biomarkers comparative effectiveness shared decision

O N L I N E R E P O R T F R O M A S C O 2 0 1 0 | 1

OVERVIEW OF EDUCATIONAL NEED

The method of making treatment decisions in oncology is evolving from a paternalistic model in which physicians make decisions based on consensus guidelines and evidence-based data to a shared model in which patients participate in choosing their own treatment. This new model requires a change in the way information is shared and treatment decisions are made. Integrating shared decision making into practice requires the healthcare professional to properly prepare patients for informed decision making, address his or her own biases, and anticipate patient values. Healthcare providers need to learn how to most effectively integrate patients into the shared decision-making process.

ACCREDITATION STATEMENT

The Institute for Continuing Healthcare Education is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.

The Institute for Continuing Healthcare Education designates this enduring material for a maximum of 1.25 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Online Report from ASCO 2010PRINCIPLES ON THE PATHWAY TO TREATMENT:

BIOMARKERS COMPARATIVE EFFECTIVENESS SHARED DECISION MAKING TREATMENT

EDUCATIONAL PLANNING COMMITTEE

Edith P. Mitchell, MD, FACP Clinical Professor of Medicine and Medical Oncology Program Leader in Gastrointestinal Oncology Thomas Jefferson University Philadelphia, Pennsylvania

Christine M. O’Leary, PharmD, BCPS Director, Clinical Services Institute for Continuing Healthcare Education Adjunct Pharmacy Practice Faculty University of the Sciences in Philadelphia Philadelphia, Pennsylvania

Release Date: January 25, 2011 | Expiration Date: January 25, 2012

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2 | P R I N C I P L E S O N T H E P A T H W A Y T O T R E A T M E N T

TARGET AUDIENCE

The target audience for this educational initiative includes U.S.-based oncologists. It is also appropriate for other healthcare providers involved in the care of cancer patients.

LEARNING OBJECTIVES

Upon completion of this activity, the participant should be able to:

• Assess the evidence on available biomarkers for cancer treatment and incorporate a biomarker screening evaluation into patient treatment plans

• Assess the evidence on the value of shared decision making on overall outcomes in cancer patients

• Integrate comparative effectiveness research into individual treatment plans

DISCLOSURE

It is the policy of the Institute for Continuing Healthcare Education (the Institute) that the education presented at Institute-provided, CME-certified activities be unbiased and based upon scientific evidence. To help participants make judgments about the presence of bias, the Institute provides information that faculty have disclosed about financial relationships they have with commercial entities that produce or market products or services related to the content of this educational activity. Any relationships faculty members may have with com-mercial entities have been disclosed and reviewed, and any potential conflicts have been resolved.

Relationships are abbreviated as follows: A, Advisor/review panel member; C, Consultant; G, Grant/research support recipient; E, Educational committee; H, Honoraria; PE, Promotional event talks; S, Stock shareholder; SB, Speakers’ Bureau; O, Other.

EDUCATIONAL PLANNING COMMITTEE

Edith Mitchell, MD, has disclosed the following relevant financial relationships that have occurred within the past 12 months: Amgen/A, SB; DiagnoCure, Response Genetics, Inc./C; Bristol-Myers Squibb, Genentech, Inc., Merck & Co., Inc./SB.

Christine M. O’Leary, PharmD, BCPS, has disclosed no relevant financial relationships specific to the subject matter of this activity that have occurred within the past 12 months.

CONTENT FREELANCER

Elizabeth Friedenwald (Medical Writer) has disclosed that she has not had any relevant financial relationships specific to the subject matter of this activity that have occurred within the past 12 months.

CONTENT PEER REVIEWER

Johanna Bendell, MD, has disclosed that she has no relevant financial relationships that have occurred within the past 12 months.

ACTIVITY DEVELOPMENT AND MANAGEMENT TEAM

Cathy Pagano, CCMEP; Scott Kober, CCMEP; Karen J. Thomas, CCMEP; Sandra Davidson; April Reynolds, MS, ELS; Tina Chiu, MEd; Melissa M. Schepa-carter, CMP; and Courtney Cohen are employees of the Institute for Continuing Healthcare Education and are collectively responsible for the planning, develop-ment, management, and evaluation of this CME activity. These individuals have disclosed that they have had no relevant financial relationships specific to the subject matter of this activity that have occurred within the past 12 months. Shunda R. Irons-Brown, PhD, MBA, also an employee of the Institute, has disclosed the following relationships: Merck & Co., Inc./S; Bristol-Myers Squibb, GlaxoSmithKline/O.

The educational content of this activity has been peer reviewed and validated to ensure that it is a fair and balanced representation of the topic, based on the best available evidence.

PRODUCT DISCLOSURE

This educational activity will include off-label discussion of the following: chemotherapeutic agents.

COMMERCIAL SUPPORT

Supported by an educational grant from

GENERAL DISCLOSURE AND COPYRIGHT STATEMENT

The opinions expressed in this activity are those of the participating faculty and not those of the Institute for Continuing Healthcare Education, Genentech, Inc., or any manufacturers of products mentioned herein.

The information is provided for general medical education purposes only and is not meant to substitute for the independent medical judgment of a healthcare professional relative to diagnostic and treatment options of a specific patient’s medical condition. In no event will the Institute for Continuing Healthcare Education be responsible for any decision made or action taken based upon the information provided through this activity.

Participants are encouraged to consult the package insert for all products for updated information and changes regarding indications, dosages, and contraindications. This recommendation is particularly important with new or infrequently used products.

Copyright 2010, Institute for Continuing Healthcare Education (the Institute). All rights reserved. No part of this presentation may be reproduced or transmitted in any other form or by any means, electronic or mechanical, without first obtaining written permission from the Institute.


INTRODUCTION

Oncology is a dynamic field of medicine that is constantly integrating new technologies and ideas in classifying tumors and tumor subtypes based on histopathologic characteristics. The evolving role of biomarkers is changing diagnostic and treatment patterns through a deeper understanding of the mechanisms of tumorigenesis and factors related to growth, progression, and development of metastases. The concept of comparative effectiveness research is designed to impact treatment and healthcare decisions by providing evidence such as potential effectiveness, benefits, and possible toxicities of different treatment options. Resulting outcomes lead to optimizing patient care — as evidenced by changing prescribing patterns and potential cost savings — by encouraging more rigorous evaluation of treatment efficacy and safety. Shared decision making is changing the way healthcare providers and patients communicate with each other because of the broad availability of information.

What information do providers need to know to incorporate these principles into practice? This monograph will describe some of the important factors that impact these diverse topics, with a focus on improving care for patients with cancer.

BIOMARKERS

The technological advances in medicine in the last decade have been numerous and dramatic. One of the most significant has been the development of technology that allows scientists to peer deeply into molecules and unravel the human genome. This improved understanding of the human genome has allowed for the development of novel therapies and has become an integral part of cancer management, with applications including patient stratification, drug regimen selection, toxicity avoidance, therapeutic monitoring, as well as detection of predisposition to disease processes. These applications have resulted in more personalized approaches to treatment for patients. As technology is becoming more efficient and accurate (though not always affordable), correlation with survival and other outcomes has led to the widespread adoption of some tests by healthcare providers. For example, in 2002, the detection of 1 million base pairs in a person’s genome required years of work and cost more than $500,000, whereas in 2010, the same project would take a few hours and cost a few hundred dollars. It is expected that the ability to sequence a person’s full genome will soon cost less than $1000 [Feero 2010].

The general availability of information about molecular pathology and genomics of a patient’s disease has led to fundamental changes in treatment and prognosis. In particular, the field of oncology is directly benefiting from the discovery and exploitation of biomarkers. Biomarkers provide molecular information about cancers that can be prognostic or predictive, which

allows for more targeted and effective treatment for individual patients. The information provided by biomarkers is creating a new paradigm of individualized treatments for patients with cancer; however, this technology is in its infancy, and only a few biomarkers, such as human epidermal growth factor receptors 2 (HER2) in breast cancer and carcinoembryonic antigen (CEA) in colorectal cancer, have been shown through rigorous testing to be clinically relevant. Many more biomarkers are being evaluated and, undoubtedly, many more have yet to be discovered. This monograph will discuss just a few.

Definition: Biomarkers are characteristics that can be

objectively measured and used as indicators of biologic

processes, both normal and pathogenic, or as predictors

of pharmacologic responses to a therapeutic intervention

[Biomarkers 2001].


TRIPLE-NEGATIVE BREAST CANCER | In the last 20 years, mortality due to breast cancer has declined substantially, in part because of the effective use of adjuvant medical therapy [EBCTCG 2005]. However, in addition to discovering new thera-pies, identifying cancers that are more likely to respond well to a particular therapy may improve patient selectivity (but not necessarily outcomes) [Dowsett 2008]. A number of biomarkers are well established in determining the prognosis and effective management of breast cancer, including the status of estrogen receptors (ERs), progesterone receptors (PRs), and HER2. In fact, these biomarkers have become a fundamental part of defining the disease; new breast cancer treatments have been developed based on the presence or absence of these markers as well as on the use of cDNA microarrays to determine molecular pheno-type [Anders 2008, Dowsett 2008, Sotiriou 2009].

In addition, a number of subtypes of breast cancer are more aggressive and difficult to treat. Genomic analyses of breast cancer phenotypes have subclassified breast cancers into 4 important categories, each with different clinical behavior. These include the luminal A (ER-positive and/or PR-positive, HER2-neg-ative), luminal B (ER-positive and/or PR-positive, HER2-positive), HER2-positive (ER-negative and PR-negative), and basal-like (ER-negative, PR-negative, HER2-negative, CK5/6-positive, and/or HER1-positive) phenotypes, which, in this order, have increasingly aggressive behaviors and worse prognoses. Triple-negative breast cancers, the majority of which are considered basal-like, account

for only a small proportion of breast cancer diagnoses (<20%) but have an increased risk of recurrence and death compared to other breast cancers [Dent 2007, Anders 2008]. It should also be noted that the majority of patients who have the BRCA1 gene mutation develop triple-negative breast cancer [Anders 2008]. A recent review of 284 women with triple-negative invasive breast cancer showed that 10.6% had BRCA1 mutations. Of these 30 patients, none had a family history of breast or ovarian cancer, and the mean age of onset was 40.2 years [Fostira 2010].

Because of the lack of expression of ER, PR, and HER in triple-negative breast cancer, there is a lack of effectiveness with many of the standard targeted therapies. Although triple-negative breast cancer has low expression of ER, PR, and HER2, it has high expression of CK5, CK14, caveolin-1, CAIX, p63, and epider-mal growth factor receptor (EGFR, HER1). It is important to note that “triple negative” and “basal-like” are not synonymous terms. “Basal-like” describes the molecular phenotype of the tumor us-ing cDNA microarrays; “triple negative” is a term based on clinical assays of ER, PR, and HER2. While most triple-negative tumors are in fact basal-like, up to 30% are not. Similarly, up to 40% of basal-like tumors are not triple negative, but rather ER-, PR-, or HER2-positive [Anders 2008].

Data presented at the ASCO 2010 Annual Meeting on serveral potential biomarkers that are under investigation in triple-negative breast cancer are shown in Table 1.

TABLE 1

DATA ON BIOMARKERS FOR TRIPLE-NEGATIVE BREAST CANCER PRESENTED AT ASCO 2010

POTENTIAL BIOMARKER

AUTHOR RESULT DESCRIPTION

Inactivation of BRCA1 by promoter methylation

Grushko 2010Predictive of potential response to a common targeted therapy such as PARP inhibitors

198 cancers analyzed for methylation of the BRAC1 promoter and expression of ER, PR, HER2, EGFR, and CK5/6. Of 191 tumors, 28% were triple negative, and 43% of these had BRCA1 methylation. Of 74 tumors, 19% were basal-like, and 48% of these had BRCA1 methylation.

PTEN, EGFR, KRAS, PIK3CA as markers of response to cetuximab

Khambata-Ford 2010

PTEN positivity associated with improved PFS in triple-negative patients. None of the biomarkers were associated with cetuximab benefit.

Retrospective review of phase II trial in 154 patients in which only TN patients showed an improved response rate in an irinotecan/carboplatin + cetuximab arm vs. a cetuximab arm alone (49% vs. 38%). In this study, tissue samples were available for 124 cases. KRAS mutations were found in 5%, PIK3CA mutations in 17% of all cases and 17% of TN cases, and ER positivity in 18%. EGFR positivity was 47%, and PTEN positivity was 48%. Only PTEN positivity in patients treated with cetuximab was associated with improved PFS.

PARP, poly (ADP-ribose) polymerase; PFS, progression-free survival; TN, triple negative

Review of Selected Biomarkers in Selected Cancers: Triple-Negative Breast, Colorectal, Pancreatic, and Non-small Cell Lung


COLORECTAL CANCER | Colorectal cancer (CRC) continues to be a leading cause of cancer morbidity and mortality, with an estimated incidence of 142,570 cases in 2010. It is the cause of mortality in 9% of patients, or 51,370 persons, with cancer each year [ACS 2010]. Although an increase in screening with colonoscopy and stool tests has contributed to a substantial decrease in incidence, the lifetime risk of developing CRC is still 5.5% [Compton 2008].

The improved understanding of the molecular underpinnings of CRC has contributed to the development of a model of CRC as a multi-step accumulation of genetic and epigenetic alterations, and several such pathways have been identified. Up to 85% of CRCs are associated with some type of genetic abnormal-ity that may be caused by mutations of a number of different genes, including KRAS, BRAF, and TP53 as well as translocation of chromosome 18q [Compton 2008, Smits 2008]. Between 15%-20% of CRCs have defects in repair genes (eg, hMLH1, hMSH2, hPMS1, hPMS2, and hMSH6), which leads to ineffective mismatch repair pathways or microsatellite instability [Compton 2008, Smits 2008]. The importance of genetic abnormalities in

CRC is reflected in practice guidelines. For example, in the 2011 National Comprehensive Cancer Network (NCCN) Guidelines™ for colon cancer [Engstrom 2011], determination of tumor KRAS gene status is recommended for all metastatic stage IV disease and before consideration of cetuximab or panitumimab therapy. In addition, although data are inconsistent, an optional screen-ing for BRAF V600E mutation might be considered. This year at ASCO, results were presented from many trials that investigated the importance of biomarkers in predicting response to therapies. Some of the key information is shown in Table 2.

PANCREATIC CANCER | Cancer of the pancreas is a devastat-ing disease that has a 5-year survival of only 6%. In 2010, an estimated 43,140 new cases will be diagnosed and 36,800 per-sons will die of pancreatic cancer. The low survival rate is partially due to the disease being diagnosed most often in later stages, as well as few effective treatments. Surgery is only possible in less than 20% of patients. Chemotherapy and radiation therapy are also options, and the combination of gemcitabine with erlotinib is considered standard of care for advanced disease [ACS 2010].

TABLE 2

INVESTIGATIONS OF BIOMARKERS IN COLORECTAL CANCER PRESENTED AT ASCO 2010

POTENTIAL BIOMARKER


BRAF and KRAS predictive of cetuximab efficacy

Van Cutsem 2010

KRAS was predictive of treatment outcome in patients with mCRC who are receiving first-line cetuximab + FOLFIRI. However, BRAF mutation status did not appear to be predictive.

In the phase III CRYSTAL trial, 1198 patients were enrolled and 1063 were evaluated for KRAS and BRAF mutational status. For the 666 patients with KRAS wild-type tumors, all efficacy endpoints were significantly improved with cetuximab + FOLFIRI compared to FOLFIRI alone. The BRAF wild-type was found in 625 of 566 patients, while KRAS wild-type and mutation was found in 59. BRAF mutation was associated with poor prognosis in both treatment arms.

KRAS, NRAS, and BRAF in patients with advanced CRC being treated with first-line EGFR

Maughan 2010

Although this trial had negative outcomes, KRAS wild-type tumors may benefit from cetuximab with oxaliplatin.

In the COIN trial of 1630 patients, 80% of patients were genotyped and 43% had KRAS mutations, 4% NRAS mutations, and 8% BRAF mutations. In this trial, the addition of cetuximab to oxaliplatin did not result in a change in OS or PFS. For patients who had KRAS wild-type, there was no change in OS or PFS, but there was an increased response rate.

KRAS and BRAF predictive of efficacy of cetuximab + chemotherapy

Bokemeyer 2010

KRAS wild-type tumors are associated with improvement in response and duration in patients given cetuximab therapy compared to those who received cetuximab alone.

In the CRYSTAL and OPUS studies, 1063 and 315 tissue samples, respectively, were evaluated for KRAS mutations. Samples that were KRAS wild-type were then evaluated for BRAF, and it was found that 625/666 in the CRYSTAL study were BRAF/KRAS wild-type vs. 175/179 in the OPUS study. KRAS wild-type tumors were associated with a significant improvement in OR, PFS, or OS in patients given cetuximab therapy compared to those who received cetuximab alone. Best outcomes were seen in patients who had both KRAS and BRAF wild-type tumors; however, BRAF mutation status did not appear to be predictive of cetuximab efficacy in combination with chemotherapy.

mCRC, metastatic colorectal cancer; OR, overall response; OS, overall survival


Improvements in the treatment of pancreatic cancer have been only incremental. Biomarkers are being widely investigated to improve prognosis and appropriate treatment. Some potential biomarkers for pancreatic cancer are shown in Table 3.

NON-SMALL CELL LUNG CANCER | Lung cancer is the lead-ing cause of death by cancer in men and women. In 2010, an estimated 222,520 new cases will be diagnosed and 157,300 people will die, which is equivalent to about 28% of all deaths by cancer. Non-small cell lung (NSCLC) cancer accounts for 85% of all cases. Despite improvements in treatment, 5-year survival for NSCLC is only 17% [ACS 2010].

For localized cases, surgery is possible, but lung cancer is diagnosed in later stages when adjuvant chemotherapy may be beneficial. The standard of care for stage II and IIIa cancer is cisplatin-based chemotherapy. However, responses are modest, and new treatments continue to be investigated. Potential biomarkers that can optimize therapy are being identified (see Table 4).

COMPARATIVE EFFECTIVENESS

As biomarkers are integrated into the treatment plan for patients with cancer, an ongoing challenge is evaluating their effective-ness. As with any emerging technology, rigorous evaluation is necessary to evaluate the clinical and cost effectiveness of biomarkers in improving treatments and patient outcomes. In 2009, the U.S. government authorized the expenditure of $1.1 billion to conduct research comparing “clinical outcomes, effectiveness, and appropriateness of items, services, and procedures that are used to prevent, diagnose, or treat diseases, disorders, and other health conditions” [Weinstein 2009]. Clinicians and patients will benefit from a system that offers comprehensive and unbiased information about new therapies and how they compare to existing standards of care.

The fundamental model of comparative effectiveness research (CER) is comprised of the well-designed and well-conducted prospective, randomized clinical trial and carefully performed meta-analyses of these trials. However, many treatments cannot be evaluated using these strategies or have yet to be rigorously studied. Other research approaches can be used instead, such as cohort, population, and modeling studies; additional approaches have also been proposed (see Table 5).

Ultimately, it is hoped that CER will improve health outcomes by helping to identify the most effective and appropriate therapies for patients while potentially minimizing exposure to ineffective interventions. The challenge for proponents of CER is the perception that it might lead to limited or loss of physician

autonomy. However, when the clinical perspective is the guiding rationale, CER should help guide healthcare providers and patients to choose treatments centered on evidenced-based data of the highest quality [Mushlin 2010, Weinstein 2009, Lyman 2010].

Patients will likely benefit from the development of well- designed studies that are integral to the comparative efficacy initiative [Garber 2009]. As an important part of the evolving concept of personalized medicine, information on biomarkers is expected to improve both treatment effectiveness and safety by identifying the optimal population. In addition, targeted use of treatments will hopefully help control healthcare costs by reducing the use of inappropriate or ineffective therapies. However, biomarkers will need to be co-developed along with potential targeted therapies, which will present special challenges. Certainly, any trials for targeted therapies should archive tissue samples for potential future research. Other recommendations that have been proposed are shown in Table 6.

SHARED DECISION MAKING

The trend in oncology is toward personalizing medicine with preventive measures and treatments that are tailored to an individual patient’s circumstances, both environmental and genomic. However, changing patient expectations complicate the integration of these new technologies, such as biomarkers, into the treatment continuum for patients with cancer. Treatment decisions in oncology are evolving from a paternalistic model, in which physicians make unilateral decisions for patients based on consensus guidelines and evidence-based data, to a shared model in which patients participate in choosing their own treatment. This new model, called shared decision making (SDM), acknowledges the growing belief that patients have a right to autonomy and self-determination as well as the realization that physicians are often not able to independently determine a patient’s values and beliefs about their care [Pieterse 2008]. The majority of patients want and expect to be included in decisions about their healthcare because they believe that they are responsible for their own well-being [Stacey 2008]. In fact, recent studies in women with breast cancer show that active patient involvement is associated with greater satisfaction with care, including improvement in quality of life, physical and social functioning, as well as fewer reported side effects [Stacey 2008].

The vast majority of patients and physicians believe in SDM. A recent study showed that, in the oncology setting, most physicians (95%) and most patients (96%) prefer SDM. In fact, the growing acceptance of SDM by the healthcare community is illustrated by its integration into recent consensus guidelines.


TABLE 3 | BIOMARKERS FOR PANCREATIC CANCER

POTENTIAL BIOMARKER


hENT— transports gemcitabine into cells

Farrell 2008Predictive marker of gemcitabine therapy

In a prospective trial of 538 patients who were randomized to either gemcitabine or 5-flourouracil, immunohistochemistry was performed on tissue to determine hENT1 status. In the gemcitabine arm, hENT1 expression was associated with OS and disease-free survival but was not in the 5-fluorourocil arm [Farrell 2008].

hENT Tan 2010

Although this trial had negative outcomes, KRAS wild-type tumors may respond to cetuximab with oxaliplatin

Prognostic for improved OS and PFS

RRM1— determinant of gemcitabine resistance

Tan 2010Shows trend toward being predictive of response to gemcitabine

Same study as above; showed trend in improved benefit for patients treated with gemcitabine

RRM1 Tan 2010 Not prognostic No prognostic value seen

HER2 over-expression — predictive of response to anti-HER2 therapy

Geissler 2010 Not predictive

After an observation that up to 82% of cases of metastatic pancreatic cancer overexpressed HER2, a trial was initiated to evaluate the safety and efficacy of trastuzumab followed by capecitabine in this patient population. After 207 patients had their disease evaluated for HER2 status, it was determined that only 11% had HER2 overexpression. In addition, response rates were not improved in the 17 patients treated. Based on this study, anti-HER2 therapy does not appear to be beneficial.

hENT, human equilibrative nucleoside transported protein; RRM1, ribonucleoside reductase subunit M1

TABLE 4 | INVESTIGATIONS OF BIOMARKERS FOR NSCLC PRESENTED AT ASCO 2010

POTENTIAL BIOMARKER


ALK for treat-ment with anti-ALK therapies

Bang 2010ALK was associated with high response and a good safety profile in patients treated with crizotinib

In this trial, 82 patients who were ALK-positive were treated with an ALK inhibitor. ORR was 57%, DCR at 8 weeks was 87%, and probable PFS at 6 months was 72%. It should be noted that the mean age of this population was 51 years, and 76% were nonsmokers.

Loss of PTEN expression in patients treated with EGFR tyrosine kinase inhibitors

Hensing 2010

PTEN loss was associated with decreased PFS and OS with treat-ment with EGFR tyrosine kinase inhibitors

In this study of 115 patients, 84 had tumors that were PTEN-positive. In patients with loss of PTEN expression compared to those who were PTEN-positive, me-dian PFS was lower (2.0 vs. 2.9 months) and OS was decreased (4.04 vs. 8.51 months, P=0.057). A multivariate analysis — which also included clinical factors such as smoking, histology, and gender — using the Cox proportional hazards model showed that PTEN was the only factor associated with improved survival (P=0.046).

EGFR and KRAS predict response to vandetinib (an EGFR inhibitor) + docetaxel

Johnson 2010

EGFR positivity (increased gene copy and mutation status) may be predictive for patients being treated with vandetinib + docetaxel; KRAS mutation showed no predictivity

In a study of 1391 patients with stage IIIb/IV NSCLC who were treated with vandetinib ± docetaxel, 570 archival tumor samples were analyzed for EGFR and KRAS status. High EGFR protein expression shown with IHC (≥1% tumor cells stained) was seen in 88% of patients, of whom 35% were FISH+ and 14% were EGFR MT (exons 18-21). In this study, 13% were KRAS MT (exons 12-13). Consistent trends in improved PFS, OS, and ORR were seen in patients with EGFR (FISH+ or MT). However, no differences were seen in treatment types or KRAS mutation status.

EGFR, KRAS, and BRAF predict response to sorafenib, a small molecule kinase inhibitor

Herbst 2010

In patients treated with sorafenib, KRAS mutation was associated with an improved DCR, and EGFR mutation was associated with a decreased DCR

A phase II trial in 105 patients with lung cancer being treated with sorafenib, erlotinib, vandetinab, and erlotinib plus bexarotene. Patients were analyzed for DCR at 8 weeks. In this study 61% (11/18) of patients with a KRAS mutation treated with sorafenib showed a DCR compared to 31% (4/13) of those treated with erlotinib. By contrast, patients with an EGFR mutation had a significantly lower response of 23% (3/13) than those with no mutation 64% (3/13) (P=0.012).

DCR, disease control rate; FISH, fluorescence in situ hybridization; IHC, immunohistochemistry; MT mutation; ORR, overall response rate


In the 2010 update of guidelines on the early detection of prostate cancer developed by the American Cancer Society, SDM has been incorporated into the process “[w]hen the evidence is not clear that the benefits of screening outweigh the risks, an individual’s values and preferences must be factored into the screening decision” [Wolf 2010].

The definition of SDM is not consistent across studies [Pieterse 2008], although the essential elements (as defined by a number of models) include presenting options and discussing patients’ values (see Table 7) [White 2007, Charles 1999, Charles 1999a]. One study of 60 patients with rectal cancer showed that whereas physicians viewed patient participation in treatment choice as reaching an agreement, 23% of patients felt that participation could be solely defined as the physician providing information. In this study, 81% of patients believed that not all patients would be able to participate in choosing treatments and 74% believed that physicians would not be able to “weigh the pros and cons of treat-ment” for their patients.

Implementing SDM in clinical practice will be challenging. Many patients are not health literate, which is defined as the “capacity to obtain, process, and understand basic health information and services needed to make appropriate health decision” [Kutner 2003]. The results of a national survey of American adults, which is shown in Figure 1, showed that only 12% were considered pro-ficient in health literacy, 53% were intermediate, 22% were basic, and 14% were below basic [Kutner 2006].

A further challenge is that patients’ perceptions of risk are complicated because they are affected by nonquantifiable issues, such as personal motivations, social and community influences, and emotional reactions [Klein 2007]. In addition, the presenta-tion of multiple options can lead to “decisional conflict,” which can cause patients to change their minds, delay making a deci-sion, regret a previous decision, and even blame a physician for a bad outcome [Stacey 2008]. A recent study reported that 66% of women with early-stage breast cancer had trouble deciding between treatment choices (eg, mastectomy or lumpectomy with radiation therapy). In another study, only 30% of patients with advanced NSCLC felt certain about a choice between chemother-apy or best supportive therapy. The key factors related to these decisional conflicts included feeling uninformed and unsupported as well as being uncertain about their personal values [Stacey 2008].

As previously mentioned, the majority of physicians believe in SDM; however, studies have shown that their treatment decisions are often influenced by their own values and professional experi-ence [Baldauf 2009]. A survey of more than 1,000 physicians showed that 93% of urologists chose radical prostatectomy as the

TABLE 5POTENTIAL SOURCES OF COMPARATIVE

EFFECTIVENESS RESEARCH

1. Randomized controlled trials (RCTs)

2. Systematic reviews of and meta-analyses of RCTs

3. Other comparative clinical trials

4. Population studies, including registries and administrative and claims data

5. Prognostic and predictive association studies

6. Quality of life studies, including patient-reported outcomes

7. Clinical decision models, including cost-effectiveness and cost-utility analyses

TABLE 6RECOMMENDATIONS FOR THE CO-DEVELOPMENT

OF TARGETED THERAPIES AND PREDICTIVE BIOMARKERS [Lyman 2010]

1. Source studies should be well-designed, large, randomized trials with appropriate control groups and clinically relevant endpoints of efficacy and safety.

2. Biomarkers should be based on a biologically plausible rationale, with good test performance and reproducibility.

3. Biomarker results should be available on a majority of subjects with a detailed accounting of the reasons for unavailable samples or results.

4. Biomarker subgroups and planned analysis should be prespecified.

5. Source studies should have adequate power to establish with confidence any differential treatment effect in subgroups based on the biomarker.

6. Biomarker measurement should be blinded to the treatment group assignment and study outcomes.

7. Appropriate adjustment for multiple testing should be reported.

8. Formal testing of any drug/biomarker interaction should be conducted.

9. When possible, results should be adjusted for all known prognostic/predictive factors.

10. Consistent findings related to the biomarker should be observed in at least two large trials.

TABLE 7ELEMENTS OF SHARED DECISION MAKING [White 2007, Charles 1999, Charles 1999a]

• Discussing the nature of the decision (What is the clinical issue being addressed?)

• Describing treatment alternatives

• Discussing the pros and cons of the choices

• Discussing uncertainty; assessing family understanding

• Eliciting patient values and preferences

• Discussing the family’s role in decision making

• Assessing the need for input from others

• Exploring the context of the decision and eliciting the family’s opinion about the treatment decision


preferred treatment for men with moderately differentiated, clini-cally localized prostate cancer, while 72% of radiation oncologists believed that surgery and external beam radiation therapy were equivalent or more appropriate treatment options. In other words, physicians chose the treatments that they were personally more familiar with and would be in charge of delivering or performing [Fowler 2000]. Based on their understanding of their patients, physicians may try to guess what a patient prefers, although stud-ies have shown that they are poor at predicting an informed pa-tient’s treatment choice [Brothers 2004, Stalmeier 2007]. These studies confirm that it is important for physicians to be willing to discuss patient preferences and not make blind assumptions.

Patient participation in treatment choices is becoming increas-ingly standard in oncology. Studies show that most patients would prefer to share decision-making responsibilities about their over-all health, and this process has been shown to favorably impact a number of factors, including quality of life. Many healthcare providers already use SDM in their practices, although some may not be using it appropriately, and others still prefer a traditional, paternalistic approach. To increase adoption of SDM, health-care providers would benefit from education and training about studies backing its use in oncology, as well as the most effective methods and materials available to appropriately implement it for all patients.

Additionally, providing well-designed decision aids can facilitate SDM. Decision aids provide patients with information about treatment options and risks and benefits, as well as tools to help clarify personal values. They may also provide more detailed information about a disease condition [IPDAS 2010, Stacey 2008]. Currently, more than 500 decision aids are available or in development [IPDAS 2010]. In order to improve the reliability of these instruments, a group of interested professionals founded the International Patient Decision Aid Standards (IPDAS) Collabo-ration, which has developed standards to improve the quality of all decision aids [IPDAS 2010]. Familiarizing oncologists with the availability and applicability of these tools may greatly improve the shared decision-making process with their patients.

CONCLUSION

Oncology treatments are complex and require weighing potential risks against potential benefits. Innovation in oncology will continue to produce new technologies and treatments. Of the multitude of factors oncology practitioners must take into consideration, the principles of shared decision making, comparative effectiveness, and utilization of biomarkers to optimize treatment have been identified as critical elements to incorporate into standards of practice. To implement these principles, the oncologist must remain up to date on the most recent evidence on tumor types and be informed about new advances and involve their patients in therapeutic decision making, with the ultimate goal of improved health outcomes in the oncology patient population.

PERCENT BELOW BASIC

ADULTS (%)

PERCENT BASIC AND ABOVE

BELOW BASIC BASIC INTERMEDIATE PROFICIENT

14 22 53 12

80 60 40 20 0 20 40 60 80 100

FIGURE 1 | HEALTH LITERACY IN AMERICAN ADULTS

1 0 | P R I N C I P L E S O N T H E P A T H W A Y T O T R E A T M E N T

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