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Predicting and treating recurrence in intermediate-risk prostate cancer patients remains a challenge despite having identified genomic instability (1) and hypoxia (2,3) as risk factors. This underlies challenges in assigning the functional impact of these risk factors to mechanisms promoting prostate cancer progression. Here we show chronic hypoxia (CH), as observed in prostate tumours (4), leads to the adoption of an androgen-independent state in prostate cancer cells. Specifically, CH results in prostate cancer cells adopting transcriptional and metabolic alterations typical of castration-resistant prostate cancer cells. These changes include increased expression of transmembrane transporters for the methionine cycle and related pathways leading to increased abundance of metabolites and expression of enzymes related to glycolysis. Targeting the Glucose Transporter 1 (GLUT1) identified a dependency on glycolysis in androgen-independent cells. Overall, we identified a therapeutically targetable weakness in chronic hypoxia and androgen-independent prostate cancer. These findings may offer additional strategies for treatment development against hypoxic prostate cancer.

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Supplementary methods. Supplementary Figures 1-9

Supplementary text S1. Supplementary Figure 1: Chromatin Profiles of Primary GC. Supplementary Figure 2: Epithelial features of GC promoters. Supplementary Figure 3: GC Somatic Promoter Features. Supplementary Figure 4: Association of Somatic Promoters with Gene Expression in GC and Other Tumor Types. Supplementary Figure 5: Changes in DNA methylation at CpG island containing promoters. Supplementary Figure 6: Expression distribution of alternative and canonical isoforms. Supplementary Figure 7: Characterization of RASA3 Isoform. Supplementary Figure 8: Characterization of MET Isoforms. Supplementary Figure 9: Immunogenicity of N-terminal peptides. Supplementary Figure 10: Immunogenicity Assay and Nanostring Profiling. Supplementary Figure 11: Functional Assessment of Peptide Immunogenicity. Supplementary Figure 12: EZH2 Inhibition. Supplementary Figure 13: Unannotated somatic promoters.

Background: In contrast to the IMpassion130 trial evaluating atezo + nab-pac [Schmid, NEJM 2018], the randomized phase 3 IMpassion131 trial (NCT03125902) did not demonstrate significantly improved progression-free survival (PFS; primary endpoint) and showed no improvement in overall survival (OS; secondary endpoint) with the addition of atezo to pac as first-line therapy for mTNBC in either the PD-L1+ or the intention-to-treat (ITT) population [Miles, Ann Oncol 2021]. In IMpassion130, enhanced PFS and OS improvement with atezo + nab-pac were seen in the basal-like immune-activated (BLIA) subtype, whereas potential resistance to atezo + nab-pac was observed in the luminal androgen receptor (LAR) subtype [Emens, ASCO 2021]. LAR may be more prevalent in Asian populations [Ding, Oncotarget 2019], which represented one-third of patients enrolled in IMpassion131 and could have influenced the overall result. PFS outcomes were numerically worse in Asian vs non-Asian subgroups in IMpassion131 [Miles, Ann Oncol 2021]. To investigate potential reasons for observing different effects in IMpassion130 and IMpassion131, we explored the prevalence and impact on clinical outcomes of TNBC molecular subtypes and race in IMpassion131. Patients and Methods: Patients with mTNBC (no prior systemic therapy or ≥12 months since [neo]adjuvant chemotherapy) were randomized 2:1 to atezolizumab 840 mg or placebo (days 1, 15), both with paclitaxel 90 mg/m2 (days 1, 8, 15), every 28 days until disease progression or unacceptable toxicity, stratified by tumor PD-L1 status, prior taxane, liver metastases, and geographic region. Molecular subtypes according to Burstein classification were determined by RNA sequencing (Illumina TruSeq RNA Access) of baseline tumor samples. Prevalence of Burstein molecular subtypes [Burstein, Clin Cancer Res 2015] was assessed in Asian (of whom 69% were enrolled in China) and non-Asian populations; clinical outcomes were assessed according to molecular subtype and race using Cox proportional hazards analysis. All analyses were performed using the final data cut-off (Sep 4, 2020; median follow-up duration: 14.4 months). Results: The biomarker-evaluable population (BEP; n=471) was representative of the ITT population (n=651) with respect to baseline characteristics and PFS hazard ratio (HR; 0.75 [95% CI 0.61-0.92] vs 0.81 [95% CI 0.68-0.96], respectively). Distribution of molecular subtypes in the BEP was: 30% BLIA, 41% basal-like immune suppressed (BLIS), 24% LAR, 5% mesenchymal (MES), similar to IMpassion130. Among the BLIA samples, 82% were PD-L1+ and 18% PD-L1-; corresponding percentages were 41% vs 59% for BLIS, 31% vs 69% for LAR, and 32% vs 68% for MES. Compared with non-Asian patients, the Asian subgroup (n=117) included more LAR (31% vs 22%) and fewer MES (1% vs 6%) samples; this was particularly pronounced in the cohort enrolled in China (n=79; 37% LAR, 0% MES). PFS was improved with atezo + pac in the BLIA subtype (HR 0.66, 95% CI 0.45-0.97). None of the Burstein subgroups derived OS benefit from atezo + pac. Findings were similar irrespective of PD-L1 status. Direction of effect for PFS and OS favored the placebo + pac arm in the LAR Asian subgroup (n=30). Conclusion: In these exploratory analyses, the distribution of molecular subtypes and enhanced effect of atezo + pac in the BLIA subtype are consistent with findings from IMpassion130. The lack of improved efficacy with the combination of atezo + pac in the IMpassion131 trial cannot be explained by overrepresentation of a Burstein subtype less sensitive to atezo in the trial population. Citation Format: Fabrice André, Regula Deurloo, Aditi Qamra, David Cameron, Joseph Gligorov, Andreas Schneeweiss, Carlos Barrios, Binghe Xu, Ching-Wei Chang, Luciana Molinero, Shilpen Patel, Andrea Liptrot, Leilani Morales, David Miles, Joyce O’Shaughnessy. Activity of atezolizumab (atezo) plus paclitaxel (pac) in metastatic triple-negative breast cancer (mTNBC) according to Burstein molecular subtype: Analysis of the IMpassion131 trial [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD10-05.

High interleukin (IL)-6 levels are associated with more severe clinical manifestations in patients hospitalized with COVID-19, but the complex role of IL-6 in antiviral and inflammatory processes has made it difficult to decipher its involvement in the disease. IL-6 receptor blockade by tocilizumab (anti-IL6R; Actemra) is used globally for the treatment of severe COVID-19, yet a molecular understanding of the therapeutic benefit remains unclear. We characterized the immune profile and identified cellular and molecular pathways directly modified by tocilizumab in peripheral blood samples collected from patients enrolled in the COVACTA study, a phase 3, randomized, double-blind, placebo-controlled trial that assessed the efficacy and safety of tocilizumab in hospitalized patients with severe COVID-19 pneumonia. We identified factors predicting disease severity and clinical outcomes, including markers of inflammation, lymphopenia, myeloid dysregulation, and organ injury. Proteomic analysis confirmed a pharmacodynamic effect for tocilizumab in addition to identifying novel pharmacodynamic biomarkers. Transcriptomic analysis revealed that tocilizumab treatment leads to faster resolution of lymphopenia and myeloid dysregulation associated with severe COVID-19, indicating greater anti-inflammatory activity relative to standard of care and potentially leading to faster recovery in patients hospitalized with COVID-19.One sentence summaryInterleukin-6 receptor blockade with tocilizumab accelerated resolution of myeloid dysfunction and lymphopenia in patients hospitalized with COVID-19

Background: In recent years, a relationship between the tumor microenvironment (TME) and patient response to targeted cancer immunotherapy has been suggested. We applied machine-learning algorithms on H&E stained tissue to study the TME in metastatic non-small cell lung cancer (NSCLC) patients. Our goal was to identify digital pathology (DP) features associated with outcome under combination treatment or monotherapy with atezolizumab (atezo), an anti-PD-L1 therapy, and relate those features to other data modalities. We analyzed patient data from two phase 3 clinical trials, OAK (docetaxel versus atezo in 2L+ NSCLC) and IMpower150 (bevacizumab, carboplatin, and paclitaxel (BCP) versus BCP+atezo (ABCP) in advanced 1L non-squamous NSCLC). Methods: As part of our effort to build a DP-based tumor-immune microenvironment atlas, digitized H&E images were registered onto the PathAI research platform. Over 200K annotations from 90 pathologists were used to train convolutional neural networks (CNNs) that classify slide-level human-interpretable features (HIFs) of cells and tissue structures from images and deployed on images from OAK and IMpower150. HIFs and PD-L1 status were associated with outcome in all samples in each arm in OAK and results were validated in IMpower150, using Cox proportional hazard models. Bulk RNAseq was run using samples extracted from the same area as the H&E slide. Results: We identified a composite feature capturing the ratio of immune cells to fibroblasts in the stroma predictive of both overall survival (OS) (HR=0.74 p=0.0046) and progression-free survival (PFS) (HR=0.87 p=0.14). While patients primarily benefit from atezo if they are PD-L1 high, we found that even PD-L1 negative patients benefited from atezo when enriched for this feature (22C3 PD-L1 assay: OS HR=0.59 p=0.015, PFS HR=0.8 p=0.25; SP142 PD-L1 assay: OS HR=0.74 p=0.12, PFS HR=0.88 p=0.45). We thus recognized a DP feature that was predictive for positive outcome with atezo treatment, independent of PD-L1 levels. This association was then validated in IMpower150 comparing ABCP to BCP, both overall (OS HR=0.69 p=0.012) and in PD-L1 negative patients (SP263 assay OS HR=0.56 p=0.034). Integrating with RNAseq, patients enriched for this DP feature showed similar enrichment for B and T gene signatures and depletion in CAF-related gene signatures, thus showing the harmonization of TME between different data modalities. Conclusions: Using a deep learning-based assay for quantifying pathology features of the TME from H&E images in two NSCLC trials, we identified a novel biomarker predictive of outcome to PD-L1 targeting therapy, even in PD-L1 low & negative patients. Importantly, our work shows how different data modalities (DP, gene expression) can be integrated to further our understanding of the TME. Citation Format: Aditi Qamra, Minu K. Srivastava, Eloisa Fuentes, Ben Trotter, Raymond Biju, Guillaume Chhor, James Cowan, Steven Gendreau, Webster Lincoln, Lisa McGinnis, Luciana Molinero, Namrata S. Patil, Amber Schedlbauer, Katja Schulze, Adam Stanford-Moore, Laura Chambre, Ilan Wapinski, David S. Shames, Hartmut Koeppen, Stephanie Hennek, Jane Fridlyand, Jennifer M. Giltnane, Assaf Amitai. Digital pathology based prognostic & predictive biomarkers in metastatic non-small cell lung cancer. [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 5705.