1
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Ajayi T, Hosseinian S, Schaefer AJ, Fuller CD. Combination Chemotherapy Optimization with Discrete Dosing. INFORMS JOURNAL ON COMPUTING 2024; 36:434-455. [PMID: 38883557 PMCID: PMC11178284 DOI: 10.1287/ijoc.2022.0207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
Chemotherapy drug administration is a complex problem that often requires expensive clinical trials to evaluate potential regimens; one way to alleviate this burden and better inform future trials is to build reliable models for drug administration. This paper presents a mixed-integer program for combination chemotherapy (utilization of multiple drugs) optimization that incorporates various important operational constraints and, besides dose and concentration limits, controls treatment toxicity based on its effect on the count of white blood cells. To address the uncertainty of tumor heterogeneity, we also propose chance constraints that guarantee reaching an operable tumor size with a high probability in a neoadjuvant setting. We present analytical results pertinent to the accuracy of the model in representing biological processes of chemotherapy and establish its potential for clinical applications through a numerical study of breast cancer.
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Affiliation(s)
| | | | - Andrew J. Schaefer
- Department of Computational Applied Mathematics and Operations Research, Rice University, Houston, Texas 77005
| | - Clifton D. Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030
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2
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Steinberg PL, Liu LY, Neiman-Golden A, Patel Y, Boutros PC. Quantifying the seed sensitivity of cancer subclonal reconstruction algorithms. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.05.579021. [PMID: 38370678 PMCID: PMC10871259 DOI: 10.1101/2024.02.05.579021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Background Intra-tumoural heterogeneity complicates cancer prognosis and impairs treatment success. One of the ways subclonal reconstruction (SRC) quantifies intra-tumoural heterogeneity is by estimating the number of subclones present in bulk DNA sequencing data. SRC algorithms are probabilistic and need to be initialized by a random seed. However, the seeds used in bioinformatics algorithms are rarely reported in the literature. Thus, the impact of the initializing seed on SRC solutions has not been studied. To address this gap, we generated a set of ten random seeds to systematically benchmark the seed sensitivity of three probabilistic SRC algorithms: PyClone-VI, DPClust, and PhyloWGS. Results We characterized the seed sensitivity of three algorithms across fourteen whole-genome sequences of head and neck squamous cell carcinoma and nine SRC pipelines, each composed of a single nucleotide variant caller, a copy number aberration caller and an SRC algorithm. This led to a total of 1470 subclonal reconstructions, including 1260 single-region and 210 multi-region reconstructions. The number of subclones estimated per patient vary across SRC pipelines, but all three SRC algorithms show substantial seed sensitivity: subclone estimates vary across different seeds for the same set of input using the same SRC algorithm. No seed consistently estimated the mode number of subclones across all patients for any SRC algorithm. Conclusions These findings highlight the variability in quantifying intra-tumoural heterogeneity introduced by the seed sensitivity of probabilistic SRC algorithms. We recommend that authors, reviewers and editors adopt guidelines to both report and randomize seed choices. It may also be valuable to consider seed-sensitivity in the benchmarking of newly developed SRC algorithms. These findings may be of interest in other areas of bioinformatics where seeded probabilistic algorithms are used and suggest consideration of formal seed reporting standards to enhance reproducibility.
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Affiliation(s)
- Philippa L. Steinberg
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Jonsson Comprehensive Cancer Centre, University of California, Los Angeles, Los Angeles, CA, 90024, USA
- Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Lydia Y. Liu
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Jonsson Comprehensive Cancer Centre, University of California, Los Angeles, Los Angeles, CA, 90024, USA
- Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 1L7, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, M5G 2C1, Canada
| | - Anna Neiman-Golden
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Jonsson Comprehensive Cancer Centre, University of California, Los Angeles, Los Angeles, CA, 90024, USA
- Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Yash Patel
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Jonsson Comprehensive Cancer Centre, University of California, Los Angeles, Los Angeles, CA, 90024, USA
- Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Paul C. Boutros
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Jonsson Comprehensive Cancer Centre, University of California, Los Angeles, Los Angeles, CA, 90024, USA
- Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Urology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
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3
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Gram SB, Alosi D, Bagger FO, Østrup O, von Buchwald C, Friborg J, Wessel I, Vogelius IR, Rohrberg K, Rasmussen JH. Clinical implication of genetic intratumor heterogeneity for targeted therapy in head and neck cancer. Acta Oncol 2023; 62:1831-1839. [PMID: 37902999 DOI: 10.1080/0284186x.2023.2272293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 10/15/2023] [Indexed: 11/01/2023]
Abstract
BACKGROUND Genomic profiling is increasingly used both in therapeutic decision-making and as inclusion criteria for trials testing targeted therapies. However, the mutational landscape may vary across different areas of a tumor and intratumor heterogeneity will challenge treatments or clinical decisions based on single tumor biopsies. The purpose of this study was to assess the clinical relevance of genetic intratumor heterogeneity in head and neck squamous cell carcinomas (HNSCC) using the ESMO Scale for Clinical Actionability of Molecular Targets (ESCAT). MATERIALS AND METHODS This prospective study included 33 whole tumor specimens from 28 patients with primary or recurrent HNSCC referred for surgery. Three tumor blocks were selected from central, semi-peripheral, and peripheral positions, mimicking biopsies in three different locations. Genetic analysis of somatic copy number alterations (SCNAs) was performed on the three biopsies using Oncoscan, focusing on 45 preselected HNSCC genes of interest. Clinical relevance was assessed using the ESCAT score to investigate whether and how treatment decisions would change based on the three biopsies from the same tumor. RESULTS The SCNAs identified among 45 preselected genes within the three tumor biopsies derived from the same tumor revealed distinct variations. The detected discrepancies could potentially influence treatment approaches or clinical decisions in 36% of the patients if only one tumor biopsy was used. Recurrent tumors exhibited significantly higher variation in SCNAs than primary tumors (p = .024). No significant correlation between tumor size and heterogeneity (p = .7) was observed. CONCLUSION In 36% of patients diagnosed with HNSCC, clinically significant intratumor heterogeneity was observed which may have implications for patient management. This finding substantiates the need for future studies that specifically investigate the clinical implications associated with intratumor heterogeneity.
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Affiliation(s)
- Signe Buhl Gram
- Department of Otorhinolaryngology, Head and Neck Surgery and Audiology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Daniela Alosi
- Center for Genomic Medicine, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Frederik Otzen Bagger
- Center for Genomic Medicine, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Olga Østrup
- Center for Genomic Medicine, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Christian von Buchwald
- Department of Otorhinolaryngology, Head and Neck Surgery and Audiology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Jeppe Friborg
- Department of Oncology, Section of Radiotherapy, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Irene Wessel
- Department of Otorhinolaryngology, Head and Neck Surgery and Audiology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Ivan Richter Vogelius
- Department of Oncology, Section of Radiotherapy, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Kristoffer Rohrberg
- Department of Oncology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Jacob Høygaard Rasmussen
- Department of Otorhinolaryngology, Head and Neck Surgery and Audiology, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
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4
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Intratumor graph neural network recovers hidden prognostic value of multi-biomarker spatial heterogeneity. Nat Commun 2022; 13:4250. [PMID: 35869055 PMCID: PMC9307796 DOI: 10.1038/s41467-022-31771-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 07/01/2022] [Indexed: 11/08/2022] Open
Abstract
AbstractBiomarkers are indispensable for precision medicine. However, focused single-biomarker development using human tissue has been complicated by sample spatial heterogeneity. To address this challenge, we tested a representation of primary tumor that synergistically integrated multiple in situ biomarkers of extracellular matrix from multiple sampling regions into an intratumor graph neural network. Surprisingly, the differential prognostic value of this computational model over its conventional non-graph counterpart approximated that of combined routine prognostic biomarkers (tumor size, nodal status, histologic grade, molecular subtype, etc.) for 995 breast cancer patients under a retrospective study. This large prognostic value, originated from implicit but interpretable regional interactions among the graphically integrated in situ biomarkers, would otherwise be lost if they were separately developed into single conventional (spatially homogenized) biomarkers. Our study demonstrates an alternative route to cancer prognosis by taping the regional interactions among existing biomarkers rather than developing novel biomarkers.
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5
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van den Bosch T, Vermeulen L, Miedema DM. Quantitative models for the inference of intratumor heterogeneity. COMPUTATIONAL AND SYSTEMS ONCOLOGY 2022. [DOI: 10.1002/cso2.1034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Affiliation(s)
- Tom van den Bosch
- Laboratory for Experimental Oncology and Radiobiology Center for Experimental and Molecular Medicine Cancer Center Amsterdam and Amsterdam Gastroenterology and Metabolism Amsterdam University Medical Centers Amsterdam The Netherlands
- Oncode Institute Amsterdam The Netherlands
| | - Louis Vermeulen
- Laboratory for Experimental Oncology and Radiobiology Center for Experimental and Molecular Medicine Cancer Center Amsterdam and Amsterdam Gastroenterology and Metabolism Amsterdam University Medical Centers Amsterdam The Netherlands
- Oncode Institute Amsterdam The Netherlands
| | - Daniël M. Miedema
- Laboratory for Experimental Oncology and Radiobiology Center for Experimental and Molecular Medicine Cancer Center Amsterdam and Amsterdam Gastroenterology and Metabolism Amsterdam University Medical Centers Amsterdam The Netherlands
- Oncode Institute Amsterdam The Netherlands
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6
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Yaung SJ, Ju C, Gattam S, Nicholas A, Sommer N, Bendell JC, Hurwitz HI, Lee JJ, Casey F, Price R, Palma JF. Plasma-Based Measurements of Tumor Heterogeneity Correlate with Clinical Outcomes in Metastatic Colorectal Cancer. Cancers (Basel) 2022; 14:cancers14092240. [PMID: 35565368 PMCID: PMC9105064 DOI: 10.3390/cancers14092240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/25/2022] [Accepted: 04/28/2022] [Indexed: 11/16/2022] Open
Abstract
Sequencing circulating tumor DNA (ctDNA) from liquid biopsies may better assess tumor heterogeneity than limited sampling of tumor tissue. Here, we explore ctDNA-based heterogeneity and its correlation with treatment outcome in STEAM, which assessed efficacy and safety of concurrent and sequential FOLFOXIRI-bevacizumab (BEV) vs. FOLFOX-BEV for first-line treatment of metastatic colorectal cancer. We sequenced 146 pre-induction and 89 post-induction patient plasmas with a 198-kilobase capture-based assay, and applied Mutant-Allele Tumor Heterogeneity (MATH), a traditionally tissue-based calculation of allele frequency distribution, on somatic mutations detected in plasma. Higher levels of MATH, particularly in the post-induction sample, were associated with shorter progression-free survival (PFS). Patients with high MATH vs. low MATH in post-induction plasma had shorter PFS (7.2 vs. 11.7 months; hazard ratio, 3.23; 95% confidence interval, 1.85−5.63; log-rank p < 0.0001). These results suggest ctDNA-based tumor heterogeneity may have potential prognostic value in metastatic cancers.
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Affiliation(s)
- Stephanie J. Yaung
- Roche Sequencing Solutions, Inc., Pleasanton, CA 94588, USA; (J.J.L.); (F.C.); (J.F.P.)
- Correspondence: ; Tel.: +1-925-523-8824
| | - Christine Ju
- Roche Molecular Systems, Inc., Pleasanton, CA 94588, USA; (C.J.); (S.G.)
| | - Sandeep Gattam
- Roche Molecular Systems, Inc., Pleasanton, CA 94588, USA; (C.J.); (S.G.)
| | - Alan Nicholas
- Genentech, Inc., South San Francisco, CA 94080, USA; (A.N.); (N.S.); (H.I.H.); (R.P.)
| | - Nicolas Sommer
- Genentech, Inc., South San Francisco, CA 94080, USA; (A.N.); (N.S.); (H.I.H.); (R.P.)
| | - Johanna C. Bendell
- Sarah Cannon Research Institute/Tennessee Oncology, Nashville, TN 37203, USA;
| | - Herbert I. Hurwitz
- Genentech, Inc., South San Francisco, CA 94080, USA; (A.N.); (N.S.); (H.I.H.); (R.P.)
| | - John J. Lee
- Roche Sequencing Solutions, Inc., Pleasanton, CA 94588, USA; (J.J.L.); (F.C.); (J.F.P.)
| | - Fergal Casey
- Roche Sequencing Solutions, Inc., Pleasanton, CA 94588, USA; (J.J.L.); (F.C.); (J.F.P.)
| | - Richard Price
- Genentech, Inc., South San Francisco, CA 94080, USA; (A.N.); (N.S.); (H.I.H.); (R.P.)
| | - John F. Palma
- Roche Sequencing Solutions, Inc., Pleasanton, CA 94588, USA; (J.J.L.); (F.C.); (J.F.P.)
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7
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Laganà A. Computational Approaches for the Investigation of Intra-tumor Heterogeneity and Clonal Evolution from Bulk Sequencing Data in Precision Oncology Applications. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1361:101-118. [DOI: 10.1007/978-3-030-91836-1_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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8
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Kashyap A, Rapsomaniki MA, Barros V, Fomitcheva-Khartchenko A, Martinelli AL, Rodriguez AF, Gabrani M, Rosen-Zvi M, Kaigala G. Quantification of tumor heterogeneity: from data acquisition to metric generation. Trends Biotechnol 2021; 40:647-676. [PMID: 34972597 DOI: 10.1016/j.tibtech.2021.11.006] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 11/26/2021] [Accepted: 11/29/2021] [Indexed: 01/18/2023]
Abstract
Tumors are unique and complex ecosystems, in which heterogeneous cell subpopulations with variable molecular profiles, aggressiveness, and proliferation potential coexist and interact. Understanding how heterogeneity influences tumor progression has important clinical implications for improving diagnosis, prognosis, and treatment response prediction. Several recent innovations in data acquisition methods and computational metrics have enabled the quantification of spatiotemporal heterogeneity across different scales of tumor organization. Here, we summarize the most promising efforts from a common experimental and computational perspective, discussing their advantages, shortcomings, and challenges. With personalized medicine entering a new era of unprecedented opportunities, our vision is that of future workflows integrating across modalities, scales, and dimensions to capture intricate aspects of the tumor ecosystem and to open new avenues for improved patient care.
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Affiliation(s)
- Aditya Kashyap
- IBM Research Europe -Säumerstrasse 4, Rüschlikon CH-8803, Zurich, Switzerland
| | | | - Vesna Barros
- Department of Healthcare Informatics, IBM Research, IBM R&D Labs, University of Haifa Campus, Mount Carmel, Haifa, 3498825, Israel; The Hebrew University, The Edmond J. Safra Campus - Givat Ram, Jerusalem, 9190401, Israel
| | - Anna Fomitcheva-Khartchenko
- IBM Research Europe -Säumerstrasse 4, Rüschlikon CH-8803, Zurich, Switzerland; Eidgenössische Technische Hochschule (ETH-Zurich), Vladimir-Prelog-Weg 1-5/10, 8099 Zurich, Switzerland
| | | | | | - Maria Gabrani
- IBM Research Europe -Säumerstrasse 4, Rüschlikon CH-8803, Zurich, Switzerland
| | - Michal Rosen-Zvi
- Department of Healthcare Informatics, IBM Research, IBM R&D Labs, University of Haifa Campus, Mount Carmel, Haifa, 3498825, Israel; The Hebrew University, The Edmond J. Safra Campus - Givat Ram, Jerusalem, 9190401, Israel
| | - Govind Kaigala
- IBM Research Europe -Säumerstrasse 4, Rüschlikon CH-8803, Zurich, Switzerland.
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9
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Benchmarking pipelines for subclonal deconvolution of bulk tumour sequencing data. Nat Commun 2021; 12:6396. [PMID: 34737285 PMCID: PMC8569188 DOI: 10.1038/s41467-021-26698-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 10/20/2021] [Indexed: 11/09/2022] Open
Abstract
Intratumour heterogeneity provides tumours with the ability to adapt and acquire treatment resistance. The development of more effective and personalised treatments for cancers, therefore, requires accurate characterisation of the clonal architecture of tumours, enabling evolutionary dynamics to be tracked. Many methods exist for achieving this from bulk tumour sequencing data, involving identifying mutations and performing subclonal deconvolution, but there is a lack of systematic benchmarking to inform researchers on which are most accurate, and how dataset characteristics impact performance. To address this, we use the most comprehensive tumour genome simulation tool available for such purposes to create 80 bulk tumour whole exome sequencing datasets of differing depths, tumour complexities, and purities, and use these to benchmark subclonal deconvolution pipelines. We conclude that i) tumour complexity does not impact accuracy, ii) increasing either purity or purity-corrected sequencing depth improves accuracy, and iii) the optimal pipeline consists of Mutect2, FACETS and PyClone-VI. We have made our benchmarking datasets publicly available for future use.
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10
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Abécassis J, Reyal F, Vert JP. CloneSig can jointly infer intra-tumor heterogeneity and mutational signature activity in bulk tumor sequencing data. Nat Commun 2021; 12:5352. [PMID: 34504064 PMCID: PMC8429716 DOI: 10.1038/s41467-021-24992-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 07/12/2021] [Indexed: 02/07/2023] Open
Abstract
Systematic DNA sequencing of cancer samples has highlighted the importance of two aspects of cancer genomics: intra-tumor heterogeneity (ITH) and mutational processes. These two aspects may not always be independent, as different mutational processes could be involved in different stages or regions of the tumor, but existing computational approaches to study them largely ignore this potential dependency. Here, we present CloneSig, a computational method to jointly infer ITH and mutational processes in a tumor from bulk-sequencing data. Extensive simulations show that CloneSig outperforms current methods for ITH inference and detection of mutational processes when the distribution of mutational signatures changes between clones. Applied to a large cohort of 8,951 tumors with whole-exome sequencing data from The Cancer Genome Atlas, and on a pan-cancer dataset of 2,632 whole-genome sequencing tumor samples from the Pan-Cancer Analysis of Whole Genomes initiative, CloneSig obtains results overall coherent with previous studies.
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Affiliation(s)
- Judith Abécassis
- Institut Curie, PSL Research University, Translational Research Department, INSERM, U932 Immunity and Cancer, Residual Tumor & Response to Treatment Laboratory (RT2Lab), Paris, France
- MINES ParisTech, PSL University, CBIO - Centre for Computational Biology, Paris, France
- Institut Curie, PSL Research University, Paris, France
| | - Fabien Reyal
- Institut Curie, PSL Research University, Translational Research Department, INSERM, U932 Immunity and Cancer, Residual Tumor & Response to Treatment Laboratory (RT2Lab), Paris, France
- Department of Surgery, Institut Curie, Paris, France
| | - Jean-Philippe Vert
- MINES ParisTech, PSL University, CBIO - Centre for Computational Biology, Paris, France.
- Google Research, Brain team, Paris, France.
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11
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Fridland S, Choi J, Nam M, Schellenberg SJ, Kim E, Lee G, Yoon N, Chae YK. Assessing tumor heterogeneity: integrating tissue and circulating tumor DNA (ctDNA) analysis in the era of immuno-oncology - blood TMB is not the same as tissue TMB. J Immunother Cancer 2021; 9:jitc-2021-002551. [PMID: 34462324 PMCID: PMC8407207 DOI: 10.1136/jitc-2021-002551] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/12/2021] [Indexed: 12/29/2022] Open
Abstract
Tissue tumor mutational burden (tTMB) is calculated to aid in cancer treatment selection. High tTMB predicts a favorable response to immunotherapy in patients with non-small cell lung cancer. Blood TMB (bTMB) from circulating tumor DNA is reported to have similar predictive power and has been proposed as an alternative to tTMB. Across many studies not only are tTMB and bTMB not concordant but also as reported previously by our group predict conflicting outcomes. This implies that bTMB is not a substitute for tTMB, but rather a composite index that may encompass tumor heterogeneity. Here, we provide a thorough overview of the predictive power of TMB, discuss the use of tumor heterogeneity alongside TMB to predict treatment response and review several methods of tumor heterogeneity assessment. Furthermore, we propose a hypothetical method of estimating tumor heterogeneity and touch on its clinical implications.
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Affiliation(s)
- Stanislav Fridland
- Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Jaeyoun Choi
- Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Myungwoo Nam
- Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | | | - Eugene Kim
- Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Grace Lee
- Northwestern University, Evanston, Illinois, USA
| | | | - Young Kwang Chae
- Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA .,Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
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12
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Chromosomal copy number heterogeneity predicts survival rates across cancers. Nat Commun 2021; 12:3188. [PMID: 34045449 PMCID: PMC8160133 DOI: 10.1038/s41467-021-23384-6] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 04/14/2021] [Indexed: 12/20/2022] Open
Abstract
Survival rates of cancer patients vary widely within and between malignancies. While genetic aberrations are at the root of all cancers, individual genomic features cannot explain these distinct disease outcomes. In contrast, intra-tumour heterogeneity (ITH) has the potential to elucidate pan-cancer survival rates and the biology that drives cancer prognosis. Unfortunately, a comprehensive and effective framework to measure ITH across cancers is missing. Here, we introduce a scalable measure of chromosomal copy number heterogeneity (CNH) that predicts patient survival across cancers. We show that the level of ITH can be derived from a single-sample copy number profile. Using gene-expression data and live cell imaging we demonstrate that ongoing chromosomal instability underlies the observed heterogeneity. Analysing 11,534 primary cancer samples from 37 different malignancies, we find that copy number heterogeneity can be accurately deduced and predicts cancer survival across tissues of origin and stages of disease. Our results provide a unifying molecular explanation for the different survival rates observed between cancer types.
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13
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Abstract
Immunotherapy has changed the landscape of cancer treatment and has significantly improved the outcome of several cancer types including breast, lung, colorectal and prostate. Neoantigen recognition and immune checkpoint inhibitors are nowadays the milestones of different immunotherapeutic regimes; however, high cost, primary and acquired resistance and the high variability of responses make their extensive use difficult. The development of better predictive biomarkers that represent tumour diversity shows promise because there is a significant body of clinical data showing a spectrum of immunotherapeutic responses that might be related back to their specific characteristics. This article makes a conceptual and historical review to summarise the main advances in our understanding of the role of the immune system in cancer, while describing the methodological details that have been successfully implemented on cancer treatments and that may hold the key to improved therapeutic approaches.
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14
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Tarabichi M, Salcedo A, Deshwar AG, Leathlobhair MN, Wintersinger J, Wedge DC, Loo PV, Morris QD, Boutros PC. A practical guide to cancer subclonal reconstruction from DNA sequencing. Nat Methods 2021; 18:144-155. [PMID: 33398189 PMCID: PMC7867630 DOI: 10.1038/s41592-020-01013-2] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2019] [Accepted: 11/09/2020] [Indexed: 01/28/2023]
Abstract
Subclonal reconstruction from bulk tumor DNA sequencing has become a pillar of cancer evolution studies, providing insight into the clonality and relative ordering of mutations and mutational processes. We provide an outline of the complex computational approaches used for subclonal reconstruction from single and multiple tumor samples. We identify the underlying assumptions and uncertainties in each step and suggest best practices for analysis and quality assessment. This guide provides a pragmatic resource for the growing user community of subclonal reconstruction methods.
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Affiliation(s)
- Maxime Tarabichi
- The Francis Crick Institute, London, United Kingdom
- Wellcome Sanger Institute, Hinxton, United Kingdom
| | - Adriana Salcedo
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Department of Human Genetics, University of California, Los Angeles
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, University of California, Los Angeles
- Institute for Precision Health, University of California, Los Angeles
- Ontario Institute for Cancer Research, Toronto, Canada
| | - Amit G. Deshwar
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, Toronto, Canada
| | - Máire Ni Leathlobhair
- Big Data Institute, University of Oxford, Oxford, United Kingdom
- Ludwig Institute for Cancer Research, University of Oxford, Oxford, United Kingdom
| | - Jeff Wintersinger
- Department of Computer Science, University of Toronto, Toronto, Canada
| | - David C. Wedge
- Big Data Institute, University of Oxford, Oxford, United Kingdom
- Oxford NIHR Biomedical Research Centre, Oxford, United Kingdom
- Manchester Cancer Research Centre, University of Manchester, Manchester, United Kingdom
| | | | - Quaid D. Morris
- Ontario Institute for Cancer Research, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, Canada
- Vector Institute, Toronto, Canada
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York
- Donnelly Centre, University of Toronto, Toronto, Canada
| | - Paul C. Boutros
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Department of Human Genetics, University of California, Los Angeles
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, University of California, Los Angeles
- Institute for Precision Health, University of California, Los Angeles
- Vector Institute, Toronto, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, Canada
- Department of Urology, David Geffen School of Medicine, University of California, Los Angeles
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15
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Bonneville R, Paruchuri A, Wing MR, Krook MA, Reeser JW, Chen HZ, Dao T, Samorodnitsky E, Smith AM, Yu L, Nowacki N, Chen W, Roychowdhury S. Characterization of Clonal Evolution in Microsatellite Unstable Metastatic Cancers through Multiregional Tumor Sequencing. Mol Cancer Res 2020; 19:465-474. [PMID: 33229401 DOI: 10.1158/1541-7786.mcr-19-0955] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 07/10/2020] [Accepted: 11/18/2020] [Indexed: 11/16/2022]
Abstract
Microsatellites are short, repetitive segments of DNA, which are dysregulated in mismatch repair-deficient (MMRd) tumors resulting in microsatellite instability (MSI). MSI has been identified in many human cancer types with varying incidence, and microsatellite instability-high (MSI-H) tumors often exhibit increased sensitivity to immune-enhancing therapies such as PD-1/PD-L1 inhibition. Next-generation sequencing (NGS) has permitted advancements in MSI detection, and recent computational advances have enabled characterization of tumor heterogeneity via NGS. However, the evolution and heterogeneity of microsatellite changes in MSI-positive tumors remains poorly described. We determined MSI status in 6 patients using our previously published algorithm, MANTIS, and inferred subclonal composition and phylogeny with Canopy and SuperFreq. We developed a simulated annealing-based method to characterize microsatellite length distributions in specific subclones and assessed the evolution of MSI in the context of tumor heterogeneity. We identified three to eight tumor subclones per patient, and each subclone exhibited MMRd-associated base substitution signatures. We noted that microsatellites tend to shorten over time, and that MMRd fosters heterogeneity by introducing novel mutations throughout the disease course. Some microsatellites are altered among all subclones in a patient, whereas other loci are only altered in particular subclones corresponding to subclonal phylogenetic relationships. Overall, our results indicate that MMRd is a substantial driver of heterogeneity, leading to both MSI and subclonal divergence. IMPLICATIONS: We leveraged subclonal inference to assess clonal evolution based on somatic mutations and microsatellites, which provides insight into MMRd as a dynamic mutagenic process in MSI-H malignancies.
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Affiliation(s)
- Russell Bonneville
- Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio.,Biomedical Sciences Graduate Program, The Ohio State University, Columbus, Ohio
| | - Anoosha Paruchuri
- Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio
| | - Michele R Wing
- Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio
| | - Melanie A Krook
- Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio
| | - Julie W Reeser
- Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio
| | - Hui-Zi Chen
- Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio.,Division of Medical Oncology, Department of Internal Medicine, The Ohio State University, Columbus, Ohio
| | - Thuy Dao
- Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio
| | | | - Amy M Smith
- Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio
| | - Lianbo Yu
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio
| | - Nicholas Nowacki
- Department of Pathology, The Ohio State University, Columbus, Ohio
| | - Wei Chen
- Department of Pathology, The Ohio State University, Columbus, Ohio
| | - Sameek Roychowdhury
- Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio. .,Division of Medical Oncology, Department of Internal Medicine, The Ohio State University, Columbus, Ohio
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Cumulative learning enables convolutional neural network representations for small mass spectrometry data classification. Nat Commun 2020; 11:5595. [PMID: 33154370 PMCID: PMC7644674 DOI: 10.1038/s41467-020-19354-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 10/05/2020] [Indexed: 12/13/2022] Open
Abstract
Rapid and accurate clinical diagnosis remains challenging. A component of diagnosis tool development is the design of effective classification models with Mass spectrometry (MS) data. Some Machine Learning approaches have been investigated but these models require time-consuming preprocessing steps to remove artifacts, making them unsuitable for rapid analysis. Convolutional Neural Networks (CNNs) have been found to perform well under such circumstances since they can learn representations from raw data. However, their effectiveness decreases when the number of available training samples is small, which is a common situation in medicine. In this work, we investigate transfer learning on 1D-CNNs, then we develop a cumulative learning method when transfer learning is not powerful enough. We propose to train the same model through several classification tasks over various small datasets to accumulate knowledge in the resulting representation. By using rat brain as the initial training dataset, a cumulative learning approach can have a classification accuracy exceeding 98% for 1D clinical MS-data. We show the use of cumulative learning using datasets generated in different biological contexts, on different organisms, and acquired by different instruments. Here we show a promising strategy for improving MS data classification accuracy when only small numbers of samples are available. Convolutional Neural Networks are powerful tools for clinical diagnosis but their effectiveness decreases when the number of available samples is small. Here, the authors develop a cumulative learning method by training the same model through several classification tasks over various small Mass Spectrometry datasets.
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