Dataset Information

Quantifying the Expanding Landscape of Clinical Actionability for Patients with Cancer.

ABSTRACT: There is a continuing debate about the proportion of cancer patients that benefit from precision oncology, attributable in part to conflicting views as to which molecular alterations are clinically actionable. To quantify the expansion of clinical actionability since 2017, we annotated 47,271 solid tumors sequenced with the MSK-IMPACT clinical assay using two temporally distinct versions of the OncoKB knowledge base deployed 5 years apart. Between 2017 and 2022, we observed an increase from 8.9% to 31.6% in the fraction of tumors harboring a standard care (level 1 or 2) predictive biomarker of therapy response and an almost halving of tumors carrying nonactionable drivers (44.2% to 22.8%). In tumors with limited or no clinical actionability, TP53 (43.2%), KRAS (19.2%), and CDKN2A (12.2%) were the most frequently altered genes.

Significance

Although clear progress has been made in expanding the availability of precision oncology-based treatment paradigms, our results suggest a continued unmet need for innovative therapeutic strategies, particularly for cancers with currently undruggable oncogenic drivers. See related commentary by Horak and Fröhling, p. 18. This article is featured in Selected Articles from This Issue, p. 5.

SUBMITTER: Suehnholz SP

PROVIDER: S-EPMC10784742 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Quantifying the Expanding Landscape of Clinical Actionability for Patients with Cancer.

Suehnholz Sarah P SP Nissan Moriah H MH Zhang Hongxin H Kundra Ritika R Nandakumar Subhiksha S Lu Calvin C Carrero Stephanie S Dhaneshwar Amanda A Fernandez Nicole N Xu Benjamin W BW Arcila Maria E ME Zehir Ahmet A Syed Aijazuddin A Brannon A Rose AR Rudolph Julia E JE Paraiso Eder E Sabbatini Paul J PJ Levine Ross L RL Dogan Ahmet A Gao Jianjiong J Ladanyi Marc M Drilon Alexander A Berger Michael F MF Solit David B DB Schultz Nikolaus N Chakravarty Debyani D

Cancer discovery 20240101 1

There is a continuing debate about the proportion of cancer patients that benefit from precision oncology, attributable in part to conflicting views as to which molecular alterations are clinically actionable. To quantify the expansion of clinical actionability since 2017, we annotated 47,271 solid tumors sequenced with the MSK-IMPACT clinical assay using two temporally distinct versions of the OncoKB knowledge base deployed 5 years apart. Between 2017 and 2022, we observed an increase from 8.9% ...[more]

PMID: 37849038

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Project description:BackgroundThe review of collateral information is an essential component of patient care. Although this is standard practice, minimal research has been done to quantify collateral information collection and to understand how collateral information translates to clinical decision making. To address this, we developed and piloted a novel measure (the McLean Collateral Information and Clinical Actionability Scale [M-CICAS]) to evaluate the types and number of collateral sources viewed and the resulting actions made in a psychiatric setting.ObjectiveThis study aims to test the feasibility of the M-CICAS, validate this measure against clinician notes via medical records, and evaluate whether reviewing a higher volume of collateral sources is associated with more clinical actions taken.MethodsFor the M-CICAS, we developed a three-part instrument, focusing on measuring collateral sources reviewed, clinical actions taken, and shared decision making between the clinician and patient. To determine feasibility and preliminary validity, we piloted this measure among clinicians providing psychotherapy at McLean Hospital. These clinicians (n=7) completed the M-CICAS after individual clinical sessions with 89 distinct patient encounters. Scales were completed by clinicians only once for each patient during routine follow-up visits. After clinicians completed these scales, researchers conducted chart reviews by completing the M-CICAS using only the clinician's corresponding note from that session. For the analyses, we generated summary scores for the number of collateral sources and clinical actions for each encounter. We examined Pearson correlation coefficients to assess interrater reliability between clinicians and chart reviewers, and simple univariate regression modeling followed by multilevel mixed effects regression modeling to test the relationship between collateral information accessed and clinical actions taken.ResultsThe study staff had high interrater reliability on the M-CICAS for the sources reviewed (r=0.98; P<.001) and actions taken (r=0.97; P<.001). Clinician and study staff ratings were moderately correlated and statistically significant on the M-CICAS summary scores for the sources viewed (r=0.24, P=.02 and r=0.25, P=.02, respectively). Univariate regression modeling with a two-tailed test demonstrated a significant association between collateral sources and clinical actions taken when clinicians completed the M-CICAS (β=.27; t87=2.47; P=.02). The multilevel fixed slopes random intercepts model confirmed a significant association even when accounting for clinician differences (β=.23; t57=2.13; P=.04).ConclusionsThis pilot study established the feasibility and preliminary validity of the M-CICAS in assessing collateral sources and clinical decision making in psychiatry. This study also indicated that reviewing more collateral sources may lead to an increased number of clinical actions following a session.

Project description:BackgroundWe aimed to develop a deep learning (DL) model to predict DNA mismatch repair (MMR) status in colorectal cancers (CRC) based on hematoxylin and eosin-stained whole-slide images (WSIs) and assess its clinical applicability.MethodsThe DL model was developed and validated through three-fold cross validation using 441 WSIs from the Cancer Genome Atlas (TCGA) and externally validated using 78 WSIs from the Pathology AI Platform (PAIP), and 355 WSIs from surgical specimens and 341 WSIs from biopsy specimens of the Sun Yet-sun University Cancer Center (SYSUCC). Domain adaption and multiple instance learning (MIL) techniques were adopted for model development. The performance of the models was evaluated using the area under the receiver operating characteristic curve (AUROC). A dual-threshold strategy was also built from the surgical cohorts and validated in the biopsy cohort. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1-score, and the percentage of patients avoiding IHC testing were evaluated.FindingsThe MIL model achieved an AUROC of 0·8888±0·0357 in the TCGA-validation cohort, 0·8806±0·0232 in the PAIP cohort, 0·8457±0·0233 in the SYSUCC-surgical cohort, and 0·7679±0·0342 in the SYSUCC-biopsy cohort. A dual-threshold triage strategy was used to rule-in and rule-out dMMR patients with remaining uncertain patients recommended for further IHC testing, which kept sensitivity higher than 90% and specificity higher than 95% on deficient MMR patient triage from both the surgical and biopsy specimens, result in more than half of patients avoiding IHC based MMR testing.InterpretationA DL-based method that could directly predict CRC MMR status from WSIs was successfully developed, and a dual-threshold triage strategy was established to minimize the number of patients for further IHC testing.FundingThe study was funded by the National Natural Science Foundation of China (82073159, 81871971 and 81700576), the Natural Science Foundation of Guangdong Province (No. 2021A1515011792 and No.2022A1515012403) and Medical Scientific Research Foundation of Guangdong Province of China (No. A2020392).

Project description:BackgroundGermline and tumor pharmacogenomics impact drug responses, but germline markers less commonly guide oncology prescribing. The authors hypothesized that a critical number of clinically actionable germline pharmacogenomic associations exist, representing clinical implementation opportunities.MethodsIn total, 125 oncology drugs were analyzed for positive germline pharmacogenomic associations in journals with impact factors ≥5. Studies were assessed for design and genotyping quality, clinically relevant outcomes, statistical rigor, and evidence of drug-gene effects. Associations from studies of high methodologic quality were deemed potentially clinically actionable, and translational summaries were written as point-of-care clinical decision support (CDS) tools and formally evaluated using the Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument.ResultsThe authors identified germline pharmacogenomic results for 56 of 125 oncology drugs (45%) across 173 publications. Actionable associations were detected for 12 drugs, including 6 that had germline pharmacogenomic information within US Food and Drug Administration labels or published guidelines (capecitabine/fluorouracil/dihydropyrimidine dehydrogenase [DPYD], irinotecan/uridine diphosphate glucuronosyltransferase family 1 member A1 [UGT1A1], mercaptopurine/thioguanine/thiopurine S-methyltransferase [TPMT], tamoxifen/cytochrome P450 [CYP] family 2 subfamily D member 6 [CYP2D6]), and 6 others were novel (asparaginase/nuclear factor of activated T-cells 2 [NFATC2]/human leukocyte antigen D-related β1 [HLA-DRB1], cisplatin/acylphosphatase 2 [ACYP2], doxorubicin/adenosine triphosphate-binding cassette subfamily C member 2/Rac family small guanosine triphosphatase 2/neutrophil cytosolic factor 4 [ABCC2/RAC2/NCF4], lapatinib/human leukocyte antigen DQ α1 [HLA-DQA1], sunitinib/cytochrome P450 family 3 subfamily A member 5 [CYP3A5], vincristine/centrosomal protein 72 [CEP72]). By using AGREE II, the developed CDS summaries had high mean ± standard deviation scores (maximum score, 100) for scope and purpose (92.7 ± 5.1) and rigour of development (87.6 ± 7.4) and moderate yet robust scores for clarity of presentation (58.6 ± 25.1) and applicability (55.9 ± 24.6). The overall mean guideline quality score was 5.2 ± 1.0 (maximum score, 7). Germline pharmacogenomic CDS summaries for these 12 drugs were recommended for implementation.ConclusionsSeveral oncology drugs have actionable germline pharmacogenomic information, justifying their delivery through institutional pharmacogenomic implementations to determine clinical utility. Cancer 2018;124:3052-65. © 2018 American Cancer Society.

Dataset Information

Quantifying the Expanding Landscape of Clinical Actionability for Patients with Cancer.

Significance

Publications

Quantifying the Expanding Landscape of Clinical Actionability for Patients with Cancer.

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