Precision Oncology Decision Support Research Articles

424P MD Anderson Cancer Center global precision oncology decision support (Glo-PODS) clinical trial genomic support: Pilot program at the Prince of Wales Hospital (Chinese University of Hong Kong - CUHK)

424P MD Anderson Cancer Center global precision oncology decision support (Glo-PODS) clinical trial genomic support: Pilot program at the Prince of Wales Hospital (Chinese University of Hong Kong - CUHK)

Implementation of an academic-community partnership model for equitable access to precision oncology decision support in rural community cancer centers.

141 Background: Comprehensive genomic profiling (CGP) is an essential tool for selecting cancer therapies and matching patients to clinical trials, however patients in rural community cancer centers are less likely to receive CGP. We developed an academic-community partnership model to expand Duke’s Molecular Tumor Board (MTB) clinical decision-making and trial-matching services to Duke Cancer Network (DCN), a series of affiliated community cancer centers across rural North Carolina. Methods: The Duke Precision Cancer Medicine Initiative created a Molecular Registry of Tumors database to aggregate CGP results from multiple vendors and enable review by a multidisciplinary MTB. Patients treated at DCN sites had their CGP results and relevant clinical data reviewed by the Duke MTB. After review, clinical recommendations were communicated in a standardized note and returned to DCN providers within one week. One year into implementation, DCN providers were anonymously surveyed to identify persistent barriers to utilization of CGP. Results: Between Jan 2022 and June 2023, 96 patients were consented, enrolled, and reviewed by Duke MTB. 29.2% were Black, 19.8% were Native American, and 5 .2% were of Hispanic ethnicity. 57.3% patients were found to have an actionable genomic biomarker. Of these, 14 of 22 (63.6%) received matched FDA-approved therapies and 0 of 17 (0%) with molecular criteria for a clinical trial at Duke successfully enrolled at time of submission. 85% DCN providers reported comfort ordering CGP and 45% reported comfort interpreting the results. Most frequently reported major barriers to clinical trial participation included distance (70%), lack of transportation (55%), and lack of local trials (50%). Conclusions: Our implementation of a novel academic-community partnership serves as a sustainable and reproducible model to expand access to MTB services. Further research and interventions are necessary to mitigate persistent urban versus rural barriers to utilizing CGP, especially those in clinical trial enrollment. By providing MTB support to historically underserved communities and a framework to identify salient barriers in the process, our model can be adopted by academic cancer centers nationally, contributing to improved and equitable outcomes in oncology.[Table: see text]

Actionability classification of variants of unknown significance correlates with functional effect

Genomically-informed therapy requires consideration of the functional impact of genomic alterations on protein expression and/or function. However, a substantial number of variants are of unknown significance (VUS). The MD Anderson Precision Oncology Decision Support (PODS) team developed an actionability classification scheme that categorizes VUS as either “Unknown” or “Potentially” actionable based on their location within functional domains and/or proximity to known oncogenic variants. We then compared PODS VUS actionability classification with results from a functional genomics platform consisting of mutant generation and cell viability assays. 106 (24%) of 438 VUS in 20 actionable genes were classified as oncogenic in functional assays. Variants categorized by PODS as Potentially actionable (N = 204) were more likely to be oncogenic than those categorized as Unknown (N = 230) (37% vs 13%, p = 4.08e-09). Our results demonstrate that rule-based actionability classification of VUS can identify patients more likely to have actionable variants for consideration with genomically-matched therapy.

Real-world study to evaluate the clinical performance of a computational method in precision oncology.

e13631 Background: In recent years, precision medicine has increasingly been integrated into routine clinical oncology care. However, interpretation of large amounts of biological information can be challenging in daily clinical practice settings. We previously demonstrated that digital drug assignment (DDA), an artificial intelligence-based computational method that ranks associated targeted therapies based on the totality of available genomic data rather than matching one drug to one biomarker, was predictive of relative benefit of the agents as used in the SHIVA01 trial (Petak et al., 2021, doi: 10.1038/s41698-021-00191-2). Here, we collected real-world clinical outcome data from patients with solid tumors who received decision support where DDA was integrated to aid a molecular tumor board (MTB) and investigated the effectiveness of recommended therapies. Methods: Between 2016 and 2021, 208 patients with solid tumors were involved in our precision oncology program (69% gastrointestinal, 14% gynecological, 9% breast, 8% other tumors). In most cases targeted panel sequencing was carried out (50-gene panel: 51%, 591-gene panel: 45%). Classification of the detected alterations and therapeutic ranking was performed by the DDA software tool as previously described. The output was assessed by the MTB that provided a strategy to the clinicians who made the final therapy decisions. DDA output scores were supportive for all molecularly targeted agents (MTAs) administered. Treatment lines after decision support with MTAs were compared with lines of standard agents (STs) retrospectively and evaluated by best overall response, log-rank test of progression-free survival (PFS), and durable clinical benefit (DCB). Results: Of all 208 patients, 81 were treated with MTAs and 59 with STs after DDA, implying 114 and 97 therapeutic lines, respectively. Disease control rate (DCR) of the suggested MTAs was 40% (0 CR, 9 PR, 37 SD, 43 PD, 25 lost to follow-up), while DCR for STs was 25% (1 CR, 3 PR, 20 SD, 48 PD, 25 lost) ( p=0.025). PFS value was available for 88 MTA and 71 ST lines. Log-rank test revealed a significantly longer median PFS for MTAs than STs: 4 vs. 2.5 months, respectively ( p<0.001). Proportion of patients who reached DCB of 6 months was 32% in the MTA and 11% in the ST group ( p<0.001). DCB at 9 months was 19% and 5% in the MTA and ST cohorts, respectively ( p=0.003). Conclusions: This study revealed clear clinical benefit for targeted therapies over conventional treatments used in daily practice, where MTB was aided by a computational tool to interpret complex molecular data. After the previous clinical validation, here we show that integration of DDA into real-world clinical setting is feasible and safe. The results fit well into the trend of targeted therapies becoming a routine procedure and underscore the importance of precision oncology decision support by advanced computational tools.

Implementation of a virtual, on-demand, molecular tumor board at a large, multi-clinic, community oncology practice.

96 Background: Despite the availability of molecularly-targeted agents for the treatment of many cancer types, gaps remain in integrating comprehensive precision oncology decision support tools and services into routine clinical practice. Molecular Tumor Boards (MTBs) have been shown to improve accurate incorporation of precision oncology and oncologic clinical trial enrolment into clinical practice. However, the traditional MTB model is a didactic meeting occurring at regular pre-scheduled cadences which may not align with treatment decisions or schedules of community-based general medical oncologists and advanced practice providers (APPs) without protected time away from clinic. Herein, we report on the utilization of an on-demand virtual MTB (vMTB) implemented at Tennessee Oncology (TO) powered by the Personalized Medicine (PM) team at the Sarah Cannon Research Institute (SCRI). Methods: “MolecularHelp” (MH) decision support services were implemented in September 2021 for oncology providers at TO – a network of over 100 oncologists and 86 APPs practicing across 34 clinics in Tennessee. The MH services request was a structured order that could be placed directly within the electronic health record (EHR) or through patient-protected email within the practice. MH orders initiated a virtual, on-demand interpretation of comprehensive genomic profiling (CGP) reports by a centralized vMTB run by SCRI's PM team and supported by Genospace, SCRI's precision medicine software platform. The PM team – comprised of pharmacologists, cell biologists, human geneticists, and molecular biologists – analyze CGP results and provide expert advice on both standard-of-care (SOC) and clinical research therapeutic options. Both SOC and clinical research targeted therapy options were relayed back to the treating physician and embedded within the EHR. Herein, we report key metrics including MH order frequency, average turnaround time, and subsequent clinical trial enrolment between September 2021 and March 2022. Results: CGP reports from 120 unique patients were reviewed by the vMTB during the collection period. MH orders were initiated by 30 TO providers from 14 different clinic locations across Middle and East Tennessee. The average turnaround time from referral to vMTB interpretation was less than 10 hours. Of the 120 patients reviewed, actionable mutations were identified by the MTB in 103 patients, of whom 27 subsequently enrolled onto clinical trials (15 phase 1 and 12 phase 2/3). Conclusions: An on-demand vMTB is feasible within an engaged community oncology practice with investments in bioinformatics, decision support software tools, and a team of precision oncology experts supported by a robust clinical trial menu. On-demand vMTBs can be widely adopted to enhance clinical trial enrolment. Future directions include studying the impact of vMTBs on patient outcomes over time.

The devil you know: A population-level analysis of tumor-based next-generation sequencing of high-grade epithelial ovarian cancer (151)

<b>Objectives:</b> Clinical use of tumor-based next-generation sequencing (tbNGS) is expanding, but the indications and implications remain vague. We aimed to assess the clinical relevance of tbNGS data in a large cohort of patients with high-grade epithelial ovarian cancer. <b>Methods:</b> Electronic medical records (both internal notes and external scanned documents) of 905 patients with high-grade serous or endometrioid ovarian cancer were queried for tbNGS data using optical character recognition (OCR) and natural language processing (NLP) technologies. Identified results and clinical-demographic information were entered into an institutional database. Data were analyzed with descriptive statistics and log-rank tests comparing selected genetic alterations to progression-free survival (PFS). <b>Results:</b> Three hundred and twelve (34.4%) patients had their tbNGS results identified. Tests used had variable breadth, ranging from 46 genes to whole-exome sequencing. The most common was an institutional panel that evaluated 146 genes. Nearly all (99.7%) patients had their genomic alterations (mutations or copy number variations) identified. On average, each tumor had 2.38 (±2.46) aberrations. Two hundred and fifty-two (80.8%) patients had actionable alterations, defined as relaying eligibility for targeted treatment or clinical trial, according to the Precision Oncology Decision Support System (PODSS) database. Twenty-seven (10.7% of those with actionable abnormalities) enrolled in relevant clinical trials. In total, 630 mutations were detected in the population, with half of these (58.6%) considered actionable. The most common type of abnormality encountered was missense substitution (52.9%), followed by amplification (14.7%) and a frameshift mutation (9.72%). The most frequently mutated genes were <i>TP53</i> (93.0%), <i>PIK3CA</i> (9.35%), <i>BRCA1/2</i> (8.30%), <i>NF1</i> (4.90%), and <i>KRAS</i> (4.52%). Among these, only <i>BRCA1/2</i> was associated with a significant improvement in PFS (20.1 vs 14.1 months, p=0.046). <i>CCNE1</i> amplification, the most prevalent copy number variant, was present in 9.4% of those tested. Specific mutation types or copy number variations were not correlated with PFS. <b>Conclusions:</b> Clinical use of tbNGS is prevalent in ovarian cancer. Of patients undergoing tbNGS at our institution, four out of five gained the opportunity for an FDA-approved treatment or genotypically relevant clinical trial. Description of population-level tumor genomics could help identify therapeutic targets and guide the development of clinical decision support tools.

Repeat large panel genomic sequencing identifies actionable alterations and characterizes the genomic landscape in patients with metastatic solid tumors.

3076 Background: The implementation of genomic profiling with next generation sequencing has revolutionized the field of precision oncology. Comprehensive genomic testing of tumors to identify actionable genomic alterations is now commonly performed in the care of patients with advanced/metastatic disease. Although the genomic profile of tumors has been shown to evolve with progression and intervening treatments, the role of repeat genomic testing is not well established. We sought to determine the evolution of actionable genomic alterations in patients undergoing repeat genomic testing on the same comprehensive genomic panel. Methods: We retrospectively examined the molecular profiles and medical records of 262 patients with metastatic solid tumors treated in MD Anderson who underwent genomic testing on the same panel (Oncomine, Thermo Fisher) for the detection of somatic mutations in the coding sequence of 143 cancer-related genes, on at least 2 separate occasions. Genomic alterations were reviewed by a central Precision Oncology Decision Support (PODS) team in order to provide annotations at the alteration level on the functional significance. Results: 262 patients underwent repeat genomic testing using the same genomic panel on samples collected at different time points from July 2010 to Dec 2021 across tumor types. Changes in alterations (gain or loss) were identified on repeat testing in most patients (66%) We then specifically assessed changes in alterations that were categorized as actionable if annotated by the PODS team at the time of reporting. A gain or loss of an actionable alteration was detected in 38% (100/262) patients. New actionable alterations were frequently identified (73%; 73/100), while 41% had loss of an actionable alteration (41/100). 14% had both loss and gain of actionable alteration on repeat testing; 58% had new actionable alteration identified alone; 27% had loss of actionable alteration only. Actionable alterations identified on repeat testing included alterations in PI3K/AKT (27%), EGFR (15%), and MAPK (16%). On repeat testing, changes in ³2 actionable alterations were frequently identified in the same test (43%). Conclusions: Repeat large panel genomic testing identifies both gain and loss of actionable alterations in patients with advanced metastatic cancers. Actionable aberrations frequently co-exist with alterations in a variety of other genes, which highlights the complexities of treating patients with metastatic cancer on progression of disease and suggests that tailored combination strategies may be necessary in these patients.

Abstract 392: Patient-specific, tiered, variant-level actionability correlates with functional effect in growth survival assay

Abstract Purpose: There is increasing recognition that variants within an actionable gene may differ in their functional impact and therapeutic implications. The Precision Oncology Decision Support (PODS) team at MD Anderson Cancer Center (MDA) curates a knowledgebase of cancer-associated genomic alterations for their functional impact and therapeutic actionability. However, a substantial number of variants are not characterized within the published literature. In the MDA-PODS classification scheme, these variants are classified as “potentially” actionable, as opposed to “unknown”, if evidence exists that other variants within the same region are oncogenic. To determine the value of this tiered actionability approach, we assessed if a variant with a “potentially” assertion is more likely to have an experimentally validated oncogenic effect within a functional genomics platform (Ng et al., Cancer Cell, 2018). Procedures: Alterations researched within the published literature and found to be of unknown significance were submitted to the functional genomics platform. These variants were tested within two cell line models (Ba/F3 and MCF10A) to assess growth factor-independent survival. Results were returned to PODS indicating whether the variant conferred a change in cell viability compared with the wildtype gene. PODS then compared the functional effect of the variant with the predetermined actionability assertion. Results: During 2015-2019, PODS received functional genomics results for 485 alterations spanning 38 genes. 115 (24%) of the alterations increased survival and were thus actionable, while 364 (75%) had no effect or suppressed cell viability. Six alterations had conflicting effects in the two cell line models and were not further considered. Of the 479 variants (in 38 genes), 208 variants (43%) in 20 genes were classified as “potentially” actionable prior to functional genomics, while 254 variants (53%) in 35 genes were classified as “unknown”. 17 (4%) were classified as Yes/No for actionability based on other criteria, such as drug response. 78 (38%) of the 208 “potentially” actionable variants were found to be oncogenic in the functional genomics platform as opposed to only 29 (11%) of the 254 variants classified as “unknown” for actionability (p&amp;lt;.01). 70% of the 78 “potentially” actionable, experimentally-validated variants were located within two amino acids of another known oncogenic variant, per our current knowledgebase. Conclusions: These data display the value of providing a tiered, variant-level actionability scheme that includes a “potentially” actionable assertion, as it is associated with a greater likelihood of being functionally validated. Although genomically-matched therapy is most compelling for patients with known actionable variants and functional testing adds value, tiered actionability predictions can inform therapeutic decisions. Citation Format: Amber Johnson, Patrick Kwok-Shing Ng, Michael Kahle, Julia Castillo, Bianca Amador, Vijaykumar Holla, Thuy Vu, Le Huang, Fei Su, Sunhee Kim, Jia Zeng, Md Abu Shufean, Tara Conway, Kenna R. Shaw, Timothy A. Yap, Jordi Rodon, Funda Meric-Bernstam. Patient-specific, tiered, variant-level actionability correlates with functional effect in growth survival assay [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 392.

Abstract B17: Activating mutations of phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) gene confer sensitivity to PD-L1 checkpoint inhibition in metastatic adenocarcinoma patients

Abstract Background: Immunotherapy has produced durable responses in advanced cancers with microsatellite instability (MSI). However, response rates remain low in many adenocarcinomas, and biomarkers predictive of response are urgently needed. Preliminary data suggest that PD-L1 checkpoint expression is increased in early-stage microsatellite stable colorectal cancers with PIK3CA mutations as compared to wild-type PIK3CA. Increased expression of PD-L1 has been reported to be associated with response to immunotherapy. The impact of activating PIK3CA mutations on response to anti-PD-L1 checkpoint inhibitors in metastatic adenocarcinomas remains unknown. The objective of our study is to evaluate the impact of activating PIK3CA mutations on treatment outcomes with anti-PD-L1 checkpoint inhibitors in metastatic adenocarcinomas without MSI. Methods: We retrospectively identified patients with metastatic adenocarcinoma treated with anti-PD-L1 antibody, either as monotherapy or in combination with anti-PD-1 antibody, in early-phase clinical trials at the MD Anderson Clinical Center for Targeted Therapy. Molecular profiling was performed on archived primary or metastatic tumor tissue using CLIA-certified targeted next-generation sequencing (FoundationOne or Oncomine), and results were annotated for functional significance by the MD Anderson Precision Oncology Decision Support Team. Fischer's exact test was used for group comparisons. Median time to treatment failure (TTF) was estimated using the Kaplan and Meier method, and compared among groups by the log-rank test. Results: A total of 27 patients (female, 20; male, 7) with metastatic adenocarcinomas without MSI from 12 different sites (breast, colon and esophageal cancers being the most common cancer types with 7, 5 and 2 patients, respectively) were treated with anti-PD-L1 antibody. Activating PIK3CA mutations were present in 6 (22%) patients (E545K, 2; H1047R, 2; E542K, 1; co-occurring R88Q and K111N, 1). Patients with PIK3CA mutations had longer median TTF than patients with a wild-type PIK3CA (8.7 vs. 2.8 months, P=0.045). In addition, 3 of 6 (50%) patients with PIK3CA mutations had stable disease on treatment for &amp;gt; 6 months as compared to 1 of 21 (4.8%) patients with a wild-type PIK3CA (P=0.02). Conclusion: Our preliminary data suggest that activating PIK3CA mutations in patients with metastatic adenocarcinoma without MSI are associated with prolonged stable disease and longer TTF on treatment with PD-L1 checkpoint inhibitors. Future studies to validate our observations are warranted. Citation Format: Maliha Nusrat, David Hong, Sarina A. Piha-Paul, Vivek Subbiah, Jordi Rodon, Apostolia Tsimberidou, Abha Adat, Yan Wang, Michael Overman, Scott Kopetz, Funda Meric-Bernstam, Filip Janku. Activating mutations of phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) gene confer sensitivity to PD-L1 checkpoint inhibition in metastatic adenocarcinoma patients [abstract]. In: Proceedings of the AACR Special Conference on Targeting PI3K/mTOR Signaling; 2018 Nov 30-Dec 8; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Res 2020;18(10_Suppl):Abstract nr B17.

Rate of change in investigational treatment options: An analysis of reports from a large precision oncology decision support effort

PurposeGenomic analysis of individual patients is now affordable, and therapies targeting specific molecular aberrations are being tested in clinical trials. Genomically-informed therapy is relevant to many clinical domains, but is particularly applicable to cancer treatment. However, even specialized clinicians need help to interpret genomic data, to navigate the complicated space of clinical trials, and to keep up with the rapidly expanding biomedical literature. To quantitate the cognitive load on treating clinicians, we attempt to quantitate the rate of change in potential treatment options for patients considering genomically-relevant and genomically-selected therapy for cancer. Materials and methodsTo this end, we analyzed patient-specific reports generated by a precision oncology decision support team (PODS) at a large academic cancer center. Two types of potential treatment options were analyzed: FDA-approved genomically-relevant and genomically-selected therapies and therapies available via clinical trials. We focused on two clinically-actionable alterations: ERBB2 (Her2/neu; amplified vs. non-amplified) and BRAF mutation (V600 vs. non-V600). To determine changes in available treatment options, we grouped patients into similar groups by disease site (ERBB2: breast, gastric and “other”; BRAF: melanoma, non-melanoma). ResultsA total of 2927 reports for 2366 unique patients were generated 8/2016-12/2018. Reports included 9902 gene variants and 150 disease classifications. BRAF mutation and ERBB2 amplification were annotated with therapeutic options in 270 reports (225 unique patients). The median survival time of a therapeutic option was nine months. ConclusionWhen compared to “traditional” clinical practice guideline recommendations, treatment options for personalized cancer therapy change seven times more rapidly; partly due to change in knowledge and partly due to logistics such as clinical trial availability.

Abstract 3172: Evaluation of cytotoxic T cell infiltration and clinical benefit from PD-L1 checkpoint inhibition among PIK3CA mutant microsatellite stable (MSS) metastatic adenocarcinoma patients

Abstract Background: Immunotherapy has produced durable responses in advanced cancers with microsatellite instability. However, response rates remain very low for microsatellite stable (MSS) adenocarcinomas, and biomarkers predictive of response are urgently needed. PI3K pathway alterations are known to modulate anti-tumor immune microenvironment. We previously reported increased cytotoxic T cell infiltration and greater clinical benefit from immunotherapy among PIK3CA mutant MSS colorectal cancer patients as compared to PIK3CA wild-type patients. Here, we investigated the immune repertoire and treatment outcomes with anti-PD-L1 checkpoint inhibitors in PIK3CA mutant MSS metastatic adenocarcinoma patients. Methods: We retrospectively identified metastatic adenocarcinoma patients treated with anti-PD-L1 antibody either as monotherapy or in combination with anti-PD-1 antibody in early phase clinical trials at the MD Anderson Center for Targeted Therapy. Molecular profiling was performed on archived primary or metastatic tumor tissue using CLIA certified targeted next generation sequencing (FoundationOne or Oncomine). PIK3CA mutations were annotated for functional significance by the Precision Oncology Decision Support Team. Density of cytotoxic T cells was determined using immunohistochemistry (IHC) on biopsy or resection specimens. Microsatellite status was assessed using IHC or polymerase chain reaction. Median time to treatment failure (TTF) was estimated using Kaplan and Meier method. Groups were compared using Fischer's exact, Student's T test or Log-rank test, as appropriate. Results: Twenty-seven patients with MSS metastatic adenocarcinoma (female, 20; male, 7) from 12 different sites were treated with anti-PD-L1 antibody. Breast, colon and esophageal cancers were the most common cancers with 7, 5 and 3 patients, respectively. Activating PIK3CA mutations were present in 7/27 (26%) patients (H1047R, 3; E545K, 2; E542K, 1; co-occuring R88Q and K111N, 1). Patients with activating PIK3CA mutations had trend towards longer median TTF as compared to PIK3CA wild-type patients (6.6 vs. 3.1 months, P=0.1). Similarly, 3/7 (42.9%) patients with activating PIK3CA mutations had stable disease (SD) &amp;gt; 6 months as compared to 2/20 (10%) patients with wild type PIK3CA (P=0.09). CD8 densities were available for 8 patients (5 PIK3CA mutant, 3 PIK3CA wild). Among PIK3CA mutant patients, mean CD8 density was higher in patients who had SD &amp;gt; 6 months as compared to those who did not (630.3 vs 305.1 cells/mm3; P=0.06). Conclusion: Activating PIK3CA mutations are enriched among MSS metastatic adenocarcinoma patients with prolonged SD (&amp;gt; 6 months) on anti-PD-L1 treatment. PIK3CA mutant patients with prolonged SD have higher CD8 densities, possibly due to high-affinity neopeptide-HLA interactions. Larger studies to validate our observations are warranted, including assessment of neopeptide load and HLA types, which may help identify an immunotherapy sensitive subset of PIK3CA mutant MSS adenocarcinoma. Citation Format: Maliha Nusrat, Coya Tapia, S. Greg Call, David S. Hong, Sarina A. Piha-Paul, Vivek Subbiah, Jordi Rodon, Apostolia M. Tsimberidou, Abha Adat, Yan Wang, Funda Meric-Bernstam, Michael J. Overman, Scott Kopetz, Filip Janku. Evaluation of cytotoxic T cell infiltration and clinical benefit from PD-L1 checkpoint inhibition among PIK3CA mutant microsatellite stable (MSS) metastatic adenocarcinoma patients [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3172.

Identifying functional loss of ATM gene in patients with advanced cancer.

3629 Background: ATM is frequently mutated in cancer, and defects may serve as a putative predictive biomarker. However, the functional impact of most ATM variants is not well known. In this study, we examined the relationship between ATM variants and ATM protein expression to better discern ATM functional defects in patients (pts) with advanced cancer. Methods: We retrospectively identified pts seen at MD Anderson Cancer Center who had ATM variants detected on CLIA-certified next generation sequencing (NGS) assays. ATM immunohistochemistry (IHC) was performed on available tumors. We then prospectively assessed ATM IHC on tumors from pts who were referred for DNA damage repair inhibitor (DDRi) trials. Functional classification of the variants was performed via published in silico tools and/or precision oncology decision support (PODS). An IHC cut-off of 100% loss in tumor cell nuclei defined ATM loss of protein (LOP). Results: Of 1394 ATM-mutant tumors identified retrospectively, ATM alterations were classified as 16% (N = 216) inactivating, 12% (N = 163) potentially inactivating, 71% (N = 993) variant of unknown significance (VUS), and 2% (N = 22) benign. Coding variants were seen across the ATM exonic structure/splice sites, and 20 individual variants were shared in &gt; 10 pts. 263/297 available retrospective tumor samples had interpretable IHC results; 27% (N = 72) had ATM LOP. LOP was most prevalent in tumors with inactivating ATM variants (39/100, 39%); but, importantly, LOP was seen in 20% (N = 33/162) of potentially inactivating/VUS, thus better clarifying their functional impact. In the prospective cohort of 217 pt tumors, 17% (N = 37) had ATM LOP. 29% (N = 62/217) of this cohort also had ATM variants. ATM LOP was seen in 48% of tumors with inactivating variants (N = 14/29), 25% of tumors with potentially/VUS(N = 9/36), and 9% (N = 14/156) of tumors without ATM variants identified. ATM LOP was detected most commonly in colorectal (24%; N = 8/34), cholangiocarcinoma (20%; N = 6/30), prostate (16%; N = 16/104) and pancreatic (9%; N = 1/11) cancers among this cohort of pts referred for DDRi trials. Conclusions: ATM coding variants occurred across the gene, with certain variants shared across tumor types. The functional impact of most ATM variants was VUS, and ATM LOP can help clarify function in up to 25% of these VUS. Also, ATM LOP can be seen even in tumors without ATM variants identified, suggesting epigenetic or post-translational loss. Future prospective studies assessing predictive capability of paired DNA and protein-level profiling of ATM are warranted.

Variety of clinical trial enrollment by actionable tumor suppressor genotype.

e15674 Background: In the last decade, genomics has been increasingly used for therapeutic management of cancer. In precision oncology, genes that can be directly or indirectly targeted with therapeutic agents are considered “actionable”. However, not all alterations affect gene function and alterations in tumor suppressor genes are especially challenging to interpret. The objective of this study was to determine frequency of trial enrollment in patients with “actionable” tumor suppressor alterations and determine whether this varied by genotype and tumor type. Methods: A single-institution retrospective review was performed on patients with metastatic, advanced, or recurrent solid tumors perceived to likely benefit from genomic profiling. Between 1/2015 and 1/2020, the top 5 tumor suppressor alterations CDKN2A, BRCA1/2, PTEN, ATM and NF1 were considered most common “actionable” tumor suppressor genes among patients reviewed by MD Anderson Precision Oncology Decision Support system. Once literature-based or inferred actionability of the tumor-suppressor alteration was determined, a clinical record review was performed to assess an institution-specific annotation detailing trial availability. This annotation matches somatic genomic alteration based on therapeutic significance and availability of matched investigative therapy based on available literature at the time. From here, patient demographics and trial enrollment was extracted from the electronic medical record. Results: 424 patients were categorized as having actionable alterations that had a trial available for enrollment based on genomic characteristics. Of these, 61 patients (14%) enrolled in a matched trial. Primary reasons for non-enrollment for a specific alteration were: enrollment in non-targeted therapeutic trial (39%), treatment with another stand of care therapy (32%), or enrollment in another genomically matched trial targeting another genomic alteration (14%). There was variability in enrollment by disease type approaching statistical significance (p = 0.073). CDKN2A alterations were the most common while BRCA1/2 had the highest rate of matched enrollment (39.5%). Conclusions: In this cohort of patients, the presence of an actionable tumor-suppressor alteration was associated with trial enrollment varying by genotype. Further work is needed to determine the impact of decision support on trial enrollment as well as strategies to increase actionability by building genomically-driven combination therapy trials.

Evaluation of a computational decision support system for molecularly targeted treatment planning by the clinical outcome data of the randomized trial SHIVA01.

3642 Background: Precision oncology requires the identification of individual molecular pathomechanisms to find optimal personalized treatment strategies for every cancer patient. Incorporation of complex molecular information into routine clinical practice remains a significant challenge due to the lack of a reproducible, standardized process of clinical decision making. Methods: To provide a standardized process for molecular interpretation, we develop a precision oncology decision support system, the Realtime Oncology Molecular Treatment Calculator (MTC). MTC is a rule-based medical knowledge engine that dynamically aggregates and ranks relevant scientific and clinical evidence using currently 26,000 evidence-based associations and reproducible algorithm scoring of drivers, molecular targets to match molecular alterations to efficient therapies. To validate this novel method and system, we used data of the SHIVA01 trial of molecularly targeted therapy (Lancet Oncol 2015 16:1324-34). Molecular profiles of participants were uploaded to MTC and aggregated evidence level (AEL) values of associated targeted treatments were calculated, including those used in the SHIVA01 trial. Results: The MTC output provided a prioritized list of drugs associated with the driver alterations in the patient molecular profile, where ranking is based on AEL values. Of 113 patients who received targeted therapy with available clinical best response data, disease control was experienced in 63 cases (PR: 5, SD: 58), while disease progression occurred in 50 cases. The average AEL score for the therapies applied was significantly higher in the responsive group than in the non-responsive group (1512 and 614, respectively (p = 0.049)). In 94 cases, drugs other than those used for therapy were ranked higher by the MTC. The average AEL difference between the top-ranked and the used drugs was in an inverse correlation with clinical response, i.e. smaller differences associated with a better outcome. Conclusions: Results indicate that the aggregation of evidence-based tumor-driver-target-drug associations using standardized mathematical algorithms of this computational tool is a promising novel approach to improve clinical decisions in precision oncology. Further validation based on the results of other targeted clinical trials and real-life data using more detailed molecular profiles is warranted to explore the full clinical potential of this novel medical solution.

Abstract C130: Precision oncology decision support within the Gastrointestinal Cancer Center at Dana-Farber Cancer Institute (GI TARGET)

Abstract Precision oncology is fast becoming the new paradigm for cancer treatment. This approach involves molecular profiling of patient tumors and tailoring of a treatment regimen based on the unique combination of targetable alterations identified therein. At Dana-Farber Cancer Institute (DFCI), over 24,000 patient tumors have been sequenced using the NGS platform OncoPanel since 2013. OncoPanel is a comprehensive genomic characterization of a tumor and it can be time-consuming to discern those alterations that are interesting from an academic or research perspective from those that can be leveraged to direct patient care. To address this issue, the GI TARGET (Treatment Assistance Regarding Genomic Evaluation of Tumors) project was initiated to provide decision support for oncologists within the DFCI Gastrointestinal Cancer Center (GCC). GI TARGET is a collaboration between the DFCI GCC, the Brigham &amp; Women’s Hospital (BWH) Center for Advanced Molecular Diagnostics (CAMD), and the DFCI Knowledge Systems Group, developers of the precision medicine clinical trial matching platform MatchMiner. Report generation involves review of OncoPanel data in the context of patient clinical history via one of two workflows; complex genomic cases are reviewed in a weekly Molecular Tumor Board comprised of oncologists, molecular pathologists, a PhD-level Clinical Genomics Scientist, and representatives from the MatchMiner team, while ‘simple’ genomic cases are reviewed by both an oncologist and a PhD-level Clinical Genomics Scientist. Since the launch of the project in January 2018, we have signed out over 1000 GI TARGET reports. We are assessing the utility of these reports using a number of metrics including clinical trial enrollment tracking via MatchMiner, follow-up on recommendations via chart abstraction, and oncologist feedback. The aims of GI TARGET are 1) to reduce the burden of genomic analysis on the treating oncologist by providing expert-led deep review of OncoPanel and exploration of associated therapy/additional patient care considerations, 2) to support clinical trial enrollment by matching the results of somatic testing with ongoing clinical studies, and 3) to provide an example framework for how precision oncology can be incorporated into standard clinical cancer care, which may be helpful to guide efforts in other care settings, particularly in community hospitals where the vast majority (an estimated 80%-85%) of cancer patients in the United States receive their care. Citation Format: Rachel B Keller, Tali Mazor, Marios Giannakis, Jonathan Nowak, Lynette Sholl, Andrew Aguirre, Adam Bass, Nilay Sethi, Ankur Nagaraja, Lauren Brais, Emma Reilly, Ethan Cerami, Brian Wolpin. Precision oncology decision support within the Gastrointestinal Cancer Center at Dana-Farber Cancer Institute (GI TARGET) [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr C130. doi:10.1158/1535-7163.TARG-19-C130

Precision Oncology Decision Support: Current Approaches and Strategies for the Future.

With the increasing availability of genomics, routine analysis of advanced cancers is now feasible. Treatment selection is frequently guided by the molecular characteristics of a patient's tumor, and an increasing number of trials are genomically selected. Furthermore, multiple studies have demonstrated the benefit of therapies that are chosen based upon the molecular profile of a tumor. However, the rapid evolution of genomic testing platforms and emergence of new technologies make interpreting molecular testing reports more challenging. More sophisticated precision oncology decision support services are essential. This review outlines existing tools available for health care providers and precision oncology teams and highlights strategies for optimizing decision support. Specific attention is given to the assays currently available for molecular testing, as well as considerations for interpreting alteration information. This article also discusses strategies for identifying and matching patients to clinical trials, current challenges, and proposals for future development of precision oncology decision support. Clin Cancer Res; 24(12); 2719-31. ©2018 AACR.

Effect of matched therapy in metastatic colorectal cancer on progression free survival in the phase I setting.

619 Background: The benefits of matching targeted treatments to aberrations identified by molecular profiling (MP) is unclear. Outcomes in phase 1 settings have been traditionally reported across tumor histologies. We report outcomes based on a metastatic colorectal cancer (mCRC) population. Methods: Patients (pts) with mCRC receiving at least one dose of treatment on a phase 1 study were annotated for variants detected by MP. A precision oncology decision support (PODS) team determined variant function and actionability. A matched therapy (MT) was defined as allocation to a novel agent that targeted the aberration or predicted pathway deemed actionable by PODS. Progression-free survival (PFS) was estimated using the Kaplan-Meier method. A Cox proportional hazards model was used to estimate hazard ratios (HR). Results: A total of 370 patients enrolled onto 467 phase 1 trials were identified from January 2012 to April 2017. 106 enrolments were assigned to MT. Pts with microsatellite instability-high (MSI-H), BRAFV600E, PIK3CA mutation were more likely to be assigned a MT, while left-sided tumors and RAS mutant patients were less likely to be treated with a MT. Molecularly-targeted regimens (MTR) were utilized more frequently in MT while immune-targeting MTR was more common in non-MT. BRAFV600E mutations and HER2 amplification/overexpression made up 44.3% of MT. There was a significant difference in PFS between the MT vs non-MT group (HR 0.65, 95% CI 0.51-0.83, p = 0.016) in univariate analysis. The 6-month PFS probability was 31% (95% CI; 23-41%) versus 13% (95% CI; 10-17%) respectively. Other significant factors in univariate analysis associated with longer PFS were MSI-H, BRAFV600E and use of a regimen containing cytotoxic chemotherapy while RAS mutations were associated with shorter PFS. In multivariate analysis, after correcting for mutation status, allocation to a MT was associated with improved PFS (HR = 0.72, 95% CI 0.50-0.99, p = 0.043). Conclusions: Matching clinical trial enrollment to MP based on dedicated decision support is associated with improved outcomes in mCRC patients. The MT strategy is still hampered by a limited number of actionable variants, and is driven by a small number of active MTs.

Physician interpretation of genomic test results and treatment selection.

Genomic testing is increasingly performed in oncology, but concerns remain regarding the clinician's ability to interpret results. In the current study, the authors sought to determine the agreement between physicians and genomic annotators from the Precision Oncology Decision Support (PODS) team at The University of Texas MD Anderson Cancer Center in Houston regarding actionability and the clinical use of test results. On a prospective protocol, patients underwent clinical genomic testing for hotspot mutations in 46 or 50 genes. Six months after sequencing, physicians received questionnaires for patients who demonstrated a variant in an actionable gene, investigating their perceptions regarding the actionability of alterations and clinical use of these findings. Genomic annotators independently classified these variants as actionable, potentially actionable, unknown, or not actionable. Physicians completed 250 of 288 questionnaires (87% response rate). Physicians considered 168 of 250 patients (67%) as having an actionable alteration; of these, 165 patients (98%) were considered to have an actionable alteration by the PODS team and 3 were of unknown significance. Physicians were aware of genotype-matched therapy available for 119 patients (71%) and 48 of these 119 patients (40%) received matched therapy. Approximately 46% of patients in whom physicians regarded alterations as not actionable (36 of 79 patients) were classified as having an actionable/potentially actionable mutation by the PODS team. However, many of these were only theoretically actionable due to limited trials and/or therapies (eg, KRAS). Physicians are aware of recurrent mutations in actionable genes on "hotspot" panels. As larger genomic panels are used, there may be a growing need for annotation of actionability. Decision support to increase awareness of genomically relevant trials and novel treatment options for recurrent mutations (eg, KRAS) also are needed. Cancer 2018;124:966-72. © 2017 American Cancer Society.

Clinical Use of Precision Oncology Decision Support

Precision oncology is hindered by the lack of decision support for determining the functional and therapeutic significance of genomic alterations in tumors and relevant clinically available options. To bridge this knowledge gap, we established a Precision Oncology Decision Support (PODS) team that provides annotations at the alteration-level and subsequently determined if clinical decision-making was influenced. Genomic alterations were annotated to determine actionability based on a variant's known or potential functional and/or therapeutic significance. The medical records of a subset of patients annotated in 2015 were manually reviewed to assess trial enrollment. A web-based survey was implemented to capture the reasons why genotype-matched therapies were not pursued. PODS processed 1,669 requests for annotation of 4,084 alterations (2,254 unique) across 49 tumor types for 1,197 patients. 2,444 annotations for 669 patients included an actionable variant call: 32.5% actionable, 9.4% potentially, 29.7% unknown, 28.4% non-actionable. 66% of patients had at least one actionable/potentially actionable alteration. 20.6% (110/535) patients annotated enrolled on a genotype-matched trial. Trial enrolment was significantly higher for patients with actionable/potentially actionable alterations (92/333, 27.6%) than those with unknown (16/136, 11.8%) and non-actionable (2/66, 3%) alterations (p=0.00004). Actionable alterations in PTEN, PIK3CA, and ERBB2 most frequently led to enrollment on genotype-matched trials. Clinicians cited a variety of reasons why patients with actionable alterations did not enroll on trials. Over half of alterations annotated were of unknown significance or non-actionable. Physicians were more likely to enroll a patient on a genotype-matched trial when an annotation supported actionability. Future studies are needed to demonstrate the impact of decision support on trial enrollment and oncologic outcomes.

Clinical utilization of precision oncology decision support for genomically-informed cancer therapy.

11605Background: Precision oncology is hindered by the lack of decision support for determining whether patients’ genomic alterations are actionable. To bridge this knowledge gap, we established a ...