Timing of treatment shapes the path to androgen receptor signaling inhibitor resistance in prostate cancer

Reviewer #1 (Public Review):

Summary:

Lee, Eugine et al. use in vivo barcoded lineage tracing to investigate the evolutionary paths to androgen receptor signaling inhibition (ARSI) resistance in two different prostate cancer clinical scenario models: measurable disease and minimal residual disease. Using two prostate cancer cell lines, LNCaP/AR and CWR22PC, the authors find that in their minimal residual disease models, the outgrowth of pre-existing resistant clones gives rise to ARSI-resistant tumors. Interestingly, in their measurable disease model or post-engraftment ARSI setting, these pre-existing resistant clones are depleted and rather a subset of clones that give rise to the treatment of naïve tumors adapt to ARSI treatment and are enriched in resistant tumors. For the LNCaP/AR cell line, characterization of pre-existing resistant clones in treatment naïve and ARSI treatment settings reveal increased baseline androgen receptor transcriptional output as well as baseline upregulation of glucocorticoid receptor (GR) as the primary driver of pre-existing resistance. Similarly, the authors found induction of high GR expression over long-term ARSI treatment in ARSI-sensitive clones for adaptive resistance to ARSI. For CWR22Pc cells, HER3/NRG1 signaling was the primary driver for ARSI resistance in both measurable disease and minimal residual disease models. Not only were these findings consistent with the authors' previous reports of GR and NRG1/Her3 as the molecular drivers of ARSI resistance in LNCaP/AR and CWR22Pc, respectively, but also demonstrate conserved resistance mechanisms despite pre-existing or adaptive evolutionary paths to resistance. Lastly, the authors show adaptive ARSI resistance is dependent on interclonal cooperation, where the presence of pre-existing resistant clones or "helper" clones is required to promote adaptive resistance in ARSI-sensitive clones.

Strengths:

The authors employ DNA barcoding, powerful a tool already demonstrated by others to track the clonal evolution of tumor populations during resistance development, to study the effects of the timing of therapy as a variable on resistance evolution. The authors use barcoding in two cell line models of prostate cancer in two clinical disease scenarios to demonstrate divergent evolutionary paths converging on common resistant mechanisms. By painstakingly isolating clones with barcodes of interest to generate clonal cell lines from the treatment of naïve cell populations, the authors are able to not only characterize pre-existing resistance but also show cooperativity between resistant and drug-sensitive populations for adaptive resistance.

Weaknesses:

While the finding that different evolutionary paths result in common molecular drivers of ARSI resistance is novel and unexpected, this work primarily confirms the authors' previous published work identifying the resistance mechanisms in these cell lines. The impact of the work would be greater with additional studies understanding the specific molecular/genetic mechanisms by which cells become resistant or cooperate within a population to give rise to resistant population subclones.

This study would also benefit from additional explanation or exploration of why the two resistance driver pathways described (GR and NRG1/Her3) are cell line specific and if there are genetic or molecular backgrounds in which specific resistance signaling is more likely to be the predominant driver of resistance.

Reviewer #2 (Public Review):

Summary

The authors aimed to characterise the evolutionary dynamics that occur during the resistance to androgen receptor signalling inhibition, and how this differs in established tumours vs. residual disease, in prostate cancer. By using a barcoding method, they aimed to both characterise the distribution of clones that support therapy resistance in these settings, while also then being able to isolate said clones from the pre-graft population via single-cell cloning to characterise the mechanisms of resistance and dependency on cooperativity.

While, interestingly, the timing of combination therapies has been shown to be critical to avoid cross-resistance, the timing of therapy has not been specifically considered as a factor dictating resistance pathways. Additionally, the role of residual disease and dormant populations in driving relapse is of increasing interest, yet a lot remains to be understood of these populations. The question of whether different clinical manifestations of therapy resistance follow similar evolutionary pathways to resistance is therefore interesting and relevant for the field.

The methods applied are elegant and the body of work is substantial. The proposed divergent evolutionary pathways pose interesting questions, and the findings on cooperativity provide insight. However, whether the model truly reflects minimal residual disease to the extent that the authors suggest may limit the relevance of the findings at this stage. Certain patterns in the DNA barcoding results also call into question whether the results fully support the strong claims of the authors, or whether alternative explanations could exist. While the potential to isolate individual clones in the pre-graft setting is a great strength of the method applied and the isolation of these clones is a huge body of work in itself, the limited number of clones that could be isolated also somewhat limits the validation of the findings.

Strengths

• Very relevant and interesting question, clear clinical relevance, applying elegant methods that hold the potential to provide a novel understanding of multiple aspects of therapy resistance, through from evolutionary patterns to intracellular and cooperative mechanisms of resistance.

• The text is clearly written, logical, and the structure is easy to follow.

Weaknesses

(1) The extent to which the model used truly mimics residual disease

The main conclusions of the paper are built upon results using a model for minimal residual disease. However, the extent to which this truly recapitulates minimal residual disease, particularly with regard to their focus on the timings of therapy, could be discussed further. If in the clinical setting residual disease occurs following the existence of a tumour and its microenvironment, there might be many aspects of the process that are missed when coinciding treatment with engraftment of a xenograft tumour with pre-castration. If any characterisation of the minimal residual disease was possible (such as histologically or through RNA sequencing), this may help demonstrate in what ways this model recapitulates minimal residual disease.

(2) Whether the observed enrichment of pre-resistant clones is truly that

The authors strongly make the case that their barcoding experiments provide evidence for pre-existing resistance in the context of minimal residual disease. However, it seems that the clones enriched in the ARSIR tumours are consistently the most enriched clones in the pregraft. Is it possible that the high selective pressure in the pre-engraftment ARSI condition simply leads to an enrichment of the most populous clones from the pregraft? Whereas in the control setting, the reduced selective pressure at the point of engraftment allows for a wider variety of clones to establish in the tumour? Additionally, is there the possibility that the clones highly enriched in the pregraft are in fact a heterogeneous group of cells bearing the same barcode due to stochastic events in the process of viral transduction? Addressing these questions would greatly improve the study.

(3) The robustness of the subsequent work based on 1-2 pre-resistant clones

While appreciating the volume of work involved in isolating and culturing individual pre-resistant clones, given the previous point, the conclusions would benefit from very robust validations with these single-cell clones. There are only two clones, and the results seem to focus more on one than the other, for which the data is less convincing. For instance, the Enz IC50 data, which in the case for pre-ARSI R2 is restricted to the supplementary, compares the clones A-D. In Figure S8 B, pre-ARSI R2 is compared to clone B, which is, of the four clones shown in the main figure when compared to R1, the one with the lowest Enz IC50. Therefore, while the resistant clones seem to have a significantly higher Enz IC50, comparing both clones to clones A-D may not have achieved this significance. It would also be useful to know how abundant the resistant clones were in the original barcode experiments.

(4) The logic used in the final section requires further explanation

In the final section, the authors suggest that a pre-ARSIR clone is able to cooperate with a pre-Intact clone to aid adaptive ARSI resistance. If this is true, then could it not be that rare, pre-resistant clones support adaptive resistance in established tumours? And, therefore, the mechanism underlying resistance could be through pre-existing resistant clones in both settings. The work would benefit from a discussion to clarify this discrepancy in the interpretation of the findings. This is particularly necessary given the strong wording the authors use regarding their findings, such as that they have provided 'conclusive evidence' for acquired resistance.

Author response:

eLife assessment

This important study provides new insight into the dynamics that underlie the development of therapy resistance in prostate cancer by revealing that divergent tumor evolutionary paths occur in response to different treatment timing and that these converge on common resistance mechanisms. The use of barcoded lineage tracing and characterization of isolated tumor clonal populations provides compelling evidence supporting the importance of clonal dynamics in a tumor ecosystem for treatment resistance. Several open questions remain, however, raising the possibility of alternative interpretations of the data set in its current form. Overall, the findings deepen our understanding of prostate cancer evolution and hold promising implications for how drug resistance can be addressed or prevented.

We are pleased the reviewers found our work reporting distinct evolutionary paths to resistance based on timing of treatment to be important and supported by compelling evidence. We also acknowledge the need for additional work to clarify some details, particularly regarding the mechanism of clonal cooperativity as a catalyst of resistance.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Lee, Eugine et al. use in vivo barcoded lineage tracing to investigate the evolutionary paths to androgen receptor signaling inhibition (ARSI) resistance in two different prostate cancer clinical scenario models: measurable disease and minimal residual disease. Using two prostate cancer cell lines, LNCaP/AR and CWR22PC, the authors find that in their minimal residual disease models, the outgrowth of pre-existing resistant clones gives rise to ARSI-resistant tumors. Interestingly, in their measurable disease model or post-engraftment ARSI setting, these pre-existing resistant clones are depleted and rather a subset of clones that give rise to the treatment of naïve tumors adapt to ARSI treatment and are enriched in resistant tumors. For the LNCaP/AR cell line, characterization of pre-existing resistant clones in treatment naïve and ARSI treatment settings reveal increased baseline androgen receptor transcriptional output as well as baseline upregulation of glucocorticoid receptor (GR) as the primary driver of pre-existing resistance. Similarly, the authors found induction of high GR expression over long-term ARSI treatment in ARSI-sensitive clones for adaptive resistance to ARSI. For CWR22Pc cells, HER3/NRG1 signaling was the primary driver for ARSI resistance in both measurable disease and minimal residual disease models. Not only were these findings consistent with the authors' previous reports of GR and NRG1/Her3 as the molecular drivers of ARSI resistance in LNCaP/AR and CWR22Pc, respectively, but also demonstrate conserved resistance mechanisms despite pre-existing or adaptive evolutionary paths to resistance. Lastly, the authors show adaptive ARSI resistance is dependent on interclonal cooperation, where the presence of pre-existing resistant clones or "helper" clones is required to promote adaptive resistance in ARSI-sensitive clones.

Strengths:

The authors employ DNA barcoding, powerful a tool already demonstrated by others to track the clonal evolution of tumor populations during resistance development, to study the effects of the timing of therapy as a variable on resistance evolution. The authors use barcoding in two cell line models of prostate cancer in two clinical disease scenarios to demonstrate divergent evolutionary paths converging on common resistant mechanisms. By painstakingly isolating clones with barcodes of interest to generate clonal cell lines from the treatment of naïve cell populations, the authors are able to not only characterize pre-existing resistance but also show cooperativity between resistant and drug-sensitive populations for adaptive resistance.

Weaknesses:

While the finding that different evolutionary paths result in common molecular drivers of ARSI resistance is novel and unexpected, this work primarily confirms the authors' previous published work identifying the resistance mechanisms in these cell lines. The impact of the work would be greater with additional studies understanding the specific molecular/genetic mechanisms by which cells become resistant or cooperate within a population to give rise to resistant population subclones.

We agree that additional insights into the mechanism of adaptive resistant and the role of cell-cell cooperativity are clear next steps for this work. We propose to do so through single cell characterization (RNA-seq, ATAC-seq) of tumor evolution in a time course experiment where we can track each clone using expressed barcodes. This will allow us to explore the dynamics of interaction between the "adaptable" and "helper" clones. Unfortunately, the barcode methodology used in this initial report is DNA-based; therefore, a follow-up study using a transcribable barcode library is needed to address these fascinating questions.

This study would also benefit from additional explanation or exploration of why the two resistance driver pathways described (GR and NRG1/Her3) are cell line specific and if there are genetic or molecular backgrounds in which specific resistance signaling is more likely to be the predominant driver of resistance.

In the case of NRG1/HER3 pathway mediated resistance, we know that this mechanism requires that the PTEN/PIK3CA pathway be wildtype. This is the case for the CWR22Pc model described in the manuscript. Furthermore, we have data showing that PTEN deletion in these cells rescues the phenotype, meaning that CWR22Pc cells with PTEN deletion are no longer dependent on NRG1/HER3 signaling for ARSI resistance.

In contrast, LNCaP/AR cells are PTEN null at baseline and therefore must evolve alternative mechanisms of ARSI resistance. Since our initial identification of the GR mechanism, we and others have extended the finding to additional models (VCaP, LAPC4) (PMID: 24315100; PMID: 28191869). Another recent insight is the importance of RB1 and TP53 status in maintenance of luminal lineage identity during ARSI therapy, and the recognition of lineage plasticity as a resistance mechanism in cell lines/tumor models that lack these two tumor suppressors. In summary, baseline genetics clearly plays a role in which ARSI resistance pathway is likely to emerge. We will clarify this point in the revision with additional discussion.

Reviewer #2 (Public Review):

Summary

The authors aimed to characterise the evolutionary dynamics that occur during the resistance to androgen receptor signalling inhibition, and how this differs in established tumours vs. residual disease, in prostate cancer. By using a barcoding method, they aimed to both characterise the distribution of clones that support therapy resistance in these settings, while also then being able to isolate said clones from the pre-graft population via single-cell cloning to characterise the mechanisms of resistance and dependency on cooperativity.

While, interestingly, the timing of combination therapies has been shown to be critical to avoid cross-resistance, the timing of therapy has not been specifically considered as a factor dictating resistance pathways. Additionally, the role of residual disease and dormant populations in driving relapse is of increasing interest, yet a lot remains to be understood of these populations. The question of whether different clinical manifestations of therapy resistance follow similar evolutionary pathways to resistance is therefore interesting and relevant for the field.

The methods applied are elegant and the body of work is substantial. The proposed divergent evolutionary pathways pose interesting questions, and the findings on cooperativity provide insight. However, whether the model truly reflects minimal residual disease to the extent that the authors suggest may limit the relevance of the findings at this stage. Certain patterns in the DNA barcoding results also call into question whether the results fully support the strong claims of the authors, or whether alternative explanations could exist. While the potential to isolate individual clones in the pre-graft setting is a great strength of the method applied and the isolation of these clones is a huge body of work in itself, the limited number of clones that could be isolated also somewhat limits the validation of the findings.

Strengths

Very relevant and interesting question, clear clinical relevance, applying elegant methods that hold the potential to provide a novel understanding of multiple aspects of therapy resistance, through from evolutionary patterns to intracellular and cooperative mechanisms of resistance.

The text is clearly written, logical, and the structure is easy to follow.

Weaknesses

(1) The extent to which the model used truly mimics residual disease

The main conclusions of the paper are built upon results using a model for minimal residual disease. However, the extent to which this truly recapitulates minimal residual disease, particularly with regard to their focus on the timings of therapy, could be discussed further. If in the clinical setting residual disease occurs following the existence of a tumour and its microenvironment, there might be many aspects of the process that are missed when coinciding treatment with engraftment of a xenograft tumour with pre-castration. If any characterisation of the minimal residual disease was possible (such as histologically or through RNA sequencing), this may help demonstrate in what ways this model recapitulates minimal residual disease.

We appreciate the reviewer's feedback on this point and acknowledge that the pre-ARSI setting used in our studies is not precisely identical to minimal residual disease (MRD) seen clinically, where a patient typically undergoes primary treatment (radical prostatectomy surgery or local radiotherapy) then relapses with distant disease from micrometastases that were not initially detectable. Having uncovered a key difference in the path to resistance using our pre-ARSI model, we believe our data provide a strong rationale to invest additional effort in designing newer MRD models that more closely mimic the clinical scenario, perhaps through surgical resection of a primary tumor that could “seed” micrometatases prior to therapy. We will highlight this aspect in our revised manuscript and provide clarity on the limitations and scope of our study.

(2) Whether the observed enrichment of pre-resistant clones is truly that

The authors strongly make the case that their barcoding experiments provide evidence for pre-existing resistance in the context of minimal residual disease. However, it seems that the clones enriched in the ARSIR tumours are consistently the most enriched clones in the pregraft. Is it possible that the high selective pressure in the pre-engraftment ARSI condition simply leads to an enrichment of the most populous clones from the pregraft? Whereas in the control setting, the reduced selective pressure at the point of engraftment allows for a wider variety of clones to establish in the tumour?

The reviewer raises an important point about enrichment of ARSI resistance clones in the pregraft but we do not believe that explains the subsequent in vivo data for the following reasons:

(1) The two most enriched clones in the Pre-ARSIR tumors are the second and third the most enriched clones in pre-graft, not first (Supplementary figure 1E). If the clones were enriched in resistant tumors based on their abundance in starting population, we expect to find the most enriched clone in the tumor.

(2) By varying the androgen concentration in the pregraft culture media, we could selectively deplete or enrich the same clones enriched in the Pre-ARSIR tumors in vivo, indicating the enrichment of these clones in the resistant tumors is unlikely to be solely based on their relative frequency in the pregraft (Supplementary figure 2).

We will clarify these points in the revised manuscript.

Additionally, is there the possibility that the clones highly enriched in the pregraft are in fact a heterogeneous group of cells bearing the same barcode due to stochastic events in the process of viral transduction? Addressing these questions would greatly improve the study.

The barcode library was deep sequenced to confirm even distribution of the barcodes before it was transferred from Novartis (PMID: 258491301) and we intentionally used a low multiplicity of infection (MOI) to generate barcode lines to ensure single copy insertion. That said, we cannot entirely rule out the possibility that the second and third most enriched clones in the pregraft originated from the same ancestral clone and subsequently acquired two different barcodes. We will clarify this point in the revised manuscript.

(3) The robustness of the subsequent work based on 1-2 pre-resistant clones

While appreciating the volume of work involved in isolating and culturing individual pre-resistant clones, given the previous point, the conclusions would benefit from very robust validations with these single-cell clones. There are only two clones, and the results seem to focus more on one than the other, for which the data is less convincing. For instance, the Enz IC50 data, which in the case for pre-ARSI R2 is restricted to the supplementary, compares the clones A-D. In Figure S8 B, pre-ARSI R2 is compared to clone B, which is, of the four clones shown in the main figure when compared to R1, the one with the lowest Enz IC50. Therefore, while the resistant clones seem to have a significantly higher Enz IC50, comparing both clones to clones A-D may not have achieved this significance. It would also be useful to know how abundant the resistant clones were in the original barcode experiments.

We acknowledge that studies relying on 1-2 biological samples indeed have limitations. Given our extensive prior work into the role of GR in the development of ARSI resistance (and that of other labs), we focused on demonstrating that both pre-ARSIR1 and pre-ARSIR2 clones exhibit pre-existing GR expression and are primed to further upregulate GR levels under ARSI conditions, thereby relying on GR function to sustain resistance. Given the redundancy of resistant mechanisms of the two clones, we made efforts to isolate additional clones enriched in Pre-ARSIR tumors. However, despite our attempts, we were unable to identify further clones. Pre-ARSIR1 and pre-ARSIR2 are second and third most enriched clones in pre-graft (2.1% and 1.7% respectively).

(4) The logic used in the final section requires further explanation

In the final section, the authors suggest that a pre-ARSIR clone is able to cooperate with a pre-Intact clone to aid adaptive ARSI resistance. If this is true, then could it not be that rare, pre-resistant clones support adaptive resistance in established tumours? And, therefore, the mechanism underlying resistance could be through pre-existing resistant clones in both settings. The work would benefit from a discussion to clarify this discrepancy in the interpretation of the findings. This is particularly necessary given the strong wording the authors use regarding their findings, such as that they have provided 'conclusive evidence' for acquired resistance.

We agree that rare, pre-resistant clones could support adaptive resistance (and therefore resistance in this adaptive setting could, technically be called “pre-existing”) but it is critical to recognize that these rare, pre-resistant “helper” clones are vastly outnumbered by pre-Intact clones that “acquire” resistance through their “help.” We find this to be fascinating biology and we will clarify this logic in the resubmission, as well as future experimental approaches to unravel the mechanism.