The Combiome Hypothesis: Selecting Optimal Treatment for Cancer Patients

Existing approaches for cancer diagnosis are inefficient in the use of diagnostic tissue, and decision-making is often sequential, typically resulting in delayed treatment initiation. Future diagnostic testing needs to be faster and optimize increasingly complex treatment decisions. We envision a fu...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Clinical lung cancer 2022-01, Vol.23 (1), p.1-13
Hauptverfasser: Hirsch, Fred R., Walker, Jill, Higgs, Brandon W., Cooper, Zachary A., Raja, Rajiv G., Wistuba, Ignacio I.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 13
container_issue 1
container_start_page 1
container_title Clinical lung cancer
container_volume 23
creator Hirsch, Fred R.
Walker, Jill
Higgs, Brandon W.
Cooper, Zachary A.
Raja, Rajiv G.
Wistuba, Ignacio I.
description Existing approaches for cancer diagnosis are inefficient in the use of diagnostic tissue, and decision-making is often sequential, typically resulting in delayed treatment initiation. Future diagnostic testing needs to be faster and optimize increasingly complex treatment decisions. We envision a future where comprehensive testing is routine. Our approach, termed the “combiome,” combines holistic information from the tumor, and the patient's immune system. The combiome model proposed here advocates synchronized up-front testing with a panel of sensitive assays, revealing a more complete understanding of the patient phenotype and improved targeting and sequencing of treatments. Development and eventual adoption of the combiome model for diagnostic testing may provide better outcomes for all cancer patients, but will require significant changes in workflows, technology, regulations, and administration. In this review, we discuss the current and future testing landscape, targeting of personalized treatments, and technological and regulatory advances necessary to achieve the combiome. Diagnosing cancer is not always an efficient process and this can cause delays in starting treatment. A more efficient approach to cancer diagnosis is proposed, called the “combiome.” The “combiome” characterizes the patients’ tumor and immune system to inform the diagnosis of cancer so that patients can be given the best treatment in a timelier manner.
doi_str_mv 10.1016/j.cllc.2021.08.011
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2582109315</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S1525730421002151</els_id><sourcerecordid>2582109315</sourcerecordid><originalsourceid>FETCH-LOGICAL-c466t-c3129e60fe6201ea3f160c5ee3e592fcdd6ec8a566cc82425022987b0c9e089b3</originalsourceid><addsrcrecordid>eNp9kE9LAzEQxYMotla_gAfZo5ddJ9lNmhUvsqgVChWs57CbnbUp-6cmqdBvb0qrR08zDO895v0IuaaQUKDibp3ottUJA0YTkAlQekLGNE9lDCKH07BzxuNpCtmIXDi3BmAipeycjNJMZJxLOiaz5QqjYugqM3QYzXabwa_QGXcfvWOL2pv-M1psvOnKNlpaLH2HvY-awUZF2Wu00VvpTTi5S3LWlK3Dq-OckI_np2Uxi-eLl9ficR7rTAgf6_BAjgIaFAwolmlDBWiOmCLPWaPrWqCWJRdCa8kyxoGxXE4r0DmCzKt0Qm4PuRs7fG3RedUZp7Ftyx6HrVOMS0YhTykPUnaQajs4Z7FRGxuK2J2ioPYE1VrtCao9QQVSBYLBdHPM31Yd1n-WX2RB8HAQYGj5bdAqpwMBjbWxAZiqB_Nf_g8uaoEy</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2582109315</pqid></control><display><type>article</type><title>The Combiome Hypothesis: Selecting Optimal Treatment for Cancer Patients</title><source>MEDLINE</source><source>ScienceDirect Journals (5 years ago - present)</source><creator>Hirsch, Fred R. ; Walker, Jill ; Higgs, Brandon W. ; Cooper, Zachary A. ; Raja, Rajiv G. ; Wistuba, Ignacio I.</creator><creatorcontrib>Hirsch, Fred R. ; Walker, Jill ; Higgs, Brandon W. ; Cooper, Zachary A. ; Raja, Rajiv G. ; Wistuba, Ignacio I.</creatorcontrib><description>Existing approaches for cancer diagnosis are inefficient in the use of diagnostic tissue, and decision-making is often sequential, typically resulting in delayed treatment initiation. Future diagnostic testing needs to be faster and optimize increasingly complex treatment decisions. We envision a future where comprehensive testing is routine. Our approach, termed the “combiome,” combines holistic information from the tumor, and the patient's immune system. The combiome model proposed here advocates synchronized up-front testing with a panel of sensitive assays, revealing a more complete understanding of the patient phenotype and improved targeting and sequencing of treatments. Development and eventual adoption of the combiome model for diagnostic testing may provide better outcomes for all cancer patients, but will require significant changes in workflows, technology, regulations, and administration. In this review, we discuss the current and future testing landscape, targeting of personalized treatments, and technological and regulatory advances necessary to achieve the combiome. Diagnosing cancer is not always an efficient process and this can cause delays in starting treatment. A more efficient approach to cancer diagnosis is proposed, called the “combiome.” The “combiome” characterizes the patients’ tumor and immune system to inform the diagnosis of cancer so that patients can be given the best treatment in a timelier manner.</description><identifier>ISSN: 1525-7304</identifier><identifier>EISSN: 1938-0690</identifier><identifier>DOI: 10.1016/j.cllc.2021.08.011</identifier><identifier>PMID: 34645581</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Antigenicity ; Co-stimulation ; Humans ; Immune activation ; Immune checkpoint ; Immunotherapy ; Lung Neoplasms - diagnosis ; Lung Neoplasms - therapy ; Microbiota ; Models, Theoretical ; Proteogenomics ; Targeted therapies ; Treatment Outcome</subject><ispartof>Clinical lung cancer, 2022-01, Vol.23 (1), p.1-13</ispartof><rights>2021 The Author(s)</rights><rights>Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c466t-c3129e60fe6201ea3f160c5ee3e592fcdd6ec8a566cc82425022987b0c9e089b3</citedby><cites>FETCH-LOGICAL-c466t-c3129e60fe6201ea3f160c5ee3e592fcdd6ec8a566cc82425022987b0c9e089b3</cites><orcidid>0000-0003-1059-0940 ; 0000-0002-4663-0951</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.cllc.2021.08.011$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,3548,27923,27924,45994</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34645581$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hirsch, Fred R.</creatorcontrib><creatorcontrib>Walker, Jill</creatorcontrib><creatorcontrib>Higgs, Brandon W.</creatorcontrib><creatorcontrib>Cooper, Zachary A.</creatorcontrib><creatorcontrib>Raja, Rajiv G.</creatorcontrib><creatorcontrib>Wistuba, Ignacio I.</creatorcontrib><title>The Combiome Hypothesis: Selecting Optimal Treatment for Cancer Patients</title><title>Clinical lung cancer</title><addtitle>Clin Lung Cancer</addtitle><description>Existing approaches for cancer diagnosis are inefficient in the use of diagnostic tissue, and decision-making is often sequential, typically resulting in delayed treatment initiation. Future diagnostic testing needs to be faster and optimize increasingly complex treatment decisions. We envision a future where comprehensive testing is routine. Our approach, termed the “combiome,” combines holistic information from the tumor, and the patient's immune system. The combiome model proposed here advocates synchronized up-front testing with a panel of sensitive assays, revealing a more complete understanding of the patient phenotype and improved targeting and sequencing of treatments. Development and eventual adoption of the combiome model for diagnostic testing may provide better outcomes for all cancer patients, but will require significant changes in workflows, technology, regulations, and administration. In this review, we discuss the current and future testing landscape, targeting of personalized treatments, and technological and regulatory advances necessary to achieve the combiome. Diagnosing cancer is not always an efficient process and this can cause delays in starting treatment. A more efficient approach to cancer diagnosis is proposed, called the “combiome.” The “combiome” characterizes the patients’ tumor and immune system to inform the diagnosis of cancer so that patients can be given the best treatment in a timelier manner.</description><subject>Antigenicity</subject><subject>Co-stimulation</subject><subject>Humans</subject><subject>Immune activation</subject><subject>Immune checkpoint</subject><subject>Immunotherapy</subject><subject>Lung Neoplasms - diagnosis</subject><subject>Lung Neoplasms - therapy</subject><subject>Microbiota</subject><subject>Models, Theoretical</subject><subject>Proteogenomics</subject><subject>Targeted therapies</subject><subject>Treatment Outcome</subject><issn>1525-7304</issn><issn>1938-0690</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kE9LAzEQxYMotla_gAfZo5ddJ9lNmhUvsqgVChWs57CbnbUp-6cmqdBvb0qrR08zDO895v0IuaaQUKDibp3ottUJA0YTkAlQekLGNE9lDCKH07BzxuNpCtmIXDi3BmAipeycjNJMZJxLOiaz5QqjYugqM3QYzXabwa_QGXcfvWOL2pv-M1psvOnKNlpaLH2HvY-awUZF2Wu00VvpTTi5S3LWlK3Dq-OckI_np2Uxi-eLl9ficR7rTAgf6_BAjgIaFAwolmlDBWiOmCLPWaPrWqCWJRdCa8kyxoGxXE4r0DmCzKt0Qm4PuRs7fG3RedUZp7Ftyx6HrVOMS0YhTykPUnaQajs4Z7FRGxuK2J2ioPYE1VrtCao9QQVSBYLBdHPM31Yd1n-WX2RB8HAQYGj5bdAqpwMBjbWxAZiqB_Nf_g8uaoEy</recordid><startdate>202201</startdate><enddate>202201</enddate><creator>Hirsch, Fred R.</creator><creator>Walker, Jill</creator><creator>Higgs, Brandon W.</creator><creator>Cooper, Zachary A.</creator><creator>Raja, Rajiv G.</creator><creator>Wistuba, Ignacio I.</creator><general>Elsevier Inc</general><scope>6I.</scope><scope>AAFTH</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-1059-0940</orcidid><orcidid>https://orcid.org/0000-0002-4663-0951</orcidid></search><sort><creationdate>202201</creationdate><title>The Combiome Hypothesis: Selecting Optimal Treatment for Cancer Patients</title><author>Hirsch, Fred R. ; Walker, Jill ; Higgs, Brandon W. ; Cooper, Zachary A. ; Raja, Rajiv G. ; Wistuba, Ignacio I.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c466t-c3129e60fe6201ea3f160c5ee3e592fcdd6ec8a566cc82425022987b0c9e089b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Antigenicity</topic><topic>Co-stimulation</topic><topic>Humans</topic><topic>Immune activation</topic><topic>Immune checkpoint</topic><topic>Immunotherapy</topic><topic>Lung Neoplasms - diagnosis</topic><topic>Lung Neoplasms - therapy</topic><topic>Microbiota</topic><topic>Models, Theoretical</topic><topic>Proteogenomics</topic><topic>Targeted therapies</topic><topic>Treatment Outcome</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hirsch, Fred R.</creatorcontrib><creatorcontrib>Walker, Jill</creatorcontrib><creatorcontrib>Higgs, Brandon W.</creatorcontrib><creatorcontrib>Cooper, Zachary A.</creatorcontrib><creatorcontrib>Raja, Rajiv G.</creatorcontrib><creatorcontrib>Wistuba, Ignacio I.</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Clinical lung cancer</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hirsch, Fred R.</au><au>Walker, Jill</au><au>Higgs, Brandon W.</au><au>Cooper, Zachary A.</au><au>Raja, Rajiv G.</au><au>Wistuba, Ignacio I.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The Combiome Hypothesis: Selecting Optimal Treatment for Cancer Patients</atitle><jtitle>Clinical lung cancer</jtitle><addtitle>Clin Lung Cancer</addtitle><date>2022-01</date><risdate>2022</risdate><volume>23</volume><issue>1</issue><spage>1</spage><epage>13</epage><pages>1-13</pages><issn>1525-7304</issn><eissn>1938-0690</eissn><abstract>Existing approaches for cancer diagnosis are inefficient in the use of diagnostic tissue, and decision-making is often sequential, typically resulting in delayed treatment initiation. Future diagnostic testing needs to be faster and optimize increasingly complex treatment decisions. We envision a future where comprehensive testing is routine. Our approach, termed the “combiome,” combines holistic information from the tumor, and the patient's immune system. The combiome model proposed here advocates synchronized up-front testing with a panel of sensitive assays, revealing a more complete understanding of the patient phenotype and improved targeting and sequencing of treatments. Development and eventual adoption of the combiome model for diagnostic testing may provide better outcomes for all cancer patients, but will require significant changes in workflows, technology, regulations, and administration. In this review, we discuss the current and future testing landscape, targeting of personalized treatments, and technological and regulatory advances necessary to achieve the combiome. Diagnosing cancer is not always an efficient process and this can cause delays in starting treatment. A more efficient approach to cancer diagnosis is proposed, called the “combiome.” The “combiome” characterizes the patients’ tumor and immune system to inform the diagnosis of cancer so that patients can be given the best treatment in a timelier manner.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>34645581</pmid><doi>10.1016/j.cllc.2021.08.011</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0003-1059-0940</orcidid><orcidid>https://orcid.org/0000-0002-4663-0951</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1525-7304
ispartof Clinical lung cancer, 2022-01, Vol.23 (1), p.1-13
issn 1525-7304
1938-0690
language eng
recordid cdi_proquest_miscellaneous_2582109315
source MEDLINE; ScienceDirect Journals (5 years ago - present)
subjects Antigenicity
Co-stimulation
Humans
Immune activation
Immune checkpoint
Immunotherapy
Lung Neoplasms - diagnosis
Lung Neoplasms - therapy
Microbiota
Models, Theoretical
Proteogenomics
Targeted therapies
Treatment Outcome
title The Combiome Hypothesis: Selecting Optimal Treatment for Cancer Patients
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-10T11%3A49%3A58IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=The%20Combiome%20Hypothesis:%20Selecting%20Optimal%20Treatment%20for%20Cancer%20Patients&rft.jtitle=Clinical%20lung%20cancer&rft.au=Hirsch,%20Fred%20R.&rft.date=2022-01&rft.volume=23&rft.issue=1&rft.spage=1&rft.epage=13&rft.pages=1-13&rft.issn=1525-7304&rft.eissn=1938-0690&rft_id=info:doi/10.1016/j.cllc.2021.08.011&rft_dat=%3Cproquest_cross%3E2582109315%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2582109315&rft_id=info:pmid/34645581&rft_els_id=S1525730421002151&rfr_iscdi=true