Author and curator: Ritu Saxena, Ph.D.
This post attempts to integrate three posts and to embed all comments made to all three papers, allowing the reader a critically thought compilation of evidence-based medicine and scientific discourse.
Dr. Dror Nir authored a post on October 16th titled “Knowing the tumor’s size and location, could we target treatment to THE ROI by applying imaging-guided intervention?” The article attracted over 20 comments from readers including researchers and oncologists debating the following issues:
- imaging technologies in cancer
- tumor size, and
- tumor response to treatment.
The debate lead to several new posts authored by:
- Dr. Bernstein’s (What can we expect of tumor therapeutic response),
- Dr. Saxena, the Author of this post’s, (Judging ‘tumor response’-there is more food for thought) and
- Dr. Lev-Ari’s post on Personalized Medicine: Cancer Cell Biology and Minimally Invasive Surgery (MIS) https://pharmaceuticalintelligence.com/2012/12/01/personalized-medicine-cancer-cell-biology-and-minimally-invasive-surgery-mis/#comment-5269
This post is a compilation of the views of authors representing different specialties including research and medicine. In medicine: Pathology, Oncology Surgery and Medical Imaging, are represented.
Dr. Nir’s post talked about an advanced technique developed by the researchers at Sunnybrook Health Sciences Centre, University of Toronto, Canada for cancer lesions’ detection and image-guided cancer treatment in the specific Region of Interest (ROI). The group was successfully able to show the feasibility and safety of magnetic resonance imaging (MRI) – controlled transurethral ultrasound therapy for prostate cancer in eight patients.
The dilemma of defining the Region of Interest for imaging-based therapy
Dr. Bernstein, one of the authors at Pharmaceuticalintelligence.com, a Fellow of the American College of Pathology, reiterated the objective of the study stating that “Their study’s objective was to prove that using real-time MRI guidance of HIFU treatment is possible and it guarantees that the location of ablated tissue indeed corresponds to the locations planned for treatment.” He expressed his opinion about the study by bringing into focus a very important issue i.e., given the fact that the part surrounding the cancer tissue is in the transition state, challenge in defining a ROI that could be approached by imaging-based therapy. Regarding the study discussed, he states – “This is a method demonstration, but not a proof of concept by any means. It adds to the cacophany of approaches, and in a much larger study would prove to be beneficial in treatment, but not a cure for serious prostate cancer because it is unlikely that it can get beyond the margin, and also because there is overtreatment at the cutoff of PSA at 4.0. I think that the pathologist has to see the tissue, and the standard in pathology now is for any result that is cancer, two pathologists or a group sitting together should see it. It’s not an easy diagnosis.”
“The crux of the matter in terms of capability is that the cancer tissue, adjacent tissue, and the fibrous matrix are all in transition to the cancerous state. It is taught to resect leaving “free margin”, which is better aesthetically, and has had success in breast surgery. The dilemma is that the patient may return, but how soon?” concludes Dr. Larry.
Dr. Nir responded, “The philosophy behind lumpectomy is preserving quality of life. It was Prof. Veronesi (IEO) who introduced this method 30 years ago noticing that in the majority of cases; the patient will die from something else before presenting recurrence of breast cancer. It is well established that when the resection margins are declared by a pathologist (as good as he/she could be) as “free of cancer”, the probability of recurrence is much lower than otherwise. He explains further, “The worst enemy of finding solutions is doing nothing while using the excuse of looking for the “ultimate solution.” Personally, I believe in combining methods and improving clinical assessment based on information fusion. Being able to predict, and then timely track the response to treatment is a major issue that affects survival and costs!
In this discussion my view is expressed, below.
- The paper that discusses imaging technique had the objective of finding out whether real-time MRI guidance of treatment was even possible and if yes, whether the treatment could be performed in accurate location of the ROI? The data reveals they were pretty successful in accomplishing their objective and of course that gives hope to the imaging-based targeted therapies.
- Whether the ROI is defined properly and if it accounts for the real tumor cure, is a different question. Role of pathologists and the histological analysis and what they bring to the table cannot be ruled out, and the absence of a defined line between the tumor and the stromal region in the vicinity is well documented. However, that cannot rule out the value and scope of imaging-based detection and targeted therapy. After all, it is seminal in guiding minimally invasive surgery.
- As another arm of personalized medicine-based cure for cancer, molecular biologists at MD Anderson have suggested molecular and genetic profiling of the tumor to determine genetic aberrations on the basis of which matched-therapy could be recommended to patients.
- When phase I trial was conducted, the results were encouraging and the survival rate was better in matched-therapy patients compared to unmatched patients. Therefore, every time there is more to consider when treating a cancer patient and who knows a combination of views of oncologists, pathologists, molecular biologists, geneticists, surgeons would device improvised protocols for diagnosis and treatment. It is always going to be complicated and generalizations would never give an answer. Smart interpretations of therapies – imaging-based or others would always be required!
To read additional comments, including those from Dr. Williams, Dr. Lev-Ari, refers to:
Knowing the tumor’s size and location, could we target treatment to THE ROI by applying imaging-guided intervention? Author and Reporter: Dror Nir, Ph.D.
Dr. Lev-Ari in her paper linked three fields that bear weight in the determination of Tumor Response to Therapy:
- Personalized Medicine
- Cancer Cell Biology, and
- Minimally Invasive Surgery (MIS)
Her objectives were to address research methodology, the heterogeneity innate to Cancer Cell Biology and Treatment choice in the Operating Room — all are related to the topic at hand: How to deliver optimal care with least invasive intervention course.
Any attempt aimed at approaching this desirable result, called Personalized Medicine, involves engagement in three strategies:
- prediction of Patient’s reaction to Drug induction
- design of Clinical Trials to validate drug efficacy on small subset of patients predicted to react favorable to drug regimen, increasing validity and reliability
- Genetical identification of patients at no need to have a drug administered if non sensitivity to the drug has been predicted
These method are to be applied to a list of 56 leading Cancer types.
While the executive task of the clinician remains to assess the differentiation in Tumor Response to Treatment, pursuit of individualized histopathology, as well as tumor molecular, genetic and functional characteristics has to take into consideration the “total” individual patients’ characteristics: age, co-morbidities, secondary risks and allergies to drugs.
In Dr. Lev-Ari’s paper Minimally Invasive Treatment (MIT) is compared with Minimally Invasive Surgery (MIS) applied for tumor resection. In many cases MIS is not the right surgical decision, yet, it is applied for a corollary of patient-centered care considerations. At present, facing the unknown of the future behavior of the tumor as its response to therapeutics bearing uncertainty related to therapy outcomes.
Forget me not – says the ‘Stroma’
Dr. Brücher, the author of review on tumor response criteria, expressed his views on the topic. He remembers that 10 years ago, every cancer researcher stated – “look at the tumor cells only – forget the stroma”. However, the times have changed, “now, everyone knows that it is a system we are looking at, and viewing and analyzing only tumor cells is really not enough.”
He went on to state “if we would be honest, we would have to declare that all data, which had been produced 8-13 years ago, dealing with laser capture microdissection, would need a rescrutinization, because the influence of the stroma was ‘forgotten’.”
He added, “the surgeon looks at the ‘free margin’ in a kind of reductionable model, the pathologist is more the control instance. I personally see the pathologist as ‘the control instance’ of surgical quality. Therefore, not the wish of the surgeon is important, the objective way of looking into problems or challenges. Can a pathologist always state if a R0-resection had been performed?”
What is the real RO-resection?
There have been many surrogate marker analysis, says Dr. Brücher, and that a substantially well thought through structured analysis has never been done: mm by mm and afterwards analyzing that by a ROC analysis. For information on genetic markers on cancer, refer to the following post by Dr. Lev-Ari’s: Personalized Medicine: Cancer Cell Biology and Minimally Invasive Surgery (MIS)
He also stated that there is no gold standard to compare the statistical ROC analysis to. Often it is just declared and stated but it is still not clear what the real RO-resection is?
He added, “in some organs it is very difficult and we all (surgeons, pathologists, clinicians) that we always get to the limit, if we try interpreting the R-classification within the 3rd dimension.”
Dr. Brücher explains regarding resectability classification, “If lymph nodes are negative it does not mean, lymph nodes are really negative. For example, up to 38% upper GI cancers have histological negative lymph nodes, but immunohistochemical positive lymph nodes. And, Stojadinovic et al have also shown similar observations at el in colorectal cancer. So the 4th dimension of cancer – the lymph nodes / the lymphatic vessel invasion are much more important than just a TNM classification, which unfortunately does often not reflect real tumor biology.”
The discussion regarding the transition state of the tumor surrounding tissue and the ‘free margin’ led to a bigger issue, the heterogeneity of tumors.
Dr. Bernstein quoted a few lines from the review article titled “Tumor response criteria: are they appropriate?, authored by Dr Björn LDM Brücher et al published in Future Oncology in 2012.
- Tumor heterogeneity is a ubiquitous phemomenon. In particular, there are important differences among the various types of gastrointestinal (GI) cancers in terms of tumor biology, treatment response and prognosis.
- This forms the principal basis for targeted therapy directed by tumor-specific testing at either the gene or protein level. Despite rapid advances in our understanding of targeted therapy for GI cancers, the impact on cancer survival has been marginal.
- Can tumor response to therapy be predicted, thereby improving the selection of patients for cancer treatment?
- In 2000, the NCI with the European Association for Research and Treatment of Cancer, proposed a replacement of 2D measurement with a decrease in the largest tumor diameter by 30% in one dimension. Tumor response as defined would translate into a 50% decrease for a spherical lesion
- We must rethink how we may better determine treatment response in a reliable, reproducible way that is aimed at individualizing the therapy of cancer patients.
- We must change the tools we use to assess tumor response. The new modality should be based on empirical evidence that translates into relevant and meaningful clinical outcome data.
- This becomes a conundrum of sorts in an era of ‘minimally invasive treatment’.
- Integrated multidisciplinary panel of international experts – not sure that that will do it.
Dr. Bernstein followed up by authoring a separate post on tumor response. His views on tumor response criteria have been quoted in the following paragraphs:
Can tumor response to therapy be predicted?
The goal is not just complete response. Histopathological response seems to be related post-treatment histopathological assessment but it is not free from the challenge of accurately determining treatment response, as this method cannot delineate whether or not there are residual cancer cells. Functional imaging to assess metabolic response by 18-fluorodeoxyglucose PET also has its limits, as the results are impacted significantly by several variables:
• tumor type
• sizing
• doubling time
• anaplasia?
• extent of tumor necrosis
• type of antitumor therapy and the time when response was determined.
The new modality should be based on individualized histopathology as well as tumor molecular, genetic and functional characteristics, and individual patients’ characteristics, a greater challenge in an era of ‘minimally invasive treatment’.
This listing suggests that for every cancer the following data has to be collected (except doubling time). If there were five variables, the classification based on these alone would calculate to be very sizable based on Eugene Rypka’s feature extraction and classification.
But looking forward, time to remission and disease free survival are additionally important. Treatment for cure is not the endpoint, but the best that can be done is to extend the time of survival to a realistic long term goal and retain a quality of life.
For detailed discussion on the topic of tumor response and comments refer to the following posts:
What can we expect of tumor therapeutic response?
Author: Larry H. Bernstein, MD, FCAP
Judging ‘Tumor response’-there is more food for thought
Reporter: Ritu Saxena, Ph.D.
Additional Sources:
Research articles:
Brücher BLDM, Swisher S, Königsrainer A et al. Response to preoperative therapy in upper gastrointestinal cancers. Ann. Surg. Oncol.16(4),878–886 (2009).
Miller AB, Hoogstraten B, Staquet M, Winkler A. Reporting results of cancer treatment. Cancer47(1),207–214 (1981).
Therasse P, Arbuck SG, Eisenhauer EA et al. New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J. Natl Cancer Inst.92(3),205–216 (2000).
Brücher BLDM, Becker K, Lordick F et al. The clinical impact of histopathological response assessment by residual tumor cell quantification in esophageal squamous cell carcinomas. Cancer106(10),2119–2127 (2006).
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Dear Ritu,
I think that what you are doing in this post is actually great. Starting and managing a debate among members of this blog, has significant added value for readers.
If I may, I would like to highlight the place of the items you have mentioned in the cancer patient’ pathway. I hope this will help readers to see the bigger picture:
1. Accurate pre-clinical assessment of cancer patients; e.g. receiving comprehensive information regarding tumor’s size, location, structure and aggressiveness is key for making the right treatment choice. To date, practitioners are using imaging and biopsies in order to receive such information. Adding to this stage of the pathway, a bio-tech based process that will predict tumor’s response to drugs treatment is the right thing to do. A good example that supports this approach, although from another field, is the way treatment of HIV patients is being managed!
2. Real-time quality control of treatment, including surgery and the associated pathology work, which are the procedures discussed in length by our colleagues, can be hugely improve by incorporating smart imaging devices. Using smart-imaging for monitoring changes in tumors’ size and texture could also enable timely indication on the effectiveness of drugs’ treatment.
3. Post treatment follow-up: this part represents today a major economical burden that can be significantly reduce by using smart-imaging devices and very specific bio-markers.
I am very pleased by this assessment. I am not so that that post treatment follow up with smart imaging is ready for prime time. I think that certain tumors at a stage of development have expected median doubling times, and the evidence is out there, but whether it is accessable, I don’t know. I think that prediction of doubling time would be a determinant of frequency of re-imaging. The ability to see structural, or texture changes may be the greatest benefit of smart-imaging. I would like to have the opinion of Prof David Avrin, a pioneer in this field, first at Stanford, and then at Univ of Utah, and finally at UC San Francisco. I would presume that the structural changes resulting in change in texture, could well precede size change. If that is so, than one might expect to better identify the surrounding tissue at risk.
A 91 year old brilliant holocaust survivor died 2 years ago with a chronic myelogenous leukemia the went into crisis, and he was prepared for the results. Another 91 year old holocaust survivor who was beat up by the guards for reaching out for bread was found to have laryngeal cancer some months ago, and they got it all. He had a relapse a few weeks ago characterized by weakness and malaise. The location of the carcinoma and the rich lymphatic and blood supply made the short tumor free survival. He has severe cancer cachexia. He was ready for death. He has not a touch of hatred, and he has a grasp of history.
We are gaining a lot of knowledge about genomics, but the understanding of the link to metabolic pathways is not on such a solid ground. That might be locked up in metabolomics as well as oxidative stress.
Dear Larry,
Smart-imaging is not yet in it’s prime time. It’s in “prime development” 🙂 among others, by myself.
If I may, I would like to refer you to references sited in my posts. you will find out scientific evidence regarding the potential of ultrasound and MRI based tissue characterization to enable quantitative follow-up of changes in tissue texture, structure and bio-chemical components. this is in addition to the more “obvious” measurement of size.
Dr. Ritu,
Thank you very much for leading the effort in this new compilation. Please note all my editorial notions inserted in this post, for future use. Thank you.
Dr. Williams,
This post will become part of the forthcoming e-Book on Cancer in a dedicated Chapter to Tumor Response to Treatment by Cancer Type
Dr. Dror Nir,
In the forthcoming e-Book on Cancer, please prepare a Chapter by Medical Imaging Technology Options, another Chapter on applications of Imaging technologies to Cancer Type. Alternatively, suggest to Dr. Williams and Myself your own organization of the Medical Imaging aspect in Cancer Diagnosis & Treatment.
Aviva, can we talk about over the phone? maybe a TCON with Dr. Williams? I do not feel I understand what you are asking for in regards to these e-Books…Thanks,
I will e-mall both, the info for a Telephone Conference, the two of you set up the time and I’ll be made available.
I am on Skype as well.
Thanks everyone for your comments and I am glad all of you liked it. Aviva, I’ll make the changes suggested by you ASAP.
Ritu, this is a great post as (I agree with Dr. Nir and Berstein) addition of the comments and expounding on them is a nice part of the discussion. As far as ROI I was wondering if this will make a difference in image-guided therapy to a cancer like bladder cancer where one cancer has not invaded through the bladder and another tumor has invaded through? It seems to me that if a tumor has invaded the wall (like bladder or prostate) radical surgery and resection is indicated. Please correct me if I am wrong. In addition I am thinking of adding a post to follow up on Dr. Berstein’s comments concerning personalized medicine and the current state of affairs on how to determine drug sensitivity to a tumor. In a nutshell, this is entailing patient derived xenografts in combination with genome sequencing.
Dr. Williams,
Thank you for the comment, we would like to have thepost that you have suggested.
Ritu, thanks for sharing this post, I may suggest adding to this debate as refer to the cancer patient’ pathway highlights, the protonomic analysis / genomic analysis study using molecular events associated with different molecules, measuring the change in one tumor versus another tumor as additional tool for Predicting Tumor Response to Treatment.
Gil,
Thank you for your comment.
Please send as a reference for the point you raised.
I e-mailed you seaprate e-mail requesting from you some information
Aviva,
Enclosed please find some reference articles for your review:
1. CONSULTATIVE PROTEOMICS Report: http://www.magicwater.org/storage/max/CP08-15UT_Finalrb_.pdf
2. Morphoproteomic demonstration of constitutive nuclear factor-kappaB activation in glioblastoma multiforme with genomic correlates and therapeutic implications. Brown RE, Law A. http://www.ncbi.nlm.nih.gov/pubmed/17127728
3. Morphoproteomic and Pharmacoproteomic Rationale for mTOR Effectors as Therapeutic Targets in Head and Neck Squamous Cell Carcinoma* http://www.annclinlabsci.org/content/36/3/273.full.pdf
4. “Chemotherapy sensitivity and resistance testing: to be “standard” or to be individualized, that is the question.” Gastric cancer : official journal of the International Gastric Cancer Vol. 9 – 2006; PubMed ID: 16767362
5. Clinical Proteomics: From Biomarker Discovery and Cell Signaling Profiles to Individualized Personal Therapy Bioscience Reports 200502/04, Volume 25, Issue 1-2, pp 107-125
6. Evolution of Proteomic Methods for Analysis of Complex Biological Samples – Implications for Personalized Medicine http://cdn.intechweb.org/pdfs/29627.pdf
I found what I had put away. One of the most elegant papers I have seen in several years, and I reviewed 16 for this journal this year.
__________________________________________________________________
Clinical Biochemistry
Manuscript Draft
Manuscript Number: CLB–12-00159
Mark J. Sarnoa and Charles S. Davis
a Vision Biotechnology Consulting, 19833 Fortuna Del Este Road, Escondido, CA 92029, USA (mjsarno@att.net)
b CSD Biostatistics, Inc., San Diego, CA, 4860 Barlows Landing Cove, San Diego, CA 92130, USA (chuck@csdbiostat.com)
___________________________________________________________________
Title: Robustness of ProsVue™ linear slope for prediction of prostate cancer recurrence: Simulation studies on effects of analytical imprecision and sampling time variation
Article Type: Full Research Paper
Section/Category: Analytical Investigation
Keywords: ProsVue, slope, prostate cancer, random variates
Financial support for this investigation was provided by Iris Molecular Diagnostics
_________________________________________________________________
Abstract: Objective: The ProsVue assay measures serum total prostate-specific antigen (PSA) over three time points post-radical prostatectomy and calculates rate of change expressed as linear slope. Slopes ≤2.0 pg/ml/month are associated with reduced risk for prostate cancer recurrence.
However, an indicator based on measurement at multiple time points, calculation of slope, and relation of slope to a binary cutpoint may be subject to effects of analytical imprecision and sampling time variation.
We performed simulation studies to determine the presence and magnitude of such effects.
___________________________________________________________________
Design and Methods: Using data from a two-site precision study and a multicenter retrospective clinical trial of 304 men,
we performed simulation studies to assess whether analytical imprecision and
sampling time variation can drive misclassification of patients with stable disease or classification switching for patients with clinical recurrence.
__________________________________________________________________
Results: Analytical imprecision related to expected PSA values in a stable disease population results in ≤1.2% misclassifications.
For recurrent populations, an analysis taking into account correlation between sampling time points demonstrates that classification switching across the 2.0 pg/ml/month cutpoint occurs at a rate ≤11%.
Lastly, sampling time variation across a wide range of scenarios results in 99.7% retention of proper classification for stable disease patients with linear slopes up to
the 75th percentile of the distribution.
___________________________________________________________________
Conclusions: These results demonstrate the robustness of the ProsVue assay and the linear slope indicator. Further, these simulation studies provide a potential framework for evaluation of future assays that may rely on the rate of change principle
_________________________________________________________________
Most recently, the ProsVue Assay has been cleared for commercial use by the US Food and Drug Administration (FDA) as “a prognostic marker in conjunction with clinical evaluation as an aid in identifying those patients at reduced risk for recurrence of prostate cancer for the eight year period following prostatectomy.”
The assay measures serum total prostate specific antigen (PSA) in post-RP samples and calculates rate of change of PSA over the sampling period, expressing the outcome as linear slope. The assay is novel in at least a few respects.
First, as the intended use implies, the assay is optimized to identify patients at reduced risk for recurrence. In order to demonstrate efficacy for this indication, the assay employs the immuno-polymerase chain reaction (immuno-PCR) [4] to achieve sensitivity an order of magnitude lower than existing “ultrasensitive” PSA assays [5]. The improved sensitivity allows quantification of PSA at levels exhibited in stable disease (<5 pg/ml), which have been historically below the
measurement range of ultrasensitive assays [6,7].
Secondly, the assay is the first to receive clearance based on linear slope of tumor marker concentration versus time post-surgery. Specifically, PSA is measured in three samples taken between 1.5 and 20 months post-RP and the slope calculated using simple least squares regression. The calculated slope is compared to a threshold of 2.0 pg/ml/month with values at or below the threshold associated with reduced risk for PCa recurrence.
_______________________________________________________________
Does analytical imprecision present a potential risk for misclassification by driving errors in the calculated slope that result in classification switching? Since excursions of precision can occur as point sources in single sampling points or in cumulative effect from the three sampling points, the question is worthy of consideration.
Similarly, does variation in the time at which samples are taken drive errors resulting in classification switching?
Both questions seek to evaluate the robustness of the ProsVue Assay and are properly presented for clinical chemists and physicians evaluating use of the assay in clinical practice. Furthermore, since future diagnostic assays may employ the rate of change principle, it is important to develop statistical methods to evaluate effects of variation.
We performed multiple simulation studies to address the questions specific to ProsVue and also provide a potential framework for evaluation of future assays.
_______________________________________________________________
Simple assays measuring a marker circulating in abundance that is directly
associated with a present clinical condition have given way to more sophisticated
methods measuring scarce analytes associated with risk for eventual clinical events.
Although PSA has been measured for decades for early detection and monitoring of PCa, accurate measurement at post-RP levels to identify patients with reduced risk of recurrence represents a new development. Furthermore, measurement of PSA at multiple time points and calculation of rate of change using linear regression extends application of the analyte markedly beyond traditional use. Such use presents certain questions of variation effects.
________________________________________________________________
Our results indicate that analytical imprecision in the range of concentrations exhibited in patients at reduced risk for recurrence (the focus of the assay) presents no significant risk of misclassification.
Classification switching in this population occurs at a frequency of ≤1.2%. Similarly, due to the highly correlated nature of PSA values between sampling time points, slopes for recurrent patients and clinical classification are substantively insensitive to analytical variation even in a subpopulation of recurrent patients with slowly rising PSA values.
Lastly, sampling time variation negligibly affects clinical classification for stable disease patients with slopes at and below the 75th percentile.
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