bims-tumhet 2022-10-16 papers

bims-tumhet

Biomed News

on Tumor Heterogeneity

Issue of 2022–10–16
eleven papers selected by
Sergio Marchini, Humanitas Research

Ovarian Cancer Therapy: Homologous Recombination Deficiency as a Predictive Biomarker of Response to PARP Inhibitors.
State of the Biomarker Science in Ovarian Cancer: A National Cancer Institute Clinical Trials Planning Meeting Report.
Deep-Learning to Predict BRCA Mutation and Survival from Digital H&E Slides of Epithelial Ovarian Cancer.
Mutations in circulating tumor DNA detected in the postoperative period predict poor survival in patients with ovarian cancer.
Liquid Biopsy and the Translational Bridge from the TIME to the Clinic.
Classification of High-Grade Serous Ovarian Cancer Using Tumor Morphologic Characteristics.
Post-Translational Modifications of cGAS-STING: A Critical Switch for Immune Regulation.
Clinical implications of T cell exhaustion for cancer immunotherapy.
Gene expression profiling of mucinous ovarian tumors and comparison with upper and lower gastrointestinal tumors identifies markers associated with adverse outcomes.
Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning.
Molecular Biomarkers for the Early Detection of Ovarian Cancer.

Onco Targets Ther. 2022 ;15 1105-1117

Ovarian Cancer Therapy: Homologous Recombination Deficiency as a Predictive Biomarker of Response to PARP Inhibitors.

Rowan E Miller, Osnat Elyashiv, Karim H El-Shakankery, Jonathan A Ledermann.

  Poly(adenosine diphosphate-ribose) polymerase (PARP) inhibitors have revolutionised the management of patients with high-grade serous and endometrioid ovarian cancer demonstrating significant improvements in progression-free survival. Whilst the greatest benefit is seen with BRCA1/2 mutant cancers, it is clear that the benefit extends beyond this group. This sensitivity is thought to be due to homologous recombination deficiency (HRD), which is present in up to 50% of the high-grade serous cancers. Several different HRD assays exist, which fall into one of three main categories: homologous recombination repair (HRR)-related gene analysis, genomic "scars" and/or mutational signatures, and real-time HRD functional assessment. We review the emerging data on HRD as a predictive biomarker for PARP inhibitors and discuss the merits and disadvantages of different HRD assays.

Keywords:  BRCA mutations; PARP inhibitors; homologous recombination deficiency; maintenance therapy; ovarian cancer

DOI:  https://doi.org/10.2147/OTT.S272199
JCO Precis Oncol. 2022 Oct;6 e2200355

State of the Biomarker Science in Ovarian Cancer: A National Cancer Institute Clinical Trials Planning Meeting Report.

Josee-Lyne Ethier, Katherine C Fuh, Rebecca Arend, Gottfried E Konecny, Panagiotis A Konstantinopoulos, Kunle Odunsi, Elizabeth M Swisher, Elise C Kohn, Dmitriy Zamarin.

PURPOSE: Despite therapeutic advances in the treatment of ovarian cancer (OC), 5-year survival remains low, and patients eventually die from recurrent, chemotherapy-resistant disease. The National Cancer Gynecologic Cancer Steering Committee identified the integration of scientifically defined subgroups as a top strategic priority in clinical trial planning.
METHODS: A group of experts was convened to review the scientific literature in OC to identify validated predictive biomarkers that could inform patient selection and treatment stratification. Here, we report on these findings and their potential for use in future clinical trial design on the basis of hierarchal evidence grading.
RESULTS: The biomarkers were classified on the basis of mechanistic targeting, including DNA repair and replication stress, immunotherapy and tumor microenvironment, oncogenic signaling, and angiogenesis. Currently, BRCA mutations and homologous recombination deficiency to predict poly (ADP-ribose) polymerase inhibitor response are supported in OC by the highest level of evidence. Additional biomarkers of response to agents targeting the pathways above have been identified but require prospective validation.
CONCLUSION: Although a number of biomarkers of response to various agents in OC have been described in the literature, high-level evidence for the majority is lacking. This report highlights the unmet need for identification and validation of predictive biomarkers to guide therapy and future trial design in OC.

DOI: https://doi.org/10.1200/PO.22.00355
Int J Mol Sci. 2022 Sep 26. pii: 11326. [Epub ahead of print]23(19):

Deep-Learning to Predict BRCA Mutation and Survival from Digital H&E Slides of Epithelial Ovarian Cancer.

Camilla Nero, Luca Boldrini, Jacopo Lenkowicz, Maria Teresa Giudice, Alessia Piermattei, Frediano Inzani, Tina Pasciuto, Angelo Minucci, Anna Fagotti, Gianfranco Zannoni, Vincenzo Valentini, Giovanni Scambia.

  BRCA 1/2 genes mutation status can already determine the therapeutic algorithm of high grade serous ovarian cancer patients. Nevertheless, its assessment is not sufficient to identify all patients with genomic instability, since BRCA 1/2 mutations are only the most well-known mechanisms of homologous recombination deficiency (HR-d) pathway, and patients displaying HR-d behave similarly to BRCA mutated patients. HRd assessment can be challenging and is progressively overcoming BRCA testing not only for prognostic information but more importantly for drugs prescriptions. However, HR testing is not already integrated in clinical practice, it is quite expensive and it is not refundable in many countries. Selecting patients who are more likely to benefit from this assessment (BRCA 1/2 WT patients) at an early stage of the diagnostic process, would allow an optimization of genomic profiling resources. In this study, we sought to explore whether somatic BRCA1/2 genes status can be predicted using computational pathology from standard hematoxylin and eosin histology. In detail, we adopted a publicly available, deep-learning-based weakly supervised method that uses attention-based learning to automatically identify sub regions of high diagnostic value to accurately classify the whole slide (CLAM). The same model was also tested for progression free survival (PFS) prediction. The model was tested on a cohort of 664 (training set: n = 464, testing set: n = 132) ovarian cancer patients, of whom 233 (35.1%) had a somatic BRCA 1/2 mutation. An area under the curve of 0.7 and 0.55 was achieved in the training and testing set respectively. The model was then further refined by manually identifying areas of interest in half of the cases. 198 images were used for training (126/72) and 87 images for validation (55/32). The model reached a zero classification error on the training set, but the performance was 0.59 in terms of validation ROC AUC, with a 0.57 validation accuracy. Finally, when applied to predict PFS, the model achieved an AUC of 0.71, with a negative predictive value of 0.69, and a positive predictive value of 0.75. Based on these analyses, we have planned further steps of development such as proving a reference classification performance, exploring the hyperparameters space for training optimization, eventually tweaking the learning algorithms and the neural networks architecture for better suiting this specific task. These actions may allow the model to improve performances for all the considered outcomes.

Keywords:  artificial intelligence; digital pathology; machine learning; ovarian cancer; somatic BRCA mutational status

DOI:  https://doi.org/10.3390/ijms231911326
Biomed J. 2022 Oct 05. pii: S2319-4170(22)00138-X. [Epub ahead of print]

Mutations in circulating tumor DNA detected in the postoperative period predict poor survival in patients with ovarian cancer.

Angel Chao, Shu-Jen Chen, Hua-Chien Chen, Kien Thiam Tan, Wen Hsiao, Shih-Ming Jung, Lan-Yan Yang, Kuan-Gen Huang, Hung-Hsueh Chou, Huei-Jean Huang, Ting-Chang Chang, An-Shine Chao, Yun-Hsien Lee, Ren-Chin Wu, Chyong-Huey Lai.

BACKGROUND: We investigated whether mutations in plasma circulating tumor DNA (ctDNA) can provide prognostic insight in patients with different histological types of ovarian carcinoma. We also examined the concordance of mutations detected in ctDNA samples with those identified in the corresponding formalin-fixed paraffin-embedded (FFPE) tumor specimens.
METHODS: Between July 2016 and December 2017, 29 patients with ovarian carcinoma were prospectively enrolled. FFPE tumor specimens were obtained from all participants. A total of 187 blood samples for ctDNA analysis were collected before surgery (C0), immediate after surgery before adjuvant chemotherapy (C1), and at six-month intervals. Progression-free survival (PFS) and overall survival (OS) served as the main outcome measures.
RESULTS: The study cohort consisted of 13 (44.8%) patients with high-grade serous carcinomas (HGSC), 9 (31.0%) with clear cell carcinoma, 2 (6.9%) with mucinous carcinomas, 4 (13.8%) with low-grade serous carcinomas, and 1 (3.4%) with endometrioid carcinoma. Twenty-four (82.8%) patients had at least one detectable ctDNA variant. The concordance rate between mutations identified in pretreatment ctDNA and corresponding FFPE tumor specimens was 92.3% for patients with HGSC and 58.6% for the entire cohort. The median follow-up time was 33.15 months (range: 0.79-46.13 months). Patients with an advanced stage disease more likely had detectable ctDNA mutations before surgery (C0) and after surgery at C1, while those with HGSC more likely had ctDNA mutations detected before surgery. The presence of ctDNA mutations at C1 was an independent predictor of worse OS with a hazard ratio of 6.56 (95% confidence interval, 1.07-40.17) for detectable versus undetectable C1 ctDNA variants, p = 0.042).
CONCLUSIONS: ctDNA mutations are common in patients with ovarian carcinoma. The presence of ctDNA mutations after surgery was an independent predictor of less favorable PFS and OS.

DOI: https://doi.org/10.1016/j.bj.2022.09.004
Cells. 2022 Oct 03. pii: 3114. [Epub ahead of print]11(19):

Liquid Biopsy and the Translational Bridge from the TIME to the Clinic.

Paul Walker.

  Research and advancing understanding of the tumor immune microenvironment (TIME) is vital to optimize and direct more effective cancer immune therapy. Pre-clinical bench research is vital to better understand the genomic interplay of the TIME and immune therapy responsiveness. However, a vital key to effective translational cancer research is having a bridge of translation to bring that understanding from the bench to the bedside. Without that bridge, research into the TIME will lack an efficient and effective translation into the clinic and cancer treatment decision making. As a clinical oncologist, the purpose of this commentary is to emphasize the importance of researching and improving clinical utility of the bridge, as well as the TIME research itself.

Keywords:  liquid biopsy; tumor immune microenvironment

DOI:  https://doi.org/10.3390/cells11193114
JAMA Netw Open. 2022 Oct 03. 5(10): e2236626

Classification of High-Grade Serous Ovarian Cancer Using Tumor Morphologic Characteristics.

Katelyn F Handley, Travis T Sims, Nicholas W Bateman, Deanna Glassman, Katherine I Foster, Sanghoon Lee, Jun Yao, Hui Yao, Bryan M Fellman, Jinsong Liu, Zhen Lu, Kelly A Conrads, Brian L Hood, Waleed Barakat, Li Zhao, Jianhua Zhang, Shannon N Westin, Joseph Celestino, Kelly M Rangel, Sunil Badal, Igor Pereira, Prahlad T Ram, George L Maxwell, Livia S Eberlin, P Andrew Futreal, Robert C Bast, Nicole D Fleming, Thomas P Conrads, Anil K Sood.

Importance: Despite similar histologic appearance among high-grade serous ovarian cancers (HGSOCs), clinical observations suggest vast differences in gross appearance. There is currently no systematic framework by which to classify HGSOCs according to their gross morphologic characteristics.
Objective: To develop and characterize a gross morphologic classification system for HGSOC.
Design, Setting, and Participants: This cohort study included patients with suspected advanced-stage ovarian cancer who presented between April 1, 2013, and August 5, 2016, to the University of Texas MD Anderson Cancer Center, a large referral center. Patients underwent laparoscopic assessment of disease burden before treatment and received a histopathologic diagnosis of HGSOC. Researchers assigning morphologic subtype and performing molecular analyses were blinded to clinical outcomes. Data analysis was performed between April 2020 and November 2021.
Exposures: Gross tumor morphologic characteristics.
Main Outcomes and Measures: Clinical outcomes and multiomic profiles of representative tumor samples of type I or type II morphologic subtypes were compared.
Results: Of 112 women (mean [SD] age 62.7 [9.7] years) included in the study, most patients (84% [94]) exhibited a predominant morphologic subtype and many (63% [71]) had a uniform morphologic subtype at all involved sites. Compared with those with uniform type I morphologic subtype, patients with uniform type II morphologic subtype were more likely to have a favorable Fagotti score (83% [19 of 23] vs 46% [22 of 48]; P = .004) and thus to be triaged to primary tumor reductive surgery. Similarly, patients with uniform type II morphologic subtype also had significantly higher mean (SD) estimated blood loss (639 [559; 95% CI, 391-887] mL vs 415 [527; 95% CI, 253-577] mL; P = .006) and longer mean (SD) operative time (408 [130; 95% CI, 350-466] minutes vs 333 [113; 95% CI, 298-367] minutes; P = .03) during tumor reductive surgery. Type I tumors had enrichment of epithelial-mesenchymal transition (false discovery rate [FDR] q-value, 3.10 × 10-24), hypoxia (FDR q-value, 1.52 × 10-5), and angiogenesis pathways (FDR q-value, 2.11 × 10-2), whereas type II tumors had enrichment of pathways related to MYC signaling (FDR q-value, 2.04 × 10-9) and cell cycle progression (FDR q-value, 1.10 × 10-5) by integrated proteomic and transcriptomic analysis. Abundances of metabolites and lipids also differed between the 2 morphologic subtypes.
Conclusions and Relevance: This study identified 2 novel, gross morphologic subtypes of HGSOC, each with unique clinical features and molecular signatures. The findings may have implications for triaging patients to surgery or chemotherapy, identifying outcomes, and developing tailored therapeutic strategies.

DOI: https://doi.org/10.1001/jamanetworkopen.2022.36626
Cells. 2022 Sep 28. pii: 3043. [Epub ahead of print]11(19):

Post-Translational Modifications of cGAS-STING: A Critical Switch for Immune Regulation.

Yang Yu, Jingyang Liu, Cun Liu, Ruijuan Liu, Lijuan Liu, Zhenhai Yu, Jing Zhuang, Changgang Sun.

  Innate immune mechanisms initiate immune responses via pattern-recognition receptors (PRRs). Cyclic GMP-AMP synthase (cGAS), a member of the PRRs, senses diverse pathogenic or endogenous DNA and activates innate immune signaling pathways, including the expression of stimulator of interferon genes (STING), type I interferon, and other inflammatory cytokines, which, in turn, instructs the adaptive immune response development. This groundbreaking discovery has rapidly advanced research on host defense, cancer biology, and autoimmune disorders. Since cGAS/STING has enormous potential in eliciting an innate immune response, understanding its functional regulation is critical. As the most widespread and efficient regulatory mode of the cGAS-STING pathway, post-translational modifications (PTMs), such as the covalent linkage of functional groups to amino acid chains, are generally considered a regulatory mechanism for protein destruction or renewal. In this review, we discuss cGAS-STING signaling transduction and its mechanism in related diseases and focus on the current different regulatory modalities of PTMs in the control of the cGAS-STING-triggered innate immune and inflammatory responses.

Keywords:  cGAS-STING; dsDNA sensing; innate immunity; post-translational modification; type I interferons

DOI:  https://doi.org/10.3390/cells11193043
Nat Rev Clin Oncol. 2022 Oct 10.

Clinical implications of T cell exhaustion for cancer immunotherapy.

Andrew Chow, Karlo Perica, Christopher A Klebanoff, Jedd D Wolchok.

Immunotherapy has been a remarkable clinical advancement in the treatment of cancer. T cells are pivotal to the efficacy of current cancer immunotherapies, including immune-checkpoint inhibitors and adoptive cell therapies. However, cancer is associated with T cell exhaustion, a hypofunctional state characterized by progressive loss of T cell effector functions and self-renewal capacity. The 'un-exhausting' of T cells in the tumour microenvironment is commonly regarded as a key mechanism of action for immune-checkpoint inhibitors, and T cell exhaustion is considered a pathway of resistance for cellular immunotherapies. Several elegant studies have provided important insights into the transcriptional and epigenetic programmes that govern T cell exhaustion. In this Review, we highlight recent discoveries related to the immunobiology of T cell exhaustion that offer a more nuanced perspective beyond this hypofunctional state being entirely undesirable. We review evidence that T cell exhaustion might be as much a reflection as it is the cause of poor tumour control. Furthermore, we hypothesize that, in certain contexts of chronic antigen stimulation, interruption of the exhaustion programme might impair T cell persistence. Therefore, the prioritization of interventions that mitigate the development of T cell exhaustion, including orthogonal cytoreduction therapies and novel cellular engineering strategies, might ultimately confer superior clinical outcomes and the greatest advances in cancer immunotherapy.

DOI: https://doi.org/10.1038/s41571-022-00689-z
Clin Cancer Res. 2022 Oct 12. pii: CCR-22-1206. [Epub ahead of print]

Gene expression profiling of mucinous ovarian tumors and comparison with upper and lower gastrointestinal tumors identifies markers associated with adverse outcomes.

Nicola S Meagher, Kylie L Gorringe, Matthew J Wakefield, Adelyn Bolithon, Chi Nam Ignatius Pang, Derek S Chiu, Michael S Anglesio, Kylie-Ann Mallitt, Jennifer A Doherty, Holly R Harris, Joellen M Schildkraut, Andrew Berchuck, Kara L Cushing-Haugen, Ksenia Chezar, Angela Chou, Adeline Tan, Jennifer Alsop, Ellen Barlow, Matthias W Beckmann, Jessica Boros, David D Bowtell, Alison H Brand, James D Brenton, Ian Campbell, Dane Cheasley, Joshua Cohen, Cezary Cybulski, Esther Elishaev, Ramona Erber, Rhonda Farrell, Anna Fischer, Zhuxuan Fu, Blake Gilks, Anthony J Gill, Charlie Gourley, Marcel Grube, Paul Harnett, Arndt Hartmann, Anusha Hettiaratchi, Claus K Høgdall, Tomasz Huzarski, Anna Jakubowska, Mercedes Jimenez-Linan, Catherine J Kennedy, Byoung-Gie Kim, Jae-Weon Kim, Jae-Hoon Kim, Kayla Klett, Jennifer Koziak, Tiffany Lai, Angela Laslavic, Jenny Lester, Yee Leung, Na Li, Winston Liauw, Belle W X Lim, Anna Linder, Jan Lubinski, Sakshi Mahale, Constantina Mateoiu, Simone McInerny, Janusz Menkiszak, Parham Minoo, Suzana Mittelstadt, David Morris, Sandra Orsulic, Sang Yoon Park, Celeste Leigh Pearce, John V Pearson, Malcolm C Pike, Carmel M Quinn, Ganendra Raj Mohan, JianYu Rao, Marjorie J Riggan, Matthias Ruebner, Stuart Salfinger, Clare L Scott, Mitul Shah, Helen Steed, Colin J R Stewart, Deepak Subramanian, Soseul Sung, Katrina Tang, Paul Timpson, Robyn L Ward, Rebekka Wiedenhoefer, Heather Thorne, Paul A Cohen, Philip Crowe, Peter A Fasching, Jacek Gronwald, Nicholas J Hawkins, Estrid Høgdall, David G Huntsman, Paul A James, Beth Y Karlan, Linda E Kelemen, Stefan Kommoss, Gottfried E Konecny, Francesmary Modugno, Sue K Park, Annette Staebler, Karin Sundfeldt, Anna H Wu, Aline Talhouk, Paul D P Pharoah, Lyndal Anderson, Anna DeFazio, Martin Köbel, Michael L Friedlander, Susan J Ramus.

PURPOSE: Advanced stage MOC have poor chemotherapy response and prognosis and lack biomarkers to aid Stage I adjuvant treatment. Differentiating primary mucinous ovarian carcinoma (MOC) from gastrointestinal (GI) metastases to the ovary is also challenging due to phenotypic similarities. Clinicopathological and gene expression data were analysed to identify prognostic and diagnostic features.
EXPERIMENTAL DESIGN: Discovery analyses selected 19 genes with prognostic/diagnostic potential. Validation was performed through the Ovarian Tumor Tissue Analysis consortium and GI cancer biobanks comprising 604 patients with MOC (n=333), mucinous borderline ovarian tumors (MBOT, n=151), upper GI (n=65), and lower GI tumors (n=55).
RESULTS: Infiltrative pattern of invasion was associated with decreased overall survival (OS) within 2-years from diagnosis, compared with expansile pattern in Stage I MOC (hazard ratio HR 2.77 (1.04-7.41, p=0.042). Increased expression of THBS2 and TAGLN were associated with shorter OS in MOC patients, (HR 1.25 (95% CI 1.04-1.51, p=0.016)) and (1.21 (1.01-1.45, p=0.043)) respectively. ERBB2 (HER2)-amplification or high mRNA expression was evident in 64/243 (26%) of MOCs, but only 8/243 (3%) were also infiltrative (4/39, 10%) or Stage III/IV (4/31, 13%).
CONCLUSIONS: An infiltrative growth pattern infers poor prognosis within 2-years from diagnosis and may help select Stage I patients for adjuvant therapy. High expression of THBS2 and TAGLN in MOC confer an adverse prognosis and is upregulated in the infiltrative subtype which warrants further investigation. Anti-HER2 therapy should be investigated in a subset of patients. MOC samples clustered with upper GI, yet markers to differentiate these entities remain elusive, suggesting similar underlying biology and shared treatment strategies.

DOI: https://doi.org/10.1158/1078-0432.CCR-22-1206
Nat Commun. 2022 Oct 10. 13(1): 5962

Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning.

Chunman Zuo, Yijian Zhang, Chen Cao, Jinwang Feng, Mingqi Jiao, Luonan Chen.

Spatially resolved transcriptomics (SRT) technology enables us to gain novel insights into tissue architecture and cell development, especially in tumors. However, lacking computational exploitation of biological contexts and multi-view features severely hinders the elucidation of tissue heterogeneity. Here, we propose stMVC, a multi-view graph collaborative-learning model that integrates histology, gene expression, spatial location, and biological contexts in analyzing SRT data by attention. Specifically, stMVC adopting semi-supervised graph attention autoencoder separately learns view-specific representations of histological-similarity-graph or spatial-location-graph, and then simultaneously integrates two-view graphs for robust representations through attention under semi-supervision of biological contexts. stMVC outperforms other tools in detecting tissue structure, inferring trajectory relationships, and denoising on benchmark slices of human cortex. Particularly, stMVC identifies disease-related cell-states and their transition cell-states in breast cancer study, which are further validated by the functional and survival analysis of independent clinical data. Those results demonstrate clinical and prognostic applications from SRT data.

DOI: https://doi.org/10.1038/s41467-022-33619-9
Int J Mol Sci. 2022 Oct 10. pii: 12041. [Epub ahead of print]23(19):

Molecular Biomarkers for the Early Detection of Ovarian Cancer.

Ruiqian Zhang, Michelle K Y Siu, Hextan Y S Ngan, Karen K L Chan.

  Ovarian cancer is the deadliest gynecological cancer, leading to over 152,000 deaths each year. A late diagnosis is the primary factor causing a poor prognosis of ovarian cancer and often occurs due to a lack of specific symptoms and effective biomarkers for an early detection. Currently, cancer antigen 125 (CA125) is the most widely used biomarker for ovarian cancer detection, but this approach is limited by a low specificity. In recent years, multimarker panels have been developed by combining molecular biomarkers such as human epididymis secretory protein 4 (HE4), ultrasound results, or menopausal status to improve the diagnostic efficacy. The risk of ovarian malignancy algorithm (ROMA), the risk of malignancy index (RMI), and OVA1 assays have also been clinically used with improved sensitivity and specificity. Ongoing investigations into novel biomarkers such as autoantibodies, ctDNAs, miRNAs, and DNA methylation signatures continue to aim to provide earlier detection methods for ovarian cancer. This paper reviews recent advancements in molecular biomarkers for the early detection of ovarian cancer.

Keywords:  CA125; HE4; RMI; ROMA; molecular biomarkers; ovarian cancer

DOI:  https://doi.org/10.3390/ijms231912041