Journal Issue

PCa Commentary | Volume 181 – September 2023

Background

The function of initial staging of prostate cancer is to accurately estimate the risk of progression in order to guide optimal therapy. The influential National Comprehensive Cancer Network (NCCN)
recommends a PSMA PET/CT as part of initial staging for men in the risk categories of intermediate- and high-risk cancer as defined by clinicopathologic features: PSA, Gleason score, clinical tumor stage and number of positive biopsy cores. For men with a life expectancy of >10 years molecular analysis (i.e., Decipher, Prolaris or OncotypeDx Prostate) is suggested by the NCCN.

PSMA PET/CT in advanced cancer identifies lesions that are missed on conventional CT and bone scans. Yaxley et al., BJU Int 2019 reported the results of primary staging by PSMA PET/CT in 1253 men, half and half with intermediate and high-risk disease in the largest published study on this subject. Metastases were identified in 12.1%; 8.2% in men with PSA values <10 ng/ml and 47% with PSA values > 20 ng/mL. Segregated by Gleason Grade, metastases were found in 6.4% with GG 2-3 (Gleason 3+3 and 3+4). Extra-pelvic lymph nodes occurred in 47%. Bone metastases were found in 5.2% in men with intermediate-risk and in 20% high-risk cancer.

Enter the Immune System — Its Influence on Cancer Behavior

The influence of immune cell infiltration within the tumor and from the surrounding microenvironment is not taken into account in assigning a Gleason score and therefore is not considered in the NCCN classifications of risk. The variety of promoter and suppressive lymphocytes and macrophages that comprise these infiltrations are not fully identified under the microscope and play no role in standard risk assessments.

The immune microenvironment of the primary tumor influences both the initiation and early progression (i.e., toward a higher Gleason score) of the developing cancer. Immune elements compete (i.e., pro-tumor and anti-tumor) to suppress malignant clones from progressing — while others promote disorganization and aggressiveness. Protective cells include immune enhancing CD8+T-cells, Killer T-cells, protective macrophages; immune suppressive cells include T-regulation cells and immune suppressive macrophages. In fact, recent studies reveal that the ratio of one particular gene in anti-tumor macrophages to a different gene in pro-tumor macrophages can by itself determine the outcome in a man’s cancer — and possibly all cancers. (Bill et al., SCIENCE, Aug 2023).

These immune cellular elements are not only found within and surrounding tumors but also can be identified in the blood to various amounts. Based on their presence in the blood, Chinese researchers have “developed and validated a machine-learning nomogram for the prediction of risk stratification of prostate cancer based on subsets of these lymphocytes in the blood.” This nomogram functioned better than the standard NCCN prognostic risk groups. (Yang et al., Journal of Translational Medicine 2023.)

The Predictive Power of Artificial Intelligence

The genomic classifier Decipher has been multiply validated to estimate prognosis based on its risk scale of 0 to 1.0. However, AI has the capability to derive a prediction — for example of an individual man’s response to a given treatment. When whole mount pathology slides, multiply stained, are digitized into a scanner, the AI process identifies standard histology but also detects the subtypes of immune cells infiltrating the tumor and the surrounding microenvironment. This comprehensive analysis presents a complete picture of a man’s cancer, its predicted behavior and supports individualized management.

Two Examples of Clinically Useful Applications of AI for Intensifying or De-intensifying
Therapy

Commentary #180 discusses a study assessing the relative benefits of adding ADT to
radiation therapy in men with advanced cancer based on a derived biomarker.
At the 2023 annual meeting of the American Society of Clinical Oncology, Armstrong and
colleagues from Duke University presented their abstract, “Development and validation of an
AI-derived digital pathology-based biomarker to predict benefit of long-term androgen deprivation therapy with radiotherapy in men with localized high-risk prostate cancer across
multiple phase III NRG/RTOG trials,” i.e., a goal “to guide ADT duration to maximize benefits
and minimize risks.”
“Pre-treatment prostate biopsy slides were digitized from six randomized trials evaluating the
benefit of radiation therapy +/- ADT” in men with localized high-risk prostate cancer with end
points of distant metastases (DM) and prostate cancer-specific mortality. The data from this
training set was then validated in a major trial comparing radiation plus 4 vs. 28 months of ADT and determined a distinguishing biomarker. Their study found that 28 months of ADT was 13% better than 4 months in the biomarker-positive group as compared to only 2% better in the biomarker-negative group, a finding that could influence clinical decision-making.
[A spokesman for Artera AI.com said the company is working on developing a test based on this
study for commercial availability.]

BOTTOM LINE

PSMA PET/CT scans and applications of AI in prostate cancer management are aiding individualized patient management.

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PCa Commentary | Volume 180 – August 2023

Background

Ever since 1675 when the Dutch lens maker, Leeuwenhoek, looked down the newly invented microscope at rainwater and reported seeing ‘animalcules – tiny dancing creatures” (bacteria), pathologists have been similarly looking down their scopes and guiding prostate management by applying their expertise in pattern recognition.

In the 1960s, Dr. Donald Gleason organized those patterns into doublets of 3, 4 (i.e., 3+4) and 5 to create the prognostic Gleason Score — now further refined. Anthony D’Amico, professor of Radiation Oncology at Harvard Medical School is credited for establishing prognostic categories segregating “risk of recurrence” into low-, intermediate- and high-risk prostate cancer (with intermediate-risk now subdivided into favorable” and “unfavorable.”)

[Google reminds us that “A prognostic biomarker provides information about the patient’s overall cancer outcome, regardless of therapy, whilst a predictive biomarker gives information about the effect of a therapeutic intervention.”]

Feng, Spratt et al., have now advanced the art by generating a personalized precision predictive model by utilizing large data sets, machine learning and the less human-dependent “deep learning” — both components of artificial intelligence. Their analysis was directed toward discovering a biomarker that can predict which men with intermediate-risk prostate cancer benefit from the addition of ADT to radiotherapy.

Their study found that some men gained much, but others only minimally. Previous data has found that only 60% of men with intermediate-risk cancer benefit from the addition of ADT to radiotherapy — although those with “unfavorable” intermediate-risk benefit more so. The challenge was identifying who benefits and who does not.

The Spratt AI Study

“Artificial Intelligence Predictive Model for Hormone Therapy Use in Prostate Cancer,” NEJM Evidence, June 29, 2023., by Dr. Daniel Spratt with many collaborators on behalf of NRG Prostate Cancer AI Consortium.

Dr. Spratt: “With the first-ever predictive biomarker of ADT benefit in prostate cancer, created with AI, we are able to further realize the ability to create a personalized approach for the treatment of cancer.” (Quoted in Precision Medicine News, 2023).

The study was based on “digitized pathology images of pretreatment prostate tissue and clinical data of 5727 patients enrolled in 5 phase 3 randomized trials in which treatment was radiotherapy with or without ADT.” The goal was to develop a binary model to predict the benefit from short-term ADT regarding the likelihood of developing distant metastases.

The article notes that androgen deprivation therapy (ADT) is customarily combined with radiation treatment in this setting. These were trials of radiation treatment for higher grades of prostate cancer, in which there are “no validated predictive models to guide its use.”

Spratt’s colleagues endeavored to establish such a guide – a binary determinant for men with localized intermediate-risk cancer predicting whether “a given patient will benefit from ADT or not“ based on the risk of time to developing distant metastases. They generated such a model by analyzing 4 major randomized clinical trials comparing radiation therapy alone or combined with ADT.

“Data from these trials were acquired and digitized and then were used to train a predictive AI model” to predict the differential benefit of ADT according to their biomarker status. Their image extraction method was described: “For each patient, the tissue across all available digital slides was divided into 256 x 256-pixel patches. … [and the] model was trained on image patches using self-supervised learning. Over 2.5 million tissue patches across four trials … were fed through the model 200 times to train the model.”

The next step was to take this predictive model and validate it using data from RTOG trial 9408 which randomized men with intermediate-risk cancer to radiation treatment plus or minus 4 months of ADT. By applying the AI-derived model, the 15-year estimate for developing distant metastases for biomarker-positive patients treated with ADT was 4% v 14.4% for biomarker-negative ADT patients. In developing their biomarker, they found the best cut-point was 67% for segregating biomarker-positive from biomarker-negative.

By applying the biomarker established in the development model, 543 men were biomarker-positive, indicating that ADT “significantly reduced (by 10.5 percentage points) the risk of distant metastases at 15 years compared to radiotherapy alone.” For 1051 biomarker-negative patients ADT conferred minimal benefit (percentage point benefit 0.5%). For prostate cancer-specific mortality, the comparison of benefits was 10.2% and 1.2%, respectively.

Dr. Spratt concluded, “Our AI-based predictive model was able to identify patients with a predominantly intermediate risk for prostate cancer likely to benefit from short-term ADT.” In an interview with ‘News Wise’ Dr. Spratt said: “We are fortunate to be already offering this test at UH Seidman Cancer Center, Case Western Reserve University, to our patients from Northwest Ohio, our nation, and around the world.” (Quoted in News Wise, June 2023)

BOTTOM LINE

The article reviewed in this Commentary is a prime example of how Artificial Intelligence can benefit the clinical management of prostate cancer.

From Artera.ai: “The ArteraAI Prostate Test is a Laboratory Developed Test that is now clinically available through a single CLIA-certified laboratory in Jacksonville, FL. Clinicians who would like to order the ArteraAI Prostate Test can contact us at support@artera.ai. Soon after, a member of our Customer Success team will reach out to set up an account in the ArteraAI Portal to enable order submission.”

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PCa Commentary | Volume 179 – July 2023

Background
Although neuroendocrine cancer (NEPC) infrequently (<2%) presents elusively in the prostate, much more commonly it develops late in the course of the disease admixed in varying extent in metastatic lesions with standard adenocarcinoma. In this situation, it is termed “treatment-emergent t-NEPC” postulated to have “transdifferentiated” from adenocarcinoma. Its development is thought to be induced by mutations that have occurred during treatment due to increased therapeutic pressure from androgen suppressive therapy on the androgen signaling pathway. The genomic characteristics of the neuroendocrine components at the various metastatic sites are heterogeneous, making it challenging to craft a single targeted therapy. The transdifferentiated cells do not express PSA or the prostate-specific membrane antigen (PSMA) and hence are negative on PSMA PET scans. t-NEPC is aggressive, and difficult to diagnose, and effective treatment is lacking. Merkens et al., J Exp Clin Cancer Res. 2022, reviewed the underlying mechanisms of neuroendocrine transdifferentiation. t-NEPC associates with an inappropriately low PSA. Clues to its presence are bulky disease in viscera (especially liver) and lymph nodes, lytic bone metastases, a PSA doubling time of <6 months and an elevated serum calcium. t-NEPC in mCRPC tissue biopsies from patients treated with, for example, Zytiga and Xtandi rose to 10.5% compared to 2.3% in patients naive to these second-generation agents. During the period from 1998 to 2011 the incidence of t-NEPC was 6.3%; in 2012-2016 after introduction of Zytiga and Xtandi the rate was 13.3%. Diagnosis Appropriate management of t-NEPC is hampered by delayed diagnoses in part due to the lack of diagnostic serum biomarkers. The best candidate biomarker (but inconsistently elevated) is chromogranin A (CgA), the main component contained in secretory granules released from NEPC cells. Plasma values less than or equal to 85-100 ng/mL are normal; values >360 suggest significant t-NEPC. A suggested strategy is to check a baseline CgA in the early treatment phase of advanced cancer, and if the disease is objectively progressing without a commensurate rise in PSA, say, 4 – 10 ng/mL, obtain an 18F-FDG PET/CT scan (which can image aggressive cancer that is negative on PSMA PET scans (Spratt et al, Prostate 2015). “CgA should be included as a tool to monitor the evolution of [t-NE]PC, wherein it may be 2-3 times above normal levels,” reviewed in “Chromogranin A:” a useful biomarker in castration-resistant prostate cancer.” Poussard et al, World Journal of Urology, 2023. 

Currently, the diagnosis of t-NEPC is based on biopsies of metastatic lesions performed when there is high suspicion for this transformation. But because of the heterogeneity among lesions, a biopsy may be misleading or inconclusive. The location of the projected biopsy may present difficulty or cause harm.

Because of these issues, an initial assay of circulating tumor cell histology may be useful (CTC, Epic Sciences), as discussed by Beltran et al, in “The Initial Detection and Partial Characterization of Circulating Tumor Cells in Neuroendocrine Prostate Cancer,” Clin Cancer Res 2016. PET Imaging targeting the delta-like ligand (DLL), a protein surface marker on NEPC cells, is under development, as is therapy with a Lutetium 177 nucleotide conjugate targeting the DLL protein. 

An elevated neuron-specific enolase (NSE) above 16 ng/ml is often, but also inconsistently, found in t-NEPC. An elevated carcinoembryonic antigen (CEA) is a commonly available serum marker associated with t-NEPC. CEACAM5, a CEA-related “cell adhesion molecule 5, is a promising t-NEPC cell surface antigen” for which the CEACAM5 directed antibody-drug conjugate labetuzumab govitecan therapy is under development, DeLucia et al. (Fred Hutchinson Cancer Research Center, Clin Cancer Res. 2021) 

Treatment
Chemotherapy offers a modest benefit in metastatic t-NEPC. The outcome of various regimens is discussed by Yamada and Beltram, Curr Oncol Rep.2021. Combination therapy with carboplatin (Paraplatin) and cabazitaxel (JEFTANA) yields objective responses of 50 – 60% with a median progression-free survival of 5.1 months and a median overall survival of 16 months. However, the management decision as to when to initiate chemotherapy in response to rising serum markers is challenging.

In an attempt to address the genomic heterogeneity of metastases, there is an ongoing trial at MD Anderson Cancer Center (NCT04592237) combining chemotherapy with carboplatin and cabazitaxel with PARP inhibition and anti-PD1 immunotherapy. A 29% rate of mutations in the BRCA family in NEPC explains the inclusion of PARP inhibitors in this regimen (Chedgy et al, J Pathol. 2018). The cell surface marker CD46 is overexpressed on NEPC cells and radioimmune therapy with an alpha-emitting Actinium 225 isotope conjugate is being developed (Bidkar et al, Clin Cancer Res. 2023).

Perspective
Androgen suppression by various means has been the mainstay of prostate cancer treatment since 1911. Even then the downside of ADT was recognized i.e., the induction of resistance to treatment through alterations of the androgen receptor.

More recently, therapy-induced NEPC has been added to the list of adverse developments. Until an effective strategy is developed to avoid the induction of t-NRPC, the only practical ways of addressing this induction are delaying the onset of ADT until a worrisome rise in the PSA doubling time is evidenced, limiting adjuvant ADT when the gain is minimal compared to no ADT (as can be assessed by using Decipher), and employing metastases directed therapy in oligometastatic relapse without accompanying ADT.

BOTTOM LINE
Successful management of treatment-induced neuroendocrine prostate cancer is limited by difficulty in diagnosis and a lack of effective therapy. Intense research is underway to address this important deficiency.

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