Clinical Use of Precision Oncology Decision Support

Molecular tumour boards - current and future considerations for precision oncology

Over the past 15 years, rapid progress has been made in developmental therapeutics, especially regarding the use of matched targeted therapies against specific oncogenic molecular alterations across cancer types. Molecular tumour boards (MTBs) are panels of expert physicians, scientists, health-care providers and patient advocates who review and interpret molecular-profiling results for individual patients with cancer and match each patient to available therapies, which can include investigational drugs. Interpretation of the molecular alterations found in each patient is a complicated task that requires an understanding of their contextual functional effects and their correlations with sensitivity or resistance to specific treatments. The criteria for determining the actionability of molecular alterations and selecting matched treatments are constantly evolving. Therefore, MTBs have an increasingly necessary role in optimizing the allocation of biomarker-directed therapies and the implementation of precision oncology. Ultimately, increased MTB availability, accessibility and performance are likely to improve patient care. The challenges faced by MTBs are increasing, owing to the plethora of identifiable molecular alterations and immune markers in tumours of individual patients and their evolving clinical significance as more and more data on patient outcomes and results from clinical trials become available. Beyond next-generation sequencing, broader biomarker analyses can provide useful information. However, greater funding, resources and expertise are needed to ensure the sustainability of MTBs and expand their outreach to underserved populations. Harmonization between practice and policy will be required to optimally implement precision oncology. Herein, we discuss the evolving role of MTBs and current and future considerations for their use in precision oncology.

Travel Time and Distance and Participation in Precision Oncology Trials at the National Cancer Center Hospital

Importance

Genotype-matched trials, which are becoming increasingly important in the precision oncology era, require referrals from institutions providing comprehensive genomic profiling (CGP) testing to those conducting these trials, and the travel burden for trial participation is significant. However, it remains unknown whether travel time or distance are associated with genotype-matched trial participation.

Objective

To assess whether travel time or distance are associated with disparities in genotype-matched trial participation following CGP testing.

Design, Setting, and Participants

This retrospective cohort study from June 2020 to June 2022 included patients with advanced or metastatic solid tumors referred to the National Cancer Center Hospital for participation in genotype-matched trials following CGP testing and discussion by molecular tumor boards. Data were analyzed from June to October 2022.

Exposures

Travel time and distance.

Main Outcomes and Measures

The primary and secondary outcomes were enrollment in genotype-matched trials and all-cancer clinical trials, respectively.

Results

Of 1127 patients (mean [range] age, 62 [16-85] years; 584 women [52%]; all residents of Japan), 127 (11%) and 241 (21%) were enrolled in genotype-matched trials and all-cancer clinical trials, respectively. The overall median (IQR) travel distance and time were 38 (21-107) km and 55 (35-110) minutes, respectively. On multivariable regression with 23 covariates, travel distance (≥100 km vs <100 km) was not associated with the likelihood of genotype-matched trial participation (26 of 310 patients [8%] vs 101 of 807 patients [12%]; odds ratio [OR], 0.64; 95% CI, 0.40-1.02), whereas in patients with travel time of 120 minutes or more, the likelihood of genotype-matched trial participation was significantly lower than those with travel time less than 120 minutes (19 of 276 patients [7%] vs 108 of 851 patients [13%]; OR, 0.51; 95% CI, 0.29-0.84). The likelihood of genotype-matched trial participation decreased as travel time increased from less than 40 (38 of 283 patients [13%]) to 40 to 120 (70 of 568 patients [12%]) and 120 or more (19 of 276 patients [7%]) minutes (OR, 0.74; 95% CI, 0.48-1.17; OR, 0.41; 95% CI, 0.22-0.74, respectively). Neither travel time nor distance were associated with the likelihood of all-cancer clinical trial participation.

Conclusions and Relevance

In this cohort study of patients undergoing CGP testing, an increased travel time was associated with a decreased likelihood of genotype-matched trial participation. This warrants further research on interventions, such as decentralization of clinical trials to mitigate travel burden.

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.

Therapeutic landscape and future direction of metastatic colorectal cancer

In the era of targeted therapy based on genomic alterations, the treatment strategy for metastatic colorectal cancer (mCRC) has been changing. Before systemic treatment initiation, determination of tumour genomic status for KRAS and NRAS, BRAF^V600E mutations, ERBB2, and microsatellite instability and/or mismatch repair (MMR) status is recommended. In patients with deficient MMR and BRAF^V600E mCRC, randomized phase III trials have established the efficacy of pembrolizumab as first-line therapy and the combination of encorafenib and cetuximab as second-line or third-line therapy. In addition, new agents have been actively developed in other rare molecular fractions such as ERBB2 alterations and KRAS^G12C mutations. In March 2022, the combination of pertuzumab and trastuzumab for ERBB2-positive mCRC was approved in Japan, thereby combining real-world evidence from the SCRUM-Japan Registry. As the populations are highly fragmented owing to rare genomic alterations, various strategies in clinical development are expected. Clinical development of a tumour-agnostic approach, such as NTRK fusion and tumour mutational burden, has successfully introduced corresponding drugs to clinical practice. Considering the difficulty of randomized trials owing to cost-benefit and rarity, a promising solution could be real-world evidence utilized as an external control from the molecular-based disease registry.

Improving public cancer care by implementing precision medicine in Norway: IMPRESS-Norway

Background

Matching treatment based on tumour molecular characteristics has revolutionized the treatment of some cancers and has given hope to many patients. Although personalized cancer care is an old concept, renewed attention has arisen due to recent advancements in cancer diagnostics including access to high-throughput sequencing of tumour tissue. Targeted therapies interfering with cancer specific pathways have been developed and approved for subgroups of patients. These drugs might just as well be efficient in other diagnostic subgroups, not investigated in pharma-led clinical studies, but their potential use on new indications is never explored due to limited number of patients.

Methods

In this national, investigator-initiated, prospective, open-label, non-randomized combined basket- and umbrella-trial, patients are enrolled in multiple parallel cohorts. Each cohort is defined by the patient’s tumour type, molecular profile of the tumour, and study drug. Treatment outcome in each cohort is monitored by using a Simon two-stage-like ‘admissible’ monitoring plan to identify evidence of clinical activity.

All drugs available in IMPRESS-Norway have regulatory approval and are funded by pharmaceutical companies. Molecular diagnostics are funded by the public health care system.

Discussion

Precision oncology means to stratify treatment based on specific patient characteristics and the molecular profile of the tumor. Use of targeted drugs is currently restricted to specific biomarker-defined subgroups of patients according to their market authorization. However, other cancer patients might also benefit of treatment with these drugs if the same biomarker is present. The emerging technologies in molecular diagnostics are now being implemented in Norway and it is publicly reimbursed, thus more cancer patients will have a more comprehensive genomic profiling of their tumour. Patients with actionable genomic alterations in their tumour may have the possibility to try precision cancer drugs through IMPRESS-Norway, if standard treatment is no longer an option, and the drugs are available in the study. This might benefit some patients. In addition, it is a good example of a public–private collaboration to establish a national infrastructure for precision oncology.

Trial registrations EudraCT: 2020-004414-35, registered 02/19/2021; ClinicalTrial.gov: NCT04817956, registered 03/26/2021.

Can artificial intelligence improve cancer treatments?

Artificial intelligence (AI) powered by the accumulating clinical and molecular data about cancer has fueled the expectation that a transformation in cancer treatments towards significant improvement of patient outcomes is at hand. However, such transformation has been so far elusive. The opacity of AI algorithms and the lack of quality annotated data being available at population scale are among the challenges to the application of AI in oncology. Fundamentally however, the heterogeneity of cancer and its evolutionary dynamics make every tumor response to therapy sufficiently different from the population, machine-learned statistical models, challenging hence the capacity of these models to yield reliable inferences about treatment recommendations that can improve patient outcomes. This article reviews the nominal elements of clinical decision-making for precision oncology and frames the utility of AI to cancer treatment improvements in light of cancer unique challenges.

Implementation of a Molecular Tumor Registry to Support the Adoption of Precision Oncology Within an Academic Medical Center: The Duke University Experience

Comprehensive genomic profiling to inform targeted therapy selection is a central part of oncology care. However, the volume and complexity of alterations uncovered through genomic profiling make it difficult for oncologists to choose the most appropriate therapy for their patients. Here, we present a solution to this problem, The Molecular Registry of Tumors (MRT) and our Molecular Tumor Board (MTB).

PATIENTS AND METHODS

MRT is an internally developed system that aggregates and normalizes genomic profiling results from multiple sources. MRT serves as the foundation for our MTB, a team that reviews genomic results for all Duke University Health System cancer patients, provides notifications for targeted therapies, matches patients to biomarker-driven trials, and monitors the molecular landscape of tumors at our institution.

RESULTS

Among 215 patients reviewed by our MTB over a 6-month period, we identified 176 alterations associated with therapeutic sensitivity, 15 resistance alterations, and 51 alterations with potential germline implications. Of reviewed patients, 17% were subsequently treated with a targeted therapy. For 12 molecular therapies approved during the course of this work, we identified between two and 71 patients who could qualify for treatment based on retrospective MRT data. An analysis of 14 biomarker-driven clinical trials found that MRT successfully identified 42% of patients who ultimately enrolled. Finally, an analysis of 4,130 comprehensive genomic profiles from 3,771 patients revealed that the frequency of clinically significant therapeutic alterations varied from approximately 20% to 70% depending on the tumor type and sequencing test used.

CONCLUSION

With robust informatics tools, such as MRT, and the right MTB structure, a precision cancer medicine program can be developed, which provides great benefit to providers and patients with cancer.

Genomic, Transcriptomic, and Proteomic Profiling of Metastatic Breast Cancer

PURPOSE

Metastatic breast cancer (MBC) is not curable and there is a growing interest in personalized therapy options. Here we report molecular profiling of MBC focusing on molecular evolution in actionable alterations.

EXPERIMENTAL DESIGN

Sixty-two patients with MBC were included. An analysis of DNA, RNA, and functional proteomics was done, and matched primary and metastatic tumors were compared when feasible.

RESULTS

Targeted exome sequencing of 41 tumors identified common alterations in TP53 (21; 51%) and PIK3CA (20; 49%), as well as alterations in several emerging biomarkers such as NF1 mutations/deletions (6; 15%), PTEN mutations (4; 10%), and ARID1A mutations/deletions (6; 15%). Among 27 hormone receptor-positive patients, we identified MDM2 amplifications (3; 11%), FGFR1 amplifications (5; 19%), ATM mutations (2; 7%), and ESR1 mutations (4; 15%). In 10 patients with matched primary and metastatic tumors that underwent targeted exome sequencing, discordances in actionable alterations were common, including NF1 loss in 3 patients, loss of PIK3CA mutation in 1 patient, and acquired ESR1 mutations in 3 patients. RNA sequencing in matched samples confirmed loss of NF1 expression with genomic NF1 loss. Among 33 patients with matched primary and metastatic samples that underwent RNA profiling, 14 actionable genes were differentially expressed, including antibody-drug conjugate targets LIV-1 and B7-H3.

CONCLUSIONS

Molecular profiling in MBC reveals multiple common as well as less frequent but potentially actionable alterations. Genomic and transcriptional profiling demonstrates intertumoral heterogeneity and potential evolution of actionable targets with tumor progression. Further work is needed to optimize testing and integrated analysis for treatment selection.

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

PURPOSE

Genomic 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 METHODS

To 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).

RESULTS

A 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.

CONCLUSION

When 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.

Medical oncologists' perspectives of the Veterans Affairs National Precision Oncology Program

BACKGROUND

To support the rising need for testing and to standardize tumor DNA sequencing practices within the U.S. Department of Veterans Affairs (VA)'s Veterans Health Administration (VHA), the National Precision Oncology Program (NPOP) was launched in 2016. We sought to assess oncologists' practices, concerns, and perceptions regarding Next-Generation Sequencing (NGS) and the NPOP.

MATERIALS AND METHODS

Using a purposive total sampling approach, oncologists who had previously ordered NGS for at least one tumor sample through the NPOP were invited to participate in semi-structured interviews. Questions assessed the following: expectations for the NPOP, procedural requirements, applicability of testing results, and the summative utility of the NPOP. Interviews were assessed using an open coding approach. Thematic analysis was conducted to evaluate the completed codebook. Themes were defined deductively by reviewing the direct responses to interview questions as well as inductively by identifying emerging patterns of data.

RESULTS

Of the 105 medical oncologists who were invited to participate, 20 (19%) were interviewed from 19 different VA medical centers in 14 states. Five recurrent themes were observed: (1) Educational Efforts Regarding Tumor DNA Sequencing Should be Undertaken, (2) Pathology Departments Share a Critical Role in Facilitating Test Completion, (3) Tumor DNA Sequencing via NGS Serves as the Most Comprehensive Testing Modality within Precision Oncology, (4) The Availability of the NPOP Has Expanded Options for Select Patients, and (5) The Completion of Tumor DNA Sequencing through the NPOP Could Help Improve Research Efforts within VHA Oncology Practices.

CONCLUSION

Medical oncologists believe that the availability of tumor DNA sequencing through the NPOP could potentially lead to an improvement in outcomes for veterans with metastatic solid tumors. Efforts should be directed toward improving oncologists' understanding of sequencing, strengthening collaborative relationships between oncologists and pathologists, and assessing the role of comprehensive NGS panels within the battery of precision tests.

A Review of Precision Oncology Knowledgebases for Determining the Clinical Actionability of Genetic Variants

The increased availability of tumor genetic testing and targeted cancer therapies contributes to the advancement of precision medicine in the field of oncology. Precision oncology knowledgebases provide a way of organizing clinically relevant genetic information in a way that is easily accessible for both oncologists and patients, facilitating the genetic-based clinical decision making. Many organizations and companies have built precision oncology knowledgebases, intended for multiple users. In general, these knowledgebases offer information on cancer-related genetic variants as well as their associated diagnostic, prognostic, and therapeutic implications, but they often differ in their information curations, designs, and user experiences. It is advisable that oncologists use multiple knowledgebases during their practice to have them complement each other. In the future, convergence toward common standards and formats is needed to ensure that the comprehensive knowledge across all sources can be unified to bring the oncology community closer to the achievement of the goal of precision oncology.

Identification of Actionable Genomic Alterations Using Circulating Cell-Free DNA

PURPOSE

Cell-free DNA (cfDNA) next-generation sequencing is a noninvasive approach for genomic testing. We report the frequency of identifying alterations and their clinical actionability in patients with advanced/metastatic cancer.

PATIENTS AND METHODS

Prospectively consented patients had cfDNA testing performed. Alterations were assessed for therapeutic implications.

RESULTS

We enrolled 575 patients with 37 tumor types. Of these patients, 438 (76.2%) had at least one alteration detected, and 205 (35.7%) had one or more alterations of high potential for clinical action. In diseases with 10 or more patients enrolled, 50% or more had at least one alteration deemed of high potential for clinical action. Trials were identified in 80% of patients (286 of 357) with any alteration and in 92% of patients (188 of 205) with one or more alterations of high potential for clinical action of whom 57.6% (118 of 205) had 6 or more months of follow-up available. Of these patients, 10% (12 of 118) had received genomically matched therapy through enrollment in clinical trials (n = 8), off-label drug use (n = 3), or standard of care (n = 1). Although 88.6% of all patients had a performance status of 0 or 1 upon enrollment, the primary reason for not acting on alterations was poor performance status at next treatment change (28.1%; 27 of 96).

CONCLUSION

cfDNA testing represents a readily accessible method for genomic testing and allows for detection of genomic alterations in most patients with advanced disease. Utility may be higher in patients interested in investigational therapeutics with adequate performance status. Additional study is needed to determine whether utility is enhanced by testing earlier in the treatment course.

Operationalization of Next-Generation Sequencing and Decision Support for Precision Oncology

Genomic testing has become a part of routine oncology care and plays critical roles in diagnosis, prognostic assessment, and treatment selection. Thus, in parallel, the variety of genomic testing providers and sequencing platforms has grown exponentially. Selection of the best-fit panel for each case can be daunting, with many factors to consider. Among them is whether alteration interpretation and therapy/clinical trial matching are included and/or sufficient. In this article, we review some common commercially available sequencing platforms for the genes and types of alterations tested, samples needed, and reporting content provided. We review publicly available resources for a do-it-yourself approach to alteration interpretation when it is not provided or when supplemental research is needed, along with resources to identify genomically matched treatment options that are approved and/or investigational. However, with both commercially provided interpretation and publicly available resources, there are still caveats and limitations that can stem from insufficient or ambiguous nomenclature as well as from the presentation of information. Use cases in which clinical decision making was affected are discussed. After treatment options are identified, it is important to assess the level of evidence for use within the patient's tumor type and molecular profile. However, numerous level-of-evidence scales have been published in recent years, so we provide a publicly available tool to facilitate interoperability. The level of evidence, along with other factors, such as allelic frequency and copy number, can be used to prioritize treatment options when multiple are identified.

Next-generation sequencing for the general cancer patient

Next-generation sequencing is a novel method of DNA sequencing that has become a cornerstone of precision oncology. This sequencing method detects differences in specific DNA sequences between a sample and a reference genome or matched normal DNA. In addition to single-nucleotide variants, other insertions, deletions, copy number changes, and fusions may be drivers of cancer growth, and thus represent therapeutic opportunities. As a result, genomic characterization has been increasingly used to guide treatment decisions, especially in patients with advanced disease. This review discusses the basic technologies involved in next-generation sequencing, the applications of this method, and limitations in the clinical realm.

Real-World Outcomes of an Automated Physician Support System for Genome-Driven Oncology

PURPOSE

Matching patients to investigational therapies requires new tools to support physician decision making. We designed and implemented Precision Insight Support Engine (PRECISE), an automated, just-in-time, clinical-grade informatics platform to identify and dynamically track patients on the basis of molecular and clinical criteria. Real-world use of this tool was analyzed to determine whether PRECISE facilitated enrollment to early-phase, genome-driven trials.

MATERIALS AND METHODS

We analyzed patients who were enrolled in genome-driven, early-phase trials using PRECISE at Memorial Sloan Kettering Cancer Center between April 2014 and January 2018. Primary end point was the proportion of enrolled patients who were successfully identified using PRECISE before enrollment. Secondary end points included time from sequencing and PRECISE identification to enrollment. Reasons for a failure to identify genomically matched patients were also explored.

RESULTS

Data were analyzed from 41 therapeutic trials led by 19 principal investigators. In total, 755 patients were accrued to these studies during the period that PRECISE was used. PRECISE successfully identified 327 patients (43%) before enrollment. Patients were diagnosed with 29 tumor types and harbored alterations in 43 oncogenes, most commonly ERBB2 (21.3%), PIK3CA (14.1%), and BRAF (8.7%). Median time from sequencing to enrollment was 163 days (interquartile range, 66 to 357 days), and from PRECISE identification to enrollment 87 days (interquartile range, 37 to 180 days). Common reasons for failing to identify patients before enrollment included accrual on the basis of molecular alterations that did not match pre-established PRECISE genomic eligibility (140 [33%] of 428) and external sequencing not available for parsing (127 [30%] of 428).

CONCLUSION

PRECISE identified 43% of all patients accrued to a diverse cohort of early-phase, genome-matched studies. Purpose-built informatics platforms represent a novel and potentially effective method for matching patients to molecularly selected studies.

Referrals to a Phase I Clinic and Trial Enrollment in the Molecular Screening Era

BACKGROUND

Enrichment of patients based on molecular biomarkers is increasingly used in early phase clinical trials. Molecular profiling of patients with advanced cancers can identify specific genomic alterations to inform decisions about investigational treatment(s). Our aim was to evaluate the outcomes of new patient referrals to a large academic solid tumor phase I clinical trial program after the implementation of molecular profiling.

MATERIALS AND METHODS

Retrospective chart review of all new referrals to the Princess Margaret Cancer Centre (PM) phase I clinic from May 2012 to December 2014. Molecular profiling using either MALDI-TOF hotspot mutation genotyping or targeted panel DNA sequencing was performed for patients at PM or community hospitals through the institutional IMPACT/COMPACT trials.

RESULTS

A total of 971 new patient referrals were included for this analysis. Twenty-seven percent of referrals assessed in clinic were subsequently enrolled in phase I trials. Of all new referrals, 41% had prior molecular profiling, of whom 11% (n = 42) were enrolled in genotype-matched trials. Patients with prior molecular profiling were younger, more heavily pretreated, and had more favorable Princess Margaret Hospital Index (PMHI) scores. Eastern Cooperative Oncology Group (ECOG) performance status 0-1 (p = .002), internal referrals within PM (p = .002), and PMHI (p ≤ .001) were independently associated with successful trial enrollment in multivariable analysis.

CONCLUSION

Although nearly half of new patients referred to a phase I clinic had prior molecular profiling, the proportion subsequently enrolled into clinical trials was low. Prior molecular profiling was not an independent predictor of clinical trial enrollment.

IMPLICATIONS FOR PRACTICE

The landscape of oncology drug development is evolving alongside technological advancements. Recently, large academic medical centers have implemented clinical sequencing protocols to identify patients with actionable genomic alterations to enroll in therapeutic clinical trials. This study evaluates patient referral and enrollment patterns in a large academic phase I clinical trials program following the implementation of a molecular profiling program. Performance status and referral from a physician within the institution were associated with successful trial enrollment, whereas prior molecular profiling was not an independent predictor.

Precision oncology: separating the wheat from the chaff

Precision oncology based on next-generation sequencing (NGS) test is growing in daily clinical practice. However, the real impact of this strategy in patients' outcome on a large scale remains uncertain. In this review, we summarise existing literature on this topic, limitations for broad NGS implementation, bottlenecks in genomic variant interpretation and the role of molecular tumour boards.

Molecular Profiling of Patients with Pancreatic Cancer: Initial Results from the Know Your Tumor Initiative

Purpose: To broaden access to and implementation of precision medicine in the care of patients with pancreatic cancer, the Know Your Tumor (KYT) program was initiated using a turn-key precision medicine system. Patients undergo commercially available multiomic profiling to determine molecularly rationalized clinical trials and off-label therapies.Experimental Design: Tumor samples were obtained for 640 patients from 287 academic and community practices covering 44 states. College of American Pathologists/Clinical Laboratory Improvement Amendments-accredited laboratories were used for genomic, proteomic, and phosphoprotein-based molecular profiling.Results: Tumor samples were adequate for next-generation sequencing in 96% and IHC in 91% of patients. A tumor board reviewed the results for every patient and found actionable genomic alterations in 50% of patients (with 27% highly actionable) and actionable proteomic alterations (excluding chemopredictive markers) in 5%. Actionable alterations commonly found were in DNA repair genes (BRCA1/2 or ATM mutations, 8.4%) and cell-cycle genes (CCND1/2/3 or CDK4/6 alterations, 8.1%). A subset of samples was assessed for actionable phosphoprotein markers. Among patients with highly actionable biomarkers, those who received matched therapy (n = 17) had a significantly longer median progression-free survival (PFS) than those who received unmatched therapy [n = 18; PFS = 4.1 vs. 1.9 months; HR, 0.47; 95% confidence interval (CI): 0.24-0.94; P _adj = 0.03].Conclusions: A comprehensive precision medicine system can be implemented in community and academic settings, with highly actionable findings observed in over 25% of pancreatic cancers. Patients whose tumors have highly actionable alterations and receive matched therapy demonstrated significantly increased PFS. Our findings support further prospective evaluation of precision oncology in pancreatic cancer. Clin Cancer Res; 24(20); 5018-27. ©2018 AACR.

Precision Medicine in Hormone Receptor-Positive Breast Cancer

In recent decades, breast cancer has become largely manageable due to successes with hormone receptor targeting. Hormone receptor-positive tumors have favorable outcomes in comparison to estrogen receptor (ESR1, ER)/progesterone receptor-negative tumors given the targetable nature of these tumors, as well as their inherently less aggressive character. Nonetheless, treatment resistance is frequently encountered due to a variety of mechanisms, including ESR1 mutations and loss of ER expression. A new era of precision medicine utilizes a range of methodologies to allow real-time analysis of individual genomic signatures in metastases and liquid biopsies with the goal of finding clinically actionable targets. Preliminary studies have shown improved progression-free survival and overall survival with implementation of this information for clinical decision making. In this review, we will discuss the opportunities and challenges in integrating precision medicine through next-generation genomic sequencing into the management of breast cancer.

Survival Outcomes by TP53 Mutation Status in Metastatic Breast Cancer

Purpose

We sought to determine the significant genomic alterations in patients with metastatic breast cancer (MBC), and survival outcomes in common genotypes.

Patients and Methods

High-depth next generation sequencing was performed for 202 genes in tumor and normal DNA from 257 patients with MBC, including 165 patients with ER/PR+ HER2- (hormone receptor positive, HR+ positive), 32 patients with HER2+ and 60 patients with triple negative (ER/PR/HER2-) cancer. Kaplan Meier survival analysis was performed in our discovery set, in breast cancer patients analyzed in The Cancer Genome Atlas, and in a separate cohort of 98 patients with MBC who underwent clinical genomic testing.

Results

Significantly mutated genes (SMGs) varied by histology and tumor subtype, but TP53 was a SMG in all three subtypes. The most SMGs in HR+ patients included PIK3CA (32%), TP53 (29%), GATA3 (15%), CDH1 (8%), MAP3K1 (8%), PTEN (5%), TGFBR2 (4%), AKT1 (4%), and MAP2K4 (4%). TP53 mutations were associated with shorter recurrence-free survival (P=0.004), progression-free survival (P=0.00057) and overall survival (P=0.003). Further, TP53 status was prognostic among HR+ patients with PIK3CA mutations. TP53 mutations were also associated with poorer overall survival in the 442 HR+ breast cancer patients in the TCGA (P=0.042) and in an independent set of 96 HR+ MBC who underwent clinical sequencing (P=0.0004).

Conclusions

SMGs differ by tumor subtype but TP53 is significantly mutated in all three breast cancer subtypes. TP53 mutations are associated with poor prognosis in HR+ breast cancer. TP53 mutations should be considered in the design and interpretation of precision oncology trials.