signatures distinguishing the subtypes. Our recent network-based approach, PathExt, demonstrates that global transcriptional changes in a disease context are likely mediated by a small number of key genes, and these mediators may better reflect functional or translationally relevant heterogeneity. We apply PathExt to 1059 BRCA tumors and 112 healthy control samples across 4 subtypes to identify frequent, key-mediator genes in each BRCA subtype. Compared to conventional differential expression analysis, PathExt-identified genes (1) exhibit greater concordance across tumors, revealing shared as well as BRCA subtype-specific biological processes, (2) better recapitulate BRCA-associated genes in multiple benchmarks, and (3) exhibit greater dependency scores in BRCA subtype-specific cancer cell lines. Single cell transcriptomes of BRCA subtype tumors reveal a subtype-specific distribution of PathExt-identified genes in multiple cell types from the tumor microenvironment. Application of PathExt to a TNBC chemotherapy response dataset identified TNBC subtype-specific key genes and biological processes associated with resistance. We described putative drugs that target top novel genes potentially mediating drug resistance. Overall, PathExt applied to breast cancer refines previous views of gene expression heterogeneity and identifies potential mediators of TNBC subtypes, including potential therapeutic targets.

Abstract 957: Prediction of patient response to targeted and immunotherapies from the tumor transcriptome in a wide set of indications and clinical trials

Background: Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers.

Methods: We present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patient’s response to a variety of therapies in multiple cancer types, importantly, without training on previous treatment response data. Consequently, in addition to its ability to predict patients' response to approved and well-studied therapies, ENLIGHT can predict the response to new treatments in early development, even before clinical data has accumulated. Accordingly, we study ENLIGHT in two translationally relevant scenarios: Personalized Oncology (PO), aimed at prioritizing approved treatments to a given patient, and Clinical Trial Design (CTD), selecting the subset of most likely responders in a patient cohort.

Results: Evaluating ENLIGHT’s performance on 21 blinded clinical trial datasets spanning 11 indications and 15 different drugs in the PO setting, we show that it can effectively predict a patient’s treatment response across multiple therapies and cancer types, with an overall odds ratio of 2.59 (p=3.41e-8), and a 36% increase in response rate over the baseline (p=3.30e-13). Its prediction accuracy is better than other state-of-the-art transcriptomics-based signatures. Unlike most signatures that are prognostic or provide insights for only very few, specific treatments, ENLIGHT provides matching scores to a broad range of treatments. Quite strikingly, its performance is comparable to that of supervised predictors developed for specific indications and drugs. In combination with the IFN-γ signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, our results show that by excluding non-responders ENLIGHT can enhance clinical trial success for immunotherapies and other monoclonal antibodies, achieving > 90% of the response rate attainable under an optimal exclusion strategy.

Conclusion: ENLIGHT is a powerful transcriptomics-based precision oncology pipeline developed by Pangea Biomed that broadly predicts response to both extant and novel targeted and immune therapies, going beyond context-specific biomarkers.

Citation Format: Gal Dinstag, Eldad D. Shulman, Efrat Elis, Doreen S. Ben-Zvi, Omer Tirosh, Eden Maimon, Isaac Meilijson, Emmanuel Elalouf, Boris Temkin, Philipp Vitkovsky, Eyal Schiff, Danh-Tai Hoang, Sanju Sinha, Nishanth Ulhas Nair, Joo Sang-Lee, Alejandro A. Schäffer, Ze'ev Ronai, Dejan Juric, Andrea B. Apolo, William L. Dahut, Stanley Lipkowitz, Raanan Berger, Razelle Kurzrock, Antonios Papanicolau-Sengos, Fatima Karzai, Mark R. Gilbert, Kenneth Aldape, Padma S. Rajagopal, Tuvik Beker, Eytan Ruppin, Ranit Aharonov. Prediction of patient response to targeted and immunotherapies from the tumor transcriptome in a wide set of indications and clinical trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 957.

Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome

BACKGROUND

Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers.

METHODS

We present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patient's response to a variety of therapies in multiple cancer types without training on previous treatment response data. We study ENLIGHT in two translationally oriented scenarios: personalized oncology (PO), aimed at prioritizing treatments for a single patient, and clinical trial design (CTD), selecting the most likely responders in a patient cohort.

FINDINGS

Evaluating ENLIGHT's performance on 21 blinded clinical trial datasets in the PO setting, we show that it can effectively predict a patient's treatment response across multiple therapies and cancer types. Its prediction accuracy is better than previously published transcriptomics-based signatures and is comparable with that of supervised predictors developed for specific indications and drugs. In combination with the interferon-γ signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, ENLIGHT can potentially enhance clinical trial success for immunotherapies and other monoclonal antibodies by excluding non-responders while overall achieving more than 90% of the response rate attainable under an optimal exclusion strategy.

CONCLUSIONS

ENLIGHT demonstrably enhances the ability to predict therapeutic response across multiple cancer types from the bulk tumor transcriptome.

FUNDING

This research was supported in part by the Intramural Research Program, NIH and by the Israeli Innovation Authority.

Cross-species identification of cancer resistance-associated genes that may mediate human cancer risk

Cancer is a predominant disease across animals. We applied a comparative genomics approach to systematically characterize genes whose conservation levels correlate positively (PC) or negatively (NC) with cancer resistance estimates across 193 vertebrates. Pathway analysis reveals that NC genes are enriched for metabolic functions and PC genes in cell cycle regulation, DNA repair, and immune response, pointing to their corresponding roles in mediating cancer risk. We find that PC genes are less tolerant to loss-of-function (LoF) mutations, are enriched in cancer driver genes, and are associated with germline mutations that increase human cancer risk. Their relevance to cancer risk is further supported via the analysis of mouse functional genomics and cancer mortality of zoo mammals' data. In sum, our study describes a cross-species genomic analysis pointing to candidate genes that may mediate human cancer risk.

Abstract 27: Sex biases in cancer and autoimmune disease incidence are strongly positively correlated with mitochondrial gene expression across human tissues

Cancer occurs more frequently in men while autoimmune diseases (AIDs) occur more frequently in women. To explore whether these sex biases have a common basis, we collected 170 AID incidence studies from many countries for tissues that have both a cancer type and an AID that arise from that tissue. Analyzing a total of 182 country-specific, tissue-matched cancer-AID incidence rate sex bias data pairs, we find that the sex biases observed in the incidence of AIDs and cancers that occur in the same tissue are positively correlated across human tissues. Second, we find that the sex bias in the expression of the 37 genes encoded in the mitochondrial genome stands out as the common key factor whose levels across human tissues are most strongly and positively associated with these incidence rate sex biases.

Citation Format: David Robert Crawford, Sanju Sinha, Nishanth U. Nair, Bríd M. Ryan, Jill Barnholtz-Sloan, Stephen M. Mount, Ayelet Erez, Kenneth Adalpe, Philip E. Castle, Padma S. Rajagopal, Chi-Ping Day, Alejandro A. Schäffer, Eytan Ruppin. Sex biases in cancer and autoimmune disease incidence are strongly positively correlated with mitochondrial gene expression across human tissues [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 27.

Abstract 985: Is the transcriptome of primary and metastatic cancers closer to their origin or target tissues

Introduction: Most cancer deaths are caused by metastasis, yet understanding how metastatic cancers adapt from their origin tissues to their target tissues remains a fundamental scientific and clinical challenge. To date, no studies have systematically analyzed the transcriptomic similarity of metastatic cancers to their target tissues in a genome wide manner. Here, we ask if the overall gene expression of primary and metastatic tumors is closer to their tissue of origin or closer to their target tissue? Next, we aim to identify the key pathways in metastatic tumors whose gene expression becomes markedly closer to their target than primary tissue of origin.

Methods: We analyzed the expression profiles of: (a) primary tumors of 9 cancer types, which have metastasized to the liver, lung, or brain (TCGA data, n = 306), (b) metastatic tumors of 12 cancer types (MET500 collection, n = 194) and (c) their origin and target normal tissue samples (GTEx, n = 5,663). We computed the similarity (Euclidean distance) between the expression profiles of the tumors (either primary or metastasis) to the mean expression of their corresponding normal tissue of origin and target tissues, termed their transcriptomic distance (TD). For each tumor sample’s expression profile (either primary tumor or metastasis), we compute the ratio of its TD to the tissue of origin over its TD to the target tissue to get its TD ratio.

Results: 1) We find that while most primary tumors are more similar to the tissue of origin than to the target tissues, there is a shift in expression patterns in metastatic tumors towards their target.

2) Across cancer types that metastasize to the liver, cell cycle and growth pathways are significantly transcriptomically closer to the tissue of origin; while the expression of pathways related to more specialized liver cellular functions, such as coagulation and bile acid metabolism are becoming significantly closer to the liver.

3) We tested our key findings by analyzing a matched cohort of primary breast cancers and metastasis samples, reassuringly finding that they overall recapitulate the key results emerging from the non-matched analysis. The key pathways altered when metastasizing to four target tissues (liver, lung, ovary, skin) are adipogenesis, fatty acid metabolism, coagulation, xenobiotic metabolism, and bile acid metabolism.

Conclusions: Quite surprisingly, this the first systematic analysis comparing the landscape of primary and metastatic transcriptional alterations of the same cancer type, providing a genome wide view of how cancers adapt to new environments during metastasis. The systematic identification of the specific pathways whose expression shifts towards the target tissues in metastatic tumors provides important leads to their potential targeting.

Citation Format: Camilo Calvo-Alcañiz, Padma S. Rajagopal, Sanju Sinha, Fiorella Schischlik, Antonios Papanicolau-Sengos, Nishanth Ulhas Nair, Eytan Ruppin. Is the transcriptome of primary and metastatic cancers closer to their origin or target tissues [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 985.

Pleiotropy-guided transcriptome imputation from normal and tumor tissues identifies candidate susceptibility genes for breast and ovarian cancer

Familial, sequencing, and genome-wide association studies (GWASs) and genetic correlation analyses have progressively unraveled the shared or pleiotropic germline genetics of breast and ovarian cancer. In this study, we aimed to leverage this shared germline genetics to improve the power of transcriptome-wide association studies (TWASs) to identify candidate breast cancer and ovarian cancer susceptibility genes. We built gene expression prediction models using the PrediXcan method in 681 breast and 295 ovarian tumors from The Cancer Genome Atlas and 211 breast and 99 ovarian normal tissue samples from the Genotype-Tissue Expression project and integrated these with GWAS meta-analysis data from the Breast Cancer Association Consortium (122,977 cases/105,974 controls) and the Ovarian Cancer Association Consortium (22,406 cases/40,941 controls). The integration was achieved through application of a pleiotropy-guided conditional/conjunction false discovery rate (FDR) approach in the setting of a TWASs. This identified 14 candidate breast cancer susceptibility genes spanning 11 genomic regions and 8 candidate ovarian cancer susceptibility genes spanning 5 genomic regions at conjunction FDR < 0.05 that were >1 Mb away from known breast and/or ovarian cancer susceptibility loci. We also identified 38 candidate breast cancer susceptibility genes and 17 candidate ovarian cancer susceptibility genes at conjunction FDR < 0.05 at known breast and/or ovarian susceptibility loci. The 22 genes identified by our cross-cancer analysis represent promising candidates that further elucidate the role of the transcriptome in mediating germline breast and ovarian cancer risk.

PhenomeXcan: Mapping the genome to the phenome through the transcriptome

Large-scale genomic and transcriptomic initiatives offer unprecedented insight into complex traits, but clinical translation remains limited by variant-level associations without biological context and lack of analytic resources. Our resource, PhenomeXcan, synthesizes 8.87 million variants from genome-wide association study summary statistics on 4091 traits with transcriptomic data from 49 tissues in Genotype-Tissue Expression v8 into a gene-based, queryable platform including 22,515 genes. We developed a novel Bayesian colocalization method, fast enrichment estimation aided colocalization analysis (fastENLOC), to prioritize likely causal gene-trait associations. We successfully replicate associations from the phenome-wide association studies (PheWAS) catalog Online Mendelian Inheritance in Man, and an evidence-based curated gene list. Using PhenomeXcan results, we provide examples of novel and underreported genome-to-phenome associations, complex gene-trait clusters, shared causal genes between common and rare diseases via further integration of PhenomeXcan with ClinVar, and potential therapeutic targets. PhenomeXcan (phenomexcan.org) provides broad, user-friendly access to complex data for translational researchers.

Development and Pilot Testing of a Simulation to Study How Physicians Facilitate Surrogate Decision Making Based on Critically Ill Patients' Values and Preferences

CONTEXT

There are no evidence-based programs to train physicians to facilitate shared decision making based on incapacitated intensive care unit patients' values and preferences.

OBJECTIVES

The objective of this study was to develop a high-fidelity simulation to fill this gap.

METHODS

Case development involved six steps: 1) drafting a case about an elderly patient receiving prolonged mechanical ventilation; 2) engaging an expert advisory board to optimize case content; 3) revising the case based on advisory board input; 4) training actors to portray the case patient's daughter; 5) obtaining physician feedback on the simulation; and 6) revising the case based on their feedback. We conducted a cross-sectional pilot study with 50 physicians to assess feasibility and acceptability, defined a priori as an enrollment rate >40 physicians/year, study procedures <75 minutes/participant, >95% actor adherence to standardization rules, and high physician ratings of realism and acceptability.

RESULTS

Advisory panel feedback yielded two modifications: 1) refocusing the case on decision making about tracheostomy and percutaneous gastrostomy and 2) making the patient's values more authentic. Physician feedback yielded two additional modifications: 1) reducing how readily the actor divulged the patient's values and 2) making her more emotional. All 50 physicians enrolled in the pilot study over 11 months completed study procedures in <75 minutes. Actor adherence to standardization rules was 95.8%. Physicians' mean ratings of realism and acceptability were 8.4 and 9.1, respectively, on a 10-point scale.

CONCLUSION

Simulation is feasible, is acceptable, and can be adequately standardized to study physicians' skills for facilitating surrogate decision making based on an incapacitated intensive care unit patient's values and preferences.

Distinct Pattern of Metastases in Patients with Invasive Lobular Carcinoma of the Breast

BACKGROUND

Invasive lobular carcinoma (ILC) comprises around 10 - 15% of invasive breast cancers. Few prior studies have demonstrated a unique pattern of metastases between ILC and the more common invasive ductal carcinoma (IDC). To our knowledge, such data is limited to first sites of distant recurrence. We aimed to perform a comparison of the metastatic pattern of ILC and IDC at first distant recurrence as well as over the entire course of metastatic disease.

METHODS

We used a prospectively collated database of patients with metastatic breast cancer. Breast cancer recurrence or metastases were classified into various sites and a descriptive analysis was performed.

RESULTS

Among 761 patients, 88 (11.6%) were diagnosed with ILC and 673 (88.4%) with IDC. Patients with ILC showed more frequent metastases to the bone (56.8 vs. 37.7%, p = 0.001) and gastrointestinal (GI) tract (5.7 vs. 0.3%, p < 0.001) as first site of distant recurrence, and less to organs such as lung (5.7 vs. 24.2%, p < 0.001) and liver (4.6 vs. 11.4%, p = 0.049). Over the entire course of metastatic disease, more patients with ILC had ovarian (5.7 vs. 2.1%, p = 0.042) and GI tract metastases (8.0 vs. 0.6%, p < 0.001), also demonstrating reduced tendency to metastasize to the liver (20.5 vs. 49.0%, p < 0.001) and lung (23.9 vs. 51.9%, p < 0.001). All associations but bone held after sensitivity analysis on hormonal status. Although patients presenting with ILC were noted to have more advanced stage at presentation, recurrence-free survival in these patients was increased (4.8 years vs. 3.2 years, p = 0.017). However, overall survival was not (2.5 vs. 2.0 years, p = 0.75).

CONCLUSION

After accounting for hormone receptor status, patients with IDC had greater lung/pleura and liver involvement, while patients with ILC had a greater propensity to develop ovarian and GI metastases both at first site and overall. Clinicians can use this information to provide more directed screening for metastases; it also adds to the argument that these two variants of breast cancer should be managed as unique diseases.

Abstract P4-10-04: Employment trends in young women following a breast cancer diagnosis

Background: Workplace concerns are particularly salient for young women with breast cancer (BC), and a cancer diagnosis (dx) and treatment may affect their careers. We sought to evaluate the perceived impact of dx on employment, describe job changes, and identify factors associated with transition out of the workforce after dx of BC at a young age.

Methods: As part of an ongoing, multi-center cohort of young women diagnosed with BC at age ≤ 40, we surveyed women with early-stage BC about their pre- and post-dx employment status. Additional items assessed socio-demographic and treatment information; tumor characteristics were ascertained via pathology and medical record review. We used logistic regression to identify predictors of transitioning from pre-dx employment to unemployment at 1 year after dx. Among women employed 1 year after dx, we evaluated job satisfaction, perceived impact of dx on job performance, accommodations made by employers, and perceived likelihood of employment in the future.

Results: 76% of women (555/730) were employed both before dx and at 1 year; 13% were not employed at either time point; 7% were employed pre-dx but unemployed at 1 year; 4% were not employed prior to dx but reported employment at 1 year. Among women employed 1 year after dx, 74% (427/581) were somewhat or completely satisfied with their job. Only 6% said cancer or treatment limited their ability to perform their job quite a bit or very much; 38% said their ability was affected a little bit. Most (63%) said their employers had made accommodations for them, and almost all women (93%) said it was very likely they would be working in 1 year. In multivariable analyses (Table 1), women with stage 3 disease (vs. stage 1), were more likely to transition out of the workforce following dx, while women with a college or graduate degree (vs. no college degree) were less likely to transition out.

Conclusion: Most young women with early stage BC remain employed and report a willingness by their employer to make accommodations following a breast cancer dx. While few women reported that their dx or treatment limited their job performance, the finding that women with more advanced disease were more likely to transition out of the workforce suggests an impact of dx/treatment burden on employment. Women without a college degree were also at risk for unemployment post-dx, suggesting that job type, socioeconomic status, and environment affect employment outcomes. Attention to these subgroups of women is warranted to ensure that they are sufficiently supported given the potential adverse psychosocial and financial impacts of unemployment on patients, families, communities, and society.

Table 1. Multivariable analysis of factors associated with transition out of workforce 1year post-dx (N=634) OR (95% CI)Stage (ref=1) 04.52 (0.60-33.85)21.11 (0.48-2.58)34.05 (1.53-10.72)*White non-Hispanic (ref=non-WNH)1.47 (0.56-3.81)College graduate (ref=no college degree)0.44 (0.22-0.90)*Married/Living as married (ref=unmarried)0.95 (0.43-2.08)Parous (ref=nulliparous)1.75 (0.83-3.69)Age at diagnosis (years)0.98 (0.90-1.06)Mastectomy (ref=lumpectomy)1.74 (0.75-4.05)Endocrine therapy (ref=none)0.75 (0.41-1.39)Chemotherapy (ref=none)5.20 (0.93-29.22)Radiation (ref=none)1.38 (0.64-2.96)*p&lt;0.05

Citation Format: Partridge AH, Rosenberg SM, Rajagopal PS, Ruddy KJ, Tamimi RM, Schapira L, Come S, Borges V, Gelber S. Employment trends in young women following a breast cancer diagnosis. [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 P4-10-04.

Adjuvant chemotherapy for invasive bladder cancer: a 2013 updated systematic review and meta-analysis of randomized trials

CONTEXT

The role of adjuvant chemotherapy remains poorly defined for the management of muscle-invasive bladder cancer (MIBC). The last meta-analysis evaluating adjuvant chemotherapy, conducted in 2005, had limited power to fully support its use.

OBJECTIVE

To update the current evidence of the benefit of postoperative adjuvant cisplatin-based chemotherapy compared with control (ie, surgery alone) in patients with MIBC.

EVIDENCE ACQUISITION

A comprehensive literature review was performed to identify all randomized controlled trials (RCTs) comparing adjuvant cisplatin-based chemotherapy with control for patients with MIBC. The search included the Medline, Embase, Cochrane Central Register of Controlled Trials databases, and abstracts from the American Society of Clinical Oncology meetings up to May 2013. An updated systematic review and meta-analysis was performed.

EVIDENCE SYNTHESIS

A total of 945 patients included in nine RCTs (five previously analyzed, one updated, and three new) were examined. For overall survival, the pooled hazard ratio (HR) across all nine trials was 0.77 (95% confidence interval [CI], 0.59-0.99; p=0.049). For disease-free survival, the pooled HR across seven trials reporting this outcome was 0.66 (95% CI, 0.45-0.91; p=0.014). This disease-free survival benefit was more apparent among those with positive nodal involvement (p=0.010).

CONCLUSIONS

This updated and improved meta-analysis of randomized trials provides further evidence of an overall survival and disease-free survival benefit in patients with MIBC receiving adjuvant cisplatin-based chemotherapy after radical cystectomy.